As a low-cost and environmentally friendly new type of fertilizer, microbial fertilizers can effectively improve soil, increase fertility, increase crop yield, and reduce crop losses caused by pathogens, playing an important role in the national agricultural green development strategy. In this paper, we summarized the development status of microbial fertilizers in China in recent years, the classification and preservation of strains, and the mechanism of soluble and volatile secondary metabolites of plant growth promoting bacteria from the aspects of classification, product application and mechanism of microbial fertilizers, and analyzed the common problems faced by the development of microbial fertilizer industry. Finally, several suggestions for promoting the development of microbial fertilizers were put forward.

【Objective】To explore the structure and evolution characteristics of cotton NLP transcription factors in the whole genome, and further understand their expressions patterns, so as to lay a foundation for the further function research and utilization of NLP genes. 【Method】The NLP transcription factor family members in the whole genomes of four cotton species, Gossypium arboreum (G. arboreum, Ga), Gossypium raimondii (G. raimondii, Gr), Gossypium barbadense (G. barbadense, Gb) and Gossypium hirsutum (G. hirsutum, GH), were identified using two strategies, BLASTP and HMM search. Further bioinformatics analysis was carried out on the confirmed cotton NLP family members. The molecular weights, theoretical isoelectric points and other physical and chemical properties were predicted using online software Expasy; the MEGA 7 software was used to build the phylogenetic tree; protein conservative motifs were analyzed through MEME website; online software GSDS 2.0 was used to analyze gene structures; TBtools was used to view the chromosome localizations; McscanX was used to analyze the replication genes of cotton NLP family members; the PlantCARE website was used to predict the cis-acting elements in the promoters of cotton NLP family genes. The heat maps of cotton NLP genes expression levels of different tissues and under abiotic stresses were drawn through TBtools to analyze the tissue expression characteristics and abiotic stresses response characteristics. The expressions of GHNLPs in cotton under nitrogen starvation and nitrogen resupply treatments were analyzed by RT-qPCR. 【Result】A total of 11, 11, 21 and 22 NLP members were screened from the four cotton protein databases of G. arboreum, G. raimondii, G. barbadense and G. hirsutum, respectively. These NLP family genes encoded 693-996 amino acids. The relative molecular masses ranged from 76.92-110.02 kDa and the theoretical isoelectric points were 5.13-7.77. The subcellular localization prediction results showed that almost all the NLP members located in the nucleus. Promoter analysis found a large number of cis-acting elements related to phytohormone and stress response. Phylogenetic analysis showed cotton NLPs were divided into three groups, I, II and III. Gene replication analysis showed that fragment replication was the main force for NLP members expansion in cotton. All the Ka/Ks values were less than 1, indicating that evolution of NLP family in cotton mainly underwent purification selection. The results of expression analysis also confirmed that GHNLPs responded to nitrogen starvation and nitrogen resupply. 【Conclusion】From the whole genome of G. arboreum, G. raimondii, G. barbadense, and G. hirsutum, 11, 11, 21 and 22 NLP transcription factor members were identified respectively. They had high conservatism and some degree of differences. The expression levels of GHNLPs changed significantly during nitrogen starvation and nitrogen resupply processes, which may play a role in the response of cotton to nitrate.

The harmless and reuse of agricultural waste not only turns waste biomass into treasure, but also reduces pollution to the ecological environment and improves the living environment, which is an effective way to achieve green, circular, low-carbon, efficient and sustainable development. In this article, we conducted bibliometric statistics of the relevant literature on agricultural waste resource utilization published in China and abroad from 1990 to 2022, and comprehensively analyzed the annual trend of the number of articles published in this field and the key words with the help of CiteSpace and VOSviewer bibliometric tools. Domestic research in this area started nearly 10 years earlier than overseas, the research intensity and importance of international research in this field since 2002 have been significantly higher than that of domestic research, the growth trend of the number of annual publications is also significantly higher than that of domestic research, and the gap between the number of publications at home and abroad is also widening year by year. The research hotspots in the field are not exactly the same at home and abroad. We focus more on the use of waste for the production of organic fertilizers to realize the recycling of green and low-carbon agriculture in China; the foreign countries focus more on biomass materialization and energy utilization of waste to compensate for the consumption of non-renewable resources. Based on China's basic condition of having more people and less land, China should strive to broaden the disposal methods of agricultural waste by classifying and disposing of agricultural waste raw materials and subsequently using them for substrate, feed, fertilizer, materialization and energy. And waste gas, heat and residue should be recycled in order to realize the multi-level utilization of harmless, reduced and diversified biomass resources. The results of the study provide a reference for the development of the field of agricultural waste resource utilization in China.

In order to comprehensively understand the current status of major diseases in freshwater aquaculture across the country, and identify the existing drawbacks in disease prevention and control, we conducted a survey at the city level in freshwater aquaculture areas. By conducting a comprehensive analysis of disease data from different regions, the types of diseases (a total of 101 species, including 25 viral diseases, 25 bacterial diseases, 6 fungal and algal diseases, 32 parasitic diseases, and 13 other diseases), epidemiological patterns, severity of damage and control measures for aquaculture species across the country were clarified. By further analyzing the existing drawbacks in disease prevention and control, the targeted recommendations for guiding the future development of aquatic disease prevention and control are put forward.

[Significance] Autonomous and intelligent agricultural machinery, characterized by green intelligence, energy efficiency, and reduced emissions, as well as high intelligence and man-machine collaboration, will serve as the driving force behind global agricultural technology advancements and the transformation of production methods in the context of smart agriculture development. Agricultural robots, which utilize intelligent control and information technology, have the unique advantage of replacing manual labor. They occupy the strategic commanding heights and competitive focus of global agricultural equipment and are also one of the key development directions for accelerating the construction of China's agricultural power. World agricultural powers and China have incorporated the research, development, manufacturing, and promotion of agricultural robots into their national strategies, respectively strengthening the agricultural robot policy and planning layout based on their own agricultural development characteristics, thus driving the agricultural robot industry into a stable growth period. [Progress] This paper firstly delves into the concept and defining features of agricultural robots, alongside an exploration of the global agricultural robot development policy and strategic planning blueprint. Furthermore, sheds light on the growth and development of the global agricultural robotics industry; Then proceeds to analyze the industrial backdrop, cutting-edge advancements, developmental challenges, and crucial technology aspects of three representative agricultural robots, including farmland robots, orchard picking robots, and indoor vegetable production robots. Finally, summarizes the disparity between Chinese agricultural robots and their foreign counterparts in terms of advanced technologies. (1) An agricultural robot is a multi-degree-of-freedom autonomous operating equipment that possesses accurate perception, autonomous decision-making, intelligent control, and automatic execution capabilities specifically designed for agricultural environments. When combined with artificial intelligence, big data, cloud computing, and the Internet of Things, agricultural robots form an agricultural robot application system. This system has relatively mature applications in key processes such as field planting, fertilization, pest control, yield estimation, inspection, harvesting, grafting, pruning, inspection, harvesting, transportation, and livestock and poultry breeding feeding, inspection, disinfection, and milking. Globally, agricultural robots, represented by plant protection robots, have entered the industrial application phase and are gradually realizing commercialization with vast market potential. (2) Compared to traditional agricultural machinery and equipment, agricultural robots possess advantages in performing hazardous tasks, executing batch repetitive work, managing complex field operations, and livestock breeding. In contrast to industrial robots, agricultural robots face technical challenges in three aspects. Firstly, the complexity and unstructured nature of the operating environment. Secondly, the flexibility, mobility, and commoditization of the operation object. Thirdly, the high level of technology and investment required. (3) Given the increasing demand for unmanned and less manned operations in farmland production, China's agricultural robot research, development, and application have started late and progressed slowly. The existing agricultural operation equipment still has a significant gap from achieving precision operation, digital perception, intelligent management, and intelligent decision-making. The comprehensive performance of domestic products lags behind foreign advanced counterparts, indicating that there is still a long way to go for industrial development and application. Firstly, the current agricultural robots predominantly utilize single actuators and operate as single machines, with the development of multi-arm cooperative robots just emerging. Most of these robots primarily engage in rigid operations, exhibiting limited flexibility, adaptability, and functionality. Secondly, the perception of multi-source environments in agricultural settings, as well as the autonomous operation of agricultural robot equipment, relies heavily on human input. Thirdly, the progress of new teaching methods and technologies for human-computer natural interaction is rather slow. Lastly, the development of operational infrastructure is insufficient, resulting in a relatively low degree of "mechanization". [Conclusions and Prospects] The paper anticipates the opportunities that arise from the rapid growth of the agricultural robotics industry in response to the escalating global shortage of agricultural labor. It outlines the emerging trends in agricultural robot technology, including autonomous navigation, self-learning, real-time monitoring, and operation control. In the future, the path planning and navigation information perception of agricultural robot autonomy are expected to become more refined. Furthermore, improvements in autonomous learning and cross-scenario operation performance will be achieved. The development of real-time operation monitoring of agricultural robots through digital twinning will also progress. Additionally, cloud-based management and control of agricultural robots for comprehensive operations will experience significant growth. Steady advancements will be made in the innovation and integration of agricultural machinery and techniques.

Trichoderma harzianum belongs to Trichoderma, it is a widely used biological agent and a fungus with various values. With the extensive application of Trichoderma harzianum in agricultural production, different functions of Trichoderma harzianum in plants have been widely concerned. This paper summarized the biological control mechanism of Trichoderma harzianum, including antibacterial effect, induced resistance and competition. It also reviewed the application of Trichoderma harzianum in biological control, plant growth promotion, quality improvement and salt tolerance. The paper further elaborated that there are still problems and deficiencies in the application of Trichoderma harzianum in plants, which need to be solved urgently. Finally, this paper discussed the application prospect of Trichoderma harzianum in plants under the background of green agriculture and pointed out the future research direction, aiming to achieve a more rational and safe use of Trichoderma harzianum in plants in agricultural production.

[Significance] Agricultural sensor is the key technology for developing modern agriculture. Agricultural sensor is a kind of detection device that can sense and convert physical signal, which is related to the agricultural environment, plants and animals, into an electrical signal. Agricultural sensors could be applied to monitor crops and livestock in different agricultural environments, including weather, water, atmosphere and soil. It is also an important driving force to promote the iterative upgrading of agricultural technology and change agricultural production methods. [Progress] The different agricultural sensors are categorized, the cutting-edge research trends of agricultural sensors are analyzed, and summarizes the current research status of agricultural sensors are summarized in different application scenarios. Moreover, a deep analysis and discussion of four major categories is conducted, which include agricultural environment sensors, animal and plant life information sensors, agricultural product quality and safety sensors, and agricultural machinery sensors. The process of research, development, the universality and limitations of the application of the four types of agricultural sensors are summarized. Agricultural environment sensors are mainly used for real-time monitoring of key parameters in agricultural production environments, such as the quality of water, gas, and soil. The soil sensors provide data support for precision irrigation, rational fertilization, and soil management by monitoring indicators such as soil humidity, pH, temperature, nutrients, microorganisms, pests and diseases, heavy metals and agricultural pollution, etc. Monitoring of dissolved oxygen, pH, nitrate content, and organophosphorus pesticides in irrigation and aquaculture water through water sensors ensures the rational use of water resources and water quality safety. The gas sensor monitors the atmospheric CO₂, NH₃, C₂H₂, CH₄ concentration, and other information, which provides the appropriate environmental conditions for the growth of crops in greenhouses. The animal life information sensor can obtain the animal's growth, movement, physiological and biochemical status, which include movement trajectory, food intake, heart rate, body temperature, blood pressure, blood glucose, etc. The plant life information sensors monitor the plant's health and growth, such as volatile organic compounds of the leaves, surface temperature and humidity, phytohormones, and other parameters. Especially, the flexible wearable plant sensors provide a new way to measure plant physiological characteristics accurately and monitor the water status and physiological activities of plants non-destructively and continuously. These sensors are mainly used to detect various indicators in agricultural products, such as temperature and humidity, freshness, nutrients, and potentially hazardous substances (e.g., bacteria, pesticide residues, heavy metals, etc. Agricultural machinery sensors can achieve real-time monitoring and controlling of agricultural machinery to achieve real-time cultivation, planting, management, and harvesting, automated operation of agricultural machinery, and accurate application of pesticide, fertilizer. [Conclusions and Prospects In the challenges and prospects of agricultural sensors, the core bottlenecks of large-scale application of agricultural sensors at the present stage are analyzed in detail. These include low-cost, specialization, high stability, and adaptive intelligence of agricultural sensors. Furthermore, the concept of "ubiquitous sensing in agriculture" is proposed, which provides ideas and references for the research and development of agricultural sensor technology.

Humic acid has the ability to improve soil, increase fertilizer efficiency and provide nutrients for plants. It plays an important role in the ecological environment. Humic acid is a complex aromatic macromolecule formed by amino acids, amino sugars, peptides and aliphatic compounds. The extraction of humic acid includes microbial dissolution method, alkali-acid precipitation method and acid extractant method. Based on this, the basic concept, properties, mechanism of humic acid and the preparation principle and function of humic acid fertilizer were summarized, and some shortcomings were discussed. It was pointed out that the research focus of humic acid fertilizer in the future would be the main components of humic acid raw materials. More cost-saving method should be studied for the production of humic acid, and a unified quality inspection standard should be established.

Abiotic stress is one of the main factors causing global grain yield reduction. It is of great significance to study the function and response mechanisms of plant stress-related proteins to improve crop stress resistance. Pentatricopeptide repeat (PPR) proteins, belong to the largest family of nuclear coding proteins in higher plants and are named because they contain highly specific PPR motifs. Depending on motif type and arrangement, PPR proteins can be classified as P and PLS, and PLS proteins can be further classified as PLS, E, E+, DYW, and other subclasses based on their carboxyl-terminal domains. PPR proteins are widely distributed in terrestrial plants, mainly in chloroplasts and mitochondria, and a few in the nucleus. As sequence-specific RNA binding proteins, PPR proteins are involved in multiple aspects of plant RNA processing, including RNA editing, splicing, stabilization, and translation. PPR protein plays a variety of important roles in the whole life process of plants, but the mechanism of its action in plant stress resistance is not well understood. Based on the localization and function of PPR proteins related to abiotic stress reported, the mechanism of PPR proteins involved in regulation of abiotic stress, including post-transcriptional regulation and retrograde signaling, was reviewed and discussed in this paper. Post-transcriptional regulation is related to the role of PPR proteins in the modification of RNA after transcription. It is generally believed that PPR affects stress resistance in plants by regulating the expression of stress-related genes via binding RNA and by regulating the metabolism of organelle RNA. In terms of retrograde signaling, damage to PPR proteins can lead to impaired mitochondrial or chloroplast function, and then produce various retrograde signals (such as ROS), thereby regulating the expression of related genes and resisting adversity. However, since plastid signaling is affected by many environmental factors, some of which are still unclear, the mechanism of the PPR protein in retrograde signaling remains to be clarified. In addition, PPR proteins are pleiotropic and some have important effects on plant growth and reproduction while acting on stress resistance. Finally, this paper further analyzed the current research status of PPR protein as an RNA editing tool, discussed the remaining problems and research prospects of PPR protein in the direction of abiotic stress, and pointed out the key points and difficulties that need to be paid attention to in future research, to provide references for further research on PPR protein and crop abiotic stress resistance breeding.

With the increase of global temperature, heat stress has emerged as one of the major factors affecting plant growth and development. The substantial losses caused by heat, particularly for staple crops like rice, pose a significant impact on economic benefits. In order to unravel the molecular mechanisms underlying plant response to heat stress, the adverse effects of heat stress on the morphology, physiology, biochemistry and photosynthesis have been presented. Furthermore, the three molecular mechanisms employed by plants to cope with heat stress, including signal transduction pathways, transcriptional factor regulatory networks and the expression of heat-resistance related genes have been introduced as well. Based on these insights, this review suggests that bioinformatics, genetic engineering, cell biology and molecular biology may be further employed as tools for understanding the molecular mechanisms of heat stress in plants. At last, this review offers a prospective outlook on future research directions in this field.

【Objective】The yield of wheat, the second-highest-yielding food product in the world, has a major impact by grain weight. This research used materials from a recombinant inbred line (RIL) population derived from Heshangtou (HST) and Longchun 23 (LC23). Based on 55K SNP genotype data, QTL mapping was performed for traits related to grain weight of wheat, and co-segregation markers of major grain length QTL were developed and verified to provide reference for molecular marker assisted selection breeding.【Method】The wheat 55K SNP microarray was used to genotype parents and RIL populations, and a high density genetic linkage map was constructed, and its correlation with Chinese spring reference genome IWGSC RefSeq v1.0 was analyzed. QTL mapping of traits related to grain weight in multiple environments based on inclusive composite interval mapping method. The analysis of variance of major effect QTLs were performed to judge the additive interaction effect among different QTLs, and to analyse its effect on traits related to grain weight. At the same time, the corresponding kompetitive allele specific PCR marker was developed according to the closely linked SNP loci of major QTL for grain length, and verified in 242 wheat accessions worldwide.【Result】In this study, a high density genetic map of Heshangtou/Longchun 23 RIL population was constructed, with full length 4 543 cM, including 22 linkage groups, covering 21 chromosomes of wheat, and the average genetic distance was 1.7 cM. There was a significant correlation between genetic map and physical map, and the Pearson correlation coefficient were 0.77-0.99 (P<0.001). A total of 51 QTLs related to grain weight were detected, among them, 4 stable major QTLs were found in multi-environments (three or more environments) and distributed on 2D, 5A, 6B and 7D chromosomes. According to the physical interval and functional markers, it is inferred that stable major QTLs Qtkw.nwafu-2D.1 and Qtkw.nwafu-7D are photoperiod gene Ppd-D1 and flowering gene FT-D1, respectively. The analysis of variance shows that there is a significant interaction between them. The favorite alleles polymerization of Qtkw.nwafu-2D.1 and Qtkw.nwafu-7D can significantly increase thousand grain weight and grain width of wheat. In addition, the corresponding KASP molecular detection marker AX-111067709 was developed based on the co-segregated SNP of the major locus Qgl.nwafu-5A for grain length, which was significantly correlated with grain length and grain weight traits in a diversity panel comprising of 242 wheat accessions, and could increase grain length by 3.33% to 4.59% and grain weight 5.70% to 10.35% in different environments (P<0.001).【Conclusion】There are several genetic loci that affect traits linked to grain weight in Heshangtou (HST) and Longchun 23 (LC23), and Qtkw.nwafu-2D.1 and Qtkw.nwafu-7D dramatically increased thousand grain weight and grain width through additive interaction effects. Qgl.nwafu-5A is significantly correlated with grain weight and grain length, and its co-segregated molecular marker AX-11106770 can be used in molecular marker assisted selection breeding.

In order to widely promote the newly approved wheat variety ‘Shannong 116' with high yield and strong gluten into production as soon as possible, we conducted an in-depth analysis from the genetic background of its hybrid parents, the performance of high and stable yield in regional trials, and the performance of strong gluten stability in many years of quality test results. The results showed that the yield of ‘Shannong 116' increased significantly compared with the control, the national test increased by 4.0% compared with ‘Zhoumai 18', and the Shandong test increased by 3.8% compared with ‘Jinan 17'. In the national wheat quality evaluation for four consecutive years from 2018 to 2021, the quality test indicators of ‘Shannong 116' all met the GB/T17892 standard for strong or medium gluten wheat. The plant height of ‘Shannong 116' is 76.9 cm, with compact plant type, neat ear layer and good ripening. It combines the excellent characteristics of the female parent of strong strength, disease resistance, early maturity and the male parent of high yield, water saving and lodging resistance. It is suitable for large-scale planting and market order acquisition and utilization in the Huang-huai wheat region.

【Objective】The temporal characteristics, spatial pattern, evolution mode, decoupling relationship and performance evaluation of China’s agricultural carbon emissions were analyzed scientifically, so as to provide a basis for helping China achieve the goal of “carbon peaking and carbon neutrality” and strengthen the construction of an agricultural power. 【Method】This study constructed an index system for assessing agricultural carbon emissions and agricultural carbon emission performance in China, and measured the systematic measurement index of agricultural carbon emissions in Chinese provinces from 2007 to 2020. The Kernel density estimation and standardized ellipsoidal visualization analysis were used to analyze the regional distribution characteristics and spatial-temporal evolution trends of agricultural carbon emissions, Tapio model was used to examine the decoupling relationship between examining agricultural carbon emissions and economic growth, and the super-efficient SBM model with non-expected output was constructed to report the agricultural carbon emission performance and decomposition efficiency of China and the seven economic regions. 【Result】 From 2007 to 2020, the overall agricultural carbon emissions in China showed an “inverted U-shaped” curve of rising and then declining, with obvious regional differences and stable distribution of ranks. The eastern region had the best emission reduction effect, the central region had a “bipolar” distribution, and the western region had a higher pressure of emission reduction, with the overall spatial pattern dominated by the northeast-southwest direction, and tended to be decentralized to the northeast and northwest. China’s agricultural carbon emissions and agricultural economic development have been maintained at a weakly decoupled level and have made a breakthrough to a strongly decoupled level, which could be divided into two stages: a stable period (2007-2016) and a breakthrough period (2017-2020). The assessment of agricultural carbon emission performance showed a trend of “rapid rise - slow decline - steady improvement”, with the Great Northwest Economic Zone and the Northern Coastal Economic Zone in the first and last positions, respectively, and the contribution of technological change in agricultural production (TC) was more prominent than that of technical efficiency change (EC). 【Conclusion】With 2017 as the inflection point, China’s agricultural carbon emissions as a whole showed a decreasing trend, and the agricultural economic development as a whole was gradually getting rid of the dependence on agricultural carbon emissions, with different agricultural bases and different emission reduction targets in each region and province. It was necessary to reasonably plan the scale and internal structure of agricultural comparative advantage industries according to local conditions, reasonably select the resource endowment production characteristics of industries in the region. At the same time, we should pay attention to technology iteration and updating in the agricultural economic development and energy conservation and emission reduction in the role of promoting, taking into account the regional ecological benefits and economic benefits.

Zinc (Zn) is an essential micronutrient for animals and plants. Zinc deficiency or excess can seriously affect the growth and development of rice. Maintaining zinc content in rice at a certain level is helpful to improve the yield and quality of rice, increase the zinc content in grain, and solve the current problem of zinc deficiency in human body to a certain extent. Therefore, it is important to understand Zn uptake, transport, distribution, and other molecular mechanisms regulating Zn homeostasis in rice. In this review, we briefly summarized the importance of zinc in plants, especially the ion transporters in rice and the molecular mechanisms. The roles of these ion transporters in the uptake of Zn from soil, the transport from root to shoot, and the distribution of Zn to various parts of rice were summarized. Some molecular mechanisms related to ion transporters were also summarized. This study provides reference for the mining of zinc homeostasis regulatory genes in rice, the study of molecular mechanism, and the creation of high zinc rice germplasm.

To improve soil nutrient utilization efficiency and deal with single manure application problems such as dosage, fertilizer efficiency, and nutrient leaching, “biochar”, “compost”, “biochar manure application”, “soil properties” and “crop nutrients” were used as keywords to search and summarize relevant literatures on sources of Web of Science, Google Scholar, China National Knowledge Internet and others. The results showed that: (1) biochar improved the maturity of composting, increased the abundance of microbial communities, and reduced the risk of nutrient leaching in organic fertilizers, thus effectively reducing the environmental impact of traditional composting; (2) manure combined with biochar could improve soil moisture condition, and increase the contents of the available phosphorus and available potassium of different types of soils. Meanwhile, it could also provide better living materials and an environment for soil organisms and microorganisms; (3) the combination of biochar and organic fertilizer could increase the yield of crops and improve the contents of nitrogen, phosphorus and potassium, while different types of crops responded to them differently. The combined application of biochar and organic fertilizer enhanced soil fertility and plant nutrition, and its effect varied with the application rate, type of soil and crop and other factors. Our studies could provide a reference for efficient utilization of livestock and poultry manure resources in agricultural production.

With the intensification of global climate change and land salinization, improving the ability of rice (Oryza sativa L.) to grow in saline and alkaline environments has become a key challenge for agricultural production. The realization of the strategy of " the adaptation of germplasm to land " requires a deep understanding of the salt tolerance mechanism of rice, then breeding improvement on this basis. In this study, we summarized the recent research results on salt tolerance regulatory genes in rice, and classified them functionally according to the biological processes involved. The perception of salt stress in rice and the subsequent activation of various physiological regulatory mechanisms, including osmotic regulation, ion homeostasis, antioxidant defense system and nutrient balance, were analyzed in detail. In this review, we focus on several key Salt stress signaling pathways in rice, including the SOS (Salt Overly Sensitive) pathway, MAPK (Mitogen-Activated Protein Kinase) cascade pathway and hormone regulatory pathway. These pathways play crucial roles in rice adaptation salt stress environment. By reviewing the existing literature, this review aims to provide a comprehensive overview of the salt tolerance regulatory genes and their functions in rice, provide scientific basis on breeding salt-tolerant rice on these grounds, and as a reference in improving the yield and quality of rice under saline and alkaline environments.

Global agriculture is facing severe challenges, and breeding technology is the foundation and key to the development of the seed industry. Gene editing technology refers to the precise modification of target genes to achieve deletion, insertion, and replacement of specific target gene fragments. It can precisely modify target genes or introduce certain excellent genes into crops to produce crops with excellent agronomic traits, which has great potential in molecular design breeding and is of great significance to ensuring food security. Weed damage has a huge impact on the yield and quality of crops. To control weed damage efficiently, safely and sustainably has always been a hot research topic. Currently, more than 200 types of chemical herbicides have emerged in the global market. Using chemical methods to control weeds has become an important part of modern agriculture, and the cost of weed control has been significantly reduced by promoting herbicide-resistant crops. However, with the large-scale promotion of herbicide-resistant crops and the long-term use of single herbicides, environmental safety problems such as weed resistance and escape of resistant genes have gradually been discovered. Currently, the development of functional genomics, bioinformatics and genetic engineering technology (especially the widespread application of gene editing technology in plants) has created conditions for the creation of herbicide-resistant crops and new efficient weed control systems. In this article, the main target genes of herbicides that inhibit amino acid biosynthesis, lipid metabolism, carotenoid, plastoquinone and tocopherol biosynthesis pathways and their action mechanisms are introduced at first. Secondly, two methods for mining new herbicide resistance genes and herbicide systems are introduced, including the directed mutation method of herbicide resistance genes within crops based on CRISPR/Cas system and the resistance gene guidance method based on the co-evolution theory of natural product and organisms in nature. Moreover, the research progress of three breeding methods for herbicide resistant crops was reviewed, including conventional breeding, transgenic breeding and CRISPR/Cas genome editing based breeding. Among them, the research progress of CIRSPR/Cas system, base editing technology, and prime editing system in cultivating herbicide resistant crops were highlighted. The main challenge faced by chemical control of weeds and herbicide resistant crops is resistant weeds and environmental safety issues, and gene escape, respectively. At present, the rapid development of genome editing technology provides new solutions and new opportunities for the development of herbicide resistant crops in the post genome era. Finally, the prospects for the future of herbicide-resistant crops were provided.

In order to elucidate the mechanism of plant response to abiotic stress and accelerate the improvement of plant breeding, the application of genomics, transcriptomics, proteomics, metabolomics and phenomics in plant response to abiotic stress was summarized. The characteristics, advantages and disadvantages of genomics, transcriptomics, proteomics, metabolomics and phenomics technologies were analyzed. The research progresses of multi-omics technologies such as genomics, transcriptomics, proteomics, metabolomics, and phenomics in plant response to abiotic stress in recent years were generalized by key points in this paper. It was pointed out that there were some problems in the application of multi-omics technology in plants, such as weak correlation, insufficient data mining and so on. The overall mechanism of plant response to abiotic stress should be fully elucidated by using multi-omics technology. It was suggested that multi-omics techniques should be integrated, and bioinformatics analysis methods should be strengthened.

【Objective】The purpose of this study is to identify the gene members of the Callosobruchus chinensis heat shock protein (HSP) superfamily, and to clarify the expression changes of HSP genes in C. chinensis after high and low temperature stress, so as to provide a theoretical basis for further exploration of HSP gene function.【Method】The CDS and protein sequences of HSP genes of different insects were downloaded from Insect Base 2.0 and used as a reference for local BLASTp and tBLASTn comparison search in the full-length transcriptome sequencing database of C. chinensis. At the same time, target sequences were screened again by combining HMMER and key words to complete the summary of search results. Bioinformatics analysis of HSP superfamily genes in C. chinensis was performed using CDD, MEGA, ProtParam, and other online analytical tools. Seven candidate HSP genes were screened out based on high and low temperature transcriptome sequencing data of C. chinensis adults and the expression characteristics of 7 CcHsps were compared and analyzed by qRT-PCR technique under different developmental stages and temperature stresses of C. chinensis.【Result】A total of 31 HSP genes were identified, including 3 HSP90s, 8 HSP70s, 8 HSP60s, and 12 sHSPs (small HSP). Physicochemical analysis showed that the proteins encoded by CcHsps contain 159-776 amino acid residues (aa), the molecular weights are about 18.4-88.9 kDa, and the theoretical isoelectric points are 4.95-9.17. Subcellular localization results showed that most CcHsps were located in the cytoplasm, while a few genes were located in the mitochondrial matrix, endoplasmic reticulum and nucleus. Phylogenetic analysis showed that different family members of HSPs in C. chinensis could integrate well with HSP in other insects, which indicating their evolutionary conservation. The results of qRT-PCR showed that the 7 candidate CcHsps were differentially expressed under different temperature stresses. After high temperature stress, the expression level of CcHsp20.102 in male and female adults was up-regulated by 1 000 and 500 times, respectively, and the expression level of CcHsp70-5 in male and female adults was up-regulated by 500 and 450 times. After the larvae undergoing high and low temperature stress, the expression level of CcHsp19.855 and CcHsp70-5 was significantly different.【Conclusion】A total of 31 complete HSP superfamily gene members were identified by the full-length transcriptome sequencing data of C. chinensis, which were divided into 4 subfamilies. Different HSP families had different gene structures, protein conserved domains and gene expression characteristics. The differential expression of 7 candidate CcHsps in different developmental stages and under different temperature stresses indicated that they played different functions and roles. It is speculated that CcHsp20.102 and CcHsp70-5 may perform important functions in the adult resistance to high temperature stress, and the high temperature tolerance of larvae may be related to the differential expression of CcHsp19.855 and CcHsp70-5.

【Objective】 The aim of this study was to identify the co expression modules of luffa fruit length and diameter development and to screen key regulatory genes, so as to provide the theoretical basis for subsequent research on the molecular mechanism of fruit shape control in luffa. 【Method】 The luffa fruits in 9 fruit development stages (2 days before anthesis, and 0, 2, 4, 6, 8, 10, 15, and 20 days after anthesis) were applied as research materials. The fruit length and diameter of each stage were measured. The WGCNA method was used to jointly analyze transcriptome and fruit length and diameter data, to identify co-expressed gene modules of fruit length and diameter development, and to screen out key regulatory genes.【Result】A total of 14 co expression modules were identified by WGCNA, among which two modules (Turquoise and Lightpink4) were significantly correlated with fruit length and diameter (absolute value of correlation coefficient=0.9); Turquoise module was significantly positively correlated, while Lightpink4 module was significantly negatively correlated. KEGG enrichment analysis found that the Turquoise module was significantly enriched in endocytosis and phenylpropanoid biosynthesis pathways, which were closely related to fruit enlargement and growth regulation, and could be used as a key gene module for studying fruit length and diameter in luffa. According to the connectivity and functional annotation of genes in Turquoise module, ten key regulatory genes were screened, including xyloglucan endotransglucosylase/hydrolase gene XTH23, actin-depolymerizing factor gene ADF2, chaperone protein gene DnaJ10, expansin gene (EXPA1, EXPA4 and EXLA5), kinesin gene kinesin-13A, auxin response genes SAUR21, and Aux/IAA11. The RT-qPCR results showed that the expression levels of ten regulatory genes significantly increased after the fruit entered the rapid growth period (8 day after anthesis), with an increase of 2-50 times approximately. Through constructing a gene interaction network, it was found that some candidate genes interacted with the WRKY, bHLH, and HSF transcription factor families.【Conclusion】The Turquoise module, an important co expression module of luffa fruit length and diameter was obtained, and ten potential candidate genes for luffa fruit shape control were screened. It was found that luffa fruit length and diameter development regulation mainly involved the processes of cell wall reconstruction, cell development and differentiation, and auxin regulation.

To provide a reference for the in-depth research and utilization of coumarin compounds in the genus Heracleum, this review summarized the types and chemical structures of the identified coumarin compounds in the genus Heracleum, concluded the research progress in aspects of anti-bacteria, anti-oxidation, anti-cell proliferation, anti-inflammation, toxicity, etc, and comparatively analyzed the difference between relevant domestic and foreign researches. It revealed the diversity of coumarin compounds in the genus Heracleum and their values in medical and food industries. It proposed during the drug development process, there are problems, such as the research in its function is not deep or systemic enough, active ingredients are unclear, etc. It suggested fortifying research on its toxicity and efficacy and promoting the development and utilization of the genus Heracleum as a plant resource for foods and medicine.

Anoectochilus roxburghii is a rare and precious medicinal and ornamental plant of the Orchidaceae. It is favored by people for the rich variety of medicinal and nutritional components, and its market demand has been increasing in recent years. In order to promote the industrial development and basic research of A. roxburghii, this paper summarizes previous studies on A. roxburghii research, briefly compares the phylogenetic differences of Anoectochilus, focus on the research results of genomics (structural genomics/functional genomics) and adversity (biotic/abiotic interaction) response, analyzes the main factors affecting the quality of A. roxburghii (processing methods, cultivation models, strains). This paper systematically reviews the studies on germplasm resources, genomics, environmental interaction, quality difference and embryo reproduction that have not been carried out in depth, and puts forward corresponding strategies and prospects for the main problems and the solutions. It provides a feasible reference for the molecular biology research of precious medicinal plants in the era of big data.

The study aims to explore the relationship between age, musk-extracted time and yield, quality of musk, and to supplement the information of musk secretion mechanism, quality traits and appropriate musk-extracted time. Stratified Random Sampling method was used to observe and statistically analyze the musk yield and color, shape of 59 forest musk deer (1-5 years old). The results showed that there was no significant effect on musk yield by age and musk-extracted time, but the musk yield of forest musk deer of 2.7-3.7 years old was higher than that of other age groups; the average musk yield of adult musk deer (>2 years old) was (17.636±6.642) g, which was 4.129 g higher than that of bred musk deer (≤ 2 years old) (13.444±3.356) g (P=0.002<0.05); the average musk yield of adult musk deer in March was (19.955±7.267) g, which was 4.759 g higher than that in September (15.196±5.0113) g (P=0.023<0.05); although there was no significant difference between the observed group and control group (P=0.230>0.05), individual forest musk deer had higher musk yield and a larger yield range. The proportion of powdery musk was 61.02%, blocky musk was 6.78%, dry mud or mud musk was 15%~17%, brown musk was 52.54%, dark brown musk was 13.56%, dark tan and tan musk were respectively11.86%, light brown musk was 6.78%, and chocolate brown and black tan musk were 1.69%, respectively; the powdery musk (85%) of bred musk deer was higher than that of adult musk deer (48.72%), and the proportion of brown musk was the highest (60%, 51.28%); Powdery musk of adult musk in March and September were 35% and 63.16%, blocky musk was 5% and 15.79%, dry mud musk was 15% and 21.05%, mud musk was 45% and 0.00%, respectively. In summary, there was no significant correlation between different ages, musk-extracted time and musk yield, musk yield of adult musk deer was significantly higher than that of bred musk deer, and musk yield in March was significantly higher than that in September; the observed musk deer population had the characteristics of wider distribution of musk yield and higher yield, which indicated that years of breeding had promoted the separation of quantitative characters of musk yield, and provided a preliminary basis for the establishment of the core population later; brown and powdery musk accounted for the highest proportion, and the musk with mud and high water content collected in March was significantly higher than that in September.

[Significance] Big Models, or Foundation Models, have offered a new paradigm in smart agriculture. These models, built on the Transformer architecture, incorporate numerous parameters and have undergone extensive training, often showing excellent performance and adaptability, making them effective in addressing agricultural issues where data is limited. Integrating big models in agriculture promises to pave the way for a more comprehensive form of agricultural intelligence, capable of processing diverse inputs, making informed decisions, and potentially overseeing entire farming systems autonomously. [Progress] The fundamental concepts and core technologies of big models are initially elaborated from five aspects: the generation and core principles of the Transformer architecture, scaling laws of extending big models, large-scale self-supervised learning, the general capabilities and adaptions of big models, and the emerging capabilities of big models. Subsequently, the possible application scenarios of the big model in the agricultural field are analyzed in detail, the development status of big models is described based on three types of the models: Large language models (LLMs), large vision models (LVMs), and large multi-modal models (LMMs). The progress of applying big models in agriculture is discussed, and the achievements are presented. [Conclusions and Prospects] The challenges and key tasks of applying big models technology in agriculture are analyzed. Firstly, the current datasets used for agricultural big models are somewhat limited, and the process of constructing these datasets can be both expensive and potentially problematic in terms of copyright issues. There is a call for creating more extensive, more openly accessible datasets to facilitate future advancements. Secondly, the complexity of big models, due to their extensive parameter counts, poses significant challenges in terms of training and deployment. However, there is optimism that future methodological improvements will streamline these processes by optimizing memory and computational efficiency, thereby enhancing the performance of big models in agriculture. Thirdly, these advanced models demonstrate strong proficiency in analyzing image and text data, suggesting potential future applications in integrating real-time data from IoT devices and the Internet to make informed decisions, manage multi-modal data, and potentially operate machinery within autonomous agricultural systems. Finally, the dissemination and implementation of these big models in the public agricultural sphere are deemed crucial. The public availability of these models is expected to refine their capabilities through user feedback and alleviate the workload on humans by providing sophisticated and accurate agricultural advice, which could revolutionize agricultural practices.

Map-based cloning is a classical and effective method to identify candidate genes for specific phenotypic variants. Map-based cloning of functional genes plays important roles in the innovative utilization of germplasm resources, molecular design breeding and improving breeding efficiency. In recent years, the whole-genome sequencing of Gossypium raimondii, Gossypium arboreum, Gossypium hirsutum, and Gossypium barbadense has been completed and improved. Map-based cloning has entered into a crucial period. In 2016, the dominant glandless gene Gl₂^e (GoPGF) was the first map-based cloning gene in cotton. So far, 20 qualitative traits genes and 5 quantitative traits genes have been identified by map-based cloning technology. In this paper, research progress was systematically reviewed in fiber, gland, nectary, leaf type, plant architecture, plant color, and fertility in terms of gene symbols, names, chromosomal positioning, and candidate genes. Moreover, map-based cloning strategies were systematically reviewed in mapping populations and bulked segregate analysis-sequencing (BSA-seq). With the reduction of sequencing cost and utilization of BSA-seq, it is believed that more and more genes will be cloned by map-based cloning technology. In addition, transformation and genome editing have been successfully used to evaluate the function of the candidate gene in the target interval. It is believed that map-based cloning could provide a theoretical basis and genetic resources for molecular design breeding in cotton.

The aim was to establish a method for the determination of twelve flavonoid components in Scutellaria baicalensis. The HPLC method was performed on an Agilent ZORBAX SB-C18 column (4.6 mm×150 mm, 5μm) with a detection wavelength of 280 nm and a mobile phase of acetonitrile-0.1% phosphoric acid aqueous solution at a flow rate of 1.0 mL/min and a sample volume of 10 μL. The established extraction and chromatographic conditions were used for the qualitative and quantitative analysis of Scutellaria baicalensis. The linearity of the twelve flavonoids in Scutellaria baicalensis was good in the mass concentration range (r>0.98), and the average spiked recoveries of scutellarin, apigenin-7-O-β-D-glucopyranoside, carthamidin, baicalin, luteolin, chrysin-7-O-glucuronide, wogonoside, apigenin, baicalein, wogonin，chrysin and oroxylin A were 99.7214%, 92.7039%, 103.9755%, 100.5837%, 92.4819%, 93.2465%, 99.6387%, 104.3866%, 95.0767%, 95.3245%, 92.2161%, 95.3995% (RSD<5). A method for the determination of twelve flavonoid components in Scutellaria baicalensis was established, which was easy to operate, reproducible and accurate.

Genomic selection is defined as using the molecular marker information that covered the whole genome to estimate individual’s breeding values. Using genome information can avoid many problems caused by pedigree errors so as to improve selection accuracy and shorten breeding generation intervals. According to different statistical models, methods of estimated genomic breeding value (GEBV) can be divided into based on BLUP (best linear unbiased prediction) theory, based on Bayesian theory and others. At present, GBLUP and its improved method ssGBLUP have been widely employed. Accuracy is the most used evaluation metric for genomic selection models, which is to evaluate the similarity between the true value and the estimated value. The factors that affect the accuracy can be reflected from the model, which can be divided into controllable factors and uncontrollable factors. Traditional genomic selection methods have promoted the rapid development of animal breeding, but these methods are currently facing many challenges such as multi-population, multi-omics, and computing. What’s more, they cannot capture the nonlinear relationship between high-dimensional genomic data. As a branch of artificial intelligence, machine learning is very close to biological mastery of natural language processing. Machine learning extracts features from data and automatically summarizes the rules and use to make predictions for new data. For genomic information, machine learning does not require distribution assumptions, and all marker information can be considered in the model. Compared with traditional genomic selection methods, machine learning can more easily capture complex relationships between genotypes, phenotypes, and the environment. Therefore, machine learning has certain advantages in animal genomic selection. According to the amount and type of supervision received during training, machine learning can be classified into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The main difference is whether the input data is labeled. The machine learning methods currently applied in animal genomic selection are all supervised learning. Supervised learning can handle both classification and regression problems, requiring the algorithm to be provided with labeled data and the desired output. In recent years, the application of machine learning in animal genomic selection has been increasing, especially in dairy and beef cattle. In this review, machine learning algorithms are divided into three categories: single algorithm, ensemble algorithm and deep learning, and their research progress in animal genomic selection were summarized. The most used single algorithms are KRR and SVR, both of which use kernel tricks to learn nonlinear functions and map data to higher-dimensional kernel spaces in the original space. Currently commonly used kernel functions are linear kernel, cosine kernel, Gaussian kernel, and polynomial kernel. Deep learning, also known as a deep neural network, consists of multiple layers of connected neurons. An ensemble learning algorithm refers to fusing different learners together to obtain a stronger supervised model. In the past decade, the related literature on machine learning and deep learning has shown exponential growth. And its application in genomic selection is also gradually increasing. Although machine learning has obvious advantages in some aspects, it still faces many challenges in estimating the genetic breeding value of complex traits in animals. The interpretability of some models is low, which is not conducive to the adjustment of data, parameters, and features. Data heterogeneity, sparsity, and outliers can also cause data noise for machine learning. There are also problems such as overfitting, large marks and small samples, and parameter adjustment. Therefore, each step needs to be handled carefully while training the model. This paper introduced the traditional methods of genomic selection and the problems they face, the concept and classification of machine learning. We discussed the research progress and current challenges of machine learning in animal genomic selection. A Case and some application suggestions were given to provide a certain reference for the application of machine learning in animal genomic selection.

Sorghum is the fifth largest grain crop in the world and can be used for food, feed, brewing and bioenergy. Sorghum genetic transformation technology is an essential and important tool in the research of sorghum functional genomics and can also serve as an important complement to traditional breeding methods. In this review, we summarize the research progress of sorghum transformation in recent years, analyze the problems in sorghum genetic transformation and propose strategic solutions to them in order to provide a reference for further improvement of sorghum genetic transformation technology. By summarizing more than 50 literatures on sorghum tissue culture and genetic transformation in recent years, we introduced the current research status of sorghum genotypes, explant sources, and regeneration system construction for genetic transformation, and compared the advantages and disadvantages of four commonly used methods for sorghum genetic transformation: electroporation, pollen-mediated transformation, particle bombardment and Agrobacterium-mediated transformation, summarized the effects of the main components of genetic transformation vectors, including promoters, target genes, selective marker genes and reporter genes, on transformation efficiency, explained the current application status of sorghum genetic transformation, analyzed the main bottleneck problemns in sorghum genetic transformation technology, and studied countermeasures. Sorghum genotypes have a significant influence on tissue culture and P898012 and Tx430 are the most widely used. Gene bombardment and Agrobacterium-mediated transformation are the most commonly used methods for sorghum genetic transformation, and the advantages of Agrobacterium-mediated transformation are gradually emerging. In vector construction, CaMV35S and ubi1 are the most commonly used promoters, and antibiotic resistance genes (nptII, hpt), herbicide resistance genes (bar), and nutrient assimilation genes are the three commonly used selection markers. With the development of sorghum genetic transformation technology and CRISPR/Cas9-mediated gene editing technology, some genes with important agronomic traits have been successfully transferred into sorghum. However, strong genotype dependence, long tissue culture cycle, and poor genetic transformation stability are the main bottlenecks that limit the genetic transformation of sorghum. By introducing morphogenesis regulatory factors, somatic cell generation can be directly performed, which shortens the tissue culture cycle, improves the transformation efficiency, and expands the source of explants. This has become a major breakthrough in sorghum genetic transformation technology. The use of morphogenesis regulatory factors and adoption of cut-dip-budding (CDB) delivery system can further improve the sorghum genetic transformation technology. Combined with the application of CRISPR/Cas9 gene editing technology, they will surely provide an important technical basis for the sorghum molecular breeding and gene function identification.

【Objective】The results of carbon sequestration studies on combining straw returning with nitrogen fertilizer are controversial. Aimed at such problem, this experiment was carried out to reveal the effects of combining straw returning with nitrogen fertilizer on Carbon sequestration capacity and mechanism of farmland, so as to provide a reference for the future research. 【Method】Based on 11 years of long-term positioning experiments, this paper adopted split-zone design, the main treatment included straw returning to soil and removal straw from field, and the subplots included three N application rate, which were no nitrogen (N0), 168 kg·hm^-2 (N168, nitrogen), and 336 kg·hm^-2 (N336, excessive nitrogen application). 【Result】Compared with wheat without nitrogen fertilizer, wheat yield increased by 14.4%-19.5% with nitrogen fertilizer. The effect of straw returning to the field on yield was not significant. Straw returning significantly increased the cumulative input of soil carbon by 70.8% (P<0.05), but had no significant effect on soil organic carbon storage. Compared N0, the nitrogen application significantly increased soil carbon accumulation input and soil organic carbon storage by 7.7%-8.5% (P<0.05) and 4.7%-8.1% (P<0.05), respectively. The application of nitrogen fertilizer significantly increased the carbon fixation rate by 32.7%-56.1% (P<0.05), and N336 significantly increased the soil carbon fixation efficiency by 51.8% (P<0.05); straw returning to the field did not significantly improve the soil carbon fixation rate, but significantly reduced the carbon fixation efficiency by 30.9% (P<0.05). Both nitrogen application and straw returning could improve soil carbon pool capacity, and N0 and N168 have reached carbon saturation. The content of soluble organic carbon (DOC), microbial biomass carbon (MBC) and easily oxidized organic carbon (EO) in the soil increased by 4.6%, 11.2% and 4.5% respectively after returning straw to the field. Compared N0, DOC under N168 and N336 increased by 14.12% and 29.54% respectively; MBC decreased by 14.0% and 28.0% on average, respectively; EO increased by 8.2% and 11.5%, respectively. Straw returning to the field was beneficial to the improvement of soil DOC/SOC and microbial entropy. Applying nitrogen fertilizer was beneficial to the increase of DOC/SOC, but reduced the microbial entropy. Both straw returning and nitrogen fertilizer application had no effect on soil EO/SOC. Both straw returning and nitrogen application were beneficial to the improvement of macroaggregates (>0.25 mm), and straw returning significantly increased the organic carbon content of macroaggregates by 5.2%. The average weight diameter (MWD) and geometric average diameter (GMD) of aggregates under non-return showed a trend of first increasing and then decreasing with the increase of nitrogen level, while under straw returning, it showed an increase with the increase of nitrogen level. Straw returning increased the MWD and GMD of aggregates by 8.8% and 7.5% respectively, and the application of nitrogen fertilizer increased the MWD and GMD by 14.1%-22.7% and 16.8%-23.4% respectively, compared with CK. Both straw returning and nitrogen application could improve the distribution of organic carbon in large aggregates. 【Conclusion】Straw returning with nitrogen fertilizer could increase carbon input, increase activated organic carbon content, reduce microbial activity, and improve the protection of organic carbon by aggregates.

【Objective】The objective of this study is to screen Trichoderma strains which have inhibitory effect on the Pythium spp. causing maize stalk rot, and to clarify their taxonomic status, control efficacy and antifungal mechanism. This study will provide important resources for the research and development of biocontrol agent against Pythium stalk rot.【Method】For the antagonistic strains screening, the inhibitory effect of Tichoderma strains on P. inflatum, P. arrhenomanes and P. aristosporum was tested by measuring the mycelia growth. The taxonomic status of Tr21 was determined by morphological and molecular characteristics. The effect of Tr21 on the mycelia morphology of Pythium spp. was observed in the laboratory. In order to analyze the effect of Tr21 fermentation broth on the membrane permeability of Pythium spp., propyridine bromide (PI) dye solution was used to stain, and the absorbance values of protein and nucleic acid in mycelia supernatant at different treatment times were detected. The effect of Tr21 fermentation broth on germination characteristics of maize seeds was tested by seed soaking with different concentrations of fermentation broth. The control efficacy of Tr21 on stalk rot was confirmed through greenhouse pot and field inoculation experiments.【Result】From the 109 strains of Trichoderma spp., seven strains were screened with antagonistic activity against P. inflatum, P. arrhenomanes and P. aristosporum, and the inhibition rate was above 60%. The inhibition rate of Tr21 to three Pythium species reached 100%, the inhibition rate of 5×, 10× and 20× diluent to three Pythium species reached 100%, and the inhibition rate of 50× diluent to three Pythium species was also more than 55.56%. Tr21 strain was identified by morphological and molecular biology as T. afroharzianum. The results of microscopic observation showed that the fermentation broth of Tr21 could cause mycelial malformations, such as rough mycelia, increased mycelial branching, shortened nodes, and overflow of mycelia contents. The result of PI fluorescence stain showed that the cell membrane of three Pythium species was damaged by Tr21 fermentation broth, and the PI dye was more likely to penetrate the damaged cell membrane into the mycelium and stain the mycelia red. The results of nucleic acid and protein leakage showed that the absorbance values of the mycelia treated by the fermentation broth changed greatly. After treatment for 5 h, the OD₂₆₀ increased by 0.08 and OD₂₈₀ increased by 0.10, 0.11 and 0.10, respectively, indicating that the membrane of the mycelia was damaged, leading to the overflow of mycelia contents. The different concentrations of Tr21 fermentation broth had no effect on the germination characteristics of maize seeds, and the 20× diluent had the best effect on germination and growth of seeds. The results of pot experiment showed that 5× diluted fermentation broth of Tr21 had the best control efficacy on Pythium stalk rot caused by three Pythium species, which was 60.67%, 63.15% and 59.66%, respectively. The control efficacy on Pythium stalk rot of 5× diluent was the highest, reaching 82.25%, with a mass ratio of 1﹕100 (5× diluent to seed).【Conclusion】An effective T. afroharzianum strain Tr21 was obtained for preventing and controlling of maize Pythium stalk rot. The fermentation broth of Tr21 can lead to mycelia malformation, breakage, cell membrane damage and contents leakage, etc. In conclusion, the T. afroharzianum strain Tr21 is a promising biocontrol microbial.

Wheat stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a devastating disease threaten food security in China and worldwide. Epidemics of wheat stripe rust have been under control through applying resistant cultivars and crop protection approaches. However, due to climate change, innovation of cropping system, improvement of breeding technology, yield level enhancement of wheat cultivars, variation in structure and frequency of virulence genes in Pst populations in the new era, the current status of stripe rust resistance genes in wheat breeding programs need to be evaluated. The results could provide useful information for applying stripe rust resistance genes to develop new wheat cultivars with broad-spectrum and durable rust resistance. After multiple year’s stripe rust resistance survey, genetic analysis, molecular tagging and mining of stripe rust resistance genes in wheat cultivars and advanced breeding lines, the current status of major stripe rust resistance genes utilization was reviewed. We summarized the present situations of major stripe rust resistance gene discovery and germplasm innovation, the most frequently used stripe rust resistance genes, new strategy for pyramiding adult plant partial resistance and all stage resistance, and molecular marker assisted selection for developing wheat cultivars with broad spectrum and durable resistance in China. This review also proposes the major research areas in wheat stripe rust resistance breeding in the new era.

With the rapid development of aquaculture in China, the environmental problems caused by the discharge of aquaculture tailwater are becoming more and more serious. The treatment of aquaculture tailwater has emerged as a crucial research area in recent years. At present, the main methods of aquaculture tailwater treatments include physical, chemical, and biological treatments, which are often combined in practical production. Based on the research status at home and abroad, the physical, chemical and biological technologies of aquaculture tail water treatment were summarized and analyzed, and the development trend of aquaculture tail water treatment technology in China was prospected, aiming to provide some references and directions for aquaculture tail water treatment.

[Objective] The rural revitalization strategy presents novel requisites for the extension of agricultural technology. However, the conventional method encounters the issue of a contradiction between supply and demand. Therefore, there is a need for further innovation in the supply form of agricultural knowledge. Recent advancements in artificial intelligence technologies, such as deep learning and large-scale neural networks, particularly the advent of large language models (LLMs), render anthropomorphic and intelligent agricultural technology extension feasible. With the agricultural technology knowledge service of fruit and vegetable as the demand orientation, the intelligent agricultural technology question answering system was built in this research based on LLM, providing agricultural technology extension services, including guidance on new agricultural knowledge and question-and-answer sessions. This facilitates farmers in accessing high-quality agricultural knowledge at their convenience. [Methods] Through an analysis of the demands of strawberry farmers, the agricultural technology knowledge related to strawberry cultivation was categorized into six themes: basic production knowledge, variety screening, interplanting knowledge, pest diagnosis and control, disease diagnosis and control, and drug damage diagnosis and control. Considering the current situation of agricultural technology, two primary tasks were formulated: named entity recognition and question answering related to agricultural knowledge. A training corpus comprising entity type annotations and question-answer pairs was constructed using a combination of automatic machine annotation and manual annotation, ensuring a small yet high-quality sample. After comparing four existing Large Language Models (Baichuan2-13B-Chat, ChatGLM2-6B, Llama 2-13B-Chat, and ChatGPT), the model exhibiting the best performance was chosen as the base LLM to develop the intelligent question-answering system for agricultural technology knowledge. Utilizing a high-quality corpus, pre-training of a Large Language Model and the fine-tuning method, a deep neural network with semantic analysis, context association, and content generation capabilities was trained. This model served as a Large Language Model for named entity recognition and question answering of agricultural knowledge, adaptable to various downstream tasks. For the task of named entity recognition, the fine-tuning method of Lora was employed, fine-tuning only essential parameters to expedite model training and enhance performance. Regarding the question-answering task, the Prompt-tuning method was used to fine-tune the Large Language Model, where adjustments were made based on the generated content of the model, achieving iterative optimization. Model performance optimization was conducted from two perspectives: data and model design. In terms of data, redundant or unclear data was manually removed from the labeled corpus. In terms of the model, a strategy based on retrieval enhancement generation technology was employed to deepen the understanding of agricultural knowledge in the Large Language Model and maintain real-time synchronization of knowledge, alleviating the problem of LLM hallucination. Drawing upon the constructed Large Language Model, an intelligent question-answering system was developed for agricultural technology knowledge. This system demonstrates the capability to generate high-precision and unambiguous answers, while also supporting the functionalities of multi-round question answering and retrieval of information sources. [Results and Discussions] Accuracy rate and recall rate served as indicators to evaluate the named entity recognition task performance of the Large Language Models. The results indicated that the performance of Large Language Models was closely related to factors such as model structure, the scale of the labeled corpus, and the number of entity types. After fine-tuning, the ChatGLM Large Language Model demonstrated the highest accuracy and recall rate. With the same number of entity types, a higher number of annotated corpora resulted in a higher accuracy rate. Fine-tuning had different effects on different models, and overall, it improved the average accuracy of all models under different knowledge topics, with ChatGLM, Llama, and Baichuan values all surpassing 85%. The average recall rate saw limited increase, and in some cases, it was even lower than the values before fine-tuning. Assessing the question-answering task of Large Language Models using hallucination rate and semantic similarity as indicators, data optimization and retrieval enhancement generation techniques effectively reduced the hallucination rate by 10% to 40% and improved semantic similarity by more than 15%. These optimizations significantly enhanced the generated content of the models in terms of correctness, logic, and comprehensiveness. [Conclusion] The pre-trained Large Language Model of ChatGLM exhibited superior performance in named entity recognition and question answering tasks in the agricultural field. Fine-tuning pre-trained Large Language Models for downstream tasks and optimizing based on retrieval enhancement generation technology mitigated the problem of language hallucination, markedly improving model performance. Large Language Model technology has the potential to innovate agricultural technology knowledge service modes and optimize agricultural knowledge extension. This can effectively reduce the time cost for farmers to obtain high-quality and effective knowledge, guiding more farmers towards agricultural technology innovation and transformation. However, due to challenges such as unstable performance, further research is needed to explore optimization methods for Large Language Models and their application in specific scenarios.

The aim of this study was to provide reference for the functional study of early nodulation protein ENOD93 in non-legumes. In this study, bioinformatics method was used to identify rice ENOD93 gene family. The physical and chemical properties, chromosome location, gene structure, protein structure, expression spectrum and evolutionary relationship of the members were analyzed. The results showed that there were 7 members of rice ENOD93 gene family, which were distributed on chromosomes 2 and 6, and the gene structure was relatively simple. Moreover, most ENOD93 genes are highly similar in the distribution and arrangement of conserved domain and motif. The results of RNA-Seq data analysis showed that ENOD93 gene family was highly expressed in pistils, seeds and embryos, and the expression level of some gene was induced by stress. Based on the phylogenetic analysis of nine monocotyledonous and dicotyledonous species, 31 ENOD93 gene family members were divided into four distinct groups. The expression of ENOD93 gene in rice was different in different tissues and at different developmental stages, and some genes were induced by stress, suggesting that ENOD93gene family was involved in the development process of many plant tissues and played an important role in the response to stress.

[Objective] To effectively tackle the unique attributes of wheat leaf pests and diseases in their native environment, a high-caliber and efficient pest detection model named YOLOv8-SS (You Only Look Once Version 8-SS) was proposed. This innovative model is engineered to accurately identify pests, thereby providing a solid scientific foundation for their prevention and management strategies. [Methods] A total of 3 639 raw datasets of images of wheat leaf pests and diseases were collected from 6 different wheat pests and diseases in various farmlands in the Yuchong County area of Gansu Province, at different periods of time, using mobile phones. This collection demonstrated the team's proficiency and commitment to advancing agricultural research. The dataset was meticulously constructed using the LabelImg software to accurately label the images with targeted pest species. To guarantee the model's superior generalization capabilities, the dataset was strategically divided into a training set and a test set in an 8:2 ratio. The dataset includes thorough observations and recordings of the wheat leaf blade's appearance, texture, color, as well as other variables that could influence these characteristics. The compiled dataset proved to be an invaluable asset for both training and validation activities. Leveraging the YOLOv8 algorithm, an enhanced lightweight convolutional neural network, ShuffleNetv2, was selected as the basis network for feature extraction from images. This was accomplished by integrating a 3×3 Depthwise Convolution (DWConv) kernel, the h-swish activation function, and a Squeeze-and-Excitation Network (SENet) attention mechanism. These enhancements streamlined the model by diminishing the parameter count and computational demands, all while sustaining high detection precision. The deployment of these sophisticated methodologies exemplified the researchers' commitment and passion for innovation. The YOLOv8 model employs the SEnet attention mechanism module within both its Backbone and Neck components, significantly reducing computational load while bolstering accuracy. This method exemplifies the model's exceptional performance, distinguishing it from other models in the domain. By integrating a dedicated small target detection layer, the model's capabilities have been augmented, enabling more efficient and precise pest and disease detection. The introduction of a new detection feature map, sized 160×160 pixels, enables the network to concentrate on identifying small-targeted pests and diseases, thereby enhancing the accuracy of pest and disease recognition. Results and Discussion The YOLOv8-SS wheat leaf pests and diseases detection model has been significantly improved to accurately detect wheat leaf pests and diseases in their natural environment. By employing the refined ShuffleNet V2 within the DarkNet-53 framework, as opposed to the conventional YOLOv8, under identical experimental settings, the model exhibited a 4.53% increase in recognition accuracy and a 4.91% improvement in F₁-Score, compared to the initial model. Furthermore, the incorporation of a dedicated small target detection layer led to a subsequent rise in accuracy and F₁-Scores of 2.31% and 2.16%, respectively, despite a minimal upsurge in the number of parameters and computational requirements. The integration of the SEnet attention mechanism module into the YOLOv8 model resulted in a detection accuracy rate increase of 1.85% and an F₁-Score enhancement of 2.72%. Furthermore, by swapping the original neural network architecture with an enhanced ShuffleNet V2 and appending a compact object detection sublayer (namely YOLOv8-SS), the resulting model exhibited a heightened recognition accuracy of 89.41% and an F₁-Score of 88.12%. The YOLOv8-SS variant substantially outperformed the standard YOLOv8, showing a remarkable enhancement of 10.11% and 9.92% in accuracy, respectively. This outcome strikingly illustrates the YOLOv8-SS's prowess in balancing speed with precision. Moreover, it achieves convergence at a more rapid pace, requiring approximately 40 training epochs, to surpass other renowned models such as Faster R-CNN, MobileNetV2, SSD, YOLOv5, YOLOX, and the original YOLOv8 in accuracy. Specifically, the YOLOv8-SS boasted an average accuracy 23.01%, 15.13%, 11%, 25.21%, 27.52%, and 10.11% greater than that of the competing models, respectively. In a head-to-head trial involving a public dataset (LWDCD 2020) and a custom-built dataset, the LWDCD 2020 dataset yielded a striking accuracy of 91.30%, outperforming the custom-built dataset by a margin of 1.89% when utilizing the same network architecture, YOLOv8-SS. The AI Challenger 2018-6 and Plant-Village-5 datasets did not perform as robustly, achieving accuracy rates of 86.90% and 86.78% respectively. The YOLOv8-SS model has shown substantial improvements in both feature extraction and learning capabilities over the original YOLOv8, particularly excelling in natural environments with intricate, unstructured backdrops. Conclusion The YOLOv8-SS model is meticulously designed to deliver unmatched recognition accuracy while consuming a minimal amount of storage space. In contrast to conventional detection models, this groundbreaking model exhibits superior detection accuracy and speed, rendering it exceedingly valuable across various applications. This breakthrough serves as an invaluable resource for cutting-edge research on crop pest and disease detection within natural environments featuring complex, unstructured backgrounds. Our method is versatile and yields significantly enhanced detection performance, all while maintaining a lean model architecture. This renders it highly appropriate for real-world scenarios involving large-scale crop pest and disease detection.

To analyze the current situation of carbon storage and carbon sink value in Yunnan Province, based on the data of the 4th Forest Resources Class II survey in Yunnan Province, the arbor forest biomass was estimated by IPCC method, and the AS index was constructed based on the age structure of the forest to assess the forest carbon sequestration potential of Yunnan Province. The results showed that the carbon storage of arbor forests in Yunnan Province was 1433.60 Tg, and the carbon sink value was 11180681.55×10⁴ yuan. The biomass and carbon storage of arbor forests were mainly concentrated in Yunnan pine, oak and other broad-leaved trees. The carbon stocks in the west of Yunnan Province were mainly concentrated in Pu’er City and Diqing Prefecture. The carbon stocks in the west of Yunnan Province were higher than those in the east, and the age structure of the forests showed a single peak with a right deviation. The AS index of Yunnan Province is 1.15, the stand is younger, forests are highly productive, the forest biomass is increasing, and the forest carbon sink potential is large.

To advance the application of genome-wide association study (GWAS) in the precise mining of the key genetic variation loci associated with important agronomic traits in cucurbit crops, this paper introduced the principles and statistical models of GWAS, outlined the advantages of GWAS in identifying genetic variation loci in crops populations, studying plant metabolic mechanisms and implementing precise genetic improvement strategies. It also systematically reviewed the recent advances of GWAS in the genetic improvement of major cucurbit crops such as watermelon, melon, cucumber, pumpkin or other kinds of cucurbit crops. Furthermore, it provided an outlook on the joint multi-omics analysis and database in breeding research of cucurbit crops, giving a basis and reference in the process of genetic improvement of cucurbit crops.

【Objective】Spike length (SL) plays an important role in determining spike structure and yield potential of wheat. Quantitative trait loci (QTL) for spike length were excavated and their genetic effects were further analyzed to provide theoretical basis for molecular breeding. 【Method】This study consisted of a population of 198 F₆ recombinant inbred lines (RIL) derived from the cross between the natural mutant msf and the cultivar Chuannong 16 (MC population). The MC population and its parents were planted in five different environments including Wenjiang in 2021 and 2022 (2021WJ and 2022WJ); Chongzhou in 2021 and 2022 (2021CZ and 2022CZ); and Ya’an in 2021 (2021YA) for spike length measurement. The 16K SNP chip-based constructed high-quality and high-density genetic linkage maps were used to map QTL for spike length. Additionally, the genotype of the flanking markers for the major spike length QTL was used to analyze its genetic effect on yield-related traits and thus to evaluate its potentiality for yield improvement.【Result】A total of 14 QTL for spike length were identified and they were mainly distributed on chromosomes 1A (one), 1B (one), 2B (one), 3D (three), 4A (one), 4D (two), 5A (one), 5B (one), 7A (one), 7B (one), and 7D (one). Among them, QSl.sau.1A was detected in four environments and the best linear unbiased prediction (BLUP) value, explained 6.46% to 20.12% of the phenotypic variation, and thus was regarded as a major QTL. The positive allele at QSl.sau.1A came from the parental line msf. QTL analysis across multiple environments also detected QSl.sau.1A, indicating it exhibits minimal environmental influence and represents a major and stably expressed QTL. The effect of QSl.sau.1A was successfully verified in two populations with different genetic backgrounds. Genetic effects analysis showed that the positive allele of QSl.sau.1A showed a significant effect on improving grain number per spike (12.68%), grain weight per spike (14.99%), 1000-grain weight (5.79%), flag leaf width (2.94%), spikelet number (1.48%), and flowering date (0.61%), and a significant effect of reducing plant height (-6.47%) and effective tiller number (-36.11%).【Conclusion】A major and stably expressed spike length QTL, QSl.sau.1A, was detected on chromosome 1A. Its positive allele significantly increased grain number per spike, grain weight per spike, thousand grain weight, and spikelet number per spike, indicating its great breeding value.

Chalkiness is one of the important indexes to evaluate the appearance quality of rice. It is a bad character that seriously affects the grinding, appearance and taste quality of rice, and plays an important role in the market value evaluation of rice. This paper summarized the effects of environmental factors, physiological mechanisms and genetic mechanisms on the formation of chalkiness in rice, and pointed out the difficulties existing in the improvement of chalkiness in rice breeding. Based on the current research results and the development of related technologies, some suggestions for improvement were put forward to provide a certain research basis for the production of high-quality rice.