Maira Kussainova
@kaznaru.edu.kz
Sustainable Agriculture Center
Kazakh National Agrarian Research University
RESEARCH INTERESTS
Agriculture, soil and ecosystem processes, and their interactive feedback to biophysical and anthropogenic activities, including precision agriculture and conservation biology, and landscape ecology. Her current research is simultaneously related to the i
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Zhenwang Li, Lei Ding, Beibei Shen, Jiquan Chen, Dawei Xu, Xu Wang, Wei Fang, Alim Pulatov, Maira Kussainova, Amartuvshin Amarjargal,et al.
Elsevier BV
Venkatesh Kolluru, Ranjeet John, Sakshi Saraf, Jiquan Chen, Brett Hankerson, Sarah Robinson, Maira Kussainova, and Khushboo Jain
Springer Science and Business Media LLC
AbstractLivestock 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.
Beibei Shen, Jingpeng Guo, Zhenwang Li, Jiquan Chen, Wei Fang, Maira Kussainova, Amartuvshin Amarjargal, Alim Pulatov, Ruirui Yan, Oleg A. Anenkhonov,et al.
MDPI AG
Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R2 = 0.26, RMSE = 0.41 m2/m2) and typical steppe (R2 = 0.32, RMSE = 0.38 m2/m2); the GEOV2 LAI performed better in desert steppe (R2 = 0.39, RMSE = 0.30 m2/m2). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R2 = 0.94), MODIS LAI (R2 = 0.92), and GLOBMAP LAI (R2 = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan.
Maira KUSSAİNOVA, Maxat TOİSHİMANOV, Gulnaz ISKAKOVA, Nursultan NURGALİ, and Jiquan CHEN
Eurasian Journal of Soil Sciences
The present study investigates the effects of different fertilization practices, including chemical and organic fertilizers, on CH4 and N2O emissions in various crop cultivation systems in Kazakhstan. The research focuses on three staple crops: wheat, barley, and corn, which are commonly grown in the region. A randomized complete block design field trial was conducted with three replications for each crop, totaling 27 plots. Gas sampling was carried out five times between June and September 2021, with cylindrical gas sampling chambers inserted into the soil at a depth of 10 cm. The concentrations of CH4 and N2O were analyzed using GS-MS. Results reveal that all three crops exhibited moderate to high CH4 and N2O emissions, with corn consistently displaying the highest emissions. Both chemical and organic fertilizers led to increased emissions of CH4 and N2O compared to control plots. The organic fertilizer treatment occasionally showed slightly higher emissions compared to chemical fertilizer treatment. However, the differences in CH4 and N2O concentrations between fertilized and unfertilized plots were not drastically significant. Notably, environmental factors, such as soil moisture and temperature, played a more prominent role in influencing CH4 and N2O production than the type of fertilizer applied. These findings underscore the significance of optimizing fertilization practices to minimize greenhouse gas emissions while maintaining crop productivity and promoting sustainable agriculture in Kazakhstan.
Lulu Hou, Xiaoping Xin, Haixia Sun, Yi Tao, Jiquan Chen, Ruirui Yan, Xiang Zhang, Beibei Shen, Ahmed Ibrahim Ahmed Altome, Yousif Mohamed Zainelabdeen Hamed,et al.
Elsevier BV
Lulu Hou, Xiaoping Xin, Beibei Shen, Qi Qin, Ahmed Ibrahim Ahmed Altome, Yousif Mohamed Zainelabdeen Hamed, Ruirui Yan, Serekpaev Nurlan, Nogayev Adilbek, Akhylbekova Balzhan,et al.
MDPI AG
(1) Estimation of grazing livestock intake is the basis for studying animal–plant relationships and the nutritional status of grazing livestock and has important implications for grassland composition and productivity. (2) We used the saturated alkanes method to determine the feed intake and vegetation nutrient digestibility of livestock at different grazing intensities and in different months. (3) We found that C31 had the highest concentration in both pasture and fecal output, and the average recovery of C31 was 77.99%. The different grazing intensities significantly affected livestock intake. As the grazing intensity increased, there was a decreasing trend of livestock intake and the highest livestock feed intake was 6.11 kg DM/day in light grazing. With the increase in grazing season months, the highest livestock intake was 6.67 kg DM/day in the cold period in September. The month also had a significant effect on the digestibility of livestock for all nutrient variables when compared to the grazing intensity. Livestock weight and medium palatability species are more important for livestock intake. (4) Our study provides a more accurate measurement of grazing livestock intake, which can be used as a reference for the scientific management of grazing livestock and the rational use of grazing pastures.
Kolluru Venkatesh, Ranjeet John, Jiquan Chen, Jingfeng Xiao, Reza Goljani Amirkhiz, Vincenzo Giannico, and Maira Kussainova
Elsevier BV
Kolluru Venkatesh, Ranjeet John, Jiquan Chen, Meghann Jarchow, Reza Goljani Amirkhiz, Vincenzo Giannico, Sakshi Saraf, Khushboo Jain, Maira Kussainova, and Jing Yuan
IOP Publishing
Abstract 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 (SNOWcSD) as an SEM moderator exhibited superior model accuracy and explained the interactions between various predictor–response combinations. Winter SNOWcSD 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 (LSKD) 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 (POPD) on vegetation productivity. Finally, we found that vegetation dynamics were more sensitive to SM, VOD, LSKD and POPD than climatic drivers, suggesting that water content and human influence drivers were more critical in Kazakhstan.
Jiquan Chen, Ranjeet John, Jing Yuan, Elizabeth A Mack, Pavel Groisman, Ginger Allington, Jianguo Wu, Peilei Fan, Kirsten M de Beurs, Arnon Karnieli,et al.
IOP Publishing
Abstract This paper synthesizes the contemporary challenges for the sustainability of the social-environmental system (SES) across a geographically, environmentally, and geopolitically diverse region—the Asian Drylands Belt (ADB). This region includes 18 political entities, covering 10.3% of global land area and 30% of total global drylands. At the present time, the ADB is confronted with a unique set of environmental and socioeconomic changes including water shortage-related environmental challenges and dramatic institutional changes since the collapse of the Union of Soviet Socialist Republics. The SES of the ADB is assessed using a conceptual framework rooted in the three pillars of sustainability science: social, economic, and ecological systems. The complex dynamics are explored with biophysical, socioeconomic, institutional, and local context-dependent mechanisms with a focus on institutions and land use and land cover change (LULCC) as important drivers of SES dynamics. This paper also discusses the following five pressing, practical challenges for the sustainability of the ADB SES: (a) reduced water quantity and quality under warming, drying, and escalating extreme events, (b) continued, if not intensifying, geopolitical conflicts, (c) volatile, uncertain, and shifting socioeconomic structures, (d) globalization and cross-country influences, and (e) intensification and shifts in LULCC. To meet the varied challenges across the region, place-based, context-dependent transdisciplinary approaches are needed to focus on the human-environment interactions within and between regional landscapes with explicit consideration of specific forcings and regulatory mechanisms. Future work focused on this region should also assess the role of the following mechanisms that may moderate SES dynamics: socioeconomic regulating mechanisms, biophysical regulating mechanisms, regional and national institutional regulating mechanisms, and localized institutional regulating mechanisms.
Buho Hoshino, Kazuki Seno, Maira Kussainova, Nobutake Nakatani, and Satoru Hobara
IEEE
Oases agriculture is one of the most vulnerable anthropogenic landscapes to climate change and human activates. Central Asia is one of the arid regions highly vulnerable to water scarcity. Located in Central Asia, Kazakhstan is characterized as a semi-arid region which includes dry steppe land in the south. Agriculture carried out in this area is typically oasis farmland with water taken from local rivers used for irrigation. During the former Soviet Union, irrigation projects were widely carried out to expand agricultural land, and large-scale irrigation projects were created in several areas. Therefore, many irrigated farmlands were abandoned due to the collapse of the former Soviet Union. However, China's investment in Kazakhstan agriculture is cultivating once abandoned agricultural land and developing new oases agricultural land. Our study focusses on the vulnerability of oasis agriculture and extract changes in agricultural land for about 30 years from 1989 to the present using Landsat series and Sentinel series and visualized them using RGB color combined techniques. The results show that agricultural land is disappeared or desertified at the Ili River basin and at the foot of the zhongar-Alatau Mountain and that there are several years of fallow even in areas where agriculture is active. Using the Zharkent region in the irrigated alluvial fan of zhongar-Alatau Moun-tain of eastern Kazakhstan as an example, we classify the farm field changing using Landsat TM and Sentilel-2 satellite imagery and identify of vulnerability to the disappearance of oases farmland. China's investment in agriculture could lead to the depletion of water resources in the region.
Jiquan Chen, Ranjeet John, Changliang Shao, Zutao Ouyang, Elizabeth A. Mack, Geoffrey M. Henebry, Gang Dong, Ginger R. H. Allington, Amber L. Pearson, Fangyuan Zhao,et al.
MDPI AG
Integrating the dynamics and interconnections of natural and human system properties into a single measure would make it simpler to reliably and repeatedly assess and compare different social-environmental systems (SES). We propose a novel metric to assess the magnitudes and variations in SES dynamics by integrating longitudinal gross domestic product, population, and ecosystem net primary production. We use annual public data across the Asian Drylands Belt (ADB) from 1992 through 2016 for 18 political entities as our testbed for assessing the efficacy of the metric. We perform cross-comparisons with existing natural and social science metrics to demonstrate the validity of the proposed metric, including the Human Development Index and the Palmer Drought Severity Index. The new metric demonstrates notable and meaningful differences in trends among the political entities that reflect major social, economic and environmental events over the 25-year period. It provides unique perspectives about the three pillar components (social, economic and environmental systems) in each of the 18 political entities (PE) of the ADB. The metric also shows meaningful associations with key economic and environmental indicators and great potential for broader application and evaluation, given additional testing in other countries, regions, and biomes.
Maira KUSSAİNOVA, Kenneth E. SPAETH, and Ermekkul ZHAPARKULOVA
Eurasian Journal of Soil Sciences
This study examined the use of a novel web-tool for Rangeland Hydrology and Erosion Model (RHEM) as a prediction runoff and erosion as a function of vegetation structure and behavior of different plant community phases and the amount of coverage for the different states in the Aydarly village of Jambul district of Almaty province. US Department of Agriculture experts and Kazakhstani scientists jointly conducted this study, where, based on the results, they received recommendations on improving rangeland. Results suggested that the model could be further improved with additional measured experimental data on infiltration, runoff, and soil erosion within key ecological sites in order to better quantify model parameters to reflect ecosystem changes and risk of crossing interdependent biotic and abiotic thresholds. These additions were further improved and implemented in other regions of Kazakhstan on other projects.
Kayo Matsui, Tetsuhiro Watanabe, Maira Kussainova, and Shinya Funakawa
Informa UK Limited
Abstract Black saxaul (Haloxylon aphyllum) is a native tree species tolerant of aridity and salinity. It is planted to alleviate environmental damage due to the formation of the Aralkum desert and improve vegetation of the Aral Sea region. To investigate the environmental factors that determine seedling mortality and growth after rooting, we focused on soil properties and topographic factors in a study plot. We found that a hard clay layer that was low in hydraulic conductivity underlay accumulated sandy sediments at different depths. The soil in low seedling mortality areas was consistently sandy and low in salinity from the surface to a depth of 100 cm. In areas of high seedling mortality (75–100%), soils with a high content of silt and clay, with high salinity were detected within 100 cm depths. This suggests that accumulated sand sediment over a depth of 100 cm is required for root development. Plant height was positively correlated with depth of the hard clay layer. Significant relationships with plant height were also detected in chemical and physical properties at 80–100 cm such as electric conductivity (ECe), sodium adsorption ratio (SAR), and sand ratio. However, these properties had no significant relationship at 0–20 cm or with the relative elevation of the plot, suggesting that the subsequent growth of seedlings depends more on the sub-soil environment than on-surface conditions. The assessment of sub-soil condition is recommended to make site selection for reforestation much more reliable.
Kayo Matsui, Yerlan Akhapov, Maira Kussainova, and Shinya Funakawa
Elsevier BV
Maira Kussainova, Marion Tauschke, and Abdulla Saparov
Springer International Publishing