The Azodyn crop model as a decision support tool for choosing cultivars

We evaluated the Azodyn wheat crop model as a cultivar decision support tool using a set of 14 genotypes, tested in 21 contrasting environments. The results showed that the Azodyn crop model satisfactorily simulated yield and grain protein content for a large range of genotypes and environments, as shown by a root mean square error of 1.4 Mg ha -1 and 1.7 g 100 g MS -1 , respectively. The comparison between the observed and the simulated rankings of genotypes showed a ranking error of the model of one rank or less. The model was able to identify the best genotype to be used to obtain the highest yield in 20 cases out of 33 and the highest grain protein content in 48 cases out of 64. As a new way to evaluate crop models as a decision support tool for cultivar choice, we compared the Azodyn predictive accuracy against the cultivar yield and grain protein average generally used by cultivar growers as a predictive model. We showed that in the main production conditions, the Azodyn predictions fit the yield and grain protein content observed better than the average.

[1]  J. Léon,et al.  Stability Analysis in Plant Breeding , 1988 .

[2]  G. Hammer,et al.  Genotype‐by‐Environment Interaction in Grain Sorghum. III. Modeling the Impact in Field Environments , 1989 .

[3]  Graeme L. Hammer,et al.  Improving Genotypic Adaptation in Crops – a Role for Breeders, Physiologists and Modellers , 1991, Experimental Agriculture.

[4]  Graeme L. Hammer,et al.  Assessing climatic risk to sorghum production in water-limited subtropical environments. I.Development and testing of a simulation model , 1994 .

[5]  R. C. Muchow,et al.  Assessing climatic risk to sorghum production in water-limited subtropical environments II. Effects of planting date, soil water at planting, and cultivar phenology , 1994 .

[6]  James W. Jones,et al.  Soybean Reproductive Development: Adapting a Model for European Cultivars , 1995 .

[7]  Holger Meinke,et al.  Improving wheat simulation capabilities in Australia from a cropping systems perspective II. Testing simulation capabilities of wheat growth , 1996 .

[8]  Francisco J. Villalobos,et al.  Evaluation of sunflower (Helianthus annuus, L.) genotypes differing in early vigour using a simulation model , 1997 .

[9]  Holger Meinke,et al.  Improving wheat simulation capabilities in Australia from a cropping systems perspective III. The integrated wheat model (I_WHEAT) , 1998 .

[10]  M. Gooding,et al.  Models of wheat grain quality considering climate, cultivar and nitrogen effects , 1999 .

[11]  Sylvie Recous,et al.  Azodyn: a simple model simulating the date of nitrogen deficiency for decision support in wheat fertilization , 1999 .

[12]  James W. Jones,et al.  DEVELOPING GENETIC COEFFICIENTS FOR CROP SIMULATION MODELS WITH DATA FROM CROP PERFORMANCE TRIALS , 2001 .

[13]  Jean-Marc Meynard,et al.  Management planning for winter wheat with multiple objectives (2): ethanol-wheat production , 2002 .

[14]  Senthold Asseng,et al.  Simulation of grain protein content with APSIM-Nwheat , 2002 .

[15]  Senthold Asseng,et al.  A simulation analysis that predicts the influence of physiological traits on the potential yield of wheat , 2002 .

[16]  James W. Jones,et al.  PCYield: model-based decision support for soybean production , 2002 .

[17]  Graeme L. Hammer,et al.  Evaluating Plant Breeding Strategies by Simulating Gene Action and Dryland Environment Effects , 2003, Agronomy Journal.

[18]  A. Condon,et al.  Evaluating the Impact of a Trait for Increased Specific Leaf Area on Wheat Yields Using a Crop Simulation Model , 2003 .

[19]  Jeffrey W. White,et al.  Improving Physiological Assumptions Of Simulation Models By Using Gene‐Based Approaches , 2003 .

[20]  H. Sinoquet,et al.  An overview of the crop model STICS , 2003 .

[21]  J. Goudriaan,et al.  ON APPROACHES AND APPLICATIONS OF THE WAGENINGEN CROP MODELS , 2003 .

[22]  James W. Jones,et al.  Genetic Coefficients in the CROPGRO–Soybean Model , 2003, Agronomy Journal.

[23]  H. Parchamy,et al.  Radiation from Interaction between Short Pulse and Ultra High Power Laser and Magnetized Plasma , 2004 .

[24]  G. M. Paulsen,et al.  A Statistical Method for Summarizing Results of Cultivar Performance Trials , 2004 .

[25]  David Makowski,et al.  A model-based approach for optimizing segregation of soft wheat in country elevators , 2004 .

[26]  Eric Justes,et al.  Evaluation of the ability of the crop model STICS to recommend nitrogen fertilisation rates according to agro-environmental criteria , 2004 .

[27]  S. Recous,et al.  Adaptation and assessment of the Azodyn model for managing the nitrogen fertilization of organic winter wheat , 2004 .

[28]  Aude Barbottin.  Utilisation d'un modèle de culture pour évaluer le comportement des génotypes: Pertinence de l'utilisation d'Azodyn pour analyser la variabilité du rendement et de la teneur en protéines du blé tendre , 2004 .

[29]  Aude Barbottin,et al.  Nitrogen Remobilization during Grain Filling in Wheat: Genotypic and Environmental Effects , 2005 .

[30]  M. Jeuffroy,et al.  The assessment of Azodyn-Org model for managing nitrogen fertilization of organic winter wheat , 2005 .

[31]  Christine Aubry,et al.  DéciBlé, a software package for wheat crop management simulation , 2005 .