In most of Nigeria's rural communities, land holdings are small and uneven; and this impacts significantly on their mechanisation potentials. This fragmented nature of the farmlands also inhibits the creation of an effective land market. This study utilised a digital orthomosaic generated from an Unmanned Aerial Vehicle (UAV) survey in evaluating the productivity levels of traditional and mechanised farmers in Okeho Community of Oyo State, South-Western Nigeria. The aerial survey was conducted with a DJI Phantom 4 Professional UAV covering 250 acres of traditional and mechanised farmlands to produce a very high resolution orthomosaic at 6cm spatial resolution. Sixty-three respondents (61 traditional farmers and 2 mechanised farmers) were also interviewed using questionnaires. Their responses were keyed into a database with the Open Data Kit (ODK) data collector. The orthomosaic was classified into farmland units and a database of the farmers land holdings was created in ArcGIS software. Some parameters influencing their productivity were computed - Crop Field Fraction (CFF) and Crop Yield Index (CYI). The results showed that very few farmers had a shared equity on land (only 3%); most farms were acquired under freehold or lease. Also, only 1% of their farm sizes was larger than 5 acres. There was a sharp disparity in the crop field fraction (traditional farms - 32.2; mechanised farms - 68.8), and the productivity from the mechanised farmers surpasses that of the traditional farmers. It is recommended that the Government should support cluster farming systems among farmers to boost productivity.
CITATION STYLE
Olayinka, D. N., Omolaye, K. L., Ilesanmi, A. J., Okolie, C. J., & Arungwa, I. D. (2021). Application of UAV surveys for evaluating the productivity levels of traditional and mechanised farmers in a customary land tenure system. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 43, pp. 617–622). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-617-2021
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