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Improved production systems for traditional food crops: the case of finger millet in western Kenya

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Abstract

Increasing agricultural productivity through the dissemination of improved cropping practices remains one of the biggest challenges of this century. A considerable amount of literature is dedicated to the adoption of improved cropping practices among smallholder farmers in developing countries. While most studies focus on cash crops or main staple crops, traditional food grains like finger millet have received little attention in the past decades. Traditional food grains have however an important potential to improve food security, reduce micronutrient deficiencies, and enhance smallholder adaptation to climate change. The present study aims to assess the factors that influence adoption decisions among finger millet farmers in western Kenya. Based on cross-sectional household data from 270 farmers, we estimated a multivariate probit model to compare the adoption decisions in finger millet and maize production. While improved practices such as the use of a modern variety or chemical fertilizer are relatively well adopted in maize production, they are less common in finger millet production. Social networks as well as access to extension services play crucial roles in the adoption of improved finger millet practices, while the same variables are of minor importance for the adoption of improved maize practices. A Cobb-Douglas production function shows a positive effect of modern varieties and chemical fertilizer on finger millet yields.

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Notes

  1. FAOstat does not differentiate between different types of millet.

  2. The administrative areas in Kenya were regularly subject to reforms that split districts into smaller units. Given the availability of data to construct a sampling frame, we refer to the number of districts and district boundaries that existed before the 2007 reform. The latest reform in 2013 classified 47 counties (based on 46 districts as defined in 1992 and Nairobi) that we refer to in brackets for greater clarity.

  3. This variable includes all income generated from skilled and unskilled self-employment as well as skilled and unskilled wage labor.

  4. Regarding extension, we included a dummy that equals one if the household received maize (not millet) related extension. Furthermore, we included a variable on the gender of the person responsible for maize (not millet) production.

  5. Alternatively, a translog production function would increase the flexibility of the model. However, in our data set the translog functional form leads to problems of multicollinearity. We therefore chose the more restrictive Cobb-Douglas functional form.

  6. This includes both hired and family labor.

  7. It is important to keep in mind that farmers who have received finger millet related extension were oversampled in our data and that the simple descriptive adoption rates presented here are therefore not representative for the whole region in the case of finger millet.

  8. We calculated the marginal effects by introducing an observation where all variables equalled the mean value of that variable. The marginal effect of a dummy variable was measured as the change in the predicted probability of that observation due to a change of the dummy value from zero to one. The marginal effect of a continuous variable was measured as the change in the predicted probability due to an increase of the mean value by 1. In the case of off-farm income, the mean value was increased by 1 % to measure the marginal effect.

  9. First stage results of the endogenous treatment effects model are presented in table 10 in the Appendix

  10. Since the dependent variable is log-dependent, coefficients of dummy variables are interpreted as [exp(coefficient)-1]*100

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Acknowledgments

The authors are grateful for financial support provided by the Courant Research Centre “Poverty, Equity and Growth in Developing Countries” (funded by the German Research Foundation) and by the Dorothea Schlözer Program of Göttingen University. Furthermore, we would like to thank the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT) in Nairobi and the Kenyan Agricultural & Livestock Research Organization (KALRO) in Kakamega for logistical support during fieldwork.

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  1. Department für Agrarökonomie und Rurale Entwicklung, Georg-August University of Göttingen, Platz der Göttinger Sieben 5, 37073, Göttingen, Germany

    Christina Handschuch & Meike Wollni

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  1. Christina Handschuch
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  2. Meike Wollni
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Correspondence to Christina Handschuch.

Appendix

Appendix

Table 10 First stage results of the endogenous treatment effects model
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Handschuch, C., Wollni, M. Improved production systems for traditional food crops: the case of finger millet in western Kenya. Food Sec. 8, 783–797 (2016). https://doi.org/10.1007/s12571-016-0577-7

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