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    Adding an intelligent component to an existing decision support system : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Information Systems at Massey University
    (Massey University, 1996) Jeffries, Anna Elizabeth
    A framework to guide the development of an intelligent component and its integration with an existing decision support system has been proposed. An initial framework was outlined, drawing concepts from the fields of decision support systems, knowledge based systems and intelligent decision support systems. This framework was applied to a problem in the domain of dairy farm management. A prototype intelligent decision support system was developed. Experiences gained during the development process enabled refinements to the framework to be made. The prototype was tested to assess the success of the framework in producing the desired results. The development framework was evaluated based on criteria drawn from relevant literature. The proposed development framework is considered to be a useful tool for intelligent decision support system development from an existing decision support system. Its success is attributed to the integration of methods and techniques drawn from a number of well established methodologies.
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    Risk in New Zealand dairy farming : perception and management : a thesis presented in partial fulfilment of the requirements for the degree of Master of Applied Science in Agricultural Systems and Management at Massey University, Palmerston North, New Zealand
    (Massey University, 2005) Pinochet Chateau, Rene Eduardo
    Many changes have taken place in New Zealand during the last 20 years. These changes have affected the dairy sector in its broadest sense, at both industry and farm level. After economic deregulation (1984), a survey was conducted in 1992 amongst a sample of pastoral New Zealand farmers to assess the perception of risk and the strategies most commonly used by them to manage risk. Dairy farmers were part of the total sample analysed. Since the 1980s agriculture, not only in New Zealand but world wide, has changed at a rapid rate with farmers facing a challenging environment. The identification of both sources of variation and management strategies for them has made risk management a high priority issue. Therefore there is a need to understand the critical aspects of the environment faced by New Zealand dairy farmers, to update our knowledge of how they are recognizing and managing risk. The main objective of this research was to assess farmers' risk perception and identify the main variables affecting risk in New Zealand dairy systems. To accomplish the objectives, the 1992 survey was replicated with another sample of dairy farmers. Additionally a logistic regression was used to analyse the ProfitWatch Database (Dexcel). The four most important sources of risk perceived by farmers in 2004 were from the market side of their operations (2), Human (1) and Financial (1 ). To control risk, farmers were mainly focused in the use of Production and Financial strategies. The risks perceived and the use of risk management strategies have changed significantly during the last twelve years. Now farmers perceive more risk in almost all the sources identified in the surveys and they also make more intensive use of almost all the strategies to cope with those sources of risk. Significant differences were also found in the perception of some of the risk sources of the different groups of farmers analysed (Sharemilkers vs. Owner-operators and; North Island vs. South Island dairy farmers). Finally the database analysis showed that of the seven variables included in the logistic regression to assess risk, measured as Return on Equity (ROE), only four of them were found to be significant for the model. In order of importance, these were: the Debt Servicing Capacity (DSC), the Debt to Asset Ratio (DTAR), the Asset Turnover Ratio (ATR) and the Operating Profit Margin (OPM). The findings of this research have confirmed that currently farmers are mainly concerned about the changes of prices, changes in world situation, accidents or health problems and changes in interest rates; however to control risk they are both production- and financial-orientated. With this clear profile, it can be stated that indeed risk perception and the way farmers manage risk has changed during the last twelve years. Additionally, farmers perceive sources of risks and manage them differently, according to their specific situation (e.g. Ownership structure, Geographic location). The analysis of the database showed that increases in farm size were not associated with a decrease in risk (ROE). Also, the use of Farm Working Expense Ratio and Economic Farm Surplus as the main variables to evaluate cost control and profitability of dairy farms overlook more useful ratios of ATR and OPM. Finally, high levels of debt can lead to reduction in the risk faced by a dairy business if non-equity capital (money borrowed) is efficiently used and high levels of efficiencies, both capital and operational, are achieved.
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    Key performance indicators for goal attainment in dairy farming : essential elements for monitoring farm business performance : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Applied Science in Agricultural Systems management at Massey University
    (Massey University, 1999) Rawlings, Karin Michelle
    The family farm is the predominant business structure within the New Zealand dairy industry. Owner-operator farms represent two thirds of the total industry. This business structure is often complicated because of the intricate interaction between business and family requirements. This research investigated whether owner-operator dairy farmers were using performance indicators that represented goal attainment. Detailed business analyses and discussions took place on ten farms. Only two farming couples had developed formal business plans including vision and mission statements. Only one of these farms was actively implementing strategic management. Amongst the remaining farming couples goal identification ranged from no identification to well defined goals. Most identified goals were of a strategic nature yet there was no strategic management in place to monitor their progress. The lack of strategic management seen amongst farming couples is possibly relates to their locus of control. To alter the locus of control a better understanding of individuals perceptions of success and control needs to be gained so that knowledge and skill deficits can be identified. Nine of the ten farming couples had an incomplete view of their business, which reflected the operational and tactical management focus. The business analyses highlighted that the area that was of the greatest concern to farm business health was the cost of capital, in particular the cost of equity. Most farming couples struggled to relate the analysis results to their goals, which again reflected their operational and tactical management focus. The Balanced Scorecard was introduced as a strategic planning tool for farmers, however, its effectiveness could not be tested because of the lack of strategy amongst the farming couples. The Balanced Scorecard was able to provide a framework to assist in the understanding of strategic management and its importance to holistic farm business management.
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    The development of an expert system for diagnosing reproductive problems in seasonal dairy herds : a thesis presented in partial fulfilment of the requirements for the degree of Master of Veterinary Science at Massey University
    (Massey University, 1997) Hayes, David
    An expert system for investigating the reproductive performance of seasonally calving dairy herds (DairyFIX) was designed and developed. The system uses information retrieved from an on-farm information system (DairyMAN) to identify the primary causes of reduced performance and make recommendations for further action. The current performance of DairyMAN user herds and a sample of herds from the National Dairy Database were initially measured. Realistic targets for each performance indicator were then calculated using this information. The associations and interactions between the performance and diagnostic indicators used in DairyMAN were more clearly defined using multivariate statistical analyses and path models. Additional information was obtained from the literature for incorporation into the DairyFIX system. The reproductive performance studies covered one complete year (1993/94 season) and were limited to spring calving seasonal herds which are typical of the New Zealand dairy industry. Such herds are managed so that all cows calve on a synchronous annual cycle as close as possible to the "ideal" calving date in that location. A typical aim is to have calvings spread over 6 to 8 weeks with a narrow spread being strongly favoured. The herd planned start of calving (PSC) date defines the beginning of this optimal period of calving and the planned start of mating (PSM) the first day of mating that must be used to achieve the desired calving pattern. Expert system development is introduced in Chapter 1 with some historical information that supports the use of on-farm information systems. The concepts and design methods used for developing expert systems are then reviewed in brief. Examples are provided to illustrate the different levels of sophistication that can be used for developing expert systems. These demonstrate that relatively simple designs are often the most successful. Examples including Bovid and Dairy Expert are discussed in detail. The methods used to monitor seasonally calving dairy herds are then reviewed with an emphasis on the main performance and diagnostic indicators used in DairyMAN. These include the four and eight week calving rates, 21 day submission rates, non-return and conceptions rates and four and eight week in-calf rates. Empty rates can be considered the final definitive measure of performance by many dairy farmers, but the difficulties with interpreting this figure are presented. The limited mating period in seasonally calving dairy herds means that the assessment of heat detection is difficult and often inaccurate. For this reason several alternative methods for assessing heat detection efficiency and accuracy are discussed in detail. A detailed description is given in Chapter 2 of herd and individual cow performance for DairyMAN user herds and the National Dairy Database sample. These data show that the reproductive performance of New Zealand herds is often below that previously reported. Calving rates are on average above industry targets, but only with a significant level of calving induction. Removal of inductions for welfare and marketing reasons will have a significant effect on the performance of many herds. Submission rates are the earliest available measures of performance during a mating season. New Zealand herds do not, on average, achieve the necessary targets. All measures of heat detection efficiency, although imprecise, show this is not a major problem with about 6% of heats missed. This has a negative effect on submission rates. Detection efficiency is an important issue for some individual herds as the consequences of poor heat detection are dramatic. The performance levels suggest that nutritional anoestrus and the effects of a spread calving pattern are the major causes of low submission rates. Conception rates of less than 60 % are reported. These are below those often suggested as typical for New Zealand herds. Much of the previous data has been taken from small study groups that may not adequately represent all herds. The common use of non-return rates may have created expectations that cannot be achieved in average herds as these are an optimistic measure of performance. Health events such as lameness are only reported for reference. Only limited health data is recorded although DairyMAN provides the flexibility to records such data. The variability in the type and degree of recording of these data is identified as a significant problem that limits the use of the available records. Some of the health events and especially lameness may have a large effect on the reproductive performance of many herds. Path models are developed in chapter 3 as an essential prerequisite to the development of the DairyFIX expert system. The models statistically confirm most of the relationships that have been previously considered important when evaluating herd reproductive performance. A number of factors including herd size and breed are shown to be associated with differing calving rates. The four week calving rate is shown to have strong indirect effects on submission rates, conception rates and herd in-calf rates. As such, it is one of the most important variables in seasonal herds. The importance of submission rates and conception rates is confirmed. Daily per cow milk production is shown to be a useful indicator of submission rates as both of these variables are directly influenced by nutrition. The models identify some limitations with using non-return rates as measures of conception. These generally give optimistic results that do not accurately reflect true performance. Such problems are compounded if reasonably accurate measures of heat detection cannot be obtained in herds with a very restricted mating period. Although the interactions between the performance and diagnostic indicators are largely understood from previous work this is the first time they have been brought together in statistically verified path models. The use of an on-farm information system (DairyMAN) was shown to be associated with improved herd performance including daily per cow milk yield and reproductive outcomes. DairyMAN user herds had cows of the same breed and genetic capacity. So DairyMAN users were able to produce more milk with animals of equivalent genetic merit, indicating that users achieved better management of the herd through improved attention to managerial details. This was associated with their adoption of DairyMAN, but not shown by this study to be a direct consequence of it. These findings are important because there is very little information confirming that on-farm information systems or central databases give true performance gains. This is despite the historical recognition of these systems and the rapid expansion in recent years. Justifying the use of more sophisticated tools such as an expert system would be more difficult if gains were not being achieved with the current technology. Calving induction was shown to be associated with some negative effects on milk yield and reproductive outcome. The New Zealand dairy industry does not currently favour the use of this management tool, but the impact any changes in management practices would have need to be evaluated with consideration of these effects. Calving induction is typically not used as recommended in New Zealand as many of the treatments are done too late to provide sufficient economic gains through increased lactation length and increasing the number of days from calving to the planned start of mating. Regional differences in performance were identified. DairyMAN user herds in the Manawatu had inferior conception rates while herds in the Taranaki had superior reproductive performance. These observed differences suggest a need to further identify causes of these differences, if performance is to be improved in some regions. The performance of individual cows and groups was examined in detail and statistical models developed for use in DairyFIX. Breed, lactation number, days calved at the start of mating and some health events were all shown to have an important impact on performance. The inferior performance of lactation 1 and 2 groups is having a large effect on performance. Cows in lactation 3 and 4 generally have the best performance with some reduction for aged cows. These aged cows do not dramatically affect overall herd performance because they are only a small proportion of most herds Jersey cows tended to show superior calving and submission rates. A number of complex models were developed for herds that pregnancy test and those that use non-return as the measure of conception. DairyFIX was developed to achieve two primary objectives. The first was to simplify the epidemiological approach to investigating herd reproductive problems. The system automates procedures that would otherwise be followed using DairyMAN and uses a graphical diagnostic interface to achieve this. DairyFIX quickly focuses on the important aspects of any performance deficit and reduces the time required to investigate a herd problem. Although DairyMAN is a comprehensive software package, users have difficulties due to its complexity and may invest a considerable amount of time exploring the program rather than focusing on the task required. The second objective of this project was to make expertise available for the user when examining herd performance. This required the use of the more sophisticated aspects of an expert system including the development of a "knowledge base" of information. DairyFIX consists of three sections. The first simply evaluates performance and determines if any problems exist. The second part considers the effect of the major components of performance such as the calving pattern and heat detection so that the primary causes of poor performance are identified. This section uses the statistical models previously developed to estimate expected performance. The third part of DairyFIX consists of several specific interrogation procedures for each area of poor performance. This section is not necessarily required for an operational system as the user can otherwise be referred to the appropriate reports in DairyMAN. Another module within DairyFIX was designed to assess the expected performance of a herd in subsequent seasons given a predicted herd profile. The models used for this purpose are similar to those that retrospectively evaluate performance. DairyFIX simplifies the investigative task and identifies the major causes of poor reproductive performance. It is anticipated that this tool will allow more dairy farmers and veterinarians to make effective use of DairyMAN while reducing the investment in time that is currently required.