Evidence map›Paper›PMID 39950573›Full record

ArticleG3 (Bethesda, Md.)2025

Assessing genotype adaptability and stability in perennial forage breeding trials using random regression models for longitudinal dry matter yield data.

Claudio Carlos Fernandes Filho, Sanzio Carvalho Lima Barrios, Mateus Figueiredo Santos, Jose Airton Rodrigues Nunes, Cacilda Borges do Valle, Liana Jank, Esteban Fernando Rios

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Claudio Carlos Fernandes FilhoSugarcane Technology Center, Piracicaba, SP 13400-970, Brazil.
Sanzio Carvalho Lima BarriosEmbrapa Beef Cattle, Campo Grande, MS 79106-550, Brazil.
Mateus Figueiredo SantosEmbrapa Beef Cattle, Campo Grande, MS 79106-550, Brazil.
Jose Airton Rodrigues NunesDepartment of Biology, Institute of Natural Sciences, Universidade Federal de Lavras, Minas Gerais 37200-900, Brazil.
Cacilda Borges do ValleEmbrapa Beef Cattle, Campo Grande, MS 79106-550, Brazil.
Liana JankEmbrapa Beef Cattle, Campo Grande, MS 79106-550, Brazil.
Esteban Fernando RiosAgronomy Department, University of Florida, Gainesville, FL 32611-0180, USA.ORCID 0000-0003-3389-7195

Funding

Agriculture and Food Research Initiative 2014-67013-22418University of FloridaUSDA
6 · The paper itself

Abstract

Genotype selection for dry matter yield (DMY) in perennial forage species is based on repeated measurements over time, referred to as longitudinal data. These datasets capture temporal trends and variability, which are critical for identifying genotypes with desirable performance across seasons. In this study, we have presented a random regression model (RRM) approach for selecting genotypes based on longitudinal DMY data generated from 10 breeding trials and three perennial species, alfalfa (Medicago sativa L.), guineagrass (Megathyrsus maximus), and brachiaria (Urochloa spp.). We also proposed the estimation of adaptability based on the area under the curve and stability based on the curve coefficient of variation. Our results showed that RRM always approximated the (co)variance structure into an autoregressive pattern. Furthermore, RRM can offer useful information about longitudinal data in forage breeding trials, where the breeder can select genotypes based on their seasonality by interpreting reaction norms. Therefore, we recommend using RRM for longitudinal traits in breeding trials for perennial species.

Indexed as

Adaptation, PhysiologicalGenotypeModels, GeneticPlant BreedingPoaceaeMedicago sativaRegression Analysisforage breedinggenotype by environmentlongitudinal datarandom regression

Identifiers

PMID39950573
PMCPMC11917486

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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.