Estimation of yield and dry matter of rapeseed using logistic model under water salinity and deficit irrigation

Abstract

The inadequacy of most models to simulate crop growth and yield is due to complexity, difficulty to understand, and lack of input data. Therefore, several simple crop growth models are presented to reduce these failures. In this investigation, yield and aboveground dry matter (DMabove) of rapeseed were simulated by two logistic growth models that were based on days after planting (DAP) and growing degree days (GDD) under water salinity and deficit irrigation, in a 2-year experiment. Data of first and second year were used for calibration and validation of the model, respectively. The coefficients of logistic function were determined as a function of irrigation water salinity and sum of applied water and rainfall in spring of the first year. Results indicated that logistic function based on GDD-predicted DMabove during growing season more accurately than logistic function based on DAP. Furthermore, seed yield of rapeseed was estimated based on harvest index with a good accuracy. Therefore, logistic function based on GDD that is based on the cumulative heat units can be used for different weather conditions and planting dates to determine rapeseed DMabove and yield under water salinity and deficit irrigation.