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The "Three Rural Issues", encompass challenges related to agriculture, farmer, and rural area, which hold significant importance in driving comprehensive rural revitalization efforts in China. Farmer entrepreneurship, as a crucial means to enhance productivity, create job opportunities, and increase residents' income, has gradually become a key driving force in promoting rural revitalization in the new stage of development in China. With the rapid development of rural e-commerce, farmer entrepreneurship has encountered new opportunities. This study utilizes the 2020 China Family Panel Studies (CFPS) data and employs a structural equation model (SEM) to analyze the direct impact of rural e-commerce participation on farmer entrepreneurial behavior, considering factors such as human capital, social capital, and network infrastructure. This study further explores the indirect effects and mechanisms of e-commerce participation as a mediating variable and analyzes the impact and mechanisms on agricultural entrepreneurship behavior. The findings are as follows: (1) E-commerce participation significantly promotes farmer entrepreneurial behavior; (2) E-commerce participation as a mediating variable has a positive indirect effect on the relationship between social trust, network infrastructure, human capital, and farmer entrepreneurial behavior; (3) E-commerce participation has a significant positive influence on farmer entrepreneurship in the agricultural sector, and farmers with higher levels of network infrastructure and human capital have a higher probability of choosing agricultural entrepreneurship under the influence of e-commerce participation. Finally, this study provides policy recommendations in terms of infrastructure construction, entrepreneurial policy environment, and education level, aiming to optimize the situation of farmer entrepreneurship and contribute to the comprehensive promotion of rural revitalization.Overall, the research in this paper effectively combines theory and empirical evidence to outline the direct and indirect impact mechanisms of rural e-commerce participation on farmers' entrepreneurial behavior and agriculture-related entrepreneurial behavior and to test the effects of their impacts. First, most of the existing literature deals with farmers in individual sample areas, while the sample selected in this paper is farmers in the whole country, which is relatively more generalizable; second, most of the previous studies explore the level of e-commerce in the inter-provincial or county areas, while this paper expands the empirical study of rural e-commerce on the entrepreneurial behavior of farmers and the micro-period of agricultural entrepreneurial behavior, and focuses on the impacts of the e-commerce activities of farmers on their entrepreneurial behavior. [ABSTRACT FROM AUTHOR]

Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture. [ABSTRACT FROM AUTHOR]

The vegetable sector is a vital pillar of society and an indispensable part of the national economic structure. As a significant segment of the agricultural market, accurately forecasting vegetable prices holds significant importance. Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode. In this paper, we exploit the average daily price data of six distinct types of vegetables sourced from seven key wholesale markets in Beijing, spanning from 2009 to 2023. Upon training an LSTM model, we discovered that it exhibited exceptional performance on the test dataset. Demonstrating robust predictive performance across various vegetable categories, the LSTM model shows commendable generalization abilities. Moreover, LSTM model has a higher accuracy compared to several machine learning methods, including CNN-based time series forecasting approaches. With R2 score of 0.958 and MAE of 0.143, our LSTM model registers an enhancement of over 5% in forecast accuracy relative to conventional machine learning counterparts. Therefore, by predicting vegetable prices for the upcoming week, we envision this LSTM model application in real-world settings to aid growers, consumers, and policymakers in facilitating informed decision-making. The insights derived from this forecasting research could augment market transparency and optimize supply chain management. Furthermore, it contributes to the market stability and the balance of supply and demand, offering a valuable reference for the sustainable development of the vegetable industry. [ABSTRACT FROM AUTHOR]

The main objective of the presented paper was the analysis of the current state of food security in the world, segmentation of 100 selected countries and determination of its main driving factors. The analysis used 27 indicators covering 5 basic areas: agricultural production, poverty, demography, economic development, and environmental indicators. The analysis was based on data from the FAO and World Bank for the most recent available period, which was year 2020. The data dimension was reduced with the application of factor analysis, and the main driving factors of food sufficiency were determined. The result was 6 factors: technological development, economic development, agricultural production, environmental factor, and physical quality of life and environment. To group similar countries in terms of selected indicators, a cluster analysis was performed, whereby countries were grouped by similarity into three clusters. The 1st cluster consisted of the most economically developed countries, where only 2.54% of the population suffers from malnutrition. The countries in this cluster were characterized by high levels of economic development, high caloric intake of the population, and high, life expectancy. On the other hand, they recorded negative development in demographic indicators such as fertility and birth rates. The 2nd cluster included the poorest areas of the African continent, which were most endangered by direct food insufficiency (23.74% of the population). In contrast to the first cluster, these countries were characterized by low levels of economic development, high prices, and low-calorie intakes of the population, as well as low life expectancy, while on the other hand, these countries had high fertility and birth rates. The third largest cluster consisted of countries with a medium threat of food insufficiency, where 6.37% of the population suffers from malnutrition. The countries in the third cluster excelled in terms of crop and livestock production volumes, but in contrast to this, they achieved lower levels of fat, protein, and calorie intake of population. [ABSTRACT FROM AUTHOR]

Sustainable agricultural economic development is the core task for achieving the objective of rural revitalization strategy in China, which cannot be separated from the support and guidance of fiscal policy, and agricultural industry integration is a key path for the fiscal promotion of sustainable agricultural economic development. This paper systematically examines the interaction mechanism between fiscal agricultural expenditures and sustainable agricultural economic development by using 31 provincial panel data in China from 2008 to 2020 and adopting a two-way fixed effect model, a panel quantile model, and a mediating effect model, respectively. The results show that the impact of fiscal agricultural expenditures on sustainable agricultural economic development is significantly positive, and appears a dynamic increasing trend with the agricultural development stage upgrading. Moreover, heterogeneity analysis shows that the effect of fiscal agricultural expenditures is more obvious for the samples in the central region and with a high share of primary industry. Further, a mediating effect test finds that agricultural industry integration plays a mediating mechanism between fiscal agricultural expenditures and sustainable agricultural economic development. Therefore, this paper proposes constructing a long-term investment mechanism for fiscal agricultural expenditures, formulating differentiated fiscal support policies for agriculture, and prioritizing support for agricultural industry integration, which provides theoretical support and policy inspiration for promoting sustainable agricultural economic development. [ABSTRACT FROM AUTHOR]

In the current context, after a pandemic that has left traces in all socio-economic fields and during major geopolitical problems, Basic research is needed in order to identify directions for rapid and sustainable socio-economic recovery. The agricultural sector has played a particularly important role in speeding economic growth and development in less developed countries. As a result, we may argue that the agriculture sector has aided these countries' integration into global trade. The proposed paper aims to identify the degree of dependence of the Moldovan economy on agriculture and how this dependence can be transformed into a challenge that will help the country's economy. Also, the paper aims to identify the link between agricultural production and gross domestic product at the level of the Moldovan economy, as well as the need to create a stability in the labour market in agriculture. The above mentioned will be achieved in the conditions in which there is already a decrease in the labour force, a decrease due to medical, geo-political and migration factors. To develop a sustainable economy, sustainable agricultural public policies must be enacted, resulting in the attraction of personnel from metropolitan regions (where unemployment is higher) to rural areas. [ABSTRACT FROM AUTHOR]

This article presents the results regarding a systematic literature review procedure on digital twins applied to precision agriculture. In particular, research and development activities aimed at the use of digital twins, in the context of predictive control, with the purpose of improving soil quality. This study was carried out through an exhaustive search of scientific literature on five different databases. A total of 158 articles were extracted as a result of this search. After a first screening process, only 11 articles were considered to be aligned with the current topic. Subsequently, these articles were categorised to extract all relevant information, using the preferred reporting items for systematic reviews and meta-analyses methods. Based on the obtained results, there are two main conclusions to draw: First, when compared with industrial processes, there is only a very slight rising trend regarding the use of digital twins in agriculture. Second, within the time frame in which this work was carried out, it was not possible to find any published paper on the use of digital twins for soil quality improvement within a model predictive control context. [ABSTRACT FROM AUTHOR]

The new development pattern has identified two key avenues for the sustained advancement of high-quality agricultural and rural development: digitalisation and low-carbon development. The measurement of the digital economy and the agricultural carbon emission performance, and their spatial and temporal heterogeneity, is a crucial step in promoting the spatial coordination and sustainable development of digitalisation and low-carbon agriculture. This paper employs the entropy value method, SBM model, and coupling coordination degree model to investigate the coupling coordination measurement and spatial-temporal heterogeneity of the performance of the digital economy and agricultural carbon emissions. The data used are provincial panel data from 2013 to 2021. The simulation results demonstrate that, between 2013 and 2021, the digital economy of all provinces exhibited varying degrees of growth, yet the development of the digital economy between provinces exhibited a more pronounced tendency to diverge. Concurrently, the agricultural carbon emission efficiency in China exhibited a fluctuating upward trend. The development of the digital economy and the efficiency of agricultural carbon emission were found to be highly coupled. Their coupling and coordination relationship showed a downward trend followed by an upward trend. In general, it is suggested that we should increase investment in digital economy infrastructure and technology, promote digital agricultural applications, strengthen policy guidance and financial support, establish a coupling coordination mechanism and strengthen farmers' digital literacy and environmental awareness. [ABSTRACT FROM AUTHOR]

For market players and policy officials, commodity price forecasts are crucial problems that are challenging to address due to the complexity of price time series. Given its strategic importance, corn crops are hardly an exception. The current paper evaluates the forecasting issue for China's weekly wholesale price index for yellow corn from January 1, 2010 to January 10, 2020. We develop a Gaussian process regression model using cross validation and Bayesian optimizations over various kernels and basis functions that could effectively handle this sophisticated commodity price forecast problem. The model provides precise out-of-sample forecasts from January 4, 2019 to January 10, 2020, with a relative root mean square error, root mean square error, and mean absolute error of 1.245%, 1.605, and 0.936, respectively. The models developed here might be used by market players for market evaluations and decision-making as well as by policymakers for policy creation and execution. [ABSTRACT FROM AUTHOR]

The paper presents the development of a conceptual information model for optimizing land use and crop forecasting in agriculture, implemented using the PHP programming language, Javascript and the MYSQL database management system. The model is designed to solve optimization problems in various fields such as logistics, manufacturing, finance, etc. As part of the work, an information model was developed, which is a set of data, algorithms and software modules designed to solve optimization problems. The model is implemented based on PHP, Javascript, and MYSQL technologies. The model allows you to solve optimization problems of varying complexity using various optimization methods such as linear programming, nonlinear programming, artificial intelligence methods, etc. The model can be applied to solve optimization problems in various fields. In addition, the model allows you to solve optimization problems with a high degree of accuracy and in a short time, and also has a user-friendly interface that allows users to easily and quickly set optimization tasks. The current optimization problems and forecasting problems are not covered in the work. How this will be implemented is not indicated in the resulting information model. The relevance of scientific research – Every year farmers face the task of how to use the land. And they face many factors, including transportation costs, seed quality, cattle grazing and many other options for using the land. But using the simulation model or information system proposed by us, it is important to understand not only the actual use of the land, but also the economic profit. The system will indicate (advise) based on historical data as an expert, what kind of crop to plant so that there is maximum benefit. Agrotechnical indicators, allows you to create more accurate models and forecasts, which contributes to making informed decisions. Such a system allows farmers and agronomists to respond quickly to changing conditions, optimize land use and improve the efficiency of agricultural production in general. An information model has been developed that will make it possible to predict the harvest taking into account data on weather, soil, crops and historical yield data, as well as optimize land use taking into account data on soil type, climate, crop rotation and financial indicators. The developed information model can improve the efficiency and sustainability of agricultural production. The model can be used by agronomists and farmers to make more informed decisions. The model takes into account data on soil type, climate, crop rotation and financial indicators to optimize land use. [ABSTRACT FROM AUTHOR]

Real-time acquisition of location information for agricultural robotic systems is a prerequisite for achieving high-precision intelligent navigation. This paper proposes a data filtering and combined positioning method, and establishes an active screening model. The dynamic and static positioning drift points of the carrier are eliminated or replaced, reducing the complexity of the original Global Navigation Satellite System (GNSS) output data in the positioning system. Compared with the traditional Kalman filter combined positioning method, the proposed active filtering–Kalman filter algorithm can reduce the maximum distance deviation of the carrier along a straight line from 0.145 m to 0.055 m and along a curve from 0.184 m to 0.0640 m. This study focuses on agricultural robot positioning technology, which has an important influence on the development of smart agriculture. [ABSTRACT FROM AUTHOR]

The purpose of the paper was to analyse the role of public agricultural spending as a panacea for agricultural subsector production in Cameroon. The data were taken from World Bank development database and processed using growth model, and error correction model. Despite the agricultural sector's significant contributions to the country's economy throughout the years, the country has made little headway in reducing it. Recent interest in the relationship between government spending and economic output has not focused on the central question of whether government spending, particularly agricultural spending increases agricultural subsector output or otherwise. With instantaneous and compound growth rates of 6.7 percent and 6.93 percent, 3.03 percent and 3.08 percent, 13.66 percent and 14.64 percent, respectively, the results demonstrated stagnation in agricultural and livestock subsector production and labor. In contrast, public agriculture spending increased at a faster rate, with instantaneous and compound growth rates of 6.09 percent and 6.27 percent, respectively. In the short run, agricultural land, labor, and public agricultural spending all have a substantial impact on animal productivity. Cutting public agricultural spending by 25% is the most cost-effective way to maintain crop and livestock production in the short term. Incentives such as tax reductions and infrastructure development should be established to attract more private investors, knowledge transfer, and significant capital inflows to ensure the agricultural sub-long-term sector's viability. In the short term, privatization of the government's agricultural development projects will be critical for efficient use of public resources. [ABSTRACT FROM AUTHOR]

This paper aims to study the impact of agricultural enterprises' digitization and the performance and bankruptcy risk. Analyzing financial insolvency and predicting bankruptcy risk is a constant focus of research. Company managers and their creditors, auditors, counterparties, etc., are very interested in this issue. Many economists are constantly looking for adequate models and methods by which to establish the financial health of companies with the most significant degree of precision. This paper answers whether some of the most commonly used bankruptcy prediction models also apply in the agricultural sector. For this purpose, a study was made of the annual statistical reports of medium and large Bulgarian agricultural enterprises operating in this sector for five years. Next, the paper's authors also look for evidence that agricultural enterprises with a higher level of digitalization and application of information and communication technologies face a lower risk of bankruptcy and better performance, applying the fixed effect and probit models. The econometric analysis clearly shows a statistically significant relationship between the risk of bankruptcy and the productivity of agricultural enterprises, measured by ROA. Applying the probit model reveals a higher probability of bankruptcy for firms with lower productivity and higher leverage. [ABSTRACT FROM AUTHOR]

State‐promoted and market‐oriented horticultural production has fundamentally reshaped land use and agricultural economy in peri‐urban Lhasa, Tibet Autonomous Region, China. This paper examines two scenarios amidst this transformation. In the first, peri‐urban land has been valorised for vegetable production through the erection of greenhouses and the introduction of agronomic science. However, Tibetans refrain from participating directly, preferring to lease the land to Han migrants, and citing structural inequalities between Han and Tibetans and Tibetan work ethic as explanation. In the second, the municipal government is more proactive in introducing agro‐science and providing technical training to enrol Tibetans. However, the participation of Tibetans is selective and partial, contingent on the same non‐market rationalities that Tibetans invoke to justify non‐participation in the first scenario. This paper explicates the diverging modes of participation for Tibetans by engaging with the Callonian approach on economisation and marketisation, and interrogating how socio‐technical relations established among humans, material infrastructure and agro‐science culminate in variegated calculative and non‐calculative agencies. Focusing in particular on the structural relations and cultural conventions that frustrate the unilinear enactment of a market order, this study contributes to the geographies of marketisation in two ways. First, it works with an analytical framework centred on the ongoing dynamic of framing, overflowing and reframing to argue that the marketisation approach can be expanded to account for the encounters between market agencements and local socio‐cultural contingencies. Geographically, second, it also contributes to the agenda of interrogating diverse market variants and the richness of economic differences in real space‐times. [ABSTRACT FROM AUTHOR]

Pollinators are critical for agricultural production and food security, leading to many ongoing surveys of pollinators (especially bees) in crop and adjacent landscapes. These surveys have become increasingly important to better understand the community of potential pollinators, quantify relative insect abundance, and secure crop ecosystem services. However, as some bee populations are declining, there is a need to align and improve bee survey efforts, so that they can best meet research and conservation goals, particularly in light of the logistical and financial constraints of conducting such studies. Here, we mined the existing literature on bee surveys in or around agricultural lands to better understand how sampling methods can be optimized to maximize estimates of 2 key measures of bee communities (abundance and richness). After reviewing 72 papers spanning 20 yr of publication, we found that study duration, number of sites, sampling time, and sampling method most significantly influenced abundance, while the number of trips per year and collection method significantly influenced richness. Our analysis helps to derive thresholds, priorities, and recommendations that can be applied to future studies describing bee communities in agroecosystems. [ABSTRACT FROM AUTHOR]

The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN). However, considering the locality of convolutional operation, CNN unable to master the interplay between global and distant semantic information. Some researches has employ Vision Transformer as a backbone in remote sensing field. Inspired by these researches, in this paper, we propose a network named Siam-Swin-Unet, which is a Siamesed pure Transformer with U-shape construction for remote sensing image change detection. Swin Transformer is a hierarchical vision transformer with shifted windows that can extract global feature. To learn local and global semantic feature information, the dual-time image are fed into Siam-Swin-Unet which is composed of Swin Transformer, Unet Siamesenet and two feature fusion module. Considered the Unet and Siamesenet are effective for change detection, We applied it to the model. The feature fusion module is designed for fusion of dual-time image features, and is efficient and low-compute confirmed by our experiments. Our network achieved 94.67 F1 on the CDD dataset (season varying). [ABSTRACT FROM AUTHOR]

To facilitate accurate prediction and empirical research on regional agricultural carbon emissions, this paper uses the LLE-PSO-XGBoost carbon emission model, which combines the Local Linear Embedding (LLE), Particle Swarm Algorithm (PSO) and Extreme Gradient Boosting Algorithm (XGBoost), to forecast regional agricultural carbon emissions in Anhui Province under different scenarios. The results show that the regional agricultural carbon emissions in Anhui Province generally show an upward and then downward trend during 2000–2021, and the regional agricultural carbon emissions in Anhui Province in 2030 are expected to fluctuate between 11,342,100 tones and 14,445,700 tones under five different set scenarios. The projections of regional agricultural carbon emissions can play an important role in supporting the development of local regional agriculture, helping to guide the input and policy guidance of local rural low-carbon agriculture and promoting the development of rural areas towards a resource-saving and environment-friendly society. [ABSTRACT FROM AUTHOR]

Multivariate time series forecasting has an important role in many real-world domains. Especially, price prediction has always been on the focus of researchers. Yet, it is a challenging task that requires the capturing of intra-series and inter-series correlations. Most of the models in literature focus only on the correlation in temporal domain. In this paper, we have curated a new dataset from the official website of Turkish Ministry of Commerce. The dataset consists of daily prices and trade volume of vegetables and covers 1791 days between January 1, 2018 and November 26, 2022. A Spectral Temporal Graph Neural Network (StemGNN) is employed on the curated dataset and the results are given in comparison to Convolutional neural networks (CNN), Long short-term memory (LSTM) and Random Forest models. GNN architecture achieved a state-of-the-art result such as mean absolute error (MAE): 1,37 and root mean squared error (RMSE): 1.94). To our knowledge, this is one of the few studies that investigates GNN for time series analysis and the first study in architecture field. [ABSTRACT FROM AUTHOR]

Despite its immense natural resources, Angola struggles to significantly improve its economy to reduce poverty. Carbon emissions have been increasing over the years, even though the country plans to reduce them by 35% by 2030. This paper attempts to assess the carbon emissions of several sectors (industries, transport, services, and residences) on economic growth, intending to find a balance between environmental protection that requires carbon emissions reduction and economic development that may add to environmental degradation. The study employed time series data on GDP, CO2, CH4, and N2O covering 1971 to 2021 and ARDL and ECM models. This is the first study at the state level in Angola on the relationship between economic development and environmental sustainability considering methane and nitrous oxide emissions. Additionally, the paper assesses the responses of GDP to deviation shock of GDP, CO2, CH4, and N2O by 2032. Phillip Perron and Augmented Dickey-Fuller tests showed that all the data are stationary at the first difference, favoring the application of the ARDL model to explore the short and long-run relationships. The result reveals that methane from agricultural activities and carbon emissions from the building sector and public services contribute to economic growth, whereas carbon emissions from industrial heat systems, non-renewable electricity production, and manufacturing industries harm economic growth. However, no relationship exists between nitrous oxide emissions and economic development. In addition, impulse response function estimates show that appropriate investments can sustain economic development over the years. Therefore, the country should diversify its economy and avoid polluting fuel sources, such as coal. Raising renewable energy's proportion in the total energy mix can support growth while considering the environmental quality. Investments in skills training, academic projects in renewable energy technologies development, agriculture mechanization, and sustainable job creation are recommended. Additionally, investing in quality seeds adapted to climate realities might help lessen climate change's adverse effects and promote growth. Manure manufacturing processes must be improved to reduce agriculture and livestock's methane and nitrous oxide emissions. The country's leaders are encouraged to promote raw material processing industries while insisting on reducing carbon emissions. [ABSTRACT FROM AUTHOR]

This paper establishes that agricultural group guarantee loans (AGGLs) are indeed an innovative tool used by Microfinance Institutions (MFIs) to extend credit to resource-disadvantaged smallholder farmers regarded as not credit-worthy by traditional lenders. It disproves popular literature that one requires assets to access formal credit and that extremely poor farmers are segregated from borrowing citing a lack of collateral to pledge to both group members and to the lender. This paper indicates that efforts to get smallholder farmers out of destitution should be redirected to addressing other group credit aspects other than increasing participation. Multi-stage sampling obtained 161 agricultural loan borrowers of Promotion of Rural Initiative Development Enterprises (PRIDE) microfinance, a formal Tier III credit institution in Uganda. Both borrowers of the group (AGGLs) and Individual (Individual Loan borrowers, IL) loans were selected for comparison purposes. Semi-structured interviews and in-depth discussions with farmer groups (focus group discussions, FGDs) collected both quantitative and qualitative data for the study. Descriptive statistics analysis presented the socioeconomic characteristics of the borrowers while the binary logistic regression model determined the factors that influence participation in AGGLs. The findings indicated that IL borrowers were better off in socioeconomic aspects such as income than AGGL borrowers. The study results revealed that the probability of participating in AGGLs decreases with an increase in the number of asset ownership and an increase in household expenses, particularly education. This implies that AGGLs are socially perceived to be a "facility for the poor", supporting the motives of MFIs. Agricultural group guarantee loans are associated with smaller loan amounts due to fear of default. These smaller amounts limit investment and consequent income improvement. This is the first paper to study participation in AGGLs offered by a formal credit institution in Uganda. Other group loans offered in Uganda do not target agriculture, those that do, are offered by savings and credit cooperative organizations (SACCOs) informally started by farmers. [ABSTRACT FROM AUTHOR]

Currently, more and more attention is being paid to the development of technologies for the satellite monitoring of land use and the state of agricultural landscapes. The lack of up-to-date information about the boundaries of individual agricultural fields does not allow us to fully assess the state of arable land and take them into account. The available statistical sources have discrepancies and do not have information about the spatial distribution of used and unused agricultural fields. The purpose of this work is to establish the spatial distribution of cultivated and uncultivated arable lands of the Volgograd region according to remote sensing data. This paper presents the results of mapping the actual boundaries of arable lands of the Volgograd region as of 2021. High-resolution Sentinel-2 and Google Earth PRO data in the geographic information program are used to decrypt arable land. As a result, 6.05 million ha of arable land are mapped. The data are compared with official statistics for 2021, and an excess of 12% is noted in comparison with the results of decryption. It is noted that, over the past 20 years, according to statistical data, the areas of arable land and deposits have practically not changed. When comparing the decryption results with the data on arable lands of the Vega service, a difference of 4% was noted, which is quite high accuracy. According to the All-Russian Agricultural Census of 2016, the area of arable land used was exceeded by 8%. According to the SRTM digital terrain model, morphometric parameters of arable land were calculated throughout the region. It is determined that agricultural fields are located mainly on the slopes of the western exposure (37%), which is due to the predominance of the general slope of the relief to the west. Most (78%) of the field areas are on slopes with a steepness of up to 1°, and about 2% occupy areas of more than 3°. Water erosion is noted on steep slopes. The smoothest relief in the Volga region is on the territory of the Caspian lowland. Using remote methods, the assessment of the areas of fallow lands was carried out: about 960 000 ha. According to various sources, from 4800 to 891 000 ha of unused arable land are noted. The resulting geoinformation basis will make it possible to fully account for and assess the condition of cultivated and uncultivated arable lands, as well as develop projects for the rational use of land resources to increase yields and prevent the degradation of agricultural landscapes. [ABSTRACT FROM AUTHOR]

Plant-soil sensing devices coupled with Artificial Intelligence autonomously collect and process in situ plant phenotypic data. A challenge of this approach is the limited incorporation of phenotype data into decision support systems designed to harness agricultural practices and forecast plant behavior within the intricate context of genotype, environment, and management interactions (G × E × M). To enhance the role of digital phenotyping in supporting Precision Agriculture, this paper proposes a sensing network based on the Internet of Things. The developed system comprises three modules: data collection, communication, and a cloud server. Several processes co-occur in the server, namely data visualization to confirm the correct sensors and data stream functioning. In addition, a crop growth model (CGM) runs on the server, which is powered by the collected data. The simulations generated by the model will support agricultural decisions, obtaining, in advance, insights about plant behavior considering several G × E × M scenarios. To assess the performance of the proposed network to provide reliable data to the model, a greenhouse was equipped with several sensors that collect plant, environment, and soil data (e.g., leaf numbers, air temperature, soil moisture). The proposed network can provide real-time causal support for advanced agricultural practices, evolving from a data-driven approach to an integrative framework where context (G × E × M) drives decision making. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a transformative technology that is reshaping industries and daily life, leading us towards a connected future that is full of possibilities and innovations. In this paper, we present a robust framework for the application of Internet of Things (IoT) technology in the agricultural sector in Bangladesh. The framework encompasses the integration of IoT, data mining techniques, and cloud monitoring systems to enhance productivity, improve water management, and provide real-time crop forecasting. We conducted rigorous experimentation on the framework. We achieve an accuracy of 87.38% for the proposed model in predicting data harvest. Our findings highlight the effectiveness and transparency of the framework, underscoring the significant potential of the IoT in transforming agriculture and empowering farmers with data-driven decision-making capabilities. The proposed framework might be very impactful in real-life agriculture, especially for monsoon agriculture-based countries like Bangladesh. [ABSTRACT FROM AUTHOR]

The current agricultural products supply chain traceability system has problems such as low data storage capacity, centralized management, poor scalability, and the inability to guarantee the privacy information of participants. This paper used the Hyperledger Fabric v2 . 0 platform to design an agricultural products supply chain traceability system based on master- slave federated chain structure to solve the above problems. This system used the constructed master-slave federated chain model to store information of the supply chain, and used the multi-channel design scheme of the federated chain to ensure the security of the private information of each participating subject in the supply chain. This system deployed IoT devices in each supply chain participant to collect, process and upload information to ensure that the information stored in the supply chain wasn' t tampered with, safe and reliable. At the same time, this paper designed a smart contract between the master chain and the slave chain to determine the rationality of the information uploaded by each participant following the " National Food Safety Standard" to ensure the quality of the food at the source. In addition, it designed aclustered transmission method to upload the large amount of data generated by IoT devices in the farm with low energy consumption. The experimental results show that the system builds the agricultural products supply chain with low latency and returns the information for consumer queries in a shorter time. Meanwhile, the clustered transmission method can significantly reduce the energy consumption generated by uploading data from IoT devices. The designed agricultural products supply chain traceability system realizes the openness and transparency of the agricultural products supply chain, improves the efficiency of supply chain information traceability, and in creases consumer s' food safety trust. [ABSTRACT FROM AUTHOR]

Economic statistics are frequently produced at an administrative level such as the subnational division. However, these measures may lack sufficient local variation for effective analysis of local economic development patterns and exposure to natural hazards. Agricultural gross domestic product (GDP) is a critical indicator for measurement of the primary sector, on which more than 2.5 billion people depend for their livelihoods, and it provides a key source of income for the entire household. Through a data-fusion method based on cross-entropy optimization, this paper disaggregates national and subnational administrative statistics of agricultural GDP into a global gridded dataset at approximately 10×10 km for the year 2010 using satellite-derived indicators of the components that make up agricultural GDP, i.e., crop, livestock, fishery, hunting and forestry production. To illustrate the use of the new dataset, the paper estimates the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP, which amounts to around USD 432 billion of agricultural GDP circa 2010, with nearly 1.2 billion people living in those areas. The data are available on the World Bank Development Data Hub (10.57966/0j71-8d56;). [ABSTRACT FROM AUTHOR]

The aim of this paper is to highlight the role of computer based information systems in managing the resources of agricultural companies in order to forecast agricultural production and to study, from several points of view, the impact of climate change, respectively the changes brought by them. The Internet of Things involves the use of the Internet to connect different devices, services, and automated systems, thus forming a network of objects. Internet of Things devices will be integrated, connected to computer based information systems to increase the efficiency of agricultural production, by remotely monitoring sensors that can detect soil moisture and crop growth, ensuring (for example) remote management of smart combine harvesters and equipment of irrigation. The data provided by different sensors will be collected and the operational data obtained will be analyzed. The analysis will be performed using artificial intelligence methods that, through specific algorithms, allow the learning of new processing procedures, using data and previous knowledge collected, correlated with information obtained from meteorological services, to improve the making process. of decisions. Climate change is affecting agriculture by decreasing agricultural areas and reducing crops in the affected areas. These changes ultimately lead to a reduction in the amount of food available to humans and animals, to starvation, a phenomenon that still affects millions of people today. [ABSTRACT FROM AUTHOR]

Sustainable development of agriculture is the basis for achieving social sustainable development. As the basic industry of national economy, green development of agriculture has become an important support for building an environment-friendly society. Agricultural fiscal expenditure is a direct channel for the government to support agriculture and promote agricultural transformation. It is important to analyze the impact of agricultural fiscal expenditure (AFE) on agricultural green total factor productivity (AGTFP) for sustainable agricultural development. Therefore, this paper employs the random effect model and spatial Durbin model to empirically analyze the direct effect and spatial spillover effect of AFE on AGTFP by using the agricultural panel data of 30 provinces in China from 2008 to 2020. Then, by taking the policy proposal as the time node, this paper also conducts a time heterogeneity analysis to measure the impact of policy enactment on AFE and AGTFP. The main conclusions are as follows: (1) AGTFP exists significant positive spatial spillover effect. The "radiation effect" of agricultural green development is significant. (2) AFE can significantly reduce the AGTFP in the local area, that is, 1% increase of AFE in the local area will reduce AGTFP by 0.037%. At present, agriculture is still yield-oriented. The improvement of AFE in the local area will lead to the expansion of local agricultural production and increase pollution emission. (3) AFE has a significant negative spatial spillover effect on AGTFP, that is, for every 1% increase in AFE, the AGTFP will decrease 0.123% in geographically similar areas, while the AGTFP will decrease by 0.116% in economically and geographically similar areas. It is obvious that AFE will promote the optimization of agricultural production conditions in the province, with the "demonstration effect" on the surrounding areas, the enthusiasm of production in the surrounding areas will increase, thus expanding the pollution emission. (4) According to the analysis of different periods, AFE has a negative impact on AGTFP mainly before the reform innovation is proposed in 2015. It indicates that reform policies have a significant impact on agricultural sustainability. [ABSTRACT FROM AUTHOR]

With the vast popularity of the deep learning models in the engineering and mathematical fields, Artificial Neural Networks (ANN) have recently attracted significant research applications in agriculture, economics, informatics and finance. In this paper, we use a deep learning method to capture and predict the unknown complex nonlinear characteristics of agricultural output based on autoregressive artificial neural network, using Nigeria as a case study. Using the proposed model, shocks in agricultural output is analyzed and modeled using data obtained for a period of forty years (1980-2019), and compared with analyses obtained from the autoregressive integrated moving average model (ARIMA). This result is significant because it justifies the superiority of the hybrid ANN model over the traditional Box-Jenkins methodology for forecasting non-stationary time series. The empirical results show that the proposed autoregressive ANN model achieves an improved forecasting accuracy over the traditional Box-Jenkins ARIMA method. It is further proposed that various types of artificial neural networks would be useful in forecasting and solving relevant tasks and problems widely defined in global agricultural production. [ABSTRACT FROM AUTHOR]

Economic statistics are frequently produced at an administrative level such as the sub-national division. However, these measures may lack sufficient local variation in the economic activities to analyze local economic development patterns and the exposure to natural hazards. Agriculture GDP is a critical indicator for measurement of the primary sector, on which more than 2.5 billion people depend on their livelihoods that provide a key source of income for the entire household (FAO, 2021). Through a data fusion method based on cross-entropy optimization, this paper disaggregates national and subnational administrative statistics of Agricultural GDP into a global gridded dataset at approximately 10 x 10 kilometers using satellitederived indicators of the components that make up agricultural GDP, namely crop, livestock, fishery, hunting and timber production. The paper estimates the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP is an estimated US$432 billion of agricultural GDP circa 2010, where nearly 1.2 billion people live. The data are available on the World Bank Development Data Hub (DOI: http://doi.org/10.57966/0j71-8d56; IFPRI and World Bank, 2022). [ABSTRACT FROM AUTHOR]

The impact of climate warming on world agricultural production and food security has become increasingly obvious. As an important source of carbon emissions and the carbon sink system, the green development of agriculture is one of the important ways to alleviate the crisis and achieve China' s goals of reaching peak carbon emissions and carbon neutrality. Exploiting the potential of agricultural carbon sinks and accurately calculating agricultural net carbon emissions are of great significance to promoting the green development of agriculture meeting the ' dual carbon' goals. Based on the dual characteristics of the agricultural carbon cycle, this paper sorted out the logical relationship between agricultural green development and the agricultural carbon cycle, incorporated the role of agricultural carbon sinks into the calculation framework of agricultural carbon emissions, and measured China' s agricultural net carbon emissions from 2000 to 2019 by time and region. The decoupling model of agricultural net carbon emissions from agricultural economic growth was constructed. The results of the study found that the total amount of carbon emissions from agricultural sources increased from 277.899 million t in 2000 to 308.383 1 million t in 2019, with an average annual growth rate of 0.52%, but the agricultural carbon emission intensity showed a trend of continuous decline; the agricultural carbon sink increased from 99.539 9 million t in 2000 to 133.039 million t in 2019, with an average annual growth rate of 1.46%, and the agricultural carbon sink resources were considerable. The intensity of agricultural carbon source emissions was higher in the west than in other regions and there was a clear gap with the eastern region. The total amount of agricultural carbon source emissions, carbon sinks, and net carbon emissions all showed a medium-high pattern. The decoupling relationship between agricultural economic growth and agricultural net carbon emissions was mainly manifested in two decoupling states: strong decoupling and weak decoupling. Nearly 50% of the provinces achieved the ideal strong decoupling state in terms of agricultural green development, while the remaining provinces were in a weak decoupling state, and the decoupling state was generally ideal. Finally, this paper puts forward targeted countermeasures and suggestions to promote the green development of agriculture and help achieve the ' dual carbon' goals: we should accurately locate the carbon source; ensure the ' double bottom line' of food security and pig production capacity under low-carbon agriculture; resolve the resource endowment differences between regions; explore the potential of the agricultural carbon trading market; create a new path for farmers to increase their income, maintain the strong and improve the weak; and strive to achieve the ' package' of strong decoupling in agriculture to help achieve the ' dual carbon' goals. [ABSTRACT FROM AUTHOR]

This paper highlights why it is crucial to determine crop production using artificial intelligence for the growth of agriculture. In this paper, an elaborated ResNet-59 model has been developed to estimate potato harvests accurately. The dataset contained a global potato and tomato production data set that began in 1961 and ended in 2021; different deep learning architectures considered were ResNet-59, GoogLeNet, VGG-19, ResNet-50, VGG-16, and MobileNet. Collectively, the outcome of this ResNet-59 model’s improvement led to a general superiority with more minor mean squared errors, which were recorded as 0.0083, and a mean absolute error of 0.0762, a median of absolute errors amounted to 0.0750 along with an R2 value equalling 99.05%. According to these results, precision agriculture is another area where ResNet-59 could be effective, thus promoting the rational distribution of resources, minimizing waste and increasing food security. It is epoch-making to deliberate on the capability of artificial intelligence to emancipate sustainable farming and future research. [ABSTRACT FROM AUTHOR]

Generating electricity from renewable energy instead of fossil fuels brings great benefits to the environment and sustainable development. Thus, assessing the potential of wind and solar energy in agricultural coastal areas can identify sustainable energy solutions for meeting energy demand and producing fresh water for agricultural applications and domestic use. However, it is difficult to accurately assess the wind and solar energy potential in Libya due to the civil war, lack of measured data, and its limited availability. Consequently, this concise work is unique because it is the first to use daily measurement data from Az-Zāwiyah, Libya, for evaluating wind and solar energy based on one year of measured data for 2022. Moreover, the present study aims to investigate the potential of wind and solar energy as promising renewable sources for meeting energy demand in coastal agricultural regions in Libya using multiple datasets for the first time. In this paper, five satellite products (TerraClimate, ERA5, ERA5-Land, MERRA-2, and CFSR) were assessed and compared against measured data for January 2022–December 2022 to understand their suitability, accuracy, and reliability. The results showed that CFSR and ERA5-Land demonstrate the most favorable performance for assessing the wind resource, while all satellite products can be utilized for preliminary solar resource assessment. Then, the assessment of wind and solar resources was evaluated in five agricultural coastal regions (Aljmail, Az-Zāwiyah, Castelverde, Msallatah, and Sabratah) based on the best satellite product for the period of 2000–2022. Furthermore, the performance of the wind and solar power systems was investigated for typical farms, which were chosen to estimate the required energy demand according to daily electrical consumption. The results show that the positive outcomes of implementing these systems were highlighted, with an emphasis on their potential benefits to the entire Libyan agricultural sector. Accordingly, scaling up and generalizing the proposed systems and generalizing them to include all farms in Libya could have a significant impact on national electricity generation, mitigate greenhouse gases, and contribute to the development of the agricultural sector and the country's economy. [ABSTRACT FROM AUTHOR]

Climate is closely related to human life, food security and ecosystems. Forecasting future climate provides important information for agricultural production, water resources management and so on. In this paper, historical climate data from 1962–2001 was used at three sites in Tianjin Baodi, Tianjin and Tanggu districts as baseline and the model parameters were calibrated by the Long Ashton Research Station Weather Generator (LARS-WG). 2m-temperatures in 2011–2020 were verified under two scenarios, representative concentration pathway (RCP) 4.5 and RCP8.5 in different atmospheric circulation models with optimal minimum 2m-temperatures at the three sites. From 2031–2050, Tianjin will be using more moderate minimum 2m-temperatures in future simulations. Support vector machines (SVM) were used to optimize the simulated data to obtain more accurate future maximum and minimum 2m-temperatures for the three sites. The results showed that the determinant coefficient of LARS-WG simulation was 0.8 and SVM optimized determinant coefficient was 0.9 which greatly improved the prediction accuracy. The minimum and maximum future 2m-temperatures optimized under European Community Earth System Model (EC-EARTH) were relatively low and the same future 2m-temperatures optimized under Hadley Centre Global Environment Model Earth System (Had-GEM2-ES4) were high especially in the RCP8.5 scenario which simulated 2051–2070 climate. The SVM optimization showed that the maximum and minimum 2m-temperatures were in general agreement with the original simulation values. [ABSTRACT FROM AUTHOR]

An efficient machine learning-based framework for crop price prediction is proposed in this paper to assist the farmers in estimating their profit-loss beforehand. The proposed work is composed of four functional blocks, such as crop yield prediction, determination of supply, demand prediction and crop price prediction. The input datasets consist of the various field values, such as yield, remaining crop at the end of the year, import, demand and price of a crop. Various time series-based algorithms, such as autoregression, moving average, autoregressive moving average, autoregressive integrated moving average and exponential smoothing, are used to forecast the crop yield. The supply of the crop is determined as a sum of three variables, i.e., the predicted crop yield, residue and import values. The demand for the crop is predicted from a year alone as the demand has more correlation with year over other factors. The crop price from demand, supply and year is predicted using different approaches, which include the time series method, statistical approaches and machine learning techniques. Finally, these three techniques for price prediction are compared to determine the best model having minimum root-mean-square error value. In the proposed work, the decision tree regressor is found to be the best model, for predicting crop price, over others. The superiority of the proposed work over existing approaches, in terms of various aspects, is shown by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we present a comparison of linear, quadratic, and exponential trend model to forecasting agricultural production 15 years later. In addition, this paper also analyses whether the land area factor or land productivity factor affects changes in the amount of agricultural production. The analysis shows that the quadratic model is more applicable to predict production trends than the linear and exponential trend models. The increase in yield per unit area as the dominant factor driving the production of several crops indicates the intensification program is running well in the research area. The increase in land area as the dominant factor affecting the production of several crops indicates that the extensification program is easy to implement due to the availability of agricultural land in agricultural locations. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the collected data on the Lepidoptera species of the Saturniidae family. The studied species are available in the four Lepidoptera collections preserved by the Natural History Museum of Sibiu, Romania: the Lepidoptera collection by Daniel Czekelius, the Palaearctic Lepidoptera collection by Daniel Czekelius, the Lepidoptera collection by Eugen Worell and the Lepidoptera collection by Rolf Weyrauch. As a result of my research, it comes out that the collected species proceed from many regions of Romania, mainly from Transylvania, especially from the surroundings of Sibiu, but were also obtained from materials in exchanges with other specialists from abroad. This paper is a contribution to the knowledge concerning the spread of the biggest butterflies in Romanian fauna: Saturnia pyri Denis & Schiffermuller, 1775; S. spini Denis & Schiffermuller, 1775; S'. pavonia Linnaeus, 1758; S. boisduvalia Eversmann 1846 belonging to the Saturniidae family with samples collected during 1886- 1974. After the centralization of the data from the labels and inventory of the four collections, a total number of 56 specimens (22cc and 34ȘȘ) were identified. Considering the important faunistic and biogeographical information, we should warn about the protection of the endangered and vulnerable species and recommend a rational application of the treatments with chemical substances in agriculture, orchards and in the forestry ecosystems. [ABSTRACT FROM AUTHOR]

The OECD-FAO's Agricultural Outlook and the European Commission DG AGRI's Medium-term agricultural outlook report provide price forecasts. Users of these forecasts may be interested in their accuracy. This paper measures the accuracy for values forecast for the following year. These are very accurate as regards the AO EU price of poultry, the EC outlook price of common wheat and feed barley, but not so accurate as regards the EC outlookon beef prices. In some cases, discrepancies between the forecasts follow a systematic pattern. The paper also discovers how the OECD-FAO's outlook projections for a common wheat world representative price are changing from year to year. Usually they are positively correlated, but there are certain exceptions where their correlation is significantly negative. This means that the price projections of some commodities may vary dramatically. [ABSTRACT FROM AUTHOR]

In order to improve the effect of agricultural economic risk forecast, this paper studies the agricultural economic risk forecast combined with data mining technology and builds an intelligent agricultural economic risk forecast system. Moreover, this paper employs a dynamic factor model to estimate common factors that drive changes in target topics. In order to construct a sentiment index that can reflect the overall operating situation of the macroeconomy, this paper improves the agricultural economic risk mining algorithm and standardizes the sentiment value corresponding to the target theme. In addition, this article analyzes the sentiment changes of its individual topics one by one in combination with the specific economic environment. The simulation study shows that the agricultural economic risk forecast system based on data mining technology proposed in this paper has a good effect. [ABSTRACT FROM AUTHOR]

Efficient management of the agricultural land fund is one of the basic priorities of each state. The given resource represents the totality of the agricultural lands within a territorial-administrative unit. The Republic of Moldova, having a specific geopolitical position, during the twentieth century was subjected to various areas of influence, which imposed their own views on how to manage the land. As a result, all the political and administrative changes involved the fundamental reform of the agricultural sector and, directly, of the agricultural land fund. The paper is a reflection of the evolution of the country's land fund in the context of social and political change during the XX-XI century. The analysis of that period showed that the only viable and efficient method of managing the land fund agricultural is private property based on the implementation of modern production technologies. The general research methods (empirical and theoretical methods) were used in the process of elaborating this report, the analysis of statistical data reflecting the evolution of the national land fund as well as of the agricultural land areas were widely applied. [ABSTRACT FROM AUTHOR]

During the research, it has been highlighted that there are no two countries in the world where the legal regulation of land relations would be identical, and the following four typical models of platform interactions have been established: closed market model, open market model, with some minor restrictions for agricultural and natural real estate (lands); closed for foreigners; open with restrictions. It has been substantiated that there is a stereotype in Ukraine as regards the efficiency of large-scale agriculture, and has been highlighted that most European countries prefer family farms, by ownership, and try to maintain small/medium-sized farm size, rather than increase the influence of monopolistic companies with large-size land use. Also, the paper highlights the factor of land consumption, in the process of which it has been found that in European countries this indicator is much better as compared to that in Ukraine. The authors’ correlation analysis revealed a close relationship between these factors and the average value of agricultural lands. SWOT analysis tools have been applied to study the institutionalization of the land market. In addition, the authors have analyzed the average value of land plots of the owners of land shares(units) after the lifting of the moratorium on the turnover of agricultural lands, which allowed to state a significant regional difference in the value of these lands. Where twelve regions of Ukraine have a lower market value of land plots compared to the lowest limit of normative monetary valuation of arable lands. Using the diagram, the authors have shown the lack of mutual coordination between the indicators of the normative monetary valuation of arable lands and the average value of sold land plots of the owners of land shares (units). [ABSTRACT FROM AUTHOR]

As an excellent category for futures trading, soybean meal futures have shown an orderly and positive development trend since its listing, which has been favored by many investors in futures market. Exploring linkage between soybean meal futures prices and spot prices is beneficial for reducing the impact of price fluctuations on soybean meal industry and providing support for stabilizing agricultural product futures market. Taking monthly data from August 2009 to August 2021 as samples, the paper uses VAR model, impulse response function and variance decomposition to test impact of soymeal spot price on futures price. The results showed that the response period of soybean meal futures price to spot price shock is 2 years, and the contribution rate of soybean meal spot price to futures price is 6.97% in the second year. The research shows that the spot price of soybean meal has a significant promoting effect on the futures price. [ABSTRACT FROM AUTHOR]

The key trend for future agricultural growth is efficient, green and sustainable high-quality development, and it is crucial to sort out the factors influencing high-quality agricultural development. Objectives: The purpose of this study is to dissect whether environmental regulation has a catalytic effect on quality rural development, through which paths it is driven and whether there is a threshold effect to be further clarified. Method: In this study, the panel data of 34 provinces in China from 2012 to 2018 are used, and 17 variables are used to construct an evaluation index system, covering four dimensions: agricultural endowment, agricultural output level, agricultural green degree and social sustainability. The high-quality development of agriculture is measured by entropy method. And further, using a baseline regression model and a mediating effects model, we empirically investigated the impact of environmental regulation on high-quality agricultural development and its mechanism of action, and empirically assessed the nonlinear effects of environmental regulation using a threshold regression model. Results: Environmental control, as noted in the study, considerably assists in the establishment of high-quality agriculture; at the same time, large-scale land management plays a role in mitigating the influence of both. There is a single income threshold impact on rural households between high-quality agricultural growth and environmental regulation, and once that threshold is reached, the influence of high-quality agricultural growth grows. Contribution: According to the research findings, recommendations are made for the design of scientific environmental regulation policies, the establishment of a sound service system for large-scale rural land management, and the establishment of a stable mechanism for rural residents to sustain their income, in order to strengthen the effect of environmental regulation and realize high-quality agricultural growth in China. The marginal contribution of this paper is to enrich the study of the relationship between environmental regulation and high-quality agricultural development, which has theoretical and practical implications for promoting sustainable agricultural development. [ABSTRACT FROM AUTHOR]

People’s daily life is significantly impacted by price changes in agricultural commodities. However, it is challenging to estimate the values of agricultural commodity futures due to the agricultural futures market’s complexity and the wide range of affecting variables. Predicting agricultural commodity prices correctly is crucial for avoiding market risk, raising agricultural revenue, and implementing macroeconomic government control. In order to provide a quick and accurate prediction system for agricultural output, this research proposes an unique method for predicting vegetable prices using neural networks. In this paper, several forms of data are considered on vegetable production, examined these data, and used Harris Hawks Optimization with Multi-layer Perceptron to forecast the future output of these vegetables. The present predictive model is evaluated with six performance metrics Precision, recall, f1-score, accuracy, MSE, MCC and kappa. [ABSTRACT FROM AUTHOR]

Reliable and timely crop-yield prediction and crop mapping are crucial for food security and decision making in the food industry and in agro-environmental management. The global coverage, rich spectral and spatial information and repetitive nature of remote sensing (RS) data have made them effective tools for mapping crop extent and predicting yield before harvesting. Advanced machine-learning methods, particularly deep learning (DL), can accurately represent the complex features essential for crop mapping and yield predictions by accounting for the nonlinear relationships between variables. The DL algorithm has attained remarkable success in different fields of RS and its use in crop monitoring is also increasing. Although a few reviews cover the use of DL techniques in broader RS and agricultural applications, only a small number of references are made to RS-based crop-mapping and yield-prediction studies. A few recently conducted reviews attempted to provide overviews of the applications of DL in crop-yield prediction. However, they did not cover crop mapping and did not consider some of the critical attributes that reveal the essential issues in the field. This study is one of the first in the literature to provide a thorough systematic review of the important scientific works related to state-of-the-art DL techniques and RS in crop mapping and yield estimation. This review systematically identified 90 papers from databases of peer-reviewed scientific publications and comprehensively reviewed the aspects related to the employed platforms, sensors, input features, architectures, frameworks, training data, spatial distributions of study sites, output scales, evaluation metrics and performances. The review suggests that multiple DL-based solutions using different RS data and DL architectures have been developed in recent years, thereby providing reliable solutions for crop mapping and yield prediction. However, challenges related to scarce training data, the development of effective, efficient and generalisable models and the transparency of predictions should be addressed to implement these solutions at scale for diverse locations and crops. [ABSTRACT FROM AUTHOR]

The Puerto Rico Census of Agriculture (Census) is the leading source of statistics about the island's agricultural production. The Census is conducted every 5 years, in years ending in 2 and 7. However, the most recent Census was administered in 2018 due to delays caused by Hurricane Maria. Because the Census list frame is incomplete, a separate survey is conducted to inform measures of undercoverage, nonresponse, and misclassification, leading to adjusted weights. At the time of Hurricane Maria, the 2017 survey had already been conducted in preparation for the planned Census that year. In 2018, the survey was repeated using the same sample. Linking and analyzing the 2017 and 2018 survey data provide insights into the impacts of Hurricane Maria on the island's agriculture. Furthermore, the 2018 survey gives an opportunity to evaluate the 2018 Census results. Although the same sample was used in 2017 and 2018, automated record linkage methods are not suitable to link records from the two surveys. This paper discusses record linkage and estimation approaches used to determine the number of farms and land in farms in Puerto Rico before and after Hurricane Maria based on the two surveys and the Census of Agriculture. [ABSTRACT FROM AUTHOR]