In the wake of the resource constraints for external farm inputs faced by farmers in developing countries, sustainable agriculture practices that rely on renewable local or farm resources present desirable options for enhancing agriculture productivity. In this study, plot‐level data from the semi‐arid region of Ethiopia, Tigray are used to investigate the factors influencing farmers' decisions to adopt agriculture practices, with a particular focus on conservation tillage, compost and chemical fertilizer. A trivariate probit model is used to analyze the determinants of adoption of these practices. In addition, stochastic dominance analysis is used to compare the productivity impacts of compost with that of chemical fertilizer based on a six‐year cross‐sectional farm‐level dataset. Our results indicate heterogeneity with regard to the factors that influence adoption decisions of the three practices and the importance of both plot and household characteristics on influencing adoption decisions. In particular, we found that household endowments and access to information, among other factors, impact the choice of sustainable farming practices significantly. Furthermore, the use of stochastic dominance analysis supported the contention that sustainable farming practices enhance productivity. They even proved to be superior to the use of chemical fertilizers — justifying the need to investigate factors that influence adoption of these practices and to use this knowledge to formulate policies that encourage adoption.

This article uses data from household- and plot-level surveys conducted in the highlands of the Tigray and Amhara regions of Ethiopia. We examine the contribution of sustainable land management (SLM) practices to net value of agricultural production in areas with low vs. high agricultural potential. A combination of parametric and non-parametric estimation techniques is used to check result robustness. Both techniques consistently predict that minimum tillage (MT) is superior to commercial fertilisers (CFs), as are farmers' traditional practices (FTPs) without CFs, in enhancing crop productivity in the low agricultural potential areas. In the high agricultural potential areas, in contrast, use of CFs is superior to both MT and FTPs without CFs. The results are found to be insensitive to hidden bias. Our findings imply a need for careful agro-ecological targeting when developing, promoting and scaling up SLM practices. Copyright (c) 2010 The Authors. Journal compilation (c) 2010 The Agricultural Economics Society.

In this paper we use farmers' actual experiences with changes in rainfall levels and their responses to these changes to assess if patterns of fertilizer use are responsive to changes in rainfall patterns. Using plot and farm level panel data from the central Highlands of Ethiopia matched with corresponding village level rainfall data; results show that both the current year's decision to adopt and the intensity of fertilizer adoption is positively associated with higher rainfall levels experienced in the previous year. Furthermore, we find a concave relationship between previous season rainfall levels and fertilizer adoption, indicating that too much rainfall discourages adoption. 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 its use of plot level panel data, which permits us to investigate the importance of plot characteristics in fertilizer adoption decisions.

An extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model, incorporating factors that drive carbon emissions, is built from the regional perspective. A spatial Durbin model is applied to investigate the factors, including population, urbanization level, economic development, energy intensity, industrial structure, energy consumption structure, energy price, and openness, that impact both the scale and intensity of carbon emissions. After performing the model, we find that the revealed negative and significant impact of spatial-lagged variables suggests that the carbon emissions among regions are highly correlated. Therefore, the empirical results suggest that the provinces are doing an exemplary job of lowering carbon emissions. The driving factors, with the exception of energy prices, significantly impact carbon emissions both directly and indirectly. We, thus, argue that spatial correlation, endogeneity and externality should be taken into account in formulating polices that seek to reduce carbon emissions in China. Carbon emissions will not be met by controlling economic development, but by energy consumption and low-carbon path.

This paper estimates a Multidimensional Poverty Index for Gauteng province of South Africa. The Alkire-Forster method is applied on Quality of Life survey data for 2011 and 2013 which offer an excellent opportunity for estimating poverty at smaller geographical areas. The results suggest that the Multidimensional Poverty Index for Gauteng is low but varies markedly by municipality and by ward, as well as across income groups. Not only are low income households more likely to be multidimensionally poor, they also suffer from higher intensities of poverty. Multidimensional poverty is highest in areas of low economic activity located on the edges of the province. However, pockets of multidimensional poverty do prevail even in better performing municipalities. Government, at all spheres, needs to devise policies that channel investments into lagging areas and avoid approaches that are indifferent to the heterogeneities that exist across localised geographical extents.