Evidence map›Paper›PMID 41096385›Full record

ArticleAnimals : an open access journal from MDPI2025

Livestock Dung Proxies Provide Insights into Grazing Density Quantification and Distribution.

Bo Huang, Yingying Liu, Yingxi Chen, Yixuan Dong, Fujiang Hou, Shenghua Chang, Shuhua Yi, Yi Sun

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Bo HuangState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China.
Yingying LiuState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China.
Yingxi ChenSchool of Geographic Science, Nantong University, 9 Seyuan Road, Nantong 226007, China.
Yixuan DongSchool of Geographic Science, Nantong University, 9 Seyuan Road, Nantong 226007, China.
Fujiang HouState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China.
Shenghua ChangState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China.
Shuhua YiState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China.
Yi SunState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China.

Funding

the National Natural Science Foundation of China 42071056, 31901393
6 · The paper itself

Abstract

Managed grazing is the most widespread and economically significant form of grassland utilization worldwide. Accurate quantification of the spatiotemporal distribution of grazing intensity (GI) is crucial for promoting sustainable management of livestock-grassland ecosystems. However, a reliable method for dynamically monitoring GI and quantifying key proxies under real-world grazing conditions is still lacking. In this study, we developed a practical approach to estimate GI using sequential unmanned aerial vehicle (UAV) monitoring and evaluated its feasibility in a typical household pasture on the Qinghai-Tibetan Plateau, China. Our findings show that: (1) yak dung is clearly identifiable in UAV image, although detection accuracy decreases with increasing flight altitude (from 100% at 2 m to 93.16% at 20 m); (2) yak dung density serves as a feasible proxy for GI, effectively capturing its temporal and spatial variability; (3) yak dung density reflects cumulative GI from May to September, and its representativeness increases with the length of accumulation. The proposed approach is characterized by high frequency, accuracy, and efficiency. It is well-suited for studying animal behavior and evaluating livestock-resource relationships, thereby providing valuable insights for sustainable grassland ecosystem management.

Indexed as

alpine meadowgrazing intensityhousehold pasturethe Qinghai–Tibet plateauunmanned aerial vehicle

Identifiers

PMID41096385
PMCPMC12523358

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.