Assessment of the protein quality of 15 new northern adapted cultivars of quality protein maize using amino acid analysis

Amino acid determinations were carried out on 15 new northern adapted cultivars of quality protein maize (QPM) containing opaque-2 modifier genes to ascertain whether their amino acid scoring patterns could be used to select high-lysine QPM genotypes and to assess their protein quality. Total protein in these cultivars ranged from 8.0 to 10.2% compared to two commercial maize varieties, Dekalb DK435 (7.9%) and Pioneer 3925 (10.3%). Four of these QPM genotypes, QPM-C26, QPM-C21, QPM-C79, and QPM-C59, contained high levels of lysine (4.43-4.58 g of lysine/100 g of protein), whereas the remaining varied from 3.43 to 4.21 g of lysine/100 g of protein, compared to Dekalb DK435 and Pioneer 3925, which contained 2.9 and 3. 1 g of lysine/100 g of protein, respectively. Although lysine is the first limiting amino acid in QPM inbreds, the high-lysine QPM genotypes may supply approximately 70.2-72.6% of human protein requirements, compared to 46.2% for Dekalb DK435 and 50.1% for Pioneer 3925, 55-63% for oats, and 59-60.3% for barley. Northern adapted QPM genotypes may have the potential to increase their lysine content even further, either by an increase in specific high-lysine-containing nonzein proteins, such as the synthesis of factor EF-1a, or by a further reduction in the 19 and 22 kDa alpha-zein in the endosperm or both. This knowledge could assist maize breeders in the selection of new high-performance QPM genotypes with improved protein quality and quantity.     

近红外光谱法测定玉米秸秆饲用品质

为了对玉米秸秆的饲用品质进行可靠、便捷、快速的分析和评价,该研究以不同品种、密度、氮肥和水分处理的不同发育时期和不同部位玉米秸秆为试验材料,应用近红外光谱（NIRS）技术和偏最小二乘法（PLS）,采用一阶导数+中心化+多元散射校正的光谱数据预处理方法,构建了玉米秸秆体外干物质消化率（IVDMD）、酸性洗涤纤维（ADF）、中性洗涤纤维（NDF） 和可溶性糖（WSC）含量的NIRS分析模型。所建立的IVDMD、ADF、NDF和WSC含量的NIRS校正模型决定系数（R2cal）分别为0.9906、0.9870、0.9931和0.9802,交叉验证决定系数（R2cv）分别为0.9593、0.9413 、0.9678和0.9342,外部验证决定系数（R2val）分别为0.9549、0.9353、0.9519和0.9191,各项标准差（SEC、SECV和SEP）为0.935～1.904,相对分析误差（RPD）均大于3。结果表明,各参数的NIRS分析模型可用于玉米秸秆饲用品质的分析和品种选育的快速鉴定。     

Quality protein maize: a biochemical study of enzymes involved in lysine metabolism.

Quality protein maize (QPM) varieties have been produced by the introduction of opaque-2 modifier genes. Two QPM varieties, BR451 and BR473, a wild type and an opaque-2 variety, have been used to study key enzymes controlling lysine metabolism in the endosperm during development. Aspartate kinase and homoserine dehydrogenase enzymes, which are involved in lysine and threonine biosynthesis, respectively, exhibited identical activity patterns during endosperm development, with a maximum specific activity at 16 days after pollination. The QPM varieties exhibited higher levels of aspartate kinase activity in the endosperm, suggesting an increased rate of lysine biosynthesis when compared to the opaque-2 and wild-type genotypes. Similar results were observed for the lysine ketoglutarate reductase and saccharopine dehydrogenase enzymes, which form a single bifunctional polypetide involved in endosperm lysine degradation. Both enzyme activities were strongly reduced in the opaque-2 maize variety when compared to the wild-type maize, whereas the QPM varieties exhibited even lower levels of lysine ketoglutarate reductase-saccharopine dehydrogenase activities when compared to the opaque-2 variety. The developmental pattern of enzyme activity showed a different profile when compared to the enzymes involved in lysine biosynthesis, with activity being detected only 12-16 days after pollination (DAP) and maximum activities approximately 24 DAP. These results also suggest that the modifier genes have intensified the effect of the opaque-2 mutation on lysine ketoglutarate reductase-saccharopine dehydrogenase. These alterations lead to an increase in soluble lysine in the endosperm of the QPM varieties when compared to the opaque-2 and wild type.     

Near-Infrared Reflectance Spectroscopy (NIRS) assessment of δ(18)O and nitrogen and ash contents for improved yield potential and drought adaptation in maize

The oxygen isotope composition (δ(18)O), accumulation of minerals (ash content), and nitrogen (N) content in plant tissues have been recently proposed as useful integrative physiological criteria associated with yield potential and drought resistance in maize. This study tested the ability of near-infrared reflectance spectroscopy (NIRS) to predict δ(18)O and ash and N contents in leaves and mature kernels of maize. The δ(18)O and ash and N contents were determined in leaf and kernel samples from a set of 15 inbreds and 18 hybrids grown in Mexico under full irrigation and two levels of drought stress. Calibration models between NIRS spectra and the measured variables were developed using modified partial least-squares regressions. Global models (which included inbred lines and hybrids) accurately predicted ash and N contents, whereas prediction of δ(18)O showed lower results. Moreover, in hybrids, NIRS clearly reflected genotypic differences in leaf and kernel ash and N contents within each water treatment. It was concluded that NIRS can be used as a rapid, cost-effective, and accurate method for predicting ash and N contents and as a method for screening δ(18)O in maize with promising applications in crop management and maize breeding programs for improved water and nitrogen use efficiency and grain quality.     

基于近红外光谱的灌浆期玉米籽粒水分小样本定量分析

玉米灌浆期含水率测定是考种育种的重要指标。为了节约样本且快速准确测定灌浆期玉米水分,该文应用近红外光谱技术,提出了基于小样本条件下的自举算法(Bootstrap)与基于x-y距离结合的样本划分方法(SPXY,sample set partitioning based on joint x-y distances)相结合的样本优化方法的偏最小二乘(PLS,partial least square)水分定量分析模型Bootstrap-SPXY-PLS模型。试验结果表明,当Bootstrap重抽样本次数等于500,样本数量大于等于10时,模型的性能稳定,并且随着样本数量增加,重抽样本次数相对减少;样本数量为10和50时,全谱Bootstrap-SPXY-PLS模型的预测均方根误差(RMSEP,root-mean-square error of prediction)均值分别为0.38%和0.40%,预测相关系数(correlation coefficients of prediction)分别为0.975 1和0.968 5,决定系数R~2分别为0.999 9和0.993 6;基于竞争性自适应重加权采样算法(CARS,competitive adaptive reweighed sampling)波长变量筛选后的CARS-Bootstrap-SPXY-PLS模型的预测均方根误差RMSEP均值分别为0.36%和0.35%,预测相关系数分别为0.973 6和0.975 0,模型决定系数R~2分别为0.924 5和0.918 0。因此,全谱Bootstrap-SPXY-PLS模型和CARS-Bootstrap-SPXY-PLS模型均具有稳定的预测能力,为玉米育种时灌浆期种子水分测定提供了一种稳定、高效的方法。     

Estimating hydrogen cyanide in forage sorghum ( Sorghum bicolor ) by near-infrared spectroscopy

Hydrogen cyanide (HCN) is a toxic chemical that can potentially cause mild to severe reactions in animals when grazing forage sorghum. Developing technologies to monitor the level of HCN in the growing crop would benefit graziers, so that they can move cattle into paddocks with acceptable levels of HCN. In this study, we developed near-infrared spectroscopy (NIRS) calibrations to estimate HCN in forage sorghum and hay. The full spectral NIRS range (400-2498 nm) was used as well as specific spectral ranges within the full spectral range, i.e., visible (400-750 nm), shortwave (800-1100 nm) and near-infrared (NIR) (1100-2498 nm). Using the full spectrum approach and partial least-squares (PLS), the calibration produced a coefficient of determination (R(2)) = 0.838 and standard error of cross-validation (SECV) = 0.040%, while the validation set had a R(2) = 0.824 with a low standard error of prediction (SEP = 0.047%). When using a multiple linear regression (MLR) approach, the best model (NIR spectra) produced a R(2) = 0.847 and standard error of calibration (SEC) = 0.050% and a R(2) = 0.829 and SEP = 0.057% for the validation set. The MLR models built from these spectral regions all used nine wavelengths. Two specific wavelengths 2034 and 2458 nm were of interest, with the former associated with C═O carbonyl stretch and the latter associated with C-N-C stretching. The most accurate PLS and MLR models produced a ratio of standard error of prediction to standard deviation of 3.4 and 3.0, respectively, suggesting that the calibrations could be used for screening breeding material. The results indicated that it should be feasible to develop calibrations using PLS or MLR models for a number of users, including breeding programs to screen for genotypes with low HCN, as well as graziers to monitor crop status to help with grazing efficiency.     

Application of near-infrared reflectance spectroscopy (NIRS) to the evaluation of carotenoids content in maize

The importance of including antioxidant compounds in the diet is well recognized. These compounds remediate the detrimental activity on animal cells of the so-called reactive oxygen substances (ROS). Many papers have reported on the determination of both hydrophilic and hydrophobic antioxidant compounds present in a large number of vegetables, and all methods involve the extraction from the matrix of the compounds to be determined. Because some problems may arise, such as the completeness of the extraction and the stability of the extracted compound during the extraction steps, the possibility of analyzing these compounds in the native matrix would be useful. Here is reported the application of near-infrared spectroscopy (NIRS) to the determination of the content of carotenoids in maize, comparing the obtained data with those derived from high-performance liquid chromatography (HPLC) determination of the extract obtained from the same samples. Equations for predicting carotenoid content in maize were derived using scores from modified partial least-squares (MPLS) as independent variables. Cross-validation procedures indicated good correlations between HPLC values and NIRS estimates. The results show that NIRS, a well-established and widely applied technique, can be applied to determine the maize carotenoids and that samples are readily analyzed in minutes, the only required step being their grinding.     

Nondestructive prediction of total phenolics, flavonoid contents, and antioxidant capacity of rice grain using near-infrared spectroscopy

Phytochemicals such as phenolics and flavonoids, which are present in rice grains, are associated with reduced risk of developing chronic diseases such as cardiovascular disease, type 2 diabetes, and some cancers. The phenolic and flavonoid compounds in rice grain also contribute to the antioxidant activity. Biofortification of rice grain by conventional breeding is a way to improve nutritional quality so as to combat nutritional deficiency. Since wet chemistry measurement of phenolic and flavonoid contents and antioxidant activity are time-consuming and expensive, a rapid and nondestructive predictive method based on near-infrared spectroscopy (NIRS) would be valuable to measure these nutritional quality parameters. In the present study, calibration models for measurement of phenolic and flavonoid contents and antioxidant capacity were developed using principal component analysis (PCA), partial least-squares regression (PLS), and modified partial least-squares regression (mPLS) methods with the spectra of the dehulled grain (brown rice). The results showed that NIRS could effectively predict the total phenolic contents and antioxidant capacity by PLS and mPLS methods. The standard errors of prediction (SEP) were 47.1 and 45.9 mg gallic acid equivalent (GAE) for phenolic content, and the coefficients of determination ( r (2)) were 0.849 and 0.864 by PLS and mPLS methods, respectively. Both PLS and mPLS methods gave similarly accurate performance for prediction of antioxidant capacity with SEP of 0.28 mM Trolox equivalent antioxidant capacity (TEAC) and r (2) of 0.82. However, the NIRS models were not successful for flavonoid content with the three methods ( r (2) < 0.4). The models reported here are usable for routine screening of a large number of samples in early generation screening in breeding programs.     

Production and Nutritional Evaluation of Liquefied Weaning Foods from Malted Sorghum,Quality Protein Maize,and Other Cereals

Different concentrations of sorghum diastatic malt (SDM) were added to pregelatinized pastes from regular maize flour with the aim of hydrolyzing the starch to produce liquefied foods with 15% solids. Viscosities of the blends decreased as the concentration of SDM increased. Addition of 6.66% SDM based on total amount of solids reduced viscosity by ≈50% when compared with a food that did not contain any SDM. Addition of 33.3 or 46.6% SDM reduced viscosity by ≈70 or 75%, respectively. Most of the reduction in viscosity occurred within 1–3 min of incubation with warm water. Weanling rats were fed a combination 33.3% SDM and 66.6% of either quality protein maize (QPM), regular maize (RMZ) or decorticated pearl millet (DPM) to estimate protein efficiency ratios (PER), protein digestibility, biological value (BV), and net protein utilization (NPU). Rat growth was positively correlated with dietary lysine content and essential amino acid (EAA) scores; therefore, animals fed QPM weanling food had significantly higher (P < 0.05) protein digestibility corrected EAA scores, PER, BV, and NPU than counterparts fed diets based on RMZ or DPM. This demonstrates that it is feasible to produce nutritious liquefied weaning foods blending 33.3% SDM with 66.6% QPM using simple processing techniques.     

Nondestructive determination of lignans and lignan glycosides in sesame seeds by near infrared reflectance spectroscopy

Sesame (Sesamum indicum L.) contains abundant lignans including lipid-soluble lignans (sesamin and sesamolin) and water-soluble lignan glycosides (sesaminol triglucoside and sesaminol diglucoside) related to antioxidative activity. In this study, near infrared reflectance spectroscopy (NIRS) was used to develop a rapid and nondestructive method for the determination of lignan contents on intact sesame seeds. Ninety-three intact seeds were scanned in the reflectance mode of a scanning monochromator. This scanning procedure did not require the pulverization of samples, allowing each analysis to be completed within minutes. Reference values for lignan contents were obtained by high-performance liquid chromatography analysis. Calibration equations for lignans (sesamin and sesamolin) and lignan glycosides (sesaminol triglucoside and sesaminol diglucoside) contents were developed using modified partial least squares regression with internal cross-validation (n = 63). The equations obtained had low standard errors of cross-validation and moderate R2 (coefficient of determination in calibration). The prediction of an external validation set (n = 30) showed significant correlation between reference values and NIRS predicted values based on the SEP (standard error of prediction), bias, and r2 (coefficient of determination in prediction). The models developed in this study had relatively higher values (more than 2.0) of SD/SEP(C) for all lignans and lignan glycosides except for sesaminol diglucoside, which had a minor amount, indicating good correlation between the reference and the NIRS estimate. The results showed that NIRS, a nondestructive screening method, could be used to rapidly determine lignan and lignan glycoside contents in the breeding programs for high quality sesame.     

Analysis of dry matter and protein contents in fresh yam bean tubers by near‐infrared reflectance spectroscopy

Abstract

Fast screening methods are needed for plant breeding. The objective of this research was to evaluate the potential of near‐infrared reflectance spectroscopy (NIRS) for the simultaneous analysis of dry matter and protein contents in intact discs of fresh yam bean (Pachyrhizus spp.) tubers. Discs from 210 tubers were extracted with a punch few hours after harvesting and scanned by NIRS using a specially designed adapter. External validation revealed a close relationship between NIRS and reference methods for dry matter content (r²=0.94; standard error of performance, SEP=1.2%) and protein content (r²=0.87; SEP=1.94%). The calibration for protein content was compared with another one developed using dried‐ground tuber samples (r²=0.97; SEP=0.97%). These results suggested that NIRS can be used to determine dry matter and protein contents in fresh tuber samples of yam beans with acceptable accuracy. Further research will have to determine if additional traits can be incorporated into this scheme.     

Rapid Prediction of Rice Quality Characteristics by Near‐Infrared Reflectance Spectroscopy for Breeding Programs

Breeding of high‐quality rice requires quick methods to evaluate the quality characteristics such as milling, grain appearance, nutritional, eating, and cooking qualities. Because routine measurements of these quality traits are time consuming and expensive, a rapid predictive method based on near‐infrared spectroscopy (NIRS) can be applied to measure these quality parameters. In this study, calibration models for measurement of grain quality were developed using a total of 570 brown and milled rice samples. The results indicated that the models developed from the spectra of brown rice for all the quality traits had the coefficient of determination for external validation (R²) larger than 0.64 except for gel consistency. The best model was developed for the protein content, with R² of 0.94 for external validation. The model for the total score of physicochemical characteristics (TSPC), a comprehensive index reflecting all other traits, had R² of 0.70 and SD/SEP of 1.70, which indicates that high or low TSPC for a given rice could be discriminated by NIRS. The models developed from brown rice were as accurate as those from milled rice. Results suggest that NIRS‐based predictions for rice quality traits may be used as indicator traits to improve rice quality in breeding programs.     

Determination of acid-detergent fiber and crude protein in forages by near-infrared reflectance spectroscopy: collaborative study

A collaborative study was conducted to determine the standard error of difference among laboratories for near-infrared reflectance spectroscopic (NIRS) determination of acid-detergent fiber (ADF) and crude protein in forages. The 6 participating laboratories were members of the USDA/ARS National Near-Infrared Reflectance Spectroscopy Forage Research Project. The NIRS calibration equations were developed in the Associate Referee's laboratory for crude protein and ADF and were transferred to the instrument in each of the other collaborating laboratories. The calibration set included over 650 diverse forage samples with crude protein and ADF calibration data; the validation set included 94 samples of bermudagrass. Among-laboratory reproducibility for the NIRS method, calculated as the relative standard deviation for reproducibility (RSDR), was 1.14% for ADF and 0.42% for crude protein. The variance component for among-laboratory variation (coefficient of variation) was 2.54% for ADF and 2.89% for crude protein. These results confirm that it is possible to calibrate, validate, and transfer (NIRS) equations and data among laboratories for the accurate determination of ADF and crude protein, and thereby demonstrate that NIRS can be used as a standard method for the analysis of forages. The method has been adopted official first action.     

Technological and Nutritional Properties of Flours and Tortillas from Nixtamalized and Extruded Quality Protein Maize (Zea mays L.)

Nixtamalized and extruded flours from quality protein maize (QPM, V‐537C) and tortillas made from them were evaluated for some technological and nutritional properties and compared with the commercial brand MASECA. Both QPM flours showed higher (P < 0.05) protein content, total color difference, pH, available lysine, and lower (P < 0.05) total starch content, Hunter L value, water absorption index, gelatinization enthalpy, resistant starch, and retrograded resistant starch than nixtamalized MASECA flour. Tortillas from nixtamalized and extruded QPM flours had higher contents of essential amino acids than tortillas from MASECA flour, except for leucine. Tortillas from processed QPM flours also showed higher (P < 0.05) values of the nutritional indicators calculated protein efficiency ratio (C‐PER 1.80–1.85 vs. 1.04), apparent and true in vivo protein digestibility (78.4‐79.1 vs. 75.6% and 76.4–77.4 vs. 74.2%, respectively), PER (2.30–2.43 vs. 1.31), net protein retention (NPR; 2.88–2.89 vs. 2.11), and protein digestibility corrected amino acid score (PDCAAS; 54–55 vs. 29% based on preschool children and 100 vs. 85% based on adults) than MASECA flour. The use of QPM for flour and tortilla preparation may have a positive effect on the nutritional status of people from countries where these products are widely consumed.     

Prediction of nutritive values,morphology and agronomic characteristics in forage maize using two applications of NIRS spectrometry

This study evaluates nutritive, morphological and agronomic characteristics of forage maize predicted by using a high-quality near-infrared (NIR) spectrometer and an NIR hyperspectral-imaging technique using partial least squares (PLS) regression models. The study includes 132 samples of dried milled whole-plant homogenates of forage maize with variation in maturity, representing two growing seasons, three locations in Sweden and three commercial maize hybrids. The samples were measured by a classical sample cup NIR spectrometer and by a pushbroom hyperspectral-imaging instrument. The spectra and a number of variables (crude protein, CP, neutral detergent fibre, starch, water soluble carbohydrates (WSC) and organic matter digestibility), morphological variables (leaves, stems & ears) and crop yield were used to make PLS calibration models. Using PLS modelling allowed the determination of how well maize variables can be predicted from NIR spectra and a comparison of the two types of instruments. Most examined variables could be determined equally well, by both instruments, but the pushbroom technique gave slightly better predictions and had higher analytical capacity. Predictions of CP, starch, WSC and the proportions of ears in the maize gave robust. The findings open new possibilities to further utilise the technology in plant breeding, crop management, modelling and forage evaluation.     

Parent and harvest year effects on near-infrared reflectance spectroscopic analysis of olive (Olea europaea L.) fruit traits

The influence of parent and harvest year on the determination of oil, moisture, oleic acid, and linoleic acid contents in intact olive fruit was studied by near-infrared spectroscopy (NIRS). Spectral data from 400 to 1700 nm were recorded on 437 fruit samples collected in 1996 and 1997 from seedling plants derived from three different female parents. Partial least squares models were developed using samples for each year and for each female parent separately and were validated against the other groups. Calibration models were accurate enough to predict all constituents in new samples from a different female parent but were not transferable across years. However, a calibration equation of sufficient accuracy was obtained from the combined data set (r values of 0.94, 0.93, 0.84, and 0.88 and RMSECV values of 1.33, 1.88, 4.73, and 2.91 for oil, moisture, oleic acid, and linoleic acid contents, respectively). These results demonstrate the utility of NIRS as a selection tool in olive breeding programs.     

Nondestructive determination of oil content and fatty acid composition in perilla seeds by near-infrared spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used as a rapid and nondestructive method to determine the oil content and fatty acid composition in intact seeds of perilla [Perilla frutescens var. japonica (Hassk.) Hara] germplasms in Korea. A total of 397 samples (about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for the oil content and fatty acid composition were measured by gravimetric method and gas-liquid chromatography, respectively. Calibration equations for oil and individual fatty acids were developed using modified partial least-squares regression with internal cross validation (n = 297). The equations for oil and oleic and linolenic acid had lower standard errors of cross-validation (SECV), higher R2 (coefficient of determination in calibration), and higher ratio of unexplained variance divided by variance (1-VR) values than those for palmitic, stearic, and linoleic acid. Prediction of an external validation set (n = 100) showed significant correlation between reference values and NIRS estimated values based on the standard error of prediction (SEP), r2 (coefficient of determination in prediction), and the ratio of standard deviation (SD) of reference data to SEP. The models for oil content and major fatty acids, oleic and linolenic acid, had relatively higher values of SD/SEP(C) and r2 (more than 3.0 and 0.9, respectively), thereby characterizing those equations as having good quantitative information, whereas those of palmitic, stearic, and linoleic acid had lower values (below 2.0 and 0.7, respectively), unsuitable for screening purposes. The results indicated that NIRS could be used to rapidly determine oil content and fatty acid composition (oleic and linolenic acid) in perilla seeds in the breeding programs for development of high-quality perilla oil.     

Development of NIRS equations for food grain quality traits through exploitation of a core collection of cultivated sorghum

A sorghum core collection representing a wide range of genetic diversity and used in the framework of a sorghum breeding and genetics program was evaluated by near-infrared reflectance spectroscopy (NIRS) to predict food grain quality traits: amylose content (AM), protein content (PR), lipid content (LI), endosperm texture (ET), and hardness (HD). A total of 278 sorghum samples were scanned as whole and ground grain to develop calibration equations. Laboratory analyses were performed on NIRS sample subsets that preserved the core collection racial distribution. Principal component analysis performed on NIRS spectra evidenced a level of structure following known sorghum races, which underlined the importance of using a wide range of genetic diversity. Performances of calibration equations were evaluated by the coefficient of determination, bias, standard error of laboratory (SEL), and ratio of performance deviation (RPD). Ground grain spectra gave better calibration equations than whole grain. PR equation (RPD of 5.7) can be used for quality control. ET, LI, and HD equations (RPD of 2.9, 2.6, and 2.6, respectively) can be used for screening steps. Even with a small SEL in whole sample analysis, a RPD of 1.8 for AM confirmed that this variable is not easy to predict with NIRS.     

Use of NIRS for quantification of mangiferin and hesperidin contents of dried green honeybush (Cyclopia genistoides) plant material

Cyclopia genistoides, normally used for the preparation of an herbal tea, honeybush, is a good source of the bio-active compounds mangiferin and hesperidin and is in demand for the preparation of xanthone-enriched extracts. Near-infrared spectroscopy (NIRS) was used to develop calibration models to predict the mangiferin and hesperidin contents of the dried green plant material. NIRS measurements of plant material and pure compounds were performed in diffuse reflectance mode. The calibration sets for mangiferin and hesperidin contents ranged from 0.7 to 7.21 and 0.64-4.80 g/100 g, respectively. Using independent validation, it was shown that the NIRS calibration models for the prediction of mangiferin (SEP=0.46 g/100 g; R2=0.74; and RPD=1.96) and hesperidin (SEP=0.38 g/100 g; R2=0.72; and RDP=1.90) contents of the dried plant material are adequate for screening purposes, based on RPD values.     

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

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