Untangling the impacts of socioeconomic and climatic changes on vegetation greenness and productivity in Kazakhstan

Dados Bibliográficos

ID15547339
AutoresVenkatesh Kolluru (0000-0002-2110-5560, University of South Dakota, autor correspondente), Ranjeet John (0000-0002-0150-8450, University of South Dakota), Jiquan Chen (0000-0003-0761-9458, Michigan State University), Meghann Jarchow (0000-0003-4978-7783, University of South Dakota), Reza Goljani Amirkhiz (0000-0001-7700-7487, University of South Dakota), Vincenzo Giannico (0000-0002-9907-3730, University of Bari Aldo Moro), Sakshi Saraf (0000-0002-6785-8381, University of South Dakota), Khushboo Jain (0009-0001-9843-1495, University of South Dakota), Maira Kussainova (0000-0002-9800-6093, Kazakh National Agrarian Research University), Jing Yuan (0000-0002-4262-4116, Michigan State University)
Ano2022
Volume17
Fascículo9
Páginas095007-095007
Data de publicação2022-08-24
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEnvironmental Research Letters (JOURNAL)
Identificadores do periódicoISSN: 1748-9326 • E-ISSN: 1748-9326
EditoraIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ac8c59
OpenAlexW4293200746
IdiomaEN
Citações recebidas1
Referências citadas96

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

Atmospheric sciences · Climate change · Climatology · Ecosystem · Evapotranspiration · Generalized additive model · Geography · Normalized Difference Vegetation Index · Physical geography · Primary production · Productivity · Vegetation (pathology · Water content · Environmental Science · Mathematics · Plant Water Relations and Carbon Dynamics · Remote Sensing in Agriculture · Tree-ring climate responses · Ecology