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In the social context of advocating a low-carbon economy, achieving sustainable growth in line with current social development requirements is an issue that agribusiness must face. In order to explore the mechanisms influencing the sustainable growth of Chinese agriculture and to optimize the quality of agribusiness decisions, this paper examines the relationship between environmental management, debt financing indicators, and financial sustainable growth of the company in Chinese agriculture. Specifically, a decision support system based on the least square dummy variable (LSDV) model, mediating effects model and threshold effects model was constructed by using annual financial reports and questionnaire data of the listed agricultural enterprises. After empirical analysis, the following results were obtained: first, both environmental management and debt financing management help Chinese agricultural firms achieve financially sustainable growth. Second, debt financing can transmit the effect of environmental management on financially sustainable growth. Third, there are significant differences in the effects of debt financing on financially sustainable growth under different environmental management conditions. Finally, in order to promote the development of Chinese agriculture, this paper suggests that agricultural enterprises should actively implement environmental management and that relevant Chinese authorities should lower the financing threshold of the agricultural industry, while ensuring risk regulation. [ABSTRACT FROM AUTHOR]

The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, the process is often inaccurate and far from perfect. This paper explores the potential of using expert forecasts to enhance the crop yield predictions of our global-to-local XGBoost machine learning system. Additionally, it investigates the ERA5 climate model's viability as an alternative data source for crop yield forecasting in the absence of on-farm weather data. We find that, by combining both the expert's pre-season forecasts and the ERA5 climate model with the machine learning model, we can—in most cases—obtain better forecasts that outperform the growers' pre-season forecasts and the machine learning-only models. Our expert-informed model attains yield forecasts for 4 weeks ahead with an average RMSE of 0.0855 across all the plots and an RMSE of 0.0872 with the ERA5 climate data included. [ABSTRACT FROM AUTHOR]

Reducing regional inequality is one of the seventeen Sustainable Development Goals (SDGs) established by the United Nations. However, a persistent regional disparity known as the "Mezzogiorno Trap" presents a significant challenge. The underdeveloped regions that fall into the "Mezzogiorno Trap", even though they can narrow the gap with other regions through substantial support, see the disparity widen again when the level of assistance starts to decline. This paper proposes a methodology for identifying the "Mezzogiorno Trap". By employing this approach and combining panel data on Chinese agriculture from 2015 to 2021, it is discovered that despite the overall development of the Chinese agricultural economy during this period, the "Mezzogiorno Trap" still exists. The paper analyzes the reasons behind the "Mezzogiorno Trap" in the Chinese agricultural economy and presents constructive recommendations based on the research findings. The research process demonstrates that this methodology is better suited for studying regional disparities in specific economic sectors, and the obtained results are more stable and reliable. [ABSTRACT FROM AUTHOR]

In order to address the significant prediction errors resulting from the substantial fluctuations in agricultural product prices and the non-linear features, this paper proposes a hybrid forecasting model based on variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), and long short-term memory networks (LSTM). This combined model is referred to as the VMD–EEMD–LSTM model. Initially, the original time series of agricultural product prices undergoes decomposition using VMD to obtain a series of variational mode functions (VMFs) and a residual component with higher complexity. Subsequently, the residual component undergoes a secondary decomposition using EEMD. All components are then fed into an LSTM model for training to obtain predictions for each component. Finally, the predictions for each component are linearly combined to generate the ultimate price forecast. To validate the effectiveness of the VMD–EEMD–LSTM model, empirical analyses were conducted for one-step and multi-step forecasts using weekly price data for pork, Chinese chives, shiitake mushrooms, and cauliflower from China's wholesale agricultural markets. The results indicate that the composite model developed in this study provides enhanced forecasting accuracy. [ABSTRACT FROM AUTHOR]

By introducing network game theory, this paper analyzes the internal relationship and interaction mechanism among environmental governance, local government competition, and agricultural carbon peak level. On the basis of theoretical analysis, a spatial panel model is constructed using panel data from 30 provincial-level regions in China for empirical analysis. The research finds that local governments have positive competition with respect to the agricultural carbon peak, they adopt complementary carbon peak competition strategies, and they are more inclined to take geographical distance to adjacent regions as the yardstick in the competition with respect to the agricultural carbon peak strategy. That is, when the carbon peak level of surrounding provinces increases, the carbon peak level of the region will also increase. Thus, there is a phenomenon of mutual imitation and convergence between neighboring provinces. Environmental governance has a significant positive direct effect and a positive spatial spillover effect. From the perspective of coefficient, its direct effect is significantly greater than the spatial spillover effect. Therefore, more attention should be paid to local environmental governance to promote the improvement of the agricultural carbon peak level. Furthermore, the agricultural industrial structure, fiscal decentralization, agricultural public investment, regional industrial structure, and the proportion of the rural population have significant spatial spillover effects. The agricultural industrial structure and fiscal decentralization are significantly positively correlated with the peak level of agricultural carbon while the proportion of the rural population is significantly negatively correlated with the peak level of agricultural carbon. The research results have important theoretical value for expanding the research in the field of agricultural carbon emissions and provide important practical reference for China to successfully achieve the goal of agricultural carbon peak and promote the high-quality development of agriculture [ABSTRACT FROM AUTHOR]

Agricultural price prediction is a hot research topic in the field of agriculture, and accurate prediction of agricultural prices is crucial to realize the sustainable and healthy development of agriculture. It explores traditional forecasting methods, intelligent forecasting methods, and combination model forecasting methods, and discusses the challenges faced in the current research landscape of agricultural commodity price prediction. The results of the study show that: (1) The use of combined models for agricultural product price forecasting is a future development trend, and exploring the combination principle of the models is a key to realize accurate forecasting; (2) the integration of the combination of structured data and unstructured variable data into the models for price forecasting is a future development trend; and (3) in the prediction of agricultural product prices, both the accuracy of the values and the precision of the trends should be ensured. This paper reviews and analyzes the methods of agricultural product price prediction and expects to provide some help for the development of research in this field. [ABSTRACT FROM AUTHOR]

Exploring the relationship between child rearing burden and farmers' adoption of climate adaptation technologies can be used to improve farmers' adoption of these technologies, thus reducing the impact of climate change on agricultural production and increasing agricultural output. However, with the full implementation of the Chinese three-child policy, the number of children in families will continue to increase and the cost of raising children will rise, which will have a crowding out effect on the adoption of climate adaptive technologies. In this context, we analyzed the impact and mechanism of child rearing burden on farmers' adoption of climate adaptive technology by Probit model and discussed its heterogeneity based on family life cycle theory. Cross-sectional survey data were collected from 511 farm households in the 3 provinces of China to produce the findings. We found that the child rearing burden had a significant negative impact on farmers' adoption of climate adaptive technology. The impact mechanism analysis showed that the child rearing burden mainly affected farmers' adoption of climate adaptive technology through three paths: risk appetite, economic capital and non-agricultural employment, with non-agricultural employment having the largest impact, followed by risk appetite and finally, economic capital. Furthermore, the effect of child rearing burden on the adoption of climate adaptive technology was heterogeneous amid different family life cycles: In the upbringing and burden period, the child support burden had a significant negative impact on the adoption of climate adaptive technology and the impact was greater in the upbringing period, while in the stable period, the child support burden had a significant positive impact on the adoption of climate adaptive technology. The influence mechanism was also heterogeneous in different family life cycles. This paper not only provides research evidence on the relationship between child rearing burden and farmers' adoption of climate adaptive technology, but also has certain empirical value for the formulation and implementation of supportive measures for improving fertility policies. [ABSTRACT FROM AUTHOR]

The article provides an overview of the accuracy of various yield forecasting algorithms and offers a detailed explanation of the models and machine learning algorithms that are required for crop yield forecasting. A unified crop yield forecasting methodology is developed, which can be adjusted by adding new indicators and extensions. The proposed methodology is based on remote sensing data taken from free sources. Experiments were carried out on crops of cereals, legumes, oilseeds and forage crops in eastern Kazakhstan. Data on agricultural lands of the experimental farms were obtained using processed images from Sentinel-2 and Landsat-8 satellites (EO Browser) for the period of 2017–2022. In total, a dataset of 1600 indicators was collected with NDVI and MSAVI indices recorded at a frequency of once a week. Based on the results of this work, it is found that yields can be predicted from NDVI vegetation index data and meteorological data on average temperature, surface soil moisture and wind speed. A machine learning programming language can calculate the relationship between these indicators and build a neural network that predicts yield. The neural network produces predictions based on the constructed data weights, which are corrected using activation function algorithms. As a result of the research, the functions with the highest prediction accuracy during vegetative development for all crops presented in this paper are multi-layer perceptron, with a prediction accuracy of 66% to 99% (85% on average), and polynomial regression, with a prediction accuracy of 63% to 98% (82% on average). Thus, it is shown that the use of machine learning and neural networks for crop yield prediction has advantages over other mathematical modelling techniques. The use of machine learning (neural network) technologies makes it possible to predict crop yields on the basis of relevant data. The individual approach of machine learning to each crop allows for the determination of the optimal learning algorithms to obtain accurate predictions. [ABSTRACT FROM AUTHOR]

The implementation of China's rural homestead withdrawal policy has been a topic of great interest in recent years. However, little is known about its impact on farmers' homestead withdrawal behavior from the perspective of psychological resilience. This study aims to evaluate the impact of psychological resilience on farmers' homestead withdrawal behavior and to explore its mechanism and heterogeneity, as well as its impact on compensation choices. The research data was collected through a survey of 657 rural households in Shaanxi and Shandong provinces. The probit model and mediation effect model were used to analyze the data. The results demonstrate that psychological resilience positively affects farmers' homestead withdrawal behavior; this finding is robust. Specifically, the positive influence of stability and adaptability dimensions of psychological resilience is more significant. Moreover, the effect of psychological resilience varies across different family life cycles, and it has a stronger promoting effect on the homestead withdrawal behavior of child-raising families. Mechanism analysis indicates that psychological resilience can promote homestead withdrawal behavior by reducing farmers' land dependence consciousness and improving farmers' risk preferences. Finally, farmers with strong psychological resilience are more inclined to choose cash compensation. Based on these findings, this paper proposes targeted policy recommendations for rural homestead system reform. [ABSTRACT FROM AUTHOR]

Nowadays, the development of the world shows that building a new countryside is the only way for all countries to move towards industrialization and urbanization. Whether from the perspective of world development or from the perspective of China's century-old villages, the development of rural agriculture is very important. Rural infrastructure construction is the key measure to promote all aspects of rural development. The purpose of this paper is to analyze the current situation of China's rural infrastructure construction, study the factors affecting the development of rural infrastructure construction and find out the key reasons, so as to put forward suggestions on rural infrastructure construction and promote rural development. Therefore, this article uses the relevant data of China Rural Statistical Yearbook 2020 to analyze the influencing factors of China's rural infrastructure. The article divides rural infrastructure into three levels: agricultural production infrastructure, peasant living infrastructure and rural social undertakings infrastructure, and eight indicators are selected for each level. The article analyzes the new indicators system constructed from 24 original indicators using factor analysis. According to the analysis results, the article comprehensively analyzes rural infrastructure construction situation of 31 provincial administrative regions from three levels and comprehensive indicators. Finally, the articled reaches the following conclusions: the overall development of rural infrastructure construction in Jiangsu, Shandong and Henan provinces is the best, while the rural infrastructure construction in Hainan and Ningxia Hui Autonomous Region, Tibet Autonomous Region, Qinghai and other provinces is the worst compared with other provinces. [ABSTRACT FROM AUTHOR]

The paper aims to explore the relationship between size, production orientation, and performance in the Czech agriculture and to answer the research question as to what extent a farm size and a product orientation of farm do matter in relation to its productivity and profitability. We use data from FADN CZ database (Farm Accountancy Data Network—Czech Republic) of conventional farms oriented on fieldcrops production, milk production, other grazing livestock and mixed production, and we cover the period from 2015–2020. Pursuing an econometric approach (ANOVA and multivariate regression analysis), we test productivity and profitability differentiation among the different-sized and different production orientation companies. Finally, subsidies and their effects on different groups of companies are assessed. The findings from testing our empirical model indicate that very large farms have statistically significantly higher total factor productivity than large farms, which perform better than medium and small farms. Average productivity of large-size farms compared to small and medium farms is 1.4 times higher in terms of total factor productivity, more than two times higher in terms of agricultural land productivity, and 3.2 times higher in terms of labour productivity. The findings show that farms with field production statistically significantly outperform farms with orientation on other grazing livestock and mixed production. Different levels of productivity are translated into differentiation in the profitability. The highest profitability ratios are achieved by large farms followed by very large, medium, and small ones. The assessment of ratio of subsidies to agricultural production shows that small farms received 2.3 times higher agricultural subsidies per unit of agricultural production compared to very large farms. [ABSTRACT FROM AUTHOR]

New players are entering the new and important digital data market for agriculture, increasing power asymmetries and reinforcing their competitive advantages. Although the farmer remains at the heart of agricultural data collection, to date, only a few farmers participate in data platforms. Despite this, more and more decision support systems (DSSs) tools are used in agriculture, and digital platforms as data aggregators could be useful technologies for helping farmers make better decisions. However, as these systems develop, the efficiency of these platforms becomes more challenging (sharing, ownership, governance, and transparency). In this paper, we conduct a case study for an accessible and scalable digital data platform that is focused on adding value to smallholders. The case study research is based on meta-governance theory and multidimensional multilayered digital platform architecture, to determine platform governance and a data development model for the Andalusian (Spain) fruit and vegetable sector. With the information obtained from the agents of this sector, a digital platform called farmdata was designed, which connects to several regional and national, and public and private databases, aggregating data and providing tools for decision making. Results from the interviews reflect the farmer's interests in participating in a centralized cloud data platform, preferably one that is managed by a university, but also with attention being paid toward security and transparency, as well as providing added value. As for future directions, we propose further research on how the benefits should be distributed among end users, as well as for the study of a distributed model through blockchain. [ABSTRACT FROM AUTHOR]

As an effective heuristic method, three-way decision theory gives a new semantic interpretation to the three fields of the rough set, which has a huge application space. To classify the information of agricultural products more accurately under certain thresholds, this paper first makes a comprehensive evaluation of the decision, particularly the influence of the attributes of the event itself on the results and their interactions. By using fuzzy sets corresponding to membership and non-membership degree, this paper analyzes and puts forward two cases of proportional correlation coefficients in the transformation of a delayed decision domain, and selects the corresponding coefficients to compare the results directly. Finally, consumers can conveniently grasp product attribute information to make decisions. On this basis, this paper analyzed the standard data to verify the accuracy of the model. After that, the proposed algorithm, based on three decision-making agricultural product information classification processing, is applied to the relevant data of agricultural products. The experimental results showed that the algorithm can obtain more accurate results through a more straightforward calculation process. It can be concluded that the algorithm proposed in this paper can enable people to make more convenient and accurate decisions based on product attribute information. [ABSTRACT FROM AUTHOR]

The agro-pastoral transitional zone in northern China is an important agricultural and pastoral production base. This study focuses on the eastern section of the agro-pastoral transitional zone in northern China. Combined with spatio-temporal analysis, stepwise regression, and gray relation analysis, we analyzed the average annual growth rate, spatio-temporal changes, and influencing factors of agricultural and pastoral production in the study area during 2000–2020. The results show that, in the past 20 years, the agricultural and pastoral production of the 50 municipal districts and counties has significantly changed, among which the agricultural production of 38 municipal districts and counties has shown an extremely significant increase. Generally, the growth rate of agricultural production is higher than that of pastoral production. Agricultural and pastoral production in the study area is influenced by socio-economic and land use/cover factors, with an average correlation degree of 0.79 and 0.88, respectively. Climate change affects agricultural production in agricultural counties, with a correlation degree of 0.85. The results of this research provide valuable insights into understanding the long-term temporal and spatial changes in agricultural and pastoral production and help to develop sound agriculture and pastoral management practices in the eastern section of the agro-pastoral transitional zone in Northern China. [ABSTRACT FROM AUTHOR]

This paper focuses on the impact of a new rural land reform, the Separation of Three Rights Reform, on changes in China's agricultural production organizations. We illustrate the impact of market and nonmarket mechanisms on allocating agricultural production factors under the new rural land market transition through a land system and factor allocation model. Based on the expansion paths of different types of factors in the model, we classify the development of Chinese agricultural production entities into "extensional expansion," "labor-intensive expansion," "land-intensive expansion," and "exit of agricultural production." These agricultural production paths correspond to agricultural enterprises, family farms, agricultural cooperatives, and small farmers' exit. Further, empirical and economic geography analysis results show that the interaction of market and nonmarket mechanisms is the main drive that induces the current diversified organizations in rural China. Thus, this paper provides a comprehensive explanation of changing patterns of an agricultural production organization under the transition of the rural land market. [ABSTRACT FROM AUTHOR]

Among the different types of households, agricultural ones are perceived as the most exposed to the problem of income instability. Income fluctuations pose a threat not only to the financial stability of farming families but also to the sustainable development of the agriculture sector. Investigating the scale and factors of the variability of farmers' household incomes is important in designing and monitoring agricultural policy measures that aim to stabilize farmers' incomes. In this context, the main objective of this article is to assess the income variability of agricultural households in Poland and to identify the main correlates of farmers' income fluctuations in the period 1996–2022. To emphasize the significance of the problem of income instability for agricultural households, a comparative assessment of income variability was performed (relative to non-agriculture households). Furthermore, two research subperiods (1996–2003; 2004–2022; before and after EU accession) were distinguished to capture the association between the Common Agriculture Policy (CAP) and the stability of agricultural income. The study focusses on associations between variables (regression and correlation analyses were used). The income of agricultural households was shown to exhibit greater variability (year-on-year) than the income of households of workers and of self-employed people, with a lower scale of fluctuations after 2004. Furthermore, the study reveals a group of factors that could be correlated with the variability of the income of agricultural households: income structure (share of income from agriculture), natural and climatic conditions (variability of the yields and agricultural production), and changes in prices of the means of agricultural production. The findings raise, however, a question about the effectiveness of CAP instruments in stabilizing agricultural income. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions is of paramount importance to the accomplishment of the 2030 Sustainable Development Goals. The effect of rural e-commerce on agricultural carbon emissions (ACEs) is controversial, and particularly the mechanism behind the effect is unknown. To identify the impact of rural e-commerce on agricultural carbon emissions and its mechanisms, we take advantage of China's Rural E-Commerce Demonstration County Program (REDCP) as a quasi-natural experiment and use the multi-period difference-in-difference (DID) model to investigate the relationship between rural e-commerce and agricultural carbon emissions. Our data are based on panel data of 355 prefecture-level cities from 2010 to 2019 in China. We identify that rural e-commerce can reduce agricultural carbon emissions by an average of 14.4%, but this effect is not long-lasting. Mechanism analyses suggest that the reduction effect of rural e-commerce on agricultural carbon emissions is mainly due to fostering agricultural economic growth, increasing the share of low-carbon industry, and improving agricultural total factor productivity (TFP). Further heterogeneity analyses demonstrate that rural e-commerce has better carbon emissions reduction performance in eastern cities as well as in non-major grain-producing cities in China. [ABSTRACT FROM AUTHOR]

Understanding the impact of the digital economy on agriculture in developing countries holds significant importance in enhancing agricultural production and addressing hunger-related challenges. This study uses panel data of 35 SSA countries from 2006 to 2021 and investigates the relationship between digital economy and agriculture using dynamic regression models. The impact mechanism is also analyzed using mediating models. The results show that the digital economy has a positive impact on agriculture through the increase of agricultural productivity, human capital accumulation and the improvement of government governance. The effects of the digital economy are larger in countries with higher and lower levels of agricultural development, significant in middle-income countries, and insignificant in low-income countries. This study may provide a better understanding of the nexus between digital economy and agricultural development, and offer valuable insights for governments in developing nations to formulate relevant and effective policies. [ABSTRACT FROM AUTHOR]

This paper conducts a comprehensive analysis of the agricultural sector's resource allocation and production decisions. This paper uses the differential systems with quasi-fixity to evaluate the complete agricultural production system, which examines the input and output linkages in terms of elasticities. The differential systems are estimated using the maximum likelihood estimation technique based on the two-step profit-maximizing procedure in theory. The results reveal that livestock production requires more intermediate inputs, but crop production depends on all the inputs, such as labor, capital, and intermediate inputs. In addition, the results show that input demand is inelastic, indicating that the agricultural sector has little flexibility in adjusting the demand for inputs in response to changes in input prices. Substitutable relationships among labor, capital, and intermediate inputs exist, which may reduce the pressures on production costs when input prices rise. Regarding the quasi-fixed input, land expansion changes the composition of labor and intermediate inputs, showing that the agricultural sector reduces the intensive margin when it pursues the extensive margin. Furthermore, the results show that agricultural supply is not very responsive to the respective price changes. Along with the inelastic output supply, there exist substitutable relationships between livestock and crop supply, showing that relative price changes can alter output composition in supply. The agricultural sector also reallocates more land areas into crop production rather than livestock production. [ABSTRACT FROM AUTHOR]

Shifting from a land-scale operation to a service-scale operation of agricultural production chain outsourcing services (APOS) is crucial to achieving innovation in agricultural-scale operation techniques. Using propensity score matching (PSM) and data from 1027 farm households in Guanzhong Plain, Shaanxi Province, we empirically assessed the impact of APOS on agricultural output level and output risk. First, age, gender, health, education, training, number of outworkers, land tenure, land contiguity, and subsidy satisfaction had a substantial beneficial influence on the involvement of farm families in APOS. Second, involvement in APOS may greatly increase the amount of agricultural production and lower the risk associated with farm families' agricultural output. Moreover, the participation in outsourcing services for agricultural machinery use and field management significantly increased agricultural output and decreased output risk, but the participation in agricultural machinery use outsourcing services increased yield and reduced risks more significantly. [ABSTRACT FROM AUTHOR]

As a raw material for sugar, ethanol, and energy, sugarcane plays an important role in China's strategic material reserves, economic development, and energy production. To guarantee the sustainable growth of the sugarcane industry and boost sustainable energy reserves, it is imperative to forecast the yield in the primary sugarcane production regions. However, due to environmental differences caused by regional differences and changeable climate, the accuracy of traditional models is generally low. In this study, we counted the environmental information and yield of the main sugarcane-producing areas in the past 15 years, adopted the LSSVM algorithm to construct the environmental information and sugarcane yield model, and combined it with WCA to optimize the parameters of LSSVM. To verify the validity of the proposed model, WCA-LSSVM is applied to two instances based on temporal differences and geographical differences and compared with other models. The results show that the accuracy of the WCA-LSSVM model is much better than that of other yield prediction models. The RMSE of the two instances are 5.385 ton/ha and 5.032 ton/ha, respectively, accounting for 7.65% and 6.92% of the average yield. And the other evaluation indicators MAE, R2, MAPE, and SMAPE are also ahead of the other models to varying degrees. We also conducted a sensitivity analysis of environmental variables at different growth stages of sugarcane and found that in addition to the main influencing factors (temperature and precipitation), soil humidity at different depths had a significant impact on crop yield. In conclusion, this study presents a highly precise model for predicting sugarcane yield, a useful tool for planning sugarcane production, enhancing yield, and advancing the field of agricultural production prediction. [ABSTRACT FROM AUTHOR]

Fluctuations in agricultural commodity prices affect the supply and demand of agricultural commodities and have a significant impact on consumers. Accurate prediction of agricultural commodity prices would facilitate the reduction of risk caused by price fluctuations. This paper proposes a model called the dual input attention long short-term memory (DIA-LSTM) for the efficient prediction of agricultural commodity prices. DIA-LSTM is trained using various variables that affect the price of agricultural commodities, such as meteorological data, and trading volume data, and can identify the feature correlation and temporal relationships of multivariate time series input data. Further, whereas conventional models predominantly focus on the static main production area (which is selected for each agricultural commodity beforehand based on statistical data), DIA-LSTM utilizes the dynamic main production area (which is selected based on the production of agricultural commodities in each region). To evaluate DIA-LSTM, it was applied to the monthly price prediction of cabbage and radish in the South Korean market. Using meteorological information for the dynamic main production area, it achieved 2.8% to 5.5% lower mean absolute percentage error (MAPE) than that of the conventional model that uses meteorological information for the static main production area. Furthermore, it achieved 1.41% to 4.26% lower MAPE than that of benchmark models. Thus, it provides a new idea for agricultural commodity price forecasting and has the potential to stabilize the supply and demand of agricultural products. [ABSTRACT FROM AUTHOR]

Much of the established literature on agricultural cooperatives describes their myriad contributions to farmers' economic performance. In Brazil, one of the world's leading agricultural exporters, there were more than 1500 agricultural cooperatives with 1 million members in 2020, and in 2017, 11% of all Brazilian farms were associated with one of these cooperatives. In this paper, we estimate the factors associated with the municipality share of cooperative membership (MSCM) and how municipality-level production value changes with MSCM. Our analysis is at the municipality level using aggregate data from the 2017 Agricultural Census. We find that in Brazil, higher education and smaller property sizes are associated with membership in agricultural cooperatives. To estimate how MSCM is associated with farm profits, we use a generalized propensity score and find that an increase in MSCM increases net municipal farm income, driven mostly by an increase in the value of agricultural production compared to a smaller increase in the cost of production. [ABSTRACT FROM AUTHOR]

The high-quality development of new agricultural business entities (NABEs) is an important driving force for realizing rural revitalization and accelerating the modernization of agriculture and rural areas. The main purpose of the study is to investigate the spatial distribution pattern, aggregation scales, development mechanism, and internal differences of various types of NABEs in different regions. It provides targeted ideas for alleviating regional differences in the development of NABEs in different agricultural regions. Kernel density estimation, nearest neighbor distance analysis, Tyson's polygon coefficient of variation, and Ripley's K function are used to study the spatial and temporal evolution, spatial aggregation, and scale divergence of various types of NABEs, and Pearson correlation analysis is incorporated to explore the specific factors affecting the development of various types of NABEs. The study results: First, family farms are the most widely distributed, and agricultural enterprises are the most sparsely distributed, being distributed "more in the southeast and less in the northwest" in all three categories. Second, the strongest aggregation scales of different NABEs are increasing, and the strongest aggregation scales of agricultural enterprises are larger than those of family farms and cooperatives in all agricultural areas. Third, the development of specialized farmers' cooperatives (abbreviated as 'cooperatives') is more constrained by traditional agricultural inputs and is a kind of agricultural input-oriented development. Family farms are more constrained by the living standards of rural residents in the region and are a kind of rural economy-oriented development. Agricultural enterprises are more subject to the economic level of the region, which is a kind of market economy-oriented development. Finally, in the process of developing NABEs, regional differences should be emphasized, and a small number of agriculturally leading enterprises and model cooperatives should drive a large number of small-scale family farms and smallholder farmers in order to become a characteristic path for China's agricultural development. [ABSTRACT FROM AUTHOR]

In response to the sustainable development goal of agriculture put forward by the United Nations, countries have introduced a series of agricultural environmental protection policies. However, the effectiveness of these policies has been hindered by insufficient responses from farmers. This study begins with the fundamental logic of farmers' production behavior and first introduces family and place identities into the theoretical analysis framework of farmers' environment-friendly production behavior (EPB). Using primary survey data for farmers from the northwestern Chinese province of Gansu, a hierarchical regression and simple slope analysis models were developed to verify how family and place identities affect farmers' EPB. The potential moderating effect of place identity on the relationship between family identity and farmers' EPB is further investigated. The results show that improving family identity has a significantly negative effect on farmers' EPB, while improving place identity can significantly promote farmers' EPB. Compared with respondents who have a low place identity, the inhibitory effect of family identity on farmers' EPB is significantly weakened for those who have a high place identity, which suggests that farmers with a high place identity are more likely to engage in EPBs in agricultural production. This study highlights the importance of considering the economic logic toward maximizing family economic interests and the social logic oriented toward collective interests in the decision-making process of agricultural environmental policies. [ABSTRACT FROM AUTHOR]

Many authors emphasize that reducing the income deprivation of the agricultural sector in relation to the non-agricultural sectors is a prerequisite to the growth of sustainability of agriculture. Thus, this raises the question: despite the impact of the Common Agricultural Policy (CAP) on the value of agricultural income in EU countries, is there still income deprivation for agriculture in relation to non-agricultural sectors? If so, is its depth comparable among farms with a different scale of production from the same EU country or among farms with the same scale of production from different countries? The answers to these questions constitute the added value of the article. The aim of the paper is to compare the ratio of agricultural income to non-agricultural income in regard to family farms in EU countries. Results show that the CAP solutions do limit the agricultural income disparities but that there are significant differences in the income deprivation of farms with different production values in the same country. These differences also apply to farms with a similar production volume in different countries. This publication includes critical analysis of literature, spatial-analysis and panel regression. The time scale of the research is 2004–2017, the spatial scope is individual EU countries and the subjective scope is representative EU Farm Accountancy Data Network (FADN) farms. [ABSTRACT FROM AUTHOR]

Macadamia is an essential commodity crop in Malawi. The nuts are a lucrative commodity and are used for household consumption, income generation among farming families and as a foreign exchange earning crop at country-level. Macadamia production in Malawi has increased significantly in recent years. Malawi is the seventh top producer of macadamia nuts valued at £23.5 million, with a global market share of 3%. In 2018, the country was the fourth-largest exporter of macadamia nuts that were valued at £18.2 million. The majority (90%) of this crop was grown by large commercial estates with smallholder's production only contributing about 10% of the total crop production. However, the smallholder sector is vital for the future growth of the macadamia sector in the country. Further, Malawian smallholders consider macadamia production as a low-input crop with large returns per unit area (£10.7 kg−1 ha−1), and it thus a lucrative commodity with high potential for poverty reduction and wealth creation among these farming families. This paper, therefore, explores: (i) the historical and current trends in macadamia nut production in Malawi; (ii) analyses the country's macadamia value chain focusing on smallholder farmer contributions; and (iii) discusses the constraints of smallholder macadamia production in Malawi for informed policymaking. We conclude that the synthesis of the Malawian macadamia sub-sector provides an understanding of the vital contributions of macadamia to Malawi's economic growth and improvement of livelihoods. [ABSTRACT FROM AUTHOR]

Family farming is an important pillar of the global primary food sector and is characterized by a significant local and regional impact. The sustainability of these businesses depends greatly on the use of effective management practices in family farming and smallholder farmer's activities. However, there is a lack of methodologies to support decision making in this sector related to the recognition of all aspects involved in the acquisition of inputs. Commonly, the existing approaches are focused on cost as the major criterion, which might impact on the agribusiness sustainability in the short or long term. Furthermore, traditional cost-focused approaches should be complemented with multi-criteria aspects within comprehensive and practical methodologies. This paper presents a Cost Management Model (CMM) integrating the concepts of Total Cost of Ownership (TCO) and Multiple Criteria Decision Analysis (MCDA), which includes both monetary and non-monetary attributes. The use of TCO was mostly focused on industrial applications and studies on costs in agribusiness are still scarce and incomplete. The model was applied to a set of agribusiness families in Southern Brazil to support the supplier selection process of the three main inputs (i.e., fertilizer, fungicide, and soybeans). A significant cost reduction of approximately 13% for fertilizers, 7% for fungicides, and 3% for soybeans can be achieved. The CMM can be applied by smallholder farmers in any region of the world, and its flexibility allows changes in weights and criteria at any time by the decision maker. Buyers are more prepared to select the best supplier for each input and the later can recognize how they are more or less competitive when all costs and relevant criteria are taken into consideration. It can be also applied from an industry-level perspective to highlight the industry's cost structure and value chain, supporting more focused and effective public policies namely the design of subsidies that mitigate relevant context costs and the investment in infrastructures. Both, subsidies and public investment, are of major importance in rural areas of developing countries. [ABSTRACT FROM AUTHOR]

Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork. [ABSTRACT FROM AUTHOR]

In the context of the increase in energy prices and, as a consequence, on other inputs on the global and European market, the study aims to analyze the performance of the European Union's agriculture through the lens of the correlations and links that are established between different determinants/factors and which provide a much clearer picture of the agricultural sector than the analysis of the result/output and its contribution to GDP. The working methodology consisted in the comparative analysis of the statistical data related to the Member States and the aggregated general data for the EU 27 using the EUROSTAT data by processing them in order to achieve the proposed goal. The results indicate a high level of performance for a number of states that are not considered very important in this area and, in contrast, a lower level of performance for a number of states considered at the top of the pyramid in terms of performance. The derived indicators used and calculated within the research can represent basic elements in the fundamental analysis of the agricultural activity performance of a country and the European Union as a whole. [ABSTRACT FROM AUTHOR]

The residual value of a tractor affects the cost of ownership. As there is not much transactional information available for used tractors, nor is there a history of new tractor prices, existing studies struggle to forecast the residual value of agricultural tractors. This is made even more challenging by the emission-regulation-related tractor price increase, low inflation in recent decades, and the complexity of the portfolio offerings from manufacturers. Using the new equivalent tractors, grouped by families of similar characteristics, bypasses these challenges and enables us to obtain larger data sets. These large data sets can be forecasted using transparent linear power regressions that offer the lowest root mean squared error (RMSE = 1.5574) and the highest combined, adjusted coefficient of determination (RSqAdj = 0.8457), outperforming all previously tested studies as well as the ensemble, Gaussian process regression, kernel, linear regression, neural network, support vector machine, and decision tree models. The accessibility of the public information required, as well as its processing using mainstream software through a model that is simple to use, yet robust, enables any stakeholder (manufacturers, sellers, financers, insurers, and, most of all, users) to reliably determine the residual value of an agricultural tractor, empowering them to make fact-based, cost-of-ownership-optimized decisions. [ABSTRACT FROM AUTHOR]

Excess fertilization is a major cause of agricultural environmental pollution. However, different studies attribute farmers' fertilization behavior to different factors, hindering theoretical and empirical development. Using a meta-analysis of 64 empirical studies on Chinese farmers' fertilization behavior, this study first systematically collects, combines, and analyzes the existing studies to identify the key influencing factors that may affect farmers' environment-friendly fertilization behavior (FEFB) in China. A meta-regression analysis is further applied to explore how the effects of the factors identified have changed over time. The results showed that the key factors include gender, health status, risk attitude, family size, farm size, environmental concern, social norms, cadre status, and policy propaganda. Over time, the influence of family size, environmental concern, cadre status, and policy propaganda is stable; the influence of social norms gradually strengthens; and the influence of risk attitude weakens. Although household income and the proportion of agricultural income have no significant effect, their influence gradually increases and changes positively and significantly over time; hence, they may become important factors in the future. Our results provide important policy implications for policymakers and agricultural managers to develop appropriate strategies to reduce the usage of chemical fertilizers in China. [ABSTRACT FROM AUTHOR]

The creation and deployment of digital technologies throughout the agro-food system contribute to achieving Sustainable Development Goal nine. However, various digitalization studies offer limited insight into the policy issues and solutions around emerging technologies and big data systems in agriculture. There is a need for an additional understanding of how agricultural policymaking should respond to the usage of digital technologies in the agri-food sector. Furthermore, evidence is limited on how existing agricultural government programs such as land reform can be linked with digitalization policy. This systematic review of literature sought to determine the transformation that is needed in the political and economic environment for the digital revolution to take place within South African land reform agriculture. A thematic analysis of data sampled from ProQuest Central, Scopus, Dimensions, and Google Scholar reveals five areas of intervention from agricultural policymaking. The digital revolution in agriculture can be brought about by transforming digital infrastructure, data interoperability and governance, digital markets, the compatibility of government incentives with the private sector, and the digital cultural landscape. This essay contributes to agricultural policy and decision-making dialogues that pay attention to digital technologies and land reform programs in South Africa. [ABSTRACT FROM AUTHOR]

This study suggests a novel hybrid model for calculating the interrelationships between factors by integrating the Fuzzy set, Delphi, the Grey theory, and Weighted Influence Nonlinear Gauge System (WINGS) approaches in agricultural green supply chain management (AGSCM). Fuzzy Delphi helps to select 12 indicators from 19 factors by defuzzification for ambiguity associated with subjective judgment by 10 experts in data collection. Grey WINGS can illustrate the relationships, direction, and strength of factors simultaneously, which illustrates that environmental law, green consciousness, product quality, and price are the most significant factors of AGSCM. The results can help operators not only to analyze these key influencing factors, but also to understand the complex cause-and-effect relationships between these factors. This integrated model will hopefully provide a useful tool to agricultural policy makers and decision makers for sustainable development. [ABSTRACT FROM AUTHOR]

Maintaining stable linkages between farmers and APS service providers is conducive to cutting transaction costs, increasing service organizations' willingness to invest in the long term and motivation to innovate on their own, improving agricultural production and resource use efficiency, and safeguarding farmers' welfare and national food security. The willingness of farmers to renew their contracts is a key factor in long-term APS partnerships. Based on research data from the Northeast region in 2018, this study uses the Theory of Planned Behavior (TPB) to construct a two-stage decision-making framework for the contracting-renewal and to determine how the process influences the formation of willingness. The Heckman two-stage model is applied. The results indicate that the mechanism of farmers' willingness to renew APS is formed in accordance with TPB's conceptual analysis framework with the dual logic of "stimulus" and "constraint". In the first stage, individual and family characteristics have a significant influence on farmers' contracting behavior. In the second stage, three exogenous latent variables, behavioral attitudes, perceived behavioral control, and subjective norms, have a better explanatory role in the formation of farmers' willingness to renew APS. The stronger the perceived control that is generated by farmers during the decision-making process, the more pronounced the behavioral attitudes were, and the stronger the inducement of willingness to renew the service. The findings suggest that governments should strengthen APS outreach and improve the regional economy and natural environment. Overall, this study helps to uncover the farmer's TPB mechanism and provide an empirical basis for how to promote APS development. [ABSTRACT FROM AUTHOR]

Agricultural insurance and green agriculture are strongly related. Agricultural insurance not only motivates farmers to adopt environmentally friendly production technology and enhances the effectiveness of production, but it also accomplishes the goal of lowering the number of chemicals that are put into the environment. This article investigates the dynamic relationship between agricultural insurance, air pollution, and agricultural green total factor productivity. To complete the aim, the authors used the panel auto-regressive distributed lags method (PMG method) and panel data from 50 states of the United States between 2005 and 2019. The empirical findings demonstrate a considerable co-integration and a cross-sectional reliance between agricultural insurance, air pollution, and agricultural green total factor production. Expanding agricultural insurance may boost agricultural green whole factor output but also exacerbate air pollution. However, significant air pollution does not increase agricultural production's green total factor productivity. The panel Granger causality test shows a one-way causal relationship between agricultural insurance, green total factor productivity, and air pollution. A one-way causal relationship exists between air pollution and agricultural green total factor productivity. The author concluded that improving agricultural insurance coverage or cutting down on air pollution will boost agricultural green total factor output. These findings have long-term policy and management repercussions, particularly for those involved in agriculture policy and environmental management. [ABSTRACT FROM AUTHOR]

The effort to increase agricultural productivity continues to receive interest in Africa as low productivity levels, poverty and food insecurity remain or even increase. This study used the Färe-Primont Index to estimate agricultural total factor productivity growth for 49 African countries. Panel data consisting of 833 observations for the period 2000 to 2016 were obtained from the United State Department of Agriculture Economic Research Service database. The results show that the average growth rate for agriculture in Africa is 0.73% per annum. The sector experienced increased growth after the Maputo Declaration, which was sustained during the global financial crisis. West Africa experienced the largest growth while Southern Africa suffered a substantial decline. The study also discovered that growth differed between countries indicating that customization of the Comprehensive Africa Agriculture Development Programme into regional and country-specific policy interventions is important to boost agricultural productivity. Finally, the growth was achieved through technical change, while efficiency change constrained growth. Policy-makers should increase investment in agricultural extension services, education and training to enhance managerial capacity (efficiency change) because improved managerial capacity could increase agricultural growth and thereby increase food security and alleviate poverty in Africa. [ABSTRACT FROM AUTHOR]

This study examines the spillover effects of China's New Rural Pension Scheme (NRPS) implemented in 2009 as a cash transfer program for agricultural production. Based on the data collected by the China Health and Retirement Longitudinal Survey (CHARLS) in four periods (2011, 2013, 2015 and 2018), we employ Seemingly Unrelated Regression to explore how China's NRPS affects agricultural production. Our findings show that NRPS pensions reduce household operating areas by 1.99 mu and agricultural investment by 1150 yuan, while increasing the labor time of their own agricultural production by 168 h, and farmers in the payment period have a similar impact. This finding is still reliable after a series of robustness tests. Gender heterogeneity analysis indicates that male participation in NRPS is more likely to reduce the actual operating area and increase the labor input of the family, while female participation in NRPS is more likely to reduce the agricultural capital input of the family. Moreover, the in-depth study of agricultural performance shows that the implementation of NRPS helps increase the average output value per mu by 700 yuan and technical efficiency by 0.2%, although this is at the cost of declining labor productivity. This study links the joint decision-making of agricultural production factor inputs with pension schemes and contributes to the development of relevant research, which may provide policy implications for how cash transfer schemes affect agricultural production and agricultural performance in other countries. [ABSTRACT FROM AUTHOR]

Due to the growth of the "urban shadow", farming systems in metropolitan areas (MAs) are characterized by specific environmental, economic, and social pressures. Urbanization pressure and its related threats, as well as the opportunities resulting from a convenient location close to consumers' markets, cause rapid farming systems changes. The present study provides a quali-quantitative overview of change in farming systems in selected Polish metropolitan areas. Various statistical methods were applied to analyze changes in farming systems. First, farming system changes were characterized for the study area by the exploratory factor analysis. In addition, experts' opinions (qualitative data) on changes in farming systems until 2027 are presented. The research conducted showed that there are a few main patterns in changes to/adaptations of farming systems: Horizontal growth of farms, farmland concentration, process of specialization (horticulture, orchards), trajectories towards more multifunctional farming practices (off-farm diversification, as well as "deepening" and "broadening" strategies), reduction of livestock farming, deintensification and farmland abandonment. Those processes vary in space, and they are only partially explained in terms of distance to the core city (location). Experts forecast that the identified trends will continue, paying attention to the growing importance of multifunctional agriculture and the intensification of recessive processes, including farmland abandonment and excessive deintensification of production. According to the experts surveyed, most farmers will continue to apply traditional development strategies, which will be ineffective without the possibility of increasing the area of farms. [ABSTRACT FROM AUTHOR]