Determining a suitable “dry sowing and wet emergence” water control program for cotton fields in the arid regions of Northwest China is of great significance in saving water resources, improving economic efficiency, and promoting sustainable development of agriculture. The objective of this study was to analyze the effects of different “dry sowing and wet emergence” water control treatments on dry matter accumulation, chlorophyll fluorescence, yield quality, and water productivity of cotton, and to determine the optimal “dry sowing and wet emergence” water control program for cotton growth in arid areas. A two-year experiment was carried out in 2021 and 2022 in mulched drip-irrigated cotton fields, with a total of 13 treatments of different seedling water quantities (2021: 67.5 mm, 90 mm, 112.5 mm; 2022: 6 mm, 10.5 mm, 15 mm) and different drip frequencies (frequencies means number of drops at seedling stage) (2021: one, two, three times; 2022: two times, four times) in the “dry sowing and wet emergence”. Results indicated a positive correlation between increased seedling water quantity and growth indexes. High seedling water quantity treatment demonstrated a 14.33% higher cotton yield than the low seedling water quantity treatment. In comparison with low-frequency treatment, the high-frequency treatment exhibited significantly larger cotton plant height, dry matter accumulation, and yield. Over two years, the average values increased by 8.69%, 16.4%, and 15.91%, respectively, with a 14.55% increase in the coefficient of photochemical quenching of the leaf blade (qP). The high frequency and larger amount of seedling water quantity treatments showed significantly higher irrigation water productivity, with increases of 39.2% and 70.2% compared to the winter irrigation control treatment. In summary, the appropriate “dry sowing wet emergence” water regulation mode (the first drip: 15 mm, the second drip: 4.5 mm, the third drip: 22.5 mm, the fourth drip: 15 mm) can ensure crop yield quality under the premise of significantly reducing the agricultural irrigation water, which can provide certain theoretical support for the green, efficient, and sustainable development of the local cotton industry.

Unveiling the variation mechanism of vegetation net primary productivity (NPP) and elucidating the underlying drivers of these changes is highly necessitated for terrestrial carbon cycle research and global carbon emission control. Taking Henan Province, renowned as the anciently central China and current China’s foremost grain producer, as an example, this study employed the Theil–Sen Median Trend Analysis to evaluate the spatiotemporal characteristics and trends of NPP. Correlation Analysis and Residual Analysis were used to explain the drivers of NPP dynamics. To deepen the inquiry, the Geodetector method was employed to scrutinize the multifaceted effects and interplay among diverse variables influencing NPP. The result showed demonstrated that approximately 85.72% of the area showed an increase in NPP, covering a broad geographical distribution. Notably, 89.31% of the province has witnessed a positive human-driven NPP change. It means human activities emerged as a driving force with a positive effect on vegetation NPP, consequently fostering an increasing trend of NPP. Among climatic factors, the correlation between NPP and precipitation was stronger than that between the temperature and NPP, the determined power of factors in Henan Province was population density, (0.341) > GDP (0.326) > precipitation (0.255) > elevation (0.167) > slope (0.136) > temperature (0.109), and a single factor had a lesser interaction effect than two factors. The implications of these findings extend beyond the realms of research, potentially offering valuable insights into the formulation of targeted ecosystem restoration measures tailored to the distinct context of Henan Province, and also expect to provide crucial references for carbon emission control in China and across the world.

Agricultural technological change plays a crucial role in food security and agricultural development. In the case of considering economic risks and technical risk tolerance, farmers will use different technologies to match production factors to achieve the optimal production state. Therefore, under different farmland management modes, farms show different characteristics of technological progress. This paper attempts to compare and analyze agricultural technology development under different farmland management modes: the unified management mode of collective organizations (UMCO) and the decentralized management mode of contracted families (DMCF). The Stochastic Frontier Analysis (SFA) of the translog average production function was applied to the 24 farms of the Hulunbuir Agricultural Reclamation Group, of which 11 farms in the western part of the Greater Khingan Mountains (Western Farms) were managed by the DMCF, and the other 13 farms in the eastern part of the Greater Khingan Mountains (Eastern Farms) were managed by the UMCO. The results are as follows: (1) without considering the resource allocation efficiency, from 2000 to 2019, the generalized technological progress rate (TFPG) of the 13 Eastern Farms (7.65%) was higher than that of the Western Farms (2.25%). (2) The returns to scale (SRC) of the Western Farms was higher than that of the Eastern Farms. (3) The technological efficiency change rate (TEC) and the technical progress (TP) of the Eastern Farms is higher than that of the Western Farms. It is recommended that farms strengthen the construction of their infrastructure and service systems, resist natural disasters, reduce the disaster’s impact on technological progress, give full play to the overall planning advantages of the collective organizations, improve the product allocation efficiency factors, and create connotative profit points.

The accurate prediction of crops’ water requirements is an important reference for real-time irrigation decisions on farmland. In order to achieve precise control of irrigation and improve irrigation water utilization, a real-time crop water requirement prediction model combining convolutional neural networks (CNNs) and the Informer model is presented in this paper, taking the real-time water demand of winter wheat–summer maize from 2017 to 2021 as the research object. The CNN model was used to extract the depth features of the day-by-day meteorological data of the crops, and the extracted feature values were inputted into the Informer model according to the time series for training and prediction to obtain the predicted water demand of winter wheat and summer maize. The results showed that the prediction accuracy of the constructed CNN–Informer combination model was higher compared to CNN, BP, and LSTM models, with an improvement of 1.2%, 25.1%, and 21.9% for winter wheat and 0.4%, 37.4%, and 20.3% for summer maize; based on the good performance of the model in capturing the long-term dependency relationship, the irrigation analysis using the model prediction data showed a significant water-saving effect compared with the traditional irrigation mode, with an average annual water saving of about 1004.3 m³/hm², or 18.4%, which verified the validity of the model, and it can provide a basis for the prediction of crops’ water demand and sustainable agricultural development.

This study aimed at investigating the applicability of a SWAT (Soil and Water Assessment Tool) model in understanding the effects of drought on winter wheat. This study is based on the water requirement of the crop and addresses the absence of a SWAT model with real-time irrigation simulation. A real-time irrigation module was developed for the downstream irrigation area of the Yellow River to estimate the real-time irrigation of crops. By further simulating the dynamic evolution process of soil moisture content, a dynamic drought evaluation model of winter wheat was established, and the relative soil moisture was set as the evaluation index to assess and analyze the dynamic variation of drought evolution during the growth of winter wheat. The results showed that the improved SWAT model has strong applicability. During the growth of winter wheat, the variation trend of droughts is consistent with that of natural precipitation. For winter wheat, drought is quite normal during all growth stages, and the average frequency is 93.5%, 89.1%, 84.8%, 89.1% and 95.7%. Severe drought and extra-severe drought usually occur during the sowing–tillering stage and the tillering–greening stage, and the frequency of occurrence is 30.4% and 19.6%, respectively. This paper provides a novel approach for the study of agricultural drought in the downstream irrigation area of the Yellow River.

Aiming at the problem that the existing channel leakage calculation methods generally ignore the dynamic changes of influencing factors, which leads to a large calculation error, this study attempts to utilize the machine learning method to accurately calculate the channel leakage loss under the dynamic changes in the influencing factors. By using the machine learning method to analyze the impact of dynamic changes in the flow rate and soil moisture content over time on the channel leakage loss in the water transmission process and quantify the impact of the selected factors on the leakage loss, a dynamic simulation model of the multi-parameter channel leakage loss was constructed, and a test was carried out in the irrigation area to verify the accuracy of the model. The test results are as follows: the actual leakage loss of the U1 channel is 1094.03 m³, the simulated value of the model is the 1005.24 m³, and the error between the simulated value and the measured value is 8.12%; the total leakage of the U2 channel is 1111.24 m³, the simulated value of the model is 1021.1 m³, and the error between the simulated value and the measured value is 6.31%. The experimental results show that the use of machine learning to construct a dynamic simulation model of channel leakage loss under the comprehensive consideration of the dynamic change in influencing factors over time has a better effect, and the calculation accuracy is high.