Controversy exists on the magnitude and variability of farm nutrient balances and quality of arable land in sub-Saharan Africa with Kenya among those affected negatively. This study investigates quality of arable land by fitting multivariate multilevel model to farm nutrient balance data collected from five agro-climatic zones of Kenya (arable lands). Objectives of the study were to investigate the magnitude and variability of Nitrogen, Phosphorus and Potassium (NPK) farm nutrient balances in arable lands of Kenya, study effects of agro-climatic zones on nutrient balances and to determine effects of household resource endowments on NPK nutrient balances. The study concludes that agro-climatic zones differ with respect to farm nutrient balances; that livestock resource endowments and hired labour have positive effects on the magnitude and direction of farm nutrient balances; and that household ownership of large capital resources do not guarantee a positive effect on farm nutrient balances. The study recommends integration of sound livestock practices and application of agro-climatic zone differentiated interventions in future strategies for addressing farm nutrient balances and arable land quality, and the use of large sample sizes and relevant factors/ covariates in future analysis to shed additional insights on farm nutrient balances and on how arable land quality can be im proved.
Stoorvogel, J.J. and Smaling, E.M.A. (1990) Assessment of Soil Nutrient Depletion in Sub-Sahara Africa: 1983-2000, 4 Volumes. Report 28, The Win and Staring Centre, Netherlands.
Smaling, E.M.A., Nandwa, S.M. and Janssen, B.H. (1997) Soil Fertility Is at Stake, in Reprenishing Soil Fertility in Sub-Saharan Africa. In: Buresh, R., Sanchez, P.A. and Calhoun, F., Eds., American Society of Agronomy and Soil Science Society of America, Madison Wisconsin, 47-61.
Mortimore, M. and Harris, F. (2005) Do Small Farmers’ Achievements Contradict the Nutrient Depletion Scenarios for Africa? Land Use Policy, 22, 43-56. https://doi.org/10.1016/j.landusepol.2003.06.003
Hartemink, A.V.K. and van Keulen, H. (2005) Soil Degradation in Sub-Saharan Africa. Land Use Policy, 22, 1. https://doi.org/10.1016/j.landusepol.2004.01.001
Faerge, J. and Magid, J. (2004) Evaluating NUTMON Nutrient Balancing in Sub-Saharan Africa. Nutrient Cycling in Agroecosystems, 69, 101-110. https://doi.org/10.1023/B:FRES.0000029680.97610.51
van den Noorgate, W., Lopez-Lopez, J.A., Marin-Martinez, F. and Sanchez-Meca, J. (2012) Three-Level Meta-Analysis of Dependent Effect Sizes. Behavioral Research, 45, 576-594. https://doi.org/10.3758/s13428-012-0261-6
Borenstein, M., Hedges, L.V., Higgins, J.P.T. and Rothstein, H.R. (2009) Comprehensive Meta Analysis (Version 2.2.064). Biostat, Englewood NJ.
Borenstein, M., Hedges, L.V., Higgins, J.P.T. and Rothstein, H.R. (2009) Introduction to Meta-Analysis. John Wiley and Sons Ltd., Hoboken. https://doi.org/10.1002/9780470743386
Scammacca, N. Roberts, G. and Stuebing, K.K. (2014) Meta-Analysis with Complex Research Designs: Dealing with Dependence from Multiple Measures and Multiple Group Comparisons. Review of Educational Research, 84, 328-364. https://doi.org/10.3102/0034654313500826
Capon, N., Farley, J.U. and Hoenig, S. (1990) Determinants of Financial Performance: A Meta-Analysis. Management Science, 36, 1143-1159. https://doi.org/10.1287/mnsc.36.10.1143
Cleophas, T.J. and Zwinderman, A.H. (2007) Meta-Analysis. Circulation, 115, 2870-2875. https://doi.org/10.1161/CIRCULATIONAHA.105.594960
Broeze, K.A., Opmeer, B.C., van der Veen, F. and Bossuyt, P.M. (2010) Individual Patient Data Meta-Analysis: A Promising Approach for Evidence Synthesis in Reproductive Medicine. In: Bhattacharya, S. and Mol, B.W.J., Eds., Human Reproduction Updata, 16, 561-567. https://doi.org/10.1093/humupd/dmq043
Batary, P., Baldi, A., Kleijn, D. and Tscharntke, T. (2010) Landscape-Moderated Biodiversity Effects of Agri-Environmental Management: A Meta-Analysis. Proceedings of the Royal Society Biological Sciences, 278, 1894-1902. https://doi.org/10.1098/rspb.2010.1923
Cooper, H., Hedges, L.V. and Valentine, J.C. (2009) The Handbook of Research Synthesis and Meta-Analysis. Russel Sage Foundation, New York.
Geist, H.J. and Lambin, E.F. (2001) What Drives Tropical Deforestation? A Meta-Analysis of Proximate and Underlying Causes of Deforestation Based on Subnational Case Study Evidence. LUCC Report Series No.4, LUCC International Project Office, University of Louvain, Belgium.
Alston, J.M., Chan-Kang, C., Marra, M.C., Pardey, P.G. and Wyatt, T.J. (2000) A Meta-Analysis of Rates of Return to Agricultural RD. International Food Policy Research Institute.
Mondelaers, K., Aertsens, J. and Van Huylenbroeck, G. (2009) A Meta-Analysis of the Differences in Environmental Impacts between Organic and Conventional Farming. British Food Journal, 111, 1098-1119. https://doi.org/10.1108/00070700910992925
Tuomisto, H.L., Hodge, I.D., Riordan, P. and Macdonald, D.W. (2012) Does Organic Farming Reduce Environmental Impacts? A Meta-Analysis of European Research. Journal of Environmental Management, 112, Article ID: 309320. https://doi.org/10.1016/j.jenvman.2012.08.018
Konstantopoulos, S. (2006) Fixed and Mixed Effects Models in Meta-Analysis. IZA DP No. 2198, Northwestern University and IZA Bonn, Bonn.
Rao, S.R., Graubard, B.I., Schmid, C.H., Morton, S.C., Louis, T.A., Zaslavsky, A.M. and Finkelstein, D.M. (2008) Meta-Analysis of Survey Data: Application to Health Services Research. Health Services Outcomes Research Method, 8, 98-114. https://doi.org/10.1007/s10742-008-0032-0
Sileshi, G., Akinnifesi, F.K., Ajayi, O.C. and Place, F. (2008) Meta-Analysis of Maize Yield Response to Woody and Herbaceous Legumes in Sub-Saharan Africa. Plant and Soil, 307, 1-19. https://doi.org/10.1007/s11104-008-9547-y
Stwardt, G. (2010) Meta-Analysis in Applied Ecology. Biology Letters, 6, 78-81. https://doi.org/10.1098/rsbl.2009.0546
Mavridis, D. and Salanti, G. (2011) A Practical Introduction to Multivariate Meta-Analysis. Statistical Methods in Medical Research, 22, Article ID: 133158.
Rabe-Hesketh, S. and Skrondal, A. (2006) Multilevel Modelining of Complex Survey Data. Journal of the Royal Statistical Society, 169, 805-827. https://doi.org/10.1111/j.1467-985X.2006.00426.x
Afshartous, D. and Wolf, M. (20015) Avoiding Data Snooping in Multilevel and Mixed Effects Models. Institute for Empirical Research in Economics, University of Zurich, Zurich.
Goldstein, H. (1986) Multilevel Mixed Linear Model Analysis Using Iterative Generalised Least Squares. Biometrika, 73, 43-56. https://doi.org/10.1093/biomet/73.1.43
Zhang, Z., Charlton, C.M.J., Parker, R.M.A., Leckie, G. and Browne, W.J. (2016) R2MLwiN: A Package to Run MLwiN from within R. Journal of Statistical Software. http://www.bris.ac.uk/cmm/media/r2mlwin/article/Zhang_et_al.pdf
Muthén, B.O. and Satorra, A. (1989) Multilevel Aspects of Varying Parameters in Structural Models. In: Bock, R.D., Ed., Multilevel Analysis of Educational Data, Academic Press, San Diego, 87-99. https://doi.org/10.1016/B978-0-12-108840-8.50009-3
De Jager, A. (2007) Practice Makes Perfect: Participatory Innovation in Soil Fertility Management to Improve Rural livelihoods in East Africa. PhD Thesis, Wageningen University, Wageningen.
Elias, E., Morse, S. and Belshaw, D.G.R. (1998) Nitrogen and Phosphorus Balances of Kindo Koisha Farms in Southern Ethiopia. Agriculture Ecosystems and Environment, 71, 93-113. https://doi.org/10.1016/S0167-8809(98)00134-0