بررسی ارتباط بین غلظت نشاسته دفعی از طریق مدفوع با سایر ترکیبات مدفوع در گاوهای هلشتاین اوایل دوره شیردهی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم دامی، دانشکدگان کشاورزی و منابع طبیعی دانشگاه تهران، کرج، البرز، ایران

2 گروه علوم دامی، دانشکدگان کشاورزی و منابع طبیعی دانشگاه تهران،کرج، البرز، ایران.

3 مدیر تولید دامپروری هلدینگ کشاورزی و دامپروری فردوس پارس، تهران، ایران

4 بخش تحقیقات علوم دامی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی آذربایجان شرقی، سازمان تحقیقات و آموزش و ترویج کشاورزی، تبریز،

چکیده

هدف از مطالعه حاضر بررسی ارتباط بین غلظت نشاسته دفعی از طریق مدفوع با سایر ترکیبات مدفوع در گاوهای هلشتاین اوایل دوره شیردهی  بود. در مطالعه حاضر ابتدا همبستگی بین غلظت نشاسته دفعی از طریق مدفوع با سایر ترکیبات مدفوع مورد مطالعه قرار گرفت و سپس انتخاب بهترین مدل برازش نشاسته مدفوعی براساس تابعیت تک و چند متغیره با استفاده از سایر ترکیبات مدفوع گاوهای اوایل دوره شیردهی هلشتاین انجام گرفت. نتایج تابعیت تک متغیره و همچنین همبستگی بین پارامترهای مدفوعی نشان داد که رابطه معکوسی بین اسیدیته مدفوع، پروتئین مدفوع و الیاف نامحلول در شوینده خنثی مدفوع با دفع نشاسته مدفوعی وجود دارد. همچنین براساس تابعیت تک متغیره نشان داده شد که اسیدیته مدفوع بالاترین ضریب تبیین را در برآورد نشاسته مدفوعی داشت (48/0 = R2). همچنین نشان داده شد بهترین برآورد نشاسته دفعی از طریق مدفوع با استفاده از تابعیت چندگانه با استفاده از سه عامل اسیدیته مدفوع، درصد پروتئین مدفوع و درصد الیاف نامحلول در شوینده خنثی با ضریب تبیین برابر با 61/0 با دقت و صحت بالایی امکان‌پذیر است. در نهایت نتایج مطالعه حاضر نشان داد که همبستگی معنی داری بین نشاسته دفع شده از طریق مدفوع با سایر ترکیبات مدفوع وجود دارد و همچنین پیش بینی غلظت نشاسته دفعی از طریق مدفوع با استفاده از مدل های تابعیت چندگانه با دقت بالا امکان پذیر می باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigation of the Relationship Between Fecal Starch Concentration and other Fecal Components in Early Lactating Holstein Cows

نویسندگان [English]

  • Ayub Mohammadi 1
  • Farhang Fatehi 2
  • kamran Reza-yazdi 2
  • Ali Sadeghi-Sefidmazgi 1
  • Mohammad Hadi Khabazan 3
  • Farhad Parnian-khajehdizaj 4
1 Department of Animal Science, University college of Agriculture and Natural Resources, University of Tehran, Karaj, Alborz,, Iran
2 Department of Animal Science, University college of Agriculture and Natural Resources, University of Tehran, Karaj, Alborz, Iran.
3 Production Manager of Ferdous Pars Agricultural and Livestock Holding, Tehran, Iran
4 Assistant Professor, Agricultural Research, Education and Extension Organization (AREEO), Tabriz, East Azerbaijan, Iran
چکیده [English]

The purpose of the current study was to investigate the relationship between fecal starch concentration and other fecal components in early Holstein lactating Cows. In current study, correlation coefficients between fecal starch concentration and other fecal components was studied and then univariate and multivariate regression approach used to select the best fecal starch fitting model using other fecal compositions in early Holstein lactating Cows. The present study conducted to estimate fecal starch excretion using other fecal components on 76 industrial dairies. The model fitting was performed based on univariate and multivariate regression. The results of univariate regression and correlation between fecal parameters showed an inverse relationship between fecal pH, fecal protein and fecal neutral detergent fiber with fecal starch excretion. Furthermore, based on univariate regression, it was shown that fecal pH had the highest coefficient of determination in estimating fecal starch (R² = 0.48). It was also demonstrated that the best estimation of fecal starch excretion is possible with high accuracy and precision using multiple regression with three factors: fecal pH, fecal protein percentage, and neutral detergent fiber percentage. Finally, the results of the present study showed that there is a significant correlation between excreted starch through feces and other compounds of feces, and also, it is possible to predict the concentration of excreted starch through feces using multivariate regression models with high accuracy.

کلیدواژه‌ها [English]

  • Fecal pH
  • Fecal starch
  • Holstein dairy cows
  • Multivariate regression

Extended Abstract

Introduction

    Nutritional management of early lactation dairy cows is one of the most challenging aspects of dietary management in modern dairy herds. This is because these early lactating cows have the highest daily dry matter intake as a percentage of body weight, and any imbalance in the diet can lead to acidosis and digestive problems (Fredin et al., 2014). In the diet of these cows, to achieve maximum milk production, a large amount of starch from grain sources is included daily. Due to the high passage rate of feed materials in the digestive system of these cows, without proper nutritional management, a significant portion of consumed starch passes through the rumen, and after partial digestion in the small intestine, undigested amounts reach the end of the digestive tract (Owens et al., 2015; Mills et al., 2017). There, it ferments, and ultimately, a considerable portion is excreted through feces. Therefore, the objective of the present study was to investigate the relationship between fecal starch concentration and other fecal components in early lactation Holstein cows and to develop predictive models for its estimation.

 

Materials and Methods

    This study was conducted on 76 Holstein dairy herds, where one pen of early Holstein lactating Cows was selected as the index pen from each herd based on average milk production, days in milk, and parity. From each pen, 10 cows were selected with body condition score of 3±0.25, days in milk of 80±10, milk production of 47±2.5, and parity of 2.5±0.5. Sampling of total mixed ration, feed ingredients, and feces was conducted for three consecutive days. Finally, data such as forage-to-concentrate ratio in the diet, as well as fecal starch values and ratios, crude protein, neutral detergent fiber, and organic matter in feces were measured. It should be noted that fecal pH was measured immediately after fecal sampling. Finally, using the obtained data, fecal excreted starch was estimated using univariate and multivariate linear regression models. The prioritization criteria for the effects of each parameter was determined using the coefficient of determination.

 

Results and Discussion

    The descriptive results of this study showed that the mean percentages of excreted starch, fecal pH, crude protein percentage, crude protein values per day, neutral detergent fiber percentage, neutral detergent fiber values per day, and fecal organic matter in early Holstein lactating Cows herds were 11.5, 6.58, 16.9, 1.65, 46.1, 4.46, and 83.1, respectively. Based on univariate prioritization, the decrease in fecal excreted starch with increasing fecal pH, fecal protein percentage, excreted protein values per day, and fecal neutral detergent fiber was demonstrated with determination coefficients of 0.48, 0.38, 0.28, and 0.19, respectively (P-value ≤0.01). It should be noted that the correlation between fecal starch percentage and other fecal parameters including fecal pH, fecal protein percentage, excreted protein values per day, and fecal neutral detergent fiber was -0.69, -0.61, -0.53, and -0.43, respectively, indicating an inverse correlation between excreted fecal starch and these parameters. Furthermore, results of multivariate regression revealed that model  containing fecal pH, fecal protein percentage, and fecal neutral detergent fiber percentage (48.52 – 3.66 pH - 3.77 CPF + 1.11 NDFF), had the highest coefficient of determination of 0.61.

 

Conclusion

    The results of current study demonstrated a significant correlation between fecal excreted starch and other fecal components. Additionally, the results showed that excreted starch could be estimated with an acceptable accuracy by multivariate regression approach.

 

Data Availability Statement

This article contains all the data that were created or evaluated during the research.

Acknowledgements

The authors would like to sincerely thank the members of the Faculty of Animal Sciences, University of Tehran Research Council for the approval and support of this research.

Conflict of interest

The author declares no conflict of interest.

REFERENCES
Abeyta, M. A., Horst, E. A., Mayorga, E. J., Goetz, B. M., Al-Qaisi, M., McCarthy, C. S., ... & Baumgard, L. H. (2023). Effects of hindgut acidosis on metabolism, inflammation, and production in dairy cows consuming a standard lactation diet. Journal of Dairy Science106(2), 1429-1440. https://doi.org/10.3168/jds.2022-22303
Abeyta, M. A., Horst, E. A., Rodriguez-Jimenez, S. J., May-Orga, E. J., Goetz, B. M., Al-Qaisi, M., ... & Baumgard, L. H. (2019, January). Effects of hindgut acidosis on metabolism, inflammation, and production in dairy cows acclimated to a low-starch diet. In JOURNAL OF DAIRY SCIENCE (Vol. 102, pp. 402-402). STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA: ELSEVIER SCIENCE INC.
Adesogan, A. T., Arriola, K. G., Jiang, Y., Oyebade, A., Paula, E. M., Pech-Cervantes, A. A., ... & Vyas, D. (2019). Symposium review: Technologies for improving fiber utilization. Journal of dairy science102(6), 5726-5755. https://doi.org/10.3168/jds.2018-15334
Allen, M. S., Longuski, R. A., & Ying, Y. (2021). Effects of corn grain endosperm type and fineness of grind on feed intake, feeding behavior, and productive performance of lactating dairy cows. Journal of Dairy Science104(7), 7630-7640. https://doi.org/10.3168/jds.2020-18991
AOAC. (2012). Official methods of analysis. Association of Official Analytical Chemists, 881-82.
Atkinson, G. A., Smith, L. N., Smith, M. L., Reynolds, C. K., Humphries, D. J., Moorby, J. M., ... & Kingston-Smith, A. H. (2020). A computer vision approach to improving cattle digestive health by the monitoring of faecal samples. Scientific Reports10(1), 17557. https://doi.org/10.1038/s41598-020-74511-0
Chen, P., Li, Y., Wang, M., Shen, Y., Liu, M., Xu, H., ... & Li, J. (2024). Optimizing dietary rumen-degradable starch to rumen-degradable protein ratio improves lactation performance and nitrogen utilization efficiency in mid-lactating Holstein dairy cows. Frontiers in Veterinary Science11, 1330876. https://doi.org/10.3389/fvets.2024.1330876
Conway, L. K., Hallford, D. M., & Soto-Navarro, S. A. (2012). Effects of wet corn gluten feed and yellow grease on digestive function of cattle fed steam-flaked corn-based finishing diets. Animal feed science and technology178(1-2), 20-26. https://doi.org/10.1016/j.anifeedsci.2012.09.003
Cueva, S. F., Wasson, D. E., Martins, L. F., Räisänen, S. E., Silvestre, T., & Hristov, A. N. (2024). Lactational performance, ruminal fermentation, and enteric gas emission of dairy cows fed an amylase-enabled corn silage in diets with different starch concentrations. Journal of dairy science107(7), 4426-4448. https://doi.org/10.3168/jds.2023-23957
Dann, H. M., Tucker, H. A., Cotanch, K. W., Krawczel, P. D., Mooney, C. S., Grant, R. J., & Eguchi, T. (2014). Evaluation of lower-starch diets for lactating Holstein dairy cows. Journal of dairy science97(11), 7151-7161. https://doi.org/10.3168/jds.2014-8341
Darabighane, B., Tapio, I., Ventto, L., Kairenius, P., Stefański, T., Leskinen, H., ... & Bayat, A. R. (2021). Effects of starch level and a mixture of sunflower and fish oils on nutrient intake and digestibility, rumen fermentation, and ruminal methane emissions in dairy cows. Animals11(5), 1310. https://doi.org/10.3390/ani11051310
Fernandez, J. A., Coppock, C. E., & Schake, L. M. (1982). Effect of calcium buffers and whole plant processing on starch digestibility of sorghum based diets in Holstein cows. Journal of Dairy Science65(2), 242-249. https://doi.org/10.3168/jds.S0022-0302(82)82183-8
Ferraretto, L. F., P. M. Crump, and R. D. Shaver. 2013. Effect of cereal grain type and corn grain harvesting and processing methods on intake, digestion, and milk production by dairy cows through a meta-analysis. J. Dairy Sci. 96:533–550. https://doi.org/10.3168/jds.2012-5932
Firkins, J. L., Eastridge, M. L., St-Pierre, N. R., & Noftsger, S. M. (2001). Effects of grain variability and processing on starch utilization by lactating dairy cattle. Journal of animal science79(suppl_E), E218-E238. https://doi.org/10.2527/jas2001.79E-SupplE218x
Fredin, S. M., Ferraretto, L. F., Akins, M. S., Hoffman, P. C., & Shaver, R. D. (2014). Fecal starch as an indicator of total-tract starch digestibility by lactating dairy cows. Journal of dairy science97(3), 1862-1871. https://doi.org/10.3168/jds.2013-7395
Gallo, A., Giuberti, G., & Masoero, F. (2016). Gas production and starch degradability of corn and barley meals differing in mean particle size. Journal of dairy science99(6), 4347-4359. https://doi.org/10.3168/jds.2015-10779
Grant, R. J., & Chazy, N. Y. (2010). Lowering cost of production with feed efficiency and cow comfort. Agricultural Research Institute Chazy, NY.
Hall, M. B. (2009). Determination of starch, including maltooligosaccharides, in animal feeds: Comparison of methods and a method recommended for AOAC collaborative study. Journal of AOAC International92(1), 42-49. https://doi.org/10.1093/jaoac/92.1.42
Harmon, D. L., & McLeod, K. R. (2001). Glucose uptake and regulation by intestinal tissues: Implications and whole-body energetics. Journal of Animal Science79(suppl_E), E59-E72. https://doi.org/10.2527/jas2001.79E-SupplE59x
Hoffman, P. C., Esser, N. M., Shaver, R. D., Coblentz, W. K., Scott, M. P., Bodnar, A. L., ... & Charley, R. C. (2011). Influence of ensiling time and inoculation on alteration of the starch-protein matrix in high-moisture corn. Journal of dairy science94(5), 2465-2474. https://doi.org/10.3168/jds.2010-3562
Hutjens, M. F. (2011). More efficient rations are headed our way-Researchers focused on more effectively feeding starch, minerals, protein, and forages to improve efficiency and health without compromising production. Hoard's Dairyman156(16), 593.
Khorrami, B., Kheirandish, P., Zebeli, Q., & Castillo-Lopez, E. (2022). Variations in fecal pH and fecal particle size due to changes in dietary starch: Their potential as an on-farm tool for assessing the risk of ruminal acidosis in dairy cattle. Research in Veterinary Science152, 678-686. https://doi.org/10.1016/j.rvsc.2022.10.001
Kljak, K., Heinrichs, B. S., & Heinrichs, A. J. (2019). Fecal particle dry matter and fiber distribution of heifers fed ad libitum and restricted with low and high forage quality. Journal of dairy science102(5), 4694-4703. https://doi.org/10.3168/jds.2018-15457
Krogstad, K. C., & Bradford, B. J. (2023). Does feeding starch contribute to the risk of systemic inflammation in dairy cattle?. JDS communications4(1), 14-18. https://doi.org/10.3168/jdsc.2022-0303
Liu, S., Wei, Z., Deng, M., Xian, Z., Liu, D., Liu, G., ... & Guo, Y. (2023). Effect of a high-starch or a high-fat diet on the milk performance, apparent nutrient digestibility, hindgut fermentation parameters and microbiota of lactating cows. Animals13(15), 2508. https://doi.org/10.3390/ani13152508
Liu, Y. F., Zhao, H. B., Liu, X. M., You, W., Cheng, H. J., Wan, F. C., ... & Zhang, X. L. (2015). Substitution of wheat for corn in beef cattle diets: digestibility, digestive enzyme activities, serum metabolite contents and ruminal fermentation. Asian-Australasian journal of animal sciences29(10), 1424. https://doi.org/10.5713/ajas.15.0866
Matamura, M., Naito, H., Morio, Y., & Kondo, M. (2024). Fecal image-based starch digestibility estimation in fattening cattle using deep learning. Computers and Electronics in Agriculture225, 109246. https://doi.org/10.1016/j.compag.2024.109246
Matthé, A., Lebzien, P., Hric, I., Flachowsky, G., & Sommer, A. (2001). Effect of starch application into the proximal duodenum of ruminants on starch digestibility in the small and total intestine. Archives of Animal Nutrition55(4), 351-369. https://doi.org/10.1080/17450390109386202
McCarthy, C. S., Dooley, B. C., Branstad, E. H., Kramer, A. J., Horst, E. A., Mayorga, E. J., ... & Baumgard, L. H. (2020). Energetic metabolism, milk production, and inflammatory response of transition dairy cows fed rumen-protected glucose. Journal of Dairy Science103(8), 7451-7461. https://doi.org/10.3168/jds.2020-18151.
Mertens, D. R. (2002). Determination of starch in large particles. Ro-Tap shaker method. US Dairy Forage Research Center, Madison, WI. Revised April.
Mills, J. A., France, J., Ellis, J. L., Crompton, L. A., Bannink, A., Hanigan, M. D., & Dijkstra, J. (2017). A mechanistic model of small intestinal starch digestion and glucose uptake in the cow. Journal of Dairy Science100(6), 4650-4670. https://doi.org/10.3168/jds.2016-12122
Mutsvangwa, T., Davies, K. L., McKinnon, J. J., & Christensen, D. A. (2016). Effects of dietary crude protein and rumen-degradable protein concentrations on urea recycling, nitrogen balance, omasal nutrient flow, and milk production in dairy cows. Journal of dairy science99(8), 6298-6310. https://doi.org/10.3168/jds.2016-10917
National Academies of Sciences, Engineering, and Medicine. (NASEM) (2021). Nutrient Requirements of Dairy Cattle.
Neubauer, V., Petri, R. M., Humer, E., Kröger, I., Reisinger, N., Baumgartner, W., ... & Zebeli, Q. (2020). Starch-rich diet induced rumen acidosis and hindgut dysbiosis in dairy cows of different lactations. Animals10(10), 1727. https://doi.org/10.3390/ani10101727
Offner, A., & Sauvant, D. (2004). Prediction of in vivo starch digestion in cattle from in situ data. Animal Feed Science and Technology111(1-4), 41-56. https://doi.org/10.1016/S0377-8401(03)00216-5
Offner, A., Bach, A., & Sauvant, D. (2003). Quantitative review of in situ starch degradation in the rumen. Animal Feed Science and Technology106(1-4), 81-93. https://doi.org/10.1016/S0377-8401(03)00038-5
Owens, C. E., Zinn, R. A., Hassen, A., & Owens, F. N. (2016). Mathematical linkage of total-tract digestion of starch and neutral detergent fiber to their fecal concentrations and the effect of site of starch digestion on extent of digestion and energetic efficiency of cattle. The Professional Animal Scientist32(5), 531-549. https://doi.org/10.15232/pas.2016-01510
Owens, F. N., & Zinn, R. A. (2005, March). Corn grain for cattle: Influence of processing on site and extent of digestion. In Proc. Southwest Nutr. Conf (Vol. 86, p. 112). Tucson: University of Arizona.
Pan, X. H., Yang, L., Beckers, Y., Xue, F. G., Tang, Z. W., Jiang, L. S., & Xiong, B. H. (2017). Thiamine supplementation facilitates thiamine transporter expression in the rumen epithelium and attenuates high-grain-induced inflammation in low-yielding dairy cows. Journal of Dairy Science100(7), 5329-5342. https://doi.org/10.3168/jds.2016-11966
Patton, R. A., Patton, J. R., & Boucher, S. E. (2012). Defining ruminal and total-tract starch degradation for adult dairy cattle using in vivo data. Journal of dairy science95(2), 765-782. https://doi.org/10.3168/jds.2011-4183
Reis, W. L., Palma, M. N., Paulino, M. F., Rennó, L. N., & Detmann, E. (2020). Investigation on daily or every three days supplementation with protein or protein and starch of cattle fed tropical forage. Animal Feed Science and Technology269, 114650. https://doi.org/10.1016/j.anifeedsci.2020.114650
Rodriguez-Jimenez, S., McCarthy, C. S., Horst, E. A., Mayorga, E. J., Al-Qaisi, M., Abeyta, M. A., ... & Baumgard, L. H. (2019, January). Relationships between fecal pH and milk production, metabolism, and acute phase protein response in periparturient dairy cows. In JOURNAL OF DAIRY SCIENCE (Vol. 102, pp. 402-402). STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA: ELSEVIER SCIENCE INC.
Salfer, I. J., Morelli, M. C., Ying, Y., Allen, M. S., & Harvatine, K. J. (2018). The effects of source and concentration of dietary fiber, starch, and fatty acids on the daily patterns of feed intake, rumination, and rumen pH in dairy cows. Journal of Dairy Science101(12), 10911-10921. https://doi.org/10.3168/jds.2018-15071
Sanz-Fernandez, M. V., Daniel, J. B., Seymour, D. J., Kvidera, S. K., Bester, Z., Doelman, J., & Martín-Tereso, J. (2020). Targeting the hindgut to improve health and performance in cattle. Animals10(10), 1817. https://doi.org/10.3390/ani10101817.
SAS Institute. (2019). SAS Certified Professional Prep Guide: Advanced Programming Using SAS 9.4. SAS institute.
Shipandeni, M. N., Paula, E. M., Esposito, G., Faciola, A. P., & Raffrenato, E. (2023). Effects of starch sources varying in particle sizes on ruminal fermentation, nutrient flow, starch digestibility, and lactation performance of dairy cows. Journal of Animal Science101, skad147. https://doi.org/10.1093/jas/skad147
Silvestre, T., Fetter, M., Räisänen, S. E., Lage, C. F. A., Stefenoni, H., Melgar, A., ... & Hristov, A. N. (2022). Performance of dairy cows fed normal-or reduced-starch diets supplemented with an exogenous enzyme preparation. Journal of dairy science105(3), 2288-2300. https://doi.org/10.3168/jds.2021-21264
Trotta, R. J., Harmon, D. L., Matthews, J. C., & Swanson, K. C. (2021). Nutritional and physiological constraints contributing to limitations in small intestinal starch digestion and glucose absorption in ruminants. Ruminants2(1), 1-26. https://doi.org/10.3390/ruminants2010001.
Van Gastelen, S., Dijkstra, J., Alferink, S. J., Binnendijk, G., Nichols, K., Zandstra, T., & Bannink, A. (2021). Abomasal infusion of corn starch and β-hydroxybutyrate in early-lactation Holstein-Friesian dairy cows to induce hindgut and metabolic acidosis. Journal of Dairy Science104(12), 12520-12539. https://doi.org/10.3168/jds.2021-20323.
Van Soest, P. V., Robertson, J. B., & Lewis, B. A. (1991). Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. Journal of dairy science74(10), 3583-3597. https://doi.org/10.3168/jds.S0022-0302(91)78551-2
Volden, H., & Larsen, M. (2011). Digestion and metabolism in the gastrointestinal tract. In NorFor-The Nordic feed evaluation system (pp. 59-80). Wageningen Academic. https://doi.org/10.3920/978-90-8686-718-9_011
Wildman, E. E., Jones, G. M., Wagner, P. E., Boman, R. L., Troutt Jr, H. F., & Lesch, T. N. (1982). A dairy cow body condition scoring system and its relationship to selected production characteristics. Journal of dairy science65(3), 495-501. https://doi.org/10.3168/jds.S0022-0302(82)82223-6
Zhao, Y., Yu, S., Zhao, H., Li, L., Li, Y., Liu, M., & Jiang, L. (2023). Integrated multi-omics analysis reveals the positive leverage of citrus flavonoids on hindgut microbiota and host homeostasis by modulating sphingolipid metabolism in mid-lactation dairy cows consuming a high-starch diet. Microbiome11(1), 236. https://doi.org/10.1186/s40168-023-01661-4
Zinn, R. A., Barreras, A., Corona, L., Owens, F. N., & Ware, R. A. (2007). Starch digestion by feedlot cattle: Predictions from analysis of feed and fecal starch and nitrogen. Journal of animal science85(7), 1727-1730. https://doi.org/10.2527/jas.2006-556
Zinn, R. A., Owens, F. N., & Ware, R. A. (2002). Flaking corn: processing mechanics, quality standards, and impacts on energy availability and performance of feedlot cattle. Journal of Animal Science80(5), 1145-1156. https://doi.org/10.2527/2002.8051145x