英国散养蛋鸡群的可持续性绩效基准

IF 6.1 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Agricultural Systems Pub Date : 2024-09-25 DOI:10.1016/j.agsy.2024.104103
L.E. Higham , I. Handel , L. Boden , D. Moran
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引用次数: 0

摘要

内容为了公平地养活世界上日益增长的人口,同时阻止进一步突破地球边界,有必要从粮食安全、环境和道德可持续性等多重且经常相互竞争的目标方面评估食品和农业系统的相对绩效。目标我们旨在为英格兰和苏格兰 2016 年至 2022 年间生产的 80 个散养蛋鸡群的可持续性绩效设定基准,并找出表现最佳的鸡群之间的共同特征,为供应链政策提供信息。标杆管理基于多标准效率评分,包含六项投入和两项产出标准,涵盖人类、动物和环境领域,包括被忽视的动物福利和抗生素使用情况。然后进行单变量和多变量回归分析,以探索与实现最高效率相关的生产因素。结果和结论与同类相比,约有一半的鸡群达到了最高效率得分。对其投入和产出要素的分析表明,与效率低下的鸡群相比,效率最高的鸡群在一系列广泛的标准方面表现出色,这表明一些鸡场成功地兼顾了生产和盈利能力以及卓越的环境、动物福利和抗生素使用性能。单变量分析和多变量回归显示,在 p < 0.05 的水平上,没有统计学意义上的效率预测因素,鸡群相对效率得分的未解释差异在 0.7 和 1.0 之间,这表明具有相似生产系统和标准的养殖户之间存在改进路径,可能是基于未测量的人为因素。然而,单变量分析表明,蛋鸡品种与效率之间的关联度为 p < 0.1,这支持了这样一种观点,即具有均衡遗传优势的品种将在寻求减轻生产外部性的食品系统中发挥重要作用。SIGNIFICANCEDEA 可将多个可持续发展维度整合为一个单一的绩效指标,对生产单位进行基准衡量和比较。它为产业界和政府提供了一种方法,以确定潜在的杠杆点,激励改进绩效,并为更好地收集与生产的市场和非市场(外部)成本影响有关的数据奠定了基础,包括动物福利和抗菌素的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Benchmarking sustainability performance in UK free-range laying hen flocks

CONTEXT

To equitably nourish the world's growing human population whilst halting further transgression of the planetary boundaries, there is a need to evaluate the relative performance of food and farming systems in terms of multiple and often competing food security, environmental, and ethical sustainability objectives.

OBJECTIVE

We aimed to benchmark the sustainability performance of 80 free-range laying hen flocks in England and Scotland, in production between 2016 and 2022, and to identify any common characteristics between the best performers to inform supply chain policy. Benchmarking was based on multi-criteria efficiency scores, incorporating six input and two output criteria covering human, animal, and environmental domains, including the neglected measures of animal welfare and antibiotic use.

METHODS

Data Envelope Analysis (DEA) was used to derive efficiency scores. Univariate and multivariate regression analyses were then applied to explore production factors that could be associated with the attainment of maximum efficiency.

RESULTS AND CONCLUSIONS

Approximately half of the flocks attained the maximum efficiency score, relative to their peers. Analysis of their component inputs and outputs demonstrated the favourable performance of the most efficient flocks across a broad array of criteria compared to inefficient flocks, indicating that some farms are successfully reconciling production and profitability with superior environmental, animal welfare, and antibiotic use performance. Univariate analysis and multivariate regression revealed no statistically significant predictors of efficiency at the level of p < 0.05, with unexplained variation in relative efficiency scores of the flocks of between 0.7 and 1.0, indicating a path of improvement amongst farmers with similar production systems and standards, potentially based on unmeasured human factors. However, univariate analysis revealed an association between laying hen breed and the attainment of efficiency at the level of p < 0.1, supporting the notion that breeds with balanced genetic merit will play an important role in food systems seeking to mitigate externalities of production.

SIGNIFICANCE

DEA allows multiple sustainability dimensions to be combined into a single performance metric to benchmark and compare production units. It offers a method for industry and government to identify potential leverage points to incentivise improved performance, and is a basis for better data collection in relation to both market and non-market (external) cost impacts of production, including animal welfare and antimicrobial use.
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来源期刊
Agricultural Systems
Agricultural Systems 农林科学-农业综合
CiteScore
13.30
自引率
7.60%
发文量
174
审稿时长
30 days
期刊介绍: Agricultural Systems is an international journal that deals with interactions - among the components of agricultural systems, among hierarchical levels of agricultural systems, between agricultural and other land use systems, and between agricultural systems and their natural, social and economic environments. The scope includes the development and application of systems analysis methodologies in the following areas: Systems approaches in the sustainable intensification of agriculture; pathways for sustainable intensification; crop-livestock integration; farm-level resource allocation; quantification of benefits and trade-offs at farm to landscape levels; integrative, participatory and dynamic modelling approaches for qualitative and quantitative assessments of agricultural systems and decision making; The interactions between agricultural and non-agricultural landscapes; the multiple services of agricultural systems; food security and the environment; Global change and adaptation science; transformational adaptations as driven by changes in climate, policy, values and attitudes influencing the design of farming systems; Development and application of farming systems design tools and methods for impact, scenario and case study analysis; managing the complexities of dynamic agricultural systems; innovation systems and multi stakeholder arrangements that support or promote change and (or) inform policy decisions.
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