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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.

Food quality and safety issues have always been imperative topics discussed by people. The anti-tampering of blockchain technology and the transparency of information make it possible to improve food traceability and safety quality. How to effectively apply blockchain traceability technology to food safety has great research significance for improving food safety and consumer quality trust.

The paper aims to analyze the differences in product quality levels and market participants’ profits before and after the use of blockchain-driven traceability technology in the food agricultural product supply chain (SC) in the dynamic game frameworks of supplier-led and retailer-led modes, respectively, and explores the willingness, social welfare and consumer surplus of each member of the agricultural product SC to participate in the blockchain. Besides, We investigate the SC performance improvement with the mechanism of central centralized decision-making and revenue-sharing contract, compared to the SC performance in dynamic games.

The results are obtained as follow: The adoption of blockchain traceability technology can help improve the quality of food agricultural products, consumer surplus and social welfare, but the application and popularization of technology is hindered by traceability technology installment costs. Compared with the supplier leadership model, retailer-led food quality level, customer surplus and social welfare are higher.

How to effectively apply blockchain traceability technology to food safety has great research significance for improving food safety and consumer quality trust.

Food quality and safety issues have always been hot topics discussed by people. The anti-tampering of blockchain technology and the transparency of information make it possible to improve food traceability and safety quality.

The research results enrich the theories related to food safety and quality, and provide a valuable reference for food enterprises involved in the decision-making exploration of blockchain technology.

Based on the characteristics of blockchain technology, the demand function is adjusted and the product loss risk of channel members is transferred through a Stackelberg game SC composed of agricultural products suppliers and retailers.

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  We introduce two features of blockchain: quality trust and product information tracking.

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  The willingness of each member of the supply chain to use blockchain for product traceability was explored.

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  The overall traceability effect of the retailer-led blockchain is better than that of the manufacturer-led blockchain.

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  The cost of blockchain technology is a barrier to its adoption.

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  Blockchain brings higher consumer surplus and social welfare.

We introduce two features of blockchain: quality trust and product information tracking.

The willingness of each member of the supply chain to use blockchain for product traceability was explored.

The overall traceability effect of the retailer-led blockchain is better than that of the manufacturer-led blockchain.

The cost of blockchain technology is a barrier to its adoption.

Blockchain brings higher consumer surplus and social welfare.

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.

With the rise of mandating climate-related disclosures (CRD), this paper aims to investigate how energy and agriculture markets are exposed to climate disclosure risk.

Using the multivariable simultaneous quantile regression and data from 1 January 2017 to 29 February 2024, the authors examine daily and monthly responses of energy and agriculture markets to climate disclosure risk, energy risk, market sentiment, geopolitical risk and economic policy risk. The sample covers the global market, Australia, Canada, European Union (EU), Hong Kong, Japan, New Zealand, Singapore, the UK and the USA.

The results show that climate disclosure risk creates both positive and negative shocks in the energy and agriculture markets, and the impacts are asymmetric across quantiles in different economies. The higher the climate disclosure risk, the greater impact of crude oil future on the energy sector in North America (Canada and the USA) and Europe (EU and the UK), but no greater effects in Asia Pacific (Australia, New Zealand and Singapore). The agriculture sector can hedge against economic policy and geopolitical risks, but it is highly exposed to climate disclosure and energy risks.

This study timely contributes to the modest literature on the asymmetric effects of climate disclosure risk on the energy and agriculture markets at the global and national levels. The findings offer practical implications for policymakers and investment practitioners in understanding financial effects of mandating CRD to diversify risks depending upon market conditions and policy uncertainty.

Under the “dual carbon” framework, the article explores the equilibrium points among the government, agricultural enterprises and village committees, and uses sensitivity analysis to reveal the dynamic factors affecting these stakeholders, thereby proposing methods to enhance agricultural disaster resilience.

The article uses MATLAB to construct a game model for the three parties with interests: agribusiness, government and village council. It examines the stability of strategies among these entities. Through graphical simulation, the paper analyzes the sensitivity of agricultural enterprises carbon emissions and village committees’ rent-seeking behaviors in the decision-making process, focusing on significant factors such as government carbon tax and regulatory policies.

A single government reward and punishment mechanism is insufficient to influence the strategic choices of enterprises and village committees. The cost of rent-seeking does not affect the strategic choices of enterprises and village committees. A key factor influencing whether the village committee engages in rent-seeking is the level of labor income of the village committee as an “intermediary”.

This paper focuses on the dynamic game between three stakeholders (the government, agricultural enterprises and village committees), seeking dynamic equilibrium and conducting sensitivity analysis through visualization to provide the government with optimal policy recommendations.

The disconnect between producers and consumers is a fundamental issue causing irregularities, inefficiencies and leakages in the agricultural sector, leading to detrimental impacts on all stakeholders, particularly farmers. Despite the potential benefits of Metaverse technology, including enhanced virtual representations of physical reality and more efficient and sustainable crop and livestock management, research on its impact in agriculture remains scarce. This study aims to address this gap by identifying the critical success factors (CSFs) for adopting Metaverse technology in agriculture, thereby paving the way for further exploration and implementation of innovative technologies in the agricultural sector.

The research employed integrated methodology to identify and prioritise critical success criteria for Metaverse adoption in the agricultural sector. By adopting a mixed-method technique, the study identified a total of 15 CSFs through a literature survey and expert consultation, focusing on agricultural and technological professionals and categorising them into three categories, namely “Technological”, “User Experience” and “Intrinsic” using Kappa statistics. Further, the study uses grey systems theory and the Ordinal Priority Approach to prioritise the CSFs based on their weights.

The study identifies 15 CSFs essential for adopting Metaverse technology in the agricultural sector. These factors are categorised into Technological, User Experience-related and Intrinsic. The findings reveal that the most important CSFs for Metaverse adoption include market accessibility, monetisation support and integration with existing systems and processes.

Identifying CSFs is essential for successful implementation as a business strategy, and it requires a collaborative effort from all stakeholders in the agriculture sector. The study identifies and prioritises CSFs for Metaverse adoption in the agricultural sector. Therefore, this study would be helpful to practitioners in Metaverse adoption decision-making through a prioritised list of CSFs in the agricultural sector.

The study contributes to the theory by integrating two established theories to identify critical factors for sustainable agriculture through Metaverse adoption. It enriches existing literature with empirical evidence specific to agriculture, particularly in emerging economies and reveals three key factor categories: technological, user experience-related and intrinsic. These categories provide a foundational lens for exploring the impact, relevance and integration of emerging technologies in the agricultural sector. The findings of this research can help policymakers, farmers and technology providers encourage adopting Metaverse technology in agriculture, ultimately contributing to the development of environment-friendly agriculture practices.

China's agricultural sector has developed very rapidly in the past 30 years and agricultural technological progress is deemed one of the most substantial factors leading to its rapid agricultural GDP growth. The purpose of this paper is to assess the impacts of China's agricultural technological changes on its regional disparity.

The study uses a computable general equilibrium (CGE) model of multiple regions and multiple sectors to investigate the impacts of agricultural technological changes on regional disparity. The CGE model structure includes production side, demand side, and market clearing conditions.

The results suggest that agricultural technological changes significantly reduced China's agricultural regional disparity and accounted for 40 percent reduction in agricultural regional disparity in terms of agricultural GDP per capita. Agricultural technological changes, however, led to an increase in China's overall regional disparity and accounted for 6 percent increase in its overall regional disparity in terms of per capita GDP.

China's GDP has been growing very rapidly since 1978 and agricultural GDP has been playing a decreasing role in China's overall GDP. Regional disparity in non‐agricultural GDP per capita overweighted the equalization of agricultural GDP per capita. The results imply that the Chinese government should resort more to non‐agricultural development to fight against the enlarging regional disparity.

China's agricultural technological changes have led to an increase in China's overall regional disparity while the changes have significantly reduced China's agricultural regional disparity.

The purpose of this paper is to investigate determinants of regional disparities in China's agricultural labor productivity growth.

This paper first decomposes the regional disparity in China's agricultural productivity growth into its components: technical change, efficiency change and input accumulation per worker. The convergence test is also used to analyze the determinants of regional disparity.

The paper finds that during 1987 and 2005, although the growth of China's agricultural labor productivity mainly depended on the accumulation of inputs, technical changes contributed more to regional disparities in agricultural productivity growth.

This paper, which studies the determinants of regional disparities in China's agricultural labor productivity growth, contributes to a better understanding of China's agricultural growth and how to reduce the regional inequality. It is indicated that improving efficiency to promote total factor productivity growth is important for agricultural labor productivity growth for the three regions – Eastern, Central and Western – of China. The increase in inputs for Western China, and the improvement in technical change for Central and Western China are significant aspects to promote the growth of agricultural productivity and narrow the gap with Eastern China.

The purpose of this paper is to examine the impact of off-farm employment on agricultural fixed assets among households in rural China.

The authors drew on panel data from two rounds of household-level surveys of more than 2,000 households in rural China. The two surveys were conducted in 2008 and 2012 in five provinces. The authors used instrumental-variable Tobit model to test whether the current value of agricultural fixed assets differ between households with different levels off-farm employment.

The authors observe that off-farm employment has a negative effect on the current value of agricultural fixed assets at the household level in rural China.

The authors believe that the results will contribute positively to the assessment of the effect of off-farm employment on the investment in agricultural fixed assets at the household level in the context of China.

Since the post-liberalization era, a noticeable structural change and transition in employment have unfolded within the Indian economy. Hence, the purpose of this paper is to understand employment transition and elucidate the evolving dynamics of rural economies and employment patterns from agriculture to more productive non-agricultural sectors. Additionally, the study investigates the underlying causes of socioeconomic disparities and their repercussions on employment trends.

To address the aforementioned issues, this study utilised secondary data from labour surveys conducted by the National Sample Survey Organisation spanning from 2004–05 to 2023. Initially, the study computed the magnitude of employment in both agriculture and non-agriculture sectors. Subsequently, the distribution of non-agricultural labour across various socioeconomic characteristics was estimated. Furthermore, a logistic regression model was employed to evaluate the impact of socioeconomic factors on employment choices. Finally, Fairlie’s decomposition model was applied to elucidate workers’ decisions to engage in non-agricultural sectors.

The study reveals a significant rise in rural non-agricultural employment, from 98.4 m in 2004–05 to 193.3 m in 2023, indicating changing job preferences. Notably, the construction and trade sectors emerge as significant drivers of this trend. However, self-employment and casual labour persist, highlighting job vulnerability. Additionally, women and marginalised individuals with low levels of education and socioeconomic status lag behind in non-agricultural employment.

This study makes a significant contribution by offering a thorough analysis of the employment transition from agriculture to non-agriculture over a span of two decades. It provides valuable insights into the evolving dynamics of employment trends.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2023-0904.