The excessive expansion of urbanized areas has resulted in haphazard land utili- zation, immoderate consumption of superior agricultural land and water resources, significant fragmentation of agricultural landscape, a...The excessive expansion of urbanized areas has resulted in haphazard land utili- zation, immoderate consumption of superior agricultural land and water resources, significant fragmentation of agricultural landscape, and gradual deterioration of the agro-ecological en- vironment. Combined, these factors cause poor land use efficiency. Under these circum- stances, comprehensively assessing land use efficiency for urban agriculture is a key issue in land use research. Currently, evaluation methods for agricultural land use efficiency narrowly concentrate on aspects of economic input and output. However, urban agro-ecosystems can provide diverse economic, social, and ecological services and functions. In particular, the social and ecological services and functions originating from agricultural land, which have a higher value than economic services, play a significant role in ensuring regional social, eco- logical, and environmental security. However, recent research has rarely taken these benefits into consideration. Therefore, land use value has been greatly underestimated, which has resulted in mishandled and poor land use policies. In this study, we apply Landsat imagery and social and economic statistical data for the Xi＇an metropolitan zone （XMZ） to investigate agricultural multi-functionality. We develop an evaluation framework for urban agricultural land use efficiency and identify agro-ecosystem services and functions as important outputs from agricultural land. The land use efficiency of urban agriculture is then evaluated using ecosystem services models, providing a mechanism for assessing spatial-temporal changes in land use efficiency in the XMZ from 1999 to 2015. Four important conclusions are reached from this analysis. First, the rapid urbanization and agricultural transformation from traditional cereal cultivation to modern urban agriculture has resulted in steadily increasing costs, out- puts, and land use efficiency of urban agriculture. The total output value increased 41% and land use efficiency per hectare increased by 33.13% on average. Second, the spatial patterns of comprehensive output and land use efficiency were dominated by economic outputs from agricultural land. Areas near cities, which are dominated by orchard and arable land, provide more economic functions. These areas support and regulate services due to the transforma- tion from extensive cereal production to intensive modern urban agriculture; therefore, they have higher output value and land use efficiency. In contrast, areas distant from cities, towns, and high traffic roads, namely, remote rural areas, provide more support and regulating ser- vices, but have relatively lower economic function due to inaccessibility tO urban markets and slow agricultural transformation. Therefore, these areas have lower output value and land use efficiency. The spatial change in agricultural output and land use efficiency in urban areas is strongly dependent on the degree of urbanization and agricultural transformation. Third, the total output value and land use efficiency of urban agriculture measured with our approach are much higher than evaluations using traditional methods. However, the spatial patterns measured using the two approaches are in agreement. The evaluation framework integrates ecological services and economic and social functions into a comprehensive output from agricultural land. This approach is more methodical and accurate for evaluating the compre- hensive efficiency of land use based on quantities and spatial scale because they are at the pixel scale. Finally, the evaluation results have important implications for enhancing current agricultural subsidies and even implementing ecological payment policies in China. Most importantly, they can be directly applied to agricultural transformation regulations, decision-making, and guidance for rational land utilization.展开更多
Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim ...Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of signifi cant,novel,and high-impact research in the fi elds of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.展开更多
Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim ...Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of significant,novel,and high-impact research in the fields of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.展开更多
Steady speed control of agricultural machinery can improve operating quality and efficiency.To address the impact of farmland slope variations on the speed stability of unmanned operation agricultural machinery,a hybr...Steady speed control of agricultural machinery can improve operating quality and efficiency.To address the impact of farmland slope variations on the speed stability of unmanned operation agricultural machinery,a hybrid control method was proposed.This method included a hybrid controller composed of a slope-based controller and a proportional-integral-derivative(PID)controller.The speed of agricultural machinery was influenced by longitudinal forces,which were divided into two parts:one part was slope-related forces and conventional resistance,and the other was hard-to-estimate forces,such as sliding friction.For the first part,a slope-based controller was designed;for the second part,a PID controller was implemented.By combining these two controllers,the system can dynamically adjust the throttle opening and the brake master cylinder pressure,ensuring steady speed travel on sloping farmland.Simulation tests at a target speed of 7 km/h demonstrated that the proposed controller maintained a stable speed,achieving a root mean square error of 0.13 km/h and a mean absolute percentage error of 1.6%.Field tests on a practical experimental platform validated the method’s effectiveness,with results showing consistent control performance across varying slope conditions.The proposed controller demonstrated superior control performance.Experimental data verified that this method can achieve precise control of the agricultural machinery’s movement speed,meeting the stability requirements for agricultural operations.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Foundation models are reshaping artificial intelligence,yet their deployment in specialised domains such as agricultural question answering(AQA)still faces challenges including data scarcity and barriers to domainspec...Foundation models are reshaping artificial intelligence,yet their deployment in specialised domains such as agricultural question answering(AQA)still faces challenges including data scarcity and barriers to domainspecific knowledge.To systematically review recent progress in this area,this paper adopts a task–paradigmperspective and examines applications across three major AQA task families.For text-based QA,we analyse the strengths and limitations of retrieval-based,generative,and hybrid approaches built on large languagemodels,revealing a clear trend toward hybrid paradigms that balance precision and flexibility.For visual diagnosis,we discuss techniques such as crossmodal alignment and prompt-driven generation,which are pushing systems beyond simple pest and disease recognition toward deeper causal reasoning.Formultimodal reasoning,we show how the fusion of heterogeneous data—including text,images,speech,and sensor streams—enables comprehensive decision-making for diagnosis,monitoring,and yield prediction.To address the lack of unified benchmarks,we further propose a standardised evaluation protocol and a diagnostic taxonomy specifically designed to characterise agriculture-specific errors.Finally,we outline a concreteAQA roadmap that emphasises safety alignment,hallucination control,and lightweight deployment,aiming to guide future systems toward greater efficiency,trustworthiness,and sustainability.展开更多
Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisiti...Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.展开更多
Ensuring national food security amidst rapid population growth and increasing extreme weather events remains a critical global challenge.However,the extent to which agricultural modernization in China enhances grain y...Ensuring national food security amidst rapid population growth and increasing extreme weather events remains a critical global challenge.However,the extent to which agricultural modernization in China enhances grain yield and contributes to food security remains unclear.Therefore,using panel data from 327 Chinese cities(2013–2021),this study employs spatial econometric models to analyze the spatial spillover effects of agricultural modernization level(AML)on grain yield and to reveal regional heterogeneity across nine major agricultural zones.The results showed a cumulative grain yield increase of 23.7 million tons,with peak productivity concentrated along the Hu Line and declining eastward and westward.AML also exhibited a steady increase but a clear spatial gradient,decreasing from coastal to inland regions,with the highest level observed in Southern China(SC).A key finding was that a 1%increase in AML directly raised local grain yield by an average of 4.185%,accompanied by significant positive spillover effects on neighboring regions.Regional variations revealed distinct patterns:the direct effects of AML were more pronounced in southern and eastern zones,while spillover effects dominated in northern and western zones.The largest positive direct impact of AML on grain yield was observed in the SC(8.499%),while Middle-Lower Yangtze Plain ranked second but exhibited the strongest positive spatial spillover effect(4.534%).These findings highlight the critical role of agricultural modernization in promoting grain production and provide a solid basis for optimizing regional agricultural systems,ensuring food security,and advancing sustainable agriculture.展开更多
The construction of new agricultural science has put forward the core requirements of"interdisciplinary integration,service industry demand,and cultivation of composite talents"for the smart agriculture majo...The construction of new agricultural science has put forward the core requirements of"interdisciplinary integration,service industry demand,and cultivation of composite talents"for the smart agriculture major.The"integration of general and specialized education"is the key path to solve the problems of"prominent disciplinary barriers,fragmented knowledge structure,and weak practical ability"in the traditional curriculum system.In this paper,the College of Smart Agriculture from Yulin Normal University is taken as the research object.Based on the characteristics of regional agricultural industry and the positioning of professional education,the prominent problems in the current professional curriculum system of smart agriculture are analyzed,the construction concept of"strong foundation in general education,precise core in professional education,and breaking through boundaries in integrated education"is proposed,and a"three dimensions and four layers"integrated curriculum system framework for general and specialized education is constructed.Moreover,practical exploration is conducted from the aspects of curriculum module design,teaching mode innovation,and guarantee mechanism construction.Practice has shown that this curriculum system effectively enhances students'interdisciplinary application abilities and industry adaptability,and provides a practical sample for the reform of smart agriculture courses in local universities under the background of new agricultural science.展开更多
N6-methyladenosine(m^(6)A)modification,the most abundant internal modification in messenger RNA(mRNA)and long non-coding RNA(lncRNA),has emerged as a critical epitranscriptomic regulatory mechanism in eukaryotes.While...N6-methyladenosine(m^(6)A)modification,the most abundant internal modification in messenger RNA(mRNA)and long non-coding RNA(lncRNA),has emerged as a critical epitranscriptomic regulatory mechanism in eukaryotes.While the importance of m^(6)A modification in various biological processes has been recognized,a comprehensive understanding of its diverse roles in plant biology and agricultural applications remains fragmented.This review analyzes recent advances inm^(6)A modification's biological functions in plants.m^(6)A modification plays crucial roles in multiple aspects of plant life,including seed germination,organ development,and reproductive structure formation.Furthermore,m^(6)A has been found to significantly influence plant responses to environmental stresses,including salt,drought,temperature,and heavy metal exposure.We also uncover m^(6)A involvement in important agricultural traits.This review provides insights into the mechanistic understanding of m^(6)A modification in plants and highlights its applications in agricultural improvement,offering a foundation for future research in crop enhancement and stress resistance.展开更多
Against the backdrop of China s accelerated green development and its pursuit of the"dual carbon"goals,green finance,as a crucial instrument for the rational allocation of resources,plays a significant role ...Against the backdrop of China s accelerated green development and its pursuit of the"dual carbon"goals,green finance,as a crucial instrument for the rational allocation of resources,plays a significant role in promoting agricultural green transformation.Taking Henan Province as a case study,this research employs panel data from 18 prefecture-level cities spanning 2014 to 2023,utilizing dynamic panel models and mediating effect models for empirical analysis.The findings reveal that green finance indirectly fosters agricultural green development by driving agricultural technological progress.This promoting effect is significant,with the mediating effect accounting for 3.67%.Based on these results,policy recommendations are proposed:expanding the coverage of green finance and constructing a diversified product system;comprehensively promoting agricultural technological innovation and mechanization development;and coordinating and integrating green financial resources to facilitate agricultural green transformation tailored to local conditions.展开更多
Reuse of irrigation water after appropriate filtration has emerged as one of the most important strategies for addressing global water scarcity and improving the sustainability of agricultural systems.This study revie...Reuse of irrigation water after appropriate filtration has emerged as one of the most important strategies for addressing global water scarcity and improving the sustainability of agricultural systems.This study reviews the research progress on filtration technologies and the reuse of secondary water through a comprehensive visual and bibliometric analysis of the relevant scientific literature.Using tools such as R Studio,VOSviewer,and the Bibliometrix R‐package,a total of 374 publications published between 2003 and 2024 were retrieved from the Web of Science database and systematically analyzed.The collected literature was examined with respect to publication trends,disciplinary distributions,leading journals,contributing countries,institutions,and authors.Additionally,an in-depth keyword analysis was conducted to explore co-occurrence networks,thematic clustering,and emerging research frontiers.The results indicate three distinct developmental stages in this field:a slow and exploratory phase beginning in 2003,followed by a period of moderate growth around 2013,and a rapid expansion phase that has been evident since 2018.Research outputs primarily span environmental sciences,engineering,water resources management,and agricultural sciences.The findings highlight an increasing global interest in sustainable water reuse and the need for continued innovation in filtration methods to enhance water quality and agricultural productivity.Future scientific efforts should emphasize the development of advanced,cost-effective filtration technologies,the reduction of environmental risks,and the promotion of large-scale water reuse practices to alleviate water shortages and support resilient agricultural systems.展开更多
In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention a...In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention and control of agricultural diseases. This paper provides a systematic review of the evolutionary development of algorithms within this field. Addressing challenges such as domain drift and limited global awareness in classical convolutional neural networks (CNNs) applied to complex agricultural environments, the paper focuses on the latest advancements in vision transformers (ViT) and their hybrid architectures to enhance cross-domain robustness and fine-grained recognition capabilities. In response to the challenges posed by scarce long-tail data and limited edge computing power in real-world scenarios, the paper explores solutions related to few-shot learning and ultra-lightweight network deployment. Finally, a forward-looking analysis is presented on the application paradigms of multimodal feature fusion, vision-based large models, and explainable artificial intelligence (AI) within smart plant protection. This analysis aims to offer theoretical insights for the development of efficient and transparent intelligent diagnostic systems for agricultural diseases, thereby supporting the advancement of digital agriculture and the construction of a robust agricultural nation.展开更多
Agricultural Intangible Cultural Heritage(AICH)embodies collective,intergenerational knowledge sys⁃tems that are increasingly exposed to risks of misappropriation and inequitable commercialization under contemporary i...Agricultural Intangible Cultural Heritage(AICH)embodies collective,intergenerational knowledge sys⁃tems that are increasingly exposed to risks of misappropriation and inequitable commercialization under contemporary intellectual property(IP)regimes.This study investigates the"cost of inaction"in protecting AICH and geographical origins through a qualitative comparative case study approach,drawing on selected cases from Africa,Asia,and Latin America,alongside Chinese practices.By combining narrative analysis and thematic synthesis,the study identi⁃fies two dominant patterns:external misappropriation driven by institutional gaps-manifested in biopiracy,trademark squatting,and exploitation of information asymmetry-and internal inequities arising from commercialization processes that marginalize indigenous communities in benefit-sharing.The findings reveal that existing international IP frame⁃works,including TRIPs and the CBD,provide fragmented and insufficient protection for collective knowledge sys⁃tems.Comparative analysis of national responses highlights three distinct governance models:defensive protection(In⁃dia),rights-based sui generis legislation(Peru),and market-oriented geographical indication systems(European Union),each with inherent limitations.In contrast,Chinese practices demonstrate strong capacity in market integra⁃tion and industrial upgrading but remain constrained by weak recognition of community rights and limited institutional⁃ization of benefit-sharing mechanisms.This study contributes to the literature by proposing a composite institutional framework that integrates existing IP regimes with a sui generis system centered on collective rights recognition,prior informed consent,and mandatory benefit-sharing.The framework also emphasizes the need for embedded integration with patent,trademark,and geographical indication systems,as well as institutional adaptation in the context of the digital economy.The findings offer theoretical insights and policy implications for developing countries seeking to bal⁃ance cultural preservation,economic development,and equitable governance of traditional knowledge.展开更多
Rwanda secured access to one of the world’s most lucrative agricultural markets this month when it finalised a trade protocol allowing fresh avocado exports to China,a deal that could fundamentally alter the trajecto...Rwanda secured access to one of the world’s most lucrative agricultural markets this month when it finalised a trade protocol allowing fresh avocado exports to China,a deal that could fundamentally alter the trajectory of the country’s trade.展开更多
Smart pest control is crucial for building farmresilience andensuringsustainable agriculture inthe faceof climate change and environmental challenges.To achieve effective intelligent monitoring systems,agricultural pe...Smart pest control is crucial for building farmresilience andensuringsustainable agriculture inthe faceof climate change and environmental challenges.To achieve effective intelligent monitoring systems,agricultural pest and disease detectionmust overcome three fundamental challenges:feature degradation in dense vegetation environments,limited detection capability for sub-32×32 pixel targets,and inadequate bounding box regression for irregular pest morphologies.This study proposes YOLOv12-KMA,a novel detection framework that addresses these limitations through four synergistic architectural innovations,specifically optimized for agricultural environments.First,we introduce efficient multi-head attention(C3K2-EMA),which reduces noise interference by 41%through selective regional attention while maintaining O(k⋅n⋅d)computational complexity vs.O(n2⋅d)for standard attention.Second,we develop A2C2f-KAN modules embedding Kolmogorov-Arnold networks(KAN)with B-spline activation functions,achieving 15%better feature representation for small targets without global distortion.Third,we propose minimum point distance intersection over union(MPDIoU)loss that resolves aspect ratio degeneration issues in complete intersection over union(CIoU),accelerating convergence by 23%for irregular pest shapes.Fourth,we implement the dynamic sampling(DySample)module that reduces computational overhead by 72%while preserving 94%feature fidelity compared to conventional interpolation methods.Comprehensive validation on 8742 annotated agricultural images demonstrates significant improvements:2.6 percentage point increase in mean average precision(mAP)@0.5(91.0%→93.6%),3.2 percentage point gain in mAP@0.5:0.95,with precision and recall improvements of 4.8%and 2.4%,respectively.Statistical analysis confirms significance(p<0.001)with large effect sizes(η2=0.78).The optimized architecture maintains real-time performance at 159 frames per second(FPS)on consumer hardware,enabling practical deployment in precision agriculture monitoring systems.展开更多
Climate change poses significant risks to agriculture,particularly in East Asia,a major crop-producing region.This study evaluates the effectiveness of near-term climate predictions in forecasting agricultural thermal...Climate change poses significant risks to agriculture,particularly in East Asia,a major crop-producing region.This study evaluates the effectiveness of near-term climate predictions in forecasting agricultural thermal conditions in East Asia for up to five years.We compare temperature-based agroclimatic indicators from atmospheric reanalysis data with the firstyear prediction of the Decadal Prediction System version 4(DePreSys4),initialized annually from November 1960 to 2024.Our analysis reveals that first-year predictions accurately represent observed spatial climatological patterns,although trends in agroclimatic indicators based on daily maximum temperature are overestimated.High skill scores are observed in predicting the beginning of the growing season,frost-free days,agricultural hot days,and heat intensity in major cropping regions.However,the end of the growing season is less predictable due to longer lead times.Notably,five-year average predictions show higher skill than first-year predictions due to smoothed interannual variability.These improved climate predictions enable farmers and policymakers to make informed decisions about crop selection and agricultural infrastructure.展开更多
Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strateg...Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strategies often result in unsatisfactory outcomes and massive engineering costs when managing diffusive pollution in agricultural catchments.To address this issue,this paper proposes a robust,handy,catchment N&P decision support system(CNPDSS),an Android-based smartphone system integrated with a web-based geographic information system(GIS).The CNPDSS aims to provide artificial intelligence-driven decisions that minimize N&P loadings and engineering costs for mitigating pollution in agricultural catchments.It consists of four components:a general user interface(GUI),GIS,N&P pollution modeling(NPPM),and a DSS.The CNPDSS simplifies the GUI and integrates GIS modules to create a user-friendly interface,enabling non-professional users to operate the system easily through intuitive actions.The NPPM uses straightforward empirical models to predict N&P loadings,enhancing efficiency by avoiding excessive parameters.Taking into account the N&P movement pathway in the catchment,the DSS incorporates three control measures:source reduction in farmland(before migration stage),process retention by ecological ditch(midway transport stage),and down-end purification by constructed wetland(waterbody discharge stage),to formulate a comprehensive ternary controlling strategy.To optimize the cost-effectiveness of any proposed N&P control strategies for sub-catchments,a differential evolution algorithm(DEA)is employed in CNPDSS to carry out a dual-objective decision-making optimization computation.In this study,the CNPDSS is applied to a case study in an agricultural catchment in Central China to develop the most cost-effective ternary N&P control strategies that ensure the catchment water quality within Criterion Ⅲ of the Chinese Surface Water Quality Standard GB3838-2002 is met(total N concentration≤1.0 mg L^(-1)and total P concentration≤0.2 mg L^(-1)).Our results demonstrate that the CNPDSS is feasible and also possesses an adaptive design and flexible architecture to enable its generalization and extension to support strong hands-on applications in other catchments.展开更多
Aims and Scope Journal of Northeast Agricultural University(English Edition)is a comprehensive academic journal on agricultural sciences sponsored by Northeast Agricultural University and distributed worldwide.It is a...Aims and Scope Journal of Northeast Agricultural University(English Edition)is a comprehensive academic journal on agricultural sciences sponsored by Northeast Agricultural University and distributed worldwide.It is a peer reviewed journal published quarterly and mainly publishes review and research articles that reflect the latest achievements on crop science,horticulture,plant protection,resource and environment,animal science,veterinary medicine,agricultural engineering and technology,agricultural water conservancy,life science,biotechnology and food science.展开更多
From lecture halls in Beijing to villages in the mountains of southwest China,a group of young rural innovators from Global South countries recently embarked on a journey that connected policy thinking,technological p...From lecture halls in Beijing to villages in the mountains of southwest China,a group of young rural innovators from Global South countries recently embarked on a journey that connected policy thinking,technological practice and lived rural experience.展开更多
基金National Natural Science Foundation of China, No.41271550 Humanities and Social Sciences Project of Ministry of Education in the West and the Frontier Areas, No. 12XJC790003
文摘The excessive expansion of urbanized areas has resulted in haphazard land utili- zation, immoderate consumption of superior agricultural land and water resources, significant fragmentation of agricultural landscape, and gradual deterioration of the agro-ecological en- vironment. Combined, these factors cause poor land use efficiency. Under these circum- stances, comprehensively assessing land use efficiency for urban agriculture is a key issue in land use research. Currently, evaluation methods for agricultural land use efficiency narrowly concentrate on aspects of economic input and output. However, urban agro-ecosystems can provide diverse economic, social, and ecological services and functions. In particular, the social and ecological services and functions originating from agricultural land, which have a higher value than economic services, play a significant role in ensuring regional social, eco- logical, and environmental security. However, recent research has rarely taken these benefits into consideration. Therefore, land use value has been greatly underestimated, which has resulted in mishandled and poor land use policies. In this study, we apply Landsat imagery and social and economic statistical data for the Xi＇an metropolitan zone （XMZ） to investigate agricultural multi-functionality. We develop an evaluation framework for urban agricultural land use efficiency and identify agro-ecosystem services and functions as important outputs from agricultural land. The land use efficiency of urban agriculture is then evaluated using ecosystem services models, providing a mechanism for assessing spatial-temporal changes in land use efficiency in the XMZ from 1999 to 2015. Four important conclusions are reached from this analysis. First, the rapid urbanization and agricultural transformation from traditional cereal cultivation to modern urban agriculture has resulted in steadily increasing costs, out- puts, and land use efficiency of urban agriculture. The total output value increased 41% and land use efficiency per hectare increased by 33.13% on average. Second, the spatial patterns of comprehensive output and land use efficiency were dominated by economic outputs from agricultural land. Areas near cities, which are dominated by orchard and arable land, provide more economic functions. These areas support and regulate services due to the transforma- tion from extensive cereal production to intensive modern urban agriculture; therefore, they have higher output value and land use efficiency. In contrast, areas distant from cities, towns, and high traffic roads, namely, remote rural areas, provide more support and regulating ser- vices, but have relatively lower economic function due to inaccessibility tO urban markets and slow agricultural transformation. Therefore, these areas have lower output value and land use efficiency. The spatial change in agricultural output and land use efficiency in urban areas is strongly dependent on the degree of urbanization and agricultural transformation. Third, the total output value and land use efficiency of urban agriculture measured with our approach are much higher than evaluations using traditional methods. However, the spatial patterns measured using the two approaches are in agreement. The evaluation framework integrates ecological services and economic and social functions into a comprehensive output from agricultural land. This approach is more methodical and accurate for evaluating the compre- hensive efficiency of land use based on quantities and spatial scale because they are at the pixel scale. Finally, the evaluation results have important implications for enhancing current agricultural subsidies and even implementing ecological payment policies in China. Most importantly, they can be directly applied to agricultural transformation regulations, decision-making, and guidance for rational land utilization.
文摘Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of signifi cant,novel,and high-impact research in the fi elds of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.
文摘Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of significant,novel,and high-impact research in the fields of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.
文摘Steady speed control of agricultural machinery can improve operating quality and efficiency.To address the impact of farmland slope variations on the speed stability of unmanned operation agricultural machinery,a hybrid control method was proposed.This method included a hybrid controller composed of a slope-based controller and a proportional-integral-derivative(PID)controller.The speed of agricultural machinery was influenced by longitudinal forces,which were divided into two parts:one part was slope-related forces and conventional resistance,and the other was hard-to-estimate forces,such as sliding friction.For the first part,a slope-based controller was designed;for the second part,a PID controller was implemented.By combining these two controllers,the system can dynamically adjust the throttle opening and the brake master cylinder pressure,ensuring steady speed travel on sloping farmland.Simulation tests at a target speed of 7 km/h demonstrated that the proposed controller maintained a stable speed,achieving a root mean square error of 0.13 km/h and a mean absolute percentage error of 1.6%.Field tests on a practical experimental platform validated the method’s effectiveness,with results showing consistent control performance across varying slope conditions.The proposed controller demonstrated superior control performance.Experimental data verified that this method can achieve precise control of the agricultural machinery’s movement speed,meeting the stability requirements for agricultural operations.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金supported by the Ningxia Natural Science Foundation(2025AAC050001)the Scientific Research Startup Project for Full-Time Introduced High-Level Talents in Ningxia(2024BEH04130)+2 种基金the National Natural Science Foundation of China(32460444)the Ningxia Hui Autonomous Region Key Research and Development Program(2024BBF0101302,2023BDE02001)Supported by the Special Fund for Basic Research Business of Central Universities of North Minzu University(2025BG234,2023ZRLG12).
文摘Foundation models are reshaping artificial intelligence,yet their deployment in specialised domains such as agricultural question answering(AQA)still faces challenges including data scarcity and barriers to domainspecific knowledge.To systematically review recent progress in this area,this paper adopts a task–paradigmperspective and examines applications across three major AQA task families.For text-based QA,we analyse the strengths and limitations of retrieval-based,generative,and hybrid approaches built on large languagemodels,revealing a clear trend toward hybrid paradigms that balance precision and flexibility.For visual diagnosis,we discuss techniques such as crossmodal alignment and prompt-driven generation,which are pushing systems beyond simple pest and disease recognition toward deeper causal reasoning.Formultimodal reasoning,we show how the fusion of heterogeneous data—including text,images,speech,and sensor streams—enables comprehensive decision-making for diagnosis,monitoring,and yield prediction.To address the lack of unified benchmarks,we further propose a standardised evaluation protocol and a diagnostic taxonomy specifically designed to characterise agriculture-specific errors.Finally,we outline a concreteAQA roadmap that emphasises safety alignment,hallucination control,and lightweight deployment,aiming to guide future systems toward greater efficiency,trustworthiness,and sustainability.
基金Under the auspices of the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28050400)Jilin Province Key Research and Development Project(No.20230202040NC)Common Application Support Platform for National Civil Space Infrastructure Land Observation Satellites(No.2017-000052-73-01-001735)。
文摘Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.
基金National Natural Science Foundation of China,No.42471455,No.42230113National Key Research and Development Program of China,No.2022YFC3800804-01。
文摘Ensuring national food security amidst rapid population growth and increasing extreme weather events remains a critical global challenge.However,the extent to which agricultural modernization in China enhances grain yield and contributes to food security remains unclear.Therefore,using panel data from 327 Chinese cities(2013–2021),this study employs spatial econometric models to analyze the spatial spillover effects of agricultural modernization level(AML)on grain yield and to reveal regional heterogeneity across nine major agricultural zones.The results showed a cumulative grain yield increase of 23.7 million tons,with peak productivity concentrated along the Hu Line and declining eastward and westward.AML also exhibited a steady increase but a clear spatial gradient,decreasing from coastal to inland regions,with the highest level observed in Southern China(SC).A key finding was that a 1%increase in AML directly raised local grain yield by an average of 4.185%,accompanied by significant positive spillover effects on neighboring regions.Regional variations revealed distinct patterns:the direct effects of AML were more pronounced in southern and eastern zones,while spillover effects dominated in northern and western zones.The largest positive direct impact of AML on grain yield was observed in the SC(8.499%),while Middle-Lower Yangtze Plain ranked second but exhibited the strongest positive spatial spillover effect(4.534%).These findings highlight the critical role of agricultural modernization in promoting grain production and provide a solid basis for optimizing regional agricultural systems,ensuring food security,and advancing sustainable agriculture.
基金Supported by the Autonomous Region-level Research and Practice Projects for New Engineering,New Medicine,New Agriculture,and New Humanities of Guangxi Department of Education(XNK202409)the Undergraduate Teaching Reform Project of Guangxi Higher Education(2024JGB332+1 种基金2024JGA304)the Guangxi Degree and Graduate Education Reform Project(JGY2025382).
文摘The construction of new agricultural science has put forward the core requirements of"interdisciplinary integration,service industry demand,and cultivation of composite talents"for the smart agriculture major.The"integration of general and specialized education"is the key path to solve the problems of"prominent disciplinary barriers,fragmented knowledge structure,and weak practical ability"in the traditional curriculum system.In this paper,the College of Smart Agriculture from Yulin Normal University is taken as the research object.Based on the characteristics of regional agricultural industry and the positioning of professional education,the prominent problems in the current professional curriculum system of smart agriculture are analyzed,the construction concept of"strong foundation in general education,precise core in professional education,and breaking through boundaries in integrated education"is proposed,and a"three dimensions and four layers"integrated curriculum system framework for general and specialized education is constructed.Moreover,practical exploration is conducted from the aspects of curriculum module design,teaching mode innovation,and guarantee mechanism construction.Practice has shown that this curriculum system effectively enhances students'interdisciplinary application abilities and industry adaptability,and provides a practical sample for the reform of smart agriculture courses in local universities under the background of new agricultural science.
基金supported by the National Nature Science Foundation of China(Grant No.31660568)Guangxi Science and Technology major project(Grant No.GuikeAA22068088)+2 种基金start-up funding for introduced talents in Guangxi University,the Guangxi Colleges and Universities Young and Middle-aged Teachers'Basic Scientific Research Ability Improvement Project(Grant No.2024KY0010)Guangxi Graduate Education Innovation Program(Grant No.YCSW2024093)the Guangxi University Student Innovation and Entrepreneurship Training Program Funding Project(Grant Nos.202310593704,202310593714,202410953044S).
文摘N6-methyladenosine(m^(6)A)modification,the most abundant internal modification in messenger RNA(mRNA)and long non-coding RNA(lncRNA),has emerged as a critical epitranscriptomic regulatory mechanism in eukaryotes.While the importance of m^(6)A modification in various biological processes has been recognized,a comprehensive understanding of its diverse roles in plant biology and agricultural applications remains fragmented.This review analyzes recent advances inm^(6)A modification's biological functions in plants.m^(6)A modification plays crucial roles in multiple aspects of plant life,including seed germination,organ development,and reproductive structure formation.Furthermore,m^(6)A has been found to significantly influence plant responses to environmental stresses,including salt,drought,temperature,and heavy metal exposure.We also uncover m^(6)A involvement in important agricultural traits.This review provides insights into the mechanistic understanding of m^(6)A modification in plants and highlights its applications in agricultural improvement,offering a foundation for future research in crop enhancement and stress resistance.
基金Supported by Zhejiang Provincial Philosophy and Social Sciences Planning Project(23FNSQ58YB).
文摘Against the backdrop of China s accelerated green development and its pursuit of the"dual carbon"goals,green finance,as a crucial instrument for the rational allocation of resources,plays a significant role in promoting agricultural green transformation.Taking Henan Province as a case study,this research employs panel data from 18 prefecture-level cities spanning 2014 to 2023,utilizing dynamic panel models and mediating effect models for empirical analysis.The findings reveal that green finance indirectly fosters agricultural green development by driving agricultural technological progress.This promoting effect is significant,with the mediating effect accounting for 3.67%.Based on these results,policy recommendations are proposed:expanding the coverage of green finance and constructing a diversified product system;comprehensively promoting agricultural technological innovation and mechanization development;and coordinating and integrating green financial resources to facilitate agricultural green transformation tailored to local conditions.
文摘Reuse of irrigation water after appropriate filtration has emerged as one of the most important strategies for addressing global water scarcity and improving the sustainability of agricultural systems.This study reviews the research progress on filtration technologies and the reuse of secondary water through a comprehensive visual and bibliometric analysis of the relevant scientific literature.Using tools such as R Studio,VOSviewer,and the Bibliometrix R‐package,a total of 374 publications published between 2003 and 2024 were retrieved from the Web of Science database and systematically analyzed.The collected literature was examined with respect to publication trends,disciplinary distributions,leading journals,contributing countries,institutions,and authors.Additionally,an in-depth keyword analysis was conducted to explore co-occurrence networks,thematic clustering,and emerging research frontiers.The results indicate three distinct developmental stages in this field:a slow and exploratory phase beginning in 2003,followed by a period of moderate growth around 2013,and a rapid expansion phase that has been evident since 2018.Research outputs primarily span environmental sciences,engineering,water resources management,and agricultural sciences.The findings highlight an increasing global interest in sustainable water reuse and the need for continued innovation in filtration methods to enhance water quality and agricultural productivity.Future scientific efforts should emphasize the development of advanced,cost-effective filtration technologies,the reduction of environmental risks,and the promotion of large-scale water reuse practices to alleviate water shortages and support resilient agricultural systems.
基金Supported by School-level Project of Shaoyang Industry Polytechnic College(SKY24A06)Science and Technology Plan(Special Fund Subsidy)of Shaoyang City(2024PT4070)General Research Project of Hunan Provincial Department of Education in 2025(25C1457).
文摘In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention and control of agricultural diseases. This paper provides a systematic review of the evolutionary development of algorithms within this field. Addressing challenges such as domain drift and limited global awareness in classical convolutional neural networks (CNNs) applied to complex agricultural environments, the paper focuses on the latest advancements in vision transformers (ViT) and their hybrid architectures to enhance cross-domain robustness and fine-grained recognition capabilities. In response to the challenges posed by scarce long-tail data and limited edge computing power in real-world scenarios, the paper explores solutions related to few-shot learning and ultra-lightweight network deployment. Finally, a forward-looking analysis is presented on the application paradigms of multimodal feature fusion, vision-based large models, and explainable artificial intelligence (AI) within smart plant protection. This analysis aims to offer theoretical insights for the development of efficient and transparent intelligent diagnostic systems for agricultural diseases, thereby supporting the advancement of digital agriculture and the construction of a robust agricultural nation.
文摘Agricultural Intangible Cultural Heritage(AICH)embodies collective,intergenerational knowledge sys⁃tems that are increasingly exposed to risks of misappropriation and inequitable commercialization under contemporary intellectual property(IP)regimes.This study investigates the"cost of inaction"in protecting AICH and geographical origins through a qualitative comparative case study approach,drawing on selected cases from Africa,Asia,and Latin America,alongside Chinese practices.By combining narrative analysis and thematic synthesis,the study identi⁃fies two dominant patterns:external misappropriation driven by institutional gaps-manifested in biopiracy,trademark squatting,and exploitation of information asymmetry-and internal inequities arising from commercialization processes that marginalize indigenous communities in benefit-sharing.The findings reveal that existing international IP frame⁃works,including TRIPs and the CBD,provide fragmented and insufficient protection for collective knowledge sys⁃tems.Comparative analysis of national responses highlights three distinct governance models:defensive protection(In⁃dia),rights-based sui generis legislation(Peru),and market-oriented geographical indication systems(European Union),each with inherent limitations.In contrast,Chinese practices demonstrate strong capacity in market integra⁃tion and industrial upgrading but remain constrained by weak recognition of community rights and limited institutional⁃ization of benefit-sharing mechanisms.This study contributes to the literature by proposing a composite institutional framework that integrates existing IP regimes with a sui generis system centered on collective rights recognition,prior informed consent,and mandatory benefit-sharing.The framework also emphasizes the need for embedded integration with patent,trademark,and geographical indication systems,as well as institutional adaptation in the context of the digital economy.The findings offer theoretical insights and policy implications for developing countries seeking to bal⁃ance cultural preservation,economic development,and equitable governance of traditional knowledge.
文摘Rwanda secured access to one of the world’s most lucrative agricultural markets this month when it finalised a trade protocol allowing fresh avocado exports to China,a deal that could fundamentally alter the trajectory of the country’s trade.
文摘Smart pest control is crucial for building farmresilience andensuringsustainable agriculture inthe faceof climate change and environmental challenges.To achieve effective intelligent monitoring systems,agricultural pest and disease detectionmust overcome three fundamental challenges:feature degradation in dense vegetation environments,limited detection capability for sub-32×32 pixel targets,and inadequate bounding box regression for irregular pest morphologies.This study proposes YOLOv12-KMA,a novel detection framework that addresses these limitations through four synergistic architectural innovations,specifically optimized for agricultural environments.First,we introduce efficient multi-head attention(C3K2-EMA),which reduces noise interference by 41%through selective regional attention while maintaining O(k⋅n⋅d)computational complexity vs.O(n2⋅d)for standard attention.Second,we develop A2C2f-KAN modules embedding Kolmogorov-Arnold networks(KAN)with B-spline activation functions,achieving 15%better feature representation for small targets without global distortion.Third,we propose minimum point distance intersection over union(MPDIoU)loss that resolves aspect ratio degeneration issues in complete intersection over union(CIoU),accelerating convergence by 23%for irregular pest shapes.Fourth,we implement the dynamic sampling(DySample)module that reduces computational overhead by 72%while preserving 94%feature fidelity compared to conventional interpolation methods.Comprehensive validation on 8742 annotated agricultural images demonstrates significant improvements:2.6 percentage point increase in mean average precision(mAP)@0.5(91.0%→93.6%),3.2 percentage point gain in mAP@0.5:0.95,with precision and recall improvements of 4.8%and 2.4%,respectively.Statistical analysis confirms significance(p<0.001)with large effect sizes(η2=0.78).The optimized architecture maintains real-time performance at 159 frames per second(FPS)on consumer hardware,enabling practical deployment in precision agriculture monitoring systems.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(Grant No.RS-2024-00342219)the Korea Meteorological Administration Research and Development Program(Grant No.RS-2025-02313090)S.-Y.JUN and B.-J.PARK were supported by Korea Polar Research Institute(KOPRI)grants funded by the Ministry of Oceans and Fisheries(Grant No.KOPRI PE25010).
文摘Climate change poses significant risks to agriculture,particularly in East Asia,a major crop-producing region.This study evaluates the effectiveness of near-term climate predictions in forecasting agricultural thermal conditions in East Asia for up to five years.We compare temperature-based agroclimatic indicators from atmospheric reanalysis data with the firstyear prediction of the Decadal Prediction System version 4(DePreSys4),initialized annually from November 1960 to 2024.Our analysis reveals that first-year predictions accurately represent observed spatial climatological patterns,although trends in agroclimatic indicators based on daily maximum temperature are overestimated.High skill scores are observed in predicting the beginning of the growing season,frost-free days,agricultural hot days,and heat intensity in major cropping regions.However,the end of the growing season is less predictable due to longer lead times.Notably,five-year average predictions show higher skill than first-year predictions due to smoothed interannual variability.These improved climate predictions enable farmers and policymakers to make informed decisions about crop selection and agricultural infrastructure.
基金financially supported by the National Key Research and Development Program of China(2024YFD1700104 and 2022YFE0209200-03)the National Natural Science Foundation of China(42161144002 and 41977156)+3 种基金the Guangxi Natural Science Foundation,China(2022GXNSFBA035625)the Guangxi Technology Base and Talent Subject,China(Guike AD22035927)the Shandong Key Research and Development Project,China(2022TZXD0045)the State Key Laboratory of Earth System Numerical Modeling and Application,Institute of Atmospheric Physics,Chinese Academy of Sciences。
文摘Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strategies often result in unsatisfactory outcomes and massive engineering costs when managing diffusive pollution in agricultural catchments.To address this issue,this paper proposes a robust,handy,catchment N&P decision support system(CNPDSS),an Android-based smartphone system integrated with a web-based geographic information system(GIS).The CNPDSS aims to provide artificial intelligence-driven decisions that minimize N&P loadings and engineering costs for mitigating pollution in agricultural catchments.It consists of four components:a general user interface(GUI),GIS,N&P pollution modeling(NPPM),and a DSS.The CNPDSS simplifies the GUI and integrates GIS modules to create a user-friendly interface,enabling non-professional users to operate the system easily through intuitive actions.The NPPM uses straightforward empirical models to predict N&P loadings,enhancing efficiency by avoiding excessive parameters.Taking into account the N&P movement pathway in the catchment,the DSS incorporates three control measures:source reduction in farmland(before migration stage),process retention by ecological ditch(midway transport stage),and down-end purification by constructed wetland(waterbody discharge stage),to formulate a comprehensive ternary controlling strategy.To optimize the cost-effectiveness of any proposed N&P control strategies for sub-catchments,a differential evolution algorithm(DEA)is employed in CNPDSS to carry out a dual-objective decision-making optimization computation.In this study,the CNPDSS is applied to a case study in an agricultural catchment in Central China to develop the most cost-effective ternary N&P control strategies that ensure the catchment water quality within Criterion Ⅲ of the Chinese Surface Water Quality Standard GB3838-2002 is met(total N concentration≤1.0 mg L^(-1)and total P concentration≤0.2 mg L^(-1)).Our results demonstrate that the CNPDSS is feasible and also possesses an adaptive design and flexible architecture to enable its generalization and extension to support strong hands-on applications in other catchments.
文摘Aims and Scope Journal of Northeast Agricultural University(English Edition)is a comprehensive academic journal on agricultural sciences sponsored by Northeast Agricultural University and distributed worldwide.It is a peer reviewed journal published quarterly and mainly publishes review and research articles that reflect the latest achievements on crop science,horticulture,plant protection,resource and environment,animal science,veterinary medicine,agricultural engineering and technology,agricultural water conservancy,life science,biotechnology and food science.
文摘From lecture halls in Beijing to villages in the mountains of southwest China,a group of young rural innovators from Global South countries recently embarked on a journey that connected policy thinking,technological practice and lived rural experience.
