Pest and Weed Suppressive Mechanisms in Complex Rice System at Malang, East Java, Indonesia

MSc Thesis Report Pest and Weed Suppressive Mechanisms in Complex Rice System at Malang, East Java, Indonesia

Supervisors: dr.ir. Egbert A. Lantinga, dr. ir. FJJA (Felix) Bianchi, Uma Khumairoh, MSc

Ike Widyaningrum 790305949030

May 2015

FSE-80436 Farming Systems Ecology MSc program Organic Agriculture Wageningen University, the Netherlands

Thesis Title : Pest and Weed Suppressive Mechanisms in Complex Rice System at Malang, East Java, Indonesia

Duration : June 2014 – May 2015

Name of Student : Ike Widyaningrum

Registration number : 790305949030

Credits : 36 ECTS

Course Code : FSE – 80436

Name of Course : MSc Thesis Farming Systems Ecology

Supervisors : dr. ir. Egbert Lantinga

dr. ir. Felix Bianchi

Uma Khumairoh, MSc

Examiner : dr. ir. Jeroen Groot

TABLE OF CONTENTS

List of Figures ...... iii List of Tables ...... iv List of Appendices ...... iv List of Abbreviations ...... v Preface and Acknowledgements ...... vi Abstract ...... vii I. Introduction ...... 1 1.1 Objectives ...... 3 1.2 Research Questions ...... 3 1.3 Hypotheses ...... 3 II. Materials and Methods ...... 4 2.1 Research Site ...... 4 2.2 Materials ...... 5 2.3 Methods ...... 7 2.3.1 Experimental Treatment Design ...... 7 2.3.2 Land Preparation ...... 8 2.3.3 Data Collection ...... 9 2.3.3.1 Rice Stem borer incidence (deadhearts and whiteheads) ...... 9 2.3.3.2 The abundance and diversity of arthropods ...... 10 2.3.3.3 Monitoring of weed population...... 10 2.3.4 Statistical Analysis ...... 11 III. Results ...... 12 3.1 Incidence of rice stem borer ...... 12 3.1.1 Deadhearts ...... 12 3.1.2 Whiteheads ...... 13 3.2 Pest Suppression ...... 14 3.3 Weed suppression ...... 18 3.4 Arthropods on sticky traps ...... 20 3.4.1 The abundance of arthropods ...... 20 3.4.2 The biodiversity indices ...... 23

3.5 Correlation and Regression ...... 25 3.5.1 Between Deadhearts and weed species...... 25 3.5.2 Deadhearts incidence in time scale regression ...... 29 3.5.3 Between deadhearts and rice stem borer ...... 30 3.5.3 Between insect pests and their natural enemies ...... 31 IV. Discussions ...... 33 4.1 Pest supression mechanisms through presence of fish, ducks, and Crotalaria in the rice field ...... 33 4.1.1 Rice Stem borer ...... 33 4.1.2 Other insect pests ...... 35 4.1.3 Correlation between natural enemies and insect pests ...... 36 4.2 The direct mechanism of weed supression through the presence of fish, ducks and Azolla in the rice field ...... 37 4.3 Biodiversity of Arthropods ...... 38 4.4 Correlation between deadhearts and weed species ...... 39 V. Conclusions and Recommendations ...... 40 References ...... 42 Appendices ...... 47

LIST OF FIGURES

Figure 1. Complex agroecosystem ...... 2 Figure 2. Research site map at Kepanjen, Malang, East Java ...... 4 Figure 3. Design layout of the experiment ...... 4 Figure 4. Land Preparation (a) Grid marking, (b) Crotalaria early growth ...... 8 Figure 5. a) Deadhearts, b) Whitehead, c) Rice stem borer’s pupae ...... 9 Figure 6. Transect rice plant’s sampling in one block ...... 9 Figure 7 a and b. Sticky yellow trap in the field ...... 10 Figure 8. a)Weed quadrant, b) fresh weed (Photos: Khumairoh) ...... 10 Figure 9. Percentage of deadhearts (m2) ...... 13 Figure 10. Ratio panicle loss to total panicle...... 14 Figure 11. Insect pests’ population found on sticky yellow traps. Dash line means the insecticides was applied in the treatment (Conventional) ...... 17 Figure 12. Dry matter of the weeds ...... 18 Figure 13. Insect pests and natural enemies’ abundance (only significantly different at Insect Pests 4 WAT). Dot filled (pink bar) is for treatment with insecticides application (Conventional) ...... 21 Figure 14. Ratio Natural Enemies to Insect Pests (N (NE)/N (IP)), Number of Insect Pests (N (IP)), Number of Natural Enemies (N (NE)). Dot filled bar is for treatment with insecticides application (Conventional) ...... 22 Figure 15. Biodiversity graph : a) Species richness, b) Simpson’s index, c) Shannon-Wienner index, d) Evennes index. The biodiversity indices is significantly different for insect pest, but not significant for natural enemy. Pattern filled bar is for treatment with insecticide application (Conventional) ...... 23 Figure 16. Regression line between% deadhearts and : a) Echinochloa glaberescens Munro (P<0,05), b) Cynodon dactylon (P< 0,05), c) Pistia stratioles (P<0,05), d) Eclipta prostata (P<0,01), e) Cyperus iria (P<0,01), f) Fimbristylis dichotoma (P<0,01) ...... 27 Figure 17. Percentage of deadhearts in time scale regression, (a) between 4 WAT and 6 WAT, (b) between 8 WAT and 10 WAT ...... 29 Figure 18. Regression line between % of deadhearts and number of rice stem borer ...... 30 Figure 19. Regression linear between: a) rice stem borer and E. Fairchildii, b) rice whorl maggot and E. Fairchildii, c) white backed plant hopper and E. Fairchildii, d) rice whorl maggot and lady bug, e) rice stem borer and lady bug, f) green leafhopper and lady bug, g) brown plant hopper and lady bug, h) white backed plant hopper and Oxytate striatipes ...... 32

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LIST OF TABLES Table 1. Fertilizer application ...... 8 Table 2. Percentage of deadhearts/ m2 ...... 12 Table 3. Whiteheads incidence in rice plant ...... 14 Table 4. Dry matter of weed species (g/m2) ...... 19 Table 5. Natural enemies and its hosts or prey ...... 20 Table 6. Correlation coefficents between % deadhearts and dry matter of weed species (g/m2) ...... 25

LIST OF APPENDICES

Appendix 1. Map of rainfall distribution in Malang (June-August, 2014) ...... 47 Appendix 2. a) Research Field, b) Ducks house, c) Fish’ pond, d) Bumble bee in Crotalaria’s plant .. 48 Appendix 3. a) Dragonfly, b) Black bug, c) wolf spider, d) Conocephalus longiconnis, e) Rice bug .... 49 Appendix 4. a) Mature Crotalaria (produce seed), b) Farmers visiting and discussion in Kepanjen’s research field ...... 50 Appendix 5. Lamp trap suggested by Indonesian Rice Research Center ...... 51 Appendix 6. a) Monochoria vaginalis, b) Ludwigia adscendens ...... 51 Appendix 7. Weed scientific name and common name ...... 52 Appendix 8. Fertilizer application ...... 52 Appendix 9. Pesticides application...... 52 Appendix 10. The abundance of arthropods in 4 WAT ...... 53 Appendix 11. The abundance of arthropods in 6 WAT ...... 54 Appendix 12. The abundance of arthropods in 8 WAT ...... 55 Appendix 13. The abundance of arthropods in 10 WAT ...... 56

LIST OF ABBREVIATIONS

C or Conv : Conventional treatment DAT : Days After Transplanting FFS : Farmer Field Shool IPM : Integrated Pest Management IRRI : International Rice Research Institute N (IP) : Number of Insect Pests N (NE) : Number of Natural Enemies N (NE)/ N (IP) : Natural enemy to pest ratio R : Rice (only) treatment RM : Rice – Manure treatment RMA : Rice – Manure – Azolla treatment RMAF : Rice – Manure – Azolla – Fish treatment RMAD : Rice – Manure – Azolla – Ducks treatment RMAFD : Rice – Manure – Azolla – Fish – Ducks treatment SRI : System of Rice Intensification WAT : Weeks After Transplanting

PREFACE AND ACKNOWLEDGEMENTS

This thesis research taught me a lot of things, particularly, in rice production and farmer’s livelihood. I never knew before, that planting rice is such difficult and risky work. Thanks to the farmers around the world who still planting food for people’s consumption, eventhough, perhaps it is not profitable for them.

The awareness for farmers’ welfare and the concern about the high use of pesticide in Indonesia brought me to this research. The soil fertility declines over time, that cause the decline of the yield. Meanwhile, human’s growth increase over time in Indonesia and in Asia in general. Consequently, the researcher and the government must find the solutions for the problems.

I cannot mention how much my thanks to Umma Khumairoh, a WUR PhD candidate, who is really patient in guiding me and giving the information about the research. She and her families are very welcome, that makes me so comfortable doing this research. From the first time I arrive in Malang until I left Blitar after finishing my internship, everything was going well under her guidance. Once again, thank you very much mbak Uma.

Many thanks also I dedicate for dr. Lantinga Egbert and dr. Felix Bianchi. Even though I work far from the campus, I still have much support from both supervisors. That makes me so close to them even though I seperate thousand miles. Also for dr. Jeroen Groot and Prof. Pablo Tittonell for their support in Uma’s project in Indonesia. Thank you very much teachers.

My husband, my children, and my big families are the reason why I do the challenge to continue my study. I left the undergraduate campus more than 12 years ago, that does not so easy to study abroad and achieve the MSc degree. But I never surrender. Like the wise word say, if there’s a will, there’s always a way.

Finally, I hope this research will be very useful for agriculture development in world wide, and for organic agriculture development in Indonesia. It is time to shift from the old business to the more promising way. Start by improving the soil fertility, and reduce the use of chemical materials which are harmful for the environment and human life.

ABSTRACT

A study of the roles of ducks, fish, Azolla and Crotalaria which is integrated in a rice field (complex agroecosystem) was conducted in Malang during dry periods (May-August 2014). This study aims to provide alternative management options to reduce the misuse of pesticide usage in Indonesia. The knowledge about local indigenous materials which are useful to reduce pest and weed disturbance is needed to solve the overuse of the pesticide’s problem. The performance of complex rice systems which are derived from local indigenous materials in suppressing pest and weed disturbance in rice field was investigated in this research. The experiment involved a complete randomized block design with seven treatments (Conventional, Rice, Rice-Manure, Rice-Manure- Azolla, Rice-Manure-Azolla-Fish, Rice-Manure-Azolla-Ducks, and Rice-Manure-Azolla-Fish-Ducks). The Ciherang rice variety was used, employing the SRI method. The Rice-Manure-Azolla-Fish-Ducks (RMAFD) treatment resulted in 1,2% of deadhearts caused by rice stem borer, while 16,4% of deadhearts was recorded in the Conventional treatment. Furthermore, the RMAFD treatment had 25% lower of panicle loss compared to Conventional treatment. The weed incidence in RMAFD, Conventional, and RM treatment were 1%, 33%, and 36%, respectively. The ratio of natural enemies to insect pest population (N(NE)/N (IP) for RMAFD treatment was four times higher than in the Conventional treatment. Linear regression indicated a positive correlations between the percentage of deadhearts and Cynodon dactylon (R2 94,5%) and lady bug Coccinellidae and green leafhopper Nephottetix virescens (R2 99,8%). The introduction of Crotalaria as the vegetation surrounding the rice field provides shelter, nectar and pollen which may benefit natural enemies and contributes indirectly to pest suppression. Increasing the complexity of agroecosystem can contribute to the natural suppression of pests and weeds in rice crops.

Key words: SRI method, Rice, Azolla, Fish, Ducks, pest management, weed control, Crotalaria.

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I. INTRODUCTION Rice is one of the most important food crops in the world, and is a traditional staple food for Indonesian people. The rice farm has also become a source of livelihood and food security for a large number of rural families in Indonesia (Pasaribu, 2010). In 2011, the total rice production in Indonesia was 66 kilo tonne (Indonesian Agricultural Statistics, 2014), which has to feed around 241 million people in 2011 (Statistics Indonesia, 2014). Since decades ago, the difficult task of rice production in feeding the people had triggered the emerge of “Green Revolution” program. The emerging green revolution in 1960’s has changed the agricultural practices and has largely replaced the traditional systems by introducing high yielding varieties, short duration rice varieties, and chemical pest control (Berg, 2002). However, pesticide usage in Indonesia during green revolution has brought the negative externalities associated with pest resistance, pest resurgence, negative human health impacts and environmental contamination(Liu et al, 2015). One of the negative effects of pesticide use is that it can eliminate natural enemies and cause resistance and resurgence of insect pests, and will trigger further use of pesticides (i.e. the pesticide treadmill) (Srivastava and Alderman, 1993). Realizing this negative effect, the Indonesian Government’s shift away from promoting chemical intensive agriculture to more environmentally sound agricultural practices such as Integrated Pest Management (IPM) (Mariyono et al., 2010). Rice production is limited by pests, diseases and weeds. The major rice pests and diseases in Indonesia include rice stem borers (Scirphopaga/Tryphoryza spp), brown plant hoppers (Nilaparvata lugens), rice field rats (Rattus-rattus argentiventer), blast disease (Pyricularia grisea), and rice tungro (tungro virus) (Directorate Food Crop Protection, Ministry of Agriculture Republic Indonesia, 2012). The estimated loss due to these five main pests and diseases in Indonesia was 191,729 tonnes or 5,59 % from total rice planting area in 2011 (Directorate Food Crop Protection, Ministry of Agriculture Republic Indonesia, 2012). To prevent rice plants from rice pest attack and diseases, many control methods have been developed. The major control methods used since the 1970s were the use of resistant rice varieties (early protection to pest and disease), cultural control (e.g. intercropping), biological control (use of natural enemies), and chemical control (use of pesticides) (Lou et al., 2013). Nowadays, many rice researchers in Indonesia are trying to find alternative ways to increase the rice yield and prevent the losses from pests and diseases disturbance, but also pay attention to biodiversity. It is important to achieve sustainable rice production in Indonesia because it is viable in ensuring food security and rural livelihoods. One alternative farming practice which is environmentally friendly is called “Mina Padi” (Javanese: mina = fish, and padi = rice). Mina Padi is an ancient rice farming practice from Indonesian farmers, which is integrate fish in rice field. In Indonesia, Mina Padi was first used in 1860 at West Java, and spread to all over Indonesian regions until the 1950s. Farmers believe that Mina Padi will balance the agroecosystem (minimize pest and disease attacks) and also increase protein diets for a farmer’s family gained from fish (Arlius and Ekaputra, 2011). The integration of ducks in rice field is a further extension of the system, which utilized the habits of ducks, such as moving and pecking to control plant diseases, pests, and weeds (Xiang et al., 2006).

Indonesian rice farmers usually manage their farm Conventionally without any crop rotation system. However, farmers often apply different rice varieties in a rotation. The fallow period is typically only 1-1,5 months between harvest and the next growing season. In total, rice is planted 2 times a year (sometimes 3 times a year if there is sufficient water available). This intensive growing rice system requires intensive irrigation.

In line with the current government’s program, complex agroecosystem consists of rice, Azolla, Crotalaria, ducks and fish, emerge to provide a solution for negative environmental impact of intensive cropping systems based on the Green Revolution. The complex agroecosystem is an alternative system for agricultural practices derived from indigenous knowledge of ancient people. Each component has a beneficial function for the system. Azolla fixes nitrogen, Crotalaria provides shelter for natural enemies of insect pests and also as green manure, fish and ducks play their roles as controlling agents for weed and aquatic insects, and contributes to soil fertility due to their activities in a rice field (churning, trampling, muddying) and their excretas as natural fertilizer (Figure 1).

The impact of Crotalaria (sunn-hemp) to arthropods population (pests and natural enemies) had been studied by Hinds and Hooks (2013). They mentioned that the population of spotted cucumber beetle and stripped cucumber beetle are lower in sunn hemp-zuccini interplanting, and vice versa for spiders as their natural enemies, found higher in sunn-hemp treatment.

Shelter for arthropods natural enemies

N fixation a

By Azolla and its excretas Green fertilizer Green fertilizer symbiotic bacteria (Anabaena azollae)

Figure 1. Complex agroecosystem

Recently, complex rice systems (rice-fish-duck-Azolla) have become a flagship of Asian sustainable- agriculture movements (Suh, 2012). This ancient agricultural farming practice needs to be re- introduced to Indonesian farmers to promote the sustainability in rice farming towards the food security in Indonesia.

This study focusses on the effects of the components of the complex agroecosystem (Crotalaria- fish-ducks-Azolla) to pest and weed suppression in rice. The System of Rice Intensification (SRI) will be used as a basic cultivation method in the research. The SRI method provides basic principles in pest control (Integrated Pest Management), good management of water and seed, minimum usage of pesticide, and is promising in generating higher yield than in conventional cultivation method (Berkelaar, 2001).

1.1 OBJECTIVES The aim of the study is to quantify the performance of complex rice systems that span a gradient from simple to complex, and quantify the direct mechanism of pest and weed suppression. The study will reveal whether the presence of Crotalaria will support arthropod natural enemies, and whether ducks and fish suppress insect pests and weeds in the rice complex system. The possible interactions between arthropods natural enemies and insect pests as their host (parasitism) or their prey (predation) is also studied in this research.

1.2 RESEARCH QUESTIONS 1. How do fish, ducks and arthropods natural enemies interact to suppress pest populations in rice crops? 2. How do fish, ducks and Azolla influence weed densities in rice fields? 3. What is the relationship between the presence of some weed species and the deadhearts incidence in the rice field?

1.3 HYPOTHESES 1. Pest incidence will be reduced through the presence of fish and ducks as controlling agents for insect pests; and through the presence of Crotalaria that will provide shelter, including nectar and pollen for natural enemies. 2. Weed populations will be reduced through the feeding activities of fish and ducks, and due to the presence of the Azolla, which inhibits the growth of aquatic weeds. 3. Some weed species can be an alternative host for the rice stem borer, therefore, the presence of a certain weed species must be controlled as soon as they found in the rice field.

II. MATERIALS AND METHODS

This study is part of WUR PhD candidate’s project (Uma Khumairoh, 2013) as Chapter 3: Mechanisms of weed, pest and disease suppression, and a continuation from Chapter 2: Mechanisms of Nutrient Cycling. 2.1 RESEARCH SITE The study was conducted at Kepanjen (Sub-district), Malang (District), East Java (Province) (Figure 2). Kepanjen is located in the south of Malang, about ± 18 KM from the center of Malang. Kepanjen has 44,68 Km² area and is located at 112º17’10,9” – 112º57’00” East and 55,11” – 8º26’34,45” South. The slope of the region is 0-40%. Soil types in Kepanjen are Alluvial and Regosol (fertile soil). Alluvial comes from a clay deposit brought from the rivers (i.e Brantas river). Regosol comes from rock weathering due to volcanic ash because of the volcanic eruption (Malang is surrounded by volcanos). In general, both soils (Alluvial and Regosol) type is suitable for all crops. The rivers across Kepanjen (Brantas river and Metro river) are large rivers with a high water debit. Also, Kepanjen has a dam, Sengguruh dam. The maximum temperature at Kepanjen is 32o C- 34oC, and minimum temperature is 26o C- 28oC (daily average), average precipitation is 2100 mm/year (Appendix 1).

4 Figure 2. Research site map at Kepanjen, Malang, East Java 2.2 MATERIALS A. Rice The rice variety used in the experiment is Oryza sativa cv. Ciherang (local variety) which has a growth period 116-125 days, an average yield 6 ton/ha with a potential yield of 8.5 ton/ha. Rice farmers in Kepanjen use Ciherang variety as one of the variety they usually used. Moreover, Ciherang is tolerant to brown planthopper biotypes 2 and 3, and blast diseases strain III and IV, and can be cultivated in both in the wet and dry season at 500 m.a.s.l (Indonesian Agricultural Research and Development, 2014).

B. Ducks A local duck variety was used (Anas platyrhynchos Javanicus, local name: Mojosari) (Khumairoh et al., 2012). This duck variety used in the research because this is the duck usually kept by Indonesian farmers, in their field or at their homes.

C. Fish The fish species was Nile tilapia (Oreochromis niloticus) and cat fish (Famili : Clariiidae). The fingerlings were bought from local fish farmer around Kepanjen. Those fish species are often found in the traditional market as protein’s diet for Indonesian people.

D. Azolla The Azolla species used in the research are a combination of Azolla pinnata and A. microphylla. Azolla spp have the ability to fix nitrogen and sequester atmospheric carbon in paddy soils (Ali et al., 2014).

E. Crotalaria Crotalaria has a dual function: refugia for natural enemies and green fertilizer. The yellow colour of Crotalaria’s flower attract arthropods predators and parasitoids. Crotalaria were planted around two weeks before seed bed.

2.3 METHODS 2.3.1 EXPERIMENTAL TREATMENT DESIGN The study was using a completely randomized block design. Blocks were arranged in three terraces and each treatment was randomly placed in a 10x10m plots in each block (Figure 3, Appendix 2). There were seven treatments with different of complexity in agroecosystem components: 1) Conventional, 2) R (rice only), 3) RM (Rice -Manure), 4) RMA (Rice-Manure-Azolla), 5) RMAF (Rice- Manure-Azolla-Fish), 6) RMAD (Rice-Manure-Azolla-Ducks), 7) RMAFD ((Rice-Manure-Azolla-Fish- Ducks), and three blocks for replication of each treatments. For the duck treatment 7 ducks/plot in the age of 14 days were introduced. For the fish treatment around 50 fingerlings/plot were used. The fingerlings were 5-7 cm size, and released 14 days after rice transplanting. The Azolla treatment involved around 20 kg/plot (Khumairoh, 2011).

30 cm 40 cm 30 cm 30 cm 10 m

10 m Conv R RMA Conv Inlet irigation Plastic border Inlet irigation

Bund between xx x x x x x x x x x x x x x x x x x x xx x x x x RM Conv RM outlet-inlet water x x x xxxxx xxxxx xxxxx xxxxx xxxxx x Outlet thrench x xxxxx xxxxx xxxxx xxxxx xxxxx x x xxxxx xxxxx xxxxx xxxxx xxxxx x RMAF x xxxxx xxxxx xxxxx xxxxx xxxxx x RM AD RMAD x xxxxx xxxxx xxxxx xxxxx xxxxx x x xxxxx xxxxx xxxxx xxxxx xxxxx x x xxxxx xxxxx xxxxx xxxxx xxxxx x x xxxxx xxxxx xxxxx xxxxx xxxxx x Lateral seepage x xxxxx xxxxx xxxxx xxxxx xxxxx x x x collector RMAF RMAF RMAFD x x x x x x x x x x x x x x x x x x x x x x x x xx

Bund Bridge Outle PVC RMA FD Weather station RMAD RMAFD Ammonia sampler Bucket & lysimeter

RM Conv : conventional R: rice R MA R M : manure A: azolla Duck house F : fish D: duck

: Crotalaria R RMA Conv

Figure 3. Design Layout of the Experiment

2.3.2 LAND PREPARATION

The System of Rice Intensification (SRI) method was employed in all treatments. The planting densities for SRI method is 160.000 hills/ha, or 16 hills/m2 (Jiaguo et al, 2013). Before the rice was transplanted, 170 kg/ha synthetic fertilizer (NPK) was provided. Adjacent to NPK, urea and duck manure was also incorporated at 0 Days After Transplanting (DAT), with 22 kg/ha and 333 kg/ha respectively. At 14 DAT, urea and duck manure were applied again with 139 kg/ha and 333 kg/ha respectively. The same application of urea and duck manure replied in 35 DAT (Table 1, Appendix 8). The total of urea which was incorporated into the soil was 300 kg urea/ha, and total ducks manure, which was integrated into the soil was 1 ton/ha. All the treatments (except R treatment) had the fertilizer application.

Table 1. Fertilizer application

Fertilizer Amount N-content Total N(kg) Phosphorous Total P Potassium Total K (kg) (kg) NPK 170 kg/ha 18% 30,6 14% 23,8 16 % 27,2 Urea 300 kg/ha 46% 138 - - - - Ducks manure 1000 kg/ha 1,72% 17,2 1,82% 18,2 2,18% 21,8

Before planting for grid marking, the experimental plots were prepared and leveled with little standing water (1-3 cm) a day. Good water leveling is essential for proper water management and good crop stand. The seedbed was prepared at small treatment (often at the corner of the treatment). After levelling the field, single hill of rice plants (14-15 days old, 2-3 leaf stage) were transplanted in a 25 cm x 25 cm planting design. Weeds were removed by handweeding starting at 10 DAT (Days After Transplanting). Before the rice transplantation was started, Crotalaria seed already planted in the dike (Figure 4).

(a) (b)

Figure 4. Land Preparation (a) Grid marking, (b) Crotalaria early growth

2.3.3 DATA COLLECTION 2.3.3.1 RICE STEM BORER INCIDENCE (DEADHEARTS AND WHITEHEADS)

Rice stem borer got the special attention because this is a major pest in East Java. Stem borer abundance was assessed by visual observation of damage in rice plants. The damage of rice plants caused by rice stem borer in the vegetative stage can be seen by “deadhearts” symptom, where the main shoots in the hills are drying and cause the deteriorate of the developing point (Figure 5a). The symptom in generative phase is called “whiteheads”, where the rice stem borer are attack the panicles at the flowering phase and results in a white or empty panicle (Figure 5b). The percentage of damage were calculated by this equation (Sama et al, 2015):

% deadhearts of 1 m2 2 2) x 100 % whiteheads of 1 m2 2 tal panicle of 1 m2) x 100 = ( ∑ of deadhearts of 1 m / ∑ of tillers of 1 m The observations of deadhearts= ( ∑ of panicle incidence loss of were1 m / started∑ of to at 4 WAT and were repeated every two weeks until 10 WAT (before mature/generative phase). The observation was done by counting the damage tillers showing deadhearts symptom in one single rice plant as the sample. During the generative phase (10 WAT) the panicle loss and filled panicle were observed. The observation of whiteheads was done by counting the white dry empty panicle and the filled panicle in one single rice plant as the sample. Samples included 10 plants were randomly chosen on a W- shaped transect (Figure 6).

(a) (b) (c)

Figure 5. a) Deadhearts, b) Whitehead, c) Rice stem borer’s pupae

Figure 6. Transect rice plant’s sampling in one block 9

2.3.3.2 THE ABUNDANCE AND DIVERSITY OF ARTHROPODS To monitor the flying arthropod community (both natural enemies and insect pests) in the plots, the sticky yellow traps were placed in the 4 corners of each plot treatment, and put around 1,5 m above the ground (Figure 7 a and b). The sticky yellow traps were tied in bamboo, and put 24 hours in the field (to observe nocturnal and diurnal arthropods). After 24 hours, sticky yellow traps were collected and the arthropods caught were identified. The height of the bamboo was 1,5 metres, and it plugged on the ground around 20 cm depth. The observation was done 4 times, at 4 WAT(19 June 2014), 6 WAT (3 July 2014), 8 WAT (17 July 2014) and 10 WAT (7 August 2014).

(a) 10 m

(b) Treatment Plot 10 m

Figure 7 a and b. Sticky yellow trap in the field 2.3.3.3 MONITORING OF WEED POPULATION The weed density was quantified using the quadrant method. The quadrant was made from plastic rope and tied in bamboo in the size of 1 m x 1 m (Figure 8 a and b). In one treatment, there was one quadrant inside the treatment. The assessment was conducted in the vegetative phase (around 2 WAT). Weeds were collected for identification. Weed also dried in the oven with 70oC for 72 hours and measured on a digital scale for weighing its dry matter.

. (a) (b)

Figure 8. a)Weed quadrant, b) fresh weed (Photos: Khumairoh)

2.3.4 STATISTICAL ANALYSIS Statistical analysis is performed to test experimental treatment effects by ANOVA using SPSS 22 software package (SPSS Inc., USA). The data collected from the research site were: a) rice stem borer incidence (deadhearts and whiteheads), b) the abundance of arthropods (insect pests and natural enemies), and c) dry matter of weed species. The normality of the data was tested by the Saphiro-Wilk test. When the data did not meet the normality requirement (dry matter of weed species) data were normalized using a Log10 transformation. All data were analysed using one way ANOVA, and posthoc testing was conducted using the Tukey HSD test borer incidence, arthropods abundance and dry matter of weed species. (at α = 0,05) for rice stem The relationship between the number of weed species and the incidence of deadhearts was investigated by regression analysis and calculation of Pearson’s correlation coefficients. Also, the relationship between the population of rice stem borer’s imago (adults) with the incidence of deadhearts. The interaction between natural enemies and insect pests as their hosts or prey also calculated using correlation and regression analysis.

To assess the diversity of arthropods Shannon-Wienner Index, Simpson’s Index, and the Evenness of biodiversity were calculated (Krebs, 1978; Ludwig and Reynolds, 1988).

( ) = ln = 1 = / ln( ) ( ) ′ 𝑆𝑆 𝑛𝑛𝑛𝑛 𝑛𝑛𝑛𝑛 𝑆𝑆 𝑛𝑛𝑛𝑛 𝑛𝑛𝑛𝑛−1 ′ 𝑖𝑖=1 𝑖𝑖=1 Where:𝐻𝐻 − ∑ 𝑁𝑁 𝑁𝑁 𝐷𝐷 − ∑ 𝑁𝑁 𝑁𝑁−1 𝐸𝐸 𝐻𝐻 𝑆𝑆

S = Species number of arthropods (insect pests or natural enemies) D = Simpson's index H = Shannon-Wienner index E = Evenness index (Source: Maryland Sea Grant, http://ww2.mdsg.umd.edu/interactive_lessons/biofilm/diverse.htm).

III. RESULTS 3.1 INCIDENCE OF RICE STEM BORER 3.1.1 DEADHEARTS A one-way ANOVA test was conducted to compare the effect of different complexity of agroecosystem (Conventional, R, RM, RMA, RMAF, RMAD, and RMAFD) to the development of rice stem borer incidence (deadhearts and whiteheads). Result showed that there were significant (P<0,05) effect of different complexity of agroecosystem components on deadhearts incidence at 4

WAT (F (6,14) = 69,995, P = 0,00); 6 WAT (F (6,14) = 13,358, P = 0,00); 8 WAT (F (6,14) = 12,942, P =

0,000) and 10 WAT (F (6,14) = 7,977, P = 0,001) (Table 2).

Table 2. Percentage of deadhearts/ m2

Period Treatment 4 WAT 6 WAT 8 WAT 10 WAT df = 6; F = 69,995; df = 6; F = 13,358; df = 6; F = 12,942; df = 6; F = 7,977; P-value = 0,00 P-value = 0,00 P-value = 0,000 P-value = 0,001 Conventional 16,4 ± 1,3 c 15,1 ± 1,1 c 13,7 ± 1,8 bc 14,7 ± 0,9 bc R 1,9 ± 0,5 a 3,9 ± 0,7 a 10,7 ± 0,5 ab 10,9 ± 0,9 ab RM 9,9 ± 0,8 b 9,7 ± 2,3 b 13,5 ± 0,8 bc 14,4 ± 2,1 abc RMA 2,5 ± 0,3 a 5,8 ± 1,0 ab 17,9 ± 0,6 c 18,9 ± 0,6 c RMAF 2,1 ± 0,8 a 6,6 ± 0,5 ab 10,3 ± 1,2 ab 9,1 ± 1,9 ab RMAD 1,5 ± 0,5 a 3,8 ± 0,5 a 7,9 ± 0,9 a 8,3 ± 1,6 ab RMAFD 1,2 ± 0,3 a 7,1 ± 0,9 ab 7,7 ± 0,4 a 7,3 ± 0,9 a The data were the average percentage in the 3 blocks; difference tests between treatments were analyzed by Tukey HSD method; values with different letters in the same column meant significant difference at 0,05 levels(P < 0,05).

In 4 WAT, post hoc Tukey HSD testing indicated that the mean value of deadhearts incidence in Conventional and RM treatment was significantly (P<0,05) different with another treatments. Nevertheless, another 5 treatments (R, RMA, RMAF, RMAD and RMAFD) showed there were no significant differences in deadhearts incidence among those treatments.

At 6 WAT, the incidence of deadhearts was still high in Conventional treatment compared to other treatments (Figure 9), while other treatments did not significantly differ in deadhearts incidence.

At 8 WAT, Conventional, RM and RMA treatments showed significance (P<0,05) different compared to RMAD and RMAFD treatments. In 8 WAT, RMA treatment showed the highest incidence of deadhearts (Figure 9).

In 10 WAT, the condition of deadhearts incidence was almost the same with 8 WAT, where in Conventional treatment (M=14,7 and SD = 1,5), RM treatment (M = 14,4 and SD = 3,6) and RMA treatment (M = 18,9 and SD = 1,1) were significantly different with another 4 treatments (R, RMAF, RMAD, and RMAFD).

25.0 Conv 20.0 R

15.0 RM

RMA 10.0 RMAF

Percentage of of /m2 Percentage deadhearts 5.0 RMAD

0.0 RMAFD 4 6 8 10 Weeks After Transplanting (WAT)

Figure 9. Percentage of deadhearts (m2)

Taken together, these results suggest that the incidence of deadhearts was high in the Conventional treatment, and followed by RM and RMA treatments. Meanwhile, the other treatments (R, RMAF, RMAD and RMAFD) did not significantly different in incidence of deadhearts among them.

3.1.2 WHITEHEADS

The advance phase of stemborer infestation after deadhearts is panicle loss, referred to as whiteheads. The panicle loss will cause reduction of the yield.

The effect of different treatment on agroecosystem complexity to whiteheads incidence was found out through one way ANOVA calculation. The result showed that there were significant (P<0,05) difference about the complexity of agroecosystem to whiteheads incidence in panicle loss (F

(6,14) = 5,860, P = 0,003); filled panicle (F (6,14) = 5,996, P = 0,003); total panicle (F (6,14) = 7,121, P =

0,001), and ratio panicle loss to total panicle (F (6,14) = 4,344, P = 0,011) ( (Table 3).

Post hoc Tukey HSD tests revealed that the Conventional treatment and RMA treatment had the highest whitehead loss rates. Moreover, although RMAFD had the lowest loss rate, it is not significantly different from R, RM, RMAF, and RMAD treatments.

In addition to whiteheads incidence, the ratio of panicle loss to total panicle was counted to investigate the losses in yield. This ratio nearly describes the losses in yield due the attack of rice stem borer. The RMAFD treatment (9,08 % panicle loss) was less affected by rice stem borer than the Conventional treatment (15,02 %). Means that the RMAFD treatment had 25% lower of whiteheads incidence compared to Conventional treatment. The RMA and R treatments having losses of 19,96 % and 18,49 %, respectively (Figure 10).

Post hoc Tukey test of ratio panicle loss to total panicle showed that RMA treatment had the highest loss, and significantly different with RMAD and RMAFD treatments. While the other treatments (Conventional, R, RM, and RMAF) did not show a significantly different for the ratio (Table 3).

25.00 b

ab 20.00 (%)

ab ab ab Panicle 15.00 a a Total / 10.00 Loss

Panicle 5.00

0.00 Conv R RM RMA RMAF RMAD RMAFD

Figure 10. Ratio panicle loss to total panicle

Table 3. Whiteheads incidence in rice plant

Ratio Panicle loss (whiteheads) Filled Panicle Total panicle (panicle loss/total Treatment df = 6; F = 5,860; df = 6; F = 5,996; df = 6; F = 7,121; panicle) P-value = 0,03 P-value = 0,03 P-value = 0,01

Conventional 49,3 ± 4,7 ab 279,2 ± 49,1 b 328,5 ± 44,7 b 15,9 ± 3,1 ab R 26,0 ± 3,1 a 114,6 ± 12,7 a 140,6 ± 14,9 a 18,5 ± 1,3 ab RM 32,0 ± 3,1 a 227,1 ± 13,6 b 259,1 ± 15, 3 b 12,4 ± 0,9 ab RMA 57,7 ± 10,9 b 231,3 ± 6,3 b 272,9 ± 12,1 b 21,1 ± 4,0 b RMAF 32,0 ± 2,3 a 212,5 ± 9,5 ab 244,5 ± 9,4 ab 13,1 ± 1,1 ab RMAD 27,7 ± 3,7 a 239,6 ± 19,8 b 267,3 ± 22,9 b 10,3 ± 0,8 a RMAFD 27,7 ± 1,5 a 277,1 ± 16,3 b 304,8 ± 16,9 b 9,1 ± 0,5 a The data were the average values in the 3 blocks; difference tests between treatments were analyzed by Tukey HSD method; values with different letters in the same column meant significant difference at 0,05 levels (P < 0,05).

The calculation of whiteheads incidence suggests that Conventional treatment and RMA treatment suffered from a high incidence of whiteheads. Furthermore, RMA treatments had the highest losses compared to other treatments, shown by the highest rate of the ratio of panicle loss to total panicle.

3.2 PEST SUPPRESSION From the observation on sticky yellow traps for arthropods abundance, it found that there were eight insect pest species which were dominant in the research site: Rice stem borer (Scirphopaga/Tryporyza spp), brown plant hopper (Nilaparvata lugens), grasshopper (Oxya hilla intricata), green leafhopper (Nephotettix virescens), white backed plant hopper (Sogatella furcifera), rice whorl maggot (Hydrellia phillipina), rice gall midge (Orseolia oryza), and rice moth (Corcyra chepalonica) (Appendix 10, 11, 12, 13).

The general trends of population dynamics of most insect pests in the research site indicate almost the same patterns, high in 4 WAT (vegetative phase) and lower in 10 WAT (generative phase) (Figure 11). However, rice gall midge had a contrasting development pattern as compared to the other pests. Starting from 6 WAT, the development of rice gall midge increases until 10 WAT (Figure 11).

Furthermore, at 4 WAT the abundance of insect pests were high compared to other observation weeks, where most insect pest population is found high in Conventional, R and RM treatments. It was significantly different (F (6,14) = 19,015, P = 0,000) with another treatments (RMA, RMAF, RMAD and RMAFD). Nevertheless, in 6, 8 and 10 WAT, all of the insect pest population was not significantly different among the treatments.

The result from statistical test (one way ANOVA test and Tukey HSD post hoc test) to eight species as major insect pest found in the research site is presented below (P<0,05): a. Rice stem borer Scirphopaga/Tryporyza spp. (Treitschke, 1832) (Lepidoptera: Crambidae)

One way ANOVA test for rice stem borer population showed that different treatments give

significant effect on the population of the pest (F (6,14) = 6,2, P = 0,002). Post hoc Tukey test showed that Conventional treatment had the highest rice stem borer population and significantly different with other treatments, except with RM treatment. However, another 5 treatments did not show significantly different among them: R, RMA, RMAF, RMAD, and RMAFD.

b. Brown planthopper Nilaparvata lugens (Stål, 1854) (Homoptera: Delphacidae).

Based on one way ANOVA calculation, different complexity of agroecosystem also give a

significant effect to the brown planthopper’s population (F (6,14) = 9,5, P = 0,000). Further test with Tukey’s test showed that Conventional treatment had significantly (P < 0,05) different effect to brown plant hopper’s population compared to RMA, RMAF, RMAD and RMAFD treatments. Nevertheless, Conventional treatment did not significantly different with R and RM treatments.

c. Rice whorl maggot Hydrellia philippina (Ferino, 1968) (Diptera: Ephydridae)

One way ANOVA test showed that rice maggot’s population at the research site was significantly

different ( F (6,14) = 9,452, P = 0,000) between treatments. Moreover, result from Tukey’s post hoc test showed that R treatment had the highest population of rice maggot among other treatments and significantly different with RMA treatment. However, R treatment did not significantly different with RM, Conventional, RMAD, RMAFD and RMAF treatments.

d. Grasshopper Oxya hilla intricata (Stål, 1861) (Orthoptera: Acrididae)

ANOVA test to compare means between treatments on the population of grasshopper showed

that different treatments give a significant effect of grasshopper’s population ( F (6,14) = 21,606 , P = 0,000). Further test with Tukey’s test showed that Conventional treatment had significance (P<0,05) different with other treatments, except with R treatment.

e. Green leafhopper Nephotettix virescens (Distant, 1908) (Hemiptera: Cicadellidae)

One way ANOVA test presented that different complexity of agroecosystem give significant

effect ( F (6,14) = 42,359 , P = 0,000) to green leafhopper’s population at the research site. Advance test with Tukey HSD showed that Conventional treatment had the highest (P<0,05) green leafhopper’s population and significantly different with other treatments. While, RMAFD treatment had the lowest rate and significantly different with other treatments, except with RMAD and RMAF treatments.

f. Rice moth Corcyra cephalonica (Stainton, 1866) (Lepidoptera: Pyralidae)

Mean comparison test using one way ANOVA showed that different treatment give significant

difference ( F (6,14) = 22, 154 , P = 0,000) effect on the population of rice maggot. Furthermore, Tukey’s test showed that Conventional and R treatments were significantly different with other treatments. RMAD showed the lowest rate of a rice moth population, and not significantly different with RMAFD, RMAF, RMA and RM treatments. g. White backed plant hopper Sogatella furcifera (Horváth, 1899) (Hemiptera: Delphacidae)

One way ANOVA test showed that white backed plant hopper’s population also had significant

difference among the treatments ( F (6,14) = 7,854 , P = 0,001). Result from Tukey’s test showed that population of white backed plant hopper at R treatment was significantly different with RMAFD, and RMAD treatments. However, R treatment was not significant difference with RM, RMA, RMAF, and Conventional treatments.

h. Rice gall midge Orseolia oryza (Wood-Mason, 1889) (Diptera: Cecidomyiidae)

Population of rice gall midge also significantly different ( F (6,14) = 3,606 , P = 0,022) between treatments based on one way ANOVA. Advance test with Tukey’s showed that R treatment was significance difference with RMAFD treatment, but not significantly different with other treatments.

Taken all together that the Conventional treatment tends to have higher (P < 0,05) pest population than the other treatments, and followed by R and RMA treatment. Furthermore, pest incidence in the RM, RMAF, RMAD and RMAFD were not significantly different.

Rice Stem Borer Brown Plant Hopper

10 20 C C 8 15 R R 6 RM 10 RM 4 RMA RMA 2 5 RMAF RMAF /plot insects of (100 m2) / insects plotof (100 m2) 0 0 RMAD RMAD 4 6 8 10 4 6 8 10 RMAFD RMAFD Week After Transplanting (WAT) Week After Transplanting (WAT) Number Number

Grasshopper Green Leafhopper

8 15 C C 6 R 10 R 4 RM RM

/plot (100 m2) 5 2 RMA RMA 0 RMAF 0 RMAF 4 6 8 10 RMAD 4 6 8 10 RMAD 0. 0. insects of o. o. of /plotinsects (100 m2) N

Week After Transplanting (WAT) RMAFD N Week After Transplanting (WAT) RMAFD

White Backed Plant Hopper Rice Whorl Maggot

6 C 12 C 5 R 10 4 R RM 8 3 6 RM 2 RMA /plot (100 m2) 0. 0. of /plotinsects (100 m2) 4 RMA N 1 RMAF RMAF 0 2 RMAD RMAD 4 6 8 10 0. insects of 0 N Week After Transplanting (WAT) RMAF 4 6 8 10 RMAFD D Week After Transplanting (WAT)

Rice Gall Midge Rice Moth

12 8 10 C R 6 8 R RM 6 4 RM /plot (100 m2) /plot (100 m2) RMA 4 RMA 2 RMAF 2 RMAF 0 RMAD 0 0. 0. insects of RMAD N 4 6 8 10 No. insects of 4 6 8 10 RMAFD RMAFD Week After Transplanting (WAT) Week After Transplanting (WAT)

Figure 11. Insect pests’ population found on sticky yellow traps. Dash line means the insecticides was applied in the treatment (Conventional)

3.3 WEED SUPPRESSION

Twelve weed species were found in the treatments (Appendix 6, 7). The three major weed species are Echinochloa glabrescens Munro ex Hook. f., Cynodon dactylon, and Cyperus iria, with dry matter weights 26,3 gr/m2; 24,1 gr/m2; and 21 gr/m2, respectively (Figure 12 and Table 4).

16.0 Ludwigia adscendens 14.0 Ludwigia octovalvis Eclipta prostrata (L.) 12.0 Ageratum conyzoides L /m2) r 10.0 Monochoria vaginalis (Burm. f.) C. Presl.

8.0 Echinochloa glabrescens Munro ex Hook. f. 6.0 Cyperus iria L. Fimbristylis dichotoma (L.) Vahl

dry matter (g dry matter 4.0 Cyanodon dactylon

2.0 Pistia stratiotes L.

0.0 Marsilea minuta L.

Conv R RM RMA RMAF RMAD RMAFD Salvinia molesta

Figure 12. Dry matter of the weeds The Conventional treatment contained all 12 weed species, while only 5 weed species were found in RMAFD treatments (Table 4). Eclipta prostrata (L.) and Ageratum conyzoides L. were only present in one treatment (Conventional and RMAF respectively), while Salvinia molesta was found in Conventional and RMAFD treatments. Based on one way ANOVA test to compare means in total dry matter of all species found in the research site, it was revealed that different complexity of agroecosystem, give significant effect (F

(6,14) = 3,971, P = 0,016) to dry matter of weed species. Further test with Tukey HSD test showed that RM treatment and Conventional treatment had significantly different of total weed biomass compared to other treatments. While RMAFD treatment had the lower value, but, not significantly different with R, RMA, RMAF and RMAD treatments (Table 4). Taken all together, the finding in weed suppression suggests that dry matter of the weed is higher in RM and Conventional treatments, and lower in RMAFD, RMAF, RMAD, RMA and R treatments.

Table 4. Dry matter of weed species (g/m2)

No. Species Statistics Conventional R (Rice) RM RMA RMAF RMAD RMAFD (Rice-Manure) (Rice-Manure- (Rice-Manure- (Rice-Manure- (Rice-Manure- Azolla) Azolla-Fish) Azolla-Ducks) Azolla-Fish- Ducks)

1 Ludwigia adscendens F = 1,534;P-value = 0,238 2,8 ± 1,5 a 0,9 ± 0,6 a 3,0 ± 2,03 a 2,4 ± 0,74 a 3,5 ± 1,4 a 0,7 ± 0,5 a 0 2 Ludwigia octovalvis F = 0,695;P-value = 0,658 0,1 ± 0,09 a 0,1 ± 0,06 a 0,2 ± 0,14 a 0 0 0 0 3 Eclipta prostrata F = 1,000;P-value = 0,463 0,1 ± 0,08 a 0 0 0 0 0 0 4 Ageratum conyzoides F = 1,000;P-value = 0,463 0 0 0 0 0,1 ± 0,07 a 0 0 5 Monochoria vaginalis F = 0,487;P-value = 0,807 3,1 ± 2,0 a 0,5 ± 0,19 a 0,8 ± 0,26 a 2,0 ± 0,69 a 1,0 ± 0,68 a 0,6 ± 0,37 a 0 6 Echinochloa glabrescens Munro F = 11,395;P-value = 0,000 10,4 ± 3,98 b 1,5 ± 0,91 a 11,7 ± 2,52 b 1,1 ± 0,69 a 0,6 ± 0,38 a 0 0 7 Cyperus iria F = 5,338;P-value = 0,005 7,4 ± 4,27 ab 0,8 ± 0,18 a 12,2 ± 5,13 b 0 0 1,6 ± 0,82 ab 0,04 ± 0,03 a 8 Fimbristylis dichotoma F = 11,630;P-value = 0,000 7,4 ± 2,83 ab 0,7± 0,42 a 12,3 ± 4,28 b 0 0,7± 0,45 a 0,6 ± 0,39 a 0 9 Cynodon dactylon F = 4,038;P-value = 0,015 8,8 ± 2,56 b 1,1 ± 0,75 a 6,7± 1,28 b 2,9 ± 1,87 a 1,7 ± 0,32 a 1,6 ± 0,86 a 0 10 Pistia stratiotes F = 0,734;P-value = 0,630 2,3 ± 1,73 a 0,2± 0,15 a 1,6 ± 0,88 a 1,0 ± 0,66 a 0,2 ± 0,11 a 0,3 ± 0,22 a 1,0 ± 0,52 a 11 Marsilea minuta F = 0,805;P-value = 0,582 2,4 ± 2,18 a 0 0,8 ± 0,49 a 3,1 ± 1,77 a 2,3 ± 1,49 a 1,1 ± 0,80 a 0 12 Salvinia molesta F = 1,606;P-value = 0,218 0,7 ± 0,46 a 0 0 0 0 0 0,8 ± 0,28 a Total (all species) F = 3,971 P-value = 0,016 45,5 ± 21,5 b 5,9 ± 1,3 a 49,2 ± 14,8 c 12,4 ± 2,0 a 10,1 ± 2,2 a 6,5 ± 0,6 a 1,9 ± 0,9 a The data were the average values in the 3 blocks; difference tests between treatments were analyzed by Tukey HSD method; values with different letters in the same row meant significant difference at 0,05 levels (P < 0,05), and value followed by the same letters was not significant.

3.4 ARTHROPODS ON STICKY TRAPS

3.4.1 THE ABUNDANCE OF ARTHROPODS Sticky trap sampling indicated the presence of 11 natural enemies identified from the field (Appendix 10, 11, 12, 13). The 11 natural enemies were found are: Staphylinid beetle Paederus fuscipes, green crab spider Oxytate striatipes sp, wolf spider Lycosa pseudoannulata, black parasitoid wasp Goniozus sp, red parasitoid wasp Echthrodelphax fairchildii, dragonfly Neurothemis tullia tullia (Appendix 3), meadow grasshopper Conocephalus longiconnis (Appendix 3), lady bug Coccinela transversalis, cricket Metioche vittaticolis, flies Argyrophylax nigrotibialis, carabid beetle Ophionea nigrofasciata. Other arthropods that were not caught in yellow trap are rice ear bug Leptocorisa acuta (Thunberg) (Appendix 3), black bug Scotinophara coarctata (Fabricious) (Appendix 3), and bumble bee Bombus terrestris. Table 5 presents the natural enemy species and their hosts or prey in rice crops.

Table 5. Natural enemies and its hosts or prey

No. Natural Enemies Hosts or prey References 1. Echthrodelphax fairchildii - Nilaparvata lugens Chandra, G (1980) (Perkins,1903) - Sogatella furcifera (Hymenoptera:Drynidae) - Nephottetix virescens - nectar and pollens

2. Coccinella transversalis - Aphis spp Shanker et al. (2013) (Fabricius, 1871 ) - Nephotettix virescens (Coleoptera: Coccinellidae) - Nilaparvata Lugens - Sogatella furcifera - aphid Rhopalosiphum padi - thrips Haplothrips sp. - pollens

3. Oxytate striatipes (L. Koch, - crickets, flies, bees, grasshoppers, moths and University of Arizona 1878) (Areneae: butterflies, nectar and pollens Thomisidae) 4. Paederus fuscipes (Fabricius, - Nilaparvata lugens Padmavathi et al. (2008) 1775) - Sogatella furcifera Laba et al. (2001) (Coleoptera: Staphylinidae) - Nephottetix virescens - nectar and pollens

5. Metioche vittaticolis (Stål) - Rice leaf folder (Cnaphalocrocis medinalis ) Chitra et al. (2002) (Orthoptera: Gryllidae) - Marasmia patnalis IRRI: - rice moth www.knowledgebankirri. - striped and gold-fringed stem borers org - nectar and pollens

6. Lycosa psedoannulata - Nilaparvata lugens Muthukumar et al. (Boesenberg and Strand) - Sogatella furcifera (2005) (Araneae: Lycosidae) - Nephottetix virescens - nectar and pollens

7. Goniozus sp nr. Triangulifer - Leafhoppers IRRI: Kieffer - Stem borers www.knowledgebankirri. (Hymenoptera) - Rice flies org - nectar and pollens

8. Neurothemis tullia tullia - Stem borer moths Bhuiyan, et al (2004) (Drury, 1773)) - Lepidopteran (Odonata: Libellulidae) - Planthoppers

9. Conocephalus longiconnis (de - rice earhead bug, Leptocorisa acuta Chitra et al. (2002), Haan, 1842) - rice stem borers IRRI: (Coleoptera: Tettigoniidae) www.knowledgebank.irri .org

10. Argyrophilax nigrotibialis - rice leaf folder Cnaphalocrosis medinalis Barrion et al. (1979) (Baranov) (Diptera: Tachinidae) 11. Ophionea nigrofasciata - leaffolders Karindah (2011) (Schmidt-. Goebel, 1846) - planthoppers IRRI: (Coleoptera: Carabidae) - pollens www.knowledgebank.irri .org

The highest species abundance of arthropods was caught on yellow traps at 4 WAT, and declined moderately until 10 WAT (Figure 13). In 4 WAT, insect pests were found abundantly in

Conventional treatment (F (6,14) = 19,015, P = 0,000), while natural enemies were found much in RMAF treatment, but not significantly different between treatments (Figure 13).

80.0 c 70.0 c

(100 m2) 60.0 bc 50.0 plot 40.0 a ab 30.0 a a 20.0 10.0 0.0

number of of number arthropods/ Insect Pests Natural Insect Pests Natural Insect Pests Natural Insect Pests Natural Enemies Enemies Enemies Enemies 4 WAT 6 WAT 8 WAT 10 WAT

Conv R RM RMA RMAF RMAD RMAFD

Figure 13. Insect pests and natural enemies’ abundance (only significantly different at Insect Pests 4 WAT). Dot filled (pink bar) is for treatment with insecticides application (Conventional)

With regard of natural enemy to pest ratio, the RMAFD, RMAD, and RMAF treatments had relatively high ratios: 1,18; 1,02 and 0,95 respectively (Figure 14). While the Conventional treatment had the lowest value of the ratio (NE) to N (IP) : 0,33.

Result from one way ANOVA test to compare means between the number of insect pests, natural enemies and the ratio of natural enemies to insect pests showed that the ratio of natural enemies to

insect pests population N (NE)/N (IP) was significantly different (F (6,14) = 4,676, P = 0,008). The number of insect pest’s (N (IP)) in the research site also showed significantly different (F (6,14) = 19,015, P = 0,000) between treatments. However, natural enemy’s population (N (NE)) did not

show significant differences (F (6,14) = 1,090, P = 0,415).

Advance test with Tukey HSD test showed that the ratio of natural enemies to insect pests in RMAFD treatment was significantly different with Conventional and R treatments, but not significantly different with RM, RMA, RMAF and RMAD treatments.

350 b 1.40 300 ab 1.20 ab ab 250 1.00 (%)

(100 m2) 200 ab 0.80

plot a 150 0.60 a

100 0.40 (IP) N (NE)/N Number/ 50 0.20

0 0.00 C R RM RMA RMAF RMAD RMAFD

N (NE)/N (IP) N (IP) N (NE)

Figure 14. Ratio Natural Enemies to Insect Pests (N (NE)/N (IP)), Number of Insect Pests (N (IP)), Number of Natural Enemies (N (NE)). Dot filled bar is for treatment with insecticides application (Conventional) The result of statistical analysis suggest that RMAFD treatment had the highest value of natural enemy to pest ratio, and significantly different with Conventional and R treatments. However, it is not significantly different with RM, RMA, RMAF and RMAD treatments (Figure 14).

3.4.2 THE BIODIVERSITY INDICES The calculation of biodiversity indices was conducted to find out which treatment had the highest index of several biodiversity indices.

The result showed that the species richness (total number of species) of all natural enemies was higher than all insect pests found in all treatments (Figure 15 a). The Simpson’s index (consider both species richness and the relative abundance of each species) indicates that natural enemies in all treatments (except RMA treatment) had higher value than insect pests (Figure 15 b).

Furthermore in biodiversity index, the Shannon-Wiener index (to distinguish species diversity in a community) showed that almost in all treatments (except RMA treatment) natural enemies had higher indexes than insect pests (Figure 15 c). While in the Evenness (show how identical of the abundance of different species) indicates that almost in all treatments (except in R and RMAF treatment) the value is higher in insect pests rather than natural enemies (Figure 15 d).

15 0.90 IP a IP b ab a NE NE b a c ab a a a 10 c bc a 0.85

5 0.80 Simpson's index index richness Species

0 0.75 C R RM RMA RMAF RMAD RMAFD C R RM RMA RMAF RMAD RMAFD (a) (b)

2.30 1.00 IP bc IP 2.20 a NE c NE

a 0.95 2.10 c ab a c bc ab

c a s index a 2.00 s 0.90 Wienner index Wienner

- a 1.90

Evenne 0.85

Shannon 1.80

1.70 0.80 C R RM RMA RMAF RMAD RMAFD C R RM RMA RMAF RMAD RMAFD (c) (d)

Figure 15. Biodiversity graph : a) Species richness, b) Simpson’s index, c) Shannon-Wienner index, d) Evennes index. The biodiversity indices is significantly different for insect pest, but not significant for natural enemy. Pattern filled bar is for treatment with insecticide application (Conventional)

Statistical analysis using one way ANOVA showed that the biodiversity indices (species richness, Simpson’s index, Shannon-Wienner index, and Evenness index) of insect pests are significant differences (P < 0,05) among the treatments. Nevertheless, the statistical analysis of biodiversity indices of natural enemy’s abundance did not show significant differences.

Species richness of insect pests at Conventional, R, and RM treatments were significantly different

(F (6,14) = 19,015, P = 0,000) with RMAF, RMAD, and RMAFD treatments.

Simpson’s index of insect pests also showed a significant difference ( F (6,14) = 13,313, P = 0,000) between Conventional, R, RM treatments and RMA, RMAF, RMAD, RMAFD treatments.

More about biodiversity index, Shannon-Wienner index showed significant differences ( F (6,14) = 22,210, P = 0,000) between Conventional, R and RM treatments with RMA, RMAF, RMAD), and RMAFD treatments.

Furthermore, the Evenness index of insect pests shown significant differences ( F (6,14) = 18,839, P = 0,000) between Conventional, R, RM treatments and RMA, RMAF, RMAD and RMAFD treatments.

Taken all together, the four biodiversity indices suggest that treatment Conventional, R, and RM are significantly different in insect pests’ abundance compared with another four treatments: RMA, RMAF, RMAD and RMAFD.

3.5 CORRELATION AND REGRESSION 3.5.1 BETWEEN DEADHEARTS AND WEED SPECIES A Pearson’s correlation test was conducted to assess the relationship between deadhearts, and weed species. This is based on the assumption that certain weed species become alternate host for the rice stem borer. Two significant levels (0,01 and 0,05) were found at the correlation coefficient between deadhearts and weed species, which is presented in Table 6.

Table 6. Correlation coefficents between % deadhearts and dry matter of weed species (g/m2)

LA LO EP AC MV EGM CI FD CD PS MM SM ,435 ,713 ,854* -,226 ,731 ,909** ,804* ,808* ,972** ,889** ,274 ,356 ,329 ,072 ,015 ,626 ,062 ,005 ,029 ,028 ,000 ,007 ,552 ,433 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). The linear regression analysis on deadhearts and 12 weed species indicated a positive correlation between three weed species (Echinochloa glabrescens Munro ex Hook. f., Cynodon dactylon and Pistia stratiotes (L) with the percentage of deadhearts and significant in 0,01 level (Figure 16 a,b,c; Table 6).

Another three weed species (Eclipta prostrata (L.), Cyperus iria L., and Fimbristylis dichotoma (L.) Vahl) also had a positive correlation with percentage of deadhearts and significant in 0,05 level (Figure 16 c, d, e; Table 6).

Three weed species Echinochloa glabrescens Munro ex Hook. F, Cynodon dactylon (L.) Pers. and Pistia stratiotes L. had a positive correlation with percentage of deadhearts with correlation coefficient are 0,909; 0,972 and 0,889 respectively (P < 0,01). Another three species of weed with P < 0,05 (Eclipta prostrata (L.), Cyperus iria L., and Fimbristylis dichotoma (L.) Vahl.) had correlation coefficient 0,854; 0,804 and 0,808 respectively (Table 6). Regression analysis was conducted for weed species that have significance value P < 0,01 and P < 0,05:

1. 0,01 significance level a. Echinochloa glabrescens Munro ex Hook. (Poales: Poaceae) The regression analysis showed that Echinochloa glabrescens Munro ex Hook significantly predicted 0,908, t (4,846) = 0,005, P < 0,01. Echinochloa glabrescens Munro ex Hook also explained a significant proportion of variance in percentage of deadhearts, R2 = 0,824, percentage of deadhearts, β= (F(1,5) = 23,480, P= 0,005). b. Cynodon dactylon (L.) Pers. (Cyperales: Poaceae) The regression analysis demonstrated that Cynodon dactylon (L.) Pers significantly predicted 0,972 , t (9,170) = 0,000, P < 0,01. Cynodon dactylon (L.) Pers also percentage of deadhearts, β=

elucidated a significant proportion of variance in percentage of deadhearts, R2 = 0,944, (F(1,5) = 84,095, P= 0,000). c. Pistia stratiotes L. (Alismatales: Aracaeae) The regression analysis presented that Pistia stratiotes L. significantly predicted percentage of 0,890, t (4,365) = 0,007, P < 0,01. Pistia stratiotes L. also interpreted a significant proportion of variance in percentage of deadhearts, R2 = 0,792, (F(1,5) = 19,053, P= 0,007). deadhearts, β=

2. 0,05 significance level a. Eclipta prostrata (L.) L. (Asterales: Asteraceae) The regression analysis presented that Eclipta prostrata (L.) L. significantly predicted percentage of 0,855, t (3,684) = 0,014, P < 0,05. Eclipta prostrata (L.) L. also interpreted a

significant proportion of variance in percentage of deadhearts, R2 = 0,731, (F(1,5) = 13,570, P= deadhearts, β= 0,015). b. Cyperus iria L. (Poales: Cyperaceae) The regression analysis showed that Cyperus iria L. significantly predicted percentage of 0,804, t (3,019) = 0,029, P < 0,05. Cyperus iria L. also explained a significant

proportion of variance in percentage of deadhearts, R2 = 0,646, (F(1,5) = 9,115, P= 0,029). deadhearts, β= c. Fimbristylis dichotoma (L.) Vahl. (Poales small: Cyperaceae) The regression analysis demonstrated that Fimbristylis dichotoma (L.) Vahl. significantly predicted 0,802, t (3,002) = 0,030, P < 0,05. Fimbristylis dichotoma (L.) Vahl. also elucidated a significant proportion of variance in percentage of deadhearts, R2 = 0,643, (F(1,5) = percentage of deadhearts, β= 9,014, P= 0,030). Taken all together, the strongest correlation is occuring between the percentage of deadhearts and Cynodon dactylon (R2: 0,94) compared to other weed species, and followed by Echinochloa glabrescens Munro and Pistia stratiotes, with R2 value are 0,82 and 0,79 respectively (Figure 16 a, b, c).

Echinochloa glaberescens Munro Cynodon dactylon Pistia stratiotes y = 0.1207x + 0.3436

y = 0.7778x - 0.0402 y = 0.5391x + 0.5217

14 10 2.5 R² = 0.7921 R² = 0.9439 R² = 0.8244 9 12 (g/m2)

8 2 10 7 8 6 1.5 5

weed species weed 6 4 1 4 3 2 0.5 2 1 dry matter of dry matter of weed species (g/m2) 0 dry matter of weed species (g/m2) 0 0 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 (a) % of deadhearts (b) % of deadhearts (c) % of deadhearts

Eclipta prostata Cyperus iria Fimbrystilis dichotoma

0.14 14 14 y = 0.6568x - 0.1765 y = 0.6653x - 0.2871 0.12 y = 0.0072x - 0.0179 12 R² = 0.6432 12 R² = 0.65 R² = 0.7307 0.1 10 10 0.08 8 8 0.06 6 6 0.04 4 4 0.02 2 2 0 0 0 dry matter of weed species (g/m2) dry matter of weed species (g/m2)

0 5 10 15 20 0 5 10 15 20 dry matter of weed species (gr/m2) 0 5 10 15 20 (d) % of deadhearts (e) % of deadhearts (f) % of deadhearts

Figure 16. Regression line between% deadhearts and : a) Echinochloa glaberescens Munro (P<0,05), b) Cynodon dactylon (P< 0,05), c) Pistia stratioles (P<0,05), d) Eclipta prostata (P<0,01), e) Cyperus iria (P<0,01), f) Fimbristylis dichotoma (P<0,01)

3.5.2 DEADHEARTS INCIDENCE IN TIME SCALE REGRESSION The correlation and linear regression of deadhearts incidence in time scale also conducted. The result showed that the deadhearts incidence in 4 WAT had positive correlation with the incidence in 6 WAT. Moreover, the incidence in 8 WAT also had positive correlation with the incidence in 10 WAT.

Result of statistical analysis using Pearson’s correlation showed that percentage of deadhearts incidence in 4 WAT were strongly correlated with deadhearts incidence in 6 WAT (Pearson’s correlation = 0,941, P = 0,002). The deadhearts incidence in 8 WAT also had strong correlation with deadhearts incidence in 10 WAT (Pearson’s correlation = 0,988, P = 0,000) (Figure 17). Both correlation were significant at the 0,01 level (2-tailed).

16 y = 0.6342x + 4.2122 25

14 R² = 0.8855 y = 1.1446x - 1.4163 20 R² = 0.977 WAT

12 10 15 8 10 6 4 5 % of deadherats in 8 8 WAT in deadherats % of % of deadhearts in 4 in deadhearts % of 2 0 0 0 5 10 15 20 0 5 10 15 20 (a) (b) % of deadhearts in 10 WAT % of deadhearts in 6 WAT

Figure 17. Percentage of deadhearts in time scale regression, (a) between 4 WAT and 6 WAT, (b) between 8 WAT and 10 WAT

The regression analysis showed that the percentage of deadhearts in 4 WAT significantly predicted percentage of deadhearts in 6 WAT, β = 0,941 , t (6,217) = 0,002, P < 0,01. The percentage of deadhearts in 4 WAT also explained a significant proportion of variance in percentage of

deadhearts in 6 WAT, R2 = 0,885 (F(1,5) = 38,649, P = 0,002).

Furthermore, the regression analysis also showed that the percentage of deadhearts in 8 WAT significantly predicted percentage of deadhearts in 10 WAT, β = 0,988 , t (14,567) = 0,000, P < 0,01. The percentage of deadhearts in 8 WAT also explained a significant proportion of variance in

percentage of deadhearts in 10 WAT, R2 = 0,977 (F(1,5) = 212,212, P = 0,000).

3.5.3 BETWEEN DEADHEARTS AND RICE STEM BORER The Pearson’s correlation and linear regression calculation between percentage of deadhearts and imago (adults) of rice stem borer found in the sticky traps also investigated to examine the relationship between rice stem borer as the pest causing the deadhearts symptom in rice plant.

Result of statistical analysis of correlation using Pearson’s correlation showed that percentage of deadhearts incidence were strongly correlated with rice stem borers population (Pearson’s correlation = 0,955, P = 0,001) (Figure 18) and significant at the 0,01 level (2-tailed).

25 y = 1.0464x + 5.6933 R² = 0.912 20

Number of rice stem borer/plot m2) stem rice of borer/plot (100 Number 0 0 5 10 15 20 % of deadhearts

Figure 18. Regression line between % of deadhearts and number of rice stem borer

The regression analysis showed that rice stem borer significantly predicted percentage of deadhearts, β = 0,955 , t (7,198) = 0,001, P < 0,01. Rice stem borer also explained a significant 2 proportion of variance in percentage of deadhearts, R = 0,912 (F(1,5) = 51,804, P = 0,001).

3.5.3 BETWEEN INSECT PESTS AND THEIR NATURAL ENEMIES The association between insect pests and their natural enemies in the rice field was assessed by Pearson’s correlation coefficients and linear regression (Figure 19). Rice stem borer Scirphopaga spp, rice whorl maggot Hydrellia phillipina, and white backed plant hopper Sogatella furcifera had a positive correlation with their parasitoid, Ecththrodeplhax fairchildii (Hymenoptera: Dryinidae, common name: pincher wasps). The R2 for regression linear between E. Fairchildii and rice stem borer, rice whorl maggot, and white backed plant hopper are 0,9873; 0,9411 and 0,9808 respectively (Figure 19 ab, b, c).

Another natural enemies which has strong positive correlation with several insect pests is lady bug (Coccinellidae). The regression linear between lady bug and rice stem borer Scirphopaga spp, rice whorl maggot Hydrellia phillipina, green leafhopper Nephottetix virescens and brown plant hopper Nilaparvata lugens showed the R2 of 0,9506; 0,9911; 0,9984 and 0,9543 respectively (Figure 19 d, e, f, g). The strong correlation between green leafhopper and lady bug (R2 0,998) suggest that lady bugs play a role in the control of green leafhoppers.

Green crab spider Oxytate striatipes (Familie: Thomisidae) also had a positive correlation with its prey, white backed plant hopper (WBPH). The R2 for the regression linear is 0,941 (Figure 19 h).

The linear regression between insect pests and their natural enemies is presented below: a. Scirphopaga spp. and Echthrodelphax fairchildii The regression analysis showed that Echthrodelphax fairchildii significantly predicted the population of Scirphopaga 0,994, t (12,488) = 0,006, P < 0,01. Echthrodelphax fairchildii also explained a significant proportion of variance in the population of Scirphopaga spp, R2 = spp, β= 0,987, (F(1,5) = 155,948, P= 0,006).

b. Scirphopaga spp. and Coccinellidae The regression analysis presented that Coccinellidae significantly predicted the population of Scirphopaga 0,975, t (6,204) = 0,025, P < 0,05. Coccinellidae also explained a significant

proportion of variance in the population of Scirphopaga spp, R2 = 0,951, (F(1,5) = 38,496, P= spp, β= 0,025).

c. Nilaparvata lugens and Coccinellidae The regression analysis demonstrated that Coccinellidae significantly predicted the population of Nilaparvata lugens 0,977, t (6,463) = 0,023, P < 0,05. Coccinellidae also explained a significant proportion of variance in the population of Nilaparvata lugens, R2 = 0,954, (F(1,5) = 41,766, P= 0,023). , β=

d. Sogatella furcifera and Echthrodelphax fairchildii The regression analysis showed that Echthrodelphax fairchildii significantly predicted the population of Sogatella furcifera 0,990, t (10,098) = 0,010, P < 0,01. Echthrodelphax fairchildii also explained a significant proportion of variance in the population of Sogatella , β= furcifera , R2 = 0,981, (F(1,5) = 101,976, P= 0,010).

e. Sogatella furcifera and Oxytate striatipes The regression analysis presented that Oxytate striatipes significantly predicted the population of Sogatella furcifera 0,970 , t (5,649) = 0,030, P < 0,05. Oxytate striatipes also interpreted

a significant proportion of variance in the population of Sogatella furcifera, R2 = 0,941, (F(1,5) = , β= 31,914, P= 0,030).

f. Hydrellia phillipina and Coccinellidae The regression analysis demonstrated that Coccinellidae significantly predicted the population of Hydrellia phillipina 0,996 , t (14,883) = 0,004, P < 0,01. Coccineliidae also explained a 2 significant proportion of variance in the population of Hydrellia phillipina, R = 0,991, (F(1,5) = , β= 221,501, P= 0,004). g. Hydrellia phillipina and Echthrodelphax fairchildii The regression analysis demonstrated that Echthrodelphax fairchildii significantly predicted the population of Hydrellia phillipina Echthrodelphax fairchildii also elucidated a significant proportion of variance in the population of Hydrellia , β= 0,970 , t (5,655) = 0,030, P < 0,05. phillipina , R2 = 0,941, (F(1,5) = 31,981, P= 0,030). h. Nephottetix virescens and Coccinellidae The regression analysis demonstrated that Coccinellidae significantly predicted percentage of 0,999 , t (35,642) = 0,001, P < 0,01. Coccinellidae also explained a significant 2 proportion of variance in the population of Nephottetix virescens, R = 0,998, (F(1,5) = 1270,387, deadhearts, β= P= 0,001). The results from Pearson’s correlation coefficient and linear regression revealed that the strong correlation is occuring between Nephottetix virescens and its predator, Coccinellidae, with R2 0,998, and of standardized coefficients 0,999.

m2)

20 20 20 y = 0.5483x + 1.7312 y = 0.342x + 1.618 y = 0.5352x + 2.3104 15 R² = 0.9873 15 R² = 0.9411 15 R² = 0.9808 plot (100 / plot (100 m2) / 10 10 10

fairchildii E. 5 5 5 number fairchildii/plot E. of (100 m2) 0 number Fairchildii E. of 0 0 number of 0 10 20 30 0 20 40 60 0 10 20 30 (a) number of rice stem borer/ plot (100 m2) (b) number of rice whorl maggot/plot (100 m2) (c) number of WBPH/ plot (100 m2)

25 25 25 y = 0.5167x - 1.1728 y = 0.7921x - 0.6218 y = 0.6396x - 3.134 20 R² = 0.9911 20 20 R² = 0.9506 R² = 0.9984 15 15 15

10 10 10

5 5 5

0 0 0 number of ladybug/plot (100 ladybug/plot (100 m2) number of m2) (100 plot bug/ lady number of 0 20 40 60 0 10 20 30 ladybug/plot (100 m2) number of 0 20 40 60 (d) number of rice whorl maggot/ plot (100 m2) (e) number of rice stem borer/plot (100 m2) number of green leafhopper/plot (100 m2) (f)

25 14 y = 0.413x - 0.9255 y = 0.4294x + 1.6567 12 20 R² = 0.9543 R² = 0.941 10 15 8 10 6 4 5 2 number of ladybug/plot (100 ladybug/plot (100 m2) number of Oxytate/plot m2) number (100 of 0 0 0 20 40 60 0 10 20 30 (g) number of brown plant hopper/ plot (100 m2) (h) number of WBPH/plot (100 m2)

Figure 19. Regression linear between: a) rice stem borer and E. Fairchildii, b) rice whorl maggot and E. Fairchildii, c) white backed plant hopper and E. Fairchildii, d) rice whorl maggot and lady bug, e) rice stem borer and lady bug, f) green leafhopper and lady bug, g) brown plant hopper and lady bug, h) white backed plant hopper and Oxytate striatipes

IV. DISCUSSIONS The research about complex agroecosystem is aligned with Indonesian Ministry of Agriculture program about green and sustainable agriculture development. In the middle of high use of chemical inputs by Indonesian farmer, the complex agroecosystem is presented with good offer in soil improvement, and pest and weed management. Improving soil quality is an important thing in Indonesian agricultural practices, due to decline of soil fertility caused by overuse of chemical materials. The findings in this research decribe the potential of complex agroecosystem with the composition of Azolla-Crotalaria-fish and ducks in supporting biodiversity of arthropods, and pest and weed suppression.

4.1 PEST SUPRESSION MECHANISMS THROUGH PRESENCE OF FISH, DUCKS, AND CROTALARIA IN THE RICE FIELD 4.1.1 RICE STEM BORER The result of the research showed that Conventional treatment had high levels of deadhearts percentage, while RMAFD is in low level of incidence. It perhaps can be explained as the role of ducks and fish as controlling agents (natural enemies) for larvae and imago (adult) of insect pests. The calculation of whiteheads incidence suggest that Conventional treatment and RMA treatment suffered from a high incidence of whiteheads. Furthermore, RMA treatments had the highest losses compared to other treatments, shown by the highest rate of the ratio of panicle loss to total panicle. However, the other treatments (R, RMAF, RMAD and RMAFD) did not significantly different in incidence of deadhearts and whiteheads among them.

This result is in agreeing with the research conducted by Hossain et al. (2014) about the role of ducks in controlling insects and weed. They mentioned that populations of some rice insect pest species were significantly lower in rice-duck treatments compared to farmers’ treatments without the ducks. The mechanism is due to the activities of ducklings in catching insects efficiently in the rice-duck treatments, so that reducing the insect population. The same studied about rice- ducks integration also reported by Choi Song Yoel et al. (1996) and Furuno (2001). Rice stem borer infestation is positively correlated with the age of the rice plants. The highest level of deadhearts will lead to panicle loss, thus will decrease the rice yield. Among all the treatments, RMA treatment had the highest incidence of whiteheads. It suggests that the RMA treatment will had the highest yield loss. Furthermore, the highest level of whiteheads incidence in RMA treatment, perhaps due to higher N availability in rice plants fixes by Azolla. High N inputs increases crop duration and susceptibility to stem borers (Chakraborty, 2011). Rice plants with high N will have longer leaves. The longer leaves will lead to denser rice field, and the condition is preferred by rice stem borer. The more N in rice plants also causes the plants susceptible to other pest and disease’ attack, due to weaker plant

tissues. The finding suggests that the Azolla integration together with ducks or fish is important, because ducks or fish will balance the population of Azolla in the rice field by consume it. Crotalaria has the indirect effect of rice stem borer’s suppression, through the presence of the natural enemies of rice stem borer surrounded in Crotalaria’s vegetation. From the statistical analysis of correlation and regression between insect pests and natural enemies, it revealed that rice stem borer Scirphopaga spp had the linear regression with its parasitoid, Echthrodelphax fairchildii and its predator, Coccinellidae. A natural enemy such as E. Fairchildii and Coccinellidae need nectar and pollens as their food source, which can be obtained from Croatalaria’s flower. The initial phase of rice damage due to rice stem borer incidence in vegetative phase or known as deadhearts is positively correlated with the occurrence of imago (adults) of rice stem borer. The R2 of linear regression of the correlation is 91% (Figure 17), suggests that the rice stem borer causing the deadhearts symptom in rice plants. Furthermore, the proof of such devastated incidence due to rice stem borer attack in generative stage is seen in panicle loss. It is clear from the graph if the RMAFD treatment had the lower panicle loss (9,08%) or 25% lower compared to Conventional treatment (15,02%). This result consistently occurs from the beginning of the infestation (infested tillers/deadhearts until panicle loss) that RMAFD had the lowest incidence among others.

The Economic Treshold Level (ETLs) for rice stem borer is 6% (percentage of deadhearts) per m2 in vegetative phase and 10% (percentage of whitehead) per m2 in generative phase (National Guidance for Pest Observation, Food Crop Protection, Ministry of Agriculture, 2011; Baehaki, 2013). In vegetative phase, the highest percentage of deadhearts was 16,4% (in Conventional treatment), while in generative phase, the highest whiteheads was 19,96% (in RMA treatment). Therefore, the control method for rice stem borer population at the research site located in Kepanjen Malang already need to be taken, because it exceeds the ETLs value for the rice stem borer. Insecticide was applied for six times in Conventional treatment plots during the research in Kepanjen. The composition of pesticides is presented at table in Appendix 8. Many methods are reported for rice stem borer’s controlling method. Baehaki (2013) as the senior rice researcher and expert owned by Indonesian Agriculture Ministry suggested several techniques to control rice stem borer: 1) Synchronize planting (Bahasa Indonesia: tanam serempak) with 15 days range’s different between one area to another area in a landscape level (around 1000 ha). This synchronize planting will reduce the opportunity for rice stem borer to grow due to availability of food all the time within a region. 2) Planting the resistant varieties. This seems hard to realize, because until now, there is no resistant variety found for rice stem borer, because not yet found the source of resistance gen to rice stem borer, either from cultivation rice or wild rice. However, Yasin et al. (2008) reported that the strains of red rice (Oryza nivara) showed its resistance to white rice stem borer (Scirphopaga innotata). 3) The use of Tetrasticus schoenobii, Telenomus rowani, Telenomus dignus, and Trichogramma japonicum as egg parasitoid for rice stem borer.

4) The use of lamp trap (Appendix 5) to monitor the development of rice stem borer in the rice field.

4.1.2 OTHER INSECT PESTS The development of the major insect pest population found in the research field showed the same patterns (high in 4 WAT and lower in 10 WAT), except for rice gall midge. The graph of development of rice gall midge (Orseolia oryzae (Wood-Mason) (Diptera: Cecidomyiidae)) showed the increasing trend since 6 until 10 WAT. According to International Rice Research Institute (IRRI), rice gall midge is a pest in tillering phase of rice crop.

The insect pests which had high population are: brown plant hopper, green leafhopper, rice whorl maggot, rice gall midge, and rice stem borer (Figure 11). Among those insect pests, only rice gall midge does not have positive correlation with the natural enemies (Figure 18). According to IRRI, the main egg parasite of Orseolia oryzae are Platygaster spp., Neanastus spp., Eurytoma spp., and Leptacis spp. There were no those kinds of species found in all treatments during the research.

The insect pest population at Conventional treatment tend to have higher population among other treatments, and followed by R and RMA treatment. In contrary for RMAFD treatment, which has the lowest rate of the population, but not significantly different with another five treatments (RM, RMA, RMAF, RMAD and RMAFD).

The strongest correlation is occuring between green leafhopper, white backed plant hopper, brown plant hopper and their predator, lady bug (Coccinellidae) with high R2 value (99,8 %; 98 %; and 95,4% respectively). These strong correlations suggest that there were effective predatism by lady bug on those hoppers in the research treatment. In agreement with a study conducted by Shanker et al. (2013) Coccinelidae is an effective predator for brown plant hopper and green leaf hopper.

The egg parasitoid Echthrodelphax fairchildii also a generalist predator and parasites the eggs of rice stem borer, rice whorl maggot, and white backed plant hopper, with R2 value are 98,7%; 94,1%; and 98,1 % respectively. Meanwhile, spider Oxytate striatipes only has correlation with white backed plant hopper as its prey (R2 value 94,1%).

Natural enemies have an important role in suppressing the pest incidence. Due to the absence of its natural enemies, rice gall midge had the increasing population until rice maturing phase. While for the other pests, the population is declining overtime until 10 WAT with the presence of their natural enemies.

The interesting fact has been found in the research result, where Conventional treatment had the highest insect pest’s population among other treatment (Figure 13). Perhaps there are three reasons behind the phenomena : 1) the absence of arthropods natural enemies due pesticide spraying, 2) the absence of biocontrol agents (ducks and fish), 3) the resistance of insect pests to insecticides.

The absence of natural enemies (either arthropods or ducks and fish) obviously will lead to high pest disturbance in the field. The resistance of brown plant hopper and rice stem borer to deltamethrin (chemical group: Cyclopropane carboxylate pyrethroids with ethenyl) were reported by Ping He et al. (2007) and Chelliah and Heinrich (1984). White backed plant hopper also reported resistance to insecticide with chemical compound methyl parathion, deltamethrin and quinalphos.

Moreover, a research carried out by Xuan Cheng et al. (2010) in Taiwan demonstrated that rice stem borer is resistant 1000 fold againts carbofuran insecticide, and in the other hand, rice stem borer is susceptible to chlorpyrifos, fipronil and permethrin. Due to high resistance to carbofuran, they suggested that this compound should be replace by other compound which has a different mode of actions and low-cross resistance such as chlorpyrifos, fipronil and permethrin.

4.1.3 CORRELATION BETWEEN NATURAL ENEMIES AND INSECT PESTS Three species of natural enemies are found to have correlation and linear regression with their hosts or prey. Figure 19 illustrates the linear regression between Echthrodelphax fairchildii (Drynidae) and its host: rice stem borer Scirphopaga spp, rice whorl maggot Hydrellia phillipina, and white backed plant hopper Sogatella furcifera. E. Fairchildii is an important biological control agent for rice pests. Dupo & Barrion (2009) stated that dryinids are the most important natural enemies of nymphal/adult delphacids in terms of numbers of taxa (10 species).

Another important generalist predator in rice plants is lady bug (Coccinellidae). Figure 18 showed that lady bug had the linear regression with four insect pests: rice whorl maggot Hydrellia phillipina, rice stem borer Scirphopaga spp, green leafhopper Nephottetix virescens, and brown plant hopper Nilaparvata lugens. Omkar and Pervez (2004) made the prey list (catalogue) of Coccinellidae. The catalogue provides the prey record of 261 known predaceous coccinellids of India belonging to 57 genera.

Oxytate striatipes (green crab spider) had a positive correlation with its prey, white backed plant hopper. There were not many reports about green crab spider and its prey, but, most of the Arachnidae are becoming a predator for hoppers, aphids, and small insect pests in rice field.

In general, parasitoids tend to be more susceptible to insecticides than predators, and the natural enemies are more suscpetible than targeted pests (University of California, 2015). If the natural enemies killed by wide-spectrum pesticide spraying, it will trigger the pest resurgence, and it leads to the outbreak of certain insect pests. The outbreak of brown plant hopper often occurs in Indonesia.

In addition to direct effects, there is indirect effect which may be more subtle or chronic compared to direct effects. Furthermore, Cloyd (2012) mentioned the indirect effects of insecticides spraying to natural enemies: inhibitation in the ability of population establish, reduce the capacity to utilize

the prey, reduce the reproduction, decrease the availability to search and recognize the prey and influence on sex ratio.

4.2 THE DIRECT MECHANISM OF WEED SUPRESSION THROUGH THE PRESENCE OF FISH, DUCKS AND AZOLLA IN THE RICE FIELD There were 4 major weed found in the research field: Echinochloa glabrescens Munro ex Hook. F, Cynodon dactylon, Cyperus iria and Fimbristylis dichotoma. Those four weed species are common weed species disturb in the Indonesian rice field. In irrigated rice field, the competition between weed and rice plants can decrease the yield loss until 10-40 %, depend on species and weed density, soil type, water availability, and weather condition (Nantasomsaran and Moody, 1993). In a traditional farming system in Indonesia, farmers are rare to use herbicide. They only do the hand weeding to manage the weed in rice field. The finding in this research about weed suppression is that the dry matter of weed species was found high in RM and Conventional, while RMAFD treatment had the lowest value. The mechanism of weed suppression by the presence of Azolla, ducks and fish already studied by Kathiresan (2007). Fish integrated in a rice field (grass carp, common carp, tilapia) were observed to be more greedy feeder on rice weeds (Echinochloa crusgalli and Monocharia vaginalis), thus will lead to the reduction of weed population in the rice field. He also mentioned that the poultry component (ducks) on rice weeds was attributed to the acidic nature and biochemical fractions of the waste that suppressed germination of rice weeds. Moreover, Singh and Singh (2000) mentioned that rearing grass carp fish can help to reduce the problems of weed. They suggest to integrate 1500 fingerlings of 100 gr each (approximately) for one hectare of rice land. Whenever the weed population is reduced, fish can consumed or moved to another plots. Biswas et al. (2005) mentioned two mechanisms for the weed suppression by Azolla: 1) the blockage of sunlight, and 2) physical resistance due to Azolla covering the field (with its thick and virtually light-proof cover). Raja et al. (2012) support the second mechanism by stated that thick mat of Azolla does not allow weeds to grow in rice field. The role of ducks in controlling the weed in rice field already studied by Nakornsri in 1998. The results showed that the presence of ducks for 21 and 25 times ( during 20 – 50 DAT) in the rice field gave similar weed control as herbicide application and hand weeding. The presence of ducks in rice field more than 7 times gave similar size of the tiller of rice and weed biomass, as herbicide application and hand weeding. The proper timing for using ducks was before panicle’s emerge. The highest dry matter of weed was founded in RM treatment can be explained by Renner’s research (2009). He stated that small weeds and hard seeds can pass quickly and through animal excretas, and the seeds are ready to cause weed problems in the next growing season. Therefore, the animal manure is better is to store before using in a rice field. Storing manure will decrease the weed seed viability (no longer able to germinate). 60 percent or more of weed seed viability will reduced by stock piling manure for three months. The ammonia gas and uric acid derived on the stack and the warm temperatures will decay the weed seed over time.

Another fact found in this research is the weed biomass (dry matter of the weeds) in R treatment is lower than in Conventional treatment. The R treatment is minus any fertilizer (chemical compound or organic compound) application since previous research. In the condition of soil nutrient depletion (without addition of any fertilizer for the soil), probably the weeds also cannot grow well.

4.3 BIODIVERSITY OF ARTHROPODS

The biodiversity of arthropods was also studied in addition to rice stem borer’s incidence in the research field. Figure 13 showed that the highest arthropod’s population size of species richness is in 4 WAT compared to the other observation week. The arthropod abundance in 4 WAT can be explained as the function of Crotalaria junceae as a refuge or shelter for arthropod natural enemies. Because Crotalaria was planted far before the rice plant was transplanted, natural enemies have already gathered in the rice field in 4 WAT. It was the period when Crotalaria have flowering period with nice yellow flowers which was might attract arthropods to come and made the Crotalaria plants as their refuge. Furthermore, the population of insect pests and natural enemies is getting smaller as time progresses. Start at 6 WAT until 10 WAT, the population of insect pests and natural enemies is declining (Figure 13). The age of Crotalaria perhaps affects this condition. Above 6 WAT, Crotalaria have already started to produce seed, it means that no more nice yellow colour of Crotalaria that attract the arthropods (Appendix 4 a). If observed carefully, the yellow colour between Crotalaria’s flower and sticky yellow trap is almost the same. The interesting fact is, a lot of bumble bees were seen in the experimental treatment, they were flying surround Crotalaria junceae. Bumble bee usually found in the horticulture field. But this bumble bee was found in large scale of rice field, where there was no horticulture field nearby. So, perhaps the bumble bees fly far away and find Crotalaria as their source of nectar. Osborne et al. (2007) mentioned that the bees explored at least 1.5 km from their colonies, and the percentage of foragers flying to one field descended, relatively linearly, with radial distance. Moreover, data from Agricultural office in Malang regency from 1995-2000 showed that Kepanjen sub-district is the production area for some fruits commodity, such as oranges, jack fruit and rambutan (rambutan is tropical fruit only grow in Indonesia). The role of natural enemies in decreasing insect pest population is one of the management strategy in Integrated Pest Management (IPM). Mass rearing of natural enemies also studied at IPM Farmer Field School. Therefore, the ratio between population of natural enemies and insect pests is important to show in this research. According to Mensah (2002), a predator to pest ratio of 0,5 or higher reduced the survival rate of Helicoverpa (cotton pest) larvae. This ratio of predators to pests and a pest treshold also become a tool to decide when the management control should be taken. Figure 14 describes the ratio natural enemies to insect pests, where in complex agroecosystem (RMAFD treatment) had the highest ratio among others, while in the Conventional treatment had the lowest ratio. It perhaps due to the complexity of the components inside RMAFD. All the components did their job, in order to achieve ecological balance in the system. It can be said that the higher the gradient complexity of the system, the more natural enemies abundance, and vice

versa for the insect pests. It strongly supports the finding that RMAFD have potential to balance the ecological system in the rice field. Figure 15 illustrated that the biodiversity indices of natural enemies are greater than insect pests. The species diversity (Shannon-Wiener and Simpson’s) index showed that natural enemies had higher indexes than insect pests, except in RMA treatment. It means a more kind of insect pests are found in RMA treatment, compared to other treatments. This finding is the same with rice borer incidence, where the highest whiteheads incidence is found in RMA treatment. So, the addition of Azolla in the rice field without ducks or fish integration should need attention. Furthermore, the Evenness index of insect pests is higher than natural enemies, except in R and RMAF treatment. Means that in R and RMAF treatment, the species of natural enemies are more diverse compared to other treatments. The interesting fact is, when the Evenness index in RMAFD treatment is almost balanced (Figure 15 d) between insect pests and natural enemies. It suggested that the diversity of insect pests and their natural enemies is balance in RMAFD treatment. However, the calculation is shown that the biodiversity’s value between treatment is almost similar or almost close each other. So, it can be said that the arthropods were spread well all over the treatment treatment, because the Crotalaria plants also planted in all over research site.

4.4 CORRELATION BETWEEN DEADHEARTS AND WEED SPECIES The correlation and regression calculation indicates that a positive correlation occurs between the percentage of deadhearts and 6 weed species: Echinochloa glabrescens Munro ex Hook. f., Cynodon dactylon and Pistia stratiotes L, Eclipta prostrata (L.), Cyperus iria L., and Fimbristylis dichotoma (L.) Vahl). The strongest correlation occurs between percentage of deadhearts and Cynodon dactylon, with R2 value is 94,5%. So, the presence of Cynodon dactylon in rice field need to remove quickly, in order not severe the rice stem borer incidence. It has been reported that Cyperus iria and Cynodon dactylon, become an alternative host or intermediate host plant for rice stem borer (host range of rice stem borers) (Directorate of Rice Research, India,_____). Moreover, the research conducted by Zhang et al. (2012) revealed that weed Echinochloa pyramidalis as the intermediate host plant of the rice stem borer Scirpophaga incertulas. This insect migrated from Echinochloa pyramidalis to the rice plants.

V. CONCLUSIONS AND RECOMMENDATIONS This study reveals some facts about about pest and weed suppression by ducks and fish, and the interactions between arthropod natural enemies and their hosts or prey. In the RMAFD treatment, which contains the highest complexity in terms of components, rice stem borer incidence and other pest’s disturbance were low compared to Conventional and RMA treatment. The integration of Azolla in a rice field is advisable together with ducks and fish, in order not lead to high N content in rice plants, that will cause the plants are susceptible to pest and disease’ attack. The existence of arthropods natural enemies is very important in the rice field. Their occurrence in the rice field can balance the population of insect pests, as illustrated in the Evenness index and ratio natural enemies to insect pests (N(NE)/N (IP)) of RMAFD treatment. The integration of Crotalaria in the rice field is very important, because it is become a shelter, which also provide nectar and pollen for arthropods natural enemies. However, this research does not directly allow formally testing the effect of Crotalaria in pest suppression, because all the treatment plots were surrounded by the Crotalaria’s vegetation. Therefore, the advance research about the Crotalaria’s role in conjunction with pest suppression is needed, because not much research was conducted in Indonesia about it. Whereas, this is an environmentally friendly technology and easy to be applied by rural farmers, and the most important thing, can reduce the use of chemical insecticides. Furthermore, because natural enemies are more susceptible to pesticide spraying, the use of pesticide should in a wise way and based on these principles: precise pest/disease/weed target, appropriate in kinds of pesticide used, appropriate pesticide quality, appropriate dose or concentrate, and appropriate application timing (Ministry of Agriculture of Republic Indonesia). Those principles in using the pesticide will decrease the risk of pesticide resistance. Moreover, to avoid the resistance, the active ingredients of chemical must rotate within the planting period. Weed suppression also showed in RMAD and RMAFD treatment, which had the lowest dry matter of weed per m2. It suggests that ducks and fish are effective controlling agents against weed disturbance. The role of complex agro-egosystem in suppressing pests and weed problem is perform well in this research. Aside to pest and weed suppression, fish and ducks in complex agroecosystem provides additional proteins for farmers, which is beneficial for their livelihood. Therefore, this useful information need to inform well to rice farmers, in order to have replication of the system in the Indonesian rice field. Farmer Field School (Appendix 4 b) perhaps one of the media, which can be used to transfer the knowledge to the farmers. The non significantly effect of treatments RMAF, RMAD and RMAFD in pest and weed suppression and the abundance of insect pests and natural enemies proposes that the farmers can choose the integration model; either fish – rice , ducks – rice or even the most complex components, Azolla- fish-ducks in their rice cultivation method. Considering the different types of soil among the islands in Indonesia, the rice farmers can choose which is the best model of the integration, based on their farming land condition. Therefore, the study about other local indigenous materials derived from

40 neighbourhood as the components in the complex agroecosystem will be another interesting research. Finally, this research is really useful for Indonesian agriculture who already saturated with the use of chemical materials. The trend of conventional rice yield is declining along the time, due to land degradation. New breakthrough or revive the old system which is beneficial for rice cultivation will become useful inputs for agricultural development in Indonesia and worldwide.

REFERENCES

Ali M.A, M. A. Sattar, M. N. Islam, K. Inubushi. 2014. Integrated effects of organic, inorganic and biological amendments on methane emission, soil quality and rice productivity in irrigated paddy ecosystem of Bangladesh: field study of two consecutive rice growing seasons. Plant Soil (378): 239–252. Arlius F., E.G Ekaputra. 2011. Agricultural system in SRI-Mina in promoting national food security. National Seminar: Integrated Agricultural Reformation Towards Food Sovereignty. Agricultural Faculty. University of Trunojoyo, Indonesia. Baehaki. 2013. Hama Penggerek Batang Padi dan Teknologi Pengendalian (Rice stem borer and it’s control techniques). Indonesian Rice Research Center. Barrion, A. T.; Bandong, J. P.; Lumaban, M. D.; Pantua, P. C.; Apostol, R. A.; Litsinger, J. A. 1979. Natural enemies of the rice leaf folder Cnaphalocrosis medinalis in the Philippines. International Rice Research Newsletter 1979 Vol. 4 No. 2 pp. 18. Berg, H. 2002. Rice monoculture and integrated rice- — economic and ecological considerations. Ecological Economics (41): 95–107. ﬁsh farming in the Mekong Delta, Vietnam Berkelaar, D. 2001. SRI, the system of rice intensification: Less can be more. In: Echo Development Notes, No. 70: 8 pp. Bhuiyan, S.I, M.Z. Abedin, V.P Singh. B. Hardy. 2004. Rice Research and Development in the Flood- Prone Ecosystem. IRRI.

Biswas M., S. Parveen, H. Shimozawa, N. Nakagoshi. 2005. Effects of Azolla species on weed emergence in a rice paddy ecosystem. Weed Biology and Management 5 , 176–183 (2005).

Bui Xuan Men, Tran Kim Tinh, T R Preston, R Brian Ogle and Jan Erik Lindberg.1999. Use of local ducklings to control insect pests and weeds in the growing rice field. Livestock Research for Rural Development (11) 2 1999.

Chakraborty, K. 2011. Influence Of Inorganic N Fertilizer On Plant Characters, Yield Generation And The Incidence Of Yellow Stem Borer Scirpophaga incertulas, Walker In The Field Of Local Scented Paddy Cultivar Tulaipanji. International Journal of Applied Biology and Pharmaceutical Technology. Volume: 2: Issue-4: Oct - Dec -2011.

Chandra, G. 1980. Dryinid parasitoids of rice leafhoppers and planthoppers in the Philippines. I. Taxonomy and bionomics. Acta Oecologica, Oecologia Applicata (1980): Vol. 1 No. 2 pp. 161- 172

Chelliah. S and E. A. Heinrichs. 1984. Factors contributing to brown planthopper resurgence. Proceedings of the FAO/IRRI workshop on Judicious and Efficient Use of Insecticides on Rice. Phillipina.

Chitra, N. K. Gunathilagaraj , R . P. Soundararajan. 2002. Prey Preference of Orthopteran Predators on Rice Insect Pests. Journal of Biological Control. Vol 16 (2002).

Cloyd, R. 2012. Pesticides-Advances in Chemical and Botanical Pesticides. Chapter 6: Indirect effects of pesticides on Natural Enemies.Edited by R.P Soundararajan. Publisher: InTech.

Directorate of Rice Research, _____. Rice stem borers in India-species composition and distribution. Technical Bulletin No.XX. India.

de Macale M. A. R. and P. L.G. Vlek. 2004. The role of Azolla cover in improving the nitrogen use Plant and Soil 263: 311–321.

Dupoefﬁciency A.L.B., Barrion of lowland A.T. rice.2009. Taxonomy and general biology of delphacid planthoppers in rice agroecosystems. In Planthoppers: New Threats to the Sustainability of Intensive Rice Production Systems in Asia, pp. 3–155. Eds K.L. Heong and B. Hardy, Los Ban˜ os, Philippines: International Rice Research Institute. Furuno,. T. 2001. The power of duck: Integrated Rice and Duck Farming. Tagari. Tasmania. Fu Zhiqiang, Huang Huang, Liao Xiaolan, Hu Ying, Xie Wei, He Baoliang. 2008. Effect of ducks on CH4 emission from paddy soils and its mechanism research in the rice-duck ecosystem Acta Ecologica Sinica. 28(5), 2107-2114.

Hinds J, C R.R. Hooks .2013. Population dynamics of arthropods in a sunn-hemp zucchini interplanting system. Crop Protection 53 (2013) 6-12. Hossain, S.T, Sugimoto, H, Ahmed G.J.U, Islam M.D. 2014. Effect of Integrated Rice-Duck Farming on Rice Yield, Farm Productivity, and Rice-Provisioning Ability of Farmers. Asian Journal of Agriculture and Development, Vol. 2, No. 1. Huai Yin Wang, Yang Yang, Jian Ya Su, Jin Liang Shena, Cong Fen Gao, Yu Cheng Zhu. 2008. Assessment of the impact of insecticides on Anagrus nilaparvatae (Pang et Wang) (Hymenoptera: Mymanidae), an egg parasitoid of the rice planthopper, Nilaparvata lugens (Hemiptera: Delphacidae). Crop Protection 27 (2008) 514–522.

International Rice Research Institute (IRRI). ______. Predators, What role do orthopterans play in the rice field? http://www.knowledgebank.irri.org/bioControl/module_3/08.htm. Accessed 17 June 2015. Jiaguo, Z, C. Zhongzhi, L. Xuyi and J. Xinlu. 2013. Agricultural Water Savings Possible through SRI for Water Management in Sichuan, China. Taiwan Water Conservancy Vol. 61, No. 4, December 2013. Karindah, S. 2011. Predation of Five Generalist Predators on Brown Planthopper (Nilaparvata lugens Stål). J. Entomol. Indon., September 2011, Vol. 8, No. 2 , 55-62. Kathiresan, R.M. 2007. Integration of elements of a farming system for sustainable weed and pest management in the tropics. Crop Protection (2): 424-429.

Khumairoh, U. 2011. Effects of duck, fish, and Azolla fully integration into an organic rice system in Malang, Indonesia. MSc thesis report. Wageningen UR, the Netherlands. Khumairoh, U. J.C.J Groot, E. A. Lantinga. 2012. Complex agroecosystems for food security in a changing climate. Ecology and Evolution 2012; 2(7): 1696–1704. Khumairoh, U. 2013. PhD thesis. Ongoing Project. Wageningen University, the Netherlands. Krebs, C.J., 1978, Ecology The Experimental Analysis Of Distribution and Abundace, Harper and Row Publisher. New York . 234 p.

Laba, I W., Djatnika K., M. Arifin. 2001. Analisis keanekaragaman hayati musuh alami pada ekosistem padi sawah (Analysis of Biodiversity of Natural Enemies in Rice Ecosystem), p. 207- 217. Dalam E. Soenarjo et al. (Eds.) Prosiding Simposium Keanekaragaman Hayati Arthropoda pada Sistem Produksi Pertanian (Symposium proceedings of Arthropods Biodiversity on Rice Production). Cipayung, 16-18 Oktober 2000. PEI – KEHATI. Liu, Y, X. Pan, J. Li. 2015. Current Agricultural Practices Threaten Future Global Food Production. J Agric Environ Ethics (2015) 28:203–216. Lou, You-Gen, Gu-Ren Zhang, Wen-Qin Zhang, Yang Hu, Jin Zhang. 2013. Biological control of rice insect pests in China. Biological Control 67: 8-20. Lu, J and X. Li. 2006. Review of rice-fish farming systems in China-One of the Globally Important Ingenious Agricultural Heritage Systems (GIAHS). Aquaculture (260): 106-113. Ludwig, J.A. and Reynolds, J.F. 1988. Statistical Ecology: A Primer in Methods and Computing. Wiley- interscience, 368p.

Marshall, E.J.P., V. Brown, N. Boatman, P. Lutman, G. Squire and L. Ward. 2003. The role of weeds in supporting biological diversity within crop fields. Weed Res. 43: 77-89. Mariyono, J, Tom K and R. Q Grafton. Shifting from Green Revolution to environmentally sound policies: technological change in Indonesian rice agriculture. Journal of the Asia Pacific Economy. Vol. 15, No. 2, May 2010, 128–147. Mahfud, M.C, Sarwono, G. Kustino. 2011. The dominance of Main Pest and Diseases in Rice Production at East Java. Indonesian Agency for Agricultural Research and Development, Ministry of Agriculture of Republic of Indonesia. jatim.litbang.deptan.go.id/ind. Accessed 30 January 2014. Maryland Sea Grant.______. Biofilms and Biodiversity. How to calculate biodiversity. http://ww2.mdsg.umd.edu/interactive_lessons/biofilm/diverse.htm). Accessed 10 April 2014.

Mensah, R. 2002. A predator to pest ratio for cotton IPM decision. The Australian Cottongrower. Vol 23, No 4, page 68. July-August, 2002.

Muthukumar K., Murthy S., Sivaraman S. 2002. Predatory potential of Lycosa pseudoannulata (Boes et st.) on rice pest, Sogatella furcifera Horvath observed under Laboratory conditions. Journal of Entomological Research. (2005) Vol. 29: 35-38.

Nakornsri K. 1998. Weed control with Khaki Campbell ducks in transplanted rice. Department of Agriculture, Bangkok (Thailand). Botany and Weed Science Div. Weed Science Group). Nantasomsaran, P and K. Moody. 1993. Weed management for Rainfed lowland Rice. Paper presented at the Second Annual Meeting of the Rainfed Lowland Rice Consortium, Semarang, Indonesia. February 1993. Omkar, A. Pervez. 2004. Predaceous Coccinellids in india: predator-prey catalogue (Coleoptera: Coccinellidae). Oriental Insects, Vol. 38: 27-61. Osborne JL, Martin AP, Carreck NL, Swain JL, Knight ME, Goulson D, Hale RJ, Sanderson RA. 2007. Bumblebee flight distances in relation to the forage landscape. J Anim Ecol 77(2):406-15. Padmavathi, C, Gururaj Katti, Padmakumari, A.P, Pasalu I.C. 2008. Prevalence, prey preference and predatory potential of Paederus fuscipes Curtis and Ophionea sp. in rice. Journal of Biological Control (2008). Vol 22 No 1: 191-193. Pasaribu S.M. 2010. Developing rice farm insurance in Indonesia. International Conference on Agricultural Risk and Food Security 2010. Agriculture and Agricultural Science Procedia 1 (2010): 33–41. Renner, K. 2009. Manure and Weed Seed. Manure Sense (2009). Michigan State University Extension.

Raja, W, P. Rathaur, S. A. John, P. W. Ramteke. 2012. Azolla-Anabaena Association and Its Significance In Supportable Agriculture. Hacettepe J. Biol. & Chem., 2012, 40 (1), 1–6.

Renner, K. 2009. Manure and Weed Seed. Manure Sense (2009). Michigan State University Extension. Rojas-Rousse, D. 2006. Persistent pods of the tree Acacia caven: a natural refuge for diverse insects including Bruchid beetles and the parasitoids Trichogrammatidae, Pteromalidae and Eulophidae. J. Insect Sci (8): 1-9.

Roubos C. R., , C Rodriguez-Saona , R. I. 2014. Mitigating the effects of insecticides on arthropod es. Biological Control xxx (2014) xxx–xxx. http://dx.doi.org/10.1016/j.biocontrol.2014.01.006. biological control at ﬁeld and landscape scal Sama, K, S. Nacro, D. Dakouo. 2015. Effect of transplanting period on the population dynamics, parasitm and damage of lepidopteran rice stem borers in irrigated rice scheme of Vallee du Kou, Burkina Faso. International Journal of Tropical Insect Science Vol. 35 No 1: 11-16.

Santosa S. J and J. Sulistyo. 2007. Peranan musuh alami hama utama padi pada ekosistem sawah (The role of main natural enemies of rice insect pests in rice field ecosystem). INNOFARM : Jurnal Inovasi Pertanian Vol. 6, No. 1, 2007 (1 - 10).

Sasmal S, Chari M S and Vardia H K. 2010: Role of duck droppings on pond productivity through fish-duck integrated farming system. Livestock Research for Rural Development. Volume 22, Article 172. Retrieved April 30, 2014, from http://www.lrrd.org/lrrd22/9/sasm22172.htm

C. Shanker, M. Mohan,, M. Sampathkumar, Ch. Lydia & G. Katti. 2012. Functional significance of Micraspis discolor (F.) (Coccinellidae: Coleoptera) in rice ecosystem. Journal of Applied Entomology. 137 (2013): 601-609. Singh, V,P and R.K Singh. 2000. Rainfed Rice: A Sourcebook of Best Practices and Strategies in Eastern India. International Rice Research Institute. 292 p. Su Pin, Liao Xiao-lan, Zhang Ya, and Huan. 2012. Influencing Factors on Rice Sheath Blight Epidemics in Integrated Rice-Duck System. Journal of Integrative Agriculture. 11(9): 1462- 1473. Suh, Jungho. 2014. Theory and reality of integrated rice-duck farming in Asian developing countries: A systematic review and SWOT analysis. Agricultural systems (125): 74-81. Srivastava, J and Alderman. H (editors). 1993. Agriculture and environmental challenges: Proceedings of Thirteenth Agricultural Sector Symposium of World Bank.

Tavares W.S. Cruz I, Silva, R.B, Figueiredo, M.L.C., Ramalho, F.S.; Serrão, J.E.V; Zanuncio, J. C. 2011. Soil organisms associated to the weed suppressant Crotalaria juncea (fabaceae) and its importance as a refuge for natural enemies. Planta daninha vol.29 no.3 Viçosa July/Sept. 2011. University of California. 2015. Protecting natural enemies and pllinators. http://www.ipm.ucdavis.edu/mitigation/protect_beneficials.html. Accessed 3 June 2015. Vromant, N., N.T. Hoai Chau. 2005. Overall effect of rice biomass and fish on the aquatic ecology of experimental rice treatments. Agriculture, Ecosystems and Environment (111): 153-165. Xuan Cheng, Cheng Chang, Shu-Mei Dai. 2010. Responses of striped stem borer, Chilo suppressalis (Lepidoptera: Pyralidae), from Taiwan to a range of insecticides. Pest Manag Sci (2010). Published online in Wiley interscience. Yasin, M., A. Bastian, dan B. Abdullah 2008. Ketahanan beberapa galur beras merah terhadap hama penggerek batang padi putih Scirpophaga innotata Walker (Resistence of red rice’s strain to white rice stem borer Scirpophaga innotata Walker). Prosiding Simposium Nasional Revitalisasi Penerapan PHT dalam Praktek Pertanian yang baik Menuju Sistem Pertanian Berkelanjutan. Balai Besar Penelitian Tanaman Padi, Fakultas Pertanian Unpad,Perhimpunan Entomologi Indonesia, PEI Cabang Bandung. p.107-110. Yue Ping He, Wen Ming Chen, Jin Liang Shen, Cong Fen Gao, Li Qin Huang, Wei Jun Zhou, Xu Gan Liu , Yu Cheng Zhu. 2007. Differential susceptibilities to pyrethroids in field populations of Chilo suppressalis (Lepidoptera: Pyralidae). Pesticide Biochemistry and Physiology: 89 (2007) 12– 19. Zhang N, B. Soulemane, Xiangping Guo, Chengli Zhu and Hui-lian Xu. 2012. Grain yield and weed- mediated pest incidence in paddy rice crops under controllable irrigation regimes. Journal Food, Agriculture and Environment (JFAE). Vol 10 (2): 626-631.

APPENDICES

Source: http://karangploso.jatim.bmkg.go.id/index .php/analisis-bulanan/189-analisis- distribusi-hujan/analisis-distribusi-curah-

hujan-malang-bulanan/analisis-distribusi- curah-hujan-malang-bulanan-tahun- 2014/467

(Bahasa Indonesia) : (English) Curah Hujan : Rainfall Rendah : Low

Menengah : Medium Tinggi : High

Appendix 1. Map of rainfall distribution in Malang (June-August, 2014)

(a) (b)

Appendix 2. a) Research Field, b) Ducks house, c) Fish’ pond, d) Bumble bee in Crotalaria’s plant

48 (a)

Appendix 3. a) Dragonfly, b) Black bug, c) wolf spider, d) Conocephalus longiconnis, e) Rice bug

(a) (b)

(c)

(d) (e)

(a) (b)

Appendix 4. a) Mature Crotalaria (produce seed), b) Farmers visiting and discussion in Kepanjen’s research field

Appendix 5. Lamp trap suggested by Indonesian Rice Research Center

(a) (b)

Appendix 6. a) Monochoria vaginalis, b) Ludwigia adscendens

Appendix 7. Weed scientific name and common name

No Code Species Common name 1 LA Ludwigia adscendens (L.) Hara Creeping water primrose 2 LO Ludwigia octovalvis (Jacq.) Raven Longfruited Primrose-Willow 3 EP Eclipta prostrata (L.) L. False daisy 4 AC Ageratum conyzoides L. Chikweed 5 MV Monochoria vaginalis (Burm. f.) C. Presl. Monochoria 6 EGM Echinochloa glabrescens Munro ex Hook. f. ECHGL 7 CI Cyperus iria L. Rice Flat Sedge 8 FD Fimbristylis dichotoma (L.) Vahl Forked Fringe-rush 9 CD Cynodon dactylon (L.) Pers. Bermuda grass 10 PS Pistia stratiotes L. Water lettuce 11 MM Marsilea minuta L. Water clover 12 SM Salvinia molesta Giant Salvinia

Appendix 8. Fertilizer application

Fertilizer 0 DAT 14 DAT 35 DAT Compound fertilizer (NPK) 170 kg/ha 0 0 Urea 22 kg/ha 139 kg/ha 139/ha Ducks manure 333 kg/ha 333 kg/ha 333 kg/ha

Appendix 9. Pesticides application

Pesticides Application/ha A. Fungicide Difenokonazol 50 gr/L 120 gr B. Rodenticide Kumatetralil 0,75% C. Insecticides 1. Fipronil 50 g/L 37,5 gr 2. Deltamethrin 25 g/ l 18,75 gr 3. Karbofuran 3% 180 gr 4. Endosulfan 350g/l 175 gr D. Herbicides 1. Paraquat 276 gr/L 207 gr 2. isopropil amina glifosat 486 gr/L 121,5 gr 3. 2,4 - DMA 75% + Metil metsulfuron 0,7% + 171,9 gr Etil Klirimuron 0,7% 4. Triasulfurom 75% 10 gr

Appendix 10. The abundance of arthropods in 4 WAT

No. Organism Conv R RM RMA RMAF RMAD RMAFD Insect Pests 1 Rice stem borers (Scirphopaga innotata) 8,0 ± 1,5 b 3,3 ± 0,7 a 4,6 ± 0,9 ab 4,6 ± 0,9 a 3,3 ± 0,9 a 2,3 ± 0,3 a 2,0 ± 0,6 a 2 Brown planthopper (Nilaparvata lugens) 17,3 ± 3,7 c 13 ± 2,0 bc 13,3 ± 2,9 bc 3 ± 0,6 a 4,0 ± 1,0 ab 4,0 ± 1,0 ab 1,7 ± 0,7 a 3 Rice whorl maggot (Hydrellia philippina) 7,0 ± 1,2 ab 10,0 ± 2,1 b 8,3 ± 0,7 ab 4,0 ± 1,5 a 4,6 ± 0,7 a 6,3 ± 0,7 ab 5,7 ± 0,9 ab 4 Grasshopper (Oxya hilla intricata) 6,0 ± 0,6 c 4,3 ± 0,3 bc 3,3 ± 0,3 b 2,3 ± 0,3 ab 2,3 ± 0,3 bc 1,0 ± 0,6 a 0,3 ± 0,2 a 5 Green Leafhopper (Nephotettix virescens) 13,7 ± 0,9 e 8,3 ± 1,2 d 7,0 ± 0,6 cd 5,3 ± 0,3 bcd 2,3 ± 0,3 ab 2,3 ± 0,3 ab 1,3 ± 0,3 a 6 Rice Moth (Corcyra chepalonica) 7,0 ± 0,6 b 7,0 ± 0,6 b 4,0 ± 0,6 a 3,3 ± 0,3 a 2,0 ± 0,6 ab 3,0 ± 0,6 a 2,0 ± 0,6 a 7 White backed plant hopper (Sogatella furcifera) 3,6 ± 0,7 abc 5,7 ± 0,7 c 5,3 ± 0,7 c 4,3 ± 0,3 bc 3,7 ± 0,3 bc 2,7 ± 0,3 ab 1,7 ± 0,3 a 8 Rice gall midge (Orseolia oryzae) 3,0 ± 0,6 ab 3,7 ± 0,3 b 3,3 ± 0,3 ab 2,7 ± 0,3 ab 2,7 ± 0,3 ab 2,0 ± 0,6 ab 1,3 ± 1,2 a

Natural Enemies 1 Green crab Spider (Oxytate striatipes sp) 1,0 ± 0,6 a 1,0 ± 0,6 a 2,0 ± 0,6 a 2,3 ± 0,9 a 3,0 ± 0,6 a 2,3 ± 0,7 a 1,0 ± 0,6 a 2 Wolf spider (Lycosa pseudoannulata) 0,7 ± 0,3 a 1,7 ± 0,3 ab 3,0 ± 0,6 b 2,0 ± 0,6 ab 2,7 ± 0,3 ab 2,7 ± 0,3 ab 2,0 ± 0,6 ab 3 Black Parasitoid wasp (Goniozus sp) 0,7 ± 0,3 a 1,7 ± 0,3 ab 3,0 ± 0,6 b 2,0 ± 0,6 ab 2,0 ± 0,6 ab 1,7 ± 0,3 ab 2,3 ± 0,3 ab 4 Red parasitoid wasp (Echthrodelphax fairchildii) 0,7 ± 0,3 a 1,3 ± 0,3 ab 2,7 ± 0,3 ab 2,3 ± 0,3 ab 3,3 ± 0,9 b 3,7 ± 0,9 b 3,0 ± 0,6 ab 5 Dragonfly (Neurothemis tullia tullia) 1,0 ± 0,6 a 0,3 ± 0,2 a 1,0 ± 0,6 a 0,3 ± 0,2 a 2,3 ± 0,9 a 1,7 ± 0,3 a 1,7 ± 0,3 a 6 Grasshopper (Conocephalus longiconnis) 0,7 ± 0,3 a 2,0 ± 0,6 a 1,3 ± 0,3a 1,7 ± 0,7 a 1,7 ± 1,2 a 1,7 ± 0,3 a 2 ± 0,6 a 7 Lady bug (Coccinela transversalis) 1,0 ± 0,6 a 2,3 ± 0,3 ab 3,3 ± 0,3 bc 3,3 ± 0,3 bc 4,3 ± 0,3 bc 4,3 ± 0,3 c 4,0 ± 0,6 bc 8 Flies (Argyrophylax nigrotibialis) 1,3 ± 0,3 a 4,0 ± 1,0 ab 5,7 ± 0,3 abc 10,7 ± 1,5 c 7,3 ± 2,0 bc 6,0 ± 1,0 abc 6,0 ± 1,6 abc 9 Long neck bug (Ophionea nigrofasciata) 1,7 ± 0,3 a 2,7 ± 0,7 a 2,3 ± 0,3 a 2,0 ± 0,6 a 3,0 ± 0,6 a 3,3 ± 0,3 a 2,3 ± 0,3 a The data were the average values in the 3 blocks; difference tests between treatments were analyzed by Tukey HSD method; values with different letters in the same row meant significant difference at 0,05 levels (P < 0,05), and value followed by the same letters was not significant.

Appendix 11. The abundance of arthropods in 6 WAT

No. Organism Conv R RM RMA RMAF RMAD RMAFD Insect Pests 1 Rice stem borers (Scirphopaga innotata) 1,3 ± 0,7 a 1,3 ± 0,7 a 3,0 ± 0,6 a 1,7 ± 0,9 a 0,7 ± 0,3 a 1,0 ± 0,3 a 1,0 ± 0,3 a 2 Brown planthopper (Nilaparvata lugens) 1,7 ± 0,9 a 1,3 ± 0,7 a 0,7 ± 0,3 a 1,3 ± 0,3 a 2 ± 0,6 a 1,7 ± 0,6 a 2 ± 0,6 a 3 Rice bug (Leptocorisa oratorius) 1,3 ± 0,9 a 0,7 ± 0,3a 0 0 0 0 0 4 Rice whorl maggot (Hydrellia philippina) 1,3 ± 0,9 a 2,0 ± 0,6 a 1,3 ± 0,3 a 1,3 ± 0,3 a 1,3 ± 0,7 a 1,7 ± 0,6 a 2,0 ± 0,6 a 5 Grasshopper (Oxya hilla intricata) 0,7 ± 0,4 a 0,3 ± 0,2 a 0,3 ± 0,2 a 0,3 ± 0,2 a 1,0 ± 0,6 a 0,3 ± 0,2 a 0,7 ± 0,3 a 6 Green Leafhopper (Nephotettix virescens) 2,0 ± 0,7 a 2,3 ± 0,3 a 2,0 ± 0,6 a 1,7 ± 0,9 a 1,3 ± 0,7 a 0,7 ± 0,3 a 1,0 ± 0,3 a 7 Rice Moth (Corcyra chepalonica) 1,7 ± 0,9 a 0 0 0 0 0 1,0 ± 0,3 a 8 White backed plant hopper (Sogatella furcifera) 1,7 ± 0,9 a 1,7 ± 0,9 a 2,0 ± 0,6 a 1,7 ± 0,9 a 1,3 ± 0,7 a 1,3 ± 0,7 a 1,3 ± 0,7 a 9 Rice gall midge (Orseolia oryzae) 3,0 ± 0,6 a 2,0 ± 0,6 a 1,7 ± 0,9 a 1,3 ± 0,3 a 3,3 ± 1,2 a 2,3 ± 0,3 a 1,7 ± 0,3 a

Natural Enemies 1 Paedorus fuscipes 1,0 ± 0,6 a 0,7 ± 0,3 a 0,7 ± 0,3 a 1,0 ± 0,6 a 1,0 ± 0,6 a 1,0 ± 0,6 a 1,3 ± 0,9 a 2 Green crab Spider (Oxytate striatipes sp) 1,0 ± 0,6 a 1,0 ± 0,6 a 1,0 ± 0,6 a 1,0 ± 0,6 a 1,0 ± 0,6 a 0,7 ± 0,3 a 2,3 ± 0,9 a 3 Wolf spider (Lycosa pseudoannulata) 1,0 ± 0,6 a 1,0 ± 0,6 a 1,3 ± 0,9 a 2,3 ± 0,7 a 1,7 ± 1,2 a 1,7 ± 0,7 a 2,3 ± 1,5 a 4 Black Parasitoid wasp (Goniozus sp) 0,3 ± 0,2 a 1,0 ± 0,6 a 0,3 ± 0,2 a 0 0 1,7 ± 0,7 a 0,3 ± 0,2 a 5 Red parasitoid wasp (Echthrodelphax fairchildii) 0,7 ± 0,3 a 0,3 ± 0,2 a 0,7 ± 0,3 a 1,3 ± 0,9 a 2 ± 0,7 a 0,3 ± 0,2 a 2,3 ± 0,9 a 6 Dragonfly (Neurothemis tullia tullia) 1,0 ± 0,7 a 1,0 ± 0,7 a 1,3 ± 0,7 a 0 2,0 ± 1,2 a 3,0 ± 0,6 a 0,7 ± 0,3 a 7 Grasshopper (Conocephalus longiconnis) 0,7 ± 0,3 a 0,7 ± 0,3 a 0,3 ± 0,2 a 1,7 ± 0,7 a 1,0 ± 0,6 a 1,3 ± 0,7 a 2,7 ± 0,9 a 8 Lady bug (Coccinela transversalis) 0,3 ± 0,2 a 0,7 ± 0,3 a 0,7 ± 0,3 a 0,3 ± 0,2 a 0,7 ± 0,3 a 1,0 ± 0,6 a 0,7 ± 0,3 a 9 Flies (Argyrophylax nigrotibialis) 0,7 ± 0,3a 1,0 ± 0,7 a 1,7 ± 0,7 a 2,3 ± 0,3 a 1,7 ± 1,2 a 1,7 ± 0,3 a 1,7 ± 0,7 a 10 Long neck bug (Ophionea nigrofasciata) 0,7 ± 0,3 a 1,0 ± 0,7 a 1,0 ± 0,7 a 0,7 ± 0,3 a 1,3 ± 0,9 a 1,7 ± 0,3 a 2,0 ± 1,0 a The data were the average values in the 3 blocks; difference tests between treatments were analyzed by Tukey HSD method; values with different letters in the same row meant significant difference at 0,05 levels (P < 0,05), and value followed by the same letters was not significant.

Appendix 12. The abundance of arthropods in 8 WAT

No. Organism Conv R RM RMA RMAF RMAD RMAFD Insect Pests 1 Rice stem borers (Scirphopaga innotata) 0,3 ± 0,2 a 1,3 ± 0,9 a 0 0 0 0 0 2 Brown planthopper (Nilaparvata lugens) 2,3 ± 0,7 a 5 ± 3,3 a 1,0 ± 0,6 a 0,7 ± 0,3 a 1,3 ± 0,9 a 0 0 3 Rice bug (Leptocorisa oratorius) 0,7 ± 0,3 a 0 0,7 ± 0,3 a 0 0 0 0 4 Rice whorl maggot (Hydrellia philippina) 0 1,7 ± 1,2 a 0 0,3 ± 0,2 a 0,7 ± 0,3 a 1,3 ± 0,9 a 1,0 ± 0,6 a 5 Grasshopper (Oxya hilla intricata) 0 0,3 ± 0,2 1 ± 0,6 a 0 0 0,7 ± 0,3 a 0 6 Green Leafhopper (Nephotettix virescens) 1,0 ± 0,6 a 2 ± 1,2 a 0,7 ± 0,3 a 0,7 ± 0,3 a 0 0,7 ± 0,3 a 1,0 ± 0,6 a 7 Rice Moth (Corcyra chepalonica) 2,0 ± 0,6 a 2 ± 0,6 ab 1,7 ± 1,2 a 0,3 ± 0,2 a 0,7 ± 0,3 a 2,3 ± 0,7 a 3,3 ± 0,7 a 8 Red insect (family of Miridae) 0 0,3 ± 0,2 a 0 0,3 ± 0,2 a 0,3 ± 0,2 a 1,3 ± 0,9 a 0 9 White backed plant hopper (Sogatella furcifera) 0 0,3 ± 0,2 a 0 0 0 0 0 10 Rice gall midge (Orseolia oryzae) 4,7 ± 2,6 a 7,3 ± 1,9 a 6,7 ± 3,5 a 7,0 ± 3,2 a 4,3 ± 1,2 a 3,7 ± 0,7a 6,7 ± 2,0 a

Natural Enemies 1 Paedorus fuscipes 0 0 0 0 0 0 0,3 ± 0,2 a 2 Green crab Spider (Oxytate striatipes sp) 1,3 ± 0,9 a 0 0 0 1,0 ± 0,6 a 0 0 3 Wolf spider (Lycosa pseudoannulata) 0 0,3 ± 0,2 a 0,3 ± 0,2 a 0 0 0 0 4 Black Parasitoid wasp (Goniozus sp) 2,0 ± 0,6 a 4,3 ± 0,9 a 3,3 ± 1,2 a 2,0 ± 0,6 a 0,7 ± 0,3 a 2,0 ± 0,7 a 4,7 ± 2,7 a 5 Red parasitoid wasp (Echthrodelphax fairchildii) 0 0,3 ± 0,3 a 1,0 ± 0,6 a 0,3 ± 0,2 a 0 0 0 6 Dragonfly (Neurothemis tullia tullia) 1,3 ± 0,9 a 0 2,0 ± 0,6 a 0,3 ± 0,2 a 0 1,3 ± 0,9 a 0 7 Grasshopper (Conocephalus longiconnis) 0 0 0 0 0 0 0,3 ± 0,2 a 8 Lady bug (Coccinela transversalis) 0,3 ± 0,2 a 0,3 ± 0,2 a 0 0 0 0 0 9 Cricket (Metioche vittaticolis) 1,3 ± 0,9 a 1,0 ± 0,6 a 0 0 1,3 ± 0,9 a 0,3 ± 0,2 a 1,3 ± 0,9 a 10 Flies (Argyrophylax nigrotibialis) 0 0 0 0 0 0 0 11 Long neck bug (Ophionea nigrofasciata) 5,3 ± 2,2 a 4,0 ± 2,1 a 2,3 ± 0,9 a 5,0 ± 1,5 a 2,3 ± 0,7 a 2,7 ± 0,9 a 4,0 ± 1,7 a The data were the average values in the 3 blocks; difference tests between treatments were analyzed by Tukey HSD method; values with different letters in the same row meant significant difference at 0,05 levels (P < 0,05), and value followed by the same letters was not significant.

Appendix 13. The abundance of arthropods in 10 WAT

No. Organism Conv R RM RMA RMAF RMAD RMAFD Insect Pests 1 Rice stem borers (Scirphopaga innotata) 0,3 ± 0,2 a 1,3 ± 0,9 a 0,3 ± 0,2 a 0 0,3 ± 0,2 a 0 0 2 Brown planthopper (Nilaparvata lugens) 3,0 ± 2,1 a 0,3 ± 0,2 a 1,0 ± 0,6 a 0,7 ± 0,3 a 1,0 ± 0,6 a 0 0 3 Rice whorl maggot (Hydrellia philippina) 0 2,0 ± 1,0 a 0,7 ± 0,3 a 0,7 ± 0,3 a 1,3 ± 0,7 a 1,0 ± 0,6 a 1,0 ± 0,6 a 4 Grasshopper (Oxya hilla intricata) 0 0,3 ± 0,2 a 0,3 ± 0,2 a 0 0 1,3 ± 0,7 a 0,3 ± 0,2 a 5 Green Leafhopper (Nephotettix virescens) 2,0 ± 0,6 a 2,7 ± 0,7 a 1,7 ± 0,3 a 1,0 ± 0,7 a 0,3 ± 0,2 a 1,3 ± 0,9 a 1,3 ± 0,3 a 6 Rice Moth (Corcyra chepalonica) 1,0 ± 0,6 a 1,7 ± 0,3 a 1,0 ± 0,6 a 0,3 ± 0,2 a 0,3 ± 0,2 a 2,7 ± 1,3 a 2,7 ± 1,3 a 7 Caterpillar 0 0 0 0 0 0 0 8 Red insect (family of Miridae) 0 0,3 ± 0,2 a 0 0 0 0 0 9 White backed plant hopper (Sogatella furcifera) 0 0 0 0 0,3 ± 0,2 a 0 0 10 Rice gall midge (Orseolia oryzae) 11,3 ± 4,5 a 7,3 ± 1,9 a 8,0 ± 3,6 a 7,0 ± 3,2 a 4,3 ± 1,2 a 3,7 ± 0,7 a 6,7 ± 2,0 a

Natural Enemies 1 Paedorus fuscipes 0,7 ± 0,3 a 0,3 ± 0,2 a 0 0 1,3 ± 0,7 a 0 0 2 Green crab Spider (Oxytate striatipes sp) 0 0 0,3 ± 0,2 a 0 0 0 0 3 Wolf spider (Lycosa pseudoannulata) 0 1,3 ± 0,7 a 1,0 ± 0,7 a 0,3 ± 0,2 a 0 0 0 4 Black Parasitoid wasp (Goniozus sp) 2,0 ± 0,6 a 4,3 ± 0,9 a 3,3 ± 1,2 a 2,0 ± 0,6a 0,7 ± 0,3 a 4,7 ± 2,7 a 4,7 ± 2,7 a 5 Red parasitoid wasp (Echthrodelphax fairchildii) 0,7 ± 0,3 a 0 2,0 ± 0,6 a 0,3 ± 0,2 a 0 0,7 ± 0,3 a 0 6 Dragonfly (Neurothemis tullia tullia) 0 0 0 0 0 0 0,3 ± 0,2 a 7 Grasshopper (Conocephalus longiconnis) 0,3 ± 0,2 a 0,7 ± 0,3 a 0 0 0 0 0 8 Lady bug (Coccinela transversalis) 0,7 ± 0,3 a 0,7 ± 0,3 a 0 0 0,7 ± 0,3 a 0,7 ± 0,3 a 0,7 ± 0,3 a 9 Cricket (Metioche vittaticolis) 0 1,0 ± 0,7 a 0 0 0 0 0 10 Flies (Argyrophylax nigrotibialis) 5,3 ± 2,2 a 4,0 ± 2,1 a 3,0 ± 0,6 a 4,3 ± 2,0 a 2,3 ± 0,7 a 2,7 ± 0,9 a 4,0 ± 1,7 a 11 Long neck bug (Ophionea nigrofasciata) 0 0 0 0,3 ± 0,2 a 0 0 0

The data were the average values in the 3 blocks; difference tests between treatments were analyzed by Tukey HSD method; values with different letters in the same row meant significant difference at 0,05 levels (P < 0,05), and value followed by the same letters was not significant.