Abstract To optimise grazing livestock nutrition, it is necessary to know both the available dry matter yield and the nutritive characteristics of pasture at the farm-scale in near real time. Previous studies have shown the potential of using field spectrophotometers that measure the reflectance of light across the visible to near infrared spectrums to gather information on pasture dry matter yield (DMY) and nutritive characteristics. This study sought to calibrate and validate new mathematical models for ten parameters including dry matter yield and nine nutritive characteristics of relevance to ruminant nutrition. As a part of the analysis process, two innovative approaches were tested: the use of a hybrid modelling approach where partial least squares regression (PLSR) outputs were used as support vector regression (SVR) inputs; and, the inclusion of covariate data. These approaches were compared with traditional stand-alone PLSR and SVR modelling approaches without covariates. The study was undertaken in six predominantly perennial ryegrass pastures on a single farm in the temperate zone of South-Eastern Australia. A total of 204 pasture samples were scanned with a field spectrophotometer over several spring growth stages in late 2019 and subsequently analysed by wet chemistry to obtain reference nutritive values. The raw reflectance spectra were initially pre-processed using a variety of techniques and then used to test the four kinds of chemometric models. In cross validation, hybrid models showed a superior fit for all variates in comparison to the other model types tested. However, the differential was reduced in independent validation where, out of 10 best-performing models for dry matter yield and key nutrient properties, six were produced by the hybrid modelling, three from SVR and one from PLSR. For every hybrid model that was built, adding covariate(s) consistently improved model performance but the increase was small (a reduction in normalised root mean square error (RMSE) of -0.36 % on average for all properties considered). The best performing models were comparable with other published literature with normalised RMSE of prediction ranging from 1.7 – 23.1 % (a mean of 9.7%). Well-predicted variates included metabolisable energy, digestible energy, DMY, and crude protein. Fibre fractions, ash and dry matter were less well-predicted but still had acceptable normalised RMSE values (

中文翻译：

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使用传统、现代和混合建模方法使用高光谱数据集原位预测牧场的干物质产量和营养特性

摘要 为了优化放牧牲畜的营养，需要近乎实时地了解农场规模的可用干物质产量和牧场的营养特性。先前的研究表明，使用现场分光光度计测量可见光到近红外光谱范围内的光反射率来收集牧场干物质产量 (DMY) 和营养特性信息的潜力。本研究旨在校准和验证十个参数的新数学模型，包括干物质产量和与反刍动物营养相关的九个营养特征。作为分析过程的一部分，测试了两种创新方法：使用混合建模方法，其中偏最小二乘回归 (PLSR) 输出用作支持向量回归 (SVR) 输入；和，包含协变量数据。这些方法与没有协变量的传统独立 PLSR 和 SVR 建模方法进行了比较。该研究是在澳大利亚东南部温带地区一个农场的六个主要为多年生黑麦草的牧场中进行的。2019 年末，使用现场分光光度计对 204 份牧场样品进行了多个春季生长阶段的扫描，随后进行了湿化学分析，以获得参考营养值。原始反射光谱最初使用各种技术进行预处理，然后用于测试四种化学计量模型。在交叉验证中，与测试的其他模型类型相比，混合模型显示出对所有变量的优越拟合。然而，在独立验证中差异减少，其中，在 10 个表现最佳的干物质产量和关键养分特性模型中，6 个是通过混合建模生成的，3 个来自 SVR，1 个来自 PLSR。对于构建的每个混合模型，添加协变量始终如一地提高了模型性能，但增幅很小（归一化均方根误差 (RMSE) 降低了 -0.36%，所有考虑的属性平均）。性能最好的模型与其他已发表的文献具有可比性，预测的归一化 RMSE 范围为 1.7 – 23.1%（平均值为 9.7%）。预测良好的变量包括代谢能、可消化能、DMY 和粗蛋白。纤维部分、灰分和干物质的预测不太好，但仍然具有可接受的归一化 RMSE 值（三个来自 SVR，一个来自 PLSR。对于构建的每个混合模型，添加协变量始终如一地提高了模型性能，但增幅很小（归一化均方根误差 (RMSE) 降低了 -0.36%，所有考虑的属性平均）。性能最好的模型与其他已发表的文献相当，预测的归一化 RMSE 范围为 1.7 – 23.1%（平均值为 9.7%）。预测良好的变量包括代谢能、可消化能、DMY 和粗蛋白。纤维部分、灰分和干物质的预测不太好，但仍然具有可接受的归一化 RMSE 值（三个来自 SVR，一个来自 PLSR。对于构建的每个混合模型，添加协变量始终如一地提高了模型性能，但增幅很小（归一化均方根误差 (RMSE) 降低了 -0.36%，所有考虑的属性平均）。性能最好的模型与其他已发表的文献相当，预测的归一化 RMSE 范围为 1.7 – 23.1%（平均值为 9.7%）。预测良好的变量包括代谢能、可消化能、DMY 和粗蛋白。纤维部分、灰分和干物质的预测不太好，但仍然具有可接受的归一化 RMSE 值（对于所有考虑的属性，平均为 36%）。性能最好的模型与其他已发表的文献相当，预测的归一化 RMSE 范围为 1.7 – 23.1%（平均值为 9.7%）。预测良好的变量包括代谢能、可消化能、DMY 和粗蛋白。纤维部分、灰分和干物质的预测不太好，但仍然具有可接受的归一化 RMSE 值（对于所有考虑的属性，平均为 36%）。性能最好的模型与其他已发表的文献相当，预测的归一化 RMSE 范围为 1.7 – 23.1%（平均值为 9.7%）。预测良好的变量包括代谢能、可消化能、DMY 和粗蛋白。纤维部分、灰分和干物质的预测不太好，但仍然具有可接受的归一化 RMSE 值（