Dry-matter partitioning in a tomato crop: Comparison of two simulation models

Abstract

SUMMARYTOMSIM(l.O) and TOMGRO(I.O) are two dynamic models for tomato growth and development. Their sub-models for dry-matter distribution between leaves, stem and fruits were compared and discussed. In both models the simulated dry-matter distribution is regulated by the relative sink strengths of the plant organs. These sink strengths are quantified by the potential growth rates of individual organs, i.e. the growth rates under conditions of non-limiting assimilate supply. This approach is general and not limited to the tomato crop. In TOMGRO(J.O), fruits, leaves and internodes stay within age classes and move from class to class during development, whereas in TOMSIM (1.0), record is kept of every fruit truss separately but leaves and internodes are lumped together (i.e. no record of weight or leaf area per age class as in TOMGRO(l.O)). In TOMSIM(1.0), vegetative sink strength is a constant, whereas in TOMGRO(l.0) it is calculated from potential area expansion rate of leaves and specific leaf area. In both models, the ratio between leaf growth and stem growth is constant. In TOMGRO(l.O) there is a feed-back mechanism which controls the vegetative/generative balance: a higher demand/supply ratio for assimilates induces higher fruit abortion rates. In TOMSIM(l.O) the number of fruits set per truss is not simulated, but is an input to the model. TOM SIM (1.0) functions representing flowering rate, fruit growth period, vegetative sink strength and fruit sink strength were compared with similar TOMGRO(l.O) functions, in their dependence on temperature and physiological plant age. A sensitivity analysis was made for the effects of temperature, flowering rate, and fruit and vegetative sink strengths on dry-matter distribution for both models. A validation of both models was based upon periodic destructive harvests in: 1) a greenhouse experiment in Wageningen, using a round tomato cultivar, consisting of a control treatment and a treatment where every second truss was removed at anthesis, and 2) two greenhouse experiments conducted in Montfavet, using a beefsteak tomato cultivar. Daily shoot dry-weight increase, average 24 h greenhouse temperatures and numbers of fruits set per truss (in TOMGRO(l.O) numbers of flowers per truss) were inputs to the models. In general dry-matter distribution was simulated well by both models for the cultivar and conditions where they were developed. TOMGRO(1.0)'s poor performance in one of the validations resulted from the absence of an assimilate storage pool. To achieve reasonable agreement between measurements and simulations for situations other than where the models were developed, parameter adjustments had to be made, most likely reflecting cultivar differences. Strong and weak points of both models are discussed.