Characterization of drivers of agricultural land use change
Major factors driving agricultural land use in Malaysia were characterized with Principal Component Analysis (PCA). Discrete variables assumed to drive agricultural land use were converted into spatial data. Vector data subsequently obtained from these conversions were later rasterized before being disaggregated. ASCII data of each of the disaggregated was derived using ArcGIS 10.3.1. A MatLab program was thereafter used to convert the ASCII data into vector column where systematic sampling was performed after Moran I test to select the samples for PCA analysis in SPSS/IBM version 23. The result of the PCA analysis finally aggregated variables driving agricultural land use into: urbanization, availability, ageing and cross sectoral mobility of labour, geophysical, accessibility, and climatic factors. These factors explained about 88 % of the cause of agricultural land use in the study area. The proposed transition of Malaysia to a high income nation will no doubt put additional pressures on the identified drivers (factors) of the agricultural land use, therefore, it is expected that the policy makers put in place measures that will minimize environmental effects of these pressures in order to make the proposed transition sustainable.
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