WAGENINGEN UR

Effects of duck density on suppression of weeds and pests in complex systems in MSc Thesis Report

WANG Nan 920805927080

Supervisors:

dr. Egbert Lantinga ; Uma Khumairoh, MSc Couse code: FSE-80436 2016.8

Thesis Title Effects of duck density on suppression of weeds and pests in complex rice systems in Indonesia Duration January 2016 – March 2016

Name of Student Nan Wang

Registration number 920805927080

Credits 36 ECTS

Course Code FSE – 80436

Name of Course MSc Thesis Farming Systems Ecology

Supervisors dr. Egbert Lantinga Uma Khumairoh, MSc

Examiner dr. Felix Bianchi

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Acknowledgement

This study was taken place in Malang, Indonesia during Jan – March, 2016. I stayed in Kepanjen, Malang since October, 2015, living in Uma Khumairoh’s place and doing experiment there with local . First of all, I would like to give my thanks to my supervisors dr. Egbert Lantinga and Uma Khumairoh (MSc). During my thesis research, they gave me good instructions and suggestions for my research and I learnt a lot from them. Secondly, the farmers Bu Siyami and her husband Pak Tamprik, as well as Pak Kaseri and Bu Sunar gave me the chance to do experiment in their and Pak Tamprik accepted my interview and shared his views towards complex rice system with me. With their help, I finished my thesis study. Thirdly, I want to give my sincere thanks to Uma’s families who helped me in my daily life and my experiment. They made me feel warm and at home during the stay in Indonesia. They made me think Indonesia is a beautiful and lovely country. At last, I want to thank all my friends and family members for their support and care.

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Abstract

During September 2015 and February 2016, a complex rice system with low duck density (100 ducklings/ha) was evaluated in Malang district, Indonesia. This system was compared with the conventional rice system and a complex rice system with high duck density (800 ducklings/ha). The main goal was to find out whether the low duck density complex rice system could provide sufficient suppression of pests and weeds for rice planting. The results showed that the low duck density complex rice system was still effective regarding . However, pest suppression efficiency differed between pest species. Despite the fact that the low duck density was efficient in suppressing most of the dominant rice pests, it was not powerful to say it can provide safe suppression of all pests in rice . For instance, the results showed low duck density complex rice system could provide sufficient suppression for rice whorl maggot and maize leafhopper; while for green leafhopper, zigzag leafhopper and brown planthopper, there was no sufficient proof to say that low duck density system was as safe enough in suppression as Conventional rice system or high duck density complex rice system. Besides, comparing pests appearance in Conventional rice system and high duck density fields, it was found that conventional ways to use insecticides was less effective than complex rice system with high duck density. This phenomenon on one hand may be due to the increasing resistance of insects to pesticides; on the other hand was because farmers are lacking professional chemical knowledge to choose effective chemicals. In addition, through natural enemies population comparison between Complex rice system and Conventional rice system, it was found insecticides had adverse effect on natural enemies population in Conventional system. The number of natural enemies in Conventional rice system was significantly lower than it in Complex rice system. Furthermore, there were two sampling methods used in this experiment, namely Sweep net test and Sticky yellow traps. It was found that more species including natural enemies and insects pests were found by yellow traps than sweep net. In terms of species difference, spiders, crickets, grasshoppers etc. were found more in Sweep net; while wasps and flies were found more in Yellow traps.

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Table of Contents Effects of duck density on suppression of weeds and pests in complex rice systems in Indonesia ...... 1 Acknowledgement ...... 3 Abstract ...... 4 1. Introduction ...... 1 1.1 Objectives ...... 4 1.2 Research questions ...... 4 1.3 Hypotheses ...... 4 2. Materials and Methods ...... 5 2.1 Experiment location ...... 5 2.2 Materials ...... 6 2.3 Experimental design ...... 8 2.4 Cultivation methods ...... 11 2.4.1 Land preparation ...... 11 2.4.2 Rice seedling and border crop ...... 12 2.4.3 Complex rice system management ...... 12 2.4.4 Weed and pest management ...... 12 2.5 Measurement ...... 13 2.5.1 Sweep net sampling ...... 13 2.5.2 Yellow traps ...... 13 2.5.3 Weed sampling ...... 13 2.6 Statistical analysis ...... 14 3. Results ...... 15 3.1 Weeds distribution in different treatments ...... 15 3.2 Pests population ...... 19 3.2.1 Sweep net sampling methods ...... 19 3.2.2 Yellow traps ...... 22 3.2.3 Comparison between yellow traps and sweep net ...... 25 3.3 Natural enemies of domain pests ...... 28 3.3.1 Natural enemies of domain pests ...... 28 3.3.2 Natural enemies population in different treatments by two sample methods ...... 29 3.3.3 NE/IP ratios in different treatments by two sample methods ...... 29 3.3.4 Species richness of insects in different treatments ...... 31 4. Discussion ...... 33 4.1 Weed suppression ...... 33 4.1.1 Weed suppression effectiveness in different treatments and mechanism ...... 33 4.1.2 Weed suppression effectiveness for different weed species ...... 35 4.2 Pests suppression ...... 36 4.2.1 Insect pests suppression effectiveness in different treatments...... 37 4.2.2 Natural enemies population in different treatments ...... 39 4.2.3 Comparison between two different sampling methods ...... 40 5. Conclusions ...... 42 5.1 Conclusions ...... 42 5.2 Limitations and recommendations...... 42 6. References ...... 44 7. Appendix ...... 47 7.1 Field conditions ...... 47 7.2 Ducks and fishes activity ...... 48 7.3 Insects in rice paddy ...... 49 7.3.1 Natural enemies in rice field ...... 49 7.3.2 Insects pests in rice field ...... 50 7.4 Weed dry matter in different treatments SPSS results ...... 51 7.4.1 Monochoria one way ANOVA ...... 51 7.4.2 Small flower umbrella sedge one way ANOVA ...... 52 7.4.3 Knotgrass one way ANOVA ...... 53 7.4.4 Water creeping primrose one way ANOVA ...... 54 7.5 Insects pests in different treatments SPSS results ...... 55 7.5.1 Insects pests_sweep net ...... 55 7.5.2 Natural enemies SPSS results ...... 58

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1. Introduction

Indonesia, as the fourth populous country in the world, owns a current population of 241 million persons (Kohler, 2015). Among these people, 60% of them are living in rural districts, depending on farming for making livings, which makes a crucial sector in economy aspect in Indonesia (IFAD,2015). Within Indonesia agriculture structure, food crops comprise the majority of arable lands; among which rice is the most crucial crop; It is planted in lots of areas in Indonesia, including , Madura, , Lombok, parts of northern and southern , and southern (Koo, 1985). Indonesia is highly dependent on rice, not only for food security, but also for social cultural reasons. Rice can be seen on Indonesian people’s table every day, in lunch, dinner, even for breakfast. Besides, in their daily lives, they use rice to socialize with neighbours or friends; for instance, people give rice as gifts when their neighbours get married or hold funerals. What’s more, in the traditional festivals, such as harvesting festival, Indonesians like to make rice cakes or other handcrafts by rice for parades. So in both daily consuming and cultural needs, rice is important in Indonesia people’s lives. In order to maintain food security in Indonesia, Indonesia government came up with the thought to raise rice yield, accomplishing rice self-sufficiency domestically (Koo, 1985). Thanks to the in 1960s, passing from western countries to the developing countries in Asia, rice production in Asia has gained great increases. For Indonesia, it has witnessed a great step in rice yield and rice harvested areas since 1961 till now. The annual production of rice in 2013 was 71,280,000 tons, standing as the third greatest producer in the world; the number was six times higher than it was in 1961 (Shiostu, 2015). But nowadays, despite the impressed results from green revolution of increasing yields, a series of related problems have been revealed. For instance, large-scale leads to high accidences of weeds, diseases and pests; continuous causes poor fertility, even deterioration of soil structure, as well as other health and pollution issues (Conway, 1988). Given the fact that high utilization of chemicals and artificial caused a series of environmental problems and therefore limited the potential production effectiveness of arable lands, people started to find a new way for agriculture which should be sustainable and coordinated with nature. As a result, organic rice farming has drawn big attention and has been implemented in several regions in Indonesia currently (Lestari, 2013). Since early 1980s, organic agriculture in Indonesia has started to be implemented, initially by NGOs cooperated with small holder farmers, especially in Java island; Until 2011, there is 0.2% of

1 the total in Indonesia under organic agricultural management; furthermore, in order to support organic agriculture development, the Indonesia government has allocated 4 million US dollars to build the organic program “Go Organic 2011” and set up limits on use of chemical (Willer, 2011). In terms of organic rice farming, the traditional rice cultivation in Asia can provide great inspiration for organic system nowadays; and the cultivation method is rice-fish culture; It was originally from Asia, but now it is hard to prove when and where the system been invented; while it has occurred in Indonesia for more than a hundred years (Coche, 1967). Afterwards, the rice fish systems became more diverse, and ruminants and non-ruminants started to be integrated in rice fields; these systems diversify products, make better use of certain resource, making it beneficial for small holder farmers to spread risks (Devendra, 1995). In integrated system, fishes, animals, crop can be sold after harvested; crop residues and by-products can be fed to animals; manure of animals can be nutrients source for fish and crop; and fish pond can provide habitat for water plants, which can feed animals (Devendra, 1995). In Java, Indonesia, the experiments were done about the on- returns comparison among rice , rice fish systems and rice-fish-duck systems; and the results turned out that rice-fish-duck systems were the most profitable treatment; despite it did not give description about other system characteristics such as weeds and pests situations and yields (Fagi, 1992). In these years, since the concept of agroecosystems raised up, complex rice system (integrated rice with fishes and ducks) has been explored more. Studies have showed that rice fish and rice duck system reduced N and increased N output by fishes, ducks and rice yield, which resulted in a better Nitrogen balance compared with conventional rice production (Li, 2008). Besides, several studies have proven that rice duck and rice fish rice production system can increase rice yield and the income of farmers. Fishes and ducks can provide nutrients into the system by their droppings; on the other hand, their eating behavior contribute to weed and pest control. For instance, in the study of Ahmed (2004), the density of some pests were significantly decreased in rice duck system compared to conventional ones, and the yield was significantly increased. Following the previous researches, Uma Khumairoh, the PhD from Wageningen University, has done a series of complete experiments exploring the improved complex rice system in Malang, Indonesia. The complex rice system integrating rice, fishes, ducks and was designed by Khumairoh (2012). From her study, it is found that the system of azolla, fish, duck and rice increased plant growth, and sharply reduced the density of six pests, namely snails, rice whorl maggots, leaf hoppers, plant hoppers, stem borers as well as 2 rice bugs. In terms of yield and gross margin, both of them were increased. In Khumairoh (2012)’s complex rice system, fishes, ducks and azolla are integrated into paddy field with compost as fertilizer. Within the system, fishes and ducks provide nutrients to rice by their excreta in the water; compost from straw and manure can provide nutrients with less risk of weed spreading compared with manure. In terms of pest control, margin plants (Khumairoh, 2014 & Khumairoh 2015) are planted before rice in the border of paddy fields to attract natural enemies; ducks and fishes play an important role in weed and pest control as well by their eating behaviors; in addition, ducks’ trampling behavior bring more oxygen into soil which stimulate the root growth of rice, so that increase the competition ability of rice plants; as for azolla, it is a floated fast-growing fern in the water that can block light, in which way; the weed seeds under the water can hardly germinate. Complex rice system reduces external inputs, increases production diversity and yield, which makes it profitable for smallholder farmers. In Indonesia, farmers have already applied this kind of system. While in complex rice systems, labor could be increased compared with conventional rice cultivation, for organic management and diverse production would require more work. Based on the research made by Zheng(2014), it was calculated that the labor input of existed complex system (rice and duck) was more than for rice monocultures, especially low- intensive labors like duck feeding. Smallholder farms usually represent farms owned by families with a small scale of land, usually less than 2 hectares; the family members provide the most of work on farm, and live from the farm finance income (IFC, 2013). For the limit of labor, increased labor costs of complex rice system will add extra costs for farmers. In Indonesia, smallholder farmers population reached a large number of 17 million, as the third Asia country owning large numbers of smallholder farmers (Thapa, 2009). As the result, labor shortage will be one of the obstacles of this large group of people to apply complex rice system. In Khumairoh (2013)’s study, farmers kept 400 birds/ha, which needed pay extra labor to raise the ducks, and thus, the profit was reduced. To solve this problem, there is room to improve the system designed by Khumairoh (2013) by lowering the numbers of ducks to reduce labor costs while maintaining the function of the system. The research explained in this report was working for this purpose; it was done in Malang, Indonesia, following the research of Uma Khumairoh. According to Khumairoh’s experiments and calculations, in complex rice systems, the duck density of 150 birds/ha could guarantee the self-sufficiency of the system, which means, no extra inputs are needed during the growth of rice; except the initial costs for compost fertilizers, azolla inoculation, ducklings, fish fingerlings, and nylon fence. Ducks do 3 not need to be supplied with external feed, means the reduce of labor for farmers. To access the feasibility of the low-duck-density system, experiments had been designed. Through the experiments, it was hoped that the pests and weeds suppression could be assessed compared with high-duck-density system.

1.1 Objectives

The study aimed at finding out the feasibility of low-duck-density complex rice systems, especially on pests and weeds suppression. Combining the rice cultivation situation in Indonesia, SRI (System of Rice Intensification) cultivation methods was used. The rice pests species were investigated and recorded during the experiments, as well as for weeds species separately in different treatments, which was summarized and analyzed to assess the effectiveness of biological control for low-duck-density system.

1.2 Research questions

- Could complex rice system with a duck density of 100 birds/ha provide same effectiveness in pests biological control as rice complex system with a duck density of 800 birds/ha, organic rice system and conventional rice system? - Could complex rice system with a duck density of 100 birds/ha provide same effectiveness in weeds biological control as rice complex system with a duck density of 800 birds/ha, organic rice system and conventional rice system? - How will the application of pesticides influence the appearance of natural enemies in rice field?

1.3 Hypotheses

The suppression of pests and weeds might be less effective in low-duck-density complex rice system compared with high-duck-density one, as the reduced number of ducks. But considering the feeding behaviour of ducks and fishes, along with azolla as a good inhibitor of weeds, the low-duck-density complex system might be sufficient in pests and weeds control compared to conventional rice system. Besides, considering the application of insecticides in conventional rice paddy, natural enemies would also be reduced since the most of the pesticides used by farmers are not species specific; the natural enemies population should be higher in complex rice system than in conventional fields.

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2. Materials and Methods

2.1 Experiment location

The research site was located in Kepanjen, Malang, East Java, Indonesia. Kepanjen is a town in the south of Malang city with lots of smallholder farmers, mainly producing rice, sugarcane, as well as tree. The soil type in this district is silty clay (Khumairoh, 2013). The experimental fields belong to a family of local farmers. Most of the work on farm is done by the couples on their seventies. In the previous years, this family had involved in Khumairoh’s complex rice system research; while last year, because the drought and the work to fix the channel, the experimental and neighbour farms were left bare. Normally, the fields are irrigated by the spring water near the fields, with tunnels connecting different plots. Malang has tropical climate, embodying in significant rainfall during most months of the year and a short dry season. Dry months here usually are May, June, July, August, and September, while the rest of the year is wet. The average temperature and average rainfall are 23.7 °C and 2088 mm/year. The climate situation including average temperature and rainfall per month, as well as rainy days per month, was shown in Figure 1. The experiment lasted from January of 2016 to March of 2016, and compared with previous weather condition, the dry season in this year is longer than before and the precipitation is lower in October and November compared with previous years.

mm 400 350.5 40 °C/days 350 35 300 250.5 250.5 30 250 25 200 175.5 20 150 125.5 15 Aver. Rain. (mm) 100 10 Aver. Temp. (deg C) 50 5 10 10 Rainy days/month 0 0 09/2015 10/2015 11/2015 12/2015 01/2016 02/2016 03/2016 Time

Resource from: Staklim Karangploso Malang, 2016

Figure 1. Climate in Malang, Indonesia

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2.2 Materials

The plant and animal materials used in this experiment included: rice variety Ciherang, azolla, crotalaria, cabbage, eggplants, lettuce, kangkung (water spinach); and local ducks (Anas platyrhynchos javanicus), fish Nile tilapia (Oreochromis niloticus). Ciherang is one of the domain rice varieties in Indonesia, despite the plant area is decreasing during these years, it is still the most planted rice variety in Indonesia, even for organic rice production (Shiotsu, 2015). Ciherang variety, with a high yield of 6.09 tons/ha, is suitable for both dry and wet season and needs a grow time around 116-125 days after transplanting (Septiningsih, 2015). Ciherang variety seeds was bought from local seed shop, and pre-germinated in fields before being transplanted. Azolla is a genus of small elegant water ferns famous for its ability to assimilate atmospheric nitrogen, which is functioned by its symbiotic relationship with a nitrogen-fixing blue-green alga, Anabaena azollae (Lumpkin, 1982). In this experiment, the azolla () was purchased from local rice (Figure 2). For each plot (200 m2), around 40kg azolla was used. Azolla needed to be inoculated in advance to reproduce before ducks and fishes going into system.

Figure 2 Azolla pinnata

Crotalaria juncea is an erect herbal plant, with a height of 1-3.5 meters (Figure 3). Despite it is considered as a tropical or subtropical crop, Crotalaria is resistant to drought and can fit in different soil types. It is native to Australia and , but it is widely used in tropical regions currently, including Indonesia. For Indonesia farmers, Crotalaria is worthy for its nitrogen fixing feature and containing high nutrient value to be used as fodder or soil improver (Orwa, 2009). Crotalaria produces high amount of seeds, the farmers can collect seeds for next year instead of buying seeds every year (Chaudhury, 2007). Besides, because that crotalaria is a fast growing legume, it can be cut for many times and the leaves and stems can be used on farm as fodder or green manure. In the experiment, crotalaria was 6 grown on the borders of each plot around two weeks before transplanting. This was because the experiment of Brévault (2007) shown that no-till system with Crotalaria as mulch favoured the establishment of detritivores like earthworms, termites, ants and predators such as spiders, carabids, staphylinidae, centipedes. Based on Khumairoh (2012)’s previous experiments, the natural enemies attracting function of crotalaria was also proved.

Figure 3 Crotalaria seeds and flowers

Local vegetables, for instance, lettuce, cabbage, kangkung and eggplants were applied to the border of each plot to diversify production. The seeds of kangkung and the seedlings of other vegetables were purchased from local farmers and planted after crotalaria.

Figure 4 Ducklings at 2 weeks old

Anas platyrhynchos Javanicus is a local duck species (Figure 4), with the local name Mojosari; it is raised for both meat and egg production. In this experiment, the ducklings were bought at two weeks old. Nile tilapia (Oreochromis niloticus) is the second consumed farmed food fish next to carp in the world; in Indonesia, it is commonly cultivated singly or with rice (Fitzsimmons, 2014). It has cheap price in Indonesia, but very popular in human’s recipe. In terms of its biological characteristics, Nile tilapia can be fed on organic waste, promote Chlorella dominance and product natural antimicrobials, so that it can improve sediment quality, improve water on quality and suppress the appearance of some microbial caused diseases (Fitzsimmons, 2014). Nile tilapia (2.5 months old, 15-20 cm in length) was bought from fish farmer for shortening

7 the grow time; and the stocking density was 500 stock/ha (Figure 5).

Figure 5 Nile tilapia

Other materials used in the experiment were: PVC square (50cm × 50cm) to measure weeds; plastic bags; sweep for insects sweep test; yellow traps; loops. For complex rice system, fish ponds and duck houses were made by farmers before the experiment; duck houses were built with local bamboo and rice straws.

2.3 Experimental design

There were two repetitions in total, and the fields belonged to two different smallholder farmers. Despite the difference between farmers, the two repetitions shared same weather condition (same location) and soil type. For each repetition, five treatments were set, namely conventional group (CONV), control group (CONT), RMAF (Rice, manure, azolla, fish) group, RMAFDL (Rice, manure, azolla, fish and 1 ducks/100 m2) and RMAFDH (Rice, manure, azolla, fish and 8 ducks/100 m2) group. CONV: The treatment following the normal practice of farmers, applying compost, artificial fertilizers, pesticides and herbicides; CONT: The treatment same with CONV group, but without implementing insecticides and herbicides; RMAF: The treatment integrated with manure, Azolla and fish; RMAFDL and RMAFDH: The treatments integrated with manure, Azolla, fish and different duck densities. In this two treatments, nutrients from last growing seasons remained and no extra compost or manure were added. The treatments were allocated in the field randomly with enough space (5-7 m) between each other. For each treatment, a 200 m2 plot was arranged; besides, for RMAF 8 group and RMAFD group, fish ponds and duck houses were prepared in advance and they are shown in Figure 6. Ducks houses and fish ponds only need to be invested for the first time, and can be constantly used in the following years. The layouts of the experimental fields were shown in Figure 7. The grey part in the figures are water tunnels for irrigation. In both of the repetition, the green areas are neighbour’s fields having conventional rice or corn cultivation. In figure 8, the details of RMAFD treatment was indicated. Fish ponds, duck houses and rice paddy were connected with water tunnels for fishes to pass through (Figure 6-right). when the water level was low, fishes would go back to fish pond; and come back to rice fields when the water level increases. Besides, caren (deeper and wider space in the middle of rice plantation) were made by farmers for fish and ducks to get through, in case ducks got lost because of messy and crowded rice plants. Border crops including crotalaria, kangkung, eggplants, cabbage and lettuce were shown in Figure 9.

Figure 6 Fish pond with inoculation of azolla and duck house, tunnels between pond and paddy

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Figure 7 Experiment layout

Figure 8 RMAFD system

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Figure 9 Border crops (Vegetables and Crotalaria)

2.4 Cultivation methods

SRI (System of Rice Intensification) methods were used in the experiment. Malang district introduced SRI methods years ago, especially in organic rice production. The System of Rice Intensification (SRI) was developed in and more than 20 countries now are using this method to raise rice production with reducing external inputs and production costs. There was research, which studied the efficient rate of farms, showing that farm using SRI methods tended to have high energy use efficiency, calculated by revue/cost, than other ordinary farms (Lestari, 2013). The concept of SRI used in Indonesia mainly consists of using seedlings, giving wider space to plant by planting one per hill and in square pattern, keep rice field moist but not constantly flooded and enhancing soil organic matter (Stoop, 2002).

2.4.1 Land preparation

Dikes, fish ponds, fences and duck houses were prepared first during October to November; in the meantime, after harvesting from last grow season, farmers ploughed the field twice before transplanting, and prepared rice seedling bed in the field. For RMAF, RMAFD (L&H) treatments, there was no compost or manure being applied, and all nutrients were provided by the nutrient left in field from last growing seasons and Crotalaria. In CONT and CONV groups, artificial fertilizers and organic fertilizer (compost) were applied by farmers’ practice. And the amount of fertilizers used were shown in Table 1.

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Fertilizers applications in treatments Item Amount (kg N /ha) Urea (N46%) 350

ZA (N21%) 0 (only in dry season)

CONT and CONV Phonska (N15%) 300

SP-36 (P2O5=36%) 100 KCL (K=65%) 100

RMAF Organic fertilizer (N) 200

Table 1 Fertilizer use in CONT&CONV fields

2.4.2 Rice seedling and border crop

Rice seedling activities including seedling bed preparation, rice seeds pre-germinating and sowing were conducted from Dec 24th to Dec 28th, 2015. In the meantime, border crops crotalaria and kangkung were sown on the bunches of RMAFD and RMAF plots. Crotalaria is a fast-growing legume herb; within one week, the seedlings of it came out and reached a height of 3-4 cm. Afterwards, from 5th to 6th of January, 2016, rice seedlings were transplanted at a space of 25 × 25 (cm2).

2.4.3 Complex rice system management

In RMAFD system, azolla, fishes and ducks were put into rice field in order, but with slightly time difference. Firstly, on 20th of December, azolla was put into fish pond, after around 5 days for azolla to adapt and reproduction; fishes went into fish pond on Dec. 25th, 2015. Ducklings were released on 15 January, 11 days after rice transplanting. For RMADFH treatment with 8 ducks/100 m2, besides fishes and ducks were fed by the insects and plankton in the field by themselves, rice brans were given twice a day. There was no extra feed used in RMADFL group (1 duck/100m2).

2.4.4 Weed and pest management

For weed management, normally farmers do twice-three times in a grow season, but in RMAFD system normally only needs one-time weeding at first cycle and completely no weeding when the rice field was immediately cultivated after harvest of the previous cycle. Thus, we followed the common practices of weeding to measure weed density, composition and DM. In control group, no pesticides or herbicides were used. And for conventional group, the chemicals applying followed the normal practice of farmers. In CONV treatment, seven times spray were done for pests suppression and the active

12 chemicals included carbofuran, difenoconazole, fipronil, deltamethrin, endosulfan and coumatetralyl.

2.5 Measurement

Weeds and pests were the main measurements, specifically for their species richness and population in different treatments and different status of plant growth. Weed was sampled by 4 times of a 50cm × 50cm PVC square in each plot to cover 1 m2; and pests were sampled with two methods, namely sweep net and sticky yellow trap. Weed were measured during the weeding process of farmers. During one growing seasons, generally farmers did two (2-3) times hand weeding. Pests were measured three times, 2 weeks after transplanting (WAT), 4 WAT and 8 WAT. At 2 WAT, rice is still in seedling stage, and at 4WAT rice is at tillering stage and is actively developing vegetative growth, and then at 8 WAT, rice goes into generative stage.

2.5.1 Sweep net sampling

Sweep net used in the experiment was made of white net cloth, and had a length of about 90cm and was 50 cm wide. During the sampling process, the track to sweep was diagonally distributed, and for each plot 25 times of “8 shape” were done. The sweeping route was shown by the arrow in Figure 10(Left). After sweeping, the insects caught in the sweep net were immediately transferred into labelled plastic bottles with alcohol. Every samplings were took place between 6:00-7:00 am in the morning.

2.5.2 Yellow traps

In this experiment, yellow traps were bought from shops and with bamboo sticks to fix on. In each plot, five yellow traps were diagonally distributed in the plot and the position was shown in Figure 10 (Left) by the white circles. In each sampling, yellow traps were put into the field at 7:00 am in the morning and taken out after 24 hours at a height of 80 cm above ground.

2.5.3 Weed sampling

For weed sampling, a square (50cm * 50cm) was used to measure the area of weed to be taken. In each plot, four sampling points were set for convenience, and the position of them were shown in Figure 10 (Right) with the black triangles.

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Rice Plot Rice plot

Figure 10 Sweeping direction, yellow traps position (Left) and weeding point (right)

2.6 Statistical analysis

All the data were put into Excel sheet to be summarized and made into forms. Bar and curve graphs were made by Excel to compare weeds and insects population. In order to test the significant difference between different treatments, statistical analysis was conducted by IBM SPSS Statistics22. For domain weeds species and pests, the data was put into SPSS data file; Normality distribution and homogeneous test (Levene’s test) were conducted firstly to determine which calculation method should be used. In this experiment, for data which was not normally distributed, non-parametric test was chosen and Kruskal-Wallis test was used. On the other hand, for normally-distributed data, one-way ANOVA was used to test significant differences; among these data, data fulfilling homogeneous test was analysed by one-way ANOVA and Tukey test for multiple comparison; data failing to meet homogeneous assumption was analysed by Welch test and Games-Howell (Post hoc) test.

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3. Results

3.1 Weeds distribution in different treatments

Most of the weeds found in rice field were herbal plants with plenty of tillers; after living in water, some of them were rotten and fragmented. As a consequence, it was difficult to distinguish one whole plant from another during sampling. That was why dry matter weight was used as measurement unit in data analysis. After two times sampling from the field, it was found that the weed population and species were similar in two samplings. There were 14 weed species found in the experimental plots, among which, some species had small population and dry weight, such as Southern cut grass, Water lettuce and Sessile joyweed; and some other species only existed in certain treatments, for example, African payal. The weed species and their distribution in different treatments are shown in Figure 11. From Figure 11, it can be seen, there was no weed found in RMAFDH treatment. In other plots, the weed species distribution had slight differences. The richest weed species were found in CONT group, which were 10 species; following by 9 species in CONV plots; and in RMAF and RMAFDL plots, there were 7 species found in the fields. Speaking of dry matter weight of different species, Monochoria had the highest dry matter weight totally in all experimental plots with a number of 266.3 g/m2; following by Knotgrass and Smallflower umbrella sedge, which separately shared a dry weight of 65.6 g/m2 and 49.4 g/m2. Only four species of these species were found in all of the treatments except RMAFDH, which were Monochoria, Knotgrass, Smallflower umbrella sedge and Creeping water primrose. Besides, some species were only found in one single treatment or even one repetition, for example African payal, Globe fringe-rush, Spreading dayflower and Saltmarsh bulrush.

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Treatments

8.1 7.1 0.1 0.1 0.2 0.8 CONT 6.2 4.6 6 30.4 26.6 120.1

0.2 0.002 CONV 1.7 2.4 16.2 7.3 70.9 0.1 1.8 2.2

RMAF 1.5 2.8 21.3 74.3 0.4 0.2 0.2

RMAFDL 3.4 0.02 7.9 2

RMAFDH

0.001 0.01 0.1 1 10 100 1000 Saltmarsh bulrush Water lettuce Spiny amaranth Water clover Spreading dayflower Southern cutgrass Globe fringe-rush Sessile joyweed Creeping water primrose Kono millet African payal smallflower umbrella sedge Knotgrass Monochoria

Figure 11. Weed species distribution

(Weeds were sampled at 4WAT/8 WAT at 1 m2 scale, dry matter is used and the unit is gr/m2. CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2.)

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Considering the amount and distribution of weeds species, the following analysis mainly focused on seven domain species, namely Monochoria, Knotgrass, Smallflower umbrella sedge, Creeping water primrose, spring amaranth, water clover and Kono millet. And the detailed information was shown in Figure 12. It could be found that for these seven species, they shared the similar pattern of emergence in different treatments. They tended to have obviously higher emergences in control group and lower emergences in RMAFDL and RMAFDH groups, especially in RMAFDH group, there was no weed shown in the plots. In addition, Monochoria, smallflower umbrella sedge, knotgrass and water primrose had emergence in four treatments except RMAFDH; while the other three species only mainly appeared in CONT group, little in CONV and RMAF treatments.

Dominant weed species

Cont Conv RMAF RMAFDL RMAFDH 160

140 120.1 120

100

80 70.974.3

60

40 30.4 26.6 21.3 16.2 20 7.3 7.9 6.2 7.1 8.1 4.6 1 0 2.80.02 0 0 1.71.5 3.4 0 0 2.20.2 0 0 1.8 0 0 2.4 0 0 0 0 a b c d e f g

Figure 12. Dominant weed species dry weight a: Monochoria (Monochoria vaginalis); b: Smallflower umbrella sedge (Cyperus difformis L.); c: knotgrass(Paspalum distichum L.); d: Water primrose(Ludwigia adscendens. Hara); e: Waterclover( L.); f: Spiny amaranth (Amaranthus spinosus L.) g: Kono millet (Paspalum scrobiculatum L.)

(Weeds were sampled at 4WAT and 8WAT at 1 m2 scale, dry matter is used and the unit is gr/m2. CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2.)

17

The data of monochoria, knotgrass, smallflower umbrella sedge and water creeping primrose were input into SPSS software for significance analysis. One-way ANOVA was chosen to compare the data set. Since the p-value of Levene’s test was less than 0.05, the data did not meet homogeneous assumption, Welch test and Games-Howell test were conducted for each species separately. Table 2 showed the results of Welch-test results of each species. It can be seen that except water creeping primrose, other species showed significant difference in weeds dry matter(gr) among different treatments.

Species Welch test P- English name Latin name F df1 df2 value Sig Monochoria Monochoria vaginalis 20.451 4 2 0.047 Significant Smallflower umbrella sedge Cyperus difformis L. 615.028 4 2.037 0.009 Significant Knotgrass Paspalum distichum L. 27.802 4 2 0.035 Significant Water creeping Ludwigia adscendens. Not primrose Hara 17.992 4 2 0.053 significant Table 2 One-way ANOVA Results of significance between different treatments

(Welch test for four species showed the significant difference among CONT, CONV, RMAF, RMAFDL and RMAFDH treatments. CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2)

In order to further study significant relationships between different treatments, multiple comparison was conducted by SPSS. Games-Howell test was used. Based on the results from Games-Howell test, it was found that only for species smallflower umbrella sedge, significances between every two treatments were found. While in other species, there was no significant difference found between every two treatments (Appendix7.4). However, from Figure 12, it could be seen that RMAFDL and RMAFDH had least appearance of weed population than other groups. Table 3 showed the results of multiple comparison of species smallflower umbrella sedge. It can be seen from the result that RMAFD treatments had significantly lower weed population of smallflower umbrella sedge than other groups, and there was no significantly difference between high and low density ducks groups.

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Smallflower umbrella sedge Mean ± SE(Lg10drymatter) Significance Cyperus difformis L. CONT 1.8 ± 0.06 a CONV 1.5 ± 0.08 a RMAF 0.8 ± 0.01 a RMAFDL 0.0 ± 0.00 b RMAFDH 0.0 ± 0.00 b Table 3 Multiple comparison of significance between treatments for Smallflower umbrella sedge

(Games-Howell test result of species smallflower umbrella sedge for multiple comparison among five treatments: CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2.)

3.2 Pests population

3.2.1 Sweep net sampling methods

The dominant pests tested by sweep net in rice field were rice whorl maggot, maize orange leafhopper, leafhopper (green leafhopper, zigzag leafhopper), brown plant hopper and grass hopper. Rice whorl maggots were found mostly appear in 4WAT, and then the number decreased; while planthoppers and leafhoppers had different pattern, the population increased from 2WAT until 8WAT. For grasshopper, they were only found in 4WAT.

Rice whorl maggot Rice whorl maggot (RWM) had an increasing population from 2 WAT to 4 WAT, and then gradually went down afterwards. And as Figure 13-a displayed, they shared this pattern in all of the five treatments. Rice whorl maggots had the largest population found in CONT group, following by CONV, RMAF, while in RMAFDL and RMAFDH group, they had least individuals found. After analyzing the data in 4 WAT by SPSS, as it had the most abundant number of the pests, the significant difference between treatments was gotten (Table 4). Based on the result of One-way Anova, it was found that the numbers of rice whorl maggot sampled by sweep net in different treatment groups were significant different (Appendix 7.5). And from the following step; Post Hoc Tests (Games-Howell), more specific difference between treatments were found. The number of RWM in RMAF, RMAFDL and RMAFDH group were significantly lower than in CONT and CONV groups; furthermore, it was also significant lower in RMAFDL and RMAFDH compared with RMAF group; and there was no significant difference between RMAFDL and RMAFDH groups (Table 4).

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Maize orange leafhopper (Cicadulina bipunctata) Basically, maize orange leafhopper (MLH) had the same growing pattern as leafhopper, which was increasing from 2 WAT to 8 WAT; despite in CONV group, the number at 8WAT was lower than at 4WAT. From Figure 13-b, it can be seen that MLH appeared most frequently in RMAF group during a long period of growing season, following by RMAFDL, CONT and CONV group. For CONV group, the numbers was relatively high at 4WAT, reaching the third abundant amount, while at 8WAT, the amount found in CONT was near 0. When looking at One-way Anova test (Appendix 7.5), it was found that at 8WAT, there was significant difference in numbers among different treatments. Furthermore, according to Games-Howell test results for 8WAT (Table 4), the number of MLH in RMAF group was significantly higher than CONV group. For other groups, despite there was no significant difference between them, it still can be indicated from figure 13-b that CONV was lower than all other groups, and there was only slightly difference between CONT, RMAFDL and RMAFDH.

Leafhopper (Cicadulina bipunctata, Nephotettix virescens & N. Cincticeps) In this paper, leafhopper represented zigzag leafhopper (Cicadulina bipunctata) and green leafhopper (Nephotettix virescens & N. Cincticeps), since they cause same damages and disease for rice plants, and they have similar natural enemies. It can be seen from Figure 13-c that leafhopper was found reaching the peak of population in 8 WAT, and most of them were found in adult stage. In terms of treatment differences, they were found most in RMAF group, followed by RMAFDL, CONT, CONV and RMAFDH. After inputting data in SPSS software for analysis (Welch test), it was found that there was no significant difference between different treatments in leafhopper population.

Brown planthopper (Nilaparvata lugens) Brown planthopper(BPH) was found little at 2 and 4 WAT, while the number in every treatment reached a peak at 8 WAT (Figure 13-d). At 8WAT, there was the most BPH found in RMAFDL group, followed by CONT, CONV, RMAF and RMAFDH group. However, based on the analyzation of one-way ANOVA, there was no significant difference among different treatments in brown planthopper population.

Grass hopper (Ailopus thalassinus tamulus) Grass hopper was only found at 4 WAT and the number of it was low. It was found in CONV, CONT, RMAF and RMAFDL plots, with a number of 2, 1, 4, 4 respectively From One- 20 way Anova test, it was said there was no significant difference between different treatments.

30 Rice Whorl Maggot in Sweep net a Number/25 sweeps 25

20 CONT CONV 15 RMAF 10 RMAFDL RMAFDH 5

0 2WAT 4WAT 8WAT Time after transplanting 16

14 Maize leafhopper Sweep net b Number/25sweeps 12

10 CONT CONV 8 RMAF 6 RMAFDL 4 RMAFDH 2

0 2WAT 4WAT 8WAT Times after transplanting 25 Leafhopper Sweep net Number/25sweeps c 20

15 CONT CONV 10 RMAF RMAFDL 5 RMAFDH

0 2WAT 4WAT 8WAT Times after transplanting

21

20 Brown planthopper Sweep net d 18 Number/25sweeps 16 14 CONT 12 CONV 10 RMAF 8 RMAFDL 6 RMAFDH 4 2 0 2WAT 4WAT 8WAT Times after transplanting Figure 13 Dominant pests species found by Sweep net

(CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2. For leafhopper, it contains the number of green leafhopper and zigzag leafhopper)

abbr. Pests Latin name CONT CONV RMAF RMAFDL RMAFDH

RWM Hydrellia philippina Ferino 27.0 ± 1.00 a 22.0 ± 2.00 ab 16.0 ± 1.00 b 3.0 ± 1.00 c 3.5 ± 1.50 c

MLH Cicadulina bipunctata 5.5 ± 0.50 ab 0.5 ± 0.50 b 14.0 ± 1.00 a 5.0 ± 1.00 ab 4.5 ± 2.50 ab

LH - 3.5 ± 0.50 2.0 ± 1.00 18.5 ± 4.50 11.5 ± 3.50 1.0 ± 1.00

BPH Nilaparvata lugens 6.0 ± 1.00 4.0 ± 2.00 1.5 ± 1.50 16.5 ± 2.50 0.5 ± 0.50 Table 4 Games-Howell result of sweep net pests

(CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2. Post hoc test results for rice whorl maggot, maize leafhopper, leafhopper including green leafhopper and zigzag leafhopper, brown planthopper. For leafhopper and brown planthopper, there was no significant difference found)

3.2.2 Yellow traps

The most dominant pest species found by yellow traps were brown planthopper, green leafhopper, zigzag leafhopper, maize orange leafhopper, rice whorl maggot and aphid. Generally speaking, the pests shared the same appearance pattern with it found by sweep net. Planthopper and leafhopper’s amount had been increased since 2 WAT and reached a peak at 8WAT; while for rice whorl maggot, the number reached a peak at 4 WAT and started to decrease since then. What’s more, aphids were not found during sweep net test, while in yellow traps experiment, it was found mainly in 2WAT, especially in RMAFDH treatment. 22

Rice whorl maggot Rice whorl maggot appeared most frequently at 4WAT, and it showed most in CONT, followed by CONV, RMAF treatments, while in RMAFD treatments they were found least, which was similar as the situation in sweep net test (Figure 14).

Leafhopper (Cicadulina bipunctata, Nephotettix virescens & N. Cincticeps) Leafhopper (green leafhopper and zigzag leafhopper) was found increase from 2 WAT to 8 WAT, except for RMAFDH group. This pattern matched the finding in sweep net method. In terms of distribution in different treatments, the situation in yellow traps was different from sweep net. First of all, same as in sweep net analysis, CONT and RMAFD treatments showed a lower population of leafhopper than RMAF treatment, while differing from sweep net, CONV showed a highest population in yellow traps methods, even higher than RMAF treatment.

Maize orange leafhopper (Cicadulina bipunctata) As the situation in sweep net test, maize orange leafhopper had most abundant of appearance in RMAF treatment, and in both test methods, CONT plot had more maize orange leafhopper than RMAFDL, and much more than RMAFDH. However, CONV showed a different thing in yellow trap test. CONV had a third large amount of maize orange leafhopper, only behind RMAF and CONT, while in sweep net test, it had least appearance of the pest, even lower than RMAFDH (Figure 14).

Brown planthopper (Nilaparvata lugens) Brown planthopper had largest number appeared in RMAF group, followed by CONV, RMAFDL, CONT and RMAFDH. compared with the situation in sweep net test, RMAF showed a great difference in two different test method, CONV as well. They both appeared much more than in sweep net test (Figure 14).

After putting the data of these four pest species into SPSS for statistical analysis, it was found the data was not normally distributed. So non-parametric method was used to test significance. However, according to Kruscal-Wallis test, the differences among these treatments were not significant.

23

20 Rice whorl maggot Yellow traps Number/5traps 15 CONT CONV 10 RMAF 5 RMAFDL RMAFDH 0 2WAT 4WAT 8WAT Times after transplanting

16 14 Leafhopper Yellow traps Number/5traps 12 CONT 10 CONV 8 6 RMAF 4 RMAFDL 2 RMAFDH 0 2WAT 4WAT 8WAT Times after transplanting

20 Maize Leafhopper Yellow traps Number/5traps 15 CONT CONV 10 RMAF 5 RMAFDL RMAFDH 0 2WAT 4WAT 8WAT Times after transplanting 20 Brown planthopper Yellow traps 15 Number/5traps CONT 10 CONV 5 RMAF

0 RMAFDL 2WAT 4WAT 8WAT RMAFDH -5 Times after transplanting

Figure 14 Dominant pests species found by yellow traps

(CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2. For leafhopper, it contains the number of green leafhopper and zigzag leafhopper) 24

3.2.3 Comparison between yellow traps and sweep net

The unit of insects population used in sweep net test was number/25 sweeps; while in yellow traps method, number/5 traps. Thus it was not appropriate to compare the population of insects pests caught in this two methods. However, considering the species caught by this two methods, it was showed that in both of the method, dominant pests caught in this experiment were rice whorl maggot, green leafhopper, zigzag leafhopper, maize orange leafhopper and brown planthopper. Besides, grasshopper was only caught by sweep net; and aphids were mainly found by sticky yellow traps. Furthermore, it can be seen from Figure 15, in general, the population of each species changed along with time, and the pattern of this change was found same in both sampling method. Besides, in both sampling methods, rice whorl maggots were caught more than leafhopper and planthopper. Especially for maize leafhopper, the number was lower in both sampling methods compared with other pests species. On the other hand, yellow traps showed less difference among different treatments compared with sweep net test. It had been proved by statistical analysis. According to the results of Kruscal-Wallis test, for these four pests species sampled by yellow traps, there was no significant difference on population found among different treatments; while in sweep net test, there were significant differences found among different treatments for rice whorl maggot and maize orange leafhopper.

a

Numbers

Week after transplanting

a. Rice whorl maggot; b. leafhopper including green and zigzag leafhopper; c. maize leafhopper; d. brown planthopper. (This graph showed the population changes by time in different treatments for two different sampling methods. SN stands for sweep net test and YT stands for yellow traps method. CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2.) 25

b

Numbers

Week after transplanting

c

Numbers

Week after transplanting

a. Rice whorl maggot; b. leafhopper including green and zigzag leafhopper; c. maize leafhopper; d. brown planthopper. (This graph showed the population changes by time in different treatments for two different sampling methods. SN stands for sweep net test and YT stands for yellow traps method. CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2.)

26

d

Numbers

Week after transplanting

Figure 15 Comparison between sweep net and yellow traps

a. Rice whorl maggot; b. leafhopper including green and zigzag leafhopper; c. maize leafhopper; d. brown planthopper. (This graph showed the population changes by time in different treatments for two different sampling methods. SN stands for sweep net test and YT stands for yellow traps method. CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2.)

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3.3 Natural enemies of domain pests

3.3.1 Natural enemies of domain pests

There were around 30 species of natural enemies found during the experiment, which belonged to different families, such as wasps, flies, spiders, hoppers or beetles and so on. Based on their prey characteristics, they could be divided into different groups by their feeding preference. Basically, there were insects specifically prey on rice whorl maggots, planthoppers, leafhoppers, stem borers, leaffolders, aphids, etc. Besides, some predators had a wide tastes, such as cricket and plant bug, which fed on hoppers, whorl maggots and other different pests species. To be specific, the natural enemies of rice whorl maggots found in this experiment were Opius sp, Trichogramma sp, Tetrastichus sp, Neoscona theisi, Oxyopes javanus, Lycosa pseudoannulata and Metioche vittaticollis. Among these insects, Opius sp, Trichogramma sp, Tetrastichus sp are parasites of rice whorl maggot; while Neoscona theisi, Oxyopes javanus, Lycosa pseudoannulata and Metioche vittaticollis are predators feeding on them (Sain, 2000). As for planthopper, there were both parasites and predators of it found by sweep net test in experimental fields. The parasites of planthopper included wasp species Gonatocerus sp., Panstenon nr. Collaris,Hapfogonatoupus apicalis Perkins, Pseudogonatopus sp.; Strepsiptera Elenchus yasumatsui. And predators named plant bug (Cyrtorhinus lividipennis), Lady beetles(Micraspis sp. and Harmonia octomaculata), ground beetle(Ophionea nigrofasciata), cricket(Metioche vittaticollis), water bug (Mesoveliidae sp.), damsel fly and grasshopper(Conocepphalus longipennis), as well as several spiders such as orb spider, dwarf spider, wolf spider and lynx spider (Shepard, 1987). Leafhoppers have very similar natural enemies as planthopper. For predators, except ladybeetles and water bugs specifically prey on planthopper, other predators of planthoppers mentioned above also prey on leafhoppers. While for parasites, besides Panstenon nr. Collaris,Hapfogonatoupus apicalis Perkins mentioned above that both parasite planthopper and leafhopper, there are species only parasite leafhopper, which are, diptera(pipunculus mutillatus, pipunculus javanensis), big-headed fly(Tornosvaryella subvirescens) and strepsiptera Halictophagus spectrus Yang ( Shepard, 1987). Furthermore, long-legged fly(Condylostylus sp.) and soldier beetles (Cantharidae) were found in RMAFD8 plots, which were natural enemies of aphids. Besides the natural enemies of planthopper, leafhopper, rice whorl maggot and aphids as noted above, there were several parasites species of stem borers and leaffolders found. Hereby they were not discussed in details, which was because despite the fact stem borers and leaffolders were found in the field, but they did not appear constantly in different treatments and repetitions.

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3.3.2 Natural enemies population in different treatments by two sample methods

From the result of sweep net, all the groups had the most abundant natural enemies at 4 WAT, except for CONV treatment. CONV group experienced a decrease from 2 WAT to 4WAT. Generally RMAFDL and RMAFDH treatments had the most appearance of natural enemies, while CONV group had the least. CONT and RMAF had the similar results (Figure 16). After applying one-way Anova test in SPSS, the result showed there were significant differences in natural enemy population among different treatments sampled by sweep net at 4 WAT. The natural enemy population in CONV group was significantly lower than it in RMAFD groups. When it came to yellow traps, the results showed some differences with sweep net method. Comparing different treatments, it was found that RMAFDL, RMAFDH and CONT groups had the most abundant natural enemies, and CONV and RMAF had the least. Furthermore, CONV group’s natural enemies increase from 2 WAT to 8 WAT, which was different from the situation in sweep net (Figure 16). However, from the results of non- parametric test (Kruscal-Wallis), it was showed that these differences among different treatments were not significant.

3.3.3 NE/IP ratios in different treatments by two sample methods

The ratio between the amount of natural enemies and insects pests is shown in Figure 17. Based on the result from sweep net, RMAF, RMAFDL and RMAFDH’s NE/IP ratios were more than 1, which meant natural enemies had more population than insect pests; while CONV and CONT’s NE/IP ratios were lower than 1 during 4 WAT. Among the five treatments, RMAFDH had the largest NE/IP ratio, and CONV had the smallest (Figure 17); furthermore, from the result of one-way ANOVA, the NE/IP ratios of RMAFDL and RMAFDH were significantly higher than in CONV groups at 4 WAT (Table 5). Things were different in yellow traps. All the treatments had the NE/IP ratio larger than 1. RMAFDH still had the largest ratio, while the smallest ratio was found to be in RMAF treatment (Figure 17). But it can be seen the ratio in different treatments were close to each other and there were no significant difference between these treatments found after checking in SPSS by Kruscal-Wallis test.

29

45

40 Sweep net - natural enemies population 35 CONT 30 25 CONV 20 RMAF 15 RMAFDL 10 RMAFDH

NUMBER OF INDIVIDUALS 5 0 2WAT 4WAT 8WAT

60 Yellow traps - natural enemies population

50 CONT 40 CONV

30 RMAF RMAFDL 20 RMAFDH

10 NUMBER OF INDIVIDUALS 0 2 WAT 4 WAT 8 WAT Figure 16 Natural enemies population

(CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2. For leafhopper, it contains the number of green leafhopper and zigzag leafhopper)

Treatment aver. ± se (NE all individuals number) NE/IP Ratio CONT 19.0 ± 1.0 ab 0.7 ± 0.3 ab CONV 2.5 ± 0.5 b 0.1 ± 0.0 b RMAF 21.0 ± 7.0 ab 0.8 ± 0.0 ab RMAFDL 38.0 ± 4.0 a 1.6 ± 0.1 ab RMAFDH 36.0 ± 1.0 a 2.5 ± 0.7 a Table 5 Significance test of Natural enemy amount and NE/IP ratio (Sweep net)

(Tukey test results for natural enemies population and NE/IP ratio comparing five different treatments in sweep net. CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2.)

30

NE/PE RATIO (SWEEP NET) 8

6 CONT

CONV 4

RMAF NE/PE 2 RMAFDL RMAFDH 0 2 WAT 4WAT 8WAT NE/PE RATIO (YELLOW TRAPS) 4

3

CONT

2 CONV

NE/PE RMAF 1 RMAFDL RMAFDH 0 2 WAT 4WAT 8WAT

Figure 17 NE/IP Ratio for different treatments (CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, 2 fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m ; RMAFDH: rice, fish, azolla,8 ducks/100 m2. For leafhopper, it contains the number of green leafhopper and zigzag leafhopper)

3.3.4 Species richness of insects in different treatments

Species richness means the number of different species. The species richness in different treatments was found no significant differences. It also can be seen from Figure 18 below that species richness was nearly same in different time stages and different treatments. It can be seen from Figure 18 a&b that the species richness of natural enemies was found higher than insects pests in every treatments, time stages and sampling methods. Besides, comparing these two methods, yellow traps tended to caught more species than sweep net test no matter for insects pests or natural enemies.

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a

Number ofspecies Number

Weeks after transplanting

b

Number ofspecies Number

Weeks after transplanting

Figure 18 Species richness in different treatments by two sampling methods

a. Species richness of natural enemies; b. species richness of insects pests. (SN stands for sweep net test and YT stands for yellow traps method. CONT: rice practice without herbicides and pesticides; CONV: conventional rice practice; RMAF: rice, fish, azolla integrated system; RMAFDL: rice, fish, azolla, 1 ducks/100m2; RMAFDH: rice, fish, azolla,8 ducks/100 m2.)

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4. Discussion

4.1 Weed suppression

4.1.1 Weed suppression effectiveness in different treatments and mechanism

Generally speaking, among the five treatments, CONT group was applied no herbicides and it turned out to be the field containing the most amount of weeds, including dry matter and species richness perspectives. Compared with other groups, RMAFD8 group had the weed dry matter amount lower than CONT group; and for RMAFD2 group, it also had lower weed emergence than CONT, CONV and RMAF groups, but compared with RMAFD8 groups, the weed suppression was less effective, despite the differences were not significant. From the results, it can be seen that rice system integrating ducks, fishes and azolla has more effective suppression on rice weeds species than conventional ways with pesticides. The low duck-density treatment, RMAFD2, was sufficient in weeds suppression compared with CONV groups, and it had nearly same effectiveness with RMAFD8 group. The functional agents of weed suppression may include fishes, ducks, azolla, border crops and other natural conditions.

Fish behaviour Fish species Nile tilapia is a kind of macrophyte-feeders, who prefers light-coloured pellets with a neither too soft nor hard texture of food and prefers larger pellets than small pieces. Besides it prone to eat filamentous algae and aquatic macrophyte floating in the upper water surface, and do not like to search food in deep water (Shripat, 2011). Based on the observation of Nile tilapia, this fish is an aggressive predator in the water, and they like seeking floating food on the water surface, sometimes feeding under the water or in the bottom mud as well. In the experiment fields, most of the weeds species found could be reproduced by seeds, stolon and rhizomes, except smallflower umbrella sedge and globe fringe-rush can only be distributed by seeds. For species such as creeping water primrose and water clover, they can germinate from plant fragments (Caton, 2004). So considering the feeding behaviour of fishes, they might be feeding on small seeds, soft plant fragments, rhizomes or stolon, therefore the chances of weeds spreading can be reduced. This may be part of the reasons that in treatments with fishes, especially RMAFD fields, there were less weeds found. In RMAF treatment, there were less weeds species than in CONV and CONT treatments, and except for Monochoria and Knotgrass, it contained less dry matter of weed species than CONV and CONT treatments, despite the differences were not significant. However, compared with RMAFD2&8 treatments, RMAF still seemed not sufficient as control methods.

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Ducks behaviour From the experiment, it can be seen that in RMAFD 2&8 plots, weeds population was significantly lower than CONT and CONV treatments. And RMAFD2 turned out to have significantly less weed population than CONV treatments, which meant it was sufficient in suppressing weeds population in rice field. This phenomenon echoes the result of Hossain (2005)’s experiment. Ducks play an important role in weed suppression by directly feeding on weeds and disturbance on weed growth (Houssain, 2005). From the field observation, ducks liked to swim in rice field in early morning(3-5 am.) and late afternoon (5-6 pm.). They preferred to move in group, being led by one duck. In the rice field, there was tunnels designed for ducks through rice plants. Ducks usually followed these tracks and searched food(weeds or insects) in the water. And it often could be observed that ducks searched food around the roots of rice plants. This behaviour can stimulate growth of rice by providing oxygen for roots and reducing competition of weeds or insects around rice plants. Besides, some weed species found in the field like smallflower umbrella sedge or knotgrass often located close to rice plants roots and caused competition with rice plants. The ducks’ behaviour of searching food on roots of rice plants might disturb the growth of weeds. The trampling behaviour ducks can provide oxygen in the water, this can not only benefit for rice growth, may also beneficial for weeds reduction. Monochoria was found a lot in the experiment, but it was found very low in dry matter in RMAFD fields. One of the reason might be that the germinate of Monochoria seeds needs a long anaerobic period, while the activities of ducks in the rice field may have disturbed this process, therefore reduced the appearance of Monochoria (Caton, 2004). Azolla Azolla is a genus of small aquatic ferns, which is famous for its symbiotic relationship with a nitrogen-fixing, blue-green alga, Anabaena. When living in suitable environment, the fern/alga combination can double in weight every 3 to 5 days and fix atmospheric nitrogen at a rate exceeding that of the legume/rhizobium symbiotic relationship, accumulating up to 2 to 4 kilograms of nitrogen ha/day (equalling to 10 to 20 kg of ammonium sulphate). And as azolla grows in aquatic conditions, it can be used as organic fertilizer for rice plants (Lumpkin, 1985). Besides being used as fertilizer, since it grows fast and can be grown to a mat floating on the surface of water, it can help block sunlight and oxygen so that control weed germination. And research showed with azolla in rice paddy had significantly reduced weed growth ranged from 4 to 72% compared with control (Satapathy, 1985). Furthermore, utilization of azolla as green manure to substitute animal manure can also reduce weed appearance (Satapathy, 1985). This might because that azolla manure is less contained by weeds seeds compared with animal manure. In terms of weed species found in this experiment, they had some characteristics related to weed suppression effectiveness. Among these species, most of the species except spreading dayflower prefer sunlight to germinate and grow, which means, sun block function of azolla could be contributed to the less emergence of weeds. In the experiment, it was found that weed appearance in RMAF, RMAFD (L&H) had less 34 weeds population compared with CONT and CONV groups; among these three groups, RMAF had relatively higher emergence of weeds. In one hand, this might because of the control function of ducks; at other hand, considering the field observation, because of the rainfall, azolla in RMAF field had lost and been rushed to the ditches or gathered along the ridges. While in RMAFD groups, with the movement of ducks and the fences around ridges, azolla grew better than it in RMAF group. This might be another reason why RMAFD had better performance on weed suppression.

Border crops Crotalaria was planted around the plots as border crop. Crotalaria is an annual rapid- growing legume, reaching a height of 1.2 m in 60 days under favourable conditions. It can provide 150-165 kg/ha nitrogen to the soil when grown for 60 days and produce 7t/ha air- dry organic matter after harvested. That is why it is widely used as green manure all over the world (Rotar, 1983). Besides, as it grows erectly and has branches, while growing around on the ridges of the plots, it can provide shade for ducks and fishes in RMAF and RMAFD treatments. In terms of weed suppression, it can inhibit the germination of some shade- sensitive or light-prefer weeds species, such as knotgrass, kono millet in early stages before rice transplanting. During the growth of rice, farmers can cut crotalaria leaves and stems for animal fodders or green manure. This in other hand can maintain crotalaria in a proper height, preventing shade from influencing the growth of rice.

Field conditions During the rice growing seasons, at 10 days after transplanting, water height in rice field was managed at a very low level, and it was just enough to make the soil stay moist. Afterwards, during weeding, fertilization and spraying activity in conventional fields, soil drainage occurred in all of the treatments.

4.1.2 Weed suppression effectiveness for different weed species

Among the 14 species found during the experiment, most of them can germinate from seeds, rhizome, stolon, even plant fragments. Only smallflower umbrella sedge and globe fringe-rush can only germinate from seeds and they prefer full sunlight to germinate. These two species were found little appeared in RMAF, RMAFD systems, which maybe because of the sunblock function of azolla floating mat. Opposite to smallflower umbrella sedge and globe fringe-rush, creeping water primrose and water clover are two species very easy to reproduce. They can be distributed by their plant fragments, rhizome, stolon and seeds. Creeping water primrose was found in CONT, CONV, RMAF and RMAFDL plots, especially it had more population in CONT and RMAFDL than in CONV and RMAF, despite the difference was not significant. Although these two species can be distributed in many ways, they prefer to be established from surface, for fragment reproduction is difficult to grow through thick muds, especially for water clover. This makes them less resistance to herbicides, and this might be why CONV system has good

35 suppression effect on them. In RMAFDL system, the movement of ducks might take some fragments on the surface, causing the germination of them. For species Small flower umbrella sedge, Kono millet, sessile joyweed and spreading dayflower, they were mostly found in CONV and CONT groups and the dry matter of these species were not high compared with other dominant weed species. At last, Monochoria and knotgrass was two domain species in this experiment. Monochoria and knotgrass were found most in CONT and CONV treatments, and the population in these two treatments were same. This meant these two species were tolerant to herbicides, same as Caton (2004) described in the book. While in RMAFD treatments, the population of these two species were significantly reduced. For Monochoria, this might because its germination needs light and long anaerobic period. So azolla and ducks can be good limits for Monochoria to reproduce. And for knotgrass, it is a shade-sensitive species, azolla can be a good suppression methods for knotgrass. Generally speaking, RMAFDL and RMAFDH systems had significantly reduced the germination of weed species, compared with CONV group. Although compared with RMAFDH group, these were still some weeds appeared in RMAFDL group, such as knotgrass, smallflower umbrella sedge, creeping water primrose, the dry matter content of these species were very low and the differences between RMAFDL and RMAFDH were not significant. So it can be said that RMAFDL was sufficient for weed suppression. And it had equal weed suppression ability compared with CONV group, and for some species, even better, such as Monochoria.

4.2 Pests suppression

In this experiment, there were six domain pest species found, namely rice whorl maggot(RWM), maize orange leafhopper (MLH), green leafhopper (GLH), zigzag leafhopper (ZLH), brown planthopper (BPH), aphids and grass hopper (GH). Rice whorl maggot (Hydrellia philippina Ferino) is a genus of leaf miner, it generally feeds on central whorl leaf of rice plant. Rice whorl maggot (RWM) was the most abundant pest found during the experiment, and it is the dominant pests of rice in throughout the year ( Pathak, 1994). Besides, RWM often appears at vegetative stage of rice as related to their eating habit, that echoes to the result that the peak number of rice whorl maggot appeared at 4 WAT, and it went down at 8WAT due to the lack of food in rice paddy. As for leafhoppers and planthoppers, they appeared most frequently at 8WAT instead of 4WAT, and increased from 2WAT to 8WAT. Firstly, brown plant hopper (BPH) is a though-out year insect pest in rice field, and it grows and damages rice crop during the whole plant growth season. While same as the situation in this experiment, MacQuillan (1974) also found in their experiment that BPH appeared most near the end of the crop. According to IRRI’s research, this might be related to the active dispersal behaviour of BPH adults at the end of grow season ( Dyck, 1979). BPH feeds on basal of rice plants, and usually stays at bottom part of rice field. However, near the end of the crop period, adults of BPH start to fly from one

36 plant to another (Pathak, 1994), which makes it easy to catch them by sweep net. When it comes to leafhoppers, they are same with planthoppers in life cycle, for they exist in rice field all year around. The population of them fluctuate with the availability of food, presence of natural enemies and environment (Pathak, 1994). In this experiment, generally speaking, from 2WAT to 4WAT, the population of MLH, GLH and ZLH stayed low, and increased to the peak at 8WAT. Mostly these insects caught by sweep net were adults. This might because adults insects were bigger in body size and easier to recognize. While due to the limit of personal skill, larvae and pupa are easy to be ignore because they are small and sometimes has light color like green leafhopper. This might be one reason of that most of the leafhoppers and planthoppers were found at 8 WAT.

4.2.1 Insect pests suppression effectiveness in different treatments

Based on the results of sweep net results, the suppression effectiveness differed by species in different treatments.

Rice whorl maggot For rice whorl maggots, the population of it in RMAFD groups was significantly lower than other groups, and the population in RMAFDL and RMAFDH was similar. It indicated that RMAFDL treatment was sufficient in controlling the appearance of rice whorl maggot compared with RMAFDH and CONV groups. And in this experiment, CONV group had slightly lower appearance of rice whorl maggots than CONT, which meant the suppression of rice whorl maggot in CONV treatment was insufficient. There were three possible reasons that why RMAFD system could suppress the population of rice whorl maggot. Firstly, there were several natural enemies of rice whorl maggot found during the experiment, including predators and parasites, which can reduce the eggs, larvae and adults population; secondly, proved by Khumairoh (2012)’s experiment, involvement of fishes and ducks as pests management agents, could reduce the population of rice whorl maggots, contributed by their feeding behaviours; thirdly, the induce of azolla as a fern floating on water surface could be a reason reducing the population of rice whorl maggots. One of the predators of rice whorl maggot Ochthera sauteri was found being most effectively prey when water drained or covered by azolla ferns ( Jahn, 2006). In addition, the effectiveness of insecticides controlling rice whorl maggot was investigated. It turned out that rice whorl maggot was difficult to control by insecticides (IRRI, 1980b). There were several chemicals were used in CONV treatment during experiment, among them only deltamethrin is an active ingredient for eliminating rice whorl maggot, which has mortality rate at 42% under repeated spraying ( Litsinger, 2014). This might be the reason why CONV group was not sufficient in suppressing the population of rice whorl maggot. More interestingly, some weed species were found to be more attractive plant host than rice for rice whorl maggot, among which, several species were found in this experiment, namely smallflower umbrella sedge, globe fringe rush, southern cutgrass and kono millet (Litsinger, 2014). This would be another reason that

37 compared with other treatments RMAFD8 group had less population of rice whorl maggot, because the population of weeds was extremely low in RMAFDH group.

Leafhopper For leafhoppers (green leafhopper and zigzag leafhopper), it was found that the population was high in RMAF treatments, and higher than other four treatments. For other four groups, the differences were quite small, there was least leafhoppers in RMAFDH group, followed by CONV, groups. Despite the differences could be seen from line chart (Figure 14), based on the results of one-way ANOVA, there was no significant difference among these treatments. Thus, it was hard to conclude whether treatments had significant influence on suppression of leafhoppers. For maize leafhopper, RMAF treatments still had the most abundant amount of it, and CONV seemed to be the most effective treatment to suppress the number of MLH. RMAFDL and RMAFDH had the similar number of MLH. This results indicated that CONV had the best performance in controlling the number of MLH, but RMAFDL and RMAFDH were still sufficient as the differences of MLH population between RMAFD treatments and CONV treatment were not significant. Besides, maize orange leafhopper’s population was found less than other species, and compared with experiments in previous years, it was a new species found in this region. This might because of the migration of insects caused by long time drought in this year. In experiment area, maize is another dominant crop besides rice, there is high chance that maize orange leafhopper migrated from nearby maize fields to rice fields to search food. In conclusion, CONV and RMAFDH turned out to be the most effective treatment on suppressing population of leafhoppers, while as there was no significant differences found among different treatments on suppression ability; so it was hard to conclude whether RMAFDL was sufficient or insufficient in suppressing appearance of leafhoppers. In RMAFD groups, ducks were an effective control agent in suppressing number of leafhoppers. Several literatures confirmed that rice paddy integrated with ducks had significantly lower appearances of leafhoppers including green leafhopper, zigzag leafhopper (Hossain, 2005; Men, 1999). This echoes to the finding that RMAFD treatments had sufficient suppression on leafhoppers, better than RMAF treatments. Besides, there were several natural enemies of leafhoppers were found in the experiment, such as crickets, damsel flies and spiders. Their eating behaviour helped reduce the population of leafhoppers. And from the result, it was said that in RMAFD L&H groups there was most abundant natural enemies (4WAT); followed by CONT and RMAF; while the natural enemies population in CONV was significantly lower than RMAFD systems. This might because of border crops Crotalaria can attract natural enemies into the field in RMAFD groups. While in RMAF system, fishes seemed not to be an effective contributor in suppressing leafhoppers; Vromant et al. ‘s research also indicated that Nile tilapia did not contribute to the decrease of the number of leafhoppers and planthoppers (Vromant, 2002). However, it was interesting in CONT group, without any control methods in this group, the population of leafhoppers was lower than RMAF group. This might on one hand maybe due to zero pesticides application protected natural enemies 38 in the field; on the other hand, the azolla in RMAF group lacked of management. The decomposed dead azolla would increase nitrogen level in RMAF plot, which had chance to attract leafhoppers, planthoppers and other pests (IRRI, 2015).

Brown plant hopper Things were different for brown plant hopper (BPH). RMAFDL turned out to be the group containing highest number of BPH, and the number of it was higher than rest of the groups. Based on line chart, RMAFDH turned out to be the most effective one to control this pests, despite the difference was not significant in SPSS. Besides the contribution from natural enemies mentioned above that made RMAFDH effectively control the population of planthopper; it might also relative to fishes and ducks behaviour. Although according to Vromant(2002), Nile tilapia did not reduce the population of planthopper and leafhopper in monoculture rice cultivation. There was study filmed the process that after falling into the water, planthoppers were eaten by fishes, indicating a removal rate of planthopper by fishes at 26% (Lansing, 2001). During the field observation, ducks preferred to search food around rice plants and they used mouth to touch the roots of rice plants, causing shake of rice leaves and stems. This behaviour would cause falling-down of insects resting on the rice plants including planthoppers and then planthoppers could be eaten by fishes. In RMAFDH treatment, plenty of ducks disturbed the rice field in a larger scale, leading to a better performance in suppressing planthoppers. On contrary, because of the low density of ducks in RMAFDL, the suppressing effectiveness might be influenced. For both brown plant hopper and leafhoppers, CONV treatment were efficient in controlling their occurrence, indicating that chemical insecticides were still sufficient in suppressing hoppers.

4.2.2 Natural enemies population in different treatments

The total natural enemy population and the ratio between natural enemies and insect pests (NE/IP) in 2,4,8 WAT were summarized and was shown in results part. There was no significant difference between treatments in yellow traps methods. Seen from the line chart, it had same patterns with sweep net test in terms of natural enemy population and NE/IP ratio, although the differences between different treatments on NE/IP ratio of yellow traps methods were extremely small. From the results of sweep net test, it could be concluded that RMAFD treatments had significantly higher appearance of natural enemies than CONV treatment. Especially at 4 WAT, during the vegetative stage of rice plants, pests were actively moving because of abundant food; and this was why natural enemies reached a highest peak in RMAFDL, RMAFDH and RMAF treatments; while due to the spray management of farmers, CONV group had a sharp decrease of natural enemies. This indicated that the use of insecticides could not only reduce the appearance of insect pests, but also cause mortality of natural enemies.

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In terms of NE/IP ratio, RMAFDH was still significantly higher than CONV treatment; while despite RMAFDL treatment had higher ratio, but the differences to CONV group was not significant. However, RMAFDL still had sufficient performance on pests management performance. RMAF group had similar performance with CONT group. This results indicated that the introduce of ducks, fishes, azolla and crotalaria might attractive more natural enemies. Although RMAF had the similar natural enemies with CONT treatment, it is too early to say azolla and fishes had less attractiveness to natural enemies. During the experiment process, azolla in RMAF field was destroyed by wind and rain. More prepared researches still needed to find out the effectiveness of fishes and azolla as pests control agents.

4.2.3 Comparison between two different sampling methods

Although there were differences between two sampling methods, generally the results still indicated several phenomenon in insect pests analysis. Both of the methods showed that RMAF was not sufficient enough for pests suppression and even possessed high occurrence of leafhoppers and brown planthoppers. For rice whorl maggots and maize leafhoppers, RMAFDH was the most effective treatment in controlling pests population, and RMAFDL turned out to be a sufficient treatment, however, for brown planthopper and leafhoppers, there was no significant difference among different treatments. To compare the differences between this two sampling methods, the differences between groups in yellow traps were small and not significant. On one hand, this might because of the bad weather during experiment period. For instance, within the experiment duration, there were several heavy rainfalls. The rush of rain may cause the loss of samples on yellow traps; and on the other hand, some insects, like small flies would be hurt by sticky yellow traps so that it became difficult to recognize and identify them. Besides, during the experiment, the bamboo sticks to fix yellow traps were at the same height (around 80cm). Thus, with the growth of rice, rice plants became higher than sticks, which might influence the efficiency of yellow traps to catch insects. On the other hands, yellow traps are designed to attract insects that have positive taxis to yellow. Different species of leafhoppers and planthoppers showed different colour taxis, there is still room to find whether yellow traps are suitable to monitor the population of hoppers and other insect pests ( Rodrigues-Saona, 2012). The fact that yellow traps method had little differences between group was different from other previous research conducted by Uma khumarioh and Ike Widyaningrum. This could because that insects numbers found by yellow traps were too small to conduct statistical analysis. Compared the experimental methods with Ike and Uma, in this experiment, the places settling yellow traps and the height of yellow traps were different. In khumarioh’s experiment, she put yellow traps on one single rice hill; and Ike put four yellow traps on the four corners per plot at a height of 1.5m. There were no studies found on the topic that how the height of yellow trap will influence the species and numbers of caught insects. But there was study on cotton indicating that the catching effectiveness of different insects species had relationship with the height of yellow traps (Atakan, 2004). Besides, on

40 the ridges and ditches around of rice plots there were lots of weeds species, which were same as the weed species mentioned in this article. As mentioned above, some of them are more attractive host plants to rice whorl maggots. They could be attracted and caught by the yellow traps close to the ridges, which affected the results of yellow traps test. However, for sweep net test, samplings were done in the middle of the field, can to some degree avoid this kind of error. In addition, there were two repetitions during the experiment. Taking statistics into account, more repetitions may improve the results. When it comes to natural enemies, it was found that the insects species detected by yellow traps and sweep net test were different. In the current experiment, it was found that Sweep net test tended to have more spiders, beetles and other relatively bigger insects; while yellow traps attracted more wasps, flies and moth; most of them were small in size. Considering the experimental environment, rice fields were quite wet during the experiment period because of the rainy season. Therefore, it was easy to wet nets while conducting sweep net test, leading the miss of tiny insects such as wasps and flies. While for sticky yellow traps, large insects physically speaking were relatively easier to escape from the glue; and the catching efficiency would also influenced by natural conditions. In addition, during the experiment, it could be felt that taking samples while walking in the mud in rice field required years of experience and skills. Personal sampling skills could also be a reason that differed the sampling results. Considering the yellow taxis of insects, it might differ from species. For instance, yellow traps are better catching small insects with wings, such as flies, parasitic wasps, thrips, as well as leafminers (Bambara, 2003). What’s more, the species richness including pests and natural enemies was found higher by yellow traps than sweep net test. This might because that for some small insects, they were easily stick on the wet net. During sampling procedure, they could be mixed with rice fragments or dust so that they were ignored; or destroyed by crassly actions. Especially when researchers lacked of experience in identifying and sweep net sampling. On the other hand, walking in the rice field while doing sweeping was not easy for beginners, the stiff movement during sampling could cause escape of insects. In addition, sweeping net was conducted in early morning, which meant usually insects preferred to move in early morning were easier to catch by sweep; while yellow traps were placed in the field for 24h, which made it have access to more varieties of insects.

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5. Conclusions

5.1 Conclusions

From this experiment, the conclusion can be made that the complex rice system with a duck density of 100 birds/ha (RMAFDL) can provide sufficient weed suppression for rice paddy. Although compared with high duck density system (RMAFDH), weeds appearance was higher in low-density group (RMAFDL), but the differences were not significant. When it comes to pests suppression ability, the situation differed by insects species. Among the dominant rice insect pests found in the experiment, RMAFDL treatment turned out to be able to provide sufficient control to rice whorl maggot, maize orange leafhoppers; while it showed no significant difference among different treatments for green leafhopper, zigzag leafhopper and brown planthopper. It was not sure whether it had bad effect on rice yield and farm income. Besides, among these pests, it was found CONV treatments had little suppression ability on rice whorl maggots; which may indicate the resistance to chemicals of rice whorl maggot in the experimental region was increased, and the use of chemicals was not effective. By comparing the appearance of the total natural enemies population in CONV and other treatments, it was found that the utilization of insecticides significantly decreased natural enemy abundant. Long terms speaking, insecticides utilization could destroy biodiversity and deteriorate ecological environment on farms.

5.2 Limitations and recommendations

Comparing yellow traps and sweep net test, there were advantages and disadvantages for both two methods. Yellow traps are able to get more species than sweep net since it can be placed in the field for 24 hours. And it is better in catching tiny insects, which could be easily ignored in sweep net, especially under wet weather conditions. However, yellow traps are not able to attract all kinds of species. Like in this experiment, they tended to have less number of crickets, spiders and other insects. In order to make up the weakness of sampling methods, for sweep net test, further experiment could conduct repeats in each plot in different lines and heights to get a more equal image of insects distribution. And the sampling could be conducted in different time of a day. Besides, researchers should pay more attention under wet weather condition to avoid missing of samples. For yellow traps, pre-tests of yellow traps should be taken to test at which height and shape yellow traps could gain best performance in measuring insects amount. For instance, in this experiment, yellow traps methods did not detect the significant differences between different treatments in insects population. Part of the reasons were contributed by improper experimental design. Furthermore, in this study, only insects living in the air were measured by sweep net and 42 yellow traps. Other sampling methods could be used to detect soil insects in rice fields. In this experiment, the fields were previously operated by complex rice systems, which may contribute to less weeds and pests species occurred during experiments. Further study could be operated in traditionally conventional field, to explore the feasibility of low-density duck complex rice system in transferring rice farms from conventional to organic. And for other possibilities, the experiment could be repeated in the same field constantly having complex rice system, which could be used for monitoring long-term performance of complex rice system.

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6. References

Atakan, E., & Canhilal, R. (2004). Evaluation of yellow sticky traps at various heights for monitoring cotton insect pests. J. Agric. Urban Entomol, 21(1), 15-24. Bambara, S., Casey, C., & Baker, J. R. (2003 ). Insects found on yellow sticky traps in the green house. Valid at: https://www.ces.ncsu.edu/depts/ent/notes/O&T/production/stickycard/sticky.pdf Brévault, T., Bikay, S., Maldes, J. M., & Naudin, K. (2007). Impact of a no-till with mulch soil management strategy on soil macrofauna communities in a cotton cropping system. Soil and Tillage Research, 97(2), 140-149. Caton, B. P., Mortimer, M., Hill, J. E., & Johnson, D. E. (2004). Weeds of Rice In Asia. International Rice Research Institute, Los Baños, , 196. Chaudhury, J., Singh, D. P., & Hazra, S. K. (2007). Sunnhemp (Crotalaria juncea L.). Monograph, Central Research Institute for Jute and Allied Fibres, 1-45. Coche, A. G. (1967). Fish culture in rice fields a world-wide synthesis. Hydrobiologia, 30(1), 1-44. Conway, G. R., & Barbie, E. B. (1988). After the Green Revolution: sustainable and equitable agricultural development. Futures, 20(6), 651-670. Devendra, C. (1995). Mixed farming and intensification of animal production systems in Asia. Proceedings of the Joint FAO/ILRI Roundtable on Development Strategies for Low Income Countries. Dyck, V. A., Misra, B. C., Alam, S., Chen, C. N., Hsieh, C. Y., & Rejesus, R. S. (1979). Ecology of the brown planthopper in the tropics. Brown planthopper: threat to rice production in Asia, 61-98. Fagi, A. M., Suriapermana, S., & Syamsiah, I. (1992). Rice- research in lowland area: the case. In Proceeding of Rice-Fish Research and Development in Asia. ICLARM Conf. Proc (pp. 273-286). Fitzsimmons, K., Cerozi, B., & Tran, L. ( 2014). Tilapia global supply and demand. Valid at: https://www.was.org/documents/MeetingPresentations/WA2014/WA2014_0871.pdf Hossain, S. T., Sugimoto, H., Ahmed, G. J. U., & Islam, M. (2005). Effect of integrated rice- duck farming on rice yield, farm productivity, and rice-provisioning ability of farmers. Asian Journal of Agriculture and Development, 2(1), 79-86. IFAD. (2015). Investing in rural people in Indonesia. Valid at: www.ruralpovertyportal.org IRRI. (1980b). Seedling age and whorl maggot control, p. 219. In: 1979 Annual Report. Islam, Z. (2001). IRRI Knowledge bank Control of rice insect pests. Valid at: http://www.knowledgebank.irri.org/ericeproduction/PDF_&_Docs/Control_of_rice_insect_p ests.pdf

IRRI. (2015). Rice is the key to Indonesia’s food security. Valid at: http://irri.org/news/media-releases/rice-is-key-to-indonesia-s-food-security-officials-say Jahn, G. C., Litsinger, J. A., Chen, Y., & Barrion, A. T. (2006). 15 Integrated Pest 44

Management of Rice: Ecological Concepts. Khumairoh, U., Groot, J. C., & Lantinga, E. A. (2012). Complex agro‐ecosystems for food security in a changing climate. Ecology and evolution, 2(7), 1696-1704. Kohler, H.P., et al. (2015). Indonesian perspectives: Population and demography. Post – 2015 consensus. OECD.(2015). Economic surveys Indonesia. Valid at: www.oecd.org/eco/surveys/economic-survey-indonesia.htm Koo, W. W., Karmana, M. H., & Erlandson, G. W. (1985). Analysis of Demand and Supply of Rice in Indonesia. Department of Agricultural Economics, North Dakota Agricultural Experiment Station, North Dakota State University. Litsinger, J. A., Barrion, A. T., Canapi, B. L., Lumaban, M. D., Pantua, P. C., & Aquino, G. B. (2014). THE RICE WHORL MAGGOT, Hydrellia philippina Ferino (DIPTERA: EPHYDRIDAE) IN THE PHILIPPINES: A REVIEW. The Philippine Entomologist, 27(1). Lansing, J. S., & Kremer, J. N. (2011). Rice, fish, and the planet. Proceedings of the National Academy of Sciences, 108(50), 19841-19842. Lestari, Y. K., & Suryana, A. T. Sustainability of Organic Rice Farming in Indonesia. Lumpkin, T. A., & Plucknett, D. L. (1982). Azolla as a green manure: use and management in crop production. Westview Press (In UK, supplied by Bowker Publishing Co, Epping). Lumpkin, T. A., & Plucknett, D. L. (1985). Azolla, a low cost Aquatic Green Manure for agricultural crops. Congress of the US Office of Technology Assessment. MacQuillan, M. J. (1974). Influence of crop husbandry on rice planthoppers (Hemiptera; Delphacidae) in the Solomon Islands. Agro-Ecosystems, 1, 339-358. Men, B. X., Tinh, T. K., Preston, T. R., Ogle, R. B., & Lindberg, J. E. (1999). Use of local ducklings to control insect pests and weeds in the growing rice field. Livestock Research for Rural Development, 11(2). Orwa. (2009). Crotalaria juncea: database 4.0. Pathak, M. Dꎬ, and Zeyaur R. Khan. Insect pests of rice. Int. Rice Res. Inst., 1994. Pimentel, D., Harvey, C., Resosudarmo, P., & Sinclair, K. (1995). Environmental and economic costs of soil erosion and conservation benefits. Science, 267(5201), 1117. Rodriguez-Saona, C. R., Byers, J. A., & Schiffhauer, D. (2012). Effect of trap color and height on captures of blunt-nosed and sharp-nosed leafhoppers (Hemiptera: Cicadellidae) and non-target arthropods in cranberry bogs. Crop Protection, 40, 132-144. Rotar, P. P., & Joy, R. J. (1983). 'Tropic Sun'Sunn Hemp; Crotalaria juncea L. Satapathy, K. B., & Singh, P. K. (1985). Control of Weeds by Azollo in Rice. Shripat, V. (2011). The online guide to the animals of Trinidad and Tobago. On-line). The University of the West Indies. Accessed May, 21, 2013. Sain, M. (2000). Bionomics and management of rice whorl maggot-hydrellia spp.(Diptera: Ephydridae): A review. AGRICULTURAL REVIEWS-AGRICULTURAL RESEARCH COMMUNICATIONS CENTRE INDIA, 21(2), 110-115. Septiningsih, E. M., Hidayatun, N., Sanchez, D. L., Nugraha, Y., Carandang, J., Pamplona, A. M., ... & Mackill, D. J. (2015). Accelerating the development of new submergence tolerant rice varieties: the case of Ciherang-Sub1 and PSB Rc18-Sub1. Euphytica, 202(2), 259-268. 45

Shepard, B. M., Barrion, A. T., & Litsinger, J. A. (1987). Helpful insects, spiders, and pathogens: friends of the rice farmer. Agribookstore. Shiotsu, F., Sakagami, N., Asagi, N., Suprapta, D. N., Agustiani, N., Nitta, Y., & Komatsuzaki, M. (2015). Initiation and Dissemination of Organic Rice Cultivation in Bali, Indonesia. Sustainability, 7(5), 5171-5181. Stoop, W. A., Uphoff, N., & Kassam, A. (2002). A review of agricultural research issues raised by the system of rice intensification (SRI) from Madagascar: opportunities for improving farming systems for resource-poor farmers. Agricultural systems, 71(3), 249-274. Vromant, N., Nhan, D. K., Chau, N. T. H., & Ollevier, F. (2002). Can fish control planthopper and leafhopper populations in intensive rice culture?. Biocontrol science and technology, 12(6), 695-703. Willer, H. (2011). The world of organic agriculture 2012: summary. the world of organic agriculture. Zheng, H., Huang, H., Li, D., Li, X., Fu, Z., & Chen, C. (2014). Assessment of nomadic rice- duck complex ecosystem on energy and economy. Ecological Processes, 3(1), 1-8. Thapa, G. (2009). Smallholder farming in transforming economies of Asia and the Pacific: Challenges and opportunities. IFAD. Feb.

46

7. Appendix

7.1 Field conditions

a b

c d

e f

47

g

a) Rice seedlings preparation; b) Rice transplanting; c) Duck tracks in rice paddy; d) Digging fishes pond in RMAFD treatment; e) Building duck house; f) Azolla inoculation; g) Rice paddy and border crops in plastic bags.

7.2 Ducks and fishes activity a b

c d

a) Ducks eating crotalaria leaf; b) Ducks searching food in rice paddy; c) Farmer feeding fishes; d) Fishes eating plankton on water surface.

48

7.3 Insects in rice paddy

7.3.1 Natural enemies in rice field

49

7.3.2 Insects pests in rice field

50

7.4 Weed dry matter in different treatments SPSS results

7.4.1 Monochoria one way ANOVA Test of Homogeneity of Variancesa Robust Tests of Equality of Meansa DM_g DM_g Levene Statistic df1 df2 Sig. Statisticb df1 df2 Sig. 85363105325030 4 5 ,000 Welch 20,451 4 2,000 ,047 86,000 a. Species = Monochoria a. Species = Monochoria b. Asymptotically F distributed.

Multiple Comparisons Dependent Variable: DM Games-Howell

Mean Difference 95% Confidence Interval (I) Treatment (J) Treatment (I-J) Std. Error Sig. Lower Bound Upper Bound 0 1 49,20000 18,66367 ,345 -112,2414 210,6414 2 45,80000 18,96002 ,384 -113,6197 205,2197 3 119,10000 15,15206 ,165 -277,6406 515,8406 4 120,07500 15,15002 ,164 -277,1621 517,3121 1 0 -49,20000 18,66367 ,345 -210,6414 112,2414 2 -3,40000 15,77244 ,999 -125,0356 118,2356 3 69,90000 10,90287 ,201 -215,2084 355,0084 4 70,87500 10,90003 ,199 -214,9226 356,6726 2 0 -45,80000 18,96002 ,384 -205,2197 113,6197 1 3,40000 15,77244 ,999 -118,2356 125,0356 3 73,30000 11,40274 ,201 -224,9491 371,5491 4 74,27500 11,40003 ,198 -224,6332 373,1832 3 0 -119,10000 15,15206 ,165 -515,8406 277,6406 1 -69,90000 10,90287 ,201 -355,0084 215,2084 2 -73,30000 11,40274 ,201 -371,5491 224,9491 4 ,97500 ,25125 ,320 -5,2895 7,2395 4 0 -120,07500 15,15002 ,164 -517,3121 277,1621 1 -70,87500 10,90003 ,199 -356,6726 214,9226 2 -74,27500 11,40003 ,198 -373,1832 224,6332 3 -,97500 ,25125 ,320 -7,2395 5,2895

51

7.4.2 Small flower umbrella sedge one way ANOVA

Robust Tests of Equality of Means DM_Lg10

Statistica df1 df2 Sig.

Welch 615,028 4 2,037 ,001 Brown-Forsythe 328,747 4 2,127 ,002

a. Asymptotically F distributed.

Multiple Comparisons Dependent Variable: DM_Lg10 Games-Howell

Mean Difference 95% Confidence Interval (I) Treatment (J) Treatment (I-J) Std. Error Sig. Lower Bound Upper Bound

0 1 ,26988 ,09855 ,320 -,5396 1,0793

2 ,96740 ,06224 ,070 -,3444 2,2792 3 1,77602* ,06087 ,045 ,1899 3,3621 4 1,76605* ,06425 ,027 ,7143 2,8178 1 0 -,26988 ,09855 ,320 -1,0793 ,5396 2 ,69752 ,07864 ,135 -1,0960 2,4910 3 1,50614 ,07756 ,067 -,5197 3,5320 4 1,49617 ,08024 ,051 -,0374 3,0297 2 0 -,96740 ,06224 ,070 -2,2792 ,3444 1 -,69752 ,07864 ,135 -2,4910 1,0960 3 ,80863* ,01333 ,018 ,5004 1,1168 4 ,79865* ,02453 ,006 ,5659 1,0314 3 0 -1,77602* ,06087 ,045 -3,3621 -,1899 1 -1,50614 ,07756 ,067 -3,5320 ,5197 2 -,80863* ,01333 ,018 -1,1168 -,5004 4 -,00998 ,02080 ,978 -,5275 ,5075 4 0 -1,76605* ,06425 ,027 -2,8178 -,7143

1 -1,49617 ,08024 ,051 -3,0297 ,0374 2 -,79865* ,02453 ,006 -1,0314 -,5659 3 ,00998 ,02080 ,978 -,5075 ,5275 *. The mean difference is significant at the 0.05 level.

52

7.4.3 Knotgrass one way ANOVA

Robust Tests of Equality of Meansa Test of Homogeneity of Variancesa DM_g DM_g Statisticb df1 df2 Sig. Levene Statistic df1 df2 Sig. Welch 27,802 4 2,000 ,035 5548838219662 4 5 ,000 a. Species = knotgrass 067,000 b. Asymptotically F distributed. a. Species = knotgrass

Multiple Comparisonsa Dependent Variable: DM_g Games-Howell

Mean Difference 95% Confidence Interval (I) Groups (J) Groups (I-J) Std. Error Sig. Lower Bound Upper Bound

1 2 43,600000 6,720119 ,080 -13,44720 100,64720

3 15,600000 9,404786 ,594 -67,19372 98,39372 4 42,400000 5,758472 ,125 -45,91403 130,71403 5 58,194500 5,400000 ,121 -83,39673 199,78573 2 1 -43,600000 6,720119 ,080 -100,64720 13,44720 3 -28,000000 8,676981 ,296 -126,58922 70,58922 4 -1,200000 4,472136 ,998 -53,85111 51,45111 5 14,594500 4,000000 ,343 -90,28789 119,47689 3 1 -15,600000 9,404786 ,594 -98,39372 67,19372 2 28,000000 8,676981 ,296 -70,58922 126,58922 4 26,800000 7,955501 ,341 -127,46701 181,06701 5 42,594500 7,700000 ,232 -159,30412 244,49312 4 1 -42,400000 5,758472 ,125 -130,71403 45,91403 2 1,200000 4,472136 ,998 -51,45111 53,85111 3 -26,800000 7,955501 ,341 -181,06701 127,46701 5 15,794500 2,000000 ,164 -36,64668 68,23568 5 1 -58,194500 5,400000 ,121 -199,78573 83,39673

2 -14,594500 4,000000 ,343 -119,47689 90,28789 3 -42,594500 7,700000 ,232 -244,49312 159,30412 4 -15,794500 2,000000 ,164 -68,23568 36,64668 a. Species = 3

53

7.4.4 Water creeping primrose one way ANOVA

Test of Homogeneity of Variancesa Robust Tests of Equality of Meansa DM_g DM_g

Levene Statistic df1 df2 Sig. Statisticb df1 df2 Sig.

25488124905853 Welch 17,992 4 2,000 ,053 4 5 ,000 08,000 a. Species = water creeping primrose a. Species = water creeping primrose b. Asymptotically F distributed.

Multiple Comparisonsa Dependent Variable: DM_g Games-Howell

Mean Difference 95% Confidence Interval (I) Groups (J) Groups (I-J) Std. Error Sig. Lower Bound Upper Bound 1 2 9,000000 1,746425 ,177 -14,24157 32,24157 3 9,400000 2,262742 ,157 -8,00979 26,80979 4 5,600000 1,835756 ,308 -13,78536 24,98536 5 12,392500 1,600000 ,168 -29,56044 54,34544 2 1 -9,000000 1,746425 ,177 -32,24157 14,24157 3 ,400000 1,746425 ,999 -22,84157 23,64157 4 -3,400000 1,140175 ,283 -12,80887 6,00887 5 3,392500 ,700000 ,264 -14,96188 21,74688 3 1 -9,400000 2,262742 ,157 -26,80979 8,00979 2 -,400000 1,746425 ,999 -23,64157 22,84157 4 -3,800000 1,835756 ,488 -23,18536 15,58536 5 2,992500 1,600000 ,596 -38,96044 44,94544 4 1 -5,600000 1,835756 ,308 -24,98536 13,78536 2 3,400000 1,140175 ,283 -6,00887 12,80887 3 3,800000 1,835756 ,488 -15,58536 23,18536 5 6,792500 ,900000 ,172 -16,80601 30,39101 5 1 -12,392500 1,600000 ,168 -54,34544 29,56044 2 -3,392500 ,700000 ,264 -21,74688 14,96188 3 -2,992500 1,600000 ,596 -44,94544 38,96044 4 -6,792500 ,900000 ,172 -30,39101 16,80601 a. Species = water creeping primrose

54

7.5 Insects pests in different treatments SPSS results

7.5.1 Insects pests_sweep net

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig.

RWM ,217 10 ,200* ,883 10 ,140 BPH ,218 10 ,195 ,847 10 ,053 MLH ,203 10 ,200* ,901 10 ,223 LH ,268 10 ,040 ,850 10 ,058

*. This is a lower bound of the true significance. a. Lilliefors Significance Correction

Test of Homogeneity of Variances

Levene Statistic df1 df2 Sig.

RWM 112589990684262 4 5 ,000 4,000 BPH 175921860444160 4 5 ,000 0,000 MLH 379991218559385 4 5 ,000 6,000 LH . 4 . .

Robust Tests of Equality of Means

Statistica df1 df2 Sig.

RWM Welch 49,527 4 2,470 ,010

BPH Welch 7,954 4 2,283 ,094

MLH Welch 21,571 4 2,403 ,028

LH Welch 3,173 4 2,299 ,229

a. Asymptotically F distributed.

55

Multiple Comparisons Games-Howell

Dependent Mean 95% Confidence Interval Variable (I) Treatment (J) Treatment Difference (I-J) Std. Error Sig. Lower Bound Upper Bound RWM cont conv 5,000 2,236 ,460 -21,33 31,33 RMAF 11,000* 1,414 ,049 ,12 21,88 RMAFD2 24,000* 1,414 ,011 13,12 34,88 RMAFD8 23,500* 1,803 ,027 7,02 39,98 conv cont -5,000 2,236 ,460 -31,33 21,33 RMAF 6,000 2,236 ,375 -20,33 32,33 RMAFD2 19,000 2,236 ,080 -7,33 45,33

RMAFD8 18,500 2,500 ,063 -2,56 39,56 RMAF cont -11,000* 1,414 ,049 -21,88 -,12 conv -6,000 2,236 ,375 -32,33 20,33 RMAFD2 13,000* 1,414 ,035 2,12 23,88 RMAFD8 12,500 1,803 ,080 -3,98 28,98 RMAFD2 cont -24,000* 1,414 ,011 -34,88 -13,12 conv -19,000 2,236 ,080 -45,33 7,33 RMAF -13,000* 1,414 ,035 -23,88 -2,12 RMAFD8 -,500 1,803 ,997 -16,98 15,98 RMAFD8 cont -23,500* 1,803 ,027 -39,98 -7,02

conv -18,500 2,500 ,063 -39,56 2,56 RMAF -12,500 1,803 ,080 -28,98 3,98 RMAFD2 ,500 1,803 ,997 -15,98 16,98 BPH cont conv 2,000 2,236 ,877 -24,33 28,33 RMAF 4,500 1,803 ,378 -11,98 20,98 RMAFD2 -10,500 2,693 ,260 -49,25 28,25 RMAFD8 5,500 1,118 ,173 -7,66 18,66 conv cont -2,000 2,236 ,877 -28,33 24,33 RMAF 2,500 2,500 ,841 -18,56 23,56 RMAFD2 -12,500 3,202 ,183 -38,54 13,54 RMAFD8 3,500 2,062 ,626 -37,26 44,26 RMAF cont -4,500 1,803 ,378 -20,98 11,98 conv -2,500 2,500 ,841 -23,56 18,56 RMAFD2 -15,000 2,915 ,141 -44,07 14,07 RMAFD8 1,000 1,581 ,950 -25,27 27,27 RMAFD2 cont 10,500 2,693 ,260 -28,25 49,25 conv 12,500 3,202 ,183 -13,54 38,54 RMAF 15,000 2,915 ,141 -14,07 44,07 56

RMAFD8 16,000 2,550 ,188 -39,33 71,33 RMAFD8 cont -5,500 1,118 ,173 -18,66 7,66 conv -3,500 2,062 ,626 -44,26 37,26 RMAF -1,000 1,581 ,950 -27,27 25,27 RMAFD2 -16,000 2,550 ,188 -71,33 39,33 MLH cont conv 5,000 ,707 ,059 -,44 10,44 RMAF -8,500 1,118 ,094 -21,66 4,66 RMAFD2 ,500 1,118 ,984 -12,66 13,66 RMAFD8 1,000 2,550 ,989 -54,33 56,33 conv cont -5,000 ,707 ,059 -10,44 ,44 RMAF -13,500* 1,118 ,048 -26,66 -,34 RMAFD2 -4,500 1,118 ,226 -17,66 8,66 RMAFD8 -4,000 2,550 ,665 -59,33 51,33 RMAF cont 8,500 1,118 ,094 -4,66 21,66 conv 13,500* 1,118 ,048 ,34 26,66 RMAFD2 9,000 1,414 ,072 -1,88 19,88 RMAFD8 9,500 2,693 ,293 -29,25 48,25 RMAFD2 cont -,500 1,118 ,984 -13,66 12,66 conv 4,500 1,118 ,226 -8,66 17,66 RMAF -9,000 1,414 ,072 -19,88 1,88 RMAFD8 ,500 2,693 ,999 -38,25 39,25 RMAFD8 cont -1,000 2,550 ,989 -56,33 54,33 conv 4,000 2,550 ,665 -51,33 59,33 RMAF -9,500 2,693 ,293 -48,25 29,25 RMAFD2 -,500 2,693 ,999 -39,25 38,25 LH cont conv 1,500 1,118 ,717 -11,66 14,66 RMAF -15,000 4,528 ,369 -126,60 96,60 RMAFD2 -8,000 3,536 ,510 -91,84 75,84 RMAFD8 2,500 1,118 ,460 -10,66 15,66 conv cont -1,500 1,118 ,717 -14,66 11,66 RMAF -16,500 4,610 ,328 -112,63 79,63 RMAFD2 -9,500 3,640 ,431 -76,42 57,42

RMAFD8 1,000 1,414 ,937 -9,88 11,88 RMAF cont 15,000 4,528 ,369 -96,60 126,60 conv 16,500 4,610 ,328 -79,63 112,63 RMAFD2 7,000 5,701 ,752 -40,04 54,04 RMAFD8 17,500 4,610 ,309 -78,63 113,63 RMAFD2 cont 8,000 3,536 ,510 -75,84 91,84 conv 9,500 3,640 ,431 -57,42 76,42

57

RMAF -7,000 5,701 ,752 -54,04 40,04

RMAFD8 10,500 3,640 ,392 -56,42 77,42 RMAFD8 cont -2,500 1,118 ,460 -15,66 10,66 conv -1,000 1,414 ,937 -11,88 9,88 RMAF -17,500 4,610 ,309 -113,63 78,63 RMAFD2 -10,500 3,640 ,392 -77,42 56,42 *. The mean difference is significant at the 0.05 level.

7.5.2 Natural enemies SPSS results

ANOVAa

Sum of Squares df Mean Square F Sig.

NATURALENEMY_SWEEP Between Groups 1667,600 4 416,900 15,498 ,005

Within Groups 134,500 5 26,900 Total 1802,100 9 NATURALENEMY_YELLO Between Groups 697,000 4 174,250 5,988 ,038 W Within Groups 145,500 5 29,100 Total 842,500 9 RATIO_SWEEP Between Groups 6,816 4 1,704 7,313 ,026 Within Groups 1,165 5 ,233 Total 7,981 9 RATIO_YELLOWTRAP Between Groups 2,156 4 ,539 1,400 ,355

Within Groups 1,925 5 ,385 Total 4,081 9 a. TIME = 4

66

58