Sunflower Modelling: A Review

Abstract

Vegetable oils are a key component of human dietary need and health worldwide. Oil quality of sunflower is better than all others because of the higher percentage of the linoleic acid that is the most appropriate character missing in all other oilseed crops. Changing climate and extreme weather events are making crop highly vulnerable and threatening global food security. Application of different crop models was evaluated to quantify the sunflower genotypes selection, assessment of phenotypic plasticity, physiology, and estimation of seed yield and oil concentration in response to the climate variability. The present study evaluated the worldwide sunflower modelling performance, and a case study of SUNFLO hybrid modelling technique was assessed for crop model adaptation to new genotypes under contrasting environment. Extended field experiment was conducted at 52 locations (28 genotypes) at the 75% of the total sunflower cultivated region in France. Compared to initial models the experiential correlation decreased mean square error (MSE) on an average of 54% for seed yield production, and 26% for oil content concentration. The study also identified smart management practices and evaluated the performance of different models and concluded with the utilization of hybrid modelling skills. Further research expresses the thrust to use system modelling for screening the existing hybrids on grounds of their responses to several growth parameters and adaptation capacity to rapidly changing climatic conditions. This will eventually minimize the yield losses and help in increasing the crop yield even in limited resources. The present study is also proposing a clear optimization framework for genetic diversity of sunflower hybrids and management practices under changing climatic scenario.