Optimising Sample Preparation and near Infrared Spectra Measurements of Soil Samples to Calibrate Organic Carbon and Total Nitrogen Content

2012 ◽  
Vol 20 (6) ◽  
pp. 695-706 ◽  
Author(s):  
Jasmin Miltz ◽  
Axel Don
Keyword(s):  
Organic Carbon ◽  
Sample Preparation ◽  
Nitrogen Content ◽  
Total Nitrogen ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Soil Samples ◽  
Total Nitrogen Content ◽  
Near Infrared Spectra

Reducing moisture effects on soil organic carbon content prediction in visible and near-infrared spectra with an external parameter othogonalization algorithm

2020 ◽  
Vol 100 (3) ◽  
pp. 253-262
Author(s):  
Yue Cao ◽  
Nisha Bao ◽  
Shanjun Liu ◽  
Wei Zhao ◽  
Shimeng Li
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Infrared Spectra ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Soil Samples ◽  
External Parameter ◽  
Moisture Effect ◽  
Field Spectroscopy ◽  
Near Infrared Spectra

Field spectroscopy and other efficient hyperspectral techniques have been widely used to measure soil properties, including soil organic carbon (SOC) content. However, reflectance measurements based on field spectroscopy are quite sensitive to uncontrolled variations in surface soil conditions, such as moisture content; hence, such variations lead to drastically reduced prediction accuracy. The goals of this work are to (i) explore the moisture effect on soil spectra with different SOC levels, (ii) evaluate the selection of optimal parameter for external parameter othogonalization (EPO) in reducing moisture effect, and (iii) improve SOC prediction accuracy for semi-arid soils with various moisture levels by combing the EPO with machine learning method. Soil samples were collected from grassland regions of Inner Mongolia in North China. Rewetting laboratory experiments were conducted to make samples moisturized at five levels. Visible and near-infrared spectra (350–2500 nm) of soil samples rewetted were observed using a hand-held SVC HR-1024 spectroradiometer. Our results show that moisture influences the correlation between SOC content and soil reflectance spectra and that moisture has a greater impact on the spectra of samples with low SOC. An EPO algorithm can quantitatively extract information of the affected spectra from the spectra of moist soil samples by an optimal singular value. A SOC model that effectively couples EPO with random forest (RF) outperforms partial least-square regression (PLSR)-based models. The EPO–RF model generates better results with R2 of 0.86 and root-mean squared error (RMSE) of 3.82 g kg−1, whereas a PLSR model gives R2 of 0.79 and RMSE of 4.68 g kg−1.

scholarly journals Long-term effect of tillage, nitrogen fertilization and cover crops on soil organic carbon and total nitrogen content

2011 ◽  
Vol 114 (2) ◽  
pp. 165-174 ◽  
Author(s):  
Marco Mazzoncini ◽  
Tek Bahadur Sapkota ◽  
Paolo Bàrberi ◽  
Daniele Antichi ◽  
Rosalba Risaliti
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Nitrogen Content ◽  
Total Nitrogen ◽  
Cover Crops ◽  
Nitrogen Fertilization ◽  
Term Effect ◽  
Total Nitrogen Content ◽  
Long Term Effect

scholarly journals FT-NIR Spectroscopic Analysis of Nitrogen in Cotton Leaves

2002 ◽  
Vol 56 (11) ◽  
pp. 1484-1489 ◽  
Author(s):  
Mark R. Riley ◽  
Loreto C. Cánaves
Keyword(s):  
Nitrogen Content ◽  
Total Nitrogen ◽  
Standard Error ◽  
Near Infrared ◽  
Total Nitrogen Content ◽  
Least Squares Regression ◽  
Nitrogen Levels ◽  
Plant Chemical ◽  
Grid Search Method ◽  
Kjeldahl Method

Near-infrared spectroscopy was evaluated as a means to quantify the nitrogen content in fresh cotton leaves ( Gossypium hirsutum L. var. Delta Pine 90) subjected to a factorial design experiment of varying nitrogen and water applications. Absorbance spectra were collected in the 10 000–4000 cm−1 (1000–2500 nm) region from fresh cotton leaves over a two month portion of the growing season. Total nitrogen content was quantified by a wet chemistry Kjeldahl method for validation purposes. Partial least-squares regression analysis, using an automated grid search method, selected the spectral region 6041 to 5651 cm−1 (1650–1770 nm) for analysis based on having the lowest standard error of prediction of total nitrogen content. This region includes protein spectral features. Nitrogen predictions resulted in a correlation coefficient of 0.83, and a standard error of prediction of 0.29% for nitrogen levels ranging from 3.1 to 5.2% total nitrogen. This approach has promise for providing rapid plant chemical analyses for cotton crop fertilization management purposes.

Near infrared spectrophotometric analysis of total nitrogen content in green tea.

1985 ◽  
Vol 32 (8) ◽  
pp. 553-559 ◽  
Author(s):  
Kenjiro IKEGAYA ◽  
Akihiro HINO ◽  
Jun UOZUMI ◽  
Hirotsugu TAKAYANAGI ◽  
Toyomasa ANAN ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Green Tea ◽  
Total Nitrogen ◽  
Near Infrared ◽  
Spectrophotometric Analysis ◽  
Total Nitrogen Content

scholarly journals Detection of leaf nitrogen content of summer corn using visible/near infrared spectra

2012 ◽  
Vol 30 (1) ◽  
pp. 48-54 ◽  
Author(s):  
Wei LIU ◽  
Qing-Rui CHANG ◽  
Man GUO ◽  
Dong-Xing XING ◽  
Yong-Sheng YUAN
Keyword(s):  
Nitrogen Content ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Content ◽  
Near Infrared Spectra

scholarly journals Farm-Scale Estimation of Total Nitrogen Content in Surface Paddy Soils by Extraction with Commercially Available Hydrogen Peroxide

Agronomy ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 40
Author(s):  
Naoki Moritsuka ◽  
Hiroki Saito ◽  
Ryosuke Tajima ◽  
Yukitsugu Takahashi ◽  
Hideaki Hirai
Keyword(s):  
Hydrogen Peroxide ◽  
Nitrogen Content ◽  
Total Nitrogen ◽  
Soil Samples ◽  
Total Nitrogen Content ◽  
Simple Method ◽  
Total N ◽  
Original Solution ◽  
Farm Scale ◽  
H2o2 Solution

We recently proposed a simple method for estimating total nitrogen content in paddy soil. In this method, soil is extracted with a commercial 3% hydrogen peroxide (H2O2) solution at 25 °C for 40 h, and electrical conductivity (EC (H2O2)) of the extract is measured. This study aimed to further evaluate the method’s applicability to soil samples collected at the farm scale by using the original and six additional H2O2 solutions that are locally and commercially available. The results obtained with the original solution indicated that the determination coefficients between EC (H2O2) and total N were statistically significant at all farms examined: Moka, 0.78 (n = 13); Kyoto, 0.50 (n = 16); Kizu, 0.43 (n = 89); and Kawatabi, 0.25 (n = 18). The EC of the tested H2O2 solutions varied from less than 0.05 to 1.4 mS cm−1 because of the addition of different stabilizers. EC (H2O2) values obtained with the less stabilized H2O2 solutions (one from Japan, one from USA, and the analytical grade 6% solution) agreed well with those obtained with the original solution. Thus, the proposed method can be useful for estimating the farm-scale variation in soil total N, provided a H2O2 solution with a low EC (<0.2 mS cm−1) is used for the extraction.

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