Purpose: The purpose of this paper is twofold: to determine the factors contributing to farm income in the Transitional and Savanna zones of Ghana and to ascertain variations between in the same and across the two locations; and to determine the impact of credit on farm income in each of the two zones and to ascertain the variation in impact of credit across the two locations.Design/methodology/approach: In order to address endogeneity and sample selection bias, the authors draw from the theory of impact evaluation in nonrandom experiment, employing the endogenous switching regression (ESR) while using the propensity score matching (PSM) to check for robustness of the results.Findings: The results show significant mean differences between some characteristics of households that have access to credit and those that did not have access. Further, the results revealed farm size, labor; gender, age, literacy, wealth and group membership as the significant determinants of both credit access and income in the two zones. With the ESR, credit access increases households farm income by GH¢206.56/ha and GH¢39.74/ha in the Transitional and Savanna zones, respectively, but with the PSM, credit increases farm income by GH¢201.50 and GH¢45.69 and in the Transitional and Savanna, respectively.Research limitations/implications: The mean differences in characteristics of the households revealed the presence of selection bias in the distribution of household’s covariates in the two zones. The results further indicate the importance of productive resources, information and household characteristics in improved access to credit and farm income. Also, the results from both methods indicate that credit access leads to significant gains in farm income for households in both zones. However, differences exist in the results of PSM and that of the ESR results.Practical implications: The presence of selection bias in the samples suggests that the use of ESR and PSM techniques is appropriate. Further, the results suggesting that enhanced credit access and farm income could be attained through improved access to household resources and information. The results also suggest the need for establishing and expanding credit programs to cover more households in both zones. The differential impact of credit between the two methods employed in each zone revealed the weakness of each model. The low values from PSM could indicate the presence of selection bias resulting from unobservable factors whiles the high values from the ESR could stem from the restrictive assumption of the model. This reinforces the importance of combining mixed methods to check robustness of results and to explore the weakness of each method employed.Originality/value: The novelty of this study lies in the use of a very extensive and unique data set to decompose the determinants of credit access and farm income and as well as the impacts of credit into zones.

Purpose: The purpose of this paper is to investigate factors affecting the adoption of agricultural technologies in Sub-Saharan Africa, specifically the role of credit market inefficiency in adoption of agricultural technologies in the region.Design/methodology/approach: Most importantly, the paper applies a 2SLS model on a unique data set on nine agrarian countries from Sub-Saharan Africa’s intensification of food crops agriculture (Afrint) to provide evidence on how credit market inefficiency affects adoption of technologies in the sub region.Findings: The study finds that the relationship between credit and technology adoption is one-way causal relation (i.e. credit access leads to technology adoption) as opposed to a two-way relation (i.e. mutual dependent relation). Further, the results indicate that credit market inefficiency can be a major barrier to the adoption of yield enhancing technologies in Sub-Saharan Africa. Further, the study showed mixed results for household variables. The results give credence to studies that highlight the importance of infrastructure and risk control in the adoption of new technologies.Research limitations/implications: The study is limited to only nine countries in Sub-Saharan Africa. Thus, the findings and interpretations should be considered as such. Further, there is the need for further research that considers all the region so as to establish whether or not there is a relationship between credit market inefficiencies and technology adoption in the region.Practical implications: The policy implication is that microfinance institutions should consider scaling up their credit services to ensure that more households benefit from it, and in so doing technology adoption will be enhanced.Originality/value: The main contribution of the study lies in its use of a unique data set from Sub-Saharan Africa’s intensification of food crops agriculture (Afrint) to investigation relationship between credit market inefficiency and technology adoption.

This study employs panel data from the sub-Saharan Africa’s Intensification (Afrint) project to examine the impacts of sustainable agricultural practices (SAPs) on crop production and hidden hunger. The dataset consists of 2368 households (4736 plots) across eight countries in sub-Saharan Africa. The study utilizes a multinomial endogenous switching regression model in the empirical estimations to account for sample selection bias caused by observed and unobserved farmer attributes. In addition, the study employed Mundlak fixed effects criteria to address plot level heterogeneity. The results show that joint adoption of SAPs improves total value of output and reduces hidden hunger, relative to adoption of SAPs in isolation. Specifically, an increase in total value of output is at most USD8,288.66/ha whiles decrease in cereal self-provisioning capacity is at most 647.69 kg per adult equivalent. The results therefore suggest that joint adoption of the SAPs should be promoted over adoption in isolation. The results also indicate that the benefits associated with adoption of SAPs, either in isolation or jointly, vary across Africa. This therefore implies that compatibility and potentials of the SAPs in various locations of Africa should be considered when promoting uptake of SAPs.

Introduction: This study investigates the effect of training hours received from Large-Scale Private Investors (LSPIs) on rice yield, farm income, and food security among smallholder farmers in Ghana. With the expansion of LSPIs in agricultural service delivery, identifying the optimal intensity of extension services is key to maximizing their impact.Methods: Using data from rice-producing households, the study applies a dose-response function (DRF) and marginal treatment effect (MTE) analysis to estimate the non-linear effects of LSPI-provided training hours on key welfare outcomes. These models allow us to explore threshold effects and heterogeneous treatment responses.Results and discussion: Findings show that rice yields peak with 1–3 h of training, after which additional interaction yields minimal gains. Farm income increases significantly beyond 4.5 h of training, with the highest returns at 6 h. Food security improves notably after 4 h, as measured by both the Food Consumption Score (FCS) and the Household Food Insecurity Access Scale (HFIAS). Beyond these thresholds, further training produces little to no additional benefit. These results emphasize the importance of well-timed, targeted training. Policymakers and LSPI programs should align training delivery with identified optimal durations and ensure appropriate follow-up mechanisms to sustain improvements in productivity and wellbeing.