An Optimal Milk Production Model Selection and Configuration System for Dairy Cows

Milk production forecasting in the dairy industry has been an independent research topic since the early 20th century. The accurate prediction of milk yield can benefit both the processor (creameries) and the producer (dairy farmer) through developing short-term production schedules, planning long-term road maps, facilitating trade and investment in the dairy industry, improving business operations, optimising the existing infrastructure of the dairy industry, and reducing operating costs. Additionally, due to the innate characteristics of the milk production process, the accurate prediction of milk yield has been a challenging issue in the dairy industry. With the abolishment of EU milk quotas in 2015, the business requirements of milk production forecasting from the dairy industry has become increasingly important. However, to date, most of the existing modelling techniques are data dependent and each case study utilises specific data based on unique conditions. Consequently, it is difficult to compare the prediction performance of each candidate model for forecasting milk as both the data types and origins are independent from study to study. This body of work proposes an integrated forecasting framework XIX concentrating on milk production forecasting using heterogeneous input data combinations based on animal data, milk production, weather variables and other possible records that can be applied to milk yield forecasting on either the herd level or the individual cow level. The first objective of this study concerned the development of the Milk Production Forecast Optimisation System (MPFOS). The MPFOS focused on data processing, automated model configuration and optimisation, and multiple model comparisons at a global level. Multiple categories of milk yield prediction models were chosen in the model library of the MPFOS. Separated databases existed for functionality and scalability in the MPFOS, including the milk yield database, the cow description database and the weather database. With the built-in filter in MPFOS, appropriate sample herds and individual cows were filtered and processed as input datasets for different customised model simulation scenarios. The MPFOS was designed for the purpose of comparing the effectiveness of multiple milk yield prediction models and for assessing the suitability of multiple data input configurations and sources. For forecasting milk yield at the herd level, the MPFOS automatically generated the optimal configuration for each of the tested milk production forecast models and benchmarked their performance over a short (10-day), medium (30-day) and long (365-day) term prediction horizon. The MPFOS found the most accurate model for the short (the NARX model), medium and long (the surface fitting model) terms with R2 values equalling 0.98, 0.97 and 0.97 for the short, medium and long term, respectively. The statistical analysis demonstrated the effectiveness of the MPFOS as a model configuration and comparison tool. For forecasting milk yield at the individual cow level, the MPFOS was utilised to conduct two exploratory analyses on the effectiveness of adding exogenous (parity and meteorological) data to the milk production modelling XX procedure. The MPFOS evaluated the most accurate model based on the prediction horizon length and on the number of input parameters such as 1) historical parity weighting trends and 2) the utilisation of meteorological parameters. As the exploratory analysis into utilising parity data in the modelling process showed, despite varying results between two cow groups, cow parity weighting profiles had a substantial effect on the success rate of the treatments. Removal of the first lactation and applying static parity weight were shown to be the two most successful input treatments. These results highlight the importance of examining the accuracy of milk prediction models and model training strategies across multiple time horizons. While the exploratory analysis into meteorological data in the modelling process demonstrated that based on statistical analysis results, 1) the introduction of sunshine hours, precipitation and soil temperature data resulted in a minor improvement in the prediction accuracy of the models over the short, medium and long-term forecast horizons. 2) Sunshine hours was shown to have the largest impact on milk production forecast accuracy with an improvement observed in 60% and 70% of all predictions (for all test cows from both groups). However, the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%. Thus, the utilisation of meteorological parameters in milk production forecasting did not have a substantial impact on the overall forecast accuracy. One possible reason for this may be due to modern management techniques employed on dairy farms, reducing the impact of weather variation on feed intake and lessening the direct effect on milk production yield. The MPFOS architecture developed in this study showed to be an efficient and capable system for automatic milk production data pre-processing, model configuration and comparison of model categories over varying prediction horizons. The MPFOS has proven to be a XXI comprehensive and convenient architecture, which can perform calculations for milk yield prediction at either herd level or individual cow level, and automatically generate the output results and analysis. The MPFOS may be a useful tool for conducting exploratory analyses of incorporating other exogenous data types. In addition, the MPFOS can be extended (addition or removal of models in the model library) and modularised. Therefore the MPFOS will be a useful benchmark platform and integrated solution for future model comparisons

Milk yield of cows in some European countries and the implementation of automatic milking systems

Received: February 14th, 2023 ; Accepted: April 17th, 2023 ; Published: April 22nd, 2023 ; Correspondence: [email protected] research study addresses the problem of implementing progress in agricultural production. This problem was developed on the basis of equipping farms with automatic milking systems (AMS). Different forms of progress can be identified on a dairy farm, including technical progress represented by AMS and biological progress expressed by milk yield of cows. The purpose of this research study was to compare whether the milk yield of cows in certain European countries meets the requirements for utilizing the milking potential of automatic milking systems. The study used information on the suggested amount of milk that an one-stall milking robot should milk per year. The second group of data was the annual milk yield of cows in the European Union countries and Great Britain. In eight countries, the annual milk yield of cows was in the range of 8,601–10,600 kg. It was found that in 2020, in these eight countries of the European Union, the milk yield of cows was at a level that meets the performance requirements of one-stall milking robot

Effect of introducing weather parameters on the accuracy of milk production forecast models

peer-reviewedThe objective of this study was to analyze the effect of adding meteorological data to the training process of two milk production forecast models. The two models chosen were the nonlinear auto-regressive model with exogenous input (NARX) and the multiple linear regression (MLR) model. The accuracy of these models were assessed using seven different combinations of precipitation, sunshine hours and soil temperature as additional model training inputs. Lactation data (daily milk yield and days in milk) from 39 pasture-based Holstein-Friesian Irish dairy cows were selected to compare to the model outputs from a central database. The models were trained using historical milk production data from three lactation cycles and were employed to predict the total daily milk yield of a fourth lactation cycle for each individual cow over short (10-day), medium (30-day) and long-term (305-day) forecast horizons. The NARX model was found to provide a greater prediction accuracy when compared to the MLR model when predicting annual individual cow milk yield (kg), with R2 values greater than 0.7 for 95.5% and 14.7% of total predictions, respectively. The results showed that the introduction of sunshine hours, precipitation and soil temperature data improved the prediction accuracy of individual cow milk prediction for the NARX model in the short, medium and long-term forecast horizons. Sunshine hours was shown to have the largest impact on milk production with an improvement of forecast accuracy observed in 60% and 70% of all predictions (for all 39 test cows from both groups). However, the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%. Thus, the utilization of meteorological parameters in milk production forecasting did not have a substantial impact on forecast accuracy

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms

peer-reviewedAn analysis into the impact of milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions on dairy farm electricity and water consumption using multiple linear regression (MLR) modelling was carried out. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, Irish commercial dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed on their ability to predict monthly electricity and water consumption, respectively. The subsets of variables that had the greatest prediction accuracy on unseen electricity and water consumption data were selected by applying a univariate variable selection technique, all subsets regression and 10-fold cross validation. Overall, electricity consumption was more accurately predicted than water consumption with relative prediction error values of 26% and 49% for electricity and water, respectively. Milk production and the total number of dairy cows had the largest impact on electricity consumption while milk production, automatic parlour washing and whether winter building troughs were reported to be leaking had the largest impact on water consumption. A standardised regression analysis found that utilising ground water for pre-cooling milk increased electricity consumption by 0.11 standard deviations, while increasing water consumption by 0.06 standard deviations when recycled in an open loop system. Milk production had a large influence on model overprediction with large negative correlations of −0.90 and −0.82 between milk production and mean percentage error for electricity and water prediction, respectively. This suggested that overprediction was inflated when milk production was low and vice versa. Governing bodies, farmers and/or policy makers may use the developed MLR models to calculate the impact of Irish dairy farming on natural resources or as decision support tools to calculate potential impacts of on-farm mitigation practises

Forecasting in Database Systems

Time series forecasting is a fundamental prerequisite for decision-making processes and crucial in a number of domains such as production planning and energy load balancing. In the past, forecasting was often performed by statistical experts in dedicated software environments outside of current database systems. However, forecasts are increasingly required by non-expert users or have to be computed fully automatically without any human intervention. Furthermore, we can observe an ever increasing data volume and the need for accurate and timely forecasts over large multi-dimensional data sets. As most data subject to analysis is stored in database management systems, a rising trend addresses the integration of forecasting inside a DBMS. Yet, many existing approaches follow a black-box style and try to keep changes to the database system as minimal as possible. While such approaches are more general and easier to realize, they miss significant opportunities for improved performance and usability. In this thesis, we introduce a novel approach that seamlessly integrates time series forecasting into a traditional database management system. In contrast to flash-back queries that allow a view on the data in the past, we have developed a Flash-Forward Database System (F2DB) that provides a view on the data in the future. It supports a new query type - a forecast query - that enables forecasting of time series data and is automatically and transparently processed by the core engine of an existing DBMS. We discuss necessary extensions to the parser, optimizer, and executor of a traditional DBMS. We furthermore introduce various optimization techniques for three different types of forecast queries: ad-hoc queries, recurring queries, and continuous queries. First, we ease the expensive model creation step of ad-hoc forecast queries by reducing the amount of processed data with traditional sampling techniques. Second, we decrease the runtime of recurring forecast queries by materializing models in a specialized index structure. However, a large number of time series as well as high model creation and maintenance costs require a careful selection of such models. Therefore, we propose a model configuration advisor that determines a set of forecast models for a given query workload and multi-dimensional data set. Finally, we extend forecast queries with continuous aspects allowing an application to register a query once at our system. As new time series values arrive, we send notifications to the application based on predefined time and accuracy constraints. All of our optimization approaches intend to increase the efficiency of forecast queries while ensuring high forecast accuracy

Load forecasting: a cross-field study on server and energy load forecasting Impact of temporal factors on generalization ability and performance of regression models

The server load prediction and energy load forecasting have available a wide range of approaches and applications, with their general goal being the prediction of future load for a specific period of time on a given system. Depending on the specific goal, different methodologies can be applied. In this dissertation, the integration of additional temporal information to datasets, as a mean to create a more generalized model is studied. The main steps involve a deep literature review in order to find the most suited methodologies and/or learning methods. A novel dataset enrichment process through the integration of extra temporal information and lastly, a cross-model testing stage, where trained models for server load prediction and energy load forecast are applied to the opposite field. This last stage, tests and analyses the generalization level of the created models through the temporal information integration procedure. The created models were both oriented to short-term load forecasting problems, with the use of data from single and combined months, regarding real data from Wikipedia servers of the year 2016 in the case of server load prediction and real data regarding the consumption levels in April 2016 of the city of Leiria/Portugal for the energy load forecasting case study. The learning methods used for creating the different models were linear regression, artificial neural networks and support vector machines oriented to regression problems, more precisely the Smoreg implementation. Results prove that it is possible to tune the dataset features, e.g., granularity and time window to improve prediction results and generalization. Results from this work, as well as an optimization approach through the use of genetic algorithms, normalization effects, split ratio vs crossvalidation influence and different granularities and time windows were peer-reviewed published