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Uncertainty assessment of soya bean yield gaps using DSSAT‐CSM‐CROPGRO‐Soybean calibrated by cultivar maturity groups

JOURNAL OF AGRONOMY AND CROP SCIENCE(2019)

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Abstract
Soya bean yield gap can be caused by different factors resulting in uncertainties when the objective is to use such information for farm decision-making and reference yield determination. Thus, this study aimed to quantify the soya bean yield gap for four sites, located in Southern and Midwestern Brazil, as well as the uncertainties of that related to cultivars, sowing dates, soil types and reference yields. The crop simulation model DSSAT-CSM-CROPGRO-Soybean was calibrated for cultivars with similar maturity groups, based on the data obtained from the best farmers at the county level. The yield gap by water deficit (YG(WD)) was obtained through the difference between potential and attainable yields, and that one caused by sub-optimum crop management (YG(CM)) by subtracting actual yield of each county, obtained from official statistics between 1989/90 and 2014/15 growing seasons, from the estimated attainable yield. The yield was simulated using four sowing dates, three soil types and two soya bean maturity groups by county. The reference yield uncertainty was quantified using yield reference from crop model and regional winners of the soya bean yield context, conducted by CESB (Brazilian Soybean Strategic Committee), for the growing seasons from 2013/14 to 2015/16. The crop model showed a good agreement between measured and simulated crop development and growth using calibration by maturity group, with low root mean square error (347 kg/ha). Southern sites had a mean YG(WD) of 1,047 kg/ha, while in the Midwest, it was lower than 100 kg/ha. The YG(CM) was 1,067, 528, 984 and 848 kg/ha, respectively, for Castro, PR, Mambore, PR, Montividiu, GO and Primavera do Leste, MT, representing the opportunity for yield gain when having the best farmers as reference. The maturity groups, sowing dates and soil types showed to be an important source of uncertainty for yield gap determination, being recommended to investigate the farms in detail for an appropriate quantification. The reference yield showed expressive uncertainties, with some farmers presenting conditions to increase their soya bean yields by more than 3,000 kg/ha, when considering as reference the yields obtained by the winners' farmers. These results show that uncertainties must be reduced when assessing farm yield gaps, in order to ensure that expected rate of soya bean yield growth could be reached by adopting the same technologies from CESB winners and best farmers in the county as a reference.
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Key words
attainable yield,crop management,crop modelling,potential yield,reference yield,water deficit
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