A combination of mechanistic and empirical models to predict growth and yield of sunflower as influenced by irrigation and moisture stress

Abstract

Sunflower (Helianthus annuus L.) is an important oil seed crop grown quite often in drylands. The variability in rainfall occurrence in these regions makes crop production risk and the crop experiences moisture stress at different growth stages. Crop simulation models help to assess such production risks. This paper described the development and testing of a combination of mechanistic and empirical models of sunflower. The model uses a few conservative relationships to define leaf area development as a function of leaf number and in turn leaf number as a function of accumulated thermal units. Biomass accumulation was simulated as a function of fraction of photosynthetically active radiation interception and radiation use efficiency. Seed growth was simulated from linear increase in harvest index with time. The model was calibrated empirically to predict the growth and yield of sunflower as influenced by irrigation and moisture stress effects by developing suitable sensitive factors. The model performed satisfactorily in predicting the aboveground biomass, leaf area and final yield of sunflower as influenced by irrigation and moisture stress.