Trade-offs between Agricultural Production
and Ecosystem Services at a Farm Level
by
Seth Somana & Steven Kraftb
a. Dickinson State University
b. Southern Illinois University Carbondale
Overview of Presentation
 Introduction
 Research Objective
 Study Area
 Methodology
 Results
 Conclusion
Multifunctionality
Multifunctionality refers to the possibility that an economic activity
may have multiple outputs, both commodity and non-commodity
outputs and consequently may contribute to several societal
objectives at once.
Example in agriculture:
positive externality (open space, landscape amenities);
negative externality (soil erosion, eutrophication)
Agricultural landscapes with riparian buffers have positive
multifunctional attributes (Boody et al., 2005, Jordan et al., 2007).
Riparian Buffers provide a variety of ecosystem services
Enhance Water Quality
Provide Terrestrial Habitat
Provide Stream Habitat
Flood Control
Carbon Sequestration
Multiple attributes or joint products of riparian buffers have
received considerable attention in the policy realm.
Problem : Public goods
Agriculture contribute to 50% of the land in the contiguous U.S
(Vitousek 1997) most of these lands are privately owned.
Economic or monetary valuation of ecosystem services is
difficult due to:
 Non availability of a functioning market
 Not all services have a market price (e.g. photosynthesis)
 Services are interrelated
 Time consuming
 High Cost
Conservation Policies
The U.S. government has made available a number of federal
programs to provide markets for these ecosystem services by
properly managing the activities within an agricultural watershed.
-- Conservation Compliance, Land retirement program, Working
land
NCBI formed in 1997 is a public and private partnership aimed at
helping farmers and landowners install conservation buffer on their
lands (USDA-ERS 2000).
The Goal of NCBI--- install 2 million miles of buffer on
environmentally sensitive lands.
By the year 2000 one million miles was installed; 2004 1.55 million
miles.
Decision Environment
A large number of factors affect land owners’ willingness to
change land use decisions to capture or maintain
environmental benefits (Lockeretz, 1990; Napier, 1991; Kraft
and Loftus, 2003)
Personal characteristics of the farm owner (age, education)
Institutional connection
Economic factors
Financial incentives
Legal rights
Research Purpose and Questions
1. Develop a methodology to capture the various
ecosystem services provided by riparian buffers and
agricultural production on a farm level.
2. How much of these services should be produced in a
socially efficient way on a farm level?
-How much of commodity and non-commodity outputs that could be
produced?
3. Develop a trade off between commodity and
non-commodity outputs.
Evaluating the trade-offs among multiple objectives
Gross Margin
Agricultural income is more important
Environmental quality and agricultural
income are equally important.
Production
possibilities curve
Ecosystem Services
Environmental quality is more
important.
Study Area
Cache Watershed encompasses, 1,944km2 of southern Illinois near the
confluence of the Mississippi and Ohio Rivers. The Watershed has
diverse ecological resources and unique natural communities. At least
100 state threatened or endangered plant and animal species are known
within the watershed (USFWS 1990).
Endangered species: Cypress and Tupelo swamps
The Big Creek is a tributary of the Lower Cache River with a drainage area of
33,088 acres (51.7 square miles). This stream originates in Union County in the
Lesser Shawnee Hills
PROBLEMS ADDRESSED
 Loss and fragmentation of natural
habitat
 Dramatically altered hydrologic
systems
 Sediment deposition in the wetlands
 Land use and economic activities that
are incompatible with the long term
maintenance of ecological function
Methodology
Integrated Modeling approach
Modeling based integrative decision making will be the
methodology that will be used in this study.
Ecosystem services
-Water quality: reduced sediment, N, and P loads
Wildlife enhancement.
Economic: Gross margin
Modeling Framework
GIS Platform
Input-output
Ecosystem services
Economic
model
Water Quality
Index
AGNPS
Wildlife Index
Optimization
model
EA
Generates the LU
pattern
IDENTIFY THE TRADE OFF
CURVE THAT MAXIMIZES
GROSS MARGIN & ESS
Ecosystem Services
• Sediment Reduction
• Nitrogen Reduction
• Phosphorous Reduction
• Wildlife Enhancement
Evolutionary Algorithms (EAs)
Simplified models of biological evolution,
implementing the principles of Darwinian theory of
natural selection (“survival of the fittest”) and genetics
Stochastic search and optimization algorithms
Key idea: computer simulated evolution as a problemsolving technique
Landuse and management choices
Number of landuse and management types: 14
Gene: Binary string of length 4
Integer Code
Binary Code
Landuse & Management
Acronym
0
1
2
3
4
5
6
0000
0001
0010
0011
0100
0101
0110
Riparian buffers
Alfalfa Hay
Corn no-till
Corn conservation till Fall
Corn conservation till Spring
Double crop conventional wheat
Conservation Reserve Program (CRP)
RIP
ALF
CNT
CVF
CVS
DVW
PCR
7
8
9
10
11
12
13
14
15
0111
1000
1001
1010
1011
1100
1101
1110
1111
Soybean no-till
Soybean conventional till fall
Soybean conventional till spring
Double crop no-till soybean
Double crop no-till
Wheat conventional tillage
Wheat no-till
Grasslands
Riparian buffers
SNT
SVF
SVS
DNS
DNT
WNB
WNT
GLM
RIP
Multi-objective optimization (MOO):
To find a large number of Pareto optimal solutions with respect to
multiple objective functions.
Multi-objective Optimization Problem
Maximize
subject to
f (x)  ( f1 (x), f 2 (x), ..., f k (x))
xX
f 2 ( x)
Goal of MOO
1. Find solutions close to Pareto optimum
2. Find as many diverse solutions as possible
Maximize
Many Pareto-optimal solutions
Pareto Optimal
Solutions
Maximize
f1 (x)
Water Quality Hydrological Model
Agricultural Non-Point
Source (AGNPS) Pollution Model –
USDA lead agency
 AGNPS
single event, empirical based distributed parameter model
 AGNPS
operates on a cell basis
 AGNPS
requires 22 input parameters
 To
simulate riparian buffer
 -- Curve number (mixed deciduous forest); Manning’s n : 0.005;
C factor (95% vegetative density & 75% canopy cover);
Surface condition factor of 1.0
Economic Model
Farm Economic Model based on
Soil specific Crop yields
Market Price
Labor and Machinery Constraints
Production (operating) costs
Wildlife Index model
USDA- NRCS
Landuse type
Tillage type
Distance to streams or water body
Distance from forested areas
Data
Digitized Fields for the Big creek watershed
Soils- SSURGO
DEM
Price of crops, yields for various crops based on soil types
Cost of Production
Labor and machinery cost on a per farm basis
Nitrogen and Phosphorous application rate
Variable Buffer Width
Buffer width = 20ft + (1.5 x (for each 1% increase in slope)
5.7% of the watershed area (1055 acres)
Wildlife Index
Distance from stream
Distance from forest
Crop type
Tillage type
Width of buffer
Economic Model
Soil Specific Crop yields
Cost of production and Market Price
Field-Farm-Cell lookups
--to capture the water quality parameters
Results Integrated Modeling approach
Population 100
Generation: 100
Cross over probability: 0.5
Mutation probability : 0.2
---- time: approximately 16 hrs
Results
Progression of GA
I st Gen
25 th Gen
50 th Gen
100 th Gen
Results
Economic profit and Water quality
76000
68000
A
Gross Margin ($)
60000
52000
B
44000
36000
C
28000
20000
20000
25000
30000
35000
40000
Wildlife Index
Economic Profit and Wildlife Index
Types of PPF relationships
Water quality
Vs
Gross margin
Competitive
No of
Farms
68
Wildlife index
Vs
Gross margin
Complementary
23
Competitive
91
Water quality
Vs
Wildlife index
Complementary
Competitive
0
58
Complementary
33
Complementary relation between
gross margin and water quality.
VARIABLES
Alfalfa (tons/acre)
Mean
Complementary
Competitive
Corn (bu/acre)
Complementary
Competitive
Soybean (bu/acre)
Complementary
Competitive
Wheat (bu/acre)
Complementary
Competitive
Area (acres)
Complementary
Competitive
Slope (percent)
Complementary
Competitive
Agent type
df
P-value
t-value
2.8695
2.9169
89
.760
-.306
89
.000
-4.244
89
.000
-6.151
89
.000
-5.435
89
.000
-6.484
89
.009
107.5450
121.3445
32.3470
38.7207
41.2305
47.5531
97.0597
262.7512
8.5502
7.5348
89
3.71
2.688
0.899
--Small farms, on highly sloped areas with low crop productivity have
a complementary relationship between gross margin and water quality
Summary of Analysis of tradeoffs done for high price scenario
It was costly to provide more ecosystem services as the price of
commodity increased.
Most of the profit maximizers and conservationist was closer to
the PPF – indication of efficiency.
With high price scenario all the farm had a competitive
relationship indicating that with high prices it is economically
profitable to have commodity crops.
Watershed scale Analysis
Associated Landuse
Landuse Acres
Maximizes
Gross margin
Maximize
Water quality
Maximize
Wildlife Index
Corn No-till
1,399.06
0.0
0.0
Corn Conservation Till
2,204.86
0.0
0.0
Soybean No Till
2,695.25
0.0
0.0
Soybean Conservation Till
2,744.51
0.0
0.0
Wheat
5,54.48
0.0
0.0
Double Crop
1,049.00
0.0
0.0
Alfalfa Hay
5,860.10
7,940.74
953.91
CRP
1,473.00
5,114.77
14,189.65
Pasture Grasslands
383.00
4,496.09
2,413
Buffer
77.00
890.00
885.00
Conclusion
In this study an integrated modeling approach (IMA) was
developed that can be utilized by various decision makers
in analyzing or designing policies that involve multifunctional
agricultural outputs.
The study demonstrated that the IMA could be effectively used
to find patterns of landuse and determine management choices that
approximately optimize sets of economic and environmental
objectives.
The IMA generates PPF for ecosystem service production and
agricultural production at the farm level.
The IMA also shows that the PPF between water quality and
gross margin can be complementary
With higher commodity prices more of an incentive is required
in the form of governmental payment/incentives and cost share to
promote environmental conservation.
Limitations
 AGNPS as an yearly average even though AGNPS calculate
the water quality for a single event rainfall rather than on an
yearly basis.
Questions
Acknowledgements :
Kanpur genetic algorithm lab(Debb): NSGA-II source code
Contact: Seth Soman
email: soman.sethuram@dsu.nodak.edu