Modern Agricultural Management Research Articles

Study of the Impact of New Rural Cooperatives on the Implementation of the Rural Revitalisation Strategy

This study aims to explore the impact of new rural cooperatives on the implementation of China's rural revitalisation strategy, focusing on the roles and challenges of new cooperatives in promoting agricultural modernisation, facilitating the optimisation of socio-economic structures, and achieving an ecological civilisation. Through literature review, case study analysis and field research, this study comprehensively assesses the importance and impact of new rural cooperatives in rural revitalisation. The study finds that new rural cooperatives have significantly improved agricultural productivity and product quality and contributed to agricultural modernisation by pooling farmers' efforts and introducing modern agricultural technologies and management models. These cooperatives have also contributed to the optimisation of rural socio-economic structures by providing employment opportunities, skills training and increasing farmers' incomes. In addition, co-operatives have demonstrated a positive role in promoting rural ecological environment protection and sustainable development, such as promoting green agriculture, conserving resources and protecting the ecological environment. At the same time, this study also points out the challenges faced by new rural co-operatives in the process of development, including management and operational efficiency, market adaptability, balance between environmental protection and sustainable development, fund raising and resource allocation. In response to these challenges, the study proposes a series of countermeasures, such as strengthening the internal management of cooperatives, broadening market channels, promoting environmentally friendly agricultural practices, enhancing the effective use of funds and resources, and strengthening policy support and guidance. Overall, new rural cooperatives play an important role in promoting rural revitalisation strategies, and their successful experiences and challenges are of great reference value in guiding the implementation of rural revitalisation strategies. Through an in-depth study of new rural cooperatives, this paper provides practical insights and suggestions for policymakers, cooperative managers and rural development researchers.

Discuss on the Construction of Digital Rural Revitalization Information Service Platform in the New Era

If we want to assume digital rural areas, the focus must be on industrial prosperity, adhere to quality first, green agriculture, and take the structural reform of the agricultural supply side as a guide to further promote the construction speed of modern agricultural industrial system, management system, and production system, thereby improving agricultural productivity, competitiveness, and innovation, and promoting the realization of an agricultural power. In the new era, the system engineering of digital rural projects is relatively complex. It is necessary to adopt advanced system engineering ideas and methods, arrange top-level design, institutional setting, innovative technology, talent cultivation, and optimize operation modes, in order to achieve an information service platform and achieve digital rural revitalization.

Chedi,The Plant Care and Crop Prediction App

The research behind the Chedi App stems from a comprehensive exploration of modern plant care and agriculture management practices. Focused on leveraging cutting-edge technologies, including machine learning, image recognition, and real-time monitoring, the research aims to revolutionize the way users interact with and care for indoor plants. Rooted in the principles of sustainability and community, the Chedi App seeks to provide a user-friendly and informative platform that not only assists in plant care but also fosters a shared passion for nature among a diverse user community. The research is driven by a commitment to creating an innovative, responsive, and future-proof application that addresses the evolving needs of plant enthusiasts and agricultural practitioners.

Climate Smart Agriculture: The Role of Fertilizer Innovations and Efficient Plant Nutrient Management

Climate change has emerged as a significant threat to the global agro-economy, adversely affecting agricultural productivity, food security, and local and global agricultural development goals. To ensure sustainable crop productivity and meet future demands, it is essential to take prompt actions to address the current challenges of lower agricultural productivity due to climate change. Climate-smart agriculture (CSA) is a novel concept that offers a promising approach to tackle these challenges by introducing sustainable farming practices that minimize negative environmental impacts and enhance resilience to climate change. It involves realigning current agricultural practices and introducing smart techniques to reduce the adverse effects of agriculture that contribute to climate change and higher carbon footprints. The importance of climate-smart fertilizers in agricultural productivity cannot be overemphasized, as the soil is susceptible to structural changes and alterations in nutrient availability due to climatic impacts. Therefore, the implementation of proper nutrient management mechanisms and advanced fertilizer innovations has become a top priority in implementing the concepts of CSA. Consequently, there has been a greater focus on introducing more climate-adaptive and resilient plant nutrient delivery systems and technologies in recent times. For instance, nanofertilizer, bio-based controlled-release and slow-release fertilizers have provided a more sustainable and climate-friendly alternative to traditional synthetic fertilizers. Despite the innovations, challenges remain in implementing CSA practices and practices of climate-smart fertilizers at scale. This is largely attributed to the lack of proper knowledge and a streamlined policy framework on climate resilience in agriculture and fertilizers. This article seeks to review the role of fertilizer innovations and nutrient management in CSA, targeting the reduction of nutrient losses due to climate changes. It also examines the current status of climate-smart fertilizers with a particular emphasis on the current implementation challenges and future prospects in fertilizer production, leading to global food security by adapting to climatic changes. By highlighting specific examples of modern climate-smart agriculture and fertilizer management, this article aims to provide insights into the potential benefits and challenges of implementing CSA practices and encourage further research and development in this important field.
 Keywords: Climate-smart agriculture, Climate-smart fertilizers, Climate change, Nutrient Management, Innovations in fertilizers

A review of climate change mitigation and agriculture sustainability through soil carbon sequestration

As a result of human activities such as burning fossil fuels, organic materials, and engaging in unsustainable land practices, atmospheric carbon dioxide (CO2) levels have been steadily rising, heightening worldwide concerns about climate change. It is expected concentrations to grow and changes in CO2 sequestration in agricultural soils as a result of the industrial revolution's acceleration of CO2 emissions. These emissions have been intensified by changes in land use, such as cutting down trees, burning biomass, altering farming operations, draining natural wetlands, and using the wrong methods for managing soil. The present study utilized the systematic literature review method to investigate soil carbon sequestration options as a possible means of reducing the impact of climate change and improving agriculture sustainability. As a result of soil degradation and poor management, soil organic carbon (SOC) levels have decreased, which in turn has increased atmospheric CO2 levels. But cutting-edge land application and modern agricultural management methods have the ability to reduce CO2 emissions. Several methods exist for replenishing depleted SOC, such as repurposing marginal lands for restoration purposes, advocating for reduced or zero-tillage methods in conjunction with cover as well as residue crops, and introducing nutrient cycling through composting, manure usage, and other environmentally friendly approaches to managing soil and water. One holistic approach to fighting climate change is long-term soil carbon sequestration. Soil carbon sequestration offers a comprehensive and efficient strategy for reducing the impact of climate change by recharging depleted soils, increasing biomass production, cleaning surface and underground water sources, and compensating for CO2 emissions through fossil fuels. Soil carbon sequestration presents an exciting opportunity for management of the problems caused by contemporary changes in the environment, and the adoption of these novel approaches is essential for meeting these issues.

Combining Multi-Source Data and Feature Optimization for Plastic-Covered Greenhouse Extraction and Mapping Using the Google Earth Engine: A Case in Central Yunnan Province, China

Rapidly increasing numbers of the plastic-covered greenhouse (PCG) worldwide ensure food security but threaten environmental security; thus, accurate monitoring of the spatiotemporal pattern in plastic-covered greenhouses (PCGs) is necessary for modern agricultural management and environmental protection. However, many urgent issues still exist in PCG mapping, such as multi-source data combination, classification accuracy improvement, spatiotemporal scale expansion, and dynamic trend quantification. To address these problems, this study proposed a new framework that progressed layer by layer from multi-feature scenario construction, classifier and feature scenario preliminary screening, feature optimization, and spatiotemporal mapping, to rapidly identify large-scale PCGs by integrating multi-source data using Google Earth Engine (GEE), and the framework was first applied to Central Yunnan Province (CYP), where PCGs are concentrated but no relevant research exists. The results suggested that: (1) combining the random forest (RF) classifier and spectrum (S) + backscatter (B) + index (I) + texture (T) + terrain (Tr) feature scenario produced the highest F-score (95.60%) and overall accuracy (88.04%). (2) The feature optimization for the S + I + T + B + Tr scenario positively impacted PCG recognition, increasing the average F-score by 1.03% (96.63% vs. 95.60%). (3) The 6-year average F-score of the PCGs extracted by the combined RF algorithm and the optimal feature subset exceeded 95.00%, and its spatiotemporal mapping results indicated that PCGs were prominently agglomerated in the central CYP and continuously expanded by an average of 65.45 km2/yr from 2016 to 2021. The research reveals that based on the GEE platform, multi-source data can be integrated through a feature optimization algorithm to more efficiently map PCG spatiotemporal information in complex regions.

Development Status and Countermeasures of Agricultural Mechanization in Hilly Mountainous Areas of Yulin

During the "14th Five-Year Plan," Yulin's agricultural mechanization developed rapidly, hilly mountain agricultural production gradually from animal power-based to rely on mechanical power transformation. However, due to the restriction of natural conditions and development basis, there are still problems such as unbalanced development of agricultural machinery and equipment, imperfect service system of agricultural mechanization, the small scale of agro-machinery industry and insufficient input of agricultural scientific research in Yulin. In view of this, put forward the policy suggestions to promote the development of agricultural mechanization in Yulin: improve the effectiveness of infrastructure construction, strengthen the innovation of agricultural science and technology, construct the modern agricultural management system, increase the investment of agricultural machinery scientific research and other countermeasures.

Photo‐Stimuli Smart Supramolecular Self‐Assembly of Azobenzene/β‐Cyclodextrin Inclusion Complex for Controlling Plant Bacterial Diseases

AbstractControllable and on‐demand delivery of supramolecular systems have received considerable attention in modern agricultural management, especially for managing intractable plant diseases. Here, an intelligent photoresponsive pesticide delivery system is reported based on β‐cyclodextrin (β‐CD) and azobenzene, which overcomes the resistance of phytopathogens caused by the irrational use of conventional pesticides. Antibacterial bioassays illustrated that designed azobenzene derivative 3a possesses the most efficient bioactivity with EC50 values of 0.52–25.31 µg mL−1 toward three typical phytopathogens. Moreover, the assembly of the supramolecular binary complex 3a@β‐CD is successfully elucidated and displays exceptional inhibitory activity on biofilm formation. Of note, this supramolecular binary complex significantly improves the water solubility, foliar surface wettability, and shows marked light‐responsive properties. In vivo anti‐Xoo assays reveal that 3a@β‐CD has excellent control efficiency (protective activity: 51.22%, curative activity: 48.37%) against rice bacterial blight pathogens, and their control efficiency can be elevated to values of 55.84% (protective activity) and 52.05% (curative activity) by UV–vis exposure. In addition, the 3a@β‐CD are non‐toxic toward various non‐target organisms. This study therefore offers new insights into the potential of host‐guest complexes as a feasible pesticide discovery strategy characterized by a safe, biocompatible, light‐responsive release, and antibiofilm properties for overcoming intractable plant bacterial diseases.

OPPORTUNITIES FOR DIGITISATION OF AGRICULTURAL AND RURAL DEVELOPMENT SOLUTIONS

Agriculture and development of rural areas are characterized by transformations, reforms, changes, and the search for innovation. Identification of solution alternatives and their effective implementation require human resources capable of concentrating and systemically setting priorities and performing necessary tasks for realization of ideas. With the help of agricultural digitization, one employee can communicate with several robotic devices at once by sending them specific instructions, thereby synchronizing their work with each other. Such activities in agriculture help to save time and achieve higher productivity. Purpose of the study is to analyse opportunities for digitisation of agricultural and rural development solutions. The focus is on one of the options, the aim is to define how in a digital environment, using decision support systems (DSS), one employee can carry out several jobs and, according to feedback signal, perform their quality monitoring. Using digital technologies in agriculture and rural change management, employees can guarantee that every modern agricultural and rural development management system will start its work on time, perform it qualitatively and finish it on time. An employee can monitor the processes of change in agriculture and rural environment with the help of information technology. With the help of internet technologies and robots, implemented advanced information systems (such as DSS) can collect, analyse and process huge data (metrological, soil conditions, market information, etc.), so one person can perform several jobs at the same time. Not all DSS are equally modern, allowing to analyse both tactical and strategic decisions. In order to successfully address the challenges of digitisation of agriculture and rural development, it is proposed to facilitate cooperation between a human (operator) and a robot when they exchange and (or) share tasks, control multifunctional processes, and, according to the obtained results, make changes to the components of the process in order to achieve the best result.

Emerging Issues and Problems of Soil Salinity and Water Logging

Soil salinity negatively affects plant growth and induces soil degradation. Saline soils indicate lower agricultural productivity and their impact on the livelihood security of farmers and the socio-economic status of the Indira Gandhi Canal region, Rajasthan. In the context of irrigation development, problems of rising groundwater levels and the development of salinity have arisen in the Indira Gandhi Nahar Priyojana (IGNP) area. The irrigation has recently decreased to about 1 meter, much higher than the specified value of 0.51 m in phase I. In phase I, severe inundation was observed. found in a variety of locations. It is anticipated that by the end of this century, roughly 49.6 percent of the region being studied will be at risk of experiencing flooding. The waterlogging, soil salinity, and alkalinity that plague India's agricultural landscape pose a challenge to the viability of irrigated farming. It has been determined that the most effective technique for halting the reduction in agricultural output is to use enhanced irrigation management in conjunction with a combination of surface and subsurface drainage. The method of reclaiming waterlogged lands by bio drainage is one that is efficient and economical. For the objectives of rehabilitation, species such as Salvador, Tamarix, Eucalyptus, and Prosopis Juliflora are among those that are recommended for planting in afflicted regions. The bio-drainage system helped in reducing the waterlogged areas and soil salinity by lowering the water table in the study area. Traditional knowledge and modern agricultural management good practices should be adopted for efficient and optimal use of water resources. This can be achieved by maintaining irrigation through the minimum pumping hours according to the minimum water requirements of the crop, as well as choosing more cost-effective planting methods. This research paper recommends policies to improve traditional and scientific knowledge into policies and improve mutual understanding, cooperation, and collaboration for better policy results for sustainable development and mitigate the emerging Issues and problems of soil Salinity and water logging in the area of Indira Gandhi Canal, Rajasthan.

USING YOLOV5 TO DETECT PLASTIC GREENHOUSES IN MOROCCO

Abstract. Globally, Increasing greenhouse production is causing producing environmental issues. Monitoring the number and determining the locations of greenhouses are essential for the modern agricultural land management. The aim of this study was to detect plastic greenhouses using remote sensing data and the YOLOv5 CNN-based object detection algorithm. In the process, 426 images of plastic greenhouses were manually downloaded from Google and Mohammed 6 satellites and were divided into three datasets, training (63%), validation (15%), and testing (22%). Plastic greenhouse images in the training and validation sets were manually labeled, with the training set being used to train the YOLO-V5 Model. Throughout the training, the validation dataset was used to evaluate how well the network performed. According to the performance results the system reached its highest level of accuracy at epoch 71, with a relatively brief training time of 40 minutes, The model’s average precision was 84.1 percent, Using satellite images. In detecting plastic greenhouses, YOLOv5 can deliver exceptional accuracy and computational performance.

Research on the Application of Virtual Simulation Technology in Ethics Literacy Education of Agricultural Economic Management Major

Based on virtual simulation experiment technology, this paper simulates the process of modern agricultural management, and discusses the cultivation of ethics literacy of students majoring in agricultural economic management in practical teaching. In the process of practical teaching, students should truly restore the agricultural management process and simulate some special situations to minimize the irrational behaviors that damage the natural ecosystem. Utilize virtual simulation experiment technology to comprehensively improve the ethics literacy of students majoring in agricultural economic management, put knowledge into daily agricultural management, repair and protect the agricultural ecosystem, realize the quality and increment of agricultural products and the development in the direction of green, sustainable and ecological civilization, assist the revitalization of villages and promote the sustainable development of agricultural natural ecosystem.

Using the internet of things to promote the sustainable development of modern agricultural management mode

From the perspective of agricultural development, this paper analyzes the role of the Internet of Things on agricultural development, and how to promote the sustainable development of modern agricultural management. Make rational use of scientific and technological resources, improve the efficiency of agricultural production, and promote agricultural development.

Dual-functional SERRS and fluorescent aptamer sensor for abscisic acid detection via charged gold nanorods.

Abscisic acid (ABA) is a plant hormone, which plays an important role in plant growth, crop cultivation and modern agricultural engineering management. Accordingly, the detection of ABA content combined with new techniques and methods has become a more and more popular problem in the field of agricultural engineering. In this work, a SERRS and fluorescence dual-function sensor based on the fluorescence quenching and Raman enhancement properties of gold nanorods (AuNRs) was developed, and applied to the detection of plant hormone ABA. The dual-function reporter molecule Rhodamine isothiocyanate (RBITC) and complementary DNA (cDNA) were modified on AuNRs (AuNRs@RBITC@cDNA) as signal probes and aptamer modified magnetic nanoparticles (Fe3O4MNPs@Apt) as capture probes. Through the specific recognition of ABA aptamer and its complementary chains, an dual-function aptamer sensor based on SERRS and fluorescence was constructed. When ABA molecules were present in the detection system, the signal probes were detached from the capture probes due to the preferential binding between aptamer and ABA molecules. SERS signal of the reporter molecules appeared in the supernatant after magnetic separation, and it increased with the increase of ABA concentration. If the etching agent that can etch AuNRs was added to the supernatant, the AuNRs was etching disappeared, then the signal molecules fall off from the AuNRs, and the fluorescence signal intensity would recovered. The intensity of fluorescence signal also increased with the increase of ABA concentration. Thus, the quantitative relationship between ABA concentration and SERRS intensity and fluorescence intensity of signal molecules was established. The linear range of SERRS detection was 100 fM–0.1 nM, the detection limit was 38 fM; The linear range of fluorescence detection was 1 pM–100 nM, the detection limit is 0.33 p.m. The constructed dual-effect sensor was used in the recovery laboratory of real ABA samples, the recovery rate was up to 85–108%.

Agricultural Internet of Things Application in the Construction of Regional Smart Cities

Smart city originated from the field of media, which refers to the use of various information technologies or innovative concepts to connect and integrate urban systems and services. It improves the efficiency of resource utilization, optimizes urban management and services, and improves the quality of life of citizens. A smart city is a new generation of information integration technologies using the internet of things, cloud computing, big data, and spatial geographic information integration. It is a new theory and new model to promote urban planning, construction, management, and service intelligence. The IoT technology originated from the third technological revolution. It refers to the connection of objects to the network through the information sensor equipment according to the agreed protocol. It communicates and circulates information through the information dissemination medium during the connection process to realize intelligent identification, tracking, supervision, and other functions. The internet of things is the internet where everything is connected. It is an extension and expansion of the internet based on the network. It is a huge network formed by combining various information sensing devices with the internet. In fact, people, machines, and things are interconnected at any time and at any place. Agricultural internet of things refers to the internet of things that participates in automatic control through various instruments and equipment as parameters of automatic control. Its essence is to use RFID, wireless data communication, and other technologies to construct an “internet of things” covering everything in the world on the basis of the computer internet. It realizes the automatic identification of items and the interconnection and sharing of information through the computer internet. This study aims to study the application of the agricultural internet of things in the construction of regional smart cities. It expects to promote the development of modern agriculture by improving the level of agricultural management with the help of smart city and internet of things technology. Based on the B/S system model, this study uses the related technologies of the internet of things to build a smart agriculture platform. By comparing the data in the database and entering the smart agriculture platform to monitor the data, it can achieve efficient and convenient management. It proposes the framework structure and system characteristics of the facility agricultural internet of things technology system. It also investigates and analyzes related products according to the system framework structure, and summarizes the current status of product development and the functions that can be achieved. According to the survey, 56 people believed that there was no after-sale service in the whole survey process. It accounts for 32.5% of the total surveyed persons, indicating the lack of local modern agricultural management. Thirty-one people are willing to buy agricultural products online, accounting for 18% of the total surveyed, indicating that the development of local online shopping is relatively backward.

Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery

Yield prediction is of great significance in agricultural production. Remote sensing technology based on unmanned aerial vehicles (UAVs) offers the capacity of non-intrusive crop yield prediction with low cost and high throughput. In this study, a winter wheat field experiment with three levels of irrigation (T1 = 240 mm, T2 = 190 mm, T3 = 145 mm) was conducted in Henan province. Multispectral vegetation indices (VIs) and canopy water stress indices (CWSI) were obtained using an UAV equipped with multispectral and thermal infrared cameras. A framework combining a long short-term memory neural network and random forest (LSTM-RF) was proposed for predicting wheat yield using VIs and CWSI from multi-growth stages as predictors. Validation results showed that the R2 of 0.61 and the RMSE value of 878.98 kg/ha was achieved in predicting grain yield using LSTM. LSTM-RF model obtained better prediction results compared to the LSTM with n R2 of 0.78 and RMSE of 684.1 kg/ha, which is equivalent to a 22% reduction in RMSE. The results showed that LSTM-RF considered both the time-series characteristics of the winter wheat growth process and the non-linear characteristics between remote sensing data and crop yield data, providing an alternative for accurate yield prediction in modern agricultural management.

Soil-degradation zoning of Lviv Oblast


 
 
 
 We propose a scheme of soil-degradation zoning of Lviv Oblast, which comprehensively illuminates the problem of degradation and focuses on regional differences in the manifestation and complexity of dominant degradation processes. The highest taxonomic unit of soil-degradation zoning is the soil-degradation country, which in turn is divided into a soil-degradation zone, province, county and district. This classification is based on the landscape features of the study area, the dominant types and kinds of degradation processes. In accordance with the zoning of Lviv Oblast, we have identified the following soil degradation taxa: 2 countries, 3 zones, 5 provinces, 14 counties and 31 districts. The soil-degradation country of the East European Plain unites two soil-degradation zones: Polissia mixed forests and broad-leaved forest zones. The soil-degradation zone (Polissia mixed forests) is best characterized by the processes of deflation and physical degradations. The processes of water erosion and physical degradation of soils dominate within the soil degradation zone (broad-leaved forest zone). Those zones also manifest pyrogenic and chemical degradations. The Carpathian soil-degradation country within Lviv Oblast (the Ukrainian Carpathian zone) is characterized by the distribution of geo-ecological anomalies, mechanical and physical soil degradations. The complex lithological features and geomorphology of the territory in the context of uneven anthropogenic impact determine the intensity of manifestation of degradation. Disturbing the natural stability of the soil cover in the conditions of the mountainous terrain contributes to the activation of the manifestation of degradation processes of geo-ecological anomalies. The problem of soil degradation in Lviv Oblast in the conditions of long-term, intense, and often consumer-orientated agricultural use is considered one of the most urgent tasks of modern agricultural production, soil science, ecology, land management, and environmental protection in general. In addition to arable land, the soils of the Carpathians are degrading due to excessive and often predatory deforestation, recreation and pressure from tourists. As evidenced by numerous scientific publications and our own research, soils are subject to water and wind erosions, overconsolidation and loss of structure, dehumification, depletion, desiccation and aridisation, pyrogenic degradation, contamination by agrochemicals, domestic and industrial wastes, etc. The status of the soils and land resources of the study area is close to critical.
 
 
 

Metal-porphyrinic framework nanotechnologies in modern agricultural management.

Metal-porphyrinic frameworks are an important subclass of metal-organic frameworks (MOFs). These porous materials exhibit a large number of applications for sustainable development and related environmental considerations. Their attractive features include (1) as a free base or metalated with zinc(II) or iron(II or III), they are environmentally benign, and (2) they absorb visible light and are emissive and semi-conducting, making them convenient tools for sensing agrochemicals. But the key feature that makes these nano-sized pristine materials or their composites in many ways superior to most MOFs is their ability to photo-generate reactive oxygen species with visible light, including singlet oxygen. This review describes important issues related to agriculture, including controlled delivery of pesticides and agrochemicals, detection of pesticides and pathogenic metals, elimination of pesticides and toxic metals, and photodynamic antimicrobial activity, and has an important implication for food safety. This comprehensive review presents the progress of the rather rapid developments of these functional and increasingly nano-sized materials and composites in the area of sustainable agriculture.

The curse of the past – What can tile drain effluent tell us about arable field management?

One challenge for modern agricultural management schemes is the reduction of harmful effects on the environment, e.g. in terms of the emission of nutrients. Sampling the effluent of tile drains is a very efficient way to sample seepage water from larger areas directly underneath the main rooting zone. Time series of solute concentration in tile drains can be linked to agricultural management data and thus indicate the efficacy of individual management measures. To that end, the weekly runoff and solute concentration were determined in long-term measurement campaigns at 25 outlets of artificial tile drains at 19 various arable fields in the German federal state of Mecklenburg-Vorpommern. The study sites were distributed within a 23,000 km2 region and were deemed representative of intense arable land use. In addition, comprehensive meteorological and management data were provided. To disentangle the different effects, monitoring data were subjected to a principal component analysis. Loadings on the prevailing principal components and spatial and temporal patterns of the component scores were considered indicative of different processes. Principal component scores were then related to meteorological and management data via random forest modelling. Hydrological conditions and weather were identified as primary driving forces for the nutrient discharge behaviour of the drain plots, as well as the nitrogen balance. In contrast, direct effects of recent agricultural management could hardly be identified. Instead, we found clear evidence of the long-term and indirect effects of agriculture on nearly all solutes. We conclude that tile drain effluent quality primarily reflected the soil-internal mobilisation or de-mobilisation of nutrients and related solutes rather than allowing inferences to be drawn about recent individual agricultural management measures. On the other hand, principal component analysis revealed a variety of indirect and long-term effects of fertilisation on solutes other than nitrogen or phosphorus that are still widely overlooked in nutrient turnover studies.

Knowledge Mapping of Machine Learning Approaches Applied in Agricultural Management—A Scientometric Review with CiteSpace

With the continuous development of the Internet of Things, artificial intelligence, big data technology, and intelligent agriculture have become hot topics in agricultural science and technology research. Machine learning is one of the core topics in artificial intelligence, and its application has penetrated every aspect of human social life. In modern agricultural intelligent management and decision making, machine learning plays an important role in crop classification, crop disease and insect pest prediction, agricultural product price prediction, and other aspects of management and decision-making processes in agriculture. To detect and recognize the latest research developing features in a quantitative and visual way, and based on machine learning methods in agricultural management, the authors of this paper used CiteSpace bibliometric methods to analyze relevant studies on the development process and hot spots. High-value references, productive authors, country and institution distributions, journal visualizations, research topics, and emerging trends were reviewed and analyzed. According to the keyword visualization and high-value references, machine learning approaches focus on sustainable agriculture, water resources, remote sensing, and machine learning methods. The research mainly focuses on six topics: learning technology, land environment, reference evapotranspiration, decision support systems for river geography, soil management, and winter wheat, while learning technology has been the most popular in recent years.