Dan Tulpan

University of Guelph | UOGuelph · Centre for Genetic Improvement of Livestock, Department of Animal Biosciences

The Human Accelerated Region 1, HAR1, is the most rapidly evolving region in the human genome. It is part of two overlapping long non-coding RNAs, has a length of only 118 nucleotides and features 18 human specific changes compared to an ancestral sequence that is extremely well conserved across non-human primates. The human HAR1 forms a stable sec...

Advanced computing and information technology have a crucial role in addressing challenges in livestock production by offering innovative solutions to improve efficiency, productivity, and animal welfare. These technologies rely on intelligent combinations of data analytics, Machine and Deep Learning (ML, DL), Internet of Things (IoT), and robotics...

Pellet quality is a crucial key performance indicator (KPI) for commercial feed manufacturing, which influences both the efficiency of the feed mill and downstream performance of animals fed these diets. However, due to the complexity of feed manufacturing and the large number of factors involved in the manufacturing process, controlling pellet qua...

Cattle methane emissions (ME) account for approximately 6% of global anthropogenic greenhouse gas emissions. Given the challenges in measuring ME directly from individual animals, there is a need for the development of novel indirect methods. Rumination time (RT) and milk mid-infrared spectral data (MIR) show promise for the indirect assessment of...

This hands-on workshop aims to offer examples of computational pipelines that apply traditional predictive machine learning modelling techniques to animal science datasets with the purpose of solving classification and/or regression problems. The objective of this workshop is to provide the attendants with fully functional and customizable computat...

Our objectives were to investigate whether prepartum cytokine responses to ex vivo lipopolysaccharide (LPS) stimulation of whole blood is associated with incidence of postpartum clinical disease. Blood samples from 332 cows were collected 15 d before the expected calving and divided into 2 aliquots. One aliquot remained as an unstimulated control,...

Introduction Microbes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learning is a promising option in that context. While am...

Machine Learning (ML) and Deep Learning (DL) are two sub-fields of that focus on creating predictive models from data. The algorithms used in ML and DL have been widely used in various livestock science studies, but it is uncommon for both ML and DL to be applied to the same problem. This talk presents examples where both ML and DL algorithms were...

Pig aggression is a major problem facing the industry as it negatively affects both the welfare and the productivity of group-housed pigs. This study aimed to use a supervised deep learning approach based on a Convolutional Neural Network (CNN) and image differential to automatically detect aggressive behaviours in pairs of pigs. Different pairs of...

Heat stress is a prominent issue in livestock production, even for intensively housed dairy herds in Canada. Production records and meteorological data can be combined to assess heat tolerance in dairy cattle. The overall aim of this study was to evaluate the possibility of genetic evaluation for heat tolerance in Canadian dairy cattle. The 2 speci...

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS)...

Nowadays, vast amounts of data representing feed intake, growth, and environmental impact of individual animals are being recorded in on-farm settings. Despite their apparent use, data collected in real-world applications often have missing values in one or several variables, due to reasons including human error, machine error, or sampling frequenc...

Within poultry production systems, models have provided vital decision support, opportunity analysis, and performance optimization capabilities to nutritionists and producers for decades. In recent years, due to the advancement of digital and sensor technologies, 'Big Data' streams have emerged, optimally positioned to be analyzed by machine-learni...

Domestic chickens may live in environments which restrict wing muscle usage. Notably, reduced wing activity and accompanying muscle weakness are hypothesized risk factors for keel bone fractures and deviations. We used radio-frequency identification (RFID) to measure duration spent at elevated resources (feeders, nest-boxes), ultrasonography to mea...

In the dairy industry, mate allocation is dependent on the producer’s breeding goals and the parents’ breeding values. The probability of pregnancy differs among sire-dam combinations, and the compatibility of a pair may vary due to the combination of gametic haplotypes. Under the hypothesis that incomplete incompatibility would reduce the odds of...

Weather station data and test-day production records can be combined to quantify the effects of heat stress on production traits in dairy cattle. However, meteorological data sets that are retrieved from ground-based weather stations can be limited by spatial and temporal data gaps. The National Aeronautics and Space Administration Prediction of Wo...

Background: Microbes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learning is a promising option in that context. While amp...

In the dairy industry, producers determine mate allocation by selecting future offspring with genetic potential aligning with their breeding goal. However, not all sire-dam combinations are equally fertile, influencing the mating outcome. Under Mendel's laws, alleles on the same locus should segregate independently and be equally transmitted to the...

Pelleted feeds are widely used in monogastric animal production systems because they not only improve animal performance (increasing digestibility and feed consumption) but are convenient to store and handle. However, pellet quality can be affected by many factors. While previous studies have reported the effect of a single or several factors on pe...

As the world population grows and the increase in food consumption creates unprecedented pressure on beef producers, innovative technologies for sustainable and efficient beef production is quintessential. Therefore, a significant effort is ongoing on developing digital precision technologies for continuous beef cattle monitoring and accurate cattl...

Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Can...

Dairy farmers are motivated to ensure cows become pregnant in an optimal and timely manner. Although timed artificial insemination (TAI) is a successful management tool in dairy cattle, it masks an animal's innate fertility performance, likely reducing the accuracy of genetic evaluations for fertility traits. Therefore, separating fertility traits...

Interest in reducing eructed CH4 is growing, but measuring CH4 emissions is expensive and difficult in large populations. In this study, we investigated the effectiveness of milk mid-infrared spectroscopy (MIRS) data to predict CH4 emission in lactating Canadian Holstein cows. A total of 181 weekly average CH4 records from 158 Canadian cows and 217...

Body dimensions are key indicators for the beef cattle fattening and breeding process. On-animal measurement is relatively inefficient, and can induce severe stress responses among beef cattle and pose a risk for operators, thereby impacting the cattle’s growth rate and wellbeing. To address the above issues, a highly efficient and automatic method...

Rumination time was assessed as an indicator of efficiency and sustainability in dairy cattle. This study comprised 7782 records on 656 mid-first lactation Holstein cows. Animal models were used for rumination time and methane emission traits, while repeatability animal models were used for feed efficiency and production traits in bivariate analyse...

Body size, weight, and body condition score parameters are key indicators for monitoring cattle growth and they can be utilized to predict beef cattle yield and evaluate economic traits. However, it is easy to lay intense stress on cattle while measuring livestock’s body size manually, also along with giving negative effects on their feeding and we...

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathe...

Ground-dwelling species of birds, such as domestic chickens ( Gallus gallus domesticus ), experience difficulties sustaining flight due to high wing loading. This limited flight ability may be exacerbated by loss of flight feathers that is prevalent among egg-laying chickens. Despite this, chickens housed in aviary style systems need to use flight...

In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating re...

The live body weight (LBW) is an important parameter providing guidance for estimation of growth and feed conversion efficiency, body condition, presence of disease, and management of housing, nutrition and animal health in different life stages of livestock. This research study explores the possibility of developing a semi-automatic analytic syste...

In the dairy industry, mate allocation is mainly based on the parents’ breeding values and inbreeding coefficients aiming to achieve the producer’s breeding goal. With artificial insemination, the portfolio of sires to choose from is large and the quality of the semen doses is standardized. However, not all sire-dam matings are equally likely to pr...

The objective of this study is to investigate how linear body measurements relate to and can be used to predict calf body weight using linear and machine learning models. To meet these objectives, a total of 103 Angus cross calves were enrolled in the study from wk 2 - 8. Calves were weighed and linear measurements were collected weekly, such as: p...

This is a hands-on workshop offered as a pre-conference training opportunity for researchers interested in applying machine learning techniques to animal science datasets with the purpose of classifying, clustering, performing linear and non-linear regressions or selecting a subset of features relevant to further studies. The objective of this work...

Genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering quantitative trait loci (QTL) associated with complex traits in plant species. Insufficient statistical power is a limiting factor that current conventional GWAS methods are suffering from, especially in narrow genetic bases plants such as soybe...

Recent advanced high-throughput field phenotyping combined with sophisticated big data analysis methods have provided plant breeders with unprecedented tools for a better prediction of important agronomic traits, such as yield and fresh biomass (FBIO), at early growth stages. This study aimed to demonstrate the potential use of 35 selected hyperspe...

Genome-wide association study (GWAS) is currently one of the important approaches for discovering quantitative trait loci (QTL) associated with traits of interest. However, insufficient statistical power is the limiting factor in current conventional GWAS methods for characterizing quantitative traits, especially in narrow genetic bases plants such...

Improving genetic yield potential in major food grade crops such as soybean (Glycine max L.) is the most sustainable way to address the growing global food demand and its security concerns. Yield is a complex trait and reliant on various related variables called yield components. In this study, the five most important yield component traits in soyb...

Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approach...

Recent substantial advances in high-throughput field phenotyping have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits at early growth stages. Nevertheless, the implementation of large datasets generated by high-throughput phenotyping tools such as hyperspectral re...

To meet the growing consumer demand for chicken meat, the poultry industry has selected broiler chickens for increasing efficiency and breast yield. While this high productivity means affordable and consistent product, it has come at a cost to broiler welfare. There has been increasing advocacy and consumer pressure on primary breeders, producers,...

Heat stress in dairy cattle is an existing issue in temperate regions that can cause reduced milk production, impaired fertility, and mortality. Genetic selection for heat tolerance using test-day production records and weather station data is a potential mitigation strategy. However, weather stations can have temporal data gaps and a low spatial r...

This is a hands-on workshop offered as a pre-conference training opportunity for researchers interested in applying machine learning techniques to animal science datasets with the purpose of classifying, clustering, performing linear and non-linear regressions or selecting a subset of features relevant to further studies. The objective of this work...

The aim of this project is to compare some of the state-of-the-art machine learning algorithms on the classification of steers finished in feedlots based on performance, carcass and meat quality traits. The precise classification of animals allows for fast, real-time decision making in animal food industry, such as culling or retention of herd anim...

To meet the growing consumer demand for chicken meat, the poultry industry has selected broiler chickens for increasing efficiency and breast yield. While this high productivity means affordable and consistent product, it has come at a cost to broiler welfare. There has been increasing advocacy and consumer pressure on primary breeders, producers,...

Mechanistic models (MMs) have served as causal pathway analysis and 'decision-support' tools within animal production systems for decades. Such models quantitatively define how a biological system works based on causal relationships and use that cumulative biological knowledge to generate predictions and recommendations (in practice) and generate/e...

Background The task of recognizing species names in scientific articles is a quintessential step for a large number of applications in high-throughput text mining and data analytics, such as species-specific information collection, construction of species food networks and trophic relationship extraction. These tasks become even more important in f...

Objectives Greater knowledge of variance and relationships of pork carcass parameters could be used to improve performance, efficiency, and profitability of the pork industry. Previous research has investigated the correlation between pork carcass parameters; however, there are still many misunderstandings, particularly in commercially representati...

This study aimed to examine the correlation of carcass weight, fat depth, muscle depth, and predicted lean yield in commercial pigs. Data were collected on 850,819 pork carcasses from the same pork processing facility between October 2017 and September 2018. Hot carcass weight was reported following slaughter as a head-on weight; while fat and musc...

The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. W...

Background Predicted RNA secondary structures are typically visualized using dot-plots for base pair binding probabilities and planar graphs for unique structures, such as the minimum free energy structure. These are however difficult to analyze simultaneously. Results This work introduces a compact unified view of the most stable conformation of...

Background: The mitogen-activated protein kinase (MAPK) family is involved in signal transduction networks that underpin many different biological processes in plants, ranging from development to biotic and abiotic stress responses. To date this class of enzymes has received little attention in Triticeae species, which include important cereal crop...