Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude

Abstract

Crop models can be used to explain yield variations associated with management practices, environment and genotype. This study aimed to assess the effect of plant densities using CSM-CROPGRO-Soybean for low latitudes. The crop model was calibrated and evaluated using data from field experiments, including plant densities (10, 20, 30 and 40 plants per m2), maturity groups (MG 7.7 and 8.8) and sowing dates (calibration: 06 Jan., 19 Jan., 16 Feb. 2018; and evaluation: 19 Jan. 2019). The model simulated phenology with a bias lower than 2 days for calibration and 7 days for evaluation. Relative root mean square error for the maximum leaf area index varied from 12.2 to 31.3%; while that for grain yield varied between 3 and 32%. The calibrated model was used to simulate different management scenarios across six sites located in the low latitude, considering 33 growing seasons. Simulations showed a higher yield for 40 pl per m2, as expected, but with greater yield gain increments occurring at low plant density going from 10 to 20 pl per m2. In Santarém, Brazil, MG 8.8 sown on 21 Feb. had a median yield of 2658, 3197, 3442 and 3583 kg/ha, respectively, for 10, 20, 30 and 40 pl per m2, resulting in a relative increase of 20, 8 and 4% for each additional 10 pl per m2. Overall, the crop model had adequate performance, indicating a minimum recommended plant density of 20 pl per m2, while sowing dates and maturity groups showed different yield level and pattern across sites in function of the local climate.

Climate change impacts on rainfed and irrigated soybean yield in Brazil’s new agricultural frontier

“…Teixeira et al (2019) obtained root mean square error (RMSE) of 347 and 567 kg ha −1 , and Willmott index (d) of 0.96 and 0.81, respectively for calibration and evaluation for soybean grain yield, while maturity date had RMSE of 6.9 days and d of 0.70. Sampaio et al (2021) had RMSE lower than 401 kg ha −1 for soybean grain yield and a bias lower than 7 days for maturity date.…”

“…Irrigation management can be used to reduce the agricultural production risk (Woznicki et al, 2015;Justino et al, 2019), mainly in the future period 2041-2070 under both RCP 2.6 (optimistic) and 8.5 (pessimist) in current and new growing areas, as east border and region of lower altitude, both with low growing intensity. Further, management strategies can be explored to offset the negative impacts of climate change, including the interaction of sowing dates, maturity groups and plant densities (Choi et al, 2016;Sampaio et al, 2021), improvement of soil management to increase root depth to access soil water (Battisti and Sentelhas, 2017), changes in crops and production systems (Labeyrie et al, 2021), and reducing yield gaps by sub-optimal management in favorable climate growing seasons (Battisti et al, 2018b;Santos et al, 2021b).…”

“…The maturity groups were 7.2 (MG7200) for latitude below or equal to 12.5° South, 7.7 (MG7700) for latitude between 12.5° and 7.5° South, and 8.8 (MG8800) for latitude above 7.5° South. The cultivars' parameters were obtained from Teixeira et al (2019) for MG 7.2 (MG7200) and from Sampaio et al (2021) for MG 7.7 (MG77PA) e 8.8 (MG8800) (Table 2). These authors obtained a good crop model performance during calibration and evaluation for phenology and crop growth.…”

“…In Brazil, studies involving climate change and soybean suggested impacts for most soybean production regions (Do Rio et al, 2016;Battisti et al, 2017a;Hampf et al, 2020;Zilli et al, 2020), but with a limitation for MATOPIBA (Pires et al, 2016;da Silva et al, 2020da Silva et al, , 2021. In relation to these study, our study improved the simulations, including thin resolution of weather data (108 points), general crop management, three maturity groups, two sowing dates and three soil types, applied to a crop simulation model properly calibrated for the region (Teixeira et al, 2019;Sampaio et al, 2021). In this context, improving knowledge regarding the impact of climate change on soybean productivity in this region is needed, adding the irrigated management as future alternative in areas with higher climate risk.…”

iCROPM 2020: Crop Modeling for the Future

“…The paper by Gurkan et al (2020) discusses a crop modelling study on the potential impact of climate change on sunflower production in Turkey for both rainfed and irrigated conditions. The paper by Sampaio et al (2020) presents a crop modelling study on the evaluation of different planting dates and plant densities for soybean grown in Brazil. The paper by Bracho-Mujica et al (2020) addresses the issue of input data requirements for crop models with emphasis on weather data and the study evaluated different solutions for scaling.…”