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The paper aimed to examine the determinants of agricultural commercialisation of farmers and measured its commercialisation level in the highland of Vietnam.

A sample size of 360 was randomly chosen from a total population of 5,771 farmers, and a structured questionnaire was developed to collect data. Descriptive and inferential statistics, including linear regression analysis, were used to analyse the data.

The descriptive statistics showed that the average commercialisation level of farmers was 56.3%. The regression model result indicated that number of off-farm income sources, farmer's risk perception, farming practices, number of agricultural activities, motorbikes value, ethnicity, distance from the city centre, number of customers, non-traded inputs value, participation in training programmes, family size, farm size, mobile phones value, traded inputs value, land tenure, distance from the local market and education of household head significantly affected agricultural commercialisation of farmers.

Any development strategies that assist farmers in adopting commercial farming in the highland of developing countries should consider these determinants.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2022-0161

The purpose of this paper is to measure technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from actual inefficiency using comprehensive household-level panel data.

This paper estimates technical efficiency based on the true random-effects stochastic production frontier estimator with a Mundlak adjustment. By utilising comprehensive panel data with 4,694 observations from 39 districts of four major maize-producing regions in Ethiopia, the author measures technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from technical inefficiency. By using competing stochastic production frontier estimators, the author provides insights into the influence of farm heterogeneity on measuring farm efficiency and the subsequent impact on the ranking of farmers based on their efficiency scores.

The study results indicate that ignoring unobservable farmer heterogeneity leads to a downwards bias of technical efficiency estimates with a consequent effect on the ranking of farmers based on their efficiency scores. The mean technical efficiency score implied that about a 34% increase in maize productivity can be achieved with the current input use and technology in Ethiopia. The key determinants of the technical inefficiency of maize farmers are the age, gender and formal education level of the household head, household size, income, livestock ownership, and participation in off-farm activities.

While the findings of this study are critical for informing policy on improving agricultural production and productivity, a few important things are worth considering in terms of the generalisability of the findings. First, the study relied on secondary data, so only a snapshot of environmental factors was accounted for in the empirical estimations. Second, there could be other sources of unmeasured potential sources of heterogeneity caused by persistent technical inefficiency and endogeneity of inputs. Third, the study is limited to one country. Therefore, future research should extend the analysis to ensure the generalisability of the empirical findings regarding the extent to which unmeasured potential sources of heterogeneity caused by persistent technical inefficiency, endogeneity of inputs and other unobservable country-specific features – such as geographical differences.

This paper contributes to the literature on agricultural productivity and efficiency by providing new evidence on the influence of unobservable heterogeneity in a farm efficiency analysis. While agricultural production is characterised by heterogeneous production conditions, the influence of unobservable farm heterogeneity has generally been ignored in technical efficiency estimations, particularly in the context of smallholder farming. The value of this paper comes from disentailing producer-specific random heterogeneity from the actual inefficiency.

The authors investigated cocoa farmers' access to subsidized fertilizer in Ghana and implications on productivity.

Primary data were sourced from 435 cocoa farmers. Cragg hurdle and two-step Tobit model with continuous endogenous regressors/covariates were applied for the drivers of cocoa farmers' participation in fertilizer subsidy programme and productivity. Propensity score matching (PSM), inverse-probability weights (IPW) and augmented inverse-probability weights (AIPW) were applied for productivity impact assessment of fertilizer subsidy.

All the farmers were aware of fertilizer subsidy for cocoa production in Ghana. Farmers became aware of fertilizer subsidy through extension officers, media and other farmers. Half of cocoa farmers benefitted from fertilizer subsidy. Averagely, cocoa farmers purchased 292 kg of subsidized fertilizer. Many socio-economic, farm-level characteristics and institutional factors determine cocoa farmers' participation in fertilizer subsidy programme, quantity of subsidized fertilizer obtained and productivity. Beneficiaries of fertilizer subsidy recorded higher cocoa productivity than non-beneficiaries. Hence, fertilizer subsidy for cocoa production in Ghana leads to a gain in productivity.

There should be more investments in fertilizer subsidy so that all cocoa farmers benefit and obtain the required quantities.

The authors provide new evidence on cocoa productivity gain or loss emanating from fertilizer subsidy by combining different impact assessment techniques for deeper analysis: PSM, IPW and AIPW.

Nonfamily farms are responsible for a disproportionate amount of US agriculture production. The importance of these operations to the volume of agriculture production in the United States has led researchers and policymakers to understand nonfamily farms as large commercial operations. This paper examines whether the distinction between family and nonfamily helps explain the financial outcomes of farm operations and households.

We test for differences in financial outcomes of the household and operations of family and nonfamily farms using an Oaxaca-Blinder decomposition. We compare these results to a decomposition of other possible typologies.

We present evidence that nonfamily farms are a heterogeneous group with a majority of small operations that are dominated by a small number of large operations. We discover that differences associated with the family-nonfamily distinction are largely explained by observable farm and operator characteristics that arise mechanically from the definition. However, we find suggestive evidence that family-nonfamily classification captures differences in economic behavior that lead to higher profitability measures to nonfamily farms. We find little evidence of any inherent structural differences between family and nonfamily farms that helps explain financial outcomes related to leverage or household finances.

We conclude that including nonfamily farms in official statistics of farm households may provide a more comprehensive overview of the farm sector, as our results suggest that family and nonfamily farms do not have innate differences that help explain many of their financial outcomes.

We incorporate previously unused data on nonfamily farm households and test the difference in mean financial outcomes between family and nonfamily farms.

This study examined the effects of adopting dairy feed technology bundles on the milk production of smallholder dairy farmers.

The study was based on Multinomial Endogenous Switching Regression (MESR) to estimate the effects of the adoption of three feed technology bundles on milk production using data collected from 518 dairy farm households in West Java, Indonesia.

The findings indicated that adopting technology bundles had positive and robust effects on milk production, with gradual positive effects between non-adoption and the adoption of different bundles of technologies.

This study focused on the association between the adoption of feed technology bundles and milk production. However, further analysis of the causal links between the adoption of feed technologies and milk production as well as the inclusion of other outcomes in the analysis, such as production costs and risk mitigation, are required.

Most of the literature on agricultural technology adoption focuses on the adoption of individual technologies, crop farming and conservation practices. Therefore, this study examined the effects of the adoption of dairy feed technology bundles.

This study assesses the extent to which integrated extension services contribute to the adoption of climate-smart agriculture (CSA) innovations within the cotton value chain in Burkina Faso.

To address the research question, a probit multivariate econometric model with sample selection is utilized. The model is applied to a random sample of farmers (n = 510), and the endogeneity is addressed through a control function approach.

The study highlights the central role of value chains, particularly in the cotton sector, in overcoming resource scarcity through integrated extension services. Findings show that smallholder farmers who benefit from sound extension services are more willing to adopt and diversify CSA technologies. These include improved seeds, conservation techniques, adapted planting dates and mechanization. This study confirms the synergistic nature of these technologies and emphasizes that effective climate risk mitigation depends on the combined adoption of CSA technologies.

The use of cross-sectional data limits the analysis of long-term farmer behavior, and due to data limitations, the focus was primarily on the contributions of cotton companies and farmers to climate risk mitigation. Future research using panel data across the value chain could provide a more robust insights for policy decision-making.

The study contributes to the existing body of knowledge by emphasizing the crucial role of integrated extension services within the cotton value chain in developing countries. This highlights the critical benefits for farmers and emphasizes the need to diversify modern technologies to effectively combat climate change and its variability in agriculture.

Off-farm employment is an important factor associated with fertility transition in many developing countries. The purpose of this paper is to investigate the impact of female off-farm employment on their fertility desire in rural China.

Based on the data from the China Labor-force Dynamics Survey, the authors adopt an instrumental variable approach to address the endogeneity issue. Desired number of children and desire for a second child are used to measure fertility desire.

The results show that off-farm employment participation significantly reduces women's desired number of children and the likelihood of their desire for a second child. Moreover, off-farm employment reduces women's fertility desire mainly through pathways including the weakening of son preference and a decrease in job autonomy, rather than the changes in leisure hours. Further evidence suggests that social health insurance plays an important role in moderating the adverse relationship between off-farm employment and the desire for a second child. The fertility-reducing effects are more pronounced among younger women, among those participating in off-farm wage employment and among families with only wives' participation in the off-farm labor market.

This paper contributes to the existing research by investigating the causal impact of off-farm employment on fertility desire in a rural developing context and the possible underlying mechanisms responsible for this relationship. This study provides important insights on this topic in developing countries and may have important implications for theory and practice.

Farm income in the agricultural sector is susceptible to natural and market risks. A large body of literature has studied the effects of cooperative membership on household welfare, technical efficiency, productivity and production behavior, yet little has been known about the impact of cooperative membership on farm income volatility. This paper aims to fill this research gap by investigating the relationship between cooperative membership and farm income volatility of Chinese pig farmers and drawing policy implications.

This paper examines the effect of cooperative membership on farm income volatility, using data from a two-round survey of pig farmers in China. The authors employ an endogenous switching regression model to address the selection bias issues associated with unobserved factors simultaneously affecting farmers' participation in agricultural cooperatives and income earning activities.

Using household panel from a two-round survey of 193 pig farmers in China, this analysis highlights two key findings: (1) agricultural cooperative membership has significant and positive effect on farm income stability and (2) the impact of cooperative membership on farm income stability varies with production scale.

This research makes two contributions to the literature. First, this study contributes to the scant literature exploring the relationship between agricultural cooperatives and farm income stability. Second, to the best of the authors' knowledge, this is the first study that explores such relationship in a livestock sector. The pig sector in China and around the developing world has been increasingly challenged by multifaceted risks (e.g. price fluctuations, epidemic diseases, environmental regulations), and understanding the role of agricultural cooperatives in farm income stability of pig farmers is of great practical and policy significance.

The agricultural extension system in Indonesia has experienced its ups and downs in line with the sociopolitical dynamics of the country. This study examines the impact of access to agricultural extension on the adoption of technology and farm income of smallholder farmers in Banten, Indonesia.

This study uses a quasi-experimental research design to estimate the impact outcomes at the farm level, with methods that form part of the counterfactual framework.

Estimation results show that farming experience, off-farm income, irrigation, group membership, mobile phones and livestock ownership significantly affect extension access. The results of this main study show the important role of extension access to technology adoption and agricultural income. These studies found consistently positive and statistically significant effects of access to extension services on technology adoption and farm income.

The consistent positive and significant effect of extension access implies that public investment by the government in agricultural extension can optimize the potential impact on technology adoption and agricultural income, which also affects the distribution of the welfare of rural smallholder farmers.

Agricultural extension as a key to increasing technology adoption. However, the impact of access to agricultural extension in Indonesia has received less attention in terms of adoption and farm income.

Beginning Farmer and Rancher programs are available for operators with ten years of experience or less on any farm. These programs support farmers who are starting operations, often without an initial asset allocation. However, some beginning farmers acquire operations that are already established, with substantial assets in place. The authors investigate whether a profitability gap exists between beginning farmers entering the industry ex novo and those operating a preexisting operation and if so, what factors contribute to the gap.

The authors utilize the Blinder-Oaxaca decomposition to determine what drives financial differences between first-generation beginning farmers, second-generation beginning farmers and established farmers using a unique farm-level panel dataset from 1997 to 2021.

Results indicate that first- and second-generation beginning farmers have similar operating profit margins, but first-generation beginning farmers have a statistically higher rate of return on assets than second-generation beginning farmers. Established farmers outperform second-generation beginning farmers on both the operating profit margin and rate of return on assets. These results suggest that economic viability for beginning farmers differs depending upon the initial status of their operation, suggesting that heterogenous policies may be more impactful in supporting various pathways to enter agriculture.

This analysis is the first to identify beginning farmers that enter the industry without an asset base and those that take over a principal operator role on an established farm through an assumed farm transition. The authors quantify differences in financial performance using detailed accrual-based financial data that tracks farms over time in one dataset.