STICS Soil Crop Model - Conceptual Framework, Equations and Uses - Chap 8 : Yield Formation

editorial reviewedThe STICS crop model has been developed since 1996 at INRA (French National Institute for Agronomic Research) in collaboration with other research and technical institutes. The model syntheses, illustrates and concretizes an important part of the French agronomic knowledge as a point of view on the field and cropping systems working. The formalisations of the STICS crop model presented in this book can be considered as references used in the framework of crop sciences. They will help professionals and students in the partitioning and understanding of the complex agronomic system. The book arrangement relies on the way the model designs the crop-soil system functioning, each chapter being devoted to a set of important functions such as growth initiation, yield onset, water uptake, transformation of organic matter etc. One chapter deals with the cropping system and long term simulations and the final chapter is about the involvement of the user in terms of option choices and parameterization. If this book is mainly intended for scientists who use the STICS model, it can also be useful for agronomists, crop modellers, students and technicians looking for elementary formalizations of the crop-soil system functioning

Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models

We compared the performance of eight widely used, easily accessible and well-documented crop growth simulation models (APES, CROPSYST, DAISY, DSSAT, FASSET, HERMES, STICS and WOFOST) for winter wheat (Triticum aestivum L.) during 49 growing seasons at eight sites in northwestern, Central and southeastern Europe. The aim was to examine how different process-based crop models perform at the field scale when provided with a limited set of information for model calibration and simulation, reflecting the typical use of models for large-scale applications, and to present the uncertainties related to this type of model application. Data used in the simulations consisted of daily weather statistics, information on soil properties, information on crop phenology for each cultivar, and basic crop and soil management information. Our results showed that none of the models perfectly reproduced recorded observations at all sites and in all years, and none could unequivocally be labelled robust and accurate in terms of yield prediction across different environments and crop cultivars with only minimum calibration. The best performance regarding yield estimation was for DAISY and DSSAT, for which the RMSE values were lowest (1428 and 1603 kg ha−1) and the index of agreement (0.71 and 0.74) highest. CROPSYST systematically underestimated yields (MBE – 1186 kg ha−1), whereas HERMES, STICS and WOFOST clearly overestimated them (MBE 1174, 1272 and 1213 kg ha−1, respectively). APES, DAISY, HERMES, STICS and WOFOST furnished high total above-ground biomass estimates, whereas CROPSYST, DSSAT and FASSET provided low total above-ground estimates. Consequently, DSSAT and FASSET produced very high harvest index values, followed by HERMES and WOFOST. APES and DAISY, on the other hand, returned low harvest index values. In spite of phenological observations being provided, the calibration results for wheat phenology, i.e. estimated dates of anthesis and maturity, were surprisingly variable, with the largest RMSE for anthesis being generated by APES (20.2 days) and for maturity by HERMES (12.6). The wide range of grain yield estimates provided by the models for all sites and years reflects substantial uncertainties in model estimates achieved with only minimum calibration. Mean predictions from the eight models, on the other hand, were in good agreement with measured data. This applies to both results across all sites and seasons as well as to prediction of observed yield variability at single sites – a very important finding that supports the use of multi-model estimates rather than reliance on single model

Respiration de croissance et respiration d'entretien au cours d'un cycle de vegetation chez le mais

Diplôme : Dr. Ing

Elaboration du rendement chez le mais

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Quantification de la respiration de plantes entières : notions de respirations de croissance et d'entretien

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Evaluation du nombre de grains potentiels a la recolte a partir d'observations de developpement floral anterieur a la floraison, chez le mais

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Respirations de croissance et d'entretien du mais dans differentes conditions artificielles de culture

National audienceIn a precedent paper, we have shown the applicability of growth and maintenance respiration concepts to a whole life cycle for an annual plant (Zea mays L.). In this paper, the method is used for 9 chamber crops under several conditions (2 varieties, 2 growth temperature and 2 roots medium). While growth efficiency (YG) variations are small with these conditions, maintenance cost (m) is largely affected by crop grain percentage. The effects of the assumptions used are discussed and coefficients for respiration modelling are proposed.Nous avons montré dans un précédent article que le fractionnement de la respiration en croissance et entretien était applicable à un cycle de végétation pour une plante annuelle, le maïs. Nous l’appliquons ici sur 9 cultures en chambre, dans différentes conditions (2 variétés, 2 températures et 2 milieux de culture). Alors que l’efficience de la croissance (YG) est peu affectée par ces conditions, le coût de l’entretien (m) varie avec le pourcentage de grains. L’effet des hypothèses de calcul est discuté et des coefficients applicables en modélisation sont proposés

Importance des reserves dans le remplissage des grains. Modalites de repartition des assimilats entre structures et reserves chez le mais

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Respirations de croissance et d'entretien du mais dans differentes conditions artificielles de culture

National audienceIn a precedent paper, we have shown the applicability of growth and maintenance respiration concepts to a whole life cycle for an annual plant (Zea mays L.). In this paper, the method is used for 9 chamber crops under several conditions (2 varieties, 2 growth temperature and 2 roots medium). While growth efficiency (YG) variations are small with these conditions, maintenance cost (m) is largely affected by crop grain percentage. The effects of the assumptions used are discussed and coefficients for respiration modelling are proposed.Nous avons montré dans un précédent article que le fractionnement de la respiration en croissance et entretien était applicable à un cycle de végétation pour une plante annuelle, le maïs. Nous l’appliquons ici sur 9 cultures en chambre, dans différentes conditions (2 variétés, 2 températures et 2 milieux de culture). Alors que l’efficience de la croissance (YG) est peu affectée par ces conditions, le coût de l’entretien (m) varie avec le pourcentage de grains. L’effet des hypothèses de calcul est discuté et des coefficients applicables en modélisation sont proposés