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In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation.

(1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers’ decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas.

By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

The growth of lending from nontraditional lenders may pose challenges for official US Department of Agriculture (USDA) farm sector debt estimates, but it is difficult to find data to assess official estimates. The purpose of this study is to examine whether debt provided by nontraditional lenders is accurately accounted for in official estimates.

We compare traditional and nontraditional lending data from farm equipment lien collateral values and the USDA Agricultural Resource Management Survey (ARMS). After analyzing trends in equipment lending implied by farm equipment lien data and ARMS, we estimate whether changes in farm equipment lien values predict changes in equipment debt reported in ARMS and whether lender type influences that relationship.

We find that credit provided by nontraditional lenders is likely underreported in ARMS. Our econometric model shows that equipment debt volumes for nontraditional lenders are consistently lower than traditional loan volumes in ARMS across a variety of model specifications. We also find that an increase in lien values for nontraditional lenders is less likely to predict an increase in ARMS equipment debt volumes than an increase for traditional lenders.

Official farm sector debt estimates may not fully account for nontraditional lenders.

This study demonstrates how the growth of nontraditional lending poses challenges for estimating US farm sector debt. We evaluate farm sector debt estimates and advance knowledge of the role of nontraditional lenders in farm equipment credit provision. The farm equipment lien dataset provides a rich source of novel data for research on local and national equipment debt and investment.

Small-scale farmers are highly heterogeneous with regard to their types of farming, levels of technology adoption, degree of commercialization and many other factors. Such heterogeneous types, respectively groups of small-scale farming systems require different forms of government interventions. This paper applies a machine learning approach to analyze the typologies of small-scale farmers in South Africa based on a wide range of objective variables regarding their personal, farm and context characteristics, which support an effective, target-group-specific design and communication of policies.

A cluster analysis is performed based on a comprehensive quantitative and qualitative survey among 212 small-scale farmers, which was conducted in 2019 in the Limpopo Province of South Africa. An unsupervised machine learning approach, namely Partitioning Around Medoids (PAM), is applied to the survey data. Subsequently, the farmers' risk perceptions between the different clusters are analyzed and compared.

According to the results of the cluster analysis, the small-scale farmers of the investigated sample can be grouped into four types: subsistence-oriented farmers, semi-subsistence livestock-oriented farmers, semi-subsistence crop-oriented farmers and market-oriented farmers. The subsequently analyzed risk perceptions and attitudes differ considerably between these types.

This is the first typologisation of small-scale farmers based on a comprehensive collection of quantitative and qualitative variables, which can all be considered in the analysis through the application of an unsupervised machine learning approach, namely PAM. Such typologisation is a pre-requisite for the design of more target-group-specific and suitable policy interventions.

This study investigates the impact of board funds, banking credit, and economic development on food production in the context of South Asian economies (India, Pakistan, Bangladesh, Sri Lanka, and Nepal).

This study used data from the World Development Indicators covering the years 1991–2019. To investigate the relationship between the variables of the study, we employed the panel unit root test, panel cointegration test, cross-sectional dependence test, fully modified least squares (FMOLS), and panel dynamic least squares (DOLS) estimators.

The empirical results indicate that board funding significantly increase food production; however, banking credit had a negative impact. Furthermore, the findings indicate that economic development, Arable land, fertilizer consumption, and agricultural employment play a leading role in enhancing food production. The results of the Dumitrescu-Hurlin causality test also show substantiated the significance of the causal relationship among all variables.

South Asian countries should prioritize board funding, bank credit, and economic development in their long-term strategies. Ensuring financial access for farmers through micro-credit and public bank initiatives can spur agricultural productivity and economic growth.

This study is the first to combine board funding, banking credit, and economic development to better comprehend their potential impact on food production. Instead of using traditional approaches, this study focuses on these financial and developmental aspects as critical determinants for increasing food production, using evidence from South Asia.

The aim of this research is to elucidate the correlation between open innovation, digital strategies and networking in enhancing agricultural enterprises within the new perspective of Agrifood 5.0. As such, it contributes to making businesses more competitive, especially in the Italian agricultural sector, where small and medium-sized enterprises are highly fragmented. Numerous studies have asserted that the competitiveness of actors operating within a specific territory is closely linked to local identity and image enhancement. Agricultural organizations are undergoing a profound transformation, with technological assets emerging as catalysts for new synergies. Advanced technologies such as robotics, the Internet of Things (IoT) and automation (AI) are emerging as differentiating elements capable of further advancing the agricultural sector, transitioning it from Agrifood 4.0 to Agrifood 5.0. The empirical analysis of the research shows a positive correlation between a collaborative attitude and a propensity for innovation. Indeed, the data demonstrated that digital strategies and open innovation positively influence competitiveness in agricultural SMEs.

The methodology employed in this study is mixed, incorporating both qualitative and quantitative approaches. The quantitative aspect involves analysis of the dataset from the Italian Statistical Institute (ISTAT) through logistic regression, while the qualitative component entails analysis of semi-structured interviews conducted with a sample of 174 agricultural cooperatives in southern Italian regions (Campania). This approach allows for a comprehensive understanding of the research topic, capturing both numerical trends and nuanced insights from interviews.

After analyzing the data from the 7th General Census of Agriculture conducted by ISTAT, a clear understanding of the sector has emerged, revealing several potential research avenues. It is evident that innovation in the agricultural sector is often driven by the largest and best-capitalized production entities, primarily located in Italy. Conversely, smaller agricultural entities can benefit from networking as new technological assets act as catalysts for new synergies, innovation and competitiveness.

Enhancing the relational contribution within the network and humanizing a fragmented sector are crucial elements for promoting open innovation. Network structuring facilitates the transmission of managerial knowledge, contributing to an overall increase in the intellectual and relational capital of the agricultural sector. These factors, combined with open innovation, enhance the competitiveness of individual firms and elevate the brand of the entire sector, creating a conducive environment for transitioning toward Agrifood 5.0. This transition is characterized by increased interconnection, continuous innovation and overall prosperity. Specific studies on this topic are lacking in Italy, particularly in the southern regions. Therefore, this contribution focuses on investigating the Campania region.

The novelty of this study lies in its investigation of the relationship between agricultural enterprises and innovation in the context of enterprises networking strategies (i.e. associationism and/or cooperation), promoting competitiveness. The limitations of this study are related to the dimension of the sample selected and its relationship with other productive sectors.

A correlation has been shown in the literature between credit constraints and the adoption of agricultural technologies, technical efficiencies and measures for adapting to climate change. The relationship between credit constraints, risk management strategy adoption and income, however, is not well understood. Consequently, the purpose of this study was to investigate how credit constraints affect the income and risk management practices adopted by Northern Nigerian maize farmers.

Cross-sectional data were collected from 300 maize farmers in Northern Nigeria using a multi-stage sampling technique. Descriptive statistics, seemingly unrelated regression and double hurdle regression models were the analysis methods.

The results showed that friends and relatives, banks, “Adashe”, cooperatives and farmer groups were the main sources of credit in the study area. The findings also revealed that the sources of risk in the study area included production risk, economic risk, financial risk, institutional risk, technological risk and human risk. In addition, the risk management strategies used to mitigate observed risks were fertilizer application, insecticides, planting of disease-resistant varieties, use of herbicides, practising mixed cropping, modern planning, use of management tools as well as making bunds and channels. Furthermore, we found that interest rate, farm size, level of education, gender and marital status were significant determinants of statuses of credit constraints while the age of the farmer, gender, household size, primary occupation, access to extension services and income from maize production affected the choice and intensity of adoption of risk management strategies among the farmers.

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income.

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income. It therefore recommends that financial service providers should be engaged to design financial products that are tailored to the needs of smallholder farmers in the study area.

This paper incorporates the role of constraints in influencing farmers’ decisions to uptake credits and subsequently their adoption behaviours on risk management strategies. The researcher approached the topic with a state-of-the-art method which allows for obtaining more reliable results and hence more specific contributions to research and practice.

Most Asians are directly or indirectly dependent on agriculture, and most live in rural areas. Since Asia is primarily an agricultural economy, rural tourism and agrotourism are critical in enabling growth. Agrotourism is a term that refers to the fusion of tourism and agriculture. The freshwater turtles (Batagur sp.) are a group of six turtles that once roamed southern Asia's great rivers and estuaries from the Indian Subcontinent to the Indonesian islands. Therefore, this review is to investigate the current state of agrotourism in Batagur sp. conservation and assess the associated challenges and opportunities. This review paper recognized the impacts and benefits for the rural site as an agrotourism industry. Moreover, three initiatives in Batagur sp. conservation programs were also discussed. Agrotourism, with a focus on the conservation of Batagur sp., is a vital driver of economic growth in rural Asian regions. It diversifies rural communities' income sources, enhancing economic stability and resilience.

The purpose of this article is to determine the regional economic factors and bank characteristics that significantly contribute to changes in bank liquidity. We also seek to identify regions that may be most susceptible to liquidity tightening.

For this article we use data on deposits from commercial banks, Federal Reserve survey data and indicators of regional and agricultural economic conditions. We specify a panel regression with fixed effects to model how liquidity at agricultural banks has changed and to identify the most significant drivers.

Our results suggest that small banks and banks with branch networks located in areas more concentrated in agricultural production bear the greatest risk of reduced liquidity.

Prior to the pandemic and more recently, lower deposit growth, combined with strong demand for agricultural loans, has led to reductions in liquidity at agricultural banks. Lower liquidity could reduce credit availability for farm borrowers and increase risks for banks that must rely on alternative sources of funding to meet loan demand.

Previous research has shown that exogenous shocks from other economic sectors, such as energy, can significantly affect bank liquidity, but research is limited on how agricultural bank liquidity is affected by downturns in the agricultural economy and other regional economic factors. Another contribution is this paper’s analysis of regional disparities in bank liquidity.

This study aims to provide an empirical investigation into rising capital flight and the role of institutional quality to mitigate its effect on the real sector in sub-Saharan Africa (SSA).

The study uses the system generalized method of moments and uses data spanning from 1989 to 2020 from 26 SSA countries.

The findings show that capital flight has no direct impact on the real sector while institutional quality adversely impacted the agricultural and industrial sectors. The study also found that institutional quality is unable to mitigate the effect of capital flight on the industrial sector.

This study investigates if institutional quality mitigates the impact of capital flight on the real sector proxied by industrial value-added and agriculture value-added.