Applications of an Ecophysiological Model for Irrigated Rice (Oryza Sativa)- Echinochloa Competition

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Agronomy & Horticulture -- Faculty Publications Agronomy and Horticulture Department

1996

Applications of an Ecophysiological Model for Irrigated Rice (Oryza sativa)- Echinochloa Competition

John L. Lindquist University of Nebraska-Lincoln, [email protected]

Martin Kropff University of Minnesota

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Lindquist, John L. and Kropff, Martin, "Applications of an Ecophysiological Model for Irrigated Rice (Oryza sativa)- Echinochloa Competition" (1996). Agronomy & Horticulture -- Faculty Publications. 617. https://digitalcommons.unl.edu/agronomyfacpub/617

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Applications of an Ecophysiological Model for Irrigated Rice (Oryza sativa)-Echinochloa Competition Author(s): John L. Lindquist and Martin J. Kropff Reviewed work(s): Source: Weed Science, Vol. 44, No. 1 (Jan. - Mar., 1996), pp. 52-56 Published by: Weed Science Society of America and Allen Press Stable URL: http://www.jstor.org/stable/4045782 . Accessed: 14/09/2012 10:47

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http://www.jstor.org Weed Science, 1996. Volume44:52-56

Applications of an Ecophysiological Model for Irrigated Rice (Oryzasativa)- Echinochloa Competition1

JOHN L. LINDQUISTand MARTINJ. KROPFF2

Abstract.A simulationmodel of rice-barnyardgrasscompeti- yield loss. These empiricalrelationships show considerablevari- tion for light was used for two managementapplications. ation among years and locations (1, 21), presumably due to First,simulations using 47 weatherdata sets fromfour loca- variationin weatherand otherenvironmental factors. A number tions in Asia were conductedto evaluate the influenceof of simulationmodels have recently been developed to quantita- weathervariation on single year economicthreshold densi- tively describemechanisms of inter-plantcompetition based on ties of barnyardgrass.Second, rapid leaf areaexpansion and fundamentalplant physiology (6, 12, 16, 20, 22, 23). These leaf area index were evaluated as potential indicatorsof ecophysiologicalmodels may be utilized to evaluatethe relative improved rice competitiveness and tolerance to barn- importanceof weather,year, and location variabilityin weed- yardgrass.Influence of weather variation on single year crop interferencerelationships. economicthresholds was small under the assumptionthat Improved cultivar competitiveness and tolerance to weeds competitionwas for light only. Increasingearly leaf area have been suggested as methods of reducingthe negative influ- expansionrate reducedsimulated barnyardgrass seed pro- ence of weeds on crop yield (2, 5, 9). Improvedrice competitive- ductionand increasedsingle year economicthresholds, sug- ness may benefit managementby reducing weed reproductive gestingthat the use of competitiverice cultivarsmay reduce output. Because fewer seeds are produced, the influence of the needfor chemicalweed control. The model predicted that barnyardgrasson rice yields in subsequent years should be rice leaf area index 70 to 75 d after planting was a good reduced. Improved tolerance to weeds aids management by indicator of early leaf area expansion rate. Nomencla- reducingthe impact of each weed on crop yield, resultingin an ture: Barnyardgrass,Echinochloa crus-galli (L.) Beauv.,#3 increase in the numberof bamyardgrassplants needed to cause ECHCG;rice, Oryzasativa L. 'IR72.' economic damage(i.e., economic thresholdweed density would Additionalindex words. Economic threshold, integrated weed increase). Ecophysiological models may be used to generate management,weed ecology,IPM, weed-cropinterference, hypotheses regarding which plant characteristicsconfer im- ECHCG. proved competitivenessor tolerancein crops. An ecophysiologicalmodel (INTERCOM)was developedfor INTRODUCTION rice-barnyardgrasscompetition for light in well-fertilizedhigh- yielding irrigatedrice ecosystems (12). Kropffet al. (17) evalu- Worldrice productionmust be increasedby as much as 67% ated INTERCOMperformance using data from an experiment to feed the projectedhuman population in 2025 (8). Weed com- with irrigateddirect seeded rice and barnyardgrass.Dry matter petition reduces currentrice productionby an estimated 25% production, leaf area development, and yield were simulated (18). Echinochloa species are among the most severe weeds in accuratelyfor all treatments.Further tests of model performance irrigatedrice cropsand most rice producersrely on handweeding were made using eight data sets collected over a wide range of for control. Owing to high costs or lack of available labor and environments.Direct seeded or transplantedrice yield loss re- herbicides, a need for alternativeweed managementstrategies sultingfrom barnyardgrass interference was predictedaccurately exists. Integrationof culturalweed managementpractices may by the model (92%of variationaccounted for) over a wide range be utilized effectively in many rice growing areas.Development of competitionsituations (14). of appropriatecultural practices requiresa quantitativeunder- In this study,INTERCOM was used to examine two applica- standingof weed-cropinterference relationships and factorsthat tions for an integratedweed managementprogram. Objectives alterthem (13). were to evaluatethe influenceof weathervariation and improved Empirical weed-crop interferencemodels (e.g., 4, 15) are early leaf areagrowth rate on simulatedrice-barnyardgrass com- commonlyused to quantifycompetitive relationships and predict petition and on single year economic threshold densities of barnyardgrass. 1Receivedfor publicationMarch 22, 1994, andin revisedform May 23, 1995. 2FormerGrad. Res. Asst., Dep. Agron.Plant Gen., Univ. Minnesota,St. Paul, MN 55108 and Systems Agron., InternationalRice ResearchInstitute, P.O. Box 933, 1099, Manila, The Philippines. Present address of authors:Dep. Agron., MATERIALSAND METHODS Univ. Nebraska,Lincoln NE 68583-0915; Dep. Theor.Prod. Ecol., Wag. Agric. Univ., Bomsesteeg 65, 6708 PD Wageningen,Netherlands and the Institutefor Model overview.Details of INTERCOMstructure have been Agrobiologicaland Soil FertilityResearch, P.O. Box 14,6700 AA, Wageningen, describedelsewhere (12). Requiredmodel inputs include daily Netherlands. weather data (maximum and minimum temperature,global ra- 3Lettersfollowing this symbol are WSSA-approvedcomputer code from Composite List of Weeds, Revised 1989. Available from WSSA, 1508 West diation, and rainfall), site latitude, plant density, planting date, UniversityAve., Champaign,IL 61821-3133. and a numberof species-specific parameters. 52 WEED SCIENCE The model simulates competition for light, based upon the Table1. Weatherdata bases used in rice-bamyardgrasscompetition simulations. profile of absorbedphotosynthetically active radiation(PAR)4 in Julian the canopy and the photosynthesis-lightabsorption response Years date of curveof individualleaves. The quantityof PARabsorbed by each Locationof station available planting species is a functionof the amountand distributionof photosyn- Beijing, China 1980 to 1988 145 theticarea (leaves, stems,reproductive organs) within the canopy KhonKaen,Thailand 1975 to 1988 45 and the light extinction coefficient. The photosynthesis-light Aduturai,India 1980 to 1992 45 Los Banos, Philippines 1980 to 1990 45 response curve is defined using a saturationfunction with the maximumvalue determinedby the nitrogencontent of leaves. Forboth rice andbarnyardgrass distribution of photosynthetic area within the canopy is assumed to be parabolicwith a peak stants).A coefficient estimateand its standarderror may be used area at 50% of plant height. This assumption is supportedby to determineET stochasticallyand provide information about the the data of Noda et al. (19). Height growth of each species variabilityof weed threshold levels. The estimate of I and its occurs independentlyof species interactionand is simulatedas standarderror obtained from fitting Cousens' equation to the an empirical function of accumulated growing degree days simulateddata in Figure 1 were used to evaluatethe influence of (GDD)4. weather variabilityon single year economic thresholdpopula- Gross CO2assimilation is integratedover canopy height. Net tions of bamyardgrass.Values of I are assumed to be normally CO2assimilation is determinedby subtractingmaintenance and distributed and therefore may be randomly generated using growthrespiration from gross CO2assimilation. Daily drymatter the Box-Muller algorithm(10). This method was used to gener- growthincrease is calculatedfrom net CO2assimilation rate and ate 1000 estimates of I. ET was then calculated iteratively for then partitionedto the roots, stems, leaves, and reproductive each I, holding all othercoefficients constantto values shown in organsbased upon empiricallyderived allocation functions. Dry Table 2. matterloss rates are determinedempirically and imposed on the Influence of early leaf area growth rate on rice competitive- growth increment of each organ group as a function of phe- ness and tolerance. INTERCOM was used to evaluate the nological stage of development. influence of improvedearly leaf area growth rate on rice com- Influence of weather variation on simulated rice-barn- petitiveness and tolerance.In the model, expansion of leaf area yardgrass competition. The influence of annualweather vari- index (LAI)4is determinedusing an exponentialgrowth function ation on rice-barnyardgrasscompetition was examined by until total canopy LAI reaches 1.0. Following this early growth repeatedlysimulating direct seeded (300 plants in-2) rice yield period,the model simulatesgrowth and competition as described loss across a range of barnyardgrassdensities (0, 5, 10, 20, 40, in the model overview section. The exponentialgrowth function 60, 80, 150, 200, or 300 plantsm-2). Both rice andbamyardgrass consists of a single coefficient that defines relative leaf area were set to emergeon the same day. Parameterestimates used in growthrate (RGRL4,LAI GDD-1, 12). simulations were identical to those used by Kropff et al. (17) The model was used to determinewhether variation in RGRL when evaluatingmodel performance.Forty-seven weather data would influence simulated barnyardgrasspanicle biomass at sets from four locations across Asia were used in these simula- maturityand the yield loss-weed density relationship.Six rice- tions. Date (Julianday) of seeding variedacross sites depending on seasonality of the weather (Table 1). Cousens' hyperbolic yield loss equation (4) was fit to the pooled simulated data. Estimatesof the I4 coefficient from Cousens'equation were used 100 in calculatingsingle year economic thresholds(ET4, 3, 24): 80 -

ET= C Y P I H _ 60 i) 40Q4 where C is total cost of herbicideand its application($ ha-1), Y is weed free crop yield (kg ha-l), P is crop price ($ kg-1), I is 20 - + ((I D)/A)) proportionalyield loss as weed density approacheszero (4), and .0 L (I D)/(1 H is herbicideefficacy (proportionof plantskilled). Coefficients used to calculate ET are often determinedem- 0 I, , I | , -- pirically and used deterministically(as if they were true con- 0 50 100 150 200 250 300 BarnyardgrassDensity (plants m-2) 4Abbreviations:ET, single year economic threshold;GDD, growing degree days; I proportionalyield loss as weed density approacheszero; LAI, leaf area Figure 1. Simulatedrice yield loss (YL)-barnyardgrassdensity (D) relationship index;PAR, photosynthetically active radiation;RGRL, relative leaf areagrowth using 47 weatherdata sets from four locations in Asia. Coefficient estimatesfor rate from emergenceuntil total canopy LAI reaches 1.0. Cousens' equationwere I = 1.16 ? 0.01, A = 102.31 ? 0.54 (n = 470).

Volume44, Issue 1 (January-March)1996 53 LINDQUISTAND KROPFF:AN ECOPHYSIOLOGICALMODEL FOR IRRIGATEDRICE-ECHINOCHLOA COMPETITION Table2. Variablesused to calculate the single season economic threshold(ET) Arkansas,and California)and found thatyield loss relationships in equation[1]. variedlittle across environments. Variablename Valuea Single year economic thresholdvalues calculatedusing 1000 randomlygenerated values of I rangedfrom 2.86 to 3.01 plants Herbicidecost (C, $ ha-l) 24.14 Weed free crop yield (Y, kg ha'l) 4000 m-2, with a mean ? standarddeviation of 2.93 ? 0.02 plantsm-2. Cropprice (P, $ kg-l) 0.198 The impactof variationin I on ET densitiesof barnyardgrasswas Yield loss (I, % weed-) 1.16 (0.01) minimalbecause the estimatedstandard error of I was very small. Herbicideefficacy (H) 0.90 Estimates of I obtained from fitting Cousens' equation to ob- Economic threshold(ET) 2.93 (0.02) serveddata will have a much largerstandard errors (e.g., 21) due to randomand experimentalerror, and microenvironmentalhet- aData provided by K. Moody at IRRI. Values in parentheses are ? one standarddeviation. erogeneity within an experiment. Methods of evaluating risk bButachlor(N-(butoxymethyl)-2-chloro-N-(2,6-diethylphenyl)acetamide) at associatedwith yield loss predictionsand herbicideapplication 1 kgha'. recommendationsneed to be more fully developed and incorpo- rated into bioeconomic decision aid models and other applied integratedweed managementprograms. barnyardgrassmixture treatments were simulatedfor each of six Influenceof earlyleaf area growthrate on rice competitive- RGRLvalues (0.005,0.007, ... 0.015 LAI 0C-1d-1). Each RGRL ness and tolerance. INTERCOM predicts that an increased value representsa hypotheticalrice cultivar.Direct seeded rice RGRL will negatively affect barnyardgrasspanicle biomass at density was assumedconstant at 300 plantsmr2. maturity (Figure 2). However, the relative effect varies as a Barnyardgrassdensity treatments of 0, 10, 20, 40, 80, and 300 function of weed density; the relationshipis nearly linear when plants m--2were set to emerge simultaneously with the crop. weed density is high and strongly curvilinearat low weed den- Simulatedoutput included weed paniclebiomass at maturityand sities. These results suggest that increasing early leaf area crop yield, from which yield loss was determined.Cousens' expansion may improverice competitivenessby reducingbarn- equation was fit to simulatedyield loss-barnyardgrassdensity yardgrassseed production.However, because some seeds are relationshipsobtained for each RGRLvalue. Resultingestimates always produced, furtherresearch is needed to determine the of I were used to calculateET deterministically. effect of increased crop competitiveness on long-term weed To determinethe best time duringthe growingseason that leaf populationdynamics. areashould be measuredto obtainmaximum differences among Simulatedrice yield loss as a function of barnyardgrassden- genetic lines, rice leaf area index was simulated for five rice sity decreases dramaticallyas rice RGRL increases (Figure 3). RGRL values (0.005, 0.007, 0.009, 0.011, and 0.015 LAI 0C-1 Estimatesof I from simulatedyield loss relationshipsin Figure d-1). Direct seeded rice density was 300 plants m-2 and barn- 3 are lower when rice RGRL is high (Table 3), suggesting that yardgrass,emerging simultaneouslywith the crop, was simu- rapid leaf area expansion will improve rice tolerance to barn- lated at 10 and 300 plants m-2. SimulatedLAI over time was yardgrasscompetition. Single year economic threshold densi- comparedamong the five RGRLvalues. ties of barnyardgrass calculated deterministically, using estimates of I shown in Table 3, range from 0.13 to 13.4 plants in in RESULTSAND DISCUSSION m-2. The impactof even small increases RGRLmay result Influence of weather variation on simulated rice-barnyardgrasscompetition. Ninety-nine percent of the totalvari- Barnyardgrass ation in simulatedyield loss acrossweather conditions was Density explained by bamyardgrassdensity based on the least squares io 3000 -__10 best fit of Cousens' equation (Figure 1). These simulateddata __ .--- 20 that environmentalvariation resulting from weather ** ~~~~~~~~~~~40 suggest _^ > ** ___--~~~~~~~~.. .. 80 alone has little influence upon the competitive relationshipbe- tween rice and barnyardgrass.In this version of INTERCOM, cn 2000 -..-300**. changes in total incident radiation (e.g., due to cloud cover) would influenceeach species only throughtheir photosynthesis- *ts light response curves and rate of development (a function of c~1000 GDD). Kropff et al. (11) conducted sensitivity analyses on Q- .I..... ,I ,. INTERCOMand found thatthe coefficients defining the photo- ...... synthesis-light response curve had little impact on crop yield c. 0 loss. Since competitionis for light only, it is not surprisingthat 0.005 0.007 0.009 0.011 0.013 0.015 weather variation had little impact on simulated rice-barn- RGRL(LAI OC-1 d-1) yardgrass interference relationships. Hill et al. (7) compiled irrigated rice barnyardgrass interference data from seven Figure 2. Simulatedmature barnyardgrass panicle biomass as a function of rice experiments conducted at four locations (Japan, Philippines, early leaf area expansionrate (RGRL)over five weed densities.

54 Volume44, Issue1 (January-March)1996 WEED SCIENCE 8 RGRL / _ (a) 100 0 . 7 .- 0.005 o 0 - 80 6 -...0.007 / ,#'

0 60- C 5 0.009%"1 / ' ""'A 7 ~0.005 -J64 0.007 0) 40 -RGRL 5-- j ,0 0.005 0.007 01) 03 2 /, _ o - a 0.009 20 C> 0.011 V 0.013 0 0 50 100 150 200 250 300 0 10 20 30 40 50 60 70 80 90 100 BarnyardgrassDensity (plants m-2) Time(DAP) . ) -- -0.015 ~/y .^ \

Figure 3. Simulated rice yield loss as a function of barnyardgrass density over six RGRL values. Lines show best fit of Cousens' equation to each simulated 8DAP RGRL (b) data set. fo1 = - ~0.005 6 ....0.007 a relativelylarge increase in ET and a reducedneed for chemical crop4t lO (a) and 0.011()pns control. .~~~ 0.015 These relationships suggest that rapid leaf area expansion may be an excellent indicatorof rice competitivenessand tolerance. However, determinationof the relative leaf area growth rate requires repeated measurementsof leaf area early in the growingseason. This is impracticalfor a breederevaluating large numbersof genetic lines. Recent reportssuggest that a measure 0 10 20 30, 40 50 60 70 80 90 100 of cropcanopy area or leaf areaindex earlyin the growing season may be a sufficientindicator of cropcompetitiveness or tolerance Time(DAP) (2, 5, 9). Plots of simulatedleaf area index as a function of days after Figure 4. Simulatedrice leaf areaindex (LAI) as a functionof days afterplanting plantingsuggest that maximumdifferences in rice LAI (among (DAP) for five RGRL values. Barnr[rdgrassemerged simultaneouslywith the crop at 10 (a) and 300 (b) plantsm7 hypotheticallines) in the presenceof barnyardgrassoccurred 70 to 75 d afterplanting, regardless of RGRL value (Figure4). At moderateweed density (10 plantsin-2), maximumdifferences in at low RGRLvalues (0.005 to 0.007 LAI C-1 rice LAI occurred significantdifferences in leaf areaindex will be detectedamong at weed density (300 plantsmi-2), maximum d-1).However, high geneticlines. values were higher differences in LAI occurred when RGRL The RGRLvalues used for rice in these simulationswere These results suggest (0.011 to 0.015 LAI 0C-1d-1, Figure 4). chosento createa rangeof earlyleaf areagrowth rates. Field maintainedduring thatboth time of sampling and weed density measuredRGRL values used to simulateour growth experiments influence upon whether a breedingtrial may have an important were0.009 and0.012 LAI 0C-1d'I for rice andbarnyardgrass, respectively.Values reported for other species range from 0.0085 to 0.019 LAI OC-1d-I (12). We assumethat genetic variation in Table 3. Influence of rice RGRL on estimated value of I obtained from fitting riceRGRL is sufficientlywide that values used in thesesimula- Cousens' equation to simulated yield loss in Figure 3, economic threshold (ET) tionsare potentially real. weed-free rice densities of barnyardgrass using [11, and simulated yield. INTERCOMpredicts that as RGRLis increased,rice yield RGRL I ET Yield also increases(Table 3). Since changesin biomassallocation arenot considered in the LAI OC-4 d-1 % yield loss plants m-2 kg ha-1 patternsamong hypothetical genotypes model,an increase in yieldcan only occur if totalabove ground 0.005 27.47 0.13 6361 biomassis increased.In practice,some genetic lines of riceare 0.007 3.50 0.97 6769 0.009 1.16 2.92 6931 likely to have very high values of RGRLaccompanied by a 0.011 0.55 6.21 7000 reductionin harvestindex, particularly if the increase in leafarea 0.013 0.32 10.75 7037 expansionresults from a tradeoffin thefraction of biomassbeing 0.015 0.25 13.44 7029 allocatedto theleaves versus other organs. This would result in

Volume44, Issue 1 (January-March)1996 55 LINDQUISTAND KROPFF:AN ECOPHYSIOLOGICALMODEL FOR IRRIGATEDRICE-ECHINOCHLOA COMPETITION a line thatis highly tolerantto weeds butdoes not yield well under 6. Graf,B., A. P. Gutierrez,0. Rakotobe,P. Zahner,and V. Delucchi. 1990. A high inputconditions. Breeders must thereforebe wary of unde- simulation model for the dynamics of rice growth and development:Part II-The competitionwith weeds for nitrogenand light. Agric. Syst. 32:367- sirable traits associated with high rice RGRL. Jordan(9) sug- 392. gested that breedingfor competitivenessand tolerancetraits is 7. Hill, J. E., S. K. De Datta, and J. G. Real. 1989. Echinochloa competition not likely to occur until the benefits are shown to be greaterthan in rice: a comparisonof studiesfrom direct-seededand transplantedflooded the potentialcosts. Such a breedingeffort may be most appropri- rice. Pages 115-129 in Auld, B. A., R. C. Umaly, and S. S. Thitrosomo,eds. Proc. Symp. on Weed Man., Biotrop Spec. Pub]. No. 38. Bogor, Indonesia. ate for low input cropping systems, crop productionsituations 8. IRRI. 1990. IRRI Rice Facts, InternationalRice Research Institute, Los where herbicides are unavailableor very costly, or where the Banos, Philippines. 10 pages. probabilityof groundwater contamination is high. Field research 9. Jordan,N. 1993. Prospectsfor weed controlthrough crop interference.Ecol. is needed to evaluatereal gains in competitivenessand tolerance Applic. 3:84-91. 10. Keen, R. E. and J. D. Spain. 1992. Page 299 in Computersimulation in among cultivarsvarying in RGRL. biology: A basic introduction.Wiley-Liss Inc., New YorkNY. This version of INTERCOMassumes high soil nutrientand 11. Kropff,M. J., N. C. van Keulen,H. H. van Laar,and B. J. Schnieders.1993. water concentrations,and thereforeonly simulatescompetition The impactof environmentaland genetic factors.Pages 137-147 in Kropff, for light. The competitive relationshipsexamined in this study M. J. and H. H. van Laar, eds. Modelling Crop-WeedInteractions. CAB Internationaland the InternationalRice ResearchInstitute. would change considerablyunder conditions where more than 12. Kropff, M. J. and H. H. van Laar (eds.). 1993. Modelling crop-weed one resourceis limiting or where light is not the most limiting interactions.CAB International,Wallingford, UK andthe InternationalRice resource.Traits that confer improvedcompetitiveness and toler- ResearchInstitute. ance in a light-limitingsystem may be ineffective or even detri- 13. Kropff, M. J. and L. A. P. Lotz. 1992. Optimizationof weed management mentalin a moisture-or nitrogen-limitingsystem. Knowledgeof systems: The role of ecological models of interplantcompetition. Weed Technol.6:462-470. the most limiting resource in a given environment and the 14. Kropff,M. J., K. Moody, J. L. Lindquist,T. Migo, and F. F. Fajardo.1994. responseof both crop and weed to thatresource in limitedsupply Models to predictyield loss due to weeds in rice ecosystems. Phil. J. Weed is extremely importantfor the identificationof traitsconferring Sci., Special Issue: 29-44. competitivenessand tolerance in other cropping systems. Ver- 15. Kropff,M. J. and C.J.T.Spitters. 1991. A simple model of crop loss by weed competition from early observationson relative leaf area of weeds. Weed sions of INTERCOMthat simulatecompetition for light, water, Res. 31:97-105. and soil nitrogenare currentlyunder development. 16. Kropff, M. J. and C.J.T. Spitters. 1992. An ecophysiological model for interspecificcompetition, applied to the influence of Chenopodiumalbum L. on sugar beet. I. Model description and parameterization.Weed Res. ACKNOWLEDGEMENTS 32:437-450. 17. Kropff, M. J., S. E. Weaver,L.A.P. Lotz, J. L. Lindquist,W. Joenje, B. J. This study was completed in part while J. L. Lindquistwas Schnieders,N. C. van Keulen, T. R. Migo, and F. E Fajardo.1993. Under- visiting IRRI on a NormanBorlaug International Research Fel- standingcrop-weed interaction in field situations.Pages 105-136 in Kropff, lowship grantedfrom the University of Minnesota (U of MN) M. J. and H. H. van Laar, eds. Modelling Crop-WeedInteractions. CAB Internationaland the InternationalRice ResearchInstitute. College of Agriculture.Support to J. Lindquistfrom the U of MN 18. Moody, K. 1988. Developing appropriateweed managementstrategies for Departmentof Agronomyand Plant Genetics duringthis visit is small-scalefarmers. Pages 319-320 in M. A. Altieri and M. Liebmann,eds. gratefullyacknowledged. We thankK. Moody and A. Dieleman Weed managementin agroecosystems:Ecological approaches.CRC Press for theirhelpful commentson the manuscript. Inc., Boca RatonFL, USA. 19. Noda, K., K. Ozawa, and K. Ibaraki.1968. Studies on the damage on rice plants due to weed competition (effect of barnyardgrasscompetition on growth, yield, and some ecophysiological aspects of rice plants). Bull. LITERATURECITED KyushuAgric. Exper.Sta., Vol. XIII, nos. 3&4, 345-367. 1. Bauer,T. A., D. A. Mortensen,G. A. Wicks, T. A. Hayden,and A. R. Martin. 20. Spitters,C.J.T. and R. Aerts, 1983. Simulationof competitionfor light and 1991. Environmentalvariability associated with economic thresholdsfor waterin crop weed associations.Asp. Appl. Biol. 4:467-484. soybeans. Weed Sci. 39:564-569. 21. Swinton, S. M., D. D. Buhler, F. Forcella, J. L. Gunsolus, and R. P. King. 2. Callaway,M. B. 1992. A compendiumof crop varietaltolerance to weeds. 1994. Estimationof crop yield loss due to interferenceby multiple weed Amer.J. Alt. Agric. 7:169-180. species. Weed Sci. 42:103-109. 3. Coble, H. D. and D. A. Mortensen. 1992. The thresholdconcept and its 22. Wiles, L. J. and G. G. Wilkerson. 1991. Modeling competition for light applicationto weed science. Weed Technol.6:191-195. between soybean and broadleafweeds. Agric. Syst. 35:37-51. 4. Cousens,R. 1985. An empiricalmodel relatingcrop yield to weed and crop 23. Wilkerson, G. G., J. W. Jones, H. D. Coble, and J. L. Gunsolus, 1990. density and a statistical comparison with other models. J. Agric. Sci. SOYWEED:A simulationmodel of soybeanand common cocklebur growth 105:513-521. and competition.Agron. J. 82:1003-1010. 5. Forcella,F. 1987. Toleranceof weed competitionassociated with high leaf 24. Zanin, G. and M. Sattin. 1988. Threshold level and seed productionof area expansionrate in tall fescue. Crop Sci. 27:146-147. velvetleaf (Abutilontheophrasti Medicus) in maize. WeedRes. 28:347-352.

56 Volume44, Issue I (January-March)1996