基于土壤颜色和磁化率的马达加斯加水稻土土壤性质预测

IF 1.9 4区 农林科学 Q3 ENVIRONMENTAL SCIENCES Soil Science and Plant Nutrition Pub Date : 2022-10-22 DOI:10.1080/00380768.2022.2136929
Hobimiarantsoa Rakotonindrina, N. Moritsuka, K. Kawamura, Y. Tsujimoto, T. Nishigaki, Haja Bruce Andrianary, T. Razafimbelo, H. Razakamanarivo, A. Andriamananjara
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引用次数: 0

摘要

准确评估土壤性质是提高作物生产肥料管理水平的必要条件。传统的实验室化学分析既昂贵又费时。土壤颜色与不同的土壤成分有关,土壤磁化率(MS)反映了与土壤性质相关的磁性矿物的丰度。改进土壤颜色和质谱分析的近端传感技术为经济实惠和快速评估土壤特性提供了机会。本研究的目的是利用逐步多元线性回归(SMLR)、随机森林(RF)和非线性回归方法,评估土壤颜色参数和MS值在马达加斯加中部高地低地和高地田预测土壤性质方面的潜在用途。目标性状包括土壤有机碳(SOC)、全氮(TN)、草酸可提取磷和铁(Feox)含量以及土壤质地。采用决定系数(R2)、均方根误差(RMSE)和性能与四分位数距离之比(RPIQ)对模型的预测精度进行评估。利用土壤颜色参数,采用RF算法对所有稻田的Feox含量(loge Feox)的预测精度均可接受(R2 = 0.54, RMSE = 0.55, RPIQ = 1.70),而SMLR方法对旱地的土壤有机碳、Feox、粘土和砂含量的预测精度最高,R2为0.43 ~ 0.67,RPIQ为1.63 ~ 1.77,可靠性可接受。在低洼地区,采用带颜色参数的SMLR预测TN含量具有可接受的准确性(R2 = 0.34, RMSE = 0.49, RPIQ = 1.71)。土壤颜色参数与MS值作为预测变量的组合提高了RF法对低地农田土壤有机碳的预测效果(R2 = 0.57, RMSE = 6.37, RPIQ = 1.96)。利用土壤颜色和质谱参数是一种很有前途的方法,可以通过RF和SMLR方法简化高地和低地生态系统土壤性质的评估。结合土壤颜色和质谱参数,提高了土壤有机碳含量的预测精度。
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Prediction of the soil properties of Malagasy rice soils based on the soil color and magnetic susceptibility
ABSTRACT Accurate assessments of soil properties are required to improve fertilizer management practices for crop production. Conventional chemical analysis in the laboratory is costly and time-consuming. Soil color is related to different soil compositions, while soil magnetic susceptibility (MS) has been found to reflect the abundance of magnetic minerals relevant to soil properties. Improving proximal sensing techniques for the analysis of soil color and MS provides opportunities for affordable and rapid assessments of soil properties. The aim of this study was to evaluate the potential use of soil color parameters and MS values to predict soil properties using stepwise multiple linear regression (SMLR), random forest (RF), and nonlinear regression approaches in lowland and upland fields in the central highlands of Madagascar. The target properties included the contents of soil organic carbon (SOC), total nitrogen (TN), oxalate-extractable phosphorus and iron (Feox), and the soil texture. The model prediction accuracy was assessed using the coefficient of determination (R2), root-mean-square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). The use of soil color parameters yielded an acceptable prediction accuracy of the Feox content (loge Feox) for all rice fields (R2 = 0.54, RMSE = 0.55, RPIQ = 1.70) using the RF algorithm, while the SMLR approach gave the most accurate prediction for upland fields with acceptable reliabilities for SOC, Feox, and clay and sand content prediction, with R2 ranging from 0.43 to 0.67 and RPIQ from 1.63 to 1.77. In lowland fields, TN content was predicted with acceptable accuracy (R2 = 0.34, RMSE = 0.49, RPIQ = 1.71) using SMLR with the color parameter. The combination of the soil color parameters with the MS value as predictor variables increased SOC prediction for lowland fields using the RF approach (R2 = 0.57, RMSE = 6.37, RPIQ = 1.96). Use of the soil color and MS parameters was revealed to be a promising way to simplify the assessment of soil properties in upland and lowland ecosystems by using RF and SMLR approaches. A combined use of the soil color and MS parameters improved the prediction accuracy for the SOC content.
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来源期刊
Soil Science and Plant Nutrition
Soil Science and Plant Nutrition 农林科学-农艺学
CiteScore
4.80
自引率
15.00%
发文量
56
审稿时长
18-36 weeks
期刊介绍: Soil Science and Plant Nutrition is the official English journal of the Japanese Society of Soil Science and Plant Nutrition (JSSSPN), and publishes original research and reviews in soil physics, chemistry and mineralogy; soil biology; plant nutrition; soil genesis, classification and survey; soil fertility; fertilizers and soil amendments; environment; socio cultural soil science. The Journal publishes full length papers, short papers, and reviews.
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