GYGA Workshop: Outputs, Products, Opportunities for Collaboration - Yield … GYGA Workshop 1... ·...
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GYGA Workshop: Outputs, Products, Opportunities for Collaboration
ILRI-Addis Ababa, September, 2015
Workshop Goals
• Report GYGA-2 activities and outputs
• Discuss uses of GYGA products and opportunities for collaboration – Taking Agronomy to scale
– Supplement to existing climate change research
– Contribute to research prioritization, impact assessment, etc.
• Explore opportunities for new initiatives to expand and apply GYGA products
Previous GYGA Workshops, 2012-2014 Naivasha, Kenya Cotonou, Benin
ILRI-HQ, Ethiopia
Countries currently in the GYGA website
Steps for up-scaling for local to global relevance
Crop-specific harvested area
Weather station buffer zones with large crop area
Soil types & cropping systems within buffer zones
Climate zones
Crop model simulations
Actual yields
Yield gaps From: Van Bussel et al. 2015. Field Crops Res. 177: 98-108
Scaling approach for long-term weather data is especially important in yield gap assessment
‘top-down’ approach
Gridded weather, soil, and crop data allows full coverage but
has large uncertainty
Simulation unit: grid
?
Too coarse to be locally relevant and difficult to
validate
Targeting a tractable number of locations for
data collection
Simulation unit: location x soil x crop system combination
within a climate zone
Upscaling from location to region or country by a
robust CZ scheme
Soil 2
Soil 3 Soil 1
LOCATION A
Full coverage without loosing local relevance
GYGA ‘bottom-up’ approach
Soil 2
Progress towards GYGA-2 Objectives
Progress towards GYGA-2 Objectives
Importance of rootable soil depth on yield and yield stability
Cu
mu
lati
ve m
aize
are
a (%
)
Mean coefficient of variation, rainfed yield potential
Coefficient of variation in yield nearly doubles if 40% of rainfed maize area has rootable soil depth <75cm*
*Analysis includes 10 countries in Sub-Saharan Africa
Maize yield stability (CV) versus yield level in US Corn Belt
Grain Yield (t/ha)
Co
effi
cien
t o
f va
riat
ion
From: Grassini et al., 2014. Crop Physiology, 2nd Edition. Academic Press, Oxford.
harsh rainfed maize environments
Technology Extrapolation Domains: CZ x root-zone plant water holding capacity spatial units
GYGA Climate zonation
Root zone water holding capacity
• GYGA technology extrapolation domains
Black lines: delimitation of GYGA- climate zones Colours: GYGA-TEDs
Estimate uncertainty at country level
Estimation is crop specific;
Estimate uncertainty for three yield parameters: Yp, Yw and Ya • Uncertainty Yg is derived from Yp, Yw, Ya uncertainties
Classify result in three categories: High, Medium and Low
Estimate uncertainty for several aspects: • For Yp: Weather, Crop area, Cropping system, Model, Scaling
• For Yw: Weather, Soil, Crop area, Cropping system, Model, Scaling
• For Ya: Yield data, Number of (recent) years, Scaling
A second indicator is estimated for these aspects: The magnitude of the effect (sensitivity) on yield, again indicated as High, Medium or Low.
There is room for comments and remarks, to underpin the estimation • Eg. used to specify spatial variation in a country
Uncertainty design
For Yp, Yw and Ya at country level ‘Uncertainty’ is available as additional variable:
Uncertainty visualization
Uncertainty visualisation
GYGA website sessions: Jan 1 – Sept 15, 2015
12,500
GYGA Website Useage: Jan 1 – Sept 15 2015 vs same period in 2014
6,000
2015 2014
Summary of GYGA Outputs and Products
• First, bottom-up estimates of yield gaps for major cereal crops in SSA and S. Asia – Open access to underpinning data – Transparent, reproducible protocols – Cadre of yield gap agronomists
• Robust geospatial framework to support ag R & D • Improved method for propagating long-term weather data
from short-term data dataset at a given location • First digital map of RZ-PAWHC for SSA
• Sowing date rules framework for rainfed cereal crop productdion
• Opportunities for collaboration with public and private sector
Building out the Global Atlas
23 Countries in the Atlas: 14 countries in progress:
Burkina Faso
Mali
Ghana
Niger
Nigeria
Ethiopia
Kenya
Uganda
Tanzania
Zambia
Australia
Morocco
Jordan
Tunisia
Denmark
Germany
Netherlands
Poland
Spain
Bangladesh
India
Argentina
Brazil
Romania
Portugal
United States
Ukraine
South Africa
Turkey
France
United Kingdom
Austria
Czech Republic
Hungary
Slovakia
Uruguay
China
Holy Grail: Robust geospatial framework for prediction of crop response to management practices and new techologies
More efficient and effective: • field research • technology transfer and adoption at scale • research prioritization for R & D investments • Ex-ante and ex-poste evaluation and impact assessment • Research on climate change impacts
field region./watershed nation field region/watershed
Major constraint: Availability of long-term daily weather data (since 1971)
1048 stations with at least 3-yrs daily weather data
706 stations with at least 15-yrs daily weather data
126 stations with 15-yrs daily weather data with < 10% missing days and < 30-consecutive day gap
Source: World Meteorological Organization and NOAA Global Summary of the Day database