In the 1980s, efforts were initiated to link more complex pest and crop models. This type of integrated modeling approach has increased our understanding of complex pest-crop systems, especially in studies where physically based modeling was combined with detailed experimental work. Rapid advancements in computer hardware and software technology has helped advance the decision-making process significantly, building on research to generate the social, economic, and biological knowledge needed to improve IPM strategies, including the development of interactive computer systems that use simulation models, databases, and decision algorithms in an integrated fashion (e.g., the Decision Support System for Agrotechnology Transfer (DSSAT) project). Considerable progress has been made over the past few decades toward integrating dynamic agroecosystem crop models with components simulating pest effects on crop growth and development at the physiological level. The integration procedure for pest-crop modeling uses the concept of potential damage mechanisms, typically introduced by quantitative equations coupling a pest variable (e.g., population number and location) to a crop variable (e.g., photosynthesis, biomass, and leaf area). Several generic damage mechanisms have been identified in the literature including competition for resources, tissue and assimilate consumption, reduction in plant density, effects on photosynthetic reactions and respiration, reduction of assimilation rate, etc. Crop models used to link effects of pests include MACROS and SUCROS for wheat, PEANUTGRO and SOYGRO for peanuts and soybeans, and CERES-Rice and ORYZA1 for rice. Another approach for linking pest-crop models is to run a population dynamics model in conjunction with a crop model, that is, the pest population model is used to quantify pest dynamics in a summarized form for subsequent input into the crop model. In many studies, however, empirical pest levels (i.e., quantitative information on pest dynamics derived from observed data) were directly introduced into the crop simulation.
A recent example of a physically based modeling approach for calculating pest-induced yield losses is the InfoCrop model. InfoCrop is a dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact assessment in tropical agroecosystems. It provides integrated assessment of the effect of weather, soil, crop type and variety, pests, and management on crop growth and yield, as well as on soil nitrogen and organic carbon dynamics. Its general structure (relating to basic crop growth and yield processes) is based on several earlier models, primarily the
SUCROS series. InfoCrop mechanistically assesses crop loss using a three-step approach evaluating pest incidence, pest damage mechanisms, and crop yield. The model does not simulate pest population dynamics; therefore, pest incidence has to be provided as an input as described above. Specific damage mechanisms considered by InfoCrop include reduction in germination and plant stand, competition for resources such as radiation and nutrients, reduction in assimilation rate, assimilate and tissue reduction, and impeded plant water and nutrient uptake. As described above, these damage mechanisms are coupled at the specific process level to the crop growth model in order to quantify losses due to pests. InfoCrop was used to simulate the effect of Russian wheat aphid (Diuraphis noxia Mordvilko) damage on winter wheat at Fort Collins and Akron, Colorado. Observed and simulated yield reductions in four experiments over a period of 2 years were found to be closely related (r = 0.85). InfoCrop was also used to simulate the loss of rice and wheat dry matter and grain yield due to pests in multiple field experiments conducted at the experimental farm of the Indian Agricultural Research Institute, New Delhi, India. The model did a very good job simulating dry matter loss due to pests for both rice and wheat; results were somewhat poorer for simulated loss in grain yield.
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