【Objective】To meet the increasing food demand driven by population growth and environmental changes, it is necessary to continuously cultivate varieties with high yield, good quality, and multiple resistances. Efficiently create new germplasm with rich genetic backgrounds and genetic diversity to provide a reference for breeding new varieties that balance multiple excellent traits. 【Method】The Sanming dominant genic male sterile material was used to simplify the hybridization procedure. It was hybridized with multiple parents with distant geographical relationships to aggregate multiple excellent traits. Aiming at problems such as a narrow genetic basis and the difficulty of applying molecular markers, S221 was successively and continuously hybridized with materials such as 09598, Ezhong 5, Yuanfengzhan, Yunxiangruan, etc. Fertile plants were selected from the offspring of the last hybridization. The new variety was cultivated by combining the pedigree method with heat-tolerance analysis, rice quality analysis, and resistance screening. The DNA of 60 selected single plants from the F₁₀ series of lines and 4 parents was extracted. Primers for the target sites were designed. The target DNA fragments were captured by PCR and sequenced. Finally, the genotyping analysis of the target sites was carried out. The SLYm1R high-density rice whole-genome SNP chip was used for the analysis of functional genes. 【Result】Genotype analysis is carried out to analyze the degree of genetic relationship or similarity based on the magnitude of the base substitution rate. The parental materials Ezhong 5 and Yunxiangruan have a relatively distant relationship with other parental materials, while 09598 has a relatively close relationship with Yuanfengzhan. The base substitution rates among the three newly obtained lines are as follows: 0.0099545 (170531-170532), 0.0338213 (170531-170533), and0.0371913 (170532-170533). Within each line, the base substitution rate is 0, indicating that there are differences among the three lines, but there is no genetic difference within each line. Through successive generations and expansion propagation, new germplasms were formed, which were named ZY531, ZY532, and ZY533 respectively. The results of functional gene analysis show that the functional genes of the ZY532 series of germplasms are respectively derived from 4 parents, aggregating excellent genes from multiple parents. For example, the Os-MOT1;1 gene is derived from Yunxiangruan, which can reduce abiotic stresses such as molybdenum accumulation; the Bph3 gene is derived from 09598 and Ezhong 5, which can enhance the resistance to brown planthoppers; the OsGSK2 gene is derived from 09598, Yuanfengzhan, and Yunxiangruan, which can increase the length of the mesocotyl and is suitable for direct seeding; the Badh2 gene is derived from Yunxiangruan, making the rice fragrant; multiple blast resistance genes are derived from different parents and can also be aggregated into the innovative resources, enabling it to obtain good blast resistance. ZY532 has excellent rice quality, good blast resistance, and strong heat resistance. ZY532 also has good heat resistance, and the heat resistance of the hybrid combination prepared reaches level 3. 【Conclusion】When using dominant genic male sterility to cultivate new varieties, due to the complex genetic background, the breeding cycle is often long. Combining high-throughput SNP marker detection can quickly screen out stable lines and more types, which not only broadens the genetic basis but also improves the breeding efficiency. It is an efficient breeding method.

【Objective】Spike-related traits constitute a key factor influencing wheat yield. This study conducted a genome-wide association study (GWAS) on wheat spike-related traits to identify significant loci controlling these traits, thereby providing theoretical references for research on genetic improvement of wheat spike-related traits. 【Method】Using a panel of 261 winter wheat varieties (lines), we measured spike-related phenotypic traits and performed genome-wide association studies (GWAS) with the wheat 90K SNP array, employing the Fixed and Random Model Circulating Probability Unification (Farm CPU) model. Stable and significant loci identified through this analysis were further subjected to haplotype analysis. 【Result】Under three environmental conditions, all 11 panicle-related traits exhibited extensive phenotypic variation, with coefficients of variation (CV) ranging from 3.63 to 64.29. The heritability estimates for these traits varied between 0.42 and 0.84. Highly significant differences (P<0.001) were observed among genotype, environment, and genotype × environment interactions. Genome-wide association study (GWAS) identified 171 loci significantly associated with the 11 traits (P<0.001), including 20 pleiotropic loci detected in two or more environments. These loci were associated with eight panicle traits: panicle length (3 loci), peduncle length (7 loci), sterile spikelet number (1 locus), fertile spikelet number (2 loci), total spikelet number (2 loci), grains per panicle (1 locus), grain weight per panicle (2 loci), and thousand-grain weight (2 loci). The phenotypic contribution rates of these loci ranged from 0.95% to 18.54%. A pleiotropic locus (Ra_c10072_677) significantly associated with both grain weight per panicle and grains per panicle was identified on chromosome 7B, demonstrating phenotypic contribution rates ranging from 2.62% to 6.16%. The marker wsnp_Ex_rep_c69639_68590556, which showed consistent association with peduncle length across two or more environmental conditions (explaining 5.94% of the genetic variation), was selected for haplotype analysis. Three haplotypes (Hap1, Hap2, and Hap3) were characterized, with distribution frequencies of 77.40%, 13.70%, and 8.80%, respectively. Phenotypic analysis revealed that 261 winter wheat cultivars (lines) carrying haplotype Hap3 (30.58 cm) exhibited significantly greater peduncle length (P<0.001) compared to those with Hap1 (28.67 cm) and Hap2 (27.49 cm). The haplotype distribution frequencies showed significant geographic divergence: Hap1 predominated in the Northern Winter Wheat Region, Hap2 was more prevalent in the Huang-Huai Winter Wheat Region, while Hap3 displayed no substantial frequency (>5%) across all winter wheat regions. For stably detected loci across three environments, candidate gene mining identified four genes associated with panicle development. These genes, functionally annotated as encoding MYB transcription factors and F-box domain-containing proteins, represent key candidates influencing panicle architecture. 【Conclusion】The spike traits of wheat exhibited significant variation across different genotypes. A total of twenty stably associated loci were identified across two or more environments. Three distinct haplotypes significantly associated with the peduncle length were detected on chromosome 7B, and four candidate genes potentially related to spike traits were screened out.

【Objective】In precision agriculture, the detection of crop seedlings can be interfered with by factors such as soil weeds, occlusion between seedling leaves, and multi-scale datasets. Based on the object detection algorithm, this paper improved the YOLOv8s algorithm and designed the wheat leaf tip detection model YOLO-Wheat to solve problems, such as leaf occlusion of wheat seedlings in the field, interference from soil weeds, and multi-view data with multiple scales, thereby enhancing the accuracy of wheat seedling leaf detection and providing a theoretical basis for wheat seedling detection at the seedling stage in precision agriculture. 【Method】Close-up and distant images of wheat seedlings were collected respectively through mobile phone cameras and on-board RGB cameras during the emergence period to construct a crop image dataset. In the network model, a pyramid structure of multi-scale feature fusion (high-level screening-feature fusion pyramid, HS-FPN) was adopted. This structure used high-level features as weights, filters low-level feature information through the channel attention module, and combined the screened features with the high-level features. Enhancing the feature expression ability of the model could effectively solve the problem of multi-scale data. Integrate the efficient local attention (ELA) local attention mechanism in the network model was used to enable the model to focus on the leaf tip information of wheat and to suppress the interference of soil background factors of weeds. Meanwhile, the loss function of YOLOv8s (complete IoULoss, CIoULoss) was optimized, and the inner-Iou Loss auxiliary bounding box loss function was introduced to enhance the network's attention to small targets and to improve the positioning accuracy of wheat leaf tips. In terms of training strategies, transfer learning was employed. The model was pre-trained using close-up images of wheat leaf tips, and then the parameters of the model were updated and optimized using distant images. 【Result】The YOLO-Wheat model was compared with five object detection models, namely Faster-RCNN, YOLOv5s, YOLOv7, YOLOv8s, and YOLOv9s. The YOLO-Wheat model was the best in wheat leaf tip detection, with a recognition accuracy rate of 92.7% and a recall rate of 85.1%, respectively. The mean Average Precision (mAP) values were 82.9%. Compared with the Faster-RCNN, YOLOv5s, YOLOv7, YOLOv8s and YOLOv9s models, the recognition accuracy mAP values of YOLO-Wheat have increased by 17.1%, 13.6%, 11.0%, 8.7% and 3.8% respectively; the recall rates increased by 13.1%, 6.7%, 4.5%, 1.8% and 1.3%, respectively. Compared with the Faw-RCNN, YOLOv5s, YOLOv7, YOLOv8s and YOLOv9s models, the mAP values of YOLO-Wheat have increased by 16.2%, 9.8%, 5.0%, 5.9% and 0.7%, respectively. 【Conclusion】This method could effectively solve the problem of multi-scale data, achieve precise detection of small targets at the leaf tips of wheat seedlings in complex field environments using unmanned aerial vehicle (UAV) images, and provide technical support and theoretical reference for intelligent leaf counting of wheat seedlings in complex fields.

【Objective】The effects of double-pressing precision uniform sowing and nitrogen (N) fertilizer interaction on wheat population construction, dry matter accumulation characteristics, and yield formation were explored, in order to provide the theoretical basis and technical support for high-yield cultivation of wheat in the Huang-Huai-Hai wheat region. 【Method】The two-factor split plot experiment was conducted in Jiyang, Shandong Province from 2021 to 2024. The conventional string sowing (S1) and double-pressing precision uniform sowing (S2) were assigned to the main plots, and the N fertilizer application rates of 0, 120, 180 and 240 kg N·hm^-2 (N0, N150, N210, N240) were assigned to the subplots. The field emergence rate, tillering characteristics per plant, population dynamics, dry matter accumulation and transport, grain yield, and economic benefits of wheat under the interaction of sowing method and N application rates were systematically tested. 【Result】Compared with S1, S2 could increase the field emergence rate of wheat and enhance the tillering ability of per plant. Under the same N fertilizer application rates, the 3-year average number of tillers at seedling, wintering, jointing, and anthesis under S2 was significantly increased by 5.7%, 24.7%, 13.1% and 18.0%, respectively, and the tiller formation spikes ratio at maturity was increased by 5.5%-7.6%. Combined with N210 or N270, S2 significantly improved the aboveground dry matter accumulation at jointing, anthesis, and maturity by optimizing the aboveground dry matter accumulation rate at each growth stage, and under the same N fertilizer application rates, S2 significantly increased by 10.5%, 9.9% and 13.3% compared with S1, respectively. The translocation amount of dry matter pre-anthesis, dry matter accumulation amounts post-anthesis, and contribution rate of dry matter accumulation amount to yield post-anthesis under the two sowing methods reached a high level under N210 and N270 treatments, and S2 was significantly higher than S1, while the 3-year average increased by 7.0%-8.6%, 18.5%-27.1% and 3.5% -5.3%, respectively. The response of the harvest index to the N fertilizer application rates was different between different sowing methods. Under the S1 and S2 sowing methods, the maximum values were reached under N150 and N210 treatments for three years, and 3-year average were 2.6%-15.0% and 1.5%-16.8% higher than the other treatments, respectively, and the S2 was higher than S1 under the same N fertilizer rates. For three years, the grain yield and economic benefit of S2 were higher than those of S1 under N150, N210, and N270, and 3-year average were significantly increased by 7.6 %, 9.2 %, 16.1 % and 12.5 %, 14.0 %, 23.1 %, respectively; meantime the combination of S2 with N210 or N270 could achieve simultaneous improvement of grain yield and economic benefit. 【Conclusion】Under nitrogen-saving principles, double-pressing precision uniform sowing combined with 210 kg N·hm^-2 fertilizer application rates improved sowing conditions, increased wheat field emergence rate, optimized population spatial distribution, and synergistically improved wheat yield and economic benefits, which provided the technical support for high-yield cultivation of wheat in the Huang-Huai-Hai wheat region.

【Objective】Aboveground biomass is an important indicator of crop growth, in order to explore the accuracy difference between single spectral parameter model and fusion of different growth stage models in silage maize above ground biomass (AGB) estimation, this study aimed to compare the effects of unmanned aerial vehicle (UAV) multispectral feature parameters and model fusion methods on silage maize AGB estimation modeling accuracy, so as to improve the accuracy of silage maize biomass monitoring in Ningxia, and to provide a feasible technological solution for silage maize biomass dynamic monitoring. 【Method】DJI UAV M300 RTK equipped with M600 Pro multispectral camera was used to acquire multispectral image data of silage maize at each growth stage under six different nitrogen levels, and the relationship between the spectral reflectance and vegetation index and the change of biomass of silage maize in the upper part of the ground under different treatments were analyzed. The data set of silage maize in the whole life cycle were classified into the nutrient growth stage data set and reproductive growth stage data set, and the correlation analysis of the two different growth stage data sets were carried out. The multispectral vegetation index with high degree of correlation was selected as the input of modeling data, and the AGB estimation model of silage maize at different growth stages were constructed by using machine learning methods, such as Random Forest (RF) and Convolutional Neural Network (BP). The model was optimized by using the Gray Wolf Optimization Algorithm, and finally optimizing the model by using the Shapley Analysis. The optimized AGB estimation model of different growth stages of silage maize was combined to obtain the AGB estimation model of silage maize with multi-spectral change characteristics for the whole reproductive period. 【Result】The division of two different growth stages could improve the correlation between silage maize biomass and multispectral vegetation index, in which the green chlorophyll vegetation index (GCVI) improved with the highest value, and the absolute values of the correlation reached 0.61 and 0.64; the accuracy of the RF model after the combination using Shapley analysis was relatively high, with R² of 0.89 and root mean square error (RMSE) of 1.31 kg·m^-2; The RF model optimized by Gray Wolf algorithm with the Shapley combination had the highest accuracy with R² of 0.92 and RMSE of 1.11 kg·m^-2. 【Conclusion】 In this study, screening the optimal spectral parameters at each growth stage of silage maize and integrating multi-stage modeling using Shapley analysis could effectively improve the accuracy of silage maize AGB prediction model.

【Objective】The objective of this study is to clone HSP 9/12 genes of small heat shock proteins without ACD domain from Setosphaeria turcica and analyze their expression patterns during fungal development, infection, and HT-toxin induction processes. 【Method】The coding genes of heat shock protein HSP 9/12 were screened and cloned from the whole genome of S. turcica. Bioinformatics methods were employed to analyze the physicochemical properties, subcellular localization, structural prediction, and phylogenetic analysis of HSP 9/12 proteins. RNA-seq and RT-qPCR were used to examine the expression of HSP 9/12 genes during fungal development, infection, and HT-toxin induction. 【Result】Two HSP 9/12 genes were screened and cloned from the S. turcica genome, encoding proteins with 99 and 100 amino acids, respectively. Based on their molecular weights, they were named StHsp10.1 and StHsp10.7. Physicochemical analysis revealed that both HSP 9/12 proteins are hydrophilic, with subcellular localization predictions indicating they are located in the cytoplasm with nuclear localization signals. They lack transmembrane domains and signal peptides, and both contain the HSP9_HSP12 (PF04119) domain. StHSP10.1 is an acidic unstable protein, while StHSP10.7 is an alkaline stable protein, both existing predominantly in α-helix-dominated secondary and tertiary structural forms. StHSP10.1 shows closer phylogenetic relationship with Saccharomyces cerevisiae HSP12, whereas StHSP10.7 exhibits closer affinity to Schizosaccharomyces pombe HSP9. The StHSP10.1 exhibited the highest expression during conidial development, followed by hypha, appressoria, and penetration peg, with the lowest expression in germ tubes. After inoculation, the fungal StHSP10.1 expression rapidly increased, reaching 6.37-fold higher FPKM at 72 h compared to 24 h post inoculation. The results of RT-qPCR analysis during the HT-toxin induction process showed that, as the induction time increased, the relative gene expression level of StHSP10.1 in the wild-type strain (WT) significantly increased being 2.9-, 14.1-, and 39.8-fold higher at 14, 21, and 28 d compared to 7 d, respectively, but remained extremely low in the STK1 gene knockout mutant (ΔSTK1). StHSP10.7 showed extremely low expression levels during fungal development, infection, and HT-toxin induction. AlphaFold 3 predicted that the region from -38 to -24 bp upstream of the transcription start site of the StHSP10.1 contains TATA-box, and binding sites for cell differentiation proteins RCD1 and bZIP transcription factor StbZIP11, simultaneously. Using the STRING online platform to construct the protein-protein interaction network for StHSP10.1, two regulatory pathways of StHSP10.1 were proposed: Ras1→STK1→StbZIP11→StHSP10.1 and Ras1→UBE2→CUE1→RCD1-like→StHSP10.1, suggesting important roles in HT-toxin synthesis and stress induction, respectively. 【Conclusion】There are significant differences in the expression patterns of HSP 9/12 genes in S. turcica. StHSP10.1 serves as a key regulatory gene in the processes of pathogen development, infection, and HT-toxin induction, whereas StHSP10.7 has no regulatory effect.

【Objective】The objective of this study is to clarify the effect of key cytochrome P450 (CYP450) genes in Aphis craccivora on the toxicity of flonicamid and validate their functions, thus providing a theoretical basis for managing insect resistance and formulating green control strategies that combine gene-targeted interference with pesticide treatment. 【Method】P450 enzyme activity was measured after flonicamid treatment to evaluate the overall metabolic response. P450 genes of A. craccivora were identified through database mining combined with phylogenetic analysis, and the qRT-PCR was used to screen the P450 genes responsive to insecticide treatment. The protein structures of up-regulated P450 enzymes were predicted using AlphaFold 3, and molecular docking of P450 enzymes with flonicamid was conducted via AutoDock to characterize binding modes and affinities. RNA interference (RNAi) was applied to silence the target P450 genes, and then the alteration of flonicamid toxicity to A. craccivora was evaluated. 【Result】After treatment with flonicamid, the activity of P450 enzymes increased significantly, peaking at 24 h, and then gradually decreased. A total of 59 P450 genes of A. craccivora were screened out, among which the expressions of CYP6CY53 in the CYP3 cluster and CYP302A1 in the Mito cluster were significantly up-regulated after flonicamid treatment, suggesting that they play a crucial role in insecticide metabolism. AlphaFold 3 predicted the structures of CYP6CY53 and CYP302A1 with confidence scores of 0.75 and 0.86, respectively. Molecular docking between the two enzymes and flonicamid was performed using AutoDock, and the results indicated that flonicamid binds to the two enzymes primarily through hydrogen bonds, with binding energy values of -15.32 and -18.17 kJ·mol^-1, respectively. After silencing CYP6CY53 and CYP302A1 in A. craccivora, the median lethal concentration (LC₅₀) of flonicamid decreased to 2.06 and 6.08 mg·L^-1, respectively, and the sensitivity ratios increased to 9.26 and 3.13 times that of the control group. 【Conclusion】The expression of CYP6CY53 and CYP302A1 was up-regulated in A. craccivora in response to flonicamid stress. These two genes play crucial roles in mitigating the toxicity of flonicamid, and the silencing of them can significantly enhance the sensitivity of A. craccivora to flonicamid.

【Objective】This study aimed to investigate the impact of different topographic factors (such as slope position and slope gradient) and ridge tillage methods (such as transverse ridge tillage and longitudinal ridge tillage) on soil nutrient content and fertility indexes of sloping arable land, which would provide a scientific basis for selecting the ridge tillage method and maintaining soil fertility of sloping arable land in this region. 【Method】The study was conducted on a typical long, gentle slope arable land in Hongxing Farm, Bei’an City, Heilongjiang Province. Grid-based sampling was conducted in plots with two different ridge tillage methods (transverse ridge tillage plot, 18 points; longitudinal ridge tillage plot, 11 points). Comparative analysis was carried out to assess the effects of slope position and slope gradient on soil nutrient contents and the Soil Fertility Index (SFI) under the two ridge tillage methods. Analysis of variance (ANOVA) was used to test the impact of topographic factors, ridge tillage methods, and their interactions on the differences in soil nutrient spatial distribution between the two plots. Finally, variance partitioning analysis (VPA) was used to quantify the contribution of each factor to the explanatory degrees of SFI. 【Result】The differences in soil nutrient content and fertility index between the two plots were significant. The mean values of soil organic matter, total nitrogen, total phosphorus, total potassium, effective phosphorus, and available potassium were all significantly higher in the transverse ridge tillage plot compared with the longitudinal ridge tillage plot. However, the pH value was significantly lower in the transverse plot than that in the longitudinal plot. Consequently, the SFI of the two plots was ranked as follows: transverse ridge tillage>longitudinal ridge tillage (P<0.05). Slope position and slope gradient significantly influenced the spatial distribution of soil nutrients in plots with different ridge tillage methods, resulting in significant differences in the explanatory degree of each influencing factors on SFI variation between the two plots. In the transverse ridge tillage plot, the slope gradient differences caused by the micro-terrain (explanatory degrees, 32.62%) were the main drivers of soil nutrient differentiation. In this plot, soil total and effective phosphorus decreased as the slope increasing. In the longitudinal ridge tillage plot, soil organic matter, total nitrogen, and total phosphorus were the highest at the middle slope, and soil organic matter, total nitrogen, total potassium, effective phosphorus, and available potassium initially decreased and then increased with rising slope. In this plot, the slope position (explanatory degrees, 6.81%) and slope gradient (explanatory degrees, 7.22%) influence the distribution of soil nutrients. A comprehensive analysis of the SFI across the entire slope surface revealed that ridge tillage method had the highest explanatory degrees for the spatial variation of SFI (15.46%), followed by slope position (9.54%). The combined explanatory degree of the interaction between ridge tillage method and the two topographic factors, slope gradient and slope position, was 9.49%. 【Conclusion】Topographic factors played a key role in soil nutrient migration in sloping arable land, and the impact of each factor (slope gradient and slope position) varied significantly under different ridge tillage methods. In transverse ridge tillage, slope differences caused by internal micro-terrain helped retain soil nutrients, while in longitudinal ridge tillage, slope gradient and slope position influenced the spatial differentiation of soil nutrients. Across the entire slope surface, the contribution of ridge tillage method to the spatial variation in soil fertility was greater than that of topographic factors. Therefore, the management of black soil sloping farmland needed to consider the combined effects of ridge tillage methods and topography.

【Objective】 This research investigated the effects of different types of fertilizers on the field traits, nitrogen absorption and utilization, yield, yield components, and processing quality of weak-gluten wheat following rice cultivation in the Jiang-Huai region. The goal was to provide a theoretical basis for the selection of optimal fertilizer types and regulation of application rates for achieving high yield and quality of weak-gluten wheat in this region. 【Method】 The two weak-gluten wheat varieties, including Yangmai 20 (YM20) and Baihumai 1 (BHM1), were used as experimental materials. The experiment was set three nitrogen levels: 150 kg·hm^-2 (N₁₀), 180 kg·hm^-2 (N₁₂), and 210 kg·hm^-2 (N₁₄), with an additional nitrogen-free treatment (N₀) to calculate nitrogen efficiency. Four types of fertilizers were applied: compound fertilizer + urea (F₁), slow-release mixed fertilizer (F₂), controlled- release fertilizer (F₃), and wheat formula fertilizer (F₄). The effects of different treatments on wheat population dynamics, nitrogen utilization, yield, and quality were analyzed. 【Result】As nitrogen application levels increased, the yield and quality indicators of weak-gluten wheat improved. The one-time basal application of slow-release mixed fertilizer, controlled-release fertilizer, and wheat formula fertilizer significantly increased the SPAD value of the flag leaf after anthesis and the number of spikes at maturity. Compared with the compound fertilizer+urea treatment, the nitrogen accumulation in the above-ground parts under slow-release mixed fertilizer, controlled-release fertilizer, and wheat formula fertilizer was increased by 5.0%-12.8%, 0.8%-6.2%, and 9.9%-17.2%, respectively; the nitrogen utilization efficiency increased by 10.3%-26.9%, 4.3%-16.5%, and 22.1%-39.8%, respectively; during anthesis, the activities of nitrate reductase (NR) in the flag leaf increased by 2.6%-8.8%, -2.9%-1.5%, and 5.1%-12.8%; glutamine synthetase (GS) activity increased by 9.0%-23.8%, 2.0%-8.9%, and 11.8%-28.7%; and glutamate synthase (GOGAT) activity increased by 3.9%-18.4%, 0.8%-8.9%, and 7.7%-24.0%; yield increased by 5.0%-11.5%, 1.8%-7.6%, and 9.7%-18.4%, respectively. At nitrogen levels of 180 and 210 kg·hm^-2, slow-release mixed fertilizer, controlled-release fertilizer, and wheat formula fertilizer all resulted in varying degrees of yield improvement compared with the compound fertilizer+urea treatment. At the same nitrogen levels, slow-release mixed fertilizer and controlled-release fertilizer significantly reduced grain protein content, wet gluten content, grain hardness, and solvent retention capacity compared with the compound fertilizer + urea treatment. They also reduced dough formation time and stability time, lowered water absorption rate, and the farinograph quality number. These treatments also enhanced the dough rheological properties and weakened the gluten strength. 【Conclusion】For weak-gluten wheat cultivation following rice in the Jiang-Huai region, it was recommended to control the nitrogen level at 180-210 kg·hm^-2. A one-time basal application of slow-release mixed fertilizer could ensure both high quality and high yield of weak-gluten wheat.

【Objective】This study aimed to compare the advantages of agricultural carbon emission density indicators, and to clarify the current situation, influencing factors, and spatial spillover effects of inter provincial agricultural carbon emission density in China, so as to provide a reference for exploring more potential space for agricultural carbon reduction. 【Method】On the basis of accurately measuring the carbon emission density of agriculture in China and its provinces, this study used methods such as kernel density estimation, spatial autocorrelation, and spatial Durbin model to investigate its spatiotemporal characteristics, influencing factors, and spatial spillover effects. 【Result】Although the agricultural carbon emission density has slightly decreased from 2005 to 2022, it was accompanied by significant interannual fluctuations, showing an overall change in three stages: "fluctuation decrease", "continuous increase", and "fluctuation decrease". Inter provincial development has shown a balanced trend, and although there was still an absolute gap, it was continuously narrowing; there was a strong and stable spatial dependence pattern in agricultural carbon emission density, with a local spatial heterogeneity pattern dominated by the "low-low" clustering type of interdependence between low-density provinces and multiple low-density provinces, and the "high-high" clustering type of interdependence between high-density provinces and multiple high-density provinces as the secondary dominant pattern; the carbon emission density of agriculture was comprehensively influenced by four levels: government responsibilities, economic development, social participation, and cultural education. There was an inverted U-shaped relationship between the level of agricultural economic development and agricultural industry agglomeration. Agricultural technological progress significantly promoted the reduction of agricultural carbon emission density, while the increases in agricultural public investment, financial support for agriculture, agricultural industrial structure, rural human capital, and industrialization level all led to an increase in agricultural carbon emission density; in terms of spatial spillover effects, the improvement of agricultural economic development level in surrounding areas would cause an initial increase and then a decrease in local agricultural carbon emission density. The increase in agricultural public investment in surrounding areas would promote a decrease in local agricultural carbon emission density, while the spatial spillover effects of fiscal support for agriculture and rural human capital would lead to an increase in local agricultural carbon emission density. 【Conclusion】There were still significant differences in agricultural carbon emission density among provinces in China; There is a strong spatial dependence between provinces across the country, and there were obvious spatial heterogeneity patterns in some regions; the carbon emission density of agriculture was influenced by four factors: government responsibilities, economic development, social participation, and cultural education. Continuously improving the level of agricultural economic development and increasing public investment in agriculture were beneficial for reducing the carbon emission density of agriculture in surrounding areas, while the spatial spillover effect of fiscal support for agriculture and rural human capital was the opposite.

【Objective】The peroxiredoxin (Prx) gene family in garden asparagus was systematically identified at the genome-wide level. The tissue-specific expression profiles and responses to abiotic stress, dormancy, and dormancy release were comprehensively investigated, providing a critical foundation for further functional characterization and breeding applications of AoPrxs. 【Method】Using the genomic data of asparagus, the members of the Prx family were identified through bioinformatics methods. The physicochemical properties, subcellular localization, gene structures, and conserved motifs of the AoPrxs were predicted using ProtParam, Cell-PLoc 2.0, SWISS-MODEL, and MEME tools, respectively. The tissue-specific expression patterns of AoPrxs and their responses to diverse stresses, dormancy, and dormancy release were systematically analyzed using RNA-Seq data and real-time fluorescent quantitative PCR (qRT-PCR). 【Result】The AoPrx family contains six members, designated AoPrx1-AoPrx6, all of which contain the conserved PXXXTXXC sequence. Their amino acid sequences ranged from 162 to 268 residues, with molecular weights ranging from 17 397.96 to 29 246.45 Da. A total of 220 (38 types) cis-acting elements were identified in AoPrxs, including those involved in plant growth and development, stress response, hormone signaling, and light response. Collinearity analysis revealed that AoPrx has a higher homology with TaPrx and OsPrx. Phylogenetic analysis classified the AoPrxs into five subfamilies, each exhibiting highly conserved gene structures, amino acid sequences, and protein structures. Notably, AoPrx1 and AoPrx2 lack the ‘resolving’ Cys residue but exhibit greater similarity in both amino acid sequence and protein structure to members of the PrxⅡB subfamily than to those of the 1-Cys subfamily. Consequently, they were classified into the PrxⅠB subfamily. Tissue expression analysis showed that AoPrx2 is ubiquitously expressed across all tissues, in contrast to AoPrx5, which showed stamen-specific upregulation in supermale flowers. The expression profile under adverse stress conditions indicated that salt-alkali stress significantly upregulated AoPrx5 expression in the roots of asparagus seedlings. Additionally, salt, drought, and low-light treatments markedly enhanced AoPrx4 expression levels in the stems and leaves of asparagus seedlings, while infection by Phomopsis asparagi exerted an opposite regulatory effect on AoPrx4 expression. Furthermore, AoPrx2, AoPrx4, and AoPrx5 may also be involved in the regulatory processes of dormancy induction and release in asparagus. 【Conclusion】Six Prx family members were identified in the asparagus genome, distributed across five chromosomes. All members contained the conserved PXXXTXXC motif and were phylogenetically classified into five subfamilies. Expression profiling revealed subfunctionalization among Prx paralogs: AoPrx5 was closely associated with stamen development and root salt tolerance. AoPrx4 played a pivotal role in the responses of stem and leaf tissues to salt, drought, low-light conditions, and phomopsis asparagi stress. Moreover, AoPrx2, AoPrx4, and AoPrx5 were also involved in the regulation of asparagus dormancy induction and release.

【Objective】Calyx abscission in Korla Xiangli contributes to the improvement of fruit quality. Through multi-omics integrated analysis, the molecular mechanism of calyx abscission in Korla Xiangli was explored. The differentially expressed genes and proteins during the abscission process, as well as the involved metabolic pathways and signal transduction pathways were identified. This enriched the understanding of the abscission mechanism of calyx and provided a theoretical basis for further research on the abscission mechanism of calyx in Korla Xiangli. It also laid the foundation for the application of physiological and molecular techniques in regulating calyx abscission in Korla Xiangli in production. 【Method】Using Korla Xiangli pear as the research material, the clayx abscission zone of decalyx fruits and the corresponding parts of the abscission zone of persistent calyx fruits at the critical period of abscission zone formation were selected as research objects. Transcriptome and proteome sequencing, along with integrated analysis were performed. Multiple bioinformatics tools were employed to screen differentially expressed genes and proteins. The genes and proteins related to abscission were analyzed in detail, and the metabolic pathways and signal transduction pathways they participated in were identified. Finally, the differentially expressed genes were verified by quantitative real-time PCR (qRT-PCR). 【Result】A total of 393 differentially expressed genes were obtained through transcriptome analysis. These genes were mainly enriched in cell wall biosynthesis, plant-type cell wall organization or biogenesis, plant-type cell wall biosynthesis, plant-type secondary cell wall biosynthesis, phenylpropanoid metabolic processes, etc., as well as metabolic pathways, biosynthesis of secondary metabolites, and mutual Pentose and glucuronate interconversions, which indicated that most differentially expressed genes were involved in cell wall metabolism. A total of 256 differentially expressed proteins were obtained through proteome analysis. The integrated analysis showed that the differentially expressed genes and proteins were mainly enriched in pathways of plant hormone signal transduction, pentose and glucuronate interconversions, zeatin biosynthesis, and phenylpropanoid biosynthesis. Plant hormone-related genes (e.g., ethylene-responsive transcription factors, indole-3-acetic O-methyltransferase gene) and genes/proteins involved in cell wall degradation (e.g., pectin lyase gene, pectin esterase gene, polygalacturonase gene, peroxidase gene) were screened out, playing critical roles in calyx abscission of Korla Xiangli. In addition, the expression trends of 10 differentially expressed genes verified by qRT-PCR were consistent with the sequencing results. 【Conclusion】The regulation mechanism of calyx abscission in Korla Xiangli was determined from the levels of gene transcription and protein expression. Genes and proteins related to plant hormones and cell wall degrading enzymes were screened out, and clarified that the calyx abscission process of Korla Xiangli mainly involved in pathways such as phenylpropanoid biosynthesis, plant hormone signal transduction, pentose and glucuronate interconversions.

【Objective】 This study aimed to investigate the co-synergistic action of different plant growth regulators compound combination on grape, optimize the increase fruit size method and reduce the concentration of gibberellin, so as to select the suitable plant growth regulators treatment for Thompson Seedless grape, and to provide the theoretical basis and technical support for flower and fruit management technology selection in Xinjiang. 【Method】 With Thompson Seedless grape as the experimental material, a split-split plot field experiment was conducted in Shanshan, Xinjiang Uygur Autonomous Region, China. The GA₃ concentration for inflorescence elongation were used as main plots, and four levels were set: 0, 60, 80, and 100 mg·L^-1. Three types of fruit-enlarging compound plant growth regulators including CPPU, TDZ, and BR were used as split-plots. The concentration levels of the fruit-enlarging compound plant growth regulators were used as split-split plots, and four levels were set: low, medium, relatively high, and high concentrations. This study focused on analyzing the effects of the three-factor combination GA₃ concentration for inflorescence elongation, type of fruit-enlarging compound plant growth regulator, and concentration of the fruit-enlarging compound plant growth regulator on fruit quality, such as single berry weight, fruit shape index, soluble solid-to-acid ratio, and color parameters at the fruit ripening stage. The quality related to raisin processing was investigated, such as dry output rate, browning rate, color parameters and fullness of raisins. 【Result】 The single berry weight under different treatments were ranged from 2.64 to 4.88 g, mainly influenced by the type of fruit-enlarging compound regulator. Significant differences were observed among the three types of regulators, with the order being TDZ > CPPU > BR; The soluble solid content (SSC) in single berry ranged from 16.30% to 23.00%, influenced by three factors and their interactions. In factor A (GA₃ concentration for inflorescence elongation), both 60, 80, and 100 mg·L^-1 treatments were significantly higher than CK. In factor B (type of fruit-enlarging compound regulator), BR treatment was significantly higher than CPPU and TDZ treatments. The solid-to-acid ratio in berry ranged from 20.40 to 35.14, mainly influenced by factor B. The dry output rate of raisin ranged from 16.10% to 27.90%, affected by both three factors and their interactions. In factor A, the dry output rate of 80 mg·L^-1 GA₃ treatments were significantly higher than CK and the other two treatments. In factor B, the dry output rate under CPPU and BR treatments were significantly higher than TDZ. In factor C (concentration of fruit-enlarging compound regulator), the low-concentration treatment was significantly higher than the other three concentration treatments. The fullness of raisins index ranged from 0.54 to 0.97, influenced by both three factors and their interactions, and in factor A, both 60, 80, and 100 mg·L^-1 GA₃ treatments were significantly higher than 0 mg·L^-1 GA₃ treatment. In factor B, the fullness of raisins with TDZ and BR treatments were significantly higher than CPPU, and the high-concentration treatment was significantly higher than the other three concentration levels. The browning rate of raisin ranged from 5.71% to 65.17%, affected by both three factors and their interactions. In factor A, the 100 mg·L^-1 GA₃ treatment was significantly lower than CK, 60 and 80 mg·L^-1 GA₃ treatments. In factor B, the BR treatment was significantly lower than CPPU and TDZ treatments, and the relatively high-concentration treatment was significantly lower than the other three concentration levels. 【Conclusion】T30 treatment (80 mg·L^-1 GA₃ for inflorescence elongation, and 50 mg·L^-1 GA₃+10 mg·L^-1 GA₄₊₇+3 mg·L^-1 TDZ for fruit enlargement) had the highest fresh fruit quality. T34 treatment (80 mg·L^-1 GA₃ for inflorescence elongation, and 50 mg·L^-1 GA₃+10 mg·L^-1 GA₄₊₇+2 mg·L^-1 BR for fruit enlargement) had the highest quality related to raisin.

【Objective】 Mounting behavior in sheep is a critical ethological indicator for identifying the estrus status of ewes, and plays an essential role in breeding management and estrus monitoring. Traditional manual observation methods suffer from subjectivity, low efficiency, and high omission rates, which limit their applicability in large-scale intelligent farming. To address the challenges of accurately recognizing mounting behavior in complex farm environments, such as dramatic illumination changes, severe occlusions, and dense sheep clusters, this study aimed to develop a high-precision and robust automatic recognition model to enable rapid detection and precise localization of mounting behavior, thereby supporting intelligent sheep reproduction management. 【Method】 Daily activity videos of 24 ewes were collected from the Jiaxiang Breeding Sheep Farm in Shandong Province between April 15 and May 15, 2024. A balanced dataset consisting of 4 700 annotated images (including both mounting and non-mounting samples) was constructed. Based on the YOLOv11 architecture, an improved detection model was proposed, named SIDS-YOLOv11, which incorporated four key modules: SCINet for low-light image enhancement, improving visual quality in dim conditions; iAFF for optimizing multi-scale semantic feature fusion; DySample for enhancing edge detail recovery via dynamic upsampling; and SEAM for improving target perception under occlusions using spatial attention. The training process employed the CIoU (Complete Intersection over Union) loss function for bounding box regression, combined with various data augmentation techniques to enhance model robustness and generalization. 【Result】 On the validation set, compared with the original YOLOv11 model, SIDS-YOLOv11 achieved a mAP@0.5 of 0.942, a Precision of 0.956, a Recall of 0.854, and a mAP@0.5-0.95 of 0.703—representing improvements of 3.5%, 4.7%, 1.7%, and 1.5%, respectively. Heatmap visualizations demonstrated that the improved model maintained accurate focus on target regions even in low-light and occluded scenarios. The attention regions of the enhanced model were more concentrated, background noise was reduced, and feature extraction capabilities were significantly improved, leading to enhanced recognition accuracy and localization stability. Compared with mainstream detection models, such as YOLOv8n, YOLOv6, Faster R-CNN, and SSD, SIDS-YOLOv11 achieved a better balance between detection accuracy and inference speed. Evaluation on low-light and heavily occluded videos further verified the model's superior performance, maintaining over 63% detection accuracy in complex scenarios, indicating strong applicability and adaptability. 【Conclusion】 The proposed SIDS-YOLOv11 model effectively integrated image enhancement, feature representation, and attention mechanisms, significantly improving the recognition accuracy of sheep mounting behavior in complex environments. The model maintained stable performance under challenging conditions, such as occlusion and low illumination, offering a high-performance visual recognition solution for estrus monitoring, behavior analysis, and breeding management in smart farming. The model held strong potential for practical deployment and large-scale application.