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.
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