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

Hetta, Mårten; Mussadiq, Zohaib; Wallsten, Johanna; Halling, Magnus; Swensson, Christian; Geladi, Paul

doi:10.1080/09064710.2017.1278782

Cited by 10 publications

(11 citation statements)

References 15 publications

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“…It appears from the literature that calibrations based on a narrow range of species tend to have lower RPD values. Examples include 1.7 for grass silages (De Boever et al 1996), 1.8 for sagebrush and 2.3 for forage maize (Hetta et al 2017). In the present study, the RPD values for in vitro DMD of annual grasses, and mixed perennial grasses and legumes could be further improved with grouping before calibration development.…”

Section: Discussionmentioning

confidence: 93%

“…There are many examples of NIRS calibrations to predict the nutritional value of forages, such as whole cereal plants (Deaville et al 2009;Stubbs et al 2010), lucerne (alfalfa, Medicago sativa) (Halgerson et al 2004;Brogna et al 2009), perennial grasses (Myer et al 2011;Burns et al 2013), forage maize (Zea mays; Hetta et al 2017) and even woody forage shrubs, such as tagasaste (Cytisus proliferus; Flinn et al 1996) and sagebrush (Artemisia tridentata; Olsoy et al 2016). These examples are all characterised by narrow taxonomic diversity with only one or two plant species within the calibration set.…”

Section: Introductionmentioning

confidence: 99%

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Broad near-infrared spectroscopy calibrations can predict the nutritional value of >100 forage species within the Australian feedbase

et al. 2020

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Context Near-infrared reflectance spectroscopy (NIRS) is a tool that permits rapid and inexpensive prediction of the nutritional characteristics of forages consumed by ruminants. Aim Our aim was to investigate the feasibility of developing a NIRS calibration to predict the nutritional value of the majority of grasses, legumes and forbs that are utilised for sheep and cattle production in southern Australia. Methods More than 100 annual and perennial forage species were grown in replicated plots at two locations over a period of 3 years. Biomass was sampled every 3–6 weeks, dried, ground and scanned with a desktop NIRS machine (n = 4385). One-quarter of these samples were subjected to laboratory analysis for calibration development or validation. Key results Despite the large variation in the taxonomy and maturity of the plants when sampled, we successfully developed broad calibrations that predicted key nutritional traits. We achieved excellent predictions for crude protein, with a ratio of standard error of performance:standard deviation (RPD) of 5.3, and standard error of cross validation (SECV) of 1.06%. Predictions of neutral detergent fibre were also excellent (RPD 4.3, SECV. 3.5%). For pepsin–cellulase DM digestibility and acid detergent fibre, predictions were very good (RPD 3.7, SECV 2.6% and RPD 3.9, SECV 2.1%). Predictions for organic matter were less reliable (RPD 2.2). We achieved very promising predictions of methane production during batch culture fermentation (RPD 3.1, SECV 3.5 mL/gDM). Predictions of ammonia and total volatile fatty acid concentrations in the post-fermentation substrate were poor. Conclusions We found that the broad calibrations predicted the nutritional traits of annual grasses, annual legumes and forb species with greater accuracy than perennial grasses or legumes. This could be associated with the accuracy of the wet chemistry methods. As a general rule, separating taxonomically similar species into groups before the development of calibrations, did not lead to more accurate predictions. Implications If more spatial and temporal diversity can be built in without a large reduction in accuracy, these broad NIRS calibrations represent a valuable tool for Australian researchers, feed testing agents and livestock producers, as they encompass nearly all of the species that appear in monocultures or mixed swards.

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Broad near-infrared spectroscopy calibrations can predict the nutritional value of >100 forage species within the Australian feedbase

et al. 2020

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“…NIRS or other spectral techniques are most accurate when using dried and homogenized (i.e., milled) plant material. For example, starch has been accurately quantified on dried cotton leaves or dry forage maize using NIRS (R 2 > 0.9; Hattey et al, 1994;Hetta et al, 2017;Lu et al, 2017). Estimating chemical compounds with spectral measurements on fresh leaf tissue is often less reliable due to masking effects of light absorption by the cuticle or the leaf water content (Curran et al, 1992;Fourty and Baret, 1998).…”

Section: Introductionmentioning

confidence: 99%

A Non-destructive Method to Quantify Leaf Starch Content in Red Clover

et al. 2020

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Grassland-based ruminant livestock production provides a sustainable alternative to intensive production systems relying on concentrated feeds. However, grassland-based roughage often lacks the energy content required to meet the productivity potential of modern livestock breeds. Forage legumes, such as red clover, with increased starch content could partly replace maize and cereal supplements. However, breeding for increased starch content requires efficient phenotyping methods. This study is unique in evaluating a non-destructive hyperspectral imaging approach to estimate leaf starch content in red clover for enabling efficient development of high starch red clover genotypes. We assessed prediction performance of partial least square regression models (PLSR) using cross-validation, and validated model performance with an independent test set under controlled conditions. Starch content of the training set ranged from 0.1 to 120.3 mg g −1 DW. The best cross-validated PLSR model explained 56% of the measured variation and yielded a root mean square error (RMSE) of 17 mg g −1 DW. Model performance decreased when applying the trained model on the independent test set (RMSE = 29 mg g −1 DW, R 2 = 0.36). Different variable selection methods did not increase model performance. Once validated in the field, the non-destructive spectral method presented here has the potential to detect large differences in leaf starch content of red clover genotypes. Breeding material could be sampled and selected according to their starch content without destroying the plant.

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“…milled) plant material. For example, starch has been accurately quantified on dried cotton leaves or dry forage maize using NIRS ( R2 > 0.9) [19,[23][24][25]. Estimating chemical compounds with spectral measurements on fresh leaf tissue is often less reliable due to masking effects of light absorption by the cuticle or the leaf water content [26,27].…”

Section: Introductionmentioning

confidence: 99%

A non-destructive method to quantify leaf starch content in red clover

Frey

Baumann

Aasen

et al. 2020

Preprint

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Background: Grassland-based ruminant livestock production provides a sustainable alternative to intensive production systems relying on concentrated feeds. However, grassland-based roughage often lacks the energy content required to meet the productivity potential of modern livestock breeds. Forage legumes, such as red clover, with increased starch content could partly replace maize and cereal supplements. However, breeding for increased starch content requires efficient phenotyping methods. This study is unique in evaluating a non-destructive hyperspectral imaging approach to estimate leaf starch content in red clover for enabling efficient development of high starch red clover genotypes.Results: We assessed prediction performance of partial least square regression models (PLSR) and validated model performance with an independent test set. Starch content of the training set ranged from 0.1 to 120.3 mg g-1 DW. The best cross-validated PLSR model explained 56% of the measured variation and yielded a root mean square error (RMSE) of 17 mg g-1 DW. Model performance decreased when applied to the independent test set (RMSE = 29 mg g-1 DW, R2 = 0.36). Different filtering methods did not increase model performance.Conclusion: The non-destructive spectral method presented here, provides a tool to detect large differences in leaf starch content of red clover. Breeding material can be sampled and selected according to their starch content without destroying the plant.

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

Cited by 10 publications

References 15 publications

Broad near-infrared spectroscopy calibrations can predict the nutritional value of >100 forage species within the Australian feedbase

Broad near-infrared spectroscopy calibrations can predict the nutritional value of >100 forage species within the Australian feedbase

A Non-destructive Method to Quantify Leaf Starch Content in Red Clover

A non-destructive method to quantify leaf starch content in red clover

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