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a USDA-ARS, Pasture Systems and Watershed Management Res. Unit, Building 3702, Curtin Road, University Park, PA 16802
b Dep. of Agron, Pennsylvania State Univ., University Park, PA 16802
Corresponding author (alrotz{at}psu.edu)
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: CERES, Crop Estimation through Resource and Environment Synthesis • DAFOSYM, Dairy Forage System Model • DM, dry matter • DSSAT, Decision Support System for Agricultural Technology • NDF, neutral-detergent fiber • NLEAP, Nitrate Leaching and Economic Analysis Package
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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A practice that affects both the crop rotation and feeding strategy is the production and feeding of soybean. The land area on Pennsylvania farms that is cropped to soybean increased 42% between 1990 and 1998 (PASS, 1999), and much of this increase occurred on dairy farms. This increase is due, in part, to improvements such as no-till drills and herbicide resistant soybean varieties. Soybean production is attractive to dairy producers because of relatively low input costs, crop rotation benefits, and the ability to replace some of the purchases of off-farm protein supplements with homegrown feedstuffs. In addition, soybean production often replaces corn grain, which recently has had a relatively low market value.
The production and feeding of soybean can have many impacts on the productivity, profitability, and environmental impact of a dairy farm. The agronomic benefits are well documented. Greater corn yields are obtained when corn follows alfalfa or soybean in a rotation. The magnitude of this increase is dependent on the climate, soil, tillage, and crop conditions. In a summary of available data, Singer and Cox (1998) noted that typical corn yield responses to crop rotation in the Midwest range from a 10 to 15% increase. A related benefit of corn following legume crops is that the N released from the decaying legume provides an excellent source of N for the corn. Maloney et al. (1999) found that added N fertilizer could not be used to compensate for all of the rotation effect. Other explanations for the increased yields include the presence of soil antibodies or phytotoxic compounds, improved soil texture and water infiltration, and reduced disease and insect damage (Maloney et al., 1999; Omay et al., 1998; and Singer and Cox, 1998).
Animal production benefits can also be obtained through the feeding of soybean. Soybean provides a high-protein, high-energy feed supplement that compliments the nutrient needs of lactating dairy cows. Heating or roasting soybean reduces the rumen degradability of this protein, providing a better match to the animals protein needs (Rotz et al., 1999b). The animal response to the supplemental feeding of soybean is variable due to differences in processing and feeding. A typical or average increase in milk production attained by feeding roasted soybean is 1.5 kg d-1 compared with balanced diets that have soybean meal as the sole protein source (Dhiman et al., 1997; Faldet and Satter, 1991). Raw (unheated) soybean has also been fed along with animal by-product proteins (high in rumen undegradable protein) to attain similar milk yields to those obtained by feeding balanced diets using roasted soybean (Grummer et al., 1994).
Environmental benefits are also possible by including soybean in crop rotations on dairy farms. Owens et al. (1995) measured substantial reductions in NO3–N loss and NO3–N concentration in the percolate from 6 yr of a corn–soybean rotation compared with previous years of continuous corn. Another issue is the overall nutrient balance of the farm. By producing more of the animals nutrient needs on the farm, the import of feed supplements can be reduced. With less importation, nutrients may be recycled more efficiently within the farm, reducing losses to the environment.
A comprehensive, multidisciplinary systems approach is required to design new production options and assess their economic and environmental benefits. The effects on all major farm components and their interactions must be considered. Such an analysis requires computer modeling as an integration tool. DAFOSYM provides a comprehensive simulation model that integrates the many biological and physical processes on a dairy farm (Rotz et al., 1989; Borton et al., 1995; Harrigan et al., 1996). This model has been used to evaluate and compare the whole-farm impacts of alternatives in manure handling (Borton et al., 1995; Harrigan et al., 1996), various levels of alfalfa and corn silage production and feeding (Borton et al., 1997), and options in protein feed supplementation (Rotz et al., 1999b). The objective of this study was to determine if the current trend in Pennsylvania to produce and feed soybean on dairy farms offers a long-term economic benefit to producers and an environmental benefit to society.
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MATERIALS AND METHODS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Model Description and Development
DAFOSYM is a whole-farm model where crop production, feed use, and the return of manure nutrients back to the land are simulated over many years of weather (Rotz et al., 1989; Harrigan et al., 1996; Rotz et al., 1999b). The growth and development of alfalfa, corn, and soybean crops are predicted on a daily time step from the soil and weather conditions. Tillage, planting, harvest, and storage operations are simulated to predict the resource use, timeliness of operations, crop losses, and nutritive changes in the feeds. The feed allocation and animal response are related to the nutritive value of available feeds and the nutrient requirements of six animal groups making up the dairy herd (Rotz et al., 1999a). Nutrient flows through the farm are modeled to predict potential nutrient accumulation and loss to the environment (Rotz et al., 1999b).
Simulated performance is used to predict the production costs, income, and net return or profit of the farm for each weather year. A simple whole-farm budget is used where investments in equipment and structures are depreciated over their useful life and annual expenditures and incomes are accounted. Possible government subsidies and income tax implications are not considered. By modeling several alternatives, the effects of system changes can be compared, including resource use, production efficiency, environmental impact, and profitability. The distribution of annual values can also be used to assess the risk involved in alternative technologies or strategies due to the effect of weather on the farm performance.
Two crop submodels were added to DAFOSYM to conduct this study. A simple yield prediction model for corn grain and silage was replaced with a phenological model of corn growth and development, and a new submodel was developed to predict the development and grain yield of soybean. Existing tillage, planting, harvest, feeding, economic, and nutrient submodels were expanded and linked to the soybean model to enable a whole-farm simulation of the new crop.
The same soil model was used for all crops simulated in DAFOSYM. The soil moisture was predicted in four soil layers considering the precipitation, runoff, evapotranspiration, moisture migration, and drainage (Jones and Kiniry, 1986). Three relatively thin layers were used near the surface with thicknesses of 30, 46, and 76 mm. The fourth layer extended from the 152-mm depth to the bottom of the soil profile or the crop rooting depth, which ever was first limiting. A typical rooting depth of 1.5 m was designated for corn and soybean. For simplicity, the physical characteristics (e.g., texture, water-holding capacity, and drainage rate) were set the same for all of the layers. Soil parameters were assigned to describe generic clay-loam, loam, sandy-loam, and loamy sand soils of deep, moderate, and shallow depths (Jones and Kiniry, 1986).
The link between soil moisture and crop growth and development was modeled using a water stress factor. This factor controlled the growth rates of various plant parts as implemented in the CERES (Crop Estimation through Resource and Environment Synthesis)-maize model (Jones and Kiniry, 1986). The factor varied between 0 and 1 where 1 represented no stress on the crop growth. The values were <1 below a critical soil moisture, which was set at half of the available water-holding capacity in the root zone. Below this level, the water stress factor declined in proportion to the decline in the available soil moisture toward zero at the lower limit of available moisture.
The soil N was tracked in two soil layers. The upper layer was the sum of the three upper layers defined for soil moisture, and the lower layer was the same as that defined for moisture. The N movement and transformation within and among soil layers was modeled using functions from the Nitrate Leaching and Economic Analysis Package (NLEAP) model (Shaffer et al., 1991). The N losses from the soil due to volatilization, leaching, and denitrification were predicted on a daily time step. The N uptake by the crop was limited by the available soil N or the N demand of the crop. Nitrogen stress factors were used to link the crop growth and development to the soil N level. These stress factors varied between 0 and 1, as defined by Jones and Kiniry (1986). The stress level on any given day was determined from the ratio of N uptake to N demand by the crop.
Corn biomass (silage) and grain yields were predicted from seeding through maturity. Functions for predicting the aboveground growth and the phenological stage were taken from the CERES-maize model (Jones and Kiniry, 1986), as implemented in the Decision Support System for Agricultural Technology (DSSAT) Version 3.0 (Tsuji et al., 1994). Thus, changes in the leaf, stem, ear, and grain mass were predicted each day based on the soil and weather conditions. For more control over the predicted grain and silage yields, yield adjustment factors were added to increase or decrease the predicted yields by a set amount each day over all of the simulated years. Therefore, the model user was able to adjust or set the long-term average yield while maintaining year-to-year variation.
A rotation effect was added to adjust the corn yield according to the preceding crop. For corn that followed corn, the grain and silage yields were reduced by 10%. This reduction represented a typical yield difference between continuous corn and corn following a legume crop (Maloney et al., 1999; Singer and Cox, 1998). The grain and silage yield adjustment factors described above were reduced by this amount times the portion of the corn crop that followed corn each year. The rotation from a legume crop also provided additional crop residue N for use by the corn crop. The added residue N was 200 kg ha-1 from rotated alfalfa and 63 kg ha-1 from rotated soybean. Considering that about 70% of the crop residue N was recycled into the succeeding crop, this provided N credits of about 140 and 45 kg ha-1 for rotated alfalfa and soybean, respectively.
Nutritive characteristics of corn were needed to link with the animal component of the model. The required nutritive values were grain and whole-plant crude protein; neutral-detergent fiber (NDF), P, and K; and stover NDF content. The crude protein of grain was set at 100 g kg-1 DM (NRC, 1989). For the whole plant, it was 6.25 times the N content where the N content was the N taken up by the crop divided by the crop mass. The crop fiber content was the total fiber established during the growth of individual plant components divided by the crop mass. The NDF levels in growing leaf, stem, cob, and grain tissues were 680, 630, 800, and 120 g kg-1 DM, respectively. Thus, the NDF levels varied with the relative rates of growth of the plant components. During grain filling, the transfer of carbohydrates (non-NDF DM) from the stover to the grain further increased the NDF levels in the stover. The stover NDF content was the total nongrain fiber in the plant divided by the DM mass of the nongrain portion of the plant. The P contents in grain and silage were set at constant levels of 2.9 and 2.2 g kg-1 DM, and the K contents were 3.7 and 9.6 g kg-1 DM, respectively.
A soybean growth model was developed that was similar in structure to the corn model but with less detail. A simpler approach was used because only grain yield predictions were required. Relationships for predicting the phenological stage were taken from the SOYGRO model (Jones et al., 1991), as implemented in DSSAT Version 3.0 (Tsuji et al., 1994). With these relationships, dates were predicted for emergence, first flower, pod initiation, seed initiation, end of vegetative growth, physiological maturity, and harvest maturity. Dates were predicted each year based on accumulated thermal time and photoperiod.
The vegetative growth of the plant from emergence to the end date of vegetative growth was predicted using a model developed by Sinclair (1986). His relationships were used to predict photosynthetic C accumulation, leaf development, vegetative mass, and N2 fixation. Our soil model was used to predict the soil moisture in multiple layers. The water stress factor proposed by Sinclair was replaced with the linear relationship described above. Because the legume crop produced the required N, N availability was assumed to never limit the crop growth. Available soil N was used by the crop, and any additional N requirement was met through N2 fixation.
The grain yield was determined by integrating the seed growth rate from the seed initiation date through physiological maturity. The seed growth rate on a given day was a function of the ambient temperature, photosynthetic C production, and water stress:
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The maximum potential seed growth rate was set by the user to a value in the range of 8 to 10 g m-2. This provided flexibility in setting the long-term average yield while maintaining the year-to-year variation from weather. The function used to predict the temperature factor for the grain growth was obtained from the SOYGRO model (Wilkerson et al., 1983). This factor varied around an optimum hourly temperature between 21 and 23.5°C. The photosynthetic factor increased in proportion to the daily photosynthetic C accumulation, attaining a value of 1.0 when the daily accumulation was >50% of the maximum potential accumulation. The maximum potential accumulation was set at 30 g m-2. The water stress factor was determined as described above.
The nutritive characteristics of soybean grain were set to typical values (NRC, 1989). The crude protein, NDF, P, and K contents were 428, 150, 6.5, and 18 g kg-1 DM, respectively. Nutrient levels were used to determine the nutrient removal by the crop, the nutrient availability in the feed, and the nutrients removed from the farm in the sold grain. Predicting the nutrient uptake of the whole plant was unnecessary because all of the nutrients other than those in the grain were returned back to the soil.
Representative Farms
The effects of various management changes were evaluated on two farms representative of well-managed small and larger dairy farms found in Pennsylvania. The soil on both farms was a Hagerstown silt loam of medium depth with an available water holding capacity of 150 mm. Simulations of the farm performance were done for 25 weather yr using Chambersburg, Pennsylvania historical data (1966–1990).
The smaller farm consisted of 132 ha of land. Alfalfa was grown on 48 ha along with 60 ha of corn and 24 ha of soybean. This represented a crop rotation where 12 ha were rotated each year, producing 4 yr of alfalfa followed by 5 yr of corn interspersed with 2 yr of soybean. This cropping system met most of the feed needs of the herd during most of the weather years.
The larger farm was 330 ha of land consisting of 120, 120, and 90 ha of alfalfa, corn, and soybean, respectively. Thus, 30 ha were rotated through 4 yr of alfalfa followed by 7 yr of alternating corn and soybean. This cropping system met most of the forage and protein needs of the herd during most of the weather years. A modification of the 400-cow farm was used to evaluate strategies with a more restricted land base. The total crop area was reduced to 240 ha (120 ha each of alfalfa and corn), and the labor requirement was reduced by one person-year. This crop area met the forage needs of the herd during most of the weather years. The effect of soybean on this land base was evaluated by shifting half of the alfalfa crop area (60 ha) to soybean.
The corn crop was a variety with a 120-d relative maturity planted at a population of 69000 plants ha-1. Most of the manure from the herd (70%) was applied to the corn land, and the remainder was spread on the alfalfa land. To better meet the N needs of the corn crop early in the season, a minimum of 20 kg ha-1 NO3–N was applied as starter fertilizer. Corn was harvested as silage and high-moisture grain to fill available silos, and additional corn was harvested as dried grain. The high-moisture corn was only produced on the small farm. Silos were sized so that the portion of the herds forage requirement obtained from corn silage was about 35% on the small farm and 60% on the large farm. More corn silage was used on the larger farm to represent the common practice for this farm size. Corn silage harvest began no earlier than 3 September and at a crop moisture content 
680 g kg-1 DM. The harvest starting dates for high-moisture corn, dry corn, and soybean were 1, 21, and 15 October, respectively. Harvest occurred on days beyond those dates when the weather and soil conditions were suitable (Harrigan et al., 1996).
Alfalfa was harvested using a five-cutting strategy in which each cutting began at a bud stage of development. All of the cuttings, except the second one on the smaller farm, were harvested as silage that was wilted to <680 g moisture (kg DM)-1. The second cutting on this farm was harvested as dry hay in large round bales. The mean and range in postharvest crop yields over the 25-yr simulations are listed in Table 1. The yields varied between farms due to differences in the timeliness of planting and harvest as well as differences in the crop rotation effects.
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For the smaller farm, the herd included 100 Holstein cows (milking and dry) plus replacement stock. The replacements were 40 animals over 1-yr-old and 45 animals under 1-yr-old. For the larger farm, there were 400 Holstein cows, 160 older heifers, and 180 younger heifers. Most analyses were done with relatively high milk production levels (about 10000 kg cow-1 yr-1) where production was a function of the nutritive content of the feeds that were fed (Rotz et al., 1999a). The cows were housed in a freestall barn and milked in a parlor. The culling rate of the herd was 35%, which set the number of first-lactation animals at 35 and 140 for the small and large farms, respectively.
A mobile mixing wagon was used to prepare the total mixed rations for each of the six animal groups. Forages were allocated in proportion to the amount of each type produced. Feed supplements included corn grain and protein feeds. Options for protein supplementation included raw soybean, roasted soybean, soybean meal, and a protein mix with lower rumen degradability (Table 2). The raw soybean was stored at a cost of $10 t-1 DM and then cracked before feeding. The roasted soybean required a further processing charge of $28 t-1 DM. The protein mix represented a common protein feed blend used in this region, consisting of one-third feather meal, one-third blood meal, and one-third fish meal.
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Cropping and Feeding Comparisons
Various evaluations were performed with DAFOSYM to determine the impact of system changes on feed production, feed use, milk production, manure production, nutrient losses, production costs, and net farm return. In the first evaluation, the impacts of alternatives in soybean production and use were examined on the small 100-cow farm. All of the farm parameters were held constant, except for soybean production, soybean use, and the type of protein supplement used to meet the protein requirements of the herd. As a basis for comparison, the farm was first simulated with 84 ha of corn and no soybean. To meet the N requirement, 100 kg ha-1 N fertilizer was applied to the corn land. This corn and alfalfa rotation met all of the forage and grain requirements of the herd during most weather years. The protein needs were met with the purchase of soybean meal and the low rumen degradable protein mix.
The farm was next simulated with 24 ha of soybean produced and sold as a cash crop. With more legume crop on the farm, the N requirement of the corn was met with 70 kg ha-1 N fertilizer. The protein needs of the animals were met using purchased supplements. The least efficient option of meeting the protein needs was to use soybean meal as the only supplement. Because the soybean meal contained high levels of rumen degradable protein, excessive supplementation was required to meet the rumen undegradable protein requirement of lactating cows (Rotz et al., 1999b). The second and more efficient option was to include both soybean meal and the low rumen degradable protein mix in the diet formulation.
The final two options of this series evaluated the feeding of the farm-grown soybean in either a raw or roasted form. Because the protein in the raw soybean was highly degradable, the low rumen degradable protein mix was included in the diet formulation with this option. Roasting reduced the degradability of the protein. For the roasted soybean option, soybean meal was used as a second supplement in the ration formulation.
The next evaluation or series of simulations was done for the 400-cow farm on either 330 or 240 ha of cropland. The three options simulated on the larger land base were no soybean (210 ha of corn), 90 ha of soybean produced as a cash crop, and 90 ha of soybean roasted and fed. Protein supplementation was efficiently met using a blend of soybean meal and the protein mix. When soybean was not grown, an additional 40 kg ha-1 N fertilizer was required for the corn crop. Two options were then compared on the more restricted land base. After simulating an alfalfa and corn rotation, 60 ha of alfalfa land were shifted to soybean, and the bunker silo capacity was reduced. Soybean was roasted and fed to the herd to meet a portion of the protein needs, and the remainder was met with purchased soybean meal and purchased roasted soybean.
Sensitivity Analysis
A final series of simulations was done to measure the sensitivity of the economic benefit of producing and feeding soybean to other farm characteristics or management changes. Sensitivity information is useful in two ways. First, sensitivity indicates the simulation error that results if an error was made when assuming the original parameter values. Second, sensitivity indicates how changing a design or management characteristic of the farm can influence the system comparisons.
To determine sensitivity, various farm changes were made and then the 100-cow farm was simulated without soybean grown on the farm and with the production and feeding of roasted soybean. The difference in the net return between these two options on the base farm was subtracted from the difference obtained with the revised farm to determine the change in the economic benefit (additional increase or decrease in farm net return). As each farm change was made, fertilizer rates were set to meet crop requirements with as little excess as possible.
The effects of 12 farm changes were independently evaluated. The first was a change in location to central Pennsylvania. This included a change in the weather data (State College, 1974–1998), a small reduction in the available water-holding capacity of the soil, and changes in the crop varieties as well as in the planting and harvest dates. The next was a change in the predominant soil type on the farm from a silt loam to a sandy loam with a similar available water-holding capacity. Next, the no-till establishment of soybean was replaced with the full tillage system used for corn. The effect of manure handling was then determined by switching from a 6-mo storage system to daily hauling of manure.
The next two scenarios involved milk production. First, milk production was set at a more moderate level of 8500 kg cow-1 both with and without the feeding of soybean. Next, a greater difference in milk production was forced between the two feeding options. In the base analysis, the animal submodel predicted about 0.5% more milk when roasted soybean was fed compared with the protein mix. This difference was increased to 1.5%.
The next two changes involved the type and amount of forage used on the farm. In the base analysis, about 35% of the forage on this farm came from corn silage while the remainder was alfalfa silage and hay. This was changed to all corn silage by switching the alfalfa crop area to corn and using all bunker silos for corn silage. In the base analysis, rations were determined using a minimum amount of forage (Rotz et al., 1999a). This was next changed to allow a maximum amount of forage in the rations. With more forage used, alfalfa hay was purchased to meet the additional forage requirement.
The last four management changes were price increases. The prices for soybean roasting, N fertilizer, corn, and milk were each independently increased by 20%. A simulation was also made where the corn price was decreased by 20%.
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RESULTS AND DISCUSSION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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To further validate the crop models, comparisons were made between the predicted crop yields and the estimated actual yields obtained from the Pennsylvania Agricultural Statistics for Centre County from 1989 to 1998 (PASS, 1999). The primary objective in this comparison was to compare the year-to-year variation and the correlation across low- and high-yielding years. The long-term average of the predicted yields was set equal to the average of the estimated yields to make this comparison.
Generally, there was a good relationship between the predicted yields and the county data (Table 3). For corn silage, the correlation was 0.69 with a slope near 1.0 and an intercept near zero. The annual variation (coefficient of variation) was 37% greater for the predicted yields than for the estimated county yields. This was expected because the county yields were averaged over many farms (more weather conditions), which would tend to reduce the annual variation. The predicted variation, therefore, was representative of that found on individual farms.
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The soybean grain yields predicted by our model were very similar to the county yield data, providing a close relationship between the predicted and estimated yields (Table 3). A correlation of 0.8 was found with a slope very near 1.0 and an intercept very near zero. The annual variation was about 30% greater for the predicted values. As discussed for corn silage, this variation was likely more representative of the yield variation found on individual farms.
For further evaluation, the DAFOSYM crop model results were compared with the yields predicted by the parent models from which they were created. Yields were predicted over the same weather years using the corn and soybean crop options of the DSSAT model (Tsuji et al., 1994). Crop and soil parameters were set as close as possible to those used in DAFOSYM. The DSSAT model predicted very high yields for corn and low yields for soybean, giving a poorer relationship with the estimated county yields (Table 3). This was particularly true for soybean where the model was very sensitive to soil moisture. The average yield predicted by the DSSAT model was 25% below the estimated county yield and the variation across years was twice that from the statistical estimates. Despite this difference, the correlation between the yields predicted by the DAFOSYM and DSSAT models was 0.91, 0.89, and 0.95 for corn grain, corn silage, and soybean crops, respectively.
Overall, this evaluation procedure showed that the new crop models in DAFOSYM predicted yields that were representative of those found on actual farms and that these yields were closely correlated to the estimated county yields as well as those predicted by the parent models in DSSAT. Because of the ability to adjust yield predictions, our model did a better job of predicting the actual farm yields than the parent models while maintaining the year-to-year variation due to weather.
Representative Farm Comparisons
The 25-yr average performance and economic results from the model include feed production and use, milk production, nutrient losses and accumulation, production costs, and the net return or profit of the farm. The important results to consider are the comparisons between the different strategies simulated, not the absolute values generated for any particular farm. The predicted values for a given farm such as N loss and net return may vary greatly depending on the model assumptions, and thus should not be used to judge the viability of a specific farm. Relative differences between the simulated systems though, provide a meaningful evaluation of the effects of system changes.
The first farm option was to grow only corn and alfalfa on the 100-cow farm (Table 4, Column 1). The protein requirements for each animal group were efficiently met using an optimal blend of soybean meal and the low rumen degradable protein mix. This production system provided essentially all of the forage and grain needed to feed the herd. Averaged over all of the weather years, no forage was purchased and about 37 t DM corn grain was sold annually to produce 10132 kg milk cow-1. The protein needs were met using 4 t DM soybean meal and 21 t DM protein mix. Efficient feeding of protein kept the N losses relatively low. Whole-farm balances showed a small requirement for P and K fertilizer. The farm net return was $556 cow-1 yr-1.
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When the low rumen degradable protein mix was included in the rations (Table 4, Column 3), annual feed use, N losses, and milk production were essentially the same as that found without soybean production where a similar feeding strategy was used. Compared with feeding soybean meal alone, the use of the more expensive but more efficient protein mix reduced the purchased feed costs by $154 cow-1, increasing the annual farm net return by $155 cow-1. Rotz et al. (1999b) reported a more extensive comparison of the whole-farm effects of using different supplemental protein feeds.
Producing and feeding raw soybean as a high-protein feed provided environmental and economic impacts that were between those obtained using the two cash crop strategies and similar to those obtained with no soybean produced on the farm. The nutrient needs of the herd were met with the soybean and the purchase of 29 t DM corn grain and 15 t DM low rumen degradable protein mix. By feeding soybean, the N import and export from the farm were reduced below that found with cash crop soybean (Table 4). The efficiency of protein use by the animals was between that obtained with soybean meal alone and that obtained with the less degradable mix, so manure N and N losses were between those found in the previous simulations.
Roasting the soybean before feeding improved the efficiency of protein use. Milk production increased by 230 kg cow-1 (2.3%) compared with the strategy where soybean meal was the sole protein source. With roasted soybean, the animal nutrient needs were met using slightly less forage and corn grain and 17 t DM purchased soybean meal. The N import, export, and losses were very similar to those found with feeding raw soybean (Table 4). The purchased feed costs were also similar, but the roasting cost reduced the farm net return by $5 cow-1 yr-1 compared with feeding raw soybean. Compared with the no soybean option, the annual net return was reduced by $2 cow-1.
Slightly different benefits for producing and feeding soybean were found on the larger 400-cow farm due to differences in the farm design. The major differences were more animals per unit of cropland and a greater use of corn silage. With a land base of 330 ha, there were 1.2 animal units (1000 kg body wt.) per unit of land, which was 60% greater than that of the smaller farm. The increase in animal density increased the whole-farm P balance from a small deficit to a small surplus. The N leaching loss also increased about 25%. On this larger farm, more corn silage was used in the animal rations, which reduced the amount of protein obtained from forage. Thus, more protein supplement was needed, and a greater portion of this supplement was met with the soybean meal (Table 5, Columns 1, 2, and 3, vs. Table 4).
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When the land area on the 400-cow farm was reduced to 240 ha, both the profitability and the potential for environmental damage increased. The total N cycled on the farm decreased, but the amount per unit of land increased about 65% (Table 5, Columns 1 and 4). With less land for manure application, P loading increased from a small accumulation of 4 kg ha-1 yr-1 to an accumulation of 12 kg ha-1 yr-1. The N volatilization loss increased about 50%, and the leaching loss increased by 35%. The annual net return increased about $23 cow-1 with the reduction in cropland.
Under this farm scenario, growing and feeding roasted soybean had little effect on N volatilization and leaching losses; P accumulation increased by 1 kg ha-1 yr-1 and K accumulation more than doubled (columns 4 and 5, Table 5). Different nutrient effects occurred on this farm primarily because soybean replaced alfalfa instead of corn. Annual farm net return was decreased by $60 cow-1. Thus, as the cropland per animal decreased, there was less economic benefit (greater economic loss) and less environmental impact through the production and feeding of roasted soybean.
A summary of all of the simulated management options indicates little or no environmental (nutrient loss and accumulation) benefit to growing and feeding soybean on dairy farms. Soybean production and use reduced the N import in feeds and fertilizer, but the N added to the system through N2 fixation by the legume crop more than offset this reduction. Protein was not fed quite as efficiently as attained with the low rumen degradable protein mix, so the N volatilization loss was slightly higher. The P and K balances for the farm were also not affected much by feeding soybean and reducing the import of the supplemental protein feed mix used in this study. Environmental benefits from feeding soybean were only found when compared with the relatively inefficient practice of using soybean meal as the sole protein feed supplement. A potential benefit not included in this analysis though, was reduced pesticide loss to the environment.
Generally, the most economical system was to produce soybean as a cash crop and purchase feed supplements to efficiently meet the animal protein needs. Compared with a cash crop system where soybean meal was the sole protein supplement, feeding either raw or roasted soybean with appropriate supplements was economical, increasing the farm net return by about $100 cow-1 yr-1. Compared with the base farm, soybean production and feeding provided little difference in the farm net return even though it resulted more efficient protein feeding. Thus, the agronomic benefits of growing soybean on these farms increased the farm profit, but when the soybean was fed either in raw or roasted form, this increased profit was lost, giving a net return similar to a corn and alfalfa rotation.
Another consideration is the risk or year-to-year variation in the annual net return. The influence of soybean production and feeding on this variation was relatively small and inconsistent across farms (Tables 4 and 5). For the corn and alfalfa cropping option on the small farm, the standard deviation in the annual net return values was $192 cow-1, or 35% of the mean net return. The lowest variation was with cash crop soybean using soybean meal as the sole protein supplement. This standard deviation was $155 cow-1, which was 34% of the mean. The standard deviation in the annual net return for the other options was about $165 cow-1, or 29% of the mean over the 25-yr simulation. Thus, the production and feeding of soybean provided a small reduction in the economic risk due to the weather effects on farm performance.
On the larger farm, there was less economic risk or year-to-year variation in the net return, and soybean production had less influence on this variation. Unlike the smaller farm, the lowest variation was found with no soybean ($109 cow-1, or 15% of the mean), and the highest was found with cash crop soybean ($122 cow-1, or 16% of the mean). The lower variation and the opposite trends on this larger farm are likely due to the use of corn silage as the predominant forage crop and a more stable supply of this forage.
Management Interactions
The influence of various farm characteristics or management changes on the economic benefit (increase or decrease in net return) received from producing and feeding roasted soybean is illustrated in Fig. 1. The first change was to move the farm north to central Pennsylvania. The change in the weather, soil characteristics, and management practices had a small but positive effect, increasing the annual economic benefit by $12 cow-1.
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When milk production was set at a more moderate level that was not affected by the feeding of soybean, the economic benefit obtained was entirely from decreases in the cropping and feeding costs. With this change, there was a small but positive change in the economic benefit (Figure 1). The decrease in feed costs under this scenario was a little greater than the value of the increased milk obtained in the original comparison. Increasing the difference in milk production received through the feeding of roasted soybean increased the economic benefit. Assuming that 1% more milk was produced with soybean feeding increased the annual economic benefit by $21 cow-1.
The production and feeding of soybean was much more beneficial when all of the forage on the farm came from corn silage. Various factors contributed to this increased benefit. Compared with an all corn farm, the growing of soybean provided a good benefit through N2 fixation and the rotation effects on the corn yield. With the greater forage yield from corn, there was less demand on the cropland for feed production. Thus, the lower relative yield of soybean did not interfere with the forage and grain needs of the herd. The protein of roasted soybean was also better utilized in the high corn silage rations. These benefits, along with differences in cropping costs, increased the annual economic benefit by $58 cow-1.
The use of more forage in the animal rations also increased the economic benefit of producing and feeding soybean on the farm. With more forage, and particularly more alfalfa hay in animal rations, the lower degradability of the protein in roasted soybean provided a little better match to the animal needs. On the base farm, the feeding of roasted soybean primarily reduced the use of grain. With maximum forage rations, soybean also replaced some forage. The net result was an increase in the economic benefit of $35 cow-1 yr-1.
Of the four price changes evaluated, only the corn price had a sizable effect on the economic benefit of producing and feeding soybean. A 20% increase in the cost of roasting the soybean caused a small decrease in the economic benefit. Twenty percent increases in N fertilizer and milk prices each increased the benefit by <$5 cow-1 yr-1. With a greater corn price (with soybean price unchanged), soybean production offered less of an economic advantage relative to corn. A 20% increase in the corn price reduced the economic benefit of the roasted soybean strategy by $16 cow-1 yr-1. Likewise, a 20% decrease in the corn price increased the economic benefit by $16 cow-1 yr-1 (data not shown).
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CONCLUSIONS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Although most of the evaluated farm scenarios showed little or no economic benefit in producing and feeding soybean, a substantial benefit was found under certain management strategies. The benefit increased by $58 cow-1 yr-1 when all of the forage on the farm was corn silage relative to the base farm where 35% of the forage was corn silage, and the remainder was alfalfa. When maximum forage diets were used to feed lactating animals (compared with minimum forage diets on the base farm), the economic benefit increased by $35 cow-1 yr-1. Lower relative corn prices also improved the benefit by $16 cow-1 yr-1.
There was normally little environmental benefit in growing soybean as a cash crop or for feed on dairy farms. Compared with an inefficient protein feeding strategy where purchased soybean meal was the sole protein feed supplement, the production and feeding of roasted soybean reduced the volatile loss of N from the farm by about 20% and resulted in a small decrease (about 7%) in the N leaching loss. Compared with more efficient feeding strategies where less degradable protein supplements were fed, the N losses increased up to 20% through the production and feeding of soybean. On all of the farms evaluated, soybean production as a cash crop or feed had little effect on the P balance and a relatively minor influence on the K balance.
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ACKNOWLEDGMENTS |
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NOTES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Received for publication January 10, 2000.
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REFERENCES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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