Technological advancements have made hand‐held near infrared (NIR) spectrometers more affordable and more accurate, creating interest in on‐farm application for forage management. The objective of this study was to evaluate the ability of a hand‐held NIR spectrometer to predict grass percentage within fresh alfalfa (Medicago sativa L.):grass mixtures. Forage samples were collected at a range of maturities and varieties during the 2021 and 2022 growing seasons from multiple locations in New York. Fresh forage samples were chopped, and pure species were combined into known proportions on a dry matter basis, resulting in 534 samples. Analysis was carried out on NIR spectra collected from a hand‐held NeoSpectra spectrometer using stationary and sliding scanning techniques. Development of calibration models was completed using partial least squares regression with cross validation. The best performing calibration model using absorbance was from the sliding scanning technique with preprocessing consisting of mean‐centering (R2 = 0.89, root mean square error of prediction [RMSEP] = 13.7%, and ratio of prediction to deviation = 2.53). A total of 84% of the samples were correctly classified when the grass component was lower than 40%. For samples with the grass component above 40%, a total of 94% of the samples were correctly classified. Correct sample classification is critical considering that the extension recommendation in New York is to reseed alfalfa fields when the grass component exceeds 40% of the sward on a botanical composition basis. This research demonstrates that NIR technology has potential to provide the agricultural industry with rapid, non‐destructive, and affordable information to allow farmers and consultants to predict grass proportion within alfalfa:grass fresh forage mixtures in real time.

中文翻译：

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预测新鲜苜蓿中的草比例：使用手持式近红外光谱仪的草混合物

技术进步使得手持式近红外 (NIR) 光谱仪更加经济实惠且更加准确，从而激发了人们对农场饲料管理应用的兴趣。本研究的目的是评估手持式近红外光谱仪预测新鲜苜蓿中草百分比的能力（苜蓿L.)：草混合物。在 2021 年和 2022 年生长季节，从纽约多个地点收集了各种成熟度和品种的饲料样本。将新鲜饲料样品切碎，并根据干物质将纯物种按已知比例组合，得到 534 个样品。使用固定和滑动扫描技术对从手持式 NeoSpectra 光谱仪收集的近红外光谱进行分析。使用偏最小二乘回归和交叉验证完成了校准模型的开发。使用吸光度的最佳性能校准模型来自滑动扫描技术，其预处理包括均值中心化（右2= 0.89，预测均方根误差 [RMSEP] = 13.7%，预测与偏差之比 = 2.53）。当草成分低于40%时，共有84%的样本被正确分类。对于草成分在40%以上的样本，总共有94%的样本被正确分类。考虑到纽约的推广建议是当草地成分超过植物成分的 40% 时，重新播种苜蓿田，正确的样本分类至关重要。这项研究表明，近红外技术有潜力为农业提供快速、无损且经济实惠的信息，使农民和顾问能够实时预测苜蓿：草新鲜饲料混合物中的草比例。