不同种群密度和各种植物性料添加剂剂量水平对生长完成猪的影响
Hyun Ah Cho1, Min Ho Song2, Ji Hwan Lee3
1Department of Animal Science, Chungbuk National University, Cheongju 28644, Korea.
Journal of animal science and technology
|June 19, 2023
概括
植物性料添加剂 (PFA) 降低了在高种群密度下养的猪的压力. 一种苦提取物和精油混合在正常剂量最有效地减轻了对生长和行为的负面影响.
科学领域:
- 动物科学动物科学
- 营养科学 营养科学
- 农业科学 农业科学
背景情况:
- 在养猪业中,高种群密度会引发压力,对动物福利和生产率产生负面影响.
- 植物性料添加剂 (PFA) 作为减轻畜牧业生产压力的天然替代品正在被探索.
研究的目的:
- 为了研究不同植物性料添加剂 (PFA) 在不同剂量水平的有效性,在受高养殖密度压力的成长终结猪中.
- 评估PFA对生长性能,营养消化能力,血液参数和拥挤条件下的行为的影响.
主要方法:
- 72只猪被分配到9个治疗组,包括对照组 (动物福利密度和高种群密度) 和用精油,苦提取物和精油或葡萄末提取物补充的组.
- 猪被安置了8周,收集和分析了体重,料摄入量,消化能力,便分数,行为和血型的数据.
主要成果:
- 高种群密度显著降低了平均每日收益,料效率和营养物质消化能力,同时增加了便得分和像咬咬这样的异常行为.
- 补充PFA,特别是苦提取物和精油 (CES1) 的混合物,减轻了高种群密度对生长表现和压力指标的负面影响.
- 治疗组之间的血液样本没有显著差异,但PFA有助于减轻与压力相关的皮质醇水平增加.
结论:
- 该研究表明,PFA可以有效地减轻高种群密度对生长完成猪的有害影响.
- 苦提取物和精油在正常剂量 (CES1) 的混合物在减轻压力和改善拥挤条件下的表现方面被证明是最有效的.
- 猪养殖系统代表了一种有前途的策略,可以在集约养殖系统中提高动物福利和生产率.
更多相关视频
07:02Providing Meaningful Environmental Enrichment and Measuring Saliva Cortisol in Pigs Housed on Slatted Flooring
Published on: September 30, 2019
7.2K
05:52Author Spotlight: Induction of Experimental Endotoxemic Shock in Pigs for Studying Hemodynamic and Respiratory Failure
Published on: December 8, 2023
997
相关概念视频
Determination of Multiple Dosing Parameters: Loading and Maintenance Doses
A loading dose is an essential pharmacological strategy to rapidly achieve the target plasma drug concentration necessary for an immediate therapeutic effect. This approach is especially critical for drugs characterized by slow absorption or extended half-lives, where delaying therapeutic plasma levels could compromise treatment outcomes. By administering a loading dose, clinicians ensure a prompt onset of drug action, even for agents with complex pharmacokinetic profiles.Achieving steady-state...
Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
