Al-Ghumaiz, N. S.

Dietz, T. S.

Leep, R. H.

English

University of Kentucky

University of Kentucky UKnowledge International Grassland Congress Proceedings 21st International Grassland Congress / 8th International Rangeland Congress Predicting Botanical Composition of Grass‐Clover Pastures Mixtures Using Near‐Infrared Reflectance Spectroscopy N. S. Al-Ghumaiz Qassim University, Saudi Arabia R. H. Leep Michigan State University T. S. Dietz Michigan State University Follow this and additional works at: https://uknowledge.uky.edu/igc  Part of the Plant Sciences Commons, and the Soil Science Commons This document is available at https://uknowledge.uky.edu/igc/21/5-2/18 The 21st International Grassland Congress / 8th International Rangeland Congress took place in Hohhot, China from June 29 through July 5, 2008. Proceedings edited by Organizing Committee of 2008 IGC/IRC Conference Published by Guangdong People's Publishing House This Event is brought to you for free and open access by the Plant and Soil Sciences at UKnowledge. It has been accepted for inclusion in International Grassland Congress Proceedings by an authorized administrator of UKnowledge. For more information, please contact UKnowledge@lsv.uky.edu. 瞯 ]708　 瞯 　 Multifunctional Grasslands in a Changing World 　 Volume Ⅰ 　Grasslands/Rangelands Resources and Ecology ——— Application of Information Technology in Monitoring and Managing Grasslands/Rangelands ResourcesPredicting botanical composition of grass‐clover pastures mixtures using near‐infrared reflectancespectroscopyN . S . A L‐Ghumaiz 1 , R . H . Leep 2 , and T . S . Dietz 21 Department o f Plant Production‐Qassim University . Buridah , Qassim .51452 . Saudi A rabia2 . Department o f Crop andSoil Sciences , M ichigan State University .East L ansing , M I . 48824 .USA . nghumai＠ msu .eduKey words :NIRS , botanical composition , binary mixtures , predication equationIntroduction The development of a reliable and rapid approach for determining botanical composition has been a research goal formany years . Several researchers have attempted to use NIRS as an approach to determine botanical compositions . Someresearchers have already proven the capability of NIRS to determine species content in legume‐grass mixtures ( Pitman et al . ,１９９１) . Our objective was to compare NIRS to other methods for separating complex mixtures of species .　Materials and methods Twenty‐four cool season clover and grass cultivars were used for this study . Clover species includedthree red clover( T ri f olium p ratense L ) cultivars , four white clover ( T rifolium repens L ) , and one kura clover ( T rifoliumambiguim . Bieb ) cultivar . The grass species included two festuloliums ( Festulolium braunii , K . A .) cultivars , fourorchardgrass ( Dacty lis glomerata L . ) , seven perennial ryegrass ( Lolium perenne L ) and three tall fescue ( Festucaarundinacea Schreb) . The experiments were arranged in a randomized complete block design ( RCBD) with three replications .Each replication consisted of ６７ entries of different combinations of grass and clover cultivars . Samples were hand clippedwithin a ０ .２５ m２ quadrat in each plot when plants were ２０ cm in height and dried at ６５ °C then ground to pass through a １mm‐screen . Calibration equations were developed using １‐artificially pure mixed samples , ２‐hand‐separated samples from onelocation and year and ３‐hand‐separated samples from multiple locations and years . Standard deviation of calibration ( SD ) ,standard error of calibration ( SEC ) and calibration coefficient of determination ( R２ ) were used to evaluate the calibrationequation . Prediction coefficient of determination ( R２ ) , standard error of prediction ( SEP ) , slope , and bias were used asvalidation criteria of the equations .Results Data from artificially pure mixtures shows a very strong calibration equation with R２ of ０ .９９ and standard error ofcalibration ( SEC) of １ .４ . However , regression analyses of the spectral data with botanical composition resulted in predictioncoefficient of determination ( R２ ) of ０ .３１ and ０ .２４ and SEP of ２８ .１ and ２６ .７ for grass and clover , respectively . The variationin plant maturity and weed content observed in the field samples were not represented in the lab mixed samples . Thus , thecalibration equation developed from the artificially mixed samples of several grass and clover species did not accuracy predict thespecies composition clipped from three different locations over three growing seasons . A higher prediction may obtain if thecalibration equation was derived from samples representing all locations and years ( Shaffer et . al . , １９９０) .Equations developed from hand‐separation from one location and year had R２ of ０ .９７ for grass and ０ .９５ for clover . However ,when this equation was applied to predict the botanical composition of samples collected from three locations and three years ,similar predictions were obtained to the results of the artificial pure mixed samples with validation R２ of ０ .２５ for grass withsomewhat improvement prediction for clovers with R２ of ０ .３７ . This clearly reflects the problem of obtaining enoughrepresentative samples that can include all spectra data from other locations .On other hand Calibration equations developed from hand‐separated samples collected from three locations over three years hadR２ of ０ .８２ for grass and ０ .８４ for both clovers and weeds . The validation statistics for these equations resulted in an R２ of ０ .６７to ０ .７２ , SEP of ６ .９ to １２ .８ , and a bias of ０ .３ to ０ .６％ . Slopes were close to one for the grass and clover . In spite of therebeing multiple species in the weeds portion , SEP for weeds was lower than grass and clover components . It is possible the weedspecies were more similar in reflectance values than the different species of grasses .Conclusions Even though we had some success in predicting grass‐clover mixtures based on multiple locations and years ascompared with the equation created from a single location and year , this study did not result in as high R２ as those observed inother studies ( Locher et al . , ２００５ ) . The lower R２ observed were due to various factors . Environmental conditions and thegrinding and storing processes can affect NIRS prediction . This study , which presented two different strategies for developinga calibration equation , has shown that creating an equation from representative samples of a larger database of previously hand‐separated mixtures can increase the prediction accuracy .

Predicting Botanical Composition of Grass‐Clover Pastures Mixtures Using Near‐Infrared Reflectance Spectroscopy

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Predicting Botanical Composition of Grass‐Clover Pastures Mixtures Using Near‐Infrared Reflectance Spectroscopy

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