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

Uso de inteligência artificial com foco em visão computacional na produção de bovinos e suínos

O presente trabalho teve como objetivo realizar uma revisão sobre a história das redes neurais artificias, assim como analisar diferentes trabalhos acadêmicos de áreas com e sem relação com as ciências veterinárias, para verificar o atual estado das redes neurais no meio acadêmico. Foi determinado, por meio da análise de quarenta artigos referentes a soluções de problemas na produção animal, através do uso de redes neurais, que o maior número de trabalhos da área abrangem a espécie bovina, que o número de dados de treinamento nos trabalhos é pequeno, se comparado com bases de dados de artigos de outras áreas do conhecimento, que a maioria dos trabalhos não entram em detalhes sobre o software utilizado para implementação das redes neurais e que a maioria dos autores principais dos trabalhos atuavam em departamentos de ciência animal. Outro objetivo do trabalho foi determinar as mais eficazes arquiteturas de redes neurais, cuja tarefa compreende a predição da ocorrência de descarte de matrizes suínas provenientes de granjas de quarto sítio e unidades produtoras de leitões. Uma base de dados com 5.013 fêmeas descartadas, classificadas em sete diferentes categorias de motivos de descarte, foi utilizada para o treinamento das redes neurais. Além disso as mais eficazes arquiteturas de redes neurais, cuja tarefa compreende a predição da ocorrência de descarte de matrizes suínas provenientes de granjas de quarto sítio e unidades produtoras de leitões. Uma base de dados com 5.013 fêmeas descartadas, classificadas em sete diferentes categorias de motivos de descarte, foi utilizada para o treinamento das redes neurais. A base foi filtrada e os dados de diferentes índices produtivos das fêmeas foram utilizados para a realização de quatro experimentos. O primeiro experimento tinha o objetivo de testar a eficácia de uma rede neural em classificar o motivo de descarte das fêmeas suínas nas sete diferentes categorias. A acurácia máxima alcançada foi de 56,35%. O segundo experimento visava determinar a eficácia de uma rede neural em estimar a probabilidade de descarte das fêmeas em apenas uma categoria. A acurácia máxima alcançada foi de 99,78%. O terceiro experimento tinha como objetivo avaliar a eficácia de uma rede neural em prever as variáveis da vida produtiva do parto seguinte das fêmeas, baseada em dados dos dois partos anteriores. O erro médio absoluto mínimo alcançado foi de 1,777 para a variável de número de desmamados. O quarto e último experimento tinha o intuito de testar a capacidade de uma rede neural em determinar se uma fêmea seria ou não descartada no parto seguinte, baseada em dados de dois partos anteriores. De maneira geral, as redes neurais demonstraram adequado desempenho em encontrar padrões em diferentes dados da vida produtiva de matrizes suínas e predizer a ocorrência e a classificação de seus descartes.The objective of this work was to review the history of artificial neural networks, as well as to analyze different academic works in areas related and unrelated to the veterinary sciences, to verify the current state of neural networks in the academia. It was determined, through the analysis of forty research works referring to solutions of problems in animal production, through the use of neural networks, that the largest number of works in the area are referring to the bovine species, that the number of training data in the research works is small, compared to databases of articles from other areas of knowledge, that most of the papers do not go into detail about the software used to implement neural networks and that most of the main authors of the papers work in animal science departments. Besides that, another objective of this work was to determine the most effective neural network architectures, whose task includes the prediction of the occurrence of culling of female swine breeders from four-site units and piglet-production units. A database of 5,013 culled gilts, classified into seven different culling reason categories, was used for the neural networks training. The base was filtered and the data from different productive indexes of the females were used to perform four experiments. The first experiment aimed to test the effectiveness of a neural network in the task of classifying the reason for culling swine gilts into seven different categories. The maximum accuracy achieved was 56.35%. The second experiment aimed to determine the effectiveness of a neural network in estimating the probability of gilt culling in only one category. The maximum accuracy achieved was 99.78%. The third experiment aimed to evaluate the effectiveness of a neural network in predicting the productive life variables of the breeder's next delivery, based on data from the previous two parities. The minimum mean absolute error reached was 1.777 for the weaned number variable. The fourth and final experiment was designed to test the ability of a neural network to determine whether or not a gilt would be culled in the next parturition based on data from two previous births. In general, neural networks have shown adequate performance in finding patterns in different data of the productive life of swine breeders and predicting the occurrence and classification of their culling events

Efecto de un sistema silvopastoril sobre la calidad de la leche, comparado con un sistema de producción convencional

En este estudio se evaluó el efecto de un sistema Silvopastoril sobre la calidad de la leche, comparado con un sistema de producción convencional, además cuáles factores de la planta afectan los niveles de grasa y proteína en la leche, asimismo se desarrollaron 6 RNA para predecir porcentajes de grasa y proteína. En el SSP, se utilizó botón de oro (Tithonia diversifolia) mientras que para el convencional uno con pasto estrella (Cynodon nlemfuensis). Las variables evaluadas para las dos pasturas fueron: MS, CEN PC, FDN, y ENER y para la calidad de la leche se midió el % de GRASA y % de PROTEINA. El análisis de varianza no mostró diferencias significativas para la fuente de variación bloques en las variables bromatológicas de las pasturas ni para las variables de calidad de leche, pero si se encontraron diferencias altamente significativas (P0,01) para las tres pasturas evaluadas (BO, ES(BO) y ES). La MS, PC y las CEN tanto para ES y ES(BO) no presentaron diferencias significativas, mientras que el BO si presentaron. Con respecto a las composiciones de FDN, ENER, la grasa y la proteína de la leche fueron significativamente diferentes para los tres tratamientos. BO tuvo un porcentaje superior al promedio en la grasa y la proteína de la leche. Por su parte, el asocio ES(BO) también mostró un porcentaje mayor, comparado con el ES. Para las RNA, se seleccionaron las que registraron el mayor R2. El R2 para la grasa y proteína de la leche en el BO fue de 0,9601 y 0,9622 respectivamente. 0,957 y 0,8957 para la grasa y la proteína de la leche respectivamente en un sistema de ES(BO); para el ES, los valores de R2 fueron de 0.9646 y 0.938 para la grasa y proteína de la leche respectivamente. Con el sistema Silvopastoril se obtuvieron los mejores valores de Resumen: grasa y proteína de la leche, lo que significa una mejor calidad de la misma, comparada con un sistema de producción convencional. Además el uso de las redes neuronales artificiales permitió predecir valores de grasa y proteína para los dos sistemas estudiados, con un alto nivel de predicción.//Abstract: In this study the effect of a Silvopastoril system on the quality of milk as compared to a conventional production system was determined as well as which plant factors were affecting the levels of fat and protein in milk, lastly six RNA to predict percentages of fat and protein were developed. For the purpose of the research the buttercup as SSP was used (Tithonia diversifolia) and for the conventional one with star grass (Cynodon nlemfuensis). The variables analyzed for pastures were MS, CEN CP, NDF, and ENER and for measuring the quality of milk the% of FAT and % of PROTEIN were evaluated. The analysis of variance exhibited no significant differences for the source of variation block in the bromatological variables of pastures or for the milk quality variables, but highly significant differences were found (P 0.01) for the three evaluated pastures (BO, ES (BO) and ES). MS, CP and CEN of the ES and ES(BO) did not showed significant differences, whereas BO did. FDN, ENER, fat and milk protein were significantly different for the three treatments. BO had a higher percentage in relation to the fat and milk protein average. Meanwhile, the union ES (BO) also showed a higher percentage compared to the ES. For RNA, the ones showing the highest R2 were selected. The R2 for fat and milk protein in the BO was 0.9601 and 0.9622 respectively. 0.957 and 0.8957 for the fat and milk protein respectively in a ES system (BO); for the ES, the R2 values were 0.9646 and 0.938 for fat and milk protein respectively. Silvopastoril system obtained the best values of fat and milk protein, which means a better quality of it, compared to a conventional production system. Furthermore, the use of artificial neural networks allowed to predict values of fat and protein for the two systems studied, with a high level of prediction.Maestrí

4.Uluslararası Öğrenciler Fen Bilimleri Kongresi Bildiriler Kitabı

Çevrimiçi ( XIII, 495 Sayfa ; 26 cm.)

Bayesian nonparametric models for data exploration

Mención Internacional en el título de doctorMaking sense out of data is one of the biggest challenges of our time. With the emergence of technologies such as the Internet, sensor networks or deep genome sequencing, a true data explosion has been unleashed that affects all fields of science and our everyday life. Recent breakthroughs, such as self-driven cars or champion-level Go player programs, have demonstrated the potential benefits from exploiting data, mostly in well-defined supervised tasks. However, we have barely started to actually explore and truly understand data. In fact, data holds valuable information for answering most important questions for humanity: How does aging impact our physical capabilities? What are the underlying mechanisms of cancer? Which factors make countries wealthier than others? Most of these questions cannot be stated as well-defined supervised problems, and might benefit enormously from multidisciplinary research efforts involving easy-to-interpret models and rigorous data exploratory analyses. Efficient data exploration might lead to life-changing scientific discoveries, which can later be turned into a more impactful exploitation phase, to put forward more informed policy recommendations, decision-making systems, medical protocols or improved models for highly accurate predictions. This thesis proposes tailored Bayesian nonparametric (BNP) models to solve specific data exploratory tasks across different scientific areas including sport sciences, cancer research, and economics. We resort to BNP approaches to facilitate the discovery of unexpected hidden patterns within data. BNP models place a prior distribution over an infinite-dimensional parameter space, which makes them particularly useful in probabilistic models where the number of hidden parameters is unknown a priori. Under this prior distribution, the posterior distribution of the hidden parameters given the data will assign high probability mass to those configurations that best explain the observations. Hence, inference over the hidden variables can be performed using standard Bayesian inference techniques, therefore avoiding expensive model selection steps. This thesis is application-focused and highly multidisciplinary. More precisely, we propose an automatic grading system for sportive competitions to compare athletic performance regardless of age, gender and environmental aspects; we develop BNP models to perform genetic association and biomarker discovery in cancer research, either using genetic information and Electronic Health Records or clinical trial data; finally, we present a flexible infinite latent factor model of international trade data to understand the underlying economic structure of countries and their evolution over time.Uno de los principales desafíos de nuestro tiempo es encontrar sentido dentro de los datos. Con la aparición de tecnologías como Internet, redes de sensores, o métodos de secuenciación profunda del genoma, una verdadera explosión digital se ha visto desencadenada, afectando todos los campos científicos, así como nuestra vida diaria. Logros recientes como pueden ser los coches auto-dirigidos o programas que ganan a los seres humanos al milenario juego del Go, han demostrado con creces los posibles beneficios que podemos obtener de la explotación de datos, mayoritariamente en tareas supervisadas bien definidas. No obstante, apenas hemos empezado con la exploración de datos y su verdadero entendimiento. En verdad, los datos encierran información muy valiosa para responder a muchas de las preguntas más importantes para la humanidad: ¿Cómo afecta el envejecimiento a nuestras aptitudes físicas? ¿Cuáles son los mecanismos subyacentes del cáncer? ¿Qué factores explican la riqueza de ciertos países frente a otros? Si bien la mayoría de estas preguntas no pueden formularse como problemas supervisados bien definidos, éstas pueden ser abordadas mediante esfuerzos de investigación multidisciplinar que involucren modelos fáciles de interpretar y análisis exploratorios rigurosos. Explorar los datos de manera eficiente abre potencialmente la puerta a un sinnúmero de descubrimientos científicos en diversas áreas con impacto real en nuestras vidas, descubrimientos que a su vez pueden llevarnos a una mejor explotación de los datos, resultando en recomendaciones políticas adecuadas, sistemas precisos de toma de decisión, protocolos médicos optimizados o modelos con mejores capacidades predictivas. Esta tesis propone modelos Bayesianos no-paramétricos (BNP) adecuados para la resolución específica de tareas explorativas de los datos en diversos ámbitos científicos incluyendo ciencias del deporte, investigación contra el cáncer, o economía. Recurrimos a un planteamiento BNP para facilitar el descubrimiento de patrones ocultos inesperados subyacentes en los datos. Los modelos BNP definen una distribución a priori sobre un espacio de parámetros de dimensión infinita, lo cual los hace especialmente atractivos para enfoques probabilísticos donde el número de parámetros latentes es en principio desconocido. Bajo dicha distribución a priori, la distribución a posteriori de los parámetros ocultos dados los datos asignará mayor probabilidad a aquellas configuraciones que mejor explican las observaciones. De esta manera, la inferencia sobre el espacio de variables ocultas puede realizarse mediante técnicas estándar de inferencia Bayesiana, evitando el proceso de selección de modelos. Esta tesis se centra en el ámbito de las aplicaciones, y es de naturaleza multidisciplinar. En concreto, proponemos un sistema de gradación automática para comparar el rendimiento deportivo de atletas independientemente de su edad o género, así como de otros factores del entorno. Desarrollamos modelos BNP para descubrir asociaciones genéticas y biomarcadores dentro de la investigación contra el cáncer, ya sea contrastando información genética con la historia clínica electrónica de los pacientes, o utilizando datos de ensayos clínicos; finalmente, presentamos un modelo flexible de factores latentes infinito para datos de comercio internacional, con el objetivo de entender la estructura económica de los distintos países y su correspondiente evolución a lo largo del tiempo.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Joaquín Míguez Arenas.- Secretario: Daniel Hernández Lobato.- Vocal: Cédric Archambea

Evaluation of the ingestive behaviour of the dairy cow under two systems of rotation with slope

The ingestive behaviour of grazing animals is modulated by the vegetation characteristics, topography and the type of stocking method. This research was carried out in 2019, at the Rumipamba CADER-UCE. It aimed to evaluate the impact of two contrasting stocking methods of dairy cows grazing a pasture with an average of slope >8.5%. Four dairy cows were set to graze a 0.4 ha paddock for 5 days for continuous stocking methods, while for the electric fence methods the dairy cows were restricted to 0.2 ha and the fence was moved uphill every 3 hours, repeating this process four times a day. Cow were equipped with activity sensors for 12 h per day. The whole procedure was repeated 2 times after realizing an equalization cuts and both paddocks, a rest time of 30 days and a random reassignment of paddocks to one of the treatments. The cows showed a difference in terms of the percentage of grazing P=0.0072, being higher with the electric fence (55% of the measurement time). From rising-plate-meter estimates of available biomass along the grazing periods, we calculated despite similar forage allowances (electric fence = 48.06 kg DM/cow/d and continuous = 48.21 DM/cow/d) a higher forage intake was obtained in the electric fence treatment (17.5 kg DM/cow/d) compared the continuous stocking (15.7 kg DM/cow/d) (P=0.006). In terms of milk production animals grazing under the differences electrical fence stocking method tended (P=0.0985) to produce more milk (17.39 kg/d) than those grazing in the continuous system (15.16 kg/d) due to the influence of the slope (P=0.05), while for milk quality the protein content was higher for the electric fence (33.7 g/l) than the continuous method (30.5 g/l) (P=0.039). None of the other milk properties differed between methods (P>0.05)

Mathematical analysis for tumor growth model of ordinary differential equations

Special functions occur quite frequently in mathematical analysis and lend itself rather frequently in physical and engineering applications. Among the special functions, gamma function seemed to be widely used. The purpose of this thesis is to analyse the various properties of gamma function and use these properties and its definition to derive and tackle some integration problem which occur quite frequently in applications. It should be noted that if elementary techniques such as substitution and integration by parts were used to tackle most of the integration problems, then we will end up with frustration. Due to this, importance of gamma function cannot be denied