Correction models for intergenotypic competition in winter wheat

Foucteau, Vincent; Brabant, Philippe; Monod, Hervé; David, Olivier; Goldringer, Isabelle

doi:10.1051/agro:2000170

Cited by 5 publications

(11 citation statements)

References 12 publications

(31 reference statements)

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“…This is in contrast to previous studies on wheat (Goldringer et al, 1994; Foucteau et al, 2000). In these studies, relative selection intensity was increased from −1 to 83% (Goldringer et al, 1994) and 59 to 84 and 78 to 87% (Foucteau et al, 2000). Of course, a different crop was used in these studies, but the yield levels (500–900 g m −2 ) were also much higher than those observed in this study.…”

Section: Discussioncontrasting

confidence: 99%

“…P value from the analysis of variance, genetic and residual variance components, and heritability for grain yield of 14 varieties evaluated in three types of plots with different row numbers in four experiments. 1994) and 59 to 84 and 78 to 87% (Foucteau et al, 2000). Of course, a different crop was used in these studies, but the yield levels (500-900 g m -2 ) were also much higher than those observed in this study.…”

Section: Discussioncontrasting

confidence: 56%

“…However, the model did not improve relative selection intensity. This is in contrast to previous studies on wheat (Goldringer et al, 1994;Foucteau et al, 2000). In these studies, relative selection intensity was increased from -1 to 83% (Goldringer et al, lower residual mean square than M 0 with significant effect of the difference in agronomic traits between the plants in a plot and its neighboring plots, which were used as covariates in the models.…”

Section: Resultsmentioning

confidence: 68%

“…Likely, such bias is high under both highyielding conditions where neighboring plants compete for light and low-yielding conditions where neighboring plants compete for nutrients and/or water. It has been proposed to use statistical models to correct for competition effect (e.g., Goldringer et al, 1994;Foucteau et al, 2000). This study considers a covariate model that includes plant characteristics associated with the competition as one or more covariates (Kempton et al, 1984;Goldringer et al, 1994).…”

Section: Does Use Of Unbordered Plots Affectmentioning

confidence: 99%

“…The covariates used equaled the difference between the value of a certain agronomic trait in the plot and the mean of that trait in the adjacent plots. Previous studies on wheat ( Triticum aestivum L.) showed that the covariate model based on height could improve the estimation of grain yield in unbordered plots (Goldringer et al, 1994; Clarke et al, 1999; Foucteau et al, 2000). It is not known whether such statistical models can improve the estimation of grain yield of upland rice grown in unbordered plots.…”

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confidence: 99%

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Does Use of Unbordered Plots Affect Estimation of Upland Rice Yield?

et al. 2015

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Advance in crop genomics have led to a demand for more accurate and precise phenotyping of a large number of genotypes for discovering genes and mechanisms underpinning important agronomic traits. The use of unbordered plots for field phenotyping is one approach for reducing the resources needed, but competition effects between neighboring lines may confound varietal performance. Four field experiments were conducted in Benin to examine whether grain yields of 14 diverse upland rice (Oryza sativa spp.) varieties determined in unbordered one‐row or two‐row plots differ from those measured in self‐bordered four‐row plots and to examine if statistical models including covariates based on plant characteristics (height, panicle number, and days to heading) for correcting competition effect can improve the estimation of the yield in unbordered plots. Mean grain yield across all varieties ranged from 118 to 378 g m−2 in four experiments. There was no significant variety × row number interaction effect on grain yield, except for the highest yielding experiment. In that experiment, the variety × row number interaction was significant for one‐row versus four‐row plots, but not for two‐row versus four‐row plots. In one‐row plots in this high‐yielding experiment, the neighborhood covariate model based on panicle number improved residual mean square by 20%, but relative selection intensity by 3% only. Similarly, the covariate models based on height or panicle number in both one‐ and two‐row plots in the other experiments improved just 4%. We conclude that unbordered one‐ or two‐row plots can provide reasonable estimates of grain yield of upland rice without any bias due to competition effects, except for high‐yielding one‐row plots (>350 g m−2).

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Section: Discussioncontrasting

confidence: 99%

Section: Discussioncontrasting

confidence: 56%

Section: Resultsmentioning

confidence: 68%

Section: Does Use Of Unbordered Plots Affectmentioning

confidence: 99%

mentioning

confidence: 99%

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Does Use of Unbordered Plots Affect Estimation of Upland Rice Yield?

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Breeding for Genetically Diverse Populations: Variety Mixtures and Evolutionary Populations

Dawson

Goldringer²

2011

Organic Crop Breeding

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Designing mixtures of varieties for multifunctional agriculture with the help of ecology. A review

Barot

Allard

Cantarel

et al. 2017

Agron. Sustain. Dev.

Self Cite

201

174

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The study of natural ecosystems and experiments using mixtures of plant species demonstrates that both species and genetic diversity generally promote ecosystem functioning. Therefore, mixing crop varieties is a promising alternative practice to transform modern high-input agriculture that is associated with a drastic reduction of within-field crop genetic diversity and is widely recognized as unsustainable. Here, we review the effects of mixtures of varieties on ecosystem functioning, and their underlying ecological mechanisms, as studied in ecology and agronomy, and outline how this knowledge can help designing more efficient mixtures. We recommend the development of two complementary strategies to optimize variety mixtures by fostering the ecological mechanisms leading to a positive relationship between biodiversity and ecosystem functioning and its stability through time, i.e., sampling and complementarity effects. (1) In the "trait-blind" approach, the design of high-performance mixtures is based on estimations of the mixing abilities of varieties. While this approach is operational because it does not require detailed trait knowledge, it relies on heavy experimental designs to evaluate mixing ability. (2) The trait-based approach is particularly efficient to design mixtures of varieties to provide particular baskets of services but requires building databases of traits for crop varieties and documenting the relations between traits and services. The performance of mixtures requires eventually to be evaluated in real economic, social, and agronomic contexts. We conclude that the need of a multifunctional low-input agriculture strongly increases the attractiveness of mixtures but that new breeding approaches are required to create varieties with higher mixing abilities, to foster complementarity and selection effects through an increase in the variance of relevant traits and to explore new combinations of trait values

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Correction models for intergenotypic competition in winter wheat

Cited by 5 publications

References 12 publications

Does Use of Unbordered Plots Affect Estimation of Upland Rice Yield?

Does Use of Unbordered Plots Affect Estimation of Upland Rice Yield?

Breeding for Genetically Diverse Populations: Variety Mixtures and Evolutionary Populations

Designing mixtures of varieties for multifunctional agriculture with the help of ecology. A review

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