Global warming is one of the greatest threats to the social development of human beings. It is a typical example of global climate change, and has profoundly affected human production and life in various aspects. As the foundation of human existence, agricultural production is particularly vulnerable to climate change, which has altered environmental factors such as temperature, precipitation, and wind speed, and affected crop growth cycles, the frequency of extreme weather events, and the occurrence patterns of pests and diseases directly or indirectly, ultimately influencing crop yield and quality. This article reviews the latest research progress in this field, summarizes the impact of global climate change on agricultural production as well as the feedback mechanisms of agricultural activities on climate change, and proposes strategies for agricultural production to cope with global climate change. This paper aims to provide a scientific basis and suggestions for ensuring the sustainable development of agricultural production.

Global agricultural drought is evolving into a compound crisis threatening food security and ecological stability that is characterized by increased frequency, intensity, duration, and spatial extent. Since 2000, the global number of drought events has increased by 29% compared with the previous two decades, and 82% of drought-related losses in developing countries are concentrated in agriculture. The UNCCD (2022) projects that drought may affect up to three quarters of the world’s population by 2050. Climate change and human activities jointly drive this escalation through higher atmospheric evaporative demand, altered precipitation regimes, land use change, groundwater overexploitation, and pollution emissions. Their interaction forms amplifying feedback loops across ecology-agriculture and climate–agriculture systems, resulting in ecosystem degradation, crop yield loss, and rising socioeconomic inequality. Addressing this crisis requires a three-dimensional framework integrating mitigation, adaptation, and collaborative governance. This review synthesizes coupled driving mechanisms, cross-system impacts, and response pathways, and it also highlights priorities in compound-drought attribution, region-specific technology scaling, cross-scale governance, and resilience building in vulnerable regions.

Two years of field experiments were conducted at the National Field Observation Experiment Station for Efficient Agricultural Water Use in the Wuwei Oasis, Gansu Province. Based on the eddy correlation system, the evapotranspiration (ET) of the cabbage agroecosystem during the growth period was obtained and the main driving factors of ET changes were determined. The Root Zone Water Quality Model 2.0 version (RZWQM2 model) was used to simulate ET during the growth period. The results showed the following: (1) The ET of cabbage during the growth period was 260. 1 ± 24.2 mm, which was basically lower than other crops planted in this area. (2) Through partial correlation analysis and principal component analysis, it can be found that environmental and physiological factors jointly drive changes in ET. The main driving factors include gross primary productivity, net radiation, and water use efficiency. (3) The RZWQM2 model can simulate the ET of the cabbage agroecosystem well, especially in simulating the total ET value and its trend. The growth period ET_s were 7.3% lower than the ET_m. Cabbage is an important cash crop in Northwest China, and ET is an important component of the water cycle in the agroecosystem. Determining the main driving factors of ET is of great significance for the sustainable utilization of agricultural water resources in Northwest China. Our results can provide a scientific basis for the cultivation of cabbage as a cash crop and the development of water saving agriculture.

Accurate diagnosis of crop water demand is a core challenge in alleviating agricultural water scarcity. Traditional diagnostic methods, which rely mainly on soil moisture sensor monitoring or empirical models based on meteorological data, suffer from limitations such as insufficient spatiotemporal representativeness and an inability to reflect crop physiological status in real time, leading to an annual water waste of 10–30%. Therefore, developing technologies that enable real-time, non-destructive, and precise monitoring of crop water status is crucial. In recent years, the rapid advancement of high-throughput phenotyping technology has provided revolutionary tools to address this challenge. By integrating multi-source sensors (e.g., thermal infrared and hyperspectral imaging), multi-dimensional response characteristics of crops under water stress can be rapidly acquired. This paper systematically reviews research progress in using high-throughput phenotyping to obtain water-sensitive phenotypic traits and construct crop water demand diagnosis models. It focuses on: (1) the connotation and acquisition techniques of key water-sensitive phenotypic indicators, such as canopy temperature, spectral indices, and chlorophyll fluorescence; (2) the advantages, limitations, and fusion strategies of multi-platform data acquisition systems, including unmanned aerial vehicles (UAVs), ground mobile platforms, and satellite remote sensing; and (3) the construction methods, performance evaluation, and practical application cases of diagnostic models based on machine learning (e.g., Random Forest, XGBoost), deep learning (e.g., CNN, LSTM), and mechanism-coupled models. The innovation of this review lies in its systematic integration of the entire technological chain—"phenotyping acquisition → model construction → decision-making"—while identifying current research challenges, including field environmental complexity, model generalization capability, data barriers, and interpretability. Future development pathways are proposed, focusing on low-cost sensing, explainable AI, multi-source data fusion, and cloud-edge collaborative decision systems. This review aims to provide a systematic theoretical and practical reference for water management in precision irrigation and smart agriculture.