1
PASTOR: introductory explanation and exercises (including
linear programming)
B.A.M. Bouman
PASTOR: introductory explanation and exercises (including linear programming).......... 1
Introduction and installation of exercises ........................................................................... 1
Part 1. PASTOR technical coefficient generation .............................................................. 3
1. PASTOR Introduction .................................................................................................... 3
1.1 Herds ......................................................................................................................... 4
1.2 Pasture ....................................................................................................................... 5
1.3 Feed supplements ...................................................................................................... 6
2 Exercises .......................................................................................................................... 7
2.1 Exercise: herd modeling ........................................................................................... 7
2.2 Exercise: unfertilized pasture modeling.................................................................... 8
2.3 Exercise: fertilized pasture modeling...................................................................... 11
Part 2. Linear programming exercises .............................................................................. 14
3. Case study description .................................................................................................. 14
3.1 Model 1: base scenario................................................................................................ 15
3.2 Model 2: labor scenario .............................................................................................. 19
3.3 Model 3: greenhouse gas reduction scenario .............................................................. 20
3.4 Model 4: pesticide reduction scenario ........................................................................ 21
3.5 Model 5: sustainability scenario: non-fertilized pastures ........................................... 21
3.6 Model 6: sustainability scenario: fertilized pastures 1 ................................................ 22
3.7 Model 7: sustainability scenario fertilized pastures 2 ................................................. 23
3.8 Conclusions ................................................................................................................. 23
Introduction and installation of exercises
This text with exercises is an introduction to the functioning of the Technical Coefficient
Generator PASTOR (Part 1), and to the use of PASTOR-generated technical coefficients
in linear programming models (Part 2). PASTOR is explained in Chapter 5 of this book,
and a complete description and user manual is provided on this CDROM (as found in
\PASTOR\PASTOR4\MANUAL\pmanual.doc; Bouman et al., 1998a). PASTOR itself
is located in the directory \PASTOR\PASTOR4\, the readme.doc file in that directory
explains how to install and use PASTOR.
In part 1, a brief explanation is given of PASTOR. Next, three exercises with PASTOR
are presented (in \PASTOR\EXERCISE\EXERC1\ of this CDROM); one to generate
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technical coefficients of herds, one to generate technical coefficients of unfertilized
pastures, and one to generate technical coefficients of fertilized pastures. The aim is to
familiarize the user with general concepts of PASTOR and to serve as example on how
PASTOR can be used to generate (in this case alternative sustainable) pasture production
systems.
In Part 2, a simple case-study is presented that illustrates how the generated technical
coefficients with PASTOR can be used in linear programming to find optimum
combinations of herds, pastures and feed supplements to maximize farmers’ profit under
certain sustainability restrictions (in \PASTOR\EXERCISE\EXERC2\ of this
CDROM). The linear programming models for this exercise are programmed in the
language GAMS 2.25 (Brooke et al., 1992). To run these models, users should have the
GAMS software (which is a commercial package, and therefore not included on this
CDROM). Since programming in GAMS is outside the scope of this exercise, a set of
linear programming models have been ‘pre-programmed’ for the various scenarios to be
run. The different scenario’s all use the same model (i.e. set of equations), but with
different restrictions or inputs (technical coefficients generated with PASTOR). Users
who are familiar with GAMS or linear programming modeling can view the model
(source code).
To install the exercises of PART 1, copy the files from CDROM onto your hard disk:
 DOS PC: copy the whole subdirectory called EXERC1 onto the root directory (C) of
your PC. The result should be:
C:\ EXERC1\ BREEDING
PASTOF
PASTOU
TEMP
 Windows(95): start up the DOS box, and follow the same procedure as above.
Use a text editor of your preference to read and adapt the input files as required in the
exercises. PASTOR should be run under DOS (or the DOS box under Windows(95)).
PASTOR was programmed in FORTRAN77, linked and compiled on PC Compaq
machines.
To install the exercises of PART 1, copy the files from CDROM onto your hard disk:
 Copy the whole subdirectory called EXERC2 onto the root directory (C) of your PC.
The result should be:
C:\ EXERC2\
FILES1
FILES2
FILES2
MODEL1
MODEL2
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MODEL3
MODEL4
MODEL5
MODEL6
MODEL7
 Windows(95): start up the DOS box, and follow the same procedure as above.
Use a text editor of your preference to read and adapt the input files as required in the
exercises. The GAMS models should be run in the appropriate way as installed on your
own PC. The GAMS models provided are programmed in GAMS 2.25 on PC Compaq
machines.
During the exercises of both Part 1 as well as Part 2, you are requested to fill-in some
Tables. The tables in this text are blank, but the correct answers are entered as ‘hidden
text’. To view these answers, change the ‘view’ option of hidden text of this file.
Part 1. PASTOR technical coefficient generation
1. PASTOR Introduction
This document and exercise is a brief introduction to the system PASTOR 2.0 (Pasture
and Animal Production Technical coefficient generatOR; Bouman et al., 1992). PASTOR
generates technical coefficients for pasture land use systems, herds and feed
supplementing in cattle livestock systems in the humid tropics. A pasture is a land use
system that entails the growing and management of pasture (i.e. grass, or grass-clover
mixtures). Technical coefficients are inputs and outputs of production systems. Examples
for pasture are costs of production, yield in terms of dry matter and nutrition for cattle,
associated sustainability indicators such as soil nutrient balance and pesticide use, and
labor use. Examples for herds are costs, labor use and production (in terms of meat and
milk) of a herd. Technical coefficients are used in linear optimization modeling to select
combinations of pastures, herds and feed supplements on specific land units that
maximize a certain goal under specified boundary conditions and restrictions. Usually,
the goal (objective function) is value added (‘profit’), while restrictions can be related to
the amount of resources available (land, labor), marketable volumes of products or some
environmental limitations (such as allowable pesticide use). Example of the use of
PASTOR generated systems are given in Chapters 6, 7 and 8 of this book. Here, the
approach to linear programming and its use in exploratory land use studies is not further
explained. Merely suffice to say that PASTOR is a tool that generates technical
coefficients for a large number of production systems in the livestock sector. A linear
programming model can select optimum combinations of the three components of a cattle
system, by maximizing value added while balancing the nutrition intake requirements by
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the herds with the nutrition provided by the pastures and feed supplements. PASTOR was
developed for livestock systems in the humid tropics using the Atlantic Zone in Costa
Rica as a case-study. However, the system was set up in a generic manner so that by
adapting the input files, users can make PASTOR suitable to other environments.
PASTOR generates technical coefficients using the ‘target oriented’ approach for
alternative production systems: a target production level is set by the user and that
subsequently the amount of required inputs is calculated by PASTOR. Inputs are
calculated on the basis of systems-analytical knowledge on input-output relations for the
production system under consideration. Target production levels may vary from potential
production levels to very low levels. In the first case, high external input levels (e.g.
fertilizers, crop protection) will be required, and in the second case, low external input
levels will be simulated. Next to desired production levels, the manner (technology) of
production can be specified. For instance, certain operations may be performed with
machines or may alternatively be done manually (or using a combination method). By
specifying a number of target production levels and a number of different technologies
that may be used, PASTOR ‘generates’ a large number of technical coefficients that
characterize production systems. ‘Classical’ input technical coefficients are the use of
resources such as fertilizers, pesticides, machines, labor, and - of course - the total costs
of production (using all the required resources). ‘Classical’ output technical coefficients
are yields and the economic value of the yield. Next to these ‘classical’ technical
coefficients, Technical PASTOR also calculates a number of other parameters that are
associated with the modeled production systems. These technical coefficients are called
‘sustainability indicators’, and quantify effects on the resource base and the environment.
Examples are the losses of nutrients and pesticides to the environment that are associated
with each generated production system. These technical coefficients can be used in linear
programming models to set restrictions on the selection of production systems based on
environmental or sustainability issues. In the following sections, a brief explanation is
given on how PASTOR computes technical coefficients for herds, pastures and feed
supplements respectively.
1.1 Herds
The HERD component of PASTOR simulates Technical Coefficients for herds. The
calculated technical coefficients are:
 Production (in terms of meat and milk)
 Nutrition requirements (in terms of energy, crude protein and phosphorus)
 Costs of production
 Labor required
So far, the HERD component can model cattle breeding (CRIA) and beef fattening
(GORDO) systems1. Based on a specification of herd structure characteristics, desired
target growth of the animals and buying/selling strategy, total production and nutrition
1
Lately, a routine was added to generate dedicated double-purpose systems. i.e. the both milk as well as
beef production.
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requirements of the herd are simulated. The specification of herd structure, birth and
death rates, buying/selling strategy and the target growth is inputted by the user, e.g. herd
size, buying/selling weight or age, daily animal growth rate etc. By ‘book-keeping’ of the
number of animals in various age-classes, and by using the desired animal growth rate,
PASTOR calculates the yearly production of the specified herd. The total nutrition
requirements of the herd are then calculated using the equations presented by the
National Research Council (NRC, 1989, 1996). It is noted that in this approach, the
source of nutrition (e.g. pasture, feed supplement) is not taken into account; i.e. the total
amounts of energy, crude protein and phosphorus are calculated that are needed to sustain
the desired target growth. These requirements must be met by a combination of the
delivery of nutrition by the pastures as simulated by the pasture component of PASTOR,
and the delivery of nutrition by the feed supplements as simulated by the feed supplement
component of PASTOR. This optimal combination is found in a linear programming
model, where also restrictions on minimum/maximum amounts and on nutrition
combination types can be set.
The costs and labor requirements of the simulated herds are calculated from maintenance
and operation specifications provided by the user (e.g. the use of corrals and troughs and
the application of inoculations).
1.2 Pasture
The PASTURE component of PASTOR simulates Technical Coefficients for pasture land
use systems There are two pasture models: one for fertilized pastures, and one for
unfertilized pastures. The technical coefficients of simulated pastures are:
 Production (in terms of dry matter, energy, crude protein and phosphorus; i.e. herd
nutrition)
 Costs of production
 Labor required
 Environmental and sustainability indicators
 Fertilizer use specifications
For fertilized pastures, PASTOR calculates the necessary amounts of external nutrients to
be supplied (in the form of fertilizer) to reach the desired target production as specified
by the user. The calculation procedure is quite complex, and involves the following steps.
First, maximum attainable production levels (quantity in terms of biomass, and quality in
terms of nutrient contents) of the pasture type under consideration are supplied by the
user for each land unit that is relevant for the area of study. Pastures can be grazed by
animals with different stocking densities (animals/ha). The stocking rate affects the
amount of pasture that is trampled (and therefore not available for consumption) and the
soil nutrient balance via manuring. Therefore, the user can specify a range of animal
stocking densities that may be ‘put’ on the pasture. Based on the specified maximum
production levels and stocking densities, PASTOR computes for each land unit the total
amount of external nutrients that are needed to reach those maximum production levels.
These computations are based on a soil nutrient balance, where the user can specify the
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amount of nutrient mining (depletion of the soil stock) that is allowed. For truly
sustainable production, the allowed nutrient mining is zero. Next, the user can specify a
range of actually applied external fertilizer quantities. Using these quantities, PASTOR
then re-calculates the actual target-yield levels (and pasture qualities) that are obtained on
each land unit for the whole range of supplied stocking rates and actual fertilizer
application levels. Also, the associated nutrient losses to the environment are computed
(environmental technical coefficients).
Next to ‘technology’ of fertilizer application, the user has to specify the
technology of weeding, i.e. amount and type of herbicides applied (if any) and amount of
labor used, for two extreme levels of production: the maximum attainable and the lowest
possible. PASTOR calculates the actual amounts applied as function of the actual targetyield levels computed above. Two environmental indicators are calculated related to the
use of herbicides: the so-called Biocide Index (Jansen et al., 1995) and the total amount
of active ingredients applied.
Costs and labor use are calculated based on specifications of operations and
maintenance as provided by the user (e.g. establishment and maintenance of fences, cost
and labor for fertilizer application, cost and labor for weeding etc.)
For unfertilized pastures, the calculation procedure of PASTOR is relatively simple. No
fertilizer is given, and the user simply specifies the target production to be realized. The
soil nutrient balance of PASTOR calculates the balance (depletion/surplus) of nutrients in
the soil as output. The unfertilized model of PASTOR is used for natural unfertilized
pastures, and for unfertilized grass-clover mixtures. The amount of nitrogen supplied by
the clover to the system is inputted by the user. All other calculations (e.g. for cost and
labor) are the same as in the fertilized pasture model.
All pastures are defined by the combination of pasture type, fertilizer application level (if
any), stocking rate, technology and land unit. PASTOR calculates for each desired
combination the corresponding technical coefficients.
1.3 Feed supplements
The FEED component of PASTOR simulates Technical Coefficients for feed
supplements. The technical coefficients of simulated feed supplements are:
 Nutrition provided (in terms of energy, crude protein and phosphorus)
 Costs of application (costs of feeds and of materials such as shovels, wheel-barrow,..)
 Labor required
The FEED model is very simple and merely ‘converts’ data supplied by the user in input
files into the correct format for reading by linear programming models. Nutrition for
cattle, costs of application and labor requirements for application are all expressed per
unit of applied feed supplement.
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2 Exercises
2.1 Exercise: herd modeling
This exercise will introduce the user to handling input data for the generation of technical
coefficients for cattle breeding herds
 Go to the directory C:\EXERC1\BREEDING. Note the data files CONTROL.DAT,
CRIAHRD.DAT and the executable file CRIA.EXE. The file CONTROL.DAT
‘controls’ the input and output files; it is beyond the scope of this exercise to detail
the functioning of this file.
 The file CRIAHRD.DAT contains characteristics of the herd that will be modeled
(i.e. for which technical coefficients will be generated). Study the contents of the file.
Note how the file contains parameters that specify the structure, selling strategy and
target growth rates of the herd..
 Run the model for generation of technical coefficients of the herd as specified in the
CRIAHRD.DAT file by typing <CRIA>. Note how four output files with extension
‘.PRN’ have been created. These files contain the generated technical coefficients:
HYIELD contains the ‘yields’ of the herd; HCOST the associate costs of production;
HLABOR the associated labor required; HNUTRIT the required nutrition by the herd.
View the contents of these files and fill-in the first row of Table 2.1 (listed as
‘STANDARD’). The headers explain the columns that are listed in the files; the first
column gives a code that identifies the herd type modeled. Next, the file
CRIAHRD.DAT will be edited to create herds, and to study the effect on the
generated technical coefficients. The idea is to find herd strategies with ‘better’
results than the standard herd, in the sense that yield may be higher, costs lower, or
labor or nutrition requirements lower.
 1. Edit CRIAHRD.DAT: find the entries for the selling strategy of the herd: ASMS
for age of selling of male calves, and ASFS for age of selling female calves. Change
these values from 8. months into 16. (Note: do not forget to put the dot (‘.’) behind
the 16.!). Run the CRIA model. Fill-in the second row (marked by ASM = 16) in
Table 2.1.
 2. Edit CRIAHRD.DAT: first put the values for ASMS and ASFS back at 8.; then
change the value for the calving interval from CI 14. to 11. (Note: do not forget to put
the dot (‘.’) behind the 11.!). Run CRIA; and fill-in Table 2.1.
 3. Edit CRIAHRD.DAT: Change the value for the calving interval CI from 11. to 17.
(Note: do not forget to put the dot (‘.’) behind the 17.!). Run CRIA; and fill-in Table
2.1.
 4. Edit CRIAHRD.DAT: first put the value for CI back at 11.; then change the value
for the target live weight gain in the first year for male calves (LWGM0) and for
female calves (LWGF0) from 0.65 and 0.52 respectively to 1. (Note: do not forget to
put the dot (‘.’) behind the 1.!). Run CRIA; and fill-in Table 2.1.
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 5. Edit CRIAHRD.DAT: first put the values for LWGM0 and LWGF0 back at their
original values, resp. 0.65 and 0.52; then change the value for the mortality rate in the
first year MRATE0 from 0.1 to 0.05. Run CRIA; and fill-in Table 2.1.
Table 2.1. Yield (live weights of calves LWCY, and live weight of ‘old’ cows LWCO;
kg/y), required nutrition (metabolizable energy HME; Mcal/month), costs (COST;
colon/y) and labor required (LABA; d/y) of simulated cattle breeding herd. HME is on
monthly basis; all other variables are on yearly basis.
LWCY
LWCO
HME
COST
LABA
STANDARD
ASM = 16
CI = 11
CI = 17
LWG = 1.0
MRATE=0.05
 Study the results obtained from the various reruns with the CRIA model in table 2.1.
Study how variation in selling strategy and properties of the animals in the herd affect
yield, but also affect the required amount of nutrition, costs and labor used. An linear
programming model would be needed (together with options for feed strategies) to
find-out which herd would give highest profits.
2.2 Exercise: unfertilized pasture modeling
This exercise will introduce the user to handling input data for the generation of technical
coefficients for unfertilized pastures. Unfertilized pastures are calculated with an ‘open’
soil nutrient balance, i.e. the depletion or accumulation of nutrients in the soil is
calculated by the model as function of the desired (target) biomass production of the
pasture, soil properties and natural inputs of nutrients (cattle manure, N in rain, possible
N supplied by clovers). The soil nutrient balance is an important element in the
consideration of sustainability of a pasture system. Modeled pastures may turn out to be
unsustainable (by analyzing generated technical coefficients) in the sense that a depletion
of soil nutrients will lead to a future reduction in yield potential, which obviously can not
go on forever and thus is ultimately unsustainable. On the other hand, modeled pastures
may be sustainable when clovers are incorporated that fix nitrogen and make it
subsequently available for pasture growth. Of course, there is ‘price’ to pay for
sustainability, in the form of input costs (clover seed) and/or higher labor requirements.
The generated technical coefficients of unfertilized pastures can help in this analysis of
sustainability. In this exercise, the issue of sustainability will be studied, together with
effects of pasture type, land unit and stocking rate on the generated technical coefficients.
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 Go to the directory C:\EXERC1\PASTOU. Note the data files CONTROL.DAT,
PAS_UNF.DAT, PAS_CLO.DAT and the executable file PASTU.EXE.
 The file CONTROL.DAT ‘controls’ the input and output files. View the contents of
CONTROL.DAT. Note that the input file listed under FILEI1 reads
‘PAS_UNF.DAT’: the PASTU model will be run for pasture characteristics as
specified in PAS_UNF.DAT.
 Study the contents of the file PAS_UNF.DAT. This file lists 1) characteristics of an
unfertilized, natural pasture, 2) effects of land unit on production, 3) effects of
stocking rate on production, and 4) management characteristics (e.g. weeding). In this
example, we will concentrate on items 1 to 3. The first ‘table’ in the file lists monthly
‘potential’ production characteristics of the pasture: dry biomass (DMP) and nutrition
values such as metabolizable energy (ME) and crude protein content (CP). Next
follows a list of responses of the pasture to particular land units. In the ‘standard’
form of the file, only entries for one land unit are given (Soil Fertile Well drained).
The entries specify the reduction of the potential yields specified above due to land
unit (RDMP), a reduction factor due to trampling by the cattle per land unit
(RDMUSE) and the amount of nitrogen supplied by any present clovers in the
mixture (NSUPL). Since this pasture contains no clovers, NSUPL is zero. Next
follows a list that specifies a number of stocking rates (animals per ha) and the effect
on above-ground dry matter loss caused by trampling by the stock. Stocking rate not
only affects attainable yield, but also affects the soil nutrient balance by the amount
of manure produced and recycled in the system.
 Run the pasture model by typing <PASTU>. Note that five output files have been
created that contain technical coefficients: PYIELD with yield data; PCOST with
production costs; PLABOR with labor required; PINDICAT with environmental and
sustainability indicators; PFERT with a summary of fertilizers applied and losses of
nutrients. View the output files; the headers explain the columns that are listed in the
files. The first column contains a code that specifies the pasture types modeled: the
first three letters give the soil abbreviation (SFW), the next three give the pasture type
and technology level (U20 - not explained further here), and the last two give the
stocking rate (R1-R3). Thus, in a single run, PASTU has produced technical
coefficients for three pastures (characterized by the three stocking densities). Refer to
the input file PAS_UNF.DAT to ‘recognize’ these three reruns in the file.
 Fill-in the first three rows of Table 2.2 from the output files PYIELD, PCOST and
PINDICAT.
 Edit the file PAS_UNF.DAT: find the entries that specify the effects of land unit on
production (SOILP (name of land unit), RDMP, RDMUSE and NSUPL). Remove the
asterix (*) for the lines with land units SIW and SFP. Now, PASTU will make reruns
over the three specified land units (and over the three specified stocking densities).
Run PASTU.
 Study the output files. Note that, now, nine pasture types have been run: three
combinations of stocking rates times three combinations of land units. Fill-in the last
six rows of Table 2.2 from the output files PYIELD, PCOST and PINDICAT.
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Table 2.2. Pasture yield (in the form of supplied metabolizable energy, HME; Mcal/y),
yield ‘surplus’ (in the form of supplied minus removed metabolizable energy, DMEHRD;
Mcal/y), costs (COST; colon/y) and soil nitrogen balance (NBAL; kg/y) of pastures
generated by PASTU. All data are for 1 hectare.
HME
DMEHRD
COST
NBAL
SFW.U20.R1
SFW.U20.R2
SFW.U20.R3
SIW.U20.R1
SIW.U20.R2
SIW.U20.R3
SFP.U20.R1
SFP.U20.R2
SFP.U20.R3
 Study the results obtained from the various reruns with the PASTU model in Table
2.2.
In the first column of Table 2.1, it is seen that for each land unit, the yield in terms of
metabolizable energy (HME) decreases with increasing stocking rate. This is caused by
the trampling effect of the cattle, that increases with stocking rate (see RDMUSE in
PAS_UNF.DAT). All simulated pastures are unsustainable: the soil nitrogen stock NBAL
decreases annually with 125 kg (SFW.U20.R1) to 18 kg (SFP.U20.R3)! This means that
in the long run, the yield levels (HME) can not be sustained without additional inputs of
nitrogen. NBAL increases (smaller negative values) with increasing stocking rate: more
animals per hectare produce more manure (nitrogen input in the system). The nutrients
that are inputted in the system via manure have been taken up by the cattle from the
pasture itself and from any feed supplements. Therefore, low levels of NBAL can only be
realized via ‘extra’ nutrient inputs obtained via feed supplements. DMEHRD quantifies
the surplus of pasture production (in terms of metabolizable energy): the amount of
energy available from the pasture (HME) minus the amount that is eaten by the cattle at
the given stocking rate. When DMEHRD is positive, the pasture produces more than the
cattle can eat, and there is ‘surplus’. When DMEHRD is negative, the pasture produces
insufficient energy to meet the requirements of the stock, and the given stocking rate can
only be realized using feed supplements. On the SFW soil, the pasture production is only
sufficient to sustain 1-2 animals/ha; on the SIW and SFP land units, only 1 animal/ha can
be sustained. The production costs per hectare are the same for all modeled systems
(main costs are for establishment and maintenance of fences, which are the same for all
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systems), whereas all pastures are supposed to receive the same treatments for e.g.
weeding and other operations2.
The next run will illustrate technical coefficients for an unfertilized grass-clover mixture.
 Edit the file CONTROL.DAT: change the file name PAS_UNF.DAT under FILEI1
into PAS_CLO.DAT. Study the file PAS_CLO.DAT; look especially at the inputs for
potential yield, effects of land unit and stocking rate. Note the amount of nitrogen that
is supplied by the clover to the system (NSUPL parameter)!. Run PASTU; study the
output files, and fill-in Table 2.3.
Table 2.3. Pasture yield (in the form of supplied metabolizable energy, HME; Mcal/y),
yield ‘surplus’ (in the form of supplied minus removed metabolizable energy, DMEHRD;
Mcal/y), costs (COST; colon/y) and soil nitrogen balance (NBAL; kg/y) of a grass-clover
mixture generated by PASTU. All data are for 1 hectare.
HME
DMEHRD
COST
NBAL
SFW.C20.R1
SFW.C20.R2
SFW.C20.R3
 Study the results obtained from the various reruns with the PASTU model in Table
2.3; compare with results in Table 2.2.
Except for a stocking rate of 1 animal unit/ha (why?), all modeled systems are sustainable
in the sense of soil nitrogen balance. The production of the grass-clover mixture is higher
than that of the ‘natural’ on the same soil, but so are the costs! (Table 2.2). An economic
analysis (using linear programming) will have to show whether the grass-clover mixture
is a feasible alternative to feed cattle production systems (e.g. as modeled in table 2.1).
2.3 Exercise: fertilized pasture modeling
This exercise will introduce the user to handling input data for the generation of technical
coefficients for fertilized pastures. In this case study, fertilized pastures are calculated
with a ‘closed’ soil nutrient balance, i.e. depletion of the soil nutrient stock is not
allowed! This is accomplished by a complex nutrient balance calculation consisting of
three main steps (simplified): 1) the model (PASTOF) calculates – per land unit and
stocking rate - the amount of fertilizer that is needed to realize the attainable production
level as indicated in the pasture input file (user supplied), 2) the user specifies how much
2
In fact, this is not true: the level of weeding (input of man-hours, chemicals etc.) depends on the yield
level of the pasture. This feed-back will be implemented in a next version of PASTOR.
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of this amount of fertilizer he/she wishes to apply (in percentage of the maximum), and 3)
the model then re-calculates the actual realized production based on the fertilizer gift,
other nutrient inputs, soil characteristics, and under the restriction that no depletion of
nutrient stock in the soil takes place3. Thus, by definition, all pasture systems modeled
with the fertilized pasture model PASTOF of PASTOR are sustainable in the sense of soil
nutrient stock. This exercise will focus on the effect of fertilizer application on simulated
technical coefficients by studying two extreme situations: zero fertilizer application and
full fertilizer application to reach potential production. Next to soil nutrient balance, the
so-called Biocide Index is introduced as an ‘environmental indicator’ technical
coefficient. The Biocide Index is a qualitative measure that incorporates the (total)
amount of pesticides used, their toxicity levels and their longevity in the system. The
higher this value, the ‘worse’ it is supposed to be for the environment.
 Go to the directory C:\EXERC1\PASTOF. Note the data files CONTROL.DAT,
PAS_FERT.DAT and the executable file PASTOF.EXE.
 View the contents of the file PAS_FERT.DAT. This file lists 1) characteristics of a
fertilized, improved pasture, 2) effects of land unit on production, 3) effects of
stocking rate on production, 4) fertilizer application level, and 5) management
characteristics (e.g. weeding). In this example, we will concentrate on items 1 to 4.
The first ‘table’ in the file lists monthly potential production characteristics of the
pasture when there is no nutrient limitation whatsoever: dry biomass (DMP) and
nutrition values such as metabolizable energy (ME) and crude protein content (CP).
This is followed by a shorter list of pasture nutrition values under complete soilnutrient depleted conditions (i.e. the minimum values below which the pasture dies).
Next follows a list of responses of the pasture to land units; the same three land units
are present as in the previous exercise. Note that the parameter NSUPL is missing
here: PASTOF is not (yet) suited to model fertilized grass-clover mixtures, and
therefore there is no nitrogen input from clovers here. Next follows the list that
specifies stocking densities and its effect on above-ground dry matter loss (for
simplicity, only 1 animal unit/ha here). Finally, the desired production - or technology
- level(s) is (are) specified: FGIFT specifies the amount of fertilizer application as
fraction of the maximum amount needed to attain the maximum production on each
land unit. In this ‘standard’ file, this FGIFT is 0; therefore, the situation will be
modeled of a completely mined soil without inputs of external nutrients in the form of
fertilizer.
 Run the pasture model by typing <PASTOF>. View the (five) output files. Refer to
the input file PAS_FERT.DAT to ‘recognize’ the three reruns in the file. Fill-in the
first three rows of Table 2.4 from the output files PYIELD, PCOST, PLABOR,
PFERT and PINDICAT.
 Edit the file PAS_FERT.DAT: find the entry that specifies the amount of fertilizer
application FGIFT; change the value of FGIFT from 0.00 into 1.00. Change - for
recognition sake - the entry under PLEVEL (indicating the technology level) from 20
into 30 (do not put a dot (‘.’) after 30!). Now, the situation will be modeled of
maximum attainable production per land unit. Run PASTOF.
For ‘advanced’ users: the ‘allowable nutrient depletion’ can actually be re-set by the user to any other
value than 0 to study long-term soil nutrient depletion effects.
3
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 Study the output files and fill-in the last three rows of Table 2.4.
Table 2.4. Pasture yield (in the form of supplied metabolizable energy, HME; Mcal/y),
costs (COST; colon/y), labor use (GLABA; d/y), fertilizer applied (FIN; nitrogen
fertilizer; kg N/y), soil nitrogen balance (NBAL; kg/y) and Biocide Index (BIOI; (-/y), of
fertilized pastures modeled by PASTOF. All data are for 1 hectare.
HME
COST
GLABA
FIN
NBAL
BIOI
SFW.F20.R1
SIW.F20.R1
SFP.F20.R1
SFW.F30.R1
SIW.F30.R1
SFP.F30.R1
 Study the results obtained from the various reruns with the PASTOF model in Table
2.4; compare with results in Tables 2.2 and 2.3.
The first series (recognized by ‘F20’ in the code) represent pastures on completely mined
soils, i.e. no nutrients can be taken up from the soil stock since this has been completely
exhausted. This situation will be the final, stable production situation of the pasture type
modeled in Table 2.2 when the soil nutrient depletion (NBAL) has continued to the point
that the soil holds no more nutrients. Note the extremely low production levels (HME)
that are realized on all three land units when the only source of nitrogen supply is
nitrogen deposit in rain, nitrogen input by free-living bacteria and (recycled) nitrogen in
manure! At this minimum production level, differences between land units have
disappeared. The ‘0’ for FIN confirms that no fertilizer nitrogen has been applied; the ‘0’
for NBAL confirms that there is no more soil nitrogen depletion. Costs and labor inputs
are low. The Biocide Index BIOI, however, is relatively high. This is explained as
follows: with a low yield level of the pasture, there is low soil cover by the crop and
hence there is a large amount of weeds present (common phenomenon in degraded
pastures!). To ‘combat’ these weeds, a relative large amount of herbicides is needed, and
this results in a relatively high Biocide Index.
The second series (recognized by ‘F30’) represents the maximum attainable yield
level, obtained with 100% fertilizer input. The yield levels as represented by HME are
relatively high, and vary largely among the land units (according to the factors RDMP
(land unit effect) and RDMUSE (trampling effect) as set in the file PAS_FERT.DAT).
The fertilizer input, FIN, to realize these maximum production levels are extremely high.
This is caused by the very low efficiencies of fertilizer use in the humid tropics. The data
for this study have been taken from the Atlantic Zone of Costa Rica, where is assumed
that due to extremely high rainfall, losses of fertilizer are in the order of 60-70%).
Because of these high fertilizer inputs, costs of production are extremely high too
(COST). Because yield levels are much higher than in the first series (above), the amount
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of herbicides applied is less, and the computed Biocide Indices (BIOI) are relatively low.
Note that higher production levels correspond with lower Biocide Indices.
Part 2. Linear programming exercises
3. Case study description
The case-study for linear optimization is for a (hypothetical) area in the Atlantic Zone
(AZ) in the Caribbean lowlands of Costa Rica. The AZ is characterized by a humid
tropical climate, with a mean daily temperature of 26 °C (variation through the year of
only 2°C), a mean annual rainfall of 3000-6000 mm, and an average relative humidity of
85-90%. There is a relative dry and cool season in January-March, though all months of
the year have a precipitation surplus. The elevation varies from sea-level to +400 m.
Three land units have been distinguished in the AZ:
 SFW: Soil Fertile Well drained
 SIW: Soil Infertile Well drained
 SFP: Soil Fertile Poorly drained
The case-study area is a small region of 16000 ha, with the following distribution of land
units: SFW 7000 ha; SIW 6000 ha, and SFP 3000 ha.
Of the current agricultural land use in the AZ, about 60% is cattle ranging specialized in
breeding and fattening systems. The AZ has been colonized relatively recently, with a
major ‘colonization push’ in the second half of this century. After deforestation, soils are
generally used for extensive cattle ranging (and for large-scale plantation banana
cultivation; about 20% of the agricultural land use). Due to the extensive nature of cattle
ranging with low to zero external inputs, pasture degradation has become a serious
problem in the area. The carrying capacity (yields) of the pastures are declining and fields
are becoming increasingly infested by weeds, resulting in decreasing returns.
Furthermore, during the last years, an aggravating factor for cattle farmers have been the
low meat prices on the (inter-)national markets. In short: the current practices of cattle
ranging are unsustainable.
Another concern in the AZ is raised by environmentalists who signal various
threats to the remaining natural forests and protected areas in the area. The large-scale
conversion of primary forest into extensive pastures has increased the emission of
greenhouse gasses such as CO2 and N2O, and of NO, which is a precursor to the
greenhouse gas ozone. Secondly, the large amounts of pesticides used in the area are
thought to threaten the ‘health’ of the ecosystem in general, and of humans in particular
(though this problem arises more from arable cropping than from animal husbandry).
Finally, the continuing expansion of agricultural land - especially extensive range land
systems - cause land use conflicts in buffer-zones around protected areas and natural
parks.
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This case-study focuses on possibilities to improve the situation for farmers in the
livestock sector while taking simultaneous account of income, sustainability and
environmental effects. PASTOR was used to generate a number of herds, pasture land
use systems, and feed supplement systems. These alternatives are offered to a (simple)
Linear Programming (linear programming) model to optimize farmers’ profits given
restrictions related to sustainability and environmental effects. Henceforth, the
combination of the optimization function and the restrictions are called ‘goals’. By
running the model a number of times with different goals, so-called scenarios are created.
The results of these scenarios quantify the trade-offs between the various goals.
Examples of scenarios include a pure farmers profit scenario, a sustainability scenario, or
an environmental scenario.
The linear programming model is programmed in the language GAMS 2.25. A set of
linear programming models have been ‘pre-programmed’ for the various scenarios to be
run. The different scenario’s all use the same model (i.e. set of equations), but with
different restrictions or inputs (technical coefficients generated with PASTOR). In brief,
the linear programming model finds the ‘optimum’ number and of herd types in
combination with pasture systems and feed supplements to feed the selected herds. The
optimum is found by maximizing ‘profits’, i.e. maximizing the sum of economic yield
(i.e. meat revenues) minus the sum of all input and labor costs (for herd maintenance, for
the pastures and for the feed supplements). Physical restrictions are set on land use (i.e.
no more of each land unit can be used than the maximum available), on labor (no more
labor can be used than available in the area), on feed balance (the sum of the nutrition
requirements of the herds must be equal to the sum of nutrition provided by the pastures
and the feed supplements), and - optionally - on sustainability and/or environmental
parameters. Prices of inputs and outputs are those prevailing in the Atlantic Zone in mid
1997.
3.1 Model 1: base scenario
First, a so-called base-scenario is run in which no restrictions are set with regard to
sustainability or environmental effects, i.e. profit is maximized under restrictions of
available land units and on herd feed balances only. It is assumed that all labor needed is
available in the area.
Herds
The following two herds have been modeled with PASTOR:
 HB150: a herd of 50 animals for breeding
 HF150: a herd of 50 animals for fattening
Herd size is here expressed in physical animals. For standardization, another unit is used
called ‘animal unit’ (AU), which is the equivalent of an animal of 400kg liveweight.
Pastures
The following alternative unfertilized pasture systems have been modeled with PASTOR:
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
N20: Natural (currently prevailing systems that are soil nutrient depleting); mixed
manual and chemical weeding is applied
 P30: Grass-clover mixture that is generally non-soil depleting with regard to nitrogen;
because of the presence of clover, only manual weeding is applied.
These pastures can occur on the following three land units (as present in the area):
 SFW: Soil Fertile Well drained
 SIW: Soil Infertile Well drained
 SFP: Soil Fertile Poorly drained (note: P30 pasture does not grow on SFP)
On these pastures, the following stocking densities have been modeled:
 R1: 1.0 AU/ha
 R2: 1.5 AU/ha
 R3: 2.0 AU/ha
 R4: 2.5 AU/ha
 R5: 3.0 AU/ha
The full code to recognize a complete pasture consists of ‘land unit name.pasture
name.stocking density’, for example SFW.N20.R1.
Feed supplements:
The following feed supplements have been modeled with PASTOR:
 MOL: sugar cane molasse
 BAN: green bananas
 CN2: feed concentrate
 P20: Phosphorus concentrate
The year has been divided into a relatively DRY season (Jan-March), and a relatively
WET season (April-December), though in all months there is a precipitation surplus. In
both seasons, the nutrition required by the herd should match the nutrition provided by
the mixture of pastures and feed supplements.
 Go to the sub-directory EXERC2. Go to the sub-directory FILES1 and note the files
with technical coefficients for the base scenario (pre-)produced by PASTOR: the
FEED* files with technical coefficients for feed supplements, GRASS* files with
technical coefficients for pastures, and HERD* files with technical coefficients for
the two herds. Use the Norton Viewer (F3) to quickly view - and recognize from the
previous exercises - the contents of the files. Especially view the file GRASS.PRN
that contains the sustainability and environmental indicators (the header in the files
explains the columns).
 Go to the directory EXERC2\MODEL1. Run the GAMS model by typing <GAMS
MODEL1>. While running GAMS writes some output to the screen that indicates the
state of model running. When finished, GAMS has produced two files:
MODEL1.LST and RESULTS.DAT. MODEL1.LST contains very detailed output
and will not be discussed here. RESULTS.DAT contains summary output. View the
contents of RESULTS.DAT.
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The first item in RESULTS.DAT is the so-called ‘objective function’, which is the
variable that has been maximized by GAMS. In our case-study, this is the total profit of
cattle farming in the whole area under consideration:
Total benefits in million colon/year
Benefits =
713.0859
This profit is the highest profit that can be obtained with cattle farming in the area since except for the restriction on maximum soil use and nutrition balance that must be
satisfied! - no restrictions on labor availability or sustainability/environmental effects
were imposed. The base run can thus be interpreted as a pure farmers’ profit scenario
(short term profit…).
Then follows the list of selected herds, pastures and feed supplements that resulted in the
highest profit realized:
Herds (APST) in numbers
Herd
Solution
HB150
0.00
HF150
842.11
Apparently, it is most profitable to concentrate on beef fattening systems. The optimum
solution of the pure farmers’ profit scenario consists of 842 beef fattening herds of 50
animals (38 AU) each, with no cattle breeding systems.
Pastures (PAST) in ha
Pasture
Stock rate
SFW N20 R1
1.00
SFW N20 R2
1.50
SFW N20 R3
2.00
SFW N20 R4
2.50
SFW N20 R5
3.00
SFW P30 R1
1.00
SFW P30 R2
1.50
SFW P30 R3
2.00
SFW P30 R4
2.50
SFW P30 R5
3.00
SIW N20 R1
1.00
SIW N20 R2
1.50
SIW N20 R3
2.00
SIW N20 R4
2.50
SIW N20 R5
3.00
SIW P30 R1
1.00
SIW P30 R2
1.50
SIW P30 R3
2.00
SIW P30 R4
2.50
SIW P30 R5
3.00
PAST Hectare
0.00
0.00
7000.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
6000.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
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SFP N20 R1
1.00
0.00
SFP N20 R2
1.50
0.00
SFP N20 R3
2.00
3000.00
SFP N20 R4
2.50
0.00
SFP N20 R5
3.00
0.00
SFP P30 R1
0.00
0.00
SFP P30 R2
0.00
0.00
SFP P30 R3
0.00
0.00
SFP P30 R4
0.00
0.00
SFP P30 R5
0.00
0.00
On all three land units, the Natural pasture is preferred to the grass-clover mixture.
Although yields of the latter are much higher, the additional costs involved apparently
preclude adoption of these mixtures. On all three selected Natural pastures, a stocking
density of 2 AU/ha is chosen.
Feeds supplements (FAST) in kg
Feed type Season
Solution
MOL
DRY
0.00
MOL
WET
0.00
BAN
DRY
13702478.82
BAN
WET
18121216.94
CN2
DRY
2909526.32
CN2
WET
6667578.95
P20
DRY
0.00
P20
WET
0.00
Banana and feed concentrate are apparently the optimum choice for feed supplements.
Note that additional feeding in the DRY season (with only three months) is relatively
higher than in the WET season (with nine months); monthly yields of pastures in DRY
season are lower than in the WET season.
Next follows a summary on restrictions realized:
Land use in ha
Land unit
Solution
Maximum
SFW
7000.00
7000.00
SIW
6000.00
6000.00
SFP
3000.00
3000.00
All land units in the case study area are fully used.
Labor use in days
Labor use
= 70780.86
Labor availability = Infinite
The amount of labor available for herd, pasture and feed supplement maintenance and
application was set at unlimited.
Sustainability indicators
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* NBAL = Soil N depletion (kg/y)
* PBAL = Soil P depletion (kg/y)
* KBAL = Soil K depletion (kg/y)
* NLEA = N leached (kg/y)
* NVOL = N volatilized (kg/y)
* NDEN = N denitrified (kg/y)
* BIOI = Biocide Index (-)
* BIOA = Active pesticides applied (kg/y)
Indicator
Solution
Maximum
NBAL
1205600
Infinite
PBAL
6790
Infinite
KBAL
560630
Infinite
NDEN
72800
Infinite
NLEA
874400
Infinite
NVOL
119200
Infinite
BIOA
26060
Infinite
BIOI
221360
Infinite
Recall that there were no restrictions on neither the sustainability nor the environmental
effect parameters. For example, the combination of selected pastures and feed
supplements leads to a total loss of soil nitrogen4 in the area of 1205600 kg - which is on
average 1205600/16000 = 75.35 kg/ha yearly! The total amount of active pesticide
ingredients amounts to 26060 kg - which is on average 26060/16000 = 1.63 kg/ha yearly.
NDEN is the total amount of nitrogen denitrified that is lost to the environment, a.o. the
greenhouse gas N2O and the ozone precursor NO.
The solution in this so-called base scenario corresponds well with the currently prevailing
livestock situation in the AZ of Costa Rica; beef fattening gives higher benefits than beef
breeding, and natural grasses are the dominant pasture type, with an average stocking
density of 1.75 AU/ha. Note, however, that these production systems are highly
unsustainable as expressed by the large amounts of soil nutrient depletion (of course, the
loss of nutrients can be counter-balanced by addition of fertilizer, and this alternative is
studied in the following exercises).
 Fill-in some summary information in Table 3 (at the end of this document) for
comparison with other scenarios (pre-filled-in as example)
3.2 Model 2: labor scenario
In this scenario, the same model as in the base scenario is run, the only difference
consisting of a restriction on the availability of labor. It is assumed that shortage of labor
in the area (heavy competition for labor from a.o. the banana plantations makes for labor
4
Note that here, NBAL, PBAL and KBAL are defined here as loss: a positive value indicates a soil nutrient
loss, and a negative value indicates an accumulation. This is opposite as in the technical coefficient file
GRASS.PRN where negative values indicate loss.
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shortage) reduces the total labor pool for the livestock sector to 200 working people
(equals 60000 working days/year).
 Go to sub-directory EXERC2\MODEL2; run model2 (enter <GAMS MODEL2>);
study the file RESULTS.DAT. Fill-in Table 3; comment on the differences with the
results from the base-run (MODEL1), as well as on the trade-offs between the various
parameters.
Due to labor shortage, cattle farming becomes more extensive: on SIW and SFP land, the
stocking density (partly) decreases from 2 AU/ha to 1.5 AU/ha. As a result, total profits
in the area are now 95% of the profits obtained in the base run. However, the decrease in
profits is lower than the decrease in labor use! The use of feed supplements changes from
banana to molasse, because the latter product requires less labor per unit of nutritional
value.
3.3 Model 3: greenhouse gas reduction scenario
The same model as in the base scenario is run, with a restriction on the emission of the
gasses N2O and NO as quantified (a.o.) by NDEN5. The scenario expresses a hypothetical
(political) wish to decrease the emission of these gasses by 10% from the current
situation. It is assumed that the current situation is quantified in the base run; therefore
the restriction on NDEN is set as 90% of 72800 = 65520 kg N/ha.
 Go to sub-directory EXERC2\MODEL3; run model3 (enter <GAMS MODEL3>);
study the file RESULTS.DAT. Fill-in Table 3; comment on the differences with the
results from the base-run (MODEL1), and comment of the trade-offs between the
various parameters.
The results look alike to those of scenario 2. The reduction in the emission of greenhouse
gasses is realized through extensification of the systems. The realized profit in the area is
94% of the profit realized in the base run. Note that, though the emission of the
greenhouse gasses has been reduced, the cattle farming systems are more unsustainable
than in the base run: the loss of soil nutrients N, P and K is 108%, 407% and 127% larger
than in the base run! This means that the realization of the objective of 10% reduction in
greenhouse gas emissions is only realized at the expense of sustainability (and at the
expense of farmers’ income). Note, finally, that in comparison with the results in scenario
2, bananas are used again as feed supplement because labor is no longer in short supply
(check also Labor use in RESULTS.DAT).
5
NDEN is still quite a crude estimate in PASTOR; work is underway to link PASTOR to dynamic models
that simulate greenhouse gas emissions in detail, e.g. CENTURY or DNDC
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3.4 Model 4: pesticide reduction scenario
The same model as in the base scenario is run, with a restriction on the use of pesticides
(here herbicides only) as quantified by BIOA. The scenario expresses a hypothetical
(political) wish to decrease the use of active pesticides by 10% from the current situation.
It is assumed that the current situation is quantified in the base run; therefore the
restriction on BIOA is set as 90% of 26060 = 23454 kg N/ha.
 Go to sub-directory EXERC2\MODEL4; run model4 (enter <GAMS MODEL4>);
study the file RESULTS.DAT. Fill-in Table 3; comment on the differences with the
results from the base-run (MODEL1), and comment of the trade-offs between the
various parameters.
Reduction of the use of pesticides is realized by changing some pastures on the SFW land
units from natural to the grass-clover mixture. Natural pastures are modeled in PASTOR
as having combined manual and chemical weeding, whereas grass-clover only has
manual weeding because of the presence of clovers. Again, have a good look at the tradeoffs between the economic (profit), sustainability and environmental effect parameters.
3.5 Model 5: sustainability scenario: non-fertilized pastures
So far, the pasture systems chosen in all previous scenarios were unsustainable. This
means that in the long run, yields will decline because of exhaustion of the soil nutrient
stock. The following scenarios explore the possibilities of sustainable cattle farming in
the case-study area by imposing restrictions on the maximum allowable soil nutrient
depletion6.
In model 5, a restriction is set on soil nitrogen depletion only: NBAL = 0. The rest of the
model is again the same as the base scenario.
 Go to sub-directory EXERC2\MODEL5; run model5 (enter <GAMS MODEL5>);
study the file RESULTS.DAT. Fill-in Table 3; comment on the differences with the
results from the base-run (MODEL1), and comment of the trade-offs between the
various parameters.
Without a possibility of fertilizing, the only alternative left to avoid soil nitrogen mining
pastures are the grass-clover mixtures, P30. These mixtures do not grow on poorly
drained land units, and therefore these land units (SFP) are taken out of production.
Because of the relatively high costs of grass-clover mixtures (mainly high establishment
costs and labor input), the cumulative profit in the area drops dramatically to 59% of that
6
Such a restriction implicitly also represents a future situation where all soils have been mined completely
(because of the use of soil mining Natural pastures, see base run), and no more soil nutrients are available
for uptake by the pasture.
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of the base run, whereas labor use increases 175%. Note that, though the soil nutrient
balance for nitrogen is ‘closed’ (i.e. no depletion of the soil nitrogen stock), there still is
depletion of the stock of potassium (KBAL), while phosphorus accumulates (negative
PBAL). There still is a need for pesticides (note BIOA and BIOI) because herbicides are
used in land preparation prior to planting/sowing of the mixture.
3.6 Model 6: sustainability scenario: fertilized pastures 1
In this scenario, a completely new set of pasture alternatives were generated using
PASTOR. To study the feasibility of using fertilizer to compensate for soil nutrient
depletion (and hence to produce sustainable pastures!), the following fertilized pastures
were modeled:
 E: Estrella (Cynodon nlemfuensis), an improved grass variety; with the following
fertilizer levels: 30, 40, 50 and 60% of the amount to reach its potential production
level (coded as E30,.., E60 respectively).
 T: Tanner (Brachiari radicans), an improved grass variety; with the following
fertilizer levels: 30, 40, 50 and 60% of the amount to reach its potential production
level (coded as T30, .., T60 respectively).
On these pastures, the following stocking rates were modeled:
 R1: 1.0 AU/ha
 R2: 2.0 AU/ha
 R3: 3.0 AU/ha
 R4: 4.0 AU/ha
 R5: 5.0 AU/ha
All other variables, as well as the herds and feed supplements, remained the same as in
the base scenario. No unfertilized pastures were taken into account in this scenario. The
technical coefficients for this scenario are stored in the FILES2 sub-directory.
In the linear programming model, restrictions on soil nutrient mining were imposed for
all three nutrients N, P and K: NBAL = 0, PBAL = 0, and KBAL = 0.
 Go to the sub-directory EXERC\FILES2 and note the files with technical coefficients
for the pastures modeled (GRASS* files). Especially view the file GRASS.PRN that
contains the sustainability and environmental indicators, and the file GRASX.PRN
that contains information on the amount of fertilizer applied (the header in the files
explains the columns).
 Go to sub-directory EXERC2\MODEL6; run model6 (enter <GAMS MODEL6>);
study the file RESULTS.DAT. Fill-in Table 3; comment on the differences with the
results from the base-run (MODEL1), and comment of the trade-offs between the
various parameters.
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All land units are taken out of production and no more cattle farming activities occur in
the area; apparently, the costs of fertilizer application (fertilizer costs, application costs)
are imperatively high for completely sustainable production. The needed fertilizer
applications are indeed very high (file GRASX.PRN). This is due to high nutrient losses
of about 60%, caused by a combination of high rainfall and permeable soils in the area.
For example, to reach potential production levels of about 25-30 ton total above-ground
dry matter per hectare, some 25000 kg of NPK nutrients are needed7!
3.7 Model 7: sustainability scenario fertilized pastures 2
In this scenario, the price of fertilizers is reduced to 30% of its regular value (as used in
the previous scenario) to study the effect of subsidies on feasibilities of sustainable cattle
farming in the study area. The generated technical coefficients for this scenario are stored
in the FILES3 sub-directory.
The same linear programming model as in the previous scenario is used.
 Go to sub-directory EXERC2\MODEL7; run model7 (enter <GAMS MODEL7>);
study the file RESULTS.DAT. Fill-in Table 3; comment on the differences with the
results from the base-run (MODEL1), and comment of the trade-offs between the
various parameters.
With fertilizers subsidized at a rate of 70%, total farmers‘ profit is similar to scenario 5
where sustainability (at least for soil N and P) was reached with grass-clover mixtures.
Note that the amount of cattle that is present in the area has increased tremendously
because of the high yield levels of the fertilized pastures.
3.8 Conclusions
The results of the scenarios (as summarized in Table 3) indicate the ‘playground’ for
sustainable development of cattle livestock activities in the area. By running different
scenarios, trade-offs between economic, sustainability and environmental objectives are
quantified. The base-run, without any restrictions to optimizing cumulative farmers
profit, quite well represents the current situation in the area. The highest profits are
realized with unsustainable, currently used natural pastures (or pastures consisting of
improved grasses introduced some decades ago which have been ‘naturalized’) and a
stocking density of 2 AU/ha. A reduction in the use of pesticide inputs or in greenhouse
gas emissions of 10% have a ‘financial trade-off’ of -6% and -4% respectively on
cumulative farmers profit (while still using the unsustainable natural pastures). Natural
pastures with a stocking rate of 2 AU/ha have an annual soil nutrient depletion rate of 75,
0.4 and 35 kg/ha of N, P and K respectively. This means that, in the long run, yields of
7
Currently in high fertilized banana plantations, fertilizer applications average about 2000-2500 kg/ha.
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the natural pastures will decline, which is in agreement with what is observed in the
area!. A sustainable way of cattle ranging in the area can be obtained by using grassclover mixtures (though still K is being mined), but this results in a loss of nearly 60% in
farmers’ profit. In the long run, however, this is the only way in which cattle-ranging is
possible (unless pastures are abandoned, left to regenerate, and taken into production
after a long period of fallow/converted forest). More research into grass-clover mixtures,
and subsequent extension to farmers would be recommendable in the area. Fertilization
of pastures given current market prices seems unfeasible.
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Table 3: summary of optimization results of various scenarios for the Atlantic Zone livestock case-study.
Model1:
Base
scenario
Model2:
Labor
Model3:
N2O and
NO
Model4:
Pesticide
Model5:
NBAL=0
Model6:
Fertilizer-1
Model7:
Fertilizer-2
Profit
Herds
713 106
842 HF150
SFW (ha-typeSR)
7000 N20 2
AU/ha
SIW (ha-typeSR)
6000 N20 2
AU/ha
SFP
NBAL
NDEN BIOA
3000 N20 2A
U/ha
1205600
72800
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