Can Spatial Modeling Substitute for Experimental Design in Agricultural Experiments?

Borges, Alejandra; Gonzalez-Reymundez, Agustin; Ernst, Oswaldo; Cadenazzi, Mónica; Terra, José A.; Gutiérrez, Lucı́a

doi:10.2135/cropsci2018.03.0177

Cited by 22 publications

(44 citation statements)

References 34 publications

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“…The ALPHA design was the best option regardless of experimental size. These results are similar to those found by other studies (Qiao et al, 2000; Casler, 2015; Borges et al, 2019). This confirms the idea that designs such as the ALPHA design are beneficial when the number of treatments to be evaluated is greater and spatial variability is present in the field.…”

Section: Discussionsupporting

confidence: 93%

“…Six experimental designs with four levels of correction for spatial variability were evaluated in six locations through a simulation process following Borges et al (2019). Yield data from genotypes were simulated according to an experimental design using real field variability maps, and genotypic effects were predicted for each location based on historical information.…”

Section: Methodsmentioning

confidence: 99%

“…The simulated plot yield was calculated according to Eq. [3]:

\underset{true_}{Y_{i j}} = G_{i} + \underset{true_}{{normalε}_{i j} *} + \underset{true_}{{normalδ}_{i j}}

where Y ij is the yield plot data simulated corresponding to the i th genotype and the j th replication, G i is the i th genotypic effect from the historical dataset corresponding to the randomly assigned treatment to the plot, ε ij * is the field experimental error that represent the spatial heterogeneity of the field and was obtained from the predicted values of the uniformity trials, and δ ij is a repeatability error following Borges et al (2019). The δ ij were assumed as independent random variables with δ ij ∼ N (0, σ 2 r ), where σ 2 r is a random noise or repeatability that represented a percentage of the total field heterogeneity of each location with

{normalσ}^{2}_{{normalr}_{LOCK}}

= 0.10 × MSE LOCk to simulate an average heritability on a mean basis of 0.86.…”

Section: Methodsmentioning

confidence: 99%

“…In agricultural experimentation, factors such as soil heterogeneity, agricultural practices, and environmental conditions influence the genotypic performance of lines and contribute to the spatial variability (Arnold and Kempton, 1979; Gilmour et al, 1997). Therefore, modeling spatial correlations might be necessary to improve genotypic effect estimation even after a good experimental design is used (Federer, 1998; Qiao et al, 2000; Campbell and Bauer, 2007; Casler, 2015; Borges et al, 2019). Several approaches have been proposed to control spatial variability such as nearest‐neighbor adjustment (Katsileros et al, 2015), smoothing techniques including penalized splines analysis (Stefanova et al, 2009; Piepho and Williams, 2010; Velazco et al, 2017), modeling the variance–covariance matrix of spatial correlations using geostatistical components (Williams, 1986; Williams et al, 2006; Piepho and Williams, 2010), or using mixed models (Smith et al, 2005).…”

mentioning

confidence: 99%

“…This argument could then be used as a means to replace experimental designs with spatial modeling. Borges et al (2019) showed that this is not efficient.…”

mentioning

confidence: 99%

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Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

2019

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The efficient use of testing resources is one of the key factors for successful plant breeding programs. Controlling micro‐ and macro‐environmental variability is an effective way of improving the testing efficiency and the selection of superior genotypes. Common experimental designs in genotypic testing usually use replicated or augmented experiments at each location, but they are balanced across locations. Some studies suggest that the increase in population size even at the expense of balanced experiments might be beneficial if genotype × environment interaction (GEI) is modeled. The objective of this study was to compare strategies for micro and macro‐environmental variability control that include GEI information to optimize resource allocation in multi‐environment trials (METs). Six experimental designs combined with four spatial correction models were compared for efficiency under three experimental sizes using simulations under a real yield variability map. Additionally, six resource allocation strategies were evaluated in terms of accuracy and the expected response to selection. The α‐lattice (ALPHA) experimental design was the best one at controlling micro‐environmental variability. The moderate mega‐environmental design (MED) strategy had the largest response to selection. This strategy uses historical mega‐environments (MEs) to unbalance genotypic testing within MEs while modeling GEI. The MED was the best resource allocation strategy and could potentially increase selection response up to 43% in breeding programs when genotypes are evaluated in METs.

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Section: Discussionsupporting

confidence: 93%

Section: Methodsmentioning

confidence: 99%

“…The simulated plot yield was calculated according to Eq. [3]:

\underset{true_}{Y_{i j}} = G_{i} + \underset{true_}{{normalε}_{i j} *} + \underset{true_}{{normalδ}_{i j}}

{normalσ}^{2}_{{normalr}_{LOCK}}

= 0.10 × MSE LOCk to simulate an average heritability on a mean basis of 0.86.…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

“…This argument could then be used as a means to replace experimental designs with spatial modeling. Borges et al (2019) showed that this is not efficient.…”

mentioning

confidence: 99%

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Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

2019

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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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Correcting experimental data for spatial trends in a common bean breeding program

et al. 2022

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In common bean (Phaseolus vulgaris L.) breeding, several trials are carried out in field conditions to predict the genotypic values, but experimental designs may not be sufficient to capture the field heterogeneity in the experimental area. The objective of this work was to evaluate the potential of spatial models to correct data from a common bean breeding program for spatial trends and improve the prediction of genotypic values. We used real data from 19 field trials from a common bean breeding program and three experimental designs. The traditional statistical model with design effects and independent errors was fitted and used as the basic model. Later, we fitted a sequence of spatial models to include different residual (co)variance structures for local trends and fixed and random effects based on plot position information to capture global and extraneous trends. The basic model and the best-fit spatial model were compared regarding the estimates of heritability, accuracy, prediction error variance, and discordance in the top-ranking genotypes. In most cases, the use of spatial models improved the estimates of heritability and accuracy or, at least, reduced the estimates of prediction error variance. Also, changes in the genotypic values classification were observed. Because no single model presented the best fit for all trials, some of the tested models were recommended for future trials based on the patterns of spatial trends observed. Thus, the use of spatial models helped to improve the data analysis and the prediction of genotypic values by capturing the field heterogeneity in our common bean field trials.

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Can Spatial Modeling Substitute for Experimental Design in Agricultural Experiments?

Cited by 22 publications

References 34 publications

Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

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

Correcting experimental data for spatial trends in a common bean breeding program

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