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Crop growth models are tools with valuable uses in research synthesis and crop management. This paper discusses genetic coefficients in the CROPGRO–Soybean model in terms of definitions, implications for genetic improvement, relationships to field performance, and linkage to genomics. As used in crop models, genetic coefficients are mathematical constructs designed to mimic the phenotypic phenotypic outcome of genes under different environments to influence: (i) life cycle including fractional allocation to different phases, (ii) photosynthetic, (iii) vegetative, (iv) rooting, and (v) reproductive processes. Model sensitivity analyses was used to hypothesize genetic coefficients of soybean [Glycine max (L.) Merr.] and impact on field performance. Yield improvement from increased leaf photosynthesis was shown to be small if coupled to specific leaf weight. Yield improvement with longer seed filling duration was enhanced by traits such as slower N mobilization to sustain leaf photosynthesis or by genetic traits and management factors allowing adequate leaf area index before seed fill. Yield improvement under water‐deficit appeared feasible from rate of root‐depth increase, shift in root profile, and a slow senesce trait. Modeled genetic coefficients showed mostly additive effects on yield when evaluated in combinations; and combinations of minor changes gave yield increases of 13 to 17%, comparable to recent genetic improvement. More than additive effects occurred under good crop management or under projected rise in global CO2. Information from genomics, physiology, and yield performance trials can be used to derive genetic coefficients for crop models. Interaction of molecular geneticists, physiologists, and crop modelers is needed to facilitate the translation of genetic knowledge to modes of action, and finally to integrated field performance under multiple stress environments.

Net radiation (R n) is a key variable for computing reference evapotranspiration and is a driving force in many other physical and biological processes. The procedures outlined in the Food and Agriculture Organization Irrigation and Drainage Paper No. 56 ͓FAO56 ͑reported by Allen et al. in 1998͔͒ for predicting daily R n have been widely used. However, when the paucity of detailed climatological data in the United States and around the world is considered, it appears that there is a need for methods that can predict daily R n with fewer input and computation. The objective of this study was to develop two alternative equations to reduce the input and computation intensity of the FAO56-R n procedures to predict daily R n and evaluate the performance of these equations in the humid regions of the southeast and two arid regions in the United States. Two equations were developed. The first equation ͓measured-R s-based (R s-M)] requires measured maximum and minimum air temperatures (T max and T min), measured solar radiation (R s), and inverse relative distance from Earth to sun (d r). The second equation ͓predicted-R s-based (R s-P)] requires T max , T min , mean relative humidity (RH mean), and predicted R s. The performance of both equations was evaluated in different locations including humid and arid, and coastal and inland regions ͑Gainesville, Fla.

These cultivar-specific traits are referred to as genetic coefficients (Table 1). Crop model testing in diverse environments is essential if modelersThe estimation of genetic and soil parameters can be wish to make applications or extrapolations to those environments. A recent study demonstrated the effectiveness of optimization tech-a tedious, time-consuming process that can be done niques for deriving cultivar coefficients for the CROPGRO-Soybean manually by adjusting some of the parameters so that model from typical information provided by soybean performance predicted data fit observations (Boote et al., 1997; Col-tests. The objectives of this study were (i) to explore the extent to son et al., 1995) or by fitting different statistical models which cultivar coefficients developed by these approaches from crop (linear or exponential) to measured data (Dardanelli performance tests are stable across different regions, (ii) to test the et al., 1997). A third approach for model parameter CROPGRO-Soybean model's ability to predict phenology and seed estimation, which is more systematic and objective, is yield using cultivar coefficients that were developed in different rethe use of optimization techniques. A predefined objecgions, and (iii) to investigate whether 3 yr of crop performance data tive function is either maximized or minimized during are adequate for developing stable genetic coefficients. A stepwise these approaches. Several studies have used optimizaprocedure was applied to derive cultivar coefficients for 10 common cultivars grown in different environments in Georgia and North Caro-tion procedures to derive soybean cultivar coefficients lina. Regarding the transportability of cultivar coefficients across for predicting flowering date (Grimm et al., 1993) and states, we found that the critical daylength coefficients were the most the occurrence of reproductive stages after flowering reliable cultivar traits. We found less stability of the cultivar traits (Grimm et al., 1994), for improving crop models (Piper that control genetic differences in seed yield potential. The estimated et al., 1998), and for estimating soil and root growth cultivar coefficients developed in Georgia enabled CROPGRO to parameters (Calmon et al., 1999a,b) by minimizing the predict yield and harvest maturity in North Carolina within 3.8% and error sum of squares between observations and pre-3.5 d, respectively, from the observed averages. Using the cultivar dictions. coefficients developed from North Carolina environments allowed us A recent study by Mavromatis et al. (2001) described to simulate the actual mean yield and harvest maturity in Georgia toan approach to estimate soil and cultivar coefficients within 2.5% and 2.0 d. Furthermore, the model's ability to predict seed yield and maturity with cultivar coefficients developed from 3 yr for CROPGRO-soybean from typical information (such of data was nearly as good as that derived from much larger data sets.as anthesis and harvest maturity dates, final seed yield, seed size, ...