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Analysis of crop residue use in small holder mixed farms in Ethiopia

Published online by Cambridge University Press:  02 November 2016

Ashraf Alkhtib*
Affiliation:
International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 5689, Addis Ababa, Ethiopia
Jane Wamatu
Affiliation:
International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 5689, Addis Ababa, Ethiopia
Girma T Kassie
Affiliation:
International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 5689, Addis Ababa, Ethiopia
Barbara Rischkowsky
Affiliation:
International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 5689, Addis Ababa, Ethiopia
*
*Corresponding author: [email protected]

Abstract

Determinants of the use of cereal and pulse residue for livestock feeding and soil mulching among smallholder farmers in the mixed farming system were analyzed. Crop residue (CR) is dual purpose resources in the mixed crop–livestock systems of the Ethiopian highlands. They serve as livestock feed and inputs for soil and water conservation. They are generated predominantly from cereals and pulses. However, in view of the allocation of CR, soil conservation and livestock are two competing enterprises. Identifying the determinants of the intensity of use of cereal and pulse residue may help in designing strategies for more efficient CR utilization. Data on CR were generated and its utilization was collected in two highland regions in Ethiopia from 160 households using a structured questionnaire. The data were analyzed using the multivariate Tobit model. Results of the study showed that farmers prefer using CR from pulses over CR from cereals for livestock feeding purposes. The proportion of CR from pulses that was used as feed was positively affected by education level of the farmer, livestock extension service, number of small ruminants and CR production from the previous season. Distance of farm plots from residences of the farm households negatively affected the proportions of cereal and pulse residue used for feed. The use of pulse residue increased significantly when the women participated in decision making on CR utilization. The proportion of cereal and pulse residue used for soil mulch was positively affected by the education level of the farmer, the distance between the homestead and the cultivated land, extension service, awareness about soil mulch, the slope of cultivated land, participation in farmer-to-farmer extension and CR generated in the preceding season. In view that pulse CR have better nutritive value compared with cereal CR, better utilization of CR could be achieved by maximizing the use of pulse residue as livestock feed and optimizing the use of cereal residue as soil mulch. More livestock extension on the nutritive value of pulse residue should be provided to the farmers who cultivate sloppy plots. Encouraging the culture of labor exchange among the farmers could result in increased labor availability in the farms that would facilitate the transport and storage of pulse residue and increase its use as livestock feed. Increasing the awareness among farmers about the superiority of the pulse residue over cereal residue as feed and encouraging use of cereal residue as soil mulch could optimize the utilization of CR in the household.

Type
Research Papers
Copyright
Copyright © Cambridge University Press 2016 

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References

Alemayehu, M. 2003. Country Pasture/Forage Resources Profiles. FAO, Ethiopia.Google Scholar
Alkemade, R., Reid, R., Van Den Berg, M., De Leeuw, J., and Jeuken, M. 2012. Assessing the impacts of livestock production on biodiversity in rangeland ecosystems. Proceedings of the National Academy of Sciences of the United States of America 110:20900–20905.Google Scholar
Arias, C. and Cox, T. 2001. Estimation of a US diary sector model by maximum simulated likelihood. Applied Economics 33:12011211.CrossRefGoogle Scholar
Cappellari, L. and Jenkins, S. 2006. Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation. The Stata Journal 6:156189.CrossRefGoogle Scholar
Collier, P. and Dercon, S. 2009. African Agriculture in 50 Years: Smallholders in a Rapidly Changing World. Expert Meeting on How to Feed the World in 2050. Food and Agriculture Organization of the United Nations (FAO), Economic and Social Development Department, Rome, Italy.Google Scholar
Cornick, J., Cox, T., and Gould, B. 1994. Fluid milk purchases: A multivariate Tobit analysis. American Journal of Agricultural Economics 76:7482.CrossRefGoogle Scholar
Drechsel, P., Gyiele, L., Kunze, D., and Cofie, O. 2001. Population density, soil nutrient depletion, and economic growth in sub-Saharan Africa (Analysis). Ecological Economics 38: 251258.CrossRefGoogle Scholar
Gebremeskel, Y., Estifano, A., and Melaku, S. 2011. Effect of selected faba bean (Vicia faba L.) varietal difference on straw DM yield, chemical composition and nutritional quality. Journal of the Drylands 4:333340.Google Scholar
Gebretsadik, H., Haile, M., and Yamoah, C. 2009. Tillage frequency, soil compaction and N-fertilizer rate effects on yield of teff (Eragrostis Tef (Zucc) Trotter) in central zone of Tigray, northern Ethiopia. Momona Ethiopian Journal of Science 1:8294.CrossRefGoogle Scholar
Geweke, J. 1989. Bayesian inference in econometric models using Monte Carlo integration. Econometrica 57:13171339.Google Scholar
Hajivassiliou, V. and Mcfadden, D. 1998. The method of simulated scores for the estimation of LDV models. Econometrica 66:863896.Google Scholar
Hajivassiliou, V. and Ruud, P. 1994. Classical estimation methods for LDV models using simulation. In Engle, R. & Mcfadden, D. (eds) Handbook of Econometrics. Elsevier, New York. Vol. 4, p. 23832441.Google Scholar
Herrero, M., Thornton, P. K., Notenbaert, A., Msangi, S., Wood, S., Kruska, R., Dixon, J., Bossio, D., Steeg, J. van de, Freeman, H. A., Li, X., and Parthasarathy Rao, P. 2012. Drivers of change in crop-livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030: A study commissioned by the CGIAR Systemwide Livestock Programme. ILRI Project Report. ILRI, Nairobi, Kenya.Google Scholar
Herrero, M., Thornton, P., Notenbaert, A., Wood, S., Msangi, S., Freeman, H., Bossio, D., Dixon, J., Peters, M., Van De Steeg, J., Lynam, J., Rao, P., Macmillan, S., Gerard, B., Mcdermott, J., Sere, C., and Rosegrant, M. 2010. Smart investments in sustainable food production: Revisiting mixed crop–livestock systems. Science 327:822825.CrossRefGoogle ScholarPubMed
Jahnke, H. 1982. Livestock production systems in livestock development in tropical Africa. Kieler Wissenschafsverlag Vauk. p. 9–11.Google Scholar
Jaleta, M., Kassie, M., and Shiferaw, B. 2013. Tradeoffs in crop residue utilization in mixed crop–livestock systems and implications for conservation agriculture. Agricultural Systems 121:96105.Google Scholar
Jaleta, M., Kassie, M., and Erenstein, O. 2015. Determinants of maize stover utilization as feed, fuel and soil amendment in mixed crop–livestock systems, Ethiopia. Agricultural Systems 134:1723.CrossRefGoogle Scholar
Keane, M. 1994. A computationally practical simulation estimator for panel data. Econometrica 62:95116.Google Scholar
Kearl, L. 1982. Nutrient requirements of ruminants in developing countries. Utah University, Utah, USA. p. 71–91.Google Scholar
Keftasa, D. 1988. Role of crop residues as livestock feed in Ethiopian highlands. In Dzowela, B. (ed.) African Forage Plant Genetic Resources, Evaluation of Forage Germplasm and Extensive Livestock Production Systems. International Livestock Centre for Africa, Addis Ababa.Google Scholar
Lal, R. 2009. Soil degradation as a reason for inadequate human nutrition. Food Security 1:4557.Google Scholar
Lee, L. 1981. Simultaneous equation models with discrete and censored variable. In Manski, C. and MCFADDEN, D. (eds). Structural Analysis of Discrete Data with Econometric Applications. The MIT Press, Massachusetts. p. 346364.Google Scholar
Revelt, D. and Train, K. 1998. Mixed logit with repeated choices: Households’ choices of appliance efficiency level. Review of Economics and Statistics 80:647657.CrossRefGoogle Scholar
Rockström, J., Kaumbutho, P., Mwalley, J., Nzabi, A. W., Temesgen, M., Mawenya, L., Barron, J., Mutua, J., and Damgaard-Larsen, S. 2009. Conservation farming strategies in East and Southern Africa: Yields and rain water productivity from on-farm action research. Soil and Tillage Research 103:2332.Google Scholar
Ryschawy, J., Choisis, N., Choisis, J., Joannon, A., and Gibon, A. 2012. Mixed crop–livestock systems: An economic and environmental-friendly way of farming? Animal 6:17221730.Google Scholar
Smil, V. 1983. Biomass energies: resources, links, constraints. Plenum Press, New York and London. p. 164–166.Google Scholar
Thornton, P. K. and Herrero, M. 2015. Adapting to climate change in the mixed crop and livestock farming systems in sub-Saharan Africa. Nature Climate Change 5:830836.CrossRefGoogle Scholar
Train, K. 2003. Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge.Google Scholar
Tullu, A., Kusmenoglu, I., Mcphee, K., and Muehlbauer, F. 2001. Characterization of core collection of lentil germplasm for phenology, morphology, seed and straw yields. Genetic Resources and Crop Evolution 48:143152.CrossRefGoogle Scholar
Zinash, S., Aschalew, T., Alemu, Y., and Tegegne, A. 2001. Status of livestock research and development in the highlands of Ethiopia. In Wall, P. (ed.) Wheat and Weeds: Food and Feed. Proceeding of Two Stockholder Workshops. Ethiopian Society of Animal Production, Mexico City. p. 227250.Google Scholar