Important Variables Influencing Milk Yields on Smallholder Farms in Western Kenya

Authors

  • Simon P. O. Wanjala Kenya Agricultural and Livestock Research Organization and Kenyatta University
  • Bernard K. Njehia
  • Festus M. Murithi

Keywords:

smallholder farms, milk yields, important variables, pair-wise ranking, beta weights, product measure, multiple linear regression

Abstract

This study sought to assess the most important variables influencing milk yields on smallholder farms in Western Kenya, a region with persistent milk insufficiency. Four approaches were assessed on ranking of important variables: Use of farmer focus groups, key informant interviews (pair-wise ranking of variables), beta weights and Product measure (multiple linear regression). The findings showed that all the four methods produced different ranking order. Using a combined weighting system, the most important variables influencing milk yields on smallholder farms were found to be fodder; dairy meal, credit, Artificial insemination, improved research technologies, group membership, policy and economic returns. Collectively, they explained 63.9% of the variance in milk yields in the area (F8, 291= 65.089, p<0.001).We argue that a combined weighting approach appears to be a sharper ranking method in selecting important variables from many components in a value chain system.

Author Biography

Simon P. O. Wanjala, Kenya Agricultural and Livestock Research Organization and Kenyatta University

Research Scientist, Kenya Agricultural and Livestock Research Organization

PhD Fellow, Agribusiness Management Kenyatta University

References

De Maris A, Regression with social data: Modelling continuous and limited response variables. John Willey & Sons Inc. ISBN 0-471-22337-9, 2004.

Government of Kenya, Kenya Vision 2030. Nairobi: Ministry of state for planning, National Development and Vision 2030, 2007.

GTZ, Value links Manual: A Methodology of Value Chain Promotion. Revised ed. Eschborn: GTZ, Germany, 2008.

Hinkle DE, Wiersma W, Jurs SG, Applied statistics for the behavioural Sciences (5th ed.). Boston, MA: Houghton Mifflin, 2003.

Jaetzold R, Schmidt H, Hornet ZB, Shisanya CA, Farm management Handbook of Kenya. Natural conditions and farm information, vol 11/C, 2nd edn. Nairobi : Ministry of agriculture/GTZ, 2006.

Karanja AM, The Dairy Industry in Kenya: The Post-Liberalization Agenda. Tegemeo Institute/Egerton University Research paper. Nairobi, Kenya, 2003.

Krueger RA, Casey MA, Focus Groups: A Practical Guide for Applied Research (3rd edn). Thousand Oaks, CA: Sage, 2000.

LeBreton JM, Ployhart RE, Ladd RT, “A Monte Carlo comparison of relative importance methodologiesâ€. Organizational Research Methods, 7(3), 258-282. Doi: 10.1177/1094428104266017, 2004.

Morgan DL, Focus Group Interviewing. In: Gubrium, J.F and Holstein J.S (Eds.), and Book of Interview Research: Context and method. Thousand Oaks, CA: Sage Publications, 2002.

Mulwa FW, Participation and evaluation of community projects. Community based

project monitoring, qualitative impact assessment and people friendly evaluation methods.

Nairobi: Kijabe Printing Press, 2006.

Muriuki H, Omore A, Hooton N, Waithaka M, Ouma R, Staal SJ, Odhiambo P, The policy environment in the Kenya dairy sub-sector: A review, 2004.

Nathans LL, Oswald FL, Nimon K, “Interpreting Multiple Linear Regression: A Guidebook of Variable Importanceâ€, Practical Assessment Research & Evaluation, 17:9, 2012.

Ndambi OA, Hemme T, Latacz-Lohmann U, “Dairying in Africa - Status and recent developments. Livestock Production for Rural Development, 19 (8), 2007.

New Partnership for African Development, Comprehensive African Agriculture Development Programme. Rome: FAO, 2002.

Nimon K., Gavrilova M, Roberts JK, “Regression results in human resource development research: Are we reporting enough?†In C. Graham and K. Dirani (Eds.), Proceedings of the Human Resource Development 2010 International Conference (pp. 803-812), Knoxville, TN: AHRD, 2010.

Pedhazur EJ, Multiple regression in behavioural research: Explanation and prediction (3rd ed.). Stamford, CT: Thompson Learning, 1997.

Pratt JW, “Dividing the indivisible: Using simple symmetry to partition variance explained†In Pukkila T, Puntanen, S, (Eds.), Proceedings of the second international Tampere conference in statistics (pp. 245-260). Tampere, Finland: University of Tampere. 2010 International Conference (pp. 803-812), Knoxville, TN: AHRD, 1987.

Rich KM, Baker D, Negassa A, Ross-Brent R, “ Concepts, applications and extensions of value chain analysis to livestock systems in developing countriesâ€, Paper presented at the International Association of Agricultural economists, Beijing, China, 16-22 August, 2009.

Saunders M, Lewis P, Thornhill A, Research Methods for Business Students, 5th Edition, Harlow, England: FT Prentice Hall-Pearson Education Limited, 2009.

Staal SJ, Pratt AN, Jabbar M, Dairy Development for the Resource Poor Part 1: A Comparison of Dairy Policies and Development in South Asia and East Africa. PPLPI Working Paper No. 44-1 ILRI, 2008.

Tabachnick BG, Fidell LS, Using Multivariate Statistics (4th Edition), Boston: Allyn & Bacon Sydney, 2001

Trienekens JA, Agricultural Value Chains in Developing Countries: A Framework for Analysis, 2011.

United Nations Economic Commission for Africa, Sustainable Development Report. Addis Ababa: UNECA, 2007.

Waithaka AM, Wokabi A, Nyaganga J, Ouma E, De Wolf T, Biwott J, Staal SJ, Ojowi M, Ogidi R, Njarro I, Mudavadi P, Characterization of dairy systems in the Western Kenya region. The Smallholder Dairy (R&D) Project, 2002.

Wanjala SPO, Njehia B, Ngichabe C, “Assessment of the Structure and Performance of the Milk Market in Western Kenyaâ€, International Journal of Current Research, 6(3):5652-5656, 2014.

Werner J, Participatory development of agricultural innovations, procedures and methods of on-farm research. Eschborn: GTZ, Germany, 1993.

Zamykal D, Steele M, Kerr D, Chaseling J, Identifying Significant Contributors to Milk

Production in the Absence of the Herd Size Effect, James cooks university, Queensland, Australia, 2007.

Zientek LR, Thompson B, Commonality analysis: Partitioning variance to facilitate better understanding of data. Journal of Early Intervention, 28, 299-307. Doi: 10.1177/105381510602800405, 2006.

Downloads

Published

2015-02-15

How to Cite

Wanjala, S. P. O., Njehia, B. K., & Murithi, F. M. (2015). Important Variables Influencing Milk Yields on Smallholder Farms in Western Kenya. Asian Journal of Agriculture and Food Sciences, 3(1). Retrieved from https://ajouronline.com/index.php/AJAFS/article/view/1777