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其动物的饮食量，动物农业运营的可变生产成本中大约 70% 是成本。然而，很少有方法可以让生产者测量动物的饲料摄入量，而且具有这种能力的地点数量有限，拟议的低成本农场工具将使农民能够识别潜在的替代动物。那些更多如果 5% 的美国牛肉生产商使用此工具，则目前无法确定大规模放牧时牧场动物的摄入量。这将涉及 40,000 次操作，并可能改善与 500,000 到 100 万头牛相关的管理决策。该 I-Corps 项目基于开发预测算法，以利用每日动物体重和水所提出的工具已经使用来自专门饲养仓的数据进行了训练，该饲养仓拥有测量饲料摄入量以及动物体重和水摄入量的设备。该系统明显高估或低估了实际采食量，该工具适用于没有昂贵的采食系统或无法称重饲料的广阔牧场的情况。在谷仓喂养约 2200 只动物近 100 只动物在小块土地上放牧，可以确定放牧采食量的基本事实。结果显示，个体每日采食量的预测准确度在 92-95% 范围内。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式获得支持。使用基金会的智力价值和更广泛的影响审查标准进行评估。