Corn, as a globally significant food crop, faces significant yield reductions due to competitive growth from weeds. Precise detection and efficient control of weeds are critical technical components for ensuring high and stable corn yields. Traditional deep learning object detection models generally suffer from issues such as large parameter counts and high computational complexity, making them unsuitable for deployment on resource-constrained devices such as agricultural drones and portable detection devices. Based on this, this paper proposes a lightweight corn weed detection model, AGRI-YOLO, based on the YOLO v11n architecture. First, the DWConv (Depthwise Separable Convolution) module from InceptionNeXt is introduced to reconstruct the C3k2 feature extraction module, enhancing the feature extraction capabilities for corn seedlings and weeds. Second, the ADown (Adaptive Downsampling) downsampling module replaces the Conv layer to address the issue of redundant model parameters; The LADH (Lightweight Asymmetric Detection) detection head is adopted to achieve dynamic weight adjustment while ensuring multi-branch output optimization for target localization and classification precision. Experimental results show that the AGRI-YOLO model achieves a precision rate of 84.7%, a recall rate of 73.0%, and a mAP50 value of 82.8%. Compared to the baseline architecture YOLO v11n, the results are largely consistent, while the number of parameters, G FLOPs, and model size are reduced by 46.6%, 49.2%, and 42.31%, respectively. The AGRI-YOLO model significantly reduces model complexity while maintaining high recognition precision, providing technical support for deployment on resource-constrained edge devices, thereby promoting agricultural intelligence, maintaining ecological balance, and ensuring food security.

Introduction: In arid areas, exploring new "dry sowing wet emergence (DSWE)" water-saving irrigation techniques may become one of the most important ways to reduce agricultural irrigation water use and improve economic efficiency.Methods: The study was conducted in a two-year field trial in 2021 and 2022, setting up three seedling emergence rates (W1: 6 mm, W2: 10.5 mm, W3: 15 mm) and two drip frequencies (D1: 2 times, D2: 4 times) for a total of six irrigation combinations.Results and discussion: The results indicate that under the "DSWE" irrigation pattern, in contrast to the low frequency treatment, the photosynthetic efficiency of cotton leaves in the high-frequency treatment is significantly higher. The stomatal conductance of cotton leaves has increased by 6.67% within two years, and the net photosynthetic rate has risen by 12.22%. Compared with the CK treatment, there is no remarkable difference in the photosynthetic indicators of the W3D2 treatment, while the net photosynthetic rate has increased by 1.68%. The population physiological indicators of each treatment group exhibit a trend of initially increasing and then decreasing as the growth period prolongs. The differences in the group population physiological indicators of cotton at the seedling stage among different seedling water treatments are relatively minor. The high frequency treatment maintains a relatively high level throughout the growth period. Compared with the low-frequency treatment, the yields of lint cotton and seed cotton in the high-frequency treatment have increased by 14.77% and 20.89%, respectively. Compared with the winter irrigation technology, there are no significant differences in the cotton yield and quality indicators of the "DSWE" high-frequency and high-seedling water treatment (W3D2). Over two years, the average unit yields of lint and seed cotton have decreased by 1.95% and 3.01%, respectively. Nevertheless, irrigation water during the growth period declined by 38.46%. The appropriate "DSWE" irrigation technology (W3D2) can significantly enhance the physiological indicators of cotton, ensuring crop yield and quality while significantly reducing the amount of agricultural irrigation water.

To analyze the water-resource limitations for crops in irrigation districts along the lower reach of the Yellow River, we used the single-crop coefficient method provided by FAO-56 to analyze crop water demand (CWD) and irrigation water requirement (IWR) for the main crops (winter wheat, summer maize, and cotton) from 1971 to 2015. The impact of climate threats on IWR was then quantified based on the standardized precipitation evapotranspiration index (SPEI), following which the conflicts between water demand and water supply were analyzed. The results show that about 75.4% of the total annual IWR volume is concentrated from March to June. Winter wheat is the largest water consumer; it used an average of 67.9% of the total IWR volume. The study area faced severe water scarcity, and severe water deficits occurred mainly between March and June, which is consistent with the occurrence of drought. With the runoff from the Yellow River Basin further decreasing in the future, the water supply is expected to become more limited. IWR is negatively correlated with the SPEI. Based on the relationship between SPEI and IWR, the water allocation for irrigation can be planned at different timescales to meet the CWD of different crops.

Conflicts between water supply and water demand are intensifying in irrigation districts along the Lower Yellow River due to climate change and human activities. To ensure both adequate food supply and water resource sustainability in China, the Soil and Water Assessment Tool (SWAT) model was used to simulate the water balance and water use of agro-ecosystems in an irrigation district of the lower Yellow River Basin, China. Simulated average annual irrigation requirements decreased from 1969 to 2010. Irrigation requirements during the winter wheat season decreased owing to reduced reference evapotranspiration and increased precipitation. Annual evapotranspiration (ET) increased with increasing irrigation volume, and differences among irrigation scenarios were mainly due to ET of winter wheat. Water deficit typically occurred during winter wheat seasons with less precipitation. Field seepage and surface runoff tended to occur in years with high precipitation, particularly during the summer maize season under full irrigation and scheduled irrigation scenarios. Frequent and heavy irrigation did not always lead to high water use efficiency. To cope with limited water resources in this region, it is necessary to properly irrigate crops based on soil water content and take full advantage of precipitation and surface runoff during the summer maize season.

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.

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.