Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective

Abstract

This study spatially assesses, quantifies, and visualizes the agricultural expansion and land use intensification in the northwestern foothills of Mount Kenya over the last 30 years: processes triggered by population growth, and, more recently, by large-scale commercial investments. We made use of Google Earth Engine to access the USGS Landsat data archive and to generate cloud-free seasonal composites. These enabled us to accurately differentiate between rainfed and irrigated cropland, which was important for assessing agricultural intensification. We developed three land cover and land use classifications using the random forest classifier, and assessed land cover and land use change by creating cross-tabulation matrices for the intervals from 1987 to 2002, 2002 to 2016, and 1987 to 2016 and calculating the net change. We then applied a landscape mosaic approach to each classification to identify landscape types categorized by land use intensity. We discuss the impacts of landscape changes on natural habitats, biodiversity, and water. Kappa accuracies for the three classifications lay between 78.3% and 82.1%. Our study confirms that rainfed and irrigated cropland expanded at the expense of natural habitats, including protected areas. Agricultural expansion took place mainly in the 1980s and 1990s, whereas agricultural intensification largely happened after 2000. Since then, not only large-scale producers, but also many smallholders have begun to practice irrigated farming. The spatial pattern of agricultural expansion and intensification in the study area is defined by water availability. Agricultural intensification and the expansion of horticulture agribusinesses increase pressure on water. Furthermore, the observed changes have heightened pressure on pasture and idle land due to the decrease in natural and agropastoral landscapes. Conflicts between pastoralists, smallholder farmers, large-scale ranches, and wildlife might further increase, particularly during the dry seasons and in years of extreme drought.

Livelihoods and Food Security Among Rural Households in the North-Western Mount Kenya Region

“…The study area is characterized by a steep ecological gradient marked by distinct altitudinal belts that drop from a height of 5,200 m in the alpine zone through the Laikipia plateau at 1,500 m to below 1,000 m in the Samburu lowlands (Kiteme and Gikonyo, 2002). The climate changes from semihumid (1,000-1,500 mm annual rainfall) in the highlands of Meru County to semi-arid (400-700 mm annual rainfall) and to arid (about 350 mm annual rainfall) northwards over the Laikipia Plateau (Eckert et al, 2017). We chose this area due to the presence of different agro-ecological zones, diverse livelihood systems, and the presence of large-agricultural investments 1 (LAIs).…”

How do large-scale agricultural investments affect land use and the environment on the western slopes of Mount Kenya? Empirical evidence based on small-scale farmers' perceptions and remote sensing

“…The towns of Nanyuki, Naro Moru, and Timau are the area's main economic centers. The presence of LAIs has increased considerably, from 24 in 2003 to 35 in 2013 (Lanari, 2014); this development has been coupled with the emergence of a remarkable number of greenhouses and open water bodies (Eckert et al, 2017;Lanari et al, 2016). To assess the impacts of LAIs on small-scale farmers' land use, we selected five of the total 35 LAIs in our study area for closer analysis.…”

“…F1 scores range between 83.6% and 87.8%. Detailed information on the classification and validation methods are provided in Eckert et al (2017). Changes in land cover and land use were assessed by applying a post-classification pixel-to-pixel comparison and creating cross-tabulation matrices for the periods from 1987 to 2002 and from 2002 to 2016.…”

Large-scale agricultural investments trigger direct and indirect land use change: New evidence from the Nacala corridor, Mozambique

“…Such seasonal composites representing key phenological time windows can be helpful in separating certain land cover and land use classes in a reliable way (Griffiths et al, 2014;Griffiths, Müller, Kuemmerle, & Hostert, 2013). A more detailed description of the method used to generate these seasonal image collections is available in (Eckert, Kiteme, Njuguna, & Zaehringer, 2017). We chose the year 2000 as the baseline situation for the LULC change analysis because gap-free and monthly data for years more immediately preceding LAI establishment were not available due to a technical failure of the Landsat ETM+ sensor between 2002 and 2014.…”