I-Corps: Predictive algorithms to determine individual feed intake in beef cattle.

I-Corps：确定肉牛个体采食量的预测算法。

• 批准号: 2348526
• 负责人: Matthew Wilson
• 金额: $ 5万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2348526&HistoricalAwards=false
• 关键词: Corps; Predictive; algorithms; determine; individual

The broader impact/commercial potential of this I-Corps project is the development of an on-farm tool to allow animal agriculture producers to determine how much their animals are eating. Approximately 70% of an animal agriculture operation’s variable cost of production is the cost of feed. However, there are few approaches that allow producers to measure their animals’ feed intake, and the limited number of locations that have that capacity are expensive. The proposed low cost, on-farm tool would allow farmers to identify potential replacement animals that are more efficient, improve how they manage animals in the feedlot and to quantify intakes of animals grazing pasture. Currently there is no way for pasture animal intake to be determined when animals are grazing at scale. If 5% of US beef producers made use of this tool that would be 40,000 operations and likely improve the management decisions related to upwards of 500,000 to a million cattle.This I-Corps project is based on the development of a predictive algorithm to make use of daily animal weight and water intake, along with weather data, to predict daily feed intake. The proposed tool has been trained using data from a specialized feeding barn that has equipment to measure feed intake as well as animal weight and water intake. Currently, state-of-the-art systems significantly over- or under-estimate the actual feed intake. The proposed tool intends to work in situations where either there is not an expensive feed intake system or in extensive grazing pasture situations where weighing feed is not possible. The system has been validated on ~2200 animals fed in the barn and almost 100 animals grazing small plots where a ground truth can be determined for grazing feed intake. Results have shown predictions of individual daily feed intake to within 92-95% accuracy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该I-Corps项目的更广泛的影响/商业潜力是开发农场工具，以允许动物农业生产者确定动物的饮食量。动物农业经营可变的生产成本约有70％是饲料成本。但是，很少有方法可以使生产者测量其动物的饲料摄入量，并且具有该容量的位置数量有限。拟议的低成本，农场工具将使农民能够识别出更有效的潜在替代动物，改善它们如何管理饲养场中的动物，并量化动物的摄入量。当前，当动物大规模磨碎时，无法确定牧场动物的摄入量。如果我们中有5％的牛肉生产商使用该工具，该工具将是40,000个操作，并且可能会改善与一百万牛相关的管理决策。此I-Corps项目是基于预测算法的开发，以利用日常动物的体重和水的摄入量，以及天气数据，以预测每日饲料饲料的摄入量。提出的工具已使用来自专门的饲养谷仓的数据进行了培训，该谷仓具有测量饲料摄入量以及动物体重和摄入量的设备。目前，最先进的系统大大过度或低估了实际的饲料摄入量。拟议的工具打算在没有昂贵的进料摄入系统或在不可能进行加权饲料的广泛放牧的牧场情况下工作。该系统已在喂食谷仓中的约2200只动物上进行了验证，近100只动物磨碎了小块，可以确定地面真相以用于摄入饲料。结果表明，个人每日饲料摄入量的预测至92-95％的准确性。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来诚实地通过评估来诚实地支持。