Evidence map›Paper›PMID 36636026›Full record

ArticleGlobal change biology. Bioenergy2022

The development of a new crop growth model SwitchFor for yield mapping of switchgrass.

Yanmei Liu, Astley Hastings, Shaolin Chen, Andre Faaij

Abstract read
In one paragraph

Article in Global change biology. Bioenergy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Yanmei LiuIntegrated Research on Energy, Environment and Soc-Energy and Sustainability Research Institute Groningen University of Groningen Groningen The Netherlands.ORCID https://orcid.org/0000-0003-4123-9002
Astley HastingsInstitute of Biological and Environmental Science University of Aberdeen Aberdeen UK.
Shaolin ChenBiomass Energy Center for Arid and Semi-Arid Lands Northwest A&F University Yangling P.R. China.ORCID https://orcid.org/0000-0001-7607-8275
Andre FaaijIntegrated Research on Energy, Environment and Soc-Energy and Sustainability Research Institute Groningen University of Groningen Groningen The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Switchgrass is a promising energy crop has the potential to mitigate global warming and energy security, improve local ecology and generate profit. Its quantitative traits, such as biomass productivity and environmental adaptability, are determined by genotype-by-environment interaction (GEI) or response of genotypes grown across different target environments. To simulate the yield of switchgrass outside its original habitat, a genotype-specific growth model, SwitchFor that captures GEI was developed by parameterising the MiscanFor model. Input parameters were used to describe genotype-specific characteristics under different soil and climate conditions, which enables the model to predict the yield in a wide range of environmental and climate conditions. The model was validated using global field trail data and applied to estimate the switchgrass yield potentials on the marginal land of the Loess Plateau in China. The results suggest that upland and lowland switchgrass have significant differences in the spatial distribution of the adaptation zone and site-specific biomass yield. The area of the adaption zone of upland switchgrass was 4.5 times of the lowland ecotype's. The yield difference between upland and lowland ecotypes ranges from 0 to 34 Mg ha

Indexed as

bio‐energybiomass productiongenotype‐by‐environment interactiongenotype‐specific plant growth modelmarginal landSwitchFor model

Identifiers

PMID36636026
PMCPMC9828430

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.