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Fruit cultivation plays a pivotal role in improvement of the agricultural economy. Pomegranate is a nutritionally rich fruit that is highly valuable because of its excellent antioxidant properties, richness in vitamins and fiber. Pomegranate is affected by diseases, which can severely affect its yield and quality. Timely disease detection is vital for effective disease control in pomegranate orchards. This study presents a novel approach for disease detection in pomegranate and categorization of pomegranate using a hybrid Convolutional Neural Network (CNN) coupled with the Honey Badger Optimization Algorithm (HBOA). The proposed hybrid CNN-HBOA approach leverages the strengths of both CNN and metaheuristic optimization algorithm HBOA. The CNN model was designed to extract high-level features from images of pomegranate affected by various diseases, and HBOA was employed to fine-tune the CNN parameters to improve the accuracy and robustness of the model. Initially, the quality of the images was enhanced using a contrast enhancement technique, and the images were segmented using the Detectron2 algorithm. Finally, the extracted features were fed as input to the disease detection stage wherein the CNN classifies the disease. Furthermore, the classification accuracy of the CNN model was enhanced using the HBOA algorithm. The performance of the hybrid CNN-HBOA algorithm was evaluated using a vast collection of images depicting multiple disease categories of pomegranate. Experimental results demonstrate the superiority of the hybrid CNN-HBOA system, which achieves a detection accuracy of 99.58%, precision of 100%, recall of 99.71% and F1 score of 99.75% compared to other existing state-of-the-art models. [ABSTRACT FROM AUTHOR]

This study goes into the essential challenge of estimating potato output in order to ensure sustainable agricultural practices while also providing vital insights into global market patterns. The potato production data series compares the accuracy of two popular forecasting models, ARIMA (AutoRegressive Integrated Moving Average) and ETS (Error-Trend-Seasonality), in predicting potato production. The study assesses the efficacy of these models with a particular focus on their relevance to the agricultural markets of India, China, and the USA, three major potato-producing countries. This research builds ARIMA and ETS models and thoroughly assesses their forecasting performance using historical production data series from these important nations. The results show that the ETS model, especially when considering the chosen countries, consistently performs better in predicting potato production for the testing data set than the ARIMA model. According to the models, China and India will keep contributing more to the potato market, solidifying their positions as key players. It is anticipated that the US economy will plateau and stabilize. For the anticipated year 2027, the expected potato output for China, India, and the USA is 100,417, 61,882, and 18,229 thousand tonnes, respectively. Nonetheless, the increasing diversity of confidence intervals in extended forecasts illustrates the intricacy of agricultural productivity and the numerous factors that could impact outcomes. We believe that this research significantly advances sustainable farming methods by offering a thorough analysis of worldwide potato production projections. It also improves our comprehension of the dynamics of the potato market, providing insightful information that can guide decision-making at different levels. In the conclusions, we stated that the studies not only have consequences for the potato sector, but they also highlight how crucial it is to use cutting-edge forecasting methods in order to promote sustainable food production and guarantee future food security. [ABSTRACT FROM AUTHOR]

The paper presents an original application of economic complexity to the agricultural sector aiming to determine its suitability to measure and depict regional structural inequalities across Brazilian regions. Using census data, we calculate agricultural complexity indexes for products (PCI-Agro) and regions (ECI-Agro) and the agricultural product space. We show that higher PCI-Agro is associated with higher levels of productivity, education, income and human development. Also, we found that ECI-Agro is associated with higher productivity in agriculture. The econometric evidence points to an inverted 'U'-shape curve relationship between ECI-Agro and agricultural productivity, which has significant policy implications. [ABSTRACT FROM AUTHOR]

Using evidence from official statistics as well as village studies on incomes from farming across regions, crops, socio-economic classes, and castes, first, I argue that the large majority of cultivators today receive abysmally low incomes and face high variability in incomes. Rising distress can also be inferred from the fact that costs of production have risen faster than government-announced minimum support prices for most crops in most regions of India. Second, I examine the myth of farming in India being highly subsidised, resulting in the overproduction of rice and wheat. I show that subsidies in India are low in comparison to the rest of the world, the European Union and North America, in particular. I also examine projections for different food crops to examine the question of 'overproduction'. The third issue examined in this article is the invisibility of women workers in agriculture. I argue that women are playing an increasingly significant role in the agricultural economy, a role that official statistics are unable to capture. [ABSTRACT FROM AUTHOR]

This article uses the time‐varying parameter vector autoregressive model to assess the impact of geopolitical risk and shocks on agricultural commodity markets, accounting for demand—exports; supply—input prices; inventory; speculation; and economic fluctuations. The results show that geopolitical risks significantly impact corn and soybean futures prices and market behaviors with context‐specific implications in the short to medium term. In addition, heightened geopolitical risk during the Russia–Ukraine war increased oil prices and indirectly elevated agricultural commodity prices. These insights are crucial for agricultural risk management, informing federal policies and forecasting future price trends in an increasingly uncertain global market environment. [ABSTRACT FROM AUTHOR]

Forecasts are common in agricultural settings where they are routinely used for decision‐making. The advent of the computer age has allowed for rapid generation of individual forecasts that can be updated in real time. It is well known that the selection and use of a single forecast can expose the forecaster to serious error as a result of model mis‐specification. Forecast combination avoids this problem by combining information from different forecasts. Although forecast combination can be as simple as averaging across forecasts, advances in machine learning have made it possible to combine forecasts according to more complicated weighting schemes and criteria. We provide an overview of forecast combination techniques, including those at the frontier of current practice and involving machine learning. We also provide a retrospective on the use of forecast combination in agricultural economics and prospects for the future. Several of the techniques are illustrated in an application to forecasting nationwide corn and soybean planted acreage and we demonstrate how forecast combination can improve expert USDA projections. [ABSTRACT FROM AUTHOR]

With economic globalization and the financialization of agricultural products continuing to advance, the interconnections between different agricultural futures have become closer. We utilize a TVP‐VAR‐DY model combined with the quantile method to measure the risk spillover between 11 agricultural futures in the United States and China from July 9, 2014, to December 31, 2022. We obtain several findings. First, CBOT corn, soybean, and wheat are identified as the primary risk transmitters, with DCE corn and soybean as the main risk receivers. Second, sudden events or increased economic uncertainty can enlarge the overall risk spillovers. Third, there is an aggregation of risk spillovers amongst agricultural futures based on the dynamic directional spillovers. Lastly, the central agricultural futures under the conditional mean are CBOT corn and soybean, while CZCE hard wheat and long‐grained rice are the two risk‐spillover centers in extreme cases, as per the results of the spillover network and minimum spanning tree. [ABSTRACT FROM AUTHOR]

Introduction: Agricultural green development (AGD) prioritizes sustainable growth by integrating economic, ecological, and social dimensions, aiming to harmonize agricultural economic development with environmental protection and social progress. Methods: This study integrates the three-stage super-efficiency DEA-SBM model with the BP algorithm, creating an advanced DEA-SBM-BP model to overcome the shortcomings of traditional DEA in evaluation and management processes. The study further applies the Dagum Gini coefficient, kernel density estimation (KDE), and Moran's index to assess and forecast the efficiency and spatiotemporal evolution patterns of green agricultural development in key cities within the Yangtze River Delta. Results: The analysis shows that AGD in the central city of the Yangtze River Delta is overall balanced; however, substantial variations exist among cities within individual provinces. Factors like macroeconomic conditions, workforce quality, and policy support play a crucial role in promoting the efficiency of AGD. Among these, macroeconomic development level has a negative impact, while labor quality and policy support exhibit bidirectional effects. Infrastructure construction, digitalization of agricultural economy, and rural security have become key factors in the green development of modern agriculture. The green advancement of agriculture in the central Yangtze River Delta region typically exhibits a marked clustering effect; however, the local clustering reveals a trend toward dispersed development. Discussion: Despite the emergence of new characteristics in agricultural production in China within the context of high-quality development, differences in resource endowments and economic structures among cities continue to be significant factors contributing to regional imbalances and changes in the agglomeration patterns of agricultural development. [ABSTRACT FROM AUTHOR]

Farm diversification is becoming increasingly important for farmers in developing countries to improve economic resilience, especially with the increasing instability of agricultural markets. Agritourism, as one of the diversification strategies, can provide farmers with an alternative source of income. However, the factors influencing this diversification decision, particularly in rural areas such as North Toraja, Indonesia, still need to be fully understood. This qualitative research aims to understand the factors that influence farmers' livelihood diversification decisions in North Toraja, focusing on the role of agritourism and its implications for economic resilience and sustainable development. Data was collected through semi-structured interviews with 30 North Toraja farmers who have yet to diversify their businesses. The results showed that farmers' diversification decisions are influenced by several factors, including internal motivation (such as income generation opportunities and lifestyle needs), resource availability (land, capital, skills), market conditions (demand for agricultural products and agritourism services), and accessibility (strategic location and good access). The analysis also revealed that 83% of farmers were motivated by income generation opportunities, while 60% considered capital and location important factors in diversification. Farm diversification through agritourism provides additional income opportunities and contributes to farmers' economic resilience. This research emphasizes that diversification decisions are complex processes influenced by economic, social, and cultural factors. The results of this study provide insights into the dynamics of farmers' diversification decisions and provide policy implications to support sustainable rural development through agritourism. [ABSTRACT FROM AUTHOR]

Wheat is a very vital cereal crop in the development of a nation's economy and agricultural sector. A crucial element that profoundly affects the growth and productivity of wheat is the soil temperature. As an insulating layer, straw mulch controls the soil temperature. Field measurement of the soil temperature is time-consuming and costly; therefore, the present study investigates the applicability of random forest (RF), support vector machine (SVM) and generalized regression neural network (GRNN) models for estimating soil temperature in wheat fields covered with straw mulch using meteorological, soil cover and agronomical parameters. A field study was conducted on the wheat cultivar with two soil cover treatments for measuring the soil temperature at three depths (5, 50 and 80 cm). Soil plant analysis development value; soil cover and depth; daily meteorological data such as average air temperature, average relative humidity, sunshine hours and vapor pressure were used as inputs for machine learning models in training and testing. Statistical performance indicators showed applicability of three models with a root mean square error (RMSE) ranging from 0.245–0.595°C, 0.867–0.886°C and 0.557–0.812°C, Nash-Sutcliffe efficiency (NSE) ranging from 0.917–0.987, 0.823–0.836 and 0.804–0.935 and Lin's concordance correlation coefficient (CCC) ranging from 0.949–0.991, 0.887–0.897 and 0.881–0.957 for the RF, SVM and GRNN, respectively. Results indicated better performance of the RF model for predicting the soil temperature at different depths in the case of both crop and soil cover conditions. Also, the uncertainty analysis indicated the better performance of RF with R-factor and P-factor value of 0.40 and 0.84, respectively. [ABSTRACT FROM AUTHOR]

The article delves into the historical significance of forests and forestry in Gondar, Ethiopia, between 1938 and 1991, shedding light on the challenges faced by the forest ecosystem. It discusses issues like deforestation, lack of resources, and poverty, which have impacted the local community's economic opportunities. Despite the forest's role in providing livelihoods and environmental benefits, factors like overgrazing and societal influences have led to its degradation. The document also covers topics like land tenure, forest management, and post-independence environmental conservation efforts, offering valuable insights for researchers interested in Ethiopian history and environmental studies. [Extracted from the article]

Ensuring food security and while achieving low-carbon development is a practical issue for the agricultural economy and the priority for carbon neutrality in the planting industry. Based on the time series data of Sichuan planting industry, the change of carbon emission and emission intensity was reviewed, and the input-output super-efficiency model was applied to evaluate the green development efficiency of Sichuan planting industry, and the influencing factors were analyzed by Tobit regression model. The results showed that carbon intensity of Sichuan planting industry decreased from 0.717 t to 0.208 t per 10000 yuan of GDP over the 2010−2022 period, with the continued expansion of the planting area being the core driver of the increase. At the same time, the green development trend of Sichuan planting industry showed the evolution characteristics of "continuous low efficiency−rapid stretching−stable development". In terms of influencing factors, the unique agricultural industrial structure and climate environment, as well as technological investment, have driven the green development of planting industry in Sichuan, while the high proportion of grain and oil crop cultivation was an objective challenge. The fiscal policy had improved the utilization level of planting industry to climate resources, but there was also a significant inhibitory effect. Therefore, while actively adapting to global warming, achieving a long-term balance between food security and green development of the planting industry, and guiding the green development willingness of planting entities with policy orientation, should be a direction that the government should continue to focus on. [ABSTRACT FROM AUTHOR]

With the proposed "double carbon" target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park. [ABSTRACT FROM AUTHOR]

Soybeans, a vital source of protein for animal feed and an essential industrial raw material, are the most traded agricultural commodity worldwide. Accurate price forecasting is crucial for maintaining a resilient global food supply chain and has significant implications for agricultural economics and policymaking. This review examines over 100 soybean price forecast models published in the last decade, evaluating them based on the specific markets they target—futures or spot—while highlighting how differences between these markets influence critical model design decisions. The models are also classified into AI-powered and traditional categories, with an initial aim to conduct a statistical analysis comparing the performance of these two groups. This process unveiled a fundamental gap in best practices, particularly regarding the use of common benchmarks and standardised performance metrics, which limits the ability to make meaningful cross-study comparisons. Finally, this study underscores another important research gap: the lack of models forecasting soybean futures prices in Brazil, the world's largest producer and exporter. These insights provide valuable guidance for researchers, market participants, and policymakers in agricultural economics. [ABSTRACT FROM AUTHOR]

Many financial markets use designated market makers (DMMs), but the impacts of DMMs on agricultural futures markets – and in particular, how to arrange DMMs among contracts expiring in different months – are largely neglected. In 2017, Chinese exchanges started recruiting DMMs for inactive contracts when they become nearby contracts to address the discontinuous trading activity of nearest‐to‐maturity contracts, which enables us to study the benefit and cost of recruiting DMMs for inactive contracts using a quasi‐experimental framework. Leveraging tick‐by‐tick data on corn and soybean meal futures, we find that DMMs improve the market quality of inactive contracts without disrupting the market quality of dominant contracts. Heterogeneity analysis in policy settings suggests that more DMMs are conducive to improving market quality for corn and soybean meal futures. We demonstrate that DMM policy is a feasible measure to facilitate continuous activeness in Chinese agricultural futures markets. Our results are important for exchanges and regulators seeking to better design and implement designated market‐making programs in agricultural futures markets. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Latin American & Caribbean Studies (Routledge) is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article explores the history of the banana industry in Jamaica and its decline due to the spread of Panama Disease. The Boston Fruit Company played a significant role in transforming the industry from smallholdings to plantations. The disease had a devastating impact on Jamaica's banana exports, leading to a decline in the industry. The article also discusses the value of environmental history in understanding Caribbean agricultural systems and the broader history of agricultural modernization. It highlights the adaptive strategies of Afro-Jamaican smallholders and challenges the notion of marginalized communities as passive victims. The field of Caribbean environmental history is still developing, and this article offers a glimpse into the complex region and its history. [Extracted from the article]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The study aims to investigate the risk transmission from COVID-19 to global agriculture, energy, natural resources/mining and environmentally/socially responsible investments. Additionally, it explores the connectedness of global energy indices with global agriculture, natural resources/mining and environmentally/socially responsible investments. The study develops a new COVID-19-based Global Fear Index (GFI) to achieve the objectives, thus contributing to the prevailing literature. Design/methodology/approach: The data of Global indices are selected from January 2020 to December 2021. The study uses multivariate BEKK-GARCH and TVP-VAR models to explore COVID-19 risk transmission and connectedness between global indices. Findings: Significant shock and volatility transmissions from COVID-19 to all global indices are observed. Results show that global agriculture, natural resource/mining markets and environmentally and socially responsible investments are safe havens during COVID-19. Furthermore, these global investment choices are barely connected with global energy indices. Practical implications: Portfolio managers and investors should invest in global indices to gauge the risk-adjusted return during the pandemic and upcoming health-related risks. Investors in energy sectors are advised to diversify the risk by adding safe-haven assets to their portfolios. Social implications: The findings shed light on the importance of environmentally and socially responsible investments as a separate asset class where ecologically friendly and socially sentimental investors could invest in diversifying the risk of their portfolios. Originality/value: The paper offers valuable insights to policymakers and investors regarding asset pricing, risk management and financial market stability during pandemic-type emergencies. [ABSTRACT FROM AUTHOR]

With the advancement of urbanization, there has been a significant reduction in cultivated land, accompanied by soil erosion. Concurrently, the regularity of rainfall in recent years has been erratic, adversely impacting the grain economy and agricultural development in certain regions. Henan Province, spanning the basins of the Yangtze River, the Yellow River, the Huaihe River, and the Haihe River, possesses complex hydrological conditions and serves as a pivotal agricultural zone in China. Therefore, this paper utilizes daily rainfall data collected over 54 years (1969–2022) from 112 rain measuring stations in Henan Province to calculate the rainfall erosivity using the Zhang model and the erosivity model from the first national water survey. Meanwhile, spatial analysis was performed using the Kriging interpolation method in the ArcGIS Geostatistical Wizard, resulting in detailed spatial distribution maps of rainfall and rainfall erosivity. The study also employed Wavelet and Mann–Kendall tests to analyze the abrupt changes, trends and periodicity of rainfall and rainfall erosivity within the target region. The findings indicate that the average rainfall (1969–2022) in Henan province was 718.26 mm, while the average rainfall erosivity (R) was 3213.46 MJ mm/(hm2 h). R values are positively correlated with rainfall intensity and volume, displaying an annual upward trend. Spatially, R values increase gradually from northwest to southeast, closely aligning with topographical variations. Additionally, the analysis revealed a predominant periodic cycle of 54 years in the precipitation patterns. These results offer valuable insights for environmental and agricultural management in other regions of central China. [ABSTRACT FROM AUTHOR]

Despite the widespread influence of the burgeoning digital economy on agricultural productivity in recent years, China's food system confronts numerous challenges. Notably, research exploring the digital economy's impact on food system resilience remains scarce, and the pivotal role of industrial agglomeration in this context remains unclear. Therefore, this article is based on the panel data of 30 provinces in China from 2011 to 2021, this paper empirically examines the direct effect, the mechanism of action, and the spatial spillover effect of the resilience of a digital economy-enabled food system using a double fixed-effects model, mediated-effects model, and spatial econometric model. The results show the following: (1) The resilience of China's urban food system shows obvious spatial differences, but the overall trend in improvement requires attention. (2) The development of a digital economy has a facilitating effect on the level of resilience of the food system, and industrial agglomeration induces an intermediary effect. (3) The digital economy has a significant positive spatial spillover effect on the resilience of the food system; i.e., the digital economy can improve the resilience of the food system in the region and the neighboring regions. Accordingly, policy recommendations have been put forward to improve infrastructure construction and promote the development of digital villages; strengthen the construction of industrial agglomerations and promote the enhancement of the quality and efficiency of industries; and promote the development of regional linkages and build a solid foundation for food security. [ABSTRACT FROM AUTHOR]

This study conducts a meta‐regression analysis to synthesize the marginal effects of 12 factors that frequently appear in empirical studies examining farmer's technology adoption behavior. The analysis includes 187 observational studies on technology adoption in agriculture, which are published in 32 peer‐reviewed journals in the broader field of agricultural economics, covering farmer's adoption in 47 countries for a diverse range of agricultural technologies. Using this broad meta‐dataset, we investigate whether each of the 12 determinants has a true effect on technology adoption rates and examine whether Type I and Type II publication bias are present in the adoption literature. Our results reveal that while most determinant factors significantly affect adoption rates, their marginal effects are generally of small magnitude and vary considerably by technology type and country group. Additionally, our results provide evidence of the presence of Type I publication bias in half of the factors considered and Type II publication bias in nearly all, underscoring the need for caution when interpreting results in the adoption literature by researchers and policymakers. Overall, the findings highlight the critical need for proactive measures to address publication bias and promote more transparent and credible research practices in agricultural economics. [ABSTRACT FROM AUTHOR]

Agribusinesses invest in harnessing the goodwill affiliated with US agrarianism and farm iconography, hoping publics transfer the goodwill they attach to farming onto corporations. To make apparent agribusiness’s rhetorical strategies, we analyze “For the Farmers,” a 2021–2022 John Deere and Anheuser-Busch collaborative campaign to raise money for the nonprofit Farm Rescue. We theorize how productive tensions—farmers as thriving vs. farmers as needy and farms as human spaces vs. farms as machine spaces—mobilize the contradictions of agribusiness mythmaking in the twenty-first century. Our analysis of this campaign’s beer packaging and promotional video offers scholars who are invested in food rhetoric and agricultural communication an invitation to notice how agribusiness atmospheres both host contradiction about imagined pasts and recenter white masculinity to evoke a secure future based on trust of industrial-scale farming and the hydrocarbon economy. By theorizing how rhetorical affect aligns drinking beer with rescuing farmers, we offer a model of critiquing agribusiness’s reliance on myths to promote their brands. [ABSTRACT FROM AUTHOR]

The article "Accidental gamblers: Risk and vulnerability in Vidarbha cotton" by Sarthak Gaurav and Thiagu Ranganathan examines the struggles of cotton farmers in Vidarbha, India, tracing the historical influences of colonialism and neoliberal policies on agriculture. The authors analyze the complexities of modern farming practices, such as the adoption of Bt-cotton and pesticide use, and how farmers employ various strategies to mitigate risks in response to economic and environmental factors. The study sheds light on the challenges faced by small and marginal farmers, particularly women, and emphasizes the importance of state interventions, seed sovereignty, and public investment in irrigation to support sustainable agriculture in the region. [Extracted from the article]

In global agricultural markets, farmers often face profit squeezes due to low bargaining power, which affects the sustainability of agriculture and the livelihoods of farmers. Cooperative intervention is seen as a key solution to improve bargaining power and optimize profit distribution in the agricultural supply chain. In this study, a two-stage dynamic game model is adopted to focus on bargaining power and compare the effects of linear pricing versus a double charging system under cooperative intervention. It is found that the cooperative is better when it has full bargaining power or when it faces downstream sellers with comparable bargaining power, and the dual-charging system is more favorable. When cooperatives bargain with sellers, the degree of differentiation of agricultural products affects the cooperatives' profitability and cooperatives tend to maintain the two-part tariff when the bargaining power is less than a threshold condition containing the degree of differentiation of agricultural products; when discounts exceed the threshold, cooperatives shift to linear pricing to safeguard their profitability. Numerical analysis validates the theory and reveals the changing pattern of cooperative profits under market forces. This study not only provides theoretical support for the study of supply chain pricing strategies under the condition of considering the bargaining power of producers (farmers) but also provides management insights for the stability and sustainable development of agricultural supply chains. Its novel dynamic game framework is cross-culturally applicable to help farmers' cooperatives cope with the challenge of profit distribution in a global context and emphasizes the importance of differentiation strategies to enhance bargaining power and promote fairness and efficiency in global agricultural supply chains. [ABSTRACT FROM AUTHOR]

This study investigates the spatio-temporal evolution of agricultural carbon emission efficiency (ACEE) in China and its relationship with agricultural economic growth (AEG). The results indicate several findings: Firstly, between 2012 and 2021, China's agricultural carbon emission efficiency exhibited an upward trend, with the mean value increased from 0.349 to 0.807. Furthermore, the distribution pattern shifted from a dispersed, point-like distribution to an aggregated and continuous distribution. Secondly, the average agricultural carbon emission efficiency in China following a decreasing order: South China, Northwest China, Southwest China, East China, North China, Central China and Northeast China. Thirdly, the relationship between agricultural carbon emission efficiency and the agricultural economy in China has transitioned from weak decoupling to negative decoupling. Based on these findings, this study proposes some recommendations to enhance agricultural carbon emission efficiency and promote its decoupling from agricultural economic growth. These recommendations aim to achieve low-carbon and high-efficiency development of agriculture. [ABSTRACT FROM AUTHOR]

The article discusses statistics and research on new and beginning producers using Census of Agriculture data. It highlights a decline in the number of U.S. farms and an increase in the average age of producers, with efforts being made to support new entrants into agriculture. The data shows that new and beginning producers tend to be younger, operate smaller farms, and face challenges in accessing farmland and financing, emphasizing the importance of government support for their success. The article concludes by emphasizing the role of new and beginning producers in addressing the challenges of an aging population and farmland transition in the U.S. agricultural sector. [Extracted from the article]

In a competitive economy, agricultural policy must be implemented with a balanced sustainability approach by supporting producers in agricultural production. In recent years, it has been argued that global climate change, local/international marketing dynamics and local/international agricultural policy changes have more impact on the agricultural sector. Governments and international organizations are making intense efforts to develop sustainable policies in the agricultural sector to accelerate progress towards the Sustainable Development Goals (SDGs). While research on the adoption of new practices and new strategies in agriculture continues to grow and develop, agricultural policy practices offer new opportunities for agricultural marketing. The main purpose of this study is to determine and rank the importance level weights of the criteria affecting agricultural policy for sustainable agricultural marketing. In determining the criteria weights, the Best Worst method (BWM) from the multi-criteria decision making (MCDM) methods was preferred. The research proposes a framework for determining the weights of the appropriate criteria for agricultural policy strategy selection through a combined approach of multi-criteria decision making involving relevant stakeholders in an environment of uncertainty. According to the research results, project costs were determined to be the most important criterion for decision makers. This was followed by social benefits and employment opportunities criteria, respectively. When determining agricultural policy strategies for sustainable agricultural marketing, economic, social and environmental criteria may influence the evaluation of policy makers and stakeholders. Therefore, when determining strategies in agricultural policy, more importance should be given to sustainability approaches, taking into account all these factors. [ABSTRACT FROM AUTHOR]

Simple Summary: A genome-wide copy number variations (CNVs) analysis using high-density Ovine BeadChip array data in 288 Chinese Merino sheep was conducted. A total of 656 CNV regions (CNVRs) with an average size of 66.88 Kbps were identified. These CNVRs contain 519 losses, 60 gains, and 77 gain–losses ranging from 1.5 Kbps to 646 Kbps and covering 43 Mbps (1.58%) of the whole Ovine genome. To validate these results, we performed a quantitative PCR to detect 11 randomly selected CNVRs and successfully verified 8 (72.7%). The gene functional enrichment analysis revealed that a total of 1592 genes in 465 CNVRs were identified as related to biological functions such as ATP binding activity. Our results expand the current CNV map of the sheep genome and provide preliminary fundamental information for carrying out CNV studies of fine wool sheep in the future. Sheep are a vital species in the global agricultural economy, providing essential resources such as meat, milk, and wool. Merino sheep (Junken type) are a key breed of fine wool sheep in China. However, research on fine wool traits has largely overlooked the role of SNPs and their association with phenotypes. Copy number variations (CNVs) have emerged as one of the most important sources of genetic variation, influencing phenotypic traits by altering gene expression and dosage. To generate a comprehensive CNVR map of the ovine genome, we conducted genome-wide CNV detection using genotyping data from 285 fine wool sheep. This analysis revealed 656 CNVRs, including 628 on autosomes and 28 on the X chromosome, covering a total of 43.9 Mbs of the sheep genome. The proportion of CNVRs varied across chromosomes, from 0.45% on chromosome 26 to 3.72% on chromosome 10. Functional annotation through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses highlighted significantly enriched GO terms, including odorant binding, ATP binding, and sulfuric ester hydrolase activity. The KEGG analysis identified involvement in pathways such as neuroactive ligand–receptor interaction, axon guidance, ECM–receptor interaction, the one-carbon pool by folate, and focal adhesion (p < 0.05). To validate these CNVRs, we performed quantitative real-time PCR experiments to verify copy number predictions made by PennCNV software (v1.0.5). Out of 11 selected CNVRs with predicted gain, loss, or gain–loss statuses, 8 (IDs 68, 156, 201, 284, 307, 352, 411, 601) were successfully confirmed. This study marks a significant step forward in mapping CNVs in the ovine genome and offers a valuable resource for future research on genetic variation in sheep. [ABSTRACT FROM AUTHOR]

This study presents the development of ecological compounds using polylactic acid (PLA) and artichoke flour with the aim of obtaining materials with properties like commercial PLA. PLA biocomposites with different concentrations of green artichoke (HV) and boiled artichoke (HH) (1, 3, 5, 7, 10 and 20% by weight) were manufactured through an extrusion and injection process. Structural, mechanical, physical and color tests were carried out to analyze the effect of lignocellulosic particles on the biopolymeric matrix. The Shore D hardness, elongation at break and heat deflection temperature (HDT) of the PLA/HV and PLA/HH samples showed similar values to pure PLA, indicating that high concentrations of both fillers did not severely compromise these properties. However, reductions in the tensile strength, impact strength and Young's modulus were observed, and both flours had increased water absorption capacity. FTIR analysis identified the characteristic peaks of the biocomposites and the ratio of the groups regarding the amount of added filler. The SEM revealed low interfacial adhesion between the polymer matrix and the filler. This study represents a significant advance in the valorization and application of circular economy principles to agricultural waste, such as artichoke waste. PLA/HV biocomposites make a substantial contribution to sustainable materials technology, aligning with the goals of the 2030 agenda to reduce environmental impacts and promote sustainable development. [ABSTRACT FROM AUTHOR]

BACKGROUND: This study employs a machine learning approach to investigate the impact of climate change on fig production in Turkey. The eXtreme Gradient Boosting (XGBoost) algorithm is used to analyze production performance and climate variable data from 1988 to 2023. Fig production is a significant component of Turkey's agricultural economy. Therefore, understanding how climate change affects fig production is essential for the development of sustainable agricultural practices. RESULTS: Despite an observed increase in fig production between 2005 and 2020, potential yield may be negatively impacted by climate variables. Identifying the specific climatic factors affecting fig production efficiency remains a challenge. In the study, two different machine learning models are created: one for fig production yield per decare and another for fig production yield per bearing fig sapling. Eight climate variables (16 variables considering day and night values) serve as independent variables in the models. The models reveal that temperature change has the highest impact, with a percentage contribution of 41.30% in the first model and 43.90% in the second model. Thermal radiation (day and night) and 2 m temperature also significantly affect individually fig production. Wind speed, precipitation and humidity contribute to a lesser extent. CONCLUSION: This study illuminates the intricate interrelationship between climate change and fig production in Turkey. The utilization of machine learning as a predictive tool for future production trends and an instrument for informing agricultural practices is a valuable contribution to the field. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Extensive farmland in Europe hosts high biodiversity levels but is threatened by land use changes associated with market and agricultural policy drivers. We show that a nationwide replacement of rainfed cereals in favor of beef production in Portugal has coincided with a nearly 80% decline in the population of a ground‐nesting raptor, the Montagu's harrier Circus pygargus, since 2002. Reduced cereal crop cover led to nesting attempts occurring mostly in fodder crops, the predominant habitat with suitable sward structure at the onset of breeding. This results in a twofold ecological trap: early hay harvesting destroys first nesting attempts, whereas late cereal harvesting destroys replacement clutches. This double phenological mismatch prevents any breeding success and likely explains the country's observed population decline of Montagu's harriers and other key farmland birds. Reforming current agroenvironmental schemes to promote practices compatible with wild population phenology is paramount to revert these changes. [ABSTRACT FROM AUTHOR]

Soil is the backbone of the agricultural economy of any country. Soil salinity refers to the higher concentration of soluble salts in the soil. Soil salinity is a ruinous abiotic stress that has emerged as a threatening issue for food security. High salt concentration causes an ionic imbalance that hampers water uptake, affecting photosynthesis and other metabolic processes, ultimately resulting in inferior seed germination and stunted plant growth. A wide range of strategies have been adopted to mitigate the harmful effects of salinity such as efficient irrigation techniques, soil reclamation, habitat restoration, flushing, leaching or using salt-tolerant crops, but all the methods have one or more limitations. An alternative and effective strategy is the exploitation of salt-tolerant plant growth-promoting bacteria (ST-PGPB) to mitigate salt stress and improve crop productivity. ST-PGPB can survive in salinity-tainted environments and perform their inherent plant growth-promoting and biocontrol functions effectively. Additionally, ST-PGPB can rescue plants via stress-responsive mechanisms including production of growth regulators, maintenance of osmotic balance, aminocyclopropane-1-carboxylate (ACC) deaminase activity, exopolysaccharides (EPS) activity, improvement in photosynthesis activity, synthesis of compatible solutes, antioxidant activity and regulation of salt overly sensitive (SOS) signaling pathway. Several well-known ST-PGPB, specifically Azospirillum, Bacillus, Burkholderia, Enterobacter, Pseudomonas and Pantoea, are used as bioinoculants to improve the growth of different crops. The application of ST-PGPB allows plants to cope with salt stress by boosting their defense mechanisms. This review highlights the impact of salinity stress on plant growth and the potential of ST-PGPB as a biofertilizer to improve crop productivity under salt stress. [ABSTRACT FROM AUTHOR]

Copyright of African Journal of Reproductive Health is the property of Women's Health & Action Research Centre and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Asian Journal of Agriculture & Development reflects on its first twenty years of operation, highlighting the importance of agriculture in Asian economies. The journal evolved from focusing on developmental and policy-related research to including basic technological research for modernization. Articles in the 20th-anniversary issue cover topics such as rice policies, agricultural contracts, world rice markets, urban agriculture, youth involvement in agriculture, technology readiness, and flood damage resilience. The journal aims to continue expanding its reach to gather diverse perspectives on agriculture and rural development in Asia and beyond. [Extracted from the article]

The Circular Economy is now the focus of attention in various sectors, and many international organisations, such as FAO, IFAD, UNICEF, WFP and WHO, are showing great interest in the issue (FAO, 2021). In the 2030 agenda published by the UN in 2015, specifically SDG 2 says 'End hunger, achieve food security, improve nutrition and promote sustainable agriculture'. Hence, research on the application of Circular Economy methods in agriculture has become increasingly relevant. The main objective of this study was to analyse the evolution of this line of research and identify current trends linking Circular Economy and Agriculture and identify the drivers in this area of study. A bibliometric analysis of the articles produced in the period 2005–2020 was therefore carried out. The results show, in an irrefutable manner, that this topic is increasingly gaining visibility and relevance, that coincides with the establishment of the SDGs. The analysis of the key words highlighted three main lines of research: sustainability and sustainable development, as the eco-sustainable management of agricultural processes; bioenergy, produced from waste or agricultural production; life-cycle assessment, methods for calculating the environmental and health impacts of agricultural production processes. Interest in the Circular Economy depends on the awareness on the topic shown by each country. This is also reflected in the research. The regions where studies on Circular Economy and Agriculture are mainly carried out are Europe, Asia, North and South America, while Russia, Africa, the Middle East and Oceania still suffer from a certain gap. The situation of studies limited to Circular Economy applied to organic agriculture was also briefly analysed, and it was pointed out that in recent years many researchers have turned towards this field of research. Many studies tend to show that organic farming is the only primary activity that allows environmentally sustainable production and consumption. [ABSTRACT FROM AUTHOR]

Purpose: Understanding farmer and consumer behavior is essential to the successful implementation of behavior change interventions. The purpose of this study is to summarize existing research, provide a comprehensive overview of nudge interventions, and identify important trends in behavioral agricultural economics. Research on nudge interventions can help policymakers encourage farmers and consumers to change their behavior in desired direction like switching to organic agriculture or eco-labeled products and promote innovation in sustainable agriculture. Design/methodology/approach: The paper contains a bibliometric analysis of nudge research in general and agricultural economics as well as a literature review of the 53 empirical studies on nudge interventions between 2003 and 2023. Findings: The frequency of nudge research in all research fields and in agricultural economics has increased over the years. This study summarizes nudge interventions that can be used by farmers and consumers on a variety of agricultural economic issues. Information, social norms, and social comparison as nudge interventions are most prominent in the publications, while green nudges have not been researched to a greater extent. Originality/value: This study provides a comprehensive overview of nudge research in agricultural economics with potential for theoretical and policy implications. It highlights potential nudge interventions among farmers and consumers that can be applied in practices. [ABSTRACT FROM AUTHOR]

The US rejoined the Paris Agreement in 2021 with a targeted 50–52% reduction in net GHG emissions in 2030 relative to 2005. Within the US's nationally determined contributions, several land-based mitigation options were submitted, targeting the removal of 0.4–1.3 GtCO2 yr−1 in 2030 compared to the net flux in 2010. Acknowledging disagreement has existed on both technological and economic feasibility levels of soil C sequestration adoption and practices, this review explores and evaluates the research findings and needs for six concepts: (1) permanence; (2) additionality; (3) leakage; (4) uncertainty; (5) transaction costs; and (6) heat-trapping ability of different gases. These concepts are crucial for the effective implementation of soil C sequestration projects since they help establish robust and integrated methodologies for measurement, verification, and issuance of carbon credits. In turn, they help ensure that environmental, social, and economic benefits are accurately assessed and credibly reported, enhancing the integrity of carbon markets and contributing to global climate mitigation efforts. This review also evaluates the existing and potential market opportunities for agricultural production with C sequestration and "climate- smart" farming practices. Current barriers to, research needs for, and policy considerations regarding soil C sequestration strategies are also stated. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simple Summary: Lumpy Skin Disease (LSD) is a highly contagious viral infection in cattle that poses a significant threat to agricultural economies, especially in countries like India. Early and accurate detection is vital to prevent widespread outbreaks and reduce economic losses. Our research leverages advancements in artificial intelligence (AI), specifically deep learning, to develop an automated system for detecting LSD in cattle. We utilized publicly available datasets containing images of healthy cattle, those affected by LSD, and, importantly, cattle with other skin diseases to ensure our model specifically identifies LSD rather than general illness signs. We evaluated over ten pretrained deep learning models, including VGG16 and MobileNetV2, using transfer learning techniques. Through detailed data preprocessing, augmentation, and balancing, we enhanced model performance and generalizability. We assessed the models using crucial medical diagnostics metrics like sensitivity and specificity to minimize false negatives and positives. VGG16 and MobileNetV2 stood out, achieving high accuracy along with excellent sensitivity and specificity, effectively detecting LSD without misclassifying other conditions. This study provides valuable insights into applying deep learning in veterinary diagnostics, contributing to developing reliable AI tools for early LSD detection, ultimately improving animal health management and safeguarding the agricultural economy. Lumpy Skin Disease (LSD) poses a significant threat to agricultural economies, particularly in livestock-dependent countries like India, due to its high transmission rate leading to severe morbidity and mortality among cattle. This underscores the urgent need for early and accurate detection to effectively manage and mitigate outbreaks. Leveraging advancements in computer vision and artificial intelligence, our research develops an automated system for LSD detection in cattle using deep learning techniques. We utilized two publicly available datasets comprising images of healthy cattle and those with LSD, including additional images of cattle affected by other diseases to enhance specificity and ensure the model detects LSD specifically rather than general illness signs. Our methodology involved preprocessing the images, applying data augmentation, and balancing the datasets to improve model generalizability. We evaluated over ten pretrained deep learning models—Xception, VGG16, VGG19, ResNet152V2, InceptionV3, MobileNetV2, DenseNet201, NASNetMobile, NASNetLarge, and EfficientNetV2S—using transfer learning. The models were rigorously trained and tested under diverse conditions, with performance assessed using metrics such as accuracy, sensitivity, specificity, precision, F1-score, and AUC-ROC. Notably, VGG16 and MobileNetV2 emerged as the most effective, achieving accuracies of 96.07% and 96.39%, sensitivities of 93.75% and 98.57%, and specificities of 97.14% and 94.59%, respectively. Our study critically highlights the strengths and limitations of each model, demonstrating that while high accuracy is achievable, sensitivity and specificity are crucial for clinical applicability. By meticulously detailing the performance characteristics and including images of cattle with other diseases, we ensured the robustness and reliability of the models. This comprehensive comparative analysis enriches our understanding of deep learning applications in veterinary diagnostics and makes a substantial contribution to the field of automated disease recognition in livestock farming. Our findings suggest that adopting such AI-driven diagnostic tools can enhance the early detection and control of LSD, ultimately benefiting animal health and the agricultural economy. [ABSTRACT FROM AUTHOR]

China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China. [ABSTRACT FROM AUTHOR]

International agricultural markets are an important part of the global food resource chain. Tapping into the potential of agricultural trade between China and countries along the "Belt and Road" (B&R) is conducive to safeguarding China's and the world's food security, but there is less literature on the potential of bilateral trade demand. This paper ranks the B&R countries according to the scale of imports and exports, and calculates the elasticity of demand for imports, the elasticity of substitution for exports, and, finally, the potential of elasticity of demand for trade between China and the major B&R countries. The results show that China's agricultural export potential to major B&R countries is ranked as follows: Indonesia, Thailand, Russia, Poland, Turkey, Vietnam, Malaysia, Ukraine, India, and Singapore. The major B&R countries are also ranked in terms of their export potential to China: Vietnam, India, Ukraine, Russia, Malaysia, Thailand, Indonesia, Poland, Singapore, and Turkey. The findings of this paper provide a decision-making basis for promoting agricultural trade between China and B&R countries. [ABSTRACT FROM AUTHOR]

This paper investigates the economic feasibility and benefits of introducing genetically modified (GM) crops into Lebanon's agricultural sector. The methodology combines a rigorous cost–benefit analysis with qualitative insights from local farmers and agricultural scientists to ensure relevance to Lebanon's unique agricultural context. Through this study, we identified tomatoes and potatoes as the most suitable crops for GM implementation. The findings indicate that GM tomatoes could increase net income by USD 10,000 per hectare in the short term and USD 50,000 over five years. These economic benefits are primarily driven by higher yields and reduced pesticide costs. This study emphasizes the necessity of a holistic approach, including financial support, infrastructure development, farmer education, and robust market access strategies, to maximize the potential of GM crops. This research provides a strategic framework for leveraging GM technology to address Lebanon's agricultural challenges, promoting sustainable practices, enhancing food security, and ensuring long-term economic stability. By integrating local context and stakeholder perspectives, this paper offers a unique and actionable pathway for successful GM crop implementation in Lebanon. [ABSTRACT FROM AUTHOR]

This study focuses on 19 provinces in the Yangtze River Basin of China. It gathers relevant data indicators from 2010 to 2021 and constructs an evaluation index system centered on agricultural science and technology innovation. The study evaluates the relationship between agricultural "science and technology innovation-economy-ecology" systems and identifies key obstacle factors using the obstacle degree model. The study draws the following conclusions: Firstly, the comprehensive development level index of the agricultural science and technology innovation system shows an overall linear upward trend (values range from 0.121 to 0.382). Secondly, the comprehensive development level index of the agricultural economic system exhibits an upward trend but with a relatively small overall magnitude (values range from 0.248 to 0.322). Thirdly, the comprehensive development level index of the agricultural ecological system demonstrates significant overall fluctuations, with notable regional disparities (values range from 0.384 to 0.414). Fourthly, the overall agricultural SEE (Science and technological innovation, Economy, Ecology) complex system exhibits a characteristic of "high coupling, low coordination", identifying the main obstacle factors influencing agricultural SEECS based on a formulated approach. Subsequently, the following policy recommendations are proposed: Firstly, enhance the agricultural technological innovation system and promote green and efficient agricultural technology research and development. Secondly, to accelerate the transformation and upgrading of modern agriculture, achieving green and high-quality development of the agricultural economy. Thirdly, to strengthen agricultural ecological environment protection, laying a solid foundation for the healthy and sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

This study offers detailed recommendations on strengthening government support without harming digital finance benefits, especially in negatively affected areas, which is critical for enhancing the inclusiveness of the digital financial landscape and reducing social disparities. This paper uses year 2011–2022 panel data from China's 31 provinces to empirically analyze digital finance's effects, mechanisms, and heterogeneity on agricultural economy resilience with a two-way, fixed-effect model. It further explores each feature's impacts using machine learning methodologies like the random forest, GBRT, SHAP value method, and ALE plot. The findings show that digital finance boosted agri-economy resilience, varying by food-producing status and marketization. Among all the features analyzed, government input, urbanization level, and planting structure emerged as the most critical factors influencing agri-economy resilience. Notably, government input negatively moderated this relationship. The ALE plot revealed non-linear effects of digital finance and planting structure on agri-economy resilience. [ABSTRACT FROM AUTHOR]

The digital economy's transformative impact on agriculture presents both opportunities and challenges for China's pursuit of high-quality agricultural and rural development. This study investigates the complex interplay between digital economy, industrial structure transformation, and agricultural advancement using panel data from 31 Chinese provinces spanning 2012–2021. We employed mediation analysis and threshold effect models to uncover several key findings: (1) The digital economy influences high-quality agricultural and rural development through the dual-mediating mechanisms of industrial structure intensification and upgrading in China. (2) These mediating effects exhibit heterogeneous patterns: while industrial intensification positively channels the digital economy's impact, industrial upgrading shows an initial negative indirect effect, suggesting potential short-term disruptions. (3) The relationship between digital economy and agricultural development is nonlinear, characterized by significant threshold effects. The digital economy's positive impact becomes more pronounced as industrial structure surpasses certain sophistication and advancement thresholds. Our findings reveal the nuanced dynamics of digital-driven agricultural transformation, highlighting the need for targeted policies that leverage industrial-structure changes while mitigating potential adverse effects. This research contributes to a more comprehensive understanding of how digitalization can be harnessed to promote sustainable and high-quality agricultural and rural development in China, with implications for other developing economies navigating similar transitions. [ABSTRACT FROM AUTHOR]

Exploring the adoption of green production technologies by farmers (GTA) is of great significance, given the context of global climate change and sustainable agricultural development. This article starts from the perspective of the transformation of the farmland transfer market, based on research data from 2076 farmers, and uses the endogenous disposal effect model to examine its impact on GTA. The results indicate that the transformation of China's farmland transfer market has a significant "greening" effect on agriculture. Specifically, the paid transfer of farmland, written leases, and clear lease forms promote GTA, while acquaintance transactions are not conducive to it. Participation in the farmland transfer market has increased the likelihood of GTA by 18.7% and is statistically significant at the 1% level. However, the adoption level of green production technology by ordinary farmers remains low, and achieving green development in agriculture is a long and arduous task. Mechanism analysis shows that the transformation of the farmland transfer market can effectively enhance GTA by increasing the scale of operations, farmers' income, and mechanization levels. When the transfer targets new business entities, such as family farms, professional cooperatives, and enterprises, it significantly promotes GTA. The findings suggest that deepening the reform of the farmland transfer market, promoting large-scale agricultural operations, strengthening the application of green production technologies, improving the level of agricultural mechanization, and accelerating the process of sustainable agricultural development are all essential steps toward enhancing GTA and, ultimately, achieving sustainable agricultural development. [ABSTRACT FROM AUTHOR]