Crop Intensification in Theni District, Southern India Using Multi-Criteria Based GIS Analysis
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Int.J.Curr.Microbiol.App.Sci (2019) 8(11): 407-423 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 8 Number 11 (2019) Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2019.811.051 Evaluating the Variables for Banana (Musa Sp.) Crop Intensification in Theni District, Southern India using Multi-Criteria based GIS Analysis R.M. Vijayakumar1*, L. Gnanappazham2*, C.B. Manjunath1, M. Kavino3, R. Jagadeeswaran4, S. Ramsubramoniam5 and P. Jeyakumar6 1Department of Fruit Crops, Horticulture College and Research Institute, Tamil Nadu Agricultural University, Coimbatore- 641 003, Tamil Nadu, India 2Department of Earth & Space Sciences, Indian Institute of Space Science & Technology, Department of Space, Govt. of India, Valiamala, Thiruvananthapuram-695 547, Kerala, India 3Department of Fruit Crops, Horticultural College and Research Institute, Tamil Nadu Agricultural University, Kamatchipuram, Periyakulam Tamil Nadu 625604, India 4Department of Soil Science and Analytical Chemistry, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Kudumiyanmalai, Pudukottai District Tamil Nadu 622104, India 5Regional Remote Sensing Centre (South), ISRO, I-Site Campus, Marthahalli, Bangalore-560 037 Karnataka, India 6Department of Crop Physiology, Tamil Nadu Agricultural University, Coimbatore- 641 003, Tamil Nadu, India *Corresponding author ABSTRACT Increasing population stresses the need for crop intensification per unit area of land to meet the demand. Over exploitation of natural resources leads to irreversible damage to crop production eco-systems. A multi-criteria decision model (MCDM) to assess the K e yw or ds parameters contributing to intensification leads to proper utilization of resources. This Crop manuscript presents the findings of Analytical Hierarchical Process (AHP) derived intensification, advanced decision model Technique for Order of Preference by Similarity to the Ideal Banana crop, Multi- Solution (TOPSIS) for evaluating the contributing intensification parameters comprising Criteria Decision slope, soil, precipitation and socio-economic indicators for banana plantation in Theni Model (MCDM) district, Tamil Nadu, India. The location of production areas has direct relation with crop Article Info collection centers, cold storage units and market access indicating that intensification is influenced by these parameters. TOPSIS derived crop suitability map is in cohesion with Accepted: crop cover map of the study area. The analysis reveals that banana area in 2016-17 was 07 October 2019 307 hectares and in 412 hectares in 2017 – 2018 & Banana intensified area was about 49 Available Online: 10 November 2019 hectares for 2016, 2017 and 2018. Topsis analysis has revealed that an area of 43732 ha is suitable for banana wherein average 14575 hectares is under banana cultivation hence an area of 29200 is available for intensification with suitable interventions. 407 Int.J.Curr.Microbiol.App.Sci (2019) 8(11): 407-423 Introduction atmospheric conditions, vegetation etc.), as well as economic and socio-cultural Agricultural intensification is defined as conditions in decision making process (Joerin improving per-acre output using et al., 2001). Geographical information system hybrids/advanced cultivation methods without (GIS) provides ample scope for integration of increasing production area (United Nations, multiple spatial variables accurately and 2010). Over exploitation of natural resources possible permutations in analyzing land for developmental activity may affect their suitability (Malczewski, 2006; Vittala, et al., availability and regeneration capacity. Ever 2010). Hence, multi-criteria decision making increasing population has affected the carrying (MCDM) methodology has been combined capacity of land and hence, optimal utilization with spatial analytics for evaluating the land and quantification of natural resources are pre- use potentials and deciphering complexities of requisite for management of resources. resource allocation/utilization (Ennaji et al., Production fluctuations occur within short 2018; Heller et al., 2012). These techniques time periods due to variations in extensively used for suitability analysis to weather/climate conditions call for timely and identify the potential lands for agriculture accurate monitoring systems (Campbell et al., intensification. GIS based analysis has been 2014). applied for analyzing Crop intensification studies (Bera et al., 2017; Ahmed et al., Cropping intensity is focused in areas where 2016), vegetable crops intensification studies suitable land, climate, capital, labour and for land/site suitability analysis (Sati and Wei connectivity to markets are enabled, to 2018; Mazahreh et al., 2018), horticultural facilitate higher yields and returns (Van fruit crops (Boitt et al., 2015; Boonyanuphap Ittersum et al., 2013). Socio-environmental et al., 2004). factors and inclusive community participation ensure success of crop intensification. The Banana is a major tropical fruit crop being future is largely dependent on land-use cultivated in Indian subcontinent. The area changes in different scenarios and assessment and output per unit of land has increased over of social, economic and environmental the years owing to availability of disease-free consequences under these scenarios (Kunwar, tissue cultured planting material. Indian 2010). bananas have gained wider acceptance in Middle East and European markets leading to Land suitability analysis (LSA) is a technique intensification of cropped area in traditional to deduce natural and interventional capacities cultivation regions (NHB, 2016). Increasing to assess extent suitability to specific crops population is posing pressure for optimizing of (Bandyopadhyay et al., 2009). Assessment of production factors like land, water and energy. land for different activities provides capability Though food production has doubled in last of land to support the requirements for five decades arable land has increased only by potential use (Malczewski 2004). GIS based ~ 9% (Godfray et al., 2010). In this context of Multi-criteria decision model (MCDM) is banana crop intensification, there is need to widely employed for land suitability studies. adopt sustainable agricultural practices to Multi-criteria evaluation of land suitability for contain the depletion of natural resources crop production involves different criteria like (Ochola et al., 2013; Tilman et al., 2011). The geological and biophysical elements (i.e., current study examines multi-criteria decision geology, soil characteristics, relief, approach (MCDA) using TOPSIS model to 408 Int.J.Curr.Microbiol.App.Sci (2019) 8(11): 407-423 achieve the objectives: (i) to determine banana Secondary data crop intensification parameters based on land accessibility, slope, soil, distance from CartoDEM, a product of Cartosat-1 stereo drainage and rainfall (ii) analyze the extent of images of 2.5m resolution was accessed from intensification for 2016 to 2018 time series Bhuvan data portal of Indian Space research period. Organization (ISRO) (Bhuvan, 2018). Digital Elevation Model data was used for extracting Materials and Methods slope and distance from drainage network. Other data used in the study are detailed in Study area Table 1. Theni district, Tamil Nadu, India lies between Socio-economic data like population, 9° 53‟ 0”N to 10° 22‟ 0” N latitudes and Government and financial institutions, 77°17‟ 0” E to 77° 67‟ 0” E longitudes, agriculture input suppliers, regulated markets, covering an area of 2889 km2 (Fig. 1). Theni crop collection centers and cold storage District is gifted with moderate climate facilities data was collated from open source suitable for cultivation of major tropical and data of Theni district. (Theni District profile, subtropical crops. 2018) The temperature ranges between 20° - 40° C The work flow consists of (i) data preparation and average annual rainfall is about 951 mm. which involve landuse land cover mapping Precipitation is the lowest in January, with an using remote sensing data and generation of average of 16 mm reaches its peak in October thematic maps of the criteria for banana with an average of 203 mm. suitability and (ii) suitability mapping for banana crop which involves MCDM with the The soils range from clay loam to sandy loam weights of criteria layers determined using suitable for most of agriculture and AHP and TOPSIS (Fig. 2). horticulture crops. Theni is ranked 2nd in Asia for Banana trading and known for the large- Data preparation scale trading of agricultural cash crops. The major crops in the region are Rice (Paddy), Remote sensing data analysis for land use Banana, Mango, Coconut and other crops classification (Theni profile, 2018). Three datasets of Sentinel2 time series data (< Data 10% cloud free) for 2016, 2017 and 2018 were used for land use land cover analysis. A subset Remote sensing data of the study area was extracted for image analysis. Random forest algorithm was Sentinel-2 satellite images (Level 1C), for the implemented in Python script for land use study area were accessed from European classification (Manjunath et al., submitted). Space Agency (ESA) Scihub-Copernicus (ESA, 2018). We selected multi-spectral Extraction of banana intensified area images with less than 10% or near zero (<1 %) Crop intensification is an increase in cloud coverage for three data sets of 10 January 2016, 23 February 2017 and 28 agricultural