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.

这个i-Corps项目的更广泛的影响/商业潜力是开发一种农场工具，使动物农业生产者能够确定他们的动物吃了多少。畜牧业的可变生产成本中，大约70%是饲料成本。然而，几乎没有方法允许生产商测量他们动物的饲料摄入量，而且有这种能力的有限地点的成本很高。拟议的低成本农场工具将使农民能够确定更高效的潜在替代动物，改善他们在饲养场管理动物的方式，并量化放牧动物的摄入量。目前还没有办法确定牧场动物的摄入量是在动物大规模放牧时确定的。如果5%的美国牛肉生产商使用这一工具，那么将有4万头牛进行操作，并可能改善与50万至100万头牛相关的管理决策。这个i-Corps项目基于一种预测算法的开发，该算法利用每日动物体重和水分摄入量，以及天气数据来预测每日饲料摄入量。拟议的工具已经使用一个专门的饲养场的数据进行了培训，该饲养场拥有测量饲料摄入量以及动物体重和水摄入量的设备。目前，最先进的系统严重高估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饲料摄取系统的情况下工作，或者在不可能称量饲料的大面积放牧情况下工作。该系统已经在大约2200只在牛舍饲养的动物和近100只放牧动物的小块土地上得到了验证，在那里可以确定放牧饲料摄入量的地面真实情况。结果显示，对个体每日饲料摄入量的预测准确率在92%-95%以内。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。