Interdependent Dynamics of Food, Energy and Water in Kazakhstan and Mongolia: Connecting LULCC to the Transitional Socioecological Systems

This study is proposed to examine the interconnections of food, energy and water (FEW), as well as their interdependent dynamics under the rapid changes in climate and intensified land use in Kazakhstan (KaZ) and Mongolia (MG) over a 40-year period (1981-2020). We will apply the concept, principles and methods of socioeconomic-ecological systems to guide our research at three hierarchical levels: local, provincial and national. Net primary production, albedo, and evapotranspiration will be used as the key indicators for food production, radiation energy, and water balance, respectively, of the rangelands that support continued increases in economies, livestock, agriculture, and human development. Our premise is that the interconnections and interdependencies of FEW measures vary significantly between KaZ and MG, among the provinces within each country, and among the herding landscapes. Four mechanisms will be examined to test three specific hypotheses: socioeconomic, biophysical, institutional, and a localized regulative mechanism. At national and provincial levels, we will take a macro-ecosystem and macroeconomic approach to examine the interdependencies of NPP, albedo, ET, LSK, and their spatiotemporal relationships (Task 1). A hierarchical Bayesian Structural Equation Model (HB-SEM) and modern econometric models will be used as our primary tools to model the complex data from a variety of remote sensing products and available socioeconomic databases. At the provincial level, we propose an innovative downscaling model generate 30-m resolution FEW measures from the coarse resolution MODIS/AVHHR products to explore the direct consequences of LCLUC (Task 2). Three provinces from each country will be studied by treating counties as the sampling units for HB-SEM. At local herding landscapes, we will apply conventional ecosystem and microeconomic methods to explore the direct impacts of herding practices on FEW measures through manipulative experiments (Task 3). We aim to identify the direct connections between land use practices and FEW measures. Our premise is that herders who are well informed about the local climate, the landscape, market accessibility, and other knowledge/information would conduct their practices more effectively than those without. This information/knowledge, consequently, will improve both their family’s livelihood and FEW functions. The experiment will be conducted in the Almaty and Tov provinces through comparing the changes in FEW measures of the two experimental herder groups. Our experiment will be stratified into four counties in each province, with 12 herders per county to be studied. We will provide timely “knowledge & information” to half of the selected herders, while the other half will continue “business as usual”. Two animals from each herding family (total = 96*2) will be randomly selected for movement tracking by installing a GPS collar. Intensive field campaigns, monitoring stations, and large scale household surveys (up to 600) will be organized through applications of UAS, GPS tracking, ground sampling of the herding landscapes, and household surveys. We aim to advance FEW science by focusing on the interactions and feedbacks of major FEW functions and drivers through examining their exogenous processes and underlying mechanisms.  Data will be distributed via our project website (which will be hyperlinked to NEFI and LCLUC webpages) along with sufficient metadata to describe the locations and methods. Our data management and access plan will be posted alongside our data both to make our procedures clearer, as well as to provide a contribution to the developing field of data science and informatics. We have organized a vibrant, multi-expertise team that has knowledge and experience in the region. Our well-established working relationships in KaZ and MG and available facilities/equipment will ensure the success of this project despite the multi-national nature of the collaboration.