Impacts of extreme agroclimatic indicators on the performance of coffee (

Fedhasa Benti Chalchissa, Girma Mamo Diga, Gudina Legese Feyisa, Alemayehu Regassa Tolossa

Abstract read

In one paragraph

Article in Heliyon, 2022. 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.

Modeling the impacts of extreme climate scenarios on soil acidity (pH and exchangeable aluminum) in Abbay River Basin, Ethiopia.Heliyon · 2024
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.

Fedhasa Benti ChalchissaJimma University, Department of Natural Resource Management, Jimma, Ethiopia.

Girma Mamo DigaEthiopia Agricultural Research Institute, Addis Ababa, Ethiopia.

Gudina Legese FeyisaAddis Ababa University, Center for Environmental Science, Addis Ababa, Ethiopia.

Alemayehu Regassa TolossaJimma University, Department of Natural Resource Management, Jimma, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Estimating crop biomass is critical for countries whose primary source of income is agriculture. It is a valuable indicator for evaluating crop yields and provides information to growers and managers for developing climate change adaptation strategies. The objective of the study was to model the impacts of agroclimatic indicators on the performance of aboveground biomass (AGB) in Arabica coffee trees, a critical income source for millions of Ethiopians. One hundred thirty-five coffee tree stump diameters were measured at 40 cm above ground level. The historical (1998-2010) and future (2041-2070) agroclimatic data were downloaded from the European Copernicus climate change services website. All datasets were tested for missing data, outliers, and multicollinearity and were grouped into three clusters using the K-mean clustering method. The parameter estimates (coefficients of regression) were analyzed using a generalized regression model. The performance of coffee trees' AGB in each cluster was estimated using an artificial neural network model. The future expected change in AGB of coffee trees was compared using a paired t-test. The regression model's results reveal that the sensitivity of

Indexed as

Agroclimatic indicatorArtificial neural network modelBove ground biomassCoffee treeMicroclimates

Identifiers

PMID36016531

DOI10.1016/j.heliyon.2022.e10136

PMCPMC9396549

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LicenceCC BY

Read underepoch 390

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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.