Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Field trial analyses of wheat and cassava benefit from spatial correction.

PloS one - 1 Jan 2026

Setotaw Tesfahun Alemu, Nyaga Christine Mwende, Waring David James, Chen Jianli, Egesi Chiedozie, Frels Katherine, Gutierrez Lucia, Jung Jinha, Kanaabi Michael, Krause Margaret, Mason R Esten, Mohammadi Mohsen, de Oliveira Eder Jorge, Olson Eric, Rutkoski Jessica E, Santantonio Nicholas, Sneller Clay, Sorrells Mark E, Mueller Lukas A, Jannink Jean-Luc

Abstract excerpt

Spatial variation is a major source of error in agricultural field experiments affecting genotype performance prediction. Implementing statistical models that account for spatial effects can improve the prediction of genotype performance. This study evaluated the impact of the P-spline spatial correction method on the estimation of genetic parameters and AIC values in two distinct crops, wheat and cassava, using...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Genotype
Share to Manihot

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Genetic

Share to Models, Statistical

Share to Plant Diseases

Share to Triticum

Source

Open in PubMed Open full text

Identifiers

PMID: 42616763
DOI: 10.1371/journal.pone.0354968
PMCID: PMC13489483

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Field trial analyses of wheat and cassava benefit from spatial correction.PMID 42616763Read ↗

Field trial analyses of wheat and cassava benefit from spatial correctionSemantic proximity Read ↗

Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis.Semantic proximity Read ↗

Accuracies of univariate and multivariate genomic prediction models in African cassava.Semantic proximity Read ↗

Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data.Semantic proximity Read ↗

The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye.Semantic proximity Read ↗

Accuracy of prediction from multi-environment trials for new locations using pedigree information and environmental covariates: the case of sorghum (Sorghum bicolor (L.) Moench) breeding.Semantic proximity Read ↗

Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model.Semantic proximity Read ↗

Spatial modeling increases accuracy of selection for Phytophthora infestans‐resistant tomato genotypesSemantic proximity Read ↗

Improving lodgepole pine genomic evaluation using spatial correlation structure and SNP selection with single-step GBLUP.Semantic proximity Read ↗

Mixed spatial models for data analysis of yield on large grapevine selection field trials.Semantic proximity Read ↗

Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision.Semantic proximity Read ↗

Leveraging Transcriptomics Data for Genomic Prediction Models in CassavaSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.