Maps, geography and spatial analysis are increasingly important for developing R&D programs, priority setting, monitoring, impacts assessment and reporting. But the community of researchers working on this topics has perhaps been less organized to carry out this type of work. This initiative propose to develop an online Atlas for R&D among the DCL community with focus on Geo-spatial Science, Technology and Applications (GeSTA).

1. Geographic priorities for research and development on
dryland cereals and legumes (DCL)
Glenn Hyman1, Elizabeth Barona2, Chandrashekhar Biradar3, Edward Guevara1, John Dixon4,
Steve Beebe1, Silvia Elena Castano1, Tunrayo Alabi5, Murali Krishna Gumma6, Shoba
Sivasankar6, Ovidio Rivera1, Herlin Espinosa1, Jorge Cardona1
1International Center for Tropical Agriculture (CIAT), Cali, Colombia
2Independent Researcher, Miami, USA
3International Center for Agricultural Research in the Dry Areas (ICARDA), Amman, Jordan
4Australian Centre for International Agricultural Research, Canberra, Australia
5International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
6International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
eAtlasDCL.cgiar.org

2. 2
Introduction
• Maps, geography and spatial
analysis are increasingly important
for developing R&D programs,
priority setting, monitoring,
impacts assessment and reporting.
• But the community of researchers
working on this topics has perhaps
been less organized to carry out
this type of work.
• This initiative propose to develop
an online Atlas for R&D among the
DCL community with focus on
Geo-spatial Science, Technology
and Applications (GeSTA).

3. 3
The DCL eAtlas www.eatlasdcl.cgiar.org

4. 4
The DCL eAtlas www.eatlasdcl.cgiar.org

5. 5
Methodology
Data sources
• CGIAR center GIS/Geo-
informatics labs
• Public domain
• FAO farming systems
Spatial analysis
• Remote sensing and GIS
• Spatial overlay
Determining priority
regions
• Overlay of DCL crop
distributions with
FAO farming systems
• Analysis of biotic,
abiotic,
socioeconomic and
management
conditions by
farming system.
Spatial overlays, zonal/geostats

6. 6
Scripts, documentation and data all
available online and cloud
Methodology

7. Results
Major drylands farming systems and
crop and regional dominance

8. Results
• Three farming systems in South Asia – rainfed mixed, rice-wheat
and dry rainfed – make up about one third of the 162 million ha of
DCL crops in the 18 priority farming systems
• A second important region is Sub-Saharan Africa, where the cereal-
root crop mixed system accounts for 21.3 million ha, the agro-
pastoral millet sorghum system accounts for 18.6 million ha, the
pastoral system accounts for 10.8 million ha and the maize mixed
system has 7.6 million ha
• In Eastern Europe and Central Asia more than 15 million ha are
cultivated, with barley figuring prominently.
• In East Asia over 22 million ha are cultivated, with groundnut and
soybean as the predominant crops.
Major drylands farming systems and
crop and regional dominance

9. 9
The farming
systems where
dryland cereals
and grain legumes
are concentrated
are particularly
prone to high
temperatures and
drought and crop
stress.
Results
Abiotic and biotic stress
climate change and extreme events

10. These dryland systems,
especially those with less than
1000 mm of annual precipitation,
tend to have a higher probability
of drought or a failed season,
when precipitation does not
meet crop requirements.
There is a general tendency of
the drier farming systems having
higher expected
temperature changes between
now and future climate scenarios
(2050) and further decrease in
precipitation
Results
Abiotic and biotic stress
climate change and extreme events

11. The soils of DCL
priority farming
systems present
a number of
abiotic
constraints to
DCL crop
production.
Results
Abiotic and biotic stress
climate change and extreme events

12. The key DCL
farming systems
are home to
about one third
of the global
population,
including an
enormous
number of
people living in
poverty.
Results Population and poverty

13. Potential additions to DCL Atlas Platform
- higher resolutions (space and time)
- crop types and granularity;
- similarity mapping and out scaling;
- tracking adoption of technologies;
- niche modelling and predictions;
- yield gap mapping across the scales
- carry out genotype-by-environment
analysis;
- land potential for investment and
implementation
-
Phase2 development and way forward
…

14. 14
Level5_mod250_2014_lulc_15cls.img
01.Rainfed-DC Maize/mixed crops
02.Rainfed-SC Maize/sorghum
03.Rainfed-SC tef, sorghum, Maize
04.Rainfee-SC-tef/wheat,barly
05.Rainfed mixed Crops
06.Irrigated-SC-sugarcane-VLS
07.Irrigated_mixedcrops
08.Rainfed_Rice
09.Rangeland/fallow
10.Range lands/Shrublands
11.Shrublands/Wasteland tress
12.Barenlands/Sanddunes
13.Forest
14.Waterbodies
15.Builtup
Mapping crop land areas.
e.g., South Asia
e.g., Ethiopia
Gumma et al., 2016. ICRISAT
Crop types, pattern and phenology

15. Tracking of technological adoption
Adoption of water harvesting
structures, in Andra Pradesh
Ahmed, I. et al., 2016. ICRISAT
Biradar, et al., 2015. ICARDA
Adoption of conservation
agriculture (zero tillage) in
Morocco

16. 16
Adoption of Chickpea
Districts
Area (ha)
MODIS-2000 MODIS-2005 Modis-2012
Anantapur 34777 51304 84493
Cuddapah 30343 69258 117903
Kurnool 68113 140511 196793
Prakasam 35129 128288 159524
168362 389361 558713
Links image analysis
to field surveys
Gumma et al., 2016. ICRISAT
Tracking of technological adoption

17. Source: Glenn, CIAT
Niche modelling and distribution

18. 18
Near real-time farm analytics at
pixel to landscape scales
March December
National>State>District>Tehsil>Block>Village
>Farm>Polygon/Point/Pixel
Cropping System
Crop Intensity
Crop Calendar
Crop Rotation
Cropped Area
Fallow Dynamics
-Fallow area
-Duration
-Start date
-End date
Yield Potential
-Current
-Achievable
Suitable Crop/Variety
-Legumes
-Oil Seeds
Soils
Soil Health (SHC)
Soil Moisture (SMAP)
Water use
Evapotranspiration
Allocation/Irri. Sch.
Markets
-ePlatform
-Ag Supply Chain
-Access (I/O)
Monitoring
-Pest/Diseases
-Crop Stress
Citizen Science
Crop Intensification & Diversification

19. 10%
26%
20%
15%
10%
7%
5%
6%
1%
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
<0.5 0.5-1 1-1.5 1.5-2 2- 2.5 2.5-3 3.5-4 4.5-5 >5
Area(mha)Millions
Grain Yield (kg/ha, '1000)
Agricultural Productivity, Production and Dynamics
Biradar, et al., 2016

20. 10%
26%
20%
15%
10%
7%
5%
6%
1%
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
<0.5 0.5-1 1-1.5 1.5-2 2- 2.5 2.5-3 3.5-4 4.5-5 >5
Area(mha)Millions
Grain Yield (kg/ha, '1000)
5%
10%
15%
20%
25%
12%
6%
4%
3%
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
<0.5 0.5-1 1-1.5 1.5-2 2- 2.5 2.5-3 3.5-4 4.5-5 >5
Area(mha)Millions
Grain Yield (kg/ha, '1000)
1%
6%
5%
7%
10%
15%
20%
26%
10%
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
<0.5 0.5-1 1-1.5 1.5-2 2- 2.5 2.5-3 3.5-4 4.5-5 >5
Area(mha)Millions
Grain Yield (kg/ha, '1000)
10%
15%
20%
26%
10%
7%
5%
6%
1%
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
<0.5 0.5-1 1-1.5 1.5-2 2- 2.5 2.5-3 3.5-4 4.5-5 >5
Area(mha)Millions
Grain Yield (kg/ha, '1000)
Current: 0% Gain
32 m tons
S-2 : 5% Gain
35 m tons
S-1 : 72% Gain
61 m tons
S-3 : 18% Gain
40 m tons
Location specific Investment, Interventions and Impacts
S=Scenarios
Biradar, et al., 2016*

21. 21
Thank Youc.biradar@cgiar.org
avoid the unmanageable and
manage the unavoidable
-IPCC Confronting Climate Change:
in an inch of land and bunch of crop