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Motivation Multivariate data are common in biological experiments and using the information on multiple traits is crucial to make better decisions for treatment recommendations or genotype selection. However, identifying genotypes/treatments that combine high performance across many traits has been a challenger task. Classical linear multi-trait selection indexes are available, but the presence of multicollinearity and the arbitrary choosing of weighting coefficients may erode the genetic gains. Results We propose a novel approach for genotype selection and treatment recommendation based on multiple traits that overcome the fragility of classical linear indexes. Here, we use the distance between the genotypes/treatment with an ideotype defined a priori as a multi-trait genotype-ideotype distance index (MGIDI) to provide a selection process that is unique, easy-to-interpret, free from weighting coefficients and multicollinearity issues. The performance of the MGIDI index is assessed through a Monte Carlo simulation study where the percentage of success in selecting traits with desired gains is compared with classical and modern indexes under different scenarios. Two real plant datasets are used to illustrate the application of the index from breeders and agronomists’ points of view. Our experimental results indicate that MGIDI can effectively select superior treatments/genotypes based on multi-trait data, outperforming state-of-the-art methods, and helping practitioners to make better strategic decisions towards an effective multivariate selection in biological experiments. Availability and implementation The source code is available in the R package metan (https://github.com/TiagoOlivoto/metan) under the function mgidi(). Supplementary information Supplementary data are available at Bioinformatics online.
Motivation Multivariate data are common in biological experiments and using the information on multiple traits is crucial to make better decisions for treatment recommendations or genotype selection. However, identifying genotypes/treatments that combine high performance across many traits has been a challenger task. Classical linear multi-trait selection indexes are available, but the presence of multicollinearity and the arbitrary choosing of weighting coefficients may erode the genetic gains. Results We propose a novel approach for genotype selection and treatment recommendation based on multiple traits that overcome the fragility of classical linear indexes. Here, we use the distance between the genotypes/treatment with an ideotype defined a priori as a multi-trait genotype-ideotype distance index (MGIDI) to provide a selection process that is unique, easy-to-interpret, free from weighting coefficients and multicollinearity issues. The performance of the MGIDI index is assessed through a Monte Carlo simulation study where the percentage of success in selecting traits with desired gains is compared with classical and modern indexes under different scenarios. Two real plant datasets are used to illustrate the application of the index from breeders and agronomists’ points of view. Our experimental results indicate that MGIDI can effectively select superior treatments/genotypes based on multi-trait data, outperforming state-of-the-art methods, and helping practitioners to make better strategic decisions towards an effective multivariate selection in biological experiments. Availability and implementation The source code is available in the R package metan (https://github.com/TiagoOlivoto/metan) under the function mgidi(). Supplementary information Supplementary data are available at Bioinformatics online.
Sulfur fertilization plays a crucial role in wheat (Triticum aestivum L.) production, influencing both protein concentration and grain yield. Wheat, being one of the most important food crops globally, requires efficient management of essential nutrients, including sulfur and nitrogen, to achieve optimal production. This study aimed to quantify the effect of sulfur fertilization on wheat protein concentration and grain yield and the relationship with nitrogen through two complementary methods: a comprehensive meta-analysis and a controlled greenhouse experiment. The meta-analysis, encompassing 55 studies from 20 countries with 545 comparisons, quantified the overall response of wheat to sulfur fertilization in diverse field environments, examining the effects based on soil texture and organic matter content. The greenhouse study investigated the effects of varying sulfur application rates and sources on protein concentration and grain yield and analyzed the relationship between sulfur and nitrogen concentrations in the grain. The meta-analysis showed overall positive effects of sulfur application on both protein concentration (2.1%) and grain yield (4.2%), with the magnitude of these effects varying based on soil texture and organic matter content. Sandy soils and soils with low organic matter content exhibited the most pronounced responses to sulfur fertilization. The greenhouse experiment revealed responses of both protein concentration and grain yield to increasing sulfur application rates, indicating an optimal rate beyond which additional sulfur may not provide further benefits. A strong positive correlation between sulfur and nitrogen concentrations in the grain highlighted their interdependence in wheat nutrition. These findings emphasize the importance of considering soil properties and the sulfur–nitrogen interaction when developing site-specific sulfur fertilization strategies for wheat. The results provide valuable insights for optimizing grain yield and protein concentration, contributing to more sustainable and efficient wheat production systems.
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