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Crop model testing in diverse environments is essential if modelers wish to make applications or extrapolations to those environments. A recent study demonstrated the effectiveness of optimization techniques for deriving cultivar coefficients for the CROPGRO-Soybean model from typical information provided by soybean performance tests. The objectives of this study were (i) to explore the extent to which cultivar coefficients developed by these approaches from crop performance tests are stable across different regions, (ii) to test the CROPGRO-Soybean model's ability to predict phenology and seed yield using cultivar coefficients that were developed in different regions, and (iii) to investigate whether 3 yr of crop performance data are adequate for developing stable genetic coefficients. A stepwise procedure was applied to derive cultivar coefficients for 10 common cultivars grown in different environments in Georgia and North Carolina. Regarding the transportability of cultivar coefficients across states, we found that the critical daylength coefficients were the most reliable cultivar traits. We found less stability of the cultivar traits that control genetic differences in seed yield potential. The estimated cultivar coefficients developed in Georgia enabled CROPGRO to predict yield and harvest maturity in North Carolina within 3.8% and 3.5 d, respectively, from the observed averages. Using the cultivar coefficients developed from North Carolina environments allowed us to simulate the actual mean yield and harvest maturity in Georgia to 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 of data was nearly as good as that derived from much larger data sets., DYNAMIC, PROCESS-LEVEL crop models are playing an increasing role in analyzing the interactions and contributions of environmental factors (such as temperature, daylength, and water supply), genetics, and physiology on seed [...]

Atmospheric [CO.sub.2] and temperature may significantly modify plant production. Grasslands occupy in excess of 25% of the Earth's land area, but grassland species have received limited attention from researchers studying climate change. A 3-yr study was conducted to determine the effects of elevated atmospheric [CO.sub.2] and temperature on dry matter (DM) harvested from the [C.sub.3] legume `Florigraze' rhizoma peanut (RP, Arachis glabrata Benth.) and the Ca grass `Pensacola' bahiagrass (BG, Paspalum notatum Flugge). Both species were field grown in Millhopper fine sand (loamy siliceous Grossarenic Paleudult) in temperature-gradient greenhouses under different [CO.sub.2] (360 and 700 µmol [mol.sup.-1]) and temperature conditions (baseline [B], B+1.5, B+3.0, and B+4.5 ° C, where B equaled ambient temperature). Plots (2 by 5 m) were harvested three times in 1996 and four times each in 1997 and 1998. Analyzed across years, yield increased 25% for RP (P = 0.02) and tended to increase for BG (15%; P = 0.18) with the near doubling of [CO.sub.2], but there was species by [CO.sub.2] interaction (P = 0.06) as a result of the greater response to [CO.sub.2] by the [C.sub.3] legume. There was a positive effect of increasing temperature on yield of both species. Averaged across species, yield increased 11% in 1996, 12% in 1997, and 26% in 1998 as temperature increased from B to B+4.5 ° C. Under well-watered conditions in this experiment, elevated [CO.sub.2] increased DM harvested of a [C.sub.3] legume and tended to increase that of a [C.sub.4] grass, while the yield response to increasing temperature was positive for both species., ATMOSPHERIC [CO.sub.2] has steadily increased from preindustrial concentrations of 280 µmol [mol.sup.-1] and is predicted to double by the end of the 21st century (Barnola et al., 1994; Keeling and [...]

Successful uses of crop models in technology transfer and decision support tools require that coefficients describing new cultivars be available as soon as the cultivars are marketed. The objectives of this study were (i) to develop an approach to estimate cultivar coefficients for the CROPGRO-Soybean model from typical information provided by crop performance tests, (ii) to evaluate the suitability of yield trial data for deriving genetic coefficients and site-specific soil traits for use in crop models, and (iii) to explore the extent to which our approach allowed the crop model to reproduce observed genotype x environment (GE) interactions, cultivar ranking, and year-to-year yield variability. Crop performance tests typically record harvest maturity date, seed yield, seed size, height, and lodging. A stepwise procedure using data on 11 cultivars grown at five sites in Georgia over 4 to 10 yr efficiently decreased the root mean square error (RMSE) between observed and predicted data. For `Stonewall', a maturity group VII cultivar, the RMSE of 769 kg [ha.sup.-1] between the actual and modeled seed yield, estimated initially by means of the existing general maturity group coefficients, was reduced to 404 kg [ha.sup.-1]. For the same cultivar, the initial RMSE of 5.3 and 9.3 d between the actual and simulated anthesis and harvest maturity dates, respectively, estimated by means of the existing general maturity group coefficients, were reduced to 2.9 and 5.8 d. In addition to deriving useful information on site characteristics and cultivar traits, our approach has enabled CROPGRO to satisfactorily mimic the genotypic yield ranking and much of observed genotype x environment interactions. Across all environments, the difference in genotype ranking based on yield between measured and predicted values was one or less for 61% of the environments., DYNAMIC CROP MODELS have potential to quantify the contribution of environmental factors, such as temperature, daylength, and water supply, on complex GE interactions observed in yield trial data. CROPGRO-Soybean v. [...]

Abstract: Brachiaria and Cynodon are two of the most important pasture grasses worldwide. Computer model simulations can be used to study pasture species growth and physiological aspects to identify gaps of knowledge for genetic improvement and management strategies. The objective of this research was to compare the performance relative to calibrated parameters of the CROPGRO‐Perennial Forage Model (CROPGRO‐PFM) for simulating three different species (“Marandu” palisadegrass, “Convert HD 364®” brachiariagrass and “Tifton 85” bermudagrass) grown under similar management. The field experiment consisted of two harvest frequencies, 28 and 42 days, under irrigated and rainfed conditions. Data used to calibrate the model included regular forage harvests, plant‐part composition, leaf photosynthesis, leaf area index, light interception and plant nitrogen concentration. The simulation of biomass production of the three grasses presented d‐statistic values higher than 0.80, RMSE ranging from 313 to 619 kg/ha and ratio observed/simulated ranging 0.968 to 1.027. Harvest frequency treatments of 28 and 42 days were well simulated by the model. A sensitivity analysis was conducted to evaluate the most influential parameters needed for model calibration and to contrast the grasses, showing that the differences among the three grasses are mostly driven by plant‐part composition and assimilate partitioning among plant organs. [ABSTRACT FROM AUTHOR]