Jujubes are a crucial characteristic industry in China. Predicting jujube production in various regions of China is significant to developing the jujube industry. This study aims to predict jujube yields across China by machine learning and optimize water‐nitrogen applications to achieve the highest yields. We utilized four machine learning methods (i.e., linear regression, support vector machine, ensemble learning and Gaussian process regression) to create predictive models based on the jujube production, irrigation, fertilization and planting density data sets. The results showed that the Gaussian process regression model best predicted jujube yield by comparing the predicted and measured data. The ensemble learning and Gaussian process regression model best optimized the optimal water and nitrogen application range. On the whole, the Gaussian process regression model is more suitable for yield prediction and water‐nitrogen management. The water‐nitrogen coupling function based on the Gaussian process regression model for predicting jujube yield in Xinjiang, Gansu and Shaanxi was developed to make suitable irrigation and fertilizer regimes. This study can provide a theoretical basis for predicting jujube production and water‐nitrogen management in China.

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Leaf area index and dry matter mass are important indicators for crop growth and yields. In order to solve the problem of predicting the summer maize growth index and yield under different soil quality and field management conditions, this study proposes a prediction model based on the supervised machine learning regression algorithm. Firstly, the data pool was constructed by collecting the measured data for maize in the main planting area. The total water input (rainfall plus irrigation water), fertilization, soil quality, and planting density were selected as the training set. Then, the maximum leaf area index (LAImax), maximum dry material mass (Dmax), and summer maize yields (Y) in the data pool were trained by using Gaussian regression (rational quadratic kernel function and Matern kernel function), support vector machine (SVM) and linear regression models. The training models were verified with the data-set not included in the data pool, and the water and fertilizer coupling functions were developed. The prediction results showed that compared to the support vector machine models and the linear regression models, the Gaussian regression prediction models comprising the rational quadratic and Matern kernel functions had good prediction accuracy. The coefficients of determination (R2) of the prediction results were 0.91,0.89 and 0.88; the root-mean-square errors (RMSEs) were 0.3, 1138.6 and 666.16 kg/hm2; and the relative root-mean-square errors (rRMSEs) were 6.3%, 5.94% and 6.53% for LAImax, Dmax and Y, respectively. The optimal total water inputs and nitrogen applications indicated by the prediction results and the water and fertilizer coupling functions were consistent with the measured range from the field tests. The supervised machine learning regression algorithm provides a simple method to predict the yield of maize and optimize the total water inputs and nitrogen applications using only the soil quality and planting density.

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