Canopy Visible and Near‐infrared Reflectance Data to Estimate Alfalfa Nutritive Attributes Before Harvest

Starks, Patrick J.; Brown, Michael A.; Turner, Kenneth E.; Venuto, Bradley C.

doi:10.2135/cropsci2015.03.0162

Cited by 13 publications

(6 citation statements)

References 20 publications

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“…Kawamura et al [19] selected 167 narrow bands (FWHM = 1 nm) for CP m estimation, while this study indicates that eleven broad bands (FWHM = 10 nm) have an equivalent or superior performance (Figure 8). Our results are also in agreement with previous studies [46], both when indicating the best-predicting spectral range (400-1100 nm) and the comparable prediction accuracy. Equally as important, for in-field conditions, the SWIR region did not consistently improve protein estimations, either when expressed as CP m or as %CP (Figure 8-SWIR and FS), which does not justify its use for CP estimation.…”

Section: Discussionsupporting

confidence: 93%

Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level

et al. 2020

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Crude protein estimation is an important parameter for perennial ryegrass (Loliumperenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model’s prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350–2500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400–1100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha−1). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha−1). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5–3.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 > 0.5), rendering them only suitable for qualitative grading.

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Section: Discussionsupporting

confidence: 93%

Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level

et al. 2020

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“…Traditional NIR devices have a benchtop‐type configuration, high resolution and associated high cost, and they have been mainly used by trained personnel working in commercial and research laboratories. Recent technological advances brought to market a variety of portable spectroscopy devices (Crocombe, 2018) that are more affordable and could potentially be a tool in the hands of farmers or consultants for in situ analysis (Pérez‐Marín, Paz, Guerrero, Garrido‐Varo, & Sánchez, 2010; Starks, Brown, Turner, & Venuto, 2015; Warburton, Brawner, & Meder, 2014). A great deal of literature has reported successful development of NIR models to predict many traits of several forage species including prediction of chemical constituents (Saha et al., 2018), nutritive value (Burns, Fisher, & Rottinghaus, 2006), ethanol yield (Vogel et al., 2011), ergot alkaloid concentration (Roberts, Benedict, Hill, Kallenbach, & Rottinghaus, 2005), and botanical composition (Coleman, Christiansen, & Shenk, 1990; Karayilanli, Cherney, Sirois, Kubinec, & Cherney, 2016).…”

Section: Introductionmentioning

confidence: 99%

Comparison of benchtop and handheld near‐infrared spectroscopy devices to determine forage nutritive value

2020

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The quality of predicted plant‐, soil‐, and animal‐response values from near‐infrared (NIR) reflectance spectra depends on the ability to generate appropriate NIR models. The first step in the development of NIR models is collection of spectral data. Limited work, however, has been reported that compares NIR models for prediction of forage nutritive value when the spectra are obtained from devices with different spectral ranges and resolutions. The objectives of this study were to (a) develop and evaluate NIR spectroscopy models using a benchtop‐type (FOSS) and two handheld NIR devices (microPHAZIR and DLP NIRscan Nano EVM) to predict crude protein (CP), acid detergent fiber (ADF), amylase and sodium sulfite‐treated neutral detergent fiber (aNDF), and in vitro true dry matter digestibility (IVTD) of dried ground forage grass samples and (b) compare predictions among the three NIR devices. Switchgrass (Panicum virgatum L.) and bermudagrass [Cynodon dactylon (L.) Pers] hay samples were scanned with the NIR devices and analyzed with wet chemistry for development of NIR prediction models. Among devices, the r2 of validation values for aNDF models ranged from .81 to .87; all other r2 values were >.86 and as high as .98 with standard error of prediction (SEP; g kg−1) ranging from 8.1 to 11.5 for CP, 19.1 to 23.8 for aNDF, 14.2 to 20.0 for ADF, and 26.8 to 49.9 for IVTD. The FOSS benchtop NIR prediction models consistently had the highest r2 and lowest SEP values; however, the predictive power for both handheld devices was similar to the benchtop‐type device.

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“…Forage quality was determined by estimating relative forage value (RFV). The RFV is used to rank forages relative to the nutritive value of alfalfa harvested at full bloom, which has a RFV value of 100 (Starks, Brown, Turner, & Venuto, 2015) and is used as an indicator of forage quality (Holman, Thompson, Hale, & Schlegel, 2010). The RFV was calculated using NIRS predicted parameters and the following equation: RFV = DDM (% of dry matter) × DMI (% of body weight)/1.29, where DDM is digestible dry matter, DMI is dry matter intake, and both estimates are calculated using ADF and NDF values previously determined with NIRS (Saha, Hancock, & Kissel, 2017).…”

Section: Methodsmentioning

confidence: 99%

Differences among eighteen winter pea genotypes for forage and cover crop use in the southeastern United States

et al. 2021

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Winter pea (Pisum sativum L.) can be used as a forage and cover crop in the southeast and mid-Atlantic United states; however, minimal effort has been devoted to optimize winter pea genetics for forage and cover crop production in these regions. Studies were conducted from 2015-2017 in Maryland and North Carolina screening 18 winter pea genotypes for forage and cover crop use. Winter pea genotypes were compared with widely grown crimson clover (Trifolium incarnatum L.) and hairy vetch (Vicia villosa Roth]. All legume genotypes were harvested across four timings. Legume winter hardiness, disease incidence, biomass production, quality, and N release were estimated. Winter hardiness was severe with many winter pea genotypes at the Maryland environments, which restricted winter pea biomass production. There was considerable variation for disease incidence among the winter pea genotypes depending on biotic stressors at each environment. At the North Carolina environments, several winter pea genotypes produced similar biomass to crimson clover and hairy vetch across harvest timings. At the Maryland environments, crimson clover and hairy vetch biomass exceeded winter pea biomass. The winter pea genotypes varied considerably for quality traits including protein, lignin, and cellulose. Relative forage value declined as biomass harvest was delayed and was generally higher with all winter pea genotypes than crimson clover or hairy vetch. These results show wide genetic variation in the winter pea genotypes screened for biomass and quality; this variation could be utilized in breeding efforts to enhance winter pea production in the region. 1 INTRODUCTION Cover crop adoption is increasing in the U.S. southeast region, with some states having surpassed 10% of cropland acreage planted with cover crops (USDA-National Agricultural Statistics Service, 2016; Wayman et al., 2016). Farm-Abbreviations: ADF, acid detergent fiber; NDF, neutral detergent fiber; NIRS, near-infrared spectroscopy; RFV, relative forage value.

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Canopy Visible and Near‐infrared Reflectance Data to Estimate Alfalfa Nutritive Attributes Before Harvest

Cited by 13 publications

References 20 publications

Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level

Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level

Comparison of benchtop and handheld near‐infrared spectroscopy devices to determine forage nutritive value

Differences among eighteen winter pea genotypes for forage and cover crop use in the southeastern United States

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