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利用近感电磁感应和伽马射线光谱数据选择最佳校准样本:在甘蔗生长土壤石灰和镁管理中的应用。

Selecting optimal calibration samples using proximal sensing EM induction and γ-ray spectrometry data: An application to managing lime and magnesium in sugarcane growing soil.

机构信息

School of Biological, Earth and Environmental Sciences, UNSW Sydney, Kensington, NSW, 2052, Australia.

Manaaki Whenua Landcare Research, P.O. Box 69040, Lincoln, 7640, New Zealand.

出版信息

J Environ Manage. 2021 Oct 15;296:113357. doi: 10.1016/j.jenvman.2021.113357. Epub 2021 Jul 28.

DOI:10.1016/j.jenvman.2021.113357
PMID:34351291
Abstract

Calcium (Ca) and magnesium (Mg) are essential for growth of sugarcane leaves and roots, as well as respiration and nitrogen metabolism, respectively. To assist farmers decide suitable application rates of lime and Mg fertiliser, respectively, the Australian sugarcane industry established the Six-Easy-Steps nutrient management guidelines based on topsoil (0-0.3 m) Ca (cmol(+) kg) and Mg (cmol(+) kg). Given the heterogeneous nature of soil, digital soil mapping (DSM) methods can be employed to allow for the precise application rate to be determined. In this study, we examine statistical models (i.e., ordinary kriging [OK], linear mixed model [LMM], quantile regression forests [QRF], support vector machine [SVM], and Cubist regression kriging [CubistRK]) to predict topsoil and subsoil (0.6-0.9) Ca and Mg, employing digital data in combination (i.e., proximal sensing electromagnetic induction (EMI) [e.g., 1mPcon, 1mHcon, etc.], gamma-ray [γ-ray] spectrometry [i.e., TC, K, U and Th] and digital elevation model [DEM] derivatives). We also investigate various sampling designs (i.e., spatial coverage [SCS], feature space coverage [FSCS], conditioned Latin hypercube [cLHS] and simple random sampling [SRS]) and calibration sample size (i.e., n = 180, 150, 120, 90, 60 and 30). The predictions are assessed using Lin's concordance correlation coefficient (LCCC) and ratio of performance to interquartile distance (RPIQ) with an independent validation dataset (i.e., n = 40). The best results were for prediction of subsoil Mg, utilising CubistRK (LCCC = 0.82) with the largest calibration sample size (n = 180), followed by LMM (0.79), SVM (0.76), QRF (0.70) and OK (0.65). This was generally the case for topsoil and subsoil Ca. We also conclude that no single sampling design was universally better, and 180 samples were necessary for predicting topsoil Ca and Mg with moderate agreement (0.65 < LCCC < 0.80). However, with FSCS, a minimum of 120 samples were enough to calibrate a CubistRK model and achieve substantial (LCCC > 0.80) agreement for predicting subsoil Ca and Mg. With respect to soil use and management according to the Six-Easy-Steps, the sandy soil in the north and south require large application rate of lime (3.5 t/ha) and Mg (125 kg/ha), respectively. Conversely, varying amounts of fertiliser rates of lime (2.0, 1.5 and 1 t/ha) and Mg (50 kg/ha) were recommended where Vertosols were previously mapped.

摘要

钙(Ca)和镁(Mg)是甘蔗叶片和根系生长以及呼吸和氮代谢所必需的。为了帮助农民分别确定石灰和 Mg 肥料的适宜施用量,澳大利亚制糖业根据表土(0-0.3 m)中的 Ca(cmol(+) kg)和 Mg(cmol(+) kg)建立了六步简易养分管理指南。考虑到土壤的非均质性,可以采用数字土壤制图(DSM)方法来确定精确的施用量。在这项研究中,我们检验了统计模型(即普通克里金[OK]、线性混合模型[LMM]、分位数回归森林[QRF]、支持向量机[SVM]和 Cubist 回归克里金[CubistRK]),以预测表土和底土(0.6-0.9)的 Ca 和 Mg,同时结合数字数据(即近地表电磁感应(EMI)[例如,1mPcon、1mHcon 等]、伽马射线[γ射线]光谱[即 TC、K、U 和 Th]和数字高程模型[DEM]衍生物)。我们还研究了各种采样设计(即空间覆盖[SCS]、特征空间覆盖[FSCS]、条件拉丁超立方[cLHS]和简单随机采样[SRS])和校准样本量(即 n=180、150、120、90、60 和 30)。使用独立验证数据集(即 n=40),通过林氏一致性相关系数(LCCC)和性能与四分位距比(RPIQ)评估预测结果。对于预测底土 Mg,使用 CubistRK(LCCC=0.82)的结果最佳,该模型采用最大的校准样本量(n=180),其次是 LMM(0.79)、SVM(0.76)、QRF(0.70)和 OK(0.65)。对于表土和底土 Ca 也是如此。我们还得出结论,没有一种采样设计是普遍更好的,预测表土 Ca 和 Mg 需要中等一致性(0.65<LCCC<0.80),则需要 180 个样本。然而,使用 FSCS,只需要 120 个样本就足以校准 CubistRK 模型,并实现对底土 Ca 和 Mg 的实质性(LCCC>0.80)预测。根据六步简易法进行土壤利用和管理,北部和南部的沙土需要大量施用石灰(3.5 t/ha)和 Mg(125 kg/ha)。相反,在以前绘制过 Vertosols 的地方,推荐使用不同数量的石灰(2.0、1.5 和 1 t/ha)和 Mg(50 kg/ha)肥料。

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