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Quantification of cropping pattern and productivity of agro-ecosystems in Central Asia

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Title Quantification of cropping pattern and productivity of agro-ecosystems in Central Asia
 
Creator Biradar, Chandrashekhar
 
Contributor Loew, Fabian
Xiao, Xiangming
Dong, Jinwei
Fliemann, Elisabeth
Patil, Prashant
Singh, Murari
Tulaymat, Mohammad Fawaz
Omary, Jalal
Thomas, Richard
 
Subject agroeconomics
 
Description Agricultural production systems in Central Asia have been evolving in response to changes in climate, land use and policies. The cropping pattern and productivities of irrigated and rainfed agro-ecosystems as well as grazing lands are highly variable in both spatial and temporal domains. Accurate and up-to-date information of these production systems on regular intervals (inter- and intra-annual) along with ever changing climate, land use/land cover, its pattern, etc. are important for understanding the food security and sustainability of agro-ecological systems in the region. The present study provides an overview of satellite and in-situ based observations, mapping and modelling of land-use dynamics, coupled with edaphic and climatic factors in dryland production systems. Our efforts highlight recent advances in satellite-based characterization of the agricultural production systems across the scale from fields, basin and the regions. We take advantage of three operational satellite remote sensing systems such as Rapid Eye (5m), Landsat (30m) and MODIS (250-500m). Secondly, the satellite-based Vegetation Photosynthesis Model (VPM) was used to estimate gross and net primary production of croplands, grasslands and tree-based systems. The study also analysed the dynamics of
the land degradation to identify prioritization hotspots for intervention and the design of improved adaptation strategy across the scales. Finally, study discusses the role of community remote sensing and citizen science in the participatory monitoring of grasslands and croplands in the dry areas. This is an ongoing study, and initial results show the annual dynamics of the vegetation flux across the study region in response to climate and extreme events. The vegetation trend analysis seems to be providing a clear predictor of the productivity in response to degree of land degradation. The vegetation index, the LSWI is much more sensitive to productivity and droughts than does NDVI and EVI. The resultant data products are also adopted for the design of spatial decision support systems for rural advisory and policy analysis towards climate-smart villages and agriculture.
 
Date 2016-02-01T21:29:31Z
2016-02-01T21:29:31Z
 
Type Conference Paper
 
Identifier https://mel.cgiar.org/reporting/downloadmelspace/hash/2E2MQW66/v/063cb831d714a1ab26afe18bb29fd45b
Chandrashekhar Biradar, Fabian Loew, Xiangming Xiao, Jinwei Dong, Elisabeth Fliemann, Prashant Patil, Murari Singh, Mohammad Fawaz Tulaymat, Jalal Omary, Richard Thomas. (11/5/2015). Quantification of cropping pattern and productivity of agro-ecosystems in Central Asia. Berlin, Germany.
https://hdl.handle.net/20.500.11766/3503
Limited access
 
Language en
 
Rights CC-BY-NC-4.0
 
Format PDF
 
Publisher The German Aerospace Center (DLR)
 
Source 36th International Symposium on Remote Sensing of Environment;