应用多变量技术,用化学和近红外校准模型估计奶牛的料效率
Valentina Novara1, Mattia Masseroni1,2, Maddalena Canossa1
1Department of Animal Science, Food and Nutrition (DIANA), Università Cattolica Sacro Cuore, 29122 Piacenza, Italy.
开发准确的料效率 (FE) 模型对于奶牛场的利能力和可持续性至关重要. 与化学成分分析相比,近红外 (NIR) 光谱数据为农场预测提供了更强大和更容易泛化的方法.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 营养科学 营养科学
背景情况:
- 料效率 (FE) 是奶牛场经济和环境绩效的一个关键指标.
- 准确的FE估计有助于优化营养管理和降低成本.
- 现有的FE预测方法在实际应用中可能受到限制.
研究的目的:
- 开发和比较两个预测模型来估计乳牛的料效率.
- 评估基于TMR化学组成的模型与近红外 (NIR) 光谱数据的模型.
- 评估模型的稳定性和通用性,以便在农场应用.
主要方法:
- 从波河谷 (2021-2024) 的奶牛场收集了144个TMR样本.
- 分析样本使用福里埃变换NIR光谱和化学测量技术 (LASSO回归).
- 使用化学成分和NIR光谱数据开发预测模型,验证性能.
主要成果:
- 化学组成模型:强度校准 (R2=0.80),但外部验证减少 (R2=0.64),表明偏差.
- 基于NIR的模型:在校准 (R2=0.73) 和外部验证 (R2=0.70) 中的性能稳定.
- NIR模型显示较低的斜率扭曲和偏移,表明更好的稳定性.
结论:
- 与化学成分相比,NIR光谱数据为农场FE预测提供了一种比化学成分更强大和更可概括的模型.
- NIR模型为奶牛农场管理提供了潜在的决策支持.
- 需要进一步改进校准,以减少系统错误和提高准确性.
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