Analysing genotype by environment interaction in Dutch potato variety trials using factorial regression

Baril, Claire; Denis, Jean-Baptiste; Wustman, R.; Eeuwijk, F.A. van

doi:10.1007/bf01677553

Cited by 22 publications

(21 citation statements)

References 13 publications

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“…Higher percentages were found for potato, soybean [Glycine max (L.) Merr. ], pea, wheat, maize and sorghum (Baril et al, 1995;Sneller et al, 1997;Brancourt-Hulmel et al, 2000;Abush, 2001;Alberts, 2004;Das et al, 2012), while similar percentages were reported for maize (Ajibade et al, 2003) and winter/spring wheat (Voltas et al, 2005).…”

Section: Tablesupporting

confidence: 73%

“…Indeed, the observed percentage was higher than that found in other crops, such as potato, winter wheat, pea (Pisum sativum L.), maize and sugarcane (Baril et al, 1995;Brancourt-Hulmel et al, 2000;Taye et al, 2000;Admassu et al, 2008;Ramburan et al, 2011). Similar percentages were otherwise reported for maize [Zea mays L.], sorghum [Sorghum bicolor L. Moench] and winter/spring wheat (Ajibade et al, 2003;Alberts, 2004;Voltas et al, 2005;Das et al, 2012).…”

Section: Tablecontrasting

confidence: 67%

“…The percentage of total SS due to G × E (7.3%) was lower than that found for potato (Solanum tuberosum L.), winter wheat (Triticum aestivum L.), sugarcane (Saccharum officinarum L.) (Baril et al, 1995;BrancourtHulmel et al, 2000;Admassu et al, 2008;Ramburan et al, 2011; ranging in the various studies from 22.8% to 56.5%). The low G × E interaction value detected in this study can be explained by considering that our experiments were carried out in the adaptation region of some of the genotypes under evaluation (LR, Syn1 4C and Syn1 8C) and in OA and LI, where no conventional agronomic inputs gave the F1 hybrid means to exploit its vigour.…”

Section: Tablementioning

confidence: 69%

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Broccoli yield response to environmental factors in sustainable agriculture

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Section: Tablesupporting

confidence: 73%

Section: Tablecontrasting

confidence: 67%

Section: Tablementioning

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“…It has been used to analyse the effect of introducing different crop varieties in agriculture (e.g. Baril et al, 1995;Vargas et al, 1999Vargas et al, , 2006Voltas et al, 2005). It is based on the concept "factorial experiment", or factorial design, in statistics, which involves two or more factors, each of which can be assigned a category or a discrete value.…”

Section: The Analysismentioning

confidence: 99%

The use of regression for assessing a seasonal forecast model experiment

Benestad¹,

Senan²,

Orsolini³

2016

Earth Syst. Dynam.

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Abstract. We show how factorial regression can be used to analyse numerical model experiments, testing the effect of different model settings. We analysed results from a coupled atmosphere-ocean model to explore how the different choices in the experimental set-up influence the seasonal predictions. These choices included a representation of the sea ice and the height of top of the atmosphere, and the results suggested that the simulated monthly mean air temperatures poleward of the mid-latitudes were highly sensitivity to the specification of the top of the atmosphere, interpreted as the presence or absence of a stratosphere. The seasonal forecasts for the midlatitudes to high latitudes were also sensitive to whether the model set-up included a dynamic or non-dynamic sea-ice representation, although this effect was somewhat less important than the role of the stratosphere. The air temperature in the tropics was insensitive to these choices.

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“…Abeledo et al (2003) used regression analyses to relate yield of nine barley varieties to different variables such as number of grains and year of release. BrancourtHulmel et al (2000) used environmental covariates to explain genotype by environment interaction in winter wheat, while Baril et al (1995) used both environmental covariates and genotypic covariates for describing the genotype by environment interaction in Dutch potato variety trials. Østergård et al (2008) used external growth characteristics from VCU testing (Values of Cultivation and Use) together with environment measures of disease pressure and weed pressure to describe their influence on yield.…”

Section: Introductionmentioning

confidence: 99%

Importance of growth characteristics for yield of barley in different growing systems: will growth characteristics describe yield differently in different growing systems?

Kristensen

Ericson

2008

Euphytica

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The interest in organic grown cereals has increased the need for variety tests under organic growing systems and/or the knowledge on whether growth characteristics describe yield differently under conventional and organic conditions. This paper is a contribution to that question by examining the relationships between some important growth characteristics in barley trials in both systems in Northern Sweden and in Denmark. Mixed model analyses were used for regressions of growth characteristics (or transformations of those) on yield (and logtransformed yield), allowing the slope to depend on the growing system. The analyses showed that diseases seemed to have a less negative effect on yield in the organic growing system than in the conventional growing system if pesticides were not applied. For other characteristics the effect depended on the country. This was the case for grain characteristics where the effect of volume weight in the Swedish trials was much larger in the conventional growing system than in the organic growing system, while a non-significant difference in the opposite direction was found for the trails from Denmark. For the trials from Denmark the effect of grain weight was much larger in the organic growing system than in the conventional growing system, but there was only a small and non-significant difference in the Swedish trials. In both countries there was a significant interaction between the two grain characteristics.

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Analysing genotype by environment interaction in Dutch potato variety trials using factorial regression

Cited by 22 publications

References 13 publications

Broccoli yield response to environmental factors in sustainable agriculture

Broccoli yield response to environmental factors in sustainable agriculture

The use of regression for assessing a seasonal forecast model experiment

Importance of growth characteristics for yield of barley in different growing systems: will growth characteristics describe yield differently in different growing systems?

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