Early-season crop mapping and information extraction is essential for crop growth monitoring and yield prediction, and it facilitates agricultural management and rapid response to agricultural disasters. However, training classifiers by remote sensing classification features for early crop prediction can be challenging, as early-season mapping can only use remote sensing image data during part of the crop growth period. In order to overcome this limitation, this study takes the Sanjiang Plain as an example to investigate the earliest identification time of rice, maize and soybean based on Sentinel-2 time-series data and the random forest classification algorithm. Crop information extraction was then performed. Following the analysis of the remote sensing classification features by the random forest importance approach and the subsequent normalization, the optimal features greater than or equal to 0.5 have yielded quite results in early crop mapping, and their overall accuracy was the highest in early-season mapping. The overall accuracy was observed to improve by 5% for 10 to 20 days of delay. In addition, rice, maize, and soybean were mapped at the irrigation transplanting period (10 May), jointing stage (9 July) and flowering (29 July), with an overall accuracy of 90.4%, 90.0% and 90.9%, respectively. This study shows that features suitable for early crop classification can be selected by random forest importance analysis as well as the ability of remote sensing to extract crop acreage information within the reproductive period.

O. decorus asiaticus is a major grasshopper species that harms the development of agriculture on the Mongolian Plateau. Therefore, it is important to enhance the monitoring of O. decorus asiaticus. In this study, the spatiotemporal variation in the habitat suitability for O. decorus asiaticus on the Mongolian Plateau was assessed using maximum entropy (Maxent) modeling along with multi-source remote sensing data (meteorology, vegetation, soil, and topography). The predictions of the Maxent model were accurate (AUC = 0.910). The key environmental variables affecting the distribution of grasshoppers and their contribution were grass type (51.3%), accumulated precipitation (24.9%), altitude (13.0%), vegetation coverage (6.6%), and land surface temperature (4.2%). Based on the assessment results of suitability by Maxent model, the model threshold settings, and the formula for calculating the inhabitability index, the 2000s, 2010s, and 2020s inhabitable areas were calculated. The results show that the distribution of suitable habitat for O. decorus asiaticus in 2000 was similar to that in 2010. From 2010 to 2020, the suitability of the habitat for O. decorus asiaticus in the central region of the Mongolian Plateau changed from moderate to high. The main factor contributing to this change was accumulated precipitation. Few changes in the areas of the habitat with low suitability were observed across the study period. The results of this study enhance our understanding of the vulnerability of different regions on the Mongolian Plateau to plagues of O. decorus asiaticus and will aid the monitoring of grasshopper plagues in this region.

The dry–hot–windy climate frequently occurs during the grain-filling stage of winter wheat on the North China Plain (NCP) and thus negatively influences wheat yield. Sprinkler irrigation can improve field temperature and humidity and can be used to mitigate dry–hot–windy climate disasters. A two-season field experiment was carried out on the NCP to test how sprinkler irrigation influences the microclimate, canopy temperature and photosynthetic traits, as well as the grain-filling process and final grain yield, when spraying 1.5–2 mm of water on dry–hot–windy days. Field experiments revealed that, compared with the no-spraying treatment, spraying with 2 mm of water each time caused the air and canopy temperatures to decrease by 2.3–7.6 °C and 4.3–9.9 °C, respectively, during and just after spraying stopped, and the temperatures returned to their previous levels approximately one hour after spraying. The air humidity increased by up to 10% during and after spraying. The photosynthesis and transpiration rates and the stomatal conductivity after spraying increased by 34–235%, 15–55% and 24–79%, respectively. The linear relationships between photosynthesis rates and transpiration rates with respect to stomatal conductivity suggest that increases in both photosynthesis and transpiration rates are the main contributors to the increase in stomatal conductivity, which is due mainly to the improved canopy temperature and humidity conditions caused by spraying practices. The grain-filling process was improved by spraying, which ultimately increased the unit grain mass by approximately 5%. One spraying event on a dry–hot–windy day influenced the field microclimate and canopy photosynthetic traits for 90 min (30 min in spraying time + 60 min after spraying). When the intensity of the dry–hot–windy climate is strong, two spraying events can be applied. Spraying 2–2.5 mm of water each time was sufficient when the leaf area index was 4–5 during the grain-filling stage of winter wheat. Spray events can have a slight effect on grain yield when a dry–hot–windy climate occurs within the last five days before harvest.

The availability of soil potassium plays a critical role in yield increases. Potassium-solubilizing microorganisms (KSM) offer a promising biological solution to improve potassium availability, but their efficacy across diverse global environments remains uncertain. Through a global meta-analysis of 102 studies (846 paired observations), we systematically evaluated the effects of KSM application on crop yields across five key dimensions: microorganism types, soil factors, crop classifications, field management, and stress types. KSM inoculation significantly increased soil available potassium (+28.9%), crop yield (+23.4%), and key growth indices, such as root length (+29.50%) and leaf area (+44.7%). This study identified Aspergillus spp. as the most suitable microorganism, and revealed that KSM efficacy is highly dependent on context: yield responses were greatest in clay loam soils, vegetable crops, and greenhouse conditions. Structural equation modeling indicated that microbial abundance, climate, soil available potassium, and plant growth (root length and leaf area) are key direct and indirect drivers of yield enhancement. The results indicate that the application of KSM is an effective strategy to increase crop yields in various environments. By identifying the optimal conditions for KSM application, the identification of optimal application parameters, derived from cross-study analysis, provides a robust strategy for leveraging microbial communities to boost soil potassium availability and nutrient efficiency, thereby contributing to the transition toward more sustainable and climate-resilient agriculture.

The water (W; W₅₀, W₇₅, and W₁₀₀)–zeolite (Z; Z₀, Z₃, Z₆ and Z₉) coupling (W-Z) regulation strategy of high-quality and high-yield tomato was explored with alternate drip irrigation under mulch. Greenhouse planting experiments were used in monitoring and analyzing tomato growth, physiology, yield, quality, and water use efficiency (WUE). Suitable amounts of W and Z for tomato growth were determined through the principal component analysis (PCA) method. Results showed that tomato plant height (Ph), stem thickness (St), root indexes, leaf area index (LAI), photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), organic acid (OA), and yield showed a positive response to W, whereas nitrate (NC), vitamin C (VC), soluble solid (SS), intercellular CO₂ concentration (Ci), fruit firmness (Ff), and WUE showed the opposite trend. The responses of Ci and Ff to Z were first negative and then positive, whereas the responses of other indexes to Z showed an opposite trend (except yield under W₅₀). The effects of W, Z, and W-Z on tomato growth, physiological, and quality indexes and yield were as follows: W > Z > W-Z; the effects on WUE were as follows: Z > W > W-Z. The two principal components of growth factor and water usage factor were extracted, and the cumulative variance contribution rate reached 93.831%. Under different treatments for tomato growth, the comprehensive evaluation score F was between −1.529 and 1.295, the highest treated with Z₆W₁₀₀, the lowest treated with Z₀W₅₀. The PCA method showed that under the condition of alternate drip irrigation under mulch, the most suitable W for tomato planting was 100% E (E is the water surface evaporation), and the amount of Z was 6 t·ha⁻¹.

Soil pH is crucial for the quality of the farmland and crop growth. The objective of this study is to analyze the spatial and temporal variations of farmland soil pH in Xinjiang (XJ), and to provide a scientific basis for soil improvement and agricultural production optimization. Based on soil pH data from XJ farmland in two periods, 2008~2010 and 2019~2021, geostatistical methods and kriging spatial interpolation techniques were employed to analyze the spatiotemporal changes in soil pH and to investigate the relationship between relevant influencing factors and pH over more than a decade. The results indicate that the spatiotemporal distribution of soil pH in XJ farmlands is uneven. Between 2019 and 2021, the average soil pH was 8.11, generally being on the higher side, with a coefficient of variation (CV) of 3.85%, indicating minimal spatial variability. In the farmland soil of Southern Xinjiang (S-XJ), the average pH value was 8.17, significantly higher than that of Northern Xinjiang (N-XJ), which was 8.10, demonstrating a spatial pattern of “higher in the south, lower in the north”. Over the past decade, soil pH in XJ has significantly increased from 8.11 to 8.13, with a 0.05 unit increase in the north and a 0.01 unit increase in the south (p < 0.05). Regionally, Altay saw the largest increase of 0.22 units, while Bortala Mongol Autonomous Prefecture (Bortala) experienced the most significant decrease, dropping by 0.59 units. Furthermore, this study found that factors such as topography, nutrients, and irrigation methods all have certain influences on the spatial distribution of soil pH in XJ farmland, while variations in climate factors and fertilization levels may affect its long-term temporal changes. These research findings will provide new insights for adjusting and updating agricultural management measures related to soil pH regulation in XJ.