Proximate causes of land-use change in Narok District, Kenya: a spatial statistical model

Abstract

This study attempts to identify how much understanding of the driving forces of land-use changes can be gained through a spatial, statistical analysis. Hereto, spatial, statistical models of the proximate causes of different processes of land-use change in the Mara Ecosystem (Kenya) were developed, taking into account the spatial variability of the land-use change processes. The descriptive spatial models developed here suggest some important factors driving the land-use changes that can be related to some well-established theoretical frameworks. The explanatory variables of the spatial model of mechanised agriculture suggest a von Thünen-like model, where conversion to agriculture is controlled by the distance to the market, as a proxy for transportation costs, and agro-climatic potential. Expansion of smallholder agriculture and settlements is also controlled by land rent, defined, in this case, by proximity to permanent water, land suitability, location near a tourism market, and vicinity to villages to gain access to social services (e.g. health clinics, schools, local markets). This difference in perception of land rent reflects the widely different social and economic activities and objectives of smallholders versus the large entrepreneurs involved in mechanised farming. Spatial heterogeneity as well as the variability in time of land-use change processes affect our ability to use regression models for wide ranging extrapolations. The models allow evaluating the impact of changes in driving forces that are well represented by proximate causes of land-use change.

Deforestation modelling using logistic regression and GIS

“…The extraction information was exported into SPSS 16 software for further analysis. A multivariate, spatially explicit model of the deforestation was developed using the logistic regression (Schneider, Pontius 2001;Serneels, Lambin 2001;Wilson et al 2005). The model was used to determine the variables that explain the spatial distribution of deforestation.…”

“…One of the most important methods to detect the factors influencing deforestation and their spatial interaction is to model their influence on the landscape using spatial data (Serneels, Lambin 2001;Laurance et al 2002;Nagendra et al 2003;Mertens et al 2004;Etter et al 2006).…”

Long-term changes in Serengeti-Mara wildebeest and land cover: Pastoralism, population, or policies?

“…Remote sensing and household survey data were linked at the household level. Spatial logistic multiple regression models were built, using as the dependent variable land conversion to mechanized agriculture between 1975-1985 and 1985-1995 and, as independent variables, distance to roads, to the nearest village, to the district capital, and to permanent water, group ranch type, population density in 1979 and 1989, change in population density 1979-1989, agro-climatic zone, elevation, and soil suitability for agriculture (16). A conceptual model of the competition between different land uses was then developed and key relationships were evaluated based on the evidence.…”

“…Multiple logistic regression models show the location of conversion to large-scale wheat farming in the Loita Plains is largely explained by agro-climatic potential (for 1975-1985, 2 ϭ 0.63, n ϭ 20,000: mechanized agriculture is progressively less likely in more arid agro-climatic zones) and proximity to Narok town (16). For 1985-1996 distance to Narok remains important (odds ratio ϭ 0.885, P ϭ 0.0001, n ϭ 20,000), agro-climatic potential becomes less so.…”

“…40 and M.T. and K.H., unpublished work), especially the expansion of broad-scale mechanized farming, which is controlled by agro-climatic potential and economic factors, such as cereal and input prices, access to the market, and transportation costs (16). Kenyan Maasai landowners can lease their land to farmers or cultivate small plots themselves.…”