Determining Optimal Nutrient Management Strategies for Dairy Farms

OUR INDUSTRY TODAY

Determining Optimal Nutrient Management Strategies for Dairy Farms'

G. M. HENRY,2 M. A. DeLORENZ0,z D. K. BEEDE,? H. H. VAN HORN,? C. B. MOSS? and W. G. BOGGESS3

University of Florida Gainesville 3261 1

ABSTRACT

A model for dairy nutrient management was constructed to evaluate strategies for nutrient recycling using farm- specific information. The model used linear programming to determine optimal cropping systems that used nutrients from dairy manure applied to cropland. The optimal system minimized total feed costs, including purchased commodities, based on the ability of crops to take up manure nutrients and supply forages for cow rations. Nutrient uptake requirements to meet mandated regulations were incorporated as constraints to the linear programming model and were for either N or P.

Results from sensitivity analyses showed that rations balanced for maximum DMI versus solutions with less than maximum DMI affected the crops used in the optimal cropping system and increased feed costs. The effect was greater on feed costs at low production than at high production. The value of additional acreage was lower with no minimum requirement on DMI. Winter was the limiting season for N and P recycling because of less uptake by crops. Feed costs were almost always lowered by increasing crop acreage be- yond the minimum required for N recycling. (Key words: nutrient uptake and recycling, manure management, linear programming, cropping systems)

Received August 18, 1994. Accepted November 2, 1994. 'Florida Agricultural Experiment Station Journal Se-

*Department of Dairy and Poultry Sciences. 3Department of Food and Resource Economics.

ries Number R-04019.

Abbreviation key: MR = minimum requirement, NMR = no minimum requirement.

INTRODUCTION

Large dairies with limited amounts of land, which import large quantities of nutrient-dense feeds and possibly commercial fertilizers, potentially develop an imbalance of N and P for the total farm because N and P from purchased feedstuffs can exceed the amounts leav- ing the farm in animal products, forages, and losses to the environment (volatilization, leaching, and runoff). Recycling of manure nutrients has been the most popular method to address the problem of nutrient balance (12, 23). The recycling system has two nutrient balances, the balance between manure nutrient and crop uptake (manure nutrient balance) and the balance between crop nutrient and animal use (crop nutrient balance). Both of these balances are essential to determine strategies for manure management.

Many aids have been developed to assist dairy producers in making decisions about nutrient management. Programs have been developed to evaluate problems associated with nutrient pollution (20), to help determine the costs of constructing facilities for manure handling facilities (19), and to determine application rates and distribution patterns (9, 13, 15, 22). The Manure Application Planner (13) used linear programming to minimize commercial fertilizer costs by substituting manure nutrients. These aids, however, only considered specific areas of the nutrient recycling system and not the overall effect of nutrient management on the profitability of the dairy.

There have been many attempts to provide values of forages for cattle consumption. Rela- tive value methods, which evaluate the nutrients in many ingredients, including forages, relative to value of equivalent amounts of nutrients in reference ingredients

1995 J Dairy Sci 78:693-703 693

694 HENRY ET AL.

uptake requirements. The model was con- strained by time, labor, land, governmental regulations, feed and manure storage capacity. forage supply, and nutrient requirements for cattle and crops.

The objective function was

(e.g., corn and soybean meal), are based on solutions of simultaneous equations similar to methods described by Morrison (16). Altema- tively, linear programming has been used to determine least cost rations and optimal strategies for formulating rations (1, 21). These formulation models determine rations based on nutrient requirements of cattle and nutrient availability of feedstuffs. Bath (1) used PC DAIRY, a ration formulation model, to evaluate the relative feeding value of certain forages based on their use in rations for cows at different milk productions while holding constant the income over feed costs. Relative values determined by this method thus were in- fluenced by many of the constrants imposed in the linear program, e.g., the potential for greater DMI and much greater NEL intake with higher quality forages than with poor quality forages. Bath (1) concluded that this method better estimated the value of high quality forages in diets for very high producing cows than did simpler estimates of relative value.

The objective of this study was to develop a model that considered both the manure nutrients and crop nutrients balanced to meet mandated nutrient recycling requirements while minimizing feed costs for a set milk production. Optimal cropping systems were chosen based on crop nutrient uptake and feeding value of forages produced. This system was to be similar to a least cost ration model, but incorporated constraints for nutrient uptake and crop utilization.

MATERIALS AND METHODS

Model Description

Linear programming was used to incor- porate as many factors surrounding dairy nutrient management as possible. The General Al- gebraic Modeling System (GAMS; The Scientific Press, Redwood City, CA) was selected as the linear programming language and Minos 5.0 (Department of Operations Re- search, Stanford University, Stanford, CA) as the solver. The objective was to minimize the cost of the dairy nutrient management system, including costs of feeding and nutrient recycling. The model minimized feed costs by selecting cropping systems based on their feeding value and their ability to meet N and P

t

MIN c Costy y=l

where cost, = total annual feed cost for year y (r = 1.J).

Within a year,

n m 3 cost = xici + YfjGfj

i= 1 f=l J=1

- P c HSj j= 1

where

+ c Pnutp,Ncostp p=l j=1 n 7

Xi = hectares grown of crop i (i = 1, ..., n).

Ci = variable cost per hectare of growing crop i,

YG = as-fed kilograms of commodity f used in season j (f = 1. ..., m; j = 1 to 31,

Gfi = price per as-fed kilograms of commodity f in season j ,

YFd = as-fed kilograms of forage on hand g (g = I , ..., r) used in season J ?

GFa = cost per as-fed kilograms of using forage on hand g,

Pnutp, = kilograms of nutrient p [p = 1 to 2 (N or P)] purchased in season j,

Ncostp = cost per kilograms of nutrient p purchased,

Tmij = tons of crop i transferred from period j to j+l,

Journal of Dairy Science Vol. 78, No. 3, 1995

OUR INDUSTRY TODAY 695

HRVLi,? = percentage of harvest loss on as- is basis for crop i harvested in season j’.

Vci, = kilograms of forage i used by cow group c (c = 1, ..., v) in season

STLij = percentage feed storage loss for

Tmi, = transfer of forage i to season j in

Scostj = cost per ton of storing crop i per

P = price per tonne of hay sold, and HS, = tonnes of hay sold in season j,

season,

Annual nutrient constraints were j,

n crop i in season j, and CXijApi + Tp, = Pnutpj + Tnavp, 1=1 tonnes.

[3]

n

CXij+lApi + Tpj+l = Pnutpj+l Seasonal dietary constraints for cow group i=l

+ Tnavp,j+l + Tp,Dp, were

[41 n rn

I= 1 r

+ Tp,j+lDp,j+l

where

+ CYF,~DC,, 2 ~ ~ ~ ~ & ~ o ~ VI g= 1 [91

n rn

Api = kilograms per hectare of nutrient p taken up by crop i,

Tp, = kilograms of nutrient p transfer from season j to j + 1,

Tnavp, = kilograms of nutrient p available from the dairy in season j, and

Dp, = percentage of storage loss of nutrient p in season j.

Annual forage supply constraints were

V

XijrYieldi(1 - HRVLijp) = ~ V , i j / 2 0 0 0 + Tmjj c=l

[61

I

+ CYFcgiDCwg I DMIcjUBwc g= 1 [ 101

where

DC,i = amount of dietary characteristic w (w = 1 ,... k; e.g., DM basis), NEL megacalories per kilogram associated with crop i, commodity f, or forage on hand g,

I,,, = requirement per cow for dietary characteristic w for cow group c in season j,

&, = number of cows in group c during season j,

Oj = number of days in season j, DMIc, = total DMI for group c in season j ,

and UB,, = maximum percentage of dietary

characteristic w allowed in cow group c rations on a DM basis.

where Annual resource constraints were

j’ = previous season Q - l), n

crop i, 1=1 Yield, = tonnes per hectare yielded by CXiQli = M1 [ I l l


where

Qli = amount of resource associated with the production of an acre of crop Xi (l = 1 to 2, land and labor), and

MI = maximum or minimum quantity of resource 1 available,

and where

Costy, Xi, Yfi, YFgi, Pnutpj,

Tmi,, HSj, Yieldi, Vcij 2 0.

The objective function [ l ] minimized feed cost over the planning horizon t, which was the sum of the yearly feed costs defined by [2]. Annual feed costs were composed of crop production costs, purchased feed costs, costs of feed on hand, purchased fertilizer costs, and sale of bermudagrass hay. Costs for feed on hand were only included for yr 1, the transitional year.

Two constraints were included to control the balances for manure and crop nutrients within the nutrient recycling system. The manure nutrient constraints ([3], [4], and [5]) ensured that the manure nutrient balance was met. These constraints allowed for transfers of N from winter to spring and from spring to fall, but forced the annual manure nutrient balance at the end of the fall growing season.

Forage supply constraints ([6], [71, and [81) ensured that the crop nutrient balance was met. These constraints allowed transfers of forage between the first and second and between second and third growing season following harvest, but forced an annual crop nutrient balance at the end of the third growing season following harvest.

Constraints for dietary characteristics were [9] and [lo]. Equation [9] forced the amount of a dietary characteristic to be greater than the amount required by the cow. The second constraint [lo] forced the percentage of a dietary characteristic in a diet to be less than a certain percentage of DMI. Two constraints for each characteristic (greater than and less than) were included to allow a range for the model to select an economical level.

Resource constraints limited the use of land and labor to be less than the amounts available. Other special constraints were included to ensure realistic cropping systems and rations.

k l AL.

Default Inputs

The model used specific information to determine optimal nutrient management strategies for dairies. General farm information included location of the dairy, percentage of herd in cattle groups @gh, medium, low, dry, heif- ers, and yearlings), crop acreage availability, labor availability, harvest and storage losses, cost of purchased N, storage losses of N, storage capacity for manure and feed, and the concentration of N and P in manure wastewater. Percentage of millung cows varied by season to reflect changes in milk production and cow management practices.

Default values for available excreted N and P were 34 kg/yr per cow of N (33.6% of total N excreted; 101 kg/yr per cow total N) and 21 kg/yr per cow of P (23). These values were slightly higher than some other excretion estimates, e.g., 92 kg/yr of N (8) and 17 kg/yr of P (14). Nutrient excretion is affected by milk production and ration nutrient concentration (W.

The amounts of N and P available for crop uptake are variable. Inorganic forms of plant nutrients are readily available for crop uptake, but organic forms must be broken down by microorganisms before they are available (11). Holloway et al. (8) estimated that between 23 and 45 kg/yr per cow of N were available for crop uptake during the initial year of application. The percentage of P from manure that is available for crop uptake during the initial year was estimated to be 70% (1 1). Nutrients may be lost during the recycling process by volatilization, leaching, and runoff or introduced by rainfall (23). Manure handling facilities have the greatest effect on nutrient losses, which can range from 10 to 80% of excreted nutrients (14). Application method is an additional factor for crop N availability. Losses of N associated with irrigation of dilute wastewater have been estimated to be between 30 and 40% of total N (14). After application, nutrient loss is depen- dent on soil pH, buffering capacity, cation exchange capacity, and urease activity, as well as temperature, soil water content, and soil air exchange capacity (4).

Crop nutrient uptake has been determined by crop yield and concentration of N or P in DM (3). Johnson et al. (10) determined N and P uptake for a rye, corn, and bermudagrass cropping system at different manure applications.

Journal of Dairy Science Vol. 78. No. 3. 1995


TABLE 1. Nutrient uptake and yields for crops grown in central Florida.]

Crop DM N P descnption Season Type DM Yield CP Uptake2 P Uptake3 Cost

(9%) (tonnes/ha)(%) (kplha) (%) (kg/ha) ($/ha) Temperate corn Spring Silage 35 18.8 8.1 244 .22 41 7 54

Tropical corn Spnng Silage 35 17.2 8.4 232 .I9 32 731 Tropical corn Fall Silage 35 18.2 8.4 244 .20 36 766 Sorghum Spring Silage 35 16.3 7.5 195 .21 35 529

Temperate corn Fall Silage 35 15.6 8.1 203 .22 35 744

Sorghum Fall Silage 35 15.0 6.0 143 .26 39 534 Sorghum4 Fall Silage 35 15.0 6.0 143 .26 39 343 Bermuda Spring Silage 35 7.8 11.5 146 .28 22 259 Bermuda Fall Silage 35 11.8 11.5 216 .28 32 314 Bermuda Fall Hay 93 9.8 12.0 188 .20 20 376 Bermuda afters Fall Silage 35 11.8 13.3 250 .20 24 395 Bermuda after Fall Hay 93 9.8 12.0 188 .20 20 457

Rye Winter Silage 35 5.6 12.8 112 .32 18 306 Oats Winter Silage 35 7.8 13.0 164 .21 17 34 1

'Source: Gallaher et al. (3). Henry and Gallaher (6). Overman et al. (18). and Johnson et al. (10). 2Uptake of N was calculated by (DM yield x 1000) x (%CP/100)/6.25. 3Uptake of P was calculated by (DM yield x 2000) x (96 P/100). 4Ratooned. 5After the spring crop

Nitrogen uptake varied from 422 to 588 kgiha, and estimated P uptake varied from 62 to 67 kgha when manure N application increased from 381 to 740 kgha. Chase et al. (2) determined that N recovery efficiency of forage and grain sorghum decreased from 104 to 22% when N fertilization rate increased from 45 to 269 kg/ha on sandy soils. The N uptake by bermudagrass in response to N fertilization, N and P uptake for double cropped corn and sorghum, and N and P uptake for oat and rye on sandy soils have been determined (3, 6, 18). Crops grown in Florida for nutrient recycling include corn, forage sorghum, bermudagrass, wheat, oat, rye, and ryegrass. These crops vary in quality and yield, depending on the area and season in which they are grown. Crop costs for dairies in central and northern Florida were estimated by Hewitt (7). A summary of nutrient uptake, yields, and costs based on cited literature (3, 6, 10, 18) are shown in Table 1.

Nutrient concentrations for forages were taken from NRC (17) and from the studies just discussed. The commodities available for use in rations were those typically available in Florida (3, and their seasonal costs were aver- ages for 1988 to 1992. Nutrient concentrations for these commodities were taken from NRC

(17). The nutrient requirements for cow and heifer groups were calculated using the Spartan Dairy Ration EvaluatorBalancer (21). Dietary limitations of forages and commodities were obtained from Harris and Staples (5) and West (25).

Sensitivity Analyses

Sensitivity analyses were performed to determine model behavioral response to changes in input variables. Variables analyzed included DMI, milk production, and crop acreage. The default dairy was a 1000-cow dairy averaging 8165 kg of milklyr per cow.

The gross economic benefit of changing from one strategy to another was calculated by

GEB% = [(Ti - T2)/(1 + i)']

r n 1

GEB = gross economic benefit between system 1 and system 2, consider- ing only variable costs,

T1 = transitional year feed costs for system 1,


698 n t N K 1 c1 AL.

recommended DMI. This study evaluated the economic implications of requiring a minimum DMI constraint to be used in balancing rations and its impact on cropping systems.

T2 = transitional year feed costs for system 2,

t = year being discounted, n = number of years discounted (n =

2 to 15). M1 = optimal year feed costs for sys-

tem 1, M2 = optimal year feed costs for sys-

tem 2, and i = interest rate.

Types of Producers

At least three situations must be considered for dairy nutrient management. The first re- quires meeting the nutrient uptake requirement on the least amount of acreage possible. For the second, acreage is fixed, and the producer wants to utilize available acreage to maximize profit. The third situation is a new producer designing a new dairy and needing to know the optimal acreage needed to grow crops. This study applies to the first two situations. The model could be used to address the new dairy producer if land were included as a resource rather than a constraint in the model.

Acreage Assumption

Dairy producers were considered risk- averse about meeting N uptake requirements for governmental regulations and would not apply more manure N or P to sprayfields during a season than crops could use. As a result, the winter growing season became the limiting season for land availability. In the studies in which the amount of N varied, acreage was adjusted so that enough was available for N uptake during winter. Furthermore, the dairy producer was assumed not to have any more acreage than the minimum required during the winter. As a result, no N was purchased for winter, and the crop acreage was likely less than optimal in the long run.

DMI

The DMI was evaluated using upper and lower bounds. The first scenario placed an upper bound of 105% of NRC recommendations (17) and no minimum, which allowed the model to determine the optimal DMI for each cattle group. The final three scenarios had minimum bounds set at 90. 95, and 100% of

Milk Productlon

Milk production was varied from 6350 to 9978 kg of milldyr per cow in 907-kg incre- ments to determine the effects of milk production on optimal manure management strategies. Excretion of N was adjusted for milk production and associated diets.

Crop Acreage

Crop acreages considered were 80.9, 101.2, 115.7, 134.8, and 202.3 ha to determine the effects of land availability on optimal manure management strategies.

RESULTS AND DISCUSSION

Variability in DMI

Results from variable DMI showed a sig- nificant change in the optimal cropping system when a minimum requirement (MR) constraint was placed on DMI compared with only an upper bound of 105% (Table 2).

Corn was the preferred spring and fall crop when no minimum requirement (NMR) was placed on DMI. When the MR on DMI was increased, bermudagrass and sorghum acreage increased to meet the DMI requirements at lower costs.

As the DMI requirements were varied from NMR to a MR of 100%, the annual total feed costs increased from $889,000 to $927,000, a difference of $38,000 (Table 2). The greatest difference in feed cost occurred when DMI was increased from 90 to 95% ($15,000) and from 95 to 100% ($19.000). There was little difference when MR was 90% ($3600).

Rations for the NMR and the MR of 100% DMI scenarios are shown in Table 3. There was little difference in the feeds used in the high cow rations for winter, and DMI was only lowered to 97.3% of the recommended DMI when unconstrained. The DMI was lowest in the cow roups with medium and low DMI for the NMR scenario, 90 and 86% of recommended DMI, respectively. The medium ration for the 100% MR scenario added sorghum to

Journal of Dairy Science Vol. 78. No. 3, 1995


TABLE 2 Cropping systems for varying DMI constraints.

Cropping system

~ ~~

DMI Requirement

Season NMR1 90%2 95902 100% ~ ~ ~ ~ ~~ ~

ala) (ha) (ha) (ha)

Temperate corn SPWz EO 1 80 1 76 5 42 1

Temperate corn Fall 38 9 30 4 15 0 Tropical corn Fall 41 3 49 8 61 5 6 9

Forage sorghum Spring 3 6 3 6 7 3 34 4

Ratooned forage sorghum Fall 3 6 3 6 7 3 34 4 Bermudagrass hay after corn Fall 10 9 Oats Winter 83 8 83 8 83 8 83 8 Total feed costs, $loo0 889 893 908 927

INMR = No nunimum requirement (no lower bound consmnt); upper bound of 105% of recommended DMI *Minimum requirement (lower bound) of 90. 95. or 100% of recommended DMI

meet DMI. The low ration for the 100% MR scenario added both sorghum and bermudagrass silage to meet the minimum DMI requirements. Sorghum was used in rations of high producing cows to meet DMI requirements because bermudagrass was excluded in high cow rations because of its negative effect on milk production. The NMR rations used corn silage and whole cottonseed to meet cow nutrient requirements. Dry cow rations were the same for both studies. All NMR rations met nutrient intake requirements and other dietary requirements that ensure normal ruminal function.

Three conclusions can be drawn. First, corn silage was the preferred forage for both nutrient uptake and cow nutrient intake when NMR was placed on DMI. Second, enforcement of DMI recommendations was more expensive than more nutrient-dense rations under condi- tions of the default dairy. And finally, if DMI requirements were enforced, growing sorghum or bermudagrass was more economical than purchased cottonseed hulls to meet DMI recommendations at the prices and costs used in this study.

Milk Production

The effects of milk production on the optimal cropping system were analyzed with and without a minimum constraint being placed on DMI. Results from these studies showed that

the optimal cropping systems changed, and feed costs, land shadow prices, and feed storage capacity were lower at all production levels when NMR was placed on DMI. Only results with NMR on DMI are reported.

The optimal cropping system used corn and oats to meet nutrient disposal and cow nutrient requirements (Table 4). Sorghum entered the optimal cropping system in the scenarios with 8165 and the 9978 kg of milk, but comprised -3% of the annual crop acreage. Two types of corn were used in the fall growing season. As milk production per cow increased, the percentage of tropical corn increased from 10% at 7258 kg to a high of 19% at 9978 kg of milk production per cow.

As milk production increased, feed costs increased, but not at a constant rate. Feed costs increased as production was increased from 7258 to 8165 kg and from 9072 to 9978 kg of milk, by $65,000 and $52,000, respectively.

Forage storage capacity requirements increased as milk production increased because of the increased crop acreage required by the greater amounts of manure nutrients that needed to be recycled as milk production increased.

The value of an additional hectare also increased as milk production increased. An additional hectare was $507 greater in value for the producer at 9978 than at 6350 kg of milk/yr.

Reduction of milk production was not eco- nomically feasible because the estimated loss of milk production ($300,000/yr) was greater


700 HENRY ET AL

TABLE 3. Winter rations for two DMI scenarios at various milk production levels.

DMI and milk oroduction status

Feed NMR1 MR NMR MR

0 (5%) 0%) (9%) (kg) (S) (kg) (To)

High Medium Temperate corn, spring 1.58 7.9 1.41 7.9 Temperate corn. fall 1.92 10.7 Tropical corn, fall 8.34 42.8 6.65 33.2 4.31 24.5 6.07 30.7 Ratooned sorghum, fall .77 4.0 2.01 10.0 3.81 19.3 Whole cottonseed 2.92 15.0 1.37 6.9 2.68 15.0 Corn, ground 2.08 10.5 Soybean meal. 48% 3.74 19.2 4.27 21.4 2.77 15.5 3.42 17.3

Citrus pulp 3.59 18.4 4.00 20.0 3.75 21.0 3.31 16.8 Purchased grass hay .89 5.0 .99 5 0

Total DMI 19.47 100.0 20.00 100.0 17.87 100.0 19.77 100.0

Dicalcium phosphate . I 1 .6 .I2 .6 .OB .4 .09 4

% of Required DMI 97.3 100.0 90.4 100.0

Low Dry Temperate corn. spring 3.84 24.7 Ratooned sorghum, fall 4.24 23.7 Bermudagrass silage 1.14 6.4 Whole cottonseed 1.56 10.0 .87 7.8 .87 7.8 Corn, ground 1.56 10.0 5.40 30.2 .51 4.6 .5 I 4.6 Soybean meal. 48% 2.16 13.9 1.46 8.1 .42 3.8 42 3.8 Corn distillers grams 1.46 8.1 Dicalcium phosphate .06 .4 .01 .1 Calcium carbonate .11 .6 Citrus pulp 3.27 21.0 1.62 9.1 .65 5.8 .65 5.8 Purchased grass hay 3.1 1 20.0 2.46 13.7 8.65 78.0 8.65 78.0 Total DMI 15.56 100.0 17.90 100.0 11.10 100.0 11.10 100.0 % of Required DMI 86.9 100.0 105.0 105.0

*NMR = No minimum requirement; MR = minimum requirement.

TABLE 4. Effects of annual milk production per cow on the optimal cropping system when no minimum constraint is placed on DMI.

~ ~ ~ ~ ~ ~~ ~~~ ~

Annual milk production per cow

Cropping system 6350 kg 7258 kg 8165 kg 9072 kg 9978 kg

ala) ala) ala) (ha) Temperate corn, spring 71.2 76.9 80.1 82.6 91.1 Forage sorghum, spring 3.6 1.2 Temperate corn, fall 71.2 53.0 38.9 33.6 47.3 Tropical corn. fall 23.9 41.3 49.0 43.7 Ratooned forage sorghum, fall 3.6 1.2 Oats, winter 71.2 76.9 83.8 83.8 91.1 Maximum land, ha per season 71.2 76.9 83.8 83.8 91.1 Feed costs, $ x 103 775 825 890 933 985 Forage storage, tonnes 3 142 3575 4103 4028 4514 Economic benefit,' $/yr

additional hectare 7074 6514 675 1 7119 7581

'Feed cost savings.


OUR INDUSTRY TODAY 70 1

than the decreases in feed costs at all milk productions.

Crop Acreage

The effect of crop acreage was evaluated with and without NMR on DMI. Only results with NMR are discussed.

All N from dairy manure was used with 83.8 ha of forages, so all additional acreage over 83.8 ha could be viewed as imgated crop acreage and not as required nutrient sprayfields. Therefore, this study addressed the ability of homegrown forages to decrease feed costs when grown in addition to crops on nutrient sprayfields. Total annual feed costs, land shadow prices, and the economic benefit of changing available crop acreage were compared.

Corn was the preferred crop when NMR was placed on DMI (Table 5). 0 ats were used exclusively for the winter crop, but were not grown on all of the available acreage in the 134.8 and 202.3 ha scenarios. Oats were grown only to meet the N uptake requirements in the winter. The model preferred to grow more corn acreage in the spring and fall with purchased N rather than to grow more oats in winter. In addition, bermudagrass was used increasingly in the optimal cropping system as more land was made available.

Feed costs decreased as hectares changed from 80.9 to 101.2 and from 101.2 to 115.7.

by $12,000 and $6000, respectively. However, feed costs were not lowered significantly as land was increased over 115.7 ha.

Shadow prices for an additional hectare of land showed an economic benefit for an additional hectare to each acreage level until 202.3 ha, at which point the economic benefit reached zero. Because no value was assigned to land in production costs, the estimated benefits could be used to evaluate adding acreage to the cropping system. If land were val- ued at $2471/ha and fixed costs of irrigation and other improvements were $988/ha, the default dairy could afford to add acreage until about 101 ha, at which point the benefit ($5683) was greater than the costs ($3470) of adding a marginal hectare. Marginal returns equaled marginal costs between 101.2 and 115.7 acres.

Feed storage costs increased as land availability increased (Table 3, and these costs should be considered when the purchase of land is evaluated. In addition, purchased N increased as more land became available.

CONCLUSIONS

Evaluation of the effects of recycling nutrients on the profitability of the whole farm is important for dairy producers who are facing environmental regulations. The model presented is a framework that can be used to

TABLE 5. Effects of land availability on the optimal cropping system with no lower bound on DMI.

Cropping system 80.9 ha 101.2 ha 115.7 ha 134.8 ha 202.3 ha ~ ~ ~~ ~ ~ ~~~~

(ha) (ha) (ha) rn) ala) Temperate corn, spring 77.3 97.5 108.9 120.2 144.5

Bermudagrass. spring 2.4 10.1 19.8 Temperate corn, fall 34.8 56.3 100.0 108.1 80.1

Forage sorghum, spring 3.6 3.6 4.8 4.8 4.5

Tropical corn, fall 42.5 41.3 7.7 2.4 2.0 Ratooned forage sorghum, fall 3.6 3.6 4.8 4.8 4.5

Bermudagrass and corn, fall 1.2 1.2 1.2 1.2 1.2

Bermuda silage. fall 2.4 7.3 1.6 Bermuda hay, fall 2.8 18.2

Oats, winter 80.9 101.2 115.7 83.8 83.8 Maximum land, ha per season 80.9 101.2 115.7 134.8 202.3 Feed costs, 5 x 103 892 880 874 873 874 Forage storage, tonnes 3917 5375 6153 6888 7098 Manure storage, kg of N 458 0 0 0 0 Purchased N, kg/yr 14,963 26,963 34.286 34,885 38,326 Economic benefits, $/15 yr

Additional hectare 7922 5683 1534 912 0

Journal of Dairy Science Vol. 78, No. 3. 1995

702 HENRY ET AL

determine the economic impact of the nutrient recycling system on farm profitability. Results from sensitivity analyses showed that many variables affect the optimal cropping system and indicate the complexity of the dairy nutrient recycling system.

The DMI study showed that placing a MR constraint on DMI changed the optimal cropping system and increased feed costs compared with allowing DMI to be selected by the model. Corn was preferred to other forages because of its higher nutrient density and its ability to be used with purchased commodities in rations when no minimum requirement was placed on DMI. Sorghum and bermudagrass were grown when DMI requirements were fixed at maximum amounts.

At the level of production and estimated commodity prices of the default dairy, homegrown forages decreased feed costs compared with purchase of high fiber feeds such as cottonseed hulls. Although a feeding requirement for DMI (17) may avoid some animal health problems, our study showed definite economic costs from forced consumption of required DM in all cases. Thus, best solutions occur when DMI is permitted to range between the minimum and the maximum limits estab- lished for production characteristics of the par- ticular dairy being studied.

The optimal cropping system changed as milk production increased when a minimum constraint was placed on DMI. As milk production increased, more corn was grown to meet the nutrient requirements and the increased N uptake requirements caused by the greater N excretion by the high producing cows. The annual feed costs were lower at low production when DMI was determined by the model than for the scenario in which a fixed constraint was placed on DMI.

Alteration of crop acreage indicated an economic benefit to increasing the amount of crop acreage above the acreage needed to recycle manure nutrients, which reflected the economic advantage of growing forages. There- fore, limiting acreage to the amount needed for N uptake for the winter season complicated the interpretation of results.

REFERENCES

1 Bath, D. L. 1993. Economics of forage quality. Page 175 in Proc. Western Large Herd Dairy Management

Journal of Dairy Science Vol. 78, No. 3. 1995

Conf., Coop. Ext. Serv., New Mexico State Univ., Las Cruces.

2 Chase, C. R., G. M. Henry. and R. N. Gallaher. 1994. Yield and nutrient concentration and content in response to nitrogen fertilization in forage and grain sorghum. Page 27 in Proc. Southern Conservation Tillage Conf., Dep. Agron. Soils, Clemson Univ. Coop. Ext., Clemson, SC.

3 Gallaher, R. N., S. A. Ford, R. McSorley, and J. M. Bennett. 1991. Corn forage and forage sorghum double cropping yield. economics, crop nutrient removal and quality. Agron. Res. Rep. AY-91-05, Dep. Agron.. Univ. Florida. Gamesville.

4Hargrove. W. L. 1988. Evaluation of ammonia volatilization in the field. J. Prod. Agnc. 1:104.

5 Harris. B.. Jr.. and C. R. Staples. 1989. Feeding by- product feedstuffs to dairy canle. Pub]. No. DS 20, Dep. Dary Sa . , Florida Coop. Ext. Ser.. Univ. Florida, Gainesville.

6Henry. G . M.. and R. N. Gallaher. 1993. Forage quality and nutrient contents for oat and rye grown in central Flonda. Agron. Res. Rep. AY-93-02, Dep. Agron., Univ. Flonda, Gainesville.

7 Hewin, T. 1988. Production and harvesting COSIS for forages for silage. Paper presented to an Escambia County dairy meeting, Dep. Food and Resource Econ.. Flonda Coop. Ext. Ser., Univ. Florida Gamesville.

8 Holloway, M. P.. A. B. Bottcher. R. A. Nordstedt, and K. L. Campbell. 1991. BMPs for mitigating nitrate Contamination of the groundwater under dairies. Paper No. 91-2134 in Summer Mtg. Am. Soc. Agric. Eng. Am. Soc. Agric. Eng., St. Joseph, MI.

9 Jacobsen. K. W., and G. D. Bubenzer. 1992. A decision support system for manure management. Page 572 in Proc. 4th Int. Conf. on Computers in Agric. Ext. Prog., Florida Coop. Ext., Univ. Florida. Gaines- ville.

IOJohnson, J. C., G. L. Newton. and J. L. Butler. 1991. Recycling liquid dairy waste to sustain annual triple crop production of forages. Page 41 in Proc. Florida Dairy Prod. Conf.. Dairy Sci. Dep., Univ. Florida Coop. Ext., Gainesville.

11 Klausner, S. D. 1989. Managing the land application of animal manures: agronomic considerations. Page 79 in Proc. Dairy Manure Manage. Symp., Northeastern Reg. Agric. Eng. Serv.. Cornell Univ., Ithaca. NY.

12Lanyon. L. E.. and D. B. Beegle. 1993. A nutrient management approach for Pennsylvania: nutrient management decision-making, Agron. Facts 38-C, Coll. Agric. Sci.. Pennsylvania State Univ.. University Park.

13 Levins. R. A., and M. A. Schmin. 1992. Manure Application Planner, Users Manual. Ctr. Farm Finan- cial Management, Minnesota Ext. Sew., Univ. Min- nesota, St. Paul.

14 Midwest Plan Service: Structures and Environment Handbook. 1987. 11th rev. ed. Iowa State Univ., Ames.

15 Miner, J. R.. R. A. Nordstedt, and R. M. Peart. 1992. An interactive program to recommend manure application rates for various confinement livestock and poultry facilities. Page 565 in Proc. 4th Int. Conf. on Computers in Agric. Ext. Prog., Florida Coop. Ext. Ser., Univ. Florida, Gainesville.


16Momson, F. B. 1957. Feeds and Feeding. 22nd ed. Morrison Publ. Co.. Ithaca, NY.

17 National Research Council. 1989. Nutrient Require- ments of Dairy Cattle. 6th rev. ed. Natl. Acad. Sci., Washington, DC.

18Overman. A. R., F. M. Rhoads. F. L. Stanley, Jr.. and C. G. Chambliss. 1991. Estimation of bermudagrass production in Florida. Circ. 938. Florida Coop. Ext. Ser., Univ. Florida, Gainesville.

19Scwhart. R. B.. Jr.. and R. D. Lacewell. 1992. Dairy Waste Management Estimator Work Sheet Manual. TAAES. Dep. Agric. Econ., Texas A&M Univ.. Col- lege Station.

20 Shaffer, M. J.. A. D. Halvorson, and F. J. Pierce. 1991. Nitrate leaching and economic analysis package (NLEAP): model descnption and application. Page 285 in Proc. Symp. Am. Soc. Agron. Managing Nitro- gen for Groundwater Quality and Farm Profitability. Soil SCI. Soc. Am.. Madison, WI.

21 Spartan Dairy Ration Evaluator/Balancer, User’s Manual, Version 2.01. 1992. Michigan State Univ.. East Lansing, MI.

22Thomson. S . J., E. R. Collins, and J. D. Harmon. 1992. Nutrient management software for Virginia farmers. Page 574 in Proc. 4th Int. Conf. on Com- puters in Agric. Ext. Prog., Florida Coop. Ext. Ser.. Univ. Florida, Gainesville.

23 Van Horn, H. H., R. A. Nordstedt, A. V. Bottcher. E. A. Hanlon. D. A. Graetz. and C. F. Chambliss. 1991. Dairy manure management: strategies for recycling nutrients to recover fertilizer value and avoid envuon- mental pollution. Circ. 1016, Florida Coop. Ext. Ser.. Univ. Florida, Gainesville.

24 Van Horn, H. H., A. C. Willue, W. J . Powers, and R. A. Nordstedt. 1994. Components of dairy manure management systems. J. Dairy Sci. 77:2008.

25 West, J. W. 1994. Considerations for using by-product feedstuffs in the Southeast. Page 13 in Proc. Flonda Ruminant Nub. Symp.. Anim. Sci. Dep., Dairy SCI. Dep., Univ. Florida, Gainesville.


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