|Salmeron, Montserrat - University Of Kentucky|
|Purcell, Larry - University Of Arkansas|
|Vories, Earl - Earl|
|Shannon, Grover - University Of Missouri|
Submitted to: Agricultural Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/19/2016
Publication Date: 11/14/2016
Citation: Salmeron, M., Purcell, L.C., Vories, E.D., Shannon, G. 2016. Simulation of genotype-by-environment interactions on irrigated soybean yields in the U.S. Midsouth. Agricultural Systems. 150:120-129. doi: 10.1016/j.agsy.2016.10.008.
Interpretive Summary: Soybean is a major crop throughout the US and genetic and environmental factors affect yield and other important properties. Crop growth models that incorporate the effect of environmental variables can potentially explain differences associated with many factors; however, extensive datasets based on labor-intensive measurements are required for proper calibration of the models. University and ARS scientists collaborated in a three-year study across nine Midsouth locations beginning in 2012 to examine the factors affecting production of irrigated soybean. Data from 2012 and 2013 were used to calibrate a growth model that was then used to predict the observations from 2014. The results were generally in good agreement, but specific factors, such as seed protein, were identified for future studies. This research will benefit producers by helping them make the best choices regarding cultivar selection for a given environment and everyone will benefit from the additional knowledge regarding optimal production of an important food crop.
Technical Abstract: Dynamic crop models that incorporate the effect of environmental variables can potentially explain yield differences associated with location, year, planting date, and cultivars with different growing cycles. Soybean (Glycine max (L.) Mer.) cultivar coefficients for the DSSAT-CROPGRO model were calibrated from two growing seasons (2012-2013) comprising 58 irrigated environments (site x year x planting date combinations) for maturity group (MG) 3 to 6 cultivars using end of season data (yield, seed weight, and seed oil and protein concentration) and previously calibrated phenology coefficients. Model accuracy after calibration of cultivar coefficients by MG (cultivars averaged within a MG) was similar compared to cultivar-specific coefficients. During the subsequent growing season (33 environments), the model efficiency (ME) for predicting yield was 0.40, with a root mean square error (RMSE) of 571 kg ha-1. The model was less efficient predicting seed number and seed weight (ME = 0.06 and -0.06, respectively). The model was able to simulate differences in seed oil concentration across environments and MGs (ME = 0.52), but not protein concentration (ME = -0.25). The analysis of yield stability had similar slopes for the observed and predicted yield regressions against an observed environmental index (EI) that were only dependent on the MG. Simulated yields were significantly different from the observed when EI>0, but yield differences in the highest yielding environments were still relatively small (245 to 608 kg ha-1). The results indicate an overall robust model performance in capturing G x E responses with coefficients calibrated by MG.