SAARC JOURNAL OF AGRICULTURE (SJA) Volume 18, Issue 1, 2020 ISSN: 1682-8348 (Print), 2312-8038 (Online) © SAC

The views expressed in this journal are those of the author(s) and do not necessarily reflect those of SAC

Published by SAARC Agriculture Centre (SAC) BARC Complex, Farmgate, Dhaka-1215, Bangladesh Phone: 880-2-8141665, 8141140; Fax: 880-2-9124596 E-mail: [email protected], Website: http://www.banglajol.info/index.php/SJA/index

Editor-in-Chief Dr. Mian Sayeed Hassan Director, SAARC Agriculture Centre BARC Complex, Farmgate, Dhaka-1215, Bangladesh

Managing Editor Dr. Ashis Kumar Samanta Senior Program Specialist, SAARC Agriculture Centre BARC Complex, Farmgate, Dhaka-1215, Bangladesh

Associate Editor Fatema Nasrin Jahan Senior Program Officer, SAARC Agriculture Centre BARC Complex, Farmgate, Dhaka-1215, Bangladesh

Printed at Natundhara Printing Press, 277/3, Elephant Road, Dhaka-1205, Bangladesh Cell: 01711019691, 01911294855, Email: [email protected] ISSN: 1682-8348 (Print), 2312-8038 (Online)

SAARC JOURNAL OF AGRICULTURE

VOLUME 18 ISSUE 1 JUNE 2020

SAARC Agriculture Centre www.sac.org.bd EDITORIAL BOARD

Editor-in-Chief Dr. Mian Sayeed Hassan Director, SAARC Agriculture Centre BARC Complex, Farmgate, Dhaka-1215, Bangladesh

Managing Editor Dr. Ashis Kumar Samanta Senior Program Specialist, SAARC Agriculture Centre BARC Complex, Farmgate, Dhaka-1215, Bangladesh

Associate Editor Fatema Nasrin Jahan Senior Program Officer, SAARC Agriculture Centre BARC Complex, Farmgate, Dhaka-1215, Bangladesh

Members

Dr. M. Jahiruddin Dr. Muhammad Musa Professor Deputy Director (Research) Department of Soil Science, Faculty of Ayub Agricultural Research Institute Agriculture, Bangladesh Agricultural Faisalabad, Pakistan University, Mymensingh, Bangladesh Email: [email protected] Email: [email protected]

Dr. P. K. Mandal Dr. Md. Abdus Samad Principal Scientist Professor National Research Centre for Plant Department of Medicine Faculty of Veterinary Science Bangladesh Agricultural IARI Campus, New Delhi, India University Email: [email protected] Mymensingh, Bangladesh

Dr. S. K. Sahoo Dr. Badar Naseem Siddiqui Principal Scientist Department of Agriculture Extension & Central Institute of Freshwater Aquaculture Communication, PMAS-Arid Agricultural Bhubaneswar, Odisha, India University Email: [email protected] Rawalpindi, Pakistan

Dr. Bhim Bahadur Khatri Dr. P.B. Dharmasena Senior Scientist and Coordinator National Consultant Food and Agriculture National Research Programme Organization, Sri Lanka Agricultural Research Council Email:[email protected] Khumaltar, Lalitpur, Nepal Email: [email protected]

Dr. Md. Shahidur Rashid Bhuiyan Dr. Pratap Singh Birthal Professor Principal Scientist Department of Genetics and National Institute of Agricultural Sher-e-Bangla Agricultural University Economics and Policy Research (NIAP), Dhaka, Bangladesh D. P. S. Marg, Pusa, New Delhi, India Email: [email protected]

Dr. A. K. Singh Dr. P. Pravin Head, Genetics Department & Joint Director Assistant Director General (Marine (Research) Fisheries) ICAR-Indian Agricultural Research Institute Krishi Anusandhan Bhawan-II Pusa-110012, New Delhi Pusa, New Delhi-110012 Email: [email protected] Email: [email protected]

CONTENTS

Title Page

COMMON RESPIRATORY DISEASES OF POULTRY IN BANGLADESH: A REVIEW 1 M.Z. Ali GENETIC VARIABILITY, CORRELATION AND PATH COEFFICIENT ANALYSIS 13 OF BRINJAL W.L. Konyak, S.P. Kanaujia, A. Jha, H.P. Chaturvedi and A. Ananda SSR MARKERS BASED GENETIC DIVERSITY IN NEPALESE 23 B.K. Joshi, J. Rawat, B. Adhikari and R. Pokhrel ROLE OF SALICYLIC ACID ON GROWTH, YIELD, QUALITYAND DISEASE 39 PEST REACTION OF ONION (ALLIUM CEPAL.) CV. AGRIFOUND LIGHT RED P. Bhasker, P.K. Gupta and H.P. Sharma PERFORMANCE OF LENTIL AS AFFECTED BY REDUCED TILLAGE AND 51 MECHANICAL SEEDING R.U. Zaman and M.R. Islam INFLUENCE OF WATER STRESS ON MORPHOLOGY, PHYSIOLOGY AND 61 YIELD CONTRIBUTING CHARACTERISTICS OF M.Z. Hossain, S. Sikder, A. Husna, S. Sultana, S. Akhter, A. Alim and J.C. Joardar STABILITY ANALYSIS FOR YIELD OF ADVANCED POTATO GENOTYPES 73 FOR COMMERCIAL CULTIVATION IN BANGLADESH B.C. Kundu, M.A. Kawochar, S. Naznin, N.U. Ahmed, S.C. Halder, M. Mostofa and H.K.M. Delowar INTEGRATED MANAGEMENT PRACTICES FOR CLUBROOT DISEASE 87 (PLASMODIOPHORA BRASSICAE WOR.) OF CAULIFLOWER IN PALUNG, MAKWANPUR, NEPAL P. Adhikari, A. Khatiwada, N. Paneru and P. Tandan ORGANIC VERSUS CONVENTIONAL – A COMPARATIVE STUDY ON 99 QUALITY AND NUTRITIVE VALUE OF SELECTED VEGETABLE CROPS OF SOUTHERN INDIA J.R. Xavier, V. Mythri, R. Nagaraj, V.C.P. Ramakrishna, P.E. Patki and A.D. Semwal FACTOR INFLUENCING THE RESURGENCE OF BROWN PLANTHOPPER IN 117 BANGLADESH A.B.M.A. Uddin, K.S. Islam, M. Jahan, A. Ara and M.A.I. Khan IMPACT OF IMPROVED CHICKPEA CULTIVATION ON PROFITABILITY AND 129 LIVELIHOOD OF FARMERS IN DROUGHT-PRONE AREAS OF BANGLADESH S.C. Sharna, M. Kamruzzaman and S.T. Siddique Title Page

ASSESSMENT OF DISEASE RESISTANCE AND HIGH YIELDING TRAITS OF 143 COMMON BUCKWHEAT GENOTYPES IN SUBTROPICAL CLIMATE OF NEPAL S. Subedi, N.B. Dhami, S.B. Gurung, S. Neupane, S. Thapa and L. Oli EFFECT OF PHOSPHORUS AND MULCHING ON YIELD OF TOMATO 153 M.M.A. Mondal and M.I. Hoque PERFORMANCE OF DIFFERENT OF MUNGBEAN IN COASTAL 161 REGION OF BANGLADESH K.N. Islam, M.M.H. Khan, M.M. Islam, M.M. Uddin and M.A. Latif ASSESSMENT OF QUALITY CHARACTERISTICS OF BOILED YAM TUBERS 173 AVAILABLE IN BANGLADESH F.N. Jahan, M.A. Rahim, M. Moniruzzaman and A.K. Samanta DIFFERENT BODY MEASUREMENT AND BODY WEIGHT PREDICTION OF 183 JAMUNA BASIN SHEEP IN BANGLADESH M.A. Sun, M.A. Hossain, T. Islam, M.M. Rahman, M.M. Hossain and M.A. Hashem INVESTIGATING THE QUALITY OF COMMERCIAL BEEF CATTLE FEEDS AND 197 FEED INGREDIENTS USED IN BANGLADESH M.T. Kamal, M.A. Hashem, M. Al-Mamun, M.M. Hossain, M.A. Razzaque and J.H. Ritu EFFECTS OF TURMERIC POWDER ON CLOSTRIDIUM PERFRINGENS LOAD IN 209 BROILER CHICKENS M.Z. Ali, M.M. Islam and S. Zaman INVESTIGATION OF SHADE TREE SPECIES USED IN TEA GARDEN IN 219 BANGLADESH M.A. Rahman, Z.R. Moni, M.A. Rahman and S. Nasreen THE SCENARIO OF SEEDLING PRODUCTION ON FLOATING BEDS IN FEW 239 SELECTED AREAS OF BANGLADESH M. Moniruzzaman, M.A. Kabir and M. Shahjahan DOCUMENTATION OF INDIGENOUS TECHNICAL KNOWLEDGE AND THEIR 251 APPLICATION IN PEST MANAGEMENT IN WESTERN MID HILL OF NEPAL K. Naharki and M. Jaishi GUIDE FOR AUTHORS 263

SAARC J. Agric., 18(1): 1-13 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48377

Review COMMON RESPIRATORY DISEASES OF POULTRY IN BANGLADESH: A REVIEW

M.Z. Ali* Animal Health Research Division, Bangladesh Livestock Research Institute Savar, Dhaka 1341, Bangladesh

ABSTRACT Now-a-days, the poultry production in Bangladesh has become a sustainable and profitable industry. The poultry sectors are contributing to meet the national demand of proteins as well as implementing food security along with employment generation. The poultry industries are frequently affected by respiratory diseases. There are five viral respiratory diseases associated with respiratory systems such as Avian influenza (AI), Newcastle disease (ND), Infectious bronchitis (IB), Infectious laryngotracheitis (ILT), and Avian metapneumovirus (AE). Among them, AI outbreaks have been occurring regularly with changing genetic characters since 2007 and atotal of 556 AI outbreaks have been reported yet in Bangladesh that placed in most AI reporter countries. Bacterial diseases like Mycoplasmosis, Ornithobacterium rhinotracheale, Fowl cholera, and Infectious coryza are mostly prevalent respiratory diseases in Bangladesh. Mycoplasmosis is become a major threat for poultry industry especially Sonali (a cross-bred) type chickens due to poor biosecurity and breeding management. With respect to fungal diseases, Aspergillosis or brooder pneumonia is a highly prevalent respiratory disease in Bangladesh with causing pneumonia in young chickens. However, respiratory diseases are prevalent in higher rates and cause outbreaks frequently. Keywords: Respiratory pathogen, Poultry, Avian influenza, Infectious bronchitis, Mycoplasmosis

INTRODUCTION The avian respiratory system begins with nostril and ends with lungs and air sacs. It is divided into the upper and lower respiratory system (Tully, 1995). It acts as a major pathway for pathogen entry and key challenges for veterinarians to diagnose the disease properly (Baskerville, 1981). The etiology of most of the respiratory

* Corresponding author: [email protected]

Received: 16.04.2020 Accepted: 19.06.2020 2 M.Z. Ali diseases is very complex, and sometimes, co-infected with more than one pathogen leads to increase severity (Yashpal et al., 2004). The most serious and paramount important economic diseases of poultry are respiratory diseases (Roussan et al., 2008). A large number of pathogens such as bacteria, virus, and fungus are associated with respiratory infection. Viral diseases like Avian influenza (AI), Newcastle disease (ND), Infectious bronchitis (IB), Infectious laryngotracheitis (ILT)and Avian metapneumovirus; bacterial diseases like Mycoplasmosis, Ornithobacterium rhinotracheale, Fowl cholera and Infectious coryza; and fungal disease like Aspergillosis are responsible for causing respiratory diseases. Every pathogen has unique trends regarding their infection pattern, transmission, clinical symptoms, control strategy and vaccination (Ali, 2018). But often, they show similar clinical signs and become difficult to differentiate based on their lesions. It is assumed that management cost of respiratory diseases is higher than any other diseases (Nooruzzaman et al., 2019a). It is estimated that around US$3,567.4 to US$4,210.8 financial loss in farms and US$266.3 losses in broiler farms of 1000 birds flock in the USA occurred due to IB (Roussan et al., 2008). Similarly, around US$ 7 million are lost because of Mycoplasma gallisepticum control program in poultry in the USA (Mohammed et al., 1987). In case of AI, financial loss increases when mortality reaches up to 100% (Nooruzzaman et al., 2019b). The poultry industry of Bangladesh is now well established and sufficient enough to meet demand of meat (production 75.14 lakh MT vs demand (72.97 lakh MT) and very close to compete the demand of eggs (production1711 crores vs. demand 1732.64 crores) (DLS, 2019). Although the sector is flourishing day by day but the improvement hinders significantly by respiratory pathogens. The study aimed to describe the prevalence, risk factors and distribution of common respiratory diseases of poultry in Bangladesh. Avian influenza Avian influenza virus (AIV) is responsible for causing pandemic threat in 1918 as around 50 million peoples died all over the world due to Spanish flu (Luthy et al., 2018). In Bangladesh, the highly pathogenic avian influenza viruses (HPAIVs) were first identified in February 2007 from Bangladesh Livestock Research Institute (BLRI) (Biswas et al., 2008). Since then poultry industry in Bangladesh is facing a significant number of outbreaks waves each year. Bangladesh has been reported a total of 556 outbreaks since 2007 in OIE (OIE, 2020). The AIV has been isolated from multiple species including chickens, ducks, turkeys, goose, quails, pigeons, crows, migratory birds, humans, bats, and monkeys in Bangladesh (Nooruzzaman et al., 2019a; Nooruzzaman et al., 2019b). The virus mutated several times and changes its genetic clades from clade 2.2.2 to clade 2.3.2.1a. Now clade 2.3.2.1a is dominating over other clades from 2013 (Nooruzzaman et al., 2019a; Giasuddin et al., 2018a). Another clade 2.3.4.4 was also identified in 2016 for the HA gene of H5N6 (Giasuddin et al., 2018b). The AIV outbreaks are mostly seasonal in nature, COMMON RESPIRATORY DISEASES OF POULTRY 3 especially more prevalent in winter and spring seasons rather than other seasons. The surveillance was conducted in the live bird market (LBM) in Dhaka and Chattogram city of Bangladesh and AIV was found nearly in all LBMs. Kim et al. (2018) reported, prevalence of A(H5) is higher in waterfowl than chickens and A(H9) is more prevalent in industrial chickens than waterfowl, and industrial broiler is more susceptible of A(H9) than indigenous and cross-breads. They also observed both A(H5) and A(H9) in LBM environments. Rahman et al. (2020) reported, both A(H5) and A(H9) and/or their co-circulation present in the air of LBMs that pose serious public health issues. Therefore, LBMs of Bangladesh act as a potential source AIV transmission (Hasan et al., 2018). The veterinary hospital cases of AIV reported as 6.4% in Chattogram (Sabuj et al., 2019), 5.4% in Kishoreganj (Rahman et al., 2018), and 0.3% in Dhaka district (Islam et al., 2014b). On the other hand, domestic ducks play a significant role to transfer AIV when they contact with migratory birds of Central Asian flyway in the wetland areas of Bangladesh (Barman et al., 2017). There are two vaccines (HVT-H5 and RE-6) against AIV that are licensed by Government in 2013 and it is using till now widely in parent stock farms but poor in marginal farms due to price and scarcity of vaccines (Giasuddin et al., 2018a). Newcastle diseases Newcastle disease (ND) is a viral endemic disease in poultry species in Bangladesh caused by Newcastle disease virus (NDV). It is the main constrains of backyard chickens farming and outbreaks may cause 100% mortality (Chukwudi et al., 2012; Biswas et al., 2005) and decreased body mass and egg production of survived chickens (Alexander, 1992). Therefore, ND is directly linked with the rural economy and food safety as well as the nutritional safety of rural peoples as it causes high mortality and morbidity in backyard chickens. The prevalence rates of ND are varied in regions. Rahman et al. (2011) reported 17.50% hospital cases were ND in Gazipur district and the highest prevalence was found in October and chickens age within 0-3 months (38.60%). Badruzzaman et al. (2015) recorded, 13.84% of ND prevalence in Sylhet district. There were also more report of ND prevalence published like 11.24% in Bogura (Talukdar et al., 2017), 7.2% in Dhaka (Giasuddin et al., 2002), 11% in Chattogram (Biswas et al., 2005), 14.1% in Dhaka division (Islam et al., 2014b), 25% in Mymensingh (Rahman et al., 2012), 26.6% in Kishoregonj (Rahman et al., 2018a), 11.78% in Kishoreganj (Mamun et al., 2019); 17.8% in Ramu (Chattogram) (Sabuj et al., 2019). Regarding type of birds, the prevalence of NDV was recorded in layer 37.5%, broiler 32.5%, native bird 55.0% and duck 27.5% (Rahman et al., 2012). By genotype data, genotype XIII NDVs are under continuing evolution in Bangladesh along with genotype VI is circulating in the poultry species (Barman et al., 2017). The field veterinarians face symptoms like Viscerotropic velogenic Newcastle Disease (VVND) regularly. Vaccination is the only protective tools to prevent NDV and Government implementing vaccination strategy for three decades. But often it is failed due to unperformed, performed incorrectly, or performed irregularly and on the other hand, village indigenous chickens are rarely vaccinated (Abraham-Oyiguh et al., 2014). 4 M.Z. Ali

Infectious bronchitis Infectious bronchitis is the most economic disease in poultry all over the world including Bangladesh caused by the Infectious bronchitis virus (IBV). It is highly contagious and causes increase mortality, low egg production, hatchability and egg quality, and increase medication cost (Bwala et al., 2018). The virus can be transmitted through aerosols and mortality could be varied from 40-90%, and can affect chickens of all ages. Bhuyan et al. (2019a) reported an overall 17.52% prevalence of IBV in Bangladesh including 42.22% in a commercial layer, 17.24% in Sonali, 11.94% in broiler, and 14.93% in broiler breeder. Bhuyan et al. (2019b) also noted higher age of chickens (41-60 weeks) are prone to IB infection (54.55%) and regarding season and flock size, winter and large flock are mostly susceptible for IB infection. According to location, IBV prevalence was 41.67% in Tangail, 24.42% in Mymensingh, 19.32% in Gazipur, 15.38% in Dhaka, 16.67% in Jamalpur, 13.68% in Bogura, 5.88% in Cumilla, and 9.26% in Rangpur (Barua et al., 2006a). Another study conducted by Bhuyan et al. (2018) revealed overall 59.30% seroprevalence of IBV in chickens of Bangladesh s including 23.82% in broiler, 97.87% in layer, 71.83% in Sonali and 83.46% in backyard chickens (Bhuyan et al., 2018). Bhuyan et al. (2019) reported overall 37.6% chickens are seropositive for IBV, whereas 31.4% in broiler and 60% in Sonali in 6 sub-districts of Mymensingh district. Several other studies also observed similar IBV seroprevalence in different districts of Bangladesh such as 58% in Fatikchori (Chattogram district) (Barua et al., 2006b), 79.38% in Northern region (Das et al., 2009), and 77.83% in North-central region (Biswas et al., 2005). Veterinary hospital cases of IB reported as 1.3% in Ramu (Cox’s Bazar district) (Sabuj et al., 2019), 0.6% in Kishoreganj (Rahman et al., 2018a). The S1 gene was sequenced by Bhuyan et al. (2019b) and phylogeny analysis demonstrated QX-like IBV and 4/91 strain of IBV were circulating in the chickens in Bangladesh. There are two types of imported vaccines available in Bangladesh, live and attenuated vaccines including H120, Ma5, 4/91, Massachusetts, and M41 strains (Ali and Hasan, 2018). Infectious Laryngotracheitis Infectious laryngotracheitis, highly contagious economically important viral disease of poultry caused by Gallidherpes virus. It is commonly known as Infectious laryngotracheitis virus (ILTV). Clinically the infected birds show gasping, extension of neck during inspiration, coughing bloody mucoid exudates, and mortality may reach up to 50% in adults, and recovered birds may act as a carrier for lifetime (Kirkpatrick et al., 2006). Rahman et al. (2018b) conducted a study in Gazipur district and reported an overall 81.47% ILTV seropositive chickens and older-aged birds were more prevalent. Another study carried out by Bhuyan et al. (2019a) in 6 sub-districts of Mymensingh revealed that ILTV was prevalent in Sonali birds (0.4%). Jahan et al. (2012) observed 92.28% ILTV seropositive chickens in 6 divisions of Bangladesh where 100% in Rajshahi, 84.37% in Rangpur, 100% in COMMON RESPIRATORY DISEASES OF POULTRY 5

Chittagong, 100% in Khulna and 100% in Barisal Divisions. Similar type of study was done by Uddin et al. (2014) in commercial layer farm of 5 sub-district of Chattogram district reported 17.33% seroprevalence of ILTV whereas the highest 24% seropositive in the winter season and highest 23.24% in age between 10-35 weeks. Poor biosecurity and birds rearing in the deep litter were most susceptible for ILTV infection. Avian metapneumovirus (AMPV) or Swollen head syndrome Avian metapneumovirus is a viral disease of chicken and turkey commonly known as swollen head syndrome or avian pneumo-virus or avian rhinotracheitis caused by Avian metapneumovirus (AMPV), a virus belonging to Paramyxo viridae family with genus Metapneumovirus (Pringle, 1998). It is highly contagious disease infect upper respiratory tract showing symptoms like sneezing, coughing, nasal discharge, watery eyes, swollen head and wattles (Shin et al., 2000). The antibody against AMPV was first identified in Bangladesh during 2014 to 2016 by Ali et al. (2019) and reported overall 53.29% of birds in 5 districts were seropositive. They described seropositive bird’s distribution as 70.07% in Gazipur, 41.26% in Bogura, 59.83% in Mymensingh, 53.44% in Rangpur, and 39.40% in Panchagar district. They also demonstrated broiler breeder was higher (72.30%) seropositive than layer (50.85%) and Sonali (35.57%) type chickens and on age groups adult age groups > 41 weeks (72.90%) and winter season (68.21%) were shown higher seropositivity. Mycoplasmosis Mycoplasma is very tiny prokaryotes devoid of cell wall and covered by a plasma membrane, resistance to antibiotics, and usually host-specific (Razin et al., 1998). Mycoplasmosis is commonly caused by Mycoplasma gallisepticum (MG) and M. synoviae (MS), infect respiratory systems and show respiratory signs in chickens also known as chronic respiratory diseases (CRD) and infectious sinusitis in turkeys (Ali et al., 2017; Ali et al., 2015a). M. gallisepticum is a highly economically important disease of poultry industry due to its increased mortality, marked production losses, downgrading of the carcass, hatchability, long time medication cost. Approximately US$ 7 million are lost for MG control program in the USA which represents a loss of US$ 5 million in consumer surplus (Mohammed et al., 1987). The seroprevalence of MG was reported by Ali et al. (2017) in Bangladesh as overall seropositive rates 64.47% including 68.77% in Sonali, 63.74% in ISA brown, 59.37% in white leghorn layer type chickens. Adult age groups of chickens along with larger flock size, and the winter season were shown higher seropositivity of MG infection. Baura et al. (2006b) reported MG seropositivity in 53% broiler and 73% layer at Lohagara sub- district and in 46% broiler and 60% layer at Satkania sub-district of Chattogram district. Islam et al. (2014a) reported 55.83% MG seropositive chickens in Bhola district and higher prevalence was found in backyard chicken (62.5%) than commercial chickens (53.61%), pullet (60.63%) shown higher positive than adults (55.63%) and old aged (51.25%) chickens and winter (60.42%) compared to summer 6 M.Z. Ali

(51.25%) season. The other researchers reported the prevalence of MG was 62.44% in Feni district (Sarkar et al., 2005), 55.13% in Rajshahi district (Hossain et al., 2007), and 46.88% Patuakhali district (Sikder et al., 2005). Mycoplasmosis was also noted in veterinary hospital cases such as 4.38% in Sylhet (Rahman and Adhikary, 2016), 11.66% in Sylhet (Badruzzaman et al., 2015), 12.79% in Bogura district (Talukdar et al., 2017), 4.8% in Kishoregonj (Rahman et al., 2018), 7.6% in Dhaka (Islam et al., 2014b), and 9.16% in Kishoregonj (Mamun et al., 2019). The average prevalence of M. synoviae was found 60% in commercial breeder farms in Chattagram district and the highest prevalence was found in larger flock size (>10000), age group >60 weeks, and winter season as 70%, 70%, and 64%, respectively (Uddin et al., 2016). Ornithobacterium rhinotracheale (ORT) Ornithobacterium rhinotracheale (ORT) is a bacterial disease of poultry belongs to the family Flavobacteriaceae causes severe respiratory distress and associated with important economic losses by growth retardation, mortality, dropped egg production and egg quality (Allymehr, 2006). The ORT was first identified in Bangladesh in 2018 by Bhuiyan et al. (2019a), reported overall 45.9% chickens were seropositive including 43.3% of broiler and 55% of Sonali type chickens. By age, 5-20 weeks and 21-40 weeks of aged groups were found 38.3% and 90% seropositive for ORT, respectively. Fowl cholera Fowl cholera is an infectious bacterial disease of poultry caused by Pasteurella multocida and has been recognized as an economic disease worldwide. It can affect the respiratory system and shown symptoms like respiratory distress, cellulitis of face and wattles, mucous discharge from mouth and nose (Ali and Sultana, 2015). In the veterinary hospital cases, prevalence of fowl cholera was 3.1% in Sylhet (Rahman and Adhikary, 2016), 2.2% in Gazipur (Hassan et al., 2016), 3.90% in Dhaka (Giasuddin et al., 2002), 5.26% in Kishoregonj (Mamun et al., 2019), 6.76% in northern and north- central districts (Biswas et al., 2005), 2.7% in Sylhet (Badruzzaman et al., 2015), 3.6% in Kishoregonj district of Bangladesh (Sabuj et al., 2019). Infectious coryza Infectious coryza is acute, occasionally a chronic highly infectious bacterial disease of poultry caused by Haemophilus paragallinarum (Blackall and Reid, 1982). It can affect the upper respiratory tract of multi-aged birds characterized by catarrhal inflammation in the nasal and sinus mucosa, swollen wattles, dyspnea, sneezing with high morbidity and low mortality up to 20%. Akter et al. (2013) reported 47.54% suspected samples were H. paragallinarum positive in the Northern region of Bangladesh during 2011-2012. They also found a prevalence of 52.8% in laying hens, 42.8% in growing hens, and 16.6% in pre-laying stage. In the veterinary hospital survey, the incidence of infectious coryza was found 0.97% in Kishoregonj COMMON RESPIRATORY DISEASES OF POULTRY 7

(Mamun et al., 2019), 0.2% in Gazipur (Hassan et al., 2016), and 2.4% in Kishoregonj (Rahman et al., 2018a). Aspergillosis Aspergillosis is an infectious, non-contagious, ubiquitous opportunistic fungal infection of poultry caused by Aspergillus spp., responsible for causing respiratory distress in early life (Sultana et al., 2014). Overall 6.14% chickens were found aspergillosis and the major risk factors are rainy season (8.22%), birds age (8.27% in 8-10 days old), use of sawdust (7.69%) as litter (Sultana et al., 2014). There are many available incidence reports of Aspergillosis was recorded in the veterinary hospital, 7.33% in Sylhet (Rahman and Adhikary, 2016), 0.33% in northern and north-central districts (Biswas et al., 2005), 7.2% in Sylhet (Badruzzaman et al., 2015), and 6.6% in Kishoregonj (Sabuj et al., 2019).

CONCLUSIONS The prevalence of respiratory diseases in both commercial and backyard chickens are higher in Bangladesh. It indicates poultry farming is more competitive regarding health management in Bangladesh. The formation of national standards for biosecurity guidelines and vaccination measures along with the strict implementation of biosecurity practices and proper vaccination could be a way of prevention of respiratory diseases in Bangladesh.

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Research Article GENETIC VARIABILITY, CORRELATION AND PATH COEFFICIENT ANALYSIS OF BRINJAL

W.L. Konyak1, S.P. Kanaujia1*, A. Jha1, H.P. Chaturvedi2 and A. Ananda1 1Department of Horticulture, SASRD, Nagaland University, Medziphema, Nagaland 2Department of Genetics and Plant Breeding, SASRD, Nagaland University Medziphema, Nagaland

ABSTRACT A field experiment was conducted during 2017-2018 for evaluating the performance of brinjal (Solanum melongena L.) genotypes under foothill condition of Nagaland following randomized block design with three replications at the experimental farm of Horticulture, SASRD, Nagaland University, Medziphema, Nagaland. Forty six brinjal genotypes viz. IIVR- 1, IIVR-2, IIVR- 3, IIVR- 4, IIVR-5, IIVR-6, IIVR-7, IIVR-8, IIVR-9, IIVR- 10, IIVR-11, IIVR-12, IIVR-13, IIVR-14, IIVR-15, IIVR-16, IIVR-17, IIVR- 18, IIVR-19, IIVR-20, IIVR-21, IIVR-22, IIVR-23, IIVR- 24, IIVR-25, IIVR- 26, IIVR-27, IIVR-28, IIVR-29, IIVR-30, IIVR-31, IIVR-32, IIVR-33, IIVR- 34, IIVR-35, IIVR-36, IIVR-37, IIVR-38, IIVR-39, IIVR-40, IIVR-41, IIVR- 42, IIVR-43, IIVR-44, IIVR-45 and Nagaland Local (check variety) were evaluated for their growth, yield, quality and genetic attributes. High phenotypic coefficient of variation, genotypic coefficient of variation and high heritability coupled with high genetic advance was found for anthocyanin content, fresh weight of fruit, number of leaves plant-1, yield plant-1, fruit length and fruit diameter. The study on path analysis revealed that maximum positive direct effect on yield is exerted by fresh weight of fruit (1.08) and number of fruits plant-1 (0.407). Based on the experimental results, it can be concluded that, genotype IIVR-31 and IIVR-7 was proved to be potential yielder under foothill condition of Nagaland. Keywords: Brinjal, Correlation, Path coefficient analysis, Heritability, Genetic advance

INTRODUCTION Brinjal (Solanum melongena L), also known as eggplant and aubergine, belongs to the family Solanaceae having chromosome number (2n=24). It is an important indigenous vegetable crop of India and is considered to have originated in Indo-

* Corresponding author: [email protected]

Received: 27.10.2019 Accepted: 05.03.2020 14 Konyak et al.

Myanmar region. It is one of the most commonly grown vegetables of India. India stands 2nd in brinjal production after China. It is grown mainly for its tender and immature fruits and has great potential as raw materials for pickle making and dehydration industries for vast domestic market as well as for export. A number of cultivars are grown throughout the country depending upon the yield, consumer’s preference about the shape, colour and size. Fruit colour varies from pure white to purple, black, green and variegated in different shades. Purple colour of eggplant is due to the presence of anthocyanin pigment, while white fruit lack this pigment. Purple variety has higher copper content and polyphenol oxidase activity whereas iron and catalyse activity is highest in the green variety. Pigmented, dark-purple brinjal has more vitamin-C than those of the white skin. Amino acid content is higher in the purple variety (Kanaujia et al., 2017). The potential of brinjal as fresh vegetable is not exploited and is still insufficient to meet the domestic needs of the people. Extensive variability found in many landraces/varieties are being cultivated in different parts of India and some of the variations are so localized that their cultivation beyond the particular zone is completely unknown. Extensive collection and evaluation of germplasm to identify superior types having desirable horticultural traits, conservation and utilization of diversity are important in improvement of this crop. Genotypes of eggplant have been characterized. Both genetic and environmental factors together cause variability. Most important aspect of the genetic constitution of the breeding material is to understand the heritable variability more particularly its genetic component which has a close connection on its response to selection. High yield can be achieved by selection of characters that have high heritability coupled with genetic advance. Selection of one trait invariably affects a number of associated traits which evokes the necessity of determining inter- relationships of various yield components among them and with yield. Yield is a composite character and dependent upon a number of ascribes. For an effective selection, it is essential to have the association of various attributes with yield and yield attributing characters. Estimation of correlation coefficient among the yield contributing characters is necessary to understand the direction of selection and maximize yield in the shortest period. Path co-efficient provides an efficient way of entangling direct and indirect causes of association of selection and measures the relative importance of each causal factor. Varietal performance of brinjal varies from place to place due to the varied agro-climatic conditions do not remain same for all the regions. Considering all the above mentioned facts, a pertinent need was felt to undertake an experiment on the performance of brinjal genotypes by calculating the genotypic correlations and determining the indirect effects of component characters on yield in brinjal under foothill condition of Nagaland so the present study was conducted as to identify, the best genotype suited for the agro-climatic condition of foothill of Nagaland.

GENETIC VARIABILITY OF BRINJAL 15

MATERIALS AND METHODS The present was carried out in experimental cum research farm of Horticulture, School of Agricultural Sciences and Rural Development, Nagaland University, Medziphema campus, Nagaland, during November 2017 to May 2018. It is situated at an altitude of 310 m above mean sea level and geographically located at 25x45’43” North latitude and 93x53’04” East longitudes, in the foothills of Nagaland. The experiments comprise of forty six genotypes of brinjal viz. IIVR-1, IIVR-2, IIVR- 3, IIVR- 4, IIVR-5, IIVR-6, IIVR-7, IIVR- 8, IIVR-9, IIVR-10, IIVR-11, IIVR-12, IIVR-13, IIVR-14, IIVR-15, IIVR-16, IIVR-17, IIVR-18, IIVR-19, IIVR-20, IIVR- 21, IIVR-22, IIVR-23, IIVR- 24, IIVR-25, IIVR-26, IIVR-27, IIVR-28, IIVR-29, IIVR-30, IIVR-31, IIVR-32, IIVR-33, IIVR-34, IIVR-35, IIVR-36, IIVR-37, IIVR- 38, IIVR-39, IIVR-40, IIVR-41, IIVR-42, IIVR-43, IIVR-44, IIVR-45 and Nagaland Local (check variety). All the forty-six genotypes were evaluated in randomized design block with three replications. Nursery was raised under low cost polyhouse, in which seed was sown on 15th October, 2017. Thirty days old, healthy and uniform seedlings free from insect pests and diseases having good root system of about 10-15 cm height with 3-4 true leaves were transplanted to well-prepared raised beds in the main field on 15th November. Plot size and spacing was maintained 1.8 m x 1.8 m and 60 cm x 60 cm, respectively accommodating 9 plants in each plot. Recommended cultural practices and plant protection measures were followed. Observations were recorded for plant height (cm), number of leaves plant-1, number of branches plant-1, Days taken from transplanting to 50% flowering, Days taken from flowering to fruit set, Days taken from fruit set to marketable stage, Crop duration, Fruit length (cm), Fruit diameter (cm), Fruit volume (cc), Number of fruits plant-1, Fresh weight of the fruits, Yield plant-1, Number of seeds fruits-1 and Anthocyanin using the formula given by Ranganna (1986). The data recorded for investigation were analysed by analysis of variance (Panse and Sukhatme, 1978). Heritability (broad sense) was estimated. Genotypic and phenotypic correlation coefficients for all possible comparisons were computed (Burton, 1952). The partitioning of genotypic correlation coefficient of traits into direct and indirect effects was carried out.

RESULTS AND DISCUSSION Genetic variability, heritability and genetic advance The analysis of variance revealed significant differences among the genotypes for all characters studied indicating a high degree of variability in the material. However, phenotypic coefficient of variation (PCV) was always higher than their respective genotypic coefficient of variation (GCV) for all the traits indicating environmental factors influencing the characters (Table 1). It is estimated that highest genotypic and phenotypic coefficient of variation was observed for anthocyanin content (76.213 and 76.229, respectively) followed by fruit volume (51.740 and 52.036, respectively) indicating presence of ample variation for these traits in the present material while 16 Konyak et al.

Table 1. Genetic parameter for yield and yield attributing traits of 46 brinjal genotypes Mean Coefficient of variation Genetic Heritability Genetic Characters ± Range Advance advance as in GCV% PCV% (%) SEm (GA) % of mean Plant height (cm) 81.044 ± 1.369 52.5-108.73 11.948 12.301 94.351 19.376 23.908 Number of leaves plant-1 82.865 ± 5.025 38-175 39.375 40.751 93.358 64.943 78.372 Number of branches 15.574 ± 0.345 11.5-22.5 13.784 14.308 92.817 4.261 27.357 plant-1 Days to 50% flowering 65.147 ± 0.905 55.67-77 8.487 8.821 92.563 10.958 16.820 Days taken from 8.136 n± 0.444 7-9.67 5.342 10.855 24.222 0.441 5.416 flowering to fruit set Days taken from fruit set 19.107 ± 0.431 15-23 10.791 11.476 88.421 3.994 20.903 to marketable stage Crop duration 148.362 ± 3.953 134.33 -164.67 4.486 6.436 48.580 9.556 6.441 Fruit length (cm) 11.647 ± 0.117 6.45-19.87 27.714 27.768 99.609 6.636 56.979 Fruit diameter (cm) 6.246 ± 0.081 2.93-12.68 36.422 36.491 99.619 4.677 74.886 Fruit volume (cc) 163.464 ±5.225 44.67-396.67 51.740 52.036 98.868 173.240 105.980 Number of fruits plant-1 17.079 ± 0.349 9-33.83 24.446 24.700 97.948 8.512 49.839 Fresh weight of fruit (g) 126.448 ± 0.889 43.17-316.17 46.214 46.230 99.930 120.337 95.167 Number of seeds fruit-1 212.196 ± 6.725 60-268.33 20.404 21.130 93.250 86.129 40.589 Anthocyanin content (mg 49.248 ± 0.442 5.70-153.43 76.213 76.229 99.958 77.303 156.967 100g-1 of fruit) Yield plot-1 (g) 2,069.716 ± 38.897 389.33-3846.58 38.825 39.118 98.504 1,642.903 79.378

GENETIC VARIABILITY OF BRINJAL 17 lowest GCV and PCV were recorded for crop duration (4.486 and 6.436), respectively. PCV was always higher than their respective GCV. The estimates of genotypic variance, phenotypic variance and component of environmental variance were utilized to compute PCV, GCV, heritability, genetic advance and genetic advance as percentage of mean. It is not always true that high heritability would always exhibit high genetic advance. Hence, that heritability in combination with genetic advance would be more reliable for predicting effects of selection because genetic advance depends on amount of genetic variability; magnitude of masking effect of genetic expression (environmental influence), and intensity of selection. However, it is not necessary that a character showing high heritability will also exhibit high genetic advance (Johnson et al., 1955). In present study, high estimates of heritability was exhibited by anthocyanin content (99.958) followed by fresh weight of fruit (99.930) and highest genetic advance were obtained from yield plot-1 (1642.903) followed by fruit volume (173.2401). Similar results reported by earlier investigation (Kumar, 2002). High heritability, along with high genetic advance as per cent over mean, indicated inheritance of those characters is controlled mainly by additive genes, and selection based on phenotypic performance may prove useful which will further help in improvement of their performance and these traits may be used as selection criteria in brinjal . Correlation coefficient It is necessary to know the correlation of yield with its economically important component for making selection in a breeding. It provides advantages of adequate selection i.e. more than one character at a time in advance generations. Correlations between character pairs are due to the linkage of genes or pleiotropy of genes. Therefore, selection of one trait influences the other linked or pleiotropically affected traits. Hence, direct selection for yield may not be effective. So, it would be necessary to have an adequate knowledge about the correlation studies in the plant improvement because they are helpful in making an effective selection. In general, the magnitude of genotypic correlation coefficient was lesser than the corresponding values of the phenotypic correlation coefficient. This indicates that the expressions of character associations had been influenced by the environment and the apparent association may not be largely due to genetic reason. The difference between genotypic and phenotypic correlation was low, indicating that the environmental effects did not have much influence on these characters. The current study revealed that at genotypic level, fresh weight of fruit (0.894), fruit volume (0.840), number of branches (0.622) and plant height (0.495) exhibited significant positive correlation with yield plant-1. Maximum significant negative correlation with yield plant-1 was revealed in anthocyanin content (-0.273) as shown in Table 3. The results were in close harmony with Patel et al. (2015). The higher magnitude of positive, direct effects, for number of fruitsplant-1, fruit length and marketable yield plant-1on total yield plant-1 indicates true, positive and significant association (Table 2). 18 Konyak et al.

Table 2. Genotypic correlation among fifteen yield and yield attributing traits of brinjal

Days from Anthocyanin Plant No. of No. of Days to Days from Crop Fruit Fruit No. of Fresh No. of fruit set to . Fruit content (mg Characters height leaves plant- branches 50% flowering to duration. length diameter fruits weight of seeds marketable vol. (cc) 100g-1 of (cm) 1 plant-1 flowering fruit set (cm) plant-1 fruit (g) fruit-1 stage (cm) fruit) Plant height (cm) No. of leaves plant-1 0.226** No. of branches 0.619** 0.287** plant-1 Days to 50% 0.025NS 0.036NS 0.183* flowering Days from flowering -0.121NS -0.332** -0.220** 0.355** to fruit set Days from fruit set 0.019NS -0.240** 0.207* 0.339** 0.268** to marketable stage Crop duration 0.221** -0.148NS 0.368** 0.403** 0.442** 0.190* Fruit length (cm) 0.199* 0.090NS 0.059NS -0.099NS 0.036NS -0.470** 0.133NS Fruit diameter (cm) 0.056NS -0.051NS 0.150NS 0.158NS -0.052NS 0.400** 0.289** -0.461** Fruit vol. (cc) 0.218* 0.069NS 0.395** -0.065NS 0.241** 0.268** 0.324** 0.034NS 0.456** No. of fruits plant-1 0.496** 0.327** 0.385** 0.070NS -0.365** -0.068NS -0.117NS 0.140NS -0.194* -0.361** Fresh weight of fruit 0.241** 0.005NS 0.387** 0.011NS 0.396** 0.282** 0.312** 0.050NS 0.412** 0.951** -0.356** (g) No. of seeds fruit-1 -0.154NS -0.193* -0.184* -0.245** -0.033NS 0.179* -0.099NS -0.164NS 0.249** 0.246** -0.221** 0.185* Anthocyanin content -0.431** 0.121NS -0.212* -0.136NS -0.222** -0.198* -0.182* -0.081NS -0.181* - -0.077NS -0.191* -0.029NS (mg 100g-1of fruit) 0.111NS Yieldplant-1 0.495** 0.156NS 0.622** 0.054NS 0.279** 0.294** 0.343** 0.134NS 0.329** 0.840** 0.071NS 0.894** 0.077NS -0.273** Note: * and **: indicated significant at 5% and 1%level of probability, respectively. NS indicates non-significant.

GENETIC VARIABILITY OF BRINJAL 19

Table 3. Direct (diagonal) and indirect effects of yield components at genotypic level in brinjal genotypes

Days Days from Fresh Anthocyani Plant No. of No. of Days to from Crop Fruit Fruit No. of No. of Genotypic fruit set to . Fruit weight n content Characters height leaves branches 50% flowering duration length diameter fruits plant- seeds correlation marketabl vol. (cc) of fruit (mg 100g-1 (cm) plant-1 plant-1 flowering to fruit (cm) 1 fruit-1 with yield e stage (cm) (g) of fruit) set Plant height (cm) -0.02251 0.01078 0.02290 -0.00143 0.00138 0.00122 0.02234 0.00196 -0.00267 -0.01885 0.20199 0.26041 0.00037 0.01712 0.495** No. of leaves plant-1 -0.00508 0.04775 0.01061 -0.00205 0.00377 -0.01548 -0.01492 0.00088 0.00247 -0.00596 0.13324 0.00498 0.00047 -0.00479 0.156NS No. of branches -0.01394 0.01369 0.03699 -0.01034 0.00249 0.01331 0.03718 0.00059 -0.00719 -0.03404 0.15664 0.41761 0.00044 0.00841 0.622** plant-1 Days to 50% -0.00057 0.00173 0.00676 -0.05664 -0.00402 0.02182 0.04066 -0.00097 -0.00761 0.00557 0.02866 0.01228 0.00059 0.00540 0.054NS flowering Days from flowering 0.00273 -0.01586 -0.00813 -0.02009 -0.01133 0.01727 0.04466 0.00036 0.00249 -0.02077 -0.14866 0.42712 0.00008 0.00881 0.279** to fruit set. Days from fruit set -0.00043 -0.01148 0.00765 -0.01919 -0.00304 0.06439 0.01914 -0.00463 -0.01924 -0.02310 -0.02762 0.30421 -0.00043 0.00785 0.294** to marketable stage Crop duration -0.00498 -0.00706 0.01362 -0.02281 -0.00501 0.01221 0.10095 0.00131 -0.01391 -0.02792 -0.04767 0.33648 0.00024 0.00725 0.343** Fruit length (cm) -0.00449 0.00428 0.00220 0.00560 -0.00041 -0.03028 0.01342 0.00985 0.02216 -0.00290 0.05696 0.05369 0.00040 0.00323 0.134NS Fruit diameter (cm) -0.00125 -0.00246 0.00553 -0.00896 0.00059 0.02576 0.02921 -0.00454 -0.04808 -0.03935 -0.07894 0.44491 -0.00060 0.00718 0.329** Fruit vol. (cc) -0.00492 0.00330 0.01459 0.00365 -0.00273 0.01724 0.03267 0.00033 -0.02192 -0.08629 -0.14700 1.02718 -0.00059 0.00442 0.840** No. of fruits plant-1 -0.01117 0.01563 0.01423 -0.00399 0.00414 -0.00437 -0.01182 0.00138 0.00932 0.03116 0.40709 -0.38424 0.00053 0.00305 0.071NS Fresh weight of fruit -0.00543 0.00022 0.01431 -0.00064 -0.00448 0.01815 0.03146 0.00049 -0.01981 -0.08210 -0.14489 1.07959 -0.00045 0.00760 0.894** (g) Number of seeds 0.00347 -0.00922 -0.00681 0.01388 0.00037 0.01152 -0.01004 -0.00162 -0.01197 -0.02119 -0.08981 0.20010 -0.00242 0.00115 0.077NS fruit-1 Anthocyanin content 0.00970 0.00576 -0.00783 0.00770 0.00251 -0.01273 -0.01841 -0.00080 0.00868 0.00961 -0.03121 -0.20665 0.00007 -0.03973 -0.273** (mg 100g-1 of fruit) Residual effect= 0.01561 Note: * and **: indicated significant at 5% and 1% level of probability, respectively. NS indicates non-significant.

20 Konyak et al.

Path coefficient analysis Path coefficient analysis has been carried out to analyse the direct and indirect effect of causal factors which affect the yield. It is simply standardized partial regression coefficient which splits the correlation coefficient into the measures of the direct and indirect effects of a set of independent variables on the dependent variable. The present study on path coefficient analysis revealed that the maximum positive direct effect on yield plant-1 was exhibited by fresh weight of fruit (1.0795) followed by number of fruit plant-1 (0.40709) and crop duration (0.10095). However, traits like days from fruit set to marketable stage, number of leaves plant-1, number of branches plant-1 and fruit length also exerted positive direct yield effect and exhibited significant positive correlation with yield plot-1 indicating a true relationship between the traits. High order of negative direct effect on yield plant-1 was exerted by fruit volume (-0.08629) followed by days to 50% flowering (-0.05664) and anthocyanin content (-0.03973). Similar observations were also recorded by past researchers (Shende et al., 2015; Patel et al., 2015; Kumar and Arumugam, 2016). The residual effect was low (0.016) indicating that traits under study are sufficient to account for variability as shown in Table 3. Thus material studied would help in designing the selection methodology which further be used in breeding program. Hence, purposeful and balanced selection based on these particular characters would be rewarding for improvement in brinjal genotypes and thereby confirmed the past findings (Flory et al., 2017; Kanaujia et al., 2018).

CONCLUSION The present study has shown significant variability and diversity in the brinjal germplasm for yield and yield related characters. From the results based on growth, yield and quality it can be concluded that the genotypes IIVR-31 and IIVR-7 was found promising and on the basis of genetic variability studies suggested that while selection emphasis should be given on fresh weight of fruit, number of fruits plant-1, crop duration, number of leaves and number of branches for increasing yield and crop improvement. An overall observation of qualitative and quantitative characters it is suggested that these genotypes IIVR-31 and IIVR-7 need to be critically analyzed as it performed much better under foothill condition of Nagaland. It would therefore, be rewarding to lay stress on these characters in hybridization program for further improvement of yield and related characters in brinjal.

ACKNOWLEDGEMENT The authors are thankful to Project coordinator, AICRP (vegetable crops), IIVR, Varanasi for providing research materials and financial assistance.

GENETIC VARIABILITY OF BRINJAL 21

REFERENCES Burton, G.W. and DeVane, E.H. (1952). Estimating heritability in tall fesue (Feslucaarundinacea) from replicated clonal material. Agronomy Journal, 45: 418-481. Flory, R., Kanaujia, S.P., Sema, A., Maiti, C.S. and Chaturvedi, H.P. (2017). Genetic diversity analysis in tomato (Solanum lycopersicum) genotypes. Indian Research journal of Genetic and Biotechnology, 9(3): 421-426. Johanson, H.W., Robinson, H.F. and Comstock, R.E. (1955). Estimates of genetic and environmental variability of soybean. Agronomy Journal, 47: 314-318. Kanaujia S.P., Devi, O.B., Jha, A., Ananda, A. and Shah, P. (2018). Genetic variability, characters association and path coefficient analysis of brinjal (Solanum melongena) Genotypes Under Foothill Condition of Nagaland. Biotech Today, 8(2): 85-91. Kanaujia, S.P., Maiti, C.S. and Narayan, R. (2017). Brinjal. Text book of vegetable production. New Delhi: Today and Tomorrow’s Printers and Publishers. Pp. 19-28. Kumar R.S. and Arumugam, T. (2016). Gene action in eggplant landraces and hybrids for yield and Quality Trait. Indian Journal of Farm Sciences, 6(1): 79-89. Panse, V.G. and Sukhatme, P.V. (1978). Statistical methods for agricultural workers. New Delhi: ICAR. Pp. 97-151. Patel, K., Patel, N.B., Patel, A., Rathod, H. and Patel, D. (2015). Study of variability, correlation and path analysis in brinjal (Solanum melongena L.). An International Quarterly Journal of Life Science,10(4): 2037- 2042. Ranganna, S. (1986). Manual of analysis of fruits and vegetables products. Tata McGraw Hill Co. Pvt. Ltd., New Delhi. Shende, R.A., Desai, S.S. and Lachyan, S.T. (2015). Genetic variability and response to selection in brinjal (Solanummelongena L.). Indian Journal of Current Research, 7(10): 21545-21547. Singh, D.K. and Kumar, R. (2002). Studies on the genetic variability in bottle gourd. Progressive Horticulture, 34(1): 99-101.

SAARC J. Agric., 18(1): 23-37 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48379

Research Article SSR MARKERS BASED GENETIC DIVERSITY IN NEPALESE MAIZE LANDRACES

B.K. Joshi1*, J. Rawat2, B. Adhikari1 and R. Pokhrel1 1National Agriculture Genetic Resources Center, NARC; Khumaltar, Kathmandu, Nepal; 2Himalayan College of Agricultural Sciences & Technology (HICAST), Kalanki Kathmandu, Nepal

ABSTRACT Knowledge on genetic diversity is necessary for developing new varieties and managing diversity for future use. Five SSR markers were used to develop the DNA finger prints and to assess the diversity of 23 Nepalese maize landraces. Five locus-based DNA finger prints have distinguished majority of the landraces. The average number of alleles was 2 per locus. Umc1333 marker had shown the highest gene diversity, heterozygosity and polymorphism information content (PIC). At level, the highest gene diversity, heterozygosity and PIC values were found in Seto Local and Seti Makai-3. 23 maize landraces formed four clusters and these clusters were related with seed color. Name of landraces also reflected genetic similarity. Genetically similar landraces can be pooled for conservation and creating dynamic diversity rich population. Distantly related landraces (Bhirkaule, Local Seto Makai, Seto Makai-1, Makai Makai-1761) can be used in breeding program. Detection of low genetic diversity might be due to bottleneck effects during the collection of these landraces from farmers. Therefore, collection strategy needs to be revised for capturing maximum diversity. Keywords: Gel analysis, Gene diversity, Maize landrace, SSR marker, DNA finger print

INTRODUCTION Maize is the second most important staple food crop in Nepal and is grown under a wide range of agro-climatic and ecological conditions both in hill and plain areas under rainfed complex farming system. The development of high yielding varieties and populations of maize always remains in the target of many countries. Exploration and collection of diversity at genetic levels are the prerequisites for maize breeding. Nepal, being climatically and socioeconomically diverse, possesses a large number of

* Corresponding author: [email protected]

Received: 19.04.2020 Accepted: 20.06.2020 24 Joshi et al. different maize landraces adapted from low altitude i.e. 60 AMSL to more than 3000 AMSL (Upadhyay et al., 2003; Joshi et al., 2017; NAGRC, 2019). But they are not given due emphasis on research and education (Joshi et al., 2020), and therefore many of them are at risk of loss from the field. This situation also demands the generation of knowledge on diversity to manage diversity both on-farm and in-situ. Diversity can be measured from the ecosystem to allelic levels using different approaches and markers. DNA based markers are found very reliable and are playing a significant role in maize breeding. Among the DNA markers, SSR marker is robust, co-dominant, hypervariable, abundant, and uniformly dispersed in plant genomes (Powell et al., 1996; Mohan et al., 1997; Senior et al., 1998). These markers have been extensively used for genetic diversity analysis, parental lines selection, , and combining ability studies in maize (George et al., 2004; Nguyen et al., 2012). In an analysis of 45 maize inbreeds involving a set of 22 primer pairs derived from maize genome and 20 primer pairs derived from rice genome generated 132 and 181 markers with an average polymorphism information content (PIC) value of 0.83 and 0.38, respectively (Ranatunga et al., 2009). Clustering pattern of these genotypes based on SSR marker profiles were different from that of morphometric traits (Ranatunga et al., 2009). Among the four important maize varieties in Nepal, 30 SSR markers revealed an average heterozygosity of 45.07% for varieties, ranging from 35.23% in Rampur Composite to 54.64% in Khumal Yellow variety (Gurung et al., 2010). Molecular marker-assisted breeding is contributing significantly to increase the grain yield of maize in USA (Prasanna et al., 2010) but, conventional breeding predominant the maize breeding in Nepal. So far 93 varieties of maize have been released and registered and 520 accessions are being conserved in National Genebank (NAGRC, 2019) but not fully utilized in breeding program (Joshi, 2017). These collections in the Genebank could be valuable resources for advancing the maize breeding for which genetic level information is necessary to generate. Knowledge of genetic variation and relationships among landraces is important to understand the genetic variability available, to estimate the rare genotypes and alleles, to adopt suitable conservation strategies, to select suitable landraces for developing as well as high yielding maize populations. Molecular markers greatly facilitate for understanding genetics of a large number of collections conserved in the Genebank. This research was, therefore, conducted to generate DNA profiles of maize landraces, to assess the genetic diversity, to relate maize landraces (accessions) in terms of their genetic information using SSR markers.

MATERIALS AND METHODS Maize landraces A total of 23 maize landraces from National Genebank, Kathmandu were used in this study. Their passport details are given in Table 1. These were collected from 11 GENETIC DIVERSITY IN NEPALESE MAIZE LANDRACES 25 districts (Ramechhap, Dadeldhura, Dailekh, Pyuthan, Kaski, Dolpa, Bajura, Lalitpur, Nuwakot, Rasuwa and Bhojpur) within an altitudinal range from 60 to 2530 AMSL. These landraces were collected before 2000 and regenerated latter in Khumaltar.

Table 1. Details of maize landraces used in this study

Latitude Longitude Altitude Regeneration SN Landrace Accession Collection site Farmer Name (D) (D) (m) year

1 Sano C2605 Thosey 6, Tapu, 27.596 86.264 1783 Khadga Bahadur 2012 Panhelimakai Ramechhap Karki

2 Makai-1761 NPGR-01761 NK NK NK NK NK NK

3 Mangre 11035 Amargadi-2, NK NK 1835 NK 2008 Dadeldhura

4 Thulochura 11044 Radimadi, Dailekh NK NK 800 NK 2008

5 Seto Local 11054 Dhalea, Pyuthan NK NK 800 NK 2008

6 Makai 11066 Rimal-7, Kaski NK NK 1200 NK 2008

7 Seto Makai-1 C0691 Likhu-4 Likhu, NK NK 2530 NainaPahadi 2011 Dolpa

8 Ratomakai NPGR-01706 Bortan, Bajura NK NK 1720 NK 1985

9 Pahenlo makai-1 C5109 Dalchoki-3, Goth NK NK 1900 Krishna Sanjel 2014 Bhanjyang, Lalitpur

10 Mailisathiyama C5051 Ratamate-1, 27.8575 85.0625 1387 Shiva Kumari Rai 2013 kai Raigaun, Nuwakot

11 Amrikane C4937 Kaule-6, Nuwakot 28.0928 85.2531 1509 Santa Lama 2013 Makai

12 Bhirkaule C5046 Dhaibung-6 27.9997 85.2089 1563 ChitraKumariNeup 2013 Dhaibung, Rasuwa ane

13 Sathiyamakai C5050 Ratamate-1, 27.8575 85.0625 1387 Shiva Kumari Rai 2013 Raigaun, Nuwakot

14 Local Seti C5169 Gupteshwor-8, NK NK 60 Meghraj Shrestha 2014 Makai Bhojpur

15 Dharim Choti C5170 Ranibash-4, Bhojpur NK NK 6o Mitra Maya Rai 2014 Paheli

16 Paheli Makai-2 C5172 Champe-5, Bhojpur NK NK 60 Chhatra Rana 2014 Magar

17 Paheli Makai-3 C5174 Pancha-2, Bhojpur NK NK NK Bahadur Rai 2014

18 Sadiya Makai C5175 Pyauli-4, Bhojpur NK NK NK Kumar Karki 2014

19 Purano Local C5176 Gupteshwor-1, NK NK NK Debi Bhakta Rai 2014 Bhojpur

20 Seti Makai-2 C5177 Mulpani-8, Bhojpur NK NK NK KopilaRasayali 2014

21 Paheli Makai-4 C5178 Mulpani-7, Bhojpur NK NK NK TribhubanTamang 2014

22 Paheli Makai-5 C5179 Bokhim-5, Bhojpur NK NK NK SubasKattel 2014

23 Seti Makai-3 C5180 Bokhim-7, Bhojpur NK NK NK Dambar Shrestha 2014 NK, not known; Some of old collections do not have complete passport information

26 Joshi et al.

DNA extraction, quantification and standard DNA template preparation About 2 g of young and healthy leaf tissue was collected from 20 days old plants. The leaf surface was disinfected with 70% alcohol. The maize leaves were washed in petri-dish with the help of distilled water and dried. Leaves were cut into fine pieces and transferred to micro-centrifuge tubes. TissueLyser (QIAGEN) was taken in the micro-centrifuge tubes. TissueLyser was run for 10 minutes at 50 oscillations per minute. DNA was extracted following the CTAB method. Briefly, 600 µl CTAB (650C pre- heated) and 8µl 2-Mercaptoethanol were added into the micro-centrifuge tube and the tube was inverted several times. Tubes were transferred into a water bath at 650C for 30-45 min. Then the tubes were centrifuged at 15000 rpm for 15 min. The supernatant was pipetted out and transferred into another micro-centrifuge tube and an equal volume of chloroform: alcohol (24:1) was added. After inverting tubes several times, tubes were centrifuged at 15000 rpm for 10 min. The supernatant was transferred to another Eppendorf tube and an equal volume of chloroform: alcohol (24:1) was added. Tubes were inverted several times and centrifuged at 15000 rpm for 10 min. The supernatant was transferred to a fresh tube and 1/3rd volume of chilled iso-propanol was added. The tubes were kept into ice and left for 5 min. Tubes were centrifuged at 15000 rpm for 3 min. Supernatant from the tube was discarded, retaining DNA pellet. DNA was washed with 70% ethanol and separated by centrifuging at 15000 rpm for 2 min. Ethanol was discarded. DNA containing tubes were left for drying. Tris-EDTA buffer (TE) solution (50µl) was added in the tube for the dilution of DNA and was kept in a deep fridge. The concentration of each DNA sample (ng/µl) was measured by Q5000 UV-Vis Spectrophotometer (Quawell). This nanodrop measures the sample in about 8 seconds with a high degree of accuracy and reproducibility. All measured data were then exported to MS Excel for further analysis. The concentrations of these DNA samples were adjusted to either 50 ng or 100 ng/µl by adding TE buffer. The required TE to be added for making standard DNA template solution (and working DNA solution) was calculated using the formula, C1 (starting concentration) x V1 (starting volume) = C2 (final concentration) x V2 (final volume) in MS Excel. DNA amplification and gel electrophoresis Ten SSR primers were selected based on potent to diversity analysis and their linkage with economical traits (e.g. Quality Protein Maize (QPM), drought tolerance, Gray Leaf Spot (GLS) disease resistance). Among them, we found good amplification from only five SSR primers (Table 2) and therefore we used only five for all maize landraces screening. DNA amplification was carried out in 15 µl reaction volumes consisting of 7.5µl 2X Green GoTaq® Reaction GENETIC DIVERSITY IN NEPALESE MAIZE LANDRACES 27

Table 2. Details of SSR primers used in this study

Ta Repeat SN Primer Sequence Chr Feature Reference (Tm), oC motifs 1 Bnlg1258 F: GGTGAGATCGTCAGGGAAAA 2 49 (53) AG (24) Related to Danson et R: GAGAAGGAACCTGATGCTGC GLS disease al., 2008 resistance 2 Phi057 F: CTCATCAGTGCCGTCGTCCAT 7 45 (56) GCC Related to Jompuk et R: CAGTCGCAAGAAACCGTTGCC opaque-2 al., 2006 and QPM 3 Bnlg1194 F: GCGTTATTAAGGCAAGCTGC 8 58 (52) AG (33) Related to Krishna et R: ACGTGAAGCAGAGGATCCAT QPM al., 2012 4 Phi031 F: GCAACAGGTTACATGAGCTGACGA 6 45 (57) GTAC Used in Shiri, R: CCAGCGTGCTGTTCCAGTAGTT drought 2011 study 5 Umc1333 F: AGGTAAGCGAGCATCTGAGGGT 7 46 (57) (CAG)4 Used in Shiri, R: TCTGGAGACTCTTCTGGGTGAACT drought 2011 study QPM, quality protein maize; GLS, gray leaf spot; Chr, Chromosome number. Ta, annealing temperature; Tm, melting temperature

Master Mix, 1.5µl forward and reverse primers each (10 picomole of each primer), 2.5µl nuclease-free water and 2µl DNA (40 ng DNA). The amplification reaction was carried out in a Thermal Cycler (MultiGene OptiMax, Labnet International, Inc.). The basic PCR program was as follows: an initial hot start and denaturing step at 94oC for 4 min followed by 30 cycles of a 1 min denaturation at 94oC, annealing temperature (depending upon primer used) for 1 min and extension at 72oC for 2 min. A final extension step at 72°C for 7 min was performed and stored at 4oC. Annealing temperature (Ta) was determined 3-5oC less than the primer’s melting temperature (Tm). Tm of the primer was calculated using Oligo calculator (http://mcb.berkeley.edu/labs/krantz/tools/oligocalc.html). PCR products were separated on a 2% agarose gel using 1x TAE buffer (2 g agarose in 100 ml 1x TAE buffer). Ethidium Bromide (EtBr) was used both in the gel and buffer for DNA staining. After cooling of the gel to 60-70°C, EtBr at a concentration of 0.5 µg ml-1 was added to the gel. The stock concentration of EtBr was 10 mg ml-1, and therefore, 5 µl stock solution (10 mg ml-1) was used for 100 ml gel. Same concentration was also used in buffer solution i.e. 5 µl stock solution for 100 ml buffer. 12 µl PCR product of each sample was loaded in each well. In the first lane, 2 µl 100 bp ladder (HiMedia) was loaded. The gels were run for 2 h at 90 V, 50 mA, and 5 W. Upon completion of the run, gels were kept in water for 5 min and placed in plastic wrap. DNA fragments were visualized under UV light and photographed using a Gel Doc system (UVDI, Major Science).

28 Joshi et al.

Gel processing, scoring and analysis Multiple photos were taken from a single gel in the Gel Doc System. The good quality gel was then selected for each set of gels. Gel was analyzed with the method called Photoshop-Excel assisted gel analysis (McDaniel and Pillai, 2002). In this method, gel image was cropped and sharpness, brightness, and contrast were adjusted in Adobe Photoshop. DNA ladder was labeled and distance travel by each band from well position was measured in mm by using Photoshop. We also printed out some gels and measured the fragment size of the ladder as per the company information to crosscheck. Then, the migration distances were measured using a scale. We found Photoshop easier and accurate. In a similar way, migration distances of each band of samples were measured and recorded. Molecular weight and distance of each band of the ladder were entered in MS Excel. DNA ladder standard (DLS) curve was generated from ladder bands’ weight and distance. There is a linear relation between LOG (DNA size) and distance (in mm). Therefore, molecular weights were transformed to LOG to base 10. Scatter plot along with trend line and equation were drawn in MS Excel between log-transformed fragment weights and migrated distances. Using this equation, fragment sizes of other bands were estimated. This equation gives the size in LOG to the base 10, and therefore, the actual size of the fragment in bp was antilog of that equation value. This estimated value was further double checked to compare with ladder size in the gel. Using a straight line in Photoshop and based on fragment size and distance, the number of alleles was estimated in the first gel. Alleles identified in the first gel were first detected in other gels of the same primer for remaining samples and if new alleles found, its size was then recorded following the above procedure. Two-way table was prepared using samples as row name and primer as column name. Actual bp (genetic allele size) was recorded either in homozygous or heterozygous state. This table was formatted as per the requirement of software and then analyzed. DNA finger print of each sample with multiple markers was prepared in MS Excel based on fragment sizes and distances. Summary statistics for primers and maize landraces were estimated along with cluster and principal coordinate analyses in GenAlEx6.5 and Power Marker 3.2. Dendrogram was viewed in Tree View.

RESULTS AND DISCUSSION SSR profiles Five primers amplified the DNA of 23 maize landraces. None of the single primers could differentiate all maize landraces. SSR profile of Bnlg1194 marker is shown in Fig 1. GENETIC DIVERSITY IN NEPALESE MAIZE LANDRACES 29

Figure 1. SSR profile of 23 maize landraces with Bnlg1194 SSR marker in agarose gel M, 100 bp marker; Lane number is indicated by the number which corresponds with landraces listed in Table 1

Most of the loci are heterozygotes. Seto Makai-1, Paheli Makai-2 and Seti Makai-3 had homozygote for Bnlg1194 marker. Multilocus DNA fingerprint distinguished the many maize landraces from each other (Fig. 2). Rato Makai and Bhirkaule had larger size alleles compare to Mangre Makai and Seto Makai-1. Rato Makai had heterozygotes for all loci except for Bnlg1258.

A. Mangre Makai B. Seto Makai-1 C. Rato Makai D. Bhirkaule 550 500 450 400 350 bp 300 250 200 150 100

Figure 2. DNA fingerprints of four maize landraces based on five SSR markers

DNA fingerprints are the most reliable testing and identification means. Fingerprint with a single SSR primer might not be always useful and powerful for discriminating genotypes. Therefore, we generated multi locus SSR markers-based fingerprints. Based on these allele size patterns, it will also be useful to select multiple traits related to genotypes. Status of loci e.g. whether heterozygotes or homozygotes can easily be determined and based on which further breeding and conservation works can be planned. DNA fingerprint has been used as a ready reference for identification of maize hybrids and authentication of genetic purity of different seed lots (Singh et al., 2014). SSR markers diversity The size of amplified bands ranged from 140 bp by Bnlg1194 to 500 bp by Phi031 marker (Table 3). All five markers revealed only 2 alleles per locus. The major allele frequency of Bnlg1258 was the highest followed by Phi057. The frequency of 400 bp allele of Bnlg1258 was the lowest (Fig. 3).

30 Joshi et al.

Figure 3. Allele frequency of five SSR markers based on 23 maize landraces

The highest heterozygosity and gene diversity were shown in Umc1333 whereas Bnlg1258 had shown the lowest heterozygosity as well as the lowest gene diversity. The average Shannon information index (I) and polymorphism information content were 0.637 and 0.345, respectively. The Shannon index and PIC of Umc1333 were the highest whereas these values of Bnlg1258 were the lowest. All the diversity measures of Umc1333 were the highest and of Bnlg1258 the lowest.

Table 3. SSR markers summary statistics based on 23 maize landraces Expected Shannon Major Observed SSR Allele Allele/ Heterozygosity informati Allele Heterozyg PIC Marker size, bp locus, n (genediversity) on index Frequency osity (Ho) (He) (I) Bnlg1258 300, 400 2 0.761 0.478 0.364 0.550 0.298 Phi057 180, 230 2 0.652 0.696 0.454 0.646 0.351 Bnlg1194 140, 180 2 0.630 0.739 0.466 0.659 0.357 Phi031 300, 500 2 0.630 0.739 0.466 0.659 0.357 Umc1333 180, 350 2 0.609 0.783 0.476 0.669 0.363 Mean 2 0.657 0.687 0.445 0.637 0.345 bp, base pair; PIC, polymorphism information content

DNA finger prints are commonly used to assess the genetic diversity among the studied genotypes. We observed narrow genetic diversity in terms of different genetical statistics e.g. allele per locus, allele frequency, gene diversity, heterozygosity, and PIC. Thee landraces were collected from different districts, agroecology, and altitudes, though, diversity is relatively captured low. This might be because of the small amount of seed samples during collection and/or controlled pollination in small plot and small populations during regeneration in Genebank. In addition, farmers in Nepal generally saved seed themselves and this might be resulted in narrowing the genetic diversity within landraces. This is also supported by observing some homozygous loci, which is not commonly assumed in highly cross- GENETIC DIVERSITY IN NEPALESE MAIZE LANDRACES 31 pollinated crops like maize. This narrow genetic background with some homozygous loci is expected to contribute to the low grain yield of these maize landraces. In addition, the small number of SSR markers used in this study could not capture total diversity. Maize landraces diversity Six landraces have shown allele sizes ranging from 140bp to 400bp. Rest of the landraces have allele sizes ranging from 140 to 500 bp (Table 4). The average allele per locus was 1.69. Seto Local and Seti Makai-3 have 2 alleles per locus whereas

Table 4. Summary genetic statistics of maize landraces based on 5 SSR markers Allele size Allele/ Major Allele Gene Heterozygo SN Landrace PIC range, bp locus, n Frequency Diversity sity 1 Sano-Paheli 140-500 1.8 0.6 0.4 0.8 0.300 2 Makai-1761 140-500 1.6 0.7 0.3 0.6 0.225 3 Mangre 140-400 1.6 0.7 0.3 0.6 0.225 4 Thulochura 140-500 1.4 0.8 0.2 0.4 0.150 5 Seto-local 140-400 2 0.5 0.5 1 0.375 6 Makai 140-500 1.6 0.7 0.3 0.6 0.225 7 Seto-Makai-1 140-400 1.6 0.7 0.3 0.6 0.225 8 Rato-Makai 140-500 1.6 0.7 0.3 0.6 0.225 9 Pahelo-Makai-1 140-500 1.8 0.6 0.4 0.8 0.300 10 Mailisathyan-Makai 140-500 1.8 0.6 0.4 0.8 0.300 11 Amrikane-Makai 140-500 1.8 0.6 0.4 0.8 0.300 12 Bhirkaule 140-500 1.8 0.6 0.4 0.8 0.300 13 Sathya-Makai 140-500 1.8 0.6 0.4 0.8 0.300 14 Local-Seto-Makai 140-400 1.6 0.7 0.3 0.6 0.225 15 Dharim-Choti 140-500 1.8 0.6 0.4 0.8 0.300 16 Pahelo-Makai-2 140-500 1.6 0.7 0.3 0.6 0.225 17 Pahelo-Makai-3 140-500 1.8 0.6 0.4 0.8 0.300 18 Sadiya-Makai 140-500 1.6 0.7 0.3 0.6 0.225 19 Purano-Local 140-500 1.6 0.7 0.3 0.6 0.225 20 Seti-Makai-2 140-400 1.6 0.7 0.3 0.6 0.225 21 Paheli-Makai-4 140-500 1.6 0.7 0.3 0.6 0.225 22 Paheli-Makai-5 140-500 1.4 0.8 0.2 0.4 0.150 23 Seti-Makai-3 140-400 2 0.5 0.5 1 0.375 Average 1.69 0.66 0.34 0.69 0.258 bp, base pair; PIC, polymorphism information content

32 Joshi et al.

Thulochura and Paheli Makai-5 have just 1.4 alleles per locus. Major allele frequency of these two landraces (Thulochura and Paheli Makai-5) was the highest whereas the lowest major allele frequency was found in Seto Local and Seti Makai-3. The average values for gene diversity, heterozygosity, and PIC were 0.34, 0.69, and 0.258, respectively. The lowest genetic diversity measures (gene diversity, heterozygosity and PIC) were observed in Thulochura and Paheli Makai-5. The highest gene diversity, heterozygosity and PIC values were found in Seto Local and Seti Makai-3. It is very common to report the diversity of each marker. But diversity over landraces combining all studied markers would be more useful to directly pick up the landraces for various purposes. We have found in Seto Local and Seti Makai-3 more diverse in comparison to other landraces. Some of these landraces have similar values indicating genetic similarity and potential candidates for pooling together for conservation and mixing populations. There are a number of studies that revealed similar genetic statistics. Gurung et al. (2010) reported 45.07% average heterozygosity at the variety level, with a maximum heterozygosity of 54.64% in Arun-4 variety. Phi109188 showed the maximum heterozygosity (81.03%). Khumal Yellow showed the highest gene diversity (0.56). The gene diversity at the variety level ranged from 0.51 in Manakamana-2 to 0.56 in Khumal Yellow and Arun-4. Reif et al. (2003)reported the gene diversity of 0.56 in tropical maize population. PIC for 34 SSRs on QPM was found from 0.50 to 0.95. The highest PIC was of bnlg1401 and the lowest of bnlg1506 (Krishna et al., 2012). A total 19 polymorphic alleles were found in 8 maize genotypes using 9 polymorphic SSR primers with 0.297 PIC and 0.373 gene diversity values (Kumari et al., 2018). The phi064 and phi053 were found as the best marker for identification of genotypes as revealed by PIC values (0.367). A total of 40 alleles (bands) with a mean of 3.33 alleles per locus were detected in 38 maize hybrids using 12 SSR markers (Shiri, 2011). Polymorphism information content (PIC) of the 12 SSR loci ranged from 0.23 (Phi080) to 0.79 (UMC2359), with a mean PIC of 0.53. Genetic relatedness among maize landraces Based on genetic distance, 23 maize landraces made 4 clusters (Fig 4). GENETIC DIVERSITY IN NEPALESE MAIZE LANDRACES 33

Mangre Local-Seto-Makai Seto-Makai-1 Seto-local I Seti-Makai-2 Seti-Makai-3 Sadiya-Makai Dharim-Choti II Purano-Local Makai-1761 Bhirkaule III Makai Mailisathyan-Makai Thulochura Sathya-Makai Amrikane-Makai Paheli-Makai-4 Paheli-Makai-5 IV Pahelo-Makai-1 Pahelo-Makai-2 Pahelo-Makai-3 Rato-Makai Sano-Paheli Figure 4. Clustering of 23 maize landraces based on 5 SSR markers using UPGMA method

Mantel test was used to analyze the ‘goodness of fit’ for the UPGMA dendrogram. Mantel test indicated the clustering of these landraces is reliable with cophenetic correlation value of 0.95. Cluster I consisted of 6 landraces, cluster II and III each consisted of 3 landraces and cluster IV has included 11 landraces. Clusters were related to seed color or local name. For example, landraces with Seto (white seed) name clustered together and name associated with Pahelo formed a separate cluster. Most closely related landraces were between Local Seto Makai and Seto Makai-1, Seti Makai-2 and Seti Makai-3, Sano-Paheli and Rato Makai. Most distantly related landraces were of Bhirkaule with Local Seto Makai and Seto Makai-1, of Makai with Local Seto Makai and Seto Makai-1, and of Makai-1761 with, Local Seto Makai and Seto Makai-1. Clustering of landraces is practically more useful to see the genetic relatedness and to select landraces in breeding and conservation programs. Two principal coordinates are sufficiently separated these 23 maize accessions into four groups as did by cluster analysis. We found that name of landraces also reflected some genetic similarity. In Nepal, farmers usually give names to landraces based on seed color and size. White seed colored landraces are grouped together and similarly, yellow seed colored landraces made a separate cluster. Many landraces in this study have shown very similar at genetic levels indicating the potentiality of pooling together for reducing the number of accessions to be managed in the Genebank. On the other hand, genetically dissimilar landraces can be selected for developing a new population as well as a composite or mixture population. It is common to observe the cluster in SSR markers-based study. By using UPGMA cluster analysis, the eight genotypes were grouped under 3 clusters (Kumari et al., 2018) based on the genetic distances ranging from 0.21 to 0.64.

34 Joshi et al.

Clustering of these landraces is also equally explained by two principal coordinates. Four groups were formed in scatter plot of two principal coordinates (Fig 5). First and second PCs accounted for 52 and 29% of the total variance. Collection districts of each landrace are indexed on top of the Fig 5.

Figure 5. Principal coordinate analysis of 23 maize landraces using five SSR markers The legend on the top indicates the collection districts. NK, not known

The geographical association was not found among the landraces collected from Nuwakot and Bhojpur. Only a single landrace was collected from the rest of the districts. Majority of these landraces were found similar and therefore, plotted in the same point e.g. Sadiya Makai, Dharim Choti and Purano Local. We have also given due priority to those markers that have linked with some economical traits. Such markers are very useful for identifying desired genotypes. The ultimate action of DNA markers is to accelerate the breeding through adopting marker assisted selection (MAS). MAS in maize is being used increasingly. SSR markers, namely UMC1862, UMC1719, UMC1447, UMC2359, and UMC1432, have significantly contributed to the differentiation of the drought-tolerant and susceptible genotypes (Shiri, 2011). The putative QTLs for GLS have been reported in association with two SSR markers (bnlg1258 and umc2019) in chromosome 2 and for common rust, the associated SSR markers were phi054, umc1319, and bnlg1451 GENETIC DIVERSITY IN NEPALESE MAIZE LANDRACES 35 in chromosome 10 (Danson et al., 2008). SSR marker has become useful on selecting opaque 2 gene in maize genotypes, therefore, there is no need of routine biochemical analysis for high lysine and tryptophan levels at each backcross populations or any other segregating lines (Danson et al., 2006). Phi057 was considered more feasible than phi112 as a marker assisted selection (MAS) for opaque-2 and to identify QPM line in the short period of time (Jompuk et al., 2006). Several thousand SSR markers of maize genomes are available in the public domain (Maize GDB; http://www.maizegdb.org). In addition to diversity related markers, MAS can be effectively applied in maize breeding using economically important traits linked markers that are available on the Maize GDB website. This online information as well as diversity reported here can greatly help to manage and utilize Nepalese maize landraces.

CONCLUSION Five SSR markers amplified the DNA of all 23 maize landraces with almost similar fragment sizes. Multiple loci-based DNA fingerprints were more discriminating power. Genetic diversity in this study was low even though landraces were collected from different districts and altitudes. Genetically similar landraces can be considered as duplicates and should be merged to maintain the long run in the genebank. Maize is a cross-pollinated crop; however, all the loci were not heterozygotes. Low genetic diversity may be due to the low number of SSR markers as well as bottleneck effects during collection from farmers. Additional markers need to screen for further validating the similarity among the landraces along with redefining the collection strategy and method.

ACKNOWLEDGMENTS We thank National Genebank for providing seeds of maize landraces. Nepal Agricultural Research Council supported financially for this study.

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Jompuk, P., Wongyai, W., Jampatong, C. and Apisitvanich, S. (2006). Detection of Quality Protein Maize (QPM) using Simple Sequence Repeat (SSR) Markers and Analysis of Tryptophan Content in Endosperm. Kasetsart Journal of Social Sciences, 40: 768 – 774. Joshi, B.K., Acharya, A., Gauchan, D. and Bhatta, M. (2017). Agrobiodiversity status and conservation options and methods. In: Joshi, B. K., H. B. KC & A. K. Acharya (eds.), Conservation and Utilization of Agricultural Plant Genetic Resources in Nepal. Proceedings of 2nd National Workshop, 22-23 May 2017, Dhulikhel. NAGRC, FDD, DoA and MoAD, Kathmandu, Nepal. Pp. 21–38. Joshi, B.K. (2017). Plant breeding in Nepal: Past, present and future. Journal of Agriculture and Forestry University, 1:1-33. Joshi, B.K., Gorkhali, N.A., Pradhan, N., Ghimire, K.H., Gotame, T.P., Kc, P., Mainali, R.P., Karkee, A. and Paneru, R.B. (2020). Agrobiodiversity and its Conservation in Nepal. Journal of Nepal Agricultural Research Council, 6: 14–33. Krishna, M.S.R., Sokka Reddy, S., Rani, C., Reddy, T. and Jabeen, F. (2012). Molecular Genetic diversity analysis of Quality Protein Maize lines using SSR markers. The Andhra Agicultural Journal, 59: 533–537. Kumari, A., Sinha, S., Rashmi, K., Mandal, S.S. and Sahay, S. (2018). Genetic diversity analysis in maize (Zea mays L.) using SSR markers. Journal of Pharmacognosy and Phytochemistry, 7: 1116–1120. McDaniel, J. and S. D. Pillai. (2002). Gel alignment and band scoring for DNA fingerprinting using Adobe Photoshop. BioTechniques, 32:120-123. Mohan, M., Nair, S., Bhagwat, A., Krishna, T.G., Yano, M., Bhatia, C.R. and Sasaki, T. (1997). Genome mapping, molecular markers and marker-assisted selection in crop plants. Molecular Breeding, 3: 87–103. NAGRC, (2019). Annual report 2075/76 (2018/19). Khumaltar, Kathmandu, National Agriculture Genetic Resources Center, NARC. Nguyen, T.V., Doan, T.T.B., Leo, A.E., Bui, C.M., Taylor, P.W.J. and Ford, R. (2012). Application of Microsatellite Markers to Fingerprint and Determine the Representational Diversity within a Recently Established Elite Maize Inbred Line Breeding Program. Journal of Agricultural Science, 4: 258–266. Powell, W., Morgante, M., Andre, C., Hanafey, M., Vogel, J., Tingey, S. and Rafalski, A. (1996). The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Molecular Breeding, 2: 225–238. Prasanna, B., Pixley, K., Warburton, M. and Xie, C. (2010). Molecular marker-assisted breeding options for maize improvement in Asia. Molecular Breeding, 26: 339–356. Ranatunga, M.A.B., Meenakshisundaram, P., Arumugachamy, S. and Maheswaran, M. (2009). Genetic diversity analysis of maize (Zea mays L.) inbreds determined with morphometric traits and simple sequence repeat markers. Maydica, 54: 113–123. Reif, J.C., Melchinger, A.E., Xia, X.C., Warburton, M.L., Hoisington, D.A., Vasal, S.K., Srinivasan, G., Bohn, M. and Frisch, M. (2003). Genetic Distance Based on Simple Sequence Repeats and Heterosis in Tropical Maize Populations. Crop Science, 43: 1275–1282. GENETIC DIVERSITY IN NEPALESE MAIZE LANDRACES 37

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SAARC J. Agric., 18(1): 39-49 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48380

Research Article ROLE OF SALICYLIC ACID ON GROWTH, YIELD, QUALITYAND DISEASE PEST REACTION OF ONION (ALLIUM CEPAL.) CV. AGRIFOUND LIGHT RED

P. Bhasker*, P.K. Gupta and H.P. Sharma Department of Plant Physiology, National Horticultural Research and Development Foundation, Chitegaon Phata, Nashik, Maharashtra, India

ABSTRACT Salicylic acid (SA) is endogenous naturally occurring plant growth hormone acting as an important signaling molecule adds tolerance against abiotic stress. A field experiment was conducted to assess the efficacy of exogenous application of SA on growth, yield and storage performances of onion during Rabi 2012-13, 2013-14 and 2014-15. The experiment was comprised of 6 different treatments of SA including control. Exogenous applications of all SA treatments significantly influenced plant growth and development. The treatment application of SA at 30 days after seed sowing and second spray at 30 days after transplanting and third spray at 60 days after transplanting performed superior in terms of growth, development and yield. Exogenous application of SA significantly influenced on thrips population and stemphylium blight disease incidence and intensity. The results also revealed that SA partially involved in post-harvest management of onion. Keywords: Foliar application, Insect, Onion, Post-harvest storage, Salicylic acid

INTRODUCTION Onion (Allium cepa L.) is one of the most important commercial vegetable crops grown in India. The highest foreign exchange earner with higher medicinal as well as consumption values (Bhasker et al., 2018) among all the vegetable crops. India ranks second in area as well as in production in the world after China. Thearea and production of onion in India is 11,81,000 hectares and 18,9,24,000 tons, respectively, with an average yield of 16 t ha-1 (National Horticultural Board, 2017). The lower productivity is due to cultivation of low yielding potential of open pollinated conventional varieties which are susceptible to biotic factors and includes bacteria,

* Corresponding author: [email protected]

Received: 13.02.2019 Accepted: 26.04.2019 40 Bhasker et al. fungi, viruses, insects, and abiotic factors i.e. drought, salinity, heat, cold and heavy metal stress. Plants have evolved broad and efficient self-mechanisms to cope with these continuous adverse challenges and to obtain an adequate defense mechanism against biotic and abiotic constrains (Nazar et al., 2011). Plants have a prominent defensive response against biotic (pathogen) attack by the synthesis of low molecular compounds with disparate functions in pathogen interactions of plant (Dixon, 2001). Salicylic Acid (SA) (2-hydroxybenzoic acid), is recently included in the category of plant growth hormones. It is an important signal molecule belonging to an extra ordinary diverse group of plant phenolic compound synthesized under different biotic and abiotic stresses (Vlot et al., 2009; Bideshki and Arvin, 2010; Miura and Tada, 2014; Shama et al., 2016). SA is distributed in wide range of plant species and act as a defensive signal that is essential for elicitor triggered immunity and the establishment of Systematic Acquired Resistance (SAR) by inducing particular enzymes involved in biosynthetic reactions (Carr et al., 2010). Thus, SA is an endogenous growth regulator, which participates in regulation of several physiological processes in crop plants viz., closing of stomata, induction of flower, ion uptake, inhibition of ethylene biosynthesis and transpiration (Khan et al., 2003; Hayat et al., 2009) and it also involved in reverses the effects of ABA on leaf abscission by this ameliorates the growth of crop.SA promotes resistance against several viral, fungal and bacterial pathogens in various crops viz. tomato (He and Zhu, 2008; Pandey and Gupta, 2012) and carrot (Hayat et al., 2009) with a diverse way of defense strategies in which SA play a core role and is the key element in both local and systemic defense, but limited information is available about SA effects on purple blotch (Alternaria porii) and stemphylium blight (Stemphylium vesicarium) in onion. In addition to its role towards biotic stresses, SA is also proved believed to play a key role in plant responses to many abiotic stresses such as heat stress in mustard (Dat et al., 1998), ozone in turnip (Kachroo et al., 2000), drought stress in (Singh and Usha, 2003), mustard (Nazara et al., 2015), soybean (Nasrin et al., 2017) and chilling stress in peach (Zhang et al., 2017). The earlier studies have reported that foliar application of SA play a significant role in plant water relations, photosynthesis (Habibi, 2012; Nazara et al., 2015) including increases in yield in several crops like garlic (Bideshki and Arvin, 2010), brinjal (Gawade and Sirohi, 2011) and tomato (Nigar et al., 2017). There is a limited literature is available of SA role on one of the most important export oriented onion crop. The pre harvest sprays of chemicals such as maleic hydrazide, ethrel, cycocel, carbendazim, benomyl, streptocycline treatments widely applied without impairing the keeping quality of onion. Pre harvest foliar application of these chemicals has gained prominence and facilitates the maintenance of quality of onion bulbs on storage with respect to inhibition of sprouting, rotting and reduction in the physiological loss in weight. Therefore, the present experiment focused on the effect of foliar application of SA on plant growth, yield, disease and insect resistance and also an attempt was made to study the role of SA in post-harvest storage of onion. ROLE OF SALICYLIC ACID ON ONION YIELD 41

MATERIALS AND METHODS A field trial was conducted to assess the influence of salicylic acid (SA) on the production of onion at the research farm of National Horticultural Research and Development Foundation (NHRDF), Nashik, Maharashtra, India during Rabi, 2012- 13, 2013-14 and 2014-15 under “All India Network Research Project on Onion and Garlic”. The field is situated at 20’. N latitude and 78.73º 57’ E longitude and 492 meter above mean sea level. The fertility status of experimental plot soil belongs to deep heavy clay with pH (7.60), electrical conductivity (0.133 ds m-1), organic carbon (0.75 mg g-1), available nitrogen (374.0 kg ha-1), available phosphorus (49.05 kg ha- 1), available potassium (414.4 kg ha-1), available sulphur (19.77 kg ha-1), available Fe (14.12 kg ha-1), available Mn (10.83 mg kg-1), available Zn (0.911 mg kg-1), available Cu (1.361 mg kg-1) and available B (0.63 mg kg-1). The water holding capacity at saturation was 62.8%, field capacity 38.9% and permanent wilting point was 24.6% on volume basis. During experimental period of three consecutive years meteorological data are listed in Fig. 1.

Temp. Max.(°C) Temp. Min.(°C) RH Max. (%) 30 160 140 25

120

C

° 20

100 Rainfall Rainfall 15 80

60

Temperatute Temperatute 10 (mm) 40 (%) & RH 5 20 0 0 Dec-12 Feb-13 Dec -13 Feb-14 Dec-14 Feb-15 …

Figure 1. Agro-meteorological data during Rabi, 2012-13, 2013-14 and 2014-15 crop growing period

Nursery preparation and transplanting in experimental plot The Agrifound Light Red onion variety developed by NHRDF were sown in the nursery beds in the month of October during three years, the size of the nursery bed was 5 m length, 1 m width and 15 cm height under broad based furrow system with drip irrigation system. The seeds were sown in lines at a distance of 5 cm at a depth of 2 cm. The seedlings were sprayed with SA with the concentration 250 mg lof-1 water at 30 days after sowing (DAS), except the control plot. This concentration is more effective on onion crop has been standardized by All India Network Research Project on Onion and Garlic. About 55 days old seedling of 15-25 cm in height were 42 Bhasker et al. transplanted in the main field with bed size 6.0 m × 1.2 m laid out in Randomized Block Design in 4 replications under drip irrigation system as the method of Bhasker et al., (2018) on dated 12.12.2013 during 1st year, 15.12.2014 during 2nd year and 18.12.2015 during 3rd year.

The experiment was comprised of six different treatments of SA i.e. T1 -Foliar application of SA at 30 DAS and second spray at 30 days after transplanting (DAT); T2 - Foliar application of SA at 30 DAS and second spray at 45 DAT; T3 - Foliar application of SA at 30 DAS and second spray at 60 DAT; T4 - Foliar application of SA at 30 DAS and second spray at 30 DAT and third spray at 45 DAT; T5 - Foliar application of SA at 30 DAS and second spray at 30 DAT and third spray at 60 DAT and T6 - Control (Water spray only).All the recommended package of practices was adapted uniformly to all the treatments as and when required. The observations were made on various growth, yield, quality, disease and insect. The bulbs were kept for post-harvest study under ambient condition for five months. The storage observations were made on physiological loss weight (PLW), sprouting, rotting and total losses.The data was recorded from 30 days after storage to 150 days and calculated as with total weight of stored bulbs, the cumulative loss in weight of bulbs were calculated and expressed as percent.

RESULTS AND DISCUSSION Influence of exogenous application of SA on morphological parameters in onion Exogenous spray of SA at different stages of onion significantly influenced the growth parameters as evidenced by the highest plant height and number of leaves (Table 1). Foliar application of SA three times at 30 DAS and second sprays at 30 DAT and third spray at 60 DAT increased plant height (66.98 cm) and number of leaves (8.95 plant-1) might be attributed to SA involved in increased photosynthetic activity which enhances the number of leaves per plant and chlorophyll content thereby plant growth and the results were found at par with foliar application of SA at 30 DAS and second spray at 60 DAT, and foliar application of SA at 30 DAS and second spray at 30 DAT and third spray at 45 DAT. These results are in agreement with the results of Amin et al. (2007). Similar results were obtained earlier in different crops like brinjal (Gawade and Sirohi, 2011), tomato (Niga ret al., 2017) and soybean (Nasrin et al., 2017). In untreated control lowest plant height (64.52 cm), numbers of leaves (8.68 plant-1) with lowest neck thickness (1.40 cm) were recorded, indicating that SA played a role in increasing the growth and development of plant by enhancing SAR within the plant due to more SA accumulated by the application of SA at 30 days interval three times. Influence of exogenous application of SA on production of dry matter Significant influence of exogenous application of SA on dry matter yield was observed in all SA treatments as compared to control. The increase in dry weight ROLE OF SALICYLIC ACID ON ONION YIELD 43 might be attributed to the increase in plant height and number leaves leading to increase in photosynthetic process causing highest dry weight of leaves (13.92 q ha- 1), dry weight of bulbs (33.14 q ha-1) and total dry biomass (47.06 q ha-1) recorded in higher rates of SA than lower rate of spraying, which might be due to better efficacy of SA in onion at higher rate of spraying which enhanced the utilization of resources and better light interception. The SA treatment also increases the TSS content as compared with control. Significantly highest TSS content (11.96%) was recorded in SA application at 30 DAS and second spray at 30 DAT and third spray at 45 DAT and found at par with SA application at 30 DAS and second spray at 30 DAT and third spray at 60 DAT, the increase in TSS content may be due to influence on sugar metabolism in plant by increasing enzyme activity of α- amylase and nitrate reductase, which accelerate the sugar translocation from the leaves to economic parts of the plant (Farouk and Osman, 2011). The results are in agreement with earlier report (Bideshki et al., 2013). Influence of exogenous application of SA on pests and diseases Among the various foliar diseases of onion, stemphylium blight (Stemphylium vesicarium) is the most destructive diseases causing severe yield reduction. At 70 DAT the lowest stemphylium blight incidence and intensity were recorded in the treatment of SA, whereas one more destructive diseases of onion i.e. purple blotch did not appear. The incidence of stemphylium blight ranged from 9.0% to 21.0% and intensity varied between 0.36% and0.84%. The lowest disease incidence and intensity were recorded in three times SA application. Less infection of stemphylium blight was recorded in all SA treatments, induces the resistance to S. vesicarium than the control, which might be due to induction of disease resistance towards SAR against pathogens by more accumulation of SA (Shang et al., 2011; Wu et al., 2012). The similar results were recorded in tomato (Pandey and Gupta, 2012; Falconi et al., 2013). Thrips (Thripstabaci Linderman) is most common serious destructive pest of onion. The impact of exogenous application of SA towards onion thrips infestation under field condition at different stages of crop growth revealed significant reduction in thrips population by application of SA at three times of spraying than two times. Entire cropping period lowest mean thrips population (18.45 nymphs plant-1) was recorded in three times SA application, which might be due to application of second and third spray of SA at 45 and 60 DAT, effectively reduced the thrips infestation (Fig. 2) and might further induce the resistance of plants, while highest thrips population was recorded at 70 DAT in all the treatments. 44 Bhasker et al.

N 30 DAT N 50 DAT N 70 DAT N 90 DAT DI (%) DINT (%) 50 25

40 20

30 15

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DAT 10 5 70 at (%) DINT & DI

0 0 T1 T2 T3 T4 T5 T6 Treatments Figure 2. Effect of foliar application of salicylic acid on pest and disease reactionof onion under field conditions * DI – Disease Incidence; DINT – Disease Intensity Influence of exogenous application of SA on yield attributing characters SA had significant influence on bulb growth and yield attributing parameters. The highest equatorial bulb diameter (5.58 cm), polar bulb diameter (4.03 cm) recorded in exogenous SA application with higher times, where equatorial bulb diameter was found at par with all the treatments except control. The increase in bulb size might be due to better photosynthetic efficacy with increased dry matter content of leaves and bulbs facilitating to better allocation of photosynthates from source towards the economic part of sink, hence higher gross yield (304.59 q ha-1) as well as marketable yield (271.08 q ha-1) with highest cost: benefit ratio (1.0: 4.84) were recorded in three times SA application (Table 2). The results of highest dry matter of leaves (13.92 q ha-1), bulbs (33.14 q ha-1) and total dry matter (47.06 q ha-1), and lower disease and pest infestation showed positive relation with yield parameters. Due to better efficacy of SA highest a grade bulb (4.0 – 6.0 cm) i.e. 60. 13%, B grade bulb (3.5 – 4.0 cm) (32.08%) and lowest C grade bulbs (2.0 – 3.5 cm) (7.85%) were recorded in three times SA application at 30 DAS and second spray at 30 DAT and third spray at 60 DAT over control. The A grade bulb showed at par results with SA application at 30 DAS and second spray at 30 DAT and third spray at 45 DAT. The grading of bulb based on size; it increases the market profit. The foliar application of SA might be due to enhanced uptake of NPKS nutrients, improved flow of photosynthesis assimilates and cell integrity which in term reflected increased bulb weight (60.56 g)The lowest bolting (0.35%) was recorded in SA application at 30 DAS and second spray at 30 DAT and third spray at 45 DAT and doubles (3.35%) in SA application at 30 DAS and second spray at 45 DAT indicates that foliar application of SA had ROLE OF SALICYLIC ACID ON ONION YIELD 45 significant effect on per cent reduction of bolters and doubles over the control. The results of yield parameters are in line with the results already reported in onion (Amin et al., 2007) garlic (Bideshki and Arvin, 2010) and soybean (Nasrin et al., 2017). Influence of exogenous application of SA on post-harvest storage losses Onion storage is one of the most important aspects for post-harvest handling. The storage quality of onion depends on the total dry matter content and total loss in weight at the time of marketing. Pre harvest application of SA towards post-harvest storage losses significantly reduced the losses (Fig. 3 a, b, c, d). During five months storage period comparatively lowest sprouting loss (5.64%), rotting loss (26.20%), physiological loss in weight (13.51%) and total losses (45.36%) were recorded in SA application at 30 DAS and second spray at 30 DAT and third spray at 60 DAT, by 30 days interval SA application enhanced SAR against disease and pest for longer time due to more accumulation of SA within the cell up to crop physiological maturity(Fig. 3e), apart from protection action of SA also associated with the reduction of transpiration rate (Singh and Usha, 2003) and the same treatment is considered one of the desirable treatments to increase storage life of onion. The treatment in which minimum loss in weight of bulb during storage is considered to be one of the desirable treatments to increase storage life of onion. The similar reports of better efficacy of SA at higher dose were reported by Noor et al. (1998) in onion and Bideshki and Arvin (2010) in garlic. Overall, the score of acceptability were highest in three times SA application.

Sprouting loss (%) Rotting loss (%) PLW (%) Total loss (%)

30

25

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15

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5 StorageLosses (%) at month 1 0 T1 T2 T3 T4 T5 T6 Treatments a

46 Bhasker et al.

Sprouting loss (%) Rotting loss (%) PLW (%) Total loss (%)

40 35 30 25 20 15 10 5 Storage Losses Losses month 2 at (%)Storage 0 T1 T2 T3 T4 T5 T6 Treatments b

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ROLE OF SALICYLIC ACID ON ONION YIELD 47

Sprouting loss (%) Rotting loss (%) PLW (%) Total loss (%)

70 60 50 40 30 20 10 0 Storage Losses Storage at (%)month 4 T1 T2 T3 T4 T5 T6 Treatments d

Sprouting loss (%) Rotting loss (%) PLW (%) Total loss (%)

80 70 60 50 40 30 20

10 Storage Losses Storage at (%)month 5 0 T1 T2 T3 T4 T5 T6 Treatments e Figure 3. Effect of foliar application of salicylic acid on storage quality of onion for five months (a, b, c, d and e)

CONCLUSION The three consecutive years study on effect of Salicylic Acid on growth, yield, quality and disease pest reaction of onion revealed that foliar application of Salicylic Acid @ 250 mg-1 per liter at 30 DAS during nursery stage, subsequently 2nd spray at 30 DAT and 3rd spray at 60 DAT had significant positive influence on vegetative as well as bulb yield and yield attributing parameters and also minimize the loss due to disease pests in field as well as physiological loss weight, rotting and sprouting in storage condition and recorded highest good bulb recovery with highest cost: benefit values. 48 Bhasker et al.

REFERENCES Amin, A.A., Rashad, M., E.L.S. and EL-Abagy, H.M.H. (2007). Physiological effect of indole-3-butyric acid and salicylic acid on growth, yield and chemical constituents of onion plants. Journal of Applied Sciences Research, 3(11):1554-1563. Bhasker, P., Singh, R.K., Gupta, R.C., Sharma, H.P. and Gupta, P.K. (2018). Effect of drip irrigation on growth and yield of onion (Allium cepa L.). Journal of Spices and Aromatic Crops, 27 (1):32-37. Bhasker, P., Tailor, A.K., Sharma, H.P., Singh, R.K. and Gupta, P.K. (2018). Medicinal, Nutraceutical Values and Consumption Pattern of Onion (Allium cepa) in India: An Over View”. International Journal of Current Microbiology and Applied Sciences (Special Issue), 6:2629-2638. Bideshki, A. and Arvin, M.J. (2010). Effect of salicylic acid (SA) and drought stress on growth bulb yield and allicin content of garlic (Allium sativum) in field. Plant Ecophysiology, 2:73-79. Bideshki, A., Arvin, M.J. and Mehrnoosh, D. (2013). Interactive effect of Indole-3- butyric acid (IBA) and salicylic acid (SA) on growth parameters, bulb yield and allicin contents of garlic (Allium sativum) under drought stress in field. International Journal of Agronomy and Plant Production, 4(2):271 – 279. Carr, J.P., Lewsey, M.G. and Palukaitis, P. (2010). Signaling in induced resistance, Virus Research, 76:57-121. Dat, J.F., Foyer, C.H. and Scote, I.M. (1998). Changes in Salicylic acid and antioxidants during induced thermo tolerance in mustard seedlings. Plant Physiology,118:1455-1466. Dixon, R.A. (2001). Natural product and plant disease resistance. Nature, 411:843-847. Falconi, T., Ferrio, J.P., Cueto, I.S.D., Gine, J., Achon, M.A. and Medina, V. (2013). Effect of salicylic acid treatment on tomato plant physiology and tolerance to potato virus X infection. European Journal of Plant Pathology, 138:331-345. Farouk, S. and Osman, M.A. (2011). The effect of plant defense elicitors on common bean (Phaseolus vulgaris L.) growth and yield in absence or presence of spider mite (Tetranychus urticae Koch) infestation. Journal of Stress Physiology and Biochemistry, 7(3):5-22. Gawade,B. and Sirohi, A. (2011). Induction of Resistance in Eggplant (Solanum melongena) by Salicylic Acid against Root-Knot Nematode, Meloidogyne incognita. Indian Journal of Nematology, 41(2):201-205. Habibi, G. (2012). Exogenous salicylic acid alleviates oxidative damage of barley plants under drought stress. Acta Biologica Szegediensis, 56:57-63. Hayat, Q., Hayat, S., Irfan, M. and Ahmad, A. (2009). Effect of exogenous salicylic acid under changing environment: a review. Environmental and Experimental Botany, 68(1):14-25. He, Y. and Zhu, Z.J. (2008). Exogenous salicylic acid alleviates NaCl toxicity and increases antioxidative enzyme activity in Lycopersicon esculentum. Biologia Plantarum, 52:792-795. ROLE OF SALICYLIC ACID ON ONION YIELD 49

Kachroo, P., Yoshioka, K., Shah, J., Dooner, H.K. and Klessig, D.F. (2000). Resistance to turnip crinkle virus in Arabidopsis is regulated by two host genes and is salicylic acid dependent but NPR1, ethylene, and jasmonate independent. Plant Cell, 12:677-690. Khan, W., Prithiviraj, B. and Smith, D.L. (2003). Photosynthetic responses of corn and soybean to foliar application of salicylates. Journal of Plant Physiology, 160:485-492. Miura, K. and Tada, Y. (2014). Regulation of water, salinity, and cold stress responses by salicylic acid. Frontiers in Plant Science, 5:1-12. Nasrin, R., Ali, E., Jahanfar, D. and Soodabeh, J. (2017). Salicylic acid induced changes on antioxidant capacity, pigments and grain yield of soybean genotypes in water deficit condition. Journal of Plant Interaction, 12(1):457-464. Nazar, R., Iqbal, N., Syeed, S. and Khan, N.A. (2011). Salicylic acid alleviates decreases in photosynthesis under salt stress by enhancing nitrogen and sulfur assimilation and antioxidant metabolism differentially in two mungbean cultivars. Journal of Plant Physiology, 168:807-815. Nazara, R., Umara, S., Khanb, N.A. and Sareera, O. (2015). Salicylic acid supplementation improves photosynthesis and growth in mustard through changes in proline accumulation and ethylene formation under drought stress. South African Journal of Botany, 98:84-94. Nigar, A., Islam, M.M., Hossain, M.E., Nizam, R., Monalesa, N., Hussain, Md. A. and Parvin, S. (2017). Response of Tomato (Solanum lycopersicum L.) to Salicylic Acid and Calcium. Journal of Applied Life Sciences International, 15(2):1-7. Noor, B., Izhar, A. and Mati, U.R. (1998). Post-harvest application of salicylic acid to onion bulbs for sprout inhibition. Sarhad Journal of Agriculture, 14(6):541-547. Pandey, K.K. and Gupta, R.C. (2012). Management of fusarium wilt of tomato by application of bioagents and salicylic acid. Indian Phytopathology, 65(1):94-96. Shama, M.A., Moussa, S.A.M., Abo, El. and Fade, N.I. (2016). Salicylic acid efficacy on resistance of garlic plants (Allium sativum, L.) to water salinity stress on growth, yield and its quality. Alexandria Science Exchange Journal, 37(2):165-174. Shang, J., Xi, D.H., Jia, S.D., Zhang, Z.W., Yuan, S. and Lin, H.H. (2011). A broad spectrum, efficient and non-transgenic approach to control plant viruses by application of Salicylic Acid and Jasmonic Acid. Planta, 233:299-308. Singh, B. and Usha, K. (2003). Salicylic acid induced physiological and biochemical changes in wheat seedlings under water stress. Plant Growth Regulation, 39:137-141. Vlot, A.C., Dempsey, D.A. and Klessig, D.F. (2009). Salicylic acid, a multifaceted hormone to combat disease. Annual Review of Phytopathology, 47:177-206. Wu, W., Ding, Y., Wel, W. and Davis, R.E. (2012). Salicylic acid-mediated elicitation of tomato defence against infection by potato purple top phytoplasma. Annals of Applied Biology, 161:36-45. Zhang, B., Ma, R., Guo, L., Song, Z. and Yu, M. (2017). Effects of exogenous salicylic acid on physiological traits and CBF gene expression. Zoology Research, 3 (1):118-122 SAARC J. Agric., 18(1): 51-60 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48381

Research Article PERFORMANCE OF LENTIL AS AFFECTED BY REDUCED TILLAGE AND MECHANICAL SEEDING

R.U. Zaman1* and M.R. Islam2 1Agricultural Engineering Division, Regional Agricultural Research Station, BARI, Ishurdi, Pabna, Bangladesh 2Agronomy Division, Regional Agricultural Research Station, BARI, Ishurdi, Pabna, Bangladesh

ABSTRACT Generally, lentil seeds are sown following the traditional farming practice with 3-4 numbers of ploughing combined with broadcasting method in lentil growing countries. This is time consuming and costly. The objective of this study was to evaluate the lentil performance as affected by different mechanical seeding system as well as seeding device. There were seven different treatments of which two tillage systems like i) broadcasting after4 times tillage (CT) and ii) broadcasting after tillage with two wheeler driven High Speed Rotary Tiller (HSRT), and five direct mechanical seeding systems like TT+BP =one tillage + bed planting seeding (BP+Pl), TBP =Direct bed planting seeding (BP), TPTOS =Two wheeler operated Seeder (PTOS), TST = Strip tillage seeding (ST) and TZ =Zero tillage seeding (Z). The experiment was carried out by a randomized complete block design (RCBD) with three replications. From the results it was revealed that yield was increased from0.56 % to 10.42% in mechanical seeding system than CT. The findings also demonstrated that BP increased yield of about 10.42% with 49.31% of lower fuel consumption which saved 48.1% time compared to CT. The HSRT gave numerically higher yield compare to ST than CT but lower than BP, BP+Pl and PTOS. Zero tillage seeding system gave the minimum seed yield compare to others which was 9.67% and 19% lower than that of CT and BP, respectively. In mechanical seeding systems, bed planting exhibited higher root volume and density compare to that of others, but lower to CT and HSRT. Keywords: Conservation agriculture, Lentil, Mechanical seeding, Reduce tillage

* Corresponding author: [email protected]

Received: 28.10.2019 Accepted: 03.03.2020 52 Zaman and Islam

INTRODUCTION Tillage has been an important aspect of technological development in the evolution of agriculture. In Bangladesh today, fossil fuel is the main energy source in agriculture sector, whilst human labor is still predominant (FAOSTAT, 2017). Lentil is one of the major winter pulse crops grown across the globe. It is the second most important pulse crop in area and production, but positions first in the user’s favorite in Bangladesh. Generally, lentil is cultivated after harvesting of puddle transplanting monsoon rice. Lentil seeds were sown with 3-4 numbers of ploughing and hand broadcasting with post sowing irrigation. This is time consuming, expensive and increases risk of soil structural degradation, erosion, and soil moisture loss (Govaerts et al., 2009). In Major lentil area planted late due long duration monsoon rice and excess soil moisture that cannot permit to drive tractor or any deep tillage device to prepare soil for seed bed to seeding lentil. Besides, agricultural labor scarcity is also causing of delay sowing for lentil. As a result, lentil cannot seeding in due time (Mid November) and faced inverse climate that lead to fall dieses and finally yield loss. It is essential to introduce new seeding technologies that overcome management problems (e.g. wet soil from previous cultures, labor scarcity) for poor lentil performance and yields. Recently, different direct seeding technologies are introduced in Bangladesh as well as Indian sub continental like strip tillage (ST), Zero or minimum tillage (Z), bed planting (BP), Power tiller Operated Seeder (PTOS) and reduce tillage device like High Speed Rotary Tiller (HSRT). Those technologies mitigated directly or indirectly of the problems of excess soil moisture at sowing time, agricultural labor scarcity, late sowing, to abuse costly fossil fuel and emission ofCO2.Research reports available in Bangladesh (Barma et al., 2014) revealed that pulses can be established and grown successfully through Conservation Agriculture (CA) technology. CA technologies especially Z and ST are more viable in drought stress areas where seeding operation and initial plants establishment can be done utilizing the residual soil moisture available immediate after monsoon rice harvest (Bell and Johansen, 2009). Therefore, this study was under taken to find out the performance of reduce tillage and mechanical seeding systems to improve the performance of lentil.

MATERIALS AND METHODS Experimental site The experiment was conducted in two consecutive years of 2018 and 2019 at the agro ecological zone of High Ganges River Floodplain (AEZ#11), Regional Agricultural Research Station, Ishurdi, Pabna (24.03° N; 89.05° E; 16 AMSL), Bangladesh. Soil characteristics and climate The textural class of soil was sandy clay loam. Having soil pH 7.2, organic matter 0.98%, field capacity 28.5%, permanent wilting point 13% and bulk density 1.49 g cm-3. Precipitation is very low (900 mm) and uneven pattern in rabi (last week of TILLAGE AND MECHANICAL SEEDING ON LENTIL PERFORMANCE 53

October to last week of March) and kharif-1 (last week of March to last week of July) season. Water table depth was 9.62m in dry season (Mid-January to April) and day by day declining. Treatments and experimental design There are seven different treatments viz., Two tillage systems like i) broadcasting after 4 times tillage (CT) and ii) broadcasting after tillage with two wheeler driven High Speed Rotary Tiller (HSRT)and five direct mechanical seeding systems like TT+BP = one tillage + bed planting seeding (BP+Pl), TBP =Direct bed planting seeding (BP), TPTOS =Two wheeler operated Seeder (PTOS), TST = Strip tillage seeding (ST) and TZ =Zero tillage seeding (Z) were evaluated at randomize complete block design (RCBD)with three replications. All the tillage and seeding devices are driven by 12 hp two-wheel tractor. Seed of BARI Masur-8 was sown in the unit plots of size was 7 m × 10 m. In CT, seeds were sown by broadcasting method and soil was tilled 4 times by 48 numbers of rotating blades at depth (20-25 cm) and one times laddering. In TT+BP , where one time tillage 15cm tilling depth by power tiller which contain 48 numbers of rotating blades for pulverizing after that seeds were sown using BARI developed two wheeler operated bed planter contained 24 tines and maintained 30 cm bed width, 20 cm distance between two beds and 20cm distance between two lines on single bed. Besides, maintained 11 cm bed height. In BP, seeds and fertilizer were directly seeding by two-wheeler operated bed planter where bed width 30 cm and bed to bed distance were 20 cm and 20cm distance between two lines on single bed. Height of the bed 11 cm was maintained from base of the furrow to the bed crown. Bed planter contained 24 times for prepared bed. In PTOS seeding system, seeds and fertilizer were sown by PTOS with 5 to 6 cm tillage depth and maintained 20 cm row to row distance used to 48 tines. PTOS performs tillage operation, seeding in line and seed covering simultaneously. In HSRT, soil tilts by BARI developed two-wheeler operated high speed rotary tiller. Rotor shaft has 48 blades and rotates at 450–500 rpm and cutting depth up to 8 cm. High-speed operations of its rotary blades produce a fine soil tilth ready for seeding. Seeds were sown by broadcasting method and one times laddering. In ST system seeds and fertilizer were sown maintained 20cm distance between two rows by using the BARI developed two-wheeler operated strip tillage seeding machine. In ST system, rotating blades were reduced to 24 numbers where 4 blades in face to face configuration remain in the gang at front position of seed furrow opener for tilling and seed, fertilizer placement in strip 5 cm to 7 cm; strip width 4 cm to 5 cm and creating tilt soil just in front of furrow openers and between the two furrow openers the soil remained untilled. The J type blades of the seeder were rotating at the speed of 450 rpm. In Z system seeds and DAP were sown by direct drilling instead of tilling. BARI developed two-wheeler operated zero tillage machine was also used for zero tillage treatment and maintained 20 cm row to row distance.

54 Zaman and Islam

Fertilizer and Crop Management The crop was fertilized with 40-40-40-55-10 kg ha-1 as form of urea, DAP, murieate of potash, gypsum, boron, respectively. In treatment CT and HSRT all the fertilizer were applied as basal dose before seed sowing. But in mechanical seeding systems where employs tilling the soil just in front of furrow opener and place DAP(Di ammonium phosphate) fertilizer in line or bed and other fertilizer was broadcasted in land surface. Glaiphosate @ 6ml per litter of water was sprayed one day before of seed sowing. Monsoon rice residues were used and maintained height 15-20 cm and utilize the residual soil moisture. At the sowing time soil moisture was recorded 29 – 30%. Lentil seeds were sown on 15 November 2017, 03 November 2018 and harvested on 8 March 2018, 03 March 2019. Sampling and laboratory analysis The crops were harvested from the central 5 m x 5 m area of each plot and the yields were converted to kg/ha. Root sample was collected at ripping time by the root sampling device which volume was (15×15×20 cm). The device was inserted into the soil by hammer and collected soil for root sampling. In bed planting system, root was collected at the middle of the bed, which is equivalent to 60 % area of bed while there was no sampling in the remaining 40 % of the furrow portion of bed. Collected soil of root sampling was soaked in water in plastic buckets for 2 to 3 hours. The slurry was washed over a fine sieve (0.5 mm) and roots were collected by hand and organic debris picked out carefully. Then the root volume was measured with water immersed method used volumetric cylinder. The sampling root was also dried by oven dry method maintained 72°C for 72 h to determine the root density. The fuel consumption of different treatment was calculated by how many fuel was used per hector of land cultivated. The field capacity of farm machine was calculated by the number of hectors that can be tilled or seeding per hour. Statistical Analysis The data were subjected to variance analysis using the computer statistical software package R. The means separations were done by LSD at 5% levels of probability when the F value was significant. MS Excel software program was used for calculated machine performance and graphical description.

RESULTS AND DISCUSSION Weather and Climate of growing session Weather and climatic parameter of both years are presented in Fig. 1 and 2. In 2017- 18, lentil faced one rain event (10.5 mm) on 9-10 December 23 days after sowing during whole growing period. In second lentil growing period, 45 mm of rainfall was marked in December (18 December, 7.2 mm) and February (24, 25 and 27 February, 37.8 mm). TILLAGE AND MECHANICAL SEEDING ON LENTIL PERFORMANCE 55

Figure 1. Climatic parameter at 2017-2018 Figure 2. Climatic parameter at 2018-2019

Operational performance of different seeding system Working Performance of different machine for seeding lentil seed were displayed in Table 1.The fuel consumption measurements were made under different tillage and seeding systems. In the treatment BP+Pl, BP, PTOS, ST, Z seeding system 33.42%, 49.31%, 55.94%, 67.54%, and 70.62% respectively, of lower fuel consumption was recorded than CT. Lower fuel consumption was found mechanical seeding system than that of CT due to less number of tillage passes, partially soil tillage, and less power requirement. Seeding depth was maintained and it could be adjusted. But there is no mechanism available to maintain the seeding depth in CT system, and it can be varied from 0-25 cm. These results were supported by Saeed et al. (2014) who reported that in conventional tillage and sowing methods for wheat, the fuel consumption is more than 5 times higher compared to zero tillage systems. Hossain et al. (2018) also reported that fuel saving by Z, ST, BP, and PTOS was about 60% than that of tilled by CT in mung bean cultivation. Besides, this study also clearly showed that seeding operation by BP+Pl, BP, PTOS, ST, Z seeding method saved time by 38.91%, 48.1%, 55.83%, 65.89% and 67.4%, respectively than that of CT. Besides, HSRT exhibited saved about 62.97% of fuel with 61.3% time compare to CT at land preparation time. Similar finding was recorded Kiel et al. (2015) who observed that the practices of zero tillage and strip tillage, each of which require specialized machinery, can also reduce time requirements for planting, largely by saving fuel, compared to conventional full tillage with tractor power and manual planting. Deep tillage is always costly in terms of fuel and time (Ozturk et al., 2006).

56 Zaman and Islam

Table 1. Working performance of different tillage and seeding system (pooled average of 2017-2018 and 2018-2019) Name of Parameters CT HSRT BP+Pl BP PTOS ST Z Total Time Requirement (h/ha) 18.5* 7.16* 11.3 9.6 8.17 6.31 6.03 Fuel Consumption (lit./ha) 17.5 6.48 11.65 8.87 7.71 5.68 5.14 Field Capacity (ha/h) 0.05 0.14 0.09 0.10 0.13 0.16 0.17 Effective Working Width (cm) - 120 60 60 120 120 120 Planting strip wide (cm) - - 30 30 - 4-5 0.5-1 Tillage Depth (cm) 20-25 8 15 11 5-6 5-7 1-1.5 Used Tine (No.) 48 48 48 24 48 4 0 CT = broadcasting with 4 times tillage, HSRT = Two wheeler driven High Speed Rotary with broadcasting, BP+Pl=1tillage + bed planting seeding, BP =Direct bed planting seeding, PTOS =Two wheeler operated Seeder, ST = Strip tillage seeding and Z=Zero tillage seeding, * = Parenthesis the only land preparation time

The performance of lentil affected by different seeding system The performance of lentil affected by different seeding system showed in the Table 2. Yield and yield contributing parameter like plant height, number of branch, pod/plant, plant population and 1000 seed weight was significantly varied among the treatments. Plant height observed maximum in HSRT (48.53 cm) followed by BP (47.91cm) and BP + Pl (47.73 cm). The lowest plant height (43.36 cm) observed in zero tillage. Highest Number of branching was detected in both bed, PTOS, Strip, HSRT system; lowest in CT and zero tillage system due to combination effect of tillage depth and seed sowing method i.e. effect of line sowing and broadcasting method. Significant differences were found in pod per plant among the treatments (Table 2). Whereas, the treatments BP+Pl, BP, PTOS, and HSRT showed statistically similar results of pods/plant. Highest pods/plant (158) was observed in BP compared to others treatment. The reason of this fact among the mechanical seeding systems BP achieved higher root growth which ensured more nutrient and water uptake from the deeper soil resulted better crop growth and development. These result agreed with (Islam, 2016) who reported that the pods/plant of BP was higher compare to CT. Though same seed rate was maintained, maximum Plant population was found in BP and BP+Pl treatment due to proper seed-soil contact which lead to maximum seed germination. Lowest in CT and Z due to remain uncovered seed at sowing time for zero and uneven seed distribution at CT. Thousand seed weight was significantly varied among the treatment. But it was statically similar between two tillage systems; among five seeding systems. The values of 1000-seed weight were not affected with different tillage system under dry soil conditions (Altikat, 2013; Hassan et al., 2015). However, maximum seed weight was observed in BP+Pl (24.04 gm) which was identical to BP (24.01 gm). The lowest was in the treatment Z. The highest yield TILLAGE AND MECHANICAL SEEDING ON LENTIL PERFORMANCE 57

(2956 Kg ha-1) was observed in BP compare to other mechanical seeding and tillage system and lowest (2392 kg ha-1) in Zero tillage seeding system. Yield increased in Bed planting seeding system about 10.42%, BP+Pl about 6.43%, and PTOS about 4.2% respectively, than that of CT. Higher yield was observed in BP due to achieving more yield contributing parameters than others. Besides, these results providing the advantages of line sowing of crops and covered the seed properly at sowing time which enhance seed soil interaction that helps to increase good seed germination. However, in BP treatment where pulverizing of soil in the seeding zone at seeding time which enhance well aeration and drainage system. Finally, combined effect of above advantages increased nutrient uptake capacity that influenced better growth and development as well as yield. These results were supported by Karim et al. (2017) who observed that BP found promising for increasing lentil yield. Islam (2016) also reported that the yield of lentil was higher by 23% in ST and 18% in BP compared with CT for lentil. Besides, Zaman et al. (2017) reported that at raised bed wheat cultivation increasing 15.66% grain yield than CT. Saeed (2013) also reported that, yield of wheat with happy seeder were 18% and 15% higher than minimum and conventional tillage respectively. Yield was statically identical in CT, HSRT and ST seeding systems but yield performance was better HSRT and ST than CT. At zero tillage seeding system yield reduced 9.67% than that of CT. These results strongly agree with Pittelkow et al. (2015), who showed from a meta-analysis of 610 studies worldwide involving 5,463 paired comparisons that ZT alone decreased yields and even with rotations and residue retention there was a small overall decrease in crop yields. From these results, it is clear that mechanical seeding system is possible for lentil where sacrificing negligible yield loss or not. Yield increased 0.56% to 10.42% in mechanical seeding system than CT except zero tillage. Tillage performance for lentil HSRT showed superior to two wheeler. BP, PTOS and ST were more suitable compared to Z where uncovered seed remained at sowing time. Effect of tillage and seeding system on root volume and root density Root volume and root density of lentil were significantly influenced by different tillage systems and seeding practices Fig. (3-4). Deep tillage (25 cm) by two wheeler like CT gave highest root volume as well as root density compare to shallow tillage (5- 15 cm) like BP+Pl, BP, PTOS, HSRT and minimum or no tillage like ST and zero. These results were supported by Comia et al. (1994), who reported that root density was greater in ploughed soil than in shallow cultivated soil. Root volume was identical (Fig. 3) between deep tillage treatments (8 to 25 cm). BP gave numerically higher root volume compare to PTOS. Besides, Root volume of ST and Z were identically and ST higher than Z. However, the root density was higher in CT compare to others and those were identically except ST and Z. ST and Z exhibited lower root density compares to others and they were identical (Fig. 4).

58 Zaman and Islam

In mechanical seeding systems, BP exhibited higher root volume and density than that of others due to BP offers a favorable condition for root growth. Initial tillage operations to form the bed loosen the soil and reduce the soil penetration resistance. However, the bed top has a unique opportunity to enhance root growth through the loosening and pulverizing of soil in the seeding zone from the beginning of the experiment. The present results are in good agreement with Singh et al. (2013), who observed higher root growth in bed.

Table 2. Performance of lentil on different seeding system (pooled average of 2017- 2018 and 2018-2019) Number of Pod/pla Plant Plant/m2 1000 seed Yield % Yield Treatment Height Branching nt weight increase (No.) (Kg/ha) (cm) (No.) (No.) (gm) over CT CT 46.50 18 119 270 22.75 2648 - BP+Pl 47.73 21 154 293 24.04 2830 6.43 BP 47.91 22 158 294 24.01 2956 10.42 PTOS 44.75 21 150 286 23.59 2764 4.20 HSRT 48.53 21 152 287 23.23 2677 1.08 ST 46.80 20 138 280 23.69 2663 0.56 Z 43.36 17 120 267 23.77 2392 -10.70 CV (%) 1.74 5.09 5.03 2.37 2.99 2.48 LSD 0.965 1.21 8.48 7.99 0.842 80.02 LS *** *** *** *** * *** ***= Significant at 0.001 level of probability *= Significant at 0.05 level of probability, CT = broadcasting with 4 times tillage, HSRT = Two wheeler driven High Speed Rotary with broadcasting, BP+Pl =1tillage + bed planting seeding, BP = Direct bed planting seeding, PTOS = Two wheeler operated Seeder, ST = Strip tillage seeding and Z=Zero tillage seeding,

Figure 3. Effect of tillage and seeding Figure 4. Effect of tillage and seeding system on root volume system on root density TILLAGE AND MECHANICAL SEEDING ON LENTIL PERFORMANCE 59

ST and Z tillage system exhibited lower root volume and density than deep (25 cm) and shallow tillage (5-15 cm) as well as seeding system. Besides, root growth between ST and Z were also identical. These results may be because of the root development was sometimes delayed by direct seeding system especially in the mid- topsoil. In ST and Z where excess surface soil moisture was maintained that influenced minimal root growth. On the other hand CT, BP, BP+Pl, HSRT and PTOS allowed drying of the surface soil to the extent that adequate initial root growth. Thus, the influence of tillage differences on plant root development remains unsatisfying.

CONCLUSION From the study, it was found that higher yield was obtained in bed planting method (BP), than that of other systems. BP increased yield about 10.42% with 49.31% of lower fuel consumption and save 48.1% time, compare to CT (broadcasting with four times tillage). Between the tillage systems HSRT gave higher seed yield than CT. Hence, among the mechanical seeding system farmers could adapt BP seeding system for lentil production for achieving higher yield.

REFERENCES Altikat, S. (2013). The effects of reduced tillage and compaction level on the red lentil yield. Bulgarian Journal of Agricultural Science,19:1161-1169. Barma, N.C.D., Malaker, P.K., Sarker, Z.I, Khaleque, M.A., Hossain, I., Sarker, M.A.Z., Bodruzzaman, M., Hakim, M.A. and Hossain, A. (2014). Adoption of power tiller operated seeder in rice wheat cropping system. WRC, BARI Annual report, Nashipur, Dinajpur. Pp 248-253. Bell, R., and Johansen, C. (2009). Addressing constraints to pulses in cereals-based cropping systems, with particular reference to poverty alleviation in north-western Bangladesh. Annual report, ACIAR (LWR/2005/001). Comia, R.A., Stenberg, M., Nelson, P., Rydberg, T. and Haekansson, I. (1994). Soil and crop responses to different tillage systems. Soil and Tillage Research, 29, 335-355. FAOSTAT. (2017). Food and Agriculture Organization. Govaerts, B., Sayre, K.D., Goudeseune, B., Corte, P.D., Lichter, K., Dendooven, L. and Deckers, J. (2009). Conservation agriculture as a sustainable option for the central Mexican highlands. Soil and Tillage Research, 103:222-230. Hasan, K.I.L.I.C., Zubeyir, T. and Songu, G. (2015). Effect of tillage and crop residues management on lentil (Lens culinaris L.) yield, some yield components and weed density in rainfed areas of Turkey. Agricultural Research Communication Centre, Legume Research, 38(6):781-790. Hossain, M.A., Mottalib, M.A., Hossain, M.I., Amin, M.N., Alam, M.M. and Saha, C.K. (2018). Appropriate Conservation Machinery for Mung bean Cultivation in the Southern Region of Bangladesh. Journal of Precision Agriculture, 1(1):1-9. 60 Zaman and Islam

Islam, M.A. (2016). Conservation Agriculture: Its effects on crop and soil in rice-based cropping systems in Bangladesh. A Ph.D. Dissertation. Department of Soil and Agronomy. Murdoch University, Australia. Karim, M.R., Gulandaz, M.A., Mahmuda, M.M. and Salahuddin, M. (2017). Performance Evaluation of Bari Inclined Plate Planter for Lentil Cultivation. Journal of Environmental Science and Natural Resources, 10(2):39-44. Kiel, A., D’souza, A. and McDonald, A. (2015). Zero-tillage as a pathway for sustainable wheat intensification in the Eastern Indo-Gangetic Plains: does it work in farmers’ fields. Food Security, 7(5):983-1001. Ozturk, H.H., Ekinci, K. and Barut, Z.B. (2006). Energy Analysis of the Tillage Systems in second crop corn production. Journal of Sustainable Agriculture, 28:25-37. Pittelkow, C.M., Liang, X., Linquist, B.A., Groenigen, L.J.V., Lee, J., Lundy, M.E., Gestel, N.V., Six, J., Venterea, R.T. and Kessel, C.V. (2015). Productivity limits and potentials of the principles of conservation agriculture. Nature, 517(7534):365-368. Qaisrani, S.A., Akbar, N., Atta, E. and Ranjha, M. (2014). Cost analysis on wheat with operational time and fuel ingestion of different tillage practices in rice-wheat cropping system of Punjab, Pakistan. Pakistan Journal of Life and Social Science,12(2):114‐119. Qaisrni, S.A. (2013). Determination of agro-management practices for wheat (Triticum aestivum L.) sown in rice-wheat cropping system. PhD Thesis. Department of Agronomy Faculty of Agriculture, University of Agriculture, Faisalabad, Pakistan. Singh, A., Kang, J.S., Kaur, M. and Goel, A. (2013). Root parameters, weeds, economics and productivity of wheat (Triticum aestivum L.) as affected by methods of planting in-situ paddy straw. International Journal of Current Microbiology and Applied Sciences, 2(10):396-405. Zaman, R., Akanda, A.R., Biswas, S.K. and Islam, M.R. (2017). Effect of deficit irrigation on raised bed wheat cultivation. Cercetari Agronomice in Moldova, 4 (172):17-28.

SAARC J. Agric., 18(1): 61-71 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48382

Research Article INFLUENCE OF WATER STRESS ON MORPHOLOGY, PHYSIOLOGY AND YIELD CONTRIBUTING CHARACTERISTICS OF RICE

M.Z. Hossain*, S. Sikder, A. Husna, S. Sultana, S. Akhter A. Alim and J.C. Joardar Soil, Water and Environment Discipline, Khulna University, Bangladesh

ABSTRACT Water stress or drought is one of the main reasons behind the lower productivity of rice–a widely popular nutritious cereal crop and the staple food for a large portion of the world’s population. A pot experiment was conducted to investigate the effect of water stress on three rice varieties e.g. Banglamoti, Vittiatash and Atash balam in a silty clay soil. To identify whether less water affects rice production, rice plants were cultivated under five different water treatments, T1: flooding at 5 cm depth, T2: flooding at 3 cm depth, T3: saturated water condition, T4: water content@75% saturation, and T5: water content @50% saturation, and were arranged in a completely randomized design with three replications. Morphology, yield and physiological parameters of the rice plants were evaluated. Treatment below saturation did not produce any yield for all the rice varieties studied. All the morphological parameters and yields (e.g. dry weight of plants, plant height, tiller number, panicle number, grain number, grain weight, 1000 seed weight, and harvest index) showed a lower value under water deficient condition. Relative water content and water use efficiency declined with declining water content which represented the variations in their physiological responses to water stress. The grain content per panicle as well as 1000 grain weight of the rice varieties was maximum at saturation condition. Highest harvest index was observed for Vittiatash rice variety at saturated condition. Flooding the soil with either 5 cm or 3 cm depth did not produce any significant change in the studied parameters which indicated that approximately 2 cm water can easily be curtailed which may not affect the production of rice. Keywords: Rice, Water Stress, Morphology, Physiology, Yield

* Corresponding author: [email protected]

Received: 27.10.2019 Accepted: 24.04.2020 62 Hossain et al.

INTRODUCTION The scarcity of water, commonly known as water stress or drought, is an alarming global problem and one of the major constraints that severely limits the sustainable production of agricultural crops. Increasing the food demand with increasing population would have consequent impact on the yield index due to water deficit. Rice (Oryza sativa L.) is one of the most important cereal crops in the world and contributes 90-91% among the total food grain production in Asia (IRRI, 2012). It is the staple food crop in Bangladesh and more than 80 % of the total agricultural lands are under rice cultivation (BBS, 2002). The shortage of irrigation water at the growing season or reproductive stage results in serious deterioration of rice yield (Suriyan et al., 2010). When plant is subjected to water stress, a varieties of physiological changes are occurred such as- reduction of transpiration rate, chlorophyll content in leaves, photosynthetic rate, pigment degradation, stomatal conductance, relative water content (RWC), water use efficiency (WUE) and resulting in decrease of plant growth (Tuna et al., 2010; Jahan et al., 2014). Moreover, under acute water stressed situation, toxic materials like reactive oxygen species (ROS) is produced at the time of photosynthesis and respiration, and combines with fats, nucleic acids, proteins resulting in damage of plant cells, protein denaturation, lipid peroxidation, DNA mutation. Response of water stress by rice plant largely depends on several factors, includes- the genotype of plant, duration and severity of stress, growth stage. In the period of early vegetative growth and flowering, if plants unable to receive sufficient water, it drastically inhibits floret initiation, plant height, tiller number, leaf area, grain filling and ultimately low grain production (Zhang et al., 2018; Yang, et al., 2019). Proper irrigation is the pre- requisite of rice production and in Asian developing countries, over 80% of freshwater is used for irrigational purpose (IRRI, 2012). Currently about 1900 to 5000 liters of water is required to produce 1 kg of rice grain and it is speculated that, almost 10% of irrigated rice will face water scarcity by 2025 (Tuong et al., 2005). For this reason, researches are conducting numerous experiments and have taken it as a challenge to find out the proper management techniques, appropriate or optimum quantity of water to irrigate a particular rice variety to avoid the use of excess water in rice cultivation. Such initiatives will ensure efficient use of water and help to achieve the motto of grow more crops per drop of water (Akram, 2013; Materu et al., 2018). Therefore, the objectives of this study were to investigate the effect of water stress on morphological, physiological characteristics and yield of three rice varieties, and a systematic evaluation was carried out to identify the rice variety which performs best under low irrigation condition that will help to minimize loss of excess water for rice cultivation.

MATERIAL AND METHODS The experiment was conducted in front of the net house of field laboratory of Soil, Water and Environment Discipline, Khulna University, Bangladesh which is INFLUENCE OF WATER STRESS ON RICE 63 geographically located at N: 22˚10ʹ north latitude and 89˚20' east latitude. The experimental site has silty clay texture. Surface soil (0-15 cm) was collected from this site and the characteristics of experimental soil are given in Table 1.

Table 1: General properties of experimental soil Properties Values Soil moisture content (%) 25 Sand 12.05 Silt 54.12 Clay 33.83 Soil Textural Class Silty clay Particle density (gcm-3) 2.5 Bulk density (gcm-3) 1.15 Porosity (%) 55 pH 7.80 EC (dSm-1) 0.13

The collected soil was dried under the sun followed by crushing, mixed thoroughly and 8 kg soil was put in each of the 12L plastic pots. The pot soil was fertilized with urea, Triple Super Phosphate (TSP), Muriate of Potash (MOP) and gypsum as sources of N, P, K and S at the rate of 100 kg N, 60 kg P2O5, 75 kg K2O and 20 kg S ha-1, respectively (BARC, 2012). The whole amount of TSP, MOP, gypsum and 1/3rdof urea was applied prior to final preparation of the pots. The remaining 2/3rdurea was top dressed in two equal installments at 25 and 50 days after transplanting. Irrigation was done very carefully when needed to maintain the water stress according to the treatments and weeding was done regularly. Plant materials and plant growth To simulate the conditions of the local farming in Khulna region, 27 days old rice seedlings in regular cultivation Banglamoti (BRRI dhan 50), Vittiatash (BRRI dhan 28 selected) and Atash balam (BRRI dhan 28) were selected for transplanting into the pots. Three seedlings were transplanted in each pot which were arranged according to completely randomized design (CRD). Treatments

In growth experiment, plants were subjected to five degrees of water stresses: T1 = pots containing soil are ponded with water up to 5 cm above the soil surface, T2 = pots containing soil are ponded with water up to 3 cm above the soil surface, T3 = pots containing soil are saturated with water, T4 = soils in the pot contain water @ of 75% of saturation, T5 = soils in the pot contain water @ of 50% of saturation. Each treatment was replicated three times. 64 Hossain et al.

Measurement of agronomic yield and yield components Data on some morphological parameters such as plant height, tiller number, panicle number, and dry weight; yield attributes such as grain panicle-1, grain weight panicle-1, 1000 grain weight, and harvest index were measured according to standard methods (Jahan et al., 2013). Two physiological characteristics like Water Use Efficiency (WUE), Relative Water Content (RWC) were recorded by following methods. Relative Water Content (RWC) estimation After collecting the leaves from the top, the RWC was calculated according to the following formula:

RWC = x 100

Where, FW = fresh weight of leaves, TW = turgid weight of leaves, DW = dry weight of leaves To avoid moisture loss from the leaves, the FW was taken as soon as possible after leaf collection. For obtaining the TW, the leaves were kept into a container filled with distilled water for 12 hours until the leaves reach a constant weight which was considered as 100% hydration. The TW was determined immediately after removing the leaves from water. The DW was taken after oven drying of fully turgid leaves for 48 hours at 70⁰C (Turner, 1986). Water Use Efficiency (WUE) estimation WUE was calculated as total dry weight divided by total amount of transpiration for each water treatment. WUE was calculated by using the following formula after observing the amount of water loss from the potson daily basis.

nd st Where, W2and W1 are the total plant dry weights after 2 and 1 harvest respectively. Water stresses were imposed by simply weighing the soil added to the pot (dry weight), the amount of water started with and all other objects (weight of pot, etc.). Finally, weighing each pot once a week to measure evaporation and add evaporated water. Statistical Analysis The collected data were taken under the analysis of variance (ANOVA) using Minitab’s ANOVA (version 17.0) and Duncan’s New Multiple Range Test (DNMRT) was used to compare treatments.

RESULTS AND DISCUSSION Effect of water stress on morphological parameters The morphological characteristics of three rice varieties were tested at different water stressed condition are presented in Fig. 1. All the morphological results of tested rice INFLUENCE OF WATER STRESS ON RICE 65 varieties reduced significantly (p<0.001) due to water stress except plant height. Fig. 1(a) represents that plant height was almost similar regardless of the water stress as well as three rice varieties. In this case, Banglamoti showed highest plant height (66.33±1.26 cm) under T3 and the lowest (45.17±4.25 cm) under T5 treatment. Plant dry weight reduced progressively with the increment of stressed condition Fig. 1(b).

Fig. 1. Effects of water stress on (a) plant height, (b) dry weight, (c) tiller number, (d) panicle number (Each bar represents the average value of three replication, vertical lines above each bar indicates standard deviations, and different letters above the bars indicate the significant differences (p=0.05). Here, T1=5 cm standing water; T2= 3 cm standing water; T3= saturated condition; T4= 75% and T5= 50% of saturation)

-1 Banglamoti showed the maximum dry weight (9.07±0.66g plant ) with T1 treatment though it is not significantly higher than Vittiatash and on the other hand, in case of Atash balam plant dry weight was comparatively very low under T1, T2 and T3 treatments than the other two varieties. Water stress did not produce any significant change in tiller number plant-1 in case of each rice variety (Fig. 1c) but tiller numbers of Banglamoti and Vittiatash were almost similar and quite higher than that of Atash balam in every treatment. (Fig. 1d) shows that the maximum panicle number -1 (6.44±1.02 plant ) was found in Atash balam rice under T2 whereas Banglamoti and Vittiatash did not show any significant change in panicle number up to saturation. Panicle initiation was drastically reduced at severe water stress irrespective of the rice varieties studied. 66 Hossain et al.

Our experimental results revealed that, water stress had no effects on the height of rice varieties and the observed results can be compared with Khairi et al. (2015) who run an experiment on rice under different water level and found the similar results. On the other hand, Zubaer et al. (2007) stated an inverse relation between plant height and water stress. Dry weight of three rice varieties decreased almost sequentially with increasing water stress (Fig. 1b). The process of oxidative deterioration due to water stress causes cell damage and reduction of nutrient uptake, photosynthesis rate and leaf area which might be responsible for the decrease in plant dry matter under lower soil moisture. The finding was also in accordance with Zubaer et al. (2007), who observed a decrease in shoot dry matter of Aman rice with increasing water stress. The soil moisture deficiency at a severe rate caused a reduction of tillers production and panicle number regardless of the three genotypes which may be caused by the reduction of assimilates production under water stress and our findings were similar with Sokoto and Muhammad, (2014). Akram et al. (2013) also found a decrease in panicle number in three basmati rice cultivars under water deficit stress. Effect of water stress on yield of three rice varieties The yields of three rice varieties influenced by different water level are presented in Fig. 2. The observed yield characteristics insignificantly differed with different water level among the rice varieties but it was noticeable that there were no yields below the saturation level in all varieties tested. The maximum grain plant-1 (49.67±7.95) -1 and grain weight plant (0.96±0.17g) were observed under T3 in Vittiatash. Banglamoti and Atash balam were in the second and third position, respectively, in both the cases Fig. 2(a, b). From Fig. 2(c) it is seen that Vittiatash rice variety had the highest 1000 grain weight (18.70±4.32g) at T3 and the lowest (16.19±2.73g) at T1 but the values did not differ significantly from Banglamoti and Atash balam. The maximum 1000 grain weight of Banglamoti and Aatshbalam was calculated under T1 and T2, respectively. Fig 2(d) represents the effects of water stress on harvest index (HI) of three rice varieties. Among the rice varieties studied, the Vittiatash rice showed the highest HI than the other varieties of rice with T3 which was (0.50 ±0.06) and Atash balam was in the lowest position in all the treatments. However, treatment did not show any significant effect on the specific rice variety. The yield parameters were adversely affected under water stressed condition in such an extent that, no yields were observed below the saturation level. Several researches on rice plant also explained the decrease of grain number panicle-1, grain weight panicle-1,1000 grain weight with increasing moisture stress (Sokoto and Muhammad, 2014; Khairi et al., 2015; Yang, et al.,2019). In some cases, the drastic reduction of grain number (up to 50%) was also found (Sarvestani et al., 2008). The reason of decrement of grain production can be narrated as, water stress reduced the starch accumulation capacity of endosperm into grain by inhibiting photosynthesis rate and

INFLUENCE OF WATER STRESS ON RICE 67

Fig. 2. Effects of water stress on yield and yield contributing characteristics of three rice varieties (a) grain content plant-1(b) grain weight panicle-1 (c) 1000 grain weight (d) harvest index at different water stress (Each bar represents the average value of three replication, vertical lines above each bar indicates standard deviations, and different letters above the bars indicate the significant differences (p=0.05). Here, T1=5 cm standing water; T2= 3 cm standing water; T3= saturated condition; T4= 75% and T5= 50% of saturation)

nutrient uptake which affected rice yield which was similar with the findings of Zhang et al. (2018). Among the different rice varieties studied, weight of 1000 grain were different in different moisture level and Vittiatash performed better in yield than that of Banglamoti and Atash balam. On the other hand, in spite of being a water loving plant, the highest amount of water level (5 cm) did not show highest production and grain yield increased from T1 to T3. So, from the experiment, it was clear that, rice need optimum water for its maximum growth and yield though the need of water depends on varieties. At the water levels below fully saturation (50% and 75% saturation), there were no yield and above saturation (5 cm and 3 cm water level), the yields were lower than that of saturated condition. The principal reasons behind the maximum grain yield were- proper oxidation mechanism, photosynthesis, sufficient nutrient uptake and increase in translocation of assimilates to the grain under saturation level. The harvest index (HI) value is a very important indicator of production efficiency which indicates the proper translocation of sufficient assimilates to the grain. The 68 Hossain et al. reason of decrease in HI value could be explained as water stress inhibit nutrient supply and increase the yield of empty grain. In our experiment, the results suggested that soil water condition above saturation did not affect harvest index as well as yield and it was clear that, the three rice varieties showed different HI values under the experimental water treatments. The result was in agreement with the findings of Zubaer et al. (2007) and Khairi et al. (2015), who observed the highest HI under well irrigated varieties compared to that of grown under water stressed condition. They also stated that, the rate of reduction of HI largely influenced by the tolerance level of rice genotypes and soil moisture level. Effect of water stress on physiological characteristics of three rice varieties Relative Water Content (RWC) The effects of water stress on relative water content (RWC) of three rice varieties are presented in Fig. 3a. From the figure, it is clear that the RWC of three rice varieties followed the similar decreasing trend with the increment of water stress and in each variety T5 showed the lowest RWC. Banglamoti, Vittiatash and Atash balam showed significantly (p<0.001) the maximum RWC (73.62±1.71, 76.45±1.80 and 76.72±4.44% respectively) under T1 condition than the other water treatments though Banglamoti did not produce any significant reduction in RWC until saturation.

Fig. 3. Effects of water stress on (a) relative water content and (b) water use efficiency of three rice varieties (Each bar represents the average value of three replication, vertical lines above each bar indicates standard deviations, and different letters above the bars indicate the significant differences (p=0.05). Here, T1=5 cm standing water; T2= 3 cm standing water; T3= saturated condition; T4= 75% and T5= 50% of saturation)

Water Use Efficiency (WUE) The results of Fig. 3b represents that the change in WUE varied with water level and rice varieties and decreased with the increasing rate of water stress. The WUE of Atash balam was higher in each case and the significantly (p<0.001) higher value -1 was observed in Atash balam under T2 (4.03± 0.03 mgl ) though T1 and T2 were statistically similar. The maximum WUE of Banglamoti (2.17± 0.09 mgl-1) and -1 Vittiatash (1.5± 0.07 mgl ) were found under T1 treatment. INFLUENCE OF WATER STRESS ON RICE 69

RWC is considered as one of the important and easily measurable parameters in agriculture that is very useful to identify the drought tolerance of plants. The high RWC value expressing the withstand capacity of plant under water stressed condition than the drought-sensitive species with low RWC (Boutraa et al., 2010). Our results were in accordance with Zulkarnain et al. (2009) and Akram et al. (2013), who revealed the decreased of RWC in rice grown under water stress treatment. By using RWC value as a criterion for finding out drought tolerance, the three rice varieties might be considered drought-tolerant up to saturated condition as the RWC values are close for T1, T2 and T3 but below this level, a noticeable decrease were observed. This result can be compared with the experimental outcome of Chelah et al. (2011), who did not find out any adverse impact on RWC in rice plant at saturation level. Water use efficiency (WUE) can be considered as one of the major physiological characteristics of rice plant that is obtained by the ratio of plant dry matter to transpiration rate which determine the yield under limited condition of water (Jaafar et al., 2000; Boutraa et al., 2010). So, it can be said that, Atash balam was the most water consuming variety, followed by Banglamoti and Vittiatash, respectively. Under the most stressed condition i.e. T4 and T5 treatments, all varieties showed very low and no significant change in WUE. The result showed that WUE was not affected at higher water content in any one of the studied rice variety. Under water stressed condition, the recession of water is responsible for stress at these treatments and results in low WUE. This inverse relationship between water stressed condition and WUE was also confirmed by earlier research (Shangguan et al., 2000; Boutraa et al., 2010) using wheat as experimental plant, but highly depends on variety. On the other hand, Akram et al. (2013) found opposite results by an experiment on rice plant and stated that, WUE was enhanced significantly under moisture stress.

CONCLUSION The response of three rice cultivars under different water treatments was investigated and yield reduction was observed in each case under water deficient condition. No yield was found in the soil water level below the saturation for all the rice varieties. The vegetative growth and dry weight of the studied plants were higher under 5 cm standing water level but grain yields and harvest index were found higher under saturated condition. The relative water content and water use efficiency also decreased with decreasing moisture level. Among the varieties, Vittiatash can be evaluated as most potential variety with the maximum grain content per panicle, 1000 grain weight and harvest index by consuming minimum quantity of water under saturated water treatment. Finally, it can be concluded that there were no significant differences in yield of rice grown under the soil flooding with 5 cm or 3 cm depth, as a result approximately 2 cm irrigation water can be curtailed easily without any adverse impact on rice production.

70 Hossain et al.

ACKNOWLEDGEMENT We greatly acknowledge Khulna University Research Cell for providing fund for this research work.

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SAARC J. Agric., 18(1): 73-86 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48383

Research Article

STABILITY ANALYSIS FOR YIELD INADVANCED POTATO GENOTYPES FOR COMMERCIAL CULTIVATION IN BANGLADESH

B.C. Kundu1, M.A. Kawochar1, S. Naznin1, N.U. Ahmed2, S.C. Halder3, M. Mostofa1*, H. K.M. Delowar4 1Tuber Crops Research Centre, Bangladesh Agricultural Research Institute Gazipur-1701, Bangladesh 2Tuber Crops Research Sub Centre, Bangladesh Agricultural Research Institute, Munshiganj, Bangladesh 3Department of Biotechnology, Bangladesh Agricultural Research Institute Gazipur-1701, Bangladesh 4Department of Environmental Science and Technology, Jashore University of Science and Technology, Jashore-7408, Bangladesh

Abstract Five advanced potato genotypes, along with three check varieties were evaluated at six locations in order to find out the stable varieties for commercial production in Bangladesh. Combined analysis exhibited a highly significant variability among the genotypes, locations and GEI. The average tuber yield at 90 DAP (days after planting) of the genotypes ranged from 31.18 t/ha in the check BARI Alu-28 to 43.8 t ha-1 in the clone 9.35. The result of AMMI analysis of tuber yield at 90 DAP also showed that the first IPCA1 captured 55.30% of the interaction SS. IPCA2, IPCA3 and IPCA4 explained 29.01, 8.55 and 7.08% of GE interaction SS, respectively. In general, the AMMI2 model contained 99.94% of the treatment SS, while the residual contained 0.06%. In ASV method, the clone 9.125, followed by the check BARI Alu-28 and clone 9.91 were more stable than 9.35. Biplot of IPCA1 and IPCA2 covers 84.3% of GE interaction. Biplot analysis also showed that clone 9.44 and 9.35 and the environment of Gazipur and Jashore had a better result in the GE interaction. Clone 9.44 had specific adaptation with the environment of Gazipur and Jashore, while 9.35 had specific adaptation with Gazipur and Jamalpur. Clone 9.125, 9.91, 9.112, BARI Alu-28 and BARI Alu-8 were located toward the center of the biplot and can be considered as stable. Based on the GSI the most desirable genotype for

* Corresponding author: [email protected]

Received: 28.10.19 Accepted: 17.04.2020 74 Kundu et al.

both stability and high tuber yield is the clone 9.125 followed by clones 9.112, 9.91 and 9.35. Keywords: AMMI analysis; G × E interactions; Potato genotypes; Stability; Tuber yield.

Introduction Potato (Solanum tuberosum L.) is the 4th largest food crop of the world and 2nd crop in Bangladesh.It isa high biological value crop that giveshigh yield with good nutrition per unit area and time than any other major crop. Thus, it can play a vitalrole in the human diet as a supplement to other food crops such as wheat and rice (Badoni and Chauhan, 2010). In Bangladesh,it is still used as a crucialvegetable. Bangladesh producedabout 9.6 million tons of potato from about 0.468 million hectares of land, with an average yield of 20.61 t ha-1during the fiscal year 2018-19 (BBS, 2019). But the on-station tuber yield is higher than that of farmers’ fields. One of the considerablefactors contributing to low yield is theinsufficiencyof improved cultivars with wide adaptability. Thus, the evaluation of genotypes across different environments isessential for the selection of varieties. Evaluating genotypes over diverse environments area universal practice to ensure the stability of the performance of the genotypes (Sadeghi et al., 2011). One of the most desirable properties of a genotype is stability in performance before releasing as a variety for wide cultivation (Singh and Chaudhary, 1977). One of the major stability measures is the static stability concept (Becker and Leon, 1988). High yield stability usually refers to a genotype’s ability to perform consistently across a wide range of environments. Genotypes are selected primarily based on the mean performances across environments for that crop year, may not be the most stable (Yau and Hamblin, 1994). However, the activity of identification, selection and recommendation of superior genotypes is complicated and severely limited by genotype × environment interactions that areinevitable in multi-environmental trials (Asfawet al., 2009). The presence of genotype x environment interaction may confound the genotypic performance with environmental effects (Thillainathan and Fernandez, 2002). A combined analysis of variance (ANOVA) can quantify the interactions and describe the main effects. Several statistical techniquesare usedto decreasethe effect of the G × E interaction.Recently, the quantification of G × E interactions and yield stability studies have been done through multivariate methods, such as principal component analysis (Rea et al., 2011). The additive main effect and multiplicative interaction (AMMI) method combineanalysis of variance (ANOVA) and principal component analysis (PCA) into a unified approach that can be used to analyze multi- location trials (Gauch and Zobel, 1996). To increase accuracy, additive main effects and multiplicative interaction (AMMI) are the models of the first choice when main effects and interaction are both important (Zobelv et al.,1988). The objective of this STABILITY ANALYSIS FOR YIELD OF POTATO GENOTYPES 75 study was to identify high yielding varieties with stable performanceacross different environments.

MATERIALS AND METHODS Five clonal hybrids developed by TCRC (Tuber Crops Research Centre), BARI (Bangladesh Agricultural Research Institute), were evaluated along with BARI Alu-7 (Diamant), BARI Alu-8 (Cardinal) and BARI Alu-28 (Lady Rosetta) as checksfollowing RCB design with three replications at six locations (Bogura, Debigonj, Gazipur, Jamalpur, Jashore, and Munshigonj). The unit plot size was 3m x 3m. Whole tubers were planted at 60cm x 25cm spacing. Planting was done in the second half of November 2013. Fertilizer doses and necessary intercultural operations were done as per TCRC recommendation (Kundu et al., 2018). The crop was harvested at 90 DAP (days after planting). Data were recorded on several parameters and subjected tocombined analysis using MSTAT-C. Treatment means were separated by DMRT. As described by Zobelet al. (1988),the phenotypic stability parameters were estimated using the AMMI model as follows:

Yij = μ + gi + ej + kαikyjk + rij + εij

Where, Yij is the mean response of genotype i in the environment j; μ is the overall mean;

gi is the fixed effect of genotype i (i = 1, 2, ... g);

ej is the random effect of environment j (j = 1, 2, ... e); th k is a unique value of the k interaction principal component analysis (IPCA), (k = 1, 2, ... p, where p is the maximum number of estimable main components), th th αik is a singular value for the i genotype in the k IPCA, th th yjk is a unique value of the j environment in the k IPCA;

rij is the error for the G × E interaction or AMMI residue (noise present in the data); and k is the characteristic non-zero roots, k = [1, 2, ... min (G - 1, E - 1)].

εij is the average experimental error; the G × E interaction is represented by the factors; The sum of squares for the G × E interaction (SSG×E) was divided into n singular axes or main components of interaction (IPCA), which described the standard portion, each axis corresponding to an AMMI model. The choice of a model that best described the G × E interaction was done based on the FR test proposed by Cornelius et al. (1992). 76 Kundu et al.

The predictive averages were estimated according to the selected model. The CROPSTAT software was used for AMMI analysis. As described by Purchase et al. (2000), AMMI stability value (ASV) was calculated as:

Where, is the weight given to the IPCA1-value by dividing the IPCA1 sum of squares by the IPCA2 sum of squares. The larger the IPCA score, either negative or positive, the more specifically adapted a genotype is to certain environments. Smaller ASV scores indicate a more stable genotype across environments. Genotype Selection Index:

Based on the rank of mean grain yield of genotypes (RYi) across environments and rank of AMMI stability value (RASVi) a selection index called GSI was calculated for each genotype (Farshadfar, 2008) which incorporates both mean grain yield and stability index in a single criterion (GSIi) as:

GSIi = RASVi+ RYi

RESULTS AND DISCUSSION The result of the combined analysis of data showed that the genotypes, locations and their interactions were statistically significant for different traits (Table 1). The effect of locations presented in Table 2 revealed that traits were significantly influenced by locations. Emergence % at 30 DAP was significantly higher at Debigonj, Gazipur, JamalpurandJashore and was minimum in Bogura. The tallest plant was found in Munshigonj and Debigonj and the lowest was in Gazipur. The highest number of stems/hill and tuber yield (t ha-1) were found in Jamalpur. The tuber number/hill, tuber weight (kg)/hill, single tuber weight, tuber number/stem and tuber yield (t ha-1) were the best at Debigonj. It can be said that Debigonj is the best place for commercial table potato cultivation. However, the high dry matter was found in Gazipur (Table 2).

STABILITY ANALYSIS FOR YIELD OF POTATO GENOTYPES 77

Table 1. Combined analysis of variance of different characters at six locations

MSS Source of df Emergence Plant height No. of Foliage Plant vigour variation % at 30 (cm) stem/hill coverage DAP (%) Location 5 256.138* 2036.814** 26.608** 1221.828** 26.524** Error-I 12 47.766 72.648 0.796 28.090 0.757 Genotypes 7 71.764** 976.165** 7.761** 97.313** 3.821** LxG 35 19.146* 87.409** 2.587** 18.542** 1.346** Error-II 84 17.100 22.960 0.486 18.186 0.404 Tuber Tuber Tuber Dry matter Seed tuber Source of df number/ hill weight / hill yield (t/ha) at 90 DAP grade (%) variation (kg) by wt. Location 5 207.425** 0.090** 375.957** 26.574** 1248.723** Error-I 12 1.396 0.004 12.487 0.474 40.375 Genotypes 7 37.429** 0.082** 358.809** 11.201** 404.652** LxG 35 8.621** 0.009* 31.549** 3.674** 97.936** Error-II 84 1.035 0.005 8.438 0.607 18.453

*Significant at 5% level of probability and **Significant at 1% level of probability Table 2. Mean performances of the genotypes over locations for different characters

Location Emergence Plant No. of stem Foliage Plant vigor % at 30 height (cm) /hill coverage DAP (%) Bogura 90.90 b 56.82 b 6.93 a 87.54 b 8.458 b Debigonj 97.36 a 75.10 a 6.51a 93.83 a 6.583 d Gazipur 97.36 a 59.26 b 5.24 b 87.29 b 8.479 b Jamalpur 97.15 a 59.43 b 7.10 a 78.13 c 7.250 c Jashore 97.15 a 59.43 b 7.10 a 78.13 c 7.250 c Munshigonj 90.96 b 77.70 a 4.65 c 94.17 a 9.417 a

SE 1.409 1.74 0.182 1.082 0.178 78 Kundu et al.

Location Emergence Plant No. of stem Foliage Plant vigor % at 30 height (cm) /hill coverage DAP (%) CV (%) 4.35 7.41 11.15 4.93 8.04 Location Tuber Tuber Tuber yield Dry matter Seed tuber number/ hill weight (t/ha) at 90 grade (%) (kg)/ hill DAP(%) by wt. (%) Bogura 12.55 b 0.516 c 34.46 c 19.00 c 86.57 bc Debigonj 15.04 a 0.636 a 42.47 a 19.46 b 89.73 b Gazipur 7.824 d 0.520 c 34.69 c 20.79 a 76.52 d Jamalpur 8.692 c 0.594 b 39.60 b 19.82 b 94.93 a Jashore 8.616 c 0.613ab 40.05 b 19.82 b 94.94 a Munshigonj 8.236 cd 0.485 c 32.42 c 17.64 d 82.67 c

SE 0.241 0.013 0.721 0.141 1.297 CV (%) 10.01 12.17 7.79 4.01 9.91 The mean foliar and tuber characteristics are presented in Table 3, which indicates that the emergence, no of stems per hill and plant vigour were more or less similar to the check varieties. The clones 9.125, 9.44 and 9.91 plants are tall. Foliage coverage was the best in the clone 9.35, but all others were similar. The no of tubers was the highest (12.73/hill) in the same clone. Tuber yield per hill also was the highest in this clone, but all the clones produced better yields compared to the checks. The proportion of seed-sized tuber was very high in all the clones except the clone 9.35 (76.5%), indicating the tuber size of this clone is bigger than the others. The mean tuber yield of all locations is presented in Table 3. On an average the clone 9.35 gave the highest yield (43.78 t ha-1) followed by clone 9.112 (41.81 t ha-1), 9.125 (39.44 t ha-1), 9.44 (38.10 t ha-1) and 9.91 (37.21 t ha-1), the lowest tuber yield (32.02 and 31.18 t ha-1, successively) were given by the check BARI Alu-7 (Diamant) and BARI Alu-28 (Lady Rosetta). The clone 9.35 gave a better yield than the check at all the locations, and the highest yield in four of the six locations. So based on yield, all the clones can be selected for commercial purposes of the other required traits and found good.

STABILITY ANALYSIS FOR YIELD OF POTATO GENOTYPES 79

Table 3. Mean performances of the genotypes for different characters (mean of six locations)

Plant Foliage Plant Emergence % at Number of Genotypes height coverage vigour (1- 30 DAP stems/hill (cm) (%) 10 score)

9.112 96.76 ab 56.90 e 6.57b 88.11 b 8.64 a 9.125 95.65 ab 72.26 ab 5.27d 85.83 bcd 7.81 bc 9.35 95.46 ab 65.63 c 7.31 a 91.00 a 8.33 a 9.44 90.83 c 74.58 a 5.48d 86.67 bcd 7.86 bc 9.91 94.16 b 71.11 b 6.40 bc 87.17 bc 8.22 ab Cardinal 95.27 ab 62.40 d 6.57 b 85.33 bcd 7.64 cd Diamant 95.64 ab 56.86 e 6.46 b 83.67 d 7.47 cd L. Rosetta 97.41 a 57.26 e 5.97 c 84.33 cd 7.28 d

SE 0.975 1.129 0.164 1.005 0.150 Tuber Seed tuber Tuber number/ Tuber yield Dry matter at Genotypes weight / sized by wt. hill (t/ha) 90 DAP (%) hill (kg) (%) 9.112 11.77 b 0.64 ab 86.57 c 41.81 b 19.21cd 9.125 8.276 f 0.59 bc 89.73 b 39.44 c 18.41e 9.35 12.73 a 0.66 a 76.52 e 43.78 a 18.80 de 9.44 9.232 e 0.5 6 c 94.93 a 38.10 cd 19.34 cd 9.91 10.29 c 0.55 c 94.94 a 37.21 d 19.06 cd Cardinal 9.403 de 0.54 c 82.67 d 34.70 e 20.03 b Diamant 9.586 cde 0.48 d 92.97 f 32.02 f 19.57 bc L. Rosetta 9.988 cd 0.47 d 79.12 e 31.18 f 20.96 a

SE 0.240 0.017 1.013 0.6847 0.1836

The mean dry matter contentis also presented in Table 3. The highest dry matter content (20.96%) was found from the check variety BARI Alu-28 (Lady Rosetta) and the lowest (18.41%) was found from clone 9.125 which was statistically similar to clone 9.35 (18.80%). Considering the average all the varieties were somewhat similar but none of the clones were better than the checks, which indicated that none of the clones is suitable for processing. 80 Kundu et al.

Yield adaptation across the environments Combined analysis of variance (Table 4) over locations resulted in highly significant differences (P<0.01) in the interaction of genotypes × environments (locations). Chandra et al. (1971) reported that GE interaction with location is more important than GE interaction with year. The significant interactions of genotypes × environments suggest that tuber yield of genotypes at 90 DAP varied across environments. Significant differences for genotypes, environments and GE interaction indicated the effect of environments in the GE interaction, genetic variability among the entries and possibility of selection for stable genotypes. Therefore, we can further proceed and estimate phenotypic stability as GE interaction was significant (Farshadfar and Sutka, 2003; Farshadfar and Sutka, 2006).

Table 4. Combined additive, multiplicative interaction and analysis of variance for tuber yield at 90 DAP of 8 potato genotypes grown at six locations Source of variance D.F. MSS TSS% Genotype 7 119.623** Environment 5 125.341** G x E 35 10.5144** IPCA 1 11 18.4994* 55.30 IPCA 2 9 11.8617* 29.01 IPCA 3 7 4.49690 8.55 IPCA 4 5 5.21070** 7.08 GXE Residual 3 0.074483 0.06 Total 47 - - Pool error 96 - - *Significant at 5% level of probability and **Significant at 1% level of probability

The average tuber yield at 90 DAP of the genotypes ranged from 31.18 t ha-1 in check BARI Alu-28 (Lady Rosetta) to 43.8 t ha-1 in clone 9.35 (Table 5). The rank of genotypes based on AMMI predicted yield and observed means for tuber yield at 90 DAP of eight genotypes grown in six locations are presented in Table 5.

STABILITY ANALYSIS FOR YIELD OF POTATO GENOTYPES 81

Table 5. AMMI predicted tuber yield and rank of genotypes based on AMMI predicted yield with observed means (in parenthesis) for tuber yield at 90 DAP of 8 genotypes grown at six locations

Tuber yield (t ha-1) Genotypes Bogura Debigon Gazipur Jamalpur Jashore Munshigonj Mean

9.112 39.1 47.14 39.02 41.46 46.5 37.62 41.81

9.125 37.47 45.91 37.19 38.74 42.47 34.84 39.44

9.35 41.41 52.19 45.45 51.56 37.45 34.6 43.78

9.44 31.65 42.94 39.68 37.51 45.61 31.19 38.1

9.91 33.17 41.6 35.38 41.93 39.74 31.44 37.21

Cardinal 32.83 39.3 29.98 36.49 38.22 3 1.4 34.7

Diamant 32.4 36.34 23.82 33.3 35.12 31.1 32.02

L. Rosetta 27.61 34.3 26.97 35.77 35.32 27.13 31.18

Site means 34.46 42.47 34.69 39.59 40.05 32.41 37.28 Rank of genotypes Genotypes Bogura Debigonj Gazipur Jamalpur Jashore Munshigonj 9.112 17 (16) 3 (3) 18 (20) 12 (12) 4 (4) 21 (26)

9.125 23 (18) 5 (7) 25 (19) 19 (17) 9 (11) 32 (31)

9.35 13 (13) 1 (1) 7 (5) 2 (2) 24 (22) 33 (32)

9.44 39 (41) 8 (9) 15 (15) 22 (21) 6 (6) 42 (42)

9.91 36 (38) 11 (8) 29 (33) 10 (10) 14 (14) 40 (43)

Cardinal 37 (36) 16 (25) 44 (40) 26 (27) 20 (23) 41 (37)

Diamant 38 (39) 27 (24) 48 (48) 35 (35) 31 (29) 43 (44)

L. Rosetta 45 (45) 34 (34) 47 (46) 28 (28) 30 (30) 46 (47)

AMMI model and pattern analysis: In the AMMI model, principal component analysis is based on the matrix of deviation from additivity or residual, while pattern analysis employs both classification and ordination techniques. In this respect, both the results of AMMI analysis, the genotype and the environment will be grouped based on their similar responses (Pourdad and Mohammadi, 2008).

82 Kundu et al.

Using ANOVA yield sum of square was partitioned into genotype, environment and GE interaction. GE interaction was further partitioned by principal component analysis (Table 6). The result of AMMI analysis also showed that the first interaction principal component axis (IPCA1) captured 55.30 of the interaction SS, IPCA2, IPCA3 and IPCA4 explained 29.01, 8.55 and 7.08% of GE interaction SS, respectively. The mean square for IPCA1 and IPCA2 were significant (p<5%) and cumulative accounted for 84.31% of total GE interaction. Therefore, the post-dictate evaluation using an F-test at P=5% suggests that two IPCA1 and IPCA2 were significant for the model with 20 df. In general, AMMI2 model contained 99.94% of the treatment SS, while the residual contained 0.06%. These results indicate that the AMMI model fits the data well and justifies the use of AMMI2.

Table 6. First, second, third IPCA scores, AMMI stability values (ASV), genotypic selection index (GSI) and rank for tuber yield at 90 DAP of 8 genotypes (mean of six locations) Genotypes Mean IPCA1 IPCA2 IPCA3 IPCA4 ASVi Rank GSIi 9.112 41.81 0.739749 0.344189 0.632290 -1.257020 1.08 5 7 9.125 39.44 0.333320 0.203258 1.314450 0.475565 0.50 1 4 9.35 43.78 -3.327110 -0.384151 0.359465 0.046747 4.61 8 9 9.44 38.10 0.533681 2.444370 -0.048793 0.233199 2.55 7 11 9.91 37.21 -0.468491 0.191527 -1.019380 -0.830650 0.67 3 8 Cardinal 34.70 0.616900 -0.593437 -0.018680 1.478990 1.04 4 10 Diamant 32.02 1.242450 -1.886600 0.275898 -0.493696 2.55 6 13 L. Rosetta 31.18 0.329501 -0.319156 -1.495250 0.346861 0.56 2 10

IPCAs crossover and non-cross over interaction: IPCA scores of genotypes and environments displayed positive and negative values (Table 6 and 7). A genotype with a large positive IPCA score in some environments must have large negative interactions in some other environments. Thus, these scores presented a disproportionate genotype response (Yan and Hunt, 2001), which was the major source of variation for any crossover (qualitative) interaction. This disproportionate genotype response is referred to as crossover GE interaction for convenience. Diversely, scores with the same sign or near zero represent a non- crossover (quantitative) GE interaction or a proportionate genotype response (Farshadfar, 2008; Mohammadi and Amri, 2008).

STABILITY ANALYSIS FOR YIELD OF POTATO GENOTYPES 83

Table 7. First, second, third IPCA scores, AMMI stability values (ASV) of six locations for tuber yield at 90 DAP Locations Mean IPCA1 IPCA2 IPCA3 IPCA4 ASVi Bogura 34.46 0.127910 -1.492190 0.849334 0.455487 2 Debigonj 42.47 -0.850205 0.069392 1.184130 -1.513630 1 Gazipur 34.69 -1.484130 2.039860 0.318982 1.147270 5 Jamalpur 39.59 -1.730330 -0.846218 -1.720510 -0.362138 4 Jashore 40.05 2.506970 1.378980 -0.651700 -0.612183 6 Munshigonj 32.41 0.127910 -1.492190 0.849334 0.885200 3

AMMI Stability Value (ASV): ASV is the distance from zero in a two-dimensional scatter-gram of IPCA1 (principal interaction component analysis axis 1) scores against IPCA2 scores. Since the IPCA1 score contributes more to GE sum of scores, it has to be weighted by the proportional difference between IPCA1 and IPCA2 scores to compensate for the relative contribution of IPCA1 and IPCA2 total GE sum of squares. The distance from zero is then determined using the theorem of Pythagoras (Purchase et al., 2000). In the ASV method, a genotype with least ASV score is the most stable; accordingly, genotype clone 9.125, followed by BARI Alu-28 (Lady Rosetta), and clone 9.91 wasthe most stable while 9.35 were undesirable. Biplot analysis and ordination techniques revealed significant differences between IPCA1 and IPCA2. The first two principal interaction components (IPCA1 and IPCA2) explained 84.31% of the variability in the GE interaction. In general, the importance of AMMI model is in the reduction of the noise even if the principal component does not cover much of the GESS (Coe, 1996). Biplot analysis (Figure1) displayed that clone 9.44 and 9.35 and the environment of Gazipur and Jashore (Table 7) had the greatest effect on the GE interaction. Clone 9.44 had specific adaptation with the environment of Gazipur and Jashore, while 9.35 had specific adaptation with the environment of Gazipur and Jamalpur. Genotypes toward the center of biplot have zero interaction; therefore have general adaptation with different mean tuber yield. Clone 9.125, 9.91, BARI Alu-28 (Lady Rosetta), BARI Alu-8 (Cardinal) and clone 9.112 were located in this category; therefore, they can be considered as stable with high performance.Biplot of IPCA1 and IPCA2 (Figure 1) covers 84.3% of GE interaction. As AMM12 has the least RMSPD (root mean square predictive difference), therefore recommendation must be based on this model (Farshdfar and Sutka, 2006). In pattern analysis, genotypes are judged in grouping from and therefore save time and precision in interpretation and selection (Farshadfar and Sutka, 2003). 84 Kundu et al.

INTERACTION BIPLOT FOR THE AMMI2 MODEL 2.5 4

3

1.62 5

0.74

1 5 2

IPCA2 2

-0.14 8 3

6

4 -1.02 6

1

7 -1.9 -2.293 -1.093 0.107 1.307 2.507 IPCA1 VARIATE: TY90 DATA FILE: A-2 MODEL FIT: 84.3% OF GXE SS Figure 1. Biplot analysis of GE interaction based on AMMI2 model for the first two interaction principal component scores. The designations for the genotypes are: 1=9.112. 2=9.125, 3=9.35, 4=9.44, 5=9.91, 6=Cardinal, 7=Diamant and 8=Lady Rosetta. The environments are represented by 1=Bogura, 2=Debigon, 3=Gazipur, 4=Jamalpur, 5=Jashore and 6=Munshigon

Genotypic Selection Index (GSI): Stability should however, not be the only parameter for selection because the most stable genotypes would not necessarily give the best yield performance (Mohammadi et al., 2007).Hence there is a need for approaches that incorporate both mean grain yield and stability in a single criteria that’s, why Kang (1991; 1993) introduced three selection criteria for simultaneous selection of yield and stability entitled: rank-sum (RSM), modified rank-sum (MRSM) and the statistics yield-stability (Ysi,). In this regard, as ASV takes into account both IPCA1 and IPCA2 that justifiesmost of the variation of GE interaction, therefore the rank of ASVi; and mean grain yield (Rji;) are incorporated in a single selection index namely Genotype Selection Index (GSI). The least GSI is considered as the most stable with high grain yield. Based on the GSI the most desirable genotype for selection of both stability and high grain yield is clone 9.125 followed by clone 9.112, 9.91and 9.35, which are in agreement with the result of biplot. Even though they are more stable and higher yielders than checks.

STABILITY ANALYSIS FOR YIELD OF POTATO GENOTYPES 85

CONCLUSION Regarding tuber yield at 90DAP, clone 9.44 and 9.35, and the environment of Gazipur and Jashore had a great effect on the GE interaction. Clone 9.44 had specific adaptation with the environment of Gazipur and Jashore, while 9.35 had specific adaptation with the environment of Gazipur and Jamalpur. The most desirable genotype for selection of both stability and high tuber yield is in clone 9.125 followed by clone 9.112, 9.91and 9.35. These four can be evaluated in the regional yield trial for releasing as stable varieties. Conflict of interest The authors declare that there is no conflict of interest with this research.

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SAARC J. Agric., 18(1): 87-98 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48384

Research Article INTEGRATED MANAGEMENT PRACTICES FOR CLUBROOT DISEASE (PLASMODIOPHORA BRASSICAE WOR.) OF CAULIFLOWER IN PALUNG, MAKWANPUR, NEPAL

P. Adhikari*, A. Khatiwada, N. Paneru and P. Tandan Faculty of Agriculture, Agriculture and Forestry University, Rampur, Chitwan, Nepal

ABSTRACT Club root is one of the most important diseases in the eastern hills of Nepal affecting the rural income as well as quantity and quality of Cauliflower production. A field experiment was conducted in Randomized Complete Block Design (RCBD) with 5 treatments and 4 replications during February to May, 2019. The experiment was conducted in the Farmer’s field in the disease prone area of Thaha municipality-4, Palung of to assess the efficacy of five different treatments; Nebijin, Hatake, Trichoderma viride, Lime and control for the management or suppressing the club root disease. The effectiveness of the treatments against club root disease was evaluated along with their influence on growth parameters and yield parameters of white top variety of cauliflower. Different parameters such as disease incidence, percent disease index (PDI) or disease severity index (DSI) and percent disease control (PDI) were recorded using disease scoring scale. The treatments showed significant effect on the yield and disease parameters, but not on the vegetative parameters of cauliflower. The lowest disease incidence (50.2%) and severity index (26.8%) and the highest percent disease control (57.6%) was recorded in Nebijin. Moreover, the Marketable yield (Mtha-1) per plot was observed highest from Nebijin (48.27Mtha-1) and Trichoderma viride (47.39 Mtha-1) treatment. From the study it may concluded that the application of Nebijin was more effective for the management of clubroot disease of Cauliflower and the use of Hatake and Trichoderma virideas bio-fungicides were also giving the potential assuring measure for the controlling disease of Cauliflower. Keywords: Cauliflower, Clubroot, Hatake, Trichoderma viride, Nebijin

INTRODUCTION Cauliflower (Brassica oleracea var. botrytis L.) is an important winter vegetable crop successfully grown in Terai to high hills in normal to off-season in Nepal (APP,

* Corresponding author: [email protected]

Received: 03.10.2019 Accepted: 10.02.2020 88 Adhikari et al.

1955). It is consumed as cooked vegetables and as pickle. It is nutritionally rich and has medicinal value(Joshi et al., 2018) and also excellent source of minerals such as calcium, iron, magnesium, potassium, sodium, and phosphorous. Among the different cruciferous vegetables grown in Nepal, cauliflower, cabbage and radish considered major one. In the year 2015/16, this group shares 32% of total area (79954.1 ha) and 35.1% of total production (3700969 Mt) of vegetables in the country whereby cauliflower has the highest share of 14.0% in area under cultivation (34967 ha) and 14.9% in total vegetables production of the country (550044.8 Mt), which is the highest among all the vegetables (MOAD, 2016). Club root of cruciferous plants caused by Plasmodiophora brassicae is an economically important soil borne disease. The disease has been reported in more than 60 countries and can result in total yield loss in heavily infested field (Dixon, 2009). The disease affects a wide host range and is very problematic in Brassica vegetable production (Burnett, 2013). In Nepal, For the first-time club root disease samples of broccoli, knolkhol, from one of the farmers of Kathmandu was received at Plant Pathology Division in 1993 (PPD, 2000). Then outbreak of this disease was reported from Bhaktapur following Palung valley of Makwanpur district through the movement of seedlings. This pathogen persists in soil as apparently very durable resting spores and is reputedly capable of remaining viable and dormant for at least 20 years. The severity of infestation and symptom expression increases with the intensity of crop production (Dixon, 2009). It is very difficult to eradicate once established in the field. High levels of club root (> 90% CI) occur at temperature of ~24°C, constant and fluctuating moisture of 70% and in Acidic soil condition (Colhoun, 1953). Club root disease has been an important constraint to production of Brassica vegetables as it causes of the economic losses, but more importantly because of the difficulties with the management of the pathogen. The control of disease is very difficult using the conventional methods and practices. In this context, Integrated management measures could be effective for the control of club root problem. An integrated Management practice includes holistic measures such as cultural, biological, chemical control measures for the control of disease. This research was conducted to evaluate the effectiveness of integrated management practices for the club root disease of cauliflower in field condition.

MATERIALS AND METHODS The experiment was conducted in a disease prone area at Palung, Makwanpur during February to May, 2019. The site locates at 27.63301 Latitude and 85.06701 Longitude with the altitude of 1788masl. White top variety of cauliflower was chosen for the experiment. The experiment setup was carried out in single factorial Randomized Complete Block Design with 5 treatments and 4 replications: Nebijin, Hatake, Trichoderma viride and lime. Seedlings were raised in a plug mix media under high tech plastic tunnel. Individual Plot size was dimensioned as 2.25×1.8 m2 with 20 plants and 45 cm ×45 cm2 spacing was maintained between row to row and INTEGRATED MANAGEMENT PRACTICES FOR CLUBROOT DISEASE 89 plant to plant. The distance between replications was maintained 1m and distance between plots was maintained 50 cm. Soil sample was compositely prepared at the time of land preparation and send for routine analysis in Regional Agriculture Research station, Pokhara. Recommended doses of Chemical fertilizer @200:120:80 kgha-1Urea, DAP, MOP and compost 20mtha-1 were applied. Side placement of half dose of nitrogen, full dose of phosphorous and full dose of potash were done as a basal after 1 week of transplanting and half dose of nitrogen was applied after 1 month of basal dose application. Irrigation was done in 1-2 days interval followed by 1 week interval. Weeding was conducted manually for 2 times during the cropping period. The occurrence of Mikania micrantha and chitlange grass was problematic weeds in research trial periphery.

Table 1. Treatments details

Microbial species Trade Treatments Application Dose Application Method or Chemical Name Name

T1 Control - Use of As per farmers method recommended doses of chemical fertilizers only

-1 T2 Lime Lime 5440 kg ha Lime was applied 2 weeks before transplanting as per pH value of soil.

-1 st T3 Trichoderma viride Biocide 5L t ha 1 dose of Trichoderma Trivi viride were applied incorporated in soil before transplanting +2nd dose was soil drenched at 30DAT

-1 T4 Flusulfamide Nebijin 30 kg ha Mixed with a soil in each transplanting hole at the time of application.

-1 T5 Bacillus Hatake 1000 g ha Soil drenching 3 days Amyloliquefaciens before transplanting and D203 then in every 15 days interval.

Vegetative parameter such as plant height, canopy coverage and leaf number was recorded at 15, 30, 45 and 60 days after transplanting (DAT) respectively whereas, yield and disease parameters such as curd diameter, fresh root weight, marketable yield per plot, percentage disease incidence, percent disease index or disease severity, and percent disease control were recorded at the time of harvesting of cauliflower. 90 Adhikari et al.

Disease scoring Severity of club root was assessed by visually estimating the degree of gall development on lateral and main root system using 0-3 disease scale given by (Kuginuki et al.,1999). Disease scoring scale is presented in table 2

Table 2. Disease scoring scale used to score club root on cauliflower

Sl. No. Score Severity 1 0 no symptoms of galling 2 1 A few small club (small galls on<1/3 of roots) 3 2 moderate clubs (small to medium galls on 1/3 -2/3 of roots 4 3 severe clubs (medium to large galls on >2/3 of roots)

Figure 1. Disease scoring of infected root based on 0-3 scale

Disease Assessment Six sampled plants per plot were uprooted and scored on a 0-3 scale (Kuginuki et al., 1999) by visual observation of gall formation of each plants roots. Club root severity of each plant was assessed.

INTEGRATED MANAGEMENT PRACTICES FOR CLUBROOT DISEASE 91

Percent Disease Index (PDI) or Disease severity Index (DSI) Individual scores used to calculate an index of disease according to formula

∑(Class no.) (No. of plants in each class) DSI (%) = (Total no. of plants per sample) (No. classes -1)

Disease Incidence (DI)

Number of plants infested DI (%) = Total number of plants observed

Percent Disease Control (PDC) or Disease control percentage

PDI in control – PDI in treatment PDC(%)= PDI in control

Statistical analysis All data were presented as mean ± SEM. Statistical analysis was done by using MS EXCEL GENSTAT 15th edition. Data were subjected to one-way analysis of variance (ANOVA) and means were compared by Duncan’s Multiple Range Test (DMRT) to compare treatments means at P = 0.05. Effectiveness of treatments was studied based on above mentioned parameter.

RESULTS AND DISCUSSION Chemical properties of experiment site soil: The soil of experiment site was found loamy in texture having pH 4.7. The soil was low in organic matter content (0.05%), poor in total nitrogen (0.05%), high in available Phosphorus content (1022.2 kg ha-1) and also high in available potash content (516.0 kg ha-1). Disease Incidence: The disease incidence of clubroot disease on cauliflower was found to be significantly influenced by treatments application. The highest disease incidence [77.4(91.7%)] was recorded in control which was statistically similar with lime [77.4(91.40%)] application followed by Trichoderma viride treatment [66.9(75.0%)] whereas, the least disease incidence was recorded in Hatake [47.4(54.20%)] which was statistically identical with Nebijin treatment [50.2(58.3%)]. Results are presented in Table 3. 92 Adhikari et al.

Table 3. Influence of treatments on the disease incidence of white top variety of cauliflower Treatments Disease incidence (%) Control 77.4(91.7)a Lime 77.4(91.7)a Trichoderma viride 66.9(75.0)ab Nebijin 50.2(58.3)b Hatake 47.4(54.2)b SEM± 7.60 LSD 23.42** C.V (%) 23.8 Grand mean 63.9 Note: SEM±, Standard Error of mean; C.V, Coefficient of variation; LSD, Least significant difference. Means in the column with same letter (s) in superscript indicate no significant difference between treatments at 0.05 level of significance; '***' Significant at 0.001level of Significance; '**' Significant at 0.01 level of Significance; '*' Significant at 0.05 level of Significance. Value in parenthesis indicates the original mean value.

Above findings shows that Hatake and Nebijin is highly effective against club root than other applied treatments .Several researches has corroborated the effectiveness of Nebijin against club root disease. Tanaka et al. (1999) investigated the mode of action of flusulfamide against P. brassicae in transplanted seedlings of Chinese cabbage. These authors highlighted that the flusulfamide acts directly on the resting spores and suppresses club root disease by inhibiting the germination of Plasmodiophora brassicae resting spores through adsorption into their cells wall. In an experiment conducted at Palung /Daman, Makwanpur to find effective management tactics in farmers infested field Timila, (2008) revealed that Nebijin is an effective treatment for reducing the club root disease incidence and severity by 40.4% and 59.5% which support the present research result of effectiveness of Nebijin. In Nepal for controlling club root, Trichoderma viride is found widely recommended but its effectiveness remains variable in the farmers’ field. Cuevas et al. (2011) reported a reduction of Club root Disease incidence by 45% by the use of Trichoderma viride, as compared to fields with conventional crop practices. Likewise, Ghimire and Shrestha (2019) reported the effectiveness of Trichoderma viride in reducing the club root by 42.26% as compared to control. Moreover, soil liming has been used against club root for more than 200 years, but its effectiveness has proven to be extremely variable (Karling, 1968). Lime application does not eradicate the pathogen, but creates unfavorable conditions affecting processes such as invasion, colonization and symptom formation (Webster and Dixon, 1991). Tremblay et al. (2005) also supported the present result of lower effectiveness of lime over other combination of treatment practices by stating that single use of lime often falls INTEGRATED MANAGEMENT PRACTICES FOR CLUBROOT DISEASE 93 short of a satisfactory control of club root disease. Timila (2006) reported that lime reduced incidence by 33%. This low effectiveness of lime could be due to frequent rainfall in the trial area which caused leaching of lime active components. Disease severity and Disease control percentage The percent disease index or disease severity of club root disease on cauliflower was found to be significantly influenced by the application of treatments. It was calculated based on a (0-3) rating scale. Comparing all the treatments, control treatment showed the highest PDI [57.3(70.83%)] statistically similar to PDI of Lime application [47.4(55.55%)] followed by PDI of Trichoderma viride treatment [40.0(41.05%]. The least PDI [33.7(31.92%)] was recorded in the Nebijin followed by Hatake treatment. Least PDI of hatake treatment was supported by a research conducted on kavre showed that hatake reduced the disease severity by 75.8 % compared to control i.e. 83.75% (Basnet et al., 2018). Hatake act as a metabolites that degrade cell walls of fungus and bacteria, to the disease cure and prevent plant diseases, and triggering the plant’s immune system (Hatake, 2018). In case of percent disease control or disease control percentage, the highest (70.7%) was recorded in Nebijin treatment which was statistically similar with Hatake (54.4%) and Trichoderma viride (41.0%) treatment followed by lime application (29.4%). The lowest PDC was recorded in control plots i.e. 0%. Results are presented in table 4

Table 4. Influence of treatments on the disease severity and disease control percentage of white top variety of cauliflower Treatments Percent disease Index or Disease control percentage Disease severity (PDI) or percent disease control (PDC) Control 57.3(70.83)a 0.0c Lime 47.4(55.55)ab 29.4b Trichoderma viride 40.0(41.05)bc 41.0ab Nebijin 26.8(20.83)d 70.7a Hatake 33.7(31.92)cd 54.4ab SEM(±) 4.01 36.1 LSD 12.34*** 6.06** C.V (%) 19.5 29.68 Grand mean 41.0 33.6 Note: SEM±, Standard Error of mean; CV, Coefficient of variation; LSD, Least significant difference. Means in the column with same letter (s) in superscript indicate no significant difference between treatments at 0.05 level of significance; '***' Significant at 0.001level of Significance; '**' Significant at 0.01 level of Significance; '*' Significant at 0.05 level of Significance. Value in parenthesis indicates the original mean value. 94 Adhikari et al.

Marketable yield The Marketable yield of cauliflower was found to be significantly influenced by different treatments applied. The highest marketable yield was recorded in Nebijin (48.27 Mt ha-1) which was statistically similar with Trichoderma viride treatment followed by Hatake (43.54 Mt ha-1) and Lime treatment (41.98 Mt ha-1) and the least Marketable yield was recorded in control (35.53 Mt ha-1). Results are presented in Table 5. Table 5. Influence of treatments on the marketable yield (Mt ha-1) of white variety of cauliflower Treatments Marketable yield (Mtha-1) Control 35.53c Lime 41.98b Trichoderma viride 47.39a Nebijin 48.27a Hatake 43.54b SEM± 0.912 LSD 2.811*** C.V (%) 4.2 Grand mean 43.34 Note: SEM±, Standard Error of mean; CV, Coefficient of variation; LSD, Least significant difference. Means in the column with same letter (s) in superscript indicate no significant difference between treatments at 0.05 level of significance; '***' Significant at 0.001level of Significance; '**' Significant at 0.01 level of Significance; '*' Significant at 0.05 level of Significance In the present study, Good performance of Trichoderma viride in yield improvement supported by Basnet et al.(2018). This high effectiveness of Trichoderma maybe it’s a free living fungus common in soil and root ecosystems, which is highly interactive in root, soil and foliar environments. It reduces growth, survival or infections caused by pathogens by different mechanisms like competition, antibiosis, myco parasitism, hyphal interactions, and enzyme secretion (Singh, 2010). Average Curd weight and Curd diameter The average curd weight (kg) of cauliflower and curd diameter (cm) both found to be significantly influenced by the treatments applied. In case of weight (kg) the highest curd weight (0.977 kg) was recorded in Nebijin treatment which was statistically similar with Trichoderma viride treatment (0.9590 kg) followed by Hatake (0.8813 kg) and lime treatment (0.8498 kg) whereas the lowest curd weight (0.7193 kg) was recorded in control. Similarly, Nebijin treatment showed the highest cauliflower curd diameter (16.54 cm) which was statistically similar with Trichoderma viride treatment (16.45 cm) followed by Hatake (15.71 cm) and lime treatment (14.96 cm) and the least curd diameter (13.56 cm) was obtained in control treatment. Results are presented in Table 6. INTEGRATED MANAGEMENT PRACTICES FOR CLUBROOT DISEASE 95

Table 6. Influence of treatments on the curd weight (kg) and curd diameter (cm) of white top variety of cauliflower Treatments Curd weight (kg) Curd diameter(cm) Control 0.7193c 13.56b Lime 0.8498b 14.96ab Trichoderma viride 0.9590a 16.45a Nebijin 0.9770a 16.54a Hatake 0.8813b 15.71ab SEM± 0.01847 0.671 LSD 0.05690*** 2.068* C.V(%) 4.2 8.7 Grand mean 0.8773 15.44 Note: SEm±, Standard Error of mean; CV, Coefficient of variation; LSD, Least significant difference. Means in the column with same letter (s) in superscript indicate no significant difference between treatments at 0.05 level of significance; '***' Significant at 0.001level of Significance; '**' Significant at 0.01 level of Significance; '*' Significant at 0.05 level of Significance Fresh root weight The fresh root weight of cauliflower was found to be significantly influenced by treatments application. The highest infested fresh root weight (0.934 kg m-2) was recorded in control treatment. Whereas, the least fresh root weight (0.41 kg m-2) was recorded in Trichoderma viride treatment which was at par with fresh root weight in Nebijin, Hatake, lime treatment which had 0.397 kg m-2,0.416 kg m-2,0.600 kg m-2, 0.636 kgm-2 respectively. This statistical analyze shows that control plots had highest root weight as compared to different treatment applied plots. Results are presented in Table 7. Table 7. Influence of treatments on the fresh root weight (kgm-2) of white top variety of cauliflower Treatments Fresh root weight(kgm-2) Control 0.934a Lime 0.636b Trichoderma viride 0.397b Nebijin 0.416b Hatake 0.600b SEM± 0.0761 LSD 0.2345** C.V (%) 25.5 Grand mean 0.597 Note: SEM±, Standard Error of mean; C.V, Coefficient of variation; LSD, Least significant difference. Means in the column with same letter (s) in superscript indicate no significant difference between treatments at 0.05 level of significance; '***' Significant at 0.001level of Significance; '**' Significant at 0.01 level of Significance; '*' Significant at 0.05 level of Significance 96 Adhikari et al.

From the fresh root weight study it shows that control plots has higher root weight compared to other treatments applied which was supported by Hwang et al.,(2012) studied that severely affected roots showed higher weight due to increase in cell numbers and cell size. Primarily symptoms are seen in the root system the form of swellings and deformations known as clubs which are caused due to cellular hyperplasia (increase in cellular number) and cellular hypertrophy (increase in cellular size).

CONCLUSION Nebijin is highly effective fungicide for the control of the club root disease of Cole crops. However, the bio-fungicides Hatake and Trichoderma had also effective control against the club root disease. The lowest disease incidence (50.2%), severity index (26.8%) and the highest percent disease control (57.6%) was recorded in Nebijin treatment. Moreover, the Marketable yield (Mtha-1) of cauliflower per plot was observed highest from Nebijin (48.27Mtha-1) and Trichoderma viride (47.39 Mtha-1) treatment. The effect of treatments was non-significant for all the vegetative parameters. Nebijin could be an effective option for thenon- organic commercial growers for bulk production of Cole crop. Likewise, bio-fungicides should be promoted for the organic production and for the long-term quality of soil. Despite the effectiveness of Nebijin in controlling disease, main pitfall of the Nebijin is cost and un-affordability of the farmers in long-term basis. Nebijin is formulated for the soil incorporation which causes the quantity losses and directly increases the cost of production. So, Researches should be conducted on the different application methods of Nebijin that would minimize the quantity loss of Nebijin and further development works should be conducted to establish it as cost- effective method against the club root disease of cole crops.

ACKNOWLEDGEMENT We would like to acknowledge our indebtedness and render our warmest thanks to Agriculture and forestry University, Rampur, Chitwan; Prime minister Agriculture Modernization project (PMAMP), Makwanpur and Pankaj Raj Dhital, Assistant Professor for guiding throughout the study period.

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Colhoun, J. (1953). A study of the epidemiology of club-root disease of Brassicae. Annals of Applied Biology, 40:262-283. Cuevas, V., Lagman, C.J. and Cuevas, A. (2011). Potential impact of the use of Trichoderma spp. on Farmer’s profit in the field control of clubroot disease of crucifers caused by Plasmodiophora brassicae Wor. The Philippine Agricultural Scientist, 94(2): 171–178. Dixon, G.R. (2009). The occurrence and economic impact of Plasmodiophora Brassicae and clubroot disease. Journal of Plant Growth Regulation, 28(3): 194-202. Ghimire, A. and Shrestha, S. (2019). Effect of Cultural and Biological Treatments in Managing Clubroot Disease of Cabbage in Sidhuwa, Nepal. International Journal of Applied Sciences and Biotechnology, 7:96-101. Hatake. (2018). Biofertilizer Biofungicide. https://hatake-global.com/our-products.Accessed on 26th October , 2019. Hwang, S.F., Strelkov, S.E., Feng, J., Gossen, B.D. and Howard, R.J. (2012). Plasmodiophora brassicae: A review of an emerging pathogen of the Canadian canola (Brassica napus) crop. Molecular Plant Pathology, 13(2): 105–113. Joshi, T.N., Budha, C.B., Sharma, S., Baral, S.R., Pandey, N.L. and Rajbhandari, R. (2018). Effect of Different Plant spacing on the Production of Hybrid Cauliflower (Brassica Oleraceae Var.Botrytis) Under the Agro- Climatic Conditions of mid-hills Region of Nepal. Plant Sciences and Crop Protection, 1(1):105. Karling, J.S. (1968). The Plasmodiophorales including a complete host index, bibliography, and a description of diseases caused by species of this order. New York Hafner Pub- Lishing Company. Kuginuki, Y., Yoshikawa, H. and Hirai, M. (1999). Variation in Virulence of Plasmodiophora Brassicae in Japan Tested with Clubroot-resistance Cultivars of Chinese Cabbage (Brassica rapa L. ssp. pekinensis). European Journal of Plant Pathology, 105(4):327- 332. MOAD, (2016). Ministry of Agricultural Development (MOAD). Statistical Information of Nepalese Agriculture .Government of Nepal. PPD. (2000). Annual Report. Latipur, Nepal:Plant Pathology Division, Nepal Agriculture Research Council. Singh, R. kumar. (2010). Trichoderma: A bio-control agent for management soil born diseases. http://agropedia.iitk.ac.in/content/trichoderma-bio-control-agent-management -soil-born-diseases. Accessed on 27th October, 2019. Tanaka, S., Kochi, S.I., Kunita, H., Ito, S.I. and Kameya-Iwaki, M. (1999). Biological mode of action of the fungicide Flusulfamide against Plasmodiophora brassicae(clubroot). European Journal of Plant Pathology, 105: 577-584. Timila, R.D. and Neupane, J. (2008). Nebijin (flusulfamide) on the Management of Clubroot Disease of Cauliflower. Proceedings of the Fifth National Seminar on Horticulture ,Nepal Agriculture Research Council (NARC), Khumaltar, Lalitpur, Nepal, 274-279. Timila, R.D. (2006). Review on root management of clubroot disease:A challenge for vegetable (Brassicas) cultivation in Nepal. Proceedings of the Plant Protection Society Nepal, 90-99. 98 Adhikari et al.

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SAARC J. Agric., 18(1): 99-116 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48385

Research Article ORGANIC VERSUS CONVENTIONAL – A COMPARATIVE STUDY ON QUALITY AND NUTRITIVE VALUE OF SELECTED VEGETABLE CROPS OF SOUTHERN INDIA

J.R. Xavier*, V. Mythri, R. Nagaraj, V.C.P. Ramakrishna P.E. Patki and A.D. Semwal Defence Food Research Laboratory, Defence Research and Development Organization, Siddharthanagar, Mysore- 570 011, India

ABSTRACT Vegetables are defined as edible plant parts generally consumed raw or cooked with a main dish, in a mixed dish, as an appetizer or as a salad. Food safety aspects related to microbial quality (total plate count, yeast and mold and food borne pathogens) and toxic residue (heavy metals) and mineral content were investigated for vegetables such as green leafy vegetable, salad vegetables, sprouts, brinjal, green chilies and French beans collected from organic and conventional outlets from Mysore region, Karnataka, India. Microbial analysis was carried out using standard procedures and mminerals (Ca, K, Fe, Cu, Mg, Mn and Zn) and heavy metals (Cd and Pb) were determined. Significant variations (p>0.05) were observed for microbial quality among organic and conventional vegetables. Mineral and vitamin C content were also significantly higher (p>0.01) in organic samples. Heavy metal contamination for lead and cadmium tested positive for conventional samples while organic samples tested negative. The variables that contributed most for the variability were heavy metal contamination, mineral and vitamin C content. Organically grown vegetables were free from heavy metals and safe for consumption, as well as they are rich in mineral and vitamin C content in comparison to conventional samples. Keywords: Conventional vegetables, Microbial safety, Nutrition, Organic vegetables, Toxic residues.

INTRODUCTION Vegetables are fresh and edible portion of herbaceous plants and form integral part of a balanced diet, utilized to build up and repair the body. Being a rich source of vitamins, minerals and dietary fiber vegetables also contain valuable food ingredients

* Corresponding author: [email protected]

Received: 20.03.2020 Accepted: 28.06.2020 100 Xavier et al.

(Eni et al., 2010). Antioxidant in vegetables is some of the important nutrients besides other vitamins, minerals, flavonoids and photochemical, which have been reported to contribute to health. Their consumption not only prevents us from many diseases but also provides taste and variety to the diet. Vegetables play a major role in increasing appetite, improve digestion and also provide valuable roughages which help in bowel movement of intestine and neutralize acids. Mysore is located in southern boundary of Karnataka state. Vegetables grown in different types of cultivation differ in quality microbial and nutritive quality as per earlier reports. Organic farming can be defined as an ecological production system that promotes and enhances biodiversity and biological cycle in soil, crop and livestock. Organic products are considered as safe for environment and human health (Somasundram et al., 2016). Heavy metal contamination of the food items is one of the most important aspects of food quality assurance. Vegetables can absorb metals from soil as well as from deposits on the parts of the vegetables exposed to the air from polluted environments (Shahid et al., 2016). Heavy metals are not biodegradable, have long biological half-lives and accumulate in different body organs leading to unwanted side effects. Lead and cadmium are among the most abundant heavy metals and are particularly toxic which leads to disruption of numerous biological and biochemical processes in the human body. The present study was carried out to evaluate the food safety aspects related to microbial quality (total plate count, yeast and mold and specific food borne pathogens), heavy metals (Cd and Pb) and mineral content (Ca, Na, K, Fe, Cu, Mg, Mn and Zn) of organically and conventionally cultivated vegetables such as green leafy vegetable, salad vegetables, sprouts (green gram, Bengal gram and horse gram), brinjal, green chilies and French beans collected from organic and conventional outlets from Mysore region, Karnataka, India. The vegetables were grouped into five major categories such as green leafy vegetables, salad vegetables, other vegetables, sprouts and protected cultivated vegetables.

MATERIALS AND METHODS Sample collection, storage and preparation A total of 145 fresh samples including 12 types of vegetables and 03 sprouts namely green leafy vegetables (GLV) such as amaranthus, coriander, fenugreek leaves, mint and palak, salad vegetables such as cucumber, cabbage and tomato, green chilli, cauliflower, French beans and brinjal were collected from certified outlets selling organic and conventionally grown vegetables from city of Mysore, Karnataka (12.2958° N, 76.6394° E) (Table 1). The samples were collected over a period of five months (January to May, 2018). The samples collected were separately packed in polythene bags, labeled with codes and brought immediately to laboratory. The organic samples were labeled with codes prefixed with letter ‘O’ while the conventionally samples were labeled with codes prefixed with letter ‘C’ followed by name and number of samples collected. ORGANIC VERSES CONVENTIONAL VEGETABLE PRODUCTION 101

Table 1. Conventional and organic samples collected from Mysore Sl. No. Sample Scientific name Sample code Method (A) Green leafy vegetables (GLV) 1. Amaranthus Amaranthus gangeticus CA01 Conventional OA01 Organic 2. Coriander Coriandrum sativum CCR02 Conventional OCCR02 Organic 3. Fenugreek Trigonella foenum CFE03 Conventional leaves OFE03 Organic 4. Mint leaves Mentha spicata CM04 Conventional OM04 Organic 5. Palak Spinacia oleracea CPA05 Conventional OPA05 Organic (B) Salad Vegetables 1. Cabbage Brassica oleracea CCB07 Conventional OCB07 Organic 2. Cucumber Cucumis sativus CCC08 Conventional OCC08 Organic 3. Tomato Lycopersicone sculentum CT09 Conventional OT09 Organic (C) Other Vegetables 1. Green chilli Capsicum annum CGC1O Conventional OGC10 Organic 2. French beans Phaseolus vulgaris CFB11 Conventional OFB11 Organic 3. Cauliflower Brassica oleracea var. CCF12 Conventional botrytis OCF12 Organic 4. Brinjal Solanum melongena CBJ13 Conventional OBJ13 Organic (E) Sprouts 1. Bengal gram Cicer arietinum SBG1 Conventional 2. Green gram Vigna radiata SHG2 Conventional 3. Horse gram Macrotyloma uniflorum SGG3 Conventional Protected cultivation Broccoli Brassica oleracea var. italic PBR06 Protected Strawberry Fragaria ananassa PSB14 Protected

102 Xavier et al.

Microbial Analysis Fifty grams of each sample were placed inside a sterile plastic bag with 450 mL of 0.1% buffered peptone water (BPW), and gently rubbed for 1 min. Mesophilic aerobic bacteria, total coliforms and yeasts and molds were enumerated and results were expressed as colony-forming units per gram log (cfu/g log). Four tenfold serial decimal dilution were made for each sample; 1 mL of each step was inoculated into duplicates of suitable enumeration media. For mesophilic plate count, the plates were incubated at 37ºC for 24 h, and for psychophilic plate count, at 6˚C for 5–7 days (Sospedra et al., 2013). Coliforms were analyzed by using Violet Red Bile Agar (VRBA) media using pour plate technique and plates were incubated at 35ºC for 24 h. Estimation of yeast and mold was performed using potato dextrose agar (PDA) containing 0.1% chloramphenicol by spread plate technique and incubated at 25ºC for 05 days. Primary characterization of bacteria was done by visual colony characters, microscopic characterization (Gram and endospore staining), motility test, oxygen tolerance test, catalase test and secondary identification of isolates was carried out based on biochemical and carbohydrate utilization test. To isolate Salmonella spp., the rinsate was used as pre-enrichment and method was carried out according to ISO 6579:2002 (2007). Salmonella Shigella Agar and EMB Agar were used and diluted samples were inoculated into the enrichment media and incubated for 24 hours at 350C. To enumerate E. coli, 1 mL of diluted sample was spread-plated onto eosin methylene blue (EMB) agar and plates were incubated at 37˚C for 24 h. Specific pathogens such as E. coli and Salmonella spp. were expressed as presence or absence.

Mineral analysis 250g of fresh samples were shade dried for 48 to 72 h up to the moisture content of 10 percent and homogenized using pestle and mortar. 10 g of the dried sample was heated in a silica crucible until the sample was carbonized, ashed at 550ºC using muffle furnace and weighed after cooling for constant weight (AOAC 900.02). The ash was dissolved using 3N for mineral and heavy metal analysis using Atomic Absorption Spectrophotometer (Varian AA 280 FS). Chemometric data processing To identify the relationship between samples studied, exploratory analysis using principal component analysis (PCA) and Hierarchical cluster analysis (HCA) were performed using Stat Soft Statistic version 6.0. All reported values are the mean of three replications and were subjected to one-way and two-way analysis of variance (ANOVA) using statistical software. Duncan's multiple range test was performed to find the differences between the means at p ≤ 0.05 significance levels (Snedecor and Cochran, 1994). Results expressed as cfu/ml and treated by the Student’s t-test and Manne Whitney test, depending on variable distribution, to determine whether ORGANIC VERSES CONVENTIONAL VEGETABLE PRODUCTION 103 microbial quality and heavy metal contamination varied significantly (p < 0.05 or p< 0.01) between conventional and organic vegetables.

RESULTS AND DISCUSSION Microbiological quality (TPS, TC and YM counts (CFU/ml)) of organic and conventionally grown vegetables (A) Green leafy vegetables (GLV) In GLV, the microbial quality in terms of TPC (cfu/ml) (average mean value) for amaranthus, fenugreek leaves and palak were not on par with each other while amaranthus and mint were on par with each other while coriander, fenugreek leaves and palak were on par with each other. The microbial quality in terms of TC (cfu/ml) (average mean value) for amaranthus and palak were not on par with each other while amaranthus and fenugreek leaves & coriander and mint are on par with each other. The microbial qualities in terms of YM (cfu/ml) (average mean value) for all five GLV are on par with each other. Highly significant differences(P<0.05) were found in microbial quality in terms of TPC count among the conventional and organically grown amaranthus, coriander, fenugreek leaves and mint while highly significant differences could be observed for TC count among conventional and organically grown amaranthus, fenugreek leaves and mint. In case of YM count highly significantly (P<0.05) difference were observed among conventional and organically grown fenugreek leaves and mint while the significant difference (P<0.01) was observed among conventional and organically grown amaranthus, coriander and palak (Fig. 1). Cultivation of GLVs is a profitable business for farmers; however, these vegetables are highly perishable. Therefore, these vegetables are usually grown in peri-urban area and may irrigate the crops with untreated sewage water which contains pathogenic microorganisms. Merlini et al., (2018) studied the microbiological profile of leafy vegetables from organic and conventional farming in Brazil. Their results indicated that leafy vegetables cultivated in a conventional way could present higher count of microorganisms even after the use of chemosynthetic pesticides and fertilizers. Our results were similar to the above study and highly significant differences were found for microbial quality in terms of TPC, TC and YM among conventionally and organically grown GLV. In the study conducted by Begum and Harikrishna (2010), the bacterial contamination of GLV sampled in different places in Bangalore, palak total bacterial count (cfu/g) varied from 0.9 to too numerous to count (TNTC) while coliforms were present in negligible number upto 16 cfu/g. Coriander also showed total bacterial count in terms 104 Xavier et al. of cfu/g the count between negligible to TNTC and 296, and from negligible to TNTC and 256 for coliforms. Total bacterial count was highest in coriander followed by palak. The result of total bacterial count of vegetables is an indication of improper pre- harvest and post-harvest handling. Pre-harvest conditions can come from irrigation water, improperly composted manure used as fertilizer, fecal contamination from human and domestic animals. Organically grown GLV are sold in outlets where the handling practices are hygienic till the produce reach the consumer which could be attributed to better microbial quality in comparison with conventionally grown produce. (B) Salad vegetables In salad vegetables, the average log mean value for TPC and TC for cucumber and tomato are on par with each other whereas average log mean value of cabbage is different. Average mean values of YM count for all salad vegetable tested are on par with each other. Highly significant differences (P<0.05) were found in microbial quality in terms of TPC and TC among the conventional and organically grown tomato, while significant differences (P<0.01) were observed among conventional and organically grown cabbage and cucumber ((Fig. 1). In contrast to their health benefits, the consumption of fresh vegetables has also been associated with risk for consumers (Weldezgina and Muleta, 2016). Vegetables are rich in carbohydrates, anti-oxidants, minerals, vitamins and fibres (Said, 2012) and often consumed uncooked. The results explain that in conventionally grown salad vegetables the microbial contamination is higher than their counterparts. In the present study, soaking of salad vegetables in sterile water for 10 min has resulted in reduction of microbial load, while TC count of cucumber and cabbage were higher in organically grown samples in comparison to conventionally grown samples. Viswanathan and Kaur (2000) conducted study on raw salad vegetables obtained from street vendors of Mumbai. The average aerobic plate count for salad vegetable was in the range between 105–1010cfu/g, their corresponding coliform counts ranged between106– 109cfu/g which is on par with the present study.

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Table 2. Test for specific pathogen in conventional, organic and protected grown vegetables Sl. No. Sample Method Salmonella sp. Shigella sp. Bacillus cereus Escherichia coli Conventionally grown 1. Amaranthus Crushed sample Positive Negative Positive Positive 2. Coriander Crushed sample Negative Negative Negative Positive 3. Fenugreek Crushed sample Positive Negative Positive Positive leaves 4. Mint Crushed sample Negative Negative Positive Positive 5. Palak Crushed sample Negative Negative Negative Positive 6. Cabbage Crushed sample Positive Positive Positive Positive 7. Cucumber Crushed sample Negative Negative Negative Positive 8. Tomato Crushed sample Positive Negative Negative Positive 9. Green chilli Crushed sample Positive Negative Positive Positive 10.French bean Crushed sample Positive Negative Positive Negative 11.Brinjal Crushed sample Negative Negative Negative Positive 12.Cauliflower Crushed sample Positive Negative Positive Positive 13.Bengal gram Crushed sample Negative Positive Positive Positive 14.Green gram Crushed sample Negative Positive Positive Positive 15.Horse gram Crushed sample Negative Positive Positive Positive Organically grown 16.Amaranthus Crushed sample Positive Negative Negative Positive 17.Coriander Crushed sample Positive Negative Negative Positive 18.Fenugreek Crushed sample Positive Negative Negative Positive leaves 19.Mint Crushed sample Positive Negative Negative Positive 20.Palak Crushed sample Negative Negative Negative Positive 21.Cabbage Crushed sample Negative Negative Negative Positive 22.Cucumber Crushed sample Negative Negative Negative Positive 23.Tomato Crushed sample Negative Negative Negative Positive 24.Green chilli Crushed sample Positive Negative Negative Positive 25.French bean Crushed sample Negative Negative Negative Positive 26.Brinjal Crushed sample Negative Negative Negative Positive 27.Cauliflower Crushed sample Positive Negative Negative Positive Protected cultivation 28.Broccoli Crushed sample Negative Negative Negative Positive 29.Strawberry Crushed sample Negative Negative Negative Positive 106 Xavier et al.

(C) Other vegetables In conventional and organically grown French bean and brinjal the average log mean values of TPC, TC are on par with each other. The microbial quality in terms of TPC and TC for green chilli and cauliflower are different from the microbial quality of French bean and brinjal. YM counts of brinjal are not similar with green chilli, French bean and cauliflower but YM counts of green chilli and cauliflower are on par with each other. Significant differences (P<0.01) were found in microbial quality in terms of TPC, TC and YM count among the conventional and organically grown cauliflower, while significant differences (P<0.01) were observed among conventional and organically grown green chilli for YM count (Fig. 1).

200

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Figure 1. Microbial content in wash water sample (10-5dilution) of conventionally and organically grown vegetables ORGANIC VERSES CONVENTIONAL VEGETABLE PRODUCTION 107

Maffei et al., (2016) briefly reviewed the current knowledge and summarized data on the occurrence of pathogenic microorganisms in organic vegetables. A number of scientific studies conducted in various countries were compared for the microbiological quality of produce from organic and conventional production and results were contradictory. A number of studies indicated that organic produce may pose a greater risk than conventional grown produce, this trend is not universal across all studies. In the present study, TPC and YM count of conventional sample has been found higher when compared with the TPC and YM of organically grown vegetables. TC values in organically grown vegetables were found to be higher in conventional samples such as cabbage, French beans, brinjal and cauliflower. (D) Conventionally grown sprouts In sprouts obtained from vendors, TPC (cfu/ml) was found maximum in green gram sprouts (77.00±22.11) and minimum in horse gram (30.33±7.09), TC was found maximum in green gram sprouts (97.00±5.57) and minimum in Bengal gram (26.33±6.43) while Yeast and Mold was found maximum in Bengal gram (2.33±1.52) and negligible in green gram and horse gram (Fig. 1). Seeds were soaked in water before being placed under warm and humid conditions which may be ideal for bacterial proliferation. The germination step is the main source of contamination in sprouts (US FDA, 1999). In various studies, the microbial loads in seeds were found to be between 3.0 and 6.0 log CFU/g; with sprouts having counts that were 2 or 3 logs greater (Ren, 2009). (E) Protected cultivated crops Among protected cultivated crops maximum TPC (cfu/ml) was found in strawberry (81.67±18.23) and minimum in broccoli (11.00±6.24). TC (cfu/ml) was found maximum in strawberry (25.3±10.21) and minimum in broccoli (3.66±1.52). YM (cfu/ml) was found maximum in strawberry (51.67±14.19) and minimum in broccoli (1.50±0.00) (Fig. 1). Microbiological quality (specific pathogens (presence/absence)) of organic and conventionally grown vegetables The test for presence of specific pathogen indicated presence of E. coli among all conventionally and organically grown vegetables. Conventionally grown cabbage, tomato and French bean tested positive for Salmonella spp. contamination while organically grown samples were free of the same. Salmonella spp. contamination was noticed in organically grown coriander, mint while conventionally grown coriander and mint were found devoid of the same. Conventionally grown cabbage tested positive for Shigella sp. while all other samples tested negative for the same. B. cereus contamination was prevalent in conventionally grown vegetables such as amaranthus, fenugreek leaves, mint, cabbage, green chilli, French beans and cauliflower while organically grown vegetables tested negative. Cucumber, coriander, 108 Xavier et al. palak, tomato, brinjal also tested negative for B. cereus for both conventional and organic cultivation (Table 2). Severe outbreaks were traced to consumption of contaminated radish sprouts and pre-packaged spinach. Similarly, infections with Salmonella sp. were linked to consumption of foods of animal origin, many outbreaks were traced to contaminated fresh produce (Berger et al., 2010). Estimation of minerals in conventional and organically grown vegetables (A) Green leafy vegetables (GLV) Minerals are naturally occurring inorganic substance present in plants. In plants, vegetables are excellent source of minerals which helps to meet RDA essential nutrients. Minerals in the crops are beside vitamins, antioxidants, flavonoids and photochemical one of the most important nutrients, which have been reported to contribute to the human health (Rembialkowska, 2007; Crinnion, 2010). Highly significant differences (P<0.05) were found in mineral contents of Mg, K, Cu, Zn, Mn and Vitamin C was observed among the conventional and organically grown GLV such as amaranthus, coriander, fenugreek leaves and mint, for Fe and Ca contents of amaranthus and fenugreek while significant differences (P<0.01) were observed among conventional and organically grown fenugreek and mint for Ca, Zn and vitamin C contents. No significant differences were observed for all tested element for palak among conventional and organic cultivation (Table 3 and 4). (B) Salad vegetables Highly significant differences (P<0.05) were found in K content among all tested conventional and organically grown salad vegetables while mineral contents such as Mn Na K Mg Zn and Vitamin C was highly significant in cucumber and Fe, K and Vitamin C content for tomato. Significant differences (P<0.01) were observed among conventional and organically grown cabbage for Ca, Fe, Mg, Zn and Vitamin C, tomato for Ca, Cu and Mn while for only Cu content in cucumber (Table 3 and 4). (C) Other vegetables and sprouts Highly significant differences (P<0.05) were found in mineral contents of Ca, Mn and Zn was observed among the conventional and organically grown Green chilli, for minerals K, Mg and Vitamin C for brinjal, Ca, K and Zn for cauliflower and only for K in French bean. Only significant differences (P<0.01) were observed among conventional and organically grown Green chilli for Mg, K and vitamin C, minerals such as Fe, Mg and Mn for French bean and Mg, Mn vitamin C for cauliflower (Table 4 and 7). Ca, Mg, K, Cu content of sprouts is significantly different from each other. The Fe and Zn content of the Bengal gram and green gram and Bengal gram and horse gram were on par but there was significant difference between sodium content of green gram and horse gram (Table 3 and 4).

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Table 3. Standard values of mineral content (mg/100g) in vegetables Sl. Potassium Magnesium Zinc Manganese Iron Copper Calcium Sodium Sample No. (mg/100g) (mg/100g) (mg/100g) (mg/100g) (mg/100g) (mg/100g) (mg/100g) (mg/100g) 1 Amaranthus 597±118 146±25.9 1.03±0.3 1.43±0.44 5.28±0.7 0.17±0.08 0.010±0.004 17.55±2.4 2 Coriander 546±134 72.68±14.2 0.68±0.16 0.96±0.25 5.30±1.5 0.24±0.04 146±13 37±8.01 3 Fenugreek leaves 226±63.0 63.67±18.5 0.54±0.06 0.84±0.34 5.69±1.2 0.54±0.06 274±27.8 47.01±4.2 4 Mint 539±2.7 110±30.9 0.75±0.08 1.06±0.57 8.56±3.2 0.37±0.18 205±31.8 16.87±5.4 5 Palak 206±5.9 86.97±8.58 0.46±0.09 1.12±0.42 2.95±0.6 0.17±0.05 82.29±6.51 42.55±4.1 6 Cabbage 14.98±2.7 17.99±3.22 0.16±0.03 0.20±0.09 0.35±0.1 0.16±0.03 51.76±4.44 14.98±2.7 7 Cucumber 183±36.6 20.38±4.95 0.17±0.02 0.08±0.03 0.46±0.2 0.04±0.01 16.39±3.74 6.33±0.2 8 Tomato 167±18.9 11.86±2.87 0.11±0.02 0.09±0.02 0.22±0.0 0.04±0.01 8.90±1.04 11.86±2.8 9 Green chilli 341±64.6 29.51±7.13 0.27±0.09 0.28±0.10 1.20±0.3 0.15±0.06 18.45±4.86 2.50±0.08 10 French bean 317.00 34.98 0.37 0.27 0.98 0.07 49.90 9.18 11 Brinjal 243±57.7 18.41±2.9 0.19±0.03 0.17±0.02 0.32±0.04 0.10±0.03 16.10±1.74 3.11±0.08 12 Cauliflower 329±56.3 23.08±4.4 0.31±0.11 0.33±0.08 0.96±0.34 0.05±0.03 33±3.47 30.72±3.8 13 Bengal gram 808.00 119.00 6.10 1.21 4.60 1.18 202.00 37.30 14 Green gram 843.00 127.00 4.60 2.47 4.40 0.39 124.00 28. .00 15 Horse gram 762.00 157.00 2.80 1.57 6.77 1.81 287.00 11.78 17 Strawberry 140±5.7 15.53±1.3 0.14±0.02 043±0.02 0.36±0.03 0.07±0.01 86.12±6.93 7.37±0.14 Longvah, T., Ananthan, R., Bhaskarachary, K., &Venkaiah, K. (2017). Indian food composition tables, National Institute of Nutrition, Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India

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Table 4. Mineral content (mg/100g) of conventional and organically grown vegetables in Mysore Sl No. Sample Method Calcium Iron Magnesium Sodium Potassium Copper Manganese Zinc Vitamin C Green leafy vegetables ** ** ** ** ** ** ** 1 Amaranthus Conventional 240.01±0.0c 6.80±0.1b 480.84±0.0d 16.31±0.0a 596.15±0.0b 0.22±0.0b 1.41±0.0 1.02±0.0d 72.34±0.1d ** ** ** ** ** ** ** Organic 388.204.2 c 3.53±0.1b 121.10±0.0d 16.70±0.2a 341.90±0.2b 0.10±0.0 b 0.30±0.0 0.17±0.0d 74.42±0.3d ** ** ** ** ** ** 2 Coriander Conventional 148.49±1.2b 8.24±0.5b 147.20±3.9b 176.97±2.5b 506.11±4.4c 0.00±0.0c 0.87±0.1 a 0.70±0.0c 28.13±0.1b ** ** ** ** ** ** Organic 184.80±0.1b 1.43±0.1b 31.91±0.1 b 48.80±0.1b 459.20±0.3c 0.10±0.0c 0.50±0.0 a 0.34±0.0c 30.01±0.1b ** ** ** ** ** * * 3 Fenugreek Leaves Conventional 284.39±0.2d 5.61±0.0b 480.84±0.0 49.12±0.2 c 258.61±0.0a 0.18±0.0ab 0.81±0.0 0.54±0.0 b 56.43±0.1 c ** ** ** ** ** * * Organic 292.00±0.0d 3.90±0.0b 54.20±0.0 45.20±0.0 c 235.20±0.0a 0.10±0.0ab 0.20±0.0 0.34±0.0 b 61.79±0.3 c * * ** ** ** ** * 4 Mint Conventional 211.70±5.9 c 8.54±1.0c 147.23±6.0c 12.75±1.3a 529.15±4.3d 0.50±0.0c 1.12±0.1 0.00±0.0a 15.77±0.2 a * * ** ** ** ** * Organic 198.10±0.2c 12.5±0.1c 60.80±0.3c 18.70±3.0a 517.80±14.7d 0.20±0.0c 0.50±0.0 0.43±0.0a 18.58±0.3 a

5 Palak Conventional 87.24±0.1a 2.96±0.0 a 480.84±0.0 b 40.53±0.0 b 625.50±0.0 e 0.17±0.0 b 1.13±0.0 0.45±0.0 b 27.46±0.3 b

Organic 74.30±0.0 a 1.13±0.0 a 65.10±0.0 b 42.10±0.0 b 606.00±0.0 e 0.10±0.0 b 0.50±0.0 0.30±0.0 b 31.56±0.4 b Salad vegeatbles * * * ** * 6 Cabbage Conventional 53.62±0.2c 0.33±0.0b 19.88±0.10.1b 14.21±0.0c 274.54±0.0c 0.03±0.0a 0.21±0.0 c 0.20±0.0b 34.50±0.1b * * * ** ** * ** * ** 7 Cucumber Organic 44.00±0.9c 0.80±0.0b 15.00±0.0 b 13.60±0.0c 248.45±0.0c 0.02±0.0a 0.20±0.0 c 0.30±0.0b 35.19±0.0b ** ** ** ** ** * ** ** ** 7 Cucumber Conventional 18.37±0.0b b 0.48±0.0 b 22.42±0.0 b 5.35±0.0a 210.39±0.0a 0.00±0.0a 0.00±0.0b 0.10±0.0a 5.40±0.2a ** ** ** ** ** * ** ** ** 8 Tomato Organic 9.10±0.1b 0.60±0.0 b 14.70±0.1 b 10.20±0.1a 49.93±0.0 a 0.09±0.0a 0.10±0.0b 0.20±0.0a 7.15±0.2a ** **** ** * * ** 8 Tomato Conventional 7.93±0.0 a 0.20±0.0a 9.85±0.0 aa 12.40±0.0 b 172.09±0.0b 0.05±0.0b 0.00±0.0 a 0.10±0.0 a 17.60±0.6b Other vegetables ** ** * * ** ** ** * 9 Green chilli Conventional 21.09±0.9c 1.08±0.0c 25.40±0.3c 2.57±0.3a 432.26±0.2d 0.16±0.0c 0.22±0.0b 0.20±0.0b 85.90±0.8d ** ** * * ** ** ** * 10 French beans Organic 32.45±0.0c 4.34±0.1c 27.10±0.0c 1.90±0.1a 396.2±1.4 d 1.40±0.0c 1.40±0.0b 1.70±0.0b 90.74±0.04d * * ** * 10 French beans Conventional 48.23±0.0b 0.91±0.0 b 33.50±0.0 d 8.15±0.0c 309.83±0.0a 0.05±0.0a 0.23±0.0 a 0.30±0.0 a 1.40±0.0 a * * ** * ** 11 Brinjal Organic 49.29±0.2b 0.62±0.0 b 38.70±0.0 d 8.00±0.0c 120.40±0.1a 0.10±0.0a 0.10±0.0 a 0.30±0.0 a 1.80±0.2 a ** ** ** 11 Brinjal Conventional 17.55±0.0a 0.34±0.0a 23.90±0.0a 3.56±0.0b 287.53±0.0b 0.14±0.0b 0.16±0.0 a 0.20±0.0 a 2.50±0.1 c ** *** ** * ** ** 12 Cauliflower Organic 16.96±0.0a 0.37±0.0a 15.20±0.0a 3.20±0.0b 247.70±0.4b 0.10±0.0b 0.10±0.0a 0.20±0.0 a 4.29±0.0 c ** * ** * ** ** 12 Cauliflower Conventional 24.17±0.0b 0.96±0.0 b 21.10±0.0b 29.71±0.0d 357.27±0.0c 0.05±0.0a 0.23±0.0 a 0.30±0.0 a 48.35±1.6 b Conventionally grown sprouts

13 Bengal gram Conventional 200.83±1.4b 4.05±0.2 b 116.40±1.0 a 34.34±1.6ab 799.99±2.1 b 1.16±0.0 b 1.20±0.0 a 5.55±0.2 b 7.5±0.1 b

14 Green gram Conventional 121.40±1.5a 4.00±0.3 a 125.83±1.5 b 45.81±26.7b 716.87±3.8 a 0.33±0.0 a 2.32±0.01 b 2.89±0.0 a 14.5±0.7 c

abc Subscript letters indicate significant differences among the mineral content between crops tested. Means that share the same subscript letter are not significantly different from one another; means with different subscript letters are significantly different (P <0.05). ** indicate highly significant differences (P<0.01) among conventional and organic vegetables for their mineral content. * indicate significant differences (P<0.05) among conventional and organic vegetables for their mineral content. ORGANIC VERSES CONVENTIONAL VEGETABLE PRODUCTION 111

Estimation of vitamin C in conventional and organically grown vegetables There were significant (p>0.05) difference in vitamin C content between organic and conventionally grown vegetables. Highly significant differences were found between conventional and organically grown brinjal, cucumber and tomato. and significant difference was found between conventional and organically grown green chilli and cauliflower (Table 3 and 4). Worthington (2001) found out that organic crops contained significantly more vitamin C, iron, magnesium, and phosphorus and significantly less nitrates than conventional crops. Masamba et al. (2008) carried out studies to determine and compare vitamin C, calcium and potassium in organically and conventionally grown cabbage, carrots, Cos lettuce and Valencia oranges from New South Wales in Australia. No significant differences were observed in vitamin C content in conventionally and organically grown cabbage, carrots and lettuce while calcium and potassium showed significant differences in all organic samples of cabbage, carrots and lettuce. Hunter et al. (2011) evaluated the micronutrient composition of organic and conventional plant foods with a systematic analysis. Organic plant foods (vegetables, legumes and fruit) were found to have a 5.7% higher content of vitamins and minerals than their conventionally grown counterparts. Irrespective of cultivar, soil type, harvest conditions, and chemical analysis, organic plant foods contained significantly higher amounts of minerals, including phosphorus, compared to conventional foods. Furthermore, it has been proposed that organically produced plants synthesize higher levels of ascorbic acid than conventionally-grown plants, in response to biological and ecological stresses, and the absence of protection conferred by synthetic pesticides. Our results were similar for ascorbic content; higher ascorbic contents were found in organically grown vegetable samples. Similar study was conducted by Brandt et al. (2011), organic plant material had higher levels of vitamins C than conventional vegetables and fruits. Thippeswamy (2013) reviewed the fact that food produced using organic methods taste better and nutritious. Prevalence of heavy metals (Pb and Cd) in conventional and organically grown vegetables Heavy metal analysis found that the lead and cadmium were present only in conventionally grown vegetable samples. The safe limit for lead and cadmium is 0.3 ppm and 0.2 ppm respectively as per FAO/WHO (2008). In conventionally grown vegetable sample maximum level of heavy metal was found in fenugreek leaves (5.0 ppm), cauliflower (4.0 ppm), amaranthus (2.0 ppm) and (1.0 ppm) in coriander, palak and brinjal. In other conventionally grown vegetables there was no presence of lead. Among sprouts, horse gram (6.0 ppm) showed maximum level of lead followed by Bengal gram (5.0 ppm). In protected cultivation crops there was no trace of heavy metals found. Cadmium level in conventionally grown vegetables was found maximum in brinjal and horse gram (1.0 ppm) only (Fig. 2).

112 Xavier et al.

Sharam et al. (2006) conducted a study on heavy metal contamination in vegetables grown in Varanasi region and reported that GLV (amaranthus, palak) and salad vegetable (cabbage) had heavy metal (µg/g dry weight) ranging between 1.55 to 6.90 for Cd and 9.00 to 28.00 for Pb. Roba et al., (2016) investigated the concentrations of Zn, Cu, Pb, and Cd in several vegetables cultivated in Baia Mare mining area (Romania) and concentration order of heavy metals was Zn>Cu>Pb>Cd. Sharma et al., (2009) reported the heavy metal concentration in vegetables from tropical urban area of India, the mean concentration of Cu in cauliflower, and of Zn and Cd in palak and cauliflower had exceeded the FSSAI standards (FSSAI, 2016). Zn at the production sites also exceeded the PFA standard in cauliflower. Heavy metals accumulation in vegetables tested were higher at market sites than crop production sites. The heavy metal concentration in palak and cauliflower for Cd varied from 0.4 to 1.5, and 0.6 to 2.1 µg/g, and of Pb varied from 0.7 to 1.4 and 0.2 to1.8 µg/g, respectively. In a study conducted by Ramesh and Murthy (2012), randomly collected waste water, soil and green leafy vegetable samples from Bangalore and analysed for the heavy metals namely Cu, Zn, Pb, Cr, Cd and Mn. Results showed that, Pb concentration was exceedingly high in palak (28.43 ppm to 149.50 ppm) and coriander (54.69 ppm to 75.50 ppm) in all sampling stations, Cr content in palak (70.79 ppm) and coriander (127.27 ppm) was alarmingly exceeding the allowable limit.

Figure 2. Heavy metal analysis of organically and conventionally grown vegetables

Principal component analysis of organic, conventional and protected cultivated vegetables and their microbial safety and nutritive value The projection of variables and cases on factor plane explains that all the factors showed a positive effect on variables tested whereas manganese and copper were found to show a negative effect (Fig. 3a and 3b). In PC1, one conventional sample (C16) and one organic sample (O18) and conventional sprouts samples had large positive and negative scores for PC2, respectively. Conventional (16) and organic (18) had higher amounts of Mg, K, Ca, Pb and Ash while conventional sprouts samples had Mg, K, Ca, Pb and Ash than the other samples. In PC2, the protected cultivation samples and conventional samples (tomato, cucumber, brinjal and French bean) had large positive and negative scores for PC2, respectively. The protected cultivated samples had higher amounts of Fe, Na and ORGANIC VERSES CONVENTIONAL VEGETABLE PRODUCTION 113

Vit C and lesser YM counts while conventional samples of tomato, cucumber, brinjal and French bean had lower amounts of Fe, Na and Vit C and higher YM counts. The scores for the samples showed no tendency towards separation of organic and conventional samples (Fig. 3b). Dendrogram for HCA results did not separate organic and conventional samples into distinct groups and graphical representations of HCA are called dendrograms (Chun et al., 2010) (Fig. 4). The linkage distances were between 440 and 500, confirmed the result obtained in PCA. This shows that the two groups in the two-dimensional projection are even more separate in real space as the dendrograms are based on real distances between samples while PCA are only projections. These techniques have been employed in characterization of foods such as broccoli (Santos et al., 2013) cabbage (Anunciaçao et al., 2013), wheat flour (Lima et al., 2010), kale (Fadigas et al., 2010) and beans (Santos et al., 2009).

(a) (b) Red color-conventional samples; Green color- organic samples and; Purple color- protected cultivation samples Figure 3. (a) Projection of variables on the factor plane (b) Score plot for factor 1 and factor 2 (projection of cases on the factor plane)

Figure 4. Dendrogram for conventional and organic sample showing wards method with Euclidean distance 114 Xavier et al.

CONCLUSIONS Our study indicated that organically cultivated GLVs such as amaranthus, coriander, fenugreek leaves and mint, tomato and cauliflower were found superior than their conventionally grown counterparts in microbial quality. E. coli contamination and internalization of the pathogen was found in all conventional, organic and protected cultivation samples except French bean which indicate the necessity of thorough surface decontamination through washing, ozonisation etc. of fresh produce prior to consumption and processing applications. Consumption of these conventionally grown produce as raw salad may lead to food borne illness while all organically grown vegetables sampled tested negative for Shigella sp. and are safe to be consumed raw. Lead (0.3 ppm) contamination exceeded the safe limit in conventionally cultivated fenugreek leaves, cauliflower, amaranthus coriander, palak and brinjal and sprouts such as horse gram and Bengal gram while brinjal and horse gram exceeded the safe limit for cadmium (0.2ppm) making these vegetables unsafe for consumption. Organically grown and protected cultivation vegetables tested negative for heavy metals and higher nutrient contents and were safe for consumption.

ACKNOWLEDGEMENT The authors are thankful to Dr GK Sharma, DFRL, Mysore for editing the manuscript and Dr MM Parida, DFRL, Mysore for providing microbiology laboratory facility. Authors are also thankful to Dr Pal Murugan M, Scientist, DFRL for statistical analysis of the data.

REFERENCES AOAC. (2000). Association of Official Analytical Chemists. Official methods of Analysis (Vol. II 17th edition) of AOAC International. Washington, DC, USA. Official methods 925.09, 923.03,979.09, 962.09, 4.5.01 and 923.05. Begum, A. and Harikrishna, S. (2010). Pathogens and Heavy Metals Concentration in Green Leafy Vegetables. Journal of Chemistry, 7(S1):S552-S558 Berger, C.N., Sodha, S.V., Shaw, R.K., Griffin, P.M., Pink, D., Hand, P. and Frankel, G. (2010). Fresh fruit and vegetables as vehicles for the transmission of human pathogens. Environmental microbiology, 12(9):2385-2397. Brandt, K. and Molgaard, J.P. (2001). Organic agriculture: does it enhance or reduce the nutritional value of plant foods? Journal of the Science of food and agriculture, 81(9): 924-931. Chun, M.H., Kim, E.K., Lee, K.R., Jung, J.H. and Hong, J. (2010). Quality control of Schizonepetatenuifolia Briq by solid-phase microextraction gas chromatography/mass spectrometry and principal component analysis. Microchemical Journal, 95(1):25-31. Crinnion, W.J. (2010). Organic foods contain higher levels of certain nutrients, lower levels of pesticides, and may provide health benefits for the consumer. Alternative Medicine Review, 15(1):4-12. ORGANIC VERSES CONVENTIONAL VEGETABLE PRODUCTION 115

Eni, A.O., Oluwawemitan, I.A., and Solomon, O.U. (2010). Microbial quality of fruits and vegetables sold in Sango Ota, Nigeria. African Journal of Food Science, 4(5):291-296. Food Safety and Standards. (2016). Related to limit of Heavy Metals in food. [Amendment in the Food Safety and Standards (Contaminants, Toxins and Residues) Regulation, 2011: Regulation 2.1, in sub-regulation 2.1.1, in clause 2]. Hunter, D., Foster, M., McArthur, J.O., Ojha, R., Petocz, P. and Samman, S. (2011). Evaluation of the micronutrient composition of plant composition of plant foods produced by organic and conventional agricultural methods. Critical Reviews in Food Science and Nutrition, 51(6):571-582 Maffei, D.F., Batalha, E.Y., Landgraf, M., Schaffner, D.W. and Franco, B.D. (2016). Microbiology of organic and conventionally grown fresh produce. Brazilian journal of microbiology, 47:99-105. Masamba, K.G. and Nguyen, M. (2008). Determination and comparison of vitamin C, calcium and potassium in four selected conventionally and organically grown fruits and vegetables. African Journal of Biotechnology, 7(16):2915-2919. Merlini, V.V., Pena, F.D.L., da Cunha, D.T., de Oliveira, J.M., Rostagno, M.A. and Antunes, A.E.C. (2018). Microbiological Quality of Organic and Conventional Leafy Vegetables. Journal of Food Quality. doi.org/10.1155/2018/4908316. Ramesh, H.L. and Murthy, Y.V. N. (2012). Assessment of heavy metal contamination in green leafy vegetables grown in Bangalore urban district of Karnataka. Advances in Life Science and Technology, 6: 2012. Rembiałkowska, E. (2007). Quality of plant products from organic agriculture. Journal of the Science of Food and Agriculture, 87(15):2757-276. Ren, G., Hu, D., Cheng, E.W.C., Vargas-Reus, M.A., Reip, P. and Allaker, R.P. (2009). Characterisation of copper oxide nanoparticles for antimicrobial applications. International Journal of Antimicrobial Agents, 33:587-90. Roba, C., Roşu, C., Piştea, I., Ozunu, A. and Baciu, C. (2016). Heavy metal content in vegetables and fruits cultivated in Baia Mare mining area (Romania) and health risk assessment. Environmental Science and Pollution Research, 23(7):6062-6073. Said, D.S. (2012) Detection of parasites in commonly consumed raw vegetables. Alexandria Journal of Medicine, 48(4): 345-352. Santos, I.F., dos Santos, A.M., Barbosa, U.A., Lima, J.S., dos Santos, D.C., and Matos, G.D. (2013). Multivariate analysis of the mineral content of raw and cooked okra (Abelmoschus esculentus). Microchemical Journal, 110:439-443. Santos, W.P.C., Castro, J.T., Bezerra, M.A., Fernandes, A.P., Ferreira, S.L.C., and Korn, M.G.A. (2009). Application of multivariate optimization in the development of an ultrasound-assisted extraction procedure for multielemental determination in bean seeds samples using ICP OES. Microchemical Journal, 91(2):153-158 Shahid, M., Camille, D., Sana, K., Eva, S., Tiantian, X. and Niazi, N.K. (2016) Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake. Journal of Hazardous Materials, 325:36-58. 116 Xavier et al.

Sharma, R.K., Agrawal, M. and Marshall, F. (2006) Heavy metal contamination in vegetables grown in wastewater irrigated areas of Varanasi, India. Bulletin of Environmental Contamination and Toxicology, 77(2):312-318. Sharma, R.K., Agrawal, M., and Marshall, F.M. (2009). Heavy metals in vegetables collected from production and market sites of a tropical urban area of India. Food and chemical toxicology, 47(3):583-591. Snedecor, G.W. and Cochran, W.B. (1994). Statistical Methods. 8th ed. Lowa State University Press, Ames, lowa, USA. Somasundram, C., Razali, Z. and Santhirasegaram, V. (2016) A Review on Organic Food Production in Malaysia. Horticulture, 2(3):12 Sospedra, I., Manes, J. and Soriano, J.M. (2012) Report of toxic shock syndrome toxin 1 (TSST-1) from Staphylococcus aureus isolated in food handlers and surfaces from foodservice establishments. Ecotoxicology and Environmental Safety, 80:288-290. Thippeswamy, E. (2013). (2013). Heavy Metal Contamination in Vegetables Grown in Wastewater Irrigated Areas of Varanasi, India Comparative Analysis of Organic and Inorganic Food. IOSR Journal of Agriculture and Veterinary Science. 4(6), Pp 53-57 USFDA. (1999). Microbiological safety evaluations and recommendations on sprouted seed. Maryland, USA: U.S. Food and Drug Administration. Viswanathan, P. and Randhir, K. (2001). Prevalence and growth of pathogens on salad vegetables, fruits and sprouts. International Journal of Hygene and Environmental Health. 203:205-213 Wadamori, Y., Ravi, G. and Malik, A.H. (2016). Outbreaks and factors influencing microbiological contamination of fresh produce. Journal of the Science of Food and Agriculture. 97(5):1396-1403. Weldezgina, D. and Muleta, D. (2016). Bacteriological Contaminants of Some Fresh Vegetables Irrigated with Awetu River in Jimma Town, Southwestern Ethiopia. Pp. 1- 11. WHO/FAO. (2008). Joint FAO/WHO Food Standard Programme Codex Alimentarius Commission 13th Session. Report of the Thirty Eight Session of the Codex Committee on Food Hygiene. Houston, United States of America, ALINORM 07/30/13. Worthington, V. (2001). Nutritional quality of organic versus conventional fruits, vegetables, and grains. The Journal of Alternative Complementary Medicine, 7(2):161-173. SAARC J. Agric., 18(1): 117-128 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48386

Research Article FACTOR INFLUENCING THE RESURGENCE OF BROWN PLANTHOPPER IN BANGLADESH

A.B.M.A. Uddin1*, K.S. Islam2, M. Jahan2, A. Ara3 and M.A.I. Khan3 1Entomology Division, Bangladesh Rice Research Institute, Gazipur 2Department of Entomology, Bangladesh Agricultural University, Mymensingh 3Plant pathology Division, Bangladesh Rice Research Institute, Gazipur

ABSTRACT Possible causes of brown plant hopper resurgence were determined at the net-house of Entomology Division of Bangladesh Rice Research Institute (BRRI) during 2015. Causes of resurgence in the form of resurgence ratios were higher with acetamiprid, acephate, chlorpyrifos, cypermethrin, deltamethrin, fenvalerate, lambda cyhalothrin, thiamethoxam insecticides compared to imidacloprid, cartap, dinotefuran, isoprocarb /MIPC, phenthoate, pymetrozine when even applied at recommended dose. However, thiamethoxam, imidacloprid, isoprocarb / MIPC and cartap applied at sub-lethal dose produced higher resurgence ratio of BPH than others. Isoprocarb / MIPC, a commonly used recommended insecticide was found to have a higher resurgence ratio with the insecticide treatment at the egg stage (1.71) and combination of all stages (0.82). These insecticides influenced on the growth and reproductive physiology of rice brown planthopper and consequently resurgence ratio ranged increased. Keywords: Brown planthopper, Dose, Insecticide, Resurgence, Stage

INTRODUCTION Rice (Oryza sativa L.) is one of the world’s most important crops providing a staple food for more than half of the global population (Muthayya et al., 2014). A large complex of pest organisms, consisting of insects, vertebrates, disease and weeds, have been associated with rice for hundreds of years (Islam and Catling, 2012). There are more than 232 insects (Ali et al., 2017) and several vertebrate pest species, which cause damage to the rice plants (Islam et al., 2003). Out of this large complex, about 20-30 species may be considered as the major pests and these have the potential to cause significant yield loss (Krishnaiah et al., 2008). Among the pests infesting rice, the brown planthopper (BPH), Nilaparvata lugens (Stal.) (Homoptera: Delphacidae)

* Corresponding author: [email protected]

Received: 03.03.2020 Accepted: 20.06.2020 118 Uddin et al. has gained major importance in several Asian countries including Bangladesh. The loss in grain yield ranges from 10 to 70% (Liu and Sun, 2016). It is also a vector in transmitting viral diseases such as grassy stunt, ragged stunt and wilted stunt (Cabauatan et al., 2009). In Bangladesh rice crops worth at least US$ 8.1 million were lost due to BPH attack during the last three widespread outbreaks in 1976, 1978 and 1983 (Alam and Karim, 1986). Nowadays its outbreak is occurring frequently in different areas of Bangladesh (Ali et al., 2014), Sri Lanka (Sivasubramaniam and Imthiyas, 2018) and many other countries. The control of this insect pest has always been emphasized and largely relied on insecticides in most rice producing countries (Ali et al., 2019) especially in countries where commercial, resistant varieties are not available. All the pesticides have different types of effect on the pest, which may lead to the differential development of the next generation of the pest (Volodymyr et al., 2018). Heavy uses of broad- spectrum chemicals also reduce the biodiversity of natural enemies, lift the natural control, induce outbreak of secondary pests and contaminate ecosystem (Ali et al., 2019). Continuous use of insecticides has resulted in BPH resistance to insecticides (Wu et al., 2018) and its outbreaks. Resurgence is one of the major causes for BPH outbreak. After application of insecticides, its resurgence was reported in Bangladesh (Alam, 2013), India (Ghosal and Chatterjee, 2018), the Philippines (Heong and Hardy, 2009) and the Poland (Wojciechowska et al., 2016). In insecticide trials in experimental stations and in farmer`s fields, hopper burn commonly occurs in treated plots while untreated areas remain relatively free of infestation (FAO, 2006). Existing literatures suggest that resurgence of BPH take place after application of some insecticides (Alam, 2013; Wojciechowska et al., 2016) that killed natural enemies (Bommarco et al., 2011) while on the other hand, it could be because of stimulated fecundity of certain pests after the applications of some insecticides (Wang et al., 2005). Improper methods of application of some insecticides also caused resurgence. To find out the reasons for resurgence of BPH, an experiment was planned, designed and conducted in the net house conditions.

MATERIALS AND METHODS The trial was conducted at entomology net house of Bangladesh Rice Research Institute during 2015. The trial cage consisted of a steel frame covered with fine mesh wire screen. The size of the cage was 152 × 66 × 84 cm. It consisted of three chambers. The size of each chamber was 51 × 65 × 84cm. The cage bottom was open and placed on a tray made of steel (183 × 91 × 15 cm). Standing water was maintained in the tray. The tray with cage was placed in an iron frame (183 × 76 × 76 cm). Twenty-five to 30-day-old plants were used as experimental units. Each pot contained three hills and the number of tillers per pot ranged from 40-50. The plants in the pot were cleaned by removing dried, diseased and insect infested leaves. RESURGENCE OF BROWN PLANTHOPPER 119

Before imposing treatments, it was also confirmed that plants were free from eggs of any insects. Each pot was covered by Mylar cage of height 76.2 cm and diameter 50.4 cm. Ten gravid BPH female were introduced in each Mylar cage followed by closing the top with net. The gravid female within the pots were allowed to lay egg for 48 hours. Egg bearing pot was kept in chamber. Three pots were introduced in each chamber, which was considered as a replication. In all growth stages, BPH were collected from rearing chamber and released into experiment chamber (ca. 100/pot). Burned tillers bearing pots were replaced by fresh tiller bearing pots during study period as and when necessary. The partition of each chamber was covered by thick polythene sheet in order to keep the original condition of each chamber. This experiment was laid out in a Randomized Complete Block Design (RCBD) with three replications. Each chamber with three pots was considered as one replication of a treatment, and each room treated as one block. Effect of different insecticides on the development of BPH resurgence Fourteen groups of single molecule containing chemical insecticides were applied at recommended dose (Table 1). Fresh water was applied on the plants of control treatments. Hand sprayer (Seesa hand pressure sprayer) was used to spray insecticide.

Table 1. Name of the insecticides with concentration used in the treatments Recommended dose Treatment Name of insecticide (g or ml/L water)

T1 Acetamiprid (Tundra 20 SP) 0.25 g

T2 Acephate (Mimpahte 75 SP) 1.50 g

T3 Imidacloprid (Admire 20 SL) 0.25 ml

T4 Cartap (Suntap 50 SP) 2.40 g

T5 Chlorpyrifos (Dursban 20 EC) 2.00 ml

T6 Cypermethrin (Cymbaz 10 EC) 1.00 ml

T7 Deltamethrin (Decis 2.5 EC) 1.00 ml

T8 Dinotefuran (Token 20SG) 0.30 g

T9 Fenvalerate (Fenfen 20 EC) 0.50 ml

T10 Isoprocarb/MIPC (Chabi 75 WP) 2.60 g

T11 Lambda cyhalothrin (Karate 2.5 EC) 1.00 ml

T12 Phenthoate (Kiron 50 EC) 2.00 ml

T13 Pymetrozine (Plenum 50 WG) 0.60 g

T14 Thiamethoxam (Spike 25 WG) 0.12 g

T15 Control Only water

120 Uddin et al.

Effect of different doses of selected insecticides on resurgence development Three doses viz. lower, recommended and higher dose of five selected insecticides were sprayed in an experimental arena as treatment with the help of a hand sprayer. A control experimental site was also maintained which was sprayed with fresh water only (Table 2).

Table 2. Name of the treatments of insecticides with different doses Low, recommended and Treatment Insecticide higher dose (g or ml/L water)

T1 Imidacloprid (Admire 20 SL) 0.20 ml

T2 Imidacloprid (Admire 20 SL) 0.25 ml

T3 Imidacloprid (Admire 20 SL) 0.30 ml

T4 Cartap (Suntap 50 SP) 2.20 g

T5 Cartap (Suntap 50 SP) 2.40 g

T6 Cartap (Suntap 50 SP) 2.80 g

T7 Isoprocarb/MIPC (Chabi 75 WP) 2.20 g

T8 Isoprocarb/MIPC (Chabi 75 WP) 2.60 g

T9 Isoprocarb/MIPC (Chabi 75 WP) 2.80 g

T10 Pymetrozine (Plenum 50 WG) 0.30 g

T11 Pymetrozine (Plenum 50 WG) 0.60 g

T12 Pymetrozine (Plenum 50 WG) 0.90 g

T13 Thiamethoxam (Spike 25 WG) 0.10g

T14 Thiamethoxam (Spike 25 WG) 0.12 g

T15 Thiamethoxam (Spike 25 WG) 0.14 g

T16 Control Only water

Effect of insecticides on different stages of BPH for the possible resurgence development st nd rd th Different stages of BPH, such as T1= egg, T2= 1 -2 instar nymph, T3= 3 -4 instar nymph, T4= adult and T5= combination of all stages was used as the treatment. Specific arena was developed according to treatment. A control chamber (T6) was also maintained consisting all growth stages of BPH. Isoprocarb / MIPC insecticide (Chabi 75 WP) was applied in each chamber at the rate of 2.6 g/L of water. The control chamber was sprayed with fresh water. Data Collection: The BPH populations were recorded before imposing treatments through counting. The BPH populations were recorded after 72 hours and 30 days of RESURGENCE OF BROWN PLANTHOPPER 121 spraying. The resurgence ratio of BPH was calculated by the following equation (Heinrichs et al., 1981): Population after application of insecticide Resurgence ratio= ------Population in check field at the same interval Statistical analysis: The collected data were arranged as required for statistical analysis. The software program STATISTIX 10 was selected to analysis the data as it is reported to be more efficient in analyzing entomological data. The mean differences among the treatments were determined by LSD test.

RESULTS AND DISCUSSION Effect of different insecticides on the development of BPH resurgence The number of BPH/pot was not significantly different among the treatments before insecticide spray (Table 3), although it varied significantly after 72 hours and one month of spraying. The data of table 3 clearly showed that the resurgence ratio was significantly higher in T6 (2.06) followed by T7, T1, T11 and T9. Moderate resurgence ratio was found in T14 (1.07) and it was not significantly different with resurgence ratio of T2 (1.03) and T5 (1.02). The lowest resurgence ratio was found in T8 and T13 (0.25 to 0.26) followed by T12 (0.71), T4 (0.82), T10 (0.83) and T3 (0.96). T8 and T13 were almost identical and T4, T10 were not significantly different among them.

Table 3. Effect of single molecule containing insecticides on resurgence development No. of BPH/pot at different time intervals Recommended Resurgenc Treatment dose (g or ml/L After one Before spray After 72 e ratio water) hours of month of (pretreatment) spray spray

T1 Acetamiprid 0.25 g 109.33a 50.00b 212.67bc 1.38bc (Tundra 20 SP)

T2 Acephate 1.50 g 109.33a 17.33f 159.00de 1.03d (Mimpahte 75 SP)

T3 Imidacloprid 0.25 ml 110.67a 37.00 149.00f 0.96e (Admire 20 SL)

T4 Cartap 2.40 g 110.33a 10.67g 126.67g 0.82f (Suntap 50 SP)

T5 Chlorpyrifos 2.00 ml 110.33a 10.67g 157.33e 1.02d (Dursban 20 EC)

T6 Cypermethrin 1.00 ml 109.67a 49.00b 318.33a 2.06a (Cymbaz 10 EC)

122 Uddin et al.

No. of BPH/pot at different time intervals Recommended Resurgenc Treatment dose (g or ml/L After one Before spray After 72 e ratio water) hours of month of (pretreatment) spray spray

T7 Deltamethrin 1.00 ml 109.67a 48.00b 215.67b 1.40b (Decis 2.5 EC)

T8 Dinotefuran 0.30 g 110.00a 3.00i 39.00i 0.25h (Token 20SG)

T9 Fenvalerate 0.50 ml 109.33a 79.00a 206.67c 1.34c (Fenfen 20 EC)

T10 Isoprocarb/MIPC 2.60 g 110.00a 10.67g 129.00g 0.83f (Chabi 75 WP)

T11 Lambda 1.00 ml 111.33a 25.67e 210.00bc 1.36bc cyhalothrin (Karate 2.5 EC)

T12 Phenthoate 2.00 ml 108.00a 17.33f 109.33h 0.71g (Kiron 50 EC)

T13 Pymetrozine 0.60 g 109.67a 6.67h 40.00i 0.26h (Plenum 50 WG)

T14 Thiamethoxam 0.12 g 107.67a 32.00d 165.00d 1.07d (Spike 25 WG)

T15 Control Only water 108.00a 79.67a 154.67ef -

Level of significance NS ** ** **

LSD0.05 9.92 3.29 6.98 0.05 CV% 5.41 6.19 2.62 2.97 **= Significant at 1% level of probability, *= Significant at 5% level of probability NS= Non-Significant, Values in a column followed by different letters are significantly different

With increased adoption of new high yielding varieties, use of insecticides were also increased and the destruction of predators and parasitoids that followed insecticide misuse resulted in resurgence of several rice pests including the BPH, Nilaparvata lugens (Heinrichs and Mochida, 1984). Anand et al. (2019) conducted an experiment with seven insecticides viz., chlorpyriphos, profenophos, cypermethrin, deltamethrin, bifenthrin, lambda cyhalothrin and imidacloprid on the growth and reproductive physiology of rice brown planthopper. They reported that bifenthrin, cypermethrin, lambda cyhalothrin and deltamethrin resulted in enhancement of fecundity of brown planthopper (227.67, 218.33, 199.00 and 191.00 nymph’s vs. 131.00 nymphs in control) and consequently resurgence ratio ranged from 1.18 to 1.74. Chlorpyriphos, cypermethrin, deltamethrin, bifenthrin and lambda cyhalothrin also significantly RESURGENCE OF BROWN PLANTHOPPER 123 increased the nymphal survival (86.67 per cent to 96.00 per cent against 80.67 per cent in control) and growth index (6.34 to 7.11 vs 5.63 in control). Effect of different doses of selected insecticides on development of BPH resurgence The data clearly showed that the number of BPH/pot was not significantly different (104 to 107) among the treatment before spray (Table 4) that differed significantly after 72 h and one month of spraying. The resurgence ratio was highest in T13 (1.61) followed by T1 (1.39) and then T14 (1.30), T7 (1.26) and T4 (1.22). T13 was significantly different with other treatments but T14 and T7 were insignificant. Resurgence ratio in T15, T10, T2, T9, T3 and T5 were 1.08, 1.02, 0.98, 0.97, 0.92, 0.91 and 0.91, respectively. Significantly the lowest resurgence ratio was found in T12 (0.65) and T11 (0.69) followed by T6 (0.84). T12 and T11 were insignificantly different among them but significantly different with T6. Use of insecticide at lower dose is common in farmers’ practice as it saves some money apparently. The practice of using low dose combined with short residual toxicity of many commercial insecticides may often cause the sub-lethal effect to the pest. The result is in conformity with the earlier findings (Heinrichs et al., 1982; Chelliah and Uthamasamy, 1986; Karns and Stewart, 2003. Heinrichs et al. 1982) and reported high BPH populations (40-fold) with lower application rates of FMC 3500 (0.2 kg a.i./ha) as compared to high rate (1.0 kg a.i./ha). Chelliah (1979) reported that low doses of resurgence-inducing insecticides increased the reproductive rate of the BPH and reduced the nymphal duration, eventually leading to resurgence. Heinrichs and Mochida (1984) reported that dose rates had a distinct effect on the degree of resurgence in both the decamethrin and methyl parathion treatments with the higher rates permitting the higher BPH populations. There were 850 BPH per hill at the high and 210 BPH per hill at the low decamethrin rate and 60 BPH per hill in the check. Present findings showed 20-50% increase in the levels of resurgence when low dose was used. Further lower dose might increase the resurgence ratio to some higher degree. The efficacy study of isoprocarb/MIPC showed it was an effective insecticide but when applied with sub-lethal dose was found to be developed resurgence. It clearly indicates that any recommended product or chemical could also be cause of resurgence development under improper doses. Bao et al. (2009) conducted an experiment on the effects of sublethal doses of four insecticides viz. triazophos, fenvalerate, imidacloprid and dinotefuran on the reproduction of BPH. Imidacloprid and dinotefuran reduced the fecundity of BPH to 68.8% and 52.4% in macropterous families, and to 57.9% and 43.1% in brachypterous families, when compared with the untreated controls. By contrast, triazophos and fenvalerate increased fecundity and consequently resurgence ratio increased.

124 Uddin et al.

Table 4. Effect of different doses of selected insecticides on resurgence development No. of BPH/pot at different time Low, intervals Recommended Resurgence Treatment After one and higher dose Before spray After 72 ratio hours of month of (g or ml/L water) (Pre-treatment) spray spray

T1 Imidacloprid (Admire 0.20 ml 104.67a 52.67bc 192.67b 1.39b 20 SL)

T2 Imidacloprid (Admire 0.25 ml 104.00a 49.00c 136.33fg 0.98fg 20 SL)

T3 Imidacloprid (Admire 0.30 ml 103.67a 30.00de 127.67gh 0.92gh 20 SL)

T4 Cartap 0.20 g 104.33a 47.00c 169.67d 1.22d (Suntap 50 SP)

T5 Cartap 2.40 g 105.67a 11.00g 126.33h 0.91h (Suntap 50 SP)

T6 Cartap 2.80 g 104.00a 8.00g 117.00i 0.84i (Suntap 50 SP)

T7 Isoprocarb/MIPC (Chabi 2.20 g 103.67a 23.00ef 174.67cd 1.26cd 75 WP)

T8 Isoprocarb/MIPC (Chabi 2.60 g 104.00a 13.00g 126.33h 0.91h 75 WP)

T9 Isoprocarb/MIPC (Chabi 2.80 g 104.67a 11.33g 135.00fgh 0.97fgh 75 WP)

T10 Pymetrozine (Plenum 50 0.30 g 104.67a 29.00de 142.00ef 1.02ef WG)

T11 Pymetrozine (Plenum 50 0.60 g 105.00a 15.00fg 96.33j 0.69j WG)

T12 Pymetrozine (Plenum 50 0.90 g 103.67a 16.00fg 90.67j 0.65j WG)

T13 Thiamethoxam (Spike 0.10g 104.00a 60.33b 223.33a 1.61a 25 WG)

T14 Thiamethoxam (Spike 0.12 g 103.67a 50.33c 180.00c 1.30c 25 WG)

T15 Thiamethoxam (Spike 0.14 g 104.33a 32.00d 150.00e 1.08e 25 WG)

T16 Control Only water 106.67a 79.67a 139.00f - Level of significance NS ** ** **

LSD0.05 7.86 8.71 9.00 0.06 CV% 4.51 15.85 3.71 3.69 **= Significant at 1% level of probability, *= Significant at 5% level of probability NS= Non-Significant, Values in a column followed by different letters are significantly different RESURGENCE OF BROWN PLANTHOPPER 125

Effect of insecticide on different stages of BPH for the development of resurgence Five different growth stages including combination of all stages were sprayed with a most common insecticide— Isoprocarb/MIPC (Chabi 75 WP) for the development of resurgence (Table 5). In treatment T1, the number of BPH/pot was nil (only bearing eggs) and there was no significant difference among the treatments (T2 to T6) before insecticide application. After 72 hours of spraying, significantly the highest number of BPH was found in control treatment; but after one month it was significantly higher with T1 (303) followed by T6 (178). Zero population was found in T2 and T3 treatments. Resurgence ratio was also significantly higher in T1 (1.71) treatment compared to other treatments. But no significant difference was found betweenT4 (0.78) and T5 (0.82) treatments.

Table 5. Effect of isoprocarb/MIPC (Chabi 75 WP) on different stage of brown planthopper for the development of resurgence No. of BPH/pot at different time intervals Resurgence Treatment Before spray After 72 After one (pre- hours of month of ratio treatment) spray spray

T1 Egg 0.00b 0.00d 303.00a 1.71a st nd T2 1 -2 instar nymph 97.00a 1.67cd 0.00d 0.00c rd th T3 3 -4 instar nymph 100.00a 5.00c 0.00d 0.00c

T4 Adult 98.67a 15.67b 138.00c 0.78b

T5 Combination of all 100.00a 19.00b 144.44c 0.82b stages

T6 Combination of all 99.67a 84.33a 177.67b - stages (control) Level of significance ** ** ** **

LSD0.05 4.97 4.99 24.30 0.19 CV% 3.31 13.09 10.5 15.4 **= Significant at 1% level of probability, *= Significant at 5% level of probability NS= Non-Significant, Values in a column followed by different letters are significantly different

Many researchers reported that the effectiveness of insecticide depends on the stage of the insect. The reproductive rates of BPH exposed to insecticide-sprayed plants during nymphal or adult stage or both varied significantly (Chelliah, 1979). The reproductive rate was significantly higher when the BPH was exposed to plants sprayed with resurgence-inducing insecticides at the fourth-and fifth-instar stage as well as the adult stage (Chelliah, 1979). The result of the present study clearly showed that the nymph of the BPH was more susceptible to insecticides than the egg

126 Uddin et al. and adult stages. Therefore, care should be taken to apply insecticides depending on stage of the pest. There may be a requirement to spray the crop again after a few days to kill the early insects of pest developed from the primary sprayed egg.

CONCLUSION The insecticide namely acetamiprid, acephate, chlorpyrifos, cypermethrin, deltamethrin, fenvalerate, lambda-cyhalothrin, thiamethoxam were responsible for higher resurgence of BPH. In contrast, imidacloprid, cartap, dinotefuran, isoprocarb/MIPC, phenthoate, pymetrozine showed low resurgence producing potentials. So, recommendation of insecticides for controlling BPH that produce low resurgence require special attention in selecting right dose at the right time because they may cause high resurgence of BPH when used at sub-lethal dose. A similar high resurgence was evident with insecticide treatment at egg and adult stage. There may be a requirement to spray the crop again after a few days to kill the early insects of pest developed from the primary sprayed egg.

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Muthayya, S., Sugimoto, J.D., Montgomery, S. and Maberly, G. (2014). An overview of global rice production, supply, trade, and consumption. Annals of the New York Academy of Sciences, 1324: 7-14. Sivasubramaniam, N. and Imthiyas, M.S.M. (2018). Evaluation of the major factors associated in controlling of brown planthopper (Nilaparvata lugens) in paddy cultivation during its outbreak in selected divisional secretariats of the Ampara District of Sri Lanka. Scholars Journal of Agriculture and Veterinary Sciences, 5(11): 595-600. Volodymyr, I.L., Tetiana, M.M., Viktor, V.H., Janet, M.S. and Kenneth. B.S. (2018). Pesticide toxicity: a mechanistic approach. EXCLI Journal, 17: 1101-1136. Wang, A.H., Wu, J.C., Yu, Y.S., Liu, J.L., Yue, J.F. and Wang, M.Y. (2005). Selective Insecticide-Induced Stimulation on Fecundity and Biochemical Changes in Tryporyza incertulas (Lepidoptera: Pyralidae). Journal of Economic Entomology, 98(4):1144-9. Wojciechowska, M., Stepnowski, P. and Gołębiowski, M. (2016). The use of insecticides to control insect pests. Information Systems Journal, 13: 210-220. Wu, S., Zeng, B., Zheng, C., Mu, X., Zhang, Y., Hu, J., Zhang, S., Gao, C. and Shen, J. (2018). The evolution of insecticide resistance in the brown planthopper (Nilaparvata lugens Stål) of China in the period 2012–2016. Scientific Report, 8(1): 1-11. SAARC J. Agric., 18(1): 129-142 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48387

Research Article IMPACT OF IMPROVED CHICKPEA CULTIVATION ON PROFITABILITY AND LIVELIHOOD OF FARMERS IN DROUGHT-PRONE AREAS OF BANGLADESH

S.C. Sharna*, M. Kamruzzaman and S.T. Siddique Department of Agricultural Economics, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706

ABSTRACT The cultivation of improved chickpea varieties has been increasing over time that kicks off the local varieties from the farmer’s field. Up-to-date socio-economic information regarding this issue is scanty in Bangladesh. That is why we analyze the profitability of improved chickpea variety and assess the impact of its cultivation on the livelihood of chickpea farmers in the high Barind region of Bangladesh. The values of benefit-cost ratio depict that the improved variety is more profitable in comparison to local chickpea variety; specifically, the benefit-cost ratio (BCR) of improved chickpea production is 1.87, while it is only 1.66 for local chickpea. To understand the wellbeing of chickpea farmers, the multidimensional livelihood index (MLI) following sustainable livelihood framework of the Department for International Development (DFID) is used, which constitutes the asset pentagon of five capitals namely human, physical, natural, financial and social capital. The MLI of improved and local chickpea growers are 0.51 and 0.39 respectively which belong in the middle livelihood category. Meanwhile, the MLI reflects that the improved variety cultivars are in a better livelihood condition than the local variety growers. Among all the five capitals of the MLI, the difference between these two groups is the largest in the case of social capital followed by financial capital. Since both groups have achieved far less MLI values than 1, the recommendation is therefore to ensure different types of facilities for the development of people of high Barind tract as well as increasing the production of improved chickpea. Keywords: High Barind region, Chickpea, Multidimensional livelihood index, Profitability

* Corresponding author: [email protected]

Received: 24.04.2020 Accepted: 22.06.2020 130 Sharna et al.

INTRODUCTION The agricultural sector is the backbone of Bangladesh’s economy employing 40.60% of the total population and contributes to 13.60% of Gross Domestic Product (BER, 2019). This sector is dominated by paddy prioritized cropping patterns rather than other high value and nutrient-rich crops. In this situation, variation in cropping patterns and raise crop productivity are needed to ensure food security and ameliorate farmer’s livelihood. With the increased production of nutritious and high-value crops like pulses and oilseeds, farmers can assure food security along with combating poverty at the grass root-level (Ahmed et al., 2012). Among all pulses, chickpea (Cicer arietinum) is importantly recognized for the northwestern high Barind region of Bangladesh because of its capacity to grow well in low moisten and unfertile soil (Saha, 2002). This area includes Rajshahi, Chapai Nawabganj, and Naogaon districts. The hard-pan soil in this area is above from the normal flood-level composed of grey terrace soiland low organic matter (0.8–1.2%) (Rashid et al., 2017). This tract comprises approximately 0.8 million ha, is also home to the poorest farmers of Bangladesh (Joshi et. al., 2001). Competent technology with institutional changes, not only shapes the agricultural sectors but also uplifts the standard of living (Barrett and Carter, 2010; Bandiera and Rasul, 2006). In particular, chickpea is attractive in this context for its capability to edifice soil quality, its low input requirements and high market price (Saha, 2002). Farmers can start growing chickpea without almost any monetary input after rice production (ICRISAT, 2017). Nonetheless, the area and quantity of chickpea cultivation have been decreasing over time that leads to a copious amount of import bill. The production of chickpea lessened from 61,485 tons (1997) to 6,237 tons (2017) in the last two decades even though yield soared from 0.73 to 1.05 t ha-1 over the period (DAE, 2019). In order to meet the consumption demand, Bangladesh imported 190322 tons chickpea in 2017 that was 96% of the total chickpea supply in the market in that year (FAO, 2019). Alternately, worldwide chickpea production in 2014 was 13 million tons, whereas it was only 7 million tons in 1971 (FAO, 2019). Hence, it is urgent to increase chickpea production in the country to fulfil people’s demands and uplift the farmer’s livelihood. For expanding the production and productivity, research institutions of Bangladesh developed many improved chickpea varieties that produce higher yields and are tolerant in different stress conditions. Since the Bangladesh Agricultural Research Institute (BARI) alone has released twenty-seven pulses varieties whereas there are nine chickpea varieties (BARI, 2019). Among all these varieties, BARI Chola-5 is the most cultivated variety in 2018-19 with 438 ha area, followed by BARI Chola-6 (275 ha) and BARI Chola-4 (31 ha) in Rajshahi district (DAE, 2019). Although many chickpea varieties were developed in the past, only one study was done by Rashid et.al. (2014) on the modern chickpea varieties cultivation and profitability in the Barind region. They disclosed that the benefit-cost ratios are about 2.1 and 1.9 for improved and traditional chickpea varieties, respectively. However, CHICKPEA CULTIVATION IN DROUGHT PRONE AREAS 131 the comparative profitability between any specific modern chickpea variety and local chickpea variety has not been computed yet. Despite only a few works that have been done on chickpea farmer's wellbeing, a plethora amount of studies is found about the livelihood of other crops farmers. Only Verkaart et al. (2017) have ascertained the welfare impacts of improved chickpea adoption in Ethiopia. They assessed that the adoption of improved chickpea increased dramatically from 30% to almost 80% of the sample throughout 2006-07 to 2013-14. Income from improved varieties contributed up to 20% of total household income and alleviated household poverty in Ethiopia. About the Barind region of Bangladesh, Saha (2002) stated that chickpea cultivation surged the total farm income of all categories of farmers except the landless farmer. In absolute value, the contribution of chickpea in whole-farm income is highest (14.0%) for small farmers followed by the earnings share of large farmers (11.4%), medium farmers (10.8%) and marginal farmers (7.7%). Whereas, Islam (2018) documented that modern T. Aman rice has a positive effect on farmer’s wellbeing in rural Bangladesh that is ensured by the increased household income, wet season rice income, wet season rice yield, and household consumption expenditure. Meanwhile, Kamruzzaman and Takeya (2008) observed that both vegetable and rice producers significantly received more profit by inaugurating modern technology and innovative practices. The capacity in terms of the technical, social, human, natural and financial capital was progressed among the farmers who adopt new agricultural practices than the other farmers. Notwithstanding, many types of research have analyzed the livelihood of farmers (Islam, 2018; Nazli et al., 2012; Kamruzzaman and Takeya, 2008), there is hardly any work about the wellbeing of chickpea farmers of the drought strike area of Bangladesh. For that reason, it is necessary to assess different chickpea farmer’s livelihood as well as the comparative profitability of improved and local chickpea variety. The outputs of this study may layout proper directions to the researchers and policymakers about the considerable factors for livelihood upliftment of the chickpea farmers of the high Barind region. Alongside, the comparative profitability between the improved and traditional chickpea variety will provide an entire scenario of cost and return of chickpea cultivation.

METHODOLOGY Data and Survey Based on the objectives of the study, a multi-stage sampling technique was employed to gather primary data. In order to reveal the most chickpea growing areas, firstly, district, Upazila, and villages were purposively selected. After that, a field survey was conducted in different villages of Godagariand Tanore Upazila of Rajshahi district located in the high Barind region. This area has a high concentration of 132 Sharna et al. different improved chickpea variety growers along with local chickpea variety producers. A total of 180 farmers were interviewed, among them, 120 respondents were improved chickpea growers (i.e. adopters) and 60 respondents were local chickpea producing farmers (i.e. non-adopters). For this study, the BARI Chola-5 variety is considered as the improved variety. For confirmation of data reliability, data were collected in two different periods. The time length is from 11th to 18th March (during chickpea cultivation) and 9thto 18thJune (after chickpea harvesting) of 2019, consecutively. Conceptualizing and Quantifying Livelihood Different agencies such as the Department for International Development (DFID), Cooperative for Assistance and Relief Everywhere (CARE), United Nations Development Program (UNDP), etc. have developed different livelihood approaches. All the organizations link their ideas to the work of Chambers and Conway (1992). According to them, livelihood comprises the capabilities, assets (including both material and social resources) and activities required for a means of living. In this study, the sustainable livelihood framework of the DFID is used comprising the asset pentagon of five different capitals namely human, physical, natural, financial and social capital (DFID, 2000). The asset is grouped into those five broad types of capital. The first one is human capital which represents the skills, knowledge, ability to labour and good health that together enable people to ascertain different livelihood strategies and obtain their goals (Table 1).

Table 1. Eight indicators of human capital used in multidimensional livelihood index Indicator Description and Measurement Household size Number of a family member in the household Education of farmer Passed year(s) of formal schooling by the farmer Education of farmer’s spouse Passed year(s) of formal schooling by farmer’s spouse Health status Number of chronic illness occurred in the household during the previous year Farming experience Farmer’s farming experience (years) Training Number of agricultural training received by farmers Mobile usage ability Having mobile money transfer account and can use the mobile internet =2; having mobile money transfer account or can use the mobile internet =1; none of this=0 Crop diversification index Crop diversification index (CDI) ; Here, Herfindahl index; is calculated from the primary data

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Table 2. Nine indicators of physical capital used in multidimensional livelihood index Indicator Description and Measurement Housing status (Wall: brick, roof: brick, floor: brick) =5; (wall: brick, roof: tin, floor: brick) =4; (wall: brick, roof: tin, floor: earth) =3; (wall: tin, roof: tin, floor: earth) =2; (wall: earth, roof: tin, floor: earth) =1 Basic sanitation Household has flash toilet=3; permanent toilet=2; temporary toilet=1 Access to paved roads Concrete road to the house =3; brick road to the house =2; earth road to the house =1 Shop Dummy, the household has shop =1; otherwise =0 Motor vehicle Dummy, the household has any motorized vehicle =1; otherwise =0 Water sources Household has water pump=2; household has tube well=1; household collect water from other house=0 Agricultural equipment Dummy, a household has tractor/power tiller, sprayer, and spade like equipment =3; household has any two of the above- mentioned equipment =2; household has only one equipment =1 Refrigerator Dummy, the household has refrigerator=1, otherwise =0 Television Dummy, the household has television =1, otherwise =0

Secondly, physical capital comprises the basic infrastructure and goods incorporating buildings, sanitation facilities, agricultural equipment, etc. which created by economic production processes (Table 2).Whereas, natural capital is the stock of natural resources available for utilization (Table 3). This encompasses both intangible public goods (e.g. the atmosphere and biodiversity) and visible assets that are used directly for production purposes (e.g. trees, land, etc.).

Table 3. Four indicators of natural capital used in multidimensional livelihood index Indicator Description and Measurement Land Farm size in acres Organic fertilizer Dummy, household uses organic fertilizer =1, otherwise =0 Trees Number of the tree(s) within the house Irrigation water Distance to the nearest irrigation sourced from farmland in meter

134 Sharna et al.

Table 4. Four indicators of financial capital used in multidimensional livelihood index Indicator Description and Measurement Other income sources Dummy, household gets remittances/pension/transfer payments =1; otherwise=0 Credit facility Dummy, the household has access to credit=1; otherwise =0 Savings account Dummy, the household has savings account=1; otherwise =0 Livestock Number of the livestock(s) in the household

Besides, financial capital involves the financial resource; simply, the stocks of liquid resources to which the household has access (e.g. savings, credit). There are two main sources of financial capital; a) available stocks such as savings, cash, credit, bank deposits, etc.) regular inflows of money like pensions or other transfers from the state, and remittances from outside home (Table 4). The last capital refers to social capital, which embraces networks and connectedness that enrich people’s trust and ability to work together and expand their access to wider institutions (Table 5).

Table 5. Description of 4 indicators of social capital used in multidimensional livelihood index Indicator Description and Measurement Association with GO Dummy, the respondent is a part of GO/NGO/any development and/or NGO program=1; otherwise=0 Network Number of agricultural information source(s) Organization Dummy, having any organization membership=1; otherwise=0 membership Women decision-making Dummy, Dummy, if the woman of the farm family can make ability decisions =1; otherwise=0

The Multidimensional Livelihood Index For providing quantitative evidence-based index, the Multidimensional Livelihood Index (MLI) is developed in this research. In order to produce indicators with an identical range of 0-1 the Min-Max normalization method is used, which uses the following calculation:

CHICKPEA CULTIVATION IN DROUGHT PRONE AREAS 135

Here, represents the value of indexed indicator i of major component j; is the value of the indicator ‘i’ of major component or capital j; is the local minimum value of the indicator, and is the local maximum value of the indicator in the survey area. Equation (1) is employed for the calculation, where the indicators are positively related to livelihood. On the opposite side, equation (2) is used for the indicators that have a negative relationship with the wellbeing of farmers. Thus, the usage of equation (2) is bound for the indicators of health status (measured through occurred chronic illness) and irrigation water (valuated by distance to irrigation sources) because these measurements are negatively related to the development of livelihood. In contrast, equation (1) is used for the rest of the indicators of the multi-dimensional livelihood index. Once indicators are standardized, they are combined using equal weighting to calculate the value of major component or capital by Equation (3):

here, is the value of capital ‘j’ and ‘n’ is the number of sub-components or indicators in major component . The composite score of the livelihood index is enumerated for a respondent by summing the score of all capitals. Equal weighting is used to combat the complexity and subjective nature of livelihood indices. The equation used for the estimation of MLI is:

RESULTS AND DISCUSSION Profitability of Chickpea Production The comparisons of costs and returns of modern chickpea variety and local chickpea variety cultivation are illustrated in Table 6. Costs of all items are higher for the traditional chickpea variety than the improved variety only except the cost of hired labour. The land-use cost is the same for all farmers as all farmers belong in the same region. The land preparation cost is a little greater for the traditional variety as more fragmented lands were used for growing local variety which increases the per-acre land preparation cost. Despite, the seed cost is more for improved variety (Tk 116/kg) than traditional variety (Tk 75-90/kg), the per-acre seed cost is higher for local variety in the surveyed area. The reason behind this is the compensation of seed from the governmental institutions for instances, Department of Agricultural Extension (DAE) and Bangladesh Agricultural Development Corporation (BADC) that reduces the per-acre seed cost of adopters. Because of the resistance characteristics of improved chickpea variety, the adopters do not have to use a lot of fertilizers and pesticides and/or insecticides that results in less fertilizer and pesticide cost for 136 Sharna et al. adopters. As the improved variety provides a better yield than local variety, the adopters have to spend more on hired labour for harvesting than the expense of non- adopters. However, the adopters expend less on family labour because they use fewer amounts of fertilizer and pesticide per acre that ultimately tends to results in less utilization of family labour.

Table 6. Per acre cost and returns of improved and local chickpea variety cultivation Improved variety Local variety Particulars Mean SD Mean SD Land preparation 1916 183 1952** 154 Seeds 2048 967 2065 177 Fertilizers 559 118 657*** 102 Pesticides 466 117 809*** 109 Hired labor 6008 788 5877* 1038 Total variable cost (TVC) 11098 1472 11363* 1098 Land use cost 9000 00 9000 00 Interest on operating capital 117 15 120 11 Family labor 1626 38 1814*** 102 Total fixed cost (TFC) 10744 44 10935*** 102 Total cost (TVC+TFC) 21843 1496 22299** 1109 Gross return (GR) 40574 2743 36937*** 2662 Gross margin (GR-TVC) 29476 2901 25574*** 2963 Net return (GR-TC) 18731 2906 14638*** 2960 Benefit cost ratio (GR/TC) 1.87 0.17 1.66*** 0.15 Note: *, **, and *** indicate that mean differences between the BARI Chola-5 variety and local chickpea variety production are significant at the 10%, 5%, and 1% confidence levels, respectively.

Meanwhile, improved chickpea variety is not only more productive but also hasa higher price. Therefore, the adopters get about 15% higher gross margin, almost 1.3 times the net return of non-adopters earn that finally results in an almost 13% higher benefit-cost ratio for adopters (Table 6).

CHICKPEA CULTIVATION IN DROUGHT PRONE AREAS 137

The livelihood of Chickpea Growers

Human 1

0.5 Social Physical Adopter 0 Non-adopter

Financial Natural

Figure 1. The multi-dimensional livelihood index of adopters and non-adopters

The results of the multidimensional livelihood index reveal that there is a significant difference between the livelihood status of modern chickpea growers and local chickpea cultivators. Table 3 shows that the Multi-dimensional Livelihood Index (MLI) of adopters and non-adopters are 0.51and 0.39 respectively and the difference between them is significant at a 95% confidence level. All five capitals of MLI indicate a better position of adopters than the conditions of non-adopters (Fig. 1). Nonetheless, unsatisfactorily, the differences between those two categories are significant only for two capitals (financial and social capital) whereas, the dissimilation is insignificant for the other three capitals namely human, physical and natural capital. However, the differences between these two groups are significant for 21 indicators among 29 of them.

Table 7. Comparison between the livelihood of adopters and non-adopters Capital Indicator Adopter Non-adopter Human 0.48 (0.21) 0.44 (0.22) Household size 0.38 (0.28) 0.56 (0.28)*** Education of farmer 0.42 (0.21) 0.35 (0.18)** Education of farmer’s spouse 0.31 (0.23) 0.22 (0.18)*** Health status 0.92 (0.20) 0.86 (0.24)* Farming experience 0.32 (0.23) 0.36 (0.24) Training 0.35 (0.25) 0.20 (0.15)*** Mobile usage ability 0.45 (0.39) 0.34 (0.36)* Crop diversification index 0.72 (0.18) 0.61 (0.21)*** Physical 0.52 (0.25) 0.43 (0.22) 138 Sharna et al.

Capital Indicator Adopter Non-adopter Housing status 0.59 (0.23) 0.45 (0.23)*** Basic sanitation 0.61 (0.22) 0.58 (0.25) Access to paved roads 0.75 (0.28) 0.60 (0.38)*** Shop 0.23 (0.42) 0.20 (40) Motor Vehicle 0.18 (0.33) 0.10 (0.26)* Water source 0.57 (0.23) 0.44 (0.21)*** Agricultural equipment 0.21 (0.31) 0.18 (0.27) Fridge 0.78 (0.42) 0.70 (0.46) Television 0.78 (0.42) 0.65 (0.48)* Natural 0.46 (0.16) 0.43 (0.18) Land 0.36 (0.18) 0.21 (0.11)*** Organic fertilizer 0.68 (0.47) 0.62 (0.49) Trees 0.32 (0.23) 0.39 (0.22)* Irrigation water 0.49 (0.24) 0.51 (0.19) Financial 0.63 (0.17) 0.42 (0.11)* Other income sources 0.40 (0.49) 0.35(0.48) Credit facility 0.68 (0.47) 0.33 (0.48)*** Savings account 0.80 (0.40) 0.58 (0.50)*** Livestock 0.63 (0.29) 0.41 (0.27)*** Social 0.48 (0.15) 0.25 (0.14)* Association with GO and/or NGO 0.31 (0.46) 0.18 (0.39)* Network 0.58 (0.33) 0.28 (0.28)*** Organization membership 0.40 (0.49) 0.12 (0.32)*** Women decision making ability 0.62 (0.49) 0.43 (0.50)** Multidimensional Livelihoods Index 0.51 (0.07) 0.39 (0.08)** Note: Figures in parentheses are standard deviations. *, **, and *** indicate that mean differences between the adopters and non-adopters are significant at the 10%, 5%, and 1% confidence levels, respectively.

In the first place, the average value of human capital is 0.48 for adopters that are higher than the non-adopters (Table 7). In this study, human capital comprises eight indicators where differences for all of the indicators are significant except farming experience. Among those, the education of farmer and farmer’s spouse, health status, training, mobile usage ability, and crop diversification index represent higher value for adopters rather than only household size. The results imply that the adopter and CHICKPEA CULTIVATION IN DROUGHT PRONE AREAS 139 their spouses have a better formal education. Some studies also found that hybrid and modern varieties adoption have a positive correlation with the educational status of the farmers (Smale et al., 2018). Furthermore, the adopters have new skills such as the utilization of mobile money transfer account and the internet on mobile. In line with those skills, they receive more training which helped them to adopt new agricultural technology. Besides, the adopter experienced less chronic illness. Again, the adopters produce different types of crops to minimize the risk and uncertainty in agriculture along with gaining more profit. However, the non-adopters have a bigger household size that provides them to grow more labour intensive crops instead of cultivating easy growing crops. Usually, the physical capital includes infrastructure, machinery, agricultural equipment, electric tools, etc. those are acting as vital for societal development. In this study, there are nine indicators to estimate the value of physical capital. Table 7 depicts that the adopters have a larger value of the physical assets (0.52) than the non-adopters (0.43) which provides improved chickpea variety farmers with a better status in the society. Meanwhile, the differences between these two groups are significant only for the indicators of housing status, the road to the house, water source, and having motor vehicle and television. The livelihood of rural people depends on the spatial factors like road facility as well as other physical factors to a great extent (Donohue and Biggs, 2015). One of the crucial capitals for the development of rural communities is natural capital, which comprised natural resources like land, pond, sources of irrigation, trees, etc. The estimated value of natural capital shows that both the adopters (0.46) and the non-adopters (0.43) stay almost in the same position (Table 7). Four indicators are set for elucidating natural capital. Among those, the differences between these two groups are significant for the indicators of land and trees. Even though adopters have more than 1.5 times land compared to the non-adopters, local chickpea variety growers have almost 21% more trees in their homestead area (Table 7). There are four sub-components of financial capital including outside income sources, credit accessibility, savings account and livestock, which is the most common liquid resource of rural households. The adopters obtain 0.63 for financial capital that is larger than the acquired value of non-adopters (Table 7). In comparison to non- adopters, the adopters have access to about two times credit for agriculture purposes and it turns into profit from the production of more agricultural products. Similarly, the proportion of farmers has a savings account is about 38% higher for improved chickpea variety producers. Besides, they have 1.5 times more livestock than non- adopters that provide not only daily inflows of money but also a large amount of money at one instance after selling that livestock (Table 7). Meanwhile, some research works indicated that the improved variety adoption and per capita household income have a positive correlation, which ultimately reduces poverty and ensures 140 Sharna et al. food security (Islam, 2018; Verkaart et al., 2017; Nazli et al., 2012; Mendola, 2007). The last capital of MLI named as social capital that reports the status of the social relationship of the sample farmers in the surveyed community. The index of social capital is almost double for adopters than non-adopters (Table 7). This capital includes four indicators, for instance, relationship with governmental and non- governmental organizations, network with different information sources, organization membership and women’s position in society. Satisfactorily, the differences between adopters and non-adopters are significant for all of those four indicators. About 31% of adopters are in association with various types of governmental, non-governmental bodies or government-authorized program. Furthermore, the adopters have a better network with different organizations (e.g. DAE, BADC, BARI, etc.), agricultural input dealers, different Non-Governmental Organizations (NGOs), neighbor, relatives, other farmers, etc. These networks work as information sources that disseminate valuable information about modern and sustainable agricultural technology. Besides, some farmers act as a member of different own managed organizations for community development. These organizations mainly deposit the member’s money and provide credit to the farmers with lower interest rate along with some other services. Compared to non-adopter’s family, the women in the adopter’s family mainly the spouse of farmers can visualize their decision about agriculture. Thus, the women of adopter's family have more influence on agricultural practices.

CONCLUSIONS This article is an exertion to divulge the profitability of improved and local chickpea as well as the livelihood scenario of different chickpea farmers in the drought strike region of Bangladesh. The comparison between the livelihood of adopters and non- adopters imparts information about the livelihood of two different groups. Undoubtedly, the improved chickpea variety provide a larger gross margin, net return and ultimately, a bigger benefit-cost ratio than its counterpart. Along with that, the higher MLI of adopters proclaims a better livelihood condition than non-adopter that demonstrates a partial intervention of improved chickpea variety in the high Barind region. As the MLI for adopters and non-adopters are much lower than their full potential, there is a plethora of scope to develop their livelihood scenario. Through promulgating the improved varieties with more productivity and different resistance characteristics, the wellbeing of miserable farmers can be upgraded considerably. In this circumstance, the more intensive policy is needed to ameliorate the condition of non-adopters through providing other facilities, for instances, better education, proper rural infrastructure, women empowerment activities, income diversifying opportunities along with increasing modern variety cultivation.

CHICKPEA CULTIVATION IN DROUGHT PRONE AREAS 141

ACKNOWLEDGEMENT The authors are grateful to the Ministry of Science and Technology (MoST), Government of the People’s Republic of Bangladesh for providing financial support to implement the research work purposively.

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Research Article ASSESSMENT OF DISEASE RESISTANCE AND HIGH YIELDING TRAITS OF COMMON BUCKWHEAT GENOTYPES IN SUBTROPICAL CLIMATE OF NEPAL

S. Subedi1*, N.B. Dhami2, S.B. Gurung2, S. Neupane1, S. Thapa1 and L. Oli1 1National Maize Research Program, Rampur, Chitwan 2Hill Crops Research Program, Kabre, Dolakha

ABSTRACT Twelve common buckwheat (Fagopyrum esculentum Moench) genotypes received from Hill Crops Research Program, Kabre, Dolakha were evaluated for resistance against major foliar fungal diseases and high yielding traits at the research field of National Maize Research Program, Rampur, Chitwan during winter seasons of two consecutive years 2017 and 2018. The design of the experiment was Randomized Complete Block having three replications. The unit plot size was 3m x 2m with 25cm row to row spacing and continues plant to plant spacing was maintained and net harvested plot was 6 sq meters. The experiment was planted at last week of October in both years. The recommended dose of fertilizer was 30:30:0 for N:P:K kg ha-1 respectively and seed rate 50 kg ha-1. Disease data were recorded for downy mildew (Perenospora fagopyri), powdery mildew (Erysiphepolygoni) and botrytis leaf blight (Botrytis cinerea) severity on 20 randomly tagged plants/plot. The yield and yield attributing traits were recorded. Buckwheat genotypes differed significantly (p<0.05) on disease severity, grain yield and yield attributing traits. Genotypes CBBP-01, KIF-72-22-520, GF5283, ACC#2234 and ACC#2213 were resistant to powdery mildew, downy mildew and botrytis leaf blight and also resulted in higher grain yield. Grain yield had found strong negative correlation with the fungal disease severity of buckwheat. These genotypes might be useful for the development of major foliar fungal disease resistant high yielding common buckwheat variety in inner terai region of Nepal. Keywords: buckwheat, Disease resistance, Genotypes, Yielding traits

INTRODUCTION Common or sweet buckwheat (Fagopyrum esculentum Moench) and bitter buckwheat (Fagopyrum tataricum (L.) Gaertn) both belongs to family Polygonacea.

* Corresponding author: [email protected]

Received: 30.03.2020 Accepted: 24.05.2020 144 Subedi et al

Globally 18 species are known to be the members of genus Fagopyrum including two cultivated species Fagopyrum esculentum and F. tataricum (Oshaki et al., 2001). It is a sixth staple food crop next to rice, wheat, maize, finger millet, and barley in term of cultivation area (10296 ha), production (11472 mt) and yield (1114 kg ha-1) in Nepal (MoAD, 2019). It is considered as a pseudo-cereal, poor man’s crop, and underexploited or neglected crops, occupying major place for Nepalese agriculture system and contributing greatly in food and nutrition security of remote areas especially in high hilly regions (Baniya, 1995).It is the best crop in higher altitude in terms of adaptation to different climatic variables and easily fitted to different cropping patterns due to short duration (Joshi, 2008). It is cultivated on marginal land in 61 out of 75 districts of Nepal from about 60 to 4500 masl, especially hilly and mountain districts. Sweet or common buckwheat varieties are generally grown in mid hill, terai and inner terai region but Tartary or bitter buckwheat varieties are grown in higher altitude (Baral et al., 2014). There are altogether 19 local landraces of sweat or common buckwheat and 37 for Tartary or bitter buckwheat listed from Nepal (Joshi, 2008). Although the scope and demand of buckwheat is increasing due to its medicinal and nutritional values but the yield potential is reducing due to several biotic and abiotic constraints. The reason behind the low productivity of most of the crops in Nepal is due to the attack of many plant diseases at different stages of crop. Disease is one of the major biotic constraints to reduce buckwheat yield and also deteriorate the quality of product that ultimately reduce the market price. Buckwheat plants are affected by wide range of pathogens with fungal diseases being the most important. Fungal diseases decrease in productivity through infection and damage to leaves, stems, roots and pods as well as reduce market value due to discolor seeds (Subedi et al., 2015). Among biotic constraints, downy mildew (Perenospora fagopyri), powdery mildew (Erysiphe polygoni), botrytis leaf blight (Botrytis cinerea), rust (Puccinia fagopyri), damping-off and root rot (Rhizocto niasolani, Pythium spp, Fusariumspp) were the major fungal diseases contributes the wide yield gap in buckwheat genotypes (Manandhar et al., 2016). Resistant varieties provide a more effective and more consistent method of disease control without causing any hazards to environment. Transfer of available resistance traits in a cultivated background is especially important for farmers lacking resources to control the disease (Subedi, 2015). Therefore, the present study was undertaken in order to find out the resistant/tolerant buckwheat genotypes against major foliar diseases and the direct and indirect contributions of these traits toward yield.

MATERIALS AND METHODS Twelve buckwheat genotypes received from Hill Crops Research Program (HCRP), Kavre, Dolakha were evaluated for sources of disease resistance and yield related traits in National Maize Research Program (NMRP), Rampur, Chitwan during winter seasons of two consecutive years 2017 and 2018. The geographical location of the research area was 27°37' N latitude and 84°25' E longitude at an altitude of 256 DISEASE RESISTANCE AND HIGH YIELDING TRAITS OF BUCKWHEAT 145

ABMSL and has sub-tropical climate. The design of the experiment was randomized complete block having three replications. The unit plot size was 3m x 2m with 25cm row to row spacing and continues plant to plant spacing was maintained and net harvested plot was 6 square meters. The experiment was planted at last week of October in both years. The recommended dose of fertilizer was 30:30:0 N:P:K kg ha-1 and seed rate 50 kg ha-1. Other agronomic practices were followed as recommended (HCRP, 2017). Disease data were recorded for downy mildew (Perenosporafagopyri) and powdery mildew (Erysiphe polygoni) severity in 0-9 scale (Manandhar et al., 2016) whereas botrytis leaf blight (Botrytis cinerea) severity in 1-9 scoring scale on 20 randomly tagged plants/plot (Morrall and Mckenzie, 1974). Yield and yield attributing traits like days to flowering, days to maturity, plant height (cm), grain yield (kg ha-1) and hundred seed weight (g) were recorded. Percent Disease Index (PDI) is computed on the basis of recorded data according to the formula (Wheeler, 1969). Sum of all numerical values 100 PDI (%) = × No of plants observed Maximum diseases rating

All data were analyzed statistically using Microsoft Excel and MSTAT-C computer package program. Treatment means were compared using duncans multiple range test (DMRT) at 5% level of significance (P≤0.05). The relationship between disease severity and yield were also calculated.

RESULTS AND DISCUSSION Experiment during 2017 Data revealed that statistically significant differences were observed in the traits flowering days, days to maturity, plant height (cm), grain yield (kg ha-1), hundred seed weight (g), powdery mildew, downy mildew and botrytis leaf blight severity among buckwheat genotypes during winter season of 2017 (Table 1). The flowering days ranged from (19-25 days), maturity days (57-68 days), plant height (96-106 cm) and hundred seed weight (3.30 - 4.20 g). Buckwheat genotypes KIF-72-22-520 (1958.56 kg ha-1), CBBP-01 (1950 kg ha-1), GF 5283 (1868.44 kg ha-1), ACC#2234 (1857.22 kg ha-1) and PL 15 (1850.44 kg ha-1) were the high yielding compare to standard check Mithe Phapar-1 (1830 kg ha-1). Out of 12 buckwheat genotypes, CBBP-01, KIF-72-22-520, ACC#2234, GF 5283, ACC#2213 and PL-15 were resistant having percent disease index (PDI) of 26.35, 27.08, 30.05, 31.45, 31.65 and 32.25% respectively whereas Chitwan local (local check) was susceptible having PDI of 56.88% to powdery mildew disease. The downy mildew severity was found lower in genotypes KIF-72-22-520 (32.42%), CBBP-01 (34.25%), GF 5283 (35.58%) and ACC#2234 (38.45%) compared to 146 Subedi et al standard check Mithe Phapar-1 (41.45%). The higher downy mildew severity was recorded in genotype ACC#6529 (65.92%) followed by Chitwan local (61.72%).Similarly, botrytis leaf blight severity was found lower in genotypes CBBP-01 (37.45%), KIF-72-22-520 (38.88%), GF 5283 (41.35%), ACC#2234 (43.95%) and ACC#2213 (45.48%) compared to standard check Mithe Phapar-1 (48.58%). The higher botrytis leaf blight severity was recorded in genotype ACC#6529 (73.35%) followed by Chitwan local (66.58%) (Table 1).

Table 1. Response of buckwheat genotypes to yield parameters and major foliar fungal diseases at NMRP, Rampur, Chitwan, Nepal during winter season of 2017 Fl_ Mat_ PHT GY HSWt Percent disease index (%) Genotypes days days (cm) (kg ha-1) (g) PM DM BLB ACC#5670 †23bc 66abc 96f 1677.11d 3.30f 47.68c 56.42c 62.28c ACC#2213 22c 64cde 99d 1755.11c 3.90bcd 31.65d 42.75d 45.48f ACC#6529 22c 60f 102cd 1438.33e 4.20a 64.12a 65.92a 73.35a ACC#2234 23bc 63de 101cde 1857.22b 4.10ab 30.05d 38.45e 43.95f PC-30 23bc 65bcd 101cde 1660.56d 3.80cde 50.02c 55.45c 62.58c GF 5283 24abc 68ab 101cde 1868.44b 3.50f 31.45d 35.58ef 41.35g CBBP-01 19d 61ef 103bc 1950.00a 4.10ab 26.35e 34.25fg 37.45h KIF-72-22-520 25a 68a 105ab 1958.56a 3.40f 27.08e 32.42g 38.88h PL 15 25a 67ab 106a 1850.44b 4.10ab 32.25d 42.62d 55.42d ACC#493 24abc 66abc 101cde 1463.33e 3.80cde 55.32b 58.22c 64.38bc Mithe Phapar-1 (SC) 22c 63de 102cd 1830.00b 3.60ef 32.52d 41.45d 48.58e Chitwan Local (LC) 19d 57g 97e 1473.56e 3.30f 56.88b 61.72b 66.58b Grand mean 22.58 64.06 101.08 1731.89 3.80 40.45 47.10 53.36 F test ** ** ** ** ** ** ** ** LSD (0.05) 2.06 2.26 2.18 38.60 0.03 2.43 2.89 2.33 CV,% 5.40 2.10 1.30 1.30 4.00 3.50 3.60 2.60 Note: † Means of 3 replications, Means in column with same superscript is not significantly differed by DMRT (P≤0.05). Fl_days – flowering days, Mat_days – maturity days, PHT- Plant height, GY- grain yield, HSWt- hundred seed weight, PM- Powdery mildew, DM- Downy mildew, BLB- Botrytis leaf blight, %- percentage, cm-centimeter, kg ha-1- kilogram per hectare, g- gram, SC- standard check, LC- local check, **- highly significant

Experiment during 2018 During 2018 also, trends of both disease development, yield and yield attributing traits were similar. The yield (kg ha-1) and yield attributing traits like flowering days, DISEASE RESISTANCE AND HIGH YIELDING TRAITS OF BUCKWHEAT 147 days to maturity, plant height (cm) and hundred seed weight (g) were significantly varied with buckwheat genotypes (Table 2). The flowering days ranged from (22-29 days), maturity days (61-71 days), plant height (100-108 cm) and hundred seed weight (3.60 - 4.50 g). Buckwheat genotypes CBBP-01 (2050 kg ha-1), KIF-72-22- 520 (2048.89 kg ha-1), GF 5283 (1984.44 kg ha-1), ACC#2234 (1953.89 kg ha-1) and ACC#2213 (1952.78 kg ha-1) were the high yielding compare to standard check Mithe Phapar-1 (1950 kg ha-1) (Table 2).

Table 2. Performance of buckwheat genotypes to yield parameters and major foliar fungal diseases at NMRP, Rampur, Chitwan, Nepal during winter season of 2018

Fl_ Mat_ PHT GY HSWt Percent disease index (%) Genotypes -1 Days days (cm) (kg ha ) (g) PM DM BLB ACC#5670 †25d 68bc 100e 1784.44c 3.60d 52.05d 63.55b 68.25c ACC#2213 26cd 67cd 103cd 1952.78b 4.20b 36.68ef 46.35e 54.28f ACC#6529 25d 63e 105bc 1526.67d 4.60a 67.48a 69.38a 75.72a ACC#2234 27bc 67cd 103c 1953.89b 4.50a 36.72ef 43.78f 51.45g PC-30 28ab 70ab 102cd 1785.56c 4.00bc 51.38d 61.48c 65.28d GF 5283 26cd 67cd 101de 1984.44ab 4.00bc 35.58f 40.72g 49.55g CBBP-01 23e 63e 106ab 2050.00a 4.20b 30.05h 35.62h 40.58i KIF-72-22-520 29a 71a 108a 2048.89a 3.80cd 33.45g 39.08g 46.22h PL 15 28ab 69ab 108a 1949.11b 4.20b 37.38e 49.42d 56.38e ACC#493 27bc 69ab 105b 1550.00d 4.10b 65.45b 69.18a 72.42b Mithe Phapar-1 (SC) 27bc 66d 103c 1950.00b 4.20b 36.55ef 47.55e 55.62ef Chitwan Local (LC) 22e 61f 100e 1586.22d 3.80cd 62.48c 64.72b 71.42b Grand mean 26.08 66.72 103.72 1843.50 4.10 45.44 52.57 58.93 F test ** ** ** ** ** ** ** ** LSD (0.05) 1.38 1.74 1.61 64.60 0.03 1.50 1.86 1.93 CV,% 3.10 1.50 0.90 2.10 3.70 1.90 2.10 1.90 Note: † Means of 3 replications, Means in column with same superscript is not significantly differed by DMRT (P≤0.05). Fl_days – flowering days, Mat_days – maturity days, PHT- Plant height, GY- grain yield, HSWt- hundred seed weight, PM- Powdery mildew, DM- Downy mildew, BLB- Botrytis leaf blight, %- percentage, cm-centimeter, kg ha-1 – kilogram per hectare, g- gram, SC- standard check, LC- local check, **- highly significant

Genotypes, CBBP-01, KIF-72-22-520, GF 5283, ACC#2213 and ACC#2234 were resistant having percent disease index (PDI) of 30.05, 33.45, 35.58, 36.68 and 36.72% respectively compared to standard check Mithe Phapar-1 (36.55%) whereas ACC#6529, ACC#493 and Chitwan local (local check) was susceptible having PDI of 67.48, 65.45 and 62.48% respectively to powdery mildew disease. The downy 148 Subedi et al mildew severity was found lower in genotypes CBBP-01 (35.62%), KIF-72-22-520 (39.08%), GF 5283 (40.72%) and ACC#2234 (43.78%) and ACC#2213 (46.35%) compared to standard check Mithe Phapar-1 (47.55%) (Table 2). The higher downy mildew severity was recorded in genotype ACC#6529 (69.38%) followed by ACC#493 (69.18%) and Chitwan local (64.72%). Similarly, botrytis leaf blight severity was found lower in genotypes CBBP-01 (40.58%), KIF-72-22-520 (46.22%), GF 5283 (49.55%), ACC#2234 (51.45%) and ACC#2213 (54.28%) compared to standard check Mithe Phapar-1 (55.62%). The higher botrytis leaf blight severity was recorded in genotype ACC#6529 (75.72%) followed by ACC# 493 (72.42%) and Chitwan local (71.42%) (Table 2). Relationship between grain yield (kg ha-1) and powdery mildew severity (%) During 2017-2018 cropping season of buckwheat, yield was found to had highly significant negative correlation (r = -0.99) with the percent disease index of powdery mildew disease. The predicted linear regression line was also displayed downward slope i.e. y = - 0.07x+167.46, with regression coefficient R2 = 0.97, where ‘y’ denoted predicted crop yield of buckwheat and ‘x’ stood for PDI of powdery mildew of buckwheat (Fig. 1). The estimated regression line indicated that the unit rise in the PDI of powdery mildew disease (within 0-9 scale), there existed possibilities of yield reduction by 167.39 kg ha-1.

70

y = -0.0697x + 167.46 65 R² = 0.9726 60 55 50 45 40 35

Powdery mildew mildew Powdery severity % 30 25 1400 1500 1600 1700 1800 1900 2000 2100 Grain Yield (kg ha-1)

Figure 1. Relationship between crop yield (kg ha-1) and PDI of buckwheat powdery mildew at Rampur, Chitwan, Nepal during 2017-2018

Relationship between grain yield (kg ha-1) and Downy mildew severity (%) A significant linear negative correlation (r = -0.96) between yield and downy mildew severity was observed representing the best fit having R2 = 92% (Fig. 2). Obviously DISEASE RESISTANCE AND HIGH YIELDING TRAITS OF BUCKWHEAT 149 the yield decreased with the increase in Percent Disease Index (PDI). The predicted linear regression line was also displayed downward slope i.e. y = - 0.06x+158, with regression coefficient R2 = 0.93, where ‘y’ denoted predicted crop yield of buckwheat genotypes and ‘x’ stood for PDI of downy mildew of buckwheat (Fig. 2). The estimated regression line indicated that the unit rise in the PDI of downy mildew disease (within 0-9 scale), there existed possibilities of yield reduction by 157.94 kg ha-1.

75

70 y = -0.0605x + 158

R² = 0.9286 65

60

55

50

45

40 Downey mildew Downey severity % 35

30 1400 1500 1600 1700 1800 1900 2000 2100 Grain yield (kg ha-1)

Figure 2. Relationship between crop yield (kg ha-1) and PDI of buckwheat downy mildew at Rampur, Chitwan, Nepal during 2017-2018

Relationship between grain yield (kg ha-1) and botrytis leaf blight severity (%) A linear negative correlation (r = -0.95) between yield and botrytis leaf blight severity was observed representing the best fit having R2 = 89% (Figure 3). Obviously the yield decreased with the increase in Percent Disease Index (PDI). The predicted linear regression line was also displayed downward slope i.e. y = - 0.06x+160.73, with regression coefficient R2 = 0.90, where ‘y’ denoted predicted crop yield of buckwheat genotypes and ‘x’ stood for PDI of botrytis leaf blight of buckwheat (Fig. 3). The estimated regression line indicated that the unit rise in the PDI of botrytis leaf blight disease (within 1-9 scale), there existed possibilities of yield reduction by 160.67 kg ha-1.

150 Subedi et al

80

75 y = -0.0585x + 160.73 R² = 0.8986 70

65

60

55

50

45 Botrytis leaf blight severity leaf Botrytis blight severity % 40

35 1400 1500 1600 1700 1800 1900 2000 2100 Grain yield (kg ha-1)

Figure 3. Relationship between yield (kg ha-1) and PDI of buckwheat botrytis leaf blight at Rampur, Chitwan, Nepal during 2017-2018

The genetic improvement of buckwheat is an important area to determine its potential for production and rapid distribution. Disease resistance has been found quite useful for the improvement of desired traits and addressing the productivity and yield related problems in buckwheat (Luitel et al., 2017). Even within a species of plant that is susceptible to a particular species of pathogen however there is considerable variation in both the susceptibility of the various plant cultivars towards the pathogen and the virulence of the various pathogen races toward the plant variety. The genetics of such host-pathogen interaction are of considerable biological interest and of the utmost importance in developing disease control strategies through breeding for resistance (Agrios, 2005). Although buckwheat is normally a cold tolerant and is not attacked by many diseases or pests but a number of diseases and pests have been reported on this crop (Joshi and Paroda, 1991). The major diseases are powdery mildew (Erysiphe polygoni), downy mildew (Peronospora ducumeti), stem rot (Botrytis cinerea), chlorotic leaf spot (Alternaria alternata), leaf spot (Septoria polygonicola), smut (Sphacelothecaj agopyri),root and stem rot (Phytophthoraj agopyri), brown leaf spot (Ascochyta italica), rust (Puccinia jagopyri), root and collar rot (Sclerotinia libertianai) and root rot (Fusarium spp.). Attacks of several viruses also cause reduction in plant height and losses in grain yield. From this experiment, buckwheat genotypes CBBP-01, KIF-72-22-520, GF5283, ACC#2234 and ACC# 2213 showed higher yield with lower foliar fungal disease severity. The findings of this experiment is in agreement with the Baniya et al. (1995) who DISEASE RESISTANCE AND HIGH YIELDING TRAITS OF BUCKWHEAT 151 reported that out of 309 common buckwheat landraces, days to 50% flowering varied from 26 to 45 while days to 95% maturity varied from 67 to 98. Plant height ranged from 25 to 116.4 cm. There was a large variation noted in thousand grain weight from 10.2 to 31.8 g. The ratings for disease were from no disease to 100% infection for powdery mildew and from no disease to 60% infection for downy mildew showing a wide variation that could be utilized in crop improvement programmes. The experiment conducted by Baral et al. (2014) showed that buckwheat genotypes T-Vaskar (1282 kg ha-1), GF 5289 (1205 kg ha-1) and IR-13 (1212 kg ha-1) as promising lines in the year 2012. Baniya et al. (1995) also reported that two main buckwheat diseases powdery mildew and downy mildew show negative correlation with yield and yield attributing traits indicating low incidence of disease is associated with high values of each respective trait. The resistance in buckwheat genotypes to different fungal diseases could be the presence of phenolic and flavonoids compounds like flavonols, anthocyanins and C-glucosyl-flavones (Mikulajova et al., 2016).

CONCLUSION Grain yield had strong negative correlation with the major foliar fungal disease severity of buckwheat. Genotypes CBBP-01, KIF-72-22-520, GF5283, ACC#2234 and ACC# 2213 were resistant to powdery mildew, downy mildew and botrytis leaf blight that resulted in higher grain yield. The study can be useful for selecting suitable genotypes for the development of major foliar fungal disease resistant high yielding common buckwheat variety in subtropical climate of Nepal.

ACKNOWLEDGEMENTS The fund for this study was achieved from Nepal Agricultural Research Council (NARC). Acknowledgements go to both NMRP and HCRP team and other technical staffs of NARC for their valuable suggestions, continuous support and facilities during experimentation period.

REFERENCES Agrios, G.N. (2005). Plant Pathology, 5th ed. Elsevier Academic Press.30 Corporate Drive, Suite 400, Burlington, MA 01803, USA. Pp. 922. Baniya, B.K. (1995). Present status of buckwheat genetic resources in Nepal. In T. Matano and A. Ujihara (Eds.). Proceedings of International Symposium on Buckwheat - Current Advances in Buckwheat Research, 6th, Vol. I-III. 24-29 August, 1995. Shinshu University Press, Sinshu. Pp. 47-53. Baniya, B.K., Dongol, D.M.S. and Dhungel, N.R. (1995). Further characterization and evaluation of Nepalese buckwheat (Fagopyrum spp.) landraces. In T. Matano and A. Ujihara (Eds.). Proceedings of International Symposium on Buckwheat - Current Advances in Buckwheat Research, 6th, Vol. I-III. 24-29 August, 1995.Shinshu University Press, Sinshu. Pp. 295-304. 152 Subedi et al

Baral, K., Koirala, K.B., Subedi, S., Pokharel, A., Dhakal, J., KC, G., Paudel, A.P., Timilsina, A., Prasai, H.K. and Pokharel, B.B. (2014). Varietal investigation on buckwheat under different agro-ecological zones of Nepal. In Y.P. Giri, Y.G. Khadka, B.N. Mahato, B.P. Sah, S.P. Khatiwada, M.R. Bhatta, B.K. Chettri, A.K. Gautam, D. Gauchan, A.R. Ansari, J.D. Ranjit, R. Shrestha and B. Sapkota (Eds.). Proceedings of National Summer Crops Workshop, 27th, Vol. 2. 18-20April, 2013. National Maize Research Program, Rampur, Chitwan, Nepal. Pp. 168-172. HCRP. (2017). Annual Report 2073/74 (2016/17). Hill Crops Research Program, NARC, Kabre, Dolakha, Nepal. Pp. 131. Joshi, B.K. (2008). Buckwheat genetic resources: status and prospects in Nepal. Agriculture Development Journal, 5:13-30. Joshi, B.D. and Paroda, R.S. (1991). Buckwheat in India. National Bureau of Plant Genetic Resources, New Delhi, India. Pp. 117. Luitel, D.R., Siwakoti, M., Jha, P.K., Jha, A.K. and Krakauer, N. (2017). An overview: distribution, production and diversity of local landraces of buckwheat in Nepal. Advances in Agriculture, 2017: 2738045. Manandhar, H.K., Timila, R.D., Sharma, S., Joshi, S., Manandhar, S., Gurung, S.B., Sthapit, S., Palikhey, E., Pandey, A., Joshi, B.K., Manandhar, G., Gauchan, D., Jarvis, D.I. and Sthapit, B.R. (2016). A Field Guide for Identification and Scoring Methods of Diseases in the Mountain Crops of Nepal. NARC, DoA, LI-BIRD and Bioversity International, Nepal. Pp. 183. Mikulajová, A., Šedivá, D., Hybenová, E. and Mošovská, S. (2016). Buckwheat cultivars phenolic compounds profiles and antioxidant properties. Acta Chimica Slovaca, 9 (2): 124-129. MOAD. (2019). Statistical Information on Nepalese Agriculture 2017/18. Agri-Business Promotion and Statistics Division, Ministry of Agriculture Development, Kathmandu, Nepal. Pp. 290. Morrall, R.A.A. and Mckenzie, D.L. (1974). A note on the inadvertent introduction to North America of Ascochytarabiei, a destructive pathogen of chickpea. Plant Disease reporter, 58:342-345. Ohsaki, T., Fukuoka, S., Bimb, H.P., Baniya, B.K., Yasui, Y. and Ohnishi O. (2001). Phylogenetic analysis of the genus Fagopyrum (Polygonaceae), including the Nepali species F. megacarpum, based on nucleotide sequence of the rbcL-accD region in chloroplast DNA. Fagopyrum, 18: 9-14. Subedi, S. (2015). A review on important maize diseases and their management in Nepal. Journal of Maize Research and Development, 1(1): 28-52. Subedi, S., Gharti, D.B., Neupane, S., and Ghimire, T.N. (2015). Management of anthracnose in soybean using fungicide. Journal of Nepal Agricultural Research Council, 1:29-32. Wheeler, B.E.J. (1969). An Introduction to Plant Diseases. John Wiley and Sons. Ltd. London, UK. Pp. 301. SAARC J. Agric., 18(1): 153-160 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48389

Research Article EFFECT OF PHOSPHORUS AND MULCHING ON YIELD OF TOMATO

M.M.A. Mondal1 and M.I. Hoque2* 1Crop Physiology Division, Bangladesh Institute of nuclear Agriculture BAU Campus, Mymensingh-2202, Bangladesh 2Plant Breeding Division, Bangladesh Institute of nuclear Agriculture BAU Campus, Mymensingh-2202, Bangladesh

ABSTRACT The field experiment was carried out at Bangladesh Institute of Nuclear Agriculture, Mymensingh during October 2017 to March 2018, to investigate the effect of different levels of phosphorus and mulches on growth, yield attributes and yield of tomato cv. Roma VF. Two factors: (i) Four levels phosphorus viz., 0, 40, 80 and 120 kg P ha-1 and (ii) four different mulches viz., no mulch (control), water hyacinth, rice straw and banana leaves. The experiment was laid out in two factors randomized complete block design with four replications. The effect of phosphorus levels and mulches on morphological characters: plant height, leaf number plant-1, reproductive characters: number of flower clusters plant-1, flowers plant-1, days to flowering and yield contributing characters: number of fruits plant-1, fruit length, single fruit weight and fruit yield both plant-1 and ha-1 was significant. The highest morphological and reproductive characters, yield contributing characters and fruit yield (71.98 t ha-1) were recorded in 120 kg P ha-1 followed by 80 kg P ha-1 (69.76 t ha-1) with same statistical rank. In contrast, the above morphological, reproductive, yield attributes and fruit yield (47.62 t ha-1) was recorded in control plant where no phosphorus was applied. Application of P @ 80 kg ha-1 was found suitable dose for tomato cultivation. Among the three mulches, water hyacinth had remarkable effect on plant growth and yield attributes which resulting the highest fruit yield (68.35 t ha-1) in tomato. So, we may use water hyacinth mulch with application of 80 kg P for maximizing tomato fruit yield during winter season for silty loam soil. Keywords: Tomato, Phosphorus, Mulching, Yield, Fertilizer

* Corresponding author: [email protected]

Received: 11.05.2020 Accepted: 20.06.2020 154 Mondal and Hoque

INTRODUCTION Tomato (Lycopersicon esculentum Mill.) is one of the popular and nutritious vegetable crops all over the world including Bangladesh. It contains vitamin A, B and C including calcium and carotene. The amount of nutrient is 1.98 g protein, 320 IU vitamin-A, 1.8 mg iron and 31 mg vitamin-C in 100 g edible tomato (Parnell et al., 2004). Lycopene in tomato is a powerful antioxidant and reduces the risk of prostate cancer (Tegen et al., 2016). Phosphorus (P) fertilizer occupies the second most important input after nitrogen for increasing crop production. Optimum level of P is essential for rapid root development and for good utilization of water and other nutrients by plant. P has pronounced effect on flower cluster production and the number of flowers that increases the yield of tomato (Zhang et al., 2007). Further many researchers reported that P fertilizer significantly increased the yield of tomato (Solaiman and Rabbani, 2006; Etissa et al., 2013, Kumar et al., 2013). The macronutrient fertilizers including P fertilizer consumption in Bangladesh are still below the level required for normal crop growth and development which resulted lower yield in tomato. To optimize the nutrient supply for proper growth and development of tomato crop, judicious fertilization is essential. Optimum rate of macronutrients including phosphorus not only increases the yield but also increase the quality of tomato (Kumar et al., 2013). Mulching is a desirable management practice which is reported to regulate soil temperature, improve soil moisture, suppress weed growth and save labor cost (Kayum et al., 2008). The practice has been reported to increase yield by creating favorable temperature and moisture regimes in different parts of the world (Biswas et al., 2015). Water is the single factor which directly affects the tomato yield, because it contains 94% water. For successful crop about 285 mm water is required especially at flowering, fruit setting and enlargement stage (Jain et al., 2000). But irrigation facilities are not sufficient in all the regions of the country. Under the situation mulch play an important role in conserving soil moisture (Kayum et al., 2008). It improves the soil physical conditions by enhancing the biological activity of soil fauna and thus increases the soil fertility (Gordon et al., 2010). Artificial mulches with straw, rice husk, water hyacinth, crop residues or plastic mulch are generally practiced in the production of horticultural crops (Biswas et al., 2015). The present study was undertaken to assess the optimum level of phosphorus and to determine suitable mulch for maximum production of tomato.

MATERIAL AND METHODS The field experiment was carried out at the Research Farm of the Bangladesh Institute of Nuclear Agriculture, Mymensingh during the period from October 2017 to March 2018. The selected site was a medium high land and the pH of the soil was 6.4 with organic matter content of 1.21%. The analytical data of the soil sample from the experimental area was organic matter 1.67%, N 0.09%, available P PHOSPHORUS AND MULCHING ON TOMATO YIELD 155

4.81ppm, exchangeable K 0.27 me/100 g and available S 13.9 ppm. The tomato variety Roma-VF was used as planting material in the experiment. The experiment consisted of two factors: Factor A: Four different levels of phosphorus such as 0, 40, 80 and 120 kg ha-1 and Factor B: three mulches such as water hyacinth, rice straw and banana leaves. The experiment was laid out in two factors randomized complete block design with four replications. Seeds were sown in seedbed on 29 October 2017 and 27-day old seedlings were transplanted in the experimental field with recommended spacing of 50 cm × 50 cm. The unit plot size was 4 m × 3 m. Urea, muriate of potash (MoP), gypsum and cow dung were applied at the rate of 280 kg ha-1, 180 kg ha-1, 80 kg ha-1 and 10000 kg ha-1, respectively (BARC, 2012). The triple superphosphate (TSP) was applied according to treatment. Whole amount of TSP, gypsum and half of MoP were applied as basal dose during final land preparation, cow dung also applied before 10 days of final land preparation. The remaining half of MoP was applied as top dressing at 45 days after transplanting (DAT). Half of urea was applied as top dress at 21 DAT and remaining half was applied at 45 DAT. Weeding, pruning, staking, pesticides spray and other intercultural operations were done when required.The first trip irrigation was given 30 days after planting (DAP) followed by irrigation at 45 DAT. Mulching was also done after second irrigation at appropriate time by breaking the soil crust.At harvest, ten plants from each plot were selected randomly for data recording on morphological and reproductive characters, yield and yield related traits. Fruit yield was collected from each plot excluding border line and converted into t ha-1. Harvesting was done at different dates depending on fruit ripening. The collected data were analyzed statistically following the analysis of variance (ANOVA) technique and the mean differences among treatments were compared by Duncan’s Multiple Range Test (DMRT) using the statistical computer package program, MSTAT-C (Russell, 1986).

RESULTS AND DISCUSSION The effect of phosphorus (P) on morphological characters such as plant height, number of leaves plant-1, reproductive characters such as number of flower clusters and flowers plant-1, number of flowers cluster-1, days to flowering was significant (Table 1). Results showed that morphological and reproductive characters were higher in phosphorus applied plants than control plants. This result indicates that P has tremendous effect on growth and development in tomato. Results further revealed that plant height, number of leaves, flower clusters and flowers plant-1 increased significantly with increasing P levels till 80 kg ha-1 and further increasing P levels did not increase significantly whereas reverse trend was observed in days required to flowering. The highest plant height (76.1 cm), number of flower clusters plant-1 (21.20) and flower number plant-1 (133.5) was recorded in 120 kg P ha-1 followed by 80 kg P ha-1 with same statistical rank. In contrast, the shortest plant (63.4 cm), the lowest number of leaves (35.43 plant-1), flower clusters plant-1 (15.14) and flowers plant-1 (76.76), longest days required to start flowering (32.55 days) was recorded in 156 Mondal and Hoque control plot. In control plot phosphorus was not sufficient for normal plant growth and development and resulted in reduction of number of leaves plant-1. This result is in agreement with that of Etissa et al. (2013) who reported that plant height and leaves number increased in applied plants than control plants.

Table 1. Effect of phosphorus levels and mulches on morphological and reproductive characters in tomato cv. Roma VF Plant Leaves Days Flower Flowers Flowers Treatment height plant-1 to clusters cluster-1 plant-1 (cm) (no.) flowering plant-1 (no.) (no.) (no.) P levels (kg ha-1) 0 63.4 c 35.43 c 32.55 a 15.14 c 5.07 c 76.76 c 40 69.6 b 42.91 b 29.83 b 17.89 b 6.08 b 92.87 b 80 75.0 a 50.96 a 25.85 c 20.42 a 7.07 a 126.4 a 120 76.1 a 50.70 a 25.75 c 21.20 a 7.10 a 133.5 a F-test ** ** ** ** ** ** Mulches No mulch 66.4 b 36.90 b 29.44 a 16.21 b 5.65 b 81.59 c Water hyacinth 74.5 a 48.65 a 27.10 b 20.40 a 6.59 a 124.4 a Rice straw 71.9 a 48.51 a 26.91 b 19.3 a 6.55 a 116.4 a Banana leaves 69.3 ab 46.00 a 26.80 b 18.70 ab 6.49 a 105.9 b F-test * ** ** ** ** ** Figure (s) in a column, within treatment indicate do not differ significantly at P ≤0.05; ** and * indicate significant at 1% and 5% level of probability, respectively.

Use of different mulches had significant influence on morphological characters i.e. plant height, number of leaves plant-1, and reproductive characters such as number flower clusters and flowers plant-1, number of flowers cluster-1 (Table 1). Result indicated that morphological and reproductive characters were higher in mulches plots over non-mulch plots. The plant height and leaf number were higher in mulches than control because of might be moisture percentage was higher in those mulches plots compared to control plot. It increased nutrient availability and plants uptake more nutrients resulting higher growth and development (Biswas et al., 2015). Among the mulches, the tallest plant (74.5 cm), the highest number of leavesplant-1 (48.65), flower cluster plant-1 (20.40) and flowers plant-1 (124.4.92) was recorded in water hyacinth mulch followed by rice straw and the lowest was recorded in no mulch. The highest flower number was recorded in water hyacinth and rice straw mulches due to production of highest flower clusters plant-1 (Table 1). However, the PHOSPHORUS AND MULCHING ON TOMATO YIELD 157 shortest plant (66.4 cm) and the lowest number of leaves plant-1 (36.90) as well as flower cluster number and flowers number plant-1 (81.59) was recorded in control plot. In control plot, soil moisture was may be less than the mulches plots that causes lesser amount of nutrient availability (Tegen et al., 2016). As a result, plant growth and development hampered and ultimately plant height was short. The highest plant height was observed in water hyacinth mulch than the other mulches as reported by Singh et al. (2006) in tomato which supported the present experimental result. The effect of P levels on yield contributing characters such as number of fruits plant-1, fruit length and single fruit weight and fruit yield both plant-1 and ha-1 was significant (Table 2). Results revealed that yield contributing characters were higher in P applied plants over control plants. The number of fruits plant-1, fruit length, single fruit weight and fruit yield increased with increasing P levels. However, application of P at the rate of 120 kg ha-1, the yield contributing characters increased non-significantly over 80 kg ha-1 P. The highest fruit number plant-1 (34.07), fruit length (7.20 cm), single fruit weight (62.24 g), fruit weight plant-1 (1.91 kg) and fruit yield ha-1 (71.98 t) was recorded in 120 kg P ha-1 followed by 80 kg P ha-1 with same statistical rank. This result indicates that 80 kg P ha-1 is sufficient for getting maximum fruits plant-1 in tomato. The lowest number of fruits plant-1 (24.24), smallest fruit size and lowest fruit yield (1.19 kg plant-1 and 47.62 tha-1) was recorded in control plot due to production of lowest flower cluster and fruits plant-1. The lowest number of fruits plant-1was recorded in control plot due to insufficient P for normal plant growth and development and resulted in reduction of number of fruits plant-1. This result is in full agreement with that of Kumar et al. (2013) who stated that the number of fruits plant-1 increased with increasing phosphorous levels from 50 to 150 kg ha-1 in tomato. Similar result was also reported by many workers (Groot et al., 2004; Nawaz et al., 2012; Etissa et al., 2013) in tomato. The highest fruit yield was found in 120 and 80 kg P ha-1 applied plants due to production of highest number of flower cluster and fruits plant-1 (Table 2). Use of different mulches had significant influence on yield attributes and fruit yield of tomato over control (Table 2). Among the mulches, the highest fruit yield was recorded in water hyacinth mulch (1.98 kg plant-1 and 68.35 t ha-1) followed by rice straw mulch (1.92 kg plant-1 and 64.60 t ha-1). The highest fruit yield was recorded in water hyacinth mulch due to production of highest number of fruits plant-1. Further, among the mulches, banana leaves mulch performed the lowest in case of yield attributes and yield in tomato. However, within the mulch treatments, there had no significant different in single fruit weight. The lowest yield attributes and fruit yield was recorded in control plot.

158 Mondal and Hoque

Table 2. Effect of phosphorus levels and mulches on yield attributes and fruit yield in tomato cv. Roma VF Fruit Single fruit Fruit weight Fruits plant-1 Fruit yield Treatments length weight plant-1 (no.) (t ha-1) (cm) (g) (kg) P levels (kg ha-1) 0 24.24 c 5.04 c 54.28 c 1.32 c 47.62 c 40 29.25 b 6.41 b 59.26 b 1.54 b 58.65 b 80 33.00 ab 7.18 a 61.80 ab 1.82 a 69.76 a 120 34.07 a 7.20 a 62.24 a 1.91 a 71.98 a F-test ** ** ** ** ** Mulches No mulch 27.37 c 5.84 c 57.78 b 1.60 b 53.75 d Water hyacinth 32.56 a 6.80 a 60.38 a 1.98 a 68.35 a Rice straw 31.11 ab 6.69 ab 59.73 a 1.92 a 64.60 b Banana leaves 29.51abc 6.52 b 59.70 a 1.78 a 61.30 c F-test ** ** * ** ** Figure (s) in a column, within treatment indicate do not differ significantly at P ≤0.05; ** and * indicate significant at 1% and 5% level of probability, respectively.

Table 3. Combined effect of phosphorus levels and different mulches on yield attributes and fruit yield in tomato cv. Roma VF

Interaction Fruits Fruit Single fruit Fruit weight -1 Fruit yield (P level × plant-1 length weight plant (t ha-1) mulches) (no.) (cm) (g) (kg)

P0 M0 21.68 h 4.55 h 51.73 f 1.12 e 40.32 h

P0 M1 25.58 g 5.35 f 55.28 e 1.41 d 50.76 g

P0 M2 25.55 g 5.23 fg 55.22 e 1.41 d 51.50 g

P0 M3 24.15 gh 5.02 g 54.92 e 1.33 de 47.88 g

P40 M0 26.46 g 5.96 e 57.92 de 1.53 d 49.57 g

P40 M1 31.48 cde 6.75 c 60.33 bc 1.90 c 64.98 de

P40 M2 30.30 def 6.60 c 59.46 cd 1.80 c 61.56 ef

P40 M3 28.76 ef 6.40 d 59.32 cd 1.71 c 58.48 f

PHOSPHORUS AND MULCHING ON TOMATO YIELD 159

Interaction Fruits Fruit Single fruit Fruit weight -1 Fruit yield (P level × plant-1 length weight plant (t ha-1) mulches) (no.) (cm) (g) (kg)

P80 M0 30.14 def 6.37 d 60.35 bc 1.82 c 62.24 ef

P80 M1 36.20 ab 7.55 ab 62.70 ab 2.27 a 77.63 ab

P80 M2 34.00 abc 7.47 ab 62.05 ab 2.11 b 72.16 c

P80 M3 31.62 cd 7.35 ab 61.94 ab 1.96 c 67.03 d

P120 M0 31.20 de 6.48 cd 61.10 ab 1.91 c 62.88 de

P120 M1 37.00 a 7.60 a 63.22 a 2.34 a 80.03 a

P120 M2 34.57 abc 7.45 ab 62.09 ab 2.14 ab 73.19 bc

P120 M3 33.51 bcd 7.31 ab 62.60 ab 2.10 b 71.82 c F-test * * * * *

-1 -1 -1 * indicate significant at 5% level of probability; P0 = 0kg Pha ; P40 = 40kg Pha ; P80 = 80kg Pha ; -1 P120 = 120kg Pha ; M0 = No mulch; M1 = Water hyacinth; M2 = Rice straw; M3 = Banana leaves.

The combined effect of phosphorus level and mulches had significant effect on fruit number, fruit length, single fruit weight and fruit yield both plant-1 and ha-1 in tomato (Table 3). The highest yield attributes were observed in the treatment combination of 120 kg P ha-1 with water hyacinth mulch which resulted highest fruit yield in tomato. The lowest number of fruits plant-1 and fruit yield both plant-1 and ha-1was observed in control plot.

CONCLUSION Tomato growth and yield were affected by different levels of phosphorus application and use of mulches. Application of P @ 80 kg ha-1 was found suitable dose for tomato cultivation. Among the three mulches, water hyacinth had remarkable effect on plant growth and yield attributes which resulting the highest fruit yield in tomato. So, we may use water hyacinth mulch with application of 80 kg P for maximizing tomato fruit yield during winter season for silty loam soil.

REFERENCES Biswas, S.K., Akanda, A.R. Rahman, M.S. and Hossain, M.A. (2015). Effect of drip irrigation and mulching on yield, water-use efficiency and economics of tomato. Plant Soil and Environment, 61 (3):97-102. BARC. (2012). Fertilizer Recommendation guide. Bangladesh Agricultural Research Council (BARC). Farmgate, Dhaka-1215. Pp.72. Etissa, E., Dechassa, N., Alamirew, T.Y., Alemayehu and Desalegn, L. (2013). Growth and yield components of tomato as influenced by nitrogen and phosphorus fertilizer applications in different growing seasons. Ethiopian Journal of Agricultural Science, 23:57-77. 160 Mondal and Hoque

Gordon, G.G., Foshee, G.W., Reed, S.T., Brown, J.E. and Vinson, E.L. (2010). The effects of colored plastic mulches and row covers on the growth and yield of okra. Horticultural Technology, 20 (1):224-233. Groot, C.C.de., Marcelis, L.F.M., Boogard, R. and Lambers, H. (2004). Response of growth of tomato to phosphorus and nitrogen nutrition. Acta Horticulturae, 633:357-364. Jain, N., Chauhan, H.S., Singh, P.K. and Shukla, K.N. (2000). Response of tomato under drip irrigation and plastic mulching. In: Proceeding of the 6th International Micro-irrigation Congress, Micro-irrigation Technology for Developing Agriculture, South Africa. Pp. 45-51. Kayum, M.A., Asaduzzaman, M. and Haque, M.Z. (2008). Effects of indigenous mulches on growth and yield of tomato. Journal of Agriculture and Rural Development, 6(12):1-6. Kumar, M., Meena, M.L., Kumar, S., Sutanu, M. and Kumar, D. (2013). Effect of nitrogen, phosphorus and potassium fertilizers on the growth, yield and quality of tomatovar. Azad T-6. The Asian Journal of Horticulture, 8(2):616-619. Nawaz, H., Zubair, M. and Derawadan, H. (2012). Interactive effects of nitrogen, phosphorus and zinc on growth and yield of Tomato. African Journal of Agricultural Research, 7(26):3792-3769. Parnell, T.L., Suslow, Trevor, V. and Harris, L. J. (2004). "Tomatoes: Safe Methods to Store, Preserve, and Enjoy. ANR Catalog, University of California: Division of Agriculture and Natural Resources. Pp. 1-15. Russell, D.F. (1986). MSTAT-C computer package programme. Crop and Soil Science Department, Michigan State Univ., USA. Singh, I.S., Awasthi, O.P. and Meena, S.R. (2006). Influence of mulch on soil hydrothermal regime, growth and fruit yield of tomato grown under arid ecosystem. Agropedology Journal, 16: 112-116. Solaiman, A.R.M. and Rabbani, M.G. (2006). Effects of NPKS and cow dung on growth and yield of tomato. Bulletin of the Institute of Tropical Agriculture, Kyushu University. Pp. 31-37. Tegen, H., Dessalegn, Y. and Mohammed, W. (2016). Influence of mulching and varieties on growth and yield of tomato under polyhouse. Journal of Horticulture and Forestry, 8:1-11. Zhang, X.S., Liao, H., Chen, Q., Christie, P., Li, X.L. and Zhang, F.S. (2007). Response of tomato on calcareous soils to different seedbed phosphorus application rates. Pedosphere, 17(1): 70-76.

SAARC J. Agric., 18(1): 161-172 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48390

Research Article PERFORMANCE OF DIFFERENT CULTIVARS OF MUNGBEAN IN COASTAL REGION OF BANGLADESH

K.N. Islam1*, M.M.H. Khan2, M.M. Islam3, M.M. Uddin4 and M.A. Latif5 1OFRD, Bangladesh Agricultural Research Institute, Patuakhali, Bangladesh 2Dept. of Entomology, Patuakhali Science and Technology University, Bangladesh 3SCWMC, Soil Resource Development Institute (SRDI), Bandarban, Bangladesh 4Dept. of Entomology, Bangladesh Agricultural University, Mymensingh, Bangladesh 5Dept. of Entomology, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh

ABSTRACT The study was conducted at farmers’ field of Itbaria, Patuakhali sadarupazila under Patuakhali district during January to April 2016under the agro ecological zone AEZ-13 (Ganges Tidal Flood plain). This site was located in between 22°14' and 22°29' North latitudes and in between 90°12' and 90°28' East longitudes of Bangladesh. Fifteenmungbean varieties viz., BARI Mung-1, BARI Mung-2, BARI Mung-3, BARI Mung-4, BARI Mung-5, BARI Mung-6, BINA Moog-4, BINA Moog-5, BINA Moog- 6, BINA Moog-7, BINA Moog-8, BU Mug-1, BU Mug-2, BU Mug-4 and Patuakhali local Mung were tested to select best suitable variety for costal region.The experiment was laid out in randomized complete block design (RCBD) with threereplications. Significant variation was observed among the different mungbeanvarities in respect of majority of the observed parameters.BU Mug-1 showed the tallest plant height while the tallest pod length was observed in BARI Mung-5. The highest number of branches per plant was found in BINA Moog-4 whereas the highest number of leaves per plant found for BARI Mung-6. The highest seed yield and yield attributes like number of pods per plant, number of seeds per pod and 1000 seed weight was recorded in BARI Mung-6 followed by BINA Moog-8 while Patuakhali local Mung produced the lowest yield and attributes. Most of the yield contributing factors of BARI Mung-6 wasfavorable for better yield in coastal region of Bangladesh. Hence the mungbean production can be increased by introducingBARI Mung-6 in costal region. Keywords: Factor, Mungbean, Varieties, Yield

* Corresponding author: [email protected]

Received: 20.04.2020 Accepted: 20.06.2020

162 Islam et al.

INTRODUCTION Mungbean (Vigna radiata L. Wilczek) alternatively known as the moog bean or green gram, is a plantspeciesin the legume family. Mungbean is one of the most important pulse crops of Bangladesh. It is considered as the best of all pulses from the nutritional point of view. It has a good digestibility and flavor. It is an excellent source of protein (24.5%) with high quality of lysine (460 mg g-1 N) and tryptophan (60 mg g-1 N). It also contains remarkable quantity of ascorbic acid and riboflavin (0.21 mg 100g-1) (Azadi et al., 2013). Besides providing protein in the diet, mungbean has the remarkable quality of helping the symbiotic root rhizobia to fix atmospheric nitrogen and hence to enrich soil fertility (Anjum et al.,2006) which not only enables it to meet its own nitrogen requirement but also benefits the succeeding crops (Ali, 1992). The rice based cropping pattern has been found as an important cropping system in our country, as such condition short duration crop mungbean can easily be fit as a cash crop between major cropping seasons. It is a major pulse crop in Asia. Countries like Bangladesh, Bhutan, China, India, Myanmar, Nepal, Pakistan, Sri Lanka and Thailand collaborated with AVRDC (Asian Vegetable Research and Development Centre) using an integrated, interdisciplinary approach to research and develop improved mungbean varieties and technologies. The agro-ecological condition of Bangladesh is favorable for growing this crop. The optimum temperature ranges from 20°- 35°C depending upon season (BARC, 2013). Mungbean is also drought tolerant and can grow with a minimum supply of nutrients.It is grown in area of 1.82 lakh hectares with total production of 1.98 lakh metric tons in Bangladesh (Krishi Diary, 2016).In spite of the best efforts for improving the mungbean varieties, the productivity of this crop is lowi.e. only around 500 kg ha-1. The low productivity can be attributed to lack of suitable genotypes for different cropping situations (Dikshit et al., 2009). It is grown in three seasons in a year in Bangladesh and more than 70% mungbean area is concentrated in the three southern districts viz. Patuakhali, Barisal, and Noakhali within AEZ 13 and 18 and Patuakhali alone occupies about 30% area (Mondolet al., 2013). Mungbean is usually sown in this region at the end of January to mid February. Most of the soils in this region are cracking clay type which becomes compact and hard on drying (Choudhury et al., 2000). The reasons for low yield of mungbean are varietal and management factors. Due to the lack of land, the scope of its wide-ranging cultivation is very inadequate. Farmers commonly grow mungbean by one ploughing and scarcely use any fertilizer and irrigation due to its lower yield and also to their poor socio-economic circumstance and lack of proper information. Thus, the yield becomes low. The shortfall situation of mungbean production in our country can be conquer either by bringing more area under mungbean cultivation or by increasing the yield through improvement of production technology, such as optimizing the high yielding variety and improved technology. Bangladesh agroclimatical condition differed in differentareas and PERFORMANCE OF MUNGBEAN IN COASTAL REGION 163 mungbeanvarities performed differently. This study done on mungbean varieties would help farmers to select and cultivate best suitable variety under coastal region of Bangladesh.

MATERIALS AND METHODS Climate and Soil The study was conducted at farmers’ field of Itbaria, Patuakhali Sadar Upazilla in the Patuakhali district, the coastal region under the agro ecological zone AEZ-13 (Ganges Tidal Flood plain) which is located between 22°14' and 22°29' North latitudes of Bangladesh. The experiment was setup during January to April 2016 in the Rabi season (October to March). Rabi season (October to March) is characterized by comparatively low temperature and plenty of sunshine from November to February. Particularly in the areas of southern coastal region, mungbean crop could be grown in late Rabi (winter period) from mid January to mid February. The winter period is short in this area due to close proximity to the Bay of Bengal. The experimental site is also adjacent to the Bay of Bengal. The area lies at 0.9 to 2.1 metre above mean sea level (Iftekhar and Islam, 2004). The soil of the experimental area was non-calcareous and slightly saline (2.6 dS/m), silty clay in texture and neutral in reaction (pH 6.7). Fertility status of soil in general is poor with a low level of organic matter (1.56). Total nitrogen (0.105%) level in soil is low, phosphorous (4.69%) very low and potassium (0.40%) high. Land type is medium high land and the major area (74.6 %) is medium high land (SRDI, 2005). Experimental layout and Plant materials The experiment was laid out in a randomized complete block design (RCBD) with 3 replications. Each replication represented a block which was divided into fifteen-unit plots. The unit plots (4 m x 2.5 m) were made ready as per design with a distance of 0.5 m between the plots and 1 m between the replications. Fifteen (15) Mungbean varieties were randomly allotted to the plot within a block. The seeds of mungbeanvarieties viz., BARI Mung-1, BARI Mung-2, BARI Mung-3, BARI Mung- 4, BARI Mung-5, BARI Mung-6 were collected from Bangladesh Agricultural Research Institute (BARI), Gazipur, BINA Moog-4, BINA Moog-5, BINA Moog-6, BINA Moog-7, BINA Moog-8 from Bangladesh Institute of Nuclear Agriculture (BINA), Mymensingh, BU Mug-1, BU Mug-2, BU Mug-4 from Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipuand Patuakhali local Mung variety from farmers’ of Patuakhali. BARI, BINA and BSMRAU have developed mungbean varieties, but these varieties earlier not tested properly in coastal region. The varieties and their important characteristics are presented below: BARI Mung-1 This variety was introduced from India (M-7706). Plant height: 45-50 cm. Tolerant to CLS. Photo insensitive. Protein: 25.50%, CHO: 47.30%. Head dhal yield: 72.0%. 164 Islam et al.

Cooking time: 14 min. Seed color is green. 1000 seed weight: 25.40g. Seed yield: 0.9-1.0 t ha-1. Duration: 65-70 days. BARI Mung-2 This variety was introduced from Philippine (M-7715). Plant height: 45-52 cm. Tolerant to YMV and CLS. Photo insensitive. Protein: 22.56%, CHO: 46.15%. Head dhal yield: 76.1%. Cooking time: 15 min. Seed color is green. 1000 seed weight: 25.33g. Seed yield: 1.1-1.35 t ha-1. Duration: 65-70 days. BARI Mung-3 This variety was developed from crossing between Sona mung and BARI Mung-2. Plant height: 50-55 cm. Tolerant to YMV and CLS. Photo insensitive. Protein: 20.81%, CHO: 49.53%. Head dhal yield: 67.5%. Cooking time: 15 min. Seed color is brownish green. 1000 seed weight: 29.4g. Seed yield: 1.0-1.3 t ha-1. Duration: 60-65 days. BARI Mung-4 This variety was developed from local cross (BMX 841121). Plant height: 52-57 cm. Resistant to YMV and CLS. Photo insensitive. Protein: 23.1%, CHO: 51.32%. Head dhal Yield: 68%. Cooking time: 17 min. Seed color is green. 1000 seed weight: 31.9g. Seed yield: 1.2-1.4 tha-1. Duration: 60-65 days. BARI Mung-5 This variety was introduced from AVRDC (NM- 92). Plant height: 41-46 cm. Resistant to YMV and CLS. Photo insensitive. Protein: 20.93%, CHO: 49.46%. Head dhal Yield: 68%. Cooking time: 18 min. Quite Synchrony in maturity. Seed color is deep green. 1000 seed weight: 41.9g. Seed yield: 1.40-1.45 t ha-1. Duration: 58-60 days. BARI Mung-6 This variety was introduced from AVRDC (NM- 94). Medium plant stature. Plant height: 40-45 cm. Resistant to YMV and CLS. Photo insensitive. Bold seed size with green seed coat. Protein: 21.2%; CHO: 46.8%. Head dhal Yield: 67.2%. Cooking time: 18 min. Synchrony in maturity and late potentiality. Recommended for cultivation in Jessore, Khulna, Faridpur, Pabna, Rajshahi and Dinajpur. Seed color is deep green. 1000 seed weight: 40.0g. Seed yield: 1.5 –1.6 t ha-1. Duration: 55-60 days. BINA Moog-4 A high yielding winter mungbean variety, released in 1997. Plant height: 28-32 cm. It matures between 75-80 days. Seed size is bigger than Bina moog 1. All the pods mature at a time. Protein: 23.0%. Plants are short and tolerant to CLS and YMV. Pods have prominent ridges. Seed color is dull green. 1000 seed weight: 37.50 g. Maximum seed yield potential is 1.2 t ha-1(av. 1.1 t ha-1). PERFORMANCE OF MUNGBEAN IN COASTAL REGION 165

BINA Moog-5 A late winter-cum-summer mungbean variety, released in 1998. Plant height: 40-45 cm. It matures between 70-80 days. Seed is green shiny and bigger than BINA Moog-1. Almost all the pods mature at a time. Protein: 23.0%. Seed color is bright green. 1000 seed weight: 35-40 g. Plants are short and tolerant to CLS and YMV. Maximum seed yield potential is 2.0 t ha-1(av. 1.5 t ha-1). BINA Moog-6 This is an early mature summer mungbean variety, released in 2005. Plant height: 35- 40 cm. It matures between 64-68 days. Almost all the pods mature at a time. Protein: 23.0%. Seed color is bright green. 1000 seed weight: 35-40 g. The variety is tolerant to CLS and YMV. Average yield is up to 1.5 t ha-1. BINA Moog-7 This is a high yielding summer mungbean variety, released in 2005. Plant height: 40- 45 cm. It matures between 70-74 days. Almost all the pods mature at a time. Protein: 23.0%. Seed color is green. 1000 seed weight: 35-40 g. The variety is tolerant to CLS and YMV. Average yield is up to 1.8 t ha-1. BINA Moog-8 This is a summer mungbean variety, released in 2010. It is obtained from seeds of MB-149 which were irradiated with 400 Gy dose of gamma ray. Maturity period ranges from 64-67 days. Maximum grain yield is about 2.0 t ha-1(av. 1.8 t ha-1). Seed is medium size with green shiny color. Seed contains higher protein (23%). Plants are short and tolerant to YMV. This variety is suitable for cultivation in pulse growing areas of Bangladesh. BU Mug-1 This variety was developed from AVRDC- VC 6372 (45-8-1). Plants are short. Almost all the pods mature at a time. Resistant to YMV and CLS. Leaf and seed color is deep green. Seed yield: 1.40-1.60 t ha-1. Duration: 55-60 days. BU Mug-2 This variety was developed from AVRDC- VC 6370 (30-65). Plants are short. Almost all the pods mature at a time. Resistant to YMV and CLS. Leaf and seed color is deep green. Seed yield: 1.50-1.70 t ha-1. Duration: 55-60 days. BU Mug-4: This variety was developed from AVRDC- GK 7. Plants are short. Tolerant to high moisture and rainfall in soil. Almost all the pods mature at a time. Resistant to YMV and CLS. Leaf and seed color is deep green. High moisture and rainfall. Seed yield: 1.80-2.00 t ha-1. Duration: 55-60 days.

166 Islam et al.

Land preparation and Fertilizer application The land was prepared at ‘joe’ condition by deep ploughing and cross-ploughing 4 times by power tiller followed by laddering until the desired tilth. All the weeds, residues and stubbles of the previous crops were removed from the field and the large clods were broken into smaller pieces to obtain a desirable tilth of soil for sowing of mungbean seeds. The fertilizers were applied as per fertilizers recommendation guide (BARI, 2011). Urea, TSP and MoP were applied in the field uniformly @ 50-85-35 kg/ha-1, respectively during the final land preparation. The fertilizers were then mixed properly with the soil by spading and individual unit experimental plots were leveled. Seed sowing and Intercultural operations The mungbean seeds were sown on the 29th January, 2016 at the rate of 30 kg/ha (BARI, 2011) with making rows maintaining spacing (30 cm x 10 cm) for mungbean cultivation. Seed sowing was done at a depth of 6-7 cm and the seeds were covered by loose soil with the help of hand. All intercultural operations were done as and when necessary to ensure normal growth and development of crops. Yield and yield attributing data collection of different mungbean varieties Five plants were selected at random from each plot to measure plant height (cm), number of branches plant-1, number of leaves plant-1,number of pods plant-1, pod length (cm), number of seeds pod-1 (5 pods),1000 seed weight (g).Plant height was measured from the base of plant upto the top of the main shoot at reproductive stage and the mean plant height was expressed in centimeter (cm). Number of branches plant-1 and number of leaves plant-1were counted from the inner rows of each plot at reproductive stage and were averaged. Number of pods plant-1 was counted from each plot at the ripening stage and average number of pods per plant was calculated. Pod length was measured by a meter scale from randomly selected 5 pods after harvesting. Mean value of them was calculated plot wise and the mean pod length was expressed in centimeter (cm).Number of seeds per pod was recorded after harvesting of the crop from the randomly selected 5 pods from pre-selected 5 plants and was calculated from their mean values.1000 grains were collected from randomly selected 5 plants of each plot and were weighed in gram by digital electric balance. Mature pods manually harvested by hand in Bangladesh when about 80% of the pods became blackish in colour. Grains obtained from 1m2 (1m x 1m) area from the selected portion of the center of each unit plot that was harvested from the plots. The harvested pods of 1 m2 area from each unit plot were kept separately. The pods were properly dried in the sun, then beaten with a stick and threshed. After threshing, grains were recorded from 1 m2 area per plot wise and the yields were expressed in kg ha-1. Statistical analysis The collected data were statistically analyzed through the analysis of variance using Web Agri Stat Package (WASP 1.0). Means were separated by critical difference (CD) values at 5% level of significance. PERFORMANCE OF MUNGBEAN IN COASTAL REGION 167

RESULTS AND DISCUSSIONS Plant height (cm) Significant variation was found in case of plant height among the different varieties (Fig. 1). Plant height ranged from 31.73 to 43.80 cm. The tallest plant was recorded in BU Mug-1 (43.80 cm) followed by BU Mug-4 (41.40 cm), BARI Mung-5 (41.26 cm) and Patuakhali local Mung (41.13 cm). The shortest plant was recorded in BINA Moog-4 (31.73 cm) which was followed by BARI Mung-2 (34.80 cm) and BINA Moog-5 (34.93 cm). This dissimilarity in plant tallness might be attributed to the inherent factors. Similar findings of plant tallness were obtained by Farghali and Hussein (1995).

50 a ab ab 45 abcd ab abc bcdef bcde abcde bcdef bcdef 40 ef def cdef f 35

Plant heighPlant(cm) 30

25

20

Mungbean varieties

Figure 1. Effect of different varieties on the plant height of mungbean

Number of branches per plant Number of branches per plant was significantly influenced by the different varieties (Fig. 2). The highest number of branches per plant (3.00) was observed in BINA Moog-4 followed by BARI Mung-2 (2.00) which was statistically similar to that of BARI Mung-3 (2.00), BARI Mung-6 (2.00), BINA Moog-5 (2.00) and BINA Moog- 7 (2.00). The lowest number of branches per plant (0.66) was obtained from BARI Mung-4 which was statistically identical to BU Mug-2 (0.66) which was statistically similar to BARI Mung-5 (1.00), BINA Moog-6 (1.00) and BINA Mung-8 (1.00). The other varieties showed intermediate type results. It might be due to their genetic capacity. The similar findings were also supported by Mishra (2003). 168 Islam et al.

3.5 a 3 2.5 b b b b b 2 bc bc bc bc 1.5 c c c 1 c c 0.5

No. of branches/plant No. 0

Mungbean varieties

Figure 2. Effect of different varieties on the number of branches of mungbean

Number of leaves per plant The number of leaves per plant counted was significantly influenced by different varieties (Fig. 3). The highest number of leaves per plant (10.00) was observed in BARI Mung-6 which was statistically identical to BINA Moog-4 (10.00) and BINA Moog-7 (10.00) which was significantly higher than all other varieties. The lowest number of leaves per plant (5.00) was obtained from BARI Mung-4 which was followed by BARI Mung-5 (5.66), BINA Moog-8 (6.00) and BU Mug-2 (6.00). The other varieties showed intermediate number of leaves in respect to the highest and the lowest values. It might be probably due to their inherent characters of varieties. Similar results reported by Hussain et al. (2011).

12 a a a

ab 10 abc ab abcd bcdef bcde cdef def 8 ef def cdef f 6

4

No. of leaves/plantNo. 2

0

Mungbean varieties

Figure 3. Effect of different varieties on the number of leaves of mungbean PERFORMANCE OF MUNGBEAN IN COASTAL REGION 169

Number of pods per plant The result of analysis of variance (Table 1) showed that the mungbean varieties differed significantly from each other in number of pods per plant. The variety BARI Mung-6 (18.33) produced the highest number of pods per plant followed by BARI Mung-2 (16.27) which was statistically similar to BINA Moog-8 (15.80). The lowest number of pods per plant was recorded in the Patuakhali local Mung (8.70) which was statistically similar to BINA Moog-7 (9.00), BARI Mung-1 (9.00) and BINA Moog-6 (9.10). Effective pods per plant were different with varieties due to genotypic variations also observed by Mondal et al. (2004) in mungbean.

Table 1. Yield and yield contributing factors of different mungbean varieties

Yield and yield contributing factors No. of pods No. of seeds 1000 seed Yield Varieties per plant per pod weight (g) (kg ha-1) BARI Mung-1 9.00 g 11.33 35.66 g 602.67 g BARI Mung-2 16.27 b 11.66 37.66 f 675.33 ef BARI Mung-3 12.93 cd 10.66 33.33 hi 713.00 def BARI Mung-4 10.33 efg 11.33 33.66 h 808.67 c BARI Mung-5 11.40 def 11.33 45.66 b 940.33 b BARI Mung-6 18.33 a 12.33 48.33 a 1065.33 a BINA Moog-4 9.80 fg 11.00 36.00 g 650.00 fg BINA Moog-5 13.53 c 11.00 44.00 c 778.67 cd BINA Moog-6 9.10 g 10.66 39.66 e 599.33 g BINA Moog-7 9.00 g 11.00 32.33 i 746.33 cd BINA Moog-8 15.80 b 11.66 37.66 f 1004.33 ab BU Mug-1 11.33 def 11.66 38.00 f 586.67 g BU Mug-2 11.63 de 10.66 41.00 d 603.33 g BU Mug-4 12.63 cd 10.66 42.00 d 725.67 de Patuakhali 8.70 g 10.66 31.00 j 490.00 h local Mung CV (%) 8.15 6.46 2.04 5.36 CD (0.05) 1.63 NS 1.31 65.71

In a column means having dissimilar letter(s) differ significantly as per 0.05 level of probability. CD – Critical Difference

170 Islam et al.

Pod length (cm) Pod length which is one of the most important yield contributing factors of mungbean differed significantly among the different varieties (Fig. 4). The tallest pod length (9.45 cm) was observed in BARI Mung-5 which was statistically identical to BARI Mung-6 (9.44 cm) followed by BINA Moog-8 (8.83) and BU Mug-2 (8.78). The shortest pod length (7.10 cm) was obtained in BINA Moog-7 which followed by Patuakhali local Mung (7.22 cm) and BARI Mung-3 (7.30 cm). These results have the agreement with the results of Parvez et al. (2013) who reported that pod length differed from varieties to varieties. The probable reason of this difference could be the genetic make-up of the varieties.

12 a a bc b bcd b 10 de cde de ef ef efg fg g fg 8

6

4 Pod lenghth Pod(cm) 2

0

Mungbean varieties

Figure 4. Effect of different varieties on the pod length of mungbean

Number of seeds per pod Number of seeds per pod did not show significant differences among the varieties (Table 1). The maximum number of seeds per pod was observed in BARI Mung-6 (12.33) followed by BARI Mung-2 (11.66) which was similar to BINA Moog-8 (11.66) and BU Mug-1 (11.66). The minimum number of seeds per pod was obtained from Patuakhali local Mung (10.66) which was similar to BARI Mung-3, BINA Moog-6, BU Mug-2 and BU Mug-4. These results are in agreement with the findings of Aslam et al. (2004) reported non-significant differences for number of seeds per pod among different genotypes.

PERFORMANCE OF MUNGBEAN IN COASTAL REGION 171

1000 seed weight (g) Variety had a significant effect in 1000 seed weight and it was also observed in studied mungbean (Table 1). The 1000 seed weight under different varieties ranged from 31.00 g to 48.33 g while highest weight was recorded in BARI Mung-6 (48.33 g) followed by BARI Mung-5 (45.66 g) and BINA Moog-5 (44.00 g). In contrast, the lowest weight of 1000 seed (g) was found in Patuakhali local Mung (31.00 g) which followed by BINA Moog-7 (32.33 g) and BARI Mung-3 (33.33 g). The variation in 1000 seed weight might be due to variation in the genetic make-up of the genotypes. Aslam et al. (2004) and Hussain et al. (2011) also reported significant differences for 1000 seed weight. Seed yield (kg ha-1) In respect of seed yield, significantly the highest seed yield (1065.33 kg ha-1) was observed in BARI Mung-6 which statistically similar with variety BINA Moog-8 (1004.33 kg ha-1) followed by BARI Mung-5 (940.33 kg ha-1). The lowest seed yield (490.00 kg ha-1) was found in Patuakhali local Mung. The BINA Moog-4 (650.00 kg ha-1), BU Mug-2 (603.33 kg ha-1), BARI Mung-1 (602.67 kg ha-1), BINA Moog-6 (599.33 kg ha-1) and BU Mug-1 (586.67 kg ha-1) produces statistically similar and intermediate type of seed yield (Table 1). The seed yield was credited to BARI Mung-6 which was significantly superior over other tested varieties. This was because higher number of pods per plant, length of pod (cm) and number of seeds per pod with highest 1000 seed weight. Patuakhali local Mung produced lowest yield might be due to lesser number of pods per plant, length of pod (cm), number of seeds per pod and poor grain development. The finding is in close conformity with the finding of Mandal et al. (2005).

CONCLUSION Based on the findings of the study, it was observed that fifteen (15) mungbean varieties influenced significantly in respect to yield and yield contributing factors. The results of the experiment signified that BARI Mung-6 showed comparatively better performance and should be considered for crop growing in coastal region of Bangladesh.

REFERENCES Ali, M. (1992). Weeds are a great threat to kharif pulses. Indian Farming, 42: 29-30. Anjum, M.S., Ahmed, Z.I. and Rauf, C.A. (2006). Effect of Rhizobium inoculation and nitrogen fertilizer on yield and yield components of moonbeam. International Journal of Agriculture and Biology, 8(2): 238–240. Aslam, M., Hussain, M., Nadeem, M.A. and Haqqani, A.M. (2004). Comparative efficiency of different mungbean genotypes under Agro – climatic condition of Bhakhar. Pakistan Journal of Life and Social Sciences, 2(1): 51-53 172 Islam et al.

Azadi, E., Rafiee, M. and Nasrollahi, H. (2013). The effect of different nitrogen levels on seed yield and morphological characteristic of mungbean in the climate condition of Khorramabad. Annals of Biological Research, 4(2): 51-55. BARC. (2013). Bangladesh Agricultural Research Council, Farmgate, Dhaka. Hand Book of Agricultural Technology. Pp.61. BARI. (2011). Krishi Projukti Hatboi (Handbook on Agro-technology), 5th edition Bangladesh Agricultural Research Institute, Gazipur-1701, Bangladesh. Pp. 164-165. Choudhury, D.A., Hamid, A., Hasem, A. and Miah, G.U. (2000). Mungbean seed germination under soil moisture regimes. Bangladesh Journal of Agrilcultural Research, 25(2): 375-377. Dikshit, H.K., Sharma, T.R., Singh, B.B. and Kumari, J. (2009). Molecular and morphological characterization of fixed lines from diverse cross in Munbean (Vigna radiata (L.) Wikzek). Genetics, 88: 3. Farghali, M.A. and Hussein, H.A. (1995). Potential of genotypic and seasonal effects on yield of mung bean (Vigna radiata L. Wilczek). Assuit Journal of Agricultural Sciences, 26(2): 13-21. Hussain, F., Malik, A.U., Haji, M.A. and Malgani, A.L. (2011). Growth and yield response of two cultivars of mungbean (Vigna radiata L.) to different potassium levels. The Journal of Animal & Plant Sciences, 21(3): 622-625. Iftekhar, S. and Islam, M.R. (2004). Managing mangroves in Bangladesh: A strategy analysis. Journal of Coastal Conservation, 10(1): 139-146. Krishi Diary. (2016). Agricultural Information Services (AIS), Ministry of Agriculture, Khamarbari, Farmgate, Dhaka- 1215. Pp.15. Mandal, S., Biswal, K.C. and Jana, P.K. (2005). Yield economics nutrient uptake and consumptive use of water by summer greengram as influenced by irrigation and phosphorous appucation. Legume Research-An International Journal, 28(2): 131-133. Mishra, S.K. (2003). Effect of rhizobium inoculation, nitrogen and phosphorous on root nodulation, protein production and nutrient uptake in cowpea (Vigna sinensisSavi). Annals of Agricultural Research: New Series, 24(1): 139-144. Mondal, M.M.A, Dutta, R.K. and Islam, M.A. (2004). Yield performance of some mungbeanmutants in the northern region of Bangladesh. Bangladesh Journal of Nuclear Agriculture, 19: 32-35. Mondol, M.E.A., Rahman, H., Rashid, M.H., Hossain, M.A. and Islam, M.M. (2013). Screening of mungbean germplasm for resistance to mungbean yellow mosaic virus. International Journal of Sustainable Crop Production, 8(1): 11-15. Parvez, M.T., Paul, S.K. and Sarkar, M.A.R. (2013). Yield and yield contributing characters of mungbean as affected by variety and level of phosphorus. Journal of Agroforestry and Environment, 7(1): 115-118. SRDI. (2005). Land and soil resource utilization guide (renewal), 2nd edition. Patuakhali sadarupazila. Soil Resource Development Institute (SRDI), Krishi khamarsarak, Farmgate, Dhaka- 1215, Bangladesh. SAARC J. Agric., 18(1): 173-182 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48391

Research Article ASSESSMENT OF QUALITY CHARACTERISTICS OF BOILED YAM TUBERS AVAILABLE IN BANGLADESH

F.N. Jahan1*, M.A. Rahim1, M.M. Hossain1, M.H. Rahman1 M.A.Z. Chowdhury2, M. Moniruzzaman1 and A.K. Samanta3 1Department of Horticulture, Bangladesh Agricultural University, Mymensingh, Bangladesh 2Bangladesh Agricultural Research Council, Farmgate, Dhaka 3SAARC Agriculture Centre, BARC Complex, Farmgate, Dhaka

ABSTRACT Tuber of yam (Dioscorea spp.) is one of the important foods widely popular in several countries. In spite of its assignment in the category of underutilized crop, the tubers of yam are consumed by the people of Bangladesh in different forms. The current paper has made an endeavor to evaluate the sensory qualities of boiled yam tubers by the panel of evaluators. The quality parameters that were considered for evaluation of boiled yam tubers were ease of peeling, poundability, discoloration of water after boiling, color and texture, bitterness, sweetness, etc. Evidently, it was possible to judge the quality of yams based on the sensory evaluation as it is one of the important steps for acceptability of foods and their subsequent usages. Around 31 tuber samples were harvested during the winter season from the Germplasm Centre of Bangladesh Agricultural University. Amongst the 31 samples, 24 came under the category of ‘easy to peel’, and ‘difficult to peel’ were only 7 samples. Regarding the color after cooking, 19 samples were “white, not colored”, whereas, 3 samples were intermediate and 9 samples were highly colored; might be attributed by the presence of phyto-compounds. Nevertheless, two samples i.e. RHMF002 and RMHF010 were found to possess most of the favorable sensory characters in terms of discoloration of water after boiling, sweetness, bitterness, texture, poundability, etc. Keywords: Yam, Tuber, Sensory evaluation, Quality characteristics

INTRODUCTION The yams have received limited attention among the global researchers during the past leading to its placement under the category of neglected/ underutilized/ orphan

* Corresponding author: [email protected]

Received: 12.02.2020 Accepted: 15.05.2020 174 Jahan et al. crops. The word “Yam” is applicable to the members of the genus Dioscorea, family Dioscoreaceae, and the order Dioscoreales (Alexander and Coursey, 1969).According to the available historical evidence, Pedanius Dioscorides, the Greek physician cum botanist, quoted the application of yams as food as well as medicines in his book entitled “De Materia Medica”, written during the period AD 50 and 70. In order to honor the outstanding thoughts of the great Greek botanist, the genus name of the yam has been assigned as Dioscorea and the family name as Dioscoreaceae. The popular name “yam” has been originated from the West African language – “nyami” means “to eat” (Coursey, 1976). The family Dioscoreaceae is believed to be the one among the earliest angiosperms, and is probably originated from Southeast Asia (Coursey, 1967). In Bangladesh, the yam is considered as one kind of alu (means potato) and therefore termed with different names such as “GachAlu”, “PestaAlu”, “MouAlu”, “GointaAlu”, “PaglaAlu”, “Sore Alu” (Alam et al., 2008). The growth of yams appears to have developed independently in two regions: West Africa and Southeast Asia. According to the available reports, Dioscorea rotundata thought to be the first species that has been domesticated around 5000 BC in West Africa (Andres et al., 2017). The most economically important species under this genus is D. alata, that is originated from Southeast Asia; possibly Myanmar and Thailand (Orkwor et al., 1998). Presently, the species of Dioscorea are distributed among Malaysia, Indonesia, Philippines, Brazil, South America, Central America, Papua New Guinea, South Pacific Islands (Kumar et al., 2017). Air potato i.e. D. bulbifera is native to South Asia and is distributed among several countries such as Bangladesh, India, China, Cambodia, Indonesia, Java, Japan, Laos, Malaysia, Nepal, Pakistan, Sri Lanka, Thailand, Taiwan, Vietnam, Ghana, Gabon, Congo, Chad, Cameroon, Nigeria, Senegal, Sudan, Zimbabwe, USA, Mexico, Cuba, Costa Rica, Jamaica, Nicaragua, Brazil, Ecuador, Peru, Venezuela, Australia, etc. The other high yielding species is D. pentaphylla, a native of tropical Asia (Ayensu and Coursey, 1972). It is found in India, Bangladesh, North America, and other tropical Asian nations. In fact, the yams (Dioscorea spp.) are perennial herbaceous vines cultivated for the consumption of their starchy tubers in Asia, Africa, Central and South America, and Oceania (Liu et al., 2008). It is the fourth largest source of carbohydrates (immediately after potatoes, cassava, sweet potato) in the category of roots and tubers which meet the energy requirement of millions of global populations in addition to its role as important ingredients as animal feed and numerous processed products for human consumption (Pradhan and Panda, 2020; Viruel et al., 2015). Even though, Dioscorea spp. is an underutilized crop, yet it is found almost all over Bangladesh and its tubers are primarily used as vegetable (Alam et al., 2008). Besides the presence of carbohydrates and essential minerals, the yam tubers are thought to be the rich source of nutraceuticals; indispensable for good health and well-being of mankind (Jahan et al., 2019). The notable feature of yam is that it could be cultivated at the roadside, homestead gardens, backyards, rooftops in addition to its natural QUALITY CHARACTERISTICS OF BOILED YAM TUBERS 175 occurrence in the forest. Hence, no extra cultivable land needs to be spared for yam cultivation like other root and tuber crops. Besides, different ethnic groups also grow the yam at the foothills of Bangladesh. In Bangladesh, it is routinely taken along with meat, fish, green vegetables, coconut, and spices, depending on the individual’s preference (Alam et al. 2008). As on date, there is frugal information available on the quality characteristics of yam tubers available in Bangladesh. Keeping in view the above perspectives, the current research was aimed to evaluate the sensory characteristic features of boiled yam tubers based on standard criteria.

MATERIALS AND METHODS The BAU-Germplasm Centre of Bangladesh Agricultural University, Mymensingh is maintaining the germplasms of different Dioscorea spp. The tubers (thirty-one) of the yam were harvested during December 2019 and subjected to sensory evaluation under the similar conditions and methods on the same day (Ojokoh and Adeleke, 2019). The mature tubers were carefully selected and thoroughly washed by potable tap water to get rid of soil and extraneous material before peeling with sharp stainless-steel knives. The peels were removed and washed once again with potable water. The peeled yam tuber was sliced into uniform pieces of 15 to 20g each. Approximately, 12- 14 pieces of yam tubers were cooked in water till its softening. After completion of cooking, water was drained out and pieces of tubers were placed over the plates for sensory evaluation by ten random panelists from the staff of the university based on their willingness to participate (IPGRI/IITA, 1997). The following quality characteristics were considered towards the sensory evaluation of yam tubers: (i) ease of peeling (1: difficult, 2: easy, 3: usually eaten unpeeled), (ii) poundability of boiled tuber (1: poor, 2: good), (iii) cooking time to softness (min), (iv) discoloration of cooking water (1: very low, 5: Intermediate, 9: very high), (v) color of tuber after cooking (1: white, not colored, 5: intermediate, 9: highly colored), (vi) texture of cooked tuber (1: smooth, 2: grainy, 3: fibrous), (vii) bitterness of cooked tuber (0: not bitter, 1: bitter, 2: very bitter), (viii) sweetness of cooked tuber (0: not sweet, 1: sweet, 2: very sweet), (ix) overall assessment of cooked tubers (3: low, 5: intermediate, 7: high).

RESULTS AND DISCUSSION With the ever-growing human population coupled with the fast depletion of natural resources, it is very imminent to diversify the present-day agriculture production system to meet the various needs of the people under changing lifestyle (Janardhan et al., 2003; Jahan et al., 2019; Shajeela et al., 2011). In that course of challenging action, Dioscorea spp. occupy significant niche because its tubers and bulbils could be used for regular food as well as for production of numerous pharmaceutical products of higher economic significance (Liu et al., 2008; Viruel et al., 2015). Albeit, the impression of food consumed by different group of people varies, nevertheless, the consumer often prefers to choose certain edible foods based on parameters relied on sensory analysis (Amare, 2016). Therefore, the current research was performed in order to evaluate the sensory quality characteristics of boiled yam tubers. The quality characteristics of boiled yam tubers are presented in Table 1. 176 Jahan et al.

Table 1: Sensory characteristics of boiled tubers originated from Dioscorea spp. Poundabil Cooking Dis- Color of texture of bitterness of sweetness overall Accession Ease of ity of time to coloration of cooked cooked cooked of cooked assessment of No. peeling boiled softness cooking tuber after tuber tuber tuber cooked tubers tuber (min) water cooking RMHF001 2 2 25 9 1 3 0 1 7 RHMF002 2 2 15 1 1 1 0 2 7 RHMF003 1 1 30 9 9 3 1 0 3 RHMF004 2 2 15 9 1 2 0 1 7 RMHF005 2 2 20 5 9 1 0 0 5 RMHF006 2 1 30 9 9 3 1 0 3 RMHF007 2 2 20 9 5 1 0 1 5 RMHF008 1 1 25 1 5 3 2 0 3 RMHF009 2 1 50 9 9 3 1 0 3 RMHF010 2 2 10 5 1 2 0 2 7 RMHF011 2 2 20 9 5 1 0 0 5 RMHF012 2 1 45 9 9 3 1 0 3 RMHF013 2 2 10 1 1 1 0 1 5 RMHF014 2 2 10 1 1 2 0 0 5 RMHF015 1 1 50 9 9 2 0 0 5 RHMF016 2 2 20 1 1 2 0 0 7 RHMF017 2 2 20 1 1 2 0 1 7 RHMF018 2 2 20 1 1 2 0 1 7 RHMF019 2 2 20 1 1 2 0 1 7 QUALITY CHARACTERISTICS OF BOILED YAM TUBERS 177

Poundabil Cooking Dis- Color of texture of bitterness of sweetness overall Accession Ease of ity of time to coloration of cooked cooked cooked of cooked assessment of No. peeling boiled softness cooking tuber after tuber tuber tuber cooked tubers tuber (min) water cooking RMHF020 2 2 10 1 5 3 0 1 7 RHMF021 2 2 30 5 9 1 0 1 5 RHMF023 1 1 35 9 9 3 1 0 3 RMHF025 1 1 45 9 9 3 0 0 3 RHMF026 2 2 20 5 1 2 0 0 5 RHMF027 1 1 45 9 5 2 0 0 3 RMHF028 2 2 10 1 1 1 0 1 7 RHMF029 2 2 10 1 1 1 0 1 7 RHMF030 2 2 15 1 1 1 0 1 7 RMHF031 2 2 10 1 1 1 0 1 7 RHMF032 1 1 30 5 5 1 0 0 5 RMHF033 2 2 15 9 5 1 0 1 7 Ease of peeling (1: difficult, 2: easy, 3: usually eaten unpeeled), (ii) poundability of boiled tuber (1: poor, 2: good), (iii) cooking time to softness (min), (iv) discoloration of cooking water (1: very low, 5: Intermediate, 9: very high), (v) color of tuber after cooking (1: white, not colored, 5: Intermediate, 9: highly colored), (vi) texture of cooked tuber (1: smooth, 2: grainy, 3: fibrous), (vii) bitterness of cooked tuber (0: not bitter, 1: bitter, 2: very bitter), (viii) sweetness of cooked tuber (0: not sweet, 1: sweet, 2: very sweet), (ix) overall assessment of cooked tubers (3: low, 5: intermediate, 7: high)

178 Jahan et al.

The first step of utilizing food is assessed based on its ease of peeling. This step was carried out by the team members of the current research. According to this criteria, 7 samples were found to be in category 1 i.e. difficult, while rest 24 samples were in category 2 i.e. easy to peel. As poundability (mealiness) is one of the important principles for the evaluation of tubers and roots, the present research assessed the particular parameter by the panel of evaluators of all the collected samples after boiling. Evidently, 11 samples were poor in terms of poundability, while rests20 were of good standing. The cooking time to softness varied from 10 to 50 min among the different samples; being lowest in RMHF010, RMHF013, RMHF014, RMHF020, RMHF028, RMHF029, and RMHF031whereas highest in RMHF009 and RMHF015. The color of tubers is the function of phytochemicals present and it is considered as one of the most important features for its acceptability and attractiveness as a food item. During boiling, part of those phytochemicals comes out of tuber and changes the color of boiling water and the remaining fraction is responsible for the appearance of the boiled tuber. This study revealed all three categories for the specific attribute i.e. “discoloration of cooking water” (Fig. 1). Nevertheless, 13 samples belonged to very low (1), while 5 samples were in the intermediate (5) rank, and rest 13 samples were of the very high category (9). Obviously, the appearance of cooked yam tuber having white color (Fig. 2) is preferred as it will not alter the final color of dishes or food preparations owing to its mere presence. In the perspectives of “color of cooked tuber after cooking”, the current research recorded 15, 7 and 9 number of samples in the category of “white, not colored”, “intermediate” and “highly colored,” respectively. The texture of the cooked tubers (smooth, grainy, or fibrous) depends upon the presence of starch and its proportion along with other nutrients. The different kinds of texture of boiled yam noticed in the current investigation are presented in Fig. 3. Out of the 31 samples of boiled yam, qualities of 12 were smooth, 10 were grainy and rests 9 were fiber type. Bitterness is the attribute of tuber which is resulted by the presence of bitter principles in the form of phenolics or other compounds. According to the taste panels, no bitter taste was found in 25 samples, while 5 samples were medium in bitter and one sample was identified as very bitter (RMHF008). Among all the tested samples of yam, 2 samples were very sweet (RMHF002 and RMHF010), 14 samples were moderately sweet, and rest 15 belonged to the category of 0 i.e. not sweet. In the perspectives of the overall assessment of the cooked tubers of yam, 14 were adjudged in the rank of “high”, 9 were ranked as intermediate, and rest 8 samples were considered as ‘low category’.

QUALITY CHARACTERISTICS OF BOILED YAM TUBERS 179

c a b Figure 1. Discoloration of cooking water; a: very low, b: intermediate, c: very high

a b c

Figure 2. Color of yam tubers after boiling; a: white, not colored, b: intermediate color and c: highly colored

b c a Figure 3. Texture of cooked tuber; a: smooth, b: grainy and c: fibrous

In the promotion of any food crops, sensory evaluation is not only the most important step but also a major determinant for acceptability of the particular variety and its subsequent adoption and usages by the farming communities (Jeannette et al., 2020). The analysis of sensory quality represents a group of physical characteristics that derive from the perceived structure of the food by different sense organs (Kohyama, 2020). The main factors of consumers’ acceptability of any food crops depend upon its ease of cleaning, color, texture, tenderness, elasticity, attractiveness, taste, etc. The current research made an endeavor to perform sensory evaluation of boiled tubers of different yam obtained from the BAU-Germplasm Collection Centre, Bangladesh Agricultural University after following the standard evaluation criteria 180 Jahan et al.

(IPGRI/IITA, 1997). The study was successful to distinguish the different samples based on the quality parameters of cooked tubers. In fact, boiling in water is routinely practiced to evaluate the quality of roots and tubers (Amare, 2016). The tubers of yam could be consumed in different forms, mainly boiled, roasted, grilled or fried, and served sliced, as balls, mashed, chipped or flake (Kapsiya et al., 2015). In some cases, the boiled yam is pounded and consumed with sauce or stew. The tubers of yam are peeled, chipped, dried and milled into flours to make numerous products of choice in different parts of the world. Recently, sensory evaluation of boiled and pounded form of yam has been successfully used in varietal improvement scheme of Dioscorea spp. (Jeannette et al., 2020). The current research outlined the characteristics of the boiled tubers of 31 samples available in Bangladesh. Amongst the tested samples, two (RHMF002 and RMHF010) were found to be superior as compared to the rest 29 samples in view of sensory evaluation criteria especially ease of peeling, poundability, discoloration of water after cooking, color of tuber after cooking, texture of the cooked tuber, bitterness of cooked tuber, sweetness of cooked tuber and overall assessment. However, evaluation of nutritional attributes coupled with medicinal values should be explored for justifying its future course of actions particularly improvement, packages of practices, product diversification and varietal release in order to uphold the principles of biodiversity vis a vis food security of indigenous people.

CONCLUSION The current research made an effort to undertake the sensory evaluation of 31tuber samples of yam after boiling in water. Evidently, differences in the sensory qualities in terms of ease of peeling, poundability, color and texture of tubers, sweetness or bitterness were noticed among the samples. Among the 31 tested samples, two (RHMF002 and RMHF010) were found to be superior in most of the sensory evaluation parameters particularly ease of peeling, poundability, discoloration of water after cooking, color of tuber after cooking, taste (either bitter or sweet) and overall assessment. Intensive research is imminent for justifying those sensory characteristics of the yam tuber samples with the application of advanced tools.

ACKNOWLEDGEMENT The research was supported by the National Agricultural Technology Program- Phase II project (NATP-2). The authors are grateful to NATP-2 for extending the financial support to carry out the present research.

REFERENCES Alam, M.S., Bhuiyan, M.K.R., Hossain, M. and Begum, S.N. (2008). Improved production technology of yam (in Bangla). Technical bulletin published by Tuber Crops Research Centre, Bangladesh Agricultural Research Institute (BARI), Joydebpur, Gazipur. Pp. 1-8.

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Alexander, J. and Coursey, D.G. (1969). The origins of yam cultivation in the domestication and exploitation of plants and animals. Proceedings of a meeting of the research seminar in archaeology and related subjects held at the Institute of Archaeology, London University. Gerald: Duckworth & Co. Ltd. Pp. 405 – 425 Amare, A. (2016). Analysis of sensory and texture properties of three potato cultivars boiled to different thermal center temperatures. MSc thesis. Department of Food Technology, Lund University, Lund, Sweden. Andres, C., Adeoluwa, O. and Bhullar, G.S. (2017). Yam (Dioscorea spp.). Encyclopedia of Applied Plant Sciences. Vol. 3, Waltham, MA. Academic Press. Pp. 435 - 441. Ayensu, E.S. and Coursey, D.G. (1972). Guinea yams: the botany, ethnobotany, use and possible future of yams in West Africa. Economic Botany. 26: 301–318 Coursey, D.G. (1967). Yams: An Account of the Nature, Origins, Cultivation and Utilization of the Useful Members of the Dioscoreaceae. London: Longmans, Greens and Co. Ltd. Coursey, D.G. (1976). The origins and domestication of yams in Africa. In: Origin of African plant domestication (eds Harlan, J.R., De Wet, J.M.J.), Stemler Mouton A.B.L. Publishers, Hague, the Netherlands. Pp. 383 - 408 IPGRI/IITA. 1997. Descriptors for yam (Dioscorea spp.). International Institute of Tropical Agriculture, Ibadan, Nigeria/International Plant Genetic Resources Institute, Rome, Italy. Pp. 1-53 Jahan, F.N., Rahim, M.A., Bokhtiar, S.M. and Samanta, A.K. (2019). Potentiality of underutilized crop Dioscorea spp.: A source of nutraceuticals. SAARC Journal of Agriculture. 17(2): 1-13 Janardhanan, K., Vadivel, V. and Pugalenti, M. (2003). Biodiversity in Indian underexploited/tribal pulses. In: Improvement Strategies for Leguminosae Biotechnology (edited by P.K. Jaiwal and R.P. Singh). Kluwer Academic Publishers. Printed in Great Britain. Pp. 353 - 405. Jeannette, F., Faouziath, S., Estelle, L.Y.L., Celestin, T., Cartney, I.C., Innocent, B.Y. and Alexandre, D.A. (2020). Sensory evaluation and consumers acceptability of some yam (Dioscorea rotundata) cultivars used as parents in a yam varietal development program in Benin. International Journal of Current Microbiology and Applied Sciences. 9(3): 2083-2100. Kapsiya, J., Gungula, D.T., Tame, V.T. and Abakura, J.B. (2015). Effects of different concentrations of chemical sprout inhibitors on the sensory evaluation and food composition of stored yam (Dioscorea rotundata Poir) Tubers. American Journal of Agricultural Science. 2(5): 189-195. Kohyama, K. (2020). Food Texture – sensory evaluation and instrumental measurement. In: Textural characteristic of world foods (edited by Katsuyoshi Nishinari). Published by John Wiley & Sons Ltd. Pp. 1–13.. Kumar, S., Das, G., Shin, H.S. and Patra, J.K. (2017). Dioscorea spp. (a wild edible tuber): a study on its ethnopharmacological potential and traditional use by the local people of Simplipal biosphere reserve, India. Frontiers in Pharmacology. 8:52

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Liu, Y.W., Shang, H.F., Wang, C.K., Hsu, F.L. and Hou, W.C. (2007). Immunomodulatory activity of dioscorin, the storage protein of yam (Dioscorea alata cv. Tainong No. 1) tuber. Food Chemistry and Toxicology.45: 2312– 2318. Ojokoh A. and Adeleke B. (2019). Processing of Yam Paste (Amala): A Product of Fermented Yam (Dioscorea rotundata) Flour. International Annals of Science. 8(1): 87-95. Orkwor, G.C., Asiedu, R. and Ekanayaka, I.J. (1998). Food Yams: Advances in Research. Ibadan: International Institute of Tropical Agriculture/NRCRI. Pradhan, B. and Panda, D. (2020). Potential of neglected and underutilized yams (Dioscorea spp.) for improving nutritional security and health benefits. Frontiers in Pharmacology.11:496. Shajeela, P.S., Mohan, V.R., Jesudas, L.L. and Soris, P.T. (2011). Nutritional and antinutritional evaluation of wild yam (Dioscorea spp.). Tropical and Subtropical Agroecosystem. 14:723-730. Viruel, J., Segarra-Moragues, J.G., Raz, L., Forest, F., Wilkin, P., Sanmartin, I. and Catalan, P. (2015). Late cretaceous-early Eocene origin of yams (Dioscorea, Diocsoreaceae) in the laurasianpalaearctic and their subsequent oligocene-miocene diversification. Journal of Biogeography. 43(4): 760 -76.

SAARC J. Agric., 18(1): 183-196 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48392

Research Article DIFFERENT BODY MEASUREMENT AND BODY WEIGHT PREDICTION OF JAMUNA BASIN SHEEP IN BANGLADESH

M.A. Sun, M.A. Hossain, T. Islam, M.M. Rahman M.M. Hossain and M.A. Hashem* Department of Animal Science, Bangladesh Agricultural University Mymensingh, Bangladesh

ABSTRACT The aim of this study was to estimate different body measurements and derive prediction equation for live weight of Jamuna basin sheep using different body measurement. A total of 520 individual records of sheep (47 males and 473 females) including all temporary at nine-month age (320), first pair permanent at 1.6 year age (161) and 2nd pair permanent at two years age (39) were collected from two Upazila of Sherpur district. Body weight (BW) was taken using a weighing scale and different measurements were taken using the tailor's tape measure and measuring stick while animals were on standing position. Data were analyzed with the help of SPSS-v-20 computer package program. Average BW, wither height(WH), rump height(RH), body length(BL), sternum height(SH), body depth(RD), bi-costal diameter(BD), ear length(EL), rump width(RW), head width(HW), rump length(RL), head length(HL), heart girth(HG), cannon bone circumference(CC) and muzzle diameter(MD) were 12.28±2.75 kg, 49.42±3.78, 54.04±3.88, 49.38±4.57, 28.06±3.00, 53.09±4.40, 15.07±2.58, 8.66±3.32, 15.72±2.33, 12.87±2.20, 14.30±2.29, 18.23±2.30, 55.02±4.71, 9.22±1.01, and 16.65±1.73cm, respectively. According to sex BW, WH, RH, SH, RD and CC were found significant (p<0.001), HG and MD were found significant(p<0.01). Stepwise multiple regression analysis according to agein all temporary groups showed that the body weight was found the best fitted equation having the highest R2value 64% (BW = - 15.53+0.24HG+0.13WH+0.16BD-.1RW+0.05EL) whereas in 1.3-1.6 and 1.9-2 years age group the R2 value for BD was 56 (BW = - 15.65+0.31HG+0.22WH)and 83% (BW = -24.51+0.21RH+0.35HG+0. 19BL-0.78RL+1.2RW-0.87HW), respectively. R2value for BD in male (BW = -28.91+0.43HG+0.35WH) and female (BW = -10.62+0.27HG+ 0.13WH+0.06BD-0.13HW+0.07BL-0.10SH-0.10RL) was 88and 59%, respectively. It is concluded that the best prediction parameter of BW was HG, WH, BD, RW, EL, RH, BL and HW.

*Corresponding author: [email protected]

Received: 03.02.2020 Accepted: 18.06.2020 184 Sun et al.

Keywords: Body weight, Body measurements, Jamuna basin sheep, Regression analysis, Sex, Age

INTRODUCTION Live body weight plays a vital role for determining several characteristics of farm animals (Pesmen and Yardimci, 2008). Estimating the live weight using body measurements is practical, faster, easier and cheaper in the rural areas where the resources are insufficient for the breeder (Nsoso et al., 2004). This fundamental knowledge of BW estimation is often unavailable to farmers due to unavailability of scales. Hence, the farmers have to rely on questionable estimates of BW of their animals leading to inaccuracies in decision making and husbandry (Moaeen-ud-Din et al., 2006). In recent years, there have been a great number of studies on the prediction of BW from various body measurements taken at different growth periods of sheep (Cam et al., 2010). The BW information can be used in determining the value of animals and efficiency of rearing. Body measurements are important data sources in terms of reflecting breed standards (Riva et al., 2004) and are also important for providing information about morphological structure and development ability of the animals. Body measurements differ according to the factors like breed, gender, productivity, age and climatic condition. There are 3.537 million sheep (DLS,2019); of which 42% are reared in three ecological zones as Barind, Jamuna basin and Coastal areas. Most of the sheep are indigenous, with few crossbreds (Bhuiyan, 2006) and are capable of bi-annual lambing and multiple births. In Bangladesh, sheep production is reputed due to their high prolificacy, early maturity, extreme disease resistance, superior skin quality, and wide range of adaptability under adverse agro-climatic condition (Sultana et al., 2011).The most widely used methods for estimating the weight of sheep under farm condition are using a regression equation developed from other linear body measurements for breed/population of interest (Melesse et al., 2013). BW is important for assessing the condition of animal and represents a criterion of selection. The dosage of medication during health care and the required amount of feed depends on the weight of animal. Though BW is an important economic trait, it is rarely measured by rural livestock farmers due to lack of weighing scales. Small scale farmers rely on guess-estimates of the animal’s BW which add to inaccuracies in animal husbandry practices (Slippers et al., 2000). Thiruvenkandan (2005) highlighted that this problem could be overcome by regressing BW on a number of body characteristics which can be measured easily. Such a method has been used by several authors for different species. The heart girth has been used to predict BW in various species of some indigenous animals like sheep of Cameroon, goats in West Africa and cattle in South Africa (Nesamvuni et al., 2000). It has been observed that different models might be BODY MEASUREMENT OF SHEEP 185 needed to predict BW in different environmental conditions, body condition and breeds (Enevoldsen and Kristensen, 1997). Little work has been done on use of linear body measurements (LBM) of Jamuna basin sheep and their possible use for estimating BW. Hasan and Talukder (2011) worked a few parameters of native sheep of Bangladesh but not stated on prediction of BW. The present study worked on 14 parameters of Jamuna basin sheep for prediction of body weight. Hence, the study was carried out to estimate the body measurement relationships and to derive prediction equations for live weight using different body measurements of Jamuna basin sheep.

MATERIALSAND METHODS Study area and selection of sheep The study was conducted at two Upazila like Sherpur Sadar and Nalitabari in Sherpur district from January 2019 to June 2019. A total of 520 individual records of sheep (47 males and 473 females) from 60 households were collected. According to the age, animals were divided into 3 groups: One to nine months (320), 1.3-1.6 years (161) and 1.9-2 years (39). The age category was determined by dentition as outlined by Abegaz and Awgichew (2009). In total, 520 sets of measurements were obtained against 14 variables. BW was taken using a weighing scale, and the linear measurements were taken as described by Abegaz and Awgichew (2009). The measurements were taken using the tailor's tape measure and measuring stick while animals were on standing position as previously used for goats (Khan et al., 2006). Data were collected before grazing in the morning. Daily routine works of farmers for sheep All sheep were kept inside the house at night and grazing 6-7 hours by day. The house was cleaned every morning. Sheep were supplied with pure drinking water ad libitum. Some supplements were fed (150 g/day) in morning. Sheep were bath with shampoo and fresh water weekly basis by using body brush. Data collection The data were collected for all body measurements directly as per Fig. 1 from individual sheep. It was directly measured with gauge tape according to Fig. 1 parameters. Fourteen parameters were collected weekly basis from farmer to farmer’s house according to age, sex and dentition. All the research team was involved in collection different body measurement of Jamuna basin sheep at Sherpur district. An introductory visit was made to study area when the aims and objects of study were explained to the most of the respondents. This helped to create a friendly atmosphere between respondents and researchers. The collected data from this experiment were entered in Microsoft Excel worksheet, organized and processed for further analysis. 186 Sun et al.

Figure 1. Wither height, 2.Rump height, 3. Body length, 4. Sternum height, 5. Body depth, 6. Bicoastal diameter,7. Ear length, 8.Rump width, 9. Head width,10. Rump length, 11.Head Length,12. Heart girth, 13. Cannon bone circumference,14. Muzzle diameter

Statistical model for live weight The following model was adopted for live weight estimation: Yijklm= μ+ Si + Mj + Rk + Eijk Where: Yijklmn: The dependent variable (individual animal record for the trait). μ: Overall mean. Si: Fixed effect of sheep sex (i = Male, Female). Mj: Fixed effect of age according to pair of teeth (j = All temporary, 2nd pair permanent, 3rd pair permanent). Rk: Fixed effect of Location (k = Sherpur Sadar, Nalitabari). Eijk: The residual error. Statistical analysis Data were tabulated and analyzed with descriptive statistical method by fulfilling the objectives of the study. Tabular technique was applied for the analysis of data using descriptive statistical tools like frequency, average and percentages, standard deviation, correlation coefficient and prediction model was developed through SPSS- v-20 version computer software. The differences in means were tested using one-way ANOVAs. RESULTS AND DISCUSSION Phenotypic characteristics Average BW of combined and individual male and female sheep was 12.28±2.75, 14.55±4.71 and 12.05±2.36 kg, respectively. Individual BW was significantly BODY MEASUREMENT OF SHEEP 187

(p<0.001) higher in male (Table 1). The HG was significantly differed between sexes (p<0.05). RH was 56.95±4.68 and 12.05±2.36 in male and female which was significantly differed (p<0.001). The WH, SH, RD, and CC were significantly differed (p<0.001) between male and female and the value of all mentioned characters were higher in male. Similar estimates for BW were found in Muzaffarnagari, Pugal, Munjal sheep (Yadav et al., 2011). Higher BW was reported in Sahel sheep breeds and Djallonke sheep of Northern Ghana (Birteeb et al., 2012)

Table 1. Body measurements of Jamuna basin sheep based on sex Combined sex Individual sex Level of Body Measurements Significant N Male (Mean ± Female (Mean ± traits Mean+SD SD) SD) Body Weight(kg) 520 12.28±2.75 14.55±4.71 12.05±2.36 0.00 *** Wither Height(cm) 520 49.42±3.78 52.10±5.37 49.15±3.48 0.00 *** Rump height(cm) 520 54.04±3.88 56.95±4.68 53.75±3.67 0.00 *** Body length(cm) 520 49.38±4.57 50.61±5.92 49.26±4.40 0.05 NS Sternum height(cm) 520 28.06±3.01 30.31±4.93 27.84±2.64 0.00 *** Body depth(cm) 520 53.09±4.40 55.31±5.68 52.87±4.20 0.00 *** Bi-coastal 520 15.07±2.58 15.0±2.97 15.07±2.55 0.84 NS diameter(cm) Ear length(cm) 520 8.66±3.32 9.74±2.25 8.55±3.39 0.01 * Rump width(cm) 520 15.72±2.33 15.63±2.90 15.73±2.27 0.78 NS Head width(cm) 520 12.87±2.20 13.34±3.10 12.83±2.09 0.13 NS Rump length(cm) 520 14.30±2.29 14.61±2.74 14.27±2.24 0.32 NS Head length(cm) 520 18.23±2.30 18.63±3.03 18.19±2.21 0.21 NS Heart girth(cm) 520 55.02±4.71 57.0±6.32 54.82±4.48 0.003 ** Canon bone 520 9.20±1.01 9.78±1.12 9.15±0.99 0.00 *** circumference(cm) Muzzle diameter(cm) 520 16.65±1.73 17.40±2.28 16.57±1.65 0.002 ** *** (p<0.001), ** (p<0.01), * (p<0.05), NS- Non significant irrespective of age and sex. Yadav et al. (2011) characterized Munjal sheep and reported average BW of males and females as 60.05 and 43.95 kg, respectively which is higher than the present findings. The mean BW obtained in this study was lower than the average BW of central highland sheep, Rift Valley sheep and Menz sheep in Amhara Regional State (Tibbo et al., 2004). Many previous studies reported significant effects of environmental factors like sex and age on BW in accordance with the present study (Tadesse and Gebremariam, 2010; Shirzeyli et al., 2013). The higher mean linear body weight (LBW) values observed in male than female might be due to relatively large physical features of male as a result of natural hormonal 188 Sun et al. variations (Maria et al., 2003).WH, RH, BL, SH, RD, BD, EL, RW, HW, RL, HL, HG, CC and MD were 49.42±3.78, 54.04±3.88, 49.38±4.57, 28.06±3.01, 53.09±4.40, 15.07±2.58, 8.66±3.32, 15.72±2.33, 12.87±2.20, 14.30±2.29, 18.23±2.30, 55.02±4.71, 9.20±1.01 and16.65±1.73cm, respectively (Table 1). Jamuna basin sheep had lower HG than Washera, Farta and Gumuz sheep (Abegaz et al., 2011). It had shorter BL than Gumuz sheep (Abegaz et al., 2011). HG continued growing up to old age; indicates that different body parts mature at different ages (Mavule et al., 2013). Gopal and Prasad (2007) stated that the overall least square means for BL, WH and chest girth were 82.9, 83.9 and 85.1 cm respectively in adult Muzaffarnagari sheep in India, which is much higher than the present findings. The present finding was in close agreement with all body measurements with the reports of Gowaneet al. (2010a) in Malpura sheep and Singh et al. (2014) in Marwari sheep. Mandal et al. (2015) found variable live weight in different sheep breeds, owing to breed differences, which are genetic in nature.

Body measurement according to age From the mean BW, BD, HL and HG (Table 2) of Jamuna basin sheep was found to increase significantly with age (p<0.001).The mean WH, RH and MD of sheep with age was also seen to be increasing at1-9 month and 1.3-1.6 years age groups which were significantly differed (p<0.001). The mean BL, RW and HW of sheep were also seen to be increasing in 1.3-1.6 years than 1-9-month age groups. The mean EL, RL and CC of sheep with age were seen almost similar in all groups. The mean of SH of sheep was found higher in 1.9-2 years age group than that of all 1-9 month and 1.3- 1.6 years age group but not significant. BW, WH, RH, BL, RD, RW, HL, HG and MD were significant (p<0.001) among different ages. RL was significant (p<0.01) among different ages. The BW of sheep (1.9-2 years) was 14.55 ± 3.43 kg showing this population to be of lower BW. Age group also affected BW of sheep. Yearlings had a lower weight than other higher age groups, and this might be because of the fact that yearlings have not yet achieved mature BW. A significant effect of sex and age of sheep on BW is reported by Mavule et al. (2013) for different breeds of sheep. Both BW and HG showed an increase at the age group which might be responsible for maturation.

BODY MEASUREMENT OF SHEEP 189

Table 2. Body measurements of Jamuna basin sheep according to age

Age groups Level of Body Measurements traits 1-9 month 1.3-1.6 years 1.9-2 years significant (ME±SD) (ME±SD) (ME±SD) Body Weight(kg) 11.49±2.44 13.3±2.5 14.55±3.43 0.00 *** Wither Height(cm) 48.72±3.77 50.59±3.33 50.35±4.31 0.00 *** Rump height(cm) 53.26±3.84 55.15±3.59 55.76±3.78 0.00 *** Body length(cm) 48.38±4.33 51.19±4.38 50.12±4.88 0.00 *** Sternum height(cm) 28.09±3.14 27.96±2.45 28.20±3.87 0.86 NS Body depth(cm) 51.94±4.36 54.72±3.86 55.84±3.49 0.00 *** Bi-coastal diameter(cm) 14.90±2.66 15.40±2.45 15.07±2.38 0.12 NS Ear length(cm) 8.67±3.23 8.67±3.23 7.84±4.15 0.23 NS Rump width(cm) 15.42±2.41 15.42±2.41 15.76±2.13 0.00 *** Head width(cm) 12.79±2.23 12.79±2.23 12.43±2.02 0.10 NS Rump length(cm) 14.05±2.35 14.05±2.35 14.35±1.72 0.005 ** Head length(cm) 17.79±2.44 17.79±2.44 19.05±1.77 0.00 *** Heart girth(cm) 53.67±4.23 53.67±4.23 58.43±5.01 0.00 *** Canon bone 0.18 NS 9.15±1.09 9.15±1.09 9.41±.88 circumference(cm) Muzzle diameter(cm) 16.33±1.76 16.33±1.76 17.07±2.00 0.00 *** *** (p<0.001), ** (p<0.01), NS- Non significant

Correlation coefficient between body weight and body measurements In all 1-9-month age group, 12 out of 14 variables were significantly correlated with BW (Table 3). This means that those sheep of 1-9-month age group had relatively high HG, r = 0.76, p<0.01, were likely to have high BW. BW was also positive correlated with RD (r = 0.69, p<0.01), WH (r = 0.60, p<0.01), RH (r = 0.54, p<0.01), BL (r = 0.43, p<0.01), SH (r = 0.38, p<0.01), MD (r = 0.29, p<0.01), CC (r = 0.28, p<0.01), HL (r = 0.21, p<0.01), RW (r = 0.15, p<0.05), RL (r = 0.14, p<0.05), EL (r = 0.13, p<0.05) where HG, RD, WH, RH, BL, SH, MD, CC, HL was strongly significant (p<0.01) and RL, EL was significant (p<0.05). In 1.9-2 years age group, 10 out of 14 variables were significantly (p<0.01) correlated with BW. This means that those sheep at1.9-2 years age group had relatively high HG, r = 0.70, p<0.01, were likely to have high BW. BW was also positively correlated with WH (r = 0.57, p<0.01), RD (r = 0.55, p<0.01), RH (r = 0.50, p<0.01), BL (r = 0.47, p<0.01), SH (r = 0.32, p<0.01), CC (r = 0.26, p<0.01), RW (r = 0.26, p<0.01), MD (r = 0.21, p<0.001), HL (r = 0.16, p<0.05) where HG, WH, RD, RH, BL, SH, CBC, RW, MD, was strongly significant (p<0.01) and HL was significant (p<0.05).In 1.9-2 years age group, 6 out of the 14 variables were 190 Sun et al. significant (p<0.01) correlated with BW. This means that those sheep at1.9-2 years age group had relatively high RH, r = 0.68, p<0.01, were likely to have high BW. BW was also positively correlated with BL (r = 0.67, p<0.01), WH (r = 0.62, p<0.001), BD (r = 0.61, p<0.01), HG (r = 0.60, p<0.01), SH (r = 0.49, p<0.01) where RH, BL, WH, RD, HG, SH were strongly significant (p<0.01). According to multiple regression analysis it was found that the best estimation age was 1.9-2 years and then 1-9 month and 1.3-1.6 years, respectively (Table 4). The correlation is one of the most common and useful statistics that describes the degree of relationship between two variables. There was a positive and significant correlation between weight and other body measurements except with BD and HW (Table 3). The highest correlation coefficient obtained was between BW and HG (r = 0.76) which was followed by weight with RD (r = 0.68). The higher correlation of LBW with BW indicates that these LBW can be used as indirect selection criteria in the absence of weighing scale (Khan et al., 2006). The observed positive (p<0.05) correlations between weight and other body measurements was in agreement with literature of Melesse et al. (2013). In general, it was seen that body measurements such as BL and chest girth had a high relationship with BW of sheep. Correlation coefficients may be affected by age, sex, season, feeding condition. So, it is not expected to achieve same results in different breeds and environments, and the effectiveness of body measurements in BW prediction could be changed (Cam et al., 2010). High positive phenotypic correlation coefficients were observed between live weight and body measurements of animals in different age groups (2–6 years) (Yilmaz et al., 2013). Fakhraei et al. (2008) reported correlation more than 0.95 between BW with chest girth, BL and height in Iranian Farahani sheep. In another Iranian sheep, Moghania noticeable relationship among body measurements was declared by Hoseini et al. (2010). Lavvaf et al. (2012) presented some reports on such correlations, while there is no accordance with our results. High correlation among body measurement was not supported in two of their investigated breeds. The relationship between BW and body measurements in Saanen goats was investigated by Pesmen and Yardimci (2008). Live weight was found to be highly correlated with HG and BL in their study. The positive correlation coefficient of BW seen in this study with most body measurements demonstrated that BW could be predicted more accurately based on the dimension of various body measurements. Similar results of this study, live weight was found to be highly correlated with body dimensional traits in sheep (Lavvaf et al., 2012). Correlation values were seen positive and significant in major studied parameters (WH, BL and HG) which were similar to the results of Cam et al.(2010). Results obtained from all temporary age group are in line with Cam et al. (2010) as they have reported high phenotypic correlation between HG and BW that strongly entails the importance of relationship between HG and BW. WH is positively significantly and strongly correlated to BW of both sexes and all age groups of breed. Most of the studied animals, girth circumferences are positively, strongly and significantly correlated to BW. The BW comes nearer of body BODY MEASUREMENT OF SHEEP 191 measurement estimation when sheep grow under same breed, age, sex, feeding, deworming, management condition etc. The accurate prediction of BW may be considered as a framework for record keeping in rural areas. The economic value of sheep distributed to a special geographical location may be estimated better.

Table 3. Correlation coefficient between body weight and body measurements in Jamuna basin sheep Age groups Body Measurements traits 1-9 month 1.3-1.6 years 1.9-2 years (ME±SD) (ME±SD) (ME±SD) Wither Height (cm) 0.604** 0.569** 0.622** Rump height(cm) 0.537** 0.497** 0.679** Body length(cm) 0.428** 0.466** 0.669** Sternum height(cm) 0.381** 0.322** 0.487** Body depth(cm) 0.691** 0.548** 0.614** Bi-coastal diameter(cm) 0.023 0.026 0.015 Ear length(cm) 0.131* 0.07 0.282 Rump width(cm) 0.147** 0.259** 0.167 Head width(cm) 0.036 0.084 -0.135 Rump length(cm) 0.142* 0.088 0.035 Head length(cm) 0.205** 0.160* -0.055 Heart girth(cm) 0.756** 0.704** 0.607** Canon bone circumference(cm) 0.278** 0.263** 0.258 Muzzle diameter(cm) 0.289** 0.211** -0.001 ** (p<0.01), *(p<0.05)

Stepwise multiple regression based on age In 1-9 month age group, the equation (Table 4); BW = -15.53+0.24HG+0.13WH+0.16BD-.1RW+0.05EL, was found to be the best fitted equation because, it has highest R2 of 64% and highest adj. R2 of 0.63, this indicates that 63% of variance in BW was explained by the model. LBW was significant (p<0.001). In 1.3-1.6 years age group, the equation; BW = -15.65+0.31HG+0.22WH, was found to be the best fitted equation because, it has highest R2 value of 56% and highest adj. R2 of 0.55, this indicates that 55% of variance in BW was explained by the model. LBW was significant (p<0.001). 192 Sun et al.

In 1.9-2 years age group, the equation; BW = -24.51+0.21RH+0.35HG+0.19BL-0.78RL+1.2RW-0.87HW, was found to be the best fitted equation because, it has higher R2 value of 83% and the highest adj. R2 of 0.80, this indicates that 80% of variance in BW was explained by the model. LBW was significant (p<0.001) (Table 5). R2 and adj. R2 can be considered as criteria important in selection of appropriate linear model. The equations with larger R2 and adj. R2 showed a range similar to the range observed in actual weight category. Among multiple regression models of Yilmaz et al. (2013), highest coefficients of determination were obtained from the models formed at BL and chest girth together in Karya sheep (R2=0.79, R2=0.87). In the literature, the most appropriate parameters to predict the BW in the established regression equations were HG and BL. When both HG and BL were considered in equations simultaneously, the highest estimation precisions were gained in goat (Tadesse et al., 2012). The greatest variation of BW was accounted by combination of WH, chest girth and BL than individually of all age groups in both sexes (Thiruvenkadan, 2005). The result was generally in agreement with literature of Thiruvenkadan (2005) that HG was the best predictor of weight. Multiple regression models estimated weight with better accuracy of prediction increased with the increased number of variables (Melesse et al., 2013).

Table 4. Stepwise multiple regression based on age Age R Adjusted N Model Sig. Group square R Square -15.43+0.35HG+0.16WH 0.61 0.61 0.000 -15.95+.27HG+0.13WH+0.11BD 0.63 0.62 0.000 1-9 -15.37+0.25HG+0.14WH+0.15BD- 320 0.63 0.63 0.000 month 0.1RW -15.53+0.24HG+0.13WH+0.16BD- 0.64 0.63 0.000 .1RW+0.05EL 1.3-1.6 -8.86+0.39HG 0.49 0.49 0.000 years 161 -15.65+0.31HG+0.22WH 0.56 0.55 0.000 -19.79+0.61RH 0.46 0.44 0.000 -28.97+0.48RH+0.48HG 0.61 0.59 0.000 -28.90+0.32RH+0.24HG+0.21BL 0.66 0.64 0.000

-23.88+0.24RH+0.28HG+0.31BL- 0.72 0.69 0.000 0.54RL 1.9-2 39 -27.19+0.23RH+0.35HG+0.26BL- years 0.77 0.74 0.000 0.92RL+0.50RW -24.51+0.21RH+0.35HG+0.19BL- 0.83 0.80 0.000 0.78RL+1.2RW-0.87HW BODY MEASUREMENT OF SHEEP 193

Stepwise multiple regression based on sex In male group, the equation (Table 5); BW = -28.91+0.43HG+0.35WH, was found to be the best fitted equation because, it has highest R2 of 88% and highest Adj. R2 of 0.88, this indicates that 88% of variance in BW was explained by the model. LBW were significant (p<0.001). In female group, the equation; BW = -10.62+0.27HG+0.13WH+0.06BD-0.13HW+0.07BL-0.10SH-0.10RL, was found to be the best fitted equation because, it was highest R2 value of 59% and highest adj. R2 of 0.58, this indicates that 58% of variance in BW was explained by the model.LBW were significant (p<0.001) (Table 5).An increase in the coefficient of determination was observed as more variables were included in the prediction equations which indicates more precision in the determination of BW based on these LBM (Tadesse and Gebremariam, 2010). Similarly, findings reported by Tadesse and Gebremariam (2010) on high land sheep in Tigray Region, North-Ethiopia, indicated that incorporating more LBM in the prediction equation has improved prediction accuracy. This means that considering more parameters of LBM especially after applying principle of Parsimony or Occams razor (which stated that a model with fewer variables (p) was preferred to the one with many variables) (Yakubu and Musa, 2013) as it was applied in this study, could provide better precision in predicting the BW using established equations under each age category.

Table 5. Stepwise multiple regression based on sex Adjusted R Sex N Model R square Sig. Square -23.93+0.67HG 0.82 0.81 0.000 Male 47 -28.91+0.43HG+0.35WH 0.88 0.88 0.000 -8.98+0.38HG 0.53 0.52 0.000 -13.46+0.28HG+0.12WH+0.07BD 0.56 0.55 0.000 -12.48+0.27HG+0.11WH+0.09BD- 0.56 0.56 0.000 0.08HW -12.48+0.27HG+0.11WH+0.09BD- 0.57 0.57 0.000 0.08HW

-12.51+0.26HG+0.09WH+0.07BD- Female 473 0.58 0.57 0.000 0.12HW+0.06BL -11.45+0.26HG+0.12WH+0.07BD- 0.58 0.58 0.000 0.14HW+0.06BL-0.06SH -10.62+0.27HG+0.13WH+0.06BD- 0.59 0.58 0.000 0.13HW+0.07BL-0.10SH-0.10RL

194 Sun et al.

CONCLUSIONS It is revealed that live weight of Jamuna basin sheep can be estimated with more accuracy using different body measurements and stepwise multiple regression. Extra costs and waste time can be saved by this method. Stepwise multiple regression analysis according to age in all 1-9 month groups showed that the body weight was found the best fitted equation having the highest R2 value 64% (BW = - 15.53+0.24HG+0.13WH+0.16BD-.1RW+0.05EL) whereas in 1.3-1.6 and 1.9-2 years age group the R2 value for body weight was 56 (BW = -15.65+0.31 HG+0.22 WH) and 83% (BW = -24.51+0.21 RH+0.35 HG+0.19 BL-0.78 RL+1.2 RW-0.87 HW), respectively. R2 value for body weight in male (BW = -28.91+0.43 HG+0.35 WH) and female (BW = -10.62+0.27 HG+0.13 WH+0.06 BD-0.13 HW+0.07 BL-0.10 SH- 0.10 RL) was 88 and 59%, respectively. So, it is recommended that the best prediction parameters of body weight were HG, WH, BD, RW, EL, RH, BL and HW.

ACKNOWLEDGEMENTS The authors are highly grateful to the Krishi Gobeshona Foundation (KGF) for funding this research project.

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Slippers, S.C., Letty, B.A. and De-Vilter J.R. (2000). Production of the body weight of Nguni goats. South African Journal of Animal Science, 30:127-128. Singh, H., Pannu, U., Narula, H.K., Chopra, A., Naharwara, V. and Bhakar, S.K. (2014). Estimates of (co)variance components and genetic parameters of growth traits in Marwari sheep. Journal of Applied Animal Research, 44 (1):27-35. Shirzeyli, S.H., Lavvaf, A. and Asad, I.A. (2013). Estimation of body weight from body measurements in four breeds of Iranian sheep. Songklanakarin Journal of Science and Technology, 35: 507-511. Tadesse, A. Tikabo, G. and Gangwar, S.K. (2012). Application of linear body measurements for predicting body weight of Abergelle goat breed in Tigray region, Northern- Ethiopia, Global Journal of Bio-science and Biotechnology, 1 (2): 314-319. Tadesse, A., and Gebremariam, T. (2010). Application of linear body measurements for live body weight estimation of highland sheep in Tigray region, North-Ethiopia. Journal of the Drylands, 3:203-207. Thiruvenkadan, A.K. (2005). Determination of best fitted regression model for estimation of body weight in Kanniadu kids under farmer’s management system. Livestock Research for Rural Development, 17: 103-107. Tibbo, M., Ayalew, W, Awgichew, K., Ermias, E. and Rege, J.E.O. (2004). On-station characterisation of indigenous Menz and Horro sheep breeds in the central highlands of Ethiopia. FAO/UNEP Animal Genetic Resources Information. Yadav, D., Arora, R., Bhatia, S. and Singh, G. (2011). Morphological characterization, production and reproduction status of Munjal-A threatened sheep population of North- West India. Indian Journal of Animal Sciences,81:943–945. Yakubu, A. and Musa-Azara, I.S. (2013). Evaluation of three mathematical functions to describe the relationship between body weight, body condition and testicular dimensions in Yankasa sheep. International Journal of Morphology, 31:1376-1382. Yilmaz, O. Cemal, I. and Karaca, O. (2013). Estimation of mature live weight using some body measurements in Karya sheep. Tropical Animal Health Production, 45:397- 403.

SAARC J. Agric., 18(1): 197-208 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48393

Research Article INVESTIGATING THE QUALITY OF COMMERCIAL BEEF CATTLE FEEDS AND FEED INGREDIENTS USED IN BANGLADESH

M.T. Kamal1, M.A. Hashem1*, M. Al-Mamun2, M.M. Hossain1 M.A. Razzaque3 and J.H. Ritu1 1Department of Animal Science, Bangladesh Agricultural University Mymensingh-2202, Bangladesh. 2Department of Animal Nutrition, Bangladesh Agricultural University Mymensingh-2202, Bangladesh. 3Desert Agriculture and Ecosystem Program, Kuwait Institute for Scientific Research Safat 13109, Kuwait

ABSTRACT The study was undertaken to evaluate the quality of commercial beef cattle feed and feed ingredients which are available in Bangladesh. For this purpose, chemical analysis and in vitro digestibility (IVD) were estimated for nine commercial feeds and different feed ingredients which were collected from different regions. Chemical analysis of samples was carried out in triplicate for dry matter (DM), crude protein (CP), crude fiber (CF), ether extract (EE), ash, acid detergent fiber (ADF) and neutral detergent fiber (NDF) content. Metabolizable energy (ME) was calculated mathematically for feed samples by using standard formula. The analysis revealed the difference between the manufacturer’s claim and actual analyzed value. In commercial feeds DM content ranged from 90-92%. While CP content of commercial feeds was either lower or higher than the written value of feed industries. In Provita feed CP content (20.72%) was higher and Care feed had much lower CP content (7.54%) than written value. In vitro digestibility of DM in feed ingredients varied from 18.27 in straw to 75.77% in soybean meal. The fiber component (NDF and ADF) was negatively correlated with IVDMD and CP was positively correlated with IVDMD because fiber rich components were less digestible than the non-fibrous component (protein). Analysis of commercial feed samples revealed that the values claimed by the manufacturers are quite different from the actual analyzed values. This is a matter of concern and needs to be checked through better quality control measures by systematic feed analysis and ensure that manufacturers declare the true composition of the marketed feeds. Keyword: Beef cattle feed, Quality, Chemical analysis, In vitro digestibility (IVD)

* Corresponding author: [email protected]

Received: 27.04.2020 Accepted: 10.06.2020 198 Kamal et al.

INTRODUCTION In Bangladesh, livestock sector is one of the major components of agricultural activities and plays a crucial role in economic development by ensuring food security and stimulating the growth of a number of subsidiary industries (Goutam et al., 2017). Approximately 64% of the livestock farmers practiced fattening round the year and rest of the farmers followed fattening for period of 3 months; before Eid-ul- Adha (Kamal et al., 2019). Malpractice of feed adulteration and use of waste contaminated feeds are common practices in Bangladesh. Feed shortage is the main reason for low productivity of livestock in Bangladesh (Rahman et al., 1998; Baset et al., 2003; Jahan et al., 2018) and at the same time farmers are not able to formulate a balanced ration leading to loss of productivity. To fulfill the requirement of farmer for concentrate feed, commercial feed industries are marketing the branded feeds to cater the market demand. Ideally the role of these industries should be to provide high quality livestock feeds to enhance production by meeting the nutrient requirements of livestock in different stages of growth or production. The recognized feed mills (ACI Godrej, Lal Teer, Care, Gain, Index, Soudia feed, Provita feed etc.) as a marketing strategy display various essential nutrients percentage on the feed package to attract/deceive the farmers. So, a question arises whether the animal feed industries maintain the composition accurately in the feed as mentioned in the package and the feed is free from harmful objects or they attempt to mislead the consumer. Misleading information in the nutritional profile of feed provided by the suppliers erodes the consumer confidence and loss of productivity. The farmers are faced with multiple challenges regarding the poor and inconsistent quality of commercial feeds, limited capital, and insufficient knowledge of nutritive value of commercial feeds (Laswai and Nandonde, 2013). Commercial feed producers tend to sell their feed by exaggerating the nutritional profile of the feed leading to poor production performance of livestock and loss to the farmers. This practice challenges the reliability of commercial feed quality besides effecting the safety of feed in cattle. It is beyond the scope of this research to provide a detailed explanation of nutritional value of manufactured cattle feed and feed ingredients, especially because this is a very specific subject area. Current research will lead to better understanding of the chemical composition of the commercial feeds for beef cattle. Findings of the present study will help the farmers of Bangladesh in understanding the quality of the purchased commercial feeds leading to better feeding. An ideal combination of ingredients in compounded feed ensures rational use of available resources while meeting the nutritional requirements of the animal. So, the approach of compounded feed can be an economic attempt for better feed production as well as increasing productivity and nutritional status of livestock. The ultimate goal of feed analysis is to assess the quality of beef cattle feed through nutrient composition.

QUALITY EVALUATION OF COMMERCIAL BEEF CATTLE FEEDS 199

MATERIALS AND METHODS Experimental site The experiment was conducted at Department of Animal Science laboratory and Animal Nutrition Laboratory, BAU, Mymensingh. Collection of samples Nine (9) manufactured beef cattle feed samples (8 concentrate and a total mixed ration) and eleven (11) feed ingredients were collected from different commercial feed mills and feed dealers of Bangladesh. It is important that samples are true representative of the whole that reflects of what livestock farmers are buying. Oven dried samples were ground in a grinding machine (Cyclotec sample mill Tecator, Sweden) by using 1.0 mm sieve for chemical analysis. The ground samples in three replicates of around 250g each were kept in air tight zip lock bag for further chemical analysis. Feed quality (nutritive value) analyses: The samples were analyzed for proximate analysis such as DM (Dry matter), CP (Crude protein) and ash following the method of AOAC (2005). All determination was done in triplicate and the mean value was reported. Acid detergent fiber (ADF) and Neutral detergent fiber (NDF) were determined by following the procedures of Goering and Van Soest (1970). In-vitro study An in-vitro study was conducted to determine the organic matter digestibility (OMD) and metabolizable energy (ME) contents of feedstuffs according to the methods of Menke et al. (1979) and Menke and Steingass (1988). Calculation of organic matter digestibility and ME contents The organic matter digestibility (OMD%) and metabolizable energy (ME, MJ/kg DM) contents were calculated from the gas volume (Gv) and Crude protein value (CP%) using the following equations proposed by Menke and Steingass (1988). Roughages samples: OMD (%) =9.00+0.9991Gp+0.0595CP+0.0181TA ME (MJ/kg DM) =2.20+0.136*Gp+0.0574*CP Concentrate samples: IVOMD (%) = 9.00+0.9991Gp+0.0595CP+0.0181TA ME=1.06+0.1570Gp+0.0054CP+0.0139EE - 0.0054TA Where, OMD=Organic matter digestibility ME=Metabolizable energy (MJ/Kg DM) Gp=24 h gas production (ml/200mgDM) 200 Kamal et al.

CP=Crude protein (%) TA=Total ash content (%) EE=Ether extract (%) Statistical analysis The collected data of proximate analysis and gas production were statistically analyzed using “Analysis of Variance” technique with the help of computer program, SAS (Version 9.1.3).

RESULTS AND DISCUSSION Chemical analysis of manufactured feed The proximate analysis of nine (9) commercial beef cattle feeds are shown in table 1. The DM (%) content ranged from 90 to 92%. It was observed that the DM content of feeds was almost same. Mean ash content ranged from 8.68 to 22.08%. Mean Ash contents of Gain feed (22.08%) and Saudia feed (19.56%) was higher than other feed. Higher ash content may be due to the bone content of feeds of animal origin and soil contamination during harvest of feeds of plant origin. Normally beef cattle feeds contain 14-18%crude protein (NRC, 2000). Crude protein content in beef cattle feed samples were found to vary widely. In Provita feed CP content (20.72%) was high while in Care feed CP content (7.54%) was much lower than manufacturers claimed value and lower than the standard protein requirements of beef cattle. CP includes both true protein and non-protein nitrogen which can be used most efficiently by ruminant animals. Some protein fractions are more digestible then others, but in general the higher the protein level, the more digestible is the feed. Mean crude fiber (CF) content ranged from 4.01 to 10.11%. Higher crude fiber content observed in Gain feed (10.11%) is not favorable. Crude fiber is poorly digested component of a feed and is made up of cellulose, hemicelluloses and lignin. Mean fat value of beef cattle feeds ranged from 2.25 to 4.67%, which is within the normal range. Higher fat content is susceptible to rancidity, leading to off flavor, low palatability and toxic effects. Mean ADF value of cattle feed was within the favorable range of 17.66 to 22.85%. ADF is the least digestible portion of fiber which affects the feeds digestibility negatively. ADF content is inversely related to the digestibility. Mean NDF value ranged from 30.42 to 33.11%, whereas the standard NDF value of beef cattle feed is 35%. NDF is a major parameter that should be controlled in cattle feed formulation as it controls the feed intake of ruminants. Across feeds ash and fiber content had a significant variation (P<0.05). CP content was lower in Care and Saudia feed than MV value (table 1). Analyzed CP content of Provita feed was higher than the manufactured value. But, EE content of feeds was lower than the manufactured value which is good for animal health. A graphical presentation is shown in fig. 1. QUALITY EVALUATION OF COMMERCIAL BEEF CATTLE FEEDS 201

20 p 18 16 e 14 r 12 c 10 e 8 6 n 4 t 2 a 0 1 2 3 4 5 6 7 8 9 Feed samples Moisture(%)(MV) Moisture(%)(AV) CP(%)(MV) CP(%)(AV) EE(%)(MV) EE(%)(AV)

Figure 1. Difference between analytical value (AV) and manufactured value (MV) of commercial feed

Chemical analysis and In-vitro digestibility of feed ingredients: The proximate composition and invitro digestibility of feed ingredients was shown in Table 2. Sesame oil cake is a valuable source of protein fed to ruminant livestock and poultry (Hansen, 2011; Oplinger et al., 1997). DM, crude protein, crude fiber and ash (%) range of analyzed sesame oil cake were 91-93%, 14-15%, 0.55-0.80% and 11.34- 13%, respectively. Wheat bran is the concentrate ruminant feed and widely used in Bangladesh in every region. Maximum recommended inclusion rates are 10% in calves, 20% in dairy cows and 25% in beef cattle (Ewing, 1997). In Bangladesh, it is used up to 50% of the total diet. It has a slightly laxative effect, partly because the bran fiber is moderately digested (Gohl, 1982). Wheat bran contains protein (12%), fat (0.5%) and minerals (2%) (Slavin, 2003). Analyzed results of wheat bran indicated that it was low in protein (7-9%), 90-92% DM and 2-2.50% fat. Maize is the main raw materials for poultry which is used approximately 50 to 60%of the total ration. Maize is also used in cattle feed formulation and is grown mainly in the northern part of Bangladesh. Mojisola (2005) reported that maize grains contained 8.96% crude protein, 4.09% crude fiber, 1.33% ash, 1.48% crude fiber and 7.15% moisture. Analyzed results for maize samples had 88-90% DM, 5-6% CP, 4- 5% CF and 2-2.50% ash. Lower crude protein (5-6) found in maize may be due to its immature grains which contain low protein. Yadav (2002) reported 4.10-12.98% CP, 4.98-5.45% CF and 1.18-1.50% ash in maize grains. Sharma (2013) reported that mustard Cake contained 91.42% dry matter (DM), 30.12% crude protein (CP), 9.29% ether extract (EE). However, analyzed MOC samples revealed lower crude protein (16% CP) due to soil contamination and EE (3 -3.5%) while DM (90-91%) was found to be comparable. 202 Kamal et al.

Table 1. Analytical value (AV) of proximate composition of commercial feeds and comparative study with the manufactured value (MV) written in bag

Feed samples* Level Parameter 1 2 3 4 5 6 7 8 9 of sig.

DM AV 92.67±0.60 90.72±0.43 92.19±0.28 90.24±0.43 92.65±0.35 91.04±0.37 90.97±0.54 92.79±0.07 91.64±0.09 NS (%) MV 89 88 89 87 88 89 88 88 89

Ash AV 22.08±0.30 9.82±0.30 10.04±0.10 8.94±0.30 19.56±0.30 14.62±0.30 8.68±0.30 10.15±0.31 9.19±0.13 *** (%) MV NA

CP AV 12.36±0.08 17.39±0.04 7.54±0.05 14.89±0.06 7.52±0.08 17.66±0.05 20.72±0.52 18.03±0.04 13.18±0.19 * (%) MV 10 18 10 15 17 18 17 18 15

CF AV 10.11±0.05 8.17±0.02 6.46±0.04 5.03±0.01 4.26± 0.00 10.32±0.20 5.54±0.55 4.01±0.04 4.64±0.07 *** (%) MV 15 5-6 5-6 NA 6-7 NA 9 NA NA

EE(% AV 2.63±0.05 2.90±0.012 2.41±0.02 4.12±0.05 2.25± 0.06 4.40± 0.11 2.28±0.08 2.30±0.04 4.67±0.06 NS ) MV 3 3-5 3-3.5 4-5 3-5 4-5 4.5 3-4 3-5

ADF AV 21.86±0.09 20.02±0.03 20.93±0.05 21.96±0.06 22.85±0.10 17.66±0.03 20.94±0.04 22.91±0.07 21.96±0.05 NS (%) MV NA

NDF AV 30.70±0.15 31.13±0.03 30.42±.10 33.11±0.12 31.49±0.27 33.08±0.14 32.92±.01 31.94±0.06 32.07±0.09 NS (%) MV NA AV 2688.89 2754.12 2644.67 2745.68 2758.36 2856.67 2690.99 2876.89 2786.78 ME MV 2750 2790 2720 2790 2850 2880 2780 2900 2800 Note: Feed samples* 1= Gain feed, 2=ACI, 3=Care feed, 4=Teer feed, 5= Saudia feed, 6=Fresh feed, 7=Provita feed, 8=Index, 9=Total Mixed Ration (TMR), MV= Manufacturer’s Value (Company supplying bag value); AV= Analytical Value (laboratory test result); CP= Crude Protein; CF= Crude Fiber; EE= Ether Extract; and ME= Metabolizable energy (Kcal/kg).

QUALITY EVALUATION OF COMMERCIAL BEEF CATTLE FEEDS 203

Table 2. Chemical analysis and in-vitro digestibility of feed ingredients IVDMD Parameter DM (%) Ash (%) CP (%) CF (%) EE (%) ADF (%) NDF (%) (%) Sesame cake 91.87±0.11 11.89±0.02 14.87±0.11 0.64±.06 8.89±.06 18.04±0.07 24.49±0.36 60.28±0.04 Wheat bran 90.85±0.36 4.35±0.08 7.12±0.03 0.40±0.029 2.43±0.10 13.91±0.06 46.98±0.11 68.56±0.04 Gram bran 90.19±0.56 4.17±0.40 5.13±0.15 24.26±0.17 1.48±0.07 18.11±0.04 23.05±0.09 50.25±.0.04 Maize 88.90±0.58 2.91±0.11 4.44±0.12 4.67±0.01 3.14±0.24 9.05±0.06 25.10±0.06 70.30±0.04 Mustard oil cake 90.91±0.47 10.26±0.07 16.07±0.04 3.18±0.10 1.82±0.09 17.19±0.10 23.00±0.12 65.76±0.30 Black gram hull 87.53±0.77 6.80±0.45 7.84±0.06 4.94±0.03 2.36±0.38 17.25±0.04 24.17±0.14 37.40±0.10 De-oiled rice bran 91.74±0.00 20.09±0.03 4.61±0.05 21.92±0.04 2.07±0.35 17.29±0.08 46.93±0.25 59.87±0.99 Rapeseed meal 90.87±0.07 7.06±0.05 34.72±0.07 9.08±0.10 1.12±0.10 9.93±0.10 19.45±0.06 70.64±0.89 Soybean meal 89.58±0.24 7.89±0.02 39.76±0.03 2.25±0.03 1.81±0.02 8.30±0.18 12.14±0.09 75.77±0.67 Rice Polish 89.25±0.19 4.95±0.04 8.60±0.10 4.71±0.02 0.87±0.01 21.35±0.24 38.34±0.96 61.78±0.03 Straw 93.98±0.12 11.95±0.03 2.66±0.04 32.89±0.06 1.71±0.04 55.40±1.21 71.04±0.66 18.27±0.02

204 Kamal et al.

Rapeseed meal (RSM) is a protein rich feed which is widely used in different feed mills of Bangladesh especially in fish and dairy feed. RSM contained90-91% DM and 1-2% fat. Mean crude protein (%) content of RSM samples obtained was 34.92 ± 3.33 and was lower than those reported by many other authors (Blair et al., 1986; Verma and Banday, 1997). Soybean meal is the by-product of soybean which is produced after extraction of oil. In Bangladesh there are two commercial oil companies namely City and Fresh producing soybean meal. It is a good source of protein which contains less than 1% fat. Analyzed Soybean meal contained 88-89% dry matter (DM), 39-40% crude protein (CP) and 1-2% ether extract (EE). Rice polish is a by-product of rice which contains 89-90% dry matter (DM), 8-9% crude protein (CP) and 0.7-0.8% fat (EE). Rice polish is the most common feed ingredient and widely used in poultry, cattle and fish feed. It is a good source of protein (13.2 to 17.1%), carbohydrate (16.1%), fiber (9.5 to 13.2%), vitamins and minerals (Vargasgonzalez, 1995; Ambashankar and Chandrasekaran, 1998). Straw is amain by-product of rice and found all over Bangladesh. Straws are usually produced after harvesting the grains and can be grazed by livestock (Suttie, 2000). Straw contains DM (93-94%), protein (2-5%), CF (32%) and ADF (55-56%) with low digestibility. Many attempts have been done to increase the digestibility of straw by urea and molasses treatment (Sarnklong et al., 2010). Feed ingredients which are in low digestibility tend to contain high fiber (ADF and NDF is high). The in vitro gas production system helps to better quantify nutrient utilization and its accuracy in describing digestibility in animals had been validated in numerous experiments (Taphizadeh et al., 2008). It was also found that type of feed and their nutritional quality enhance or reduce the CH4 gas production. According to this principle, some researchers formulate eco-friendly rations with low CH4 producing feed ingredients (Kim et al., 2012; Rahman et al., 2013). Relationship between chemical components with IVDMD

80.00

70.00

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Invitro 40.00

30.00

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R Sq Linear = 0.749

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0.00 10.00 20.00 30.00 40.00 50.00 60.00 ADF Figure 2. Relationship between ADF and In vitro dry matter digestibility (IVDMD) of feed ingredients QUALITY EVALUATION OF COMMERCIAL BEEF CATTLE FEEDS 205

There were significant negative correlations between neutral detergent fiber (NDF) and acid detergent fiber (ADF) with in vitro dry matter digestibility (IVDMD) (Fig. 2 and 3), while significant positive correlation was observed for crude protein (CP, r = 0.59) (Fig.4).

80.00

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50.00 Invitro 40.00

30.00

20.00 R Sq Linear = 0.386

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10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 NDF Figure 3. Relationship between NDF and in vitro dry matter digestibility (IVDMD) of feed ingredients

80.00

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50.00 Invitro 40.00

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20.00 R Sq Linear = 0.291

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0.00 20.00 40.00 CP Figure 4. Relationship between CP content and In vitro dry matter digestibility (IVDMD) of feed ingredients

The fiber component (NDF and ADF) was negatively correlated with IVDMD because those components were less digestible than the non-fibrous component (protein). 206 Kamal et al.

CONCLUSIONS In the commercial feed, analyzed CP level was lower than the manufacturer’s claimed value. It was found that the feed ingredients of the local market were not of good quality and this affected the quality of finished feeds. In vitro digestibility of the feeds and feed ingredients varied with their fiber content. Feeds which were high in fiber were low in digestibility. There is a large amount of variability in the feed constituents of compound feeds. This could be due to the use of low-quality feed stuffs due to lack of strict quality control measures. So, there is a need for better quality standards for feed ingredients and compound feeds through analysis. There is a need for creating awareness among the feed compounders and consumers regarding maintaining the feed quality and its benefits in creating the trust between the compounders and farmers leading to better livestock productivity.

ACKNOWLEDGEMENT The investigation was supported by ‘National Science and Technology (NST) Fellowship’, awarded by the Ministry of Science and Technology (MoST), Government of Bangladesh and IFAEL.

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Research Article

EFFECTS OF TURMERIC POWDER ON CLOSTRIDIUM PERFRINGENS LOAD IN BROILER CHICKENS

M.Z. Ali1*, M.M. Islam2 and S. Zaman3 1Animal Health Research Division, Bangladesh Livestock Research Institute (BLRI) Savar, Dhaka 1341, Bangladesh. 2Nourish Poultry Diseases Diagnostic Laboratory, Nourish Poultry & Hatchery Ltd. Dhaka, Bangladesh. 3Center for Advanced Research in Sciences, University of Dhaka, Dhaka 1000, Bangladesh.

ABSTRACT Necrotic enteritis (NE) is a major economic problem in broiler industry globally and is caused by Clostridium perfringens. The aim of the study was to know the effects of turmeric on C. perfringens in broiler chickens. A total of 3000-day-old Cobb 500 broiler chicks were divided into 6 groups and reared in environment control sheds with similar management. Each group contains 500 chicks and again divided into two subgroups as control and treatment with 250 chicks. In treatment groups added 2gm/kg turmeric powder with basal feeds and reared up to 30 days and follow standard vaccine schedule. Intestinal samples were collected every week from each group to detect C. perfringens load. Bodyweight gain, feed conversion ratio (FCR) and mortality rates were also calculated. The findings were loads of C. perfringens in treatment groups decreased significantly compared to control groups. At 4th week average count of C. perfringens was 4.44±0.12 log CFU/g and 2.68±0.17 log CFU/g in control and treatment groups, respectively. Average chick’s mortality decreased significantly in treatment groups. The flock mortality was decreased significantly in treatment groups (1.40%) compared to control groups (2.17%). The FCR become significantly decreased in treatment groups (1.490) compared to control groups (1.571). Therefore, use of turmeric powder in broiler ration can reduce NE by decreasing C. perfringens loads and it could be a good source of non-antibiotic growth promoter in poultry towards reduce antibiotic resistance and consumer will get a pathogen free rich protein source. Keywords: Clostridium perfringens, Necrotic enteritis, Broiler, Turmeric, Feed conversion ratio (FCR)

* Corresponding author: [email protected]

Received: 05.05.2020 Accepted: 16.06.2020 210 M.Z. Ali

INTRODUCTION Necrotic enteritis (NE) is a major concern for the broiler industry due to it has significant health and economic impact (Skinner et al., 2010). It can reduce 12.0% body weight and not only that it increases 10.9% feed conversion ratio (FCR) (Wade and Keyburn, 2015). Thus, NE causes about USD878.19 to USD1480.52 financial loss per flock in the USA and USD 2 to 6 billion losses annually in the globe (Wade and Keyburn, 2015; Yang et al., 2019). Maximum digestion, absorption and metabolism of feeds are the key principal of broiler nutrition. NE hampered this physiology by necrosis of intestinal mucosa as well as nutrient absorption become decreased resulting in reduce FCR (Bhuiyan et al., 2019; Yang et al., 2019). The major cause of NE is Clostridium perfringens, rod-shaped, spore-forming, gram-positive, anaerobic bacteria, and ubiquitously harbor in gut (Freedman et al., 2015; Miller et al., 2010). It could become pathogenic and causes NE in poultry when changing the dynamic balance of gut environment by starvation, stress, sudden change of feeds, and/or use of antibiotics as therapeutic purposes (Prescott, 2016; Ali, 2018). So, to prevent NE use of antibiotics as a growth promoter is the easy way of feed manufacturer (Silva et al., 2009). But bacteria become antibiotic resistance due to repeated use of antibiotics with feeds (Diarra et al., 2007; Nhung et al., 2017). To save lives and microbial biodiversity an alternative use of antibiotics is the prime demand. Turmeric (Curcuma longa) is a natural herb under family- Zingiberaceae, extensively used as spice and coloring material in Asian countries. It can act as an important source for an alternative to antibiotics due to its antimicrobial properties. It is less toxic, residue-free and natural compared to synthetic antibiotics or inorganic chemicals (Khan et al., 2012). The active ingredients of turmeric are curcumin, tetrahydrocurcuminoids, demethoxycurcumin and bisdemethoxycutcumin and these are proven against bacteria (Niranjan and Prakash, 2008). Application of turmeric as a supplement in poultry feeds can play an important role in control NE by inhibiting C. perfringens and as well as feed efficiency becomes increased (Niranjan and Prakash, 2008; Khan et al., 2012). So, there is a need to explored and elucidate the safe alternatives of antibiotics to control NE. Therefore, the aim of the experimental study was to evaluate the effects of the turmeric powder on C. perfringens in intestines to control the NE and use of it instead of antibiotics.

MATERIALS AND METHODS Turmeric powder trial Experiment of turmeric powder trial as feed additives, a total 3000-day old Cobb500 unsexed broiler chicks were collected from Nourish Poultry and Hatchery Ltd. Dhaka, Bangladesh with history of same pedigree line. The chicks were divided randomly into 6 groups (replication) as G1, G2, G3, G4, G5, and G6 where 500 chicks present in each group and reared in 6 different sheds. Aga in chicks were EFFECTS OF TURMERIC POWDER ON BROILER CHICKENS 211 divided into two subgroups within the group by metal wire net as treatment group (n=250) and control group (n=250). Birds were reared in artificial controlled house (Cumberland, USA) maintaining daylight, ventilation, air speed, humidity, temperature etc. In the treatment groups used 2g turmeric powder per kg basal feed and in control groups fed only basal feed. Broiler pre starter (a mash feed) were fed during 0-11 days old age, broiler starter (a mash feed) were fed during 12-21 days age and broiler grower feed (pellet feed) were fed up to 30 days of age as basal feed. These are corn based ready feed manufactured by Nourish Poultry & Hatchery Ltd. Dhaka, Bangladesh with 2950 kcal, 3000 kcal and 3050 kcal energy in broiler pre starter, broiler starter and broiler grower feed, respectively. Artificial brooder was provided during first 10 days and standard management of feeder, waterer and floor space was provided uniform for all birds. Only rice husk used as bedding till 10th days of age and then mixed dried saw dust 50% of total bedding materials. The vaccine schedule was eye drop at day 1 of age by Nobilis® Ma5 + Clone 30 (Intervet International B.V., The Netherlands), at 11th and 18th days of age Nobilis® Gumboro D78 (Intervet International B.V., The Netherlands) in drinking water, and finally used Nobilis® ND Clone 30 (Intervet International B.V., The Netherlands) at 22th day of age in drinking water, there was no use of any antibiotic during experimental period. Sample collection and processing for C. perfringens load analysis To assess the effects of turmeric powder on C. perfringens three healthy birds were selected randomly from each treatment and control group at 0-day, 7th day, 14th day, 21th day and 28th day of age. Euthanized the birds with cervical dislocation by the help of veterinarian and ensure that all muscle contractions have ceased before beginning dissection. Used sterile scissors carefully made an incision through the skin and muscle from the mid abdomen to the breast to expose the abdominal cavity. Then the liver and spleen were separated from the gastrointestinal tract (GI tract). Cut the GI tract from the duodenal loop, just distal of gizzard and just proximal of cloacal vent. Removed the entire GI tract by gently freeing any remaining connective tissue still bound to the body cavity or any other organs and placed the GI tract in a sterile Whirl-Pac bag (Nasco, USA) for further processing. Then GI tract was placed onto a clean and sterile dissecting tray and cut into 10 cm sections from each duodenum, jejunum and ileum. The lumens of GI tract were spread and flush out the digesta by sterile PBS, and kept in sterile stomacher bags. Weight out the 25 g GI tract sections and stomached at 230 rpm for 2 min by adding 250 ml brain heart infusion (BHI) broth (Oxoid, UK) and then 6- fold decimal dilution was done. Then incubated for 24 h at 37oC in anaerobic vented jar with appropriate amount of anaerobic gas generating packs (Mitsubishi Gas Chemical America Inc., New York). 212 M.Z. Ali

Subsequentially, 100 µl of pre-enriched BHI broth from each dilution was inoculated into plates of tryptose sulphite cycloserine (TSC) agar base (Oxoid, UK) enriched with 5% egg yolk and supplemented with β D-cycloserine (Oxoid, UK). The plates were sealed tightly, incubated for 24 h at 37oC upside down in anaerobic condition. The black colonies on plates were examined and counted for typical colonies of C. perfringens and results were recorded as log CFU/ml (Mwangi et al., 2019). Finally, the pure isolates were confirmed by the biochemical (lactose fermentation) and gram staining test. Body weight gain At the age of 30th day, total live body weight and total feed consumption of all living birds from treatment and control pans of all 6 replicated were measured. According to the collected parameters the effects of turmeric powder on body weight gain and feed conversion ratio were calculated. Statistical analysis The data from sample collection and laboratory results were recorded as coding into Microsoft Excel spreadsheet 2010 (Microsoft Corporation, WA, USA). The C. perfringens colony count data were converted into log CFU/ml by Log CFU/mL = Log10 (CFU / (dilution factor*aliquot)). Data regarding total feed intake and body weight gain were recorded daily and feed conversion ratio (FCR) was calculated on weekly basis. The FCR was calculated by dividing the feed intake by weight gain. Then the data of Log CFU/mL and FCR on six treatment and control groups were subjected to one-way ANOVA (analysis of variance) by using GLM (General Linear Model). The mean comparison on treatment and control groups were calculated by using Duncan’s multiple range tests. All statistical analysis was done in statistical package SPSS version 25 (2017) (IBM corp. New York, USA). The P<0.05 was used to determine the significance.

RESULTS AND DISCUSSION The C. perfringens counts were decreased significantly (p= 0.003) in all six replications of turmeric trial compared to the control groups (Table 1). At day 1 of age in both control and treatment groups and at 1st week of age in treatment groups C. perfringens counts was below detection limit (<1.0 log CFU/g) in all six groups. On 2nd week of age average count was 4.24±0.14 log CFU/g in control groups whereas below detection limit (<1.0 log CFU/g) in 4 treatment groups including G2, G4, G5, and G6. During at 4th week of age average count declined significantly from 4.44±0.12 log CFU/g to 2.68±0.17 log CFU/g in control groups and treatment groups respectively. EFFECTS OF TURMERIC POWDER ON BROILER CHICKENS 213

Table 1. The effects of turmeric powder on C. perfringens counts of broiler chicken. Count of C. perfringens (log CFU/g) Mortality (%) DOC 1st week 2nd week 3rd week 4th week Group C T C T C T C T C T C T G1 <1.0 <1.0 3.47±0.01 <1.0 4.40±0.11 2.47±0.291 4.34±0.61 <1.0 4.25±0.55 2.69±0.21 1.40 0.80 G2 <1.0 <1.0 3.85±0.00 <1.0 4.17±0.21 <1.0 4.25±0.38 1.6±0.44 4.50±0.08 2.84±0.73 1.80 1.20 G3 <1.0 <1.0 4.04±0.06 <1.0 4.30±0.70 2.69±0.17 4.17±0.13 1.77±0.49 4.60±0.71 2.84±0.10 2.00 1.60 G4 <1.0 <1.0 3.95±0.10 <1.0 4.34±0.61 <1.0 4.40±0.19 <1.0 4.47±0.49 2.47±0.26 2.60 2.00 G5 <1.0 <1.0 3.95±0.02 <1.0 4.20±0.22 <1.0 4.40±0.09 1.90±0.64 4.44±0.18 2.77±0.34 2.40 1.80 G6 <1.0 <1.0 3.95±0.03 <1.0 4.00±0.13 <1.0 4.34±0.22 1.60±0.42 4.36±0.06 2.47±0.47 2.80 1.00 Average <1.0 <1.0 3.95±0.04 <1.0 4.24±0.14 3.58±0.09 4.32±0.14 1.72'±0.12 4.44±0.12 2.68±0.17 2.17 1.40 p 0.003* 0.000* *Significant at the 0.05 level; <1.0= not detected; CFU= colony forming unit; DOC= Day old chicks; T= Treatment (2gm turmeric powder per kg basal feed); C= Control (only basal feed) 214 M.Z. Ali

The chicken mortality rates became significantly (p= 0.000) decreased in treatment group compared to the control groups. Average chicken mortality rates reported 2.17% in control groups and 1.40% in treatment groups (Table 1). The effects of turmeric powder on FCR of broiler chicken have been demonstrated a significant (p= 0.002) association. The average FCR of control and treatment groups recorded 1.571 and 1.490 respectively (Table 2). About 17.33 g (2.22%) weight gain occurs in treatment groups compared to control groups.

Table 2. The effects of turmeric powder with basal feeds of broiler chickens on feed conversion ratio (FCR). Group Data Average G1 G2 G3 G4 G5 G6 Control 1.533 1.504 1.685 1.583 1.56 1.563 1.571 Treatment 1.501 1.458 1.537 1.566 1.444 1.43 1.49 Differences (g) 16 28 21 9 11 19 17.33 (2.22%) p 0.002 *Significant at the 0.05 level

Necrotic enteritis (NE) is a cause of C. perfringens that affects on feed utilization by disrupting the intestinal epithelium (Mwangi et al., 2019). The major clinical signs of NE have suddenly increased mortality, reduce weight gain, poor feed conversion, and necroses of intestinal mucosa are major postmortem lesions (Immerseel et al., 2004). The prevalence of C. perfringens type A is 64.73% in poultry and well known as a cause of foodborne illness and gas gangrene to humans (Khan et al., 2012; Songer, 1996). The prevalence of NE is demonstrated to be 8% in broiler chickens in Bangladesh (Miah et al., 2011). Historically herbs and spices are being used as feed additives for farming animals due to its medicinal properties (Frankic et al., 2009). These natural plant products have been proven less toxic, with no residue, and antioxidant and antimicrobial effects so it is now being considered as the ideal feed supplement of livestock worldwide (Khan et al., 2012). Turmeric (Curcuma longa) is a natural herb and popular for medicinal properties. The active ingredients of turmeric are tetrahydrocurcuminoids, curcumin, demethoxycurcumin and turmerones that are proven with their anti-inflammatory, anti-carcinogenic, and antibacterial properties (Mitsch et al., 2004). Mitsch et al. (2004) shows the antibiotic property of turmeric on C. perfringens and concluded 2gm/kg turmeric in feed supplements can inhibit the growth of C. perfringens. There is a rapidly growing interest to produce broiler meat without antibiotics and use of non-antibiotic growth promoters worldwide in order to maintain a bird’s health and growth performance (Khan et al., 2012). EFFECTS OF TURMERIC POWDER ON BROILER CHICKENS 215

The findings of research study included the use of turmeric powder in feed can reduce C. perfringens count significantly compared to control group. Researchers identified turmeric as having anti-inflammatory (Lee, 2016), wound healing, and anticancer (Kim et al., 2012) properties. Intestinal health was normal in treatment groups that help to increase absorption of dietary nutrients through smooth intestinal epithelium. As a result, FCR became significantly lower in treatment groups compared to control groups. These results are strongly supported by the experiment of Kafi et al. (2017) they demonstrated that use of turmeric in broiler feed at the rate of 0.75% level as feed additives can improve growth performance as well as increase profitability. Turmeric use in broiler ration can improve growth performance although its concentration, dose, and duration of use varies in different experiments like AL-Kassie et al. (2011) used as 0.75% to 1%, AL-Sultan (2003) used as 0.5 to 1%, and Ahmadi (2010) used as 0.9% in basal feed. Antimicrobial resistance is an important health issue for both humans and animals globally including Bangladesh (Ahmed et al., 2019; Ali and Hasan, 2018). The use of antibiotics in animal and fish feeds as growth promoter though improve feed efficiency and increase growth but they are modifying intestinal flora and creating a selective pressure to develop antibiotic resistance which favor a resistant bacteria (Collier et al., 2003). So several European countries banned or restricted the use of antibiotic growth promoters in feed (Casewell et al., 2003). The Government of Bangladesh also completely banned antibiotics in animal and fish feeds as a growth promoter by Government of Bangladesh through Fish and Animal Feed Act-2010 (Fish and Animal Feed Act, 2010). The use of anticoccidial drugs and antibiotics in animal feeds to control coccidiosis and NE was the prime focus of animal feed manufacturers (M'Sadeq et al., 2015). Therefore, the application of turmeric in poultry feeds could be a good solution to prevent antimicrobial resistance. Finally, 2% turmeric powder supplements as feed additives in broiler ration could increase feed efficiency, production performance and decrease C. perfringens growth in intestine.

CONCLUSIONS Turmeric (Curcuma longa) has been using as traditional herbal medicine and antimicrobial agents. It can be used as a natural growth promoter in broiler diet due to it can reduce C. perfringens load in gut, decrease necrotic enteritis as well as increase feed efficiency. However, further study on pharmacological activity, proper dose, and duration of use is required.

ACKNOWLEDGEMENT The work was supported by grants of the Bangladesh Livestock Research Institute and Nourish Poultry & Hatchery Ltd. 216 M.Z. Ali

CONFLICTS OF INTEREST None of the authors have any conflict of interest to declare.

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Research Article INVESTIGATION OF SHADE TREE SPECIES USED IN TEA GARDEN IN BANGLADESH

M.A. Rahman1*, Z.R. Moni2, M.A. Rahman3 and S. Nasreen1 1Forest Protection Division, Bangladesh Forest Research Institute Chattogram-4000, Bangladesh 2Senior Scientific Officer, Bangladesh Agricultural Research Council (BARC) Farmgate, Dhaka 1215, Bangladesh 3Seed Orchard Division, Bangladesh Forest Research Institute, Chattogram-4000, Bangladesh

ABSTRACT The shade trees are an integral component of tea cultivation in Bangladesh. The shade trees are essential for modulating the environment of the tea ecosystem, enriching the soil fertility, reducing temperature and the evaporative capacity, conserve soil moisture and helps in the control of certain pests and diseases which are positively thermotropic in nature. The shade trees provide partial shade to the tea plants, which is important for improving the quality of the tea leaf. The right type of shade trees and their proper management is a prerequisite for successful tea crop growing. For this persists, a floristic exploration of shade trees was carried out at fifteen tea gardens in Chattogram and Moulvibazar District of Bangladesh from November 2017 to December 2018. During the investigation, a total of 44 species of Angiosperm representing 31 genera of 9 families was enlisted. For every species, scientific name, vernacular name, status, and necessary photographs are mentioned. In the assessment, the Fabaceae family shows the highest number of shade trees comprising 19 genera and 31 species. The most common permanent shade tree species among the tea gardens are Albizia odoratissima, A. chinensis, A. lebbeck, A. lucidior, A. procera, and Derris robusta. Indigofera teysmannii is frequently using as a temporary shade species in all investigated tea gardens. Cajanus cajan, Tephrosia candida, Tephrosia candida, Gliricidia sepium, Erythrina lithosperma and Desmodium gyroides species are also used as temporary shade trees in many tea gardens. Further investigations, however,are required to find out the right type of shade tree species on the growth and yield of tea plants in tea cultivation areas of Bangladesh. Keywords: Tea, Tea garden, Shade tree species, Fabaceae

* *Corresponding author: [email protected]

Received: 04.03.2020 Accepted: 17.06.2020 SHADE TREE SPECIES OF TEA GARDEN 220

INTRODUCTION Tea (Camellia sinensis L.) is one of the most popular and favored beverages (nonalcoholic) in the world. It plays an important role in Bangladesh economy. There are 162 tea estates and 746 small cultivators having about 59,018 hectares of tea plantation generating about 85.05 million kg of over tea per annum with an average yield of about 1,587 kg per hectare in Bangladesh (BTB, 2017). It is now ranked 8th, 10th, and 12th position in the world in respect of the area, production, and export respectively (ITC, 2015). The tea industry of Bangladesh is frolicking a major role in fulfilling the domestic consumption as well as an important source of export earnings and contributing about 0.8 percent of the total GDP of the country (Rahman, 2016). Tea gardens and smallholding tea cultivators in Bangladesh have created employment for about 1,33,000 people every year (Rahman, 2016). The tea plant is traditionally grown under the shade condition as they are shade- loving plants. Planting of shade trees has become an integral component of tree plantation and management in India, Sri Lanka, Indonesia, Bangladesh and some parts of Africa. The shade trees provide 50 % to 70 % of diffused solar insolation to the tea cultivation area (Sana, 1989). It results in improves the quality of the tea leaves due to an increase in the concentration of amino acids with lowers the content of catechin in the plant and it also inhibits the concentration of flavonoid (Ku et al., 2010; Wang et al., 2012). Barua (1979) reported that tree shade can increase photosynthetic rate and yield by 10 to 30 percent, especially during hot summer in north-eastern India. Shade trees not only used as shade provider, but it also produces fuel wood timber, conserve soil from erosion, the impact of rainfall drop, enrich soil fertility, support diverse flora and fauna, producing foods (leaves, pods or flowers) for people, creation of tannins, gums, medicines and services like living fences, ornamentals and environmental protection (FAO, 1987). Tea cultivation is one of the important Agroforestry systems well known in Bangladesh, which was started in the 19thcentury.The shade trees play an important role in increasing the productivity of the tea under the environment of Bangladesh. Without shade trees, the yield of tea is limited. Thus, to increase tea yields, a large number of shade tree species are planted in various tea gardens of Bangladesh (Kalita et al., 2014). The right type of shade trees and their proper management is possibly one of the most well-investigated research problems in all tea cultivation areas of Bangladesh. Therefore, the present study was undertaken to investigate the shade tree species in the tea gardens of Bangladesh.

MATERIALS AND METHODS Study area The present study was conducted in fifteen tea gardens of Chattogram and Moulvibazar District of Bangladesh (Table 1). This study was conducted in several tea estates from September 2017 to December 2018. 221 Rahman et al.

Table 1. Investigated tea estates in Bangladesh SL no. Name of tea garden Location District 1. Ramgarh tea estate Fatikchari Chattogram 2. Dantmara tea estate Bhojpur, Fatikchari Chattogram 3. BTRI substation Ooadalia, Fatikchari Chattogram 4. Oodalia tea estate Katirhat, Fatikchari Chattogram 5. Bhojpur tea estate Bhojpur, Fatikchari Chattogram 6. Naseha tea estate Dantmara, Fatikchari Chattogram 7. Haldavalley tea estate Najirhat, Fatikchari Chattogram 8. Neapchun tea estate Narayanhat, Fatikchari Chattogram 9. Panchabati tea estate Bhojpur, Fatikchari Chattogram 10. Aasia tea estate Bhojpur, Fatikchari Chattogram 11. Majan tea estate Datmara, Fatikchari Chattogram 12. Andhar manik tea estate Chikancherra, Fatikchari Chattogram 13. BTRI main campus Srimangal Maulvibazar 14. BTRI Bilashchara Srimangal Maulvibazar Experimental Farm 15. Finlay tea estate Srimangal Maulvibazar

Specimen collection and plant identification During the study, a large number of fertile specimens of different shade trees have been collected for taxonomic identification. The collected specimens were identified consulting with the experts of (Bangladesh Forest Research Institute) BFRI Herbarium (BFRIH) and pertinent literature (Ahmed et al., 2009; Pasha and Uddin, 2013; Hooker, 1872-1897; Prain, 1903; Brandis 1906; Kanjilal et al.,1939, 1940; Heining, 1925). Also, we compared identified samples with specimens preserved at Bangladesh Forest Research Institute Herbarium (BFRIH) Chattogram, Bangladesh.

RESULTS AND DISCUSSION During the present investigation, mainly two types of shade trees viz., permanent shade trees, and temporary shade trees were found in all investigated tea gardens of Bangladesh. Permanent shade trees are planted for a longer period (about 40 years). It takes a long period to be established with their full canopy. That’s why at the initial stage of plantation temporary shade trees along with the permanent shade trees are planted to protect the tea plants from direct sunlight. When the permanent shade trees become established after 5/6 years, the temporary shade trees are removed. In the present assessment, a total of 44 species representing 31 genera of 9 families of angiosperms were enlisted (Table 2). The Morphological characteristics of shade tree species at investigated tea estates are presented at Table 3. SHADE TREE SPECIES OF TEA GARDEN 222

Table 2. Types and abundance of shade trees at investigated tea estates Scientific Vernacular Tea garden name Habits Family Status Name Name Permanent shade trees Acacia RMG, DM, BP, HV, NPC,AS, ADM Tree Akashmoni Fabaceae Less auriculiformis common A. hybrid BP, MJ, DM Tree Acacia Fabaceae Rare hybrid Albizia RMG, DM, BTRISS, ODL, BP, Tree Chakua koroi Fabaceae Most chinensis NDFC, HV, NPC, PACB, AS, MJ, common ADM, BTRIMCS, BEFBTRIS, FLA A. lebbeck RMG, DM, BTRISS, ODL, BP, Tree Sirish Fabaceae Most NDFC, HV, NPC, PACB, AS, MJ, common ADM, BTRIMCS, BEFBTRIS, FLA A. lucidor RMG, DM, BTRISS, ODL, BP, Tree Potka Siris Fabaceae Most NDFC, HV, NPC, PACB, AS, MJ, common ADM, BTRIMCS, BEFBTRIS, FLA Acacia BP, HV, NPC, PACB Tree Mangium Fabaceae Rare mangium Albizia RMG, BP, NDFC, HV, NPC, Tree Moluccan sau Fabaceae Less moluccana PACB, AS, MJ, ADM Common A. procera RMG, DM, BTRISS, ODL, BP, Tree Silkoroi Fabaceae Most NDFC, HV, NPC, PACB, AS, MJ, common ADM, BTRIMCS, BEFBTRIS, FLA A. richardiana ODL,NPC, AS, MJ Tree Gagan shiris Fabaceae Rare A. falcataria RMG, DM, ODL, BP, NDFC, HV, Tree Malakana Fabaceae Less NPC, AS, ADM, BEFBTRIS, FLA Koroi Common Adenanthera RMG, ODL, BP, HV, NPC, AS, MJ, Tree Rakta Fabaceae Less pavonina ADM Kambal common Albizia RMG, DM, BTRISS, ODL, BP, Tree Kali Sirish Fabaceae Most odoratissima NDFC, HV, NPC, PACB, AS, MJ, common ADM, BTRIMCS, BEFBTRIS, FLA Alstonia RMG, DM, ODL, BP, HV, PACB, Tree Chatim Apocynace Less scholaris AS, MJ, ADM ae common Artocarpus DM, ODL Tree Chapalish Moraceae Rare chaplasha Azadirachta RMG, DM, ODL, BP, NDFC, HV, Tree Neem Meliaceae Less indica NPC, PACB, AS, MJ, ADM common Bauhinia DM, ODL, BP, AS, MJ Tree Rakta Fabaceae Rare purpurea kanchan Butea DM, ODL, AS Tree Palash Fabaceae Rare monosperma Cassia fistula RMG, BP, NDFC, HV, NPC, AS, Tree Badar lathi Fabaceae Less 223 Rahman et al.

Scientific Vernacular Tea garden name Habits Family Status Name Name MJ, ADM common Cassia siamea BP, NDFC, HV, AS Tree Minjiri Fabaceae Rare Chukrasia DM, BP, AS Tree Chikrassi Meliaceae Rare tabularis Dalbergia RMG, DM, NPC Tree Mouhita Fabaceae Rare assamica Dalbergia RMG, DM, ODL, BP, HV, NPC, Tree Silky Fabaceae Less sericea PACB, AS, MJ, ADM Dalbergia common Dalbergia RMG, ODL, BP, , NPC, PACB, AS, Tree Shisu Fabaceae Less sissoo MJ, ADM common Delonix regia DM, BP, AS, MJ Tree Gulmohar Fabaceae Rare Derris robusta RMG, DM, BTRISS, ODL, BP, Tree Miringa Fabaceae Most NDFC, HV, NPC, PACB, AS, MJ, common ADM, BTRIMCS, BEFBTRIS, FLA Erythrina RMG, ODL, HV Tree Madar Fabaceae Rare variegata Eucalyptus DM, ODL, AS Tree Eucalyptus Myrtaceae Rare robusta Eucalyptus DM, ODL Tree Red gum Myrtaceae Rare camaldulensis Gravillea RMG, DM, ODL, BP Tree Silky oak Proteaceae Rare robusta Lagerstroemia RMG, BP, AS, MJ Tree Jarul Lythraceae Rare speciosa Leucaena DM, ODL, NDFC, HV, NPC, AS, Tree Ipilipil Fabaceae Less leucocephalla MJ, ADM common Mangifera RMG, BP, AS Tree Aam Anacardiac Rare indica L. eae Melia DM, ODL, BP, AS Tree Ghora neem Meliaceae Rare azedarachta Phyllanthus DM, ODL Tree Amlaki Phyllanthac Rare emblica eae Samanea DM, ODL Tree Shirish/Rain Fabaceae Rare saman tree Swietenia RMG, BTRISS, ODL, BP, MJ, ADM Tree Mahagoni Meliaceae Rare macrophylla Toona ciliata AS, MJ Tree Tun Meliaceae Rare Temporary shade trees Indigofera RMG, DM, BTRISS, ODL, BP, Shrubs Indigofera Fabaceae Most teysmannii NDFC, HV, NPC, PACB, AS, MJ, common ADM, BTRIMCS, BEFBTRIS, FLA SHADE TREE SPECIES OF TEA GARDEN 224

Scientific Vernacular Tea garden name Habits Family Status Name Name Cajanus cajan RMG, DM, ODL, BP, NDFC, HV, Shrubs Arhor Fabaceae Common NPC, PACB, AS, MJ, ADM, Tephrosia AS, MJ Shrubs White Fabaceae Rare candida tephrosia Gliricidia RMG, DM, ODL, BP, NDFC, HV, Shrubs Gliricidia Fabaceae Less sepium NPC, AS, MJ, ADM common Erythrina RMG, ODL Small Dadap Fabaceae Rare lithosperma tree Desmodium RMG, BP, NPC Mediu Tick clover Fabaceae Rare gyroides m size tree RMG- Ramgarh; DM-Dantmara; BTRISS- BTRI substation, Fatikchari; ODL- Oodalia; BP- Bhojpur; NDFC- Naseha; HV- Haldavalley; NPC- Neapchun;PACB- Panchabati; AS- Aasia; MJ- Majan; ADM- Andhar manik;BTRIMCS- BTRI Main campus, Srimangal; BEFBTRIS-Bilashchara Experimental Farm, BTRI, Srimangal; FLA- Finlay tea estate, Srimangal.

Table 3. Morphological characteristics of shade tree species at investigated tea estates Shade trees Morphological characteristics species Akashmoni A medium-sized, heavily branched, evergreen tree that grows between (Acacia to 15–30 m tall. Bark greyish brown, more or less smooth in young auriculiformis) trees. Adults phyllodes alternate, straight or falcate, acute or subacute, 10-15 cm long and 1.2- 2.5 cm wide, glabrous. Flowers in a spike up to 7.5 cm long, many in pairs in the upper leaf-axils, golden colored. Fruit a pod, initially straight, but maturity become very much irregularly twisted and coiled. Seed elliptical, 3-5 x 2-3 mm, almost encircled by a long orange funicle. Acacia hybrid (A. The tree is a medium-sized. The tree is capable of reaching a height of hybrid) 8–10 m and a diameter at breast height of 7.5–9.0 cm. Phyllode is about 4–6 cm wide and 15–20 cm long, with four veins. The flowers are creamy to whitish and arranged in a straight, or slightly bent, 8–10 cm spike. The pod (fruit) is usually very curly and twists. A pod holds 5–9 seeds (Fig. 4d). Chakua koroi The tree is a deciduous or evergreen. Bark dark brown or greenish- (Albizia grey with many vertical and horizontal fissures. Leaves bipinnate, chinensis) rachis 7-30 cm long with a large gland near the base and sometimes one or more between the pinnae. Its flowers are stalked heads that aggregate into a yellow panicle. Flowers are sessile, yellowish white in pedunculated, terminal or axillary heads. The fruits are indehiscent pods (Fig. 3 e).

225 Rahman et al.

Shade trees Morphological characteristics species Sirish (A. The tree can attain a height of 30 m and a diameter of 1 m. Bark lebbeck) yellowish grey to dark brown, almost blackish. Leaves bipinnate rachis 7-15 cm long, usually with a large gland near the base or between the upper pinnae. Flowers greenish white, very fragrant in peduncled heads, solitary or in fascicles of 2-4 from the upper leaf axils. Fruit a pod, 20-30 x 2.5-5 cm, very compressed and flat. Seeds 4-12 (Fig. 3 a). Potka Siris (A. A medium sized to large unarmed, glabrous evergreen or semi- lucidor) deciduous tree with spreading branches. This tree species grows up to 40 m. Leaves dark green, bi-pinnae. The leaves are (single) pinnate, in 1-3 pairs. The seed pods are yellow and glabrous. Seeds 6-8, round (Fig. 3 f). Mangium It is a fast-growing evergreen tree with a dense, spreading crown. The (Acacia bole is usually straight, often fluted near the base, free of branches. mangium) phyllode large, thicky coriaceous, up to 25 cm long and 5-10 cm broad, whitish or yellowshish green, glabrous. Flowers creamy white in a loose spike up to 10 cm long. Fruits is a pod, blackish brown and woody. Moluccan sau It is about 30m tall tree in nature with a massive trunk and an open (Albizia crown. Leaves are twice pinnately compound with small leaflets. moluccana) Flowers are creamy white small flowers are faintly fragrant. Fruits are pods that fall from the trees when mature (Fig. 4a). Silkoroi (A. A large deciduous tree with tall cylindrical boles and rather small procera) crown. Bark yellow shish brown to greenish white. Leaves bipinnate, pinnae 3-5 pairs, 12-25 cm long. Flowers sessile, yellowish white, in fascicled heads of 2-5, sometimes solitary, arrange in terminal and axillary pinnacles. Fruits a pod, flat, flexible, 7-12 x 1.2-2.5 cm. Seeds 6-12 (Fig. 3c). Gagan shiris (A. A lofty hand some evergreen tree with horizontal dichotomous richardiana) branching which from a beautiful crown. Bark yellowish gray. Leaves bipinnate, 6-9 cm long, pinnae 8-14, stipules small. Flowers small, greenish white, sessile in axillary corymbose, much shorter than leaves. Fruits a pod, dull brownish grey, firm, flat about 10 x 2 cm, shortly beaked (Fig. 4b). Malakana Tall deciduous tree to 30 m tall, 1 m in diameter. Leaves alternate, Koroi (A. bipinnate, 23–30 cm long, rufose pubescent, the pinnae 20–24, 5–10 falcataria) cm long, each with 30–40 paired leaflets, sessile, obliquely oblong, 6– 12 mm long, 3–5 mm broad, shortly acute. Flowers sessile, white, ca 10–12 mm long. Pods 10–13 cm long, 2 cm wide, flat, acute, green, turning brown, papyraceous, dehiscent. Seeds 15–20 per pod.

SHADE TREE SPECIES OF TEA GARDEN 226

Shade trees Morphological characteristics species Rakta Kambal A medium sized, unarmed, deciduous tree. Bark brown or greyish (Adenanthera brown, corky. Leaves bipinnate, rachis 20-25 cm long, red; pinnae 4-6 pavonina) pairs, 7-8 cm long, opposite; leaflets alternate, 12-20, elliptic, obtuse, base unequal, dark green and glabrous above. Flowers in long panicled racemes, greenish yellow, fragrant, minute. Fruit a pod, 15-24 x 1.5 cm, flat, linear, curved and much twisted when opening. Seeds 8-15. Kali Sirish A large semi deciduous or semi evergreen tree with spreading crown. Bark (Albizia thick, rough yellowish brown. Leaves bi-pinnate, rachis up to 30 cm long odoratissima) with 3-8 pairs of pinnae and has a large gland at the base and 1-2 between the upper pinnae. Flowers small, sessile, yellowish in heads of compact corymbs, arranged in large terminal panicles. Fruit a pod, 15-20 x 2.5 cm, dehiscent, thin, flexible, tomentose. Seed 8-12 (Fig. 3d). Chatim (Alstonia A medium sized evergreen trees with copious white latex, trunk often scholaris) flutted; branches whorled and twigs with white streaks of lenticles which are containing. Bark grey and smooth. Leaves in whorls of 4-8, oblancelate or elliptic-oblong. Flowers greenish white in compact umbellately branched pubescent panicled cymes. Fruits of follicles, 30- 60 cm long, pendulous, in clusters, terete. Chapalish A large deciduous tree with tall cylindrical bole and milky latex. Bark (Artocarpus thick, greyish brown or ashy-gray with large white patches deep chaplasha) verticle furrows peeling off in rounded flakes. Leaves are juvenile, elliptic ovate 15-20 cm long, hispid, base-subcordate or rounded. Flowers monoecious, densely crowded on globose receptacles. Fruits a globes, tuberculate, syncarp, pubescent (Fig. 4c). Neem A medium sized to large glabrous evergreen to semi deciduous tree with (Azadirachta large spreading crown. Bark greyish to blackish color. Leaves indica) imparipinnate, 18-45 cm long, crowded towards the end of branchlets. Flowers short, white, honey-scented, pentamerous, in axillary panicles shorten than leaves. Fruit a drupe, 1-15 cm long, ovoid, pericarp smooth. Rakta Kanchan A small to medium sized, evergreen to semi evergreen tree. Leaves (Bauhinia deeply cleft, leaf cleft halfway or more down. Flowers very fragrant, purpurea) deep rose or lilac. Fruits a pod, 15-30 cm long, flat. Seeds 12-15, flattened, roundish, dark brown, smooth. Palash (Butea A small to medium size tree. Bark rough, bluish-grey, exfoliating in monosperma) irregular pieces; inner bark fibirous, pinkish, exuding reddish juice when injured. Leaves pinnately 3-foliate, common petiole 12-25 cm long, geniculate, pubescent, stipules short, tomentose. Flowers large, reddish-orange, in rigid axillary racemes, 10-15 cm long and crowded towards the ends of leafless branchlets. Fruits a pod, 10-15 x 3-4 cm, rigid, pendulous and borne in large clusters in the leafless branches. Seed oval, compressed, dark brown. 227 Rahman et al.

Shade trees Morphological characteristics species Badar lathi A small to medium sized deciduous tree, often with crooked bole. (Cassia fistula) Young shoots and leaves silky. Bark greenish to ash grey. Leaves pinnate, 24-40 cm long; rachis slightly hairy; stipule minute, hairy, early falling; Flowers yellow, in pendulous, lax, axillary, 20-60 cm long racemes. Fruit a pod, 30-60 cm long and about 25 cm across, calendric, indehiscent, smooth, dark brown to black when ripe. Seeds 40-100, flattened, ovoid, about 1 cm across, embedded in a dark brown or black sweetish pulp. Minjiri (Cassia A medium size to large, fast growing, evergreen or semi deciduous siamea) tree. Bark ash grey or blackish brown. Leaves pari-pinnate, 15-30 cm long. Flowers Sulphur colored, in stout corymbose racemes arranged in a large pyramidal terminal panicles, often 60 cm long. Fruit a pod, flat, ribbon like, thickened at sutures, 10-25 cm long; greenish brown velvety when mature. Seeds many, pentagonally elliptical, flat, blackish brown, glossy. Chikrassi A tall, handsome (due to beautiful foliage), deciduous tree with tall (Chukrasia cylindrical bole and spreading crown; branchlets lenticellate. Bark tabularis) thick, rusty brown. Leaves pinnately compound, 30-50 cm long; softly pubescent when young, rachis terete. Flowers white in terminal, panicles, shorten than leaves, slightly hairy. Fruit a capsule, ellipsoidal, woody, brown or blackish outside, speckled with white lenticels. Seeds numerous, transversely and closely packed, elliptic, flat with dark brown wings. Mouhita D. assamica is a tree with a spreading crown; it can grow 7 - 10 metres (Dalbergia tall. Branches horizontally spreading. Leaves 25-30 cm; stipules assamica) caducous, large, leaflike, ovate to ovate-lanceolate; leaflets 13- 21; Flowers 6-8 mm. Legume broadly ligulate or oblong to strap- shaped, 5-9 × 1.2-1.8 (-2.5) cm, leathery, base attenuate, cuneate, apex acute, inconspicuously reticulate opposite 1 or 2(-4) seeds. Seeds reniform, compressed, ca. 6 × 2.5 mm. Silky Dalbergia Dalbergia is a medium to large-sized trees, 40-60 m tall; trunk up to 2 (Dalbergia m. Leaves are imparipinnate, alternate, 14-22 cm long. Flowers are sericea) borne in silky-hairy, dense-flowered panicles, 3-4 cm long, in leaf axils. Pods are elliptic-oblong or oblong, 2.2-6 x 0.5-0.7 cm, acute- obtuse at tip, tapering towards base into a 3-4 mm long slender stalk, flat, glabrous, reticulately nerved especially on seed portion. Shisu (Dalbergia A medium sized to very large deciduous tree often with curved bole or sissoo) crooked bole. Bark thick, grey or light brown, recticulately and longitudinally furrowed. Leaves imparipinnate, 10-20 cm long, rachis zigzag. Flowers yellowish white, in 2.5-4.0 cm long cymose racemes. Fruit a pod, strap-shaped, pale brown, 5.8 x 0.7-1.2 cm. Seeds flat, kidney shaped. SHADE TREE SPECIES OF TEA GARDEN 228

Shade trees Morphological characteristics species Gulmohar A medium sized handsome, deciduous or evergreen tree with (Delonix regia) spreading, dome shaped or flat-top crown. Leaves bi-pinnate, 15-40 cm long, pinnae 11-18 pairs. Flowers large, 7-10 cm across, scarlet. Fruit a sessile pod, 30-60 cm long, flat, linear, obliquely acuminate, woody when dry but dehiscent. Seeds many, oblong, slightly compressed, about 1.5 cm long; brown and white variegated, bony when dry. Miringa (Derris A medium sized deciduous tree with tall cylindrical bole and robusta) dichotomous branching at the top. Bark greyish white. Leaves pinnately compound, 7-20 cm long. Flowers white in selender axillary pubescent racemes, 12-25 cm long; pedicles filiform, grey downy. Fruits a pod, 2.5-7 x 3 cm, linear, narrowed at both end. Seeds brown, orbicular, compressed (Fig. 3 b). Madar (Erythrina A small to medium sized armed, deciduous tree. Bark smooth, variegate) yellowish or greenish grey, peeling off in thin. Leaves tri foliate, common petiole 15-20 cm long, unarmed, terete. Flowers showy, scarlet red, in 15-25 cm long racemes. Fruit a pod, 10-12 cm long, cylindrical with constrictions. Seeds kidney shaped, large, deep red. Eucalyptus It is a medium to large tree with a dense crown and long, spreading (Eucalyptus branches when grown in open ground. The bark is rough and persistent robusta) to the small branches, thick, held in coarse, soft, spongy, elongated slabs with deep longitudinal furrows, grey or reddish grey-brown. The juvenile leaves are petiolate, ovate, up to 19 × 8 cm, strongly discolorous, green, opposite for several pairs, then alternate. The inflorescence is axillary, 9-15 flowered, the peduncles are strongly flattened, up to 3 cm in length. The fruit is a woody capsule, with prominent stalk, cylindrical, 1.8 × 1.1 cm Red gum A large boled, medium sized to tall evergreen tree. Bark smooth white, (Eucalyptus grey, buff, reddish patches. Leaves bluish green; juvenile alternate, camaldulensis) petiolate, broad lanceolate. Flowers in axillary, 5-10 flowered umbels on a slender, terate, 0.5-2.5 cm long peduncles. Fruits a capsule, pedicillate, ovoid or tuncate globular, 0.3 – 0.6 x 0.4-1 cm disc broad. Seeds smooth yellow. Silky oak A medium sized to tall, semi deciduous tree with pyramidal or conical (Gravillea crown. Bark dark grey to blackish, furrowed, lenticellete. Leaves robusta) alternate, pinnately compound, petiolate, graceful and fin like, pinnatifid, divided with 11-23 primary segments. Flowers bright orange or orange yellow glabrous, in one sided 7-10 cm long racemes and borne in dense cluster on short leafless branches. Fruit boat shadeped 2 seeded follicles, compressed and oblique and tipped with a selender persistent style. Seed brown, flat with a shiny centre surrounded by a light brown papery wing. 229 Rahman et al.

Shade trees Morphological characteristics species Jarul A medium sized to large much branched deciduous tree. Bark greyish (Lagerstroemia to brown, smooth, peeling off in thin irregular flakes, blaze whitish. speciose) Leaves opposite, elliptic or oblong-lanceolate, 10-20 cm long, acuminate, glabrous on both surface. Flowers large, showy, mauve purple, 5-7 cm across, in ample terminal panicles, ultimate branches mostly 1-3 flowered. Fruit a capsule, sub globes, smooth, seated on accrescent woody. Seeds pale brown, 3 angular, laterally expanded into an oblong wing. Ipilipil A medium sized to tall, large, unarmed, deciduous tree. Bark light (Leucaena brownish grey, smooth. Leaves bipinnate, 7-18 cm long; pinnae 8-16 leucocephalla) cm long; leaflets 20-30, about 12 cm long, glaucous, linear, acute, finely hairy. Flowers dirty white or yellowish in auxillary or sub terminal, dense globose heads. Fruit a pod, produced very copiously, straight, 7-15 x 1.0-1.4 cm flat, glabrous, shiny brown when mature. Seeds 15-20, lenticular, shiny. Aam (Mangifera A medium-sized to large evergreen tree with a large dense crown and indica L.) rather short buttressed trunk. Bark brown or ashy-grey, vertically cracked, exfoliating in scales. Leaves crowded at the end of branchlets, oblong-lanceolate, 15-20 cm long, blunty acuminate, coriaceous, glossy, green above, paler beneath, entire with a undulated margin. Flowers in erect pubescent panicle. Fruit a drupe, ovoid or globose, laterally compressed. Ghora neem A medium-sized to large glabrous evergreen to semi-deciduous trees (Melia with large spreading crown. Bark greyish brown to blackish with azedarachta) longitudinal and oblique furrows and many scattered tubercles, blaze greenish pink with characteristic smell. Leaves imparipinnate, 18-45 cm long, crowded towards the end of branchlets. Flowers short, white, honey-scented, pentamerous, in auxillary panicles shorter than leaves. Fruit a drupe, 1-1.5 cm long, ovoid, pericarp smooth, greenish yellow when ripe enclosing white visid pulp and a smooth whitish stony endocarp almost 1-seeded. Amlaki A small to medium sized deciduous tree. Bark of young tree grey- (Phyllanthus smooth, in older trees exfoliating in irregular rounded pieces and in emblica) long strips. Leaves not compound, although individual small leaves look very much like the leaflets of pinnately compound leaf, light green, bluntish, 0.6-1.3 cm long, glabrous, paler beneath with apprised pubescence, closely arranged in two rows on opposite side of twigs. Flowers small, greenish yellow, monoecious, in axillary clusters, usually crowded towards the lower naked portion of branchlets, male flowers on short, slender pedicles; female flowers sub-sessile. Fruit a drupe, globose, about 2.5 cm across, obscurely 6-lobed, 3 celled. Seeds dark brown, trigonous. SHADE TREE SPECIES OF TEA GARDEN 230

Shade trees Morphological characteristics species Shirish/Rain tree A very large, deciduous to semi-deciduous tree with spreading crown (Samanea and large bole. Bark rough, blackish-grey, exfoliated in old trees. saman) Leaves bipinnate, pinnae 3-7 pairs, largest uppermost, shorter downwards with a gland between each pair of pinnae. Flowers in dense rose colored heads on pubescent peduncles, 1-3 together from the upper axils, and 5-8 cm long. Fruit a pod, 12-20 x 1.5 cm. indehiscent, suture thick, ripe pod blackish brown, smooth with septa between seeds and containing sweet sticky pulp. Mahagoni A tall evergreen to semi deciduous tree. Bark greyish brown, rough (Swietenia and flakes off in small patches. Leaves paripinnate, up to 60 cm long; macrophylla) leaflets 6-16, ovate-lanceolate with oblique base, up to 20 cm long with a very short panicles. Fruit a woody capsule, erect, brown. Seeds deep brown, winged at one end only. Tun (Toona A medium to large deciduous tree. Bark greyish brown, exfoliating in ciliata) irregular flakes when old. Leaves paripinnate, 30-60 cm long, somewhat crowded at the ends of branchlets; common petiole, glabrous, terete. Flowers white, sweet scented, in terminal pyramidal cymose panicle. Fruit a capsule, dark brown, smooth outside with white lenticles. Seed brown with lighter brownish wing at the ends. Indigofera Erect shrub or small tree, up to 12 m tall. Branches subsericeous with (Indigofera minute brown or white, biramous, appressed hairs. Leaves teysmannii) imparipinnate; stipules linear, up to 8 mm long; leaflets 11-23, elliptical to ovate, 2-8 cm × 1-3 cm. Inflorescence an axillary, many- flowered raceme, 8-10 cm long; flowers about 0.5 cm long; calyx brown-sericeous, 2 mm long; corolla whitish, pink or dark purple; standard ovate, up to 5 mm × 4 mm, dorsally sericeous. Pod subcylindrical, 2.5-4.5 cm × 0.5 cm, glabrous, beaked, about 16- seeded. I. zollingeriana occurs mainly on coral strands and sandy beaches, up to 850 m altitude (Fig. 4 e). Arhor(Cajanus The plant is an erect, short-lived perennial leguminous shrub that cajan) usually grows to a height of about 1-2 m, but can reach up to 2-5 m high. It quickly develops a deep (2 m depth) poisonous taproot. The stems are woody at the base, angular and branching. The leaves are alternate and trifoliate. The leaflets are oblong and lanceolate, 5-10 cm long x 2-4 cm wide. Leaves and stems are pubescent. The flowers (5 to 10) are grouped in racemes at the apices or axils of the branches. The flowers are papilionaceous and generally yellow in colour. They can also be striated with purple streaks. The corolla is about 2-2.5 cm. The fruit is a flat, straight and pubescent pod, 5-9 cm long x 12-13 mm wide. It contains 2-9 seeds that are brown, red or black in colour, small and sometimes hard-coated.

231 Rahman et al.

Shade trees Morphological characteristics species White Tephrosia candida is a deciduous Shrub growing to 3 m (9ft) by 3 m tephrosia(Tephro (9ft) at a medium rate. Leaves alternate, 17-25-foliolate; rachis 15-25 sia candida) cm, including petiole; leaflets oblong, 3-6 × 0.6-1.4 cm, abaxially densely sericeous, adaxially glabrous, secondary veins 30-50 on each side of midvein. Flowers ca. 2 cm. Calyx ca. 5 × 5 mm; teeth equal, ca. 1 mm, apex rounded. Petals white, rarely yellow or pale pink; standard densely sericeous. Ovary tomentose, with numerous ovules. Fruit linear, 8-10 cm × 7.5-8.5 mm, straight, brown tomentose with a mixture of long and short trichomes, apex truncate and with a straight ca. 1 cm beak; seeds 10-15 per legume, olive-green with dark patches, ellipsoid, ca. 5 × 3.5 mm, smooth. Gliricidia A small to medium-sized, deciduous tree with a short trunk and long (Gliricidia slender branches at first tend to rise almost vertically from the base. sepium) Bark soft, grey or ashy with longitudinal cracks. Bark of branches brownish-grey speckled with white spots like lenticels. Leaves imparipinnate, to 30 cm long. Flowers mauve pinkish; produced in great clustered racemes on leafless branches. Fruit a pod, to 20 x 2 cm, flat without wing and dehiscent seeds up to 10 or more (Fig. 4 f). Dadap (Erythrina The plant is deciduous growing to 20 m (65ft) by 20 m (65ft) at a fast lithosperma) rate. The branches and the branchlets stout and armed with short, few to many sharp prickles. Leaflets are broadly ovate and 8 to 18 centimeters long, with pointed tip and broad base. Racemes are terminal, hairy, dense, and up to 2.5 centimeters long. Flowers are papillonaceous, large and numerous. Calyx is about 4 centimeters long and minutely 5-toothed at the tip, the mouth being very oblique. Petals are bright red and shorter than the calyx, the standard being 7 to 9 centimeters long and the wings and keels subequal. Stamens are 10, upper filaments free nearly to the base or more or less connate with others. Ovary many-ovuled, style incurved. Racemes terminal, hairy, dense and up to 2.5 cm long. Fruits are pods, 10 to 25 centimeters long, 1.5 to 2 centimeters in diameter, and distinctly constricted between the seeds. Tick clover Leaves are compound. Leaflets are hairy, more densely hairy along (Desmodium major veins on the underside, to 3 inches long and 1 inch wide with a gyroides) rounded or slightly tapering base and blunt point at the tip. Flowers are pea-shaped, about ½ inch long, pink to purple with 2 yellow spots near the base of the broad upper lobe. The stamens and pistil form a curving tube that protrudes from the center, between the 2 lateral petals. The calyx behind the flower and the short flower stalk are reddish green and hairy. Fruit is a flat pod 1 to 2½ inches long with 3 to 5 sections, the sections convex on the upper edge and well rounded on the lower, and each containing a single seed. Seeds are kidney-shaped, about 4 mm long, and mature to brown. The pod is densely covered in tiny hooked hairs that latch onto anything that passes by. SHADE TREE SPECIES OF TEA GARDEN 232

Among the collected species, Fabaceae shows the highest percentage with 31 species comprising 19 genera (61.29%) followed by 5 species of Meliaceae comprising 5 genera (16.12%) and 2 species of Myrtaceae comprising 1 genus (3.22%) (Fig. 1 and 2). Apocynaceae, Moraceae, Protaceae, Lythraceae, Anacardiaceae, and Phyllanthaceae each family show the one species of one genera (Fig. 1 and 2).

Genera Species 35 30 25 20 15 10 5

0 Number of and species genera Number

Family name

Figure 1. Family wise showing the number of genera and speciesof shade trees at the surveyed tea garden.

Anacardiaceae 3% Proteaceae Phyllanthaceae 3% Lythraceae 3% 3% Moraceae Myrtaceae 3% 3% Apocynaceae 4%

Meliaceae 16% Fabaceae 62%

Generic percentages

Figure 2. Family wise generic percentage of available shade trees of the study areas. 233 Rahman et al.

a a b b

c c d d

f e e f

Figure 3. Morphologicaland leaf characteristics of different types of shade trees at investigated tea gardens in Bangladesh. a, b, c, d, e and f indicates Albizia lebbeck, Derris robusta, A. procera, A. odoratissima, A. chinensis and A. lucidor. SHADE TREE SPECIES OF TEA GARDEN 234

a a b b

c c d d

c

e e f f Figure 4. Morphological and leaf characteristics of different types of shade trees at investigated tea gardens in Bangladesh. a, b, c, d, e and f indicates A. moluccana,A. richardiana, Artocarpus chaplasha, A. hybrid, Indigofera teysmanniiand Gliricidia sepium. 235 Rahman et al.

Apocynaceae, Moraceae, Protaceae, Lythraceae, Anacardiaceae, and Phyllanthaceae shows 3.22% generic components of the entire tree flora of the tea plantations areas (Fig.2). The popular permanent shade trees among the tea gardeners area. odoratissima, A. chinensis, A. lebbeck, A. lucidior, A. procera, and D. robusta (Fig. 3 and 4).Indigofera teysmannii is frequently using as a temporary shade species in all investigated tea gardens.Cajanus cajan, Tephrosia candida, Tephrosia candida, Gliricidia sepium, Erythrina lithosperma and Desmodium gyroides species are also used as temporary shade trees in many tea gardens. This finding shows that Fabaceae is the dominant family in all investigated tea garden of Bangladesh. These types of shade trees not only provide shade to tea plants but also helps in replenishing nitrogen loss and controls insect pest due to biopesticide properties of the tree (Pangging and Mandal, 2017; Ahmed et al., 1993). In a similar study, Chowdhury et al. (2015) were enlisted 45 species of angiosperm representing 34 genera of 15 families of shade trees in the tea garden of West Bengal, India. In their assessment, the Leguminosae family showed the highest number of shade trees comprising 13 genera and 22 species and the most dominant shade tree species were Albizia odoratissima, Albizia chinensis, Albizia lebbeck, Albizia procera, Erythrina indica, Dalbergia sissoo,and Melia azedarach.Likewise, Pangging and Mandal (2017) investigated shade tree species and socioeconomic status around the Banderdewa forest range, Arunachal Pradesh, India. The common shade trees found in tea estate around the Banderdewa forest range, Arunachal Pradesh was Melia azedarach L., Albizia procera (Roxb.) Benth. and Albizia lebbeck (L.) Benth. Fabaceae was the most dominant family. Mulugeta (2017) reported that Albizia chinensis, Aleurites fordii and Calophyllum elantus species can be used as shade trees for the successful establishment of a new tea plantation.

CONCLUSION A total of 44 species of Angiosperm representing 31 genera of 9 families was enlisted at fifteen tea estates of Bangladesh. Among the estates, Fabaceae family shows the highest number of shade trees comprising 19 genera and 31 species. Further investigations, however, are required to find out the appropriate shade tree species on the growth and yield of tea plants and improvement of quality of made tea.

ACKNOWLEDGMENTS The research was supported by the NATP (Phase-II) sub-project of the Bangladesh Agricultural Research Council (BARC), Farmgate, Dhaka, Bangladesh. We are grateful to all estate management persons for their cooperation during the study. Special thanks are due to Forest Botany Division, Bangladesh Forest Research Institute, Chattogram, Bangladesh for their kind assistance in identifying plant species.

SHADE TREE SPECIES OF TEA GARDEN 236

REFERENCES Ahmed, Z.U., Begum, Z.N.T., Hassan, M.A., Khondker, M., Kabir, S.M.H., Ahmad, M., Ahmed, A.T.A., Rahman, A.K.A. and Haque, E.U. (2009). Encyclopedia of Flora and Fauna of Bangladesh. Angiosperms. Vols. 5-12. Asiatic Society of Bangladesh. Ahmed, M., Haq, M., Haq, M.I. and Ali, M.A. (1993). Influence of shade on the incidence of pests in Tea plantation in Bangladesh. Proc. Of the Tea Science & Human Health. TEATECH 1993, TRI, Kolkata, India. Pp. 326-329. Barua, D.N. (1979). Effect of long term application of nitrogen fertilizer on yield of tea. Two and a Bud, 37:1-3. Brandis, D. (1906). Indian Trees, Bishen Singh Mahendra Pal Singh, Dehradun. Pp. 767. BTB. (2019). Monthly Bulletin of Statistics on tea. January 2019. Bangladesh Tea Board, Baizid Bostami Road, Nasirabad, Chittagong. Chowdhury, A., Sarkar, S., Roy, P., Mondal S. and Chowdhury, M. (2015). Inventory of shade trees in Tea gardens of Sub-Himalayans region of West Bengal, India. The International Journal of Science and Technology, 3 (12): 164-168. FAO. (1987). Nitrogen fixing trees. Regional office for Asia and the pacific. Food and agricultural organization of the United Nations. Bangkok, Pp. 61-70. Heining, J.D. (1925). List of Plants of Chittagong Collectorate and Hill tracts. Darjeeling, India. Hooker, J.D. (1872-1897). Flora of British India. Vol. 1-7, Reeve and Co. London. ITC. (2015). Annual Bulletin of Statistics (September 2015). International Tea Committee. London, U.K. Pp. 158. Kalita, M.R., Das, K.A. and Nath, J.A. (2014). Comparative study on growth performance of two shade trees in tea agro forestry system. Journal of Environmental Biology, 35: 699- 702. Kanjilal, U.N., Das, A., Kanjilal, P.C. and De. R.N. (1939). Flora of Assam, vol.3, A Von Book Co., Delhi, India, Pp.578. Kanjilal, U.N., Das, A., Kanjilal, P.C. and De. R.N. (1940), Flora of Assam, vol.4, A Von Book Co., Delhi, India, Pp. 377. Ku, K.M., Choi, J.N., Kim, J., Kim, J.K., Yoo, L.G. and Lee, S.J. (2010). Metabolomics analysis reveals the compositional differences of shade grown tea (Camellia sinensis L.). Journal of Agricultural and Food Chemistry, 58: 418–426 Mulugeta, G. (2017). Effect of different shade tree species on the growth and yield of China hybrid Tea (Camellia sinensis L.) at Palampur Tea Research Station, H.P., India. Journal of Natural Sciences Research, 7 (4): 15-22. Pangging, G. and Mandal, S. (2017) Assessment of shade trees and socio-economic condition of the Tea workers: a case study of tea estate around Banderdewa forest range. Bulletin of Arunachal Forest Research, 32(1&2), 62-65. Pasha, M.K. and Uddin, S.B. (2013). Dictionary of Plant Names of Bangladesh (Vascular Plants). Janokalyan Prokashani. Chittagong, Dhaka, Bangladesh. 237 Rahman et al.

Prain, D. (1903). Bengal Plants. Vol. 1 & 2. Govt. press Calcutta. Rahman, M.A. (2016). An enquiry into the living conditions of tea garden workers of Bangladesh: A case study of Khan Tea estate. BRAC Institute of Governance and Development, BRAC University, Dhaka, Bangladesh. M. A Thesis. Sana, D.L. (1989). Tea Science. 2nd Edition, Ashrafia Boi Ghar, Bangla Bazar, Dhaka, Bangladesh, Pp. 7-81. Wang, Y.S., Gao, L.P. and Shan, Y. (2012) Influence of shade on flavonoid biosynthesis in Tea (Camellia sinensisL.). Scientia Horticulturae, 141: 7–16. SAARC J. Agric., 18(1): 239-250 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48396

Research Article THE SCENARIO OF SEEDLING PRODUCTION ON FLOATING BEDS IN FEW SELECTED AREAS OF BANGLADESH

M. Moniruzzaman1*, M.A. Kabir2 and M. Shahjahan3 1Dhaka School of Economics, Eskaton Garden Rd, Dhaka-1000, Bangladesh; 2University of Dhaka, Dhaka-1000, Bangladesh; 3Bangladesh Livestock Research Institute, Regional Station, Baghabari, Shahjadpur, Sirajganj-6770, Bangladesh

ABSTRACT The study was conducted to reveal various seedling production scenario on floating beds including environmental aspects associating seedling production. Data of various seeding production were collected from total 50 households (HHs) of two villages at Nazirpur Upazila in Pirojpur district of Bangladesh by a pre-tested survey questionnaire. The study showed that 68% farmers did seedling production for business purpose, and 30% as both own and business. During floating cultivation on beds about 50% farmers used their own producing seed and 26% from market. The farmers cultivated 21 different types of vegetables and spices seedlings where highest number of seedling was Bottle gourd (19.11%) followed by Papaya (13.82%) and Chili (12.60%). They used urea as a common fertilizer on floating bed which enriched by TSP (46%) and DAP (40%) during cultivation. It was observed that 32% farmers did seedling cultivation solely as own source of money while 26% got the help from NGOs. After end of the cultivation, 25% beds were sold as compost fertilizer for winter cultivation, 5% were used as own field and 17% farmers utilized the fertilizer as both business and own purpose. The study also revealed that, about 64% of respondent farmers were not suffered by any environmental complications. Adopting modern agro- technology and minimization of initial cost through subsidy can make this traditional Vasoman Chas has a sustainable agricultural practice. Keywords: Climate change, Compost fertilizer, Vegetable, Vasoman Chash, Sustainable

* Corresponding author: [email protected]

Received: 27.10.2019 Accepted: 18.04.2020 240 Moniruzzaman et al.

INTRODUCTION The people of Bangladesh depend upon agriculture and have being coping with these conditions for generations (Hossain, 2014). The farmers of southern districts (Barisal, Pirojpur, Gopalgonj, Shatkhira etc.) of Bangladesh invented an indigenous cultivation practice (Haqet al., 2004; Irfanullah, 2013), known as floating cultivation referred to “Vasoman Chash” (Haq et al., 2016) an age-old traditional practice continuing for centuries. These methods are widely used in Lake Inle in Myanmar, the Tonle Sap in Cambodia, and various fresh-water wetlands in Bangladesh (Islam and Atkins, 2007). For seedling production both natural and artificial floating beds are used (Slide et al., 2007; John et al., 2009; Mushatq et al., 2013) for agriculture in many tropical wetlands of the world. Water hyacinth is the major ingredient of soilless cultivation (Irfanullah et al., 2008; Irfanullah, 2013) and it is collected from May to July from the nearby river, canals, ditches, lagoons and from the wetland where it grows profusely to make floating bed locally known as dhap. Sometimes farmer use aquatic semi-decomposed plants such as Topapana, Kutipana, Dulalilota, Indurkanipana and immature small water hyacinth etc. There are two widely used methods for seed germination, one is ball (guti, tema) method another is to spread seed directly on the bed. The seedling production helps to control weeds and enrich soil organic nutrients in dry season, utilization of women labor, provides food and nutritional safety. However, heavy rain fall and flood may flash out or break the beds, non-availability of quality seeds, scarcity of matured water hyacinth, lack of other aquatic weeds, harms of predator (Irfanullah et al., 2011). Several studies have been conducted on floating agriculture in different districts of Bangladesh, but limited findings found specially for seeding production on floating bed. Therefore, this study was performed to know the vegetable seedling production on floating beds in few selected areas of Bangladesh.

MATERIALS AND METHODS The survey work was performed in two villages at Nazirpur Upazila in Pirojpur district during October, 2017. The Nazirpur Upazila has an area of233.63 sq. km. The temperature ranges from minimum 19°C to maximum 29.9o C, and annual rainfall 1975 mm. Nazirpur has an average literacy rate of 57.50%. Ownership of agricultural land classified as landowner 76.10% and landless 23.90%. The ecology of these villages of Nazirpur was almost same. Economy of Nazirpur was mostly based on farming and fishing. Most of the lands were lowland and marshy. Fishing wetlands for fishing were available in Mugarjhor and Manoharpur villages. However, all the collected data from the survey of 50 households (HHs) from those two villages were checked, compiled and coded in MS Excel (Microsoft version 10, SEEDLING PRODUCTION ON FLOATING BEDS 241

Redmond, USA). Qualitative data were converted into quantitative form by means of suitable scoring and then conducted analysis. Descriptive statistics were performed using SPSS version 16 (SPSS Inc. Chicago, USA).

RESULTS AND DISCUSSION Attributes, purpose and types of seedling production on floating beds The attributes, purpose and types of seedling production on floating beds of the surveyed areas are shown in Table 1 and Fig 1. About 9 days required to prepare a floating bed after purchase for seedling production with a cost 4000 Taka per bed. As individual cost about TK 261 and TK 249 were required for bamboo and land rent per season, respectively. Cost of seed, dulalilata, tupapana, bira, lata, fertilizer, pest

Table 1. Attributes of floating seedling production Variables Min Max Mean SD Floating seedling production experience (y) 2 37 18.08 8.46 Bed preparing time (d) 5 14 8.68 2.25 Number of bed 4 30 12.54 7.06 Number of cycle 3 6 4.20 0.76 Each bed cost per season (TK) 4000 4000 4000 0 Bamboo cost per bed per season (TK) 200 320 260.90 33.92 Land rent per bed per season (TK) 170 334 248.68 35.59 Seed cost per bed per cycle (TK) 175 275 221.50 24.35 Dulalilata cost per bed per cycle (TK) 300 400 375.90 22.85 Topapana cost per bed per cycle (TK) 580 700 661.80 39.56 Bira cost per bed per cycle (TK) 0 150 96.80 61.29 Lata cost per bed per cycle (TK) 0 175 137.20 43.10 Fertilizer cost per bed per cycle (TK) 40 70 48.00 5.98 Pest cost per bed per cycle (TK) 200 350 240.20 23.26 Labor cost per bed per cycle (TK) 0 550 120.44 183.87 Total cost annually per farmer (TK) 44692 378390 158325.36 93097.60 Total income annually per farmer (TK) 52000 612000 228174.04 134294.44 Net income annually per farmer (TK) 5908 233610 69848.68 50319.42 and labor cost per cycle recorded as 221.50, 376, 662, 97, 137, 48, 240 and 120 TK, respectively. However, annual net income from seedling production found TK 69,849 while total cost and income identified, respectively, TK 1,58,325 and TK 2,28,174.The average size of 44 sq. m bed of this study was purchased at TK 4000 and including seedling preparation cost per square meter cost was about TK 287, and TK 127 net income per square meter. Hossain (2014) estimated about TK 95 per 242 Moniruzzaman et al. square meter net income, which has similarity considering the time of present study. It was observed that 68% farmers did seedling production as business purpose, and 30% as both own and business (Fig. 2).

a) Seeds for germination b) Germinated seeds

c) Floating beds to sow germinated seeds d) Sowed seedlings on floating beds

e) Growing seedlings on beds f) Selling vegetables from floating cultivation Figure 1. Seedling production and floating vegetable cultivation on constructed beds SEEDLING PRODUCTION ON FLOATING BEDS 243

100 90 80 68

70

60 50 40

Percentage 30 30 20

10 2 0 Business Own use and business Others Purpose of floating cultivation Figure 2. Purpose of seedling production on floating bed

There are 21 vegetables and spices seedlings (Fig.3 and Fig. 4) were cultivated among the surveyed farmers while highest number of seedling was Bottle gourd (19.11%) followed by Papaya (13.82%) and Chili (12.60%).The findings of IUCN (2009) were in agreement with the present study for total number of seedling production and noted 31 seedlings of various vegetables and spices in four districts of Bangladesh. In other studies, Pavel et al. (2014) found 17 types of seedling production on floating beds and Haq et al. (2004) found 28 types of seedlings. A study in Kotalipara Upazila in Gopalganj district (Haq et al., 2016) evaluated the yield performances of some vegetables and spice crops on floating bed and found the average growth of red amaranth, Indian spinach, okra and turmeric were 15.3-33.0 tha−1, 34.00-59.88 tha−1, 37.5-49.0 tha−1 and 34.20-36.10 tha−1, respectively. They also compared the growth of red amaranth, Indian spinach, okra and turmeric 9.88- 12.36 tha−1, 49.40-74.10 tha−1, 13.59-16.06 and 12-13 tha−1, respectively, in high and medium high land of Bangladesh which were in agreed with the study of Ullah et al. (2010). 244 Moniruzzaman et al.

Wax gourd 1.22

Ridged luffa 4.47

Red Amaranth 4.88

Pumpkin 2.85

Potato 0.41

Papaya 13.82

Ladies finger 1.63

Kohlrabi 0.81

Hyacinth bean 9.76

Cucumber 3.25

Cowpea 1.63

Chilli 12.60 Name seedlings ofName Ceylon spinach 2.44

Cauliflower 1.22

Carrot 0.41

Cabbage 1.22

Brinjal 12.20

Bottle gourd 19.11

Bitter gourd 4.07

Arum 0.41

Amaranth 1.63

0.00 5.00 10.00 15.00 20.00 25.00 Percentage

Figure 3. Name of the seedlings cultured by the farmers on floating beds SEEDLING PRODUCTION ON FLOATING BEDS 245

a) Red amaranth b) Papaya

c) Chilli d) Bottle gourd

e) Brinjal f) Tomato Figure 4. Types of different seedlings cultivated on floating beds

Investment source of seedling production and return from floating beds The findings revealed that 32% farmers did seedling cultivation solely as own source of money while 26% got the help from NGOs (Fig.5a). During floating cultivation on 246 Moniruzzaman et al. beds about 50% farmers used their own producing seed and 26% from market (Fig. 5b).

50 45 24%

40 32 35 30 26 26 50% 25 20 12

15 Percentage 10 4 5 0 26% Own NGO Own and Own and Other NGO other person Own Purchased Both person a) Source of money for FSA b) Source of seed Figure 5. Investment sources of seedling production

Urea was a common fertilizer used on floating bed which enriched by TSP (46%) and DAP (40%) during cultivation (Fig.6). At the end of floating cultivation, the floating bed usually used as compost fertilizer. By this study, it was observed that 50% beds were sold by as compost fertilizer for winter cultivation, 10% were used as own field, and 34% farmers utilized the fertilizer as both business and own purpose (Fig.7). It also revealed from the survey that insecticide (Fig.8a) and rat killing trap (Fig.8b) were used in the floating cultivation beds.

50 46 45 40 40 35

30

25 20 15 8 Percentage 10 6 5 0 Urea , TSP Urea, DAP Urea, TSP, DAP Urea, TSP, others Fertilizer name Figure 6. Different types of fertilizer used on floating bed for seedling production SEEDLING PRODUCTION ON FLOATING BEDS 247

No use 6%

Sell Sell & own 50% use 34%

Own use 10% Return of floating bed

Figure 7. The byproducts (return) of floating beds

a) Insecticide spreading on beds b) Trap using on floating bed Figure 8. Protection of floating cultivation by different methods

These results are indicated that seedling production in the surveyed areas are mainly covered by economic stable farmers having own produced seedlings rather than other sources which might be the reason of previous experience, economical beneficial and acceptability of farmers in last floating cultivations. Moreover, floating bed uses as good compost fertilizer in winter season on Kandi (a piece of high land for winter vegetable cultivation). Constraints of floating seedling production The problems on floating cultivation (FC) revealed various constraints including unavailability of government aid, higher interest rate and inadequate capital. Land scarcity and heavy rainfall were documented as major problems for floating cultivation in four district of Bangladesh (IUCN, 2009) which slightly agreed with this study. In additionally, it was also reported the problems of quality seed, lack of irrigation water, poor communication during marketing as barriers for floating cultivation. Infestation by predator, scarcity of matured water hyacinth, lack of 248 Moniruzzaman et al.

Salvenia (Indurkanilata), Najaj and Dulalilata, infestation of different aquatic weeds, frogs and birds eat seedlings and do great harms to the production were reported as the problems of floating bed seedling production by Haq (2009).

Floating seedling cultivation effective to climate change adaptation About 64% farmers associating floating cultivation replied that climate change did not hamper seedlings production and 36% farmers replied various natural issues regarding their seedling production encompassing 6% flood, 4% heavy rain fall and 26% both flood and heavy rain fall. Irfanullah et al. (2011) in his study documented that devastating flood and excessive river current destroyed 23 floating bed in haor areas but Hoque et al.(2016) revealed that 74.3% respondents relied floating agriculture is effective to combat climate change which agree with the present study. Findings indicate that floating cultivation in haor area affected because they were novice about this technique. It is predicted that about 38 cm water rising from now by 2080 which will cover 22% of the world’s coastal wetlands (Warrick, 1996; Nicholls et al., 1999). Scientists are clarifying the possible reasons and solutions including evaluating the socio-economic and ecological implications of such a rise in sea-level (Hoozemans and Hulsbergen, 1995).

CONCLUSION Seedling production on floating bed is very economical and a good source of organic food which could be a good movement for the coming days and a solution of food insecurity. Geographical vulnerability and climate change issue are reducing the scope of seedling production. So a comprehensive study and research is needed to facilitate profitable and sustainable seedling raising one floating bed.

REFERENCES Haq, A.H.R.M. (2009). Some thoughts on soilless agriculture in Bangladesh Wetland Resource Development Society, ActionAid Bangladesh. Pp. 25. Haq, A.H.M.R., Ghosal, T.K. and Ghosh, P. (2004). Cultivating wetlands in Bangladesh. Leisa Magazine, 20(4): 18-20. Haq, M.T., Nabi, M.M. and Kumar, D. (2016). Yield Performances of Vegetable and Spice Crops on Floating Bed. International Journal of Business, Social and Scientific Research, 4(4): 286-291. Hoozemans, F.M. and Hulsbergen, C.H. (1995). Sea-level rise: a worldwide assessment of risk and protection costs. Climate Change: Impact on Coastal Habitation (Eisma D., ed). London: Lewis Publishers.Pp.137-163. Hoque, M.Z., Haque, M.E., Afrad, M.S.I. and Islam, M.N. (2016). Effectiveness of Floating Agriculture for Adapting Climate Change in Southern Bangladesh. International Journal of Economic Theory and Application, 3(1): 14-25.

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Hossain, M.A. (2014). Floating cultivation: An indigenous technology for adapting to water logging situation towards sustainable livelihood security in the low lying areas of Bangladesh. Journal of Bioscience and Agriculture Research, 1: 54-58. Irfanullah, H.M. (2013). Floating Gardening: a local lad becoming a climate celebrity? Clean Slate,Auckland, New Zealand. Pp. 26-27. Irfanullah, H.M., Adrika, A., Ghani, A., Khan, Z.A. and Rashid, M.A. (2008). Introduction of floating gardening in the north-eastern wetlands of Bangladesh for nutritional security and sustainable livelihood. Renewable Agriculture and Food Systems, 23(2): 89-96. Irfanullah, H.M., Azad, M.A.K., Kamruzzaman, M. and Wahed, M.A. (2011). Floating gardening in Bangladesh: a means to rebuild lives after devastating flood. Indian Journal of Traditional Knowledge, 10 (1): 31-38. Islam, T. and Atkins, P. (2007). Indigenous floating cultivation: a sustainable agricultural practice in the wetlands of Bangladesh. Development in Practice, 17(1): 130-136. IUCN. (2009). 2nd Phase Final Report (April 2008–June 2009): Organizing Resource Generation and Nutritional Support (ORGANS). IUCN Bangladesh Country Office, Dhaka, Bangladesh. Pp. 33. John, C.M., Sylas, V.P., Paul, J. and Unni, K.S. (2009). Floating islands in a tropical wetland of peninsular India. Wetlands Ecology and Management, 17(6): 641-653. Mushatq, B., Raina, R., Yaseen, T., Wanganeo, A. and Yousuf, A.R. (2013). Variations in the physico-chemical properties of Dal Lake, Srinagar, Kashmir. African Journal of Environmental Science and Technology, 7(7): 624-633. Nicholls, R.J., Hoozemans, F.M. and Marchand, M. (1999). Increasing flood risk and wetland losses due to global sea-level rise: regional and global analyses. Global Environmental Change, 9: S69-S87. Pavel, M.A.A. Chowdhury, M.A. and Mamun, A.A.A. (2014). Economic evaluation of floating gardening as a means of adapting to climate change in Bangladesh. International Journal of Environmental Studies, 71 (3): 261-269. Sidle, R.C., Ziegler, A.D. and Vogler, J.B. (2007). Contemporary changes in open water surface area of Lake Inle, Myanmar. Sustainability Science, 2(1): 55-65.

Ullah, M.A., Anwar, M. and Rana, A.S. (2010). Effect of nitrogen fertilization and harvesting intervals on the yield and forage quality of elephant grass (Pennisetum purpureum) under mesic climate of Pothowar plateau. Pakistan Journal of Agricultural Sciences, 47(3): 231-234. Warrick, R.A. (1996). Changes in sea level. Climate Change 1995. The Science of Climate Change. Pp. 363-405. SAARC J. Agric., 18(1): 251-261 (2020) DOI: https://doi.org/10.3329/sja.v18i1.48397

Research Article DOCUMENTATION OF INDIGENOUS TECHNICAL KNOWLEDGE AND THEIR APPLICATION IN PEST MANAGEMENT IN WESTERN MID HILL OF NEPAL

K. Naharki* and M. Jaishi Institute of Agriculture and Animal Science, Lamjung Campus, Tribhuvan University, Nepal

ABSTRACT Indigenous technical knowledge (ITK) are based on the experiences of the local people being passed on from one generation to next and has been used for management of pest since ancient times. A study was conducted to collect and document the ITK and their application in pest management from indigenous communities in western mid hill of Nepal. A total sample size of seventy-five respondents from the indigenous communities of Magar, Gurung, and Newar in Tanahun, Lamjung and Kaski districts of Nepal were interviewed with a semi structured questionnaire. The study revealed that subsistence farmers and semi commercial farmers usually made the most use of ITK while commercial farmers rarely used such indigenous knowledge. Use of ITK was highest (85%) by the subsistence farmers, followed by semi-commercial farmers (60%), and lowest (10%) by commercial farmers in pest management. The transfer of ITK was mostly found to be through past generations. The indigenous technological knowledge being applied in pest management should be documented, promoted, and encouraged in combination with scientific knowledge among the farming communities. Keywords: Documentation, ITK, Pest Management

INTRODUCTION Indigenous technical knowledge (ITK) is the fundamental part of the culture and history of a local community (Borthakur and Singh, 2012). It is the knowledge of the indigenous people residing in different geographical regions of the world with their own techniques, practices, skill and culture (Bruchac, 2014). ITK are distinctive, traditional and local knowledge prevailing within and developed around specific communities indigenous to a particular area (Ghosh, 2011). We need to learn from local communities to augment the development process (Pongel, 2011). ITK are those skills and practices which are developed over a period of time through accumulation of experience and understanding by the native communities and are

* Corresponding author: [email protected]

Received: 23.05.2020 Accepted: 22.06.2020 252 Naharki and Jaishi inherited from generation to generation (Devi et al., 2014). ITK are the storage house of knowledge, skills and techniques for management of farming system. They are transferred through sharing of cultural and traditional information (Singh, 2007). Nepal is inhabited by a multiethnic, multi-cultural and multi-lingual communities endowed with rich ITK for agricultural practices (MoFSC, 2014). Fifty-Nine indigenous nationalities have been recognized in Nepal referred as ‘Adivasi Janajati’ which consist of 35.8 percent of the total population of Nepal (NEFIN, 2018). Gurungs, known as Tamu are the major indigenous inhabitant of western mid hill and Magar are the indigenous inhabitant in the western and mid-western Nepal (Bhattachan, 2012). All of these indigenous communities have some kind of traditional knowledge associated with their life from time immemorial. But none of the national policies in Nepal has emphasized the documentation of these ITK and related issues (Sharma et al., 2009). ITK is sustainable as it has evolved after many years of observation and experience. They are important tools for advancement as ITK are farmer friendly, innovation of site-specific crop management practices, conservation of natural resource base, resilient and adaptation to changing climate and food security (UNFCCC, 2013).Traditional farm practices should be promoted and encouraged among the farming communities as an effort not only to tap the local knowledge but also to make use of locally available resources in crop management (Narayanasamy, 2001).Documentations will help in developing the contents of indigenous technological knowledge which will be disseminated to the entire community for its future implication (Pradhan et al., 2017). Plant protection has become a serious matter due to changes in climate. The ecology and biology of different insect pests are also changing which makes pest control mechanisms more difficult and complex (Singh et al., 2019). Chemical pesticides are used in agricultural field indiscriminately which has resulted in to resistance development in pests along with environmental degradation (Gill and Garg, 2014). Pest management approaches are different among traditional farmers practicing traditional farming systems in different regions of the country (Chhetry and Belbahri, 2009). Indigenous pest management practices generally involve use of locally available resources for the successful cultivation of crop plants and hence are eco- friendly and sustainable. ITK being applied in pest management have inherent characteristics of cultural and environmental compatibility as well as sustainability with cost-effectiveness (Pradhan et al., 2017). Transformation of socio-economic conditions of rural people and the resulting changes in agricultural processes have resulted in decreased use of associated traditional knowledge with shift to modern practices (Uprety, 2012).Lack of documentation of ITK, low awareness about its scientific rationality and poor research advances are the major constraints of wider ITK applicability (Devi et al., 2014). The recognition of the creativity of the traditional communities is essential for INDIGENOUS TECHNICAL KNOWLEDGE IN PEST MANAGEMENT 253 the conservation of biodiversity as well as conservation of intellectual diversity by the farmers of the community to continue to the further generations (Jena, 2007). The present study attempts to document the ITK and their application related to pest management as used by farmers of indigenous communities in western mid western hill of Nepal.

MATERIALS AND METHOD Study area and design A study was conducted to collect and document ITK and their application on pest management from indigenous farmers and local information resources. The survey was conducted in the indigenous community’s inhabitant in Tanahun, Lamjung and Kaski district of Nepal (Fig. 1). A total sample size of seventy-five respondent was interviewed with a semi structured and open-ended questionnaire between November 2019 and January 2020.The qualitative data were recorded from the survey. Twenty- five respondents from each communities of Magar, Gurung and Newar were selected on the basis of purposive random sampling in indigenous communities.

Figure 1. Map of Nepal showing study area

Documentation and statistical analysis Focus group discussion (FGD), transect walks, observation and key informant interview were qualitative research methods used for the validation and documentations of ITK. Secondary data were collected reviewing different scholarly publications, journals, books and local information. The analysis of data was performed using SPSS (Version-25.0) and MS Excel 2016. Rationality refers to the degree of perceived effectiveness of ITK or explanation and support of ITK with scientific rationale (Husain and Sundaramari, 2011). Rationality

254 Naharki and Jaishi scale (Hiranand, 1979) was used to judge the rationality of the indigenous practices. FGDs were conducted in each of three indigenous communities with participation respondents and key informants to determine the rationality of the ITK. The ITK validated for the rationality by discussions based on perceived effectiveness were documented.

RESULTS AND DISCUSSION Sources of ITK From the survey, it was found that ITK are transferred from past generations such as elder persons, parents and grandparents. About 82% of the respondents received the knowledge on ITK from their past generations where as 6% of the respondents received the knowledge through studies, journals, newspapers and articles (Fig. 2). About 12% of the respondents received the knowledge from other sources such as imitation of their neighbors, community leaders, extension agenesis and seeing it being performed by other people.

Sources of ITK

12% 6%

Past Generation

Studies, Journals 82% and Newspapers

0thers

Figure 2. Sources of ITK

There are many sources of ITK hidden in our village and societies (Satapathy et al., 2002). ITK is irregularly dispersed in communities and the valuable sources of ITK are farmers, community members and importantly elders. They are perceived by next generation through word of mouth, sights and writings (Arora et al., 2016). ITK used by the indigenous communities of western mid hill of Nepal Technical methods used in indigenous communities of western mid hill of Nepal in pest management are: INDIGENOUS TECHNICAL KNOWLEDGE IN PEST MANAGEMENT 255

Drying of harvested products Originally the traditional practice was that the maize cobs were kept dried by keeping them on the top floor where kitchen heat would facilitate drying of the seeds. They are also hanged outside forming a cluster called ‘Suli’ and ‘Thangra’. Drying reduces the crop moisture content and susceptibility of the storage insect pest in grains (Asemu et al., 2020). Dusting of ash Ash is dusted to control aphids. It is also used to control ants in potato field. Application of ash repels insects and discourages surface feeding insects (Chandola et al., 2011). Ethnobotanical use of locally available plants Use of herbs like Neem, Bojho, Marigold, Titepatiare used for the management of various pests depending on their local availability. Field burning This method is performed to kill any insects or eggs of insects that may be present in the field. When population of insects increases beyond limit and becomes out of control, entire farm infested with insect pest are burnt to prevent the pest from spreading elsewhere. Field burning improves yield and reduce the requirement of pesticides and fertilizers. Slash and burn is still being practiced in different parts of Nepal including western mid hills of Nepal (Kafle, 2011). Foliar spray of cow urine Cow urine is sprayed over the plant as it acts as pest repellant. Cow urine, ash and soil mixture is useful for the treatment of cabbage plants. Cow urine is also used for the preparation locally made compost called ‘Jhol Mol’. Nitrogen present in the urine also favors the development of the crops. Cow urine is used in rice field with damage of snails and slugs as they have molluscicidal effect (Deshmukh, 2015). Irrigation Application of fresh irrigation water helps to control pest during outbreak. Drying and wetting of rice field for few days is also one of the indigenous practices followed by the farmers especially against case worm and leaf folder in rice. The grounded pulp of the ‘Khirro’ leaf is used in irrigation channel to control Rice stem borer. Planting attractant/repellent crops on border of field Traps crops are used in the borders as attractants and repellents to protect the main crop field from insect pests. Mustard crops are planted on border of wheat field and coriander on border of Cole to attract the crop away from the major field. Marigold plants are also used as trap crop in many vegetable and cereal crops.

256 Naharki and Jaishi

Ploughing Ploughing with traditional plough called ‘Halo’ is performed before plantation to remove the weeds and killing of eggs of insect. Preparation of indigenous pesticide Indigenous pesticide is prepared using cow urine and jaggery. The toxicity of the mixture kills the insects and pests that come in contact with it. Botanicals, cow urine and cow dung are mixed in various ratios for the preparation of indigenous pesticides. Storage in bamboo container Bamboo containers made up of bamboo called ‘Bhakari’ are used to keep away the storage pests. Use of cow dung Cow dung is also used as seed protector. Seed is covered in cow dung before they are planted. It helps to protect seeds from pests. It is reported that cow dung extract @ 2% concentration is effective against ear head bug, BPH, leaf folder, stem borer, caterpillars and other chewers in rice (Narayanasamy, 2001). Use of scare crow to scare birds and animals Scare crow are used to scare the birds and animals that destroy the crops. These are specially kept in cereal crop field like rice, wheat and maize or vegetable and fruit fields. Using stick Rice moths are found to create clusters on paddy plants. They are removed with sticks and the moths submerged on the water in the field to kill them. ITK in weed management Weeds are the major host of pests in agriculture. Indigenous communities perform management of weed for the control of pest in field. During intercultural operations, hand or manual weeding is carried out and burning is still prevalent. Flooding is carried out in rice field to suppress the growth weeds. Slicing of bunds and terrace raisers is carried out before sowing in the field. On rainfed field, flooding through irrigation is carried out for effective weed control. Wooden rake called ‘Dante’ is used to collect the weed in the field during ploughing. Mixed cropping and inter cropping to utilize the inter row space is also practiced which helps to suppress weeds and increase weed control efficiency (Ghuman et al., 2008). Retention of weeds in field and the crop residues before sowing also occurs which helps in increase in nutrient content of soil and enhancement in soil quality (Duke et al., 2002)

INDIGENOUS TECHNICAL KNOWLEDGE IN PEST MANAGEMENT 257

Application of ITK in pest management Among the respondents, there were 10 commercial farmers, 32 semi commercial farmers and 33 subsistence farmers. From the survey, the result revealed subsistence farmers and semi commercial farmers usually make the most use of the ITK and is seldom used by the commercial farmers. Use of ITK was found to be 85% in subsistence farmers, 60% in semi commercial farmers and 10% among commercial farmers (Fig. 3).

Use of ITK in pest maangement

100 90% 85% 80 60% 60 40% 40 15% 20 10%

0

Commercial Farmers Semi Commercial Farmers Subsistance Farmers Percentage of Percentage Respondent Yes No

Figure 3. Use of ITK by different types of farmers

The communities have used the indigenous knowledge system to conserve and utilize the biological diversity of their surroundings (Jena, 2007). ITK are cheap, local and generally perceived through parents which makes it easier to adopt and use for the subsistence and semi commercial farmers (Nkunika,2002) whereas commercial farmers prefer use of costly pesticides which give immediate results (Palikhe, 2002) Application of botanicals for pest management Farmers use plant materials for protection of plants from pests and for improving the nutritional status of the soil. Among the plants available locally, titepati, banmara, neem and asuro were found to play an important role in pest management in indigenous communities. Botanical pesticides can be a good alternative to minimize the use of synthetic pesticides as they possess the properties of pest repellence, toxicity, anti-feedance, and insect growth regulatory activities against pests of agricultural importance (Matharu and Chahil, 2013). Moreover, they are safe and easier to use, locally available and cost effective. Plants like Azardirachta indica, Vitex nedundo, Ricinus communis, Gliricidia sepium, Euphorbia milli, Euphorbia tirucalli are proved to be the best natural pest control plants (Deshmukh, 2015). Many botanicals are being used as ITK for pest management in different crops and most of them have medicinal properties which can keep the environment safe (Joshi and Joshi, 2002).

258 Naharki and Jaishi

Table 1. Locally available botanicals used for insect control in the study area Local Common Scientific Name Insects Controlled Name Name Asuro Malabar nut Adhatoda visica Grasshopper, Scale Insects, Stored pest, Rice pest Bakaino China berry Melia azedarach Army worm, Mustard saw-fly, Tobacco caterpillar Banmara Wild Sage Lantana camara Corn weevil, Beetle Bojho Sweet Flag Acorus calamus Ants, Maize Weevil Gande Billy goat Aegeratum conizoides Diamond back moth, Stored Grain Pest weed Khirro Milk Tree Sapium insigne Stem borer Nim Neem Azadirachta indica Rice pests, Diamond back moth, Stored grain pests including weevil and grain moth. Sayapatri Marigold Tagetes erecta Rice green leafhopper, Brown planthopper, Diamond back moth, aphid Titepati Mugwort Artemisia vulgaris Corn borer, Leaf mines and galls control Tulsi Sweet Basil Ocimum basilicum Rice Pest, Beetles, Fruit flies

Constraints in ITK Documentation The knowledge on agricultural systems and pest management is accumulated, improved and preserved through personal experiences by farmers themselves and is documented very little in particular. Although, several technical methods are used in pest management since ancient time, they lack scientific validation and documentation in detail. There are no written documents on ITK available with the local farmers. Agricultural agencies, extension workers, research institutions and government bodies have felt short to explore this topic. Lack of research activities related to ITK in pest management practices and their protection for future application acts as major constraints in documentation. ITK are in the threat of extinction due to lack of systematic documentation, lacking incorporation in agricultural research and development by researcher and extension worker (UNDRIP, 2007).

CONCLUSION ITK are valuable assets to indigenous communities. They are used combining traditional skills, culture, knowledge and tools of the native communities. The documented ITK showed importance and effectiveness in controlling insects and pest management. ITK-based practices are used by the majority of the subsistence and semi commercial farming community without the knowledge of its scientific rationality. Effective actions should be taken to preserve and promote ITK with INDIGENOUS TECHNICAL KNOWLEDGE IN PEST MANAGEMENT 259 blending of indigenous and scientific knowledge. There is necessity to combine ITK with the scientific cognizance strategy with its proper documentation and apply them in pest management with rationality for their use of ITK in future.

ACKNOWLEDGEMENT Sincere gratitude is expressed to Prof. Megha N Parajulee and Mr. Kapil Kafle for devoting their valuable time during the research work and preparation of the article. Sincere thanks to Sudip Regmi, Sabina Regmi,Shikshya Parajuli and Bhanubhakta Adhikari for their valuable suggestion and guidance throughout the work.

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SAARC Journal of Agriculture Affiliated organization: SAARC Agriculture Centre (a regional institute under the framework of South Asian Association for Regional Cooperation)

DESCRIPTION SAARC Journal of Agriculture (SJA) is intended to be a leading international journal focusing on all aspects of agrarian sector in South Asian region. The SJA publishes original article, review, short communication on the fundamental, applied and management areas of crop science, horticulture, animal science, fisheries, natural resource management, post-harvest processing, rural development, climate change, social sciences etc. The aim of SJA is to advance and disseminate the knowledge in all spheres of agriculture towards achieving the sustainable development goals. PERIODICITY Two issues per year. AUDIENCE Researchers, academics, doctoral and post-doctoral scholars, policy makers, developmental agencies in broad areas of agriculture including livestock and fishery.

GUIDE FOR AUTHORS INTRODUCTION SAARC Journal of Agriculture (SJA) is the official journal of the South Asian Association for Regional Cooperation (SAARC) and published from SAARC Agriculture Centre (SAC), Dhaka, Bangladesh. It is an open accessed, peer-reviewed international journal with two issues in each year. The aim of the journal is to advance and share scientific knowledge in all spheres of agricultural sciences. The SJA welcomes the manuscripts primarily from the SAARC Member States namely 264 GUIDELINES OF THE JOURNAL

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SUBMISSION OF PAPER Papers from all SAARC Member Countries are invited. Nevertheless, manuscript from rest of the world with direct or indirect impact on agriculture/ livestock/ fisheries will also be considered for publication subject to its applicability on the SAARC region. For all purpose regarding the submission, potential authors are requested to contact with editorial desk of SAARC Journal of Agriculture ([email protected]).

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ABSTRACT The manuscript should contain an abstract. It should be self-contained and citation- free, and should not exceed 250 words. An ideal abstract should contain introductory lines, objectives, materials & methods, results and conclusions of the experiment. Abbreviations that appear in the abstract must be defined before they are first used.

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GUIDELINES OF THE JOURNAL 267

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ARTICLE STRUCTURE (Short communication) It should restrict the word count of 2500 including title, authors name and affiliation, abstract (150 words), Brief introduction, materials methods, results and discussion (combined), references, relevant tables and figures. Reference style in Text 1. Single author: the author's surname followed by year of publication (Singh, 2020) 2. Two authors: both authors' surnames followed by the year of publication (Singh and Jahan, 2020) 3. Three or more authors: first author's surname followed by 'et al.' and the year of publication (Singh et al., 2020)

REFERENCES 1. Restrict the references which are relevant to the particular topic. Recent and relevant not more than 20 years old references are encouraged. 2. All references quoted in the text must appear at the end of the article and vice- versa. The spellings author’s names and dates or years at the two places should be carefully checked. 3. The references should include names of all authors, years (within brackets), full title of the article, full name of the journal (in italics) (no abbreviations), volume 268 GUIDELINES OF THE JOURNAL

number, issue number in bracket, and pages. For book or monograph, the name of the publisher should also be given as well as its volume, edition and relevant pages. 4. The references cited together in the text should be arranged chronologically. The list of references should be arranged alphabetically on author’s names, and chronologically per author. 5. The references in the bibliography should be listed alphabetical order. 6. A few examples for correct citation of references are mentioned herewith.

Journal Article Khan, M.M.H. (2018). Abundance, damage severity and management of guava mealybug, Ferrisiavirgata CkII. SAARC Journal of Agriculture, 16(2): 73-82. Khan, M.M.H., Islam, M.M., Asaduzzaman, M. and Uddin, M.N. (2018). Mutants and weather parameters affecting the population dynamics of three major insect pests of mungbean. SAARC Journal of Agriculture, 16(2): 1-12.

REFERENCE TO A JOURNAL PUBLICATION WITH AN ARTICLE NUMBER Van der Geer, J., Hanraads, J.A.J. and Lupton, R.A. (2018). The art of writing a scientific article. Heliyon, 19: e00205. https://doi.org/10.1016/j.heliyon.2018.e00205.

BOOK/ BULLETIN Siddiky, N.A. (2017). Sustainable goat farming for livelihood improvement in South Asia. SAARC Agriculture Centre, Dhaka, Bangladesh. Pp 190 FAO. (2019). Averting risks to the food chain – A compendium of proven emergency prevention methods and tools. Second edition. Rome. Pp 104 FAO. (2018). The state of Mediterranean and black sea fisheries 2018. Rome. Pp 36

CHAPTER IN A BOOK Mettam, G.R. and Adams, L.B. (2009). How to prepare an electronic version of your article. In B.S. Jones and R.Z. Smith (Eds.). Introduction to the electronic age. New York: E- Publishing Inc. Pp. 281–304.

CONFERENCE/SYMPOSIUM/ PROCEEDINGS Joshi, B.K. (2004). Crossing frequency and ancestors used in developing Nepalese mid and high hill rice cultivars: Possible criteria for yield improvement and rice genes conservation. Proceedings of 4thNational Conference on Science and Technology held at Kathmandu, Nepal. Pp 502-523

REFERENCE TO A WEBSITE Cancer Research UK. Cancer statistics reports for the UK. (2003). https://www.can cerresearchuk.org/about-cancer/bowel-cancer. Accessed 8th June, 2019. GUIDELINES OF THE JOURNAL 269

Venugopal, D. (2000). Nilgiri tea in crisis: Causes consequences and possible solutions. http://www.badaga.org. Accessed 8th June, 2019.

Figures The number of figures should be restricted to 4 and it should have title. Citation of figures should appear within the main manuscript. It should be appeared after the reference section.

Tables The number of tables should be restricted to 4 and should be numbered and title. Suggested to avoid large tables. In case of large tables, the data could be presented through graphs for better understanding. Citation of figure should be appeared within the main manuscript. It should be placed after figures.

Abbreviations Should use internationally accepted abbreviations. For example: h for hour. New abbreviation for manuscript should be defined during their first use.

AUTHORSHIP AND ITS SEQUENCE Before submission, authors are anticipated to consider carefully the list and order of authors and mention the definitive order of authors at the time of the original submission based on their contribution. Any addition, deletion or rearrangement of author names in the authorship list will lead to rejection of the manuscript by the editorial board of the journal. Only in exceptional circumstances, the corresponding author can submit such request through their head of organization stating the reason.

MANUSCRIPT PREPARATION Authors should follow guide for authors stringently, else the manuscript would be rejected without peer review. Editors reserve the right to adjust the style to certain standards of uniformity.

ARTICLE PROCESSING CHARGE Articles of any type (research paper, review, short communication, case study) are published in the SAARC Journal of Agriculture based on scientific merit and relevance to SAARC region at free of cost.

SUBMISSION CHECKLIST Before sending the manuscript to the designated mail id of SJA, the authors are requested to undertake final check of the prepared manuscript: (i) Covering letter 270 GUIDELINES OF THE JOURNAL

(ii) Among all the authors, one has been designated as corresponding author (iii) Affiliations along with mail id of all authors at the cover page (iv) Full postal address of the organization where particular research has been approved and carried out (v) Cover page including title of manuscript, authors name/ address/ mail id, abstract, key words, corresponding author (vi) Entire file should two parts Cover page: as mentioned before. Main manuscript: title, running title, authors name (initial followed by surname), abstract, key words, introduction, materials and method, results and discussion (combined), conclusion including future researchable issues, acknowledgment, conflict of interest if nay, reference, tables with title, figure with caption (vii) Manuscript has been spell checked and grammar checked (viii) Permission is taken for the use of copyright material (ix) All references mentioned in the body of the manuscript are being cited in the reference list and vice versa