Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging

Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Using a bias-corrected random forest we were able to reproduce the OC distribution in the soil cores with very good to excellent model goodness-of-fit, enabling us to map the spatial distribution of OC in the soil cores at very high resolution (~53 × 53 µm). Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. In contrast to the relatively homogeneous distribution of OC in the plough horizon, the subsoil was characterized by distinct regions of OC enrichment and depletion, including biopores which contained ~2–10 times higher SOC contents than the soil matrix in close proximity. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns

Visible and near infrared spectroscopy in soil science

This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis–NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis–NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic. The development of the Global Soil Spectral Library might be a step in the right direction

On-line Vis-Nir sensor determination of soil variations of sodium, potassium and magnesium

Among proximal measurement methods, visible and near infrared (Vis-Nir) spectroscopy probably has the greatest potential for determining the physico-chemical properties of different natural resources, including soils. This study was conducted to determine the sodium, potassium and magnesium variations in a 10. Ha field located in Karacabey district (Bursa Province, Turkey) using an on-line Vis-Nir sensor. A total of 92 soil samples were collected from the field. The performance and accuracy of the Na, K and Mg calibration models was evaluated in cross-validation and independent validation. Three categories of maps were developed: 1) reference laboratory analyses maps based on 92 points 2) Full-data point maps based on all 6486 on-line points Vis-Nir predicted in 2013 and 3) full- data point maps based on all 2496 on-line points Vis-Nir predicted in 2015. Results showed that the prediction performance in the validation set was successful, with average R2 values of 0.82 for Na, 0.70 for K, and 0.79 for Mg, average root mean square error of prediction (RMSEP) values of 0.02% (Na), 0.20% (K), and 1.32% (Mg) and average residual prediction deviation (RPD) values of 2.13 (Na), 0.97 (K), and 2.20 (Mg). On-line field measurement was also proven to be successful with validation results showing average R2 values of 0.78 (Na), 0.64 (K), and 0.60 (Mg), average RMSEP values of 0.04% (Na), 0.13% (K), and 2.19% (Mg) and average RPD values of 1.57 (Na) 1.68 (K) and 1.56 (Mg). Based on 3297 points, maps of Na, K and Mg were produced after N, P, K and organic fertilizer applications, and these maps were then compared to the corresponding maps from the previous year. The comparison showed a variation in soil properties that was attributed to the variable rate of fertilization implemented in the preceding year

Using Near-Infrared Spectroscopy in Agricultural Systems

This chapter provides a review on the state of art of the use of the visible near-infrared (vis-NIR) spectroscopy technique to determine mineral nutrients, organic compounds, and other physical and chemical characteristics in samples from agricultural systems—such as plant tissues, soils, fruits, cocomposted sewage sludge and wastes, cereals, and forage and silage. Currently, all this information is needed to be able to carry out the appropriate fertilization of crops, to handle agricultural soils, determine the organoleptic characteristics of fruit and vegetable products, discover the characteristics of the various substrates obtained in composting processes, and characterize byproducts from the industrial sector. All this needs a large number of samples that must be analyzed; this is a time-consuming work, leading to high economic costs and, obviously, having a negative environmental impact owing to the production of noxious chemicals during the analyses. Therefore, the development of a fast, environmentally friendly, and cheaper method of analysis like vis-NIR is highly desirable. Our intention here is to introduce the main fundamentals of infrared reflectance spectroscopy, and to show that procedures like calibration and validation of data from vis-NIR spectra must be performed, and describe the parameters most commonly measured in the agricultural sector

Spectroscopic determination of major nutrients (N, P, K) of soil

Thesis (Master)--Izmir Institute of Technology, Food Engineering, Izmir, 2004Includes bibliographical references (leaves: 84-88)Text in English; Abstract: Turkish and Englishxvi, 99 leavesThe aim of this study was to determine the major soil nutrients (nitrogen, phosphorus and potassium) which mainly affect the raw material quality of food, using near infrared reflectance spectroscopy (1000-2500 nm). Genetic inverse least squares and partial least squares were used to predict the concentrations of major soil nutrients.The soil samples, collected from Menemen Application and Research Farms, were prepared for the near infrared analysis by using two different methods. According to the first method, two experiments were performed. The soil samples of which were oven dried and screened through a 2 mm sieve, were mixed with NPK fertilizer in the concentration range of 1-15% (wt/wt) (first experiment), and with NH4NO3 and TSP fertilizers in the concentration range of 0.075-0.3% (wt/wt) (second experiment). Using genetic inverse least squares method, regression coefficients of 0.9820, 0.9779 and 0.9906 were obtained for the prediction of nitrogen, phosphorus and potassium concentrations in samples containing NPK fertilizer, respectively. In the second experiment, prediction of nitrogen concentration in samples containing NH4NO3 fertilizer was done reliable with a regression coefficient of 0.8409 using genetic inverse least squares method. On the other hand, regression coefficient of 0.6005 was obtained for the prediction of phosphorus concentration in samples containing TSP fertilizer with the same statistical method.The second method differed from the first one by eliminating the drying of soil samples and moisturizing step following the addition of fertilizers into soil samples. The aim was to prevent baseline shifts in the spectra arising from the moisture changes in the samples. Five types of fertilizer [KNO3, CaNO3, TSP, (NH4)2SO4, NPK] were used in the preparation of samples in the concentration range of 0.02-0.5% (wt/wt). Using genetic inverse least squares method, calibration models produced between the reflectance spectra and the nutrient concentrations had regression coefficients greater than 0.80, however the prediction ability of the models was poor (R2<0.50) except for the samples containing (NH4)2SO4 and NPK fertilizers. The regression coefficients for the prediction of nitrogen and sulfur concentrations in (NH4)2SO4 containing samples were found as 0.8620 and 0.8555, respectively. For the prediction of nitrogen, phosphorus and potassium concentrations in NPK containing samples, the regression coefficients were found as 0.6737, 0.7633 and 0.8724, respectively. The partial least squares method was also used for the prediction of nutrient concentrations in the samples prepared according to the second method. Except samples containing (NH4)2SO4 fertilizer, nitrogen, phosphorus and potassium amounts could not be predicted in the other samples using partial least squares method (R2<0.20). The regression coefficients obtained for the prediction of nitrogen and sulfur amounts in (NH4)2SO4 containing samples were 0.9301.An additional work was carried out with laboratory analyzed soil samples collected from several points of two agricultural fields in Menemen Application and Research Farms. Total nitrogen, extractable phosphorus and exchangeable potassium amounts were determined by Agricultural Engineering Department of Ege University according to the Kjeldahl method, Bingham method and ammonium acetate method, respectively.Predictions of these nutrient concentrations by genetic inverse least squares method were poor (R2< 0.20). Using partial least squares method, the nutrient concentrations could not be predicted (factor number . 0).The results of this study indicate that, near infrared reflectance technique provided rapid, non-destructive and simultaneous determination of nitrogen, phosphorus and potassium concentrations in soil- fertilizer mixtures depending on the sample preparation steps, fertilizer types and concentrations and multivariate calibration methods (genetic inverse least squares and partial least squares methods)

Hyper Spectral Analysis of Soil Iron Oxide using PLSR Method: A Review

Spectroscopy is a rapid, simple, non-destructive and analytical technique, which provides a good alternative that may be used to replace conventional methods of soil analysis. Soil iron oxides occur in almost all type�s soils and they re?ect different environmental conditions by the high variability of their mineralogy and concentration. Soil iron oxide, being an important pedogenic indicator of the soil, measurement of Iron Oxide content can be used as an index of soil fertility. Analytical Spectral Device (ASD) Field Spec 4 Spectroradiometer is used which has 350-2500 nm spectral wavelength range to estimate iron oxide content from the soil sample. The Vis-NIR reflectance spectroscopy requires less effort and it is quick innovation to predict the soil iron oxide content. For collecting the soil iron oxide content from spectral data we are utilizing PLSR which is statistical regression method. This paper states the work that is done on different soil types at different places to observe the iron oxide content in soil

Estimating Macronutrient Content of Paddy Soil Based on Near-Infrared Spectroscopy Technology Using Multiple Linear Regression

This study investigates the feasibility of employing near-infrared (NIR) spectroscopy with multiple linear regression (MLR) to estimate macronutrients in paddy soil compared with partial least squares (PLS) and principal component regression (PCR). Seventy-nine soil samples from West Java Province, Indonesia, are subject to conventional nutrient analysis and NIR spectroscopy (1000-2500 nm). The reflectance data undergoes various pretreatment techniques, and MLR models are calibrated using the forward method to achieve correlations exceeding 0.90. The best model calibrations are selected based on high correlation coefficients, determination coefficients, RPD, and low RMSE values. Meanwhile, the comparison of performance MLR is made with the PLS and PCR models. Results indicate that simple MLR models perform less than PLS for all nutrients, better than PCR for nitrogen, and below PCR for phosphorus and potassium. However, MLR reliably estimates soil nitrogen, phosphorus, and potassium content with ratio of performance to deviation (RPD) exceeding 2.0. This study demonstrates the potential of MLR for precise macronutrient estimation in paddy soil

Improved farm soil mapping using near infrared reflection spectroscopy

Information on soil texture, soil organic matter content (SOM), nutrient status and pH is fundamental for efficient crop production and for minimising negative effects on the environment. Farmers obtain this information, on which decisions on fertiliser and lime requirements are based, through farm soil mapping. Although there is a general awareness that within-field and within-farm variations might not be adequately captured using conventional sample point density, simply increasing the number of sample pointes would increase the cost to unacceptable levels. In this thesis, near infrared reflection (NIR) spectroscopy was used to obtain more accurate information on within-field or within-farm variations in a number of soil properties. One central objective was to estimate the within-field variation in N mineralisation, to allow for improved N fertilisation strategies. Another was the development of economically feasible strategies for increasing sample point density in conventional farm soil mapping for improved decision support in precision agriculture. The results presented here show that NIR spectroscopy can be used to estimate N mineralisation (measured as plant N uptake) in fields with large variations in SOM, and that the additional predictive capacity of NIR compared with SOM is related to variations in soil texture. The results also demonstrate that it is possible to make small farm-scale calibrations with a very limited number of calibration samples for clay and SOM content, producing information at a considerably higher density than conventional farm soil mapping. Within-field calibrations for pH and easily available P, K and Mg-AL also proved possible, but more calibration samples were needed. Predictions for silt failed regardless of the number of calibration samples