Near infrared reflectance spectroscopy (NIRS) could be used for characterization of soil nematode community

Studying soil nematofauna provides useful information on soil status and functioning but requires high taxonomic expertise. Near infrared reflectance (NIR) spectroscopy (NIRS) has been reported to allow fast and inexpensive determination of numerous soil attributes. Thus the present study aimed at assessing the potential of NIRS for determining the abundance and diversity of soil nematodes in a set of 103 clayey topsoil samples collected in 2005 and 2006 from agricultural soils in the highlands of Madagascar.The morphological characterization of soil nematofauna involved extraction through elutriation then counting under binoculars and identification at family or genus level using microscopy, on ca. 150-g fresh soil samples. Taxa were assigned to five trophic groups, namely bacterial feeders, fungal feeders, obligate plant feeders, facultative plant feeders, and omnivores and predators (together). In addition, four ecological indexes were calculated: the Enrichment index, Structure index, Maturity index, and Plant parasitic index.Oven-dried (40 °C) < 2-mm sieved 5-g soil subsamples were scanned in the NIR range (1100-2500 nm), then spectra were fitted to nematofauna data using partial least square regression. Depending on the sample set considered (year 2005, year 2006, or both years), NIRS prediction of total nematode abundance was accurate (ratio of standard deviation to standard error of cross validation, i.e. RPD ≥ 2) or acceptable (RPD ≥ 1.6). Predictions were accurate, acceptable, or quasi-acceptable (RPD ≥ 1.4) for several of the six most abundant taxa, and to a larger extent, for most trophic groups (except facultative plant feeders); but they could not be made for taxa present in a small number of samples or at low abundance. By contrast, NIRS prediction of relative abundances (in proportion of total abundance) was poor in general, as was also the prediction of ecological indexes (except for the 2006 set). On the whole, these results were less accurate than NIRS predictions of soil attributes often reported in the literature. However, though not very accurate, NIRS predictions were worthwhile considering the labor-intensity of the morphological characterization. Most of all, NIRS analyses were carried out on subsamples that were probably too small (5 g) to allow representative sampling of nematofauna. Using larger samples for NIRS (e.g. 100 g) would likely result in more accurate predictions, and is therefore recommended. Scanning un-dried samples could also help improve prediction accuracy, as morphological characterization was carried out on samples not dried after sampling.Examining wavelengths that contributed most to NIRS predictions, and chemical groups they have been assigned to, suggested that NIRS predictions regarding nematofauna depended on constituents of both nematodes and preys’ food. Predictions were thus based on both nematofauna and soil organic properties reflected by nematofauna.     

Near infrared reflectance spectroscopy: A tool to characterize the composition of different types of exogenous organic matter and their behaviour in soil

In addition to total organic carbon and nitrogen, potential organic carbon mineralization under controlled laboratory conditions and indicators such as the indicator of remaining organic carbon in soil (I_ROC), based on Van Soest biochemical fractionation and short-term carbon mineralization in soil, are used to predict the evolution of exogenous organic matter (EOM) after its application to soils. The purpose of this study was to develop near infrared reflectance spectroscopy (NIRS) calibration models that could predict these characteristics in a large dataset including 300 EOMs representative of the broad range of such materials applied to cultivated soils (plant materials, animal manures, composts, sludges, etc.). The NIRS predictions of total organic matter and total organic carbon were satisfactory (R²_P = 0.80 and 0.85, ratio of performance to deviation, RPD_P = 2.2 and 2.6, respectively), and prediction of the Van Soest soluble, cellulose and holocellulose fractions were acceptable (R²_P = 0.82, 0.73 and 0.70, RPD_P = 2.3, 1.9 and 1.8, respectively) with coefficients of variation close to those of the reference methods. The NIRS prediction of carbon mineralization during incubation was satisfactory and indeed better regarding the short-term results of mineralization (R²_P = 0.78 and 0.78, and RPD_P = 2.1 and 2.0 for 3 and 7 days of incubation, respectively). The I_ROC indicator was predicted with fairly good accuracy (R²_P = 0.79, RPD_P = 2.2). Variables related to the long-term C mineralization of EOM in soil were not predicted accurately, except for I_ROC which was based on analytical and well-identified characteristics, probably because of the increasing interactions and complexity of the factors governing EOM mineralization in soil as a function of incubation time. This study demonstrated the possibility of developing NIRS predictive models for EOM characteristics in heterogeneous datasets of EOMs. However, specific NIRS predictive models still remain necessary for sludges, organo-mineral fertilizers and liquid manures.     

Use of Near Infrared Reflectance Spectroscopy (NIRS) for Predicting Soil Fertility and Historical Management

This study tests the potential of near infrared reflectance spectroscopy (NIRS) for predicting soil fertility and management history from topsoil (0–10 cm deep) spectra. Soil fertility was assessed by measuring the growth of a test plant, and soil management history was determined through inquiries with farmers. Moreover, NIRS predictive value was compared with that of a group of topsoil parameters: total carbon and nitrogen, nitrate, potential respiration and denitrification, and microbial biomass. Modelling used partial and modified partial least square regressions to ensure comparisons between predictions by NIRS versus by soil parameters. Soil fertility and management history were well predicted by NIRS (Q² = 0.78 and R² = 0.89 both; Q² and R² are cross-validation and calibration coefficients of determination, respectively), as were the soil parameters (Q² = 0.79–0.92 and R² = 0.86–0.98). Soil fertility and management history were more accurately predicted by NIRS than by the set of soil parameters.     

Applicability of near infrared spectroscopy as an alternative to acid detergent analysis for cattle and swine manure compost

Abstract

Recently, acid detergent analysis has been reported to provide valid data to evaluate decomposition properties and to determine the available nitrogen (AVN) of organic materials, such as compost. However, this methodology requires complex procedures and creates considerable costs. As an alternative, near infrared spectroscopy (NIRS) was evaluated as a simple method to determine acid detergent fiber (ADF), acid detergent lignin (ADL) and acid-detergent-soluble organic matter (ADSOM), in order to evaluate the decomposition properties of cattle and swine manure compost. To establish an easy and accurate method of estimating AVN in cattle and swine manure compost, the accuracies of direct estimations of AVN by NIRS in incubation experiments and indirect estimations by NIRS based on acid-detergent-soluble nitrogen (ADSN) or total nitrogen (TN) were examined. The reflectance spectra of freeze-dried and milled compost samples were determined using a scanning monochromator. Second derivative spectra and multiple regression analysis were used to develop calibration equations for each constituent. The calibration equations for ADF, ADL and ADSOM were “successful” according to commonly applied criteria. Acid-detergent-soluble nitrogen was found to be more suitable than TN for estimating AVN in cattle and swine manure compost. As the accuracies of the estimations of ADSN and TN by NIRS were comparable, the estimation of AVN based on ADSN as determined by NIRS was more accurate than that based on TN determined by NIRS. The direct prediction of AVN through NIRS was not as accurate as the estimation of AVN based on ADSN determined by NIRS. We conclude that NIRS is a practicable alternative to the time-consuming acid detergent analysis of cattle and swine compost, and that ADSN as determined by NIRS is useful for estimating AVN in the compost.     

Applicability of near infrared spectroscopy as an alternative to acid detergent analysis for cattle and swine manure compost

Recently, acid detergent analysis has been reported to provide valid data to evaluate decomposition properties and to determine the available nitrogen (AVN) of organic materials, such as compost. However, this methodology requires complex procedures and creates considerable costs. As an alternative, near infrared spectroscopy (NIRS) was evaluated as a simple method to determine acid detergent fiber (ADF), acid detergent lignin (ADL) and acid-detergent-soluble organic matter (ADSOM), in order to evaluate the decomposition properties of cattle and swine manure compost. To establish an easy and accurate method of estimating AVN in cattle and swine manure compost, the accuracies of direct estimations of AVN by NIRS in incubation experiments and indirect estimations by NIRS based on acid-detergent-soluble nitrogen (ADSN) or total nitrogen (TN) were examined. The reflectance spectra of freeze-dried and milled compost samples were determined using a scanning monochromator. Second derivative spectra and multiple regression analysis were used to develop calibration equations for each constituent. The calibration equations for ADF, ADL and ADSOM were "successful" according to commonly applied criteria. Acid-detergent-soluble nitrogen was found to be more suitable than TN for estimating AVN in cattle and swine manure compost. As the accuracies of the estimations of ADSN and TN by NIRS were comparable, the estimation of AVN based on ADSN as determined by NIRS was more accurate than that based on TN determined by NIRS. The direct prediction of AVN through NIRS was not as accurate as the estimation of AVN based on ADSN determined by NIRS. We conclude that NIRS is a practicable alternative to the time-consuming acid detergent analysis of cattle and swine compost, and that ADSN as determined by NIRS is useful for estimating AVN in the compost.     

贵州喀斯特山区旱地绿肥聚垅秸秆还土的培肥增产效应研究

在贵州喀斯特山区黄壤和石灰土上进行定位试验 .研究结果表明 ,绿肥聚垅、秸秆还土和绿肥聚垅 +秸秆还土均能在一定程度上提高土壤有机质及养分含量 ,改善土壤物理性状 ,提高土壤肥力 ;促进作物 (特别是玉米 )生长发育 ,改善作物经济性状 ,提高产量 ,各处理的年平均粮食复合产量比对照增加36 .3～ 1 1 7.0kg/ 6 6 7m2 ,增加 5.6 8%～ 2 0 .97% .其培肥增产作用明显     

Mineralization of carbon and nitrogen from cowpea leaves decomposing in soils with different levels of microbial biomass

Soils with greater levels of microbial biomass may be able to release nutrients more rapidly from applied plant material. We tested the hypothesis that the indigenous soil microbial biomass affects the rate of decomposition of added green manure. Cowpea (Vigna unguiculata L.) Walp.] leaves were added to four soils with widely differing microbial biomass C levels. C and N mineralization of the added plant material was followed during incubation at 30°C for 60 days. Low levels of soil microbial biomass resulted in an initially slower rate of decomposition of soil-incorporated green manure. The microbial biomass appeared to adjust rapidly to the new substrate, so that at 60 days of incubation the cumulative C loss and net N mineralization from decomposing cowpea leaves were not significantly affected by the level of the indigenous soil microbial biomass.     

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.     

Comparison of Near Infrared Reflectance Spectroscopy with Combustion and Chemical Methods for Soil Carbon Measurements in Agricultural Soils

As interest in soil organic carbon (SOC) dynamics increases, so do needs for rapid, accurate, and inexpensive methods for quantifying SOC. Objectives were to i) evaluate near infrared reflectance (NIR) spectroscopy potential to determine SOC and soil organic matter (SOM) in soils from across Tennessee, USA; and ii) evaluate potential upper limits of SOC from forest, pasture, no-tillage, and conventional tilled sites. Samples were analyzed via dry-combustion (SOC), Walkley–Black chemical SOM, and NIR. In addition, the sample particle size was classified to give five surface roughness levels to determine effects of particle size on NIR. Partial least squares regression was used to develop a model for predicting SOC as measured by NIR by comparing against SOM and SOC. Both NIR and SOM correlated well (R² > 0.9) with SOC (combustion). NIR is therefore considered a sufficiently accurate method for quantifying SOC in soils of Tennessee, with pasture and forested systems having the greatest accumulations.Abbreviations SOC, soil organic carbon; NIR, Near Infrared Reflectance Spectroscopy; MTREC, Middle Tennessee Research and Education Center; RECM, Research and Education Center at Milan; PREC, Plateau Research and Education Center; PLS, Partial least squares.     

Impact of cultivation and sugar-cane green trash management on carbon fractions and aggregate stability for a Chromic Luvisol in Queensland, Australia

Technological advances in sugar-cane harvesting and processing is bringing about rapid changes in production systems which could impact on soil physical conditions. An increasing incidence of soil structural decline and depletion of soil carbon levels has increased the risk of soil erosion and crop yield reductions. Soil carbon (C) and aggregate stability were studied on a sugar-cane (Saccharum officinarum L.) green trash blanket trial that had been established on a Chromic Luvisol soil at Mackay, Qld, Australia in 1992. The experiment consisted of blocks with two blocks being harvested early and the remaining two blocks harvested late in the crushing season. Within each block, treatment combinations of trash burnt or green trash blanket, which are either cultivated between rows or not cultivated after harvest, were included. Cropping and cultivation of the soil reduced the different C fractions in the surface 0–100 mm layer by 66–67% when compared to an adjacent uncropped reference soil. The labile C (C_L) concentration was 11% lower in the burnt treatment compared to the trash returned treatment but the opposite was found for total C (C_T). After four years, the no cultivation treatment had higher concentrations of all C fractions measured, compared to the cultivated treatment. When compared to the uncropped reference soil, cropping resulted in marked reductions in aggregate mean weight diameter (MWD) and aggregates >250 μm and an increase in aggregates <125 μm determined by both immersion and tension wetting. The return of the green trash resulted in a 30% greater MWD and a 28% increase in aggregates >250 μm and an 18% reduction in aggregates <125 μm compared to the burnt treatment when immersion wetting was used. Four years of cultivation reduced the MWD, as determined by immersion wetting, by 26% compared to the no cultivation treatment. No significant correlations were found between any measured C fraction and aggregate stability. This study indicates that sustainable sugar-cane cropping systems will likely be those where cultivation is kept to a minimum and trash is retained in the system.