ABSTRACT
For oil palm, yield variation is in large part due to variation in the number of harvested bunches. Each successively-produced phytomer carries a female (productive), male or aborted inflorescence. Since phytomer development takes 3-4 years and nearly two phytomers are produced per month, many inflorescences develop in parallel but have different phenological stages. Environment-dependent developmental rate, sex and abortion probability determine bunch productivity, which, in turn, affects other phytomers via source-sink relationships. Water deficit, solar radiation, temperature and day length are considered key external factors driving variation. Their impact is difficult to predict because of system complexity. To address this question we built a simple model (ECOPALM) to simulate the variation in number of harvested bunches. In this model, trophic competition among organs, expressed through a plant-scale index (Ic), drives sex determination and inflorescence abortion during specific sensitive phases at phytomer level. As a supplemental hypothesis, we propose that flowering is affected by photoperiod at phytomer level during a sensitive phase, thus, contributing to seasonal production peaks. The model was used to determine by parameter optimisation the influence of Ic and day length on inflorescence development and the stages at which inflorescences are sensitive to these signals. Parameters were estimated against observation of number of harvested bunches in Ivory Coast using a genetic algorithm. The model was then validated with field observations in Benin and Indonesia. The sensitive phases determined by parameter optimisation agreed with independent experimental evidence, and variation of Ic explained both sex and abortion patterns. Sex determination seemed to coincide with floret meristem individualisation and occurred 29-32 months before bunch harvest. The main abortion stage occurred 10 months before harvest - at the beginning of rapid growth of the inflorescence. Simulation results suggest involvement of photoperiod in the determination of bunch growth dynamics. This study demonstrates that simple modelling approaches can help extracting ecophysiological information from simple field observations on complex systems.
ABSTRACT
Present work focussed on improving the description of organogenesis, morphogenesis and metabolism in a biophysical plant model (SUNFLO) applied to sunflower (Helianthus annuus L.). This first version of the model is designed for potential growth conditions without any abiotic or biotic stresses. A greenhouse experiment was conducted to identify and estimate the phenotypic traits involved in plant productivity variability of 26 sunflower genotypes. The ability of SUNFLO to discriminate the genotypes was tested on previous results of a field survey aimed at evaluating the genetic progress since 1960. Plants were phenotyped in four directions; phenology, architecture, photosynthesis and biomass allocation. Twelve genotypic parameters were chosen to account for the phenotypic variability. SUNFLO was built to evaluate their respective contribution to the variability of yield potential. A large phenotypic variability was found for all genotypic parameters. SUNFLO was able to account for 80% of observed variability in yield potential and to analyse the phenotypic variability of complex plant traits such as light interception efficiency or seed yield. It suggested that several ways are possible to reach high yields in sunflower. Unlike classical statistical analysis, this modelling approach highlights some efficient parameter combinations used by the most productive genotypes. The next steps will be to evaluate the genetic determinisms of the genotypic parameters.
