Problem: The Decision Support System for Agrotechnology Transfer (DSSAT) contains a sunflower model based on CROPGRO. This model assumes some parameters values out of the crop species variability. Besides, this model has not been assessed for simulating grain yield and grain oil content in contrasting environments. Objective: The main goal of this study was to generate and test a revised CROPGRO-Sunflower model. In addition, we used the revised CROPGRO-Sunflower to quantify crop responses to environmental and management variables. Methods: Three sunflower models: a revised CROPGRO-Sunflower, the original CROPGRO-Sunflower and the OILCROP-SUN were calibrated and evaluated across contrasting environments. We compared the revised CROPGRO-Sunflower with the original CROPGRO-Sunflower and OILCROP-SUN in terms of ability to simulate crop development, growth, grain yield and grain oil content. Crop responses to soil depth, sowing date, and El Nino-Southern Oscillation (ENSO) effects were quantified using the revised CROPGRO-Sunflower in combination with climatic records for 37 growing seasons to simulate yield in two contrasting environments of Argentina: Balcarce and Reconquista. Results: Crop growth, grain yield and grain oil content were better simulated by the revised CROPGRO-Sunflower than by OILCROP-SUN. Simulated yield had a root mean square error (RMSE) of 48 g m-2 with revised CROPGRO-Sunflower and of 119 g m-2 with OILCROP-SUN. Moreover, RMSE for simulated grain oil concentration was 2% for revised CROPGRO-Sunflower and 11% for OILCROP-SUN. Deep soils and late sowing dates resulted in higher grain yield at Balcarce. Sowing date did not affect grain yield at Reconquista. An effect of the ENSO phases on sunflower grain yield was found. "La Nina" ˜ phase was associated with the lowest grain yields at both sites. Conclusions: Modifications made to the original CROPGRO-Sunflower improved model performance. The revised CROPGRO-Sunflower model can be utilized to simulate crop phenology, growth, grain yield and grain oil concentration over a wide range of environmental conditions. Implications: This calibrated and evaluated crop simulation model will allow to advance in the quantification of yield gaps and to study the impact of other management practices on sunflower crop production.
Rodriguez, I. M. R.; Mercau, J. L.; Cipriotti, P. A.; Hall, A. J. & Monzón, J. P. (2023).Fine - tuning the CROPGRO - Sunflower model and its application to the quantification of crop responses to environmental and management variables.Field Crops Research,300,art.108986
10.1016/j.fcr.2023.108986
Rodriguez, Ignacio M. R., Mercau, Jorge Luis, Cipriotti, Pablo Ariel, Hall, Antonio Juan, Monzón, Juan Pablo.2023. "Fine - tuning the CROPGRO - Sunflower model and its application to the quantification of crop responses to environmental and management variables".Field Crops Research 300:art.108986.
Recuperado de http://ri.agro.uba.ar/greenstone3/library/collection/arti/document/2023rodriguez