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This study analyzed the various determinants of land management practices in Chikun LGA of Kaduna State and determined most sustainable practice(s), with the specific focus on: Socioeconomic characteristics of farmers; types of land management practices in relation to the determinant factors; effects of land management practices on the farm productivity; determine the most sustainable land management practice in the study area. A purposive sampling technique was adopted in collecting data from three hundred and eighty (380) rural farmers with structured questionnaires administered in Buruku, Chikun Local Government area of Kaduna State, Nigeria. Five land management practices showed positive effects on the farm productivity as compared to the mean of 3 points; these were cover crop, crop rotation, irrigation, organic manure and fertilizer application. Two land management practices as perceived to have good effects on farm productivity of respondents were agro-forestry and bush fallow as shown in the component one of Original Research Article Awobona et al.; AJAAR, 14(2): 18-28, 2020; Article no.AJAAR.60898 19 the result of Principal Component Analysis (PCA). It was concluded that the farming population was ageing and was adversely affected the choice of best practices due to lack of education and knowledge to adopt the best land management practice. Sustainable land management practices has the potential to reverse the trend of food scarcity and environmental disasters, help to improve local livelihoods, restore natural ecosystems and also contribute significantly to climate change adaptation and mitigation. There is clear evidence that the productivity of soils in Buruku Village will continue to decline if strategic measures are not put in place to manage soil fertility in different soil units to support agricultural land.

asian journal of advances in agricultural research

Analysis of Environmental Factors Determining the Adoption of Land Management Practices in Guinea Savanna Agro-Ecological Zone of Kaduna Using Principal Component Analysis (PCA)