Simulation for response of crop yield to soil moisture and salinity with artificial neural network

In saline fields, irrigation management often requires understanding crop responses to soil moisture and salt content. Developing models for evaluating the effects of soil moisture and salinity on crop yield is important to the application of irrigation practices in saline soil. Artificial neural network (ANN) and multi-linear regression (MLR) models respectively with 10 (ANN-10, MLR-10) and 6 (ANN-6, MLR-6) input variables, including soil moisture and salinity at crop different growth stages, were developed to simulate the response of sunflower yield to soil moisture and salinity. A connection weight method is used to understand crop sensitivity to soil moisture and salt stress of different growth stages. Compared with MLRs, both ANN models have higher precision with RMSEs of 1.1 and 1.6 t ha−1, REs of 12.0% and 17.3%, and R2 of 0.84 and 0.80, for ANN-10 and ANN-6, respectively. The sunflower sensitivity to soil salinity varied with the different soil salinity ranges. For low and medium saline soils, sunflower yield was more sensitive at crop squaring stage, but for high saline soil at seedling stage. High soil moisture content could compensate the yield decrease resulting from salt stress regardless of salt levels at the crop sowing stage. The response of sunflower yield to soil moisture at different stages in saline soils can be understood through the simulated results of ANN-6. Overall, the ANN models are useful for investigating and understanding the relationship between crop yield and soil moisture and salinity at different crop growth stages.

Research highlights
► The ANN produced a more precise and accurate result.
► The ANN-10 performed better than ANN-6.
► ANN-6 is more convenient to use in saline soil due to the less input variables.
► Sunflower sensitivity to different soil salinity varied at crop phenological stages.
► High soil moisture reduced the yield decrease resulting from salt stress.