Ecological mechanisms underlying the sustainability ofthe agricultural heritage rice–fish coculture systemJian Xiea, Liangliang Hua, Jianjun Tanga, Xue Wua, Nana Lia, Yongge Yuana, Haishui Yanga, Jiaen Zhangb, Shiming Luob,and Xin Chena,1

aCollege of Life Sciences, Zhejiang University, Hangzhou 310058, China; and bDepartment of Ecology, South China Agricultural University, Guangzhou510642, China

Edited by Stephen R. Carpenter, University of Wisconsin, Madison, WI, and approved October 13, 2011 (received for review July 12, 2011)

For centuries, traditional agricultural systems have contributed tofood and livelihood security throughout the world. Recognizing theecological legacy in the traditional agricultural systems may help usdevelop novel sustainable agriculture. We examine how rice–fishcoculture (RF), which has been designated a “globally importantagricultural heritage system,” has been maintained for over 1,200y in south China. A field survey demonstrated that although riceyield and rice-yield stability are similar in RF and rice monoculture(RM), RF requires 68% less pesticide and 24% less chemical fertilizerthan RM. A field experiment confirmed this result. We documentedthat a mutually beneficial relationship between rice and fish devel-ops in RF: Fish reduce rice pests and rice favors fish by moderatingthe water environment. This positive relationship between rice andfish reduces the need for pesticides in RF. Our results also indicate acomplementary use of nitrogen (N) between rice and fish in RF,resulting in low N fertilizer application and low N release into theenvironment. These findings provide unique insights into how pos-itive interactions and complementary use of resource between spe-cies generate emergent ecosystem properties and how modernagricultural systems might be improved by exploiting synergiesbetween species.

Global food security is becoming an acute problem because ofthe increasing world population (1), the limitation of agri-

cultural resources (e.g., land and water) (2), and the effects ofglobal climate change on crop production (3, 4). World agriculturecurrently faces great challenges in producing sufficient food whileminimizing the negative environmental effects of crop cultivation.In the past 50 y, crop yields have substantially increased, mainly

resulting from the use of chemical fertilizers and pesticides, thedevelopment of new crop varieties, and the improvement in cul-tivation methods. The heavy application of chemical fertilizers andpesticides for long periods, however, negatively affects the envi-ronment, induces pest resistance to pesticides, and increases ag-ricultural costs (5, 6). As a consequence, modern agriculture nowrequires “rethinking” (1, 7), and such rethinking should includereconsideration of traditional agricultural systems (8–10).For many centuries, traditional agricultural systems have con-

tributed to food and livelihood security throughout the world (8).Because traditional agricultural systems have been created, shaped,and maintained by generations of farmers who used managementpractices that were matched to local conditions, and because thesesystems are based on diverse species and species interactions, tra-ditional agricultural systems reflect a successful adaptation to dif-ferent environments and are rich in biological diversity (8, 11, 12).The recognition of the ecological legacy of these traditional agri-cultural systems and the integration of these unique experiencesinto our future farm designs could help us to develop more sus-tainable agriculture. In fact, studies of such traditional systems havealready helped scientists create novel farm designs (5, 13–15).During the recent expansion of modern agriculture based on

substantial inputs of fertilizer and pesticides, however, many ofthese traditional agriculture systems have been disappearing (8). Topreserve these important agriculture systems, the Food and Agri-culture Organization, the United Nations Development Program,

and the Global Environment Facility developed a program for“globally important agricultural heritage systems (GIAHS)” in 2005(http://www.fao.org/nr/giahs/giahs-home/en/) (16). One of theseGIAHS is the rice–fish coculture system that has been practiced byfarmers in south Zhejiang province, China, for >1,200 y (17).In this rice–fish coculture system, the fish is an indigenous, red,

soft-scaled common carp (Cypinius carpia color var.) with highgenetic diversity (18, 19). The rice varieties in the system have beenchanging over time. In the last decade, high-yielding hybrid ricevarieties have been dominant (20). The rice–fish coculture isconsidered a sustainable form of agriculture because it maximizesthe benefits of scarce land and water resources by using relativelyfew chemical inputs, by producing both carbohydrate and proteinproducts, and by conserving biodiversity (20–24). Despite otherchanges resulting from rapid development over the last 30 y inChina, farmers continue to practice rice–fish coculture, in partbecause this system is an important component of traditionalculture and local customs (e.g., rice–fish festivals) (21). Althoughthe value of the rice–fish coculture has been recognized (for ex-ample, by its inclusion in the GIAHS project), the ecologicalmechanisms underlying the system have not been studied in depth.The main purpose of our present study was to estimate the

ecosystem stability of this rice–fish system and to determine howthe stability is maintained. Our hypothesis is that the stability ofthis system results from mutually beneficial relationships betweenrice and fish. The study was conducted at the GIAHS pilot site ofthe rice–fish system in China (SI Text, section S1, and Fig. S1).

ResultsQuantity and Temporal Stability of Rice Yield in Rice–Fish Coculture.We compared ecosystem stability of rice monoculture (RM) andrice–fish coculture (RF) with a farmer field survey and a field ex-periment. The temporal stability of rice yield was measured as thedegree of constancy of yield around its mean over the same timeinterval (25). In the survey of farmer fields, we determined thetemporal stability of rice yield with data collected annually from 31sampling units (each unit containing 3–5 subsamples) during2005–2010. In the field experiment (experiment 1), we determinedthe temporal stability of rice yield with data from each experi-mental plot during 2006–2010.Survey results from farmer fields showed that rice yield did not

differ betweenRMandRF over the 6 y (Fig. 1A, F1,60 = 0.092, P=

Author contributions: J.T., J.Z., S.L., and X.C. designed research; J.X., L.H., J.T., X.W., N.L.,Y.Y., H.Y., and X.C. performed research; J.X., L.H., J.T., X.W., N.L., H.Y., J.Z., S.L., and X.C.analyzed data; and J.X., L.H., J.T., S.L., and X.C. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Freely available online through the PNAS open access option.

See Commentary on page 19841.1To whom correspondence should be addressed. E-mail: chen-tang@zju.edu.cn.

See Author Summary on page 19851.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1111043108/-/DCSupplemental.

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0.763) but that the temporal stability of rice yield was higher in RFthan in RM (Fig. 1A, F1,61 = 7.031, P = 0.013). There were 68%more pesticides (F1,60 = 514.360, P = 0.000) (Fig. 1A) and 24%more fertilizers (F1,60 = 132.228, P = 0.000) (Table S1) used inRM than in RF. Pesticide application was also more variable inRM than in RF (F1,61 = 112.069, P = 0.001) (Table S1).In the field experiment (experiment 1), in which pesticides were

not applied, rice yield (F1,30 = 29.876, P = 0.001) and temporalstability of rice yield (F1,6 = 6.691, P = 0.04) were both higher inRF than in RM (Fig. 1B).Many factors can cause temporal variation in rice yield including

year-to-year changes in climate, pest incidence, use of new ricevarieties, and fertilizer rate (26, 27). During our study, pairedfarmer fields in the survey and plots in field experiment 1 did not

change in rice variety, irrigation scheme, and fertilization level (20,23) (Table S1). In addition, weather (temperature, precipitation,and relative humidity) during the rice growing seasons did not varywidely over the 6 y (Fig. S2). In the survey, the factor that variedmost was quantity of pesticides (Fig. 1A and Table S1), which maybe due to the substantial year-to-year variation in the abundance ofrice pests (Fig. 2 and Fig. S3). Hence, the relationship between thetemporal stability and the quantity of pesticides used in the 6-yperiod was analyzed. We found that the temporal stability of riceyield was positively correlated with the quantity of pesticides ap-plied in RM (R2 = 0.526, P = 0.001, n = 31) but not in RF (R2 =0.104, P = 0.077, n = 31) (Fig. 1C). The results indicate thattemporal stability of rice yield in RM may largely depend onpesticides and that the greater stability in RF than in RM maypartly depend on the presence of fish.

Occurrence of Rice Pests in Rice–Fish Coculture (Experiment 1). Totest why RF can maintain the same yield stability as RM with lowpesticides, we examined the occurrence of insect pests, diseases,and weeds of rice in a 5-y field experiment without pesticide ap-plication. We found that rice planthoppers (including Nilaparvatalugen, Sogatella furcifera, and Laodelphax striatellus) were moreabundant in RM than in RF during the outbreak period of riceplanthoppers (from late August to early September of each year)(P < 0.05, Fig. 2A), although the abundances of the rice stem borer[Chilo supperssalis (Walker)] (F1,30 = 0.166, P = 0.687) and therice leaf roller (Cnaphalocrocis medinalis Guenee) (F1,30 = 0.011,P = 0.918), which mainly attack the upper part of the rice plants,did not significantly differ between RM and RF in experiment 1(Fig. S4 A and B).Rice sheath blight [caused by Thanatephorus cucumeris (Frank)

Donk] and rice blast (caused by Pyricutaria oryzae Cav.) are im-portant rice diseases in this area. Incidence of rice sheath blightwas higher in RM than in RF (F1,30 = 11.706, P= 0.002, Fig. 2B).Rice blast incidence did not differ (F1,30 = 2.714, P = 0.110) be-tween RM and RF in the early growing season (from June to earlyAugust, when leaf area was small), but the incidence was higher inRM than in RF (F1,30 = 12.992, P = 0.023) in the later growing

Fig. 1. Rice yield and stability of rice yield (S) in rice monoculture (RM) andrice–fish coculture (RF). (A) Rice yield and S and pesticide use (Inset) in a fieldsurvey. (B) Rice yield and S (Inset) in experiment 1. (C) The relationship be-tween S and the use of pesticides as determined in the survey. a.i., active in-gredient. Error bars are SE.

Fig. 2. Rice pests in rice monoculture (RM) and rice-fish coculture (RF) inexperiment 1. (A) Density of rice planthoppers. (B) Sheath blight incidence.(C) Rice blast incidence. (D) Weed infestation. Error bars are SE.

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period (from late August to early September) (Fig. 2C). Weedbiomass was significantly lower (F1,30 = 9.915, P = 0.004) in RFthan in RM in experiment 1 (Fig. 2D).

Removal of Rice Planthoppers by Fish Activity (Experiment 1). Be-cause we noticed that planthoppers often fell into the water whenfish hit the rice stems, we reasoned that the reduction of plan-thoppers in RF was due to the fish activity. To quantify this effect,we established quadrats that contained four rice hills in both RM(without fish hitting rice plants) and RF (with fish hitting riceplants). Video recordings of the quadrats were used to quantify thenumber of times that fish hit the rice stems in RF. In addition, therice planthoppers that fell onto the water surface in RF and RMwere counted (SI Text, section S2, and Fig. S5). The video re-cording of the RF quadrats indicated 26.8 ± 2.4 hits per rice hillper day (from early morning 5:00 AM to evening 6:00 PM) andthat the peak in hitting occurred from early morning 6:00 AMto late morning 11:00 AM (Fig. 3A). The number of rice plan-thoppers that dropped to the water surface per day (from earlymorning 5:00 AM to evening 6:00 PM) was 79± 6 per RMquadratand 174 ±15 per RF quadrat (Fig. 3A).We estimated the numbers of planthoppers that fell into the

water in 1 d because of fish activity in RF. To do this, we reasonedthat rice planthoppers fell onto the water surface inRMbecause of“nonfish” effects (probably wind), and we assumed that nonfisheffects were the same in RM and RF. Thus, we calculated thenumbers of rice planthoppers that fell into the water because offish activity by subtracting the numbers collected from the watersurface in RM quadrats from those collected from the water sur-face in RF quadrats (SI Text, section S2). On the basis of sub-traction and as indicated by the shading in Fig. 3A, a mean of 96 ±8 rice planthoppers per RF quadrat per day was removed from riceplants as a consequence of fish hitting the rice stems. When thesedata were collected, there were 117 ± 13 and 91 ± 8 planthoppers

per hill on rice plants in RM and RF, respectively. For RF, thetotal number in a quadrat at the time of observation was 364 ± 33(91 ± 8 per hill × 4 hills per quadrat). The removal rate of riceplanthoppers by fish was calculated by dividing the number re-moved by fish by the total number of rice planthoppers in a quadrat(SI Text, section S2). Thus, the removal rate of rice planthoppersby fish was ∼26 ± 2%.

Activities of Fish in Rice–Fish Coculture (Experiment 1). To testwhether the activity of fish living with rice differs from the activityof fish living without rice, we monitored and compared the activ-ities of fish in RF and fish monoculture (FM) (SI Text, section S2,and Fig. S5). On the basis of video recordings, fish were moreactive in RF than in FM in a day (F1,42 = 9.754, P = 0.003) (Fig.3B). Between 12:00 PM and 2:00 PM (early afternoon), noswimming and feeding activity was observed in FM but substantialactivity was observed in RF (Fig. 3B).

Microenvironment in Rice Fields (Experiment 1). To determine whyfish were more active in RF than in FM, we compared the fieldenvironment in RF and FM (without rice). From July 29 to August18, 2007 (which were sunny days and were the hottest days in thestudy area during that year), the surface water temperature (Fig.4A, F1,6 = 437.587, P = 0.000) and light density (Fig. 4B, F1,6 =254.531, P = 0.000) from 12:00 PM to 2:00 PM were significantlyhigher in FM than in RF.Ammonia-N levels in water were significantly lower inRF than in

FM during the rice growing season (F1,30 = 10.620, P= 0.000, Fig.5A). Total N in soil tended to increase in FM but did not sub-stantially change inRF during the 5-y experiment (Fig. 5B). Total Nin soil accumulated over time in FM and was greater than in RF atthe end of the experiment (Fig. 5B, F1,30 = 2.783, P = 0.044).

Nitrogen (N) Use Efficiency (Experiment 2). To test why coculture ofrice and fish can also reduce the use of N fertilizers in RF as in-

Fig. 3. Fish activity and rice planthopper removal in experiment 1. (A) Frequency at which fish hit rice plants (bars, hits per hill per hour) and total numbers ofrice planthoppers (lines, numbers per quadrat) collected from the quadrats B and D (Fig. S5). Shading between the lines indicates the numbers of plan-thoppers falling into the water, presumably because of fish activity. (B) Frequency of fish (bars) occurring in quadrats A and C (SI Text, section S2, and Fig. S5)in rice–fish coculture (RF) and fish monoculture (FM), and air temperature (line) in experiment 1. Error bars are SE.

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dicated in the field survey (Table S1), we conducted an experimentto examine the fate of input N in RM, RF, and FM systems. Wefound that rice yield was significantly higher in RF plots with fishfeed input than in RF plots without feed input (Fig. 6A, F2, 11 =6.566, P = 0.031). Rice yield also tended to be higher in RF plotswith feed than in RMplots, even though N input was 36.5% higherin the RM plots than in the RF plots with fish feed (Fig. 6A). Fishfeed input significantly increased fish yield in both FM and RFplots (Fig. 6A, F3,15 = 22.545, P = 0.001).Only 11.1% and 14.2% of the N in fish feed was assimilated

into fish bodies in RF and FM, respectively (Fig. 6C). In RF,however, rice plants used the unconsumed N in fish feed andreduced fish feed N in the environment (i.e., in soil and water)(Fig. 6B). A comparison of RF with and without fish feed in-dicated that 31.8% of the N contained in rice grain and straw wasfrom fish feed (Fig. 6C). Subtraction of the fish N in RF from thefish N in FM indicated that 2.1% of the fertilizer N was assim-ilated into fish bodies in the RF.

DiscussionAccording to our survey of farmer fields and our first field ex-periment, the same level of temporal stability of rice yieldrequires substantially less pesticide input in RF than in RM. Thisstability of the RF system is associated with positive interactionsbetween rice and fish that generate some emergent ecosystemproperties. These emergent properties help explain the stabilityand sustainability of this traditional system.On the one hand, fish can be biocontrol agents in rice (28). In

our first field experiment, fish benefited rice by reducing insectpests, diseases, and weeds. Although fish did not reduce somepests (e.g., rice stem borer and rice leaf roller), fish substantiallyreduced rice planthoppers, rice sheath blight, and a variety ofweeds. Our results further indicated that the reduction in rice

planthoppers was partly due to fish hitting rice plant stems. Thishitting caused rice planthoppers to fall into the water where theywere possibly consumed by the fish (Fig. 3A). Whether fish couldlearn to hit the stems to feed on planthoppers in rice–fish systemremains to be determined. The hitting activity caused by fish could

Fig. 4. Temperature of surface water (A) and light intensity under the rice plant canopy (B) in experiment 1. RF, rice–fish coculture; FM, fish monoculture.

Fig. 5. Ammonium N in water (A) and total N in soil (B) in experiment 1. RF,rice–fish coculture; FM, fish monoculture. Error bars are SE.

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also shake down dew drops from rice leaves in early morning andhence reduce the risk of spore germination and mycelium pene-tration of rice blast disease in rice leaves, which is worth furtherstudy. Fish can also disrupt or eat mycelia of the pathogenic fungusRhizoctonia solani Kuhn and thus inhibit the development of ricesheath blight (Fig. 2B). In addition, fish in rice fields are capableof removing weeds by eating or uprooting them, resulting in analmost weed-free paddy field (Fig. 2D).On the other hand, rice benefits fish. First, rice improves the

environment for fish by providing shade and reducing water tem-perature during the hot season (Fig. 4 A and B). This moderationof light and temperature was associated with a substantial increasein fish activity (Fig. 3B). Second, rice acts as a N sink and helps

reduce the concentration of ammonia in the water (Fig. 5A) andtotal N in the soil (Fig. 5B); such reductions in N could make thewater more suitable for fish. Rice also may benefit fish by provid-ing a supplemental food source in the form of planthoppers andother herbivorous insects that fall from the rice plants into thewater. These positive interactions between fish and rice wouldrepresent a form of positive feedback.Complementary N use between rice and fish explains why the

RF system requires less chemical N fertilizers than the RM system.Although substantially less chemical N fertilizer was applied to RFthan to RM in experiment 2, rice yields were similar in the twosystems (Fig. 6A). RF can produce a high rice yield with lesschemical fertilizer partly because of the unconsumed N in the fishfeed (as indicated in Fig. 6C, 32% of the N in rice grain and strawwas from fish feed in RF). At the same time, the fertilizer N in RFthat is not taken up by the rice plants can stimulate plankton, whichis consumed by fish. Thus, N use is efficient in RF apparentlybecause both rice and fish use different forms of N.Positive interactions and complementary nutrient use between

rice and fish are the foundation of this “long-lived” coculturesystem and help explain why this coculture system can remainproductive over time with low input of pesticides and chemicalfertilizers. In modern, intensive agriculture, however, mono-cultures are planted, and positive interactions and complementaryuse of resources between species are largely ignored (29). Main-taining high and stable production in these intensive agriculturalsystems usually requires a high input of chemical fertilizers andpesticides (6). Rice–fish coculture, in contrast, exploits synergiesbetween species to minimize chemical inputs and to maintain highand stable crop production. Study of the rice–fish coculture systemsuggests that modern agricultural systems might be improved byadding species to monocultures that result in positive interactionsbetween the components. These interactions should improve thefunctioning of the agricultural ecosystem.Rice production is a key component of global food security

because rice is the main ingredient in the daily diets of ∼3 billionpeople. Moreover, >90% of worldwide rice production occurs indeveloping areas (27), where populations are growing rapidly andwhere the land available for agriculture is limited (2, 4). Rice–fishsystems similar to the system described in this paper are nowpracticed in Egypt, India, Indonesia, Thailand, Vietnam, thePhilippines, Bangladesh, Malaysia, and other countries (30, 31).The rice–fish systems are important in these areas because theyprovide food security, reduce the impact of agriculture on theenvironment, and may be less affected than conventional systemsby climate change (32, 33). Rice–fish coculture on a large scale,however, will require the development of machinery for rice cul-tivation, field facilities that provide fish refuge, and new technologyfor high-yield fish culture.Our study suggests that higher yield, lower chemical use, and

more efficient utilization of land, water, and nutrients could berealized if some of the ecological components from traditionalagriculture are appropriately incorporated into modern agricul-ture along with biotechnology, information-based technology, andother new technologies (34, 35). If we can apply the philosophybehind these traditional systems, future agriculture may havemorechance tomeet the escalating global food demand while protectingthe environment.

Materials and MethodsField Survey. We assessed the quantity and stability of rice yield in rice–fishcoculture by surveying farmerfields in south Zhejiang Province, China, which isthe location of the rice–fish coculture GIAHS site (120°26′–121°41′E, 27°25′–28°57′N) (the study area is described in SI Text, section S1, and Fig. S1A). During2005–2010, we randomly selected 31 villages (each village is located in a smallwatershed; 25 are in hilly areas and 6 are in flat areas) as sampling unitsthroughout the study area (Fig. S1B). In each village, we selected three to fivepairs of fields with RM vs. RF as subsample units. The two fields of each pair

Fig. 6. (A–C) Yield of rice and fish (A), N balance (B), and the fate of N input(C) in experiment 2. Bars in B show balance of N input and output in RM, RFwithout fish feed (RF feed 0), RF with fish feed (RF feed 1), FM without feed(FM feed 0), and FM with feed application (FM feed 1). A negative value forN in the environment means that some fraction of N in rice or fish was fromthe environment, and a positive value for N in the environment means thatsome portion of the input N was not used by rice and fish but remained inthe field. Pie charts in C show partitioning of N derived from fish feed inharvested rice, harvested fish, and the environment in RF and FM (e.g.,11.1% and 14.2% of the N supplied by fish feed was estimated to be con-tained in fish in RF and FM, respectively). The calculations for the balanceof N output and input within each system (RM, RF, or FM) are described inSI Text, section S5. The calculations used to determine the fate of N aredescribed in SI Text, section S6. In A, means for rice yield with the sameuppercase letter or means for fish yield with the same lowercase letter arenot significantly different (P > 0.05). RM, rice monoculture; RF, rice–fishcoculture; FM, fish monoculture. Error bars are SE.

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were locatedwithin the same village in a small watershed of∼100 ha andweresimilar in size (0.3–0.5 ha), weather, and soil type. Eachfield pair was owned byone farmer or owned separately by two farmers. The same fields were sur-veyed each year.

Without influencing normal field operations, we recorded farming ac-tivities during the rice growing season at each field; we recorded each ap-plication of fertilizers and pesticides, the varieties planted, and the irrigationscheme used. The total application of pesticides was expressed as activeingredient (a.i.) per hectare. The total application of fertilizers was calculatedand expressed as N, phosphorus (P), or potassium (K) per hectare.

In the first 2 y of the study (2005 and 2006), rice yields were obtained byharvesting sample plots (a 2-m2 plot in each field) and by obtaining harvestinformation from the farmers. Rice yields obtained by these two methodswere highly correlated. Thus, during 2007–2010, data for rice yields wereobtained only from farmers. All rice yield data used in this paper wereobtained from farmers. Data for fish yield were also obtained from farmers.Rice and fish yields were expressed as tons per hectare and kilograms perhectare, respectively.

Data for precipitation, temperature, and relative humidity were obtainedfrom three meteorological stations within the study site (Fig. S1B). Data forrice planthoppers were collected from three monitoring stations within thestudy site (Fig. S1B).

The temporal stability of rice yield (S) in RM and RF was compared withdata from each sampling unit (village; the average from the subsample unitsin that village) for 6 y (2005–2010). S for each village was calculated as S =μ/δ, where μ is the mean yield value for a time period and δ is its temporal SDover the same time interval. The temporal coefficient of variation (CV) in thequantity of pesticides and fertilizers applied for RM and RF in each sampleunit was calculated as CV = (SD/mean) × 100. Simple linear regression wasused to determine the relationship between S and the quantity of pesticide(a.i.) applied in RM and RF.

Analysis of variance (ANOVA) was conducted with the general linearmodel (GLM) in SPSS (V.17.0). Repeated measures ANOVAs were performedon rice yield and quantities of pesticides and fertilizers. Two-way ANOVAswere used to analyze the effects of culture types (RM and RF) and paired plotson temporal stability of rice yield and on CVs (percentages) of pesticidesand fertilizers over 6 y. Before analysis, data were log-transformed to meetassumptions of normality and homogeneity of variance.

Experiment 1. We conducted a 5-y field experiment at the GIAHS pilot site (SIText, section S1, and Fig. S1B) to compare the stability of the rice–fish co-culture system with two other systems and to determine how this coculturesystem maintains stability with minimal pesticide input. The experiment wasa split-plot design with three main plots (three culture systems), five subplots(five experimental years, 2006–2010), and four replicates. The three culturesystems were RM without pesticides, RF without pesticides, and FM. Blockswere arranged in four terraces, and each treatment was randomly placed ina 25 × 20-m plot within each block. Plots were separated by 5-m buffer stripsplanted with soybean.

The hybrid variety Zhong-Zhe-You No.1, a dominant variety in the studyarea,was used. Fourweeks after germination, rice seedlingswere transplantedin thefieldwith 35 cmbetween rows and30 cmbetweenhills (one seedlingperhill) within the rows. Immediately after rice was transplanted, 250 young fish(each ∼70 g) were introduced to each plot. A compound N:P:K fertilizer(15:15:15) was broadcast as a basal fertilizer before furrowing at 450 kg·ha−1.No topdressing was applied. No pesticide was used in any plot during theexperiment. Plots were flush irrigated at transplanting and were kept floodedto 20 cm depth until harvest. Fish were fed every day with 1.75 kg of local feed(a mixture of rice, corn, and wheat grain) per RF and FM plot.

Rice planthoppers (including N. lugen, S. furcifera, and L. striatellus), ricestem borers [C. supperssalis (Walker)], and rice leaf rollers (C. medinalisGuenee) were quantified every week in each experimental year. Themethod described by Frei et al. was used to quantify rice planthoppers (36).For the rice stem borer, 100 hills of rice in each plot were sampled by theparallel line sampling method and then dissected and examined for borers.For the rice leaf roller, 10 sites with a total number of 40 rice hills from eachplot were sampled at random; the larvae were recorded for each plantsampled, and the number of larvae per hill were calculated for each treat-ment. Every 2 wk after transplanting, the incidence of rice sheath blight[caused by T. cucumeris (Frank) Donk] and rice blast (caused by P. oryzaeCav.) was determined by examining 1,000 stems per plot (200 stems at fivelocations per plot) and calculating the percentage of symptomatic stems.Plots were evaluated for weed infestation 2 wk before harvest by placing a1-m2 quadrat at five locations in each plot. The aboveground dry biomass ofweeds in each quadrat was determined.

In 2007, we monitored fish activity and removal of rice planthoppers byfish activity (SI Text, section S2) and temperature of the surface water andsolar radiation intensity under the rice plant canopy in each plot (SI Text,section S3). In July, August, and September of each year, we determined theconcentration of ammonia N in water sampled from RF and FM plots (SIText, section S4). Immediately after each rice harvest, we determined total Nin soil sampled from RF and FM plots (SI Text, section S4).

In each experimental year, rice yields were measured by harvesting andweighing the rice grain from each plot. When rice was harvested, fish yieldswere measured by collecting and weighing all of the fish from each plot. Thetemporal stability of rice yield (S) in RM and RF was calculated with data fromeach plot for 5 y (2006–2010). S for each plot was calculated as described inField Survey.

The GLM in SPSS (V.17.0) was used to conduct the statistical analysis. Alldata were first subjected to a homogeneity test and were log-transformedif they did not meet the assumptions of normality and homoscedasticity.ANOVAswith split-plot design (culture types RM, RF, and FM as themain plotsand experimental years as the subplots) were performed on rice yield, fishyield, weed biomass, and soil total N. Repeated measures (sampling severaltimes in a year) ANOVAswith split-plot designwere performed on the densityof insect pests, disease incidences, and the concentration of ammonia N in thewater. For each experimental year, the density of rice planthoppers betweenRM and RFwas compared at time of the outbreak period of rice planthoppers(from late August to early September) and the other times (from early June tolate July and frommiddle to late September). The frequency of fish activity ina day and the water temperature and light intensity under the rice canopyaround noon were compared in RF vs. FM plots with repeated measuresANOVA. The numbers of rice planthoppers collected from the water surfacewere compared in RM vs. RF plots with repeated measures ANOVA.

Experiment 2. To test the effect of N application in the form offish feed on riceand fish yield, we compared RM, RF (± fish feed), and FM (± fish feed) inexperiment 2. The experiment had a completely randomized block designwith five treatments and four replicates. The treatments were (i) RM, (ii)rice–fish coculture without feed application (RF feed 0), (iii) rice–fish co-culture with feed application (RF feed 1), (iv) fish monoculture without feedapplication (FM feed 0), and (v) fish monoculture with feed application (FMfeed 1). Blocks were arranged in four terraces, and each treatment wasrandomly placed in a 6.5 × 10-m plot within each block. Plots were separatedby 5-m buffer strips planted with soybean.

Rice cultivation (e.g., variety, seedling preparation, transplantation, andirrigation) was the same as in experiment 1. For RM, a compound N:P:Kfertilizer (15:15:15) was broadcast as a basal fertilizer before furrowing at450 kg·ha−1. A topdressing of urea containing 46% N was applied to RM 30 dafter transplanting at 112.5 kg·ha−1. RF feed 0 and RF feed 1 received thesame basal fertilizer as RM but did not receive a topdressing. For treatmentsof fish monoculture (FM feed 0 and FM feed1) and rice–fish coculture (RFfeed 0 and RF feed 1), 60 young fish (each ∼12 g) were added to each plotimmediately after rice was transplanted. A compounded fish feed was pre-pared with ingredients purchased at the local market. The feed contained,on a dry matter basis, 9.13% crude protein, 3.25% crude lipid, and 14.5%crude ash. The N and P content was 1.46% and 5.37%, respectively. In thetreatments RF feed 1 and FM feed 1, feed was applied once each day at 15,30, 45, 60, 75, and 90 d after fish were added at a rate of 30, 60, 90, 120, 150,and 200 g per plot, respectively (the quantity added was increased as the fishgrew). The total amount of feed supplied during the entire experiment was9.73 kg per plot.

Rice yield was determined by harvesting the rice from each plot. Fordetermination of N content in rice grain and straw, five rice hills were col-lected per plot at harvest. The grain and aboveground straw were dried at65 °C and ground. Fish yield was estimated by collecting all fish from thewhole plot at rice harvest. Five fish were sampled from each plot for analysisof N content. Dry matter content was determined by drying the samples at105 °C for 24 h. The Kjeldahl method was used for determination of the Ncontents in rice grain, rice straw, and fish.

The balance of N output and input within each system (RM, RF, or FM) wascalculated (SI Text, section S5), and the fate of N contained in fish feed in RFor FM was analyzed (SI Text, section S6). The quantity of fertilizer N thatcould potentially stimulate plankton (which is consumed by fish) was cal-culated by subtracting the fish N in RF from the fish N in FM.

Two-way ANOVAs in the GLM (SPSS, V.17.0) were used to analyze theeffect of treatment or block on yields of rice and fish and on the N contentsof rice and fish. Means were compared by Tukey’s method at the 5%confidence level.

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ACKNOWLEDGMENTS. We thank J. H. Chen, M. F. Wu, and X. H. Wang forhelping with the field experiment in the GIAHS site. Y. L. Jin, Q. Zhang, L. Liu,Y. F. Li, L. M. Xu, W. J. Zhu, and S. S. Zhang were also involved with the fieldexperiments. We also thank Bruce Jaffee in the United States for helpful

comments on the text and English revision. This research was supported bythe National Basic Research Program of China (2011CB100406), the Scienceand Technology Department of Zhejiang Province (2008C12064), and theState Environmental Protection Administration of China (201009020-04).

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