Transcript Mining historical yield data to steer crop adaptation

Mining historical yield data to steer crop
adaptation strategies for climate change
Refining climate change impact estimates while
generating climate-change-adaptive technologies
E.g. CIMMYT has distributed approx 1,000 new wheat
genotypes p.a. in targeted environments for over 30 years
International yield data –if matched with
weather data- can help:
1) Identify factors associated with drastic
reductions in productivity, e.g.:
•temperature thresholds (e.g. Lobell et al., 2011)
•extreme in-season weather variation
•specific geographic regions/communities
•vulnerable stages of crop development
International yield data –if matched with
weather data- can help (cont)… :
2) Pinpoint ‘analog’ sites where new technologies
can be developed and tested
3) Integrate diverse datasets (biophysical, genetic,
and socioeconomic) to help make crop and bioeconomic models decision making more relevant.
4) Deploy climate-ready technologies
Germplasm deployment
GxE analysis to identify favorable “outliers” for:
•Immediate deployment of germplasm to collaborators/
farmers in climate vulnerable regions (via NARES)
•Crossing with locally-adapted material (via NARS)
•Targeting genetic resource exploration (via gene banks)
•Basic research addressing genetic bottlenecks (via AIs)
Crop management innovations
•Identify environments for
which crop management
interventions may be
complementary or
superior to genetic
strategies.
•Through identification of
susceptible growth stages,
target most appropriate
crop management
intervention(s).
(in partnership with environmental crop modelers, NARES, NGOs, farmers)
Links of yield analysis to GEC community
● Simulation of climate data
● Use of climate and socioeconomic models to
prioritize crop adaptation strategies:
Breeding objectives
Use of genetic resources (where low genetic variance
identified)
Genetic resource collection in terms of priority targets
and rate of climate change (how urgent is it to collect
genetic resources)
Crop management interventions where genetic
solutions may not be feasible.
Poverty and vulnerability focus.
Links of yield analysis to GEC community
● Stratification of analogue sites over time (10y,
20y, 30y) as well as space
● Understand environmental basis of biological
(rather than physical) analog sites (based on
behavior of genotypes, GxE etc).
● Food security modeling (e.g. Lobell, Batisti, etc)
Mining historical yield data to steer crop
adaptation strategies for climate change
Objectives
● Use simulated climate data to identify adaptation needs
of crops in a changing climate.
● Predict potential resilience of crops and cultivars to future
climates using historic yield and climate data.
● Integrate climate and crop models into a calibration and
validation “reality check”.
Objectives cont
● Use climate models to pinpoint specific analogue sites:
 Based on temperature thresholds
 Extreme weather variation
 Crop sensitive stages
● Assess the full spectrum of environmental factors that
determine crop adaptation (e.g. soil chemistry, salinity,
water quality, pollution, soil degradation, altitude,
maritime versus continental climate, etc)
● Use climate models to identify regions with promising
gene pools and to map genetic resource collection
priorities.
Objectives cont
● Map adaptation potential of resilient crops and
germplasm.
● Map adaptation gaps -i.e. environments where zero
genetic resilience is expressed related to biophysical
factors- to prioritize other types of intervention.
● Map apparent yield gaps –of on farm trials- related to
agronomic (fertility, irrigation, rotation etc),
socioeconomic factors (poverty, population pressures,
gender), and institutional factors (subsidies, corruption,
political regimes), while looking at the potential for
climate change mitigation.
Objectives cont
● Map cropping system mosaic based on predicted
adaptive potential of specific crops in different
regions in future climate scenarios (10y, 20y,
30y)
TOOLS/RESOURCES
● Meteorological data bases
● Weather simulation groups
● Yield data (National programs, GCIAR, private
sector)
● GxE analytical tools (PLS, factorial regression)
CROPS
● Selected CG crops for which good historic
performance data exist (on station/on farm)
● Trees- Provenance Trials (Agro-forestry)
SPIN-OFFS
● Use variance parameters to develop confidence
parameters on network data and to develop
additional ‘special focus’ networks
Mining historical yield data to steer crop
adaptation strategies for climate change
The wealth of data from decades of international crop
yield trials –if collated and matched with weather datacan help:
● Identify climatic/geographic factors associated with drastic
reductions in productivity.
● Identify climate-ready technologies; e.g. germplasm, crop
management innovations.
● Pinpoint ‘analogue’ sites where networks can be
established and new technologies can be developed/tested
● Pinpoint areas where policy interventions may help
accelerate adaptation of cropping systems
Science content (interdisciplinary)
Ag/resource management
● Genetic resources management through
improved knowledge of adaptation targets
Climate Change/uncertainty
● Adding a genetic and GxE dimension to climate
projection based crop models
Institutions/governance
● Pinpoint highly vulnerable regions as targets for
institutional/governance interventions
Science content (interdisciplinary)…
Gender Social differentiation
● Pinpoint highly vulnerable regions with extreme
poverty/gender issues
Spatial temporal scales
● 10, 20, 30y maps of high risk zones of present
cropping systems to provide suites of options
Adaptation potential of crops to biotic/abiotic stress
Modify crop management
Design new crops/cropping system mosaic (e.g.
involving South-South partnerships)
Policy interventions: irrigation, extension, etc
Science content relating to outcomes for:
Food security (availability, access, utilization, stability)
● Analyses permit timely and ground-based
recommendations for interventions
Environment (hydrology, biodiversity, BGC incl GHG)
● Analyses will pinpoint changes in:
 Demand for water, issues of water quality
 Need for genetic resources exploration
 Risk of soil degradation/desertification
 Risk of extreme weather events causing crop failure
 Threats and opportunities to mitigation strategies
Science content relating to outcomes for...
Livelihoods (socioeconomic capitals)
● Recommendation to stabilize/ improve
productivity
● Communication platforms developed to
disseminate findings
Selling points
Development value
● Fine tune deployment of agricultural technologies
GEC science value (interdisciplinary)**
● Develop an easily accessible data base of field trials for future
hypothesis testing within data or within the trial networks
Ag Science value
● Meta-analysis of yield trials in context of climate change predictions
How advancing CC agenda **
● Adding an evidence base from genetic resources creating potential
for greater impacts from crop-climate model outputs
How helps deliver CCAFS agenda
● Significant syngergy with Theme 1
Link to stakeholders**
● Better use of genetic resources nationally and internationally
● Provides crop adaptation information to help shape policy