Assessing Climate Change Impact on Cropland Suitability in Kyrgyzstan: Where Are Potential High-Quality Cropland and the Way to the Future

Abstract

Climate change is one of the greatest challenges in Kyrgyzstan. There have been negative spillover effects in agriculture. This study aims to assess the climate change impacts on cropland suitability in Kyrgyzstan. We used the random forest algorithm to develop a model that captures the effects of multiple climate and environment factors at a spatial resolution of 1 km2. The model was then applied in the scenario analysis for an understanding of how climate change affects cropland distribution. The potential high-quality cropland was found to be included in existing croplands, while the remaining were distributed around the Chu-Talas valley, the Issyk-kul area, and the Fergana valley. These potential high-quality croplands comprise grasslands (47.1%) and croplands (43.7%). In the future, the potential high-quality cropland exhibited inland trends at the periphery of original cropland category, with grassland and cropland as the primary land components. Due to climate change, potential high-quality cropland is expected to gradually reduce from the 2050s to the 2070s, exhibiting the largest reduction in potential high-quality areas for the Representative Concentration Pathway 8.5 scenario. Therefore, the short- and long-term adaptation strategies are needed for prioritizing the croplands to ensure food security and agricultural resilience.

Fungal Pathogens Associated with Crown and Root Rot in Wheat-Growing Areas of Northern Kyrgyzstan

“…Water from the Syr Darya and Amu Darya rivers is diverted and used to cultivate lowland agricultural areas where wheat is planted under semi-supplementary irrigation conditions. As a result, the country’s main abiotic stressors are drought and heat stress caused by water scarcity, which is a global issue [ 3 , 4 ]. Stripe or yellow rust produced by Puccinia striiformis f. sp.…”

Reconstruction of Hydrometeorological Data Using Dendrochronology and Machine Learning Approaches to Bias-Correct Climate Models in Northern Tien Shan, Kyrgyzstan

“…Kyrgyzstan is the third-most-vulnerable country in Eastern Europe and Central Asia to climate change, mainly owing to its climate-sensitive agricultural systems and a lack of an adaptive capacity [8]. Kyrgyzstan is threatened with glacier melting and a lack of freshwater balance, which are accelerated by global warming [9]. Due to climate changes, frequent natural hazards, strained water resources, and fragile ecosystems in Kyrgyzstan, it is imperative to project regional climate change based on emission scenarios for application to vulnerability, impacts, and adaption assessments.…”

“…Different statistical approaches have been applied in these hydrometeorological reconstructions using tree-ring data. Several studies employed Machine Learning (ML) methods to solve different earth and climate related problems [9,14,[43][44][45][46]. Our research is the first to use ML methods in Kyrgyzstan to reconstruct hydrometeorological data by comparing different statistical-regression and ML models.…”

Bias correction of Sentinel-2 with unmanned aerial vehicle multispectral data for use in monitoring walnut fruit forest in western Tien Shan, Kyrgyzstan

“…11,12 In addition to these anthropogenic influences, climate change is increasing drought occurrence frequency and thus changing natural habitats. [13][14][15][16] Kyrgyzstan is the third most vulnerable country in Eastern Europe and Central Asia to climate change, mainly owing to its climate-sensitive agricultural systems and a lack of an adaptive capacity. 17 If no measures are taken, the sparse populations of wild apples can be severely damaged and further decreased.…”

“…21 In this context, remote sensing (RS) has already shown its potential and effectiveness in spatiotemporal vegetation monitoring. 19,[22][23][24] Additionally, many satellite platforms (e.g., Landsat, MODIS, Aster, SPOT, Sentinel-1, and Sentinel-2) are now providing free datasets, thus promoting satellite imagery for many agricultural applications, 14,22,23,[25][26][27] including those with multisensor data fusion approachs. 28 For example, remotely sensed images from Sentinel-2 used in our research offer decametric resolution in terms of space and time, with a ground sample distance of up to 10 m and a revisit time of 6 days.…”