Evidence map›Paper›PMID 39650557›Full record

ArticlePeerJ2024

Effect of mulching and organic manure on maize yield, water, and nitrogen use efficiency in the Loess Plateau of China.

Yingying Xing, Jintao Fu, Xiukang Wang

Abstract read
In one paragraph

Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Yingying XingKey Laboratory of Applied Ecology of Loess Plateau, College of Life Science, Yan'an University, Yan'an, Shaanxi, China.
Jintao FuKey Laboratory of Applied Ecology of Loess Plateau, College of Life Science, Yan'an University, Yan'an, Shaanxi, China.
Xiukang WangKey Laboratory of Applied Ecology of Loess Plateau, College of Life Science, Yan'an University, Yan'an, Shaanxi, China.ORCID 0000-0001-5638-809X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current agricultural practices prioritize intensive food production, often at the expense of environmental sustainability. This approach results in greenhouse gas emissions and groundwater pollution due to over-fertilization. In contrast, organic agriculture promotes a more efficient use of non-renewable energy, improves soil quality, and reduces ecological damage. However, the effects of mulching and organic manure on maize yield, water use efficiency (WUE), and nitrogen use efficiency (NUE) in China's Loess Plateau have not been sufficiently researched. In 2017 and 2018, an experiment utilizing a randomized complete block design with two factors (two mulching levels × three organic nitrogen application rates) was conducted. The water content of the upper soil layer was found to be 12.6% to 19.4% higher than that of the subsoil layer. Across all soil depths and years, the soil nitrate-N content in mulched treatments was 10% to 31.8% greater than in non-mulched treatments with varying organic nitrogen rates. Additionally, mulching resulted in an increase in grain yield of 9.4% in 2017 and 8.9% in 2018 compared to non-mulched treatments. A significant interaction was observed between mulching and organic nitrogen application rate concerning WUE, alongside a negative correlation between WUE and NUE. These findings suggest that the application of 270 kg N ha

Indexed as

FertilizersManureNitrogenSoilWaterZea maysChinaOrganic AgricultureFertilizersManureNitrogenSoilWaterGrain yieldMulchingOrganic fertilizerSustainabilityWater and nitrogen use efficiency

Identifiers

PMID39650557
PMCPMC11623062

What OpenQuestion holds

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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.