Use of NIRS in Soil Properties Evaluation Related to Soil Salinity and Sodicity in Colombian Caribbean Coast

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Abstract

The banana sector contributes to 3% of the Colombia total exports, becoming the most important crop in the north of the country, benefiting more than 2.5 million families. Banana production is concentrated on the Colombian Caribbean coast where most of 90% of the soils are affected to some degree of salt affection in soils. This study was carried out in the municipality of Zona Bananera, Department of Magdalena (Colombia) elsewhere eleven geomorphological units were delimited through geomorphological surveying with geopedological methods. Given the high costs of implementing salt monitoring and management programs in the field, the implementation of Near Infrared spectroscopy (NIR) and image analysis are proposed as an alternative for mapping soils salt affectation. Geostatistical methods, traditional soil laboratory methods and multispectral analysis of 697 soil samples were analyzing using machine learning and spectral models. The Orthogonal Partial Linear Square- Discriminant Analyses (OPLS-DA), Principal Component Regression (PCR), Partial Linear Square (PLS–PLSR), Least Absolute Shrinkage and Selection Operator (LASSO) were implemented. Soil cartographies for SAS were designed in areas under banana cultivation, determining the affectation degree. The results obtained showed that 45.1% of the soils are affected by salts, with R2 0.76 and RMS 0.15 for the applied of supervised models. OPLS-DA had a better performance being the high above sea level was the principal covariable to improve the model accuracy. LASSO and PLS were useful to Mg+2 and K+ with RMSE 0.92 and 0.34 and R2 of 0.37 and 0.44, while Saitsky&Golay filter improved the predictions model for pH and Ca+2. The use of combined techniques of geopedology, geostatistics and spectroscopy were efficient, practical and cheap methodologies for evaluate soil properties associate to SAS in the stablished banana crops.

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利用近红外光谱技术评估哥伦比亚加勒比海沿岸与土壤盐碱度相关的土壤特性
摘要 香蕉产业占哥伦比亚出口总额的 3%,是该国北部最重要的作物,惠及 250 多万个家庭。香蕉生产集中在哥伦比亚加勒比海沿岸,那里 90% 的土壤都受到一定程度的盐分影响。这项研究在哥伦比亚马格达莱纳省 Zona Bananera 市进行,在其他地方通过地貌测量和地质学方法划定了 11 个地貌单元。鉴于在实地实施盐分监测和管理计划的成本较高,建议采用近红外光谱(NIR)和图像分析作为绘制土壤盐分影响图的替代方法。利用机器学习和光谱模型对地质统计方法、传统土壤实验室方法和 697 个土壤样本的多光谱分析进行了分析。采用了正交部分线性方程-判别分析(OPLS-DA)、主成分回归(PCR)、部分线性方程(PLS-PLSR)、最小绝对收缩和选择操作器(LASSO)。在香蕉种植区设计了用于 SAS 的土壤制图,以确定影响程度。结果表明,45.1% 的土壤受盐分影响,应用监督模型的 R2 为 0.76,RMS 为 0.15。由于海拔高度是提高模型准确性的主要协变量,OPLS-DA 具有更好的性能。LASSO 和 PLS 对 Mg+2 和 K+有帮助,其 RMSE 分别为 0.92 和 0.34,R2 分别为 0.37 和 0.44,而 Saitsky&Golay 滤波器则改善了 pH 和 Ca+2 的预测模型。使用地质学、地质统计学和光谱学的综合技术是评估稳定的香蕉作物中与 SAS 有关的土壤特性的高效、实用和廉价的方法。
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