To evaluate the impact of climate change on maize production, accurately measuring the radiation use efficiency (RUE) of maize is critical.  This study focused on three maize cultivars in Jilin Province, China: Zhengdan 958 (ZD958), Xianyu 335 (XY335), and Liangyu 99 (LY99).  Under the optimal growing conditions for high density planting (9 plants m^–2), the maize RUE was determined during the vegetative and reproductive phases, and the entire growth period.  The results showed that the canopy light interception for maize peaked during anthesis.  After anthesis, maize plant biomass continued to accumulate.  The maize RUE was calculated based on the absorbed photosynthetically active radiation (APAR).  During the entire growth period, maize RUE averaged 5.71 g MJ^–1 APAR among the three cultivars, with a high-to-low order of ZD958 (5.85 g MJ^–1 APAR)>XY335 (5.64 g MJ^–1 APAR)>LY99 (5.07 g MJ^–1 APAR).  Within the vegetative and reproductive growth periods, maize RUE averaged 6.85 and 5.64 g MJ^–1 APAR, respectively.  When utilizing maize models that depend on RUE to predict aboveground biomass accumulation, such as APSIM, the current RUE value of 3.6 g MJ^–1 APAR is considerably lower than the measured value obtained under high-density optimal growing conditions.  Consequently, to derive the optimal potential yield for maize in such planting conditions, we recommend adjusting the RUE to a range of 5.07–5.85 g MJ^–1 APAR.

The ongoing commercialization of genetically modified (GM) crops continues to enhance global grain yields, improve crop quality, and reduce pesticide usage. These technological advancements have effectively propelled agricultural production systems toward sustainable transformation. Specifically, GM crops address core challenges such as pest infestations, weed proliferation, and arable land constraints, emerging as a pivotal new productive force in agriculture. This study systematically examines the global spatial distribution patterns of GM crops in 2024 and provides an indepth analysis of the driving forces and evolving regional trends, offering critical informational support and strategic guidance for innovation in agricultural science and technology. In 2024, the global GM crop cultivation area reached 209.8 million hectares, a 1.7% year-on-year increase. GM Glycine max (soybean) and Zea mays (maize) dominated the landscape, accounting for 50.0 and 32.5% of the total area, respectively. Among them, maize with stacked traits of insect resistance and herbicide tolerance accounts for 92.5% of GM maize. The share of cultivation in developing countries expanded substantially, with Brazil and Vietnam emerging as regional growth drivers. Policy support and the diffusion of advanced technologies were identified as core driving forces. Concurrently, applications of gene-editing technology accelerated, and several countries approved novel tr aits such as drought tolerance and disease resistance, marking substantial progress in the commercialization of next-generation GM crops. This research provides multidimensional insights and strategic guidance to support global agricultural biotechnology development, promoting the transition of biotechnology breeding into the ‘4.0 era’.

The Loxostege sticticalis (Lepidoptera: Pyralidae) is a major migratory pest of agriculture and animal husbandry in Asia and Europe.  Utilizing plant volatile organic compounds (pVOCs) as attractants for monitoring and controlling pests is considered an environmentally friendly and effective method.  However, limited knowledge exists regarding applying pVOCs to manage L. sticticalis.  Here, volatile compounds released by Chenopodium album, Setaria viridis, and Medicago sativa, the three preferred oviposition plants for L. sticticalis females, were collected using dynamic headspace sampling techniques.  A total of 55 distinct compounds were identified through gas chromatography-mass spectrometry (GC-MS), and 16 compounds in the concentration range from 0.001 to 100 µg µL^–1 elicited consistently enhanced electrophysiological responses in both male and female L. sticticalis.  Subsequently, the attraction potential of four bioactive compounds - linalool, cis-anethole, trans-2-hexenal, and 1-octen-3-ol - were further confirmed by indoor behavioral bioassays.  The blends of linalool, cis-anethole, trans-2-hexenal, and 1-octen-3-ol mixed at ratios of 5:1:5:10 (formulation No. 25) and 5:1:1:10 (formulation No. 21) were highly attractive to L. sticticalis adults.  Field-trapping assays indicated that lure No. 2 baited with formulation 21 demonstrated superior efficacy in field trapping.  These findings suggest that pVOC-based attractants can be effectively employed for monitoring and mass trapping L. sticticalis adults, providing insights into the development of botanical attractants.

The wheat above-ground biomass (AGB) is an important index that shows the life activity of vegetation, which is of great significance for wheat growth monitoring and yield prediction.  Traditional biomass estimation methods specifically include sample surveys and harvesting statistics.  Although these methods have high estimation accuracy, they are time-consuming, destructive, and difficult to implement to monitor the biomass at a large scale.  The main objective of this study is to optimize the traditional remote sensing methods to estimate the wheat AGB based on improved convolutional features (CFs).  Low-cost unmanned aerial vehicles (UAV) were used as the main data acquisition equipment.  This study acquired RGB and multi-spectral (MS) image data of the wheat population canopy for two wheat varieties and five key growth stages.  Then, field measurements were conducted to obtain the actual wheat biomass data for validation.  Based on the remote sensing indices (RSIs), structural features (SFs), and convolutional features (CFs), this study proposed a new feature named AUR-50 (Multi-source combination based on convolutional feature optimization) to estimate the wheat AGB.  The results show that AUR-50 could more accurately estimate the wheat AGB than RSIs and SFs, and the average R² exceeded 0.77.  AUR-50_MS had the highest estimation accuracy (R² of 0.88) in the overwintering period.  In addition, AUR-50 reduced the effect of the vegetation index saturation on the biomass estimation accuracy by adding CFs, where the highest R² was 0.69 at the flowering stage.  The results of this study provide an effective method to evaluate the AGB in wheat with high throughput and a research reference for the phenotypic parameters of other crops.

Salt stress is a major constraint to crop productivity and quality.  The limited availability of salt-tolerant genes poses significant challenges to breeding programs aimed at enhancing salt tolerance.  Sorghum displays a remarkable ability to withstand saline conditions; therefore, elucidating the genetic underpinnings of this trait is crucial.  This study entailed a comprehensive resequencing of 186 sorghum accessions to perform a genome-wide association study (GWAS) focusing on relative root length (RL) and root fresh weight (RFW) under salt stress conditions.  We identified eight candidate genes within a co-localized region, among which SbTEF1 - a gene encoding a transcription elongation factor protein - was deemed a potential candidate due to its annotation and expression pattern alterations under salt stress.  Haplotype analysis, gene cloning, linkage disequilibrium (LD) analysis, and allele effect analysis revealed that PAV284, located in the promoter region of SbTEF1, modulated gene expression under salt stress, which, in turn, influenced sorghum seedlings’ salt tolerance.  PAV284 holds promise as a genetic marker for selecting salt-tolerant germplasm via marker-assisted breeding, enhancing the development of salt-tolerant sorghum cultivars.

Nitrogen (N) is a key factor in the positive response of cereal crops that follow leguminous crops when compared to gramineous crops in rotations, with the nonrecyclable rhizosphere-derived N playing an important role.  However, quantitative assessments of differences in the N derived from rhizodeposition (NdfR) between legumes and gramineous crops are lacking, and comparative studies on their contributions to the subsequent cereals are scarce.  In this study, we conducted a meta-analysis of NdfR from leguminous and gramineous crops based on 34 observations published worldwide.  In addition, pot experiments were conducted to study the differences in the NdfR amounts, distributions and subsequent effects of two major wheat (Triticum aestivum L.)-preceding crops, corn (Zea mays L.) and soybean (Glycine max L.), by the cotton wick-labelling method in the main wheat-producing areas of China.  The meta-analysis results showed that the NdfR of legumes was significantly greater by 138.93% compared to gramineous crops.  In our pot experiment, the NdfR values from corn and soybean were 502.32 and 944.12 mg/pot, respectively, and soybean was also significantly higher than corn, accounting for 76.91 and 84.15% of the total belowground nitrogen of the plants, respectively.  Moreover, in different soil particle sizes, NdfR was mainly enriched in the large macro-aggregates (>2 mm), followed by the small macro-aggregates (2–0.25 mm).  The amount and proportion of NdfR in the macro-aggregates (>0.25 mm) of soybean were 3.48 and 1.66 times higher than those of corn, respectively, indicating the high utilization potential of soybean NdfR.  Regarding the N accumulation of subsequent wheat, the contribution of soybean NdfR to wheat was approximately 3 times that of corn, accounting for 8.37 and 4.04% of the total N uptake of wheat, respectively.  In conclusion, soybean NdfR is superior to corn in terms of the quantity and distribution ratio of soil macro-aggregates.  In future field production, legume NdfR should be included in the nitrogen pool that can be absorbed and utilized by subsequent crops, and the role and potential of leguminous plants as nitrogen source providers in crop rotation systems should be fully utilized.

The commercialization of genetically modified (GM) crops has increased food production, improved crop quality, reduced pesticide use, promoted changes in agricultural production methods, and become an important new production strategy for dealing with insect pests and weeds while reducing the cultivated land area.  This article provides a comprehensive examination of the global distribution of GM crops in 2023.  It discusses the internal factors that are driving their adoption, such as the increasing number of GM crops and the growing variety of commodities.  This article also provides information support and application guidance for the new developments in global agricultural science and technology.

The tea plant [Camellia sinensis (L.) O. Kuntze] is an industrial crop in China.  The Anhui Province has a long history of tea cultivation and has a large resource of tea germplasm with abundant genetic diversity.  To reduce the cost of conservation and utilization of germplasm resources, a core collection needs to be constructed.  To this end, 573 representative tea accessions were collected from six major tea-producing areas in Anhui Province.  Based on 60 pairs of simple sequence repeat (SSR) markers, phylogenetic relationships, population structure and principal coordinate analysis (PCoA) were conducted.  Phylogenetic analysis indicated that the 573 tea individuals clustered into five groups were related to geographical location and were consistent with the results of the PCoA.  Finally, we constructed a core collection consisting of 115 tea individuals, accounting for 20% of the whole collection.  The 115 core collections were considered to have a 90.9% retention rate for the observed number of alleles (Na), and Shannon’s information index (I) of the core and whole collections were highly consistent.  Of these, 39 individuals were preserved in the Huangshan area, accounting for 33.9% of the core collection, while only 10 individuals were reserved in the Jinzhai County, accounting for 8.9% of the core set.  PCoA of the accessions in the tea plant core collection exhibited a pattern nearly identical to that of the accessions in the entire collection, further supporting the broad representation of the core germplasm in Anhui Province.  The results demonstrated that the core collection could represent the genetic diversity of the original collection.  Our present work is valuable for the high-efficiency conservation and utilization of tea plant germplasms in Anhui Province

Soy sauce is a traditional Chinese seasoning with a history spanning 3,000 years Increasing the utilization efficiency of soy sauce-separated oil (SSO), a by-product of soy sauce processing, is essential for promoting its application potential. Therefore, this study is the first to investigate the use of SSO instead of soybean oil (SO) in the diets of finishing pigs (SSO-SO) to evaluate its impact on the safety and nutritional value of roasted pork meat via systemic tests (from breeding to processing and digestion). The results indicated that regarding nutrition, the SSO-SO reduced the ∑n-6/∑n-3 in the roasted meat and digestion product by 15 and 14%, respectively, and increased the essential amino acids (∑EAAs) content in the digestion product by 6%. In terms of safety, the SSO-SO promoted protein oxidation and non-polar heterocyclic amine (HAs) formation to some extent, while reducing the thiobarbituric acid reactive substance (TBARs) value by 20% and decreasing cholesterol oxide product (COPs) content by 20-70% in the roasted meat. This study suggests that SSO shows promise as an alternative oil for n-3 polyunsaturated fatty acid (PUFA)-rich pork processing without compromising safety and nutrition.

Strip configurations play a crucial role in mediating crop productivity and resource utilization in intercropping systems. However, there remains a substantial knowledge gap concerning the mechanization-adaptive strip widths for cotton-soybean intercropping systems. Specifically, understanding how these strip widths can enhance synergies in crop productivity and land use efficiency is imperative. This study evaluated the impact of row ratio (strip) configurations on crop growth, physiology, productivity and land use efficiency in intercropped and monoculture systems. Treatments included two intercropping treatments (two rows of cotton plants alternating with three rows of soybean plants (2C3S), and three rows of cotton alternating with five rows of soybean (3C5S)), and two monoculture controls (monoculture cotton (MC), and monoculture soybean (MS)). Compared with monoculture cotton, the 3C5S system significantly increased both years averaged based chlorophyll content (SPAD value) by 6.64% at the peak boll-setting stage with increased leaf area index (LAI) and canopy photosynthetically active radiation interception ratio (In) during the early flowering stage. Furthermore, at the boll-opening stage, this system further enhanced boll and total plant nitrogen uptake. Intercropping significantly increased cotton boll density by enhancing dry matter translocation to reproductive organs with high lint yield. The 3C5S configuration outperformed 2C3S, increased the land equivalent ratio by 9.2% and net revenue by 15.87% over both years. The PCA results showed stronger relationships between cotton harvest index and other physiological parameters in 3C5S. The Mantel test indicates that yield of cotton-soybean intercropping was closely associated with cotton leaf area index and soybean aboveground biomass. Structural equation modeling identified nitrogen uptake as the key driver of yield in 3C5S. Overall, 3C5S improved crop productivity and land use efficiency compared to both 2C3S and monoculture systems, representing the optimal cotton-soybean intercropping strategy. The 2C3S and 3C5S intercropping systems were designed with a standard 2:1 row spacing (76 cm for cotton and 38 cm for soybean), compatible with mainstream agricultural machinery in China. A 55 cm operational clearance was maintained between crop strips to support fully mechanized sowing and harvesting, thereby reducing labor cost with high production revenue.

Drought imposes a severe impediment to plant growth and development, cause yield and quality to decline. Xyloglucan endotransglucosylase/hydrolase (XTH) is a kind of cell wall-modifying protein, and contributes to cell wall assembly. However, whether XTHs are involved in the drought stress of tomato (Solanum lycopersicum L.), and its mechanism and upstream regulatory factors remain unclear. Here, SlXTH23 is identified to negatively respond to drought stress in tomato. SlXTH23 knockout tomato plants increase the content of cellulose and hemicellulose, as well as the thickness of secondary cell wall in roots, and enhance drought tolerance. In contrast, SlXTH23 overexpressed transgenic tomato plants are sensitive to drought stress. Two basic helix-loop-helix transcription factors, SlbHLH086 and SlbHLH096, are identified to directly bind and regulate SlXTH23. Silencing SlbHLH086 alone or in combination with SlbHLH096 enhances drought tolerance by stimulating the expression of SlXTH23 and promoting the thickness of secondary cell wall in tomato roots. Silencing SlbHLH096 renders plants sensitive to drought stress. In addition, SlbHLH086 interacts with SlbHLH096, and SlbHLH086 prevents the inhibitory effect of SlbHLH096 on the expression of SlXTH23. In summary, this study revealed the molecular mechanism that SlbHLH086/SlbHLH096-SlXTH23 module regulates the drought tolerance of tomato by altering cell wall components and thickness, providing a novel mechanistic insight for breeding drought tolerant tomato cultivars.

The root system is an important organ for cotton to absorb water and nutrients. Different cotton varieties respond differently to drought stress. Therefore, this study firstly conducted an indoor experiment using 384 cotton varieties as materials, to screen long and short root varieties. Subsequently, a field experiment was performed to analyze the differences in drought responses between these two types of varieties. And then through genome-wide association analysis (GWAS), screened for candidate genes. The research results showed that, based on the total root length (TRL) as the main indicator in the indoor experiment, five long-root type varieties PD2164, B557, CCRI No.30, Super Jijiao Dezi Mian and Dunn HS120, and five short-root type varieties Bole 34, Henan No.79, CCRI No.50, V83-013 and Ari3696 were selected. The results of the drought stress experiment showed that under drought conditions, the average TRL increase of long-root type varieties (5.49%) was smaller than that of short-root type varieties (15.45%, P<0.05); the yields of long-root type varieties and short-root type varieties decreased by 19-35% and 10-37% respectively. It is notable that under drought conditions, the TRL increase of short-root type variety HN79 was the highest, at 69%, and the yield decrease was the lowest, at 10%, demonstrating higher drought resistance. We also identified SNPs related to the primary root traits in the At02 region 101.2-101.6 Mb through GWAS, and determined that GhAIL6 is a root development-related gene. This study identified ten cotton varieties exhibiting extreme long-root and short-root phenotypes. Further analysis showed that some short-root varieties exhibited greater increases in total root length and smaller reductions in yield under drought stress, indicating stronger drought resistance. Additionally, the study elucidated the pivotal role of GhAIL6 in promoting root growth during the cotton seedling stage. 

Being one of the most crucial food crops globally, accurate yield prediction of wheat is essential for ensuring food security, enabling precision agricultural management, and addressing climate change challenges. Previous studies mainly focused on single-period feature extraction or time-series remote sensing features for yield prediction, but lacked in-depth explanation of the yield formation mechanism. Therefore, this study aimed to develop a yield prediction model based on growth curve parameters of aboveground biomass (AGB). A logistic S-shaped growth curve was fitted using measured AGB, and key growth parameters (K, V_max, S_GIP, S_RIP, S_SIP, V_GIP, V_RIP, V_SIP, etc.) were extracted and integrated into machine learning models for yield prediction. Results showed that this approach achieved high accuracy (R²=0.97, RMSE=355.38 kg ha^-1, MAE=255.74 kg ha^-1), and the extracted parameters had clear physiological significance. To enable rapid AGB acquisition, an AGB estimation model was further developed using multi-source remote sensing features, including vegetation indices (VIs), texture indices (TIs), canopy structure (CS), and canopy temperature (CT). As the growing season progressed, these multi-source features exhibited strong complementarity, reaching the highest accuracy at 30 days after anthesis (R²=0.83) and effectively alleviating the saturation problem of VIs. Moreover, growth parameters derived from the fitted curves of the estimated AGB also achieved accurate yield prediction (R²=0.87, RMSE=746.07 kg ha^-1, MAE=570.16 kg ha^-1). The model further demonstrated stable performance across different regions and years (R²=0.85, RMSE=784.52 kg ha^-1, MAE=569.56 kg ha^-1). In conclusion, this study introduced novel AGB growth curve parameters for wheat yield estimation, which improved prediction accuracy and enhanced physiological interpretability, providing insights for efficient field-scale management and yield prediction across regions.

Enhancing photosynthetic efficiency under fluctuating light is critical for improving crop productivity in intercropping systems; however, the role of phosphorus (P) nutrition in regulating photosynthetic induction remains poorly understood. Field and pot experiments with three P supply levels were conducted in a maize–soybean intercropping system to quantify gas exchange in soybean [Glycine max (L.) Merr.] during photosynthetic induction under controlled fluctuating light protocols and to identify P-related drivers of induction kinetics. Adequate P supply increased soybean yield by 26.7%–55.4% relative to P deficiency treatments and concurrently reduced carbon loss during the low-to-high light transitions by 26.5%–37.3%. Photosynthetic limitation analysis revealed that biochemical limitation dominated the induction response under P deficiency, accounting for 72.3% of the total limitation, with stomatal limitation playing a comparatively minor role. Foliar and chloroplast inorganic phosphate (P_i) contents both increased with P supply; notably, chloroplast P_i exhibited a strong, nonlinear relationship with induction performance. A critical threshold was identified at 0.18 mg g⁻¹ FW chloroplast P_i; below which induction time increased sharply. Collectively, these findings identify chloroplast P_i availability as a proximal physiological determinant of photosynthetic induction under fluctuating light and point to improving chloroplast P_i status as a promising strategy for reducing carbon loss and enhancing soybean productivity in intercropping systems exposed to highly dynamic light environments.

Cold stress negatively affects melon growth and development. Previous studies have shown that exogenous trehalose (Tre) alleviates the cold damage in melon seedlings; however, the specific mechanism is not fully clarified. Here, Tre treatment increased the indole-3-acetic (IAA) content and upregulated an early response factor of IAA (small auxin up RNA gene, CmSAUR1) gene expression. Inhibition of IAA signal transport or silencing of CmSAUR1 decreased the activities of superoxide dismutase, ascorbate peroxidase and glutathione reductase. This led to increased susceptibility to cold damage in melon seedlings and diminished the beneficial impact of Tre. Through the analysis of transcriptome and promoter of CmSAUR1, the upstream transcription factor CmTCP9/12/25 of CmSAUR1 was excavated. Protein–DNA interaction experiments further verified that CmTCP9/12/25 could transcriptionally regulate CmSAUR1 expression. Yeast two-hybrid, luciferase complementation imaging and bimolecular fluorescence complementation experiments further confirmed that CmTCP9/12 interacted with CmTCP25 to form protein complexes. Silencing CmTCP9/12/25 genes significantly aggravated the cold damage of melon seedlings and weakened the efficacy of Tre. All of them downregulated the expression of CmSAUR1. In conclusion, application of Tre induced the upregulation of CmTCP9/12/25, and CmTCP9/12/25 could bind to the CmSAUR1 promoter and activate its expression. These eventually increased the cold tolerance of melon seedlings.

Excessive application of conventional chemical nitrogen (N) fertilizers tends to cause a series of problems such as soil acidification and compaction, and restrict further yield gains. New-type fertilizers such as carbon-based fertilizer (CBF) and slow-release fertilizer (SRF) have been shown to improve soil fertility and increase wheat yield. However, systematic comparisons of their yield-enhancing potential and the mechanisms by which they improve soil properties remain limited. In this study, two CBFs (CBF1, N-P₂O₅-K₂O=24%-12%-8%; CBF2, N-P₂O₅-K₂O=24%-10%-10%), polymer-coated urea (PCU, 45% N), sulfur-coated urea (SCU, 37% N), and conventional urea (urea, 46% N) were used as materials to elucidate mechanistic differences among fertilizer types in the regulation of soil nitrate-N dynamics, soil physicochemical properties, and soil microbial community structure in wheat fields. Our objective was to identify fertilization strategies that simultaneously enhance wheat yield and improve soil quality. The results showed that CBF1 and CBF2 reduced the early peak concentrations of soil nitrate-N following basal and topdressing fertilization relative to Urea, while providing a more balanced nitrate-N supply across early and late wheat growth stages, which maintained higher soil nitrate-N levels than Urea from overwintering to jointing and from anthesis to maturity. Compared with CBF1, CBF2 showed higher soil nitrate-N from anthesis to maturity, which was similar to PCU. With an appropriate N supply, CBF2 facilitated coordinated yield formation, significantly increasing grains per spike and total grain number. Adequate nutrient availability post-anthesis in CBF2 also facilitated grain filling, resulting in 4.08% and 6.77% increases in grain yield compared with SCU and Urea, respectively. Compared with urea, CBFs application effectively mitigated soil pH decline, enhanced soil electrical conductivity, and modulated soil enzyme activities, as well as soil bacterial diversity and community composition. On the one hand, CBFs decreased the relative abundance of nitrifying bacteria (e.g., Nitrospirota_A), thereby suppressing soil nitrification, regulating soil nitrate concentrations, and consequently reducing the relative abundance of denitrifying bacteria such as Proteobacteria, Actinobacteriota, and Firmicutes_D, which decreased the potential risk of N₂O emissions. On the other hand, CBFs application altered the relative abundance of microbial groups involved in soil carbon cycling such as Bacteroidota and Gemmatimonadota, thereby enhancing soil nutrient availability and increasing the contents of soil organic matter, available P, and available K. In general, both CBF2 and PCU optimized soil nutrient supply and increased wheat yield, whereas CBF2 was more favorable for improving soil physicochemical properties and enhancing soil fertility, which is expected to promote the synergistic improvement of wheat production potential and soil quality.

Wheat is a vital global staple crop, and the condition of its seedlings before overwintering significantly influences its yield potential.  Accurate and timely assessment of pre-winter seedling conditions is essential for effective wheat field management.  Currently, agricultural departments rely on traditional methods to classify seedlings based on indicators like leaf age, tiller count, and root number, but these methods are labor-intensive and lack high-throughput capabilities.  This study proposes a novel approach to improve seedling condition classification by integrating soil pixel removal and canopy cover with vegetation indices.  Additionally, a local optimized features (LOFs) method is introduced to enhance classification by quantifying local spectral differences in the ratio vegetation index (RVI), overcoming the limitations of traditional mean vegetation indices.  A series of sowing date treatments from 2022 to 2024 established wheat populations with varied seedling conditions.  High-resolution multispectral UAV imagery was used to derive remote sensing parameters, such as vegetation indices (VIs), pure vegetation indices (PVIs), and canopy cover (cc).  Through evaluation of various classification models, we identified PVIs combined with cc as the optimal feature set.  Among these, RVI was found to be the most significant index, as determined by SHapley Additive exPlanations (SHAP).  Building upon the optimal feature set, a Quadratic Discriminant Analysis model integrating PVIs, cc, and LOFs was ultimately developed to achieve accurate classification of seedling conditions, improving the accuracy from 0.86 (with PVIs and cc) to 0.99.  This research provides an efficient high-throughput method for pre-winter seedling classification and offers insights into estimating other agronomic parameters.

African swine fever (ASF), caused by African swine fever virus (ASFV), is a highly contagious disease that has spread globally, posing a significant threat to swine production and international trade. As rapid diagnosis is crucial for controlling ASF, its major capsid protein, p72, has become a key target for diagnostic and vaccine development. In this study, we generated five monoclonal antibodies (mAbs) against the p72 protein by immunizing mice with inactivated virus. Using phage display technology, we identified the epitope for one mAb as a novel linear B-cell epitope within amino acids 130-152 of the p72 protein. Structural and homology analyses revealed that this epitope is highly conserved across diverse ASFV genotypes and is exposed on the surface of the p72 trimer. Importantly, the epitope showed strong reactivity with sera from ASFV-positive swine. These findings offer a foundation for creating improved serological diagnostics and designing epitope-based vaccines against ASFV.

Nitrogen (N) leaching is a major pathway of N loss in subtropical crop production systems, contributing to groundwater pollution and thus posing serious threats to human health. However, the characteristics of annual N leaching in subtropical open-field vegetable systems and the effectiveness of integrative N fertilization management practices in reducing N leaching remain poorly understood. In this study, two plot-based field experiments were conducted with open-field Chinese cabbage-pepper rotation system in subtropical southwest China to quantify annual N leaching and evaluate the effectiveness of integrated N fertilization management practices. Experiment 1 compared five N fertilizer application rates using conventional urea, while Experiment 2 compared different N sources including conventional urea, organic fertilizer, nitrification inhibitor-based fertilizer, and controlled-release urea which were all applied at the optimized N rate. Results showed that the annual N leaching under farmers’ N practice (FNP) was 251 kg N ha⁻¹, with contributions of 55, 31, and 14% from the pepper season, Chinese cabbage season, and fallow period, respectively. Total N leaching increased exponentially with N rate. The seasonal N leaching factor was 32% for pepper and 17% for Chinese cabbage in the FNP treatment, respectively. Compared to FNP, optimizing N rate based on crop requirement and soil supply significantly reduced N leaching by 68% and gray water footprint by 66−75%, while improving N use efficiency (NUE) from 35% to 54%. In Experiment 2, mixing organic and inorganic fertilizers, applying nitrification inhibitor, and using controlled-release urea further reduced annual N leaching by 27, 54, and 25%, respectively, compared to conventional urea. These practices also improved crop yields by 2−11% and NUE by 10−13%, and lowered gray water footprint by 28−58%. In summary, integrative N stewardship practices, particularly use of nitrification inhibitors under optimized N rates, effectively reduced N leaching while achieving high NUE and vegetable yields, providing a promising strategy for sustainable subtropical vegetable production.