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NIR spectroscopy, mineral nitrogen analysis and soil incubations for the prediction of crop uptake of nitrogen during the growing season
Abstract
To predict the amount of N taken up in above-ground plant parts during the growing season, initial mineral soil N, a soil
incubation method, soil organic matter and NIR data were compared as predictors. Soil samples were taken from 15 plots cropped
with winter wheat on a farm in south-western Sweden. The plots were not fertilized with N during the 1997 growing season.
N contents in above-ground plant parts were measured in mid-June and in mid-August. All methods were capable of predicting
the crop uptake of N reasonably well. NIR data gave at least as good predictions as the best traditional method, initial soil
NO3-N. The most important wavelengths, around 1400 and 1700 nm, and above 2000 nm, coincide with the wavelengths reported earlier
to be important for the prediction of soil organic matter. However, the data suggest that other soil components influencing
mineralization are also spectrally active. Since very few samples were taken, the studies need to be extended in order to
be able to use the method in practice. It is recommended that further studies be instigated for the possibility of using the
same NIR calibration over several years and to clarify the spatial regions that the calibrations can cover.
... The soil spectra can be characterized by high reflectance in the visible range (approximately 400–800 nm) and three major peaks in the near infrared range (approximately 1000–2500 nm). These spectra are largely similar to those reported in other studies (Börjesson et al., 1999; Chang et al., 2001), and only differ in ...
... None of the indices explained more than 23% of the variability , with total soil C in the top layer performing with similar or higher accuracy. It is therefore remarkable that Börjesson et al. (1999) reported promising results in using NIR spectrometry for predicting N uptake of winter wheat in 15 different soils. Using cross-validation, they reported a correlation coefficient (r 2 ) of 0.81, compared to 0.60, 0.76 and 0.58 for incubations , mineral N and soil organic matter content, respectively. ...
... In contrast, our N uptake varied from 20 to 52 kg N ha −1 . Again, it is interesting to note that our SEP of 6.5 kg N ha −1 is actually lower than the 15.7 kg N ha −1 reported by Börjesson et al. (1999). ...
The increasing popularity of site-specific management (SSM) calls for fast, inexpensive, simultaneous analyses of large numbers of soil variables. The objective of this study was to assess the potential of near infrared (NIR) and diffuse reflectance Fourier transformed in the mid-infrared range (DRIFT-MIR) spectrometry for predicting crop and soil parameters in a flooded California rice field. Two transects of 400 m each were left unfertilized, and 100 sample locations were established. Soil samples were taken in spring, and crop and weed samples at harvest. IR spectra were linked to total soil C and N, mineralizable N, P Olsen, effective cation exchange capacity (eCEC) and exchangeable cations (Ca, Mg, Na and K), as well as yield, N uptake, biomass and weed biomass using partial least squares regression (PLSr). The PLSr models were calibrated using 50 random observations, and validated using the remaining 50 observations. For soil, predictions for eCEC, Ca and Mg were the most accurate, with r
2 values of 0.83, 0.80 and 0.90 for NIR and 0.56, 0.60 and 0.61 for DRIFT-MIR. Correlations for P Olsen were 0.71 and 0.55, and for mineralizable N 0.46 and 0.21, respectively. No significant correlations were found for total soil C or N. For crop parameters, only weed pressure (r
2 of 0.55 and 0.44) and straw biomass (0.30 and 0.34) yielded significant correlations. The correlation with weed pressure was an indirect effect due to better competition by weeds compared to rice under low soil fertility levels. For most parameters, standard errors of prediction were lower than reported in the literature. This indicates that the small range of variability within a field might be the limiting factor in predicting these parameters. It also illustrates the limited use of correlation coefficients in PLSr model validations. We concluded that NIR spectrometry shows promise for SSM, although its predictive power for parameters may vary from site to site. Moreover, predictive models remain unique for specific agroecosystems, and therefore have to be calibrated for every area. The fast and accurate predictions for Ca and Mg concentrations in the soil could be especially important in diagnosing and combating grass tetany, which strongly depends upon Ca and Mg concentrations in the soil.
... Considerable amounts of soil N have the potential to be mineralized during the growing season (Lindén et al., 1992). Large variations between fields, but also within fields, have been encountered (Börjesson et al., 1999). Thus, site-specific inputs of nutrients can save resources and the impact on the environment can be minimized without lowering total yields or putting product quality at risk (Robert, 1999). ...
... However, there are few examples of good relationships demonstrated between such laboratory tests and the situation in the field. In recent years, attempts have been made to relate NIR-spectra to N-uptake in crops within single fields, with promising results (Börjesson et al., 1999;Dunn et al., 2000). NIR-spectra have been related to several soil properties that can be expected to have influence on plant available N, such as amount and quality of organic matter (Chang and Laird, 2002;Fystro, 2002;Palmborg and Nordgren, 1993), soil texture (Chang et al., 2001;Stenberg et al., 1995) and nutrient status (Malley et al., 1999). ...
... NIR-spectra have been related to several soil properties that can be expected to have influence on plant available N, such as amount and quality of organic matter (Chang and Laird, 2002;Fystro, 2002;Palmborg and Nordgren, 1993), soil texture (Chang et al., 2001;Stenberg et al., 1995) and nutrient status (Malley et al., 1999). Börjesson et al. (1999) compared the performance of NIR-models with potential net N-mineralization, organic matter content and initial mineral N present in the soil profile at the beginning of the cropping season in order to predict N-uptake by the crop, and found NIR and initial mineral N to have the best predictive ability and to be equally good. Initial mineral N in combination with NIR did not improve the predictive ability. ...
In this study, the ability to predict N-uptake in winter wheat crops using NIR-spectroscopy on soil samples was evaluated.
Soil samples were taken from unfertilized plots in one winter wheat field for three years (1997–1999) and in another winter
wheat field nearby for one year (2000). Soil samples were analyzed for organic C content and their NIR-spectra. N-uptake was
measured as total N-content in aboveground plant materials at harvest. Models calibrated to predict N-uptake were internally
cross-validated and validated across years and across fields. Cross-validated calibrations predicted N-uptake with an average
error of 12.1 to 15.4kg N ha−1. The standard deviation divided by this error (RPD) ranged between 1.9 and 2.5. In comparison, the corresponding calibrations
based on organic C alone had an error from 11.7 to 28.2kg N ha−1 and RPDs from 1.3 to 2.5. In three of four annual calibrations within a field, the NIR based calibrations worked better than
the organic C based calibrations. The prediction of N-uptake across years, but within a field, worked slightly better with
an organic C based calibration than with a NIR based one, RPD = 1.9 and 1.7, respectively. Across fields, the corresponding
difference was large in favour of the NIR-calibration, RPD = 2.5 for the NIR-calibration and 1.5 for the organic C calibration.
It was concluded that NIR-spectroscopy integrates information about organic C with other relevant soil components and therefore
has a good potential to predict complex functions of soils such as N-mineralization. A relatively good agreement of spectral
relationships to parameters related to the N-mineralization of datasets across the world suggests that more general models
can be calibrated.
... Vis-NIR spectroscopy has been used to predict N uptake in greenhouse pot experiments with promising results, cross-validated R 2 of about 0.8 (Russell et al., 2002;Wagner et al., 2001). In the field, comparable results (R 2 ¼ 0.7-0.8 and RMSE ¼ 6-21 kg N ha À 1 ) have been found for N uptake in cereal and rice crops within single or nearby fields (Börjesson et al., 1999;Dunn et al., 2000;Stenberg et al., 2005;Wetterlind et al., 2008a), thus indicating the feasibility of calibrations over smaller areas with similar soil and weather conditions. However, one out of two rice experiments in south-eastern Australia only resulted in R 2 of 0.5 (Dunn et al., 2000). ...
... Notably, the range in N uptake in the Californian field was considerably smaller than that reported in other studies. In addition, the range of total soil C (9-16 g kg À 1 ) was generally small compared to those reported by Börjesson et al. (1999), Stenberg et al. (2005) and Wetterlind et al. (2008a). Wetterlind et al. (2008a) failed to predict N uptake of winter wheat at one field with large variations in N uptake but only small variations in SOM content (corresponding to 12-32 g kg À 1 C in the top 30 cm) suggesting that vis-NIR predictions of N uptake could be limited to fields with quite large variations in SOM content. ...
... Wetterlind et al. (2008a) failed to predict N uptake of winter wheat at one field with large variations in N uptake but only small variations in SOM content (corresponding to 12-32 g kg À 1 C in the top 30 cm) suggesting that vis-NIR predictions of N uptake could be limited to fields with quite large variations in SOM content. Similarly to the studies on other biological characteristics, vis-NIR predictions could explain more of the variation in N uptake compared with organic C (Börjesson et al., 1999;Stenberg et al., 2005). Results from the study by Wetterlind et al. (2008a) indicated that the additional predictive capacity of vis-NIR was related to soil texture. ...
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 pretratments, covariations 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.
... These soil properties are more or less spectrally featureless, but might be estimated indirectly due to correlations to more spectrally active properties (Chang et al., 2001; Ben-Dor & Banin, 1995a). NIR has also been related to potentially mineralisable N derived from aerobic and anaerobic incubations (Fystro, 2002; Shepherd & Walsh, 2002; Chang et al., 2001; Dunn et al., 2000) and has been used with promising results to estimate N uptake in crops (Stenberg et al., 2005; Dunn et al., 2000; Börjesson et al., 1999). The NIR technique requires calibration against values from conventional analyses. ...
... ) (Stenberg et al., 2005; Dunn et al., 2000; Börjesson et al., 1999). The results support the feasibility of plant N uptake calibrations over smaller areas with similar soil and climatic conditions. ...
... Application of pig slurry in the autumn 2002 at Ribbingsberg is the most likely explanation for the poor results in 2003, possibly masking the variation in soil mineralisation (Table 6). Similar to the studies on other biological characteristics, NIR predictions were able to explain more of the variation in N uptake compared with using organic C content as predictor (Stenberg et al., 2005; Börjesson et al., 1999). The results in Paper I indicate that the additional predictive capacity of NIR could be related to soil texture. ...
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.
... Considerable amounts of N can be mineralised during the growing season and the variation between and within fields can be significant (Börjesson et al. 1999; Delin and Lindén 2002). Adjusting fertiliser applications with respect to this variation can therefore both reduce costs and decrease nutrient losses to the environment. ...
... NIR spectra have also been related to other soil properties that can be expected to influence N mineralisation, such as the quality of SOM (Hartmann and Appel 2006; Reeves et al. 2006; Terhoeven-Urselmans et al. 2006) and soil texture (Chang et al. 2001; Sorensen and Dalsgaard 2005; Stenberg et al. 1995). NIR spectroscopy has also been used with promising results to determine both potentially mineralisable N, measured as accumulated mineral N in aerobic or anaerobic incubations (Chang et al. 2001; Dunn et al. 2000; Fystro 2002; Shepherd and Walsh 2002), as well as N uptake in crops within and between fields (Börjesson et al. 1999; Dunn et al. 2000; Stenberg et al. 2005). Börjesson et al. (1999) suggested that models based on NIR spectra could indirectly take into account the water supply function of a soil through e.g. its texture. ...
... Stenberg et al. (2005) found that even though within-field variation in SOM constituted a large part of the NIR spectra for predicting N uptake, NIR spectra included more relevant information and thus performed better. The fields in the studies by Börjesson et al. (1999) and Stenberg et al. (2005) had high variation in SOM and further investigations are needed to study the possibility of obtaining equally accurate results in fields with lower SOM content. Even though Stenberg et al. (2005) included two different fields in their investigation, these fields were located in the same river basin. ...
Adjusting fertiliser applications to within-field variations in nitrogen (N) mineralisation during the growing season can increase yields, improve crop quality, reduce costs and decrease nutrient losses to the environment. Predicting such variations at a reasonable cost is therefore important. In a 3-year study, Near Infrared Reflectance (NIR) spectroscopy was compared with soil organic matter (SOM) and clay content as predictors of plant N uptake using cross-validated PLS (Partial Least Squares) regression models. Plant N uptake was measured as total nitrogen in aboveground plant parts at harvest, in plots without N fertilisation within three different fields in southern Sweden. NIR spectroscopy and combined clay and SOM content resulted in equally good estimations of plant N uptake in fields with large variation in SOM content. Cross-validated NIR calibrations for plant N uptake within fields for separate years resulted in r
2 values of 0.75–0.85 and average cross-validation errors of 11–16 kg N ha−1 for two fields (1 year excluded at one field because of farmyard manure application). No significant improvements were seen when NIR-spectra, clay and SOM were included in the same model, suggesting that the additional predictive capacity of NIR over SOM relates to soil texture variations. NIR calibrations also performed poorly in one field where plant N uptake could not be explained by SOM or clay content. Predictions within fields between years produced r
2 values of 0.56–0.89 and prediction errors of 12–26 kg N ha−1 for one field. These results confirm that N uptake prediction accuracy can be improved by using NIR spectroscopy in fields with large SOM variations. However, good estimations could not be made between fields, indicating difficulties in creating more general calibration models for large geographical areas.
... To the authors' knowledge, there are no studies that seek to explain variance in crop-yield metrics from soil properties estimated by reflectance spectra, however, work has been done using soil reflectance spectra directly to determine crop characteristics. These include predictions of grain yield in rice (Van Groenigen et al. 2003) and plant N uptake (Börjesson et al. 1999;Stenberg et al. 2005;Terhoeven-Urselmans 2008;Wetterlind et al. 2008). ...
... It seems that the IR spectra capture more information about the soil relevant for crop growth than the soil properties included in the LM. This effect aligns with studies that compare crop predictions from IR spectra with crop predictions from laboratory reference values, in which the first outperforms the latter (Börjesson et al. 1999;Wetterlind et al. 2008). ...
How well could one predict the growth of a leafy crop from reflectance spectra from the soil and how might a grower manage the crop in the light of those predictions? Topsoil from two fields was sampled and analysed for various nutrients, particle-size distribution and organic carbon concentration. Crop measurements (lettuce diameter) were derived from aerial-imagery. Reflectance spectra were obtained in the laboratory from the soil in the near- and mid-infrared ranges, and these were used to predict crop performance by partial least squares regression (PLSR). Individual soil properties were also predicted from the spectra by PLSR. These estimated soil properties were used to predict lettuce diameter with a linear model (LM) and a linear mixed model (LMM): considering differences between lettuce varieties and the spatial correlation between data points. The PLSR predictions of the soil properties and lettuce diameter were close to observed values. Prediction of lettuce diameter from the estimated soil properties with the LMs gave somewhat poorer results than PLSR that used the soil spectra as predictor variables. Predictions from LMMs were more precise than those from the PLSR using soil spectra. All model predictions improved when the effects of variety were considered. Predictions from the reflectance spectra, via the estimation of soil properties, can enable growers to decide what treatments to apply to grow lettuce and how to vary their treatments within their fields to maximize the net profit from the crop.
... Accounting for N mineralization deeper in the soils may improve the predictability of EONR. For example, accounting for mineralization in the top 50 cm had R 2 values from 0.61 to 0.67 when relating PMN an to N uptake in wheat, whereas the R 2 was only 0.21 when PMN an was calculated only from the top 20 cm (Börjesson et al., 1999). Also, it may be necessary to split the soil sampling depth into smaller increments because N mineralization decreases with depth as the C/N ratio normally increases due to less organic N content in deeper layers of the soil (Paul et al., 2001;Purnomo et al., 2000aPurnomo et al., , 2000b. ...
... Also, it may be necessary to split the soil sampling depth into smaller increments because N mineralization decreases with depth as the C/N ratio normally increases due to less organic N content in deeper layers of the soil (Paul et al., 2001;Purnomo et al., 2000aPurnomo et al., , 2000b. The lower organic N content and changing C/N ratio as soil depth increases may have diluted our deeper (30 cm) PMN an samples, causing them to be lower than other studies that sampled the top 20 cm (Börjesson et al., 1999;Orcellet et al., 2017;Williams et al., 2007). ...
Estimates of mineralizable N with the anaerobic potentially mineralizable N (PMNan) test could improve predictions of
corn (Zea mays L.) economic optimal N rate (EONR). A study across eight US midwestern states was conducted to quantify the predictability of EONR for single and split N applications by PMNan. Treatment factors included different soil sample timings (pre-plant and V5 development stage), planting N rates (0 and 180 kg N ha–1), and incubation lengths (7, 14, and 28 d) with and without initial soil NH4–N included with PMNan. Soil was sampled (0–30 cm depth) before planting and N application and at V5 where 0 or 180 kg N ha–1 were applied at planting. Evaluating across all soils, PMNan was a weak predictor of EONR (R2 ≤ 0.08; RMSE, ≥67 kg N ha–1), but the predictability improved (15%) when soils were grouped by texture. Using PMNan and initial soil NH4–N as separate explanatory variables improved EONR predictability (11–20%) in fine-textured soils only. Delaying PMNan sampling from pre-plant to V5 regardless of N fertilization improved EONR predictability by 25% in only coarse-textured soils. Increasing PMNan incubations
beyond 7 d modestly improved EONR predictability (R2 increased ≤0.18, and RMSE was reduced ≤7 kg N ha–1). Alone,
PMNan predicts EONR poorly, and the improvements from partitioning soils by texture and including initial soil NH4–N
were relatively low (R2 ≤ 0.33; RMSE ≥ 68 kg N ha–1) compared with other tools for N fertilizer recommendations.
... Recently, promising results have also been published for NIR-based predictions of crop uptake of nitrogen. 15,16 In this paper the performance of NIR calibrations for clay and SOM matter contents on a large set of samples, covering most of the variation of agricultural mineral soils in Sweden, are tested. In addition the stability of NIR calibrations for N-uptake in winter wheat over years and across fields has been studied. ...
... The RMSEP and RPD were also significantly better for the NIR model. This difference between organic carbon and NIR as the predictors supports that the information in the NIR spectra represents a variety of properties of the soil matrix, as suggested by Börjesson et al. 15 That the bias generally remains across years is natural, as weather-conditioned differences for crops in different years could not be expected to influence the NIR-spectra. ...
... Therefore, direct quantitative estimates of NIR-spectra are impossible. Data are interpreted with multivariate statistics such as partial least squares regression, multiple linear regression, and principal components analysis (Börjesson et al., 1999;Chang et al., 2001). Coefficient of determination (R 2 ), ratio of performance deviation (RPD), ratio of error range (RER), and regression coefficient (b) were used by Nduwamungu et al. (2009b) to compare NIRS predictions with soil attributes in Eastern Canada. ...
... Nduwamungu et al. (2009b) accurately predicted soil texture, CEC, total C, total N, and potentially mineralizable N in noncalcareous soils in Quebec, Canada, and NIRS successfully predicted soil nutrient concentrations and properties in other soils Cozzolino and Morón, 2003;Malley et al., 1999;Martin et al., 2002). The NIRS was used to predict N supply in corn (R 2 ¼ 0.49; Fox et al., 1993) and winter wheat (R 2 ¼ 0.81; Börjesson et al., 1999). ...
Knowledge of the nitrogen (N) available to crops during the growing season is essential for improving fertilizer-use efficiency and minimizing the adverse impacts of N losses on the environment. In humid temperate regions, soil N supply is dominated by in-season N mineralization because plant-available N (NH4–N and NO3–N) is transformed to nonlabile forms or lost from the soil–plant system during fall and winter. The microbially mediated reactions that generate the soil N supply in agroecosystems are affected by system-specific conditions, including soil properties, agricultural management (crop rotation, tillage system, organic amendments), and most importantly, climate. Potentially mineralizable N (N0) determined from long-term soil incubation is regarded as the standard measure of soil N mineralization potential and may provide a good approximation of the soil N supply. However, this method is time consuming and not practical for routine use. Several chemical methods to estimate the N mineralization potential of soils are discussed in this chapter. The major limitation of chemical methods is that they cannot simulate the microbial-mediated release of plant-available N under field conditions. Consequently, any single chemical method may not be a good predictor of soil N supply. Thus, we suggest a holistic approach to estimate soil N supply in humid temperate regions, which involves (1) the use of a combination of N indices together with weather data and (2) identification and quantification of a specific fraction (s) of organic N that is the dominant contributor (s) to N supply in a particular system.
... Other soil properties such as plant mineral nutrients and pH have been estimated with NIR in a number of studies with promising, though varying, results [17,27282930. NIR has also been related to potentially mineralisable N derived from aerobic and anaerobic incubations [22, 23,313233 and has been used with promising results to estimate N uptake in crops333435. Shepherd and Walsh [21] proposed an IR approach based on building soil spectral libraries and illustrated the approach for African soils. ...
... Other soil properties such as plant mineral nutrients and pH have been estimated with NIR in a number of studies with promising, though varying, results [17,27282930. NIR has also been related to potentially mineralisable N derived from aerobic and anaerobic incubations [22, 23,313233 and has been used with promising results to estimate N uptake in crops333435. Shepherd and Walsh [21] proposed an IR approach based on building soil spectral libraries and illustrated the approach for African soils. ...
Wise decision-making on resource allocation and intervention targeting for soil management cannot rely solely on trial and error methods and field observations used by small-scale farmers: cost-effective soil fertility survey methods are needed. This study aimed to test the applicability of infrared spectroscopy (IR) as a diagnostic screening tool for making soil fertility recommendations in small-scale production systems. Soil fertility survey of 150 small-scale groundnut farms in western Kenya was conducted using a spatially stratified random sampling strategy. Soil properties examined were pH in water (pH w), total carbon (C), total nitrogen (N), extractable phosphorus (P), exchangeable potassium (K), calcium (Ca), magnesium (Mg) and texture. These properties were calibrated to mid-infrared (MIR) diffuse reflectance using partial least square regression (PLSR). Cross-validated coefficient of determination (r 2) values obtained from calibration models were > 0.80 for all properties, except P and K with 0.66 and 0.50 respectively. Soil nutritional deficiencies were evaluated using critical nutrient limits based on IR predictions and composite soil fertility indices (SFIs) developed from the soil properties using principal component analysis. The SFIs were calibrated to MIR soil spectral reflectance with cross-validated r 2 values > 0.80. The survey showed that 56% of the groundnut farms had severe soil nutrient constraints for production, especially exchangeable Ca, available P and organic matter. IR can provide a robust tool for farm soil fertility assessment and recommendation systems when backed up by conventional reference analyses. However, further work is required to test direct calibration of crop responses to spectral indicators and to improve prediction of extractable P and K tests.
... The potential use of either NIR or Vis-NIR spectroscopy for the analysis of soil organic matter content (Ben-Dor and Banin, 1995;Bowers and Hanks, 1965;Couillard et al., 1997), organic C and total N contents (Dalal and Henry, 1986;Morra et al., 1991), leaf litter chemistry (Joffre et al., 1992) and quality (Joffre et al., 2001), litter mass remaining during decomposition stages (Gillion et al., 1993), and organic matter quality induced variation in rates of respiration (Palmborg and Nordgren, 1993) has already been demonstrated. Correlation is also found between collected spectra and soil N supplying capacity, measured under field conditions as plant N uptake in wheat (NIR region used; Börjesson et al., 1999) and corn (Vis-NIR region used; Fox et al., 1993). Successful NIR/Vis-NIR predictions are often seen when soils of homogeneous origin are analysed. ...
... improved prediction accuracy of a range of relevant soil properties. Improved prediction accuracy is sometimes achieved by removing the water peak region around 1900 nm (e.g., Börjesson et al., 1999). However, in this investigation such a removal did not bring any general improvement. ...
The development of a rapid, accurate and cost-effective method for the prediction of constituents related to soil nitrogen (N) supply is considered important. The potential of using visible (Vis) and near infrared reflectance (NIR) spectroscopy (400–2500 nm) as such a method was investigated. Vis–NIR calibrations were performed for organic carbon (Corg) and total N (Ntot) content and their potential mineralisation using 80 grassland soil samples of rather heterogeneous origin. Prediction accuracy was tested using a 'take-out-four' validation strategy (48 samples). Within investigated variables a ratio of standard deviation of reference data to standard error of bias corrected prediction (RPD) within 1.7 (r2=0.65) and 2.7 (r2=0.87) were achieved. Apparent differences in Vis–NIR prediction accuracy among the variables were partly due to errors in the reference values. Thawed moist samples tend to be more accurately predicted than dried samples, and no benefit was derived from the grinding of sieved (4 mm) and dried samples. Prediction accuracy did not differ using two different systems for sample presentation to the Vis–NIR analyses. Comparative predictions of Corg and Ntot and their potential mineralisations were performed using the take-out-four validation strategy and simple linear regression to loss on ignition (LOI) values and hot KCl extracted NH4-N (NhotKCl) values as predictors. Likewise, the reference values of Corg and Ntot were also used as predictors for each other and for the potential C and N mineralisation constituents. Accuracy obtained for the Vis–NIR predictions of investigated constituents was in general equal or better than prediction accuracy obtained by these comparative methods. The Vis–NIR method provided promising predictions of variables important for the soil N supply.
... These regions are well in agreement with the major absorption features assigned for organic matter (Ben-Dor et al., 1997). Furthermore, they show similarity to the most important wavelength ranges in the prediction of N uptake into plants by soil NIR analysis (Bo¨rjesson et al., 1999). The absorption at around 1420 nm in the first derivate of the spectra (the slope of the peak at 1460 nm in the raw spectra) can be attributed to OH groups in water or cellulose, or to CH 2 groups in lignin (Ben-Dor et al., 1997 and references therein). ...
... Compared to ergosterol analysis, NIR is much faster and more inexpensive. Furthermore, analytical errors in NIR analysis are usually smaller than in conventional analyses (Bo¨rjesson et al., 1999). ...
Applicability of near infrared reflectance (NIR) and fluorescence spectroscopic techniques was tested on highly organic arctic soil. Soil samples were obtained at a long-term climate change manipulation experiment at a subarctic fell heath in Abisko, northern Sweden. The ecosystem had been exposed to treatments simulating increasing temperature (open-top greenhouses), higher nutrient availability (NPK fertilization) and increasing cloudiness (shading cloths) for 15 years prior to the sampling. For each of the 72 samples from the 0 to 5 cm soil depth and 36 samples from the 5 to 10 cm depth, the wavelength range of 400–2500 nm (visible and near infrared spectrum) was scanned with a NIR spectrophotometer and fluorescence excitation-emission matrices (EEMs) were recorded with a spectrofluorometer.
... Bakgrund Ett av de centrala problemen i växtnäringsförsörjningen är anpassningen av kvävegivan till ett skifte och dess olika delar. Stora mängder kväve kan mineraliseras under växtsäsongen och variationen inom fält kan vara betydande (Börjesson et al., 1999, Delin & Lindén, 2002). Detta gäller inte minst fält med stor mullhaltsvariation. ...
... NIR-spektrum har tidigare relaterats till både mullhalt och mullhaltskvalitet (Chang & Laird, 2002, Fystro, 2002) samt marktextur (Chang et al., 2001, Broge et al., 2004). Lovande resultat har även visats i studier där NIR har använts för att prediktera kväveupptag hos grödor inom och mellan fält (Börjesson et al., 1999, Dunn et al., 2000). I dag finns även andra mättekniker tillgängliga som ger möjlighet till relativt billig mätning över hela fältet med hög provtäthet. ...
... The ability to predict crop production and nutrient management using soil health indicators is inconsistent across the literature, with some studies being more successful (Börjesson et al., 1999;Culman et al., 2013;Franzluebbers et al., 2020a) than others (van Groenigen et al., 2003;D'Hose et al., 2014;Roper et al., 2018). Our study showed that either traditional measures of soil health or mid-DRIFTS approaches (specific peaks and partitioned spectra absorbances) can be used to assess early season soil health status in order to estimate corn grain yield (Fig. 3) as well as N-driven yield gap (Fig. 5). ...
Context: Developing high-throughput methods to quantify soil health can support decision making to minimize soil constraints for crop growth and increase sustainability in agroecosystems. Objective: This study evaluated the ability of soil health assessment conducted at the beginning of the crop growing season to predict corn agronomic performance in nutrient fertilization trials. We compared different methods to assess soil health, from the most traditional approach of measuring individual soil biochemical indicators to more novel approaches using diffuse reflectance infrared Fourier transform spectroscopy (mid-DRIFTS). Methods: Soil health was quantified using: I) traditional and emerging biochemical soil health indicators (up to 20 soil indicators), II) mineral and organic functional groups abundances estimated as peak areas in mid-DRIFTS, and III) mid-DRIFTS with partitioned spectra and partial least squares regression. Corn response to fertilizer was evaluated in a) phosphorus, potassium, and sulfur omission trials (2016-2018; 41 sites across 17 Ohio counties) as well as in b) N rates trials (2016-2017; 25 fields across 14 Ohio counties). Results: Modeling using either biochemical soil health indicators or mid-DRIFTS accurately predicted corn yield across nutrient treatments in omission trials (R 2 V =0.61-0.84 in validation sets) and unfertilized controls in N rates trials (R 2 V =0.68-0.81) as well as corn N-driven yield gap (i.e., delta yield defined as grain yield at agronomic optimum N rate minus grain yield of the control, R 2 V =0.64-0.76) in N rates trials. Conclusion: Mid-DRIFTS approaches equaled or outperformed the predictive ability of measured biochemical soil health indicators in estimating crop performance (up to 38 % improvement in R 2 V), unveiling a promising method to more efficiently assess soil health. Implications: Early season soil health assessment via mid-DRIFTS has potential to directly estimate corn agro-nomic performance.
... En un estudio llevado a cabo por Delin y Linden (2002) encontraron que el contenido de materia orgánica del suelo y la mineralización de N presentaron una débil correlación (r = 0,26), lo cual contrasta con lo encontrado por Börjesson et al. (1999) y Stenberg et al. (2002), quienes encontraron una correlación más fuerte (r = 0,69 y r = 0,83, respectivamente) para las mismas variables. Por otra parte, Sano et al. (2006) encontraron que el potencial de mineralización de N correlacionaba significativamente con el contenido de N orgánico del suelo, para suelos de diferente origen. ...
The application of organic amendments is a practice that is done regularly in crop systems in order to improve the physical and chemical properties of the soil and provide nutrients such as nitrogen (N). In order for plants to absorb it, much of the N that is contributed through organic amendments must be transformed from its organic form to its inorganic form in a process called mineralization. A great amount of research has been developed to understand this process and how the contribution of organic amendments influences its dynamics. This review provides an overview of soil and amendment factors involved in the N mineralization process when organic amendments, from different sources, are applied to the soil.
... Analyses that combined spectral data with EC a and/or CI data incorporated an additional preprocessing step known as block scaling (Wold et al., 2001). In block scaling, variables of different types are grouped into blocks, and then a different scaling is applied to each block so that the final results are not unduly influenced by any one variable type (Börjesson et al., 1999). In this study, the vector of spectral variables was defined as one block (n = 374), while EC a and CI were each defined as a one-variable block. ...
Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (EC a ) sensors. Previous field research has related these sensor measurements to soil properties such as bulk density, water content, and texture. A commercial instrument that can simultaneously collect reflectance spectra, EC a , and soil strength data is now available. The objective of this research was to relate laboratory-measured soil properties, including bulk density (BD), total organic carbon (TOC), water content (WC), and texture fractions to sensor data from this instrument. At four field sites in mid-Missouri, profile sensor measurements were obtained to 0.9 m depth, followed by collection of soil cores at each site for laboratory measurements. Using only DRS data, BD, TOC, and WC were not well-estimated (R ² = 0.32, 0.67, and 0.40, respectively). Adding EC a and soil strength data provided only a slight improvement in WC estimation (R ² = 0.47) and little to no improvement in BD and TOC estimation. When data were analyzed separately by major land resource area (MLRA), fusion of data from all sensors improved soil texture fraction estimates. The largest improvement compared to reflectance alone was for MLRA 115B, where estimation errors for the various soil properties were reduced by approximately 14% to 26%. This study showed promise for in-field sensor measurement of some soil properties. Additional field data collection and model development are needed for those soil properties for which a combination of data from multiple sensors is required. Keywords: NIR spectroscopy, Precision agriculture, Reflectance spectra, Soil properties, Soil sensing.
... Scientists have tried to improve the reliability of determining N fertilizer requirements before sowing and during growing periods. Because, soil N has the potential to be mineralized during the growing season (Lindén, et al., 1992), and large variation between and within fields have been also observed (Börjesson, et al., 1999). This variation needs to be quantified for inputs ...
Inefficient use of inputs and conventional management practices that are applied during crop production is leading the depletion of soil quality. The inability of conventional farming to address soil variation not only has a detrimental economic impact due to reduced yield but also adversely affects the environment. The need of sustainable management appeals for alternative approaches which will increase the productivity and resources efficiency; and will be desirable in the ecological context as well. Thus, sustainable development has reinforced the process to evolve as a more quantitative mannerto optimize soil management and impart spatially differentiated treatments. There is a global need to develop tools that will evaluate soil quality and contribute in the effectiveness and sustainability of farm management.Soil properties delineation at field and landscape scale is extremely important for a variety of agronomic and environmental concerns. The existing knowledge and recent researches (Stenberg et al., 2010)on soil-NIR measurements for soil analysis by the NIR spectroscopic technique could be used potentially for estimation of available nitrogen (N) and cation exchange capacity (CEC) of soils which will optimize fertilization strategy and farmer decision supports. However, this is worth mentioning that NIR measurements are not new in this scientific arena but it has not been practiced so far for estimation of soil properties in Bangladesh. Exploration of its potential would be beneficial for soil property analysis (and or estimation).
... The NIRS is commonly used in plant analysis, speci fi cally to determine the nutritive value of feedstuffs, but its application in soil analysis is still under investigation (Malley et al. 2002 ;Nduwamungu et al. 2009a, b ) . The soil N availability as measured by NIRS was previously demonstrated to be closely related to soil N supply as measured by crop N uptake in unfertilized plots for corn (R 2 = 0.49; Fox et al. 1993 ) and winter wheat (R 2 = 0.81; Börjesson et al., 1999 ) . In eastern Canada, Nduwamungu et al. ( 2009a ) accurately predicted potentially mineralizable N calculated from soil organic matter and clay content (Simard et al. 2001 ) under corn production. ...
Appropriate fertilizer nitrogen (N) management can optimize tuber yield and quality, and reduce the risk of environmental N losses. However, the optimal fertilizer N management can vary among fields and years. Plant- and soil-based tests are examined in this chapter as diagnostic tools to improve fertilizer N management in rain-fed potato production in eastern Canada. Plant-based diagnostic tests assess potato N sufficiency and can be used to guide in-season fertilizer N management. The nitrogen nutrition index (NNI) based on whole plants, the petiole nitrate concentration, and the leaf chlorophyll meter reading (SPAD) have been shown to successfully diagnose the level of potato N nutrition during the growing season in eastern Canada. The use of gene expression, a promising tool for a direct measurement of potato N sufficiency compared with chemical or optical methods, is also examined. Soil-based tests can be used to provide an estimate of soil N supply to adjust the at-planting fertilizer N rate. The use of pre-plant and in-season soil nitrate tests, ion exchange membranes, indices of soil mineralizable N, and near-infrared reflectance spectroscopy (NIRS) are examined. A combination of a soil-based test to guide at-planting fertilizer N application and a plant-based test to guide in-season N management may be most effective.
... Det finns ingen tillförlitlig kemisk metod eller någon annan laborationsmetod för att prognostisera kvävemineralisering under fältförhållanden, och sambandet mellan mullhalt och kvävemineralisering är ofta mycket svagt. Det pågår försök med att mäta kvävemineraliseringsförmågan med NIR (Nära Infraröd Reflektans), som redovisas av Börjesson, et al. (1999). Dessa mätte reflektansen av ljus i våglängdsområdet 1100-2500 nm. ...
... Compared to other methods, the integrative function of the NIR calibrations may explain the low analytical errors in NIR spectroscopy. In addition, it is a rapid, low price analytical technique and no hazardous chemicals are used (Börjesson et al., 1999). ...
... NIR spectroscopy has proven capable of predicting forage quality parameters such as chemical digestion fractions, crude protein and digestibility [5][6][7] and is routinely used commercially for forage quality analyses. 8 NIR spectroscopy has also been shown to have a good potential for describing organic matter quality in agricultural soils, [9][10][11] the decomposition stage of forest litter [12][13][14][15][16][17] and halophytic litter 18 and the degradability of forest leaves. 14 Consequently, NIR spectroscopy shows much promise for predicting the C and N fractions of plant materials used in C and N mineralisation studies. ...
About 250 plant litters were sampled, representing 50 different species expected to cover a wide quality range. NIR spectra were measured for all samples and Mahalanobis selectiondistances (H) between samples used to select 120 representative items for analysis of C and N distribution among Van Soest fractions. Selected samples proved to represent a wide range in residue quality, with low co-variations between parameters, but a large and fairly constant proportion (>90%) of the N is in the netutral detergent (ND) soluble fraction. NIR-calibrations performed well for N in total plant and in the ND soluble fraction and for C in ND soluble and in holocellulose . However, NIR-calibrations did not perform sufficiently well for N in holocellulose or for C and N in lignin. The independent validation indicated good predictive performance of NIR-calibrations, but in some cases there was a strong bias, possibly due to differences in methodology for Van Soest fractionation of the independent samples. Perspectives for use of the NIR method for characterising plant residue quality seem promising.
... Predicting crop P-uptake and P-budget from soil spectra would eliminate the need to establish relationships between soil test P and crop response to P fertilization. Börjesson et al. (1999), Terhoeven-Urselmans et al. (2008), and more recently St. Luce et al. (2012) link NIRS soil spectra to winter cereal N-uptake and report good predictions (R v 2 ≥ 0.70), but to our knowledge, the prediction of crop P-uptake and annual P-budget by NIRS has not been documented. The objective of this study was to evaluate the potential of NIRS to predict soil P-related properties (total soil P, soil P extracted using a Mehlich 3 solution or water, annual crop P-uptake, and annual P-budget) and other soil properties (pH, TC, TN, K, Al, Fe, Ca, Mg, Mn, Cu, and Zn). ...
Near-infrared reflectance spectroscopy (NIRS) is a rapid, inexpensive, and accurate analysis technique for a wide variety of materials, and it is increasingly used in soil science. The objectives of our study were to examine the potential of NIRS to predict (i) soil P extracted by two methods [Mehlich 3 (M3P) and water (Cp)], soil total P (TP), annual crop P-uptake, and annual P-budget, and (ii) other soil chemical properties [total C (TC), total N (TN), pH, and K, Al, Fe, Ca, Mg, Mn, Cu, and Zn extracted by Mehlich 3]. Soil samples (n = 448) were taken over a 7-yr period from an experimental site in Lévis (Québec, Canada) where timothy (Phleum pratense L.) was grown under four combinations of P and N fertilizer. The NIRS equations were developed using 80% of the samples for calibration and 20% for validation. The predictive ability of NIRS was evaluated using the coefficient of determination of validation (Rv2) and the ratio of standard error of prediction to standard deviation (RPD). Results show that M3P, Cp, crop annual P-uptake, and annual P-budget were not accurately predicted by NIRS (Rv2 < 0.70 and RPD < 1.75). Similar results were found for K and Cu. However, NIRS predictions were moderately useful for TP, TN, Fe, and Zn (0.70 ≤ Rv2 < 0.80 and 1.75 ≤ RPD < 2.25), moderately successful for TC and Al (0.80 ≤ Rv2 < 0.90 and 2.25 ≤ RPD < 3.00), successful for pH and Mg (0.90 ≤ Rv2 ≤ 0.95 and 3.00 ≤ RPD ≤ 4.00), and excellent for Ca and Mn (Rv2 > 0.95 and RPD > 4.00). The NIRS predictive ability of several soil properties appears to be related to their relationship with soil organic C. Although NIRS can predict several soil properties, prediction of total P was the only soil P-related property, correlated to soil C, that was moderately useful.
... It characterizes the molecular composition of OM in soil samples by analyzing reflected spectra of material exposed to radiative energy in the infrared region (Reeves et al., 1999;Gillon et al., 1999;Joffre et al., 2001). It integrates information on organic C with other relevant soil components (Börjesson et al., 1999;Dunn et al., 2000), and is a physical non-destructive, rapid, reproducible and low-cost method to characterize materials according to their reflectance in the wavelength range between 800 and 2500 nm (Roberts et al., 2004;Brunet et al., 2007;Barthès et al., 2010). ...
... NIR spectroscopy has proven capable of predicting forage quality parameters such as chemical digestion fractions, crude protein and digestibility [5][6][7] and is routinely used commercially for forage quality analyses. 8 NIR spectroscopy has also been shown to have a good potential for describing organic matter quality in agricultural soils, [9][10][11] the decomposition stage of forest litter [12][13][14][15][16][17] and halophytic litter 18 and the degradability of forest leaves. 14 Consequently, NIR spectroscopy shows much promise for predicting the C and N fractions of plant materials used in C and N mineralisation studies. ...
For environmental, as well as agronomic reasons, the turnover of carbon (C) and nitrogen (N) from crop residues, catch crops and green manures incorporated into agricultural soils has attracted much attention. It has previously been found that the C and N content in fractions from stepwise chemical digestion of plant materials constitutes an adequate basis for describing a priori the degradability of both C and N in soil. However, the analyses involved are costly and, therefore, unlikely to be used routinely. The aim of the present work was to develop near infrared (NIR) calibrations for C and N fractions governing decomposition dynamics. Within the five Nordic countries, we sampled a uniquely broad-ranged collection representing most of the fresh and mature plant materials that may be incorporated into agricultural soils from temperate regions. The specific objectives of the current study were (1) to produce NIR calibrations with data on C and N in fractions obtained by stepwise chemical digestion (SCD); (2) to validate these calibrations on independent plant samples and (3) to compare the precision and robustness of these broad-based calibrations with calibrations derived from materials within a narrower quality range. According to an internal validation set, plant N, soluble N, cellulose C, holocellulose (hemicellulose + cellulose) C, soluble C and neutral detergent fibre (NDF) dry matter were the parameters best predicted (r2=0.97, 0.95, 0.94, 0.91, 0.90 and 0.94, respectively). However, the calibrations for soluble C and NDF were regarded as unstable, as their validation statistics were substantially poorer than the calibration statistics. The calibrations for all structural N fractions and lignin C were considered poor (r2=0.47-0. 70). By comparing our broad-based calibrations for plant N and NDF with similar calibrations for a sample set representing a commercial forage database, it was evident that the broad-based calibrations predicted a narrow-based sample set better than vice versa. For plant N, the residual mean squared error of prediction (RMSEP), when testing the broad-based calibration with the narrow-based validation set, was substantially smaller than the RMSEP obtained when validating the broad-based calibration internally (1.8 vs 2.7 mg N g -1 dry matter). Overall, the calibrations that performed best were those concerning the parameters most strongly influencing C and N mineralisation from plant materials.
... The moderate variation in SOM (Table 1) may be one reason for the weaker relation obtained. In two studies on nearby fields with larger variation in SOM (2.0 -22.6% and 2.8 -17.2%) the correlation with SOM was better (r= 0.69 and r 2 = 0.83, respectively) (Bö rjesson et al., 1999;Stenberg et al., 2002). The within-field variations in N uptake by plants were also larger in these investigations (SD =30 kg N ha − 1 on average for both investigations). ...
Within-field variations in plant-available soil nitrogen (N) are likely to be affected by differences in soil characteristics. To study this, a 3- year field investigation was conducted during 1998-2000 on a 15 ha arable field in Sweden with considerable within-field soil texture variability. In 34 plots soil N uptake by crops, net nitrogen mineralization (Nm) during the growing season and soil mineral N in spring and shortly after harvest were determined. Beside these parameters, topography, soil organic matter content (SOM), clay content, pH(H 2 O) and grain yield were recorded. The variations in Nm were considerably large both within the field and between years. The within-field variation in Nm could partly be explained by the variation in SOM and clay content (adjusted coefficient of determination = 0.23, P <0.001). The pattern in Nm differed between years, partly because of seasonal variations in soil moisture. For these reasons, the pattern of Nm is difficult to predict without seasonal adjustments.
... Several studies have predicted indices of biochemical composition from NIR spectra (Wessman et al., 1988;McLellan et al., 1991;Gillon et al., 1994Gillon et al., , 1999aStenberg et al., 2004), whereas other studies have attempted to predict decomposition patterns or model parameters from the spectra (Gillon et al., 1999b;Joffre et al., 2001). Nitrogen mineralization patterns of agricultural soils have also been predicted by NIR spectroscopy (Börjesson et al., 1999;Dunn et al., 2002;Russell et al., 2002). ...
Prediction of carbon (C) and nitrogen (N) mineralization patterns of plant litter is desirable for both agronomic and environmental reasons. Near infrared reflectance (NIR) spectroscopy has recently been introduced in decomposition studies to characterize biochemical composition. The purpose of the current study was to use empirical techniques to predict C and N mineralization patterns of a wide range of plant materials incubated under controlled temperature and moisture conditions. We hypothesized that the richness of information in the NIR spectra would considerably improve predictions compared to traditional stepwise chemical digestion (SCD) or C/N ratios. Initially, we fitted a number of empirical functions to the observed C and N mineralization patterns. The best functions fitted with R2=0.990 and 0.949 to C and N, respectively. The fractions of C and N mineralized at different points in time were then either predicted directly with regression functions or indirectly by prediction of the parameters of the empirical functions fitted to incubation data. In both cases, partial least squares (PLS) regressions were used and predictions were validated by cross-validations. We found that the NIR spectra (best R2=0.925) were able to predict C mineralization patterns marginally better than the SCD fractions (best R2=0.911), but considerably better than the C/N ratios (best R2=0.851). In contrast, N mineralization was better predicted by SCD fractions (best R2=0.533) than the C/N ratio (best R2=0.497), which was better than NIR predictions (best R2=0.446). Although the predictions with the NIR spectra were only slightly better for C and worse for N mineralization compared to SCD fractions, NIR spectroscopy still holds advantages, as it is a much less laborious and cheaper analytical method. Furthermore, exploration of the applications of NIR spectroscopy in decomposition studies has only just begun, and offers new ways to gain insights into the decomposition process.
... Det finns ingen tillförlitlig kemisk metod eller någon annan laborationsmetod för att prognostisera kvävemineralisering under fältförhållanden, och sambandet mellan mullhalt och kvävemineralisering är ofta mycket svagt. Det pågår försök med att mäta kvävemineraliseringsförmågan med NIR (Nära Infraröd Reflektans), som redovisas av Börjesson, et al. (1999). Dessa mätte reflektansen av ljus i våglängdsområdet 1100-2500 NIR-mätningarna jämförd nm. ...
... The moderate variation in SOM may be one reason for the weak relationship obtained. In two studies on nearby fields with larger variation in SOM, the correlation with SOM was better (r 2 = 0.69 and r 2 = 0.83, respectively) (Börjesson et al., 1999;Stenberg, Jonsson & Börjesson, 2002). Another possible explanation for the poor but significant correlation between SOM and Nm in this investigation could be that the properties of the SOM vary within the field, but then more similar patterns of Nm should have been found between the three years. ...
In precision agriculture, inputs are adjusted to the varying demand across a field in order to optimize net returns and avoid losses to the environment. For site-specific N application, it is useful to know how fertilizer N demand, plant available soil N (Np, i.e. soil N taken up by plants) and potential yield relate to each other and to different soil characteristics within a field. A 3-year field investigation was carried out on a 15-ha arable field with large soil texture differences in south-west Sweden, on which winter wheat and spring barley were grown. Variation in Np was considerable both within the field and between years but could only partly be explained by variations in soil organic matter, clay and elevation. Maps of yield, grain protein content and Np differed between years, partly due to differences in seasonal variation in soil moisture. Together, protein and yield maps indicated where N supply was sufficient and where factors other than N were limiting, which allowed the accuracy of the N fertilization to be evaluated retrospectively. Differences in yield response to N between areas with different soil texture were small when soil moisture was sufficient. In a dry year, yields were smaller at sandy sites, while in a wet year Np, and thereby yield, was lower on clayey sites. Soil moisture is related to soil electrical conductivity (SEC) and elevation, which are easily measured densely within the field. Therefore, these parameters are useful for dividing the field into zones with different risks for drought and waterlogging and can be used for variable N application, assuming that the season can be defined as dry, normal or wet at the time of fertilization. Average values in zones created from a densely measured variable proved to be a better alternative for many variables in this field than interpolation of sparsely collected soil data without respect to distinct borders.
Non-agricultural food products like poultry not only give an alternative source of income and employment but also help individuals in raising the standard of their living bringing millions of poor households out of poverty. For churning profits, farms tend to ignore proper feed, and chicken feces disposal, which harms the overall surroundings. Nitrogen in chicken feces are a major source of atmospheric ammonia along with some other gases involving greenhouse gases. The present paper focusses on calculating the nitrogen content of chicken feces whenever the diet of chicken is altered. It is observed that when protein is increased in diet the nitrogen in the chicken feces shows increasing pattern. However, the highest protein diet may not result into highest nitrogen content in the feces. The increase in nitrogen between morning and evening ranged from 0.46 to 0.64 % of 10 g of feces. On an average the 5-days increase in nitrogen ranged from 0.4 to 0.64 %. The chicken feces also act as a perfect feed for fishes. This may help in reducing
the waste, increasing the profit for farmers and will help in addressing food security concerns.
Consensus modeling averages the results of multiple independent models to obtain a single prediction, which avoids the instability of a single model. Based on the philosophy of consensus modeling, a consensus partial least squares regression(cPLS) method was proposed and applied to building the quantitative model of NIR spectra of tobacco samples. Through an investigation of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of chlorine in tobacco samples. With repeated independent runs, cPLS model was found to be more robust and credible than PLS model. Furthermore, compared with PLS method, cPLS model gives more stable and accurate prediction results.
Adjustment of nitrogen fertiliser according to the within-field variation in N- mineralization during the growing season can reduce costs and decrease nutrient losses to the environment. The ability to predict that variation, particularly at low cost, is therefore of interest. In this study, SOM, texture and Near Infrared Reflectance (NIR)-spectroscopy were evaluated as tools for prediction of plant N-uptake using cross-validated PLS (Partial Least Squares) models. In 2 out of 3 fields, models based on NIR-spectra alone or in combination with SOM and texture gave predictions with r2 from0.5 to 0.9 and average errors of cross-validation between 10 and 30 kg N ha -1. Including NIR did not improve the results in calibrations based on SOM and texture in the third field with very low r2 (<0.1). The results indicate that NIR and SOM plus clay are able to predict N-uptake with similar r2. Increasing the number of samples would be expected to improve the NIR-calibrations and reduce the need for separate clay and SOM measurements in addition to NIR.
Environmental studies often require analyses of numerous chemical, physical and biological properties in large numbers of soil, litter and plant samples. Such analyses may be expensive and time consuming and therefore rapid and cost-effective methods may be required. Near infrared spectroscopy (N1RS) is a non-destructive analytical method known for rapidity, simplicity and low costs, which could be used along with classical analytical methods in order to improve efficiency of large-scale environmental research. In this review, principals of NIRS are described, examples of NIRS applications are presented and the possibilities and limitations of the method are discussed.
The aim of this study is to establish the applicability of NIR-spectroscopy as a rapid method for an accurate estimation of N-potentials in agricultural soils. Furthermore, the usefulness of this method to determine other important soil parameters was tested. Three subsequent N-uptakes of by oat in rye grass in pot trials conducted with 106 soils were used to define short-term or long-term N-potentials. Using NIR-spectra (3850–10000 cm−1), models were calculated to predict N-potentials and other soil-parameters (i.e. clay). The results demonstrate that the prediction of short-term N-potentials by NIRS-models is inadequate (R2=0.33), whereas models for long-term N-potentials are good (R2=0.77). In addition, valuable information concerning the N-turnover as Nt,Corg or soil texture (i.e. clay,silt) (R2=0.91) can be obtained. NIR-spectroscopy is a useful tool to estimate long-term soil nitrogen supply.
Precision farming aims at reducing environmental risks and increasing productivity. Soils are multi-functional media, in which air, water and biota occur together to form an essential part of the landscape, with a fundamental role in the environment. The requirement for herbicides and fertilizers can vary within a field in response to spatial differences in soil properties. Near infrared (NIR) spectroscopy is widely used today as a nondestructive analytical technique, which is capable of determining a number of physio-chemical parameters. The objective of this study was to develop optimal models for predicting chemical properties of paddy soils by visible and NIR reflectance spectra. Reflectance spectra, moisture contents, pH, total nitrogen, organic matter, available phosphate, exchangeable potassium, ex. calcium, ex. magnesium, ex. sodium, iron, manganese, zinc, and copper of soil samples were measured. The reflectance spectra were measured in the wavelength range of 400–2,500 nm with 2-nm intervals. The method of moving window partial least square (MWPLS) analysis, which is a wavelength interval selection method for multi-component spectra analysis, was used to determine the soil properties. MWPLS models showed the possibility to predict chemical properties of soil samples in the wavelength range of 1,000–2,500 nm, offering the possibility of considerable cost savings and increased efficiency over the conventional analysis method.
A new algorithm (WFCE) was proposed for simultaneously eliminating background and noise based on wavelet packet transform (WPT) and information entropy theory. At first, WPT algorithm and reconstruction algorithm were employed to split the raw spectra into different frequency components. Then the information entropy of each frequency component was calculated, showing the uncertainty to the measured analyte concentration. At last, based on comparison of information entropy, the importance of each frequency component to the whole spectra was evaluated and the suitable wavelet components representing background and noise can be determined for removal. WFCE algorithm was validated by measuring the original extract concentration of beer using the NIR spectra. The results show that the prediction ability and robustness of models obtained in subsequent partial least squares calibration using WFCE were superior to those obtained using other algorithm, and the root mean square errors of prediction can decrease by up to 38.6%, indicating that WFCE is an effective method for elimination of background and noise.
In order to provide references for leaf nutrition diagnosis of fingered citron, the technique of near infrared reflectance spectroscopy (NIRS) was introduced to analyze nitrogen (N), phosphorus (P), potassium (K), iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) in the dry-leaf samples of fingered citron. The best calibration model for N was developed with high RSQCAL (0.90), SD/SECV (2.73) and low SEC (1.06 mg g), good calibration models were obtained for P, K, Fe and Mn, and no significant correlations were found between the spectra and the individual amounts of Zn and Cu. When tested using a validation set (n = 38), N was well predicted with low values of SEP (1.21 mg g) and high RPD (2.5). The values of SEP and RPD were also acceptable for the external validation of P, Fe and Mn. Near-infrared spectroscopy analysis technique shows potential of diagnosing minerals in fingered citron, particularly for N, P, Fe and Mn.
Soil fertility diagnostics rely not only upon measurement of available nutrients but also upon the ability of the soil to retain these nutrients. Near infrared (NIR) reflectance spectroscopy is a rapid and non-destructive analytical technique which allows the simultaneous estimation of standard soil characteristics and does not require the use of chemicals. Previous studies showed that NIR spectroscopy could be used in local contexts to predict soil properties. The main goal of our research was to build a methodological framework for the use of NIR spectroscopy on a more global scale. The specific goals of this study were (i) to identify the best spectral treatment and processing-LOCAL versus GLOBAL-regression methods, (ii) to compare the performance of NIR to standard chemical protocols and (iii) to evaluate the ability of NIR spectroscopy to predict soil total organic carbon (TOC), total nitrogen (TN), clay content and cationic exchange capacity (CEC) for a wide range of soil conditions. We scanned 1300 samples representative of the main soil types of Wallonia under crop, grassland or forest. Various sample preparations were tested prior to NIR measurement. The most appropriate options were selected according to analysis of variance and multiple means comparisons of the spectra principal components. Fifteen pre-treatments were applied to a calibration set and the prediction accuracy was evaluated for GLOBAL and LOCAL modified partial least square (MPLS) regression models. The LOCAL MPLS calibrations showed very encouraging results for all the characteristics investigated. On average, for crop soil samples, the prediction coefficient of variation (CV(p)) was close to 15% for TOC content, 7% for TN content and 10% for clay content and CEC. The comparisons of repeatability and reproducibility of both NIR and standard methods showed that NIR spectroscopy is as reliable as reference methods. Prediction accuracy and technique repeatability will allow the use of NIR spectroscopy within the framework of the soil fertility evaluation and its replacement of standard protocols. LOCAL MPLS can be applied within global datasets, such as the International global soil spectral library. However, the performance of LOCAL MPLS is linked to the number of similar spectra in the dataset and more standard measurements are needed to characterise the least widespread soils.
Near infra-red reflectance spectroscopy (NIRS) offers the potential for rapid and cost-effective soil analysis. Unfortunately, soil NIRS calibrations have not performed well across soil types, and this is believed to be due to the differences in soil particle size and, or, soil mineralogy. In this study we evaluated the influence of grinding, removal of organic matter, removal of mineral component, and scanning soils through plastic bags, on the prediction of three soil organic matter properties across 25 soils. The three properties were mineralizable nitrogen, total soil carbon, and total soil nitrogen. Seven soil preparations were made from coarse whole soil (CWS) samples, and their spectra acquired. The preparations were as follows: CWS, finely ground whole soil (FWS), CWS scanned through plastic (PWS), and two methods for the approximation of mineral and soil organic matter (SOM) within the CWS. Mineral soil spectra were derived from combustion or chemical treatment. SOM spectra were approximated by the subtraction of mineral spectra from CWS spectra. The preparations influenced soil NIRS calibrations in different ways for each property, and their performance was compared with the predictive ability derived from calibrations with CWS. FWS, PWS, and mineral preparations did little to enhance the predictions, and were more likely to reduce predictive ability. SOM spectra gave the best predictions of total soil nitrogen and mineralizable nitrogen (20–30% lower in error), while total soil carbon was best predicted from the CWS spectra. The best calibrations for all three properties suggest they can be predicted within at least 8 classes. It was concluded that approximated SOM spectra could improve predictions of some soil properties by reducing soil mineralogical noise in NIRS calibration development.
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.
The feasibility of near infrared (NIR) reflectance spectroscopy in determining various soil constituents such as total organic carbon, total nitrogen, exchangeable potassium and available phosphorus has been investigated, to monitor their concentration during a long-term agronomic trial. Soil samples previously analysed by conventional chemical methods were scanned using a NIRSystems 5000 monochromator and spectra were treated using several algorithms. The first derivative of each NIR spectrum was used for all statistical analyses. Step-up, stepwise and modified partial least squares (MPLS) regression methods were applied to develop reliable calibration models between the NIR spectral data and the results of wet analyses. MPLS almost always gave the most successful calibrations. The results demonstrated that NIR reflectance spectroscopy can be used to determine accurately two important soil constituents, namely total nitrogen and carbon content. This technique could be employed as a routine testing method in estimating, rapidly and non-destructively, these constituents in soil samples, demonstrating soil variations within a long-term field experiment. For other determinations, such as exchangeable potassium and available phosphorus content, our results were less successful but may be useful for separation of samples into groups.
The objective of this work was to test whether a dynamic soil C and N model using site-specific information improved estimates of apparent net N mineralization compared with regressions only based on static soil properties. This comparison was made using data from a 34-point sampling grid within a Swedish arable field during two growing seasons, using a simple carbon balance and nitrogen mineralization model (ICBM/N) for the dynamic approach. Three free model parameters were simultaneously optimized using non-linear regression to obtain the best model fit to the data from all grid points and both years. Calculated annual mean net mineralization (Nm_sim) matched the measured Nm mean exactly, and was 44 and 71 kg N ha(-1) for the two growing seasons 1999 and 2000, respectively. However, the variability in calculated Nm_sim values among the 34 grid points was smaller than that measured, and only a small proportion of the variation within a single year was explained by the model. Despite this, the model explained 56% of the total variation in Nm during the two growing seasons, mainly due to the good fit to the seasonal overall difference. Significant factors influencing net mineralization included the soil environment controlling mineralization, total N in soil organic matter and N in crop residues. Uncertainties in estimation of theta(fc) and theta(wp) (soil water content at saturation and wilting point) and the possible influence of unknown horizontal and vertical water flows made high-precision calculations of soil water content difficult. The precision and general applicability of the actual measurements thus set limits for estimating critical parameters, and the limitations of both the experimental design and the model are discussed. It is concluded that improvements in precision in sampling and analysis of data from the grid points are needed for more critical hypothesis testing.
Near infrared reflectance spectroscopy (NIRS) was tested as a method of predicting potentially mineralizable N (PMN) in organic potting mixes. Initially, calibration models were developed using the near infrared spectra of 100 potting mix formulations and the amount of N recovered by ryegrass herbage after 168 d of growth in the formulations, focusing on which combination of a range of regression, derivatization and scatter correction techniques gave the best calibration model. In the second part of the study, a validation exercise was performed, where the 100 formulations were split into separate calibration and validation sets.In the initial calibration exercise, the most effective combination of regression technique and spectral pretreatments was modified partial least squares in conjunction with a second order derivative and a standard normal variate and detrend scatter correction technique, which had an R of 0.99. However, in the validation study the coefficient of determination was much lower (r=0.73), and results were not in line with expectations. The explanation offered for the poor validation results relates to the constriction of the set of calibration samples. Nevertheless, NIRS appears to have great potential for predicting the performance of organic potting mixes, and despite several practical drawbacks is worthy of investigation by other workers.
The vast number of chemicals used in society today urge for simple and rapid tests to assess toxic effects on the soil ecosystem. Several microbial methods for soil toxicity testing and soil quality testing exist today. Surprisingly, few of these tests have, though, reached the level of becoming international standards. By using such tests for screening purposes and dose-response testing, it is possible to identify chemicals, and concentrations of these chemicals, that could be allowed without seriously threatening the productivity and sustainable use of soils.
Mechanistic, multi-compartment decomposition models require that carbon (C) and nitrogen (N) in plant material be distributed among pools of different degradability. For this purpose, measured concentrations of C and N in fractions obtained through stepwise chemical digestion (SCD) and values predicted from near-infrared (NIR) spectra or total plant N concentration were compared. Seventy-six cash, forage, green manure and cover crop plant materials representing a wide range in biological origin and chemical quality were incubated in a sandy soil at 15 °C and −10 kPa water potential for 217 d. A mechanistic decomposition model was calibrated with data from soil without plant material and initialised by data on amounts of C and N in fractions obtained from SCD directly or C and N in SCD fractions as predicted from NIR spectroscopy or plant N concentration. All model parameters describing C and N flows from plant material were kept at default values as defined in previous, independent works with the same model. When results from SCD were used directly to initialise the decomposition model, C and N mineralisation dynamics were predicted well (r2=0.76 and 0.70 for C mineralisation rates and accumulation of inorganic N, respectively). When a NIR calibration was used to predict the SCD data, this resulted in nearly equally good model performance (r2=0.76 and 0.69 for C and N mineralisation, respectively). This was also the case when SCD data were predicted from plant material N concentration (r2=0.76 and 0.69 for C and N). We conclude that the combined use of a mechanistic decomposition model and quality data from SCD is a highly adequate basis for an a priori description of the mineralisation of both C and N from common agricultural plant materials, and that both NIR spectroscopy and measurement of total N concentration offer good and cost-effective alternatives if they are calibrated with SCD data.
Prediction of nitrogen (N) mineralization is important for specifying the optimum rate of N fertilizer for flooded rice at the time of sowing. To develop a predictive test, soils (0–0.1 m) were sampled from 22 farms throughout the rice-growing region of southern Australia over a 4-year period. Near infrared reflectance (NIR) spectra of the soils were compared with sixteen biological and chemical soil tests for the prediction of N-uptake by rice plants from these soils in the field and glasshouse. The aim of the study was to develop a soil-NIR calibration as an accurate, rapid and economical mineralization test. Nitrogen uptake by field-grown and glasshouse-grown plants was poorly correlated (r = 0.30), even though significant NIR calibrations were developed with both. Since N uptake by rice in the field was affected by varying weather and management, the field calibration is probably spurious. The calibration of soil NIR spectra with N uptake by glasshouse plants was satisfactory, with a standard error (SE) of 13 kg ha–1 over a range of 11 – 95 kg ha–1, and a correlation between calculated and measured N uptake (r = 0.87, P–1, range 52–175 mg kg–1). Analysis of the soil spectra showed that similar wavelengths were correlated with both plant-N uptake and mineralization. NIR spectroscopy shows considerable potential to predict soil N mineralization, and may assist future fertiliser decision support.
Productivity and response to nitrogen (N) fertilizer were measured on 84 irrigated rice crops growing over two seasons on farms in the Riverina region of south-eastern Australia. The results were correlated with 18 tests of the N status of the top 10 cm of soil to develop a method to specify optimum N fertilizer application before sowing. In this environment, nitrogen fertilizer applied before sowing produces higher yields than the existing pre-sowing-topdressing split, provided the application rate is not so high that it leads to cold damage or lodging. Yield, biomass and N uptake varied greatly between the 84 crops, but the mean apparent N recovery of 50–60% and the N-use efficiency of 22 kg grain kg N−1 were high by commercial standards. Of the 18 soil-N tests, the one most closely correlated to crop productivity used anaerobic incubation for 21 days at 40 °C. The standard error of this test for predicting crop biomass was at least 74 kg N ha−1, which is double the standard error of the current plant test and is unacceptably high for providing recommendations. A comparison of commercial N-application rates and the economic optimum rates showed that ricegrowers tended to overfertilize crops growing on the most fertile fields and underfertilize crops growing on the least fertile fields. Suggested strategies to increase N-use efficiency are to improve the accuracy of the soil test by considering factors such as the status of other nutrients, to inform ricegrowers of their tendency to overfertilize high-yielding crops and underfertilize low-yielding crops, and use a soil test only to discourage overfertilization.
When monitoring microbial biomass and activities in soil, the storage conditions of the soil samples prior to analysis may be decisive for the results. Studies made on freshly collected soils are generally preferred but these are not always possible for practical reasons, since sampling is often restricted to short periods of the year, e.g. due to climatic conditions. The most commonly used methods to store soils for microbiological analyses are refrigeration or freezing of field moist soil. There are, however, studies that warn against any kind of storage, although other studies do not indicate any drawbacks to it. We have compared the microbial biomass and activities in 12 different, annually frozen, agricultural mineral soils when fresh and when stored at +2±2°C and at −20±2°C for 1 d and for 1, 3, 6 and 13 months. The results showed that the effects of freezing generally were smaller than those of refrigeration. The biomass estimated by chloroform fumigation–extraction and biomass index estimated by substrate induced respiration differed in that chloroform fumigation extracted carbon had decreased with 27% after 3 months at +2°C, while substrate induced respiration showed only small deviations from the results from fresh soils. Basal respiration rate and potential denitrification activity showed a similar pattern, with a pronounced decrease in values for refrigerated soils. The nitrogen mineralisation capacity was the only measure that was greatly influenced by freezing. After 6 months N mineralisation in the frozen soils was 25% higher than that of the fresh soils. Potential ammonium oxidation and the degradation rate of the herbicide linuron were affected only a little or not at all by storage for 13 months. We concluded that storage at −20°C for 13 months does not affect the microflora in annually frozen soils in any decisive way. We have also discussed the possible reasons for the contradictory results between different studies made on storage effects.
The reflectance spectra of organic matter in the VIS-NIR-SWIR regions (400–2500 nm) were investigated with regard to possible changes that might occur during a biological decomposition process. Two different groups of organic matter were used in this study: a grape mart (CGM) and a separated cattle manure (CSM) that simulated pure organic matter endmembers in soils. Exposing the two materials for different decomposition durations (0–378 days) visually yielded color sequences as the compost aged. Significant changes in the reflectance spectra of both materials were also observed during the composting period, which provided parameters for controlling the composting process. The slopes in the VIS-NIR region were found to be basic parameters for monitoring changes and were found to be highly correlated with other chemical parameters often used for assessing organic matter conditions in the field (such as the CIN ratio). It was found that during the initial composting stage (0–60 days) the slope parameters were strongly affected by the decomposition activity and, hence, errors in the assessment of organic matter content of soils using slope (or band ratio) parameters are likely. Careful observation of the major spectral features reveals that the reflectance spectrum in the VIS-NIR-SWIR region is a very sensitive tool for monitoring slight changes. Application of the near-infrared analysis (NIRA) pathways revealed that OH and CH groups combined with hygroscopic water, starch, cellulose, and lignin are the components having the highest correlations with composting time within the conditions used. Because of the small number of samples in each testing group a complete NIRA employing validation tests could not be carried out. We concluded that the reflectance spectrum in the VIS-NIR-SWIR region is a promising tool for monitoring the composting process and that the composting process may provide invaluable spectral information about soil organic matter during its biochemical degradation.
The near infrared analysis (NIRA) approach was studied to examine its capability for predicting spectral feature soil properties from the reflectance curves in the near infrared (NIR) region (1-2.5 μm) of arid and semiarid soils. High-resolution diffuse reflectance spectra (3113 spectral points) in the NIR region were recorded for 91 soil samples from Israel. It was concluded that NIRA is a promising method for rapid and nonrestrictive analysis of soil materials, and further study of the synergism between NIRA and soil materials is recommended. -from Authors
Near-infrared reflectance analysis (NIRA) is a "sleeper" among spectroscopic techniques. David Wetzel of Kansas State University discusses the theory and practice of NIRA.
In attempting to analyze, on digital computers, data from basically continuous physical experiments, numerical methods of performing familiar operations must be developed. The operations of differentiation and filtering are especially important both as an end in themselves, and as a prelude to further treatment of the data. Numerical counterparts of analog devices that perform these operations, such as RC filters, are often considered. However, the method of least squares may be used without additional computational complexity and with considerable improvement in the information obtained. The least squares calculations may be carried out in the computer by convolution of the data points with properly chosen sets of integers. These sets of integers and their normalizing factors are described and their use is illustrated in spectroscopic applications. The computer programs required are relatively simple. Two examples are presented as subroutines in the FORTRAN language.
An evaluation of incubation and chemical methods of obtaining an index of soil N availability showed that incubation procedures involving estimation of the total mineral N produced on incubation of soil under aerobic conditions at 30C for 14 days or estimation of the ammonium N produced on incubation of soil under waterlogged conditions at 40C for 7 days provided a good index of the availability of the N in Iowa soils to ryegrass. It also showed that the results obtained with these soils by a chemical procedure involving estimation of the N extracted by boiling water and potassium sulfate solution were closely related to N uptake by ryegrass and indicated that this procedure deserves consideration as a routine method of obtaining an index of soil N availability. Air-drying and air-dry storage of soil samples had marked effects on the results obtained by the incubation methods studied, but had little effect on the results obtained by the chemical methods found to provide a good index of soil N availability.
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MANY methods of obtaining an index of the availability of soil nitrogen to plants have been proposed, but it is generally accepted that the most satisfactory methods currently available are those involving estimation of the mineral nitrogen formed when soil is incubated under conditions which promote mineralization of the organic forms of soil nitrogen by soil micro-organisms1,2. Numerous incubation techniques have been used for estimation of mineralizable soil nitrogen, but methods involving determination of the nitrate produced by incubation under aerobic conditions at 28°–35° C for 2–4 weeks have been generally preferred2. However, recent work has shown that these nitrate-product ion methods are open to serious criticisms, and that the total mineral nitrogen produced during incubation should be determined2. Also, the use of aerobic incubation techniques is complicated by problems in establishment and maintenance of satisfactory conditions of incubation1,2. In an attempt to eliminate these problems and to develop an incubation method of estimating mineralizable nitrogen that is suitable for routine analysis of soils, we have investigated the possibility that methods involving estimation of the ammonium produced by incubation of soil under waterlogged conditions may provide an index of nitrogen availability. This investigation led to development of the following method of assessing mineralizable soil nitrogen:
A low cost strategy for objective and rapid selection of soil samples from a large population was evaluated. The purpose of the strategy was to retain a maximum of the original variation in important soil properties with only a small selection of samples. The evaluation was made with emphasis on clay content, soil organic matter, cation exchange capacity, and base saturation, all of which are important factors for biochemical activities in the soil and, therefore, for soil fertility. The strategy involved use of near infrared (NIR) spectroscopy combined with principal component analysis (PCA). A 2-nm interval spectrum between 1300 and 2398 nm was recorded on 146 air-dried soil samples from the most important cultivated areas in Sweden. The samples were considered mainly Cambisols and Regosols. The first derivative of each NIR spectrum was used for PCA. Twenty soils were selected by visual examination of two-dimensional score-plots from PCA. Score-plots were made from NIR data alone, from NIR data combined with pH, and from the eight significant score vectors from PCA on NIR data, combined with pH. Two criteria for selection from these plots were applied: (i) one sample from each apparent group was selected and (ii) samples evenly distributed at the periphery of the total sample population, and one in the center, were selected. In all, six selections were made. The distributions in soil properties in the selections were compared with random selection and with the original population. It was clear that NIR could help to improve the diversity in sample selections compared with random selection. In general, peripheral selections generated a higher recovery of range and a more even distribution in soil parameters than cluster selections. For clay content and cation exchange capacity, PCA on NIR data alone gave the best results, but to improve the distribution in pH and the pH-dependent base saturation, pH had to be included in PCA. To select soil samples that are distributed in all five soil parameters to the best extent possible, we propose peripheral selection from a two-dimensional PCA plot calculated from score vectors and pH data. In the present study, this method would have reduced costs about 70% compared with wet chemistry analyzes.
(C) Williams & Wilkins 1995. All Rights Reserved.
The principal properties, here called the ρ-scales, of peat have been calculated on the basis of chemical analysis. The scales were derived from quantitative contents of carbohydrates, Klason lignin, amino acids, amino sugars and conventional chemical peat measurements. The variation in the chemical parameters was compressed using principal component analysis (PCA). Partial least squares (PLS) regression was used for prediction of botanical, microbial, physical and dewatering data. A rapid estimation of the scales has been made from near-infrared (NIR) spectroscopy and offers, indirectly, rapidly obtainable, chemically interpretable, biological information. A reduced scale based on carbohydrate data was also tested. The ρ-scales offer an interface between different areas of peat research. Strategies are outlined for the selection of a subset of chemical measurements among the variables used for characterization. A multivariate strategy based on these ideas is discussed.
Soil respiration rates before and during glucose decomposition in samples from the A0 horizon of podzolized forest soils were monitored with computerized equipment. Near infrared reflectance (NIR) spectra of the organic matter composition of the same samples were used to model the variation in basal respiration rate, substrate induced respiration (SIR) and lag time. Basal respiration rate was determined for intact sample cores, after sieving the samples, and after adjustment of the water content to 2.5 times the organic matter content. Multivariate regression with the Partial Least Squares algorithm was used in the modelling. Before sieving, 81.8% of the variation in basal respiration could be modelled by 15 NIR wavelengths measured on freeze-dried and milled soil. After sieving, 93.0–97.7% of the variation was explained by the same 15 wavelengths, which also explained 88.4% of the variation in SIR. 93.1% of the variation in lag time could be modelled by 83 NIR wavelengths measured on soil with a water content of 2.5 times the organic matter content.
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