与巴西中西部肉牛胴体质量相关的环境因素和管理方法。

IF 1.3 Q3 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Translational Animal Science Pub Date : 2024-08-12 eCollection Date: 2024-01-01 DOI:10.1093/tas/txae120
Thaís B Amaral, Alain P Le Cornec, Guilherme J M Rosa
{"title":"与巴西中西部肉牛胴体质量相关的环境因素和管理方法。","authors":"Thaís B Amaral, Alain P Le Cornec, Guilherme J M Rosa","doi":"10.1093/tas/txae120","DOIUrl":null,"url":null,"abstract":"<p><p>The \"Precoce MS\" program, established by the Brazilian government in Mato Grosso do Sul in 2017, aims to encourage beef producers to harvest animals at younger ages to enhance carcass quality. About 40% of the beef produced in the state now comes from this program, which offers tax refunds ranging from 49% to 67% based on carcass classification and production system. Despite the program success, with participants delivering younger animals (with a maximum of 4 incisors), there remains significant variability in carcass quality. This paper investigates management practices and environmental factors affecting farm performance regarding carcass quality. Data from all animals harvested between the beginning of 2017 and the end of 2018 were analyzed, totaling 1,107 million animals from 1,470 farms. Farm performance was assessed based on the percentage of animals achieving grades \"AAA\" and \"AA.\" Each batch of harvested cattle from each farm was categorized into two groups: high farm performance (HFP, with more than 50% of animals classified as \"AAA\" or \"AA\") and low farm performance (LFP, with less than 50% classified as such). A predictive logistic model was developed to forecast farm performance (FP) using 14 continuous and 15 discrete pre-selected variables. The most effective model, obtained through backward stepwise variable selection, had an <i>R</i> <sup>2</sup> of 0.18, accuracy of 71.5%, and AUC of 0.715. Key predictors included animal category, production system type, carcass weight, individual identification, traceability system, presence of a feed plant, location, and the Normalized Difference Vegetation Index (NDVI) from the 12-mo average before harvest. Developing predictive models of carcass quality by integrating data from commercial farms with other sources of information (animal, production system, and environment) can improve our understanding of production systems, optimize resource allocation, and advance sustainable animal production. Additionally, they offer valuable insights for designing and implementing better sectorial, social, and environmental policies by public administrations, not only in Brazil but also in other tropical and subtropical regions worldwide.</p>","PeriodicalId":23272,"journal":{"name":"Translational Animal Science","volume":"8 ","pages":"txae120"},"PeriodicalIF":1.3000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11401279/pdf/","citationCount":"0","resultStr":"{\"title\":\"Environmental factors and management practices associated with beef cattle carcass quality in the mid-west of Brazil.\",\"authors\":\"Thaís B Amaral, Alain P Le Cornec, Guilherme J M Rosa\",\"doi\":\"10.1093/tas/txae120\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The \\\"Precoce MS\\\" program, established by the Brazilian government in Mato Grosso do Sul in 2017, aims to encourage beef producers to harvest animals at younger ages to enhance carcass quality. About 40% of the beef produced in the state now comes from this program, which offers tax refunds ranging from 49% to 67% based on carcass classification and production system. Despite the program success, with participants delivering younger animals (with a maximum of 4 incisors), there remains significant variability in carcass quality. This paper investigates management practices and environmental factors affecting farm performance regarding carcass quality. Data from all animals harvested between the beginning of 2017 and the end of 2018 were analyzed, totaling 1,107 million animals from 1,470 farms. Farm performance was assessed based on the percentage of animals achieving grades \\\"AAA\\\" and \\\"AA.\\\" Each batch of harvested cattle from each farm was categorized into two groups: high farm performance (HFP, with more than 50% of animals classified as \\\"AAA\\\" or \\\"AA\\\") and low farm performance (LFP, with less than 50% classified as such). A predictive logistic model was developed to forecast farm performance (FP) using 14 continuous and 15 discrete pre-selected variables. The most effective model, obtained through backward stepwise variable selection, had an <i>R</i> <sup>2</sup> of 0.18, accuracy of 71.5%, and AUC of 0.715. Key predictors included animal category, production system type, carcass weight, individual identification, traceability system, presence of a feed plant, location, and the Normalized Difference Vegetation Index (NDVI) from the 12-mo average before harvest. Developing predictive models of carcass quality by integrating data from commercial farms with other sources of information (animal, production system, and environment) can improve our understanding of production systems, optimize resource allocation, and advance sustainable animal production. Additionally, they offer valuable insights for designing and implementing better sectorial, social, and environmental policies by public administrations, not only in Brazil but also in other tropical and subtropical regions worldwide.</p>\",\"PeriodicalId\":23272,\"journal\":{\"name\":\"Translational Animal Science\",\"volume\":\"8 \",\"pages\":\"txae120\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11401279/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Translational Animal Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/tas/txae120\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"AGRICULTURE, DAIRY & ANIMAL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational Animal Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/tas/txae120","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
引用次数: 0

摘要

巴西政府于2017年在南马托格罗索州设立了 "Precoce MS "计划,旨在鼓励牛肉生产者在较小的年龄段收获牲畜,以提高胴体质量。目前,该州生产的牛肉中约有 40% 来自该计划,该计划根据胴体分类和生产系统提供 49% 至 67% 的退税。尽管该计划取得了成功,但由于参与者交付的牲畜年龄较小(最多有 4 颗门牙),胴体质量仍存在很大差异。本文研究了影响农场胴体质量表现的管理方法和环境因素。本文分析了 2017 年初至 2018 年底期间收获的所有动物数据,共计来自 1470 个农场的 11.07 亿头动物。农场绩效根据达到 "AAA "和 "AA "等级的动物比例进行评估。每个农场的每批收获牛被分为两组:高农场绩效(HFP,50%以上的动物被评为 "AAA "或 "AA "级)和低农场绩效(LFP,50%以下的动物被评为 "AAA "或 "AA "级)。利用预选的 14 个连续变量和 15 个离散变量,建立了一个预测性逻辑模型来预测猪场绩效(FP)。通过逆向逐步选择变量得出的最有效模型的 R 2 为 0.18,准确率为 71.5%,AUC 为 0.715。主要预测因素包括动物类别、生产系统类型、胴体重量、个体识别、可追溯系统、是否有饲料厂、地点以及收获前 12 个月平均归一化植被指数(NDVI)。通过将来自商业农场的数据与其他信息来源(动物、生产系统和环境)进行整合,开发胴体质量预测模型,可以提高我们对生产系统的了解,优化资源配置,促进可持续动物生产。此外,这些模型还为公共管理部门设计和实施更好的部门、社会和环境政策提供了宝贵的见解,这些政策不仅适用于巴西,也适用于全球其他热带和亚热带地区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Environmental factors and management practices associated with beef cattle carcass quality in the mid-west of Brazil.

The "Precoce MS" program, established by the Brazilian government in Mato Grosso do Sul in 2017, aims to encourage beef producers to harvest animals at younger ages to enhance carcass quality. About 40% of the beef produced in the state now comes from this program, which offers tax refunds ranging from 49% to 67% based on carcass classification and production system. Despite the program success, with participants delivering younger animals (with a maximum of 4 incisors), there remains significant variability in carcass quality. This paper investigates management practices and environmental factors affecting farm performance regarding carcass quality. Data from all animals harvested between the beginning of 2017 and the end of 2018 were analyzed, totaling 1,107 million animals from 1,470 farms. Farm performance was assessed based on the percentage of animals achieving grades "AAA" and "AA." Each batch of harvested cattle from each farm was categorized into two groups: high farm performance (HFP, with more than 50% of animals classified as "AAA" or "AA") and low farm performance (LFP, with less than 50% classified as such). A predictive logistic model was developed to forecast farm performance (FP) using 14 continuous and 15 discrete pre-selected variables. The most effective model, obtained through backward stepwise variable selection, had an R 2 of 0.18, accuracy of 71.5%, and AUC of 0.715. Key predictors included animal category, production system type, carcass weight, individual identification, traceability system, presence of a feed plant, location, and the Normalized Difference Vegetation Index (NDVI) from the 12-mo average before harvest. Developing predictive models of carcass quality by integrating data from commercial farms with other sources of information (animal, production system, and environment) can improve our understanding of production systems, optimize resource allocation, and advance sustainable animal production. Additionally, they offer valuable insights for designing and implementing better sectorial, social, and environmental policies by public administrations, not only in Brazil but also in other tropical and subtropical regions worldwide.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Translational Animal Science
Translational Animal Science Veterinary-Veterinary (all)
CiteScore
2.80
自引率
15.40%
发文量
149
审稿时长
8 weeks
期刊介绍: Translational Animal Science (TAS) is the first open access-open review animal science journal, encompassing a broad scope of research topics in animal science. TAS focuses on translating basic science to innovation, and validation of these innovations by various segments of the allied animal industry. Readers of TAS will typically represent education, industry, and government, including research, teaching, administration, extension, management, quality assurance, product development, and technical services. Those interested in TAS typically include animal breeders, economists, embryologists, engineers, food scientists, geneticists, microbiologists, nutritionists, veterinarians, physiologists, processors, public health professionals, and others with an interest in animal production and applied aspects of animal sciences.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信