Integrating Genomics with Nutrition Models to Improve the Prediction of Cattle Performance and Carcass Composition under Feedlot Conditions

Journal of Animal Science

2019

This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models ( MM ) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler’s ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence ( AI ) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.

Section: Extant Mathematical Models In Ruminant Productionmentioning

confidence: 99%

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

Journal of Animal Science

2019

“…With the advancements in the Precision nutrition of ruminants identification of single nucleotide polymorphism panels, specific molecular breeding values have been computed for relevant traits for the beef cattle industry. The integration of nutrition models and genomics has been conceptualised and reported to improve the predictability of deposition of carcass fat and protein in growing cattle (Tedeschi, 2015). In addition, new sensor technologies show great potential to inform both animal nutrition models and genetic predictions, as these can facilitate the collection of phenotypic data (Greenwood et al, 2016), and increase their accuracy such as in the case of X-ray body scanners for automated measurements of carcass composition and yield (Scholz et al, 2015).…”

Section: Simulation Modelling: the Role Of Ruminant Nutrition Modelsmentioning

confidence: 99%

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

Gonzalez

Kyriazakis

2018

Animal

A plethora of sensors and information technologies with applications to the precision nutrition of herbivores have been developed and continue to be developed. The nutritional processes start outside of the animal body with the available feed (quantity and quality) and continue inside it once the feed is consumed, degraded in the gastrointestinal tract and metabolised by organs and tissues. Finally, some nutrients are wasted via urination, defecation and gaseous emissions through breathing and belching whereas remaining nutrients ensure maintenance and production. Nowadays, several processes can be monitored in real-time using new technologies, but although these provide valuable data ‘as is’, further gains could be obtained using this information as inputs to nutrition simulation models to predict unmeasurable variables in real-time and to forecast outcomes of interest. Data provided by sensors can create synergies with simulation models and this approach has the potential to expand current applications. In addition, data provided by sensors could be used with advanced analytical techniques such as data fusion, optimisation techniques and machine learning to improve their value for applications in precision animal nutrition. The present paper reviews technologies that can monitor different nutritional processes relevant to animal production, profitability, environmental management and welfare. We discussed the model-data fusion approach in which data provided by sensor technologies can be used as input of nutrition simulation models in near-real time to produce more accurate, certain and timely predictions. We also discuss some examples that have taken this model-data fusion approach to complement the capabilities of both models and sensor data, and provided examples such as predicting feed intake and methane emissions. Challenges with automatising the nutritional management of individual animals include monitoring and predicting of the flow of nutrients including nutrient intake, quantity and composition of body growth and milk production, gestation, maintenance and physical activities at the individual animal level. We concluded that the livestock industries are already seeing benefits from the development of sensor and information technologies, and this benefit is expected to grow exponentially soon with the integration of nutrition simulation models and techniques for big data analysis. However, this approach may need re-evaluating or performing new empirical research in both fields of animal nutrition and simulation modelling to accommodate a new type of data provided by the sensor technologies.

“…Oltjen et al (2000) integrated genetic parameters (breeding value) into a growth model Oltjen et al, 1986;Soboleva et al, 1999). Recently, Tedeschi (2015) used SNP panels to improve the predictability of days on feed to reach desired carcass composition of growing beef cattle by the Cattle Value Discovery System . In their analysis, the inclusion of the molecular breeding value of ribeye area increased by 13% the precision to predict body weight at 28% empty body fat.…”

Section: Integrating Genomics Into Nutrition Modelingmentioning

confidence: 99%

A glimpse of the future in animal nutrition science. 2. Current and future solutions

Fonseca

Muir³

et al. 2017

R. Bras. Zootec.

-Despite tremendous advancements in the livestock sector, additional opportunities exist to improve even further livestock production around the globe. Forecasting is not an exact science and it relies heavily on past and current knowledge. Improvements in the nutritional sciences (both human and animal) include a better understanding of agents that cause deterioration of human health, improving the quality of animal products, applying effective fetal programming, developing new feeds and feeding strategies, and revisiting longstanding technologies. Improvements in the understanding of the rumen microbiome will enable scientists to increase the fermentation efficiency and, hopefully, select microbial species of greater interest. Improvements in remote sensing and ground-based instrumentation, telecommunications, and weather forecasting technologies will aid in the continued improvements of early warning systems to assist livestock producers in reducing risk and adapting to the changing environment. Broad utilization of sensor technologies will allow scientists to collect real-time data and, when combined with mathematical modeling, decision support systems will become an indispensable managerial tool for livestock production with the possibility to automate low-level decisions on the farm, such as supplementation schedules, sorting of animals, and early detection of disease and outbreaks. The identification of feed efficient animals may be the single most impactful advancement towards long-term livestock sustainability and the promise of feeding the world animal products. We contend that education across societal levels is the first step to solve current and future challenges of the livestock industry. The dilemma has been who will take the first step forward.