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This study aims to investigate the landscape of family-based organic farm businesses in the Kashmir Valley, India, analyzing the factors that either facilitate or hinder their adoption. The research also intends to uncover sources of information seeking. The primary purpose is to provide qualitative evidence to address existing knowledge gaps and offer insights for promoting sustainable farm practices in the region.

The research employs a qualitative approach, drawing on focus group interviews. The study thoroughly explores the background and relevant literature, utilizing a comprehensive research framework. Data is collected from family based farmers engaged in organic farming practices in the Kashmir Valley. The data is analyzed using content analysis ensuring a robust and thorough exploration of the subject matter.

This study reveals a notable transition in the agricultural landscape of the Kashmir Valley, showcasing a widespread adoption of organic farming on considerable land. The study reveals that key facilitators for organic farming among family-based farms are farm productivity, entrepreneurial intention, governance, environmental consciousness, and health concerns. The exchange of information, both through formal and informal channels, is found to be a crucial factor influencing the adoption of organic farming. The study also unveiled significant inhibitors that hinder the adoption of organic farming on commercial scales, including on-farm challenges such as difficulties in acquiring inputs and facing reduced yields, market-related concerns, and a lack of support and assistance from government agencies.

This research contributes significantly to the existing literature by advancing the understanding of organic farm business and agri-entrepreneurship. It unveils key factors that either support or hinder family-based organic farms, identifying crucial information sources and presenting valuable insights for policymakers. Furthermore, this study provides practical guidance for overcoming obstacles, enhancing infrastructure, and translating identified facilitators into successful agri-ventures in the Kashmir region.

This study aims to provide a detailed characterization of Argentinean exporting wineries using a new rich firm-level data set to understand how capabilities and business strategies differ among firms with different levels of involvement in the export market.

A survey was distributed among all wineries along all wine regions of Argentina; the 45-min questionnaire was answered by 230 wineries, a representative sample with a response rate of 26.3% of the total population of Argentinean wineries. The survey assessed the interaction between wineries’ dynamics and characteristics and their participation in export markets. In the comparative analysis, the results are presented by dividing the sample into four categories according to the export intensity of the wineries.

High-intensity exporting wineries in Argentina differ from other Argentinean wineries in several dimensions. In particular, the most internationalized Argentinean wineries are the most endowed with higher capabilities; they follow a specific business model emphasizing product differentiation, quality upgrading, brand building and the development of distribution channels. Exporting wineries from Argentina adopt business practices that differ from those that prevail among wineries that only target the domestic market. They have developed firm capabilities such as human capital and technology to play a critical role in quality upgrading for their participation in global wine markets.

To the best of the authors’ knowledge, this paper is the first to study the Argentinean exporting wineries using a firm-level sizeable representative sample.

The choice of crops to produce at a location depends to a large degree on the climate. As the climate changes and food demand evolves, farmers may need to produce a different mix of crops. This study assesses how much cropland may be subject to such upheavals at the global scale, and then focuses on China as a case study to examine how spatial heterogeneity informs different contexts for adaptation within a country.

A global agricultural economic model is linked to a cropland allocation algorithm to generate maps of cropland distribution under historical and future conditions. The mix of crops at each location is examined to determine whether it is likely to experience a major shift.

Two-thirds of rainfed cropland and half of irrigated cropland are likely to experience substantial upheaval of some kind.

This analysis helps establish a global context for the local changes that producers might face under future climate and socioeconomic changes. The scale of the challenge means that the agricultural sector needs to prepare for these widespread and diverse upheavals.

Anhui Winall Hi-Tech Seed Co., Ltd., a high-tech seed enterprise integrating crop seed research, production, processing and marketing at home and abroad, is the first seed company listed on GEM in China. Its main business is research and development, breeding and marketing of seeds of hybrid rice, edible rape, cotton, melon and vegetable, with hybrid rice as its leading product. In terms of business model, Winall Hi-tech is engaged in procurement, production, sales and promotion of modified varieties and after-sales service. However, Winall Hi-tech also has to face a few potential problems.

To address the challenges of the agricultural sector, innovation is necessary. This study aims to focus on knowledge circulation as a basis to facilitate innovation in viticulture in the context of climate change.

We have conducted interviews with viticulture stakeholders in Central Spain (Madrid region) on their perceptions and concerns about climate change, knowledge on practices to mitigate its effects on this crop and their relationship with each other for knowledge exchange. A map showing the knowledge nodes and their relationships with other stakeholders has been drawn based on the answers obtained.

Winegrowers have already noticed the effects of climate change, and they are changing some agricultural practices. Drip irrigation was the most frequently mentioned option to minimize these effects. The map of knowledge identifies the main nodes in the information flow. Results also highlight different approaches to climate change and interesting nuances in the maps of knowledge among winegrowers with and without winery.

This paper is focused on the Madrid region, a territory that is still consolidating its wine sector at the economic and marketing levels. We understand that regions with more consolidated or stronger sectors involve maps of knowledge more complex than that obtained in this study.

Showing the nodes of knowledge, as well as the weaknesses and strengths of the information circuit in the wine sector in the Madrid region, is very relevant to developing strategies aimed at supporting innovation in this sector. From a practical point of view, strategies for knowledge generation and circulation are only one part of the innovation process – policies for financial and technical support are key complementary measures.

Identification of key agents in the innovation process in the wine sector is essential to foster innovation processes. Ultimately, this will lead to more efficient adaptation to new challenges in the sector.

The Agriculture Knowledge and Innovation Systems (AKIS) approach has a consolidated theoretical framework that pays great attention to knowledge flows, but specific studies are needed to capture the reality of AKIS by sector and by region.

Financial inclusion and digital finance go side by side and help enhance agricultural activities; however, the magnitude of digital financial services varies across countries. In line with this argument, this study aims to examine whether financial inclusion enhances agricultural participation and decompose the significance of the difference in determinants of agricultural participation between financially included – not financially included households and digital finance – no digital finance households.

This study uses Pakistan’s household integrated economic survey 2018/19 to test hypotheses. The logit model is used to examine the effect of financial inclusion on agriculture participation. Moreover, this study employs a nonlinear Fairlie Oaxaca Blinder technique to investigate the difference in determinants of agricultural participation.

This study reports that financial inclusion positively influences agricultural participation, meaning households may have access to financial services and participate in agricultural activities. The results suggest that the likelihood of participating in agriculture in households with mobiles and smartphones is higher. Moreover, household size, income, age, gender, education, urban, remittances from abroad, fertilizer, pesticides, wheat, cotton, sugarcane, fruits and vegetables are the significant determinants of agricultural participation. To distinguish the financially included – not financially included households’ gap, this study employs a nonlinear Fairlie Oaxaca Blinder decomposition and finds that differences in fertilizer explain the substantial gap in agricultural participation. Likewise, this study tests the digital finance – no digital finance gap and finds that the difference in fertilizer is a significant contributor, describing a considerable gap in agricultural participation.

Empirically identified that various factors cause agricultural participation including financial inclusion and digital finance. Regarding the research limitation, this study only considers a developing country to analyze the findings. However, for future research, scholars may consider some other countries to compare the results and identify their differences.

The accessibility of fertilizer can reduce the agricultural participation gap. However, increased income level, education and cotton and sugar production can also overcome the differences in agriculture participation between digital finance and no digital finance households.

This is the first study to decompose the difference in determinants of agricultural participation between financially and not financially included households.

In this paper we examine the validity of the J-curve hypothesis in four Southeast Asian economies (Indonesia, Malaysia, the Philippines and Thailand) over the 1980–2017 period.

We employ the linear autoregressive distributed lags (ARDL) model that captures the dynamic relationships between the variables and additionally use the nonlinear ARDL model that considers the asymmetric effects of the real exchange rate changes.

The estimated models were diagnostically sound, and the variables were found to be cointegrated. However, with the exception of Malaysia, the short- and long-run relationships did not attest to the presence of the J-curve effect. The trade flows were affected asymmetrically in Malaysia and the Philippines, suggesting the appropriateness of nonlinear ARDL in these countries.

The previous research tended to examine the effects of the real exchange rate changes on the agricultural trade balance and specifically the J-curve effect (deterioration of the trade balance followed by its improvement) in the developed economies and rarely in the developing ones. In this paper, we address this omission.

Farmers producer organisations (FPOs) play the most crucial role in the agriculture supply chain system, aiming to redress the balance between farming and marketing activities of agricultural produce. The purpose of this study is to assess the performance of FPOs using data envelopment analysis (usually referred to as DEA) on 34 FPO units selected from the state of Rajasthan.

One of the most commonly used techniques to examine business performance is the application of DEA. The application of DEA requires the selection of inputs and outputs. This study takes three inputs and three outputs based on the insights drawn from the field survey. While the input variables consist of total assets, paid-up capital and the number of economic activities, the three output variables are turnover, net profit and number of members benefitted. Broadly, these variables encapsulate the operational performance of the business units.

This study’s findings reveal that the estimated relative efficiency score of the input-oriented CCR (Charnes, Cooper, and Rhodes) model ranges from 0.06 to 1. Interestingly, only one FPO has reported a relative efficiency (RE) score of one, whereas the remaining FPOs fall below the efficiency frontier. However, 15 FPOs report an RE score of one in the output-oriented CCR approach. Considering the estimates obtained in the input- and output-oriented BCC (Banker, Charnes and Cooper) models, this study found that about 20% of the FPOs report an efficiency score greater than 0.80. Moreover, three FPOs are on the frontier line. An examination of the scale efficiency score in the input-oriented model, 45% of the FPOs have an efficiency score greater than 0.80, whereas almost all FPOs achieve a scale efficiency score greater than 0.80 in the output-oriented model. Overall, the results imply that the FPOs should place greater emphasis on the efficient utilisation of the inputs to enhance the overall business performance and productivity.

The findings of this study provide vital insights into the specific inputs and outputs that determine the performance efficiency of FPOs and identify the potential areas for improving the existing inefficient FPOs.

This study contributes to the repository of the existing empirical studies in three distinct ways. First, the authors hardly found any previous studies that quantitatively assess the business performance of FPOs using the DEA technique. Second, the effort to identify the slacks associated with each input and output variable in input- and output-oriented models gives insights on improvable areas for inefficient FPOs. Third, the authors attempt to demystify the empirical obfuscations by highlighting the major challenges FPOs face in the state of Rajasthan.

The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.

Despite the susceptibility of cotton crops to pest attacks in the Malwa Region of Indian Punjab, no crop insurance policy has been implemented there– not even the Pradhan Mantri Fasal Bima Yojana (PMFBY), which is a central scheme. Therefore, this paper attempts to gauge the likely impact of the PMFBY on Punjab cotton farmers and assess the changes needed for greater uptake and effectiveness of PMFBY.

The authors have conducted a primary survey to conduct this study. Initially, the authors compared the costs of cotton production with the returns in two scenarios (with and without insurance). Additionally, the authors have applied a logistic regression framework to examine the determinants of the willingness of farmers to participate in the crop insurance market.

The study finds that net returns of cotton crops are conventionally small and insufficient to cope with damages from crop failure. Yet, PMFBY will require some modifications in the premium rate and the level of indemnity for its greater uptake among Punjab cotton farmers. Additionally, using the logistic regression framework, the authors find that an increase in awareness about crop insurance and farmers' perceptions about their crop failure in the near future reduces the willingness of the farmers to participate in the crop insurance markets.

The present study looks for the viability of PMFBY in Indian Punjab for the cotton crop, which can also be extended to other crops.

Punjab could also use crop insurance to encourage diversification in agriculture. There is a need for special packages for diversified crops under any crop insurance policy. Crops susceptible to volatility due to climate-related factors should be identified and provided with a special insurance package.

There exist very scant studies that have discussed the viability of a central crop insurance scheme in the agricultural-rich state of India, i.e. Punjab. Moreover, they do not also focus on crop losses accruing due to pest and insect attacks.