Measuring Farm and Market Level Economic Impacts of Improved Maize Production Technologies in Ethiopia: Evidence from Panel Data

While it is often recognised that agricultural technology adoption decisions are intertwined and best characterised by multivariate models, typical approaches to examining adoption and impacts of agricultural technology have focused on single technology adoption choice and ignored interdependence among technologies. We examine farm‐ and market‐level impacts of multiple technology adoption choices using comprehensive household survey data collected in 2010/11 and 2012/13 in Ethiopia. Economic surplus analysis combined with panel data switching endogenous regression models are used to compute the supply shift parameter (K‐shift parameter), while at the same time controlling for the endogeneity inherent in agricultural technology adoption among farmers. We find that our improved technology set choices have significant impacts on farm‐level maize yield and maize production costs, where the greatest effect appears to be generated when various technologies are combined. The change in maize yield and production costs results in an average 26.4% cost reduction per kilogram of maize output (the K‐shift parameter). This increases the producer and consumer surpluses by US$ 140 and US$ 105 million per annum, respectively. These changes in economic surplus help to reduce the number of poor people by an estimated 788 thousand per year. We conclude that deliberate extension efforts and other policies that encourage integration of technologies are important for maize technologies to yield their full potential at both farm and market levels.     

Resource saving and productivity enhancing impacts of crop management innovation packages in Ethiopia

Crop management innovations are often not discrete fixed stand‐alone options—and their adoption may imply various combinations and adaptations. This potentially confounds their impact assessment. This article assesses the resource saving and productivity enhancing impacts of a crop management package revolving around minimum tillage in maize‐based farming systems in northwest Ethiopia. An endogenous switching regression model was applied to plot‐ and household‐level survey data collected from 290 rural households operating 590 maize plots during the 2012 production year. Controlling for variations in plot and household characteristics, the average effect of minimum tillage package (minimum tillage package) on maize productivity is 0.44 t/ha. Compared to conventional practice (CP), adoption of the MTP decreased the average male and female labor use in maize production by 14.4 and 8.2 person‐days per ha, respectively. Similarly, MTP adoption decreased draft power use for land preparation by 13.2 pair of oxen‐days per ha. Compared to CP, in general, there is a considerable short‐run maize productivity gain and reduction in labor and draft power use under MTP.     

Adoption of Multiple Sustainable Agricultural Practices in Rural Ethiopia

The adoption and diffusion of sustainable agricultural practices (SAPs) has become an important issue in the development‐policy agenda for sub‐Saharan Africa, especially as a way to tackle land degradation, low agricultural productivity and poverty. However, the adoption rates of SAPs remain below expected levels. This study analyses the factors that facilitate or impede the probability and level of adoption of interrelated SAPs, using recent data from multiple plot‐level observations in rural Ethiopia. Multivariate and ordered probit models are applied to the modelling of adoption decisions by farm households facing multiple SAPs, which can be adopted in various combinations. The results show that there is a significant correlation between SAPs, suggesting that adoptions of SAPs are interrelated. The analysis further shows that both the probability and the extent of adoption of SAPs are influenced by many factors: a household’s trust in government support, credit constraints, spouse education, rainfall and plot‐level disturbances, household wealth, social capital and networks, labour availability, plot and market access. These results imply that policy‐makers and development practitioners should seek to strengthen local institutions and service providers, maintain or increase household asset bases and establish and strengthen social protection schemes in order to improve the adoption of SAPs.     

Understanding farmers’ preference for traits of chickens in rural Ethiopia

Traditional poultry production plays an irreplaceable role in the sustenance of livelihoods in rural Ethiopia. Ironically, however, much has been done to replace indigenous poultry breeds with exotic genetic resources regardless of the importance producers and consumers attach to attributes of the resources. This study aims at informing policy to establish effective indigenous poultry breeding and conservation programs. Discrete choice experiment (DCE) was employed to generate data. Designing of the DCE involved identification, definition, and measurement of attributes of adaptive, productive, and sociocultural importance considering the multiple functions of village chickens. Random parameters logit and the generalized multinomial logit (G‐MNL) models were used to estimate taste parameters. Economic values of traits of chickens were estimated using the utility in willingness‐to‐pay (WTP) space approach, based on G‐MNL model formulation. The results show that important traits of chickens to farmers are mothering ability, diseases resistance, and meat and eggs taste. These findings question the appropriateness, at least in the prevailing production system, of the Ethiopian national government's effort to improve productivity in village poultry by targeting specialized egg‐layer improved chickens. The findings also suggest that poultry breeding programs aiming to provide readily acceptable breed technology by farmers need to prioritize traits of adaptive importance, and mothering ability, instead of focusing on egg productivity only. The key implication is that the unique qualities of the indigenous poultry breeds need to be carefully identified and prioritized before resorting to those that proved to be successful in different production systems.     

One size does not fit all: Heterogeneous economic impact of integrated pest management practices for mango fruit flies in Kenya—a machine learning approach

Most previous studies evaluating agricultural technology adoption focus on estimating homogeneous average treatment effects across technology adopters. Understanding the heterogeneous effects and drivers of impact heterogeneity should enable interventions to be better targeted to maximise benefits. We apply machine learning using data from a randomised controlled trial to estimate the heterogeneous treatment effect of fruit fly IPM practices (i.e., parasitoids, orchard sanitation, use of food bait, biopesticides, male annihilation technique, and their combinations) in Central Kenya. Results suggest significant heterogeneity in the effect of IPM practices conditioned on household characteristics. The most important covariates explaining differences in treatment effects are wealth, distance to the mango fruit market, age of the household head, labour and experience in mango farming. Results further indicate that those with fewer mango trees benefit more from most IPM practices. Additional analysis across other covariates shows mixed results but generally suggests significant differences between households benefiting the most and those benefiting the least from IPM practices.     

Agricultural Technology, Crop Income, and Poverty Alleviation in Uganda

This paper evaluates the ex post impact of adopting improved groundnut varieties on crop income and poverty in rural Uganda. The study utilizes cross-sectional data of 927 households, collected in 2006, from seven districts in Uganda. Using propensity score matching methods, we find that adopting improved groundnut varieties (technology) significantly increases crop income and reduces poverty. The positive and significant impact on crop income is consistent with the perceived role of new agricultural technologies in reducing rural poverty through increased farm household income. This study supports broader investment in agriculture research to address vital development challenges. Reaching the poor with better technologies however requires policy support for improving extension efforts, access to seeds and market outlets that simulate adoption.     

Sharecropping efficiency in Ethiopia: threats of eviction and kinship

We tested a theoretical model with the Marshallian inefficiency (H1) and threat of eviction (H2) hypotheses having opposite effects on land productivity on sharecropped plots. The model also assumes that kinship contracts may eliminate or reduce the Marshallian inefficiency (H3) and threat of eviction (H4) effects on land productivity. Our empirical findings were consistent with H2 and H4 being true. We found higher land productivity on sharecropped plots than on share tenants' own plots and higher land productivity on sharecropped plots of nonkin than of kin tenants. The nature of the data allowed controlling for unobservable household characteristics through household fixed effects and for observable plot characteristics. Analyses with and without plot characteristics revealed that these findings were stronger with plot characteristics than without them. Based on the plausible assumption that observable plot characteristics are positively correlated with unobservable plot characteristics this strengthens our conclusion. The results are also supported by first-order stochastic dominance analysis. Sharecropped plots' output value distribution unambiguously dominated the output value distribution from share tenants' own plots. Nonkin sharecropped plots' output value distribution also first-order stochastically dominated the output value distribution from kin sharecrop plots.     

Does fertilizer use respond to rainfall variability? Panel data evidence from Ethiopia

In this article, we use farmers' actual experiences with changes in rainfall levels and their responses to these changes to assess whether patterns of fertilizer use are responsive to changes in rainfall patterns. Using panel data from the Central Highlands of Ethiopia matched with corresponding village‐level rainfall data, the results show that the intensity of current year's fertilizer use is positively associated with higher rainfall levels experienced in the previous year. Rainfall variability, on the other hand, impacts fertilizer use decisions negatively, implying that variability raises the risks and uncertainty associated with fertilizer use. Abundant rainfall in the previous year could depict relaxed liquidity constraints and increased affordability of fertilizer, which makes rainfall availability critical in severely credit‐constrained environments. In light of similar existing literature, the major contribution of the study is that it uses panel data to explicitly examine farmers' responses to actual weather changes and variability.     

Adoption and Impacts of Sustainable Agricultural Practices on Maize Yields and Incomes: Evidence from Rural Zambia

This paper uses a multinomial endogenous treatment effects model and data from a sample of over 800 households and 3,000 plots to assess the determinants and impacts of adoption of sustainable agricultural practices (SAPs) on maize yields and household incomes in rural Zambia. Results show that adoption decisions are driven by household and plot level characteristics and that the adoption of a combination of SAPs raises both maize yields and incomes of smallholder farmers. Adoption of improved maize alone has greater impacts on maize yields, but given the high cost of inorganic fertiliser that limits the profitability of adoption of improved maize, greater household incomes are associated rather with a package involving SAPs such as maize–legume rotation and residue retention.     

Market imperfections,access to information and technology adoption in Uganda: challenges of overcoming multiple constraints

Limited empirical evidence exists on how multiple binding constraints influence the adoption of improved technologies by smallholder farmers. This article uses the case of groundnut variety adoption in Uganda to investigate the role of information, seed supply, and credit constraints in conditioning technology uptake. New data from a household survey in seven groundnut growing districts (n = 945) indicate that 8% of farmers lack information on new varieties, while 18% and 6% of farmers, respectively, cannot adopt mainly due to seed supply and capital constraints. A tobit‐type specification that considers all nonadopters as being uninterested in the technology (i.e., corner solutions) would lead to inconsistent parameter estimates and incorrect conclusions in this context. We therefore estimate a modified multi‐hurdle specification of demand for new varieties, taking into account how information, seed supply, and capital constraints jointly determine adoption probability and intensity. The study reveals new empirical insights on why agricultural technology adoption in Africa has lagged behind: slow uptake is not mainly due to a lack of economic incentives, but rather a reflection of information, seed supply, and credit constraints that prevent farmers from translating their desired demand into adoption of modern varieties. Policy implications are discussed.