Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/215843 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
GlobalFood Discussion Papers No. 142
Verlag: 
Georg-August-Universität Göttingen, Research Training Group (RTG) 1666 - GlobalFood, Göttingen
Zusammenfassung: 
Integrated Soil Fertility Management (ISFM) is a technology package consisting of the joint use of improved seeds, organic and inorganic fertilizers. It is increasingly promoted to enhance soil fertility, crop productivity and income of smallholder farmers. While studies find positive effects of ISFM at the plot level, to date there is little evidence on its broader welfare implications. This is important since system technologies like ISFM mostly involve higher labor and capital investments, and it remains unclear whether these pay off at the household level. Using data from maize, wheat and teff growing farmers in two agroecological zones in Ethiopia, we assess the impact of ISFM on crop and household income, and households' likelihood to engage in other economic activities. We further study effects on labor demand, food security and children's education. We use the inverse probability weighting regression adjustment method, and propensity score matching as robustness check. We find that ISFM adoption for maize, wheat or teff increases income obtained from these crops in both agroecological zones. Yet, only in one subsample, it also increases household income, while in the other it is associated with a reduced likelihood to achieve income from other crops and off-farm activities. Results further show that ISFM increases labor demand. Moreover, we find positive effects of ISFM on food security and primary school enrollment in those regions where it goes along with gains in household income. We conclude that welfare effects of agricultural innovations depend on farmers income diversification strategies.
Schlagwörter: 
Technology adoption
household income
food security
education
labor
inverse probability weighting regression adjustment
JEL: 
J23
O13
O33
Q12
Q16
Dokumentart: 
Working Paper

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