Route optimization as an instrument to improve animal welfare and economics in pre-slaughter logistics

Investigating the impact of pre-slaughter management factors on indicators of fed beef cattle welfare – a scoping review

Introduction

The impact of pre-slaughter management practices on fed beef cattle welfare is a multifaceted and well researched subject matter. Factors such as transportation, handling, lairage time and several animal characteristics can directly impact the cattle’s behavior, mobility, blood lactate and cortisol levels, likelihood of injury and ultimately overall cattle welfare. Animal welfare continues to grow as a critical component of slaughter in the fed beef industry, yet a formal review of related research does not exist.

Methods

A scoping review was performed in order to (1) catalog pre-slaughter management factors that impact fed beef cattle welfare at the time of slaughter, (2) identify indicators used to evaluate the impact of pre-slaughter management on fed beef cattle welfare at slaughter, and (3) gain further understanding of the relationship between pre-slaughter management factors and fed beef cattle welfare outcome indicators at slaughter. Three data bases were ultimately searched: PubMed, CAB Abstracts, and Web of Science. The concepts used in the database searches were the population of interest (i.e., fed beef cattle), the location in the supply chain, preslaughter management factors, and welfare outcomes.

Results

A total of 69 studies were included in final analysis for this review, including studies from six geographic regions around the globe. Studies involving alternative slaughter methods (e.g., religious stunning or mobile slaughter) were not included in the formal analysis of this review, but still merited an in-depth discussion within this paper. After reviewing the studies, a total of 37 pre-slaughter factors and 69 indicators of welfare were measured throughout. Pre-slaughter management factors were then categorized by: animal characteristics; environmental characteristics; handling; lairage; transportation; and water/feed. Outcomeindicators of welfare were categorized into: behaviors; health, injury and disease; physiological; and stunning and insensibility.

Discussion

Pre-slaughter factors relating to transportation and handling, and welfare outcomes measured by behaviors and physiology were of the most researched throughout the studies.The results of this review offer a catalogue of commonly researched factors and indicators of welfare measured during the pre-slaughter phase, as well ast he relationships between them. This review also offers further substantial evidence that a multitude of events in the pre-slaughter phase affect fed beef cattle welfare and a collection of highly applicable welfare indicators to expedite further research on the effects of pre-slaughter factors and the application of improved practices.

Logistics Distribution Route Optimization Model Based on Recursive Fuzzy Neural Network Algorithm

In recent years, more and more attention has been paid to the utilization of data and information in the logistics distribution path optimization system of e-commerce, but it is difficult to have scientific guarantee in the process of determining the optimal distribution path scheme of e-commerce. How to realize the optimization and adaptive setting of distribution path by using intelligent algorithm has become a hot spot. To battle these issues, this paper studies the logistics distribution path optimization model based on recursive fuzzy neural network algorithm. This paper analyses the research status of logistics distribution path determination scheme and applies the recursive fuzzy neural network algorithm in the selection of e-commerce logistics distribution path scheme. The experimental results show that the recursive fuzzy neural network algorithm can realize the optimization of e-commerce logistics distribution path, and the best distribution route can be made according to the characteristic difference of logistics distribution route, and its distribution accuracy can reach more than 97%.