Spatial correction is a statistical technique employed in plant breeding to mitigate the impact of environmental variability on plant performance. By adjusting observed trait values based on plant positions within the experimental field, spatial correction helps counter the effects of environmental and trial design-related factors, such as soil, climate, diseases, plot patterns, replications, and blocks. This correction ensures fair comparisons among tested varieties and facilitates the selection of those best suited to specific growing conditions. Discover in detail how spatial correction enhances precision and reliability in plant breeding.
To apply spatial correction, we can use smooth two-dimensional surfaces to model spatial variation. For example, anisotropic P-splines can be used to distinguish large-scale spatial trends (global trend) from small-scale trends (local trend). The spatial field includes the effects of genotypes, blocks, replications, and/or other sources of spatial variation described by a classical mixed model. Each component of the model has an effective dimension, which is related to variance estimation and helps characterize the importance of model components. An important result of this method is the formal relationship between several definitions of heritability and the effective dimension associated with the genetic component. This method was developed and illustrated by Rodríguez-Álvarez et al. (2018) in their article "Correcting for spatial heterogeneity in plant breeding experiments with P-splines."
Why do plant breeders need spatial correction?
Spatial correction helps breeders account for the spatial variation that exists within field trials. In agricultural research, field trials are often conducted on large plots of land, and spatial heterogeneity can arise due to differences in soil fertility, microclimate, disease pressure, or other environmental factors. Without proper spatial correction, these variations can introduce bias and confound the estimation of genotypic effects.
By applying spatial correction techniques, breeders can account for the spatial structure within their phenotypic data. This involves modeling and removing the systematic spatial trends present in the field trials, thereby reducing the influence of environmental factors and improving the accuracy of the analysis.
Spatial correction methods can be implemented using various approaches, such as spatial analysis of variance (ANOVA), spatial regression models, or spatial mixed models. These techniques allow breeders to explicitly model the spatial autocorrelation that exists between neighboring plots and estimate the residual variation that is truly attributable to genetic effects.
Incorporating spatial correction in the analysis of phenotypic data also helps in the identification and elimination of outlier observations. Outliers may arise due to localized environmental factors, measurement errors, or other sources of variation. By detecting and removing these outliers, breeders can ensure that their analyses are based on reliable and representative data, leading to more accurate conclusions and better-informed breeding decisions.
Furthermore, spatial correction aids in the integration of multi-environment trials (METs). METs involve evaluating genotypes across different locations or years to assess their performance under diverse environmental conditions. Spatial correction techniques can help harmonize the data collected from different environments, enabling breeders to make valid comparisons and to identify and predict the potential behavior of various genotypes in different environments by getting more stable genetic values, reducing Genotype x Environment interactions, achieve higher adaptive values, and more.
In the end, spatial correction is essential for plant breeders to improve the quality and reliability of their phenotypic data analysis. By accounting for spatial variation, breeders can enhance experimental design, strengthen selection processes, increase genetic gain, and facilitate the accurate evaluation of genotypes across diverse environments by helping to calculate narrower confidence intervals and smaller p-values for instance. Incorporating spatial correction techniques into their breeding programs empowers breeders to make more informed and accurate decisions, leading to the development of improved cultivars that pave the way for tomorrow’s agriculture.
To illustrate these advantages, here are some concrete examples of spatial effects that can be corrected using the P-splines method
a) Soil effects
- Slope effect: variation in yield or other agronomic traits based on altitude or slope of the terrain. For example, a decrease in yield with increasing altitude due to water or heat stress, or an increase in yield with slope due to better sunlight exposure or improved soil drainage.
- Fertility effect: variation in yield or other agronomic traits based on soil nutrient richness. For example, an increase in yield with higher fertility due to better growth or disease resistance, or a decrease in yield with higher fertility due to nitrogen excess or mineral imbalance.
- Heterogeneity effect: variation in yield or other agronomic traits based on soil structure or texture. For example, an increase in yield with homogeneity due to better water or air distribution, or a decrease in yield with heterogeneity due to poor establishment or competition.
b) Effects related to experimental design
- Border effect: variation in yield or other agronomic traits based on the position of plots relative to field boundaries. For example, an increase in yield on borders due to buffer or microclimate effects, or a decrease in yield on borders due to competition or pest effects.
- Gradient effect: variation in yield or other agronomic traits based on the direction of plots relative to an environmental factor. For example, an increase in yield along a north-south gradient due to temperature or sunlight differences, or a decrease in yield along an east-west gradient due to precipitation or wind differences.
- Block effect: variation in yield or other agronomic traits based on the arrangement of plots into homogeneous blocks. For example, an increase in yield within a block due to better soil quality or improved management practices, or a decrease in yield within a block due to poor soil quality or potential inadequate management practices.
c) other examples
- Year effect: variation in yield or other agronomic traits based on the year of the experiment. For example, an increase in yield in a favorable year due to optimal climate or low pest pressure, or a decrease in yield in an unfavorable year due to stressful climate or high pest pressure.
- Site effect: variation in yield or other agronomic traits based on the location of the experiment. For example, an increase in yield in a suitable site due to good genotype-environment matching, or a decrease in yield in an unsuitable site due to poor genotype-environment matching.
- Reaction/interaction effect: variation in yield or other agronomic traits based on the combination of two experimental factors. For example, an increase in yield in a positive reaction or interaction due to synergistic or complementary effects, or a decrease in yield in a negative interaction due to antagonistic or competitive effects.
Hiphen can provide you with spatial correction for your phenotypic data
If you are interested in spatial correction and want to apply it to your breeding experiment trials, at Hiphen, we can help you implement this method and interpret the results. Hiphen has developed the tools for spatial analysis using P-splines and mixed models. We can also advise you on choosing the most suitable experimental design that will help you maximize the value of your field trial.
Spatial correction features are coming to Cloverfield™ in 2024, you will then be able to apply spatial correction on your selected traits to get instant visualization of the corrected data and start making data-driven decisions fast.
Sincerely,
Your Hiphen Team.
Topic brought to you by Don Ced OGOUMOND - Imaging Solutions Specialist @Hiphen.
