This study aimed to systematically evaluate the effects of different nitrogen application rates and irrigation practices on water-saving and yield enhancement in winter wheat production in the North China Plain (NCP) using a meta-analysis. By quantifying the impacts on crop yield, nitrogen use efficiency (NUE), and water use efficiency (WUE), the research provides a scientific basis for optimizing management practices in winter wheat production in this region. A comprehensive literature search was conducted across multiple databases, resulting in the inclusion of 94 eligible studies from 2018 to 2023. A random-effects model was employed to calculate the combined effect sizes, followed by subgroup and sensitivity analyses to further investigate the influence of nitrogen application rates, irrigation methods, and study regions on winter wheat production efficiency. The findings reveal that increasing nitrogen application rates and adopting deficit irrigation practices significantly improved winter wheat yield (combined effect size: 4.53 t·ha⁻¹), NUE (43.29%), and WUE (0.013 t·ha⁻¹·mm⁻¹). The subgroup analysis further elucidated the critical roles of nitrogen application ratios, irrigation methods, and study regions in determining winter wheat production efficiency, while the sensitivity analysis confirmed the robustness of these findings, as the pooled effect sizes decreased by merely 0.69% and increased by 0.61% after excluding small-sample or highly biased studies, respectively. The above meta-analysis did not incorporate long-term field trials; hence, two-year field experiments with designed irrigation and organic–inorganic fertilizer treatments were conducted, which provided further validation for the meta-analysis. Under short-term conditions (excluding CO₂ effects), we observed that chemical fertilizer exhibited a measurable inhibitory effect on crop water uptake and optimal water–fertilizer management was achieved with a 7:3 inorganic–organic fertilizer ratio combined with 450 m³·ha⁻¹ irrigation. This study demonstrates the effectiveness of optimizing nitrogen fertilization and irrigation management in enhancing winter wheat yield and resource utilization efficiency. The findings offer actionable insights for sustainable agricultural practices in the NCP and similar regions, contributing to improved crop productivity and resource conservation.

This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 images, 4096 × 3072 pixels) covering key growth stages (heading, grain filling, and maturity) of winter wheat (Triticum aestivum L.) during 2022–2023 using a DJI M300 RTK equipped with multispectral sensors. The dataset encompasses diverse field scenarios under five fertilization treatments (organic-only, organic–inorganic 7:3 and 3:7 ratios, inorganic-only, and no fertilizer) and two irrigation regimes (full and deficit irrigation), ensuring representativeness and generalizability. For model development, we replaced conventional VGG16 with ResNet-50 as the backbone network, incorporating residual connections and channel attention mechanisms to achieve 92.1% mean average precision (mAP) while reducing parameters from 135 M to 77 M (43% decrease). The GFLOPS of the improved model has been reduced from 1.9 to 1.7, an decrease of 10.53%, and the computational efficiency of the model has been improved. Performance tests demonstrated a 15% reduction in missed detection rate compared to YOLOv8 in dense canopies, with spike count regression analysis yielding R² = 0.88 (p < 0.05) against manual measurements and yield prediction errors below 10% for optimal treatments. To validate robustness, we established a dedicated 500-image test set (25% of total data) spanning density gradients (30–80 spikes/m²) and varying illumination conditions, maintaining >85% accuracy even under cloudy weather. Furthermore, by integrating spike recognition with agronomic parameters (e.g., grain weight), we developed a comprehensive yield estimation model achieving 93.5% accuracy under optimal water–fertilizer management (70% ETc irrigation with 3:7 organic–inorganic ratio). This work systematically addresses key technical challenges in automated spike detection through standardized data acquisition, lightweight model design, and field validation, offering significant practical value for smart agriculture development.

Consideration of crop yield variability caused by long-term climate change offers a way to quantify the interplay between climate change, crop growth, and yield. This study employed the APSIM model to simulate the potential winter wheat yield under varying climate scenarios in 1961–2100 in the Loess Plateau. It also evaluated the long-term response and adaptation differences of winter wheat yield to climate change. The results show that there is a slight downward trend in inter-annual precipitation during the winter wheat growth period, with a reduction of −2.38 mm·decade⁻¹ under the S245 scenario (abbreviated SSP2-4.5) and −2.74 mm·decade⁻¹ under the S585 scenario (abbreviated SSP5-8.5). Interestingly, the actual yield of winter wheat was positively correlated with precipitation during the growth period but not with temperature. By contrast, climatic yield exhibits a significant correlation with both factors, suggesting that future crop yield will largely depend on its sensitivity to climate change. In addition, climate change may marginally improve yield stability, although regional variations are evident. Notably, potential yields in water-restricted areas, such as Qinghai and Gansu, are significantly influenced by precipitation. This study provides an important reference for formulating long-term adaptation strategies to enhance the resilience of agricultural production against climate change.

This study examines the impact of climate change on winter wheat production in Henan Province. The analysis, under the utilization of GLASS LAI data, focuses on shifts in the planting areas of winter wheat. In addition, a comprehensive assessment of the spatiotemporal trends in meteorological factors during the winter wheat growth period has also been conducted. The findings reveal a fluctuating increase in accumulated temperature across Henan Province, ranging from 3145 °C to 3424 °C and exhibiting a gradual rise from north to south. In particular, precipitation patterns from 1980 to 2019 showed limited significant trends, while notable abrupt changes were observed in 1983, 2004, 2009, and 2016. Geographically, southwestern Henan Province experiences greater precipitation than the northeast. Moreover, a fluctuating downward trend in sunshine hours has been observed, gradually decreasing from north to south. The study further highlights an increase in winter wheat planting frequency in the northwestern region of Luoyang and the northeastern part of Zhumadian, contrasted by a decrease in Zhengzhou and Kaifeng. Accumulated temperature is positively correlated with the expansion of winter wheat planting areas (R² = 0.685), while sunshine hours exert a suppressive effect (R² = 0.637). Among meteorological factors, accumulated temperature emerges as the most crucial determinant, followed by precipitation, with sunshine hours having a relatively minor influence. Yield demonstrates a positive association with accumulated temperature (R² = 0.765) and a negative correlation with sunshine hours (R² = −0.614). This finding is consistent with the impact of meteorological factors on winter wheat production. The results of this study enhance the understanding of how the underlying mechanisms of climate change impact crop yields.

The possible influence of global climate changes on agricultural production is becoming increasingly significant, necessitating greater attention to improving agricultural production in response to temperature rises and precipitation variability. As one of the main winter wheat-producing areas in China, the temporal and spatial distribution characteristics of precipitation, accumulated temperature, and actual yield and climatic yield of winter wheat during the growing period in Shanxi Province were analysed in detail. With the utilisation of daily meteorological data collected from 12 meteorological stations in Shanxi Province in 1964–2018, our study analysed the change in winter wheat yield with climate change using GIS combined with wavelet analysis. The results show the following: (1) Accumulated temperature and precipitation are the two most important limiting factors among the main physical factors that impact yield. Based on the analysis of the ArcGIS geographical detector, the correlation between the actual yield of winter wheat and the precipitation during the growth period was the highest, reaching 0.469, and the meteorological yield and accumulated temperature during this period also reached its peak value of 0.376. (2) The regions with more suitable precipitation and accumulated temperature during the growth period of winter wheat in the study area had relatively high actual winter wheat yields. Overall, the average actual yield of the entire region showed a significant increasing trend over time, with an upward trend of 47.827 kg ha⁻¹ yr⁻¹. (3) The variation coefficient of winter wheat climatic yield was relatively stable in 2008–2018. In particular, there were many years of continuous reduction in winter wheat yields prior to 2006. Thereafter, the impact of climate change on winter wheat yields became smaller. This study expands our understanding of the complex interactions between climate variables and crop yield but also provides practical recommendations for enhancing agricultural practices in this region

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale.