Mechanistic modelling of sustainability metrics and performance in broilers

肉鸡可持续性指标和性能的机械建模

• 批准号: RGPIN-2020-04985
• 负责人: Ellis, Jennifer
• 金额: $ 1.75万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740330
• 关键词: Mechanistic; modelling; sustainability; metrics; performance

As the global population escalates towards an estimated 9.5 billion in 2050 (FAOSTAT, 2013), the need to efficiently produce animal protein (milk, meat, eggs) for human consumption whilst minimizing the impact on the environment (methane, nitrogen, phosphorous, water & land use) will only continue to increase. These animal production systems must also manage increasing societal pressures to reduce production intensity (e.g. pasture-based systems, slow-growing breeds) and remove antibiotics and hormones from animal feed - strategies that may directly counter efforts to improve efficiency and production. Such complexities are difficult to optimize within individual research trials or even to extract from whole bodies of scientific research literature. With complex biological systems, such as the digestive and metabolic pathways that drive nutrient utilization and animal performance, models play a key role in distilling knowledge from information and identifying optimized feeding and mitigation strategies. Mechanistic models (MM) have served as decision-support and opportunity analysis tools within animal production, across species, for decades. However, they must also evolve to meet the wave of the future - utilizing new `big data' streams, hybridizing with machine learning methodologies, as well as aid the movement towards precision nutrition (via capturing additional sources of performance variation) as a strategy to reduce inefficiency in animal production. The objective of this research program is to therefore to develop models that (1) support the efficient, sustainable and ethical production of animal protein for human consumption, and that (2) serve to increase our knowledge of complex biological systems. Anticipated Impact: More efficient, sustainable, ethical and intelligent production of animal protein for human consumption. To this end, this research program proposes the development of a dynamic mechanistic broiler model able to consider sources of performance variation from (1) genetics (including new slow growing broiler breeds, as well as address between-animal variation in genetic potential), (2) nutrient digestion kinetics and non-nutritional feed attributes (large source of performance variation largely not captured or utilized in growth models), and to predict (3) the resulting nutrient excretion (nitrogen, phosphorous, methane) under varying nutritional and management scenarios (slow growing vs conventional breeds, physical feed attributes, etc). This research program will also explore the utilization of current big data streams and machine learning approaches to parameterize the developed model stochastically and capture between animal variation. Anticipated Impact: The development of an openly available mechanistic broiler model that can be used to assess nutritional, environmental, management, and nutrient excretion strategies in order to optimize performance and reduce the environmental impact of broiler production.

随着全球人口在2050年达到95亿（FAOSTAT，2013），高效生产动物蛋白（牛奶，肉类，鸡蛋）供人类消费，同时最大限度地减少对环境的影响（甲烷，氮，磷，水和土地使用）的需求只会继续增加。这些动物生产系统还必须应对日益增加的社会压力，以降低生产强度（例如，以牧场为基础的系统，生长缓慢的品种），并从动物饲料中去除抗生素和激素-这些策略可能直接对抗提高效率和产量的努力。这种复杂性很难在单个研究试验中优化，甚至很难从整个科学研究文献中提取。对于复杂的生物系统，例如驱动营养利用和动物性能的消化和代谢途径，模型在从信息中提取知识以及确定优化的饲养和缓解策略方面发挥着关键作用。几十年来，机械模型（MM）一直是跨物种动物生产中的决策支持和机会分析工具。然而，它们还必须发展以满足未来的浪潮-利用新的“大数据”流，与机器学习方法杂交，以及帮助实现精确营养（通过捕获性能变化的其他来源）作为减少动物生产效率低下的战略。因此，该研究计划的目标是开发模型，（1）支持人类消费的动物蛋白质的高效，可持续和道德生产，（2）有助于增加我们对复杂生物系统的了解。 预期影响：更高效、可持续、道德和智能地生产供人类消费的动物蛋白。 为此，本研究计划提出了一个动态机械肉鸡模型的发展，能够考虑来自（1）遗传学的性能变化来源（包括新的生长缓慢的肉鸡品种，以及解决动物间遗传潜力的变化），（2）营养消化动力学和非营养饲料属性（性能变化的大来源在很大程度上没有被捕获或利用在生长模型中），并预测（3）由此产生的营养排泄（氮、磷、甲烷）在不同的营养和管理情景下（生长缓慢与常规品种、物理饲料属性等）。该研究计划还将探索利用当前的大数据流和机器学习方法来参数化所开发的模型随机和捕获动物之间的变异。 预期影响：开发一种公开可用的机械肉鸡模型，可用于评估营养，环境，管理和营养素排泄策略，以优化性能并减少肉鸡生产对环境的影响。