The study focuses on incentives to produce crops under reduced tillage systems on highly erodible soils. A mixed integer, mathematical programming model was developed to identify optimal resource use under alternative farm program provisions. A positive counter cyclical payment only reinforces the incentive to comply with NRCS soil erosion constrains.Crop Production/Industries,

Dabney, Seth

Robinson, John R.C.

Triplett, Glover

English

Research Papers in Economics

Impact of New Farm Bill Provisions on Optimal Resource Allocation 
on Highly Erodible Soils
John R. C. Robinson
Agricultural Economist, Texas Cooperative Extension Service, Weslaco TX
jrcr@tamu.edu
Glover B. Triplett
Agronomist, Plant and Soil Science Department, Mississippi State University
gtriplett@pss.msstate.edu
Seth M. Dabney
Agronomist, National Sedimentation Laboratory, Oxford, MS
Selected Paper prepared for presentation at the Southern Agricultural Economics Association Annual Meeting,
Mobile, Alabama, February 1-5, 2003
Copyright 2003 by John Robinson.  Readers may make verbatim copies of this document for non-commercial
purposes by any means, provided that this copyright notice appears on all such copies.1 John Robinson is an Extension Economist and Associate Professor at the Texas A&M Research and
Extension Center at Weslaco, TX.  Glover B. Tripplet is a Professor of Plant and Soil Sciences, Mississippi State
University, Starkville, MS.  Seth M. Dabney is a Research Agronomist, USDA ARS National Sedimentation
Laboratory, Oxford, MS.
The authors acknowledge Cotton Incorporated for support of this research.
Impact of New Farm Bill Provisions on Optimal Resource Allocation 
on Highly Erodible Soils
John R. C. Robinson, Glover B. Triplett, and Seth M. Dabney
1
Abstract
The study focuses on incentives to produce crops under reduced tillage systems on highly erodible soils.  A mixed
integer, mathematical programming model was developed to identify optimal resource use under alternative farm
program provisions.  A positive counter cyclical payment only reinforces the incentive to comply with NRCS soil
erosion constraints.
Introduction
 The 1996 farm bill authorized payments to farmers that included, in the case of the marketing loan
program, price subsidies on actual production of specified program crops.  By supporting prices, such provision
provide incentives to produce those crops when market prices are below loan rates.  The 1996 farm program was
innovative in providing decoupled payments which participating growers or landowners received on the basis of
enrolled acreage (i.e., independent of crop production or market prices).  In addition to these two forms of subsidy,
the 2002 farm bill authorized a counter cyclical payment which is also calculated independently of crop production,
but is an inverse function of season average cash price.  For more explanation of farm program provisions see
Outlaw (2003).  The 2002 farm bill can be seen as an addition to the “safety net” for growers.
This paper examines the linkages between decoupled payments and on-farm  resource allocation with a
focus on incentives for conservation tillage.  The majority of  economic studies of alternative tillage systems have
made partial budgeting or whole farm budgeting comparisons to show that conservation tillage systems sometimes
are more profitable than conventional tillage systems (Crosson, Hanthorn and Duffy).  Partial budgeting approachestypically assume that hourly labor to perform field operations is neither limiting nor slack., which is often unrealistic
since many farms hire full-time seasonal or annual labor. While differences in fixed equipment costs can be
evaluated using partial budgeting, the comparisons are often not robust because they are made, often implicitly, on
the basis of a single farm scale.  Equipment is assumed to be used efficiently, regardless of farm size.  Mixed integer
programming models have also been used to solve for the optimal allocation of crops and tillage systems, plus
related activities such as labor and equipment acquisition (Robinson and Falconer, Triplett et al.).  The advantage of
mathematical programming is that the economies of scale in resource allocation can be explicitly modeled.
On high erodible soils, the choice of crop/tillage system has implications for long-term productivity and
short-run compliance with federal farm programs.  The 2002 farm bill continued the conservation compliance
provisions which link farm program eligibility to, among other things, maintenance of an  soil erosion plan as
approved by USDA Natural Resources Conservation Service (NRCS) technicians.    For example, parameters
reflecting soil loss under alternative crop/tillage combinations can be specified and used to constrain erosion below
local limits (“T-values”) specified by NRCS.  Alternatively, the objective function could be modeled to only include
farm program payments when the model solution contains crop/tillage systems that are withing NRCS compliance.
In this way, “decoupled” farm program payments may be linked to crop selection choices.
This paper presents results from a mixed integer programming model of a representative row crop farm in
Tate County, Mississippi.  The study area was the site of a 10-year field trial examining the productivity and
economics of alternative tillage systems (Triplett et al.).   Soils on the site were dominantly Grenada silt loam but
included small amounts of associated Memphis and Loring silt loams, all of which are both very fertile and very
subject to erosion.
Data Development
 Crop budget data from the 10-year field trials were collected and used to specify model parameters
including typical yields, gross returns, field operations, machinery performance rates, variable costs and fixed cost. 
Crop yields and historic commodity prices were used to calculate gross returns for crop and tillage combinations. 
Crops included corn, cotton, grain sorghum, soybean, and wheat in a doublecrop system with soybeans.  Tillage
systems included conventional (chisel plow, disk 2x, smooth, plant, cultivate postemergence 2x), No-tillage (spray,
plant, harvest) and a hybrid reduced tillage systems which involved minimal tillage.  More background information
on the field trials is provided by  Dabney et al. ( 1993) and Triplett et al. (1996).  To specify labor and equipment usage, the calendar was divided into thirteen 4-week periods with
assignment of typical field operations. Days suitable for field operations in each period (Spurlock et al. 1995) was
coupled with equipment performance rates to calculate potential crop acreage per worker. 
Soil erosion parameters were estimated using NRCS crop “t-factors” and “ T-values”.  These were used to
calculate the limit of soil erosion that will still be within NRCS compliance.  Estimated soil loss for each crop/tillage
system was estimated using the Revised Universal Soil Loss Equation.
Tate County average farm program yield and acreage information  were obtained from the USDA Farm
Services Agency (FSA) and used to model the farm program payments of a representative farm.  Other farm program
information collected were local crop loan rates, loan repayment rates, AMTA payment rates (for 1996 farm bill
scenarios), and target prices (for 2002 farm bill scenarios).  The online Base and Yield Analyzer software (Outlaw)
was used to identify the likely base acreage revision for a representative farm under the 2002 farm program sign-up. 
The results of the Base and Yield analysis indicated that farm program payments under the 2002 farm program were
highest by only adding oilseeds to reflect the 1998-2001 pattern of soybean plantings in Tate County.  Thus for 2002
farm bill scenarios, the parameter for government payments was calculated based on a revised base acres estimate
for the representative farm.
Model Development
A mathematical programming model was developed to reflect the general set of choices and tradeoffs faced
by a farm manager in this study area.  These include choices about tillage systems that potentially influence NRCS
compliance and thus eligibility for farm program payments.  Other more basic choices include how to allocate land
and “lumpy” capital resources (e.g., equipment, full-time labor) to the most profitable activity.  The objective
function for this model is presented below in Equation (1) where:
! i= crops (cotton, sorghum, and conservation reserve program as a default option)
! j= previously discussed tillage systems
! p=previous crop, to specify rotation constraints within a tillage system
! k=tractors, tillage implements and other equipment
! t=time periods for labor constraints and Machine Trips parameter.
(1)   Max Profits = Si Sj Sp ((GrossReturnsijp- NonMachineryVariableCostsijp)*Xijp)
  + DecoupledPayment*ComplianceIndicatorijp  - Si Sj Sk Sp St (MachineryVariableCostsk* MachineTripsk,t,i,j*Xijp )
 -  Sk (MachineryFixedCostsk* MachineTripsk,t,i,j*Buyk ) - AnnualSalary* Hire.
The continuous variable Xijp represents acres of cropping/tillage activities, while the integer variables Buyk 
and Hire reflect purchases of lumpy resources. The binary variable ComplianceIndicatorijp is zero when the model
solution violates an NRCS constraint on soil erosion, thus removing decoupled payments from the objective
function.  (In reality, non-compliance would disqualify any loan deficiency payments as well, but these were not
included for simplicity of calculation.)  The parameter MachineTripsk,t,i,j is trips across one acre of a given
crop/tillage combination with specified equipment. The AnnualSalary parameter is specified as $20,000 per year for
a full-time equipment operator.  The remaining  parameters in the objective function specify typical crop budget cost
or return values, in addition to the decoupled payment parameter which was discussed previously.  In summary, the
objective function maximizes profits by choosing crop/tillage combinations,  where farm income (net of non-
machinery  variable costs) is further reduced by the costs associated with acquiring machines or full-time labor.
Equation (1) was specified in conjunction with the following land, labor, machinery capacity, rotation, and
NRCS compliance constraints:
(2) Si Sj Sp Xijp  <  Land Scalar
(3) Si Sj Sk Sp (PerfRatek*MachineTripsk,t,i,j*Xijp) <   Sk (AvailableFieldHoursk*HIRE)
(4) Si Sj Sk St (PerfRatek*MachineTripsk,t,i,j*Xijp) <  St (CapacityParameterk,t*BUYk )
(5a) Si Xijp  <  PREPjp
(5b) PREPip   <  Si Xijp         
(6)  Si Sj Sp X
*
ijp   <  Land*ComplianceIndicatorijp for all X
*
ijp such that SOILLOSS(i,j)>TFACTOR(i)*TVALUE is true.
Equation (2) is a straightforward constraint on land resources.    Equation (3) constrains labor demand to be
less than or equal to labor supply.  This constraint is built around by two key parameters:   equipment performance
rates (hours per acre) and days available for fieldwork.  Hiring one machine operator acquires a supply of driver
hours to satisfy the labor demands of specific crops.
Equation (4) represents a set of k equations (i.e., one for each capital item) constraining machine hours
demand to be less than or equal to supply of available capacity of that given machine.  As in the labor constraint, themachinery capacity parameter was also a function of the machinery performance rate multiplied by available hours. 
Buying one machine gives the maximum potential machinery capacity in each time period.
Equations (5) are rotation sequencing constraints that require predecessor crops to likewise be in  solution,
thus generating an optimal rotation.  A conservation compliance parameter was formulated using estimates of soil
erosion under each crop/tillage combo.   When any soil loss from a crop in solution exceeds the NRCS limit, a 0/1
indicator variable is generated by Equation (6) that eliminated government payments from the objective function. 
The model was written and run in  GAMS using the XA solver.  Alternative runs were made to compare
results under 1996 and 2002 farm program provisions at 1,500 acres using forecasted market prices for 2003.  In
addition, the objective function results under both farm program payment provisions were compared over a range of
acreage sizes.
Results and Discussion
Table 1 presents market prices, calculated counter cyclical payment (CCP) rates, and the representive base crop
shares for the representative farm.  For this farm, cotton has the largest share of base acres with 40% of the total farm
acreage.  Furthermore, the CCP rate for cotton was at it’s maximum of $0.134 per lb due to the low expected price
for cotton.  Thus the CCP under the 2002 program payment calculation was positive and represented an increase
over what would have been paid under the 1996 program.  In terms of the model, the DecoupledPayment parameter
in Equation (1) was larger under the 2002 program payment scenario compared to the 1996 program payment
scenario (Table 2).
 In terms of resource allocation, Figure 1 shows the distribution of whole farm net returns under the two farm
program payment regimes, for solutions for operations scales between 100 and 1,500 acres.  The 2002 farm program
payment distribution is obviously larger.  Points a through g indicate changes in the slope of this curve, caused by
either shifts in the optimal crop mix (from no-tillage soybean:corn rotation to additions of no-tillage soybean:wheat
rotation), hirings of full-time labor, or purchases of equipment.  The pattern of resource allocation was similar for the
lower distribution of the 1996 program payment scenario.  Points h and i specifically denote the acreage at which
additional full-time labor was hired.
 The addition of the CCP reinforces the incentive to comply with NRCS soil erosion constraints.  This will
be the case as long as the CCP is positive, which is a function of season average prices received and the official target
price.  Under the 1996 farm bill, the totally decoupled AMTA payment was the only incentive, and it was probablyinsignificant compared to other incentives for compliance such as eligibility for crop insurance.  Yet this study and
previous applications of this model (Triplett, Robinson and Dabney) demonstrated that the AMTA payment alone was
enough incentive to choose soil conserving crop/tillage activities on highly erodible soils.  
The influence of the CCP under the new farm program is similar in its effect to crop insurance.   Access to
both is contingent on compliance with NRCS erosion constraints.  Both can provide an indemnity to growers when
certain risky situations prevail (low yields for crop insurance, or low prices for the CCP).  The question might be
asked under what price conditions would this representative farm decision maker ever choose conventional tillage
treatments.  In several model runs (not shown) a 5X to 10X price increase (depending on the crop) resulted in a
sufficient dwarfing of the government payments to bring a conventional tillage, non-compliance crop mix into
solution.  However, besides being an unrealistic price scenario, such high prices would likely increase growers’
demand for crop insurance (also requiring NRCS compliance). 
References
Crosson, P., M. Hanthorn, and M. Duffy. 1986. The economics of conservation tillage. In M. A. Sprague and G. B.
Triplett (eds.) No-Tillage and Surface Tillage Agriculture: The Tillage Revolution. J. Wiley & Sons: New
York..
Dabney, S. M., C. E. Murphree, G. B. Triplett, E. H. Grissinger, L. D. Meyer, L. R. Reinschmeidt, and F. E. Rhoton,
1993.  Conservation production systems for silty upland soils. In Proceedings of the 1993 Southern
Conservation Tillage Conference for Sustainable Agriculture 15-16 June 1993.  Monroe, LA.
Outlaw, Joe.  2003.  Farm Bill Education Materials.  http://agecoext.tamu.edu
Robinson, John R. C. and Lawrence L. Falconer.  2003.  “Optimal Land, Equiment, and Labor Allocation Under 
Alternative Tillage Systems in South Texas.”  2003 Proceedings of the Beltwide Cotton Conferences. 
National Cotton Council: Menphis.  
Spurlock, S. R., N. W. Buehring, and D. F. Caillavet.  1995.  "Days suitable for fieldwork
in Mississippi."  MAFES Bulletin 1026.  May 1995.
Triplett, G. B., S. M. Dabney, and James H. Siefker. 1996. Tillage systems for cotton on silty upland soils. Agronomy
Journal. 88(1996):507-512.
Triplett, G. B., J. R. C. Robinson, and S. M. Dabney. 2002.  “Whole Farm Analysis of No-Tillage and Tilled
Production Systems.”  Proceedings paper at SR- Conference on , Auburn, ALTable 1.  Farm Program Parameters for Representative Farm Model.
Corn Cotton Soybeans Wheat
Market Price $2.25/bu $0.404/lb $5.03/bu $3.67
CCP Rate $0.07/bu $0.137/lb $0/bu $0/bu
Share of Base 20% 40% 20% 20%
Table 2.  Comparative Model Solutions at the 1,500 Acre Scale for Alternative Farm Program Payment
Scenarios
Solution w/ 1996 Payments Solution w/ 2002 Payments
Whole Farm Profits (Obj. 
Function)
$117,709.20 $167,576.18
Crop/Tillage Mix no-till corn:soybean rotation no-till corn:soybean rotation
Equipment Complements 1 1
Full-time Labor Hires 1 1
NRCS Compliance? YES YES
Avg. Decoupled Payment/Acre $28.82 $62.07

Conservation production systems for silty upland soils.

Days suitable for fieldwork in Mississippi.&quot;

Farm Bill Education Materials. http://agecoext.tamu.edu

The economics of conservation tillage.

Tillage systems for cotton on silty upland soils.

Whole Farm Analysis of No-Tillage and Tilled Production Systems.”

IMPACT OF NEW FARM BILL PROVISIONS ON OPTIMAL RESOURCE ALLOCATION ON HIGHLY ERODIBLE SOILS

IMPACT OF NEW FARM BILL PROVISIONS ON OPTIMAL RESOURCE ALLOCATION ON HIGHLY ERODIBLE SOILS

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