Effects of land use land cover changes on land quality in Khwisero sub county, Kakamega County, Kenya

Abstract

Land use land cover changes (LULCCs) have to a great extent, changed the world's landscapes, rebuilding environments and what they provide to humans during the time spent supporting the rising population across the globe. However, the LULCCs affect land quality in terms of loss of vegetation cover and alteration of soil quality. Misango Hills natural forest and other forms of vegetation cover types in Khwisero Sub County have been subjected to LULCCs and changed over to farming and built-up regions. Conversion of land into settlement and urban areas in the study area has led to depletion of essential soil nutrients and a decline in soil pH, affecting agricultural activities. The factors driving these changes differ from one location to another, resulting in varied effects that in turn have significant implications on vegetation cover type and soil quality, particularly soil nutrients, moisture, and temperature. Notably, no clear investigations have been done in the study area on these varied effects that challenge land quality’s fundamental design and functional capacity. Therefore, the purpose of this study was to examine the effects of LULCCs on land quality in Khwisero Sub County, Kakamega County. The specific objectives of this study were to; examine the driving forces that influence land use land cover changes, determine the effects of land use land cover areal changes on vegetation cover types, assess the effects of land use land cover areal changes on soil nutrient availability (pH, NPK and SOM) and determine the effects of land use land cover areal changes on soil moisture levels availability and soil temperature. This study adopted the land rent tenet of Von Thunen's agricultural land use theory, Alonso's bid rent theory, and Ricardian economic theory. Multiple research designs (cross-sectional descriptive, longitudinal, and experimental) were used. Purposive sampling was used to select the key informants. Spatial random sampling was used to identify soil sampling points. A random sampling technique was used to select a minimum sample size of 384 respondents from a study population of 113,476. A supervised classification algorithm and a post-classification comparison change detection were used to measure land use land cover (LULC) percentage area change over time. Primary data was collected through questionnaires, key informant interviews, field observations, field measurements, and laboratory procedures. Secondary data involved downloading Landsat images (Landsat 7, 8, and 9; 30-meter multispectral), journal articles, annual reports, and the internet. Quantitative data analysis involved measures of central tendency, measures of dispersion, analysis of variance (ANOVA), and regression. Qualitative data was analysed by organizing it into patterns and themes relevant to the current study. LULCC drivers were analysed through a sensitivity analysis. The current study revealed four land use land cover classes of agriculture, forest, built up, and bare land. Accuracy assessment for the land use land cover classes for 2002 was 85.45% with a Kappa coefficient of 0.756, 2012 was 83.64% with a Kappa coefficient of 0.5454, while 2023 was 81.82% with a Kappa coefficient of 0.6034. The findings revealed that LULCCs are driven by settlement, poverty, and climate change, mostly affecting cropland, vegetation, and soil fertility. In addition, LULCCs affected all the vegetation cover types and soil fertility due to the decrease of NPK, SOM, SMC, an increase of soil temperatures, and a shift from moderate alkaline soils to moderately acidic and highly acidic. About 26.8% and 11.3% of the variance of SOM (R2 = 0.268, p<0.001) and TK (R2 = 0.113, p<0.046), respectively, can be explained by NDVI. About 67.1% of the variance of pH (R2 = 0.671, p<0.000) can be explained by SI, while about 47.5% variance of SMC (R2 = 0.475, p<0.000) can be explained by SMI. The stepwise Multiple Linear Regression Model of NDVI and NPK together with SOM revealed that about 26.8% variance of SOM (R2 = 0.268, p<0.001) could be explained by NDVI amongst the variables in the model. About 33.6% and 32% variance of SMC (R2 = 0.336 and 0.320, p<0.000) could be explained by soil pH amongst all the variables in this model. The current study concluded that drivers of LULCCs vary from place to place, affecting vegetation types, soil nutrients, soil moisture, and soil temperature, key determinants of land quality. The study recommends the creation of awareness among the local community for a better understanding of the importance of land quality, specifically soil nutrients of NPK, SOM, SMC, pH, and soil temperature. A review of the forest policy, particularly on reafforestation, would save vegetation cover, while the soil management policy should incorporate free soil testing for locals, educating them on methods of improving the already damaged soils in Khwisero Sub County.