scholarly journals Prediction of Canola Residue Characteristics Using Near-Infrared Spectroscopy

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tami L. Stubbs ◽  
Ann C. Kennedy
Keyword(s):  
Infrared Spectroscopy ◽  
Pacific Northwest ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cropping Systems ◽  
Farming Systems ◽  
Neutral Detergent Fiber ◽  
Residue Decomposition ◽  
Spring Canola ◽  
The Pacific

Little work has been done to characterize and quantify the residue traits affecting decomposition of winter and spring canola (Brassica napus L.) residue in dryland farming systems of the Pacific Northwest United States. Traditional methods of characterizing residue fiber and nutrients are time-consuming and expensive and require large quantities of chemical reagents. The goal of this research was to determine whether near-infrared spectroscopy (NIRS) could accurately predict neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), carbon (C), and nitrogen (N) of canola stems, litter, and roots and decomposition of canola stems. Canola residue varied in decomposition, fiber, and nutrients by year, location, and type. NIRS predictions were successful for NDF and ADF in 2011 (standard error of prediction SEP<2.67; R2>0.95) and NDF, ADF, and N in 2012 (SEP<2.38; R2>0.91). Other predictions for residue fiber and nutrient characteristics were considered moderately successful. Prediction of canola residue decomposition with NIRS was useful for screening purposes. Near-infrared spectroscopy shows promise for rapidly and reproducibly predicting some canola residue fiber and nutrient traits and may be useful for estimating residue decomposition potential in dryland conservation cropping systems.

Prediction of neutral detergent fiber content in corn stover using near-infrared spectroscopy technique

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xuyang Pan ◽  
Laijun Sun ◽  
Guobing Sun ◽  
Panxiang Rong ◽  
Yuncai Lu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Prediction Model ◽  
Spectral Data ◽  
Corn Stover ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Fiber Content ◽  
Partial Least Square ◽  
Neutral Detergent Fiber

AbstractNeutral detergent fiber (NDF) content was the critical indicator of fiber in corn stover. This study aimed to develop a prediction model to precisely measure NDF content in corn stover using near-infrared spectroscopy (NIRS) technique. Here, spectral data ranging from 400 to 2500 nm were obtained by scanning 530 samples, and Monte Carlo Cross Validation and the pretreatment were used to preprocess the original spectra. Moreover, the interval partial least square (iPLS) was employed to extract feature wavebands to reduce data computation. The PLSR model was built using two spectral regions, and it was evaluated with the coefficient of determination (R2) and root mean square error of cross validation (RMSECV) obtaining 0.97 and 0.65%, respectively. The overall results proved that the developed prediction model coupled with spectral data analysis provides a set of theoretical foundations for NIRS techniques application on measuring fiber content in corn stover.

scholarly journals Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 666
Author(s):  
Rafael Font ◽  
Mercedes del Río-Celestino ◽  
Diego Luna ◽  
Juan Gil ◽  
Antonio de Haro-Bailón
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Cicer Arietinum ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Neutral Detergent Fiber ◽  
Acid Detergent Fiber ◽  
Cicer Arietinum L

The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.

scholarly journals Near-Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures

2021 ◽  
Vol 3 (1) ◽  
pp. 73-91
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
Ângelo Carapau ◽  
Ana Elisa Rato
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Optical Sensors ◽  
Near Infrared ◽  
Production Systems ◽  
External Validation ◽  
Neutral Detergent Fiber ◽  
Reference Laboratory ◽  
Pasture Quality ◽  
Animal Grazing

Dryland pastures provide the basis for animal sustenance in extensive production systems in Iberian Peninsula. These systems have temporal and spatial variability of pasture quality resulting from the diversity of soil fertility and pasture floristic composition, the interaction with trees, animal grazing, and a Mediterranean climate characterized by accentuated seasonality and interannual irregularity. Grazing management decisions are dependent on assessing pasture availability and quality. Conventional analytical determination of crude protein (CP) and fiber (neutral detergent fiber, NDF) by reference laboratory methods require laborious and expensive procedures and, thus, do not meet the needs of the current animal production systems. The aim of this study was to evaluate two alternative approaches to estimate pasture CP and NDF, namely one based on near-infrared spectroscopy (NIRS) combined with multivariate data analysis and the other based on the Normalized Difference Vegetation Index (NDVI) measured in the field by a proximal active optical sensor (AOS). A total of 232 pasture samples were collected from January to June 2020 in eight fields. Of these, 96 samples were processed in fresh form using NIRS. All 232 samples were dried and subjected to reference laboratory and NIRS analysis. For NIRS, fresh and dry samples were split in two sets: a calibration set with half of the samples and an external validation set with the remaining half of the samples. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantifying pasture quality parameters, with greater accuracy in dry samples (R2 = 0.936 and RPD = 4.01 for CP and R2 = 0.914 and RPD = 3.48 for NDF) than fresh samples (R2 = 0.702 and RPD = 1.88 for CP and R2 = 0.720 and RPD = 2.38 for NDF). The NDVI measured by the AOS shows a similar coefficient of determination to the NIRS approach with pasture fresh samples (R2 = 0.707 for CP and R2 = 0.648 for NDF). The results demonstrate the potential of these technologies for estimating CP and NDF in pastures, which can facilitate the farm manager’s decision making in terms of the dynamic management of animal grazing and supplementation needs.

scholarly journals Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem

2020 ◽  
Vol 10 (13) ◽  
pp. 4463
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
José Marques da Silva ◽  
Luís Paixão ◽  
Emanuel Carreira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Service Providers ◽  
Quality Parameters ◽  
Neutral Detergent Fiber ◽  
Pasture Quality ◽  
Reference Methods

Pasture quality monitoring is a key element in the decision making process of a farm manager. Laboratory reference methods for assessing quality parameters such as crude protein (CP) or fibers (neutral detergent fiber: NDF) require collection and analytical procedures involving technicians, time, and reagents, making them laborious and expensive. The objective of this work was to evaluate two technological and expeditious approaches for estimating and monitoring the evolution of the quality parameters in biodiverse Mediterranean pastures: (i) near infrared spectroscopy (NIRS) combined with multivariate data analysis and (ii) remote sensing (RS) based on Sentinel-2 imagery to calculate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). Between February 2018 and March 2019, 21 sampling processes were carried out in nine fields, totaling 398 pasture samples, of which 315 were used during the calibration phase and 83 were used during the validation phase of the NIRS approach. The average reference values of pasture moisture content (PMC), CP, and NDF, obtained in 24 tests carried out between January and May 2019 in eight fields, were used to evaluate the RS accuracy. The results of this study showed significant correlation between NIRS calibration models or spectral indices obtained by remote sensing (NDVIRS and NDWIRS) and reference methods for quantifying pasture quality parameters, both of which open up good prospects for technological-based service providers to develop applications that enable the dynamic management of animal grazing.

Diseases of wheat and barley in conservation cropping systems of the semiarid Pacific Northwest

1996 ◽  
Vol 11 (2-3) ◽  
pp. 95-103 ◽  
Author(s):  
Richard W. Smiley
Keyword(s):  
Soil Erosion ◽  
Disease Management ◽  
Pacific Northwest ◽  
Conservation Tillage ◽  
Cropping Systems ◽  
Farming Systems ◽  
Tillage Practices ◽  
Conservation Farming ◽  
The Pacific ◽  
Summer Fallow

AbstractDiseases continue to be important constraints in wheat and barley conservation cropping systems in the semiarid Pacific Northwest. Several diseases are more damaging in highthan low-residue seedbeds, and in crops planted during early autumn to reduce soil erosion during winter, especially unirrigated winter wheat in rotation with summer fallow in low rainfall zones (250–400 mm). Changes in cropping systems in the region have made disease management and maintenance of yield goals and farm profitability more challenging because disease management often is more complex and expensive with conservation tillage than inversion tillage. Practices being developed to meet this challenge are reviewed for diseases that are particularly trouble some in conservation farming systems of the Pacific Northwest.

Use of LOCAL algorithm with near infrared spectroscopy in forage resources for grazing systems in Colombia

2017 ◽  
Vol 26 (1) ◽  
pp. 44-52 ◽  
Author(s):  
C Ariza-Nieto ◽  
OL Mayorga ◽  
B Mojica ◽  
D Parra ◽  
G Afanador-Tellez
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Neutral Detergent Fiber ◽  
Local Algorithm ◽  
Forage Plants ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer ◽  
Global And Local

This study used a total of 2020 Colombian forage resources of three families (Grass forages, legume forages, and other forage plants) to develop near infrared spectroscopy calibrations for predicting the nutritional value. Spectra were collected at 2 nm increments using a scanning visible/near infrared spectrometer. The reference data used for each forage were crude protein, crude ash, neutral detergent fiber, acid detergent fiber, acid detergent lignin, measured according to the Association of Official Analytical Chemists. Two chemometric tools for developing near infrared spectroscopy prediction models were compared: the GLOBAL modified partial least squares, and the calibration strategy known as LOCAL. The LOCAL procedure is designed to select, from a large database, samples with spectra resembling the sample being analyzed. Selected samples were used as calibration sets after one-tenth of the samples were selected for validation from each database. Predictions of nutrition indicators in validation samples using generic and specific calibrations were compared with both GLOBAL and LOCAL procedures. For all predicted forages, LOCAL resulted in a significant improvement in both standard error of prediction and bias values compared with GLOBAL. Determination coefficient values (r2) also improved using the LOCAL algorithm, exceeding 0.9 for most forage sets. LOCAL calibration was then used with only one database (n 2020) comprising all the forage samples and SEP and r2 were similar to those obtained in the three databases using LOCAL algorithm. Therefore, LOCAL can accurately predict the composition of different forages using only one database, and could offer a practical way to develop robust equations taking into account the biodiversity of Colombian forages.

scholarly journals Use of near-infrared spectroscopy for prediction of chemical composition of Tifton 85 grass

2021 ◽  
Vol 42 (3) ◽  
pp. 1287-1302
Author(s):  
Camila Cano Serafim ◽  
◽  
Geisi Loures Guerra ◽  
Ivone Yurika Mizubuti ◽  
Filipe Alexandre Boscaro de Castro ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Chemical Composition ◽  
Spectral Data ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Neutral Detergent Fiber ◽  
Mineral Matter ◽  
Data Set

The reduction in the quality, consumption, and digestibility of forage can cause a decrease in animal performance, resulting in losses to the rural producer. Thus, it is important to monitor these characteristics in forage plants to devise strategies or practices that optimize production systems. The aim of this study was to develop and validate prediction models using near-infrared spectroscopy (NIRS) to determine the chemical composition of Tifton 85 grass. Samples of green grass, its morphological structures (whole plant, leaf blade, stem + sheath, and senescent material) and hay, totaling 105 samples were used. Conventional chemical analysis was performed to determine the content of oven-dried samples (ODS), mineral matter (MM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose (CEL), hemicellulose (HEM), and in vitro dry matter digestibility (IVDMD). Subsequently, all the samples were scanned using a Vis-NIR spectrometer to collect spectral data. Principal component analysis (PCA) was applied to the data set, and modified partial least squares was used to correlate reference values to spectral data. The coefficients of determination (R2) were 0.74, 0.85, 0.98, 0.75, 0.85, 0.71, 0.82, 0.77, and 0.93, and the ratio of performance deviations (RPD) obtained were 1.99, 2.71, 6.46, 2.05, 2.58, 3.84, 1.86, 2.35, 2.09, and 3.84 for ODS, MM, CP, NDF, ADF, ADL, CEL, HEM, and IVDMD, respectively. The prediction models obtained, in general, were considered to be of excellent quality, and demonstrated that the determination of the chemical composition of Tifton 85 grass can be performed using NIRS technology, replacing conventional analysis.

scholarly journals A single calibration of near-infrared spectroscopy to determine the quality of forage for multiple species

2021 ◽  
Vol 10 (10) ◽  
pp. e548101018990
Author(s):  
Caroline Massignani ◽  
Bruna Búrigo Vandresen ◽  
Júlia Vargas Marques ◽  
Ricardo Kazama ◽  
Milene Puntel Osmari ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Single Species ◽  
Neutral Detergent Fiber ◽  
Time Saving ◽  
Calibration Curves ◽  
Scattering Correction ◽  
Multiple Species

Near-infrared spectroscopy (NIRS) is an efficient and chemical-free technique for quickly assessing forage quality. However, calibration curves are usually validated for the forage of a single species, while few studies have reported on the forage of multiple species. Therefore, this work aimed to develop a broad system of calibrating curves by NIRS to predict neutral detergent fiber (NDF), acid detergent fiber (ADF) and crude protein (CP) values from single and mixed forage. To accomplish this, single and mixed forage (32 forage species) were sampled over six years (2013 to 2019) from different regions of Santa Catarina state in southern Brazil. Forage samples were chemically analyzed for NDF, ADF and CP levels, followed by performing spectroscopy. Next, calibration curves were calculated as Second Derivative for NDF, First Derivative + Multiplicative Scattering Correction for ADF, and, Multiplicative Scattering Correction for CP. Approximately 200 sample forage, resulted in determination coefficient (R2) values of 0.94, 0.95, and 0.98 and validation values of 0.94, 0.95, and 0.97 for NDF, ADF, and CP, respectively. Thus, calibration curves were properly developed for quality assessment of single or mixed forage for multiple species, resulting in a chemical-free and time-saving tool for routine laboratory use.

Sign in / Sign up

Export Citation Format

Share Document