Evidence map›Paper›PMID 32339184›Full record

SynthesisPloS one2020

A meta-analysis of the effects of crop residue return on crop yields and water use efficiency.

Xingli Lu

Abstract readMeta-Analysis
In one paragraph

Synthesis in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  4. Review
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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

1 author.

Xingli LuCollege of Agronomy, Ningxia University, Yinchuan, Ningxia, China.ORCID 0000-0002-5163-9088

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After harvesting agricultural crops, the residue can be returned to the soil as mulch. This study performed a meta-analysis of previous research to investigate the effects of crop residue return and other factors on crop yields and water use efficiency (WUE). Overall, the results show that crop residue return increases crop yields by 5.0% relative to crops grown without it. The greatest increases in yield for crops grown with returned residue were associated with average annual temperatures < 10 °C (yield increase = 7.6%), rainfall ≥ 800 mm (9.5%), plowing depth ≥ 20 cm (6.5%), corn crops (8.0%), growth of a single crop per year (10.1%), no irrigation (11.9%), nitrogen (N), and potassium (K) fertilization (20.0%), and low nitrogen application rates of 0-100 kg N ha-1 (10.8%). The effects of crop residue return on crop yields were found to vary according to the following soil properties: organic matter content ≥ 15 g kg-1 (yield increase = 9.4%), available nitrogen content ≥ 100 mg kg-1 (10.3%), and pH ≤ 6.5 (11.2%). The greatest magnitudes of increase in WUE associated with crop residue return were associated with corn (yield increase = 13.7%), medium nitrogen content (100-150 kg ha-1; 23.3%), high soil organic matter (≥ 15 g kg-1; 25.5%) and low air temperatures (< 10 °C; 19.9%). In addition, our results suggest that crop residue return might be most effective in increasing crop yields and WUE in corn crops, crops with a tillage depth ≥ 20 cm, crops grown with moderate nitrogen fertilization (0-150 kg ha-1), growth of a single crop per year, high soil organic matter content (≥ 15 g kg-1), and cold conditions (< 10 °C). Overall, the results of this meta-analysis suggest that crop residue return can increase crop yields and WUE, with the relationship being mainly affected by climatic conditions, plowing depth, fertilization management, crop types, and soil properties.

Indexed as

Agricultural IrrigationCrop ProductionCrops, AgriculturalSoilSoil MicrobiologyTemperatureSoil

Identifiers

PMID32339184
PMCPMC7185903

What OpenQuestion holds

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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.