Within the soil spectroscopy community, there is an ongoing discussion addressing the comparison of the performance of prediction models built on a global calibration database, versus a local calibration database. In this study, this issue is addressed by spiking of global databases with local samples. The soil samples were analysed with MIR and XRF sensors. The samples were further measured using traditional wet chemistry methods to build the prediction models for seventeen major parameters. The prediction models applied by AgroCares, the company that assisted in this study, combine spectral information from MIR and XRF into a single ‘fused-spectrum’. The local dataset of 640 samples was split into 90% train and 10% test samples. To illustrate the benefits of using local calibration samples, three separate prediction models were built per element. For each model, 0%, 50% (randomly selected) and 100% of the local training samples were added to the global dataset. The remaining 10% local samples were used for validation. Seventeen soil parameters were selected to illustrate the differences in performance across a range of soil qualities, using the validation set to measure performance. The results showed that many models already exhibit an excellent level of performance (R² ≥ 0.95) even without local samples. However, there was a clear trend that, as more local calibration samples were added, both R² and ratio of performance to interquantile distance (RPIQ) increase.

在土壤光谱学界，正在讨论比较建立在全球校准数据库和本地校准数据库上的预测模型的性能。在这项研究中，这个问题是通过在全球数据库中添加本地样本来解决的。土壤样品用 MIR 和 XRF 传感器进行分析。使用传统的湿化学方法对样品进行进一步测量，以建立十七个主要参数的预测模型。协助本研究的公司 AgroCares 应用的预测模型将来自 MIR 和 XRF 的光谱信息结合到一个单一的“融合光谱”中。640 个样本的本地数据集被分成 90% 的训练样本和 10% 的测试样本。为了说明使用本地校准样本的好处，每个元素建立了三个独立的预测模型。对于每个模型，将 0%、50%（随机选择）和 100% 的本地训练样本添加到全局数据集中。其余 10% 的本地样本用于验证。使用验证集来衡量性能，选择了 17 个土壤参数来说明一系列土壤质量的性能差异。结果表明，许多模型已经表现出出色的性能水平（R²  ≥ 0.95) 即使没有本地样本。然而，有一个明显的趋势是，随着更多的局部校准样本的添加，R ²和性能与分位数间距离的比率 (RPIQ) 都会增加。