Smart Livestock Nutrition

Description

This book highlights the latest findings and techniques related to nutrition and feed efficiency in animal agriculture. It addresses the key challenges facing the nutrition industry to achieve high animal productivity with minimal environmental impact. The concept of smart nutrition involves the use of smart technologies in the feeding and management of livestock. The first chapters focus on advances in biological fields such as molecular agriculture and genotype selection, as well as technologies that enhance or enable the collection of relevant information. The next section highlights applications of smart nutrition in a variety of livestock systems, ranging from intensive indoor housing of broilers and pigs to extensive outdoor housing of cattle and sheep, and marine fish farms. Finally, because of the worldwide attention to this issue, the authors address the environmental consequences. This work, which takes a serious look at how nutrition can be used to improve sustainability in animal agriculture, is a key literature for readers in animal and veterinary sciences, the food industry, sustainability research, and agricultural engineering. Preface Contents 1: Putting Smart into Nutrition 1.1 Introduction 1.2 Animal Genotype and Phenotype Assessment 1.2.1 Phenotype Assessment 1.3 Assessment of the Nutritional Environment 1.3.1 Indoor-Managed Livestock 1.3.2 Outdoor-Managed Livestock 1.4 Environmental Monitoring 1.4.1 Monitoring the Infectious Environment 1.5 Granularity of Assessments 1.5.1 Frequency of Assessment 1.5.2 Resolution of Assessment 1.6 The Way Forward References 2: Matching Feed Characteristics to Animal Requirements Through Plant Breeding 2.1 Introduction 2.2 Nutrition, Digestion and Utilization 2.2.1 Forage Composition and Digestibility 2.2.2 Supply of Nutrition to the Animal 2.2.3 Protein Supply 2.2.3.1 Forages 2.2.3.2 Grain and Pulse Legumes 2.2.4 Nutrition from Cereal Crops 2.2.5 Other Essential Nutrients 2.3 Plant Breeding 2.3.1 Breeding Methodology 2.3.1.1 Outbreeding Forage Crops 2.3.1.2 Cereals and Grain Legumes 2.3.2 Genomic Selection 2.3.3 Breeding for Forage Quality 2.3.3.1 Water-soluble Carbohydrates and Digestibility 2.3.3.2 Fatty Acid Content 2.3.3.3 Phosphorus 2.3.3.4 Grass Staggers or Hypomagnesaemia 2.3.3.5 Phytoestrogens 2.3.3.6 Condensed Tannins 2.4 Building on Successes - What Plant Breeding Can Deliver for Livestock Nutrition 2.4.1 Forage Legumes 2.4.1.1 White Clover 2.4.1.2 Red Clover 2.4.1.3 Birdsfoot Trefoil 2.4.1.4 Festulolium 2.4.1.5 Multi-species Swards 2.4.2 Breeding Crops for Non-ruminants 2.4.2.1 Protein in Cereals 2.4.2.2 Protein in Grain Legumes 2.4.2.3 Protein for Non-ruminants from Forage 2.5 Conclusions and Future Outlook References 3: Circular Feed Production and Consumption in the Context of Smart Animal Nutrition 3.1 Introduction 3.2 General Characteristics of Food Leftovers Re-used in Animal Nutrition 3.3 Nutritional Quality of FFPs 3.4 Food Leftovers in Pig Diets 3.5 Food Leftovers in Ruminant Diets 3.6 Implications of the Use of Leftovers in Animal Feedstuffs 3.6.1 Safety Issues 3.6.2 Logistical Issues 3.7 Conclusions References 4: Assessment of the Nutritive Value of Individual Feeds and Diets by Novel Technologies 4.1 Introduction 4.2 Principles in the Development of NIRS Calibrations 4.3 Determination of Macronutrient, Digestibility, and Energy Value by NIRS 4.3.1 Macronutrients 4.3.2 Digestibility and Energy Value 4.3.3 Faecal Composition and Digestibility 4.4 Determination of Amino Acids and Their Digestibility by NIRS 4.4.1 Amino Acids 4.4.2 Ileal and Total Tract Digestibility of Amino Acids 4.5 Perspectives in Using NIRS for the Determination of the Nutritive Value and Incorporation into Smart Nutrition References 5: Large-Scale Phenotyping and Genotyping: State of the Art and Emerging Challenges 5.1 Introduction 5.2 Big Data 5.3 Relationship Between Penotype and Genotype 5.3.1 Increasing Heritability Through Better Phenotyping 5.4 Advances in Genomic Tools 5.4.1 Use of Genomics in Livestock Production 5.4.1.1 Genomic Evaluations 5.4.1.2 Parentage Assignment and Traceability 5.4.1.3 Breed Composition 5.4.1.4 Monitoring of Major Genes and Congenital Effects 5.4.1.5 Karyotyping 5.4.1.6 Inbreeding and Mating Advice 5.4.1.7 Precision Management 5.5 Phenomic Tools 5.5.1 The Emerging Phenotypes 5.6 Case Studies of the Marriage of Genotype and Phenotype 5.6.1 Breeding Objectives of the Future 5.6.2 Precision or Personalised Management 5.7 Challenges 5.7.1 Return-on-Investment 5.7.2 Useful and Meaningful Decision-Support Tools 5.7.3 Data Ownership 5.8 Conclusions References 6: Mathematical and Statistical Approaches to the Challenge of Forecasting Animal Performance for the Purposes of Precision Li... 6.1 Introduction 6.2 Data Description 6.3 Determining What to Forecast 6.4 Fitting Models to Data: Parameter Estimation Methods 6.4.1 Maximum Likelihood Estimation 6.4.2 Bayesian Estimation 6.4.2.1 Markov Chain Monte Carlo 6.5 Model Evaluation 6.6 Current Forecasting Approaches 6.6.1 Double Exponential Smoothing 6.6.2 The Local Linear Trend Model 6.6.3 Dynamic Linear Regression and Recursive (Rolling) Window Linear Regression 6.6.4 Possible Limitations of the Current Forecasting Approaches 6.7 Alternative Approaches 6.7.1 Other Exponential Smoothing Models 6.7.2 Machine Learning: Neural Networks 6.7.3 Deterministic Trend Models 6.8 Concluding Remarks References 7: Smart Pig Nutrition in the Digital Era 7.1 Introduction 7.2 Principles of Precision Farming Adapted to Pig Nutrition 7.2.1 Data Collection 7.2.2 Data Processing 7.2.3 Algorithm Development 7.2.4 Implementation Through Automation 7.3 Data Collection on Animals, Their Environment, and Their Feed Use: The (R)evolution of Sensors 7.3.1 Measuring Performance 7.3.1.1 Individual Identification 7.3.1.2 Body Weight 7.3.1.3 Electronic Feeding Stations 7.3.1.4 Water Consumption 7.3.1.5 Estimation of Body Composition 7.3.2 Activity 7.3.3 Pig Physiological/Health Status 7.3.3.1 Pig Body Temperature 7.3.3.2 Sound Analysers 7.3.3.3 Metabolic Biosensors 7.3.3.4 Detection of Infectious Agents 7.3.3.5 Saliva 7.3.3.6 Urine 7.3.4 Digestibility and Feed Efficiency Assessment Through Faecal Analysis 7.3.5 Characterizing the Environment of Pigs 7.3.5.1 Sensors to Measure/Evaluate Feed Quality (NIRS) 7.3.5.2 Temperature and Humidity Sensors 7.3.5.3 Air Analysers 7.3.6 Challenges in Data Collection for Smart Pig Nutrition 7.4 Evolution of Nutritional Models 7.4.1 Fattening Pigs 7.4.2 Sows in Gestation and Lactation 7.4.3 Modelling Mineral Requirements 7.4.4 Inclusion of Models in a Whole System for Practical Application 7.5 Conclusion References 8: Smart Poultry Nutrition 8.1 Introduction 8.2 Current State of Smart Poultry Nutrition 8.3 Matching Nutrient Supply to the Nutrient Requirements of Poultry 8.3.1 Smart Diet Formulation 8.3.2 Variability in Nutrient Composition of Feedstuffs 8.3.3 Margin of Safety and Stochastic Programming 8.3.4 Grain Handling 8.3.5 `Nutrient Response´ Thinking Is Critical to Deal with Marketplace Variability 8.4 Mathematical Models to Aid Smart Poultry Nutrition 8.4.1 Growth and Egg Production 8.4.2 Energy and Nutrient Requirements 8.4.3 Long-Term Effects of Nutrition 8.4.4 Further Developments 8.5 Big Data 8.6 Machine Learning 8.7 The Future of Smart Poultry Nutrition References 9: Advanced Technology in Aquaculture - Smart Feeding in Marine Fish Farms 9.1 Introduction 9.1.1 Scope and Structure 9.1.2 Choosing a Model Species: Sea-Based Atlantic Salmon Farming 9.2 Intensive Cage-Based Aquaculture of Atlantic Salmon 9.2.1 The Natural Life Cycle of Atlantic Salmon 9.2.2 Current Practices in Aquaculture: From Egg to Market 9.2.2.1 The Salmon Production Cycle 9.2.2.2 Main Industrial Challenges 9.2.3 Fish Growth: The Core Process in Intensive Fish Farming 9.2.4 The Feeding Process: From Factory to Fish Gut 9.2.4.1 From Raw Materials to Feeding Barges 9.2.4.2 From Barge to Cage 9.2.4.3 From Surface to Fish 9.2.5 Developmental Trends and New Concepts for Modern Fish Farming 9.3 Using Digital Technology to Improve Aquaculture Feeding Practices 9.3.1 Precision Fish Farming: A Framework for Applying Digital Technology to Intensive Fish Farming 9.3.2 Intelligent Sensors and Instrumentation: From Data to Information 9.3.2.1 Aim: Quantifying Key Properties in the Feeding Process 9.3.2.2 Optical Methods 9.3.2.3 Acoustics 9.3.2.4 Biosensors and Telemetry 9.3.3 Modelling and Information Fusion: Unveiling the Unobservable 9.3.3.1 Aim: Simulate and Estimate States and Dynamics in Feeding That Are Difficult to Measure 9.3.3.2 Mathematical Modelling 9.3.3.3 Sensor Fusion and State Estimation 9.3.4 Automated Solutions and Autonomous Systems: Closing the Loop 9.3.4.1 Aim: Make Operational Actions Autonomous 9.3.4.2 Robotic Systems and Vehicles 9.3.4.3 Feedback Controlled/on Demand Feeding Systems 9.4 Future Prospects and Developments 9.4.1 The Intelligent Feeding Methods of the Future 9.4.1.1 Observing and Interpreting Fish States 9.4.1.2 Deciding and Delivering 9.4.2 New Solutions for New Production Concepts References 10: Smart Nutrition of Extensively Kept Ruminants 10.1 Introduction 10.2 A Suggested Framework for Smart Nutrition of Extensively Raised Livestock 10.3 Feed Availability and Quality 10.3.1 Vegetation Reflectance and Vegetation Indexes 10.3.2 Biomass and Growth Rate of Pastures 10.3.3 Pasture Quality 10.4 Energy and Nutrient Requirements 10.4.1 Nutrient Requirements for Maintenance 10.4.1.1 Remote Monitoring of Body Weight and Composition 10.4.1.2 Requirements for Physical Activities 10.4.1.3 Total Heat Production or Maintenance Requirements 10.4.2 Energy and Nutrient Requirements for Production 10.4.2.1 Requirements for Body Growth 10.4.2.2 Requirements for Gestation 10.4.2.3 Requirements for Lactation 10.5 Feed, Energy, and Nutrient Intake 10.5.1 On-Animal Sensors to Measure Behaviour 10.5.2 Faecal Near-Infrared Spectroscopy (NIRS) 10.5.3 Metabolizable Energy Intake 10.5.4 Supplement Intake of Grazing Animals 10.6 Feed and Nutrient Excretion 10.7 Smart Animal Nutrition and Production in Extensive Conditions 10.8 Conclusion References 11: The Potential Contribution of Smart Animal Nutrition in Reducing the Environmental Impacts of Livestock Systems 11.1 Introduction 11.2 How to Quantify the Potential Environmental Impacts of Smart Nutrition 11.2.1 Life Cycle Assessment and Livestock Systems 11.2.2 Functional Units 11.2.3 Applying LCA to New Technologies 11.2.3.1 How to Scale Up a Model for a Product or Technology When the Available Data Is Likely Based on Small-Scale Pilots? 11.2.3.2 How to Account for Changes Over Time in the Background Databases Used in LCA Modelling? 11.3 Precision Feeding in Pig and Poultry Production 11.4 Smart Nutrition in Ruminant Systems 11.4.1 Smart Grazing Systems 11.5 Nutritional Strategies that Target Reductions in Environmental Impacts 11.6 Conclusions References