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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.

This study has empirically investigated the impacts of climate change on agricultural productivity worldwide, considering the ranking of agriculture productivity. Additionally, the study has estimated the extent to which climate change favoured agriculture productivity from a global perspective.

The study prepared a suitable econometric model and employed the quantile panel Autoregressive Distributed Lag technique with a two-step Error Correction Mechanism to assess the influence of global warming on worldwide agrarian productivity.

The estimated results provide evidence for the nonlinear impacts of climate change on agriculture productivity across all quantiles. Moreover, threshold levels of average annual temperature rise with the improvement of agricultural productivity, depicting that low-productive areas are highly vulnerable to global warming. Additionally, agricultural inputs like labour, capital and irrigated land are positively related to agricultural productivity, with relatively substantial marginal productivity in highly productive regions. Nevertheless, technological innovations are found to be more productive in low-productive areas.

Policymakers should prioritize region-specific climate-smart agriculture by targeting policies to increase agricultural productivity and minimize the effects of climate change on food security and nutrition.

Despite significant research in this area, there remains a knowledge gap on the nature of this relationship, especially regarding productivity thresholds under warming. The study aims to fill this gap, offering valuable insights to guide policy actions and adaptation strategies to mitigate the adverse impacts of climate change on agriculture productivity.

The purpose of this paper is to emphasize the needs to understand the barrier and determinant factors in knowledge sharing (KS), to find the common ones and subsequently to build a general framework that can be referred to in designing a KS tool that addresses the common factors.

The approach comprises of two major steps which are to survey the past literature to determine the most common barriers and determinant factors from various unique KS domains and to qualify the factor as the common one based on its presence in at least three to five KS domains. The grounded theory is used to analyze the past literature and to perform categorization.

This paper helps in the summarization of categories and subcategories of barriers and determinants and demonstration on the mapping between them.

This paper has not proved the actual use of the framework in building a KS tool based on the framework.

The common factors are based on at least 60 references of KS implementation such that it is useful for large area of application domains that require building KS tools.

This paper presents the understanding on the common factors and association between the barriers and determinants in building the general framework in which the application of the framework is demonstrated using actor network theory.