The uncertainty weather condition could pose some challenge in achieving environmental target. In this study, we use a bioeconomic model to calculate the impacts of alternative management systems. Under different safety-first constraints on the levels of environmental runoff, obtaining from APEX, optimal net return of alternative cropping practices is estimated.Environmental Economics and Policy,

Hite, Diane

Intarapapong, Walaiporn

Renck, Ashley Wood

English

Research Papers in Economics

Environmental Risk Assessment under Environmental Standard
and Safety-First Constraints
Walaiporn Intarapapong, Postdoctoral Research Assistant
Department of Agricultural Economics, Mississippi State University
Box 5187, Mississippi State, MS, 39762
Phone: 662-325-8746
e-mail: Intarapapong@agecon.msstate.edu
Diane Hite, Assistant Professor
Department of Agricultural Economics and Rural Sociology, Auburn University
209-B Comer Hall
Auburn, AL 36849-5406
Phone: 334-884-4800
e-mail: dhite@acesag.auburn.edu
Ashley Renck
Department of Agricultural Economics, Mississippi State University
Box 5187, Mississippi State, MS, 39762
Phone: 662-325-9560
e-mail: Renck@agecon.msstate.edu
Presented at Western Agricultural Economics Association Annual Meeting
July 28-31, 2002
Long Beach, CaliforniaEnvironmental Risk Assessment under Environmental Standard and 
Safety-First Constraints 
 
 
Walaiporn Intarapapong, Postdoctoral Research Assistant 
Department of Agricultural Economics, Mississippi State University, 
Box 5187, Mississippi State, MS 39762 
Phone: 662-325-8746 
e-mail: Intarapapong@agecon.msstate.edu 
 
Diane Hite, Assistant Professor,  
Department of Agricultural Economics and Rural Sociology, Auburn University, 
209-B Comer Hall 
Auburn, AL 36849-5406 
Phone: 334-844-4800 
e-mail: dhite@acesag.auburn.edu 
 
Ashley Renck 
Department of Agricultural Economics, Mississippi State University, 
Box 5187, Mississippi State, MS 39762 
Phone: 662-325-9560 
e-mail: Renck@agecon.msstate.edu
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Environmental Risk Assessment under Environmental Standard and 
Safety-First Constraints 
 
Abstract 
 
The uncertainty weather condition could pose some challenge in achieving environ-
mental target.  In this study, we use a bioeconomic model to calculate the impacts of alterna-
tive management systems. Under different safety-first constraints on the levels of environ-
mental runoff, obtaining from APEX, optimal net return of alternative cropping practices is 
estimated.   
 
I. Background 
A concern of adverse environmental impacts in association with agricultural practices 
in the United States has been steadily increasing.  A number of government programs to 
lessen such problems have been introduced, including Total Maximum Daily Load environ-
mental standards (TMDLs), which soon will be effective.  TMDLs are under consideration to 
reduce environmental runoff of nutrients, chemicals and sediment.  Best management prac-
tices, including crop rotations and alternative tillage practices (no-till and conservation till-
age), may help farmers comply with TMDL standards, while minimizing losses in farm prof-
its.  However, due to the uncertainty of agricultural nonpoint pollution, which depends on a 
number of factors, such as weather, environmental standards may be achieved only at a cer-
tain level.  The uncertainty weather condition could pose some challenge in achieving envi-
ronmental target.  Since achieving environmental standard is unlikely, safety-first plays sig-
nificant role in environmental policies.    
To estimate the environmental and economic impacts of various cropping practices 
while taking into account the stochastic nature of environmental amenities, a number of risk 
models have been applied including chance-constrained, Target MOTAD, and Upper Partial 
Moment (UPM).  To apply chance-constrained, a specific functional form of the environ-mental variables is required, which could pose some limitation to the model.  Environmental 
variables could vary from site to site due to weather and other physical conditions.  This 
specified of functional form of environmental variables has significant impact on choices of 
agricultural practices.  For Target MOTAD, instead of specifying the distribution functional 
form, it treats the sample of variables as an empirical distribution, and the results of the opti-
mization are valid as long as the empirical distribution represents the true distribution.  How-
ever, the environmental risk level is chosen exogenously, which pose skepticism to the model.   
For this study, an upper partial moment (UPM) is applied.  Unlike Target MOTAD, the envi-
ronmental risk level is endogenously determined after the desired compliance probability with 
the objective is specified.  To obtain environmental runoff, Agricultural Policy Environmental 
Extender, or APEX (Blackland Research Center, 1999; Williams et al., 2000) has been used 
to estimate nitrate runoff and sediment from various cropping practices of Deep Hollow wa-
tershed, Mississippi.  We use a bioeconomic small watershed model to calculate the impacts 
of alternative management systems.  The model merges physical data and biological data to 
analyze various management decisions and to simultaneously determine optimal management 
in terms of profit and environmental quality. Under different safety-first constraints on the 
levels of environmental runoff, obtaining from APEX, optimal net return of alternative crop-
ping practices is estimated.   
II. Analytical Approach 
  Our analytical approach is a two-part process.  In the first stage of analysis, we de-
velop the biophysical model in which we use APEX to estimate environmental runoff and 
yields under a number of scenarios.  The outputs of interest from this model are expected crop 
yields and expected runoff of nitrogen and sediment.  In addition we have developed scenar-ios in which filter strip practices are examined.  In the second stage of analysis the optimal net 
return under safety-first constraint is calculated using the Generalized Algebraic Modeling 
System (GAMS) along with information on yields, crop prices, production costs and envi-
ronmental parameters derived from APEX.  Optimality of the system is determined by maxi-
mizing net returns across the entire watershed. 
Watershed Level Physical Model 
  Site information such as cropping practices, soil types, topography and meteorological 
data has been collected over a number of years in the project, but in this paper, we focus on 
the year 1999 as the basis for our analyses.  Traditional farm models assume that a farmer’s 
production decisions are constrained by various factors such as amount of land, labor and 
other available inputs.  An extension of the traditional model that we use in our analysis is a 
bioeconomic model.  Our model is developed for the Deep Hollow watershed, and we ex-
trapolate the model results over a 25-year time period.  The underlying physical simulation 
model incorporates nearby weather conditions in the watershed, nutrient uptake and the tim-
ing of planting and harvesting of crops. 
    The bioeconomic model uses the Agricultural Policy Environmental Extender, or 
APEX (Blackland Research Center, 1999; Williams et al., 2000), which was developed as an 
extension of the EPIC (Erosion-Productivity Impact Calculator) model to small watershed 
level by the US Department of Agriculture’s Agricultural Research Service (ARS), Soil Con-
servation Service (SCS), and Economic Research Service (ERS) in the early 1980's (Sharply 
and Williams 1990 (a and b)).  APEX is designed to simulate biophysical processes and the 
interaction of cropping systems with management practices, soils and climates over long time 
periods.  APEX captures timing of planting and harvesting and the use of cultural BMPs, and produces environmental parameters w here water flows through small watersheds as surface, 
channelized and subsurface flow.  APEX has flexibility in allowing for model calibration with 
existing data.  In this study, we are interested in calibration of our model to correspond with 
onsite empirical measures of environmental parameters. 
    The watershed level model uses data inputs that replicate physical, meteorological and 
agricultural characteristics of the Deep Hollow Watershed.  The watershed consists of 10 
fields in which the primary crops g rown have been cotton and soybeans. Within the water-
shed, there are 6 different soil types: Alligator, Arents, Arkabutla, Dubbs, Dundee and Tensas.  
In each field is a combination consisting of 2 to 3 soil types resulting in 22 subfields of unique 
soils (Table 1).   
  Approximately 20 inputs into the APEX model are needed for each subfield in order 
to perform simulations from which to obtain expected yields and nutrient and sediment run-
off.  The inputs include weather, soil type, soil erodibility factors, topography (as measured 
by average slope length and steepness), distance from fields to watercourses, relative geo-
graphic location of fields within the watershed, crop rotation, tillage practices and fertilizer 
and chemical use.  As part of the MDMSEA project, the soils and topography of these fields 
have been measured to a high degree of accuracy.   
  The crops considered are continuous cotton and continuous soybeans under conven-
tional tillage.  We generated these outputs from the APEX model in order to use them as i n-
puts to the economic optimization model described in the next section. 
In our study, we will also calibrate our model to correspond with onsite empirical 
measures of environmental parameters.  Uncertainty environmental impacts due to stochastic 
weather conditions will be simulated using APEX.  Historical data of precipitation, collected from a nearby weather station (Greenwood Lefore Art), are divided up to a number of inter-
vals, which correspond to the state of nature.  The probabilities will be determined by dividing 
the number of observations in each interval by the total number of years. 
Optimization with Safety-first Constraint 
The optimal net returns of total watershed under safety-first constraints are estimated.  
The safety-first concept is applied to investigate economic decision under environmental un-
certainty.  Under safety-first rules, the decision maker concern with the probability of envi-
ronmental variables falling below target values.  The UPM model to evaluate environmental 
risk applied by Qui et al. (2001) can be written as  
Maximize  ￿ =
=
n
j
j jx c z E
1
) (  
Subject to  ￿ = £
=
n
j
k j kj K k b x a
1
,...., 1  
         ￿ = ‡ + -
=
n
j
r j rj s r d x t
1
,...., 1 0 n  
      ￿ = -
=
s
r
r r t d p
1
0 ) ( q  
      e G t L t £ + ) (
*q  
for all xj and dr greater than zero, where t is an endogenously determined reference level for 
the environmental variable, dr is zero or deviation above t for state r, and qt= q(1,t) 
In our study, nutrient and sediment runoff are simulated using, which is then used in a 
mathematical optimization program using GAMS.  Therefore, an economic optimization 
model and assess environmental risk of reduced nutrient and sediment runoff by incorporating 
the safety-first model constraints. III. Preliminary Results and Conclusions 
  Regarding uncertainty of environmental impacts (sediment and nitrate runoff) of crop-
ping practices due to stochastic weather condition are simulated under 15 states of nature, us-
ing APEX.  Information on variable costs is obtained from on site data.  To calculate net re-
turns, five years (1995-1999) average market prices of cotton and soybean in Mississippi are 
used.   
  For the baseline scenario, total watershed net returns along with amount of sediment 
and nitrate runoff are calculated.  The optimal net returns of the whole watershed subject to 
land constraint are estimated, using mathematical program, GAMS.  The environmental goals 
are to reduce sediment and nitrate runoff by 25% and 50% from the baseline levels (0% re-
duction in pollutants).  Under UPM model, probability of achieving such goal as well as envi-
ronmental goals is incorporated to safety-first constraint.  In this study, probabilities of com-
pliance with environmental goals are set to 0.50, 0.75, 0.85, and 0.95.  Even under the base-
line scenario of 0% reduction in sediment and nitrate runoff, there are 4 possible compliance 
probabilities of 0.50, 0.75, 0.85, and 0.95.  The environmental constraint becomes more re-
strictive as the reduction level and the probability of compliance increase.  GAMS are used to 
solve the optimal net returns under the UPM environmental safety-first constraint. 
  For the baseline scenario, under conventional tillage practice, the net returns, sediment 
and nitrate runoff are $16,535, 12.6 tons, and 44 lbs, respectively.  The environmental goal 
are 12.60, 9.45, and 6.30 tons for sediment and 44.40, 33.30, and 22.20 pounds for nitrate 
runoff, which correspond to a 0%, 25%, and 50% reduction in the baseline environmental re-
duction levels (Table 2 and 3).    Target value (t) of sediment and nitrate runoff, and sediment and nitrate risk levels q(t) 
are also reported in Table 2 and 3.  As the compliance probability to the sediment and nitrate 
runoff goals becomes higher the expected deviation q(t) above the reference t value becomes 
smaller.  In other words, a less deviation from reference value is allowed when the compli-
ance probability is higher.  For instance, the expected deviation falls from 1.31 tons to 0.03 
tons as compliance probability for achieving 25% sediment reduction increases from 0.50 to 
0.95, which implies a reduction in the sediment risk level (Table 2).  In this exercise, only 
conventional tillage practice is considered.  For further study, conservation and no tillage 
practices will be included, which the optimal land allocation among the various tillage prac-
tices will be estimated. 
 
 
 
 
 
 
 
 
 
 
 
 
 References 
Brook, A., D. Kendrick, A. Meeranous, and R. Raman, “GAMS: A User’s Guide”, GAMS  
           Development Corporation, 1998, http://www.gams.com/docs/document.htm. 
Balckland Research Center. 1999. APEX User’s Guide and Technical Documentation, Ver-  
           sion 8190. Texas Agricultural Experimental Station. 
 
Qui, Zeyuan, Tony Prato, and Francis McCamley. 2001.  Evaluating Environmental Risk    
            Using  Safety-First Constraints. American Journal of Agricultural Economics 83(2),  
            May 2001: 402-413. 
 
Sharply, A.N. and J.R. Williams, eds., 1990-a. EPIC-Erosion /Productivity Impact Calculator  
          Vol. I, Model Documentation. USDA Technical Bulletin No. 1768, 1990. 
 
Sharply, A.N. and J.R. Williams, eds., 1990-b. EPIC-Erosion /Productivity Impact Calculator  
         Vol. II, User Manual. USDA Technical Bulletin No. 1768, 1990. 
 
 
 
 
 
 
 
 
 
 
 
 
 
                           Table 1:  Composition of Subfields in Deep Hollow Watershed, MS 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Field 
ID 
 
 
 
Acres 
 
Soil 
 
% Soil 
 
XP3A  24.8  Dubbs  7.75   
XP3A    Tensas  3.11 
XP3B  12.0  Dubbs  2.25 
XP3B    Tensas  1.55 
XP3B    Dundee  1.04 
XP3C  12.4  Dubbs  0.66 
XP3C    Dundee  1.04 
XP10  37.1  Tensas  6.99 
XP10    Dundee  8.30 
XP10    Dubbs  1.80 
XP1  17.2  Arkabutla   12.27 
XP2W  29.5  Tensas   14.09 
XP2W    Alligator  3.18 
XP2W    Arkabutla  1.24 
XP2E  29.5  Tensas  14.50 
XP2E    Alligator  3.28 
XP2E    Arkabutla  1.24 
XP8  9.0  Alligator  2.36 
XP9A  12.6  Arkabutla  6.04 
XP9A    Arents  2.10 
XP9B  10.6  Arents  1.57 
XP9B    Arkabutla  3.64  
 
                                    Table 2. Upper Partial Moment Model for Sediment Reduction 
 
                         
Prob.  Sed. Goal      Net Returns        t  q(t) 
b  (tons)  $  (tons)  (tons) 
         
0.50  12.6  16,275  9.31  1.65 
  9.45  15,744  6.82  1.31 
  6.3  15,212  4.33  0.99 
0.75  12.6  15,930  10.46  0.54 
  9.45  15,467  7.8  0.41 
  6.3  15,004  5.13  0.29 
0.85  12.6  15,771  10.86  0.26 
  9.45  15,347  8.12  0.2 
  6.3  14,919  5.36  0.14 
0.95  12.6  15,533  12.56  0 
  9.45  15,170  9.16  0.01 
  6.3  13,407  5.79  0.03 
 
 
 
 
 
                                   Table 3. Upper Partial Moment Model for Nitrate Reduction 
 
Prob.  Nitr Goal  Net Return  t  q(t) 
b  (lbs)  $  (lbs)  (lbs) 
         
0.5  44.4  15,987  30.51  6.94 
  33.3  15,482  21.06  6.12 
  22.2  14,296  13.84  4.18 
0.75  44.4  15,546  31.62  3.19 
  33.3  14,769  26.11  1.8 
  22.2  12,623  16.99  1.3 
0.85  44.4  15,414  36.21  1.23 
  33.3  14,487  26.98  0.95 
  22.2  12,003  17.92  0.64 
0.95  44.4  14,773  42.16  0.11 
  33.3  13,373  32.15  0.06 
  22.2  10,631  21.66  0.03 
 

ENVIRONMENTAL RISK ASSESSMENT UNDER ENVIRONMENTAL STANDARD AND SAFETY-FIRST CONSTRAINTS

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