A crop model-based approach for sunflower yields

Abstract

Pushed by the Brazilian biodiesel policy, sunflower (Helianthus annuus L.) production is becoming increasingly regarded as an option to boost farmers' income, particularly under semi-arid conditions. Biodiesel related opportunities increase the demand for decision-making information at different levels, which could be met by simulation models. This study aimed to evaluate the performance of the crop model OILCROP-SUN to simulate sunflower development and growth under Brazilian conditions and to explore sunflower water-and nitrogen-limited, water-limited and potential yield and yield variability over an array of sowing dates in the northern region of the state of Minas Gerais, Brazil. For model calibration, an experiment was conducted in which two sunflower genotypes (H358 and E122) were cultivated in a clayey soil. Growth components (leaf area index, above ground biomass, grain yield) and development stages (crop phenology) were measured. A database composed of 27 sunflower experiments from five Brazilian regions was used for model evaluation. The spatial yield distribution of sunflower was mapped using ordinary kriging in ArcGIS. The model simulated sunflower grain productivity satisfactorily (Root Mean Square Error ≈ 13 %). Simulated yields were relatively high (1,750 to 4,250 kg ha −1 ) and the sowing window was fairly wide (Oct to Feb) for northwestern locations, where sunflower could be cultivated as a second crop (double cropping) at the end of the rainy season. The hybrid H358 had higher yields for all simulated sowing dates, growth conditions and selected locations.

A model-based approach to assist variety evaluation in sunflower crop

“…On this dataset, the model performance was comparable to other crop models calibrated and evaluated on a small number of good experimental data (García-López et al, 2014; e.g. for sunflower Leite et al, 2014).…”

Development and validation of a sunflower crop growth module for the Daisy model

“…The R 2 calculated for the treatments of validation indicated the high correlation obtained near one. In a similar study, the crop model OILCROP-SUN was calibrated and evaluated according to the experimental data of two sunflower genotypes cultivated under semi-arid conditions, and the measured and simulated parameters of growth components and crop phenology were compared by RMSE (Leite et al, 2014). The aggregate result of the statistical indices for validation in the present study illustrated that the developed crop model applied for sunflower in the Daisy model qualified for simulating the plant physiological indicators and the soil water content of the FI treatments.…”