A Plant Process Economic Model for Weed Management Decisions in Irrigated Onion

A simulation model was built as a decision aid for management of five weed species in direct seeded irrigated onion (Allium cepa L.). The model uses the state variable approach and simulations are driven by temperature and sunlight as photosynthetically active radiation ( PAR). It predicts yield reduction caused by competition for PAR according to the ratio of crop leaf area index (LAI) to weed LAI and respective light extinction coefficients (k). Input variables are plant density by species and average number of leaves by species. Number of leaves per plant is used by the model to provide an estimate of initial leaf area per plant. The model calculates initial species LAIs by multiplying species density times average leaf area per plant. The model accurately describes competitive interactions, taking into account respective plant densities, time of emergence, and time of weed removal. It permits economic evaluation of management factors such as handweeding, chemical weed control, herbicide phytotoxicity due to early application, and control of weed flushes during the season. The model is also used to evaluate mechanisms of plant competition for sunlight. In a sensitivity analysis, onion yield loss was more sensitive to weed PAR interception than to PAR use efficiency, the latter a species-dependent constant in the model. 1970; Forrester, 1961; Thornley and Johnson, 1990). The objective of this paper is to provide a detailed description of the model structure, its calibration, verification, and validation. Furthermore, we discuss its practical use as an aid to onion growers in making decisions about handweeding, chemical weed control, and control of several weed cohorts over the cropping season. Such applications should ultimately lead to reduction in herbicide usage. Materials and Methods MODEL DESCRIPTION. The model is written in Visual Basic and is organized in interactive screens that allow users to enter input data and to view output. Ever since Harper (1977) suggested that in most agricultural systems sunlight drives competition, scientists have designed experiments to study this factor in terms of the weed-crop interface. This model simulates the dynamics of the competition for light between onion and five annual weed species assuming no water or nutrient limitations to plant growth. Mathematical models for weed management are being devel- oped as an approach to manage and eventually reduce herbicide use. Yield loss prediction models due to weed competition are needed to evaluate the potential economic return of weed manage- ment strategies. Models that describe and predict the outcome of weed-crop competition range from empirical regression equations to mecha- nistic models with various levels of resolution (Cousens, 1985; Dunan et al., 1994; Kropff, 1993; Kropff and Spitters, 1991, 1992; Spitters and van den Bergh, 1982; Wiles and Wilkerson, 1991). The type of model and level of resolution depends on the objectives associated with building the model and on the characteristics of the system being studied. If the objective is management, the model should include input variables that growers can easily obtain. Furthermore, a multispecies model provides greater utility. Never- theless, the model must account for variables that are relevant and significantly impact the system. For example, if the time of weed and crop emergence affects the outcome of competition, then the model should consider these variables. Building a model entails the formulation of hypotheses about the behavior of the system studied. Under irrigated and fertilized cropping situations, competition among plants has been shown to be mainly for light (Harper, 1977). Our working hypothesis is that weed-crop competition for light explains much of the onion yield reduction due to weed interference. The model presented here is a dynamic, plant process model that simulates the development of onion and weed leaf area index (LAI) to predict crop yield losses due to competition for light. The model is both dynamic and deterministic and follows the state variable principle (de Wit,