Development of soil spectroscopy models for the Western Highveld region, South Africa: Why do we need local data?

IF 4 2区 农林科学 Q2 SOIL SCIENCE European Journal of Soil Science Pub Date : 2024-11-24 DOI:10.1111/ejss.70014
Anru-Louis Kock, Prudence Dimakatso Ramphisa-Nghondzweni, George Van Zijl
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Abstract

The increasing global demand for sustainable agriculture requires accurate and efficient soil analysis methods. Conventional laboratory techniques are often time-consuming, costly and environmentally damaging. To address this challenge, we developed and validated locally calibrated mid-infrared (MIR) spectroscopy models for predicting key soil properties pH, phosphorus (P) and exchangeable cations in soil samples from South Africa's Western Highveld region, using a dataset of 979 soil samples and machine learning algorithms Cubist, partial least squares regression (PLSR) and random forest (RF). A subset of spectra was also submitted to the newly developed Open Soil Spectral Library's (OSSL) prediction models to determine whether global prediction models could be used for local soil property prediction. Accurate predictions for pH, calcium (Ca) and magnesium (Mg), with coefficient of determination (R2) values exceeding 0.76 were obtained with the local calibration algorithms. The predictions for P, potassium (K) and sodium (Na) did not meet the requirements for reliability. Soil spectroscopic prediction models calibrated with local soils outperformed the corresponding global prediction models considered. The OSSL prediction results were inaccurate, with a RPIQ <1, and consistently underpredicted all soil properties. Furthermore, the OSSL collection of prediction models does not include a pH (KCl) model, the routinely used pH measurement method in South Africa. These findings highlight the importance of local calibration for accurate soil property prediction and underscore the need for regional representation in global spectral libraries. This research serves as the first local calibration of MIR spectroscopy models for the Western Highveld region of South Africa and provides a foundation for future local soil property inference model development. It also serves as a potential starting point for a comprehensive South African soil spectral library that can be contributed to global spectral libraries.

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为南非西部高地地区开发土壤光谱模型:为什么需要当地数据?
全球对可持续农业的需求日益增长,这就需要准确高效的土壤分析方法。传统的实验室技术往往耗时长、成本高且破坏环境。为了应对这一挑战,我们开发并验证了经过本地校准的中红外(MIR)光谱模型,用于预测南非西部高地(Highveld)地区土壤样本中的关键土壤属性 pH 值、磷(P)和可交换阳离子,使用的数据集包括 979 个土壤样本和机器学习算法 Cubist、偏最小二乘回归(PLSR)和随机森林(RF)。此外,还将光谱子集提交给新开发的开放土壤光谱库(OSSL)预测模型,以确定全球预测模型是否可用于本地土壤特性预测。利用本地校准算法对 pH 值、钙(Ca)和镁(Mg)进行了精确预测,判定系数(R2)值超过 0.76。对磷、钾(K)和钠(Na)的预测不符合可靠性要求。根据当地土壤校准的土壤光谱预测模型优于相应的全球预测模型。OSSL 的预测结果并不准确,RPIQ 为 1,对所有土壤特性的预测都偏低。此外,OSSL 预测模型集合不包括 pH(氯化钾)模型,而 pH 是南非常用的 pH 测量方法。这些发现凸显了本地校准对于准确预测土壤属性的重要性,并强调了全球光谱库中区域代表性的必要性。这项研究是对南非西部高地地区近红外光谱模型的首次本地校准,为未来本地土壤性质推断模型的开发奠定了基础。它也是建立一个全面的南非土壤光谱库的潜在起点,可为全球光谱库做出贡献。
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来源期刊
European Journal of Soil Science
European Journal of Soil Science 农林科学-土壤科学
CiteScore
8.20
自引率
4.80%
发文量
117
审稿时长
5 months
期刊介绍: The EJSS is an international journal that publishes outstanding papers in soil science that advance the theoretical and mechanistic understanding of physical, chemical and biological processes and their interactions in soils acting from molecular to continental scales in natural and managed environments.
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