a joint project of uc cooperative extension, lawrence ... · a joint project of uc cooperative...
TRANSCRIPT
Healthy
Food S
yste
ms
Healthy
Environ
ments
Healthy
Com
mu
nitie
s
Healthy
Calif
orn
ians
Kripa Jagannathan1; Tapan Pathak2; David Doll3 ; Amber Kerr4; Andrew Jones5
1 University of California - Berkeley, 2 University of California - Merced, 3 University of California Cooperative Extension - Merced, 4 USDA Climate Sub Hub for California, 5 Lawrence Berkeley National Laboratory
Acknowledgements
We would like to thank the University of California’s Department of
Agriculture and Natural Resources, UC Berkeley’s College of Natural
Resources and the Energy and Resources Group for their funding and
support to this project under Graduate Training in Cooperative
Extension - Pilot Program 2015-16.
This poster template was created by the Medical Illustration Unit,
Prince of Wales Hospital, University of New South Wales, Sydney,
Australia and edited for our use.
Context & rationale
In the last decade, many advances have been made in climate
modeling and projections. Yet, several sources still report that
current tools and models are not widely used, as they may not
be meeting farmers’ needs1,2
. This suggests that there is a gap
between farmers’ information needs and model outputs.
Literature also indicates that very little work is being done on
verifying the information being provided for decision-making2,
which further reduces the utility of climate projections.
Preliminary findings from this project indicate that not all
climate models are equally credible, especially at a local scale
and for a specific metric [Figure 1].
For climate information to be relevant, it is important to
understand how farmers might use prospects of future climate
and data on past climate in their decision-making3. Currently,
information flows to the end-user in a top-down manner with
limited bottom-up feedback about the farmers’ ability to apply
the projections in their decision-making process [Figure 2].
A collaborative approach for provision of climate information,
with feedback from various agricultural stakeholders, can help
improve information usability. In addition, an assessment of the
skill of different climate models in predicting relevant metrics
can be a step in the right direction for providing farmers with
the best available information for their precise needs.
Bridging the gap between climate model outputs and farmer decision-makingA joint project of UC Cooperative Extension, Lawrence Berkeley National Lab and the USDA Climate Sub Hub
HadGEM2-AO Model vs. Observed
y = -10.279x + 1157.6
y = 12.576x + 733.39
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Observed Chill hours Modeled Chill hours
Linear (Observed Chill hours) Linear (Modeled Chill hours)
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Observed Chill hours Modeled Chill hours
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CCSM4 Model vs. Observed
Figure 1. Illustration of varying skill of two different climate models in predicting chill hours for Fresno, CA*
Chill hours refer to the accumulated amount of hours with temperatures 45°F or lower during winter. The graphs indicate that the
CCSM4 predicts the declining trend in yearly chill hours better than the HadGEM2-AO
* Preliminary data - not to be cited or distributed.
Climate
models
Downscaled
results
Impact on
crops
Farmers’
decision
context
GHG
projections
Figure 2. Chain of climate information. Solid arrows represent the
current ‘top-down’ approach in provision of climate information. The
dotted red arrows illustrate the ‘bottom-up’ feedback that this project
aims to provide.
Significance
This project will be the first collaborative effort between cooperative
extension, climate modelers, and end-users in California, to produce
climate information products that are specifically tailored to growers.
The extension outputs developed will provide useful information for
many perennial crop growers, and can be used as templates to deliver
actionable climate predictions and adaptation advice for other kinds
of tree crops.
Activity 1: Identify relevant
climate information for
almond growers
• Understand if and how
farmers have used/ will use
climate information.
• Identify relevant climatic
parameters; appropriate time-
scales; time-frame for
decision-making; and the
acceptable uncertainty levels.
• Methods: Literature review,
focus group discussions, and
semi-structured interviews,
with farmers & farm advisors.
Activity 2: Calculate
metrics of relevance from
climate models
• Retrieve past climate
observations and analyze
trends.
• Analyze climate projections
for various GHG emission
scenarios, from models
such as Community
Climate System Model
(CCSM), Community Earth
System Model, and Hadley
Global Environment Model
(HadGEM).
Activity 3: Evaluate the
skill of climate models
• Evaluate selected climate
models for their accuracy
in predicting different
metrics.
• Create a framework for
selecting and ranking the
models.
Activity 4: Develop
decision support materials
for almond growers
• Create customized fact
sheets on past and future
climate patterns.
• Develop a chapter on
climate change for the
almond growers’ manual.
• Produce a climate change
training module for
extension educators.
Project activities, methods and outputs
Project goals
• Understand how almond growers (and other perennial
orchard crops) might use climate information.
• Evaluate the skills of different climate models in
capturing information that is relevant to growers.
• Generate decision-support material that assists almond
growers in adapting to climate variability and change.
References
1. Mase, A. S. & Prokopy, L. S. Unrealized Potential: A Review of Perceptions and Use of Weather and Climate Information in Agricultural Decision
Making. Weather. Clim. Soc. 6, 47–61 (2013).
2. National Research Council. A National Strategy for Advancing Climate Modeling. (2012). doi:10.2172/1056475
3. Ortiz, S. in Agricultural Decision Making - Anthropological Contributions to Rural Development (ed. Barlett, P.) 177–202 (Academic Press, 1980).
4. Almond Board of California. California Almond Industry facts. (2015)
5. Luedeling, E., Zhang, M. & Girvetz, E. H. Climatic changes lead to declining winter chill for fruit and nut trees in California during 1950-2099. PLoS
One 4, e6166 (2009).
6. Geisseler, D. & Horwath, W. R. Almond Production in California. (2011).
Almonds as the case study
While the topic is relevant to many crops, this project
focuses on almonds in California [Figures 3 & 4].
Almonds have an estimated industry value of 4.5
billion USD in the state4. It is a perennial crop, and
changes in climate within their 25-30 year lifetime
can have an adverse impact on crop yield, making
almond growers vulnerable to medium and long-term
climate change5. There is evidence that patterns of
climatic parameters such as chilling hours, bloom
times, frost dates, and water availability have been
changing in the past few decades, all of which impact
the crop negatively6.
Role of climate information in selection of crop
varieties & management practices.
Significance of past climate observations
versus forecasts and future projections.
Type and source of information, including
content, format and credibility.
Importance of accuracy of information
including acceptable error levels.
Methods for reporting key metrics i.e. annual
or decadal trends versus mean estimates.
Key interview themes
Figure 4. Almond trees in bloom
- February 2015.
Figure 3. Almonds in Merced county -
May 2015.