Mapping and assessing crop diversity in the irrigated Fergana Valley, Uzbekistan. (September 2017)

• Record Type: Journal Article
• Authors: Conrad, Christopher; Löw, Fabian; Lamers, John P.A.
• Is Part Of: Applied geography. Volume 86(2017)
• Page Start: 102
• Page End: 117
• Publication Date: 2017-09
• Subjects: Crop diversity -- Crop rotations -- Multi-sensor mapping -- Random forest -- Conditional variable importance -- Conditional inference trees -- Aral Sea Basin -- Fergana Valley; Geography -- Periodicals; Human geography -- Periodicals; Human ecology -- Periodicals; 910
• DOI: 10.1016/j.apgeog.2017.06.016
• Languages: English
• ISSNs: 0143-6228

Abstract

Abstract: Crop diversity (e.g. the number of agricultural crop types and the level of evenness in area distribution) in the agricultural systems of arid Central Asia has recently been increased mainly to achieve food security of the rural population, however, not throughout the irrigation system. Site-specific factors that promote or hamper crop diversification after the dissolvent of the Soviet Union have hardly been assessed yet. While tapping the potential of remote sensing, the objective was to map and explain spatial patterns of current crop diversity by the example of the irrigated agricultural landscapes of the Fergana Valley, Uzbekistan. Multi-temporal Landsat and RapidEye satellite data formed the basis for creating annual and multi-annual crop maps for 2010–2012 while using supervised classifications. Applying the Simpson index of diversity (SID) to circular buffers with radii of 1.5 and 5 km elucidated the spatial distribution of crop diversity at both the local and landscape spatial scales. A variable importance analysis, rooted in the conditional forest algorithm, investigated potential environmental and socio-economic drivers of the spatial patterns of crop diversity. Overall accuracy of the annual crop maps ranged from 0.84 to 0.86 whilst the SID varied between 0.1 and 0.85. The findings confirmed the existence of areas under monocultures as well as of crop diverse patches. Higher crop diversity occurred in the more distal parts of the irrigation system and sparsely settled areas, especially due to orchards. In contrast, in water-secure and densely settled areas, cotton-wheat rotations dominated due to the state interventions in crop cultivation. Distances to irrigation infrastructure, settlements and the road network influenced crop diversity the most. Spatial explicit information on crop diversity per se has the potential to support policymaking and spatial planning towards crop diversification. Driver analysis as exemplified at the study region in Uzbekistan can help reaching the declared policy to increase crop diversity throughout the country and even beyond. Highlights: Mapping orchards (perennial crops) over multiple years improves annual crop maps. Multi-sensor remote sensing backs detailed land use analyses. Remotely sensed crop maps permit a multi-scale analysis of the Simpson index of diversity. Crop diversity varies over the landscapes of the Fergana Valley, Uzbekistan. Distance to settlements and irrigation infrastructure impact on crop diversity.