Use of near‐ and mid‐infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest‐floor material in soils

The presence of relatively inert organic materials such as char has to be considered in calibrations of soil C models or when calculating C‐turnover times in soils. Rapid and cheap spectroscopic techniques such as near‐infrared (NIRS) or mid‐infrared spectroscopy (MIRS) may be useful for the determination of the contents of char‐derived C in soils. To test the suitability of both spectroscopic techniques for this purpose, artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals) and forest‐floor Oa material were produced. The total C content of these mixtures (432 samples) ranged from 0.5% to 6% with a proportion of char‐derived C amounting to 0%, 20%, 40%, 50%, 60%, or 80%. All samples were scanned in the visible and near‐IR region (400–2500 nm). Cross‐validation equations for total C and N, C and N derived from char (C_char, N_char) and Oa material were developed using the whole spectrum (first and second derivative) and a modified partial least‐square regression method. Thirty‐six samples were additionally scanned in the middle‐IR and parts of the near‐IR region (7000–400 cm^–1 which is 1430–25,000 nm) in the diffuse‐reflectance mode. All properties investigated were successfully predicted by NIRS as reflected by RSC values (ratio of standard deviation of the laboratory results to standard error of cross‐validation) > 4.3 and modeling efficiencies (EF) ≥ 0.98. Near‐infrared spectroscopy was also able to differentiate between the different coals. This was probably due to structural differences as suggested by wavelength assignment. Mid‐IR spectroscopy in the diffuse‐reflectance mode was also capable to successfully predict the parameters investigated. The EF values were > 0.9 for all constituents. Our results indicated that both spectroscopic techniques applied, NIRS and MIRS, are able to predict C and N derived from different sources in soil, if closed populations are considered.     

Determination of total soil organic C and hot water‐extractable C from VIS‐NIR soil reflectance with partial least squares regression and spectral feature selection techniques

The calibration of soil organic C (SOC) and hot water‐extractable C (HWE‐C) from visible and near‐infrared soil reflectance spectra is hindered by the complex spectral interaction of soil chromophores that usually varies from one soil or soil type to another. The exploitation of spectral variables from spectroradiometer data is further affected by multicollinearity and noise. In this study, a set of soil samples (Fluvisols, Podzols, Cambisols and Chernozems; n = 48) representing a wide range of properties was analysed. Spectral readings with a fibre‐optics visible to near‐infrared instrument were used to estimate SOC and HWE‐C contents by partial least squares regression (PLS). In addition to full‐spectrum PLS, spectral feature selection techniques were applied with PLS (uninformative variable elimination, UVE‐PLS, and a genetic algorithm, GA‐PLS). On the basis of normalized spectra (mean centring + vector normalization), the order of prediction accuracy was GA‐PLS ? UVE‐PLS > PLS for SOC; for HWE‐C, it was GA‐PLS > UVE‐PLS, PLS. With GA‐PLS, acceptable cross‐validated (cv) prediction accuracies were obtained for the complete dataset (SOC, , RPD_cv = 2.42; HWE‐C_cv, , RPD_cv = 2.13). Splitting the soil data into two groups with different basic properties (Podzols compared with Fluvisols/Cambisols; n = 21 and n = 23, respectively) improved SOC predictions with GA‐PLS distinctly (Podzols, , RPD_cv = 3.14; Fluvisols/Cambisols, , RPD_cv = 3.64). This demonstrates the importance of using stratified models for successful quantitative approaches after an initial rough screening. GA selection frequencies suggest that the spectral region over 1900 nm, and in particular the hydroxyl band at 2200 nm are of great importance for the spectral prediction of both SOC and HWE‐C.     

Prediction of model pools for a long‐term experiment using near‐infrared spectroscopy

Fourty‐one soil samples from the “Eternal Rye” long‐term experiment in Halle, Germany, were used to test the usefulness of near‐infrared spectroscopy (NIRS) to differentiate between C derived from C₃ and C₄ plants by using the isotopic signature (δ¹³C) and to predict the pools considered in the Rothamsted Carbon (RothC) model, i.e., decomposable plant material, resistant plant material, microbial biomass, humified organic matter, and inert organic matter. All samples were scanned in the visible‐light and near‐infrared region (400–2500 nm). Cross‐validation equations were developed using the whole spectrum (first to third derivative) and a modified partial least‐square regression method. δ¹³C values and all pools of the RothC model were successfully predicted by NIRS as reflected by RSC values (ratio between standard deviation of the laboratory results and standard error of cross‐validation) ranging from 3.2 to 3.4. Correlations analysis indicated that organic C can be excluded as basis for the successful predictions by NIRS in most cases, i.e., 11 out of 16.     

The use of visible and near‐infrared spectroscopy for the analysis of soil water repellency

This study investigated the potential of visible/near‐infrared reflectance spectroscopy (Vis‐NIRS) to predict soil water repellency (SWR). The top 40 mm of soils (n = 288) across 48 sites under pastoral land‐use in the North Island of New Zealand, which represented 10 soil orders and covered five classes of drought proneness, were analysed by standard laboratory methods and Vis‐NIRS. Soil WR was measured by using the molarity of ethanol droplet (MED) and the water drop penetration time (WDPT) tests. Soil organic carbon content (%C) was also measured to examine a possible relationship with SWR. A partial least squares regression (PLSR) model was developed by using Vis‐NIRS spectral data and the reference laboratory data. In addition, we explored the power of discrimination based on WDPT classes using partial least squares discriminant analysis (PLS‐DA). The PLSR of the processed spectra produced moderately accurate prediction for MED (R²_val = 0.61, RPD_val = 1.60, RMSE_val = 0.59) and good prediction for %C (R²_val = 0.82, RPD_val = 2.30, RMSE_val = 2.72). When the data from the 10 soil orders were considered separately and based on soil order rather than being grouped, the prediction of MED was further improved except for the Allophanic, Brown, Organic and Ultic soil orders. The PLS‐DA was successful in classifying 60% of soil samples into the correct WDPT classes. Our results indicate clearly that Vis‐NIRS has the potential to predict SWR. Further improvement in the prediction accuracy of SWR is envisaged by increasing the understanding of the relationship between Vis‐NIRS and the SWR of all New Zealand soil orders as a function of their physical properties and chemical constituents such as hydrophobic compounds.     

Use of mid‐infrared spectroscopy in the diffuse‐reflectance mode for the prediction of the composition of organic matter in soil and litter

Mid‐infrared spectroscopy (MIRS) is assumed to be superior to near‐infrared spectroscopy (NIRS) for the prediction of soil constituents, but its usefulness is still not sufficiently explored. The objective of this study was to evaluate the ability of MIRS to predict the chemical and biological properties of organic matter in soils and litter. Reflectance spectra of the mid‐infrared region including part of the near‐infrared region (7000–400 cm^–1) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by ¹³C CPMAS‐NMR spectroscopy. A partial least‐square method and cross‐validation were used to develop equations for the different constituents over selected spectra ranges after several mathematical treatments of the spectra. Mid‐infrared spectroscopy predicted well the C : N ratio: the modeling efficiency EF was 0.95, the regression coefficient (a) of a linear regression (measured against predicted values) was 1.0, and the correlation coefficient (r) was 0.98. Satisfactorily (EF ≥ 0.70, 0.8 ≤ a ≤ 1.2, r ≥ 0.80) assessed were the contents of C, N, and lignin, the production of dissolved organic carbon, and the contents of carbonyl C, aromatic C, O‐alkyl C, and alkyl C. However, the N mineralization rate, the microbial biomass and the alkyl–to–aromatic C ratio were predicted less satisfactorily (EF < 0.70). Limiting the sample set to mineral soils did generally not result in improved predictions. The good and satisfactory predictions reported above indicate a marked usefulness of MIRS in the assessment of chemical characteristics of soils and litter, but the accuracies of the MIRS predictions in the diffuse‐reflectance mode were generally not superior to those of NIRS.     

Near‐infrared spectroscopy can predict the composition of organic matter in soil and litter

The usefulness and limitations of near‐infrared reflectance spectroscopy (NIRS) for the assessment of several soil characteristics are still not sufficiently explored. The objective of this study was to evaluate the ability of visible and near‐infrared reflectance (VIS‐NIR) spectroscopy to predict the composition of organic matter in soils and litter. Reflectance spectra of the VIS‐NIR region (400–2500 nm) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by ¹³C‐CPMAS‐NMR spectroscopy. A modified partial least‐square method and cross‐validation were used to develop equations for the different constituents over the whole spectrum (1st to 3rd derivation). Near‐infrared spectroscopy predicted well the C : N ratios, the percentages of O‐alkyl C and alkyl C, the ratio of alkyl C to O‐alkyl C, and the sum of phenolic oxidation products: the ratios of standard deviation of the laboratory results to standard error of cross‐validation (RSC) were greater than 2, the regression coefficients (a) of a linear regression (measured against predicted values) ranged from 0.9 to 1.1, and the correlation coefficients (r) were greater than 0.9. Satisfactorily (0.8 ≤ a ≤ 1.2, r ≥ 0.8, and 1.4 ≤ RSC ≤ 2.0) assessed were the contents of C, N, and production of DOC, the percentages of carbonyl C and aromatic C and the ratio of alkyl C to aromatic C. However, the N‐mineralization rate and the microbial biomass were predicted unsatisfactorily (RSC < 1.4). The good and satisfactory predictions reported above indicate a marked usefulness of NIRS in the assessment of biological and chemical characteristics of soils and litter.     

Determination of Rapid Visco Analyser Parameters in Rice by Near‐Infrared Spectroscopy

The objective of these studies was to find alternative Rapid Visco Analyser (RVA) viscoelastic parameters that are predictable by near‐infrared spectroscopy (NIRS). Currently, RVA instruments are widely used in assessing cooking and processing characteristics in rice. The ability to predict RVA parameters by NIRS would be useful in rapidly determining rice pasting qualities, but NIRS does not correlate with the traditional parameters (peak viscosity, final viscosity, breakdown, consistency, and setback). Alternative RVA parameters were sought by collecting RVA and NIRS data for a total of 86 short, medium, and long grain rice cultivars. The amylose contents were 0.41–24.90% (w/w) and protein concentrations were 8.47–11.35% (w/w). Partial least squares (PLS) regression models generated for the entire NIR spectrum against the RVA curve showed viscosity at 212–228 sec (80°C ± 1) varied linearly with NIR spectra (1,100 to ‐2,500 nm). Regression coefficient values were R = 0.961 for 212 sec and R = 0.903 for 228 sec. The PLS correlation coefficient for the prediction of amylose at 212–228 sec decreases along with the NIRS correlation to the same time frame. An opposite trend was observed for the correlation with protein at 212–228 sec. This comparison suggests the importance of amylose and protein in water absorption during this time frame.     

Use of near infrared spectroscopy to determine biological and chemical characteristics of organic layers under spruce and beech stands

The chemical composition of organic layers of forest soils shows a high spatial variability and fast methods may be required for its study at a landscape level. The objective was to assess the applicability of near infrared spectroscopy (NIRS) to measure several chemical and biological properties of organic layers in spruce, beech, and mixed spruce‐beech stands. Spectra in the VIS‐NIR region (400—2500 nm) were recorded for 406 samples representing Oi, Oe, and Oa layers of forest soils from Solling (Germany), 195 of them were used for calibration and 211 for validation. The calibration equations for each constituent were developed using the whole spectrum (0^th to 3^rd derivative). Humus samples were analyzed for contents of C and N and contents of P, S, Na, K, Ca, Mg, Mn, Fe, and Al after pressure digestion in HNO₃. Additionally, basal respiration and microbial C (C_mic) were measured. NIRS predicted well the contents of C, N, P, S, Ca, Na, K, Fe, and Al and C/N and C/P ratios: the regression coefficients (a) of a linear regression (measured against predicted values) ranged from 0.9 to 1.1, and the correlation coefficients (r) were greater or equal 0.9. C_mic (a = 0.87, r = 0.83) was predicted satisfactorily, whereas the prediction of the basal respiration (a = 0.74, r = 0.87) was less satisfactory. Due to liming of some of the plots NIRS failed to predict contents of Mg (a = 1.27, r = 0.68). For all chemical and biological characteristics the best prediction performances were achieved using the whole sample population. Splitting the samples into smaller groups according to a dominant tree species or an organic layer did not improve the predictions.<?show $6#>     

Indicative properties of fly‐ash affected forest soils in Northeastern Germany

Many forest ecosystems in Germany are strongly influenced by emissions of pollutants like SO₂ and alkaline dusts. To quantify and evaluate the consequences of long‐term fly ash deposition on forest soils, a study was conducted in pine stands (Pinus sylvestris) in the Dübener Heide in Northeastern Germany. This forest area has been influenced mainly by emissions from coal‐fired power plants and the chemical industry of the industrial region Bitterfeld‐Wolfen‐Zschornewitz since the early 1900. The study sites are located along a fly ash deposition gradient of 8, 16, 14, 18, and 25 km away from the main emission source in Bitterfeld (sites 1, 2, 3, 4, and 5, respectively). Samples of the organic horizons (Oi, Oe, and Oa) and mineral topsoil (0—10 cm) were taken in fall 1998 and analyzed for their ferromagnetic susceptibility and total ash content. Scanning electron microscopy (SEM) and energy dispersive X‐ray microanalysis (EDX) were performed on selected samples to differentiate between the pedogenic and atmospheric origin of the mineral components in the organic horizons. As a result of the long‐term deposition, ferromagnetic fly ash components are mainly accumulated in the Oe and Oa horizons of the forest soils studied. Ferromagnetic susceptibility was significantly higher (p ≤ 0.05) in the Oe horizon of sites 1 and 2 compared to sites 3, 4, and 5. Unusually high total ash contents for organic horizons of > 74 % were determined in the Oa at all sites. SEM revealed 3 distinct features of persistent fly ash deposits from coal‐fired power plants within the organic horizons that can be defined as ”︁stable glasses” with magnetic properties, aluminum‐silicate‐minerals, and slag fragments. SEM and EDX indicated that a great portion of the mineral particles found in the organic horizons of forests soils influenced by fly ash are from atmospheric sources. For detection of atmospheric lignite‐derived deposition into forest soils, the Oe and Oa horizons have to be considered as specific diagnostic horizons because they show indicative properties for such soils.     

Development and Evaluation of a Near‐Infrared Instrument for Single‐Seed Compositional Measurement of Wheat Kernels

A single‐kernel, near‐infrared reflectance instrument was designed, built, and tested for its ability to measure composition and traits in wheat kernels. The major objective of the work was targeted at improving an existing design concept of an instrument used for larger seeds such as soybeans and corn but in this case designed for small seeds. Increases in throughput were sought by using a vacuum to convey seeds without compromising measurement accuracy. Instrument performance was evaluated by examining measurement accuracy of wheat kernel moisture, protein content, and kernel mass. Spectral measurements were obtained on individual wheat kernels as they were conveyed by air through an illuminated tube. Partial least squares (PLS) prediction models for these constituents were then developed and evaluated. PLS single‐kernel moisture predictions had a root mean square error of prediction (RMSEP) around 0.5% MC wet basis; protein prediction models had an RMSEP near 0.70%. Prediction of mass was not as good but still provided a reasonable estimate of single‐kernel mass, with RMSEP values of 2.8–4 mg. Data showed that kernel mass and protein content were not correlated, in contrast to some previous research. Overall, results showed the instrument performed comparably to other single‐seed instruments or methods based on accuracy but with an increased throughput at a rate of at least 4 seeds/s.     

Rapid Determination of Carbon,Nitrogen, Silicon,Phosphorus, and Potassium in Sugar Mill By-products,Mill Mud,and Ash using Near Infrared Spectroscopy

This article describes a proof-of-concept exercise to examine the ability of near infrared spectroscopy (NIRS)–based methods to predict the major nutrient properties of sugar mill by-products, particularly mill mud, ash, and mixtures of mud and ash. Sixty mill mud, mixed mud/ash, and ash samples were subsampled three times and analyzed using traditional analytical techniques for carbon (C), nitrogen (N), silicon (Si), phosphorus (P), and potassium (K), and the NIR spectra were recorded. Two different partial least squares (PLS) regression models were constructed, one using all samples and the other without the ash samples included in the model development. Three mud, one mixed mud/ash, and two ash samples were retained for predictive purposes and were not included in the model development process. R² values in the range of 0.77 to 0.98 were obtained for all constituents across both sets of PLS models. The standard errors of prediction (SEP) were similar for both models for N (0.10 and 0.08), P (0.17 and 0.16), and K (0.05 and 0.05). However, the SEP obtained for Si (3.53 and 1.04) and C (1.92 and 1.00) varied between the two models. These preliminary results are very encouraging. Future research will extend to robust NIRS calibrations for these nutrients and develop applications for their use within laboratory or field situations to permit nutrient monitoring in various sugar mill by-products.     

Rainforest litter quality and chemical controls on leaf decomposition with near‐infrared spectrometry

Plant‐litter chemical quality is an important driver of many ecosystem processes, however, what actually constitutes high‐ or low‐quality litter (chemical potential for fast and slow decomposition, respectively) is often interpreted by the indices available. Here, near‐infrared spectroscopy (NIRS) was used to explore leaf‐litter chemical quality and the controls on decomposition in the tropical rainforest region of north Queensland Australia. Leaf‐litter samples from litterfall collections and litterbag studies were used. NIRS was used to calibrate the chemical compositions of the material (N, P, C, Mg, Ca, acid detergent fiber, acid detergent lignin, α‐cellulose, and total phenolics) from a smaller sample set covering the spectral range in the full set of samples. Calibrations were compared for both separate (local) and combined models, for litterbags, and litterfall. Coefficients of determination (r²) in the local models ranged from 0.88 (litterbag Mg) to 0.99 (litterfall N), with residual prediction deviation ratios > 3 for all constituents except Mg (≈ 2.5). Mass loss in the litterbags was strongly related to the NIR spectra, with model r²'s of 0.75 (in situ leaves) and 0.76 (common control leaf). In situ decomposability was determined from modeling the initial NIR spectra prior to decomposition with litterbag exponential‐decay rates (model r² of 0.81, n = 85 initial samples). A best subset model including litter‐quality, climate, and soil variables predicted decay better than the NIR decomposability model (r² = 0.87). For litter quality alone the NIR model predicted decay rate better than all of the best predictive litter–chemical quality indices. The decomposability model was used to predict in situ decomposability in the litterfall samples. The chemical variables explaining NIR decomposability for litterfall were initial P, C, and phenolics (linear model r² = 0.80, n = 2471). NIRS is a holistic technique that is just as, if not more accurate, than litter–chemical quality indices, when predicting decomposition and decomposability, shown here in a regional field study.     

Medium‐ and short‐term available organic matter,microbial biomass,and enzyme activities in soils under Pinus sylvestris L. and Robinia pseudoacacia L. in a sandy soil in NE Saxony,Germany

Total, mobile, and easily available C and N fractions, microbial biomass, and enzyme activities in a sandy soil under pine (Pinus sylvestris L.) and black locust (Robinia pseudoacacia L.) stands were investigated in a field study near Riesa, NE Germany. Samples of the organic layers (Oi and Oe‐Oa) and the mineral soil (0–5, 5–10, 10–20, and 10–30 cm) were taken in fall 1999 and analyzed for their contents of organic C and total N, hot‐water‐extractable organic C and N (HWC and HWN), KCl‐extractable organic C and N (C_org(KCl) and N_org(KCl)), NH ‐N and NO ‐N, microbial‐biomass C and N, and activities of β‐glucosidase and L‐asparaginase. With exception of the HWC, all investigated C and N pools showed a clear response to tilling, which was most pronounced in the Oi horizon. Compared to soils under pine, those under black locust had higher contents of medium‐ and short‐term available C (HWC, C_org(KCl)) and N (HWN, N_org(KCl)), mineral N (NH ‐N, NO ‐N), microbial‐biomass C and N, and enzyme activities in the uppermost horizons of the soil. The strong depth gradient found for all studied parameters was most pronounced in soils under black locust. Microbial‐biomass C and N and enzyme activities were closely related to the amounts of readily mineralizable organic C (HWC and C_org(KCl)). However, the presented results implicate a faster C and N turnover in the top‐soil layers under black locust caused by higher N‐input rates by symbiotic N₂ fixation.     

Charcoal in Amazonian paddy soil—Nutrient availability,rice growth and methane emissions

We investigated the effects of charcoal under flooded (anoxic) rice cultivation at low and high fertilizer levels during 2 y in the Maranhão lowlands, eastern periphery of Amazonia. Two applications (at onset of first and second year) of 15 Mg ha^–1 of fine (< 2 mm) charcoal derived from the endocarp of the babassu (Attalea speciosa Mart.) palm nut had little influence on soil fertility, rice growth, yield, and nutritional status. Exception to this were negative impacts of charcoal on first‐year N availability, with lower sub‐superficial soil NH$ _4^+ $ availability paired with lower rice tissue N and a responsiveness of grain yields to (mainly N‐) fertilization following charcoal application. This N‐limitation effect was, however, limited to the first year and—though statistically significant—without agronomic relevance. The most consistent charcoal effect on flooded‐soil fertility was the strong increase in K availability in the second year, at low and to a lesser extent at intermediate, but not at high fertilizer level. Low K concentrations of our charcoal exclude the possibility of direct K inputs via charcoal, suggesting other indirect mechanisms for K availability increases. Methane fluxes in the second year were significantly reduced (–43.8%) by charcoal application, charcoal‐induced reductions were stronger under high‐ (–47.3%) than under low‐fertilizer regime (–26.0%). Thus, charcoal could be a valuable tool for reducing methane emissions associated with intensely fertilized flooded rice, without significantly affecting grain yields.     

Detecting and Segregating Black Tip‐Damaged Wheat Kernels Using Visible and Near‐Infrared Spectroscopy

Detection of individual wheat kernels with black tip symptom (BTS) and black tip damage (BTD) was demonstrated with near‐infrared reflectance spectroscopy (NIRS) and silicon light‐emitting‐diode (LED) based instruments. The two instruments tested, a single‐kernel NIRS instrument (SKNIRS) and a silicon LED‐based single‐kernel high‐speed sorter (SiLED‐SKS) were both developed by the Stored Product Insect and Engineering Research Unit, Center for Grain and Animal Health Research, USDA Agricultural Research Service. BTD was classified into four levels for the study ranging from sound, symptomatic (BTS) at two levels, and damaged (BTD). Discriminant analysis models for the SKNIRS instrument could distinguish sound undamaged kernels well, correctly classifying kernels 80% of the time. Damaged kernels were classified with 67% accuracy and symptomatic kernels at about 44%. Higher classification accuracy (81–87%) was obtained by creating only two groupings: 1) combined sound and lightly symptomatic kernels and 2) combined heavily symptomatic and damaged kernels. A linear regression model was developed from the SiLED‐SKS sorted fractions to predict the percentage of combined BTS and BTD kernels in a sample. The model had an R² of 0.64 and a standard error of prediction of 7.4%, showing it had some measurement ability for BTS and BTD. The SiLED‐SKS correctly classified and sorted out 90% of BTD and 66% of BTS for all 28 samples after three passes through the sorter. These instruments can serve as important tools for plant breeders and grading facilities of the wheat industry that require timely and objective determination and sorting of different levels of black tip present in wheat samples.     

Gaseous emissions of nitrogen and carbon from urban vegetable gardens in Bobo‐Dioulasso,Burkina Faso

Urban and peri‐urban agriculture (UPA) is an important livelihood strategy for the urban poor in sub‐Saharan Africa and contributes to meeting increasing food demands in the rapidly growing cities. Although in recent years many research activities have been geared towards enhancing the productivity of this land‐use system, little is known about turnover processes and nutrient efficiency of UPA. The aim of our study therefore was to determine horizontal fluxes of N, P, K, and C as well as gaseous N and C emissions in urban vegetable gardens of Bobo‐Dioulasso, Burkina Faso. Two gardens referred to as “Kodéni” and “Kuinima” were selected as representative for urban and peri‐urban systems classified as: (1) “commercial gardening + field crops and livestock system” and (2) “commercial gardening and semicommercial field crop system”, respectively. A nutrient‐balance approach was used to monitor matter fluxes from March 2008 to March 2009 in both gardens. Ammonia (NH₃), nitrous oxide (N₂O) and carbon dioxide (CO₂) emissions from the respective soils were measured during the coolest and the hottest period of the day using a closed‐chamber system. Annual partial balances amounted to 2056 kg N ha^–1, 615 kg P ha^–1, 1864 kg K ha^–1, and 33 893 kg C ha^–1 at Kodéni and to 1752 kg N ha^–1, 446 kg P ha^–1, 1643 kg K ha^–1, and 21 021 kg C ha^–1 at Kuinima. Emission rates were highest during the hot midday hours with peaks after fertilizer applications when fluxes of up to 1140 g NH₃‐N ha^–1 h^–1, 154 g N₂O‐N ha^–1 h^–1, 12 993 g CO₂‐C ha^–1 h^–1 were recorded for Kodéni and Kuinima. Estimated annual gaseous N (NH₃‐N + N₂O‐N) and C (CO₂‐C + CH₄‐C) losses reached 419 kg N ha^–1 and 35 862 kg C ha^–1 at Kodéni and 347 kg N ha^–1 and 22 364 kg C ha^–1 at Kuinima. For both gardens, this represented 20% and 106% of the N and C surpluses, respectively. Emissions of NH₃, largely emitted after surface application of manure and mineral fertilizers, accounted for 73% and 77% of total estimated N losses for Kodéni and Kuinima. To mitigate N losses nutrient‐management practices in UPA vegetable production of Bobo‐Dioulasso would greatly benefit from better synchronizing nutrient‐input rates with crop demands.     

Application of diffuse reflectance FT-IR spectroscopy and partial least-squares regression to predict NMR properties of soil organic matter

Carbon 13 nuclear magnetic resonance spectroscopy (¹³C NMR) is a powerful technique for studying the structure and turnover of soil organic matter, but is time consuming and expensive. It is therefore worth seeking swifter and cheaper methods. Diffuse reflectance FT‐IR spectroscopy (DRIFT), along with partial least squares (PLS) algorithms, provides statistical models to quantify soil properties, such as contents of C, N and clay. I have applied DRIFT?PLS to quantify soil organic C species, as measured by solid state ¹³C NMR spectroscopy, for several bulk soils and physical soil fractions. Calibration and prediction models for organic C and for particular NMR regions, namely alkyl C, O?alkyl C and carboxyl C, attained R² values of between 0.94 and 0.98 (calibration) and 0.70–0.93 (cross‐validation). The prediction of unknown soil samples, after pre‐selection by statistical indices, confirmed the applicability of DRIFT?PLS. The prediction of aromatic C failed, probably because of superimposition of aromatic bands by signals from minerals. Results from fractions of particulate organic matter suggest that the chemical homogeneity of the material hampers the quantification of its constituting C species by DRIFT?PLS. For alkyl C, prediction of carbon species by DRIFT?PLS was better than direct peak‐area quantification in the IR spectra, but advantageous in parts only compared with a linear model correlating C species with soil C contents. In conclusion, DRIFT?PLS calibrated with NMR data provides quantitative information on the composition of soil organic matter and can therefore complement structural studies by its application to large numbers of samples. However, it cannot replace the information provided by more specific methods. The actual potential of DRIFT?PLS lies in its capacity to predict unknown samples, which is helpful for classification and identification of environmental outliers or benchmarks.     

Application of Near Infrared Reflectance Spectroscopy for the Assessment of Soil Quality in a Long‐Term Pasture

Abstract

The objective of the present study was to assess the ability of near infrared reflectance spectroscopy (NIRS) to analyze chemical soil properties and to evaluate the effects of different phosphorus (P) and potassium (K) fertilization rates on soil quality in different layers of a long‐term pasture. The NIRS calibrations were developed for humus, total Kjeldahl nitrogen (N_Kjeldahl), and several humic substances (HA1, “mobile” humic acids fraction; ΣHA, sum of humic acids; FA1, “mobile” fulvic acids; ΣFA, sum of fulvic acids, etc.) using soil samples of rather heterogeneous origin, collected during 1999–2003. Different spectral preprocessing and the modified partial least squares (MPLS) regression method were explored to enhance the relation between the spectra and measured soil properties. The equations were employed for the quality prediction of a sod gleyic light loam (Cambisol) in five PK fertilization treatments. The soil was sampled in 2000 and 2003 in three field replicates at depths of 0–10, 10–20, 20–30, and 30–50 cm, n=60 samples yr^?1. The best coefficients of correlation, R², between the reference and NIRS‐predicted data were as follows: for N_Kjeldahl, 0.965; humus, 0.938; HA1, 0.903; HA2, 0.905; HA3, 0.924; ΣHA, 0.904; and FA1, 0.911; and ΣFA, 0.885. Our findings suggest that it is feasible to use NIRS for the assessment of the effects of the inorganic PK fertilizer on the soil quality in different depths of a long‐term pasture.     

Quantitative Analysis of Fat Content in Rice by Near‐Infrared Spectroscopy Technique

Fat content in rice is one of the most important nutritional quality properties. But the chemical analysis of fat content is time‐consuming and costly and could result in poor reproduction between replicates. Near‐infrared spectroscopy (NIRS) can solve those problems by providing a rapid, nondestructive, and quantitative analysis. Based on the NIRS technique and partial least squares (PLS) algorithm, four calibration models were established to quantitatively analyze fat content in brown rice grain and flour and milled rice grain and flour with 248 representative samples. The determination coefficients (R²) of these calibration models were 0.79, 0.84, 0.89, and 0.91, respectively, with the corresponding root mean square errors 0.16, 0.14, 0.09, and 0.08%. The R² were 0.73, 0.81, 0.81, and 0.89 with the corresponding root mean square errors 0.17, 0.15, 0.12, and 0.09%, respectively, in cross validation. The R² were 0.62, 0.80, 0.81, and 0.87, respectively, with the root mean square errors 0.25, 0.31, 0.28, and 0.30% in external validation. These results indicate that the method of NIRS has relatively high accuracy in the prediction of rice fat content. The four calibration models established in the present study should be useful for nutrient quality improvement in rice breeding.     

Environmental Effects on Developing Wheat as Sensed by Near‐Infrared Reflectance of Mature Grains

For 30 years, near‐infrared (NIR) spectroscopy has routinely been applied to the cereal grains for the purpose of rapidly measuring concentrations of constituents such as protein and moisture. The research described herein examined the ability of NIR reflectance spectroscopy on harvested wheat to determine weather‐related, quality‐determining properties that occurred during plant development. Twenty commercial cultivars or advanced breeding lines of hard red winter and hard white wheat (Triticum aestivum L.) were grown in 10 geographical locations under prevailing natural conditions of the U.S. Great Plains. Diffuse reflectance spectra (1,100–2,498 nm) of ground wheat from these samples were modeled by partial least squares one (PLS1) and multiple linear regression algorithms for the following properties: SDS sedimentation volume, amount of time during grain fill in which the temperature or relative humidity exceeded or was less than a threshold level (i.e., >30, >32, >35, <24°C; >80%, <40% rh). Rainfall values associated with four pre‐ and post‐planting stages also were examined heuristically by PLS2 analysis. Partial correlation analysis was used to statistically remove the contribution of protein content from the quantitative NIR models. PLS1 models of 9–11 factors on scatter‐corrected and (second order) derivatized spectra produced models whose dimensionless error (RPD, ratio of standard deviation of the property in a test set to the model standard error for that property) ranged from 2.0 to 3.3. Multiple linear regression models, involving the sum of four second‐derivative terms with coefficients, produced models of slightly higher error compared with PLS models. For both modeling approaches, partial correlation analysis demonstrated that model success extends beyond an intercorrelation between property and protein content, a constituent that is well‐modeled by NIR spectroscopy. With refinement, these types of NIR models may have the potential to provide grain handlers, millers, and bakers a tool for identifying the cultural environment under which the purchased grain was produced.