Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
Section of Soil and Crop Sciences, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.
Sensors (Basel). 2024 Aug 8;24(16):5136. doi: 10.3390/s24165136.
This study investigates the efficacy of handheld Near-Infrared Spectroscopy (NIRS) devices for in-field estimation of forage quality using undried samples. The objective is to assess the precision and accuracy of multiple handheld NIRS instruments-NeoSpectra, TrinamiX, and AgroCares-when evaluating key forage quality metrics such as Crude Protein (CP), Neutral Detergent Fiber (aNDF), Acid Detergent Fiber (ADF), Acid Detergent Lignin (ADL), in vitro Total Digestibility (IVTD)and Neutral Detergent Fiber Digestibility (NDFD). Samples were collected from silage bunkers across 111 farms in New York State and scanned using different methods (static, moving, and turntable). The results demonstrate that dynamic scanning patterns (moving and turntable) enhance the predictive accuracy of the models compared to static scans. Fiber constituents (ADF, aNDF) and Crude Protein (CP) show higher robustness and minimal impact from water interference, maintaining similar R2 values as dried samples. Conversely, IVTD, NDFD, and ADL are adversely affected by water content, resulting in lower R2 values. This study underscores the importance of understanding the water effects on undried forage, as water's high absorption bands at 1400 and 1900 nm introduce significant spectral interference. Further investigation into the PLSR loading factors is necessary to mitigate these effects. The findings suggest that, while handheld NIRS devices hold promise for rapid, on-site forage quality assessment, careful consideration of scanning methodology is crucial for accurate prediction models. This research contributes valuable insights for optimizing the use of portable NIRS technology in forage analysis, enhancing feed utilization efficiency, and supporting sustainable dairy farming practices.
本研究旨在探讨手持式近红外光谱(NIRS)设备在使用未干燥样本进行现场估测饲料质量方面的功效。研究目的是评估 NeoSpectra、TrinamiX 和 AgroCares 等多种手持式 NIRS 仪器在评估关键饲料质量指标(如粗蛋白(CP)、中性洗涤纤维(aNDF)、酸性洗涤纤维(ADF)、酸性洗涤木质素(ADL)、体外总消化率(IVTD)和中性洗涤纤维消化率(NDFD))时的精度和准确性。研究样本取自纽约州 111 个农场的青贮料仓,采用不同方法(静态、动态和旋转台)进行扫描。研究结果表明,与静态扫描相比,动态扫描模式(移动和旋转台)可提高模型的预测准确性。纤维成分(ADF、aNDF)和粗蛋白(CP)表现出更高的稳健性,受水分干扰的影响较小,与干燥样本相比,仍保持相似的 R2 值。相比之下,IVTD、NDFD 和 ADL 会受到水分含量的不利影响,导致 R2 值降低。本研究强调了理解未干燥饲料中水分影响的重要性,因为水在 1400 和 1900nm 处的高吸收带会引入显著的光谱干扰。需要进一步研究偏最小二乘回归(PLSR)加载因子,以减轻这些影响。研究结果表明,手持式 NIRS 设备有望用于快速、现场饲料质量评估,但为了建立准确的预测模型,必须仔细考虑扫描方法。本研究为优化便携式 NIRS 技术在饲料分析中的应用提供了有价值的见解,有助于提高饲料利用效率,支持可持续的奶牛养殖实践。