**3. Decision Support Systems Development: Challenges and Trade-offs**

A number of methodologies and software applications were used to develop the decision support tools above described (Table 1). The goal always remained to provide solid, but user-friendly DSS tools. The methodology as well as the software application approach fol‐ lowed the tool development and the ultimate goal pursued and not vice versa. It was usual to combine and adapt methodologies within a particular tool development. Following is a succinct description of the most important methodologies used for the DairyMGT.info DSS tools and a discussion of the approaches used for the software applications.

### **3.1. Methodologies used for the Decision Support System Tools**

#### *3.1.1. Partial Budgeting*

Partial budgeting compares a current with an alternative technology by balancing the eco‐ nomics of 4 elements that are assessed before and after the adoption of the alternative tech‐ nology: 1) additional returns (adds), 2) reduced costs (adds), 3) returns foregone (subtracts), and 4) additional costs (subtracts) [23]. Partial budgeting could be a robust methodology when a direct change is expected from the new technology without major interaction with other system components beyond the analyzed variables. Partial budgeting is the underline methodology to assess the break-even level of using feed additives, the economic benefit of milking 3 times a day, the economic evaluation of using rbST, the assessment of corn feed‐ ing strategies, and the assessment of income over feed cost by different diets under com‐ modity price changes.

**Decision Support System Tool**

Dairy Ration Feed Additive Break-Even

Dairy Cattle

Reproductive Efficiency

Income over Feed Cost

Production and Productivity

Grouping Strategies for Feeding Lactating

Exploring Pregnancy Timing Impact on

Economic Value of Sexed Semen for Dairy

Heifer Management and Cow Replacement

Economic Analysis of Switching from 2X to 3X

DSS Program for Dairy Production and

Price Risk Management and Financial

Expansion

Assessment

LGM-Analyzer

LGM-Premium Estimator

**Underline Methodology**

Mathematical Simulation

Mathematical Simulation

FeedVal 2012 Matrix Solution Online Dairy Extension Feed Cost Evaluator Database Management Online Optigen® Evaluator Matrix Solution Online

Analysis Partial Budgeting Flash

Heifers Decision Analysis Flash/Online UW-DairyRepro\$ Decision Analysis Spreadsheet Dairy Reproductive Economic Analysis Markov Chains Online

Heifer Break-Even Enterprise Budgets Online/Spreadsheet

Heifer Replacement Decision Analysis Spreadsheet/Online The Economic Value of a Dairy Cow Markov Chains Online/Spreadsheet

Cost-Benefit of Accelerated Feeding Programs Cost Benefit Flash/Online

Milk Curve Fitter Nonlinear Optimization Installation3

Milking Partial Budgeting Flash Economic Analysis of using rbST Partial Budgeting Flash

LGM-Least Cost Optimizer Nonlinear Optimization Online

Lactation Benchmark Curves for Wisconsin Database Management Flash/Spreadsheet

Markov Chains Spreadsheet

Online

Online

Mathematical Simulation

Mathematical Simulation

**Software Application**

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155

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

Online

Online

#### *3.1.2. Cost Benefit*

The cost benefit methodology is similar to partial budgeting but determines profitability of a new technology over longer periods of time and therefore requires the specification of a dis‐ count rate that is used to calculate a net present value [23]. The cost benefit is the underline methodology for calculating the value of adopting accelerated heifer liquid feeding pro‐ grams and is as a supporting methodology to find out the economic value of sexed semen for dairy heifers, the value of reproductive programs in adult cows, and to assess the net present value of alternative scenarios of possible dairy farm expansion or modernization.

#### *3.1.3. Decision Analysis*

The decision analysis is appropriate when probabilistic distributions are important factors in determining the final outcomes [24] as it occurs when analyzing the value of using sexed se‐ men on heifers, comparing the value of reproductive programs in adult cows, or projecting the replacement flow needed to maintain the herd size. In the first two cases, conditional probabilities were used to successively determine populations of pregnant, non-pregnant, and eligible to breed animals along with their respective expected monetary contributions. In the case of the replacement flow tool, transition probabilities are used to dynamically project the herd dynamics across time.



**3.1. Methodologies used for the Decision Support System Tools**

Partial budgeting compares a current with an alternative technology by balancing the eco‐ nomics of 4 elements that are assessed before and after the adoption of the alternative tech‐ nology: 1) additional returns (adds), 2) reduced costs (adds), 3) returns foregone (subtracts), and 4) additional costs (subtracts) [23]. Partial budgeting could be a robust methodology when a direct change is expected from the new technology without major interaction with other system components beyond the analyzed variables. Partial budgeting is the underline methodology to assess the break-even level of using feed additives, the economic benefit of milking 3 times a day, the economic evaluation of using rbST, the assessment of corn feed‐ ing strategies, and the assessment of income over feed cost by different diets under com‐

The cost benefit methodology is similar to partial budgeting but determines profitability of a new technology over longer periods of time and therefore requires the specification of a dis‐ count rate that is used to calculate a net present value [23]. The cost benefit is the underline methodology for calculating the value of adopting accelerated heifer liquid feeding pro‐ grams and is as a supporting methodology to find out the economic value of sexed semen for dairy heifers, the value of reproductive programs in adult cows, and to assess the net present value of alternative scenarios of possible dairy farm expansion or modernization.

The decision analysis is appropriate when probabilistic distributions are important factors in determining the final outcomes [24] as it occurs when analyzing the value of using sexed se‐ men on heifers, comparing the value of reproductive programs in adult cows, or projecting the replacement flow needed to maintain the herd size. In the first two cases, conditional probabilities were used to successively determine populations of pregnant, non-pregnant, and eligible to breed animals along with their respective expected monetary contributions. In the case of the replacement flow tool, transition probabilities are used to dynamically

> **Underline Methodology**

Corn Feeding Strategies Partial Budgeting Flash1 Income Over Feed Cost Partial Budgeting Flash

Income over Feed Supplement Cost Linear Programming Spreadsheet/

**Software Application**

Online2

*3.1.1. Partial Budgeting*

154 Decision Support Systems

modity price changes.

*3.1.3. Decision Analysis*

project the herd dynamics across time.

Feeding and Nutrition

**Decision Support System Tool**

*3.1.2. Cost Benefit*


tools, as these are less computationally demanding than alternative methods. Markov chains are therefore important part of the DairyMGT.info DSS tools and are the backbone structure of the tools: seasonal manure prediction, dynamic dairy farm model, reproductive economic analysis, and the economic value of a dairy cow. Markov chains are also important part of the tools dealing with expansion and modernization and the one comparing the value of dif‐

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157

Mathematical simulation and projection is a general description that encompass a group of di‐ verse and integrated empirical techniques and algorithms that have as main goal to represent observed data as it happens in real-life situations when not a single method fits this condition to satisfaction. Mathematical simulation and projection is used in most of the DairyMGT tools. However, it is a core methodology in a group of them. For example, mathematical simulation is used in the grouping tool to calculate feed nutrient requirements for every single cow in a herd; in the timing of pregnancy tool to aggregate the overall milk production and feed consump‐ tion a cow will have depending on the time of pregnancy; and in all LGM related tools to gener‐ ate thousands of replicates and calculate the statistics of net margins that will determine insurance premiums [27]. Also mathematical simulation and projection is important to predict cash flows within the expansion tool and to perform nutrient balances in tools such as dairy

Nonlinear optimization deals with finding an objective function of maximizing or minimiz‐ ing a variable within a set of simultaneous constraints, where the objective function or some of the constraints have nonlinear relationships. Nonlinear optimization adds a set of com‐ plexity to the implementation of decision support tools because it is computational demand‐ ing. However, for some applications it is required. Since finding the global maxima for nonlinear problems it is not always possible, a compromise between finding a satisfactory answer and maintain the applications as user-friendly as possible is needed. Nonlinear opti‐ mization is used in the grouping, milk curve fitter, and LGM least cost optimizer tools. For the grouping tool, a nonlinear optimization algorithm groups lactating cows according to nutritional requirements with the objective function of finding the aggregated maximum in‐ come over feed cost through recursive iterations by allocating cows to size-defined groups. In the milk curve fitter tool, the user enters farm herd milk production and a nonlinear algo‐ rithm minimizes the residual difference between the farm observed data and the predicted data adjusted to a pre-defined milk lactation function such as Wood [28] or MilkBot [29]. The results are coefficients of the defined function that best represent farm-specific lactation curves. The LGM-least cost uses a nonlinear optimization to find out the minimum premi‐ um price to a defined target guarantee net income over feed cost according to future project‐ ed commodity prices and farm specific conditions, replicating the rules governing the insurance product. The result is the least cost premium for a determined level of coverage

ferent reproductive programs for adult cows.

*3.1.7. Mathematical Simulation and Projection*

dynamic model, dairy nutrient manager, and grazing-N.

within the LGM-Dairy insurance structure [30-31].

*3.1.8. Nonlinear Optimization*

**Table 1.** Principal methodology and software application of DairyMGT.info decision support system tools.1Flash: Macromedia Flash. 2Online tools use a combination of software including HTML, PHP, JavaScript, C, CSS, and MySQL. 3Requires software installation in local machine.

#### *3.1.4. Enterprise Budgets*

Enterprise budgets are a systematic way to list returns and costs and evaluate profits from inside a specific business enterprise [25] within the dairy farm. This methodology is used to calculatethe heifer break-even by contrasting heifers' rearing costs with potential benefits. This methodology is also used, in more detail, in the tool working capital to project the cash flow of a dairy farm enterprise.

#### *3.1.5. Linear Programming*

Linear programming is a mathematical optimization algorithm to maximize or minimize a goal (e.g., maximum profit or minimum costs) within a set of constraints represented as linear relationships [26]. Linear programming is at the core of the tool income over feed supplementation cost in determining the diet composition that results in the maximum net return within a set of constraints of available feed ingredients. Linear programming is also used recursively in the dynamic dairy farm model to maximize the farm net return while minimizing nitrogen leaching.

#### *3.1.6. Markov Chains*

Markov chains are a mathematical system that undergoes transitions from one state to the next within a finite space of states as random processes. In dairy farming, Markov chains are widely used for decision-making to predict herd demographics or to project cows' probabil‐ istic life [2, 10, 12, 19-20]. Markov chains are also very useful to implement decision support tools, as these are less computationally demanding than alternative methods. Markov chains are therefore important part of the DairyMGT.info DSS tools and are the backbone structure of the tools: seasonal manure prediction, dynamic dairy farm model, reproductive economic analysis, and the economic value of a dairy cow. Markov chains are also important part of the tools dealing with expansion and modernization and the one comparing the value of dif‐ ferent reproductive programs for adult cows.

#### *3.1.7. Mathematical Simulation and Projection*

**Decision Support System Tool**

Environmental Stewardship

Dairy Nutrient Manager

3Requires software installation in local machine.

Grazing-N

156 Decision Support Systems

*3.1.4. Enterprise Budgets*

flow of a dairy farm enterprise.

while minimizing nitrogen leaching.

*3.1.5. Linear Programming*

*3.1.6. Markov Chains*

LGM-Net Guarantee Income Over Feed Cost

**Underline Methodology**

Mathematical

Mathematical

Mathematical

Wisconsin Dairy Farm Benchmarking Tool Database Management Online/Spreadsheet

Working Capital Decision Support System Enterprise Budgets Spreadsheet

Dynamic Dairy Farm Model Markov Chains Spreadsheet

Seasonal Prediction of Manure Excretion Markov Chains Spreadsheet

**Table 1.** Principal methodology and software application of DairyMGT.info decision support system tools.1Flash: Macromedia Flash. 2Online tools use a combination of software including HTML, PHP, JavaScript, C, CSS, and MySQL.

Enterprise budgets are a systematic way to list returns and costs and evaluate profits from inside a specific business enterprise [25] within the dairy farm. This methodology is used to calculatethe heifer break-even by contrasting heifers' rearing costs with potential benefits. This methodology is also used, in more detail, in the tool working capital to project the cash

Linear programming is a mathematical optimization algorithm to maximize or minimize a goal (e.g., maximum profit or minimum costs) within a set of constraints represented as linear relationships [26]. Linear programming is at the core of the tool income over feed supplementation cost in determining the diet composition that results in the maximum net return within a set of constraints of available feed ingredients. Linear programming is also used recursively in the dynamic dairy farm model to maximize the farm net return

Markov chains are a mathematical system that undergoes transitions from one state to the next within a finite space of states as random processes. In dairy farming, Markov chains are widely used for decision-making to predict herd demographics or to project cows' probabil‐ istic life [2, 10, 12, 19-20]. Markov chains are also very useful to implement decision support

Simulation Spreadsheet

Simulation Spreadsheet

Simulation Spreadsheet

LGM-Dairy Feed Equivalent Matrix Solution Online

**Software Application**

> Mathematical simulation and projection is a general description that encompass a group of di‐ verse and integrated empirical techniques and algorithms that have as main goal to represent observed data as it happens in real-life situations when not a single method fits this condition to satisfaction. Mathematical simulation and projection is used in most of the DairyMGT tools. However, it is a core methodology in a group of them. For example, mathematical simulation is used in the grouping tool to calculate feed nutrient requirements for every single cow in a herd; in the timing of pregnancy tool to aggregate the overall milk production and feed consump‐ tion a cow will have depending on the time of pregnancy; and in all LGM related tools to gener‐ ate thousands of replicates and calculate the statistics of net margins that will determine insurance premiums [27]. Also mathematical simulation and projection is important to predict cash flows within the expansion tool and to perform nutrient balances in tools such as dairy dynamic model, dairy nutrient manager, and grazing-N.

#### *3.1.8. Nonlinear Optimization*

Nonlinear optimization deals with finding an objective function of maximizing or minimiz‐ ing a variable within a set of simultaneous constraints, where the objective function or some of the constraints have nonlinear relationships. Nonlinear optimization adds a set of com‐ plexity to the implementation of decision support tools because it is computational demand‐ ing. However, for some applications it is required. Since finding the global maxima for nonlinear problems it is not always possible, a compromise between finding a satisfactory answer and maintain the applications as user-friendly as possible is needed. Nonlinear opti‐ mization is used in the grouping, milk curve fitter, and LGM least cost optimizer tools. For the grouping tool, a nonlinear optimization algorithm groups lactating cows according to nutritional requirements with the objective function of finding the aggregated maximum in‐ come over feed cost through recursive iterations by allocating cows to size-defined groups. In the milk curve fitter tool, the user enters farm herd milk production and a nonlinear algo‐ rithm minimizes the residual difference between the farm observed data and the predicted data adjusted to a pre-defined milk lactation function such as Wood [28] or MilkBot [29]. The results are coefficients of the defined function that best represent farm-specific lactation curves. The LGM-least cost uses a nonlinear optimization to find out the minimum premi‐ um price to a defined target guarantee net income over feed cost according to future project‐ ed commodity prices and farm specific conditions, replicating the rules governing the insurance product. The result is the least cost premium for a determined level of coverage within the LGM-Dairy insurance structure [30-31].

#### *3.1.9. Matrix Solution to Multiple Equations*

Matrix or algebra simultaneous equation solution is helpful in the area of nutrition and feed‐ ing to replace feed ingredients and maintain same level nutritional of the diet and same lev‐ el of feed intake. It is also useful to value feeds depending on their nutrients content. Each feed ingredient is defined in function of its nutrient contents and its market price. When the number of nutrients equals to the number of feed ingredients (same number of equations as unknowns) the result is an exact value for each nutrient and therefore the predicted value of a feed ingredient is equal to the input value as it is the case in the Optigen Evaluator tool [32]. Similar approach is used for the LGM-feed equivalent, which converts any feed ingre‐ dient into equivalents of corn and soybean meal, as it is required for LGM-Dairy insurance contracts. The tool FeedVal 2012 goes beyond and analyzes a set of user-defined matrix be‐ tween 2 and 50 ingredients and between 2 and 13 nutrients to find out the difference be‐ tween the feed ingredient market price and the estimated price based on the nutrient composition value of the ingredient.

the National Research Council model of nutrient requirement for dairy animals [34] accord‐

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159

According to the type of application, the methodologies used in the tool, and, most impor‐ tantly, the goal of the tool as a DSS, different software application approaches were used (Table 1). Most of the tools have been developed in different software applications with the objective of better meeting user styles and therefore capture larger audiences of users.

Spreadsheet applications are a very popular format among dairy farmers and consultants because of their familiarity with them, the possibility of using the same spreadsheet for fur‐ ther analyses, and the capacity of save and maintain a copy of it in a personal computer. Spreadsheet application was the elected method for a number of DairyMGT.info tools (Table 1). Most of the spreadsheet applications, however,required some type of Visual Basic code

Other group of tools uses Macromedia Flash as the software application. Macromedia Flash has the advantage of having a nice interactive visual interface connected with a calculator. From the point of view of the user, Flash tools are probably the easiest to use. They havethe ad‐ ditional advantage of becoming stand-alone applications and therefore of being used offline or embedded in Power Point presentations or Portable Document Format (PDF) files. One prob‐ lem with Flash applications is, however, its limited computational functionality. Flash applica‐ tions have only a set of limited mathematical functions without the possibility of using macros or combinethemwith code programming. Also Flash applications are not compatible with Ap‐ ple smart phones and tablets. Current tools that are only Flash applications within the Dai‐

Other group of tools can be classified in the general category of online tools. These use an array of different software applications. What they all have in common is that these work in any web browser and eventually in any device and in any platform including smart phones, tablets, Ap‐ ple, Linux, PC, etc. Calculations and analyses are normally performed in the DairyMGT.info web server, so the online tool is only an interface between the device of the user and the server. In general, online tools are very efficient and reliable tools that have the advantage to be al‐ ways up-to-date: users always experience the latest version of the tool. Other important ad‐ vantage is that complex processes and mathematical calculations can be managed using a combination of web code such as HTML (hyper text markup language), PHP (hypertext pre‐ processor), JavaScript (prototype-based scripting language), C (general-purpose language), CSS (style sheet language), MySQL (relational database management system), or others. An‐ other advantage of online tools is that their design layout can be very efficient and solid once the tool is deployed. A drawback for developing online tools, however, is the need of expertise in web-based code writing. Nonetheless, online tools are very efficient and probably a trend to

ryMGT.info DSS tools will eventually be converted also to be online applications.

which many of the tools of DairyMGT.info will continue to gravitate.

ing to a set of characteristics that include age, production, and live weight.

**3.2. Software Applications**

embedded into the application (macros).

#### *3.1.10. Database Management and Analysis*

Some tools require a database interface and some mechanism of querying the database to retrieve information and to perform analysis dynamically and efficiently. Databases are per‐ manently being updated. Database tools are the lactation benchmark curves and the dairy farm ratio benchmarking. The user does not update these database applications directly, but a server manager. The user queries the database and is able to compare specific farm data with a set of filtered information within the databases. Other type of database application is the feed evaluator tool that registers users in the system and allows them to enter and save their data. The users update the database and the queries retrieve real-time information any‐ time. Users can then compare their own data against to a filtered group of other farms. A different concept is portrayed in all LGM related tools for which all the data (commodity prices of milk, corn, and soybean meal from the future markets) is retrieved real-time from the official sources anytime the user performs an analysis [29]. The calculation of either LGM premiums or least cost premiums changes depending not only on the user inputs, but also based upon the time of the query. The system saves historical information, so users can also do retrospective analyses.

#### *3.1.11. External Simulation Models*

Some tools require to be integrated with more complex, fully developed and established models. That is for example the case of the Dynamic Dairy Farm Model and the Grazing-N tools. In the first case, model requires assessments of crop production (corn, soybean, pas‐ tures, etc.), which are performed by using external crop simulation models from the family of Decision Support System for Agrotechnology Transfer [33]. The dynamic dairy farm model feeds the crop simulation model with data of soils, weather, and crop management schemes and the crop simulation models return predicted biomass produced, nutrient uti‐ lization, and nitrogen leaching from the soil. The Grazing-N application is integrated with the National Research Council model of nutrient requirement for dairy animals [34] accord‐ ing to a set of characteristics that include age, production, and live weight.

#### **3.2. Software Applications**

*3.1.9. Matrix Solution to Multiple Equations*

158 Decision Support Systems

composition value of the ingredient.

also do retrospective analyses.

*3.1.11. External Simulation Models*

*3.1.10. Database Management and Analysis*

Matrix or algebra simultaneous equation solution is helpful in the area of nutrition and feed‐ ing to replace feed ingredients and maintain same level nutritional of the diet and same lev‐ el of feed intake. It is also useful to value feeds depending on their nutrients content. Each feed ingredient is defined in function of its nutrient contents and its market price. When the number of nutrients equals to the number of feed ingredients (same number of equations as unknowns) the result is an exact value for each nutrient and therefore the predicted value of a feed ingredient is equal to the input value as it is the case in the Optigen Evaluator tool [32]. Similar approach is used for the LGM-feed equivalent, which converts any feed ingre‐ dient into equivalents of corn and soybean meal, as it is required for LGM-Dairy insurance contracts. The tool FeedVal 2012 goes beyond and analyzes a set of user-defined matrix be‐ tween 2 and 50 ingredients and between 2 and 13 nutrients to find out the difference be‐ tween the feed ingredient market price and the estimated price based on the nutrient

Some tools require a database interface and some mechanism of querying the database to retrieve information and to perform analysis dynamically and efficiently. Databases are per‐ manently being updated. Database tools are the lactation benchmark curves and the dairy farm ratio benchmarking. The user does not update these database applications directly, but a server manager. The user queries the database and is able to compare specific farm data with a set of filtered information within the databases. Other type of database application is the feed evaluator tool that registers users in the system and allows them to enter and save their data. The users update the database and the queries retrieve real-time information any‐ time. Users can then compare their own data against to a filtered group of other farms. A different concept is portrayed in all LGM related tools for which all the data (commodity prices of milk, corn, and soybean meal from the future markets) is retrieved real-time from the official sources anytime the user performs an analysis [29]. The calculation of either LGM premiums or least cost premiums changes depending not only on the user inputs, but also based upon the time of the query. The system saves historical information, so users can

Some tools require to be integrated with more complex, fully developed and established models. That is for example the case of the Dynamic Dairy Farm Model and the Grazing-N tools. In the first case, model requires assessments of crop production (corn, soybean, pas‐ tures, etc.), which are performed by using external crop simulation models from the family of Decision Support System for Agrotechnology Transfer [33]. The dynamic dairy farm model feeds the crop simulation model with data of soils, weather, and crop management schemes and the crop simulation models return predicted biomass produced, nutrient uti‐ lization, and nitrogen leaching from the soil. The Grazing-N application is integrated with

According to the type of application, the methodologies used in the tool, and, most impor‐ tantly, the goal of the tool as a DSS, different software application approaches were used (Table 1). Most of the tools have been developed in different software applications with the objective of better meeting user styles and therefore capture larger audiences of users.

Spreadsheet applications are a very popular format among dairy farmers and consultants because of their familiarity with them, the possibility of using the same spreadsheet for fur‐ ther analyses, and the capacity of save and maintain a copy of it in a personal computer. Spreadsheet application was the elected method for a number of DairyMGT.info tools (Table 1). Most of the spreadsheet applications, however,required some type of Visual Basic code embedded into the application (macros).

Other group of tools uses Macromedia Flash as the software application. Macromedia Flash has the advantage of having a nice interactive visual interface connected with a calculator. From the point of view of the user, Flash tools are probably the easiest to use. They havethe ad‐ ditional advantage of becoming stand-alone applications and therefore of being used offline or embedded in Power Point presentations or Portable Document Format (PDF) files. One prob‐ lem with Flash applications is, however, its limited computational functionality. Flash applica‐ tions have only a set of limited mathematical functions without the possibility of using macros or combinethemwith code programming. Also Flash applications are not compatible with Ap‐ ple smart phones and tablets. Current tools that are only Flash applications within the Dai‐ ryMGT.info DSS tools will eventually be converted also to be online applications.

Other group of tools can be classified in the general category of online tools. These use an array of different software applications. What they all have in common is that these work in any web browser and eventually in any device and in any platform including smart phones, tablets, Ap‐ ple, Linux, PC, etc. Calculations and analyses are normally performed in the DairyMGT.info web server, so the online tool is only an interface between the device of the user and the server. In general, online tools are very efficient and reliable tools that have the advantage to be al‐ ways up-to-date: users always experience the latest version of the tool. Other important ad‐ vantage is that complex processes and mathematical calculations can be managed using a combination of web code such as HTML (hyper text markup language), PHP (hypertext pre‐ processor), JavaScript (prototype-based scripting language), C (general-purpose language), CSS (style sheet language), MySQL (relational database management system), or others. An‐ other advantage of online tools is that their design layout can be very efficient and solid once the tool is deployed. A drawback for developing online tools, however, is the need of expertise in web-based code writing. Nonetheless, online tools are very efficient and probably a trend to which many of the tools of DairyMGT.info will continue to gravitate.
