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Observationobservedoriginal resultmeasured

Claim · model-written summary

Genotype by environment interaction accounted for 43% of variation in the multi-environment evaluation.

Taxon
Vicia fabaVicia fabat0
Outcome
Genotype × environment interactione0
Value
43%e0

Verbatim

e0

“Genotype × environment interaction accounted for 43% of the sum of squares of the multi-environment evaluation”

✓ located verbatim in the source · abstract, characters 430–540
t0

“Vicia faba”

✓ located verbatim in the source · title, characters 97–107

Title

Identification and multi-environment validation of resistance to rust (Uromyces viciae-fabae) in Vicia faba

Josefina C. Sillero; María M. Rojas-Molina; Amero A. Emeran; et al. Crop and Pasture Science 2017. doi:10.1071/cp17099

Other claims from this paper (10)
Recommendationauthor interpretationoriginal resultmeasured

Claim · model-written summary

Multi-environment evaluation integrating performance, stability and adaptability analyses is necessary to support site-specific cultivar recommendations.

Taxon
MaizeZea mayst0
Outcome
multi-environment evaluatione0

Verbatim

t0

“Maize”

✓ located verbatim in the source · title, characters 80–85
e0

“demonstrate the necessity of multi-environment evaluation that integrates performance, stability and adaptability analyses”

✓ located verbatim in the source · abstract, characters 1459–1581

Title

Genotype–Environment Interaction in Shaping the Agronomic Performance of Silage Maize Varieties Cultivated in Organic Farming Systems

Katarzyna Marcinkowska; Karolina Kolańska; Konrad Banaś; et al. Agriculture 2026. doi:10.3390/agriculture16010123

Other claims from this paper (11)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The BMTME package allows parameter estimation and prediction performance evaluation of multi-trait and multi-environment data.

Outcome
prediction performancee0
Qualifier
multi-trait and multi-environment datae0

Not reportedComparatorValue

Verbatim

e0

“The package allows parameter estimation and evaluates the prediction performance of multi-trait and multi-environment data in a reliable, efficient and user-friendly way.”

✓ located verbatim in the source · abstract, characters 1131–1301

Title

An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-Based Prediction

Osval A Montesinos-López; Abelardo Montesinos-López; Francisco Javier Luna-Vázquez; et al. G3 Genes Genomes Genetics 2019. doi:10.1534/g3.119.400126

Other claims from this paper (2)
Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

Expanded germplasm and multi-environment evaluation were recommended to validate stability and refine selection.

Taxon
tarwit0
Outcome
expanded germplasm evaluation and multi-environment trialse0

Verbatim

e0

“expanded germplasm evaluation and multi-environment trials are required to validate genotype stability and refine selection criteria”

✓ located verbatim in the source · abstract, characters 1727–1859
t0

“tarwi”

✓ located verbatim in the source · title, characters 26–31

Title

Phenotypic variability of tarwi (Lupinus mutabilis S.) in Peruvian germplasm collections

Kevin Ortega-Quispe; Eunice Peña-Elme; Carolina Girón-Aguilar; et al. Genetic Resources 2026. doi:10.46265/genresj.tdic2170

Other claims from this paper (5)
Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

Further multi-environment evaluation in several locations in Indonesia is required before a stable adaptive maize hybrid is selected for intercropping with sweet potato.

Taxon
maizeZea mayse4
Outcome
further multi-environment evaluatione4
Setting
several locations in Indonesiae4

Verbatim

e4

“Selection of stable and adaptive maize hybrid variety for intercropping with sweet potato requires further multi-environment evaluation in several locations in Indonesia”

✓ located verbatim in the source · abstract, characters 1505–1674

Title

Genotype×Environment Interaction of Maize Hybrids under Intercropping With Sweet Potato in Indonesia

Yuyun Yuwariah; Nyimas Poppy Indriani; Mira Ariyanti; et al. Journal of Agronomy 2019. doi:10.3923/ja.2020.31.39

Other claims from this paper (2)
Recommendationauthor interpretationoriginal resultpositivebasis n/a

Claim · model-written summary

The authors support multi-environment evaluation and stability-based selection to improve wheat seed quality.

Outcome
multi-environment evaluation and stability-based selectione0

Verbatim

e0

“the results support the use of multi-environment evaluation and stability-based selection to improve wheat seed quality in a predictable and targeted manner”

✓ located verbatim in the source · abstract, characters 1706–1862
t0

“Wheat”

✓ located verbatim in the source · title, characters 11–16

Title

Evaluating Wheat Seed Quality: Performance, Stability, and Genetic Control Across Six Greek Environments Using Multiple Selection Designs

Vasileios Greveniotis; Elisavet Bouloumpasi; Adriana Skendi; et al. Agriculture 2026. doi:10.3390/agriculture16040476

Other claims from this paper (13)
Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

The multi-trait multi-environment Bayesian model is recommended for genetic evaluation of flood-irrigated rice genotypes.

Taxon
flood-irrigated ricee0
Outcome
genetic evaluation of flood-irrigated rice genotypese5

Verbatim

e0

“The biggest challenge for the reproduction of flood-irrigated rice”

✓ located verbatim in the source · abstract, characters 0–66
e5

“we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops”

✓ located verbatim in the source · abstract, characters 1655–1777

Title

Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice

Antônio Carlos da Silva Júnior; Isabela de Castro Sant’Anna; Michele Jorge Silva Siqueira; et al. PLoS ONE 2022. doi:10.1371/journal.pone.0259607

Other claims from this paper (10)
Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

The authors recommend GGE biplot and HMRPGV for multi-environment genotype evaluation.

Taxon
maizeZea mayst0
Outcome
GGE biplot and HMRPGV for genotype evaluatione0

Verbatim

e0

“Therefore, we recommend the GGE biplot and HMRPGV for genotype evaluation based on multi‐environment trials data.”

✓ located verbatim in the source · abstract, characters 1675–1788
t0

“maize”

✓ located verbatim in the source · title, characters 85–90

Title

Assessment of yield performance and stability of hybrids and populations of tropical maize across multiple environments in Southeastern Brazil

Alison Uberti; Wemerson Mendonça Rezende; Diego Gonçalves Caixeta; et al. Crop Science 2023. doi:10.1002/csc2.20964

Other claims from this paper (9)
Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

EUP 32 is recommended for further multi-location evaluation in a warmer environment and possible release.

Taxon
pearl milletCenchrus americanuse1
Material
EUP 32e2
Outcome
further multi-location evaluatione2
Setting
warmer environmente2

Verbatim

e1

“Thirty six pearl millet genotypes were evaluated”

✓ located verbatim in the source · abstract, characters 0–48
e2

“Genotypes EUP 32 was the highest yielding across all sites followed by EUP 35 and could be recommended for further multi-location evaluation in warmer environment and possible release for commercial production”

✓ located verbatim in the source · abstract, characters 1277–1486

Title

Evaluation of Genotype x Environment Interaction and Stability of Grain Yield and Related Yield Components in Pearl Millet (Pennisetum glaucum (L.) R.Br.)

N Lagat; P Kimurto; O Kiplagat; et al. Journal of Experimental Agriculture International 2018. doi:10.9734/jeai/2018/24311

Other claims from this paper (18)
Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

Iksan is an optimal area for multi-environmental adaptation evaluation in sesame breeding.

Outcome
optimal area for multi-environmental adaptation evaluatione6
Setting
Iksane6

Verbatim

t0

“Genotype-by-Environment Interaction in yield of Sesame”

✓ located verbatim in the source · title, characters 0–54
e6

“Iksan is optimal area for multi-environmental adaptation evaluation in sesame breeding programs”

✓ located verbatim in the source · abstract, characters 647–742

Title

Genotype-by-Environment Interaction in yield of Sesame

Kang-Bo Shim; Churl-Whan Kang; Kim DongHee; et al. The Korean Journal of Crop Science 2003

Other claims from this paper (8)

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