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