Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Application of near-infrared spectroscopy for hay evaluation at different degrees of sample preparation

  • Eun Chan Jeong (Graduate School of International Agricultural Technology, Seoul National University) ;
  • Kun Jun Han (School of Plant, Environmental, and Soil Sciences, Louisiana State University, Agricultural Center) ;
  • Farhad Ahmadi (Research Institute of Eco-friendly Livestock Science, Institute of GreenBio Science Technology, Seoul National University) ;
  • Yan Fen Li (Graduate School of International Agricultural Technology, Seoul National University) ;
  • Li Li Wang (Graduate School of International Agricultural Technology, Seoul National University) ;
  • Young Sang Yu (Graduate School of International Agricultural Technology, Seoul National University) ;
  • Jong Geun Kim (Graduate School of International Agricultural Technology, Seoul National University)
  • Received : 2023.11.06
  • Accepted : 2024.01.08
  • Published : 2024.07.01
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Abstract

Objective: A study was conducted to quantify the performance differences of the near-infrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations. Methods: A total of 227 imported alfalfa (Medicago sativa L.) and another 360 imported timothy (Phleum pratense L.) hay samples were used to develop calibration models for nutrient value parameters such as moisture, neutral detergent fiber, acid detergent fiber, crude protein, and in vitro dry matter digestibility. Spectral data of hay samples prepared by milling into 1-mm particle size or unground were separately regressed against the wet chemistry results of the abovementioned parameters. Results: The performance of the developed NIRS calibration models was evaluated based on R2, standard error, and ratio percentage deviation (RPD). The models developed with ground hay were more robust and accurate than those with unground hay based on calibration model performance indexes such as R2 (coefficient of determination), standard error, and RPD. Although the R2 of calibration models was mainly greater than 0.90 across the feed value indexes, the R2 of cross-validations was much lower. The R2 of cross-validation varies depending on feed value indexes, which ranged from 0.61 to 0.81 in alfalfa, and from 0.62 to 0.95 in timothy. Estimation of feed values in imported hay can be achievable by the calibrated NIRS. However, the NIRS calibration models must be improved by including a broader range of imported hay samples in the modeling. Conclusion: Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparations compromising analysis accuracy by NIRS.

Keywords

Acknowledgement

This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) through (the Livestock Industrialization Technology Development Program), funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (No. 121036-02-1-SB010). Also, the calibration model development was partially supported by the Hatch project fund (LAB 94398).

References

  1. Kim JD, Seo M, Lee SC, Han KJ. Review of the current forage production, supply, and quality measure standard in South Korea. J Kor Grassl Forage Sci 2020;40:149-55. https://doi.org/10.5333/KGFS.2020.40.3.149
  2. Ahmadi F, Lee YH, Ko MJ, Choi DY, Kwak WS. In situ ruminal degradation characteristics of dry matter and crude protein of imported hays marketed to the Korean cattle industry: a field study. J Food Agric Environ 2017;15:80-5.
  3. Lee BH, Kim JH, Oh M, et al. A study on the distribution of feed value and quality grade of imported hay. J Kor Grassl Forage Sci;40:1-6.
  4. Poppi DP, Norton BW, Minson DJ, Hendrickson RE. The validity of the critical size theory for particles leaving the rumen. J Agric Sci 1980;94:275-80. https://doi.org/10.1017/S0021859600028859
  5. Yakubu HG, Kovacs Z, Toth T, Bazar G. The recent advances of near-infrared spectroscopy in dairy production-a review. Crit Rev Food Sci Nutr 2022;62:810-31. https://doi.org/10.1080/10408398.2020.1829540
  6. Murphy DJ, O' Brien B, O' Donovan M, Condon T, Murphy MD. A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures. Inf Process Agric 2021;9:243-53. https://doi.org/10.1016/j.inpa.2021.04.012
  7. Norman HC, Hulm E, Humphries AW, Hughes SJ, Vercoe PE. Broad near-infrared spectroscopy calibrations can predict the nutritional value of >100 forage species within the Australian feedbase. Anim Prod Sci 2020;60:1111-22. https://doi.org/10.1071/AN19310
  8. Cherney JH, Digman MF, Cherney DJ. Handheld NIRS for forage evaluation. Comput Electron Agric 2021;190:106469. https://doi.org/10.1016/j.compag.2021.106469
  9. Bremner JM. Nitrogen-total. In: Sparks DL, Page AL, Helmke PA, editors. Methods of soil analysis: part 3 chemical methods. Madison, USA: ASA, CSSA, and SSSA Books; 1996. pp.1085-121. https://doi.org/10.2136/sssabookser5.3.c37
  10. Van Soest PJ, Robertson JB, Lewis BA. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J Dairy Sci 1991;74:3583-97. https://doi.org/10.3168/jds.S0022-0302(91)78551-2
  11. Goering HK, Van Soest PJ. Forage fiber analysis (apparatus, reagents, procedures, and some applications). Washington, DC, USA: US Department Agriculture-Agricultural Research Service (USDA-ARS); 1970. U.S. Agricultural Handbook no. 379.
  12. Shenk JS, Westerhaus MO. The application of near infrared reflectance spectroscopy (NIRS) to forage analysis. In: Fahey GS editor. Forage quality, evaluation, and utilization. Madison, USA: ASA, CSSA, and SSSA Books; 1994. pp. 406-49. https://doi.org/10.2134/1994.foragequality.c10
  13. Guo T, Dai L, Yan B, et al. Measurements of chemical compositions in corn stover and wheat straw by near-infrared reflectance spectroscopy. Animals 2021;11:3328. https://doi.org/10.3390/ani11113328
  14. Karoui R, Mouazen AM, Dufour E, et al. Mid-infrared spectrometry: a tool for the determination of chemical parameters in Emmental cheeses produced during winter. Lait 2006;86:83-97. https://doi.org/10.1051/lait:2005040
  15. Yu P, Christensen DA, McKinnon JJ, Markert JD. Effect of variety and maturity stage on chemical composition, carbohydrate and protein subfractions, in vitro rumen degradability and energy values of timothy and alfalfa. Can J Anim Sci 2003;83:279-90. https://doi.org/10.4141/A02-053
  16. Rego G, Ferrero F, Valledor M, et al. A portable IoT NIR spectroscopic system to analyze the quality of dairy farm forage. Comput Electron Agric 2020;175:105578. https://doi.org/10.1016/j.compag.2020.105578
  17. Berzaghi P, Cherney JH, Casler MD. Prediction performance of portable near infrared reflectance instruments using preprocessed dried, ground forage samples. Comput Electron Agric 2021;182:106013. https://doi.org/10.1016/j.compag.2021.106013
  18. Vough LR, Marten GC. Influence of soil moisture and ambient temperature on yield and quality of alfalfa forage. Agron J 1971;63:40-2. https://doi.org/10.2134/agronj1971.00021962006300010014x
  19. Prananto JA, Minasny B, Weaver T. Near infrared (NIR) spectroscopy as a rapid and cost-effective method for nutrient analysis of plant leaf tissues. Adv Agron 2020;164:1-49. https://doi.org/10.1016/bs.agron.2020.06.001
  20. Campbell M, Ortuno J, Koidis A, Theodoridou K. The use of near-infrared and mid-infrared spectroscopy to rapidly measure the nutrient composition and the in vitro rumen dry matter digestibility of brown seaweeds. Anim Feed Sci Technol 2022;285:115239. https://doi.org/10.1016/j.anifeedsci.2022.115239
  21. Stuth J, Jama A, Tolleson D. Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy. Field Crops Res 2003;84:45-56. https://doi.org/10.1016/S0378-4290(03)00140-0
  22. Barnes RJ, Dhanoa MS, Lister SJ. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl Spectrosc 1989;43:772-7. https://doi.org/10.1366/0003702894202201
  23. Andueza D, Picard F, Jestin M, Andrieu J, Baumont R. NIRS prediction of the feed value of temperate forages: efficacy of four calibration strategies. Animal 2011;5:1002-13. https://doi.org/10.1017/S1751731110002697
  24. Roberts CA, Stuth J, Finn PC. NIRS applications in forages and feedstuffs. In: Roberts CA, Workman J, Reeves JB, editors. Near infrared-spectroscopy in agriculture. Madison, USA:Agronomy Monographs; 2003. pp. 229-408.
  25. Cozzolino D, Labandera M. Determination of dry matter and crude protein contents of undried forages by near-infrared reflectance spectroscopy. J Sci Food Agric 2002;82:380-4. https://doi.org/10.1002/jsfa.1050
  26. Brogna N, Palmonari A, Canestrari G, Mammi L, Dal Pra A, Formigoni A. Technical note: near infrared reflectance spectroscopy to predict fecal indigestible neutral detergent fiber for dairy cows. J Dairy Sci 2018;101:1234-9. https://doi.org/10.3168/jds.2017-13319
  27. Yang Z, Nie G, Pan L, et al. Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum. Peer J 2017;5:e3867. https://doi.org/10.7717/peerj.3867
  28. Workman JJ Jr. NIR spectroscopy calibration basics. In: Burns DA, Ciurczak EW, editors. Handbook of near-infrared analysis. 3rd ed. Boca Raton, FL, USA: CRC Press; 2007. pp. 141-68.
  29. Buxton DR, Mertens DR. Errors in forage-quality data predicted by near infrared reflectance spectroscopy. Crop Sci 1991;31:212-8. https://doi.org/10.2135/cropsci1991.0011183X003100010047x
  30. Lundberg KM, Hoffman PC, Bauman LM, Berzaghi P. Prediction of forage energy content by near infrared reflectance spectroscopy and summative equations. Prof Anim Sci 2004; 20:262-9. https://doi.org/10.15232/S1080-7446(15)31309-7