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Assessment of statistical analysis of Swedish cultivar testing

a cross-validation study for model selection

Buntaran, Harimurti (2019). Assessment of statistical analysis of Swedish cultivar testing. Uppsala : Sveriges lantbruksuniv. , Rapport (Institutionen för energi och teknik, SLU), 1654-9406 ; 107
ISBN 978-91-576-9714-1
eISBN 978-91-576-9715-8
[Licentiate thesis]

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The Swedish official cultivar testing conducts multienvironmental trials (MET) to makerecommendations of cultivars that are well adapted to farmers’ regional conditions. Inthe MET, a large number of cultivars are tested in several geographical regions. Thetested cultivars perform differently in varying soil types and climates, a phenomenonknown as genotype×environment interactions. The MET data structure is often large andhighly imbalanced, which causes computational problems when applying some statisticalmethods. Several issues, such as prediction of crop variety performance and efficientcomputation of measure of cultivar stability are urgent to be tackled by developingcomprehensive and robust statistical methods. This study aims to address these issuesand provide a gold standard for MET analysis in Swedish official cultivar testing.

In this study, we investigated several linear mixed models by using cross-validation(CV). We proposed to use random cultivar effects, known as best linear unbiasedprediction (BLUP) method to replace the current fixed cultivar effects, known as bestlinear unbiased estimation (BLUE). In theory, BLUP provides more accurate rankingsand predictions than BLUE. The current-practice analysis strategy, i.e., two-stageunweighted strategy, was also compared to several strategies such as single-stagestrategy and two-stage weighted strategies that comprise some weighting methods. In theCV, mean squared error of differences (MSEP) was used to assess the performance ofestimation of cultivar effects by BLUP and BLUE to select a model that provides bestprediction accuracy. A new inter-zone stability measure was also proposed to tacklecomputational burden and provide additional useful information regarding cultivarstability across zones and years.

The MSEP revealed that BLUP outperformed the current-practice method, BLUE,and so improved the accuracy of zone-based prediction. Also, the single-stage and twostage weighted strategies outperformed the current strategy. The proposed stabilitymeasure offered a less computational resource, and provided more flexible stabilitymeasure for practical purpose.

Authors/Creators:Buntaran, Harimurti
Title:Assessment of statistical analysis of Swedish cultivar testing
Subtitle:a cross-validation study for model selection
Series Name/Journal:Rapport (Institutionen för energi och teknik, SLU)
Year of publishing :2019
Number of Pages:71
IBuntaran H.*, Piepho, H.-P., Hagman, J & Forkman J. (2018). Performance of empirical BLUE and empirical BLUP in Swedish crop variety trials. Biuletyn Oceny Odmian, 35, pp. 15–17, ISBN 978-83-86224-12-8
IIBuntaran H.*, Piepho, H.-P., Hagman, J & Forkman J. (2019). A crossvalidation of statistical models for zoned-based prediction in cultivar testing. Crop science, 59 (4), pp. 1544–1553. doi: 10.2135/cropsci2018. 10.0642
IIIBuntaran H.*, Piepho, H.-P., Schmidt, P., Rydén, J., Halling, M., Forkman, J. (2019). One-stage or two-stage? Cross-validation says, “It does not matter, if the two-stage is weighted.” (manuscript).
Place of Publication:Uppsala
Publisher:Department of Energy and Technology, Swedish University of Agricultural Sciences
ISBN for printed version:978-91-576-9714-1
ISBN for electronic version:978-91-576-9715-8
Publication Type:Licentiate thesis
Article category:Other scientific
Full Text Status:Public
Agris subject categories.:F Plant production > F01 Crop husbandry
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Agricultural Science
Keywords:BLUE, BLUP, cross-validation, genotype×environment interactions, linear mixed models, multienvironment trials, stability, stage-wise analysis
Permanent URL:
ID Code:16295
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Energy and Technology
Deposited By: SLUpub Connector
Deposited On:28 Aug 2019 08:44
Metadata Last Modified:21 Jan 2020 11:56

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