Modeling Adaptive Agricultural Management
for Climate Change
in Montana’s Flathead County
Tony Prato, University of Missouri-Columbia
Dan Fagre, U.S. Geological Survey
Zeyuan Qiu, New Jersey Institute of Technology
Duane Johnson, Montana State University
Acknowledgement
The project is supported by the National
Research Initiative of the USDA Cooperative
State Research, Education and Extension
Service, grant number 2006-55101-17129.
Context
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Impacts of climate change on mountain
ecosystems are typically evaluated in terms of
alpine areas, forests, and wildlife.
However, agriculture is a key part of mountain
ecosystems and their responses to climate
change.
Agriculture is a major user of land and water in
mountain ecosystems of the western U.S.
There are significant interactions between how
agriculture and mountain ecosystems respond to
climate change.
Problem
Agricultural producers, service providers, and
input suppliers (e.g., custom fertilizer and
pesticide applicators, cooperatives, and lending
institutions) in Montana’s Flathead County lack
the knowledge, information, and tools needed to
capitalize on benefits and minimize adverse
impacts of future climate change and variability
on agricultural production and natural resources.
Project Goals
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Develop an adaptive agricultural management
model that identifies best agricultural systems
for coping with future climate change in
Montana’s Flathead County.
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Enhance the capacity of agricultural producers
to adaptively manage their operations for
climate change.
Project Objectives
1. Construct plausible future climate change
scenarios for the next 50 years in terms of
agriculturally-sensitive climate variables (i.e.,
precipitation, temperature, and CO2
concentrations).
2. Develop an Adaptive AGricultural
ManaGEment Model (AG-GEM) that
determines the best agricultural systems for
adapting representative farms to the climate
change scenarios.
3. Create an interactive spatial decision-support
tool that makes AG-GEM and associated
geospatial databases useable and accessible to
agricultural interests.
Study Area
Farmland Use in Flathead County
(2002)
Size Distribution of Farms
(Flathead County)
Average size farm
was 218 acres in 2002.
Crops in Flathead County, 2004
(ha)
Crop
Total
Irrigated
All hay
17,814
8,178
Wheat
8,138
3,725
Barley
2,510
1,053
Lentils
363
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Canola
50
200
28,875
13,156
Total
Elements of Project
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Future climate change scenarios
Representative farms
Producer panels
Climate evaluation periods
Agricultural systems
Evaluating agricultural systems
Selecting best agricultural systems
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Adapting agricultural systems to climate change
Possible adaptations to climate change
Examples of adaptive management
Comparing agricultural systems
Decision support tool
Future Climate Change Scenarios
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Incorporate several
scenarios for annual
and seasonal patterns
of temperature,
precipitation, CO2
concentrations, and
other climate
variables for the
period 2005-2050.
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Scenarios are defined using a combination of
methods and results based on:
¾ downscaling the Hadley Center’s HadCM3 climate
model;
¾ using the eight scenarios for precipitation,
temperature, and CO2 changes for the Pacific
Northwest developed by the Climate Impacts Group
at the University of Washington; and
¾ research by Fagre, Kang et al., and others.
Representative Farms
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Distinguishing features of representative farms:
¾ size (acres, head of livestock);
¾ tenure (acres owned and leased) and asset values;
¾ enterprise mixes (crops, livestock, dairy, etc.);
¾ mix of dryland and irrigated acreage;
¾ costs of production for each enterprise;
¾ fixed costs for the overall operation;
¾ yields and a history of yields and farm program
participation;
¾ machinery complement and replacement strategy;
and
¾ policy history (base acres and payment yields, if any).
ƒ Three representative
farms will be
selected: a small to
moderate-scale farm
with crop production
only; a moderate-scale
farm with crop and
livestock production;
and a large-scale farm
with crop and
livestock production.
Producer Panels
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A producer panel is
established for each
representative farm.
A panel consists of 4
to 5 producers who
are familiar with the
operations of the
representative farm.
Climate Evaluation Periods
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Baseline climate
period: 1976-2005 (30
years)
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Future climate period:
2005-2055 (50 years).
Agricultural Systems
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An agricultural system is defined by the mix of
enterprises for a farm.
An enterprise is defined by the acreage
devoted to a particular crop or forage
operation, and costs and returns for that
enterprise.
Crop enterprises include cereal (wheat, barley)
production and forage (hay, permanent
pasture, and grazing allotments) production.
Evaluating Agricultural Systems
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Multiple criteria evaluation is used to evaluate
agricultural systems for representative farms.
Four criteria are assessed for the baseline and
future climate periods:
¾ average annual net farm income;
¾ variance in average annual net farm income;
¾ soil erosion rate; and
¾ water quality (e.g., nitrogen, phosphorus).
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Criteria weights are determined by the producer
panels
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Economic/financial
effects of agricultural
systems are evaluated
using the Farm Level
Income Policy
Simulation Model
(FLIPSIM).
FLIPSIM provides a 10-year projected income
statement, cash flow, and balance sheet for a
representative farm. Cash flow used to determine
net farm income.
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Agricultural Policy-Environmental Extender
(APEX) model used to simulate crop yields, soil
erosion and water quality for agricultural
systems.
Hypothetical Simulation of Crop Yields
Baseline
climate
period
Future
climate
period
Selecting Best Agricultural Systems
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Alternative agricultural systems for the baseline
and future climate periods are determined by the
producer panels.
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Multiple criteria evaluation is used to determine
the best agricultural system in the baseline
climate period and future climate period for
each climate change scenario.
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Best agricultural systems for the future climate
period are selected at the beginning of each of
the five 10-year farm planning horizons in the
50-year future climate period.
Adapting Agricultural Systems to
Climate Change
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Alternative agricultural systems for the future
climate period incorporate adaptations of
agricultural systems to climate change.
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To assist the producer panels in identifying
alternative future agricultural systems, they are
given simulation results for the effects of various
adaptation strategies on the four criteria used to
evaluate agricultural systems.
Possible Adaptations to
Climate Change
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using later maturing cultivars to take advantage
of longer growing seasons;
planting crops earlier and using higher seeding
rates to take advantage of higher spring
temperatures and higher precipitation;
changing the mix of crops planted;
adopting new crops;
altering tillage practices and scheduling of field
operations to better cope with earlier and wetter
springs;
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reducing pumping of irrigation water due to
higher precipitation;
altering nutrient and pesticide management
practices in response to higher temperatures and
greater precipitation;
increasing crop drying and pesticide use due to
hotter, wetter summers;
increasing field drainage because of higher
precipitation; and
increasing stocking rates to take advantage of
higher productivity of forage areas.
Example of Adaptive Management
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Suppose, due to climate change, agricultural
system 3 is the best agricultural system in the
first 10-year planning horizon, and agricultural
system 2 is the best agricultural system in the
second 10-year planning horizon for a
representative farm.
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Farm performance can be improved by
switching from system 3 to system 2 at the
beginning of the second 10-year planning
horizon.
Comparing Agricultural Systems
The extent to which agricultural systems are
adapted to climate change for a representative
farm is determined by comparing agricultural
systems for the baseline and future climate
periods for each planning horizon and climate
change scenario.
Decision Support Tool
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Since the study is only
three years long, it is
not possible to fully
implement the
adaptive management
feature of the project.
ƒ Accordingly, the adaptive management feature will
be incorporated in a web-based interactive decision
support tool developed in the project.
The tool will allow
farmers to adapt
agricultural systems for
the representative farms
as new information
becomes available
about the agricultural impacts of past climate
change, the nature of future climate change, and
development of adaptation strategies.
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Conclusion
The development and web-based
implementation of the AG-GEM is expected to
improve the capacity of agricultural producers to
adapt agricultural systems to climate change,
thereby allowing them to capitalize on the
benefits and minimize the adverse impacts of
future climate change on agricultural production
and natural resources.