Biotic Stress and Yield Loss

per unit biomass (1/W i)(dW i/dt) of species i depends on the resource dependent netgrowth function (f i(R)) and its loss rate (m i):1 d Wi f W dti(R) m i[13.1]iwhere f i(R) can be defined as a function of resource supply using a number ofapproaches. 17 The dynamics of the growth limiting resource (dR/dt) depend on thedifference between the resource supply rate (y(R)) and resource consumption (C i)summed over all species: d R y(R) ∑[RCdti(Rf i(R))] [13.2]where dR/dt is the rate of change in resource concentration and C i(f i(R)) indicatesthat resource consumption is itself resource dependent. Note that these equations area simple representation of a mechanistic plant growth model. The beauty of their simplicityis that they are analytically tractable (an equation for R* can be obtained analytically).However, the net growth function (f i(R)) depends not only on supply of theresource in question, but also on the past and present physiological status of the plant(e.g., tissue nutrient concentration, age, etc.), canopy morphology, and on environmentalfactors such as temperature and quantity of available radiation. The loss rate(respiration and senescence) will further depend on environmental factors and onstage of plant development. Resource supply rate will depend upon soil physical andchemical characteristics as well as environmental conditions. Resource consumptiondepends on the dynamics of resource supply and the demand of the plant, which isdependent upon previous growth and the partitioning of nutrients and photosynthateto different organs within the plant. All of these factors will vary temporally. Fullunderstanding of the dynamics of crop and weed growth and competition requiresthat all of these factors be studied throughout the growth period and incorporated intoour models.Models that include these physiological components as well as the effects ofdynamic environmental conditions are more appropriately dealt with through simulation,which involves stringing several mathematical functions together into algorithmsthat describe a particular process (e.g., soil water balance). Simulation modelsare excellent tools for gaining improved understanding of the mechanisms of interplantcompetition because they typically function on a daily time step, are responsiveto edaphic factors and to daily inputs of weather data, and can be used to test hypothesesabout the contribution of specific morphological and physiological factors tocompetitive outcome. A number of simulation models have been developed in whichthe mechanisms of interplant competition are described based on underlying physiologicalprocesses. 18 Although most of these models have focused on competition forlight, a few authors have presented quantitative procedures for incorporating competitionfor water and soil nitrogen. Because many of the quantitative procedures havebeen presented by others, I will briefly outline some of these procedures and focus