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Mining historical yield data to steer crop adaptation strategies for climate change

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Title: Mining historical yield data to steer crop adaptation strategies for climate change


1
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
2
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

3
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
    bio-economic models decision making more
    relevant.
  • 4) Deploy climate-ready technologies

4
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)

5
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)
6
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.

7
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)

8
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9
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.
  •  

10
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.

11
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.

12
Objectives cont
  • Map cropping system mosaic based on predicted
    adaptive potential of specific crops in
    different regions in future climate scenarios
    (10y, 20y, 30y)

13
TOOLS/RESOURCES
  • Meteorological data bases
  • Weather simulation groups
  • Yield data (National programs, GCIAR, private
    sector)
  • GxE analytical tools (PLS, factorial regression)
  •  

14
CROPS
  • Selected CG crops for which good historic
    performance data exist (on station/on farm)
  • Trees- Provenance Trials (Agro-forestry)

15
SPIN-OFFS
  • Use variance parameters to develop confidence
    parameters on network data and to develop
    additional special focus networks

16
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17
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 data- can 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
  •  

18
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

19
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

20
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

21
Science content relating to outcomes for...
  • Livelihoods (socioeconomic capitals)
  • Recommendation to stabilize/ improve productivity
  • Communication platforms developed to disseminate
    findings

22
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
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