**1. Introduction**

Dairy farming is a highly dynamic and integrated production system that requires contin‐ uous and intense decision-making. Several dairy farm components that include 1) cattle, 2) crops, 3) soils, 4) weather, 5) management, 6) economics, and 7) environment are ex‐ tremely interrelated [1]. These components and their sub-components dynamically affect and are affected among them. Therefore, an efficient decision support system (DSS) framework within an integrated systems approach is critical for successful dairy farming management and decision-making [2-5].

This chapter describes the development, application, and adoption of a suite of more than 30 computerized DSS or decision support tools aimed to assist dairy farm managers and dairy farm advisors to improve their continuous decision-making and problem solving abilities. These DSS emerged in response of dairy farm managers' needs and were shaped with their input and feedback [6-7]. No single or special methodology was used to develop each or all of these DSS, but instead a combination and adaptation of methods and empirical techni‐ ques with the overarching goal that these DSS were: 1) highly user-friendly, 2) farm and user specific, 3) grounded on the best scientific information available, 4) remaining relevant throughout time, and 5) providing fast, concrete, and simple answer to complex farmers' questions [2, 8-11]. After all, these DSS became innovative tools converting expert informa‐ tion into useful and farm-specific management decisions taking advantage of latest software and computer technologies.

All the DSS object of this chapter are hosted at http://DairyMGT.info, *Tools* section and are categorized within dairy farming management and decision making such as: 1) nutrition and feeding, 2) reproductive efficiency, 3) heifer management and cow replacement, 4) pro‐ duction and productivity, 5) price risk management and financial analysis, and 6) environ‐

© 2012 Cabrera; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Cabrera; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

mental stewardship. Depending on the complexity, the specific purpose, and the requirements of dairy farm decision makers, some DSS are completely online applications, others are Macromedia Flash tools, others are Spreadsheets, and others are self-extractable and installable programs.

feed efficiency levels [13], would solely depend on the ever-changing price relationship of milk and corn. The tool "*Corn Feeding Strategies*" shows these relationships in a graphical, dynamic, and interactive way so dairy farmers can optimize the amount of corn grain in

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

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

145

Take as another example the price of the main dairy cattle feed commodities and their rela‐ tionship with milk price according to feed efficiency changes throughout lactation states. Re‐ search data indicate that the use of concentrates (i.e., corn, soybean meal) have a substantially higher impact on milk production during early or mid-lactation than in late lactation [14]. Under this premise, increased use of forages is justified in late lactation to maximize the overall milk income over feed cost, which however depends on ever-changing feed commodity prices. The tool "*Income Over Feed Cost*" graphs interactively the milk in‐ come over feed cost weekly for entire lactations and shows the impact of feed commodity prices on the dynamic milk income over feed cost value. Therefore, dairy farmers can finetune their feeding strategies to maximize their milk income over feed cost according to lacta‐

Sometimes dairy farmers need additional help on formulating their diets to optimize feed con‐ centrate supplementation. Research trails indicate that the optimal level of concentrate supple‐ ments in a diet could be achieved by using milk production response to crude protein (CP) and its components of rumen un-degradable protein (RUP), and rumen degradable protein (RDP), according to particular cow-group rations [15]. The tool "*Income over Feed Supplement Cost*" per‐ forms an optimization according to defined feed ingredients, prices, and CP (RUP, RDP) re‐ strictions to maximize the net return. The tool helps dairy farm decision makers to select the most cost effective concentrate supplements in the diet, especially from the point of view of providing adequate amounts of RUP and RDP, which not only optimizes the net return, but al‐

Dairy farmers also want to know what are the best-priced feed ingredient choices in the mar‐ ket. This information would drive farmer feed purchase decisions. The tool called "*FeedVal 2012*" is a dynamic and interactive matrix that finds the estimated price of a feed as an aggre‐ gated sum of its individual nutrients values according to the nutrient content and prices of a set of defined feed ingredients available in the market. The tool then compares the actual price of a feed ingredient with its calculated price. The result is a list of ingredients with their relative pri‐

Another critical factor in the quest for feed efficiency and maximum milk income over feed cost is the analysis of "benchmarking" with respect to feed efficiency, milk income, and feed costs [16]. Results from surveying dairy farm rations and farm prices reveals an impressive difference regarding to feed costs, feed consumption, and overall milk income over feed cost among otherwise similar dairy farms. A large and important opportunity exists then to im‐ prove the milk value net of the feed costs by comparing performance among farms. There‐ fore an online database structure and DSS was developed: "*Dairy Extension Feed Cost Evaluator*," Figure 1. This tool performs advanced benchmarking analyses for a group of users within a region, state, or country throughout a defined timeline by querying an online database, which is permanently being updated by the users. The tool allows users to "drill-

so reduces the amount of nitrogen excretion and hence environmental impacts.

ces, indicating if an ingredient is a bargain or an expensive proposition.

each farm feeding group according to ever-changing market price conditions.

tion states and feed prices swings.

This chapter discusses the challenges on the development of these DSS with respect to the trade-offs among user-friendly design, computational detail, accuracy of calculations, and bottom line efficiency performance and effective decision-making. It portrays DSS develop‐ ment strategies, within the computational resources available, that succeeded in their pri‐ mary objective of providing dairy farm mangers fast and reliable responses to perform efficient and effective decision-making.

The chapter reveals practical and real-life applications of a number of these DSS to demon‐ strate satisfactory system assessment, acceptable future predictability, adequate scenario evaluation, and, consequently, satisfactory decision-making.

The chapter also covers aspects of DSS dissemination and adoption evaluation, including the inception and development of a dedicated webpage; local, national and international us‐ age, requested presentations, and academic publications.

The chapter also infers the possible role of emerging and evolving new technologies such as smart phones and tablets in the intersection of DSS, real-time applications, and mobile devi‐ ces, which is a fast growing area of development within the dairy farming industry.
