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Agronomy for Sustainable Development

, Volume 30, Issue 3, pp 667–677 | Cite as

Basis for designing a tool to evaluate new cultivars

  • Christophe LecomteEmail author
  • Lorène Prost
  • Marianne Cerf
  • Jean-Marc Meynard
Research Article

Abstract

Seed scientists and decision-makers must evaluate the behaviour of new cultivars. They are thus always seeking improvements in multi-parameter trials. In particular, there is a need for cultivar evaluation tools that include both environmental characterisation of the trials and advanced statistical analysis of genotype by environment interaction. Therefore, in this investigation we gathered agronomists and ergonomists to analyse the functioning, i.e. the activity system, of cultivar evaluation, and to define the specifications of a new tool. We interviewed 21 actors in order to describe and analyse the diversity of evaluation actions such as the objectives of evaluation, criteria to judge cultivars, and configuration of experimentation; and to identify contradictions that appear in the whole activity system to reveal constraints. We deduced the following specifications: (1) to take into account the very short period after harvest in which analyses have to be returned, the tool has to perform automated identification and quantification of environmental constraints in each trial (crop diagnosis) and automated analysis of genotype by environment interaction. (2) The tool has to come up to different actors’ expectations concerning environmental or cultivar characterisation or experimental design optimisation. (3) The tool has to be flexible enough to integrate particular knowledge or expertise.

cultivar evaluation genotype by environment interaction tool design activity system crop diagnosis 

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References

  1. Béguin P. (2009) L’ergonomie en conception: cristallisation, plasticité, développement, in: Weil B., Hatchuel A. (Eds.), Les nouveaux régimes de la conception, Paris: Vuibert.Google Scholar
  2. Biarnès-Dumoulin V., Denis J.-B., Lejeune-Hénaut I., Etévé G. (1996) Interpreting yield instability in pea using genotypic and environmental covariates, Crop Sci. 36, 115–120.CrossRefGoogle Scholar
  3. Blanchet A., Gotman A. (1992) L’enquête et ses méthodes: l’entretien, Nathan Université Sociologie, Collection 128, Paris.Google Scholar
  4. Brancourt-Hulmel M., Biarnès-Dumoulin V., Denis J.-B. (1997) Points de repère dans l’analyse de la stabilité et de l’interaction génotype-milieu en amélioration des plantes, Agronomie 17, 219–246.CrossRefGoogle Scholar
  5. Brancourt-Hulmel M., Lecomte C., Meynard J.-M. (1999) A Diagnosis of Yield-Limiting Factors on Probe Genotypes for Characterizing Environments in Winter Wheat Trials, Crop Sci. 39, 1798–1808.CrossRefGoogle Scholar
  6. Cerf M., Meynard J.-M. (2006) Les outils de pilotage des cultures: diversité de leurs usages et enseignements pour leur conception, Natures, Sciences, Sociétés 14, 16–29.CrossRefGoogle Scholar
  7. Clermont-Dauphin C., Cabidoche Y.-M., Meynard J.-M. (2004) Diagnosis on low-input cropping systems in a tropical upland of Southern Haiti, Agr. Ecosys. Environ. 105, 221–234.CrossRefGoogle Scholar
  8. David C., Jeuffroy M.-H., Henning J., Meynard J.-M. (2005) Yield variation of organic winter wheat: a diagnostic study in the Southeast of France, Agronomie 25, 213–223.Google Scholar
  9. Denis J.-B. (1980) Analyse de régression factorielle, Biométrie Praximétrie 20, 1–34.Google Scholar
  10. Denis J.-B. (1988) Two way analysis using covariates, Statistics 19, 123–132.CrossRefGoogle Scholar
  11. Desclaux D. (1996) De l’intérêt de génotypes révélateurs de facteurs limitants dans l’analyse des interactions génotype milieu chez le soja (Glycine max. L. Merill), Thèse de doctorat, Institut national polytechnique de Toulouse, Toulouse, France, 227 p.Google Scholar
  12. Doré T., Sebillotte M., Meynard J.-M. (1997) A diagnostic method for assessing regional variations in crop yields, Agr. Syst. 54, 169–188.CrossRefGoogle Scholar
  13. Engeström Y. (1987) Learning by expanding: An activity-theoretical approach to developmental research, Orienta-Konsultit, Helsinki.Google Scholar
  14. Epinat-Le Signor C., Dousse S., Lorgeou J., Denis J.-B., Bonhomme R., Carolo P., Charcosset A. (2001) Interpretation of Genotype Environment Interactions for Early Maize Hybrids over 12 Years, Crop Sci. 41, 663–669.CrossRefGoogle Scholar
  15. Finlay K.-W., Wilkinson G.-N. (1963) The analysis of adaptation in a plant-breeding program, Aust. J. Agr. Res. 14, 742–754.CrossRefGoogle Scholar
  16. Gauch H.-G. (1992) Statistical analysis of regional yield trials: AMMI analysis of factorial designs, Elsevier, Amsterdam.Google Scholar
  17. GEVES (2005) Les variétés de Céréales du Catalogue Officiel français, Valeur Agronomique et Technologique des variétés de la liste A, n∘ 4-juillet 2005, 397 p.Google Scholar
  18. Landau S., Mitchell R.-A.-C., Barnett V., Colls J.-J., Craignon J., Payne R.-W. (2000) A parsimonious, multiple regression model of wheat yield response to environment, Agr. Forest Meteorol. 101, 151–166.CrossRefGoogle Scholar
  19. Lecomte C. (2005) L’évaluation expérimentale des innovations variétales, Proposition d’outils d’analyse de l’interaction génotype — milieu adaptés à la diversité des besoins et des contraintes des acteurs de la filière semences, Thèse de doctorat, INA P-G, Paris, France.Google Scholar
  20. Mandel J. (1969) The partitioning of interaction in analysis of variance, J. Res. Nat. Bureau of Standards B. Mathematical Sciences 73B, 309–328.Google Scholar
  21. Meynard J.-M., David G. (1992) Diagnostic sur l’élaboration du rendement des cultures, Cah. Agr. 1, 9–19.Google Scholar
  22. Meynard J.-M., Jeuffroy M.-H. (2006) Quel progrès génétique pour une agriculture durable? in: Quelles variétés et semences pour des agricultures paysannes durables? Les dossiers de l’Environnement, INRA, Paris 30, 15–25.Google Scholar
  23. Parisot-Baril C. (1992) Étude de la stabilité du rendement chez le blé tendre d’hiver (Triticum aestivum L. Thell.), Thèse de Doctorat de l’Université Paris-Sud, 210 p.Google Scholar
  24. Prost L. (2008) Modéliser en agronomie et concevoir des outils en interaction avec de futurs utilisateurs: le cas de la modélisation et de l’outil DIAGVAR, Thèse de doctorat, AgroParisTech, Paris, France.Google Scholar
  25. SAS® System, Release 8.01.01 (1999–2000) SAS® Institute Inc., Cary, NC, USA.Google Scholar
  26. Seppänen L. (2002) Creating tools for farmers’ learning: an application of developmental work research, Agr. Syst. 73, 129–145.CrossRefGoogle Scholar
  27. Van Eeuwijk F.-A. (1995) Linear and bilinear models for the analysis of multi-environment trials: I. An inventory of models, Euphytica 84, 1–7.CrossRefGoogle Scholar
  28. Van Eeuwijk F.-A., Malosetti M., Yin X., Struik P.-C., Stam P. (2004) Modelling differential phenotypic expression, in: New discussions for a diverse planet, Proceedings of the 4th International Crop Science Congress, 26 Sept.–1 Oct. 2004, Brisbane.Google Scholar
  29. Wricke G. (von) (1962) Über eine Methode zur Erfassung der ökologischen Streubreite in Feldversuchen, Z. Pflanzenzücht 47, 92–96.Google Scholar

Copyright information

© INRA, EDP Sciences 2010

Authors and Affiliations

  • Christophe Lecomte
    • 1
    Email author
  • Lorène Prost
    • 2
  • Marianne Cerf
    • 2
  • Jean-Marc Meynard
    • 3
  1. 1.INRAUMRLEGDijon CedexFrance
  2. 2.UMR 1048 SAD-APTINRAThiverval-GrignonFrance
  3. 3.Département SADINRAThiverval-GrignonFrance

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