The influence of climate variability, soil and sowing date on simulation-based crop coefficient curves and irrigation water demand

Modern global agriculture depends heavily on irrigation water to maintain crop productivity. The widely used crop evapotranspiration estimation approach (i.e. FAO-56) can be applied to estimate the irrigation water demand of crops under optimal growth conditions by multiplying the reference evapotranspiration with often empirically determined crop-specific crop coefficients (Kc). To improve the transferability of the Kc approach while keeping the Kc concept as a relatively simple and visual tool for irrigation scheduling, a simulation-based approach to estimate site-specific continuous Kc curves was developed. The presented modeling framework consists of the process-based soil–crop–atmosphere system simulation model DAISY calibrated and validated against an extensive field data set and stochastic weather data to consider climate variability. The simulation-based Kc curves were exemplary estimated for common bean (two sowing dates) and white cabbage for several soils at an experimental site in Eastern Germany. The Kc curve ensembles (each 300 growth periods) generated by the modeling framework provided information on the probability of the Kc curves caused by the site-specific climate variability under recent climate conditions. By changing factors while fixing others (soil characteristics, sowing date), the influence of these factors on the Kc curves and the irrigation water requirement were evaluated. We found that the influence of the factors varied between the crops. The two tested common bean sowing dates had an influence on the Kc curves mainly due to varying growth rates and growth durations. The presented Kc curves may support farmers to make accurate in-season but also long-term estimations of the daily irrigation water demand under local conditions. The probability distribution of the curves provides additional information on the variability of the irrigation water demand due to climate variability.