Evidence mapPaperPMID 35511692Full record

ReviewJournal of animal science2022

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production.

Hector M Menendez, Jameson R Brennan, Charlotte Gaillard, Krista Ehlert, Jaelyn Quintana, Suresh Neethirajan, Aline Remus, Marc Jacobs, Izabelle A M A Teixeira, Benjamin L Turner and 1 more

Abstract readReview
In one paragraph

Review in Journal of animal science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Using artificial intelligence to make feeding and management decisions in dairy herds.Animal frontiers : the review magazine of animal agriculture · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Hector M MenendezDepartment of Animal Science, South Dakota State University, Rapid City, SD 57702, USA.ORCID 0000-0001-9092-7237
Jameson R BrennanDepartment of Animal Science, South Dakota State University, Rapid City, SD 57702, USA.
Charlotte GaillardInstitut Agro, PEGASE, INRAE, 35590 Saint Gilles, France.ORCID 0000-0002-7896-2520
Krista EhlertDepartment of Natural Resource Management, South Dakota State University, Rapid City, SD, 57702, USA.
Jaelyn QuintanaDepartment of Animal Science, South Dakota State University, Rapid City, SD 57702, USA.
Suresh NeethirajanFarmworx, Adaptation Physiology, Animal Sciences Group, Wageningen University, 6700 AH, The Netherlands.
Aline RemusSherbrooke Research and Development Centre, Sherbrooke, QC J1M 1Z3, Canada.ORCID 0000-0003-1177-5680
Marc JacobsFR Analytics B.V., 7642 AP Wierden, The Netherlands.
Izabelle A M A TeixeiraDepartment of Animal, Veterinary, and Food Sciences, University of Idaho, Twin Falls, ID 83301, USA.
Benjamin L TurnerDepartment of Agriculture, Agribusiness, and Environmental Science, King Ranch® Institute for Ranch Management, Texas A&M University-Kingsville, Kingsville, TX 78363, USA.
Luis O TedeschiDepartment of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA.ORCID 0000-0003-1883-4911

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.

Indexed as

Animal Nutritional Physiological PhenomenaLivestockAgricultureAnimalsFarmsModels, Theoreticalconfineddigital monitoringextensivelivestock productionmodelingsensors

Identifiers

PMID35511692
PMCPMC9171331

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.