Evidence map›Paper›PMID 37835675›Full record

ArticleAnimals : an open access journal from MDPI2023

UAV Assisted Livestock Distribution Monitoring and Quantification: A Low-Cost and High-Precision Solution.

Wenxiang Ji, Yifei Luo, Yafang Liao, Wenjun Wu, Xinyi Wei, Yudie Yang, Xiong Zhao He, Yutong Shen, Qingshan Ma, Shuhua Yi and 1 more

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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.

Wenxiang JiInstitute of Fragile Eco-Environment, School of Geographic Science, Nantong University, 9 Seyuan Road, Nantong 226019, China.
Yifei LuoInstitute of Fragile Eco-Environment, School of Geographic Science, Nantong University, 9 Seyuan Road, Nantong 226019, China.ORCID 0009-0008-2038-6373
Yafang LiaoInstitute of Fragile Eco-Environment, School of Geographic Science, Nantong University, 9 Seyuan Road, Nantong 226019, China.
Wenjun WuInstitute of Fragile Eco-Environment, School of Geographic Science, Nantong University, 9 Seyuan Road, Nantong 226019, China.
Xinyi WeiInstitute of Fragile Eco-Environment, School of Geographic Science, Nantong University, 9 Seyuan Road, Nantong 226019, China.
Yudie YangInstitute of Fragile Eco-Environment, School of Geographic Science, Nantong University, 9 Seyuan Road, Nantong 226019, China.
Xiong Zhao HeSchool of Agriculture and Environment, College of Science, Massey University, Private Bag 11222, Palmerston North 4442, New Zealand.ORCID 0000-0003-3956-4498
Yutong ShenInstitute of Fragile Eco-Environment, School of Geographic Science, Nantong University, 9 Seyuan Road, Nantong 226019, China.
Qingshan MaForestry Station of Huangnan Prefecture of Qinghai Province, 14 Regong Road, Tongren 811300, China.
Shuhua YiInstitute of Fragile Eco-Environment, School of Geographic Science, Nantong University, 9 Seyuan Road, Nantong 226019, China.
Yi SunInstitute of Fragile Eco-Environment, School of Geographic Science, Nantong University, 9 Seyuan Road, Nantong 226019, China.ORCID 0000-0003-0932-5218

Funding

a grant from State Key Laboratory of Grassland Agro-Ecosystems (Lanzhou University) SKLGAE201906National University' Innovation and Entrepreneurship Training Program 202310304145YNational University' Innovation and Entrepreneurship Training Program 202320304054ZNatural Science Foundation of Jiangsu Province BK20201439the National Natural Science Foundation of China 31901393the National Natural Science Foundation of China 42071056
6 · The paper itself

Abstract

Grazing management is one of the most widely practiced land uses globally. Quantifying the spatiotemporal distribution of livestock is critical for effective management of livestock-grassland grazing ecosystem. However, to date, there are few convincing solutions for livestock dynamic monitor and key parameters quantification under actual grazing situations. In this study, we proposed a pragmatic method for quantifying the grazing density (GD) and herding proximities (HP) based on unmanned aerial vehicles (UAVs). We further tested its feasibility at three typical household pastures on the Qinghai-Tibetan Plateau, China. We found that: (1) yak herds grazing followed a rotational grazing pattern spontaneously within the pastures, (2) Dispersion Index of yak herds varied as an M-shaped curve within one day, and it was the lowest in July and August, and (3) the average distance between the yak herd and the campsites in the cold season was significantly shorter than that in the warm season. In this study, we developed a method to characterize the dynamic GD and HP of yak herds precisely and effectively. This method is ideal for studying animal behavior and determining the correlation between the distribution of pastoral livestock and resource usability, delivering critical information for the development of grassland ecosystem and the implementation of sustainable grassland management.

Indexed as

dispersion degreefarming animal husbandryherding proximitieskernel density estimationprecision livestockreasonable management

Identifiers

PMID37835675
PMCPMC10571782

What OpenQuestion 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 OpenQuestion graph.