Spatial targeting of irrigation development and water resource management to mitigate vector-borne disease.

Scaling irrigated agriculture is a global strategy to mitigate food insecurity concerns. While expanding irrigated agriculture is critical to meeting food production demands, it is important to consider how these land use and land cover changes (LULCC) may alter the water resources of landscapes and impact the spatiotemporal epidemiology of disease. Here, a generalizable method is presented to inform irrigation development decision-making aimed at increasing crop production through irrigation while simultaneously mitigating malaria risk to surrounding communities. Changes to the spatiotemporal patterns of malaria vector (Anopheles gambiae s.s.) suitability, driven by irrigated agricultural expansion, are presented for Malawi's rainy and dry seasons. The methods presented may be applied to other geographical areas where sufficient irrigation and malaria prevalence data are available. Results show that approximately 8.60% and 1.78% of Malawi is maximally suitable for An. gambiae s.s. breeding in the rainy and dry seasons, respectively. However, the proposed LULCC from irrigated agriculture increases the maximally suitable land area in both seasons: 15.16% (rainy) and 2.17% (dry). Proposed irrigation development sites are analyzed and ranked according to their likelihood of increasing malaria risk for those closest to the schemes. Results illustrate how geospatial information on the anticipated change to the malaria landscape driven by increasing irrigated agricultural extent can assist in altering development plans, amending policies, or reassessing water resource management strategies to mitigate expected changes in malaria risk.

A Bayesian maximum entropy model for predicting tsetse ecological distributions.

BACKGROUND: African trypanosomiasis is a tsetse-borne parasitic infection that affects humans, wildlife, and domesticated animals. Tsetse flies are endemic to much of Sub-Saharan Africa and a spatial and temporal understanding of tsetse habitat can aid surveillance and support disease risk management. Problematically, current fine spatial resolution remote sensing data are delivered with a temporal lag and are relatively coarse temporal resolution (e.g., 16 days), which results in disease control models often targeting incorrect places. The goal of this study was to devise a heuristic for identifying tsetse habitat (at a fine spatial resolution) into the future and in the temporal gaps where remote sensing and proximal data fail to supply information. METHODS: This paper introduces a generalizable and scalable open-access version of the tsetse ecological distribution (TED) model used to predict tsetse distributions across space and time, and contributes a geospatial Bayesian Maximum Entropy (BME) prediction model trained by TED output data to forecast where, herein the Morsitans group of tsetse, persist in Kenya, a method that mitigates the temporal lag problem. This model facilitates identification of tsetse habitat and provides critical information to control tsetse, mitigate the impact of trypanosomiasis on vulnerable human and animal populations, and guide disease minimization in places with ephemeral tsetse. Moreover, this BME analysis is one of the first to utilize cluster and parallel computing along with a Monte Carlo analysis to optimize BME computations. This allows for the analysis of an exceptionally large dataset (over 2 billion data points) at a finer resolution and larger spatiotemporal scale than what had previously been possible. RESULTS: Under the most conservative assessment for Kenya, the BME kriging analysis showed an overall prediction accuracy of 74.8% (limited to the maximum suitability extent). In predicting tsetse distribution outcomes for the entire country the BME kriging analysis was 97% accurate in its forecasts. CONCLUSIONS: This work offers a solution to the persistent temporal data gap in accurate and spatially precise rainfall predictions and the delayed processing of remotely sensed data collectively in the - 45 days past to + 180 days future temporal window. As is shown here, the BME model is a reliable alternative for forecasting future tsetse distributions to allow preplanning for tsetse control. Furthermore, this model provides guidance on disease control that would otherwise not be available. These 'big data' BME methods are particularly useful for large domain studies. Considering that past BME studies required reduction of the spatiotemporal grid to facilitate analysis. Both the GEE-TED and the BME libraries have been made open source to enable reproducibility and offer continual updates into the future as new remotely sensed data become available.

Estimating spatio-temporal distributions of mosquito breeding pools in irrigated agricultural schemes: a case study at the Bwanje Valley Irrigation Scheme.

BACKGROUND: The association between irrigation and the proliferation of adult mosquitoes including malaria vectors is well known; however, irrigation schemes are treated as homogenous spatio-temporal units, with little consideration for how larval breeding varies across space and time. The objective of this study was to estimate the spatio-temporal distribution of pools of water facilitating breeding at the Bwanje Valley Irrigation Scheme (BVIS) in Malawi, Africa as a function of environmental and anthropogenic characteristics. METHODS: Irrigation structure and land cover were quantified during the dry and rainy seasons of 2016 and 2017, respectively. These data were combined with soil type, irrigation scheduling, drainage, and maintenance to model suitability for mosquito breeding across the landscape under three scenarios: rainy season, dry season with limited water resources, and a dry season with abundant water resources. RESULTS: Results demonstrate seasonal, asymmetrical breeding potential and areas of maximum breeding potential as a function of environmental characteristics and anthropogenic influence in each scenario. The highest percentage of suitable area for breeding occurs during the rainy season; however, findings show that it is not merely the amount of water in an irrigated landscape, but the management of water resources that determines the aggregation of water bodies. In each scenario, timing and direction of irrigation along with inefficient drainage render the westernmost portion of BVIS the area of highest breeding opportunity, which expands and contracts seasonally in response to water resource availability and management decisions. CONCLUSIONS: Changes in the geography of breeding potential across irrigated spaces can have profound effects on the distribution of malaria risk for those living in close proximity to irrigated agricultural schemes. The methods presented are generalizable across geographies for estimating spatio-temporal distributions of breeding risk for mosquitoes in irrigated schemes, presenting an opportunity for greater geographically targeted strategies for management.

Explaining variation in adult Anopheles indoor resting abundance: the relative effects of larval habitat proximity and insecticide-treated bed net use.

BACKGROUND: Spatial determinants of malaria risk within communities are associated with heterogeneity of exposure to vector mosquitoes. The abundance of adult malaria vectors inside people's houses, where most transmission takes place, should be associated with several factors: proximity of houses to larval habitats, structural characteristics of houses, indoor use of vector control tools containing insecticides, and human behavioural and environmental factors in and near houses. While most previous studies have assessed the association of larval habitat proximity in landscapes with relatively low densities of larval habitats, in this study these relationships were analysed in a region of rural, lowland western Kenya with high larval habitat density. METHODS: 525 houses were sampled for indoor-resting mosquitoes across an 8 by 8 km study area using the pyrethrum spray catch method. A predictive model of larval habitat location in this landscape, previously verified, provided derivations of indices of larval habitat proximity to houses. Using geostatistical regression models, the association of larval habitat proximity, long-lasting insecticidal nets (LLIN) use, house structural characteristics (wall type, roof type), and peridomestic variables (cooking in the house, cattle near the house, number of people sleeping in the house) with mosquito abundance in houses was quantified. RESULTS: Vector abundance was low (mean, 1.1 adult Anopheles per house). Proximity of larval habitats was a strong predictor of Anopheles abundance. Houses without an LLIN had more female Anopheles gambiae s.s., Anopheles arabiensis and Anopheles funestus than houses where some people used an LLIN (rate ratios, 95% CI 0.87, 0.85-0.89; 0.84, 0.82-0.86; 0.38, 0.37-0.40) and houses where everyone used an LLIN (RR, 95% CI 0.49, 0.48-0.50; 0.39, 0.39-0.40; 0.60, 0.58-0.61). Cooking in the house also reduced Anopheles abundance across all species. The number of people sleeping in the house, presence of cattle near the house, and house structure modulated Anopheles abundance, but the effect varied with Anopheles species and sex. CONCLUSIONS: Variation in the abundance of indoor-resting Anopheles in rural houses of western Kenya varies with clearly identifiable factors. Results suggest that LLIN use continues to function in reducing vector abundance, and that larval source management in this region could lead to further reductions in malaria risk by reducing the amount of an obligatory resource for mosquitoes near people's homes.

Nature-based agricultural solutions: Scaling perennial grains across Africa.

Modern plant breeding tends to focus on maximizing yield, with one of the most ubiquitous implementations being shorter-duration crop varieties. It is indisputable that these breeding efforts have resulted in greater yields in ideal circumstances; however, many farmed locations across Africa suffer from one or more conditions that limit the efficacy of modern short-duration hybrids. In view of global change and increased necessity for intensification, perennial grains and long-duration varieties offer a nature-based solution for improving farm productivity and smallholder livelihoods in suboptimal agricultural areas. Specific conditions where perennial grains should be considered include locations where biophysical and social constraints reduce agricultural system efficiency, and where conditions are optimal for crop growth. Using a time-series of remotely-sensed data, we locate the marginal agricultural lands of Africa, identifying suboptimal temperature and precipitation conditions for the dominant crop, i.e., maize, as well as optimal climate conditions for two perennial grains, pigeonpea and sorghum. We propose that perennial grains offer a lower impact, sustainable nature-based solution to this subset of climatic drivers of marginality. Using spatial analytic methods and satellite-derived climate information, we demonstrate the scalability of perennial pigeonpea and sorghum across Africa. As a nature-based solution, we argue that perennial grains offer smallholder farmers of marginal lands a sustainable solution for enhancing resilience and minimizing risk in confronting global change, while mitigating social and edaphic drivers of low and variable production.

Using meta-quality to assess the utility of volunteered geographic information for science.

BACKGROUND: Volunteered geographic information (VGI) has strong potential to be increasingly valuable to scientists in collaboration with non-scientists. The abundance of mobile phones and other wireless forms of communication open up significant opportunities for the public to get involved in scientific research. As these devices and activities become more abundant, questions of uncertainty and error in volunteer data are emerging as critical components for using volunteer-sourced spatial data. METHODS: Here we present a methodology for using VGI and assessing its sensitivity to three types of error. More specifically, this study evaluates the reliability of data from volunteers based on their historical patterns. The specific context is a case study in surveillance of tsetse flies, a health concern for being the primary vector of African Trypanosomiasis. RESULTS: Reliability, as measured by a reputation score, determines the threshold for accepting the volunteered data for inclusion in a tsetse presence/absence model. Higher reputation scores are successful in identifying areas of higher modeled tsetse prevalence. A dynamic threshold is needed but the quality of VGI will improve as more data are collected and the errors in identifying reliable participants will decrease. CONCLUSIONS: This system allows for two-way communication between researchers and the public, and a way to evaluate the reliability of VGI. Boosting the public's ability to participate in such work can improve disease surveillance and promote citizen science. In the absence of active surveillance, VGI can provide valuable spatial information given that the data are reliable.

Understanding spatio-temporal variation of vegetation phenology and rainfall seasonality in the monsoon Southeast Asia.

The spatio-temporal characteristics of remote sensing are considered to be the primary advantage in environmental studies. With long-term and frequent satellite observations, it is possible to monitor changes in key biophysical attributes such as phenological characteristics, and relate them to climate change by examining their correlations. Although a number of remote sensing methods have been developed to quantify vegetation seasonal cycles using time-series of vegetation indices, there is limited effort to explore and monitor changes and trends of vegetation phenology in the Monsoon Southeast Asia, which is adversely affected by changes in the Asian monsoon climate. In this study, MODIS EVI and TRMM time series data, along with field survey data, were analyzed to quantify phenological patterns and trends in the Monsoon Southeast Asia during 2001-2010 period and assess their relationship with climate change in the region. The results revealed a great regional variability and inter-annual fluctuation in vegetation phenology. The phenological patterns varied spatially across the region and they were strongly correlated with climate variations and land use patterns. The overall phenological trends appeared to shift towards a later and slightly longer growing season up to 14 days from 2001 to 2010. Interestingly, the corresponding rainy season seemed to have started earlier and ended later, resulting in a slightly longer wet season extending up to 7 days, while the total amount of rainfall in the region decreased during the same time period. The phenological shifts and changes in vegetation growth appeared to be associated with climate events such as EL Niño in 2005. Furthermore, rainfall seemed to be the dominant force driving the phenological changes in naturally vegetated areas and rainfed croplands, whereas land use management was the key factor in irrigated agricultural areas.

An agent-based model to simulate tsetse fly distribution and control techniques: a case study in Nguruman, Kenya.

BACKGROUND: African trypanosomiasis, also known as "sleeping sickness" in humans and "nagana" in livestock is an important vector-borne disease in Sub-Saharan Africa. Control of trypanosomiasis has focused on eliminating the vector, the tsetse fly (Glossina, spp.). Effective tsetse fly control planning requires models to predict tsetse population and distribution changes over time and space. Traditional planning models have used statistical tools to predict tsetse distributions and have been hindered by limited field survey data. METHODOLOGY/RESULTS: We developed an Agent-Based Model (ABM) to provide timing and location information for tsetse fly control without presence/absence training data. The model is driven by daily remotely-sensed environment data. The model provides a flexible tool linking environmental changes with individual biology to analyze tsetse control methods such as aerial insecticide spraying, wild animal control, releasing irradiated sterile tsetse males, and land use and cover modification. SIGNIFICANCE: This is a bottom-up process-based model with freely available data as inputs that can be easily transferred to a new area. The tsetse population simulation more closely approximates real conditions than those using traditional statistical models making it a useful tool in tsetse fly control planning.

Modeling larval malaria vector habitat locations using landscape features and cumulative precipitation measures.

BACKGROUND: Predictive models of malaria vector larval habitat locations may provide a basis for understanding the spatial determinants of malaria transmission. METHODS: We used four landscape variables (topographic wetness index [TWI], soil type, land use-land cover, and distance to stream) and accumulated precipitation to model larval habitat locations in a region of western Kenya through two methods: logistic regression and random forest. Additionally, we used two separate data sets to account for variation in habitat locations across space and over time. RESULTS: Larval habitats were more likely to be present in locations with a lower slope to contributing area ratio (i.e. TWI), closer to streams, with agricultural land use relative to nonagricultural land use, and in friable clay/sandy clay loam soil and firm, silty clay/clay soil relative to friable clay soil. The probability of larval habitat presence increased with increasing accumulated precipitation. The random forest models were more accurate than the logistic regression models, especially when accumulated precipitation was included to account for seasonal differences in precipitation. The most accurate models for the two data sets had area under the curve (AUC) values of 0.864 and 0.871, respectively. TWI, distance to the nearest stream, and precipitation had the greatest mean decrease in Gini impurity criteria in these models. CONCLUSIONS: This study demonstrates the usefulness of random forest models for larval malaria vector habitat modeling. TWI and distance to the nearest stream were the two most important landscape variables in these models. Including accumulated precipitation in our models improved the accuracy of larval habitat location predictions by accounting for seasonal variation in the precipitation. Finally, the sampling strategy employed here for model parameterization could serve as a framework for creating predictive larval habitat models to assist in larval control efforts.

Land surface temperature and transboundary air pollution: a case of Bangkok Metropolitan Region.

In a rapidly urbanizing world, heavy air pollution and increasing surface temperature pose significant threats to human health and lives, especially in densely populated cities. In this study, we took an information theory perspective to investigate the causal relationship between diel land surface temperature (LST) and transboundary air pollution (TAP) from 2003 to 2020 in the Bangkok Metropolitan Region (BMR), which includes Bangkok Metropolis and its five adjacent provinces. We found an overall increasing trend of LST over the study region, with the mean daytime LST rising faster than nighttime LST. Evident seasonal variations showed high aerosol optical depth (AOD) loadings during the dry period and low loadings at the beginning of the rainy season. Our study revealed that TAP affected diel surface temperature in Bangkok Metropolis significantly. Causality tests show that air pollutants of two adjacent provinces west of Bangkok, i.e., Nakhon Pathom and Samut Sakhon, have a greater influence on the LST of Bangkok than other provinces. Also, the bidirectional relationship indicates that air pollution has a greater impact on daytime LST than nighttime LST. While LST has an insignificant influence on AOD during the daytime, it influences AOD significantly at night. Our study offers a new approach to understanding the causal impact of TAP and can help policymakers to identify the most relevant locations that cause pollution, leading to appropriate planning and management.

Regional health care planning: a methodology to cluster facilities using community utilization patterns.

BACKGROUND: Community-based health care planning and regulation necessitates grouping facilities and areal units into regions of similar health care use. Limited research has explored the methodologies used in creating these regions. We offer a new methodology that clusters facilities based on similarities in patient utilization patterns and geographic location. Our case study focused on Hospital Groups in Michigan, the allocation units used for predicting future inpatient hospital bed demand in the state's Bed Need Methodology. The scientific, practical, and political concerns that were considered throughout the formulation and development of the methodology are detailed. METHODS: The clustering methodology employs a 2-step K-means + Ward's clustering algorithm to group hospitals. The final number of clusters is selected using a heuristic that integrates both a statistical-based measure of cluster fit and characteristics of the resulting Hospital Groups. RESULTS: Using recent hospital utilization data, the clustering methodology identified 33 Hospital Groups in Michigan. CONCLUSIONS: Despite being developed within the politically charged climate of Certificate of Need regulation, we have provided an objective, replicable, and sustainable methodology to create Hospital Groups. Because the methodology is built upon theoretically sound principles of clustering analysis and health care service utilization, it is highly transferable across applications and suitable for grouping facilities or areal units.

Measuring geographic access to health care: raster and network-based methods.

BACKGROUND: Inequalities in geographic access to health care result from the configuration of facilities, population distribution, and the transportation infrastructure. In recent accessibility studies, the traditional distance measure (Euclidean) has been replaced with more plausible measures such as travel distance or time. Both network and raster-based methods are often utilized for estimating travel time in a Geographic Information System. Therefore, exploring the differences in the underlying data models and associated methods and their impact on geographic accessibility estimates is warranted. METHODS: We examine the assumptions present in population-based travel time models. Conceptual and practical differences between raster and network data models are reviewed, along with methodological implications for service area estimates. Our case study investigates Limited Access Areas defined by Michigan's Certificate of Need (CON) Program. Geographic accessibility is calculated by identifying the number of people residing more than 30 minutes from an acute care hospital. Both network and raster-based methods are implemented and their results are compared. We also examine sensitivity to changes in travel speed settings and population assignment. RESULTS: In both methods, the areas identified as having limited accessibility were similar in their location, configuration, and shape. However, the number of people identified as having limited accessibility varied substantially between methods. Over all permutations, the raster-based method identified more area and people with limited accessibility. The raster-based method was more sensitive to travel speed settings, while the network-based method was more sensitive to the specific population assignment method employed in Michigan. CONCLUSIONS: Differences between the underlying data models help to explain the variation in results between raster and network-based methods. Considering that the choice of data model/method may substantially alter the outcomes of a geographic accessibility analysis, we advise researchers to use caution in model selection. For policy, we recommend that Michigan adopt the network-based method or reevaluate the travel speed assignment rule in the raster-based method. Additionally, we recommend that the state revisit the population assignment method.

Tsetse Fly Control in Kenya's Spatially and Temporally Dynamic Control Reservoirs: A Cost Analysis.

Human African trypanosomiasis (HAT) and animal African trypanosomiasis (AAT) are significant health concerns throughout much of sub-Saharan Africa. Funding for tsetse fly control operations has decreased since the 1970s, which has in turn limited the success of campaigns to control the disease vector. To maximize the effectiveness of the limited financial resources available for tsetse control, this study develops and analyzes spatially and temporally dynamic tsetse distribution maps of Glossina subgenus Morsitans populations in Kenya from January 2002 to December 2010, produced using the Tsetse Ecological Distribution Model. These species distribution maps reveal seasonal variations in fly distributions. Such variations allow for the identification of "control reservoirs" where fly distributions are spatially constrained by fluctuations in suitable habitat and tsetse population characteristics. Following identification of the control reservoirs, a tsetse management operation is simulated in the control reservoirs using capital and labor control inputs from previous studies. Finally, a cost analysis, following specific economic guidelines from existing tsetse control analyses, is conducted to calculate the total cost of a nationwide control campaign of the reservoirs compared to the cost of a nationwide campaign conducted at the maximum spatial extent of the fly distributions from January 2002 to December 2010. The total cost of tsetse management within the reservoirs sums to $14,212,647, while the nationwide campaign at the maximum spatial extent amounts to $33,721,516. This savings of $19,508,869 represents the importance of identifying seasonally dynamic control reservoirs when conducting a tsetse management campaign, and, in the process, offers an economical means of fly control and disease management for future program planning.

Urbanization, economic development, and environmental changes in transitional economies in the global south: a case of Yangon.

Background: Transitional economies in Southeast Asia-a distinct group of developing countries-have experienced rapid urbanization in the past several decades due to the economic transition that fundamentally changed the function of their economies, societies and the environment. Myanmar, one of the least developed transitional economies in Southeast Asia, increased urbanization substantially from 25% in 1990 to 31% in 2019. However, major knowledge gaps exist in understanding the changes in urban land use and land cover and environment and their drivers in its cities. Methods: We studied Yangon, the largest city in Myanmar, for the urbanization, environmental changes, and the underlying driving forces in a radically transitioned economy in the developing world. Based on satellite imagery and historic land use maps, we quantified the expansion of urban built-up land and constructed the land conversion matrix from 1990 through 2020. We also used three air pollutants to illustrate the changes in environmental conditions. We analyzed the coupled dynamics among urbanization, economic development, and environmental changes. Through conducting a workshop with 20 local experts, we further analyzed the influence of human systems and natural systems on Yangon's urbanization and sustainability. Results: The city of Yangon expanded urban built-up land rapidly from 1990 to 2000, slowed down from 2000 to 2010, but gained momentum again from 2010 to 2020, with most newly added urban built-up land appearing to be converted from farmland and green land in both 1990-2000 and 2010-2020. Furthermore, the air pollutant concentration of CO decreased, but that of NO2 and PM2.5 increased in recent years. A positive correlation exists between population and economic development and the concentration of PM2.5 is highly associated with population, the economy, and the number of vehicles. Finally, the expert panel also identified other potential drivers for urbanization, including the extreme climate event of Cyclone Nargis, capital relocation, and globalization. Conclusions: Our research highlights the dramatic expansion of urban land and degradation of urban environment measured by air pollutants and interdependent changes between urbanization, economic development, and environmental changes.

Crop climate suitability mapping on the cloud: a geovisualization application for sustainable agriculture.

Climate change, food security, and environmental sustainability are pressing issues faced by today's global population. As production demands increase and climate threatens crop productivity, agricultural research develops innovative technologies to meet these challenges. Strategies include biodiverse cropping arrangements, new crop introductions, and genetic modification of crop varieties that are resilient to climatic and environmental stressors. Geography in particular is equipped to address a critical question in this pursuit-when and where can crop system innovations be introduced? This manuscript presents a case study of the geographic scaling potential utilizing common bean, delivers an open access Google Earth Engine geovisualization application for mapping the fundamental climate niche of any crop, and discusses food security and legume biodiversity in Sub-Saharan Africa. The application is temporally agile, allowing variable growing season selections and the production of 'living maps' that are continually producible as new data become available. This is an essential communication tool for the future, as practitioners can evaluate the potential geographic range for newly-developed, experimental, and underrepresented crop varieties for facilitating sustainable and innovative agroecological solutions.

Leveraging big data for public health: Mapping malaria vector suitability in Malawi with Google Earth Engine.

In an era of big data, the availability of satellite-derived global climate, terrain, and land cover imagery presents an opportunity for modeling the suitability of malaria disease vectors at fine spatial resolutions, across temporal scales, and over vast geographic extents. Leveraging cloud-based geospatial analytical tools, we present an environmental suitability model that considers water resources, flow accumulation areas, precipitation, temperature, vegetation, and land cover. In contrast to predictive models generated using spatially and temporally discontinuous mosquito presence information, this model provides continuous fine-spatial resolution information on the biophysical drivers of suitability. For the purposes of this study the model is parameterized for Anopheles gambiae s.s. in Malawi for the rainy (December-March) and dry seasons (April-November) in 2017; however, the model may be repurposed to accommodate different mosquito species, temporal periods, or geographical boundaries. Final products elucidate the drivers and potential habitat of Anopheles gambiae s.s. Rainy season results are presented by quartile of precipitation; Quartile four (Q4) identifies areas most likely to become inundated and shows 7.25% of Malawi exhibits suitable water conditions (water only) for Anopheles gambiae s.s., approximately 16% for water plus another factor, and 8.60% is maximally suitable, meeting suitability thresholds for water presence, terrain characteristics, and climatic conditions. Nearly 21% of Malawi is suitable for breeding based on land characteristics alone and 28.24% is suitable according to climate and land characteristics. Only 6.14% of the total land area is suboptimal. Dry season results show 25.07% of the total land area is suboptimal or unsuitable. Approximately 42% of Malawi is suitable based on land characteristics alone during the dry season, and 13.11% is suitable based on land plus another factor. Less than 2% meets suitability criteria for climate, water, and land criteria. Findings illustrate environmental drivers of suitability for malaria vectors, providing an opportunity for a more comprehensive approach to malaria control that includes not only modeled species distributions, but also the underlying drivers of suitability for a more effective approach to environmental management.

Optimum land cover products for use in a Glossina-morsitans habitat model of Kenya.

BACKGROUND: Tsetse flies are the primary vector for African trypanosomiasis, a disease that affects both humans and livestock across the continent of Africa. In 1973 tsetse flies were estimated to inhabit 22% of Kenya; by 1996 that number had risen to roughly 34%. Efforts to control the disease were hampered by a lack of information and costs associated with the identification of infested areas. Given changing spatial and demographic factors, a model that can predict suitable tsetse fly habitat based on land cover and climate change is critical to efforts aimed at controlling the disease. In this paper we present a generalizable method, using a modified Mapcurves goodness of fit test, to evaluate the existing publicly available land cover products to determine which products perform the best at identifying suitable tsetse fly land cover. RESULTS: For single date applications, Africover was determined to be the best land use land cover (LULC) product for tsetse modeling. However, for changing habitats, whether climatically or anthropogenically forced, the IGBP DISCover and MODIS type 1 products where determined to be most practical. CONCLUSION: The method can be used to differentiate between various LULC products and be applied to any such research when there is a known relationship between a species and land cover.

Re-evaluating the Malawian Farm Input Subsidy Programme.

The Malawian Farm Input Subsidy Programme (FISP) has received praise as a proactive policy that has transformed the nation's food security, yet irreconcilable differences exist between maize production estimates distributed by the Food and Agriculture Organization of the United Nations (FAO), the Malawi Ministry of Agriculture and Food Security (MoAFS) and the National Statistical Office (NSO) of Malawi. These differences illuminate yield-reporting deficiencies and the value that alternative, politically unbiased yield estimates could play in understanding policy impacts. We use net photosynthesis (PsnNet) as an objective source of evidence to evaluate production history and production potential under a fertilizer input scenario. Even with the most generous harvest index (HI) and area manipulation to match a reported error, we are unable to replicate post-FISP production gains. In addition, we show that the spatial delivery of FISP may have contributed to popular perception of widespread maize improvement. These triangulated lines of evidence suggest that FISP may not have been the success it was thought to be. Lastly, we assert that fertilizer subsidies may not be sufficient or sustainable strategies for production gains in Malawi.

Evaluating Michigan's community hospital access: spatial methods for decision support.

BACKGROUND: Community hospital placement is dictated by a diverse set of geographical factors and historical contingency. In the summer of 2004, a multi-organizational committee headed by the State of Michigan's Department of Community Health approached the authors of this paper with questions about how spatial analyses might be employed to develop a revised community hospital approval procedure. Three objectives were set. First, the committee needed visualizations of both the spatial pattern of Michigan's population and its 139 community hospitals. Second, the committee required a clear, defensible assessment methodology to quantify access to existing hospitals statewide, taking into account factors such as distance to nearest hospital and road network density to estimate travel time. Third, the committee wanted to contrast the spatial distribution of existing community hospitals with a theoretical configuration that best met statewide demand. This paper presents our efforts to first describe the distribution of Michigan's current community hospital pattern and its people, and second, develop two models, access-based and demand-based, to identify areas with inadequate access to existing hospitals. RESULTS: Using the product from the access-based model and contiguity and population criteria, two areas were identified as being "under-served." The lower area, located north/northeast of Detroit, contained the greater total land area and population of the two areas. The upper area was centered north of Grand Rapids. A demand-based model was applied to evaluate the existing facility arrangement by allocating daily bed demand in each ZIP code to the closest facility. We found 1,887 beds per day were demanded by ZIP centroids more than 16.1 kilometers from the nearest existing hospital. This represented 12.7% of the average statewide daily bed demand. If a 32.3 kilometer radius was employed, unmet demand dropped to 160 beds per day (1.1%). CONCLUSION: Both modeling approaches enable policymakers to identify under-served areas. Ultimately this paper is concerned with the intersection of spatial analysis and policymaking. Using the best scientific practice to identify locations of under-served populations based on many factors provides policymakers with a powerful tool for making good decisions.

Smallholder Farms and the Potential for Sustainable Intensification.

The sustainable intensification of African agriculture is gaining momentum with the compelling need to increase food and agricultural production. In Southern Africa, smallholder farming systems are predominately maize-based and subject to erratic climatic conditions. Farmer crop and soil management decisions are influenced by a plethora of complex factors such as market access resource availability, social relations, environment, and various messages on sustainable farming practices. Such factors pose barriers to increasing sustainable intensification in Africa. This paper characterizes smallholder farming practices in Central Malawi, at Africa Research in Sustainable Intensification for the Next Generation (Africa RISING) project sites. We present findings from a survey of 324 farmers, located within four Africa RISING sites selected in a stratified random manner to represent (1) low agricultural potential (high evapotranspiration, variable rainfall), (2) medium agricultural potential (two sites), and (3) high agricultural potential (well-distributed rainfall). Soil fertility was low overall, and certain farming practices appeared to limit the sustainability of agricultural production. Nearly half of farmers did not value legume residues as a high nutrient value resource for soil amelioration, as legume residues were removed (17.9%) or burned (21.4%). Conversely, maize residues were rarely removed (4.5%) or burned (10.4%). We found that farmers do not allocate soil amendment resources to legume fields (zero instances of mineral fertilizer or manure application to legumes compared to 88 and 22% of maize systems, respectively). Policy makers in Malawi have led initiatives to intensify agricultural systems through subsidizing farmer access to mineral fertilizer as well as maize hybrid seed, and only rarely to improved legume seed. In this survey, farmers allocate mineral fertilizer to maize systems and not legume systems. There is urgent need to invest in education on sustainable reinvestment in natural resources through complementary practices, such as maximization of biological nitrogen fixation through improved legume agronomy and better organic resource and crop residue management. Recent efforts by Malawi agricultural services to promote doubled-up legumes as a sustainable intensification technology are encouraging, but benefits will not accrue unless equal attention is given to an extension campaign on management of organic resources such as crop residues.