Comparison of fecal near-infrared with conventional methods to estimate intake and digestibility in sheep
DOI:
https://doi.org/10.15517/am.v34i2.51436Keywords:
ruminants, forage, spectral analysis, feces, markersAbstract
Introduction. Intake and digestibility are parameters that define the quality of a forage; however, they are difficult and expensive to estimate. Near infrared spectroscopy technology applied to feces (NIRSf) is an alternative to conventional reference methods to estimate dry matter voluntary intake (DMVI) and digestibility (DMD) in sheep. Objective. To compare NIRSf technology with conventional methods for estimation of DMVI and DMD in confinement sheep. Materials and methods. Six bioassays were carried out at the Tibaitata research center, Cundinamarca, Colombia, during 2019 and 2021 with five sheep (LW 58.28±11 kg) to estimate DMVI and DMD by three methods: gravimetry, markers and NIRSf. The animals were fed six diets contrasting in their nutritional value. Forage and feces samples were collected, dried, and ground for subsequent chemical and spectral analysis. Results. The estimation of DMVI and DMD was different (p<0.001) in the six evaluated feeding regimenes, where the DMVIMW ranged from 37.54 to 82.58 g/kg LW0.75, and the DMD ranged from 36.32 to 58.81 %. In the comparison of the estimation of DMVI and DMD by the referent method (gravimetric) with marker and NIRSf methods, shows that the NIRSf method presented a better adjustment compared to the marker method, presenting less root mean square error value (-1.53 and -1.75, respectively), lower mean absolute error (-3.01 and -0.5, respectively), and higher determination coefficient (+0.09 and +0.28, respectively). Conclusion. The estimation of the DMVI and the DMD by means of the NIRSf equations presented a better fit compared to the marker method, however, it is necessary to improve the accuracy of the calibrations using feces samples from animals under different productive contexts.
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Andueza, D., Noziere, P., Herremans, S., de La Torre Capitan, A., Froidmont, E., Picard, F., Pourrat, J. Constant, I., Martin, C., & Cantalapiedra-Hijar, G. (2019, August). Faecal-NIRS for predicting digestibility and intake in cattle: efficacy of two calibration strategy [Conference presentation]. 70 Annual meeting of the European Association for Animal Production (EAAP), Ghent, Belgium. https://hal.archives-ouvertes.fr/hal-02383603
Andueza, D., Picard, F., Dozias, D., & Aufrère, J. (2017). Fecal near-infrared reflectance spectroscopy prediction of the feed value of temperate forages for ruminants and some parameters of the chemical composition of feces: Efficiency of four calibration strategies. Applied Spectroscopy, 71(9), 2164–2176. https://doi.org/10.1177/0003702817712740
Ariza-Nieto, C., Mayorga, O. L., Mojica, B., Parra, D., & Afanador-Tellez, G. (2018). Use of LOCAL algorithm with near infrared spectroscopy in forage resources for grazing systems in Colombia. Journal of Near Infrared Spectroscopy, 26(1), 44–52. https://doi.org/10.1177/0967033517746900
Bender, R. W., Cook, D. E., & Combs, D. K. (2016). Comparison of in situ versus in vitro methods of fiber digestion at 120 and 288 hours to quantify the indigestible neutral detergent fiber fraction of corn silage samples. Journal of Dairy Science, 99(7), 5394–5400. https://doi.org/10.3168/jds.2015-10258
Church, D. C., Pond, W. G., & Pond, K. R. (2002). Fundamentos de nutrición y alimentación de animales (2ª ed.). Limusa Wiley
de Oliveira Franco, M., Detmann, E., de Campos Valadares Filho, S., Darlisson Batista, E., de Almeida Rufino, L. M., Barbosa, M. M., & Lopes, A. R. (2017). Intake, digestibility, and rumen and metabolic characteristics of cattle fed low-quality tropical forage and supplemented with nitrogen and different levels of starch. Asian-Australasian Journal of Animal Sciences, 30(6), 797–803. https://doi.org/10.5713/ajas.16.0629
de Souza, J., Batistel, F., Welter, K. C., Mendes Silva, M., Fleury Costa, D., & Portela Santos, F. A. (2014). Evaluation of external markers to estimate fecal excretion, intake, and digestibility in dairy cows. Tropical Animal Health and Production, 47, 265–268. https://doi.org/10.1007/s11250-014-0674-6
Decruyenaere, V., Lecomte, P., Demarquilly, C., Aufrere, J., Dardenne, P., Stilmant, D., & Buldgen, A. (2009). Evaluation of green forage intake and digestibility in ruminants using near infrared reflectance spectroscopy (NIRS): Developing a global calibration. Animal Feed Science and Technology, 148(2–4), 138–156. https://doi.org/10.1016/j.anifeedsci.2008.03.007
Decruyenaere, V., Peters, M., Stilmant, D., Lecomte, P. H., & Dardenne, P. (2003). Near infrared reflectance spectroscopy applied to faeces to predict dry matter intake of sheep under grazing, comparison with n-alkanes and direct biomass measurement methods. Tropical and Subtropical Agroecosystems, 3, 471–475.
Decruyenaere, V., Planchon, V., Dardenne, P., & Stilmant, D. (2015). Prediction error and repeatability of near infrared reflectance spectroscopy applied to faeces samples in order to predict voluntary intake and digestibility of forages by ruminants. Animal Feed Science and Technology, 205, 49–59. https://doi.org/10.1016/j.anifeedsci.2015.04.011
Detmann, E., Gionbelli, M. P., & Huhtanen, P. (2014). A meta-analytical evaluation of the regulation of voluntary intake in cattle fed tropical forage-based diets. Journal Animal Science, 92(10), 4632–4641. https://doi.org/10.2527/jas.2014-7717
dos Santos Cabral, Í., Gomes Azevêdo, J. A., dos Santos Pina, D., Ribeiro Pereira, L. G, Moreira de Almeida, F., Lins Souza, L., & de Lima, R. F. (2017). Evaluation of internal markers as determinants of fecal dry matter output and digestibility in feedlot sheep. Semina: Ciencias Agrarias 38(5), 3331–3340. https://doi.org/10.5433/1679-0359.2017v38n5p3331
Jancewicz, L. J., Swift, M. L., Penner, G. B., Beauchemin, K. A., Koening, K. M., Chibisa, G. E., He, M. I., McKinnon, J. J., Yang W. -Z., &McAllister, T. A. (2017). Development of near-infrared spectroscopy calibrations to estimate fecal composition and nutrient digestibility in beef cattle. Canadian Journal of Animal Science, 97(1), 51–64. https://doi.org/10.1139/cjas-2016-0107
Jarque-Bascuñana, L., Bartolomé, J., Serrano, E., Espunyes, J., Garel, M., Calleja Alarcón, J. A., López-Olvera, J. R., & Albanell, E. (2021). Near infrared reflectance spectroscopy analysis to predict diet composition of a mountain ungulate species. Animals, 11(5), Article 1449. https://doi.org/10.3390/ani11051449
Johnson, J. R., Carstens, G. E., Prince, S. D., Ominski, K. H., Wittenberg, K. M., Undi, M., Forbes, T. D. A., Hafla, A. N., Tolleson, D. R., & Basarab, J. A. (2017). Application of fecal near-infrared reflectance spectroscopy profiling for the prediction of diet nutritional characteristics and voluntary intake in beef cattle. Journal of Animal Science, 95(1), 447–454. https://doi.org/10.2527/jas2016.0845
Lahart, B., McParland, S., Kennedy, E., Boland, T. M., Condon, T., Williams, M., Galvin, N., McCarthy, B., & Buckley, F. (2019). Predicting the dry matter intake of grazing dairy cows using infrared reflectance spectroscopy analysis. Journal of Dairy Science, 102(10), 8907–8918. https://doi.org/https://doi.org/10.3168/jds.2019-16363
Lunesu, M. F., Decandia, M., Molle, G., Atzori, A. S., Bomboi, G. C., & Cannas, A. (2021). Dietary starch concentration affects dairy sheep and goat performances differently during mid-lactation. Animals, 11(5), Article 1222. https://doi.org/10.3390/ani11051222
Lyons, R. K., & Stuth, J. W. (1992). Fecal NIRS equations for predicting diet quality of free-ranging cattle. Journal of Range Management, 45(3), 238–244. https://doi.org/doi:10.2307/4002970
Mejía-Díaz, E., Mahecha-Ledesma, L., & Angulo-Arizala, J. (2017). Consumo de materia seca en un sistema silvopastoril de Tithonia diversifolia en trópico alto. Agronomía Mesoamericana, 28(2), 389–403. https://doi.org/10.15517/ma.v28i2.23561
Mertens, D. R., & Ely, L. O. (1979). A dynamic model of fiber digestion and passage in the ruminant for evaluating forage quality. Journal of Animal Science, 49(4), 1085–1095. https://doi.org/10.2527/jas1979.4941085x
Norris, K. H., Barnes, R. F., Moore, J. E., & Shenk, J. S. (1976). Predicting forage quality by Near Infrared Reflectance spectroscopy. Journal Animal Science, 43(4), 889–897. https://doi.org/10.2527/jas1976.434889x
Núñez-Sánchez, N., Carrion, D., Peña Blanco, F., Domenech García, V., Garzón Sigler, A., & Martínez-Marín, A. L. (2016). Evaluation of botanical and chemical composition of sheep diet by using faecal near infrared spectroscopy. Animal Feed Science and Technology, 222, 1–6. https://doi.org/10.1016/j.anifeedsci.2016.09.010
Ozaki, Y., McClure, W. F., & Christy, A. A. (Eds.). (2006). Near-infrared spectroscopy in food science and technology. John Wiley & Sons.
Showers, S. E., Tolleson, D. R., Stuth, J. W., Kroll, J. C., & Koerth, B. H. (2006). Predicting diet quality of white-tailed deer via NIRS fecal profiling. Rangeland Ecology & Management, 59(3), 300–307. https://doi.org/10.2111/04-069.1
Velásquez, A. V., da Silva, G. G., Sousa, D. O., Oliveira, C. A., Martins, C. M. M. R., dos Santos, P. P. M., Balieiro, J. C. C., Rennó, F. P., & Fukushima, R. S. (2018). Evaluating internal and external markers versus fecal sampling procedure interactions when estimating intake in dairy cows consuming a corn silage-based diet. Journal of Dairy Science, 101(7), 5890–5901. https://doi.org/10.3168/jds.2017-13283
Williams, P. (2014). The RPD statistic: A tutorial note. NIR News, 25(1), 22–26. https://doi.org/10.1255/nirn.1419
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