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基于饲粮和奶牛因素预测奶牛亚急性瘤胃酸中毒风险的模型:一项荟萃分析。

Models to predict the risk of subacute ruminal acidosis in dairy cows based on dietary and cow factors: A meta-analysis.

机构信息

Institute of Animal Nutrition and Functional Plant Compounds, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, 1210 Vienna, Austria; Department of Animal and Poultry Sciences, College of Aburaihan, University of Tehran, 3391653755 Tehran, Iran.

Institute of Animal Nutrition and Functional Plant Compounds, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, 1210 Vienna, Austria.

出版信息

J Dairy Sci. 2021 Jul;104(7):7761-7780. doi: 10.3168/jds.2020-19890. Epub 2021 Apr 8.

Abstract

The present research aimed at developing practical and feasible models to optimize feeding adequacy to maintain desired rumen pH conditions and prevent subacute ruminal acidosis (SARA) in dairy cows. We conducted 2 meta-analyses, one using data from recent published literatures (study 1) to investigate the prediction of SARA based on nutrient components and dietary physical and chemical characteristics, and another using internal data of our 5 different published experiments (study 2) to obtain adjustments based on cow status. The results of study 1 revealed that physically effective neutral detergent fiber inclusive of particles >8 mm (peNDF >8) and dietary starch [% of dry matter (DM)] were sufficient for predicting daily mean ruminal pH {y = 5.960 - (0.00781 × starch) + (0.03743 × peNDF >8) - [0.00061 × (peNDF >8 × peNDF >8)]}. The model for time of pH suppression (<5.8 for ruminal pH or <6.0 for reticular pH, min/d) can be predicted with additionally including DMI (kg/d): 124.7 + (1.7007 × DMI) + (20.9270 × starch) + (0.2959 × peNDF >8) - [0.0437 × (DMI × starch × peNDF >8)]. As a rule of thumb, when taken separately, we propose 15 to 18% peNDF >8 as a safe range for diet formulation to prevent SARA, when starch or NFC levels are within 20 to 25% and 35 to 40% ranges, respectively. At dietary starch content below 20% of DM, grain type was insignificant in affecting ruminal pH. However, increasing dietary starch contents by using corn as the sole grain source could lead to more severe drops of pH compared with using grain mix based on barley and wheat, as underlined by an interaction between starch content and grain type. Data from study 2 emphasized an increased risk of SARA for cows in the first and second lactation with lower mean pH (0.2 units) and double amounts of time at pH <5.8 compared with the cows with ≥3 parities. Given that a lower ruminal pH is expected in these high-risk cows, it is advisable to keep the lower end of recommended starch (20%) and higher peNDF >8 (18%) contents in the diet of these cows. Overall, the present study underlines the possibility of predicting SARA based on dietary factors including peNDF >8 and starch contents, as well as DMI of the cows, which can be practically implemented for optimal diet formulation for dairy cows. With more data available, future studies should attempt to improve the predictions by including additional key dietary and cow factors in the models.

摘要

本研究旨在开发实用且可行的模型,以优化饲料的充足性,维持奶牛理想的瘤胃 pH 条件,并预防亚急性瘤胃酸中毒(SARA)。我们进行了 2 项荟萃分析,其中一项使用最近发表的文献中的数据(研究 1)来研究基于营养成分和日粮物理化学特性的 SARA 预测,另一项使用我们 5 个不同已发表实验的内部数据(研究 2)来根据牛的状态进行调整。研究 1 的结果表明,物理有效中性洗涤纤维(包含>8mm 的颗粒)(peNDF>8)和日粮淀粉[干物质(DM)的%]足以预测每日平均瘤胃 pH 值{y=5.960-(0.00781×淀粉)+(0.03743×peNDF>8)-[0.00061×(peNDF>8×peNDF>8)]}。对于 pH 抑制时间(瘤胃 pH<5.8 或网胃 pH<6.0,min/d),可以通过额外包含 DMI(kg/d)来预测:124.7+(1.7007×DMI)+(20.9270×淀粉)+(0.2959×peNDF>8)-[0.0437×(DMI×淀粉×peNDF>8)]。作为一个经验法则,当单独考虑时,我们建议将 15%至 18%的 peNDF>8 作为饲料配方的安全范围,以预防 SARA,同时淀粉或非纤维碳水化合物(NFC)水平分别保持在 20%至 25%和 35%至 40%的范围内。在日粮淀粉含量低于 DM 的 20%时,谷物类型对瘤胃 pH 没有显著影响。然而,与使用大麦和小麦为基础的谷物混合物相比,仅使用玉米作为单一谷物来源增加日粮淀粉含量可能会导致 pH 更严重下降,这突出了淀粉含量与谷物类型之间的相互作用。研究 2 的数据强调,初产和二胎奶牛的 SARA 风险增加,其平均 pH 值低 0.2 个单位,pH<5.8 的时间是产奶 3 胎以上奶牛的两倍。鉴于这些高风险奶牛的瘤胃 pH 值预计会更低,因此建议在这些奶牛的饮食中保持推荐淀粉(20%)和更高的 peNDF>8(18%)含量的较低端。总的来说,本研究强调了基于包括 peNDF>8 和淀粉含量以及奶牛的 DMI 等饮食因素预测 SARA 的可能性,这可实际应用于奶牛的最佳饲料配方。随着更多数据的可用,未来的研究应尝试通过在模型中包含其他关键饮食和奶牛因素来提高预测能力。

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