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Abstract Decomposing the responses of ecosystem structure and function in drylands to changes in human-environmental forcing is a pressing challenge. Though trend detection studies are extensive, these studies often fail to attribute them to potential spatiotemporal drivers. Most attribution studies use a single empirical model or a causal graph that cannot be generalized or extrapolated to larger scales or account for spatial changes and multiple independent processes. Here, we proposed and tested a multi-stage, multi-model framework that detects vegetation trends and attributes them to ten independent social-environmental system (SES) drivers in Kazakhstan (KZ). The time series segmented residual trend analysis showed that 45.71% of KZ experienced vegetation degradation, with land use change as the predominant contributor (22.54%; 0.54 million km2), followed by climate change and climate variability. Pixel-wise fitted Granger Causality and random forest models revealed that sheep & goat density and snow cover had dominant negative and positive impacts on vegetation in degraded areas, respectively. Overall, we attribute vegetation changes to SES driver impacts for 19.81% of KZ (out of 2.39 million km2). The identified vegetation degradation hotspots from this study will help identify locations where restoration projects could have a greater impact and achieve land degradation neutrality in KZ.

Abstract Livestock rearing is a major source of livelihood for food and income in dryland Asia. Increasing livestock density (LSKD) affects ecosystem structure and function, amplifies the effects of climate change, and facilitates disease transmission. Significant knowledge and data gaps regarding their density, spatial distribution, and changes over time exist but have not been explored beyond the county level. This is especially true regarding the unavailability of high-resolution gridded livestock data. Hence, we developed a gridded LSKD database of horses and small ruminants (i.e., sheep & goats) at high-resolution (1 km) for Kazakhstan (KZ) from 2000–2019 using vegetation proxies, climatic, socioeconomic, topographic, and proximity forcing variables through a random forest (RF) regression modeling. We found high-density livestock hotspots in the south-central and southeastern regions, whereas medium-density clusters in the northern and northwestern regions of KZ. Interestingly, population density, proximity to settlements, nighttime lights, and temperature contributed to the efficient downscaling of district-level censuses to gridded estimates. This database will benefit stakeholders, the research community, land managers, and policymakers at regional and national levels.

As the two largest landlocked countries, Kazakhstan and Mongolia have similar biophysical conditions and socioeconomic roots in the former Soviet Union. Our objective is to investigate the direction, extent, and spatial variation of land cover change at three administrative levels over three decades (1990–2020). We selected three provinces from each country (Aktobe, Akmola, and Almaty province in Kazakhstan, and Arkhangai, Tov, and Dornod in Mongolia) to classify the land cover into forest, grassland, cropland, barren, and water. Altogether, 6964 Landsat images were used in pixel-based classification method with random forest model for image processing. Six thousand training data points (300 training points × 5 classes × 4 periods) for each province were collected for classification and change detection. Land cover changes at decadal and over the entire study period for five land cover classes were quantified at the country, provincial, and county level. High classification accuracy indicates localized land cover classification have an edge over the latest global land cover product and reveal fine differences in landscape composition. The vast steppe landscapes in these two countries are dominated by grasslands of 91.5% for Dornod in Mongolia and 74.7% for Aktobe in Kazakhstan during the 30-year study period. The most common land cover conversion was grassland to cropland. The cyclic land cover conversions between grassland and cropland reflect the impacts of the Soviet Union’s largest reclamation campaign of the 20th century in Kazakhstan and the Atar-3 agriculture re-development in Mongolia. Kazakhstan experienced a higher rate of land cover change over a larger extent of land area than Mongolia. The spatial distribution of land use intensity indicates that land use hotspots are largely influenced by policy and its shifts. Future research based on these large-scale land use and land cover changes should be focused the corresponding ecosystem and society functions.

Studies examining the joint interactions and impacts of social-environmental system (SES) drivers on vegetation dynamics in Central Asia are scarce. We investigated seasonal trends and anomalies in drivers and their impacts on ecosystem structure and function (ESF). We explored the response of net primary production, evapotranspiration and normalized difference vegetation index (NDVI) to various SES drivers—climate, human influence, heat stress, water storage, and water content—and their latent relationships in Kazakhstan. We employed 13 predictor drivers from 2000 to 2016 to identify the interactions and impacts on ESF variables that reflect vegetation growth and productivity. We developed 12 models with different predictor–response variable combinations and separated them into two approaches. First, we considered the winter percent snow cover (SNOWc) and spring rainfall (P_MAM) as drivers and then as moderators in a structural equation model (SEM). SNOWc variability (SNOWc _SD ) as an SEM moderator exhibited superior model accuracy and explained the interactions between various predictor–response combinations. Winter SNOWc _SD did not have a strong direct positive influence on summer vegetation growth and productivity; however, it was an important moderator between human influence and the ESF variables. Spring rainfall had a stronger impact on ESF variability than summer rainfall. We also found strong positive feedback between soil moisture (SM) and NDVI, as well as a strong positive influence of vegetation optical depth (VOD) and terrestrial water storage (TWS) on ESF. Livestock density (LSK _D ) exhibited a strong negative influence on ESF. Our results also showed a strong positive influence of socioeconomic drivers, including crop yield per hectare (CROPh), gross domestic product per capita (GDPca), and population density (POP _D ) on vegetation productivity. Finally, we found that vegetation dynamics were more sensitive to SM, VOD, LSK _D and POP _D than climatic drivers, suggesting that water content and human influence drivers were more critical in Kazakhstan.