The TWO volumes of the THIRD edition was released in May 2020

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The "blue" book

The Ruminant Nutrition System has two volumes now. To facilitate the dissemination of the computer model, we are publishing An Applied Model for Predicting Nutrient Requirements and Feed Utilization in Ruminants (RNS) as Volume I (the "blue" book) and the Tables of Equations and Coding (TEC) as Volume II (the "red" book) of The Ruminant Nutrition System publications. You can get the latest books as hardcover or paperback editions from Amazon.com (Volume I and Volume II) or XanEdu.com (Volume I and Volume II).

The first edition of The Ruminant Nutrition System: An Applied Model for Predicting Nutrient Requirements and Feed Utilization in Ruminants was published in October 2016. Since then we have received much positive feedback, which has encouraged us to revise and expand it. In the second edition, we updated concepts and added new information, clarified and enhanced the discussions of important topics, included new and improved and standardized existing graphics and illustrations, rearranged some of the text, and included indexes for subjects and authors. Although we believe the second edition of The Ruminant Nutrition System was a lot more inclusive and complete, we were always seeking for new ideas and how to implement them. At that time, some branches of sciences were experiencing rapid progress because of their economic relevance, as well as the pace of their development and application of novel technologies. One example of such progress was the ability to manipulate microorganisms genomically to produce biofuel more efficiently and in a more sustainable way. In 2016, researchers successfully added a laccase, or lignin-degrading enzymes, from the aerobic bacterium Thermobifida fusca into a designer cellulosome, a multienzyme complex structure commonly found in anaerobic bacteria. The resultant chimera had the ability to degrade cellulose, hemicellulose, and lignin simultaneously. This use of laccase may be an early example of the rapid application of this long-known enzyme in biofuel production and other industries, such as pulp and paper and crop biotechnology. Applications of such technology in ruminant nutrition could yield enormous benefits not yet realized, even though its adoption may not happen in the near future. This technology may eventually change the way we understand indigestible dietary compounds. Rapid scientific developments such as this pose an interesting challenge for nutrition modeling. They require nutritionists to be constantly aware of discoveries and determine how to adopt them in the livestock industry. It is imperative that nutrition modeling follow the same pace of technological evolution and be responsive to new breakthroughs.

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The "red" book

Since the publication of the second edition of The Ruminant Nutrition System: An Applied Model for Predicting Nutrient Requirements and Feed Utilization in Ruminants in January 2018, we have continued to receive encouraging feedback from readers around the world. We learned that The Ruminant Nutrition System book has been adopted as a textbook for advanced courses in ruminant nutrition and modeling. In the third edition, we have thoroughly revised the text, equations, and illustrations for correctness and clarity. Virtually every page had a modification. Sometimes we made a minor correction, but sometimes we rephrased a whole paragraph or updated an illustration. There are also a few new textual additions. We wrote new sentences to corroborate or supplement the text, highlight pertinent findings from recent research, and provide additional information and clarification while, as much as possible, maintaining intact the structure and pagination of the second edition. We changed the background color of all equations to improve their visibility within the text. We also revised and updated the references by reducing their font size and adding the digital object identifiers when avail-able. There are 2,427 references in the Volume I (the "blue" book). Artificial intelligence, a data-intensive computational process, has received growing attention in the last decade, more specifically the machine learning and deep learning approaches. Because farms have been developing large databases, artificial intelligence has gained some traction in agriculture. The publicity around artificial intelligence promotes it as a solution for real-world problems, which may reduce the interest in mechanistic modeling. However, the integration of mathematical modeling and artificial intel-ligence is likely to spur an avant-garde technological wave in predictive analytics, resulting in a paradigm shift in the mathematical modeling domain. This shift will be so great that in the near future we will likely be using hybrid knowledge- and data-driven models. With this in mind, our goal is to make the information in The Ruminant Nutrition System book and the associ-ated computer model ready for integration with artificial intelligence. Since we started writing the first edition of the Ruminant Nutrition System, we planned to include the computer model’s equations and the calculation logic. The book, however, quickly became a comprehensive document of the published research used to identify essential equations and variables for the under-lying calculation logic of the RNS model. It was a rich, dense source of information about the biology and nutrition of ruminants and the mathematical modeling concepts behind the computer model. As a result, we scattered the RNS model equations throughout the book, within the appropriate chapters containing the pertinent scientific discussions, sometimes making it difficult for the reader to reconstruct the computer model. Soon after the publication of the first edition of the book, readers wishing to see the RNS model equations, their linkages, the calculation logic, and how they were implemented into the computer software started requesting more details. To meet this need, we needed to produce a companion book focused on describing the RNS model’s equations and code. Before releasing it to the public, however, we had to make sure that the equations accurately reflected the concepts (i.e., the validation step in mathematical modeling) delineated in the RNS’s Volume I. The RNS’s Volume II Tables of Equations and Code (the "red" book) arose because of our commitment to document and disseminate the mathematics composing the RNS model clearly and in detail. Each part of Volume II presents the RNS model’s equations and the calculation logic in two ways. The first, more traditional approach lists the equations in a tabular form, including an equation number, the independent variable with its description, and a mathematical formulation (in the form of the equation) that follows a logical sequence for calculation and execution. The second approach embeds the equations into a modern, highly aggre-gated method of an actual computer programming language structure, the R script. This second approach presents the sequence and the calculation logic for the equations more systematically and coherently than the first approach for those wishing to understand how the RNS calculations were programmed.

Comments about The Ruminant Nutrition System book:

“…it is an impressive work and very useful for student and also for more experienced scientists. I hope to have sometimes time to read it thoroughly and extracts ideas for improving Karoline model… Congratulations of such impressive work.”––Pekka Huhtanen, Professor; Swedish University Agriculture Science, Sweden. November 2016.

“… this book is a great achievement and is definitely the most advanced available on nutritional modeling and feeding systems. It is much more complete than the sum of the various NRC books and it provides a lot of new and integrated information. What I liked a lot is your ability in explaining all the biology behind the phenomena, and linking it to the many mathematical models described and their development over time. The introductory historical part is also unique, I am not aware of any other similar description of the integrated history of nutritional models. All this will be extremely valuable for many categories of scientists and professionals: researcher specialized in the area of nutritional modelling, researchers in ruminant nutrition with focus on other areas, Master and PhD students, whom will find a lot of knowledge, documentation and inspiration to develop their own research, professional that want to understand what they do.”––Antonello Cannas, Professor; University of Sassari, Italy. January 2017.

“Congratulations on a task very well done. I have cracked open your new book and only wish I could go on vacation from my day job for a few years to digest all of the scientific knowledge you have poured into it… I know and have an appreciation for all the hard work the both of you plus others within your teams have done thru the years and to get it documented and made available for others to use and learn from has to be very fulfilling and rewarding. Job very well done… I did a quick analysis of approximately how many cattle we have sorted with your models thru the years starting in 1994… It would be safe to say over 10 million head sorted with various versions of the Cornell Value Discovery System (CVDS) under our multiple packaged processes… That is a fair sum of money your base scientific technology has put in our clients pockets thru the last 24 years… I know many other business entities are using your work in various production systems. You and your associates have had a huge positive impact on the efficiency of production within the cattle industry… We (PCC, PCC clients, and our business partners) have identified numerous research and development projects we plan to develop with your models being a key element of technology packaged processes for commercial cattle operations. We plan for the processes to be simple to implement, run at the speed of commerce, improve production efficiency, produce high quality beef and add more profitability to the enterprise. (My simple definition of Sustainability)”––Max D. Garrison, DVM, CEO; Performance Cattle Company, LLC, Amarillo. March 2017.

“This book provides an excellent reference to the structure, philosophy and history behind the original Cornell Net Carbohydrate and Protein System project and its further evolution and expansion into the Ruminant Nutrition System. This effort successfully integrated knowledge from a wide variety of distinguished scientists and disciplines into a cohesive framework around which animal scientists can extend their understanding and apply the embedded concepts to real world situations. The significance of that achievement cannot be overstated, and in my humble opinion, this work describes the agricultural equivalent of the Manhattan project. While the mathematics in some sections may not be for the faint of heart, this book represents a comprehensive ‘state of the art’ of our current understanding of ruminant nutrition in very fine detail. Even the most seasoned of animal scientists will not be able to get through this book in one pass, not so much due to difficulty, but because it serves to stimulate the generation of new ideas to move the science forward in such a positive way.”––Michael C. Barry, CEO; AgModels LLC, Tully, NY. April 2017

Drs. Tedeschi and Fox have “broadened the Cornell model and integrated it with related fields of biology, a nutritional system with wide application in the nutritional sciences.”––Peter J. Van Soest, Professor Emeritus; Cornell University, Ithaca, NY. September 2017

“The Ruminant Nutrition System is an exceedingly worthwhile tool for all scientists interested in physiology and nutrition of ruminants. It is highly recommendable for teaching and research of graduate students at the master and PhD levels in animal sciences, but also in life sciences, wildlife and fisheries sciences, ecosystem sciences and management, veterinary medicine as well as biology and zoology. Moreover, the book will also be valuable to practicing nutritionists who are looking for advanced information on applied ruminant nutrition and wish to understand biological and nutritional modelling of nutrient requirements by ruminants and nutrients supplied by feedstuffs undergoing ruminal fermentation, postruminal digestion, and nutrient absorption.”––Gerhard Flachowsky, Professor; Federal Research Institute for Animal Health, Braunschweig, Germany; September 2017.

About the authors:

Dr. Luis O. Tedeschi

Luis Tedeschi is a professor in the Department of Animal Science at Texas A&M University. He received his Bachelor of Science degree in Agronomy Engineering and Master of Science degree in Animal and Forage Sciences from the University of São Paulo (Piracicaba, Brazil), and his Doctor of Philosophy degree in Animal Science from Cornell University (Ithaca, NY). His research focuses on the integration of accumulated scientific knowledge of ruminant nutrition into mathematical models to solve contemporary problems. The nutrition models he has developed are being used to develop more efficient production systems while reducing resource use and impact on the environment. He has published more than 250 articles in peer-reviewed journals and book chapters and presented at more than 80 modeling nutrition conferences and workshops worldwide. Tedeschi is a Texas A&M AgriLife Research Faculty Fellow and recipient of the 2011 Sir Frederick McMaster Fellowship and the 2013 J. William Fulbright Scholarship. He received the 2017 American Feed Industry Association in Ruminant Nutrition Research Award and the 2019 Texas A&M University Chancellor EDGES Fellowships. He served on the committee of the 2016 Nutrient Requirement of Beef Cattle by the National Academies of Sciences, Engineering, and Medicine.

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Dr. Danny G. Fox

Danny Fox is a professor emeritus of the Department of Animal Science at Cornell University. He received his Bachelor of Science, Master of Science, and Doctor of Philosophy degrees from The Ohio State University. His 35 years of research focused on the development of data, methods, models, and computer programs to accurately predict cattle nutrient requirements, as well as nutrients derived from feeds to meet cattle requirements in unique production situations worldwide. His team at Cornell developed the Cornell Net Carbohydrate and Protein System cattle nutrition model and software, which has users in more than 42 countries, for formulating rations for beef and dairy cattle. Fox has been a member of numerous national committees, including National Research Council committees on Animal Nutrition, Feed Intake, and the 1996 Nutrient Requirements of Beef Cattle. His growth and energy reserves models were adapted by both the 1996 Beef Cattle National Research Council committee and the 2001 Dairy Cattle National Research Council committee. Fox received numerous awards during his 35-year career. In 2019, he was inducted into The Ohio State University Animal Sciences Hall of Fame and received the Plains Nutrition Council Legends of Feedlot Nutrition Award.

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The Table of Contents of the Third Edition of Volume I is here and Volume II is here.

 

Brief Synopsis of Mathematical Nutrition Models

For approximately 55 years, computer models have been used as Decision Support Systems (DSS) to apply scientific knowledge to virtually every branch of science: from life sciences (e.g., development of the molecular structure of drugs and the management and planning for sustainable production of foods) to earth sciences (e.g., space exploration and global warming). Humankind has benefited tremendously by using DSS in specific areas for which experimentation is practically impossible or infeasible. Decision Support Systems (also referred to as Smart Decision Tools) can be broadly categorized into five classes: communication-driven, data-driven, document-driven, knowledge-driven, and model-driven (D. J. Powers). In the late 1960s, data-driven and model-driven DSS were built based on scientific knowledge, theory development, and operational research concepts. However, it was not until the advancement of microcomputers and software in the mid-1980s that DSS became user friendly and started being applied practically. The development of DSS was tightly connected to the evolution of the architecture and processing power of microcomputers.

Ruminant animals are widely utilized to convert human-inedible feedstuffs to nutritious food under widely varying conditions around the world. The goals of enhancing ruminant nutrition are to improve productivity, reduce resource use, and protect the environment. However, scientists often have to extrapolate nutrient requirements and feed values developed under standardized, controlled, laboratory research conditions to all combinations of cattle types, feeds, and environmental and management conditions. In these cases, DSS can be used as virtual simulators to predict nutritional requirements and feed utilization in a variety of production settings.

The Large Ruminant Nutrition System (LRNS) is a computer model that estimates beef and dairy cattle nutrient requirements and supply under specific conditions of animal type, environment (climatic factors), management, and physicochemical composition of available feeds. Accounting for farm-specific management, environmental, and dietary characteristics has enabled more accurate prediction of cattle growth, milk production, and nutrient excretion in diverse production situations have been possible. The LRNS uses the basic computational engine of the Cornell Net Carbohydrate and Protein System (CNCPS) model, version 5, with additional modifications and implementations.

In collaboration with Cornell University and the University of Sassari in Italy, we developed the Small Ruminant Nutrition System (SRNS). The SRNS, based on the structure of the CNCPS for Sheep, is a computer model for predicting the nutrient requirements of sheep and feed biological values on farms. The SRNS predicts energy, protein, calcium and phosphorus requirements, accounting for animal factors (e.g., body weight, age, insulation, movement, milk production and composition, body reserves, mature weight, and pregnancy) and environmental factors (e.g., current and previous temperature, wind, and rainfall) factors. Feed biological values are predicted based on the pool size and fractional degradation and passage rates of carbohydrate and protein fractions, ruminal microbial growth, and physically effective fiber. The system predicts dry matter intake separately for different sheep categories based on equations developed for sheep fed indoors and on pasture. Based on this information, the SRNS predicts the energy balance of the animals. Energy balance is used to predict adult sheep’s body condition score, body weight variations, and, in lactating ewes, the amount of milk produced. For growing sheep, based on the energy balance and on the relative size of the lambs, the SRNS predicts average daily gain and the composition of the gain (fat, protein, water, and minerals). For feed biological values, the SRNS predicts ruminal pH based on dietary physically effective fiber, rumen nitrogen and peptide balances, the digestibility of each nutrient by the rumen and by the whole digestive tract, metabolizable protein from ruminal microbial protein and ruminally undegraded feed protein, and the energy cost of urea production and excretion. The system also predicts fecal and urinary excretions for each nutrient.

The Cattle Value Discovery System for growing cattle (CVDSgc) represents an evolution of a growth model first published by Fox and Black (1984) to account for differences in breed type and mature size when predicting performance and profitability of feedlot cattle with alternative management systems. Since then, modifications to the system, summarized by Tedeschi, et al. (2004), have improved its accuracy to account for more of the variation in nutrient requirements and performance of growing beef cattle. The CVDS was developed for use in individual cattle management for growing beef cattle, and it provides (1) prediction of daily gain, incremental cost of gain and days to finish to optimize profits and marketing decisions while marketing within the window of acceptable carcass weights and composition; (2) predictions of carcass composition during growth to avoid discounts for under or over-weight carcasses and excess backfat; and (3) allocation of feed fed to pens to individual animals for the purpose of sorting of individuals into pens by days to reach target body composition and maximum individual profitability. This allows mixed ownership of individuals in pens, determination of individual animal cost of gain for the purposes of billing feed and predicting incremental cost of gain, and provision of information that can be used to select for feed efficiency and profitability.

A more detailed discussion of the history of these and other mathematical nutrition models as well as their future applications can be found in this article.

 

Why Mathematical Nutrition Models?

Mathematical ruminant nutrition models can be used to integrate our knowledge of feed, intake, and digestion and passage rates upon feed energy values, escape of dietary protein, and microbial growth efficiency. They can be valuable tools for estimating animal requirements and nutrients derived from feeds in each unique farm production scenario, and thus can have an important role in providing information that can be used in the decision-making process to enhance the feeding system (Tedeschi et al., 2005b). By accounting for farm-specific animal, feed, and environmental characteristics, more accurate prediction of dietary nutrient requirements for maintenance, growth and milk production of cattle and nutrient excretion in diverse production situations is possible (Fox et al., 2004).

In the United States, livestock farms are under increasing pressure to reduce nutrient accumulation on the farm and manure nutrient excretions in order to meet environmental regulations (Fox et al., 2006). The Natural Resources Conservation Service (NRCS), an office of the United States Department of Agriculture (USDA), has identified the need to improve feed management in concentrated animal feeding operations (CAFO) to reduce manure nutrients. The USDA-NRCS has developed a national conservation practice standard for feed management (#592; USDA-NRCS, 2003) to be used as part of the nutrient management (#590; USDA-NRCS, 2006) planning process. The purpose of a feed management plan is (1) to supply the quantity of available nutrients required by livestock while reducing the quantity of nutrients excreted, and (2) to improve net farm income by feeding nutrients more efficiently.

The development of feeding and nutrient management plans is complex and requires the integration of a large amount of research and knowledge information. Therefore, mathematical nutrition models can be used to assist in the deployment of technology that meets governmental regulations by facilitating the application and development of site-specific plans. Furthermore, mechanistic models more accurately account for animal and crop requirements, and manure and soil management than fixed, tabular guidelines because they can be customized and calibrated for site-specific characteristics and recommendations (Tedeschi et al., 2005a; Tedeschi et al., 2005b).

The identification of cattle requirements and formulating diets to meet those requirements more accurately is the best current strategy to minimize nutrient output per kg of meat or milk produced. The terms precision feeding and phase feeding have been widely used to describe nutrient management practices that result in reduced excretion of nutrients by CAFO. Both terms refer to a more precise nutrition system, where nutritionists meet cattle nutritional needs without supplying nutrients in excess, reducing outputs and inputs. Phase feeding of protein or protein withdrawal is a systematic method that applies precision feeding concepts to different phases of animal growth to accurately meet their nutrient requirements during the feeding period. Phase feeding involves formulating and providing more specific rations during growth-specific periods as the animal matures (Vasconcelos et al., 2007).

Mathematical models of ruminant nutrition have been employed for over three decades (Chalupa and Boston 2003) and have stimulated improvements in feeding cattle. More complete data sets available in recent years combined with different mathematical approaches have allowed us to improve nutrition models. Several mathematical models of ruminant nutrition have been develop in the past (Tedeschi et al. 2005b) and it is likely that frequency of use will increase to support decision making not only in the nutrition of cattle, but also for other aspects including farm economics, animal management, and assessment of environmental impact (Tylutki et al. 2004).

The development and application of mathematical models are essential in several branches of the scientific research domain. Notably, predictive models are used to estimate the outcomes of experiments that cannot be practically (or ethically) conducted, directly measured, are cost prohibitive, or simply because there is plenty of available data and the collection of new data is neither justifiable nor acceptable. Even though, models are generally accepted by the scientific community, the identification of their adequacy for predictive purposes is extremely important in building confidence and acceptance of the predictions in broader situations.

The need to evaluate the correctness of model predictions has been widely discussed and several techniques have been proposed (Easterling and Berger, 2002; Hamilton, 1991; Tedeschi, 2006). Nonetheless, most evaluations are superficial and provide little or no information regarding the ability of a model in predicting future outcomes. This can be partially explained because most mathematical models are designed to be static, deterministic, and range-dependent, implying that there is a range of optimum predictive ability and often they have a narrower and site-specific application rather than a broader one. A second reason is related to the difficult in assessing the suitability of mathematical models due to the intrinsic unaccounted for variation of the database; thus, affecting the results of the evaluation process. A thorough and unbiased evaluation of a model is a requisite not only to build confidence in the model’s predictions, but also in designing more resilient models. Lastly, a third reason lies in using the evaluation process to prove the rightness and robustness of a mathematical model or even to promote its acceptance and usability by others (Sterman, 2002).

 

Modelling a Sustainable Future for Livestock Production

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"Most of our current food production systems are based on maximising productivity and profitability with inadequate concern for protecting or regenerating the environment in the process. With a world population that is predicted to reach 9.55 billion by 2050, increasing pressure is being placed on global food production. Doing so while reducing the impact on the environment requires crop, soil and animal scientists around the world to come up with quick and effective solutions.

Livestock farming alone is one of the critical global contributors to greenhouse gases – accounting for up to 14% of emissions, depending on the production system. Other negative environmental impacts of the industry include nutrient run-off that pollutes water bodies, soil erosion, and the consumption of non-renewable resources.

These adverse environmental changes quickly offset improved agricultural productivity, through degradation of soil quality, increased warming, the resurgence of diseases and depletion of biodiversity, among many other outcomes. Indeed, meeting the future food requirements of our global population is not possible without environmental protection.

In short, to ensure that human population growth does not outstrip our ability to produce food, we must look after the natural resources that are at the very heart of the industry – so that they will be available for future generations. Furthermore, it is clear that any increase in food production must be achieved through enhanced yield, rather than expanding land area, as the latter would further increase the burden on the environment."

Read the complete article at Scientia, or download the PDF here.

 

Press Releases and Newsletters

October 22, 2020. Innovative agricultural solutions necessary to advance human health, sustain natural resources. AgriLife Today by Carrie Baker.

September 22, 2020. Can we produce more animal protein without damaging the environment? Research@Texas A&M by Key Ledbetter.

September 18, 2020. AgriLife Research expert uses math to predict environmental impacts of livestock production. AgriLife Today By Kay Ledbetter.

July 14, 2020. Modelling a Sustainable Future for Livestock Production. Scientia.

September 4, 2019. Teaching animal science through interactive gaming. Animal Science Newsletters.

December 21, 2017. Second Edition of The Ruminant Nutrition System published. Animal Science Newsletters.

March 8, 2017. AgriLife Research projects evaluate feeder cattle on yeast-grain diet. AgriLife Today by Blair Fannin.

January 11, 2017. Tedeschi earns Faculty Fellow during the Texas A&M AgriLife Conference. AgriLife Today by Kathleen Phillips.

February 27, 2016. Tedeschi reciprocal technical-scientific visit to Southern Vietnam. Animal Science Newsletters.

October 22, 2014. Modeling Research Helping Reduce Emissions, Add Profit to Beef Production. Beef Magazine.

October 20, 2014. Nutrition modeling helps reduce cattle emissions (PDF). Feedsfuffs by Tim Lundeen.

October 2, 2014. How the Beef Industry can Reduce Methane Emissions. AgWeb by University News Release.

September 30, 2014. Modeling systems help feedlots keep feed costs in check. SouthWest Farm Press by Blair Fannin.

September 28, 2014. The utility of applied nutrition models: A Brief history and future perspectives. Department of Animal Science.

September 23, 2014. Applied nutrition modeling producing beef more profitably, helping reduce methane emissions in feedlots. AgriLife Today by Blair Fannin.

April 3, 2013. Body condition score modeling system part of broodmare equine research. AgriLife Today by Blair Fannin.

 

Other Links

A comprehensive list of modeling environments is available here, includingAnyLogic, ExtendSim, Mathematica and System Modeler, MATLAB, NetLogo, PowerSim, Stella and iThink, Vensim and Ventity, and VisSim.

Big Data, System Dynamics and XMILE

BioModels:  A repository of mathematical models of biological and biomedical systems

National Animal Nutrition Program (NANP) - National Research Support Project (NRSP-9)

System Dynamics Society

The Utility of Applied Nutrition Models: A Brief History and Future Perspectives

 

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