While there has been a considerable reduction in the number of undernourished people in the past two decades, india still has among the largest malnutrition rates in the world. Increasing pressures…

This federal contract award, with ID NNX17AK75G, was granted by the National Aeronautics and Space Administration (NASA) to the University of Florida (UF) Division of Sponsored Research, a major research institution with expertise across various disciplines. The $588,948.72 grant is for a research project titled "Landscapes in Flux: The Influence of Demographic Change and Institutional Mechanisms on Land Cover Change" and does not have a set-aside designation.

The research aims to generate spatially-explicit data on key demographic and socioeconomic indicators related to food security in India, develop an integrated modeling framework to assess the impact of various factors on food security, and evaluate the influence of food security indicators on land cover change patterns. This work is expected to provide critical insights to support geographically differentiated strategies for addressing issues such as food insecurity, land degradation, and the impact of climate change on agriculture in the region. The project has a completion date of June 8, 2023.

While there has been a considerable reduction in the number of undernourished people in the past two decades, india still has among the largest malnutrition rates in the world. Increasing pressures from population growth and urbanization have subsequently affected land use patterns in india, with as much as 36.6% of all land in india degraded. Climate change will likely exacerbate these issues through additional stresses on food production. Regionally, a range of socioeconomic factors also play a role, such as: lack of availability of and access to resources, land degradation, food insecurity and landlessness. Geographically differentiated strategies are needed to address these issues, but current strategies are severely hampered by the paucity of spatially-explicit information on food security and agriculture. This information is crucial for early detection of trends and to disentangle the complex relationships between food security and land use. Of critical need are spatially-explicit data on the factors that define dimensions of food security, and methods that allow the combination of these data into holistic synthetic indicators that explain causal factors in an integrated manner. Identifying, estimating and mapping spatial variations in the proportional strengths of these interrelationships will help identify the factors influencing food security and eventually land-use and land-cover change in rural as well as peri-urban areas. The specific objectives of this proposal are to: 1) generate spatially downscaled data of key demographic and socio-economic indicators that putatively define dimensions of food security in india, 2) use a hypothesis-driven approach to integrate economic, social, policy, infrastructural, and behavioral facets of food security into a holistic modeling framework, and, finally to 3) assess land cover change as an emergent outcome of patterns of socio-economic, demographic and policy instruments at local to regional scales. To do this, we will use small area estimation techniques to spatially disaggregate household-scale data on critical demographic, socioeconomic and food security indicators to the village scale. Subsequently, using a structural equation modeling framework, we will integrate indicators of food security with extant socioeconomic and demographic patterns, indicators of climate adaptability and metrics of infrastructural and policy instruments. Maps of latent vectors of the SEM will allow the first-ever spatialized representation of the combined effects of institutional support, accessibility to markets and extension services on regional indicators of poverty and malnutrition. Further, we will develop a generalized methodology for mapping land cover and producing probabilistic pixel-wise maps of classification uncertainties. We will eventually combine land cover change probabilities with indicators of food security derived from structural equation models to assess the influence of food security indicators on patterns of land cover change. These analyses will provide the first-ever assessments of the relative strengths of drivers of land cover change in the study regions at local to regional scales.