Ct Omuto

Estimating water infiltration and retention characteristics using a computer program in R

Infiltration and water retention functions are widely used soil hydraulic properties in the geosciences. They contain coefficients known as hydraulic parameters that are traditionally obtained through curve-fitting. Computer programs for the curve-fitting process are available for certain infiltration or water retention models. However, these programs are either not freely accessible or do not estimate certain hydraulic parameters. They are also inefficient and prone to errors for applications involving large datasets. This paper discusses the use of a freely accessible HydroMe package for fast, efficient, and accurate estimation of soil hydraulic parameters in some commonly used infiltration and water retention models. The package is executable in the freely downloadable R programming software. It contains a program for estimating the parameters in infiltration models previously proposed. The program is capable of estimating parameters from arrays of grouped data in one single pass without having to enter the data each time for the parameter estimation. It incorporates mixed-effects and covariate modelling techniques for improved estimation accuracy. These techniques are not common in any other computer programs in the geosciences. Through covariate modelling, the package provides opportunity to include environmental correlates in the estimation of soil hydraulic parameters. Therefore, HydroMe not only improves the estimation accuracy and efficiency, but also provides insight into environmental risk factors that influence the management of soil and water resources.

A new approach for using time-series remote-sensing images to detect changes in vegetation cover and composition in drylands: a case study of eastern Kenya

Vegetation cover and composition are important aspects of the dryland environment because they provide livelihood to humans and also protect soil resources against erosion. Currently, scientists are advancing various techniques for detecting vegetation degradation in the drylands and the possibilities for its control. This study contributed through the testing of time-series mixed-effects modelling of the normalized difference vegetation index (NDVI) and rainfall relationship to trace the footprints of vegetation dynamics in the drylands. The approach aimed at providing guidelines for quick diagnosis of the changes in vegetation cover and composition to trigger necessary action. The mixed-effects technique used in this study is a novel regression approach for simultaneous modelling of the NDVI–rainfall relationship in different dominant vegetation types. Its time-series application with Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images between 1982 and 2008 was tested in eastern Kenya. The results show how the original dominant vegetation types had been converted to cereal croplands, open grasslands, or reduced to bare ground in a span of 27 years. In some places, it shows how the changes in vegetation composition resulted in the overall loss of vegetation cover. Field validation positively confirmed these observations; thus, indicating that the method was a promising tool for tracing vegetation dynamics in the drylands. In spite of its success, the method was found to be only useful in detecting changes in large areas with dominant vegetation types. The technique can therefore be recommended for regional analysis, and can be used as a first approximation to guide more detailed subsequent analysis.

Re-tooling of regression kriging in R for improved digital mapping of soil properties

Regression analysis and kriging are popular spatial estimation methods often used in soil science to provide soil information at different spatial resolutions and extent. Attempts have been made to combine them into a method known as regression kriging (RK). With the increasing acceptance of digital soil mapping paradigm, utilization of spatial estimation method such as RK is bound to rise. Although RK is versatile and popular, its current format has deficiencies which can hinder the quality of estimated soil properties. One of the deficiencies of RK is the failure of its regression model to recognize that natural soil occurs in groups with unique response characteristics to soil forming factors. Ideally, these groups should be represented as a family of curves when modelling the landscape. However, the current applications tend to use average models which either block/control the grouping effects or do not statistically recognize them. In this paper, mixed-effects modelling technique is shown for ingenious recognition of soil groupings and consequent improvement of RK accuracy. Mixed-effects modelling allows for simultaneous regression estimation for individual models in a group and for different groups in the landscape. Its implementation in RK has been illustrated using executable scripts in R. It gives better mapping accuracy and reliable maps than the current application in RK. The new RK and its easy implementation in R software are anticipated to provide potential for wide application and eventual contribution to improved soil mapping and application of DSM.

Chapter 23 - Monitoring Drought with the Combined Drought Index in Kenya

Abstract A first step in any drought management system is to monitor the state and the evolution of the drought. This study addresses the problem of nonexistent operational drought monitoring systems and presents a new methodology for monitoring the evolution and severity of drought with the new, Combined Drought Index (CDI). It is based on the fact that drought is a natural phenomenon created by a combination of several factors, such as deficiency in rainfall amount, persistence of below average rainfall, temperature excess and soil moisture characteristics. By combining the factors in the preceding text, the CDI compares present conditions with multiyear average (normal) conditions for the same time period. The methodology was applied at selected locations of different climate zones in Kenya. The results were compared with available official records of drought events (impacts), showing a very good positive relationship between the two. An attempt to detect the long-term trends of drought events using the CDI indicates that there is an increasing trend of drought events in the country, while the drought severity is not necessarily getting worse in all stations. The CDI method also revealed the possibility of drought early warning and drought-related climate change analysis in Kenya.

Chapter 11 – Major Soil and Data Types in Kenya

Abstract Soil is a natural resource that supports food production and numerous types of support to life on earth. It occurs on the earth’s surface as groups or types, which have special capabilities. To identify these capabilities, soil scientists have developed tools for mapping soil types in the landscape so that their potential uses can be maximised. However, the mapping tool needs sufficient input data that many countries in the world do not have. In Kenya, the input data for soil mapping can be found from several governmental and nongovernmental organisations. This study identified and described publicly available soil data and new tools that can be used to produce high-resolution soil map of Kenya. The spatial distribution of the locations of these soil information sources showed that the northeastern parts of the country have been poorly represented in soil information development. Furthermore, using the available soil data, this study developed a new soil map of Kenya at a higher scale than the currently available area-class map. This soil map depicts the country as consisting of 22 main soil groups according to the FAO-UNESCO classification. These groups are dominated by soil types that have strong crop production limitations under rain-fed agriculture but are good for the development of pastoral resources. This implies that rain-fed crop production in the country cannot adequately sustain the consumptive demand of over 40 million people unless improved farming methods are applied.