Efficiency of using spatial analysis for Norway spruce progeny tests in Sweden

Chen, Zhiqiang; Helmersson, Andreas; Westin, Johan; Karlsson, Bo; Wu, Harry X.

doi:10.1007/s13595-017-0680-8

Cited by 24 publications

(18 citation statements)

References 34 publications

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“…On average, spatial autocorrelation values were medium and low in magnitude, which corroborates the findings of Paget et al. (2015) with potato and in Stringer and Cullis (2002) with sugarcane ( Saccharum officinarum L.) but were much smaller than other studies (Dutkowski et al., 2002; Chen et al., 2018). The spatial autocorrelation values were influenced by the trait, that is, for TTY and MTY being higher than SG.…”

Section: Discussionsupporting

confidence: 86%

“…This improvement in error control varied according to the trait studied and was more pronounced for TTY and MTY and slightly smaller for SG. Dependence between different efficiencies and different traits has also been detected in similar studies (Chen, Helmersson, Westin, Karlsson, & Wu, 2018; Dutkowski, Costa e Silva, Gilmour, Wellendorf, & Aguiar, 2006; Paget et al., 2015).…”

Section: Discussionsupporting

confidence: 69%

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Accounting for spatial trends to increase the selection efficiency in potato breeding

et al. 2020

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A crucial point in agricultural experimentation is to compare treatments with high accuracy. However, agricultural experimentation is prone to field heterogeneity, and a common source of error is the spatial variation between the plots used in an experiment. With plant breeding experiments, the high number of tested genotypes requires breeders to use large areas, which invariably increases the likelihood of spatial variation. The use of models that do not address this variation can lead to errors in selecting the best genotypes. Our goal was to evaluate the effects of two spatial models—first‐order autoregressive (AR1) and spatial analysis of field trials with splines (SpATS)—to control the spatial variation in 30 experiments from potato (Solanum tuberosum L.) breeding programs. Specifically, we sought to control for three traits: total tuber yield (TTY), marketable tuber yield (MTY), and tuber specific gravity (SG). The results obtained with the use of spatial models were compared with the base model (independent errors) based on precision, heritability, and the impact on the selection of the best clones. Spatial models were effective in controlling local and global errors and achieved greater accuracy and efficiency over the base model. The spatial approach also showed greater heritability for all analyzed traits. The spatial models led to differences in the clone ranking and consequently in the selection of the best clones. Thus, spatial analysis has the power to make more precise analyses, which leads to more accurate selections and should be used to analyze phenotype data of potato breeding programs.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionsupporting

confidence: 69%

Accounting for spatial trends to increase the selection efficiency in potato breeding

et al. 2020

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“…As already mentioned, an RCBD experimental design was applied in the progeny trial, in accordance with its frequent use in trials covering large physical areas with numerous individuals, including multiple individuals from each family, and large spacing between individuals, with significant between-microsite variation [49]. This experimental design and environmental variation lead to significant block (fixed) and spatial (random) effects on phenotypic performance.…”

Section: Spatial Analysis and Genotype Imputationmentioning

confidence: 99%

Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados

Sawitri

Tani

Na’iem

et al. 2020

Forests

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Shorea platyclados (Dark Red Meranti) is a commercially important timber tree species in Southeast Asia. However, its stocks have dramatically declined due, inter alia, to excessive logging, insufficient natural regeneration and a slow recovery rate. Thus, there is a need to promote enrichment planting and develop effective technique to support its rehabilitation and improve timber production through implementation of Genome-Wide Association Studies (GWAS) and Genomic Selection (GS). To assist such efforts, plant materials were collected from a half-sib progeny population in Sari Bumi Kusuma forest concession, Kalimantan, Indonesia. Using 5900 markers in sequences obtained from 356 individuals, we detected high linkage disequilibrium (LD) extending up to >145 kb, suggesting that associations between phenotypic traits and markers in LD can be more easily and feasibly detected with GWAS than with analysis of quantitative trait loci (QTLs). However, the detection power of GWAS seems low, since few single nucleotide polymorphisms linked to any focal traits were detected with a stringent false discovery rate, indicating that the species’ phenotypic traits are mostly under polygenic quantitative control. Furthermore, Machine Learning provided higher prediction accuracies than Bayesian methods. We also found that stem diameter, branch diameter ratio and wood density were more predictable than height, clear bole, branch angle and wood stiffness traits. Our study suggests that GS has potential for improving the productivity and quality of S. platyclados, and our genomic heritability estimates may improve the selection of traits to target in future breeding of this species.

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“…Spatial autocorrelation is useful for analysing and examining randomness of residuals [67]. Moran’s I is commonly used for checking spatial autocorrelation and cluster detection which ranges between − 1 and 1 (Eq.…”

Section: Methodsmentioning

confidence: 99%

“…Moran’s I is commonly used for checking spatial autocorrelation and cluster detection which ranges between − 1 and 1 (Eq. (12)) [67]: where W ij is the spatial weight between i th and j th provinces; z i and z j are the values of z-score in i th and j th provinces, respectively; Y i is the number of cases for i th province; and S is the sum of all spatial weights. Moran’s I is used to determine the spatial autocorrelation of residuals for investigating the model deficiencies.…”

Section: Methodsmentioning

confidence: 99%

Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches

et al. 2019

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Background: Recent reports of the National Ministry of Health and Treatment of Iran (NMHT) show that Gilan has a higher annual incidence rate of leptospirosis than other provinces across the country. Despite several efforts of the government and NMHT to eradicate leptospirosis, it remains a public health problem in this province. Modelling and Prediction of this disease may play an important role in reduction of the prevalence. Methods: This study aims to model and predict the spatial distribution of leptospirosis utilizing Geographically Weighted Regression (GWR), Generalized Linear Model (GLM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) as capable approaches. Five environmental parameters of precipitation, temperature, humidity, elevation and vegetation are used for modelling and predicting of the disease. Data of 2009 and 2010 are used for training, and 2011 for testing and evaluating the models. Results: Results indicate that utilized approaches in this study can model and predict leptospirosis with high significance level. To evaluate the efficiency of the approaches, MSE (GWR = 0.050, SVM = 0.137, GLM = 0.118 and ANN = 0.137), MAE (0.012, 0.063, 0.052 and 0.063), MRE (0.011, 0.018, 0.017 and 0.018) and R 2 (0.85, 0.80, 0.78 and 0.75) are used. Conclusion: Results indicate the practical usefulness of approaches for spatial modelling and predicting leptospirosis. The efficiency of models is as follow: GWR > SVM > GLM > ANN. In addition, temperature and humidity are investigated as the most influential parameters. Moreover, the suitable habitat of leptospirosis is mostly within the central rural districts of the province.

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Efficiency of using spatial analysis for Norway spruce progeny tests in Sweden

Cited by 24 publications

References 34 publications

Accounting for spatial trends to increase the selection efficiency in potato breeding

Accounting for spatial trends to increase the selection efficiency in potato breeding

Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados

Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches

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