Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profits - Gao, Yang; Chang, Lin; Zeng, Mei; Hu, Quanze; Hui, Jiaojiao; Jiang, Qingsong (Frontiers in Plant Science, 2024)
Soil salinization seriously affects the efficiency of crops in absorbing soil nutrients, and the cotton production in southern Xinjiang accounts for more than 60% of China’s total. Therefore, it is crucial to monitor the dynamic changes in the salinity of the soil profile in cotton fields in southern Xinjiang, understand the status of soil salinization, and implement effective prevention and control measures. The drip-irrigated cotton fields in Alaer Reclamation Area were taken as the research objects. The multivariate linear regression model was used to study the relationship between soil salinity and soil depth in different periods, and the Kalman filter algorithm was used to improve the model accuracy. The results showed that the month with the highest improvement in model accuracy was July, with the model accuracy R2 increasing by 0.26 before and after calibration; followed by June and October, with the model accuracy R2 increasing by 0.19 and 0.18 respectively; the lowest improvement was in March, which was only 0.01. After the model was calibrated by the Kalman filter algorithm, the fitting accuracy (R2) between the predicted value and the actual value was as high as 0.79, and the corresponding RMSE was only 96.17 μS cm-1, and the measured value of soil salinity was consistent with the predicted value. Combined with the predicted conductivity data of each soil layer, the total yield of the study area was predicted to be 5,203-5,551 kg hm-2, and the income was about 4,953-7,441 RMB hm-2. It can be seen that Kalman filtering can improve the prediction accuracy of the model and provide a theoretical basis for studying the mechanism of soil salt migration in drip-irrigated cotton fields at different stages. It is of great significance for evaluating the potential relationship between cotton yield and deep soil salinity and guiding the efficient prevention and control of saline soil in cotton fields.