Wright Ryan K, Thompson Riley K, Chen Chun-Peng James, White Robin R
School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
J Anim Sci. 2025 Jan 4;103. doi: 10.1093/jas/skaf151.
Management surveys suggest that few cow-calf producers in the Southeastern United States submit forage samples for laboratory analysis due to time and labor constraints. Although tools like near infrared reflectance spectroscopy have helped reduce costs associated with nutritive value determination in stored feeds, their performance for pasture analysis has been limited. Our objective was to explore the efficacy of spectral sensing in predicting the dry matter (DM), acid detergent fiber (ADF), neutral detergent fiber (NDF), and crude protein (CP) of fresh forages during the growing season. Weekly from May through October, two random samples were collected from each of 12 fields. Spectral readings were taken above canopy level in-field and again in-lab, followed by bench chemistry analyses of DM, ADF, NDF, and CP. Chemistry results and spectral readings were aligned by field, sample, and date. The 18 individual light spectra and lidar-measured distance were used as features in a random forest regression fit to predict each nutrient and separate models were developed for in-field and in-lab spectral readings. Data were randomly split for hyperparameter tuning (15%), model training (55%), and independent evaluation (30%). The root mean squared prediction error (RMSPE), calculated on the independent evaluation data, was used to explore the viability of this system to predict forage nutritive value. The in-field and in-lab models performed similarly for each forage nutritive value. To evaluate the prediction capability of the system under various atmospheric conditions, cloud cover was added as a feature in each in-field regression. The RMSPE of DM, ADF, NDF, and CP with cloud cover were 21.8%, 9.88%, 10.1%, and 21.9%, respectively. These models were also evaluated on new, unseen data from nine subplots and used to explore the implications of the prediction errors. The NASEM (2018) Beef Cattle Nutrient Requirements model was used to simulate diet nutritional adequacy using forage nutritive value estimated from the spectral sensor compared with forage nutritive value measured by bench chemistry. These forage nutritive value estimation methods resulted in a 4.48% and 3.03% difference in metabolizable energy and metabolizable protein allowable gain, respectively. Considerable future data collection and model refinement efforts are necessary to determine the value of the spectral sensing system in supporting low-cost, in-field nutritive value monitoring.
管理调查显示，由于时间和劳动力限制，美国东南部很少有肉牛养殖户提交草料样本进行实验室分析。尽管近红外反射光谱等工具有助于降低与储存饲料营养价值测定相关的成本，但其在牧场分析中的表现有限。我们的目标是探索光谱传感在预测生长季节新鲜草料的干物质（DM）、酸性洗涤纤维（ADF）、中性洗涤纤维（NDF）和粗蛋白（CP）方面的功效。从5月到10月每周从12块田地中各随机采集两个样本。在田间冠层上方和实验室再次进行光谱读数，然后对DM、ADF、NDF和CP进行实验室化学分析。化学分析结果和光谱读数按田地、样本和日期进行比对。将18个单独的光谱和激光雷达测量的距离用作随机森林回归拟合中的特征，以预测每种养分，并针对田间和实验室光谱读数开发单独的模型。数据被随机划分为超参数调整（15%）、模型训练（55%）和独立评估（30%）。根据独立评估数据计算的均方根预测误差（RMSPE）用于探索该系统预测草料营养价值的可行性。田间和实验室模型对每种草料营养价值的表现相似。为了评估该系统在各种大气条件下的预测能力，在每个田间回归中添加云量作为特征。含云量情况下DM、ADF、NDF和CP的RMSPE分别为21.8%、9.88%、10.1%和21.9%。这些模型还在来自九个子地块的新的、未见过的数据上进行了评估，并用于探索预测误差的影响。与通过实验室化学分析测量的草料营养价值相比，使用光谱传感器估计的草料营养价值，利用NASEM（2018）肉牛营养需求模型来模拟日粮营养充足性。这些草料营养价值估计方法在可代谢能量和可代谢蛋白质允许增重方面分别导致4.48%和3.03%的差异。需要大量未来的数据收集和模型优化工作，以确定光谱传感系统在支持低成本田间营养价值监测方面的价值。
