Research paperPlanting date and soybean yield: evaluation of environmental effects with a crop simulation model: SOYGRO☆
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Dual ensemble approach to predict rice heading date by integrating multiple rice phenology models and machine learning-based genetic parameter regression models
2024, Agricultural and Forest MeteorologyCROPGRO-soybean model – Validation and application for the southern Amazon, Brazil
2024, Computers and Electronics in AgricultureEvaluating short-season soybean management adaptations for cover crop rotations with a crop simulation model
2020, Field Crops ResearchCitation Excerpt :Similarly, under irrigated conditions in Missouri and Tennessee, MG 3 cultivars had similar yields to MG 4 cultivars, and greater than MG 5 and 6 (Salmerón et al., 2016). One explanation for the relatively high productivity of short-season MG cultivars under irrigated conditions is that reproductive stages start earlier in the season allowing them to grow under more optimal environmental conditions (i.e., higher solar radiation intensity) compared to full-season MGs (Egli and Bruening, 1992; Kantolic et al., 2013), and avoid end of season low temperatures and/or frost damage (Heatherly, 1999). Under rainfed conditions, the year-to-year variability in the timing and intensity of water stress can influence the relationship between yield and MG.
Simulation of genotype-by-environment interactions on irrigated soybean yields in the U.S. Midsouth
2017, Agricultural SystemsCitation Excerpt :CROPGRO simulates seed oil and protein concentration based on a carbon and nitrogen balance, cultivar specific target protein and oil concentrations, and considers a temperature effect (Piper and Boote, 1999). The CROPGRO model has been previously used to simulate the effect of planting date on the yield of soybean (Boote et al., 1997; Calvino et al., 2003; Egli and Bruening, 1992) (the last citation with an earlier version, SOYGRO), and to study the effect of management strategies and environmental conditions on soybean productivity and irrigation requirements (Peart et al., 1995). However, the model has not been previously tested for its accuracy predicting seed oil and protein concentration across different environments.
Simplifying the prediction of phenology with the DSSAT-CROPGRO-soybean model based on relative maturity group and determinacy
2016, Agricultural SystemsCitation Excerpt :The DSSAT-CROPGRO model (Boote et al., 1998b; Hoogenboom et al., 2012; Jones et al., 2003) was selected for this study since it allows sufficient complexity and flexibility to modify temperature and photoperiod sensitivity during different stages of development. Moreover, the DSSAT model has been previously tested in soybean (Boote et al., 1997), for studying the effect of management and/or environmental conditions (Curry et al., 1995; Egli and Bruening, 1992), and it offers the feasibility to study crop rotations (Salmerón et al., 2014b). Despite a large number of crop coefficients considered in the DSSAT-CROPGRO model, only three coefficients related to photoperiod sensitivity (CSDL, PPSEN, and R1PPO), and four coefficients related to photothermal duration of life phases (EM-FL, FL-SH, FL-SD, SD-PM) are usually calibrated at the cultivar level (Boote et al., 2001).
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Contribution from Kentucky Agric. Exp. Stn. Paper No. 91-3-157.