**4. Illustration and Practical Decision-Making**

#### **4.1. Group Feeding**

The value grouping feeding strategies was analyzed by applying the grouping tool to 30 dairy farms in Wisconsin. Test records were collected and adjusted to datasets consisting of cow identification, lactation, days after calving, milk production, and milk butterfat for each cow in each farm. The aim of this exercise was to demonstrate the value of grouping com‐ pared to no grouping without knowing studied farms' actual feeding strategies. Therefore, same procedure and assumptions were followed on each analyzed farm: 1) comparison of no grouping versus 3 same-size groups, 2) prices at \$15.89/45.4 kg milk, \$0.14337/0.454 kg CP, and \$0.1174/4.19 mega joules (MJ) net energy, 3) average body weight of 500 kg for first lactation cows and 590 kg for cows in second and later lactations, 4) requirements of CP and net energy at the 83rd percentile level of the group (mean + 1 standard deviation), and 5) a cluster grouping criterion (grouping cows depending on their CP and net energy require‐ ments for maintenance and milk production).

**4.2. Sexed Semen**

The *Economic Value of Sexed Semen for Dairy Heifers* tool was used for general conditions of Wis‐ consin dairy farms based on data of a sample of 309 dairy farms and 38 custom heifer growers, a survey performed by county extension agents [35]. At the time of the analysis, using the ag‐ gregated data of the 347 operations, the average economic benefit of using sexed semen, as cal‐ culated by the tool, was \$30 per heifer. Results confirmed that most of these farmers were using optimally this new technology. They were using it for first and second service only, which was the same optimal strategy found by the tool [35]. A main conclusion of this analysis was that the sexed semen technology has an economic benefit, but it would be mostly recommended when the conception rate of the sexed semen is at least 80% of the conventional semen, the val‐ ue of the heifer calf is high, and when the price of the sexed semen is twice or less than that of the conventional semen. Due that the conception rate of both the conventional and sexed se‐ men and the market prices are important determinant parameters, a main recommendation was that the analysis should be performed on a farm-specific basis and on a permanent basis,

DairyMGT: A Suite of Decision Support Systems in Dairy Farm Management

http://dx.doi.org/10.5772/50801

161

Published data along with dairy farm records were collected and summarized to create a representative farm to assess the value of improving reproductive efficiency measured as improving the 21-day pregnancy rate using the tool *Dairy Reproductive Economic Analysis*. Data consisted of detailed information on transition probabilities arrays of replacement and abortion risks; definition of lactation curves, and several economic parameters. Then, the DSS was used multiple times to represent incremental gains in reproductive efficiency.

**Figure 7.** Projected net economic gain of improving 21-day pregnancy rate from a baseline of 10% assessed by the

Figure 7 portrays a marginally decreasing trend of economic gain with respect to 21-day preg‐ nancy rate: the higher the original 21-d pregnancy rate, the lower the gain. Nonetheless the tool shows clearly that even at 30% 21-day pregnancy rate, an extremely (and unusual) good preg‐ nancy rate, there is still an opportunity of additional gains because of improved reproductive efficiency. The tool, furthermore presents the main factors from which the additional value

for which the decision support tool plays an important role.

**4.3. Dairy Reproductive Economic Analysis**

tool: *Dairy Reproductive Economic Analysis*.


Evaluations clearly and consistently demonstrated that the income over feed cost (IOFC) in all analyzed farms was greater for the 3 feeding groups strategy than the no groping strategy (Table 2).

**Table 2.** Comparison of income over feed cost (IOFC) of no grouping versus 3 same-size feeding groups for Wisconsin dairy farms assessed by the tool: *Grouping Strategies for Feeding Lactating Dairy Cattle*.

The analysis indicated that farms could realize between \$161 and \$580/cow per year (mean = \$396) of additional IOFC by switching from no grouping to 3 same-size feeding groups us‐ ing the cluster criterion for grouping. These values represented an increase of between 7 and 52% of farm calculated IOFC. It was concluded then that grouping would have important economic implications in farm profitability and that further analysis should be done at farmspecific level and in a permanent basis by using the *Grouping Strategies for Feeding Lactating Dairy Cattle* DSS tool.

#### **4.2. Sexed Semen**

**4. Illustration and Practical Decision-Making**

ments for maintenance and milk production).

**Number of lactating cows on analyzed farms (n = 30)**

dairy farms assessed by the tool: *Grouping Strategies for Feeding Lactating Dairy Cattle*.

The value grouping feeding strategies was analyzed by applying the grouping tool to 30 dairy farms in Wisconsin. Test records were collected and adjusted to datasets consisting of cow identification, lactation, days after calving, milk production, and milk butterfat for each cow in each farm. The aim of this exercise was to demonstrate the value of grouping com‐ pared to no grouping without knowing studied farms' actual feeding strategies. Therefore, same procedure and assumptions were followed on each analyzed farm: 1) comparison of no grouping versus 3 same-size groups, 2) prices at \$15.89/45.4 kg milk, \$0.14337/0.454 kg CP, and \$0.1174/4.19 mega joules (MJ) net energy, 3) average body weight of 500 kg for first lactation cows and 590 kg for cows in second and later lactations, 4) requirements of CP and net energy at the 83rd percentile level of the group (mean + 1 standard deviation), and 5) a cluster grouping criterion (grouping cows depending on their CP and net energy require‐

Evaluations clearly and consistently demonstrated that the income over feed cost (IOFC) in all analyzed farms was greater for the 3 feeding groups strategy than the no groping

> **No grouping IOFC**

Mean 788 2,311 2,707 396 Minimum <200 697 1,059 161 Maximum >1,000 2,967 3,285 580

**Table 2.** Comparison of income over feed cost (IOFC) of no grouping versus 3 same-size feeding groups for Wisconsin

The analysis indicated that farms could realize between \$161 and \$580/cow per year (mean = \$396) of additional IOFC by switching from no grouping to 3 same-size feeding groups us‐ ing the cluster criterion for grouping. These values represented an increase of between 7 and 52% of farm calculated IOFC. It was concluded then that grouping would have important economic implications in farm profitability and that further analysis should be done at farmspecific level and in a permanent basis by using the *Grouping Strategies for Feeding Lactating*

**3 same-size feeding groups IOFC**

**---------------------\$/cow per year-----------------------**

**Additional IOFC of doing 3 samesize feeding groups**

**4.1. Group Feeding**

160 Decision Support Systems

strategy (Table 2).

*Dairy Cattle* DSS tool.

The *Economic Value of Sexed Semen for Dairy Heifers* tool was used for general conditions of Wis‐ consin dairy farms based on data of a sample of 309 dairy farms and 38 custom heifer growers, a survey performed by county extension agents [35]. At the time of the analysis, using the ag‐ gregated data of the 347 operations, the average economic benefit of using sexed semen, as cal‐ culated by the tool, was \$30 per heifer. Results confirmed that most of these farmers were using optimally this new technology. They were using it for first and second service only, which was the same optimal strategy found by the tool [35]. A main conclusion of this analysis was that the sexed semen technology has an economic benefit, but it would be mostly recommended when the conception rate of the sexed semen is at least 80% of the conventional semen, the val‐ ue of the heifer calf is high, and when the price of the sexed semen is twice or less than that of the conventional semen. Due that the conception rate of both the conventional and sexed se‐ men and the market prices are important determinant parameters, a main recommendation was that the analysis should be performed on a farm-specific basis and on a permanent basis, for which the decision support tool plays an important role.

#### **4.3. Dairy Reproductive Economic Analysis**

Published data along with dairy farm records were collected and summarized to create a representative farm to assess the value of improving reproductive efficiency measured as improving the 21-day pregnancy rate using the tool *Dairy Reproductive Economic Analysis*. Data consisted of detailed information on transition probabilities arrays of replacement and abortion risks; definition of lactation curves, and several economic parameters. Then, the DSS was used multiple times to represent incremental gains in reproductive efficiency.

Figure 7 portrays a marginally decreasing trend of economic gain with respect to 21-day preg‐ nancy rate: the higher the original 21-d pregnancy rate, the lower the gain. Nonetheless the tool shows clearly that even at 30% 21-day pregnancy rate, an extremely (and unusual) good preg‐ nancy rate, there is still an opportunity of additional gains because of improved reproductive efficiency. The tool, furthermore presents the main factors from which the additional value comes (in order): higher milk income over feed cost, lower culling costs, higher calf revenues, and lower reproductive costs. These results are being used in a large extension undertaking to promote improved reproductive efficiency in hundreds of dairy farms, but always with the fi‐ nal recommendation that specific farm data and information from current market conditions should be used with the DSS tool to have a more precise assessment.

for every single animal in the herd. Finally, the calculated salvage value was added to the cow value. The farmer was then able to use these data for continued monetary support from

> **Cow Value of a non-pregnant, 7-month after calving cow, \$**

> > **2nd Lactation**

**3rd Lactation**

http://dx.doi.org/10.5772/50801

163

**Cow Value of a 2-month pregnant, 8-month after calving cow, \$**

> **2nd Lactation**

 120 2,458 2,038 2,002 1,973 1,485 1,462 100 1,045 877 829 1,109 857 814 80 -380 -284 -345 244 230 165 120 1,891 1,499 1,477 1,184 796 809 **100 479 338 304 320 168 161** 80 -934 -823 -870 -545 -460 -487 120 1,325 961 952 395 106 157 100 -88 -200 -221 -469 -521 -491 80 -1,501 -1,361 -1,395 1,344 1,149 -1,139

**Table 3.** Impact of expected milk production on the cow value of a 2-month pregnant, 8-month after calving cow and a non-pregnant, 7-month after calving cow assessed by the tool *Economic Value of a Dairy Cow*. Bolded values represent the cow with average production in the herd (100%). 1Cow's expected milk production (% of the average cow) from the current state to the end of the present lactation. 2Cow's expected milk production (% of the average

During the months LGM-Dairy revenue insurance program was offered in year 2011, the average savings when using the LGM-Dairy Least Cost tool was 27.8% (Table 4). The tool was used during those months to assess the premium cost for a 200-cow farm producing 31 kg milk/cow per day. Based on experience and expertise with a number of dairy farmers and consultants in Wisconsin, the strategy was to insure a minimum income over feed cost of \$5/46.4 kg milk during the effective insurance period that is 10 month s per contract (start‐

Considering that the level of insurance protection is exactly the same whether to paying the regular premium or a least cost premium in Table 3, the savings are substantial. The main difference between regular and least cost premiums is the allocation of milk and feed being insured according to the covered months in the future. In the regular premi‐ um, the default situation is to assign the same level of milk quantity for protection every month. The least cost optimization, however, finds a better allocation that based on the

**3rd Lactation**

**1st Lactation**

DairyMGT: A Suite of Decision Support Systems in Dairy Farm Management

a financial institution.

**Rest of Lactation1**

cow) in all successive lactations.

**4.6. The LGM-Dairy Least Cost**

ing 2 months after the contract month).

**Expected Milk Production (% of the average cow)**

> **Successive Lactations2**

**1st Lactation**

#### **4.4. Decision Support System for Expansion**

Three hundred dairy farms completed a mailed questionnaire regarding their desires and needs of expansion or modernization [36]. Seventy eight percent of farms (26% of respond‐ ents) indicated that were planning to expand or modernize their installations and listed as the most important reason of doing that the expected increase on farm net return. Impor‐ tantly, they acknowledge largely the uncertainty of the process of expansion as a large hin‐ drance and therefore they asked for decision support tools that would allow them project systematically their options and analyze scenarios. More than 20 of these farmers were then contacted and offered to perform those projections by using the tool *Decision Support System Program for Dairy Production and Expansion*. The overall outcome was that all farmers visited agreed that the tool represented reasonably well their farm sand therefore they would trust its future projections. Further analyses were used to confirm or reject their pre-conceived evaluations and to assist farmers to make more informed decisions throughout the process of expansion or modernization. More than 10 farmers did some adjustments in their expan‐ sion or modernization process because of the tool and all of them indicated will continue using the DSS tool throughout their expansion or modernization operation.

#### **4.5. The Economic Value of a Dairy Cow**

Representative data from Wisconsin farms were collected from official sources, farm re‐ cords, and market reports to become a baseline scenario [20] from which users could select modifications according to their own conditions. Results of these data contained in the tool *Economic Value of a Dairy Cow* indicated that the expected milk production of the cow was the single most important factor for replacement decisions. The impacts of increasing or de‐ creasing up to 20% (120 to 80 in Table 3) the average milk production of a cow, a reasonable assumption, are portrayed in Table 3. It is evident that the milk production expectancy of following lactations is a much more important factor for pregnant cows whereas the impact of milk production expectancy of this lactation and future lactations are similarly important factors for non-pregnant cows.

Although these numbers are good indicators for farm decision-making, the need of using the tool with specific farm conditions and under current market condition could not be over emphasized.

This tool *Economic Value of a Dairy Cow* was also used to value the animal farm assets in a farm. The tool was first set with all parameters concerning to the specific farm and with eco‐ nomic variables representing the market conditions. Followed, the farmer created a list of all cows in the farm including their current state (lactation, month after calving, and pregnancy status) and, importantly, their projected milk production. Then, a cow value was calculated for every single animal in the herd. Finally, the calculated salvage value was added to the cow value. The farmer was then able to use these data for continued monetary support from a financial institution.


**Table 3.** Impact of expected milk production on the cow value of a 2-month pregnant, 8-month after calving cow and a non-pregnant, 7-month after calving cow assessed by the tool *Economic Value of a Dairy Cow*. Bolded values represent the cow with average production in the herd (100%). 1Cow's expected milk production (% of the average cow) from the current state to the end of the present lactation. 2Cow's expected milk production (% of the average cow) in all successive lactations.

#### **4.6. The LGM-Dairy Least Cost**

comes (in order): higher milk income over feed cost, lower culling costs, higher calf revenues, and lower reproductive costs. These results are being used in a large extension undertaking to promote improved reproductive efficiency in hundreds of dairy farms, but always with the fi‐ nal recommendation that specific farm data and information from current market conditions

Three hundred dairy farms completed a mailed questionnaire regarding their desires and needs of expansion or modernization [36]. Seventy eight percent of farms (26% of respond‐ ents) indicated that were planning to expand or modernize their installations and listed as the most important reason of doing that the expected increase on farm net return. Impor‐ tantly, they acknowledge largely the uncertainty of the process of expansion as a large hin‐ drance and therefore they asked for decision support tools that would allow them project systematically their options and analyze scenarios. More than 20 of these farmers were then contacted and offered to perform those projections by using the tool *Decision Support System Program for Dairy Production and Expansion*. The overall outcome was that all farmers visited agreed that the tool represented reasonably well their farm sand therefore they would trust its future projections. Further analyses were used to confirm or reject their pre-conceived evaluations and to assist farmers to make more informed decisions throughout the process of expansion or modernization. More than 10 farmers did some adjustments in their expan‐ sion or modernization process because of the tool and all of them indicated will continue

Representative data from Wisconsin farms were collected from official sources, farm re‐ cords, and market reports to become a baseline scenario [20] from which users could select modifications according to their own conditions. Results of these data contained in the tool *Economic Value of a Dairy Cow* indicated that the expected milk production of the cow was the single most important factor for replacement decisions. The impacts of increasing or de‐ creasing up to 20% (120 to 80 in Table 3) the average milk production of a cow, a reasonable assumption, are portrayed in Table 3. It is evident that the milk production expectancy of following lactations is a much more important factor for pregnant cows whereas the impact of milk production expectancy of this lactation and future lactations are similarly important

Although these numbers are good indicators for farm decision-making, the need of using the tool with specific farm conditions and under current market condition could not be

This tool *Economic Value of a Dairy Cow* was also used to value the animal farm assets in a farm. The tool was first set with all parameters concerning to the specific farm and with eco‐ nomic variables representing the market conditions. Followed, the farmer created a list of all cows in the farm including their current state (lactation, month after calving, and pregnancy status) and, importantly, their projected milk production. Then, a cow value was calculated

should be used with the DSS tool to have a more precise assessment.

using the DSS tool throughout their expansion or modernization operation.

**4.4. Decision Support System for Expansion**

162 Decision Support Systems

**4.5. The Economic Value of a Dairy Cow**

factors for non-pregnant cows.

over emphasized.

During the months LGM-Dairy revenue insurance program was offered in year 2011, the average savings when using the LGM-Dairy Least Cost tool was 27.8% (Table 4). The tool was used during those months to assess the premium cost for a 200-cow farm producing 31 kg milk/cow per day. Based on experience and expertise with a number of dairy farmers and consultants in Wisconsin, the strategy was to insure a minimum income over feed cost of \$5/46.4 kg milk during the effective insurance period that is 10 month s per contract (start‐ ing 2 months after the contract month).

Considering that the level of insurance protection is exactly the same whether to paying the regular premium or a least cost premium in Table 3, the savings are substantial. The main difference between regular and least cost premiums is the allocation of milk and feed being insured according to the covered months in the future. In the regular premi‐ um, the default situation is to assign the same level of milk quantity for protection every month. The least cost optimization, however, finds a better allocation that based on the underline simulated data determines a better plan that results in a much lower premium, but the same level of protection.

**5. Evaluation of Dissemination and Adoption: Potential Impact**

(1.3%), Michigan (1.3%), and Washington (1.0%).

Following is some evidence that indicates the DairyMGT.info Website has become the placeto-go for decision-making tools related to dairy farm management in Wisconsin and a trust‐ ed reference with increased visibility in other states and internationally. The DairyMGT website was officially launched at the end of 2009. A predecesor webpage existed since June 2008. between April 2012, and a rate of when email registration was required. According to *Google Analytics (http://www.google.com/analytics/)* the Wisconsin Dairy Management domain (DairyMGT.info or DairyMGT.uwex.edu) received 45,307 page views during the year period ending on April 30, 2012. Fifty nine percent were visitors from the U.S.A. and the rest from other 135 countries. From these, the most important countries were: India (5.5%), Australia (3.3%), Argentina (2.6%), Canada (1.9%), Mexico (1.8%), Kenya (1.6%), United Kingdom (1.5%), Italy (1.5%), Turkey (1.3%), Brazil (1.2%), Peru (1.2%), South Africa (1.0%), Pakistan (1.0%), and Spain (1.0%). Inside the U.S.A., visitors came from all states, but 63% of them were from Wisconsin. Other important states were: California (7.4%), Minnesota (3.1%), Illi‐ nois (2.8%), New York (2.6%), Iowa (1.6%), Texas (1.5%), Florida (1.3%), Pennsylvania

DairyMGT: A Suite of Decision Support Systems in Dairy Farm Management

http://dx.doi.org/10.5772/50801

165

During the same period of time, May 2011 to April 2012, 1,635 users of decision support tools elected to register their emails on the DairyMGT.info system. A thousand and fifty five did it during the months of 2011, a period in which email registration was optional. During January‐ to April 2012 a rate of 5 emails registrations a day was recorded. During the one year period May 2011 to April 2012 there were 9,336 downloads of the top 25 DSS tools as shown in Table 5.

> **Rank Decision Support Tool Downloads** The Wisconsin Dairy Farm Ratio Benchmarking Tool 1,280 LGM-Dairy Insurance Related Tools 1,279 Dairy Reproductive Economic Analysis 1,030 Corn Feeding Strategies 655 UW-DairyRepro\$: A Reproductive Economic Analysis Tool 592 Optigen® Evaluator 482 Economic Analysis of Switching from 2X to 3X Milking 479 Lactation Benchmark Curves for Wisconsin 454 Grouping Strategies for Feeding Lactating Dairy Cattle 432 Heifer Break-Even 346 Milk Curve Fitter 313 The Economic Value of a Dairy Cow 312

13 Decision Support System Program for Dairy Production and

14 Economic Value of Sexed Semen Programs for Dairy Heifers 245

252

Expansion


**Table 4.** Savings on premiums when insuring net margins using the LGM-Dairy insurance program during the year 2011 assessed by the tool *LGM-Dairy Least Cost* using default amounts of corn and soybean meal as feed insured and assuming a reasonable insurance deductible of \$1/46.4 kg milk for a 200-cow dairy farm producing 31 kg milk/cow per day.

#### **4.7. Dynamic Dairy Farm Model**

The *Dynamic Dairy Farm Model* was applied on a typical North Florida dairy farm of 400 cows with a production of 7,711 kg/cow per year having 62 ha of crop fields and pastures. A dual op‐ timization including maximization of profit while relaxing N leaching indicated that the nitro‐ gen leaching ranged between 4,800 to 5,000 kg/year whereas the profit would change between \$70,000 and \$70,600 (Figure 8) [2]. Furthermore, strategies to reduce nitrogen leaching would compromise profit. Depending on the farm goals and environmental regulations, the *Dynamic Dairy Farm Model* proved to be an effective tool to screen options and study whole farm man‐ agement strategies. As in previous cases, farm specific conditions along with current market conditions need to carefully be defined before doing those assessments.

**Figure 8.** Dual optimization of profit maximization by relaxing nitrogen leaching assessed by using the tool *Dynamic Dairy Farm Model*. NL is average nitrogen leaching and SD is standard deviation.
