Drought disaster space agglomeration assessment is one of the important components of meteorological disaster prevention and mitigation. Agriculture affected by drought disaster is not only a serious threat to world food security, but also an obstacle to sustainable development. Additionally, China is an important agricultural import and export country in the world. Therefore, we used the global Moran’s I and the local indicators of spatial autocorrelation (LISA) to reveal the spatial agglomeration of agricultural drought disaster in China from1978 to 2016, respectively. The results showed that China’s agricultural drought disaster presents local spatial autocorrelation of geographical agglomeration at national level during the study period. The spatial agglomeration regions of China’s agricultural drought disaster were in Inner Mongolia, Jilin province, Heilongjiang province, Liaoning province, Shanxi province, Hebei province, Shandong province, Shaanxi province and Henan province, indicating that agricultural drought disaster mainly distributed in North and Northwest China, especially occurred in the Yellow River Basin and its north areas. We also found that the overall movement direction of agricultural drought disaster agglomeration regions was northwest, and the maximum moving distance was 722.16 km. Our results might provide insight in early warning and prevention for drought disaster.

Introduction: In coastal saline lands, organic matter is scarce and saline stress is high. Exploring the promotion effect of intervention with organic acid from biological materials on soil improvement and thus forage output and determining the related mechanism are beneficial to the potential cultivation and resourceful, high-value utilization of coastal mudflats as back-up arable land.Method: Three exogenous organic acids [humic acid (H), fulvic acid (F), and citric acid (C)] were combined with four kinds of biomass materials [cottonseed hull (CH), cow manure (CM), grass charcoal (GC), and pine needle (PN)] and applied to about 0.3% of medium-salt mudflat soil. The salinity and nutrient dynamics of the soil and the growth and physiological differences of sweet sorghum at the seedling, elongation, and heading stages were observed under different treatments to screen for efficient combinations and analyze the intrinsic causes and influencing mechanisms.Results: The soil salinity, nutrient dynamics, and forage grass biological yield during sweet sorghum cultivation in saline soils differed significantly (p < 0.05) depending on the type of organic acid–biomass composite applied. Citric acid–pine needle composite substantially reduced the soil salinity and increased the soil nutrient content at the seedling stage and improved the root vigor and photosynthesis of sweet sorghum by increasing its stress tolerance, allowing plant morphological restructuring for a high biological yield. The improvement effect of fulvic acid–pine needle or fulvic acid–cow manure composite was manifested at the elongation and heading stages.Discussion: Citric acid–pine needle composite promoted the growth of saline sweet sorghum seedlings, and the effect of fulvic acid–pine needle composite lasted until the middle and late stages.

With the rapid development of China’s industrialization and urbanization, the problem of heavy metal pollution in soil has become increasingly prominent, seriously threatening the safety of the ecosystem and human health. The development of hyperspectral remote sensing technology provides the possibility to achieve the rapid and non-destructive monitoring of soil heavy metal contents. This study aimed to fully explore the potential of ground and satellite image spectra in estimating soil heavy metal contents. We chose Xushe Town, Yixing City, Jiangsu Province as the research area, collected soil samples from farmland over two different periods, and measured the contents of the heavy metals Cd and As in the laboratory. At the same time, under field conditions, we also measured the spectra of wheat leaves and obtained HuanJing-1A HyperSpectral Imager (HJ-1A HSI) satellite image data. We first performed various spectral transformation pre-processing techniques on the leaf and image spectral data. Then, we used genetic algorithm (GA) optimized partial least squares regression (PLSR) to establish an estimation model of the soil heavy metal Cd and As contents, while evaluating the accuracy of the model. Finally, we obtained the best ground and satellite remote sensing estimation models and drew spatial distribution maps of the soil Cd and As contents in the study area. The results showed the following: (1) spectral pre-processing techniques can highlight some hidden information in the spectra, including mathematical transformations such as differentiation; (2) in ground and satellite spectral modeling, the GA-PLSR model has higher accuracy than PLSR, and using a GA for spectral band selection can improve the model’s accuracy and stability; (3) wheat leaf spectra provide a good ability to estimate soil Cd (relative percent difference (RPD) = 2.72) and excellent ability to estimate soil As (RPD = 3.25); HJ-1A HSI image spectra only provide the possibility of distinguishing high and low values of soil Cd and As (RPD = 1.87, RPD = 1.91). Therefore, it is possible to indirectly estimate soil heavy metal Cd and As contents using wheat leaf hyperspectral data, and HJ-1A HSI image spectra can also identify areas of key pollution.

Breeding improvement in alfalfa (Medicago sativa L.) is often constrained by the complexity of agronomic traits and trade-offs among yield-related characteristics. Conventional single-trait selection rarely captures the full range of phenotypic variation or the interactions among traits. To address this, we developed a principal component analysis (PCA)-based framework for multivariate selection in hybrid breeding. Six yield-related traits—plant height, branch number, fresh/hay yield ratio (FHR), leaf/stem ratio (LSR), multifoliolate leaf frequency, and dry weight per plant—were quantified in two parental lines and their F₁/F₂ generations. PCA identified three principal components (PC1–PC3) with eigenvalues >1, explaining 71.14% of the total phenotypic variance: PC1 (32.43% variance) was predominantly loaded with positive contributions from dry weight per single plant, height, and branches, biologically representing overall plant vigor and biomass accumulation; PC2 (21.77% variance) showed strong negative loadings for LSR, capturing architectural trade-offs between stem dominance and leaf production; PC3 (16.94% variance) had positive loadings on multifoliolate leaf rate and fresh/dry ratio, embodying quality and physiological resilience traits. Based on PCA scores, a composite selection index was constructed, and the top 31.1% of F1 hybrids were selected. Their F₂ progeny showed significant improvements in dry weight (+15.56%, p < 0.01), multifoliolate leaf frequency (+74.78%, p < 0.001), and reduced FHR (–8.2%, p < 0.05), accompanied by lower yield decline (−7.2% versus −14.1% in controls). These results show that PCA-based multivariate selection effectively balances trait trade-offs, enhances intergenerational stability, and improves selection efficiency. This framework offers a practical tool for alfalfa breeding.

Coastal mudflats are characterized by high salinity and alkalinity, along with low mineral nutrient availability, making it challenging to achieve high biomass or effective yields when directly cultivating food or fodder crops. Exogenous complex saline soil amendments can enhance forage production, but their effects on soil salinity reduction and nutrient activation remain unclear. This study used pot experiments and laboratory analyses to investigate these effects. A 0.3% saline–alkali soil was treated with a combination of organic acids (fulvic acid and citric acid), bio-based materials (cow dung and pine needles), and beneficial microbial mixtures (Priestia megaterium + Trichoderma harzianum, Bacillus subtilis + Aspergillus niger, and Bacillus pumilus + Paecilomyces lilacinus). The organic acid bio-modifier significantly alleviated salinity stress in sweet sorghum, reducing soil salinity, increasing soil nutrient levels, enhancing root vigor and photosynthesis, and improving plant morphology, resulting in higher biomass yields. Among the factors tested, bio-based materials had the most pronounced effect. Citric acid, pine needles, Priestia megaterium, and Trichoderma harzianum enhanced sweet sorghum growth during the seedling stage, whereas fulvic acid, pine needles, Bacillus pumilus, and Paecilomyces lilacinus were more beneficial during the elongation stage.

In the traditional farming systems, the excessive application of chemical fertilizers to boost crop yields has resulted in a range of issues, such as soil quality degradation, soil structure deterioration, and pollution of the farmland ecological environment. Green manure, as a high-quality biological fertilizer source with rich nutrient content, is of great significance for enhancing the soil quality and establishing a healthy farmland ecosystem. However, there are few studies on the effects of different green manures on the soil nutrient levels, enzyme activities, and soil bacterial community composition in the rice–wheat rotation areas in southern China. Thus, we planted Chinese milk vetch (MV; Astragalus sinicus L.), light leaf vetch (LV; Vicia villosa var.), common vetch (CV; Vicia sativa L.), crimson clover (CC; Trifolium incarnatum L.), Italian ryegrass (RG; Lolium multiflorum L.), and winter fields without any crops as a control in the Taihu Lake area of Jiangsu. The soil samples collected after tilling and returning the green manure to the field during the bloom period were used to analyze the effects of the different green manures on the soil nutrient content, enzyme activity, and the structural composition of the bacterial community. This analysis was conducted using chemical methods and high-throughput sequencing technology. The results showed that the green manure returned to the field increased the soil pH, soil organic matter (SOM), alkali-hydrolyzed nitrogen (AN), available phosphorus (AP), available potassium (AK), sucrose (SC), urease (UE), and neutral phosphatase (NEP) contents compared to the control. They increased by 1.55% to 10.06%, 0.26% to 9.31%, 20.95% to 28.42%, 20.66% to 57.79%, 12.38% to 37.94%, 3.11% to 58.19%, 6.49% to 32.99%, and 50.0% to 80.36%, respectively. In addition, the green manure field increased the relative abundance of the genera Proteobacteria and Haliangium while decreasing the relative abundance of Gemmatimonadetes, Chloroflexi, SBR1031, and Anaeromyxobacter in the soil bacteria. Both the number of ASVs (amplicon sequence variants) and α-diversity of the soil bacterial communities were higher compared to the control, and the β-diversity varied significantly among the treatments. Alkali-hydrolyzed nitrogen and neutral phosphatase had the greatest influence on the soil bacterial community diversity, with alkali-hydrolyzed nitrogen being the primary soil factor affecting the soil bacterial community composition. Meanwhile, the results of the principal component analysis showed that the MV treatment had the most significant impact on soil improvement. Our study provides significant insights into the sustainable management of the soil quality in rice–wheat rotations. It identifies MV as the best choice among the green manure crops for improving the soil quality, offering innovative solutions for reducing chemical fertilizer dependence and promoting ecological sustainability.