The document presents findings from a workshop on climate-smart agriculture in Central Asia, focusing on remote sensing assessments of crop productivity, particularly for cotton and wheat. It highlights challenges such as environmental degradation, climate change impacts on water resources, and the potential for improving water productivity. The use of satellite Earth observation is promoted for enhancing agricultural monitoring and decision-making, with plans for a fully automated online tool to provide timely crop condition information.

1. Fabian Löw and Chandrashekhar Biradar
ICARDA.CGIAR.ORG
geoagro.icarda.org
13 July 2016 | Central Asia Climate Smart Agriculture Workshop | Bishkek, Kyrgyzstan

2. Remote sensing based assessment of the dynamics
of crop productivity and spatial production pattern
in the Fergana Valley, Central Asia

3. o Focus: cotton and wheat cultivation
o 100-200mm / 500mm, ET 1.300mm
o Environmental degradation, soil salinity, decreasing crop yields,
yield gaps, potential for improving water productivity up to 28%
(Reddy et al.2012, Abdullaev et al. 2009)
o Thread food security of a growing population
o Climate change likely to pose an additional challenge: pressure on
water resources (increased temperature, melting glaciers)
Status quo and challenges

4. Sustainable land and water resource management
o Guided by information-based /data driven decision making
o Can be supported by a data base containing spatial information
about agricultural production:
 Productivity (yield)
 Water demand and use of crops
 “Water productivity”
o Can hardly achieved by manual ground sampling

5. o Satellite earth observation (EO) – what can we get from it?
 Synoptic view over large regions with a high spatial detail
 Objective data with high revisit frequencies
 Mapping crop productivity in space and time

6. GIS-Tool: www.geoagro.icarda.org
o Free online access to digital maps of agricultural production
at the field level (tables and charts…)
o Data visualization (e.g. plot your own maps)
o Basic analytical tools
Where and how can I get the data?

7. Land use: crop type, crop acreage
Crop yield: production and productivity
Water productivity
Earth observation based agricultural monitoring tool:
portfolio for agricultural monitoring

8. o Annual maps of land use, based on field calibration data
o Crop acreage (e.g. irrigated / harvested area)

9. o Compare maps form different years
o “Land use intensity”, e.g. Number of years not under irrigation (abandoned land)

10. o Aggregate statistics (e.g. administrative units: oblast, rayon)
o Change in acreage across observation years
o Outlook: user defined zones (polygons), water distribution
(“Planning zones”, WUA, smaller channel levels UIS…)
2010
2011
2012
2013
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Proportioncultivatedarea[%]
Cotton Wheat-other

11. Land use: crop type, crop acreage
Crop yield: production and productivity
Water productivity

12. o Crop yield (cotton and wheat)
o Spatial pattern of crop productivity
 Crop yield (tons per hectare per field)
 Identification of low / high productivity areas

13. Monitoring
o Spatial and temporal trends / variability of
agricultural production
o Yield gap assessments
Early estimation
o Biomass,
o crop stress
2010
2011
2012
2014

14. Land use: crop type, crop acreage
Crop yield: production and productivity
Water productivity

15. o Spatial pattern of water productivity (kg/m3)
 SEBAL model (ETact)
 Ratio of crop yield per unit area to water use or ETact to
produce such a yield
 Identify opportunities to improve WP
Average WP Wheat

16. Conclusions
o Satellite EO is a potentially useful tool to support spatial
planning / sustainable water and land use management by
providing geoinformation about agricultural production from
field to regional scale
o Not a hydro-operations tool at the field level / not a precision
agriculture tool (i.e. within field)
o Prioritize focus regions (e.g. affected by low productivity),
spatial targeting of land rehabilitation, restructuring of land use,
focus investments , evaluate impact of management practices

17. Outlook:
o Development of a fully automated online tool:
 High resolution satellite image download (NASA+ESA) and
“pre-processing”
 Crop stress indication
o Crop condition information at early stages in the crop growing
season: early estimation of crop area, crop stress indication,
every 2-3 weeks, reduce planning uncertainties, increase
resilience

18. Спасибо за внимание
Contact:
Fabian Löw, PhD
Chandrashekar Biradar, PhD
Web:
ICARDA.CGIAR.ORG
geoagro.icarda.org
E-Mail:
C.Biradar@cgiar.org
fabian.loew@gmx.de
fabian@maptailor.net