Estimation of soil properties with mid-infrared soil spectroscopy across yam production landscapes in West Africa

Abstract. Low soil fertility is challenging the sustainable production of yam and other staple crops in the yam belt of West Africa. Quantitative soil measures are needed to assess soil fertility decline and to improve crop nutrient supply in the region. We developed and tested a mid-infrared (mid-IR) soil spectral library to enable timely and cost-efficient assessments of soil properties. Our collection included 80 soil samples from four landscapes (10 km × 10 km) and 20 fields per landscape across a gradient from humid forest to savannah and 14 additional samples from one landscape that had been sampled within the Land Health Degradation Framework. We derived partial least squares regression models to spectrally estimate soil properties. The models produced accurate cross-validated estimates of total carbon, total nitrogen, total sulfur, total iron, total aluminum, total potassium, total calcium, exchangeable calcium, effective cation exchange capacity, and diethylenetriaminepentaacetic acid (DTPA)-extractable iron and clay content (R2>0.75). The estimates of total zinc, pH, exchangeable magnesium, bioavailable copper, and manganese were less predictable (R2>0.50). Our results confirm that mid-IR spectroscopy is a reliable and quick method to assess the regional-level variation of most soil properties, especially the ones closely associated with soil organic matter. Although the relatively small mid-IR library shows satisfactory performance, we expect that frequent but small model updates will be needed to adapt the library to the variation of soil quality within individual fields in the regions and their temporal fluctuations.

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Estimation of soil properties with mid-infrared soil spectroscopy across yam production landscapes in West Africa

10.5194/soil-2020-100 ◽

2021 ◽

Author(s):

Philipp Baumann ◽

Juhwan Lee ◽

Emmanuel Frossard ◽

Laurie Paule Schönholzer ◽

Lucien Diby ◽

...

Keyword(s):

West Africa ◽

Soil Fertility ◽

Soil Properties ◽

Regional Scale ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Total Carbon ◽

Humid Forest ◽

Mid Infrared

Abstract. Low soil fertility is challenging the sustainable production of staple crops in the yam belt of West Africa. Quantitative soil measures are needed to assess soil fertility decline and to improve crop fertilization management in the region. We developed and tested a mid-infrared (mid-IR) soil spectral library to enable timely and cost-efficient assessments of soil properties. Our collection included 80 soil samples from four landscapes (10 km × 10 km) and 20 fields/landscape across a gradient from humid forest to savanna, and 14 additional samples from one landscape that had been sampled within the Land Health Degradation Framework. We derived partial least square regression models to estimate soil properties with spectra.The models produced accurate cross-validated estimates of total carbon, total nitrogen, total sulfur, total iron, total aluminum, total potassium, total calcium, exchangeable calcium, effective cation exchange capacity, diethylenetriaminepentaacetic acid (DTPA) extractable iron and clay content (R2 > 0.75). The estimates of total zinc, pH, exchangeable magnesium, bioavailable copper and manganese were less predictable (R2 > 0.50). Our results confirm that mid-IR spectroscopy is a reliable and quick method assess the regional-scale variation in most soil properties, especially the ones closely associated with soil organic matter. Although the relatively small mid-IR library shows satisfactory performance, we expect that frequent but small model updates will be needed to adapt the library to the variation of soil quality attributes within individual fields in the regions and their temporal fluctuations.

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Prediction of the CEC to clay ratio using mid-infrared spectroscopy

Soil Research ◽

10.1071/sr11137 ◽

2012 ◽

Vol 50 (1) ◽

pp. 1 ◽

Cited By ~ 7

Author(s):

Philip M. Bloesch

Keyword(s):

Infrared Spectroscopy ◽

Mean Squared Error ◽

Clay Mineralogy ◽

Exchange Capacity ◽

Diffraction Measurement ◽

Least Squares Regression ◽

Mid Infrared ◽

Mid Infrared Spectroscopy ◽

Laboratory Determination ◽

Validation Set

The ratio of cation exchange capacity to clay (CCR) has been used as an index of clay mineralogy in subsoils low in organic matter in place of the standard X-ray diffraction measurement. Laboratory determination of this ratio is time-consuming and expensive and involves two analyses. In this paper, the CCR has been successfully predicted from mid-infrared diffuse reflectance spectra using partial least-squares regression (PLSR) with a square-root transformation of the CCR values (R2 = 0.860; root mean squared error of prediction = 0.089; relative per cent deviation = 2.660 for an independent validation set). The most important wavelengths used in the PLSR calibration were identified. The prediction of CCR using mid-infrared spectroscopy provides a cheaper and faster alternative to laboratory determination.

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Relationship of soil properties to fractionation, bioavailability and mobility of lead and zinc in soil

Plant Soil and Environment ◽

10.17221/2201-pse ◽

2008 ◽

Vol 53 (No. 5) ◽

pp. 225-238 ◽

Cited By ~ 28

Author(s):

N. Finžgar ◽

P. Tlustoš ◽

D. Leštan

Keyword(s):

Organic Matter ◽

Regression Analysis ◽

Soil Organic Matter ◽

Soil Properties ◽

Multiple Regression ◽

Contaminated Soils ◽

Organic Matter Content ◽

Clay Content ◽

Exchange Capacity ◽

Matter Content

Sequential extractions, metal uptake by Taraxacum officinale, Ruby’s physiologically based extraction test (PBET) and toxicity characteristic leaching procedure (TCLP), were used to assess the risk of Pb and Zn in contaminated soils, and to determine relationships among soil characteristics, heavy metals soil fractionation, bioavailability and leachability. Regression analysis using linear and 2nd order polynomial models indicated relationships between Pb and Zn contamination and soil properties, although of small significance (P < 0.05). Statistically highly significant correlations (P < 0.001) were obtained using multiple regression analysis. A correlation between soil cation exchange capacity (CEC) and soil organic matter and clay content was expected. The proportion of Pb in the PBET intestinal phase correlated with total soil Pb and Pb bound to soil oxides and the organic matter fraction. The leachable Pb, extracted with TCLP, correlated with the Pb bound to carbonates and soil organic matter content (R2 = 69%). No highly significant correlations (P < 0.001) for Zn with soil properties or Zn fractionation were obtained using multiple regression.

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Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

Soil Systems ◽

10.3390/soilsystems4030052 ◽

2020 ◽

Vol 4 (3) ◽

pp. 52

Author(s):

Gustavo M. Vasques ◽

Hugo M. Rodrigues ◽

Maurício R. Coelho ◽

Jesus F. M. Baca ◽

Ricardo O. Dart ◽

...

Keyword(s):

Soil Properties ◽

Ordinary Kriging ◽

Soil Property ◽

Clay Content ◽

Exchange Capacity ◽

Organic C ◽

Covariate Data ◽

Independent Validation ◽

Soil Sensors ◽

Dense Grid

Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data—except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps.

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Machine Learning Algorithms for Soil Properties Prediction with Treated Vis–NIR Spectrums from the Itatiaia National Park

10.20944/preprints201911.0053.v1 ◽

2019 ◽

Author(s):

Yuri Andrei Gelsleichter ◽

Lúcia Helena Cunha dos Anjos ◽

Elias Mendes Costa ◽

Gabriela Valente ◽

Paula Debiasi ◽

...

Keyword(s):

Machine Learning ◽

Soil Properties ◽

National Park ◽

Near Infrared ◽

Learning Algorithms ◽

Management Plan ◽

Machine Learning Algorithms ◽

Total Carbon ◽

Least Squares Regression ◽

Near Infrared Reflectance

Visible and near-infrared reflectance (Vis–NIR) techniques are a plausible method to soil analyses. The main objective of the study was to investigate the capacity to predicting soil properties Al, Ca, K, Mg, Na, P, pH, total carbon (TC), H and N, by using different spectral (350–2500 nm) pre-treatments and machine learning algorithms such as Artificial Neural Network (ANN), Random Forest (RF), Partial Least-squares Regression (PLSR) and Cubist (CB). The 300 soil samples were sampled in the upper part of the Itatiaia National Park (INP), located in Southeastern region of Brazil. The 10 K-fold cross validation was used with the models. The best spectral pre-treatment was the Inverse of Reflectance by a Factor of 104 (IRF4) for TC with CB, giving an averaged R² among the folds of 0.85, RMSE of 1.96; and 0.67 with 0.041 respectively for H. Into the K-folds models of TC, the highest prediction had a R² of 0.95. These results are relevant for the INP management plan, and also to similar environments. The good correlation with Vis–NIR techniques can be used for remote sense monitoring, especially in areas with very restricted access such as INP.

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Development of a composite soil degradation assessment index for cocoa agroecosystems in southwestern Nigeria

Solid Earth ◽

10.5194/se-8-827-2017 ◽

2017 ◽

Vol 8 (4) ◽

pp. 827-843 ◽

Cited By ~ 3

Author(s):

Sunday Adenrele Adeniyi ◽

Willem Petrus de Clercq ◽

Adriaan van Niekerk

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Soil Properties ◽

Cation Exchange ◽

Cation Exchange Capacity ◽

Soil Degradation ◽

Clay Content ◽

Exchange Capacity ◽

Southwestern Nigeria ◽

Assessment Index

Abstract. Cocoa agroecosystems are a major land-use type in the tropical rainforest belt of West Africa, reportedly associated with several ecological changes, including soil degradation. This study aims to develop a composite soil degradation assessment index (CSDI) for determining the degradation level of cocoa soils under smallholder agroecosystems of southwestern Nigeria. Plots where natural forests have been converted to cocoa agroecosystems of ages 1–10, 11–40, and 41–80 years, respectively representing young cocoa plantations (YCPs), mature cocoa plantations (MCPs), and senescent cocoa plantations (SCPs), were identified to represent the biological cycle of the cocoa tree. Soil samples were collected at a depth of 0 to 20 cm in each plot and analysed in terms of their physical, chemical, and biological properties. Factor analysis of soil data revealed four major interacting soil degradation processes: decline in soil nutrients, loss of soil organic matter, increase in soil acidity, and the breakdown of soil textural characteristics over time. These processes were represented by eight soil properties (extractable zinc, silt, soil organic matter (SOM), cation exchange capacity (CEC), available phosphorus, total porosity, pH, and clay content). These soil properties were subjected to forward stepwise discriminant analysis (STEPDA), and the result showed that four soil properties (extractable zinc, cation exchange capacity, SOM, and clay content) are the most useful in separating the studied soils into YCP, MCP, and SCP. In this way, we have sufficiently eliminated redundancy in the final selection of soil degradation indicators. Based on these four soil parameters, a CSDI was developed and used to classify selected cocoa soils into three different classes of degradation. The results revealed that 65 % of the selected cocoa farms are moderately degraded, while 18 % have a high degradation status. The numerical value of the CSDI as an objective index of soil degradation under cocoa agroecosystems was statistically validated. The results of this study reveal that soil management should promote activities that help to increase organic matter and reduce Zn deficiency over the cocoa growth cycle. Finally, the newly developed CSDI can provide an early warning of soil degradation processes and help farmers and extension officers to implement rehabilitation practices on degraded cocoa soils.

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Is Standardization Necessary for Sharing of a Large Mid-Infrared Soil Spectral Library?

Sensors ◽

10.3390/s20236729 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6729

Author(s):

Shree R. S. Dangal ◽

Jonathan Sanderman

Keyword(s):

Soil Properties ◽

Predictive Models ◽

Soil Samples ◽

Soil Survey ◽

Least Squares Regression ◽

Spectral Library ◽

Calibration Transfer ◽

Mid Infrared ◽

Calibration Models ◽

Spectral Libraries

Recent developments in diffuse reflectance soil spectroscopy have increasingly focused on building and using large soil spectral libraries with the purpose of supporting many activities relevant to monitoring, mapping and managing soil resources. A potential limitation of using a mid-infrared (MIR) spectral library developed by another laboratory is the need to account for inherent differences in the signal strength at each wavelength associated with different instrumental and environmental conditions. Here we apply predictive models built using the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (NSSC-KSSL) MIR spectral library (n = 56,155) to samples sets of European and US origin scanned on a secondary spectrometer to assess the need for calibration transfer using a piecewise direct standardization (PDS) approach in transforming spectra before predicting carbon cycle relevant soil properties (bulk density, CaCO3, organic carbon, clay and pH). The European soil samples were from the land use/cover area frame statistical survey (LUCAS) database available through the European Soil Data Center (ESDAC), while the US soil samples were from the National Ecological Observatory Network (NEON). Additionally, the performance of the predictive models on PDS transfer spectra was tested against the direct calibration models built using samples scanned on the secondary spectrometer. On independent test sets of European and US origin, PDS improved predictions for most but not all soil properties with memory based learning (MBL) models generally outperforming partial least squares regression and Cubist models. Our study suggests that while good-to-excellent results can be obtained without calibration transfer, for most of the cases presented in this study, PDS was necessary for unbiased predictions. The MBL models also outperformed the direct calibration models for most of the soil properties. For laboratories building new spectroscopy capacity utilizing existing spectral libraries, it appears necessary to develop calibration transfer using PDS or other calibration transfer techniques to obtain the least biased and most precise predictions of different soil properties.

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Soil properties and lodgepole pine growth on rehabilitated landings in northeastern British Columbia

Canadian Journal of Soil Science ◽

10.4141/s03-013 ◽

2003 ◽

Vol 83 (4) ◽

pp. 465-474 ◽

Cited By ~ 1

Author(s):

C. E. Bulmer ◽

M. Krzic

Keyword(s):

British Columbia ◽

Soil Properties ◽

Lodgepole Pine ◽

Clay Content ◽

Exchange Capacity ◽

Soil Productivity ◽

Mechanical Resistance ◽

Average Height ◽

Water Storage Capacity ◽

Pine Trees

We determined post-establishment tree growth and soil properties on rehabilitated log landings and forest plantation sites with medium texture in northeastern British Columbia. Six years after rehabilitation treatments were applied, 60% of rehabilitated landing plots had more than 1000 stems ha-1, while 17% had fewer than 600 stems ha-1. The average height of undamaged lodgepole pine trees on rehabilitated landings was consistently lower than for trees of the same age on plantations. Surface (0–7 cm) and subsurface (10–17 cm) soil bulk densities were higher for rehabilitated landings than for adjacent plantations. Rehabilitated landing and plantation soils had similar values of total and aeration porosity. Plantation soils had higher available water storage capacity (AWSC) than rehabilitated soils. Soil mechanical resistance after landing rehabilitation was often higher than for plantation soils at the same depth. Soils on both rehabilitated landings and plantations showed an increase in mechanical resistance from June to September 2001. With the exception of June 2001, soil mechanical resistance after landing rehabilitation was often higher than 2500 kPa. For surface mineral soils, there were no differences in total C, N, or cation exchange capacity (CEC) between rehabilitated landings and plantations. Rehabilitated landing soils had significantly higher total C and N at 10–17 cm depth than plantation soils, which coincided with higher clay content for the landing subsoils. Key words: Forest soil rehabilitation, soil degradation, soil productivity, soil conservation

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Zinc Adsorption in Relation to Soil Properties, Analysed with Langmuir and Freundlich Models in Soils of Kashmir Valley

Current Agriculture Research Journal ◽

10.12944/carj.7.1.09 ◽

2019 ◽

Vol 7 (1) ◽

pp. 68-73

Author(s):

Syed Sadat ◽

N. Z. Rehman ◽

M. A. Bhat ◽

M.A. Wani

Keyword(s):

Soil Properties ◽

Chemical Properties ◽

Clay Content ◽

Exchange Capacity ◽

Sorption Data ◽

Average Value ◽

Physico Chemical ◽

Zinc Concentrations ◽

Langmuir And Freundlich Equations ◽

Zinc Adsorption

The phenomenon of fixation of added zinc in soils considerably affects the availability and efficiency of applied zinc. Pertaining to this situation, different land-use soil samples across the valley were analysed for various physico-chemical properties and adsorption capacities. The results showed that the soils were slightly acidic to alkaline in reaction and differ far and wide in other soil properties. Cation exchange capacity (CEC) of the soils showed little variation between the samples and varied from13.3 to 17.2 cmol(p+) kg-1 with an average value of 15.1 cmol(p+) kg-1of soil. The maximum of zinc adsorption were greatly influenced by soil organic matter, clay content and CEC of the soils. The data was fitted to Langmuir and Freundlich equations and the results yielded that the Freundlich equation showed better fit to the sorption data at higher zinc concentrations. However, both the models were having satisfactory results for the obtained data.

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VARIABILITY OF SOIL PROPERTIES IN RELATION TO SIZE OF MAP UNIT DELINEATION

Canadian Journal of Soil Science ◽

10.4141/cjss82-072 ◽

1982 ◽

Vol 62 (4) ◽

pp. 657-662 ◽

Cited By ~ 3

Author(s):

C. WANG

Keyword(s):

Organic Carbon ◽

Soil Properties ◽

Cation Exchange Capacity ◽

Surface Soil ◽

Boundary Effect ◽

Clay Content ◽

Soil Characteristics ◽

Exchange Capacity ◽

Coefficient Of Variability ◽

Range Of Variation

The results of a study which compared some selected soil characteristics of small and large map unit delineations are presented. Color, organic carbon, pH, cation exchange capacity and clay content were measured. Properties, such as pH and CEC of surface soil and pH of subsoil, were found to be significantly different between large and small delineations. Although Brandon is selected to be a simple and relatively uniform map unit, the range of variation was wide for all of the selected soil properties. For each property measured the coefficient of variability was always larger in surface soils than in subsoils. However, variance of measured soil properties did not differ between the two groups of delineations. Consequently, the soil boundary effect is considered to be insignificant for the Brandon unit of the Dalhousie association studied.

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