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农业科学学报（英文）
中国农业科学院农业信息研究所
农业科学学报（英文）

中国农业科学院农业信息研究所

翟虎渠

月刊

2095-3119

zgnykx@mail.caas.net.cn

010-82106283 82106280

100081

北京中关村南大街12号

农业科学学报（英文）/Journal Journal of Integrative AgricultureCSCDCSTPCD北大核心SCI
查看更多>>本刊创刊于2002年，由中国农业科学院、中国农学会主办，中国农业科学院农业信息研究所承办。刊登农牧业基础科学和应用科学的研究论文，覆盖作物科学、动物科学、农业环境、农业经济与管理等领域。
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    The Clausena Iansium genome provides new insights into alkaloid diversity and the evolution of the methyltransferase family

    Yongzan WeiYi WangFuchu HuWei Wang...
    3537-3553页
    查看更多>>摘要：Wampee (Clausena lansium) is an important evergreen fruit tree native to southern China that has a long history of use for medicinal purposes.Here,a chromosome-level genome of C.lansium was constructed with a genome size of 282.9 Mb and scaffold N50 of 30.75 Mb.The assembled genome contains 48.70％ repetitive elements and 24,381 protein-coding genes.Comparative genomic analysis showed that C.lansium diverged from Aurantioideae 15.91-24.95 million years ago.Additionally,some expansive and specific gene families related to methyltransferase activity and S-adenosylmethionine-dependent methyltransferase activity were also identified.Further analysis indicated that N-methyltransferase (NMT) is mainly involved in alkaloid biosynthesis and O-methyltransferase (OMT) participates in the regulation of coumarin accumulation in wampee.This suggested that wampee's richness in alkaloids and coumarins might be due to the gene expansions of NMT and OMT.The tandem repeat event was one of the major reasons for the NMT expansion.Hence,the reference genome of C.lansium will facilitate the identification of some useful medicinal compounds from wampee resources and reveal their biosynthetic pathways.
      原文链接:
    • 万方数据

    Identification of banana leaf disease based on KVA and GR-ARNet

    Jinsheng DengWeiqi HuangGuoxiong ZhouYahui Hu...
    3554-3575页
    查看更多>>摘要：Banana is a significant crop,and three banana leaf diseases,including Sigatoka,Cordana and Pestalotiopsis,have the potential to have a serious impact on banana production.Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases.Therefore,this paper proposes a novel method to identify banana leaf diseases.First,a new algorithm called K-scale VisuShrink algorithm (KVA) is proposed to denoise banana leaf images.The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds,the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image.Then,this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net (GR-ARNet) based on Resnet50.In this,the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed;the ResNeSt Module adjusts the weight of each channel,increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification;the model's computational speed is increased using the hybrid activation function of RReLU and Swish.Our model achieves an average accuracy of 96.98％ and a precision of 89.31％ applied to 13,021 images,demonstrating that the proposed method can effectively identify banana leaf diseases.
      原文链接:
    • 万方数据
    • 维普

    Machine learning ensemble model prediction of northward shift in potato cyst nematodes (Globodera rostochiensis and G. pallida) distribution under climate change conditions

    Yitong HeGuanjin WangYonglin RenShan Gao...
    3576-3591页
    查看更多>>摘要：Potato cyst nematodes (PCNs) are a significant threat to potato production,having caused substantial damage in many countries.Predicting the future distribution of PCN species is crucial to implementing effective biosecurity strategies,especially given the impact of climate change on pest species invasion and distribution.Machine learning (ML),specifically ensemble models,has emerged as a powerful tool in predicting species distributions due to its ability to learn and make predictions based on complex data sets.Thus,this research utilised advanced machine learning techniques to predict the distribution of PCN species under climate change conditions,providing the initial element for invasion risk assessment.We first used Global Climate Models to generate homogeneous climate predictors to mitigate the variation among predictors.Then,five machine learning models were employed to build two groups of ensembles,single-algorithm ensembles (ESA) and multi-algorithm ensembles (EMA),and compared their performances.In this research,the EMA did not always perform better than the ESA,and the ESA of Artificial Neural Network gave the highest performance while being cost-effective.Prediction results indicated that the distribution range of PCNs would shift northward with a decrease in tropical zones and an increase in northern latitudes.However,the total area of suitable regions will not change significantly,occupying 16-20％ of the total land surface (18％ under current conditions).This research alerts policymakers and practitioners to the risk of PCNs'incursion into new regions.Additionally,this ML process offers the capability to track changes in the distribution of various species and provides scientifically grounded evidence for formulating long-term biosecurity plans for their control.
      原文链接:
    • 万方数据

    The microbial community,nutrient supply and crop yields differ along a potassium fertilizer gradient under wheat-maize double-cropping systems

    Zeli LiFuli FangLiang WuFeng Gao...
    3592-3609页
    查看更多>>摘要：Soil microorganisms play critical roles in ecosystem function.However,the relative impact of the potassium (K) fertilizer gradient on the microbial community in wheat-maize double-cropping systems remains unclear.In this long-term field experiment (2008-2019),we researched bacterial and fungal diversity,composition,and community assemblage in the soil along a K fertilizer gradient in the wheat season (K0,no K fertilizer;K1,45 kg ha-1 K2O;K2,90 kg ha-1 K2O;K3,135 kg ha-1 K2O) and in the maize season (K0,no K fertilizer;K1,150 kg ha-1 K2O;K2,300 kg ha-1 K2O;K3,450 kg ha-1 K2O) using bacterial 16S rRNA and fungal internally transcribed spacer (ITS) data.We observed that environmental variables,such as mean annual soil temperature (MAT) and precipitation,available K,ammonium,nitrate,and organic matter,impacted the soil bacterial and fungal communities,and their impacts varied with fertilizer treatments and crop species.Furthermore,the relative abundance of bacteria involved in soil nutrient transformation (phylum Actinobacteria and class Alphaproteobacteria) in the wheat season was significantly increased compared to the maize season,and the optimal K fertilizer dosage (K2 treatment) boosted the relative bacterial abundance of soil nutrient transformation (genus Lactobacillus) and soil denitrification (phylum Proteobacteria) bacteria in the wheat season.The abundance of the soil bacterial community promoting root growth and nutrient absorption (genus Herbaspirillum) in the maize season was improved compared to the wheat season,and the K2 treatment enhanced the bacterial abundance of soil nutrient transformation (genus MND1) and soil nitrogen cycling (genus Nitrospira) genera in the maize season.The results indicated that the bacterial and fungal communities in the double-cropping system exhibited variable sensitivities and assembly mechanisms along a K fertilizer gradient,and microhabitats explained the largest amount of the variation in crop yields,and improved wheat-maize yields by 11.2-22.6 and 9.2-23.8％ with K addition,respectively.These modes are shaped contemporaneously by the different meteorological factors and soil nutrient changes in the K fertilizer gradients.
      原文链接:
    • 万方数据
    • 维普

    Water and nitrogen footprint assessment of integrated agronomic practice management in a summer maize cropping system

    Ningning YuBingshuo WangBaizhao RenBin Zhao...
    3610-3621页
    查看更多>>摘要：The footprints of water and nitrogen (WF and NF) provide a comprehensive overview of the type and quantity of water consumption and reactive nitrogen (Nr) loss in crop production.In this study,a field experiment over two years (2019 and 2020) compared three integrated agronomic practice management (IAPM) systems:An improved management system (T2),a high-yield production system (T3),and an integrated soil-crop management system (ISCM) using a local smallholder farmer's practice system (T1) as control,to investigate the responses of WF,Nr losses,water use efficiency (WUE),and nitrogen use efficiency (NUE) to IAPM.The results showed that IAPM optimized water distribution and promoted water use by summer maize.The evapotranspiration over the whole maize growth period of IAPM increased,but yield increased more,leading to a significant increase in WUE.The WUE of the T2,T3,and ISCM treatments was significantly greater than in the T1 treatment,in 2019 and 2020 respectively,by 19.8-21.5,31.8-40.6,and 34.4-44.6％.The lowest WF was found in the ISCM treatment,which was 31.0％ lower than that of the T1 treatment.In addition,the ISCM treatment optimized soil total nitrogen (TN) distribution and significantly increased TN in the cultivated layer.Excessive nitrogen fertilizer was applied in treatment T3,producing the highest maize yield,and resulting in the highest Nr losses.In contrast,the ISCM treatment used a reduced nitrogen fertilizer rate,sacrificing grain yield partly,which reduced Nr losses and eventually led to a significant increase in nitrogen use efficiency and nitrogen recovery.The Nr level in the ISCM treatment was 34.8％ lower than in the T1 treatment while NUE was significantly higher than in the T1 treatment by 56.8-63.1％ in 2019 and 2020,respectively.Considering yield,WUE,NUE,WF,and NF together,ISCM should be used as a more sustainable and clean system for sustainable production of summer maize.
      原文链接:
    • 万方数据
    • 维普