Healthy

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Healthy

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Calif

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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

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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.