Visible-to-near-infrared spectroscopy for prediction of soil nitrogen mineralization after sample stratification by textural homogeneity criteria

IF 6.1 1区 农林科学 Q1 SOIL SCIENCE
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Abstract

On-time and accurate estimation of the soil nitrogen mineralization rate (SNMR) is critical for nitrogen (N) management and protecting the environment. This study evaluated the performance of a visible-to-near-infrared reflectance (vis-NIR) spectroscopy for predicting SNMR for four texture groups. A total of 62 topsoil samples were collected from 17 management zones distributed over four fields and incubated with seven destructive sampling events. Samples were analysed for total mineral N (NH4++NO3) content and scanned using a vis-NIR sensor simultaneously at each of the seven-sampling times. Four partial least squares regression models were calibrated and validated for four textural groups (groups- 1– 4) identified over the United State Department of Agriculture (USDA) texture triangle. Prediction accuracies indicated that vis-NIR sensor was moderately to highly accurate for predicting SNMR, while observing variable accuracies across texture groups. The highest accuracy was obtained for group 1 (sandy-loam; coefficient of determination, R2 = 0.90; root mean square error, RMSE = 0.04 mg N kg−1 soil day−1), successively followed by group 2 (mostly loam; R2 = 0.80, RMSE = 0.05 mg N kg−1 soil day−1) group 4 (mostly silt; R2 = 0.66, RMSE = 0.08 mg N kg−1 soil day−1), and group 3 (silt-loam; R2 = 0.44, RMSE = 0.08 mg N kg−1 soil day−1). Variable importance in projection score revealed that the key spectral bands to predict SNMR were in 2150 – 2260 nm and 2470 – 2480 nm, resembling the key bands associated with soil organic compounds and clay minerals. In-advance texture information required for soil stratification is regarded a limitation of the proposed approach. In conclusion, vis-NIR holds potential for a rapid estimation of SNMR when samples are stratified into similar texture groups in advance, however, confirmatory research will be needed to validate the current findings for soils from different origin and under different management.

利用可见近红外光谱仪预测按纹理均匀性标准对样品进行分层后的土壤氮矿化度
及时准确地估算土壤氮矿化率(SNMR)对于氮(N)管理和环境保护至关重要。本研究评估了可见光-近红外反射率(可见光-近红外)光谱在预测四个质地组的土壤氮矿化率方面的性能。从分布在四块田地的 17 个管理区共采集了 62 个表层土样本,并进行了七次破坏性取样培养。在七次取样中的每一次取样中,都同时使用可见光近红外传感器对样品进行矿物氮总量(NH+NO)分析和扫描。根据美国农业部(USDA)纹理三角形确定的四个纹理组(1-4 组),对四个偏最小二乘法回归模型进行了校准和验证。预测准确度表明,可见光近红外传感器预测 SNMR 的准确度从中度到高度不等,同时观察到不同纹理组的准确度各不相同。第 1 组(沙壤土;判定系数 R = 0.90;均方根误差 RMSE = 0.04 毫克氮千克土壤日)的准确度最高,其次是第 2 组(大部分为壤土;判定系数 R = 0.80,均方根误差 = 0.05 毫克氮千克土壤日)、第 4 组(大部分为淤泥;R = 0.66,均方根误差 = 0.08 毫克氮千克土壤日)和第 3 组(淤泥质壤土;R = 0.44,均方根误差 = 0.08 毫克氮千克土壤日)。预测得分中的变量重要性表明,预测 SNMR 的关键光谱波段在 2150 - 2260 nm 和 2470 - 2480 nm 之间,类似于与土壤有机物和粘土矿物相关的关键波段。土壤分层所需的先期纹理信息被认为是建议方法的一个局限。总之,如果事先将样本分层为相似的质地组,则可见近红外光谱具有快速估算信噪比的潜力,但还需要进行确证研究,以验证目前针对不同来源和不同管理条件下的土壤得出的结论。
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来源期刊
Soil & Tillage Research
Soil & Tillage Research 农林科学-土壤科学
CiteScore
13.00
自引率
6.20%
发文量
266
审稿时长
5 months
期刊介绍: Soil & Tillage Research examines the physical, chemical and biological changes in the soil caused by tillage and field traffic. Manuscripts will be considered on aspects of soil science, physics, technology, mechanization and applied engineering for a sustainable balance among productivity, environmental quality and profitability. The following are examples of suitable topics within the scope of the journal of Soil and Tillage Research: The agricultural and biosystems engineering associated with tillage (including no-tillage, reduced-tillage and direct drilling), irrigation and drainage, crops and crop rotations, fertilization, rehabilitation of mine spoils and processes used to modify soils. Soil change effects on establishment and yield of crops, growth of plants and roots, structure and erosion of soil, cycling of carbon and nutrients, greenhouse gas emissions, leaching, runoff and other processes that affect environmental quality. Characterization or modeling of tillage and field traffic responses, soil, climate, or topographic effects, soil deformation processes, tillage tools, traction devices, energy requirements, economics, surface and subsurface water quality effects, tillage effects on weed, pest and disease control, and their interactions.
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