Diffuse reflectance spectroscopy (DRS) is an emerging soil testing approach. Although several studies have validated the DRS approach, limited efforts are made to assess the applicability of calibrated DRS models on new samples collected at different locations and/or time. To test such spatio-temporal applicability of calibrated DRS models, we collected surface soil samples from 1,112 smallholder farms during 2018 (T2018) and 607 farms during 2021 (T2021) covering seven districts of the Bundelkhand region of central India. The T2018 samples covered 7 development blocks; the T2021 samples were also collected from these blocks but from different sampling locations. Additionally, a new sampling site (Jhansi-Bamour block) was added during 2021 to create an independent test dataset. Collected samples were analysed for 17 soil parameters (basic soil properties, macronutrients, and micronutrients) and spectral reflectance over the visible to near-infrared region. Corresponding soil test crop response (STCR) ratings were also estimated. The Cubist model was calibrated in the T2018 dataset and tested against the T2021 dataset using the coefficient of determination (R2), root-mean-squared error (RMSE), and percentage relative error deviation (PRED) at 30% error threshold as performance statistics. Model applicability was assessed at each block level (site-specific), by dividing the study site into their two geology-specific regions, and by treating the entire dataset as a regional-scale spectral library. Results showed that DRS models calibrated on a finer scale (site-specific) are less efficient in estimating soil parameters in broader scale (geology-specific and regional-scale) test T2021 samples although their STCR ratings may safely be estimated at local scales. When site-specific data were aggregated to broader scales and T2018 dataset was spiked with 20% samples from the T2021 dataset, model performance improved for critical soil parameters such as soil organic carbon (SOC) contents and several plant nutrients and their ratings; application of such large-scale models also improved the estimation accuracy when applied to site-specific datasets. Exchangeable Ca and Mg, clay and SOC contents were frequently well-estimated with R2 values ranging from 0.54 to 0.93. Fine sand was the next best estimated soil property with R2 values in the range of 0.40–0.75. The STCR ratings estimated in the DRS approach matched the wet chemistry-based STCR ratings to the tune of 43 to 100%. Overall, as many as 60% of all new samples could be estimated with more than 70% accuracy for 8 out of 17 parameters. With the DRS approach tested on both spatially- and temporally-independent test datasets and, specifically, with high estimation accuracy of STCR ratings, our results suggest that the DRS approach may safely be used as a viable alternative to conventional soil testing in smallholder farms.

中文翻译：

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校准漫反射光谱模型跨空间和时间边界的适用性


漫反射光谱 （DRS） 是一种新兴的土壤检测方法。尽管有几项研究已经验证了 DRS 方法，但在评估校准的 DRS 模型对在不同地点和/或时间收集的新样本的适用性方面所做的努力有限。为了测试校准 DRS 模型的这种时空适用性，我们在 2018 年 （T2018） 和 2021 年 （T2021） 期间从 607 个农场收集了表层土壤样本，覆盖了印度中部 Bundelkhand 地区的七个地区。T2018 示例涵盖 7 个开发模块;T2021 样本也是从这些区块收集的，但来自不同的采样地点。此外，2021 年还增加了一个新的采样点（Jhansi-Bamour 区块）以创建独立的测试数据集。对收集的样品进行了 17 个土壤参数（基本土壤特性、宏量营养素和微量营养素）和可见光到近红外区域的光谱反射率分析。还估计了相应的土壤测试作物反应 （STCR） 评级。Cubist 模型在 T2018 数据集中进行了校准，并使用决定系数 （R2）、均方根误差 （RMSE） 和百分比相对误差偏差 （PRED） 在 30% 误差阈值下作为性能统计数据在 T2021 数据集上进行了测试。通过将研究地点划分为两个地质特定区域，并将整个数据集视为区域尺度的光谱库，在每个区块级别（特定地点）评估模型适用性。结果表明，在更精细的尺度（特定地点）上校准的 DRS 模型在更广泛尺度（地质特异性和区域性尺度）测试 T2021 样本中估计土壤参数的效率较低，尽管它们的 STCR 评级可以在局部尺度上安全地估计。 当特定地点的数据被聚合到更广泛的尺度，并且 T2018 数据集中添加了来自 T2021 数据集的 20% 样本时，关键土壤参数（如土壤有机碳 （SOC） 含量和几种植物养分及其评级的模型性能得到了改善;当应用于特定地点的数据集时，这种大规模模型的应用也提高了估计的准确性。可交换的 Ca 和 Mg、粘土和 SOC 含量通常得到很好的估计，R2 值在 0.54 到 0.93 之间。细沙是次好的估计土壤特性，R2 值在 0.40-0.75 之间。在 DRS 方法中估计的 STCR 评级与基于湿化学的 STCR 评级相匹配，为 43% 至 100%。总体而言，对于 17 个参数中的 8 个，可以估计多达 60% 的新样本，准确率超过 70%。通过在与空间和时间无关的测试数据集上测试 DRS 方法，特别是 STCR 评级的高估计精度，我们的结果表明 DRS 方法可以安全地用作小农农场传统土壤测试的可行替代方案。