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Cambodia is witnessing a “Goldilocks moment” in demographic change concurrent with shifts in land use, hydrology, and climate. These trends interact and affect food production, food costs, and food security. Drivers of these trends are typically examined separately with interacting factors considered along disciplinary margins. While science models to explore these interacting effects have been proposed, there remains an applied research gap in integrating these pieces and assessing interdisciplinary opportunities for developing food security solutions. Developed following a request from USAID to elucidate food security conditions in Cambodia, here the authors present their geospatial synthesis of the biophysical and socioeconomic drivers of current food security risk, as well as explore future trends for those conditions. The overall structure shows several interlocking or mutually reinforcing trends in systems that point towards a significant intensification of food insecurity in the near future. They offer an assessment of future targets for food systems innovation.

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.

Sustainable water management is a core sustainable development goal (SDG) that also contributes to other SDGs, including food and water security, ecosystem health, and climate adaptation. To achieve these synergies, policies must target efforts to regions that best correspond with development objectives. This study designs a targeting strategy for irrigation expansion in southern Mexico—a region long considered to have strong potential for sustainable irrigation development. We use an integrated farm typology and decision tree approach to identify priority municipalities for irrigation expansion. We use multivariate statistics to examine the relationships among farm characteristics in 933 municipalities, classifying each according to four farm types: lowland, midland, midland-irrigated, and highland. We then partition municipalities into 11 farm-type subgroups, each ranked by priority level for receiving irrigation interventions following Mexico’s National Water Program guidelines. Results identify a ‘highest-priority’ subgroup of 73 municipalities comprised mostly of midland and highland farm types. These types are characterized by low irrigation use, small farmland areas, high vulnerability to climate, high marginalization (poverty), strong representation from indigenous communities, low maize yield, and high rates of subsistence production. Findings provide a crucial first approximation of where irrigation expansion would best address water policy priorities and sustainable development objectives in southern Mexico. This study also provides a useful framework for scaling organizations tasked with targeting development efforts across large spatial scales.

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.

Species distribution models (SDMs) are a method for predicting the spatial distribution of a target species. While previous literature highlights predominantly biophysical variables in deve...

Successful scaling of agricultural development strategies is fundamental to increased production and yields, yet targeting efforts frequently fail to fully consider the underlying biophysical drivers of agricultural marginality, particularly at fine spatial resolutions. We present a heuristic for intelligent targeting, utilizing remotely sensed information to identify the intersection between marginal conditions for performance of a staple crop and the optimal niche for technologies that improve crop performance. Here, we explore the geographic potential of maize diversification with pigeonpea, a crop with soil productivity enhancing properties. Overall, 79 percent of agricultural land in Malawi exhibits climate conditions optimal for pigeonpea cultivation and, in total, approximately 51 percent of Malawian maize-based farming is expected to receive some benefit from pigeonpea integration, with 9 percent receiving predictable and substantial benefits. These findings illustrate the geographic scaling poten...

Scaling up development measures to target global food insecurity has a distinctly spatial character and is often cited as a solution to the global hunger crisis. Development does not occur without scaling and consensus on the ontological meaning of scaling up is a vital component to developing sustainable solutions to the global hunger crisis across geographical scales. Yet ‘scaling up’, while frequently used throughout Research and Development (R&D) and Natural Resource Management (NRM) literature, lacks ontological agreement. We begin by considering the noun, ‘scale’ and existing literature on scaling up, then present a visual analysis of definitions provided for scaling up across development institutions. Our study finds that the organization of terms used across these definitions falls into three distinct categories: Interventions, Mechanisms, and Outcomes. Further, we contend that the continued uncertainty is linked to scale being applied in two fashions: as a noun (outcome) and verb (process). Rather than calling for reformed definitions, we argue for precision of definitions. To that end, we present a conceptual framework of scaling up that gives greater emphasis on separating the noun scale, from the verb, to scale. Further, Monitoring and Evaluation (M&E) in our model complements scaling efforts beginning with how scaling up is defined by program, through to final evaluation of success.

Under-five child mortality declined 47% since 2000 following the implementation of the United Nation’s (UN) Millennium Development Goals. To further reduce under-five child mortality, the UN’s Sustainable Development Goals (SDGs) will focus on interventions to address neonatal mortality, a major contributor of under-five mortality. The African region has the highest neonatal mortality rate (28.0 per 1000 live births), followed by that of the Eastern Mediterranean (26.6) and South-East Asia (24.3). This study used the Demographic and Health Survey Birth Recode data (http://dhsprogram.com/data/File-Types-and-Names.cfm) to identify high-risk districts and countries for neonatal mortality in two sub-regions of Africa – East Africa and West Africa. Geographically weighted Poisson regression models were estimated to capture the spatially varying relationships between neonatal mortality and dimensions of potential need i) care around the time of delivery, ii) maternal education, and iii) women’s empowerment. In East Africa, neonatal mortality was significantly associated with home births, mothers without an education and mothers whose husbands decided on contraceptive practices, controlling for rural residency. In West Africa, neonatal mortality was also significantly associated with home births, mothers with a primary education and mothers who did not want or plan their last child. Importantly, neonatal mortality associated with home deliveries were explained by maternal exposure to unprotected water sources in East Africa and older maternal age and female sex of infants in West Africa. Future SDG-interventions may target these dimensions of need in priority high-risk districts and countries, to further reduce the burden of neonatal mortality in Africa.