Analysis and Optimization of Crop Planting Structure in Ningxia

[Objectives] To analyze and optimize the crop planting structure in Ningxia based on the shortage of water resources and the large proportion of agricultural water consumption in Ningxia. [Methods] The change trend of crop planting area and planting structure in Ningxia in 2004-2018 was analyzed, and a multi-objective optimization model was constructed with the objectives of maximum crop profit and minimum water demand. The STEM method was applied to solve the problem, and the optimization scheme of crop planting in Ningxia was obtained. [Results] In Ningxia in 2004-2018, the planting area showed the characteristics of "increase-decrease-increase"; the area and proportion of cash crops were increasing, and the proportion of grain crops was gradually decreasing, but the proportion of crops with high water consumption was still high. After the planting structure was optimized, the economic benefit was increased by 34.85×108 yuan, and the water demand was reduced by 3.9×108 m³. [Conclusions] Under the premise of ensuring food security, the optimized scheme not only saves water resources but also obtains higher economic benefits. It provides a reference for alleviating water shortage and increasing farmers' income

Evaluation of Empirical Equations and Machine Learning Models for Daily Reference Evapotranspiration Prediction Using Public Weather Forecasts

Although the studies on model prediction of daily ETo based on public weather forecasts have been widely used, these studies lack the comparative evaluation of different types of models and do not evaluate the seasonal variation in model prediction of daily ETo performance; this may result in the selected model not being the best model. In this study, to select the best daily ETo forecast model for the irrigation season at three stations (Yinchuan, Tongxin, and Guyuan) in different climatic regions in Ningxia, China, the daily ETos of the three sites calculated using FAO Penman–Monteith equations were used as the reference values. Three empirical equations (temperature Penman–Monteith (PMT) equation, Penman–Monteith forecast (PMF) equation, and Hargreaves–Samani (HS) equation) were calibrated and validated, and four machine learning models (multilayer perceptron (MLP), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and gradient boosting with categorical features support (CatBoost)) were trained and validated against daily observed meteorological data (1995–2015 and 2016–2019). Based on public weather forecasts and daily observed meteorological data (2020–2021), the three empirical equations (PMT, PMF, and HS) and four machine learning models (MLP, XGBoost, LightGBM, and CatBoost) were compared in terms of their daily ETo prediction performance. The results showed that the daily ETo performance of the seven models in the irrigation season with a lead time of 1–7 days predicted by the three research sites decreased in the order of spring, autumn, and summer. PMT was the best model for the irrigation seasons (spring, summer, and autumn) at station YC; PMT and CatBoost with C3 (Tmax, Tmin, and Wspd) as the inputs were the best models for the spring, autumn irrigation seasons, and summer irrigation seasons at station TX, respectively. PMF, CatBoost with C4 (Tmax, Tmin) as input, and PMT are the best models for the spring irrigation season, summer irrigation season, and autumn irrigation season at the GY station, respectively. In addition, wind speed (converted from the wind level of the public weather forecast) and sunshine hours (converted from the weather type of the public weather forecast) from the public weather forecast were the main sources of error in predicting the daily ETo by the models at stations YC and TX(GY), respectively. Empirical equations and machine learning models were used for the prediction of daily ETo in different climatic zones and evaluated according to the irrigation season to obtain the best ETo prediction model for the irrigation season at the study stations. This provides a new idea and theoretical basis for realizing water-saving irrigation during crop fertility in other arid and water-scarce climatic zones in China

Changes in Large Livestock Breeding Industry and Their Influences on Gross Output Value of Animal Husbandry in Guyuan City of Ningxia

[Objectives] To study the changes in large livestock breeding industry and their influences on gross output value of animal husbandry in Guyuan City of Ningxia. [Methods] This study explored the change trends of large livestock breeding industry and their impacts on animal husbandry income based on a linear trend method and ridge regression model. [Results] The numbers of sold and slaughtered cattle and sheep in Guyuan presented a significant upward trend, while the number for hog had a significant downward trend, during 2000-2019. The gross output value of animal husbandry (GOVAH) in Guyuan and its 5 counties had increased by 2-3 times in recent 20 years, which was mainly driven by large-scale livestock breeding. The cattle and sheep breeding sectors had positive effects on GOVAH, while the hog breeding sector had negative effect. [Conclusions] This study provides a reference for the structural adjustment and large-scale development of animal husbandry