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Integrated Farm System Model
Entry ID: USDA_ARS_IFSM
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Summary
Abstract: The Integrated Farm System Model is a farm simulation model that predicts the
long-term performance, environmental impact and economics of dairy, beef and
crop farms over multiple years of weather. This model is an extension of the
Dairy Forage System Model (DAFOSYM), which includes a beef animal component and
the option of simulating crop production without an animal component. The
Integrated Farm System Model and its predecessor, DAFOSYM, were created as
research and teaching aids for evaluating and comparing production systems.

The simulation includes the growth, harvest, handling and storage of alfalfa,
grass, corn, small grain and soybean crops. Farm produced feeds are
supplemented with purchased feeds to meet a given level of production in a
dairy or beef herd. Manure is returned back to the land where nutrients are
lost, accumulated in the soil or used in crop production. Production costs are
compared to milk, animal and feed sales to determine a net return for the farm.
To provide an understanding of the model and its capabilities, a brief overview
is provided. More detail can be found in the references provided or in the
reference manual for the model (Rotz and Coiner, 2003).

Major submodels include crop growth, harvest, feed storage, feed use, manure
handling, tillage and economic analysis. The model begins with input
information describing the farm, available machinery and weather data for a
selected location. Simulation of crop production and harvest is based on a
daily time step following historical weather data. Simulation begins in early
spring of the first year. Manure spreading, tillage and planting operations are
performed on days when soil and weather conditions are suitable for fieldwork.
Operations are done in sequence until all are completed. The crop growth models
determine the accumulation of dry matter and the change in quality (nutrient
content) in the crops based upon the weather of each day and moisture available
in the soil profile. Growth models cycle through the daily time step until the
crop is ready for harvest.

Alfalfa or grass harvest begins, weather permitting, on a calendar date or
quality level specified by the user. After the harvest of a particular cutting
is complete, the growth model is reset for regrowth of the next cutting. The
daily cycle of growth and harvest continues through all cuttings. Alfalfa
harvest is first followed by grass, corn, small grain and soybean crops as
required. Corn and small grain harvests begin with the harvest of silage and
proceed with the harvest of high moisture grain and dry grain as requested. At
the completion of harvest, feed changes during storage are determined. Based
upon the quantity and quality of feeds available from storage, animal diets are
formulated and feed disappearance is determined. The model increments to the
next year and repeats the simulation for the weather conditions of that year.

If grazing of grass or alfalfa is requested, growth of the pasture crop is
predicted for each month with the appropriate growth model. The amount of
pasture produced each month is assumed to be available that month for feeding.
Animal rations are formulated each month assuming balanced diets. Pasture is
supplemented with total mixed rations that meet the protein and energy
requirements of the animals.

An economic analysis is performed for each weather year. The total costs of
production are subtracted from the various farm incomes to obtain the net
return above feed and manure costs and the overall return to management and
unpaid factors for the modeled farm. Simulation results are reported for each
weather year.
Crop Growth

Alfalfa growth is simulated using ALSIM1 level 2 (Fick, 1977). Daily
accumulation of both leaf and stem dry matter is predicted based upon
soil-water availability and growing degree-days above 5 C. Quality (CP and NDF)
is modeled with empirical functions of the accumulated growing degree-days
during growth (Rotz et al., 1988). Separate relationships are used for leaves
and stems; so leaf to stem ratio also affects quality. Grass growth and
development are predicted on a daily basis as functions of photosynthesis and
temperature as influenced by soil water and nitrogen availability (Mohtar et
al., 1994). Crude protein concentration is related to nitrogen uptake and the
total accumulation of dry matter. NDF is predicted from the developmental stage
of the crop and the partitioning of carbohydrates among leaf and stem
components.

Corn and small grain growth and development are predicted using the
CERES-maize, CERES-wheat and CERES-barley models essentially as implemented in
the DSSAT version 3 model. Developmental staging is primarily a function of the
accumulation of thermal time. Grain and silage yields are predicted each day as
functions of the available solar radiation, temperature, day length, available
soil moisture and available soil nitrogen (Jones and Kiniry, 1986). Grain and
high-moisture grain are assigned values for crude protein (CP) and neutral
detergent fiber (NDF) (NRC, 1989). Silage quality is a function of the total
nitrogen uptake and the partitioning of carbohydrates among plant components.

Soybean development is predicted using functions from SOYGRO as implemented in
DSSAT version 3. Stage of development is predicted from the accumulation of
thermal time, photothermal time, and day length. Vegetative growth is predicted
using a model developed by Sinclair (1986). The accumulation of grain dry
matter is a function of the length of the stage for grain development,
vegetative production, temperature and day length as influenced by moisture
stress. Grain crude protein (CP) and neutral detergent fiber (NDF) are assigned
typical values (NRC, 1989).
Crop Harvest

A machinery submodel estimates harvest rate, speed of operation and energy
requirements at six levels of yield ranging from very low to very high yields
for all harvest operations (Savoie, 1982). This information is used to link
harvest with crop growth such that harvest rate is a function of yield. Forage
harvest operations are simulated on a three-hour time step and grain crop
harvest is simulated on a daily time step. Harvest operations occur when
weather and crop conditions are suitable. Crop yield is influenced by the
timeliness of harvest to account for preharvest losses. Timeliness is a
function of the suitable days available for fieldwork and the size of the
machines used for these operations (Harrigan et al., 1996). Machine hours, fuel
use and labor requirements are totaled as each operation is completed.

Field drying and rewetting processes that occur following mowing influence
alfalfa and grass harvest rates. Drying rate is a function of the daily weather
conditions, swath density and the type of conditioning (Rotz and Chen, 1985).
Rewetting from dew is a function of the crop moisture content before nightfall
and the humidity and wind conditions over the night period. Rain induced
rewetting is a function of the crop moisture content and the amount of rainfall
(Rotz, 1985). Forage dry matter and nutrient losses during field curing include
respiration, rain and machine induced losses. Dry matter loss from plant
respiration is a function of crop moisture content, ambient air temperature and
curing time (Rotz, 1995). Dry matter lost through respiration is assumed to be
totally digestible, non-protein and non-NDF material (available carbohydrate).
Dry matter and nutrient losses caused by rain consist of leaf shatter and
leaching losses. Shatter losses due to machine operations are set according to
the type of machine used with leaf and stem losses determined separately (Rotz,
1995). Crop quality during harvest changes according to the change in leaf to
stem ratio and the relative change of other plant constituents.
Feed Storage

Harvested feeds are stored as either dried or ensiled material. Following
harvest, alfalfa and grass hay or silage can be separated between two levels of
quality for storage and feed allocation. All forage harvested with an NDF
content greater than a preselected value (normally about 42%) is considered low
quality feed and the remaining material is considered high quality. Separation
of feeds by quality level enables more efficient allocation of the feeds to
animals at various stages of growth and lactation.

The hay storage model includes dry matter and nutrient losses due to microbial
activity on the hay and nutrient changes due to heating of the hay. Dry matter
lost is again non-NDF material, so NDF concentration increases as dry matter is
lost. Crude protein loss is 40% of the loss of other dry matter, which causes a
small decrease in the concentration of protein. A portion of the protein is
bound to carbohydrate during the heating process so less is available for
animal utilization (Buckmaster et al., 1989a). Losses and quality changes in
large round bales are also a function of storage method, weather conditions and
bale size (Harrigan et al., 1994).

Silo losses and forage quality changes are modeled for alfalfa, grass and corn
silages. Both tower and bunker types of structures can be used as well as
bagged silage and bale silage. Loss and quality of forage are modeled for each
plot (material harvested in three hours) throughout the storage period. Losses
occur in four phases: preseal, fermentation, infiltration and feedout
(Buckmaster et al., 1989b). Preseal losses are due to aerobic respiration in
material on the upper surface of the silo. This loss occurs until the next plot
placed into the silo covers the previous plot or until the silo is covered with
plastic. After the silage is sealed, anaerobic fermentation begins. During
fermentation, some hemicellulose is broken down, non-protein nitrogen is formed
and a small amount of dry matter is lost.

Throughout the storage period, oxygen can infiltrate through the silo wall,
plastic silo cover or bag allowing additional aerobic respiration. This
infiltration loss is often the predominant loss during ensiling. At feedout the
surface of the silo is again exposed to oxygen, which stimulates aerobic
respiration. This loss is related to the exposed surface area and the rate at
which the silo is emptied. Dry matter lost from the four phases is primarily
respirable substrate, i.e. not CP or NDF. Therefore, the concentration of CP
increases with the loss of dry matter. The breakdown of hemicellulose partially
offsets the gain in NDF concentration that occurs through the loss of non-NDF
constituents.

Silage handling and feeding losses are assumed to occur uniformly from all
plant constituents. Due to uniform distribution of this loss, feed quality is
not affected. In the case of hay, animals can selectively reject lower quality
particles thus increasing the quality of the hay consumed.
Feed Allocation and Animal Performance

Farm produced feeds are allocated to the dairy or beef herd according to animal
requirements and feed availability. Possible feeds include: 1) low-quality
forage (hay or silage), 2) high quality forage (hay or silage), 3) grain crop
silage (corn or small grain), 4) high-moisture grain, and 5) dry grain. When
required, these feeds are supplemented with purchased feeds of 1) a degradable
protein supplement, 2) an undegradable protein supplement, 3) vegetable oil or
animal fat, 4) corn grain and 5) purchased hay.

A dairy herd is split into six groups for feed allocation, and a feeding order
is strategically chosen to allocate feeds where they are best used by the
animal. Dry cows are fed first, heifers or steers greater than one year of age
are fed second and those under one year old are third with a mix of low quality
forages and grain crop silage as the preferred forage. Feed requirements for
these groups are determined first so that if there is a shortage of forage, the
higher quality hay purchased will be fed to lactating cows. The remaining three
groups are lactating cows at three stages of lactation. Highest producers are
fed first and lowest producers last. The preferred forage is a mix of high
quality forage and grain crop silage.

The preferred forage mix for any group is used when available. If not, an
alternative forage mix is established. When high quality forage is preferred,
low quality forage is the alternative, and vice-versa. If both low and high
quality forage stocks are depleted, purchased hay is used. The ratio of hay to
silage and/or the amount of alfalfa, grass or corn silage in the forage mix is
determined from the amount of each left in storage. The preferred grain in the
ration is always high-moisture grain. The first alternative is stored dry grain
and the second is purchased grain.

Rations are formulated for each of the six animal groups. If the feeds
available cannot provide the necessary nutrients for the given production level
(yet satisfy intake and fiber limitations), the production level is decreased
to that level which can be met with the given feeds. The following five
criteria are used to determine rations (Rotz et al., 1999): 1) animal intake is
limited by physical fill or energy consumption, 2) adequate forage must be fed
to maintain a roughage requirement in the rumen, 3) energy requirement must be
met, 4) ammonia pool in the rumen must be adequate for microbial growth, and 5)
substrate must be available in the rumen for microbial growth. Physical fill
and roughage are functions of the NDF, digestibility of the NDF and particle
size distribution in the feeds. The absorbed protein system is used to
determine protein requirements (NRC, 1989). Several modifications to this
system were made to improve agreement with crude protein requirements, and
protein digestibility and to insure that reasonable rations are formulated
(Rotz et al., 1999).

Rations are determined with a linear programming algorithm. For high forage
diets, forage use is maximized while using as little energy and protein
supplements as necessary. To achieve this objective, ration costs are minimized
using relative prices of forages, grain, and supplements with homegrown forages
having no cost. For a high concentrate diet, the relative price of forage is
set high for lactating cow diets forcing greater use of corn and protein
supplements.

The beef component model functions similar to the dairy animal component. The
model predicts nutrient requirements, feed intake and growth as a function of
animal age and size and the available feeds. The herd is divided into six
groups, which include suckling calves, weaned calves, replacement heifers,
stockers, finishing animals and cows. For each group, feed intake is limited by
either physical fill or energy intake. Fill is constrained by the sum of the
fill units of consumed feeds, where fill units are a function of the neutral
detergent fiber content, fiber digestibility, and particle size distribution in
feeds. Forages are allocated by their energy content, and diets are formulated
to meet the animals' intake constraints along with their energy, degradable
protein, and undegradable protein requirements (NRC, 1996).

The growth and body composition of animals is modeled based upon the work of
Williams and Jenkins (1998). Growth rate on monthly intervals is limited by the
energy content of available feeds consumed. If physical fill is not limiting,
the animal attains its maximum growth potential. Otherwise, gain is limited by
the energy available in the total feed intake. Animal gain is partitioned as
empty body weight in fat and fat-free matter, and a body condition score is
predicted based upon this composition.
Manure Production and Use

Manure production is modeled as feed DM consumed minus the digestible DM
extracted by the animals plus urine DM and feed DM lost into the manure (Borton
et al., 1995). The total quantities of silage, hay, grain and supplements
consumed by each animal group are multiplied by the fraction of indigestible
nutrients (1 - TDN) of each feed. The sum of indigestible dry matters for all
animal groups gives the fecal DM. Urinary DM is set as 5.7% of total urine with
a fecal/urine ratio of 1.2 for growing animals and 2.2 for cows. Manure DM is
increased an additional 3% of the feed DM intake to account for feed losses
into the manure. The quantity of wet manure handled is the sum of the manure
and bedding dry matters divide by the manure DM content.

Nutrients in the fresh manure are determined through a mass balance of the six
animal groups (Borton et al., 1995). Manure nutrients equal the nutrient intake
minus nutrients contained in milk produced and in meat produced through animal
growth. Nutrient losses are subtracted to determine that available for plant
growth. Nitrogen (N) losses during collection, storage and application are each
modeled as functions of temperature, storage method and the time between
spreading and incorporation. Phosphorus (P) and potassium (K) losses are
restricted to that lost during manure handling or through runoff. Since good
management is assumed, uncontrolled runoff is presumed to be small. Losses of P
and K from the farm are set at 5% of that applied in manure and fertilizer.

Crop nutrient requirements are based on the nutrients removed by field crops
and the crop yield. These requirements are met with purchased fertilizer minus
credits from legume crop carryover and manure. Due to N fixation, no N is
required for alfalfa and soybean land. On land rotated from alfalfa into corn,
small grains or grass, the N requirement is reduced by about 112 kg/ha (100
lb/ac) as a credit for the soil N remaining from the previous crop. Fertilizer
nutrients are added to that available from legumes and manure to predict that
available for crop uptake.
Tillage and Planting

Up to six sequential operations are used for establishment of each crop
(Harrigan et al., 1996). On any given parcel of land, the operations must occur
in a sequence but more than one operation can occur simultaneously if the user
allows. The submodel predicts machine hours, fuel and labor use for all tillage
and planting operations. Manure application must occur prior to the tillage
sequence, which links manure and tillage predicting the timing of spring and
fall operations. A delay in planting due to untimely operations results in a
decrease in grain crop yields.

Moisture in the upper 15 cm (6 in) of the soil is tracked through time to
predict days suitable for fieldwork. Field operations are allowed only on days
when the soil moisture is below a critical level (a little less then field
capacity). The soil model is that used to predict crop growth (Jones and
Kiniry, 1986). Soil moisture is increased by rainfall and decreased through
evaporation and moisture flow to lower soil layers.
Economics

A partial budgeting format is used to account for all costs associated with
growing, harvesting, storing and feeding of crops to the animal herd and the
collection, storage and application of manure back to the crop land. A total
feed and manure cost is determined as the sum of all costs associated with
these processes. For a dairy farm, a net return over feed and manure costs is
calculated as the difference between the income from milk sales and the net
cost of feeding the animals and handling the manure. To estimate whole farm
profit for dairy or beef farms, the costs for animal housing, animal care and
milking are subtracted from the incomes of milk, crop and animal sales to
obtain the overall return to management and unpaid factors. For a crop farm
without animals, the net return is the income from crop sales minus all costs
associated with crop production.

Production costs include capital investments in machinery and structures.
Annual costs for capital investments are determined by amortizing the initial
price over a given life with a given real interest rate. Annual operating costs
include costs of labor, fuel and electricity, maintenance and repair of
machinery, land, seed, fertilizer, chemicals, and supplemental feeds. Annual
requirements for each of these categories are determined by the model and
multiplied by a given price to determine annual costs.

All production costs and the net return over these costs are determined for
each simulated year of weather conditions. The distribution of annual values
obtained can be used to assess the risk involved in alternative technologies or
strategies as weather conditions vary. A wide distribution in annual values
implies a greater degree of risk for a particular alternative. The selection
among alternatives can be made based upon the average net return and the
probability of attaining that net return.

[Summary provided by the USDA.]

Related URL
Link: GET SERVICE > GET SOFTWARE PACKAGE
Description: Download form to access the Integrated Farm System Model.

Link: VIEW RELATED INFORMATION
Description: The Integrated Farm System Model reference manual.
Service Citation
Title: Integrated Farm System Model
Provider: USDA Agricultural Research Service
URL: http://www.ars.usda.gov/Main/docs.htm?docid=8519
ISO Topic Category
FARMING
ECONOMY
ENVIRONMENT
Access Constraints
IFSM execution requires a Windows 95 or higher operating system. At least
12 MB of fixed disk space is required to install and store the program and
associated files.

Before downloading the model, you must fill out the registration so that the
USDA can keep a record of model users.
Service Provider
Agricultural Research Service, U. S. Department of Agriculture

Service Provider Personnel
Name: USDA-ARS
Phone: 254-770-6500
Fax: 254-770-6561
Contact Address:
USDA-ARS
Grassland Soil and Water Research Laboratory
808 E. Blackland Rd.
City: Temple
Province or State: TX
Postal Code: 76502
Country: USA
Distribution Media
Distribution_Media: Online
Fees: No fees
Personnel
TYLER B. STEVENS
Role: SERF AUTHOR
Phone: (301) 614-6898
Fax: 301-614-5268
Email: Tyler.B.Stevens at nasa.gov
Contact Address:
NASA Goddard Space Flight Center
Global Change Master Directory
City: Greenbelt
Province or State: MD
Postal Code: 20771
Country: USA

USDA-ARS
Role: TECHNICAL CONTACT
Phone: 254-770-6500
Fax: 254-770-6561
Contact Address:
USDA-ARS
Grassland Soil and Water Research Laboratory
808 E. Blackland Rd.
City: Temple
Province or State: TX
Postal Code: 76502
Country: USA
Publications/References
Borton, L.R., C.A. Rotz, H.L.Person, T.M. Harrigan and W.G. Bickert. 1995.
Simulation to evaluate dairy manure systems. Applied Engineering in Agric.
11(2):301-310.

Buckmaster, D.R. 1989. Value of forage losses in the dairy forage system. Ph.D.
Thesis, Michigan State University, East Lansing.

Buckmaster, D.R., C.A. Rotz and J.R. Black. 1990. Value of alfalfa losses on
dairy farms. Trans. ASAE 33(2):351-360.

Buckmaster, D.R., C.A. Rotz and D.R. Mertens. 1989a. A model of alfalfa hay
storage. Trans. ASAE 32(1):30-36.

Buckmaster, D.R., C.A. Rotz and R.E. Muck. 1989b. A comprehensive model of
forage changes in the silo. Trans. ASAE 32(4):1143-1152.

Fick, G.W. 1977. The mechanisms of alfalfa regrowth: a computer simulation
approach. Search. Agric. 7(3):1-28.

Harrigan, T.M., W.G. Bickert and C.A. Rotz. 1996. Simulation of dairy manure
management and cropping systems. Applied Engineering in Agric. 12(5):563-574.

Harrigan, T.M., C.A. Rotz, and J.R. Black. 1994. A comparison of large round
bale storage and feeding systems on dairy farms. Applied Engineering in Agric.
10(4):479-491.

Jones, C.A. and J.R. Kiniry, ed. 1986. CERES-Maize: a simulation model of maize
growth and development. Texas A&M Univ. Press, College Station.

Mertens, D.R. 1987. Predicting intake and digestibility using mathematical
models of rumen function. J. Anim. Sci. 64(5):1548-1558.

Mohtar, R., D. Buckmaster and S. Fales. 1994. A model for grass growth under
grazing system. ASAE Paper No. 947525, ASAE, St. Joseph, MI.

National Research Council. 1989. Nutrient requirements of dairy cattle, 6th ed.
Natl. Acad. Sci., Washington, D C.
National Research Council. 1996. Nutrient requirements of beef cattle. 6th rev.
ed.: Natl. Acad. Sci., Washington, D.C.

Parsch, L.D. 1982. DAFOSYM: A system simulation model for analyzing the
economics of forages on commercial dairy farms. Ph.D. Thesis, Michigan State
University, East Lansing.

Rotz, C.A. 1985. Economics of chemically conditioned alfalfa on Michigan dairy
farms. Trans. ASAE 28(4):1024-1030.

Rotz, C.A. 1995. Loss models for forage harvest. Trans. ASAE 38(6):1621-1631.

Rotz, C.A. ed. 1989. DAFOSYM: the dairy forage system model. Reference Manual.
Agric. Engineering Dept., Michigan State University, East Lansing.

Rotz, C.A., J.R. Black, D.R. Mertens and D.R. Buckmaster. 1989a. DAFOSYM: A
model of the dairy forage system. J. Production Agric. 2(1):83-91.

Rotz, C.A., D.R. Buckmaster, D.R. Mertens and J.R. Black. 1989b. DAFOSYM: A
dairy forage system model for evaluating alternatives in forage conservation.
J. Dairy Sci. 72:3050-3063.

Rotz, C.A. and C.U. Coiner. 2003. Dairy Forage System Model: Reference Manual.
USDA Agricultural Research Service, University Park, PA .

Rotz, C.A. and Y. Chen. 1985. Alfalfa drying model for the field environment.
Trans. ASAE 28(5):1686-1691.

Rotz, C. A., D.R. Mertens, D.R. Buckmaster, M.S. Allen, and J.H. Harrison.
1999. A dairy Herd model for use in whole farm simulations. J. Dairy Sci.
82:2826-2840.

Rotz, C.A., L.D. Satter, D.R. Mertens, and R.E. Muck. 1999. Feeding strategy,
nitrogen cycling, and profitability of dairy farms. J. Dairy Sci. 82:2841-2855.

Savoie, P.H. 1982. The analysis of forage harvest, storage and feeding systems.
Ph.D. Thesis, Michigan State University, East Lansing.

Savoie, P., L.D. Parsch, C.A. Rotz, R.C. Brook and J.R. Black. Simulation of
forage harvest and conservation on dairy farms. Agric. Systems 17:117-131.

Sinclair, T.R. 1986. Water and nitrogen limitations in soybean grain production
I. Model development. Field Crops Research 15:125-141.

Williams, C.B. and T.G. Jenkins. 1998. A computer model to predict composition
of empty body weight changes in cattle at all stages of maturity. J. Anim. Sci.
76:980-987.
Creation and Review Dates
SERF Creation Date: 2007-04-09
SERF Last Revision Date: 2012-04-24
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