Malaria in Africa: Vector Species' Niche Models and Relative Risk Maps

A central theoretical goal of epidemiology is the construction of spatial models of disease prevalence and risk, including maps for the potential spread of infectious disease. We provide three continent-wide maps representing the relative risk of malaria in Africa based on ecological niche models of vector species and risk analysis at a spatial resolution of 1 arc-minute (9 185 275 cells of approximately 4 sq km). Using a maximum entropy method we construct niche models for 10 malaria vector species based on species occurrence records since 1980, 19 climatic variables, altitude, and land cover data (in 14 classes). For seven vectors (Anopheles coustani, A. funestus, A. melas, A. merus, A. moucheti, A. nili, and A. paludis) these are the first published niche models. We predict that Central Africa has poor habitat for both A. arabiensis and A. gambiae, and that A. quadriannulatus and A. arabiensis have restricted habitats in Southern Africa as claimed by field experts in criticism of previous models. The results of the niche models are incorporated into three relative risk models which assume different ecological interactions between vector species. The “additive” model assumes no interaction; the “minimax” model assumes maximum relative risk due to any vector in a cell; and the “competitive exclusion” model assumes the relative risk that arises from the most suitable vector for a cell. All models include variable anthrophilicity of vectors and spatial variation in human population density. Relative risk maps are produced from these models. All models predict that human population density is the critical factor determining malaria risk. Our method of constructing relative risk maps is equally general. We discuss the limits of the relative risk maps reported here, and the additional data that are required for their improvement. The protocol developed here can be used for any other vector-borne disease

Factors affecting runoff and soil loss under simulated rainfall on a sandy Bainsvlei Amalia soil

Published ArticleThis study was conducted on a long-term tillage experiment that was designed to evaluate appropriate tillage practices for sustainable dryland crop production. Measurements of runoff and soil loss were conducted on a sandy Bainsvlei Amalia soil (88.0% sand, 3.6% silt, and 8.4% clay) with a rainfall simulator on three tillage practices, namely no-tillage (NT), stubble mulch (ST) and conventional tillage (CT), each combined with four levels of wheat residue cover. The measurements were replicated twice for each of the three tillage practices on 1 m2 area plots. The simulator used for the study produced raindrops at a constant intensity of 60 mm h-1 with a veejet type nozzle, which had kinetic energy comparable to natural rainfall. A sharp decline in runoff and soil loss occurred with an increase in residue ground cover from bare to about 70%, above which the effect was less dramatic. Generally, runoff and soil loss was higher on NT plots compared with ST and CT plots. It is recommended that a crop residue covering at least 70% should be maintained on the soil surface when conservation tillage is practiced to ensure higher infiltration and lower runoff on this type of soil. Tilling the soil to a depth of at least 150 to 250 mm below the mulch gave the lowest runoff and soil loss

Tillage–crop residue management and rainfall–runoff relationships in the Alemaya catchment of Eastern Ethiopia

Published ArticleA field study was conducted in the Alemaya catchment, Eastern Ethiopia, with the objective of determining the effect of tillage practices, crop reside cover and rainfall characteristics on runoff during two rainfall seasons on a clayey Regosol with 5% slope. Relationships between runoff and rainfall characteristics, such as the amount, intensity and erosivity were established from a two-year data set. The results on fields without crop showed that a minimum of 2 t ha-1 of fresh wheat residue is required to reduce runoff from low intensity storms and 4 t ha-1 of residue for high intensity storms with peak intensities of more than 55 mm h- 1. These norms can be used for estimating runoff under comparable soil and climatic conditions. A technique was also developed to estimate runoff from the rainfall intensity- duration curve and the mean soil infiltration rate

Conceptualisation of the consequences of land use decisions on water resources in the central region of South Africa: an agent based modelling perspective

Published ArticleOver the past few decades, numerous researchers have improved measurements of land use change through representation of much more complex processes of land use and its impact on water resources. Understanding the causes of land use change has moved from a simplistic representation of a few driving forces to a much more detailed understanding that involves situation-specific interactions among a large number of factors at different spatial and temporal scales using agent-based models. The agent-based perspective is centred on the general nature and rules of land use decision making by individuals and represents the motivations behind decisions and the external factors that influence decisions about land use. In this paper, an attempt is made to conceptualise the social and biophysical interactions as the driving forces that lead to decisions of land use, and its potential impact on water resource, including factors such as interventions and technologies that influence the decision of land use change in rural agricultural areas. The development of the conceptual model was done through a series of meetings and workshops and by visualising the relationships between the different factors, such as biophysical and socio-economic factors, using a brain mapping technique. The resulting conceptual model illustrates the main domains of the environment, the socioeconomic factors, and captures all the factors and their interaction that lead to decision in land use change. The socio-economic factors and their interaction will be captured by the ABM module while the biophysical factors that have direct impact on runoff and stream flow could be handled by the hydrologic module which will then be integrated into the ABM model. This, however, is a primary effort in the development of an ABM within the Modder River Basin system and needs continues refinement for optimum functionality and simulation of the real world

Conceptualisation of the consequences of land use decisions on water resources in the central region of South Africa: an agent based modelling perspective

Over the past few decades, numerous researchers have improved measurements of land use change through representation of much more complex processes of land use and its impact on water resources. Understanding the causes of land use change has moved from a simplistic representation of a few driving forces to a much more detailed understanding that involves situation-specific interactions among a large number of factors at different spatial and temporal scales using agent-based models. The agentbased perspective is centred on the general nature and rules of land use decision making by individuals and represents the motivations behind decisions and the external factors that influence decisions about land use. In this paper, an attempt is made to conceptualise the social and biophysical interactions as the driving forces that lead to decisions of land use, and its potential impact on water resource, including factors such as interventions and technologies that influence the decision of land use change in rural agricultural areas. The development of the conceptual model was done through a series of meetings and workshops and by visualising the relationships between the different factors, such as biophysical and socio-economic factors, using a brain mapping technique. The resulting conceptual model illustrates the main domains of the environment, the socioeconomic factors, and captures all the factors and their interaction that lead to decision in land use change. The socio-economic factors and their interaction will be captured by the ABM module while the biophysical factors that have direct impact on runoff and stream flow could be handled by the hydrologic module which will then be integrated into the ABM model. This, however, is a primary effort in the development of an ABM within the Modder River Basin system and needs continues refinement for optimum functionality and simulation of the real world