Tomato growth and yield : quantitative analysis and synthesis

Research output: Thesisinternal PhD, WU

Abstract

<br/>In this thesis, the responses of tomato crop growth and yield to greenhouse climate (light, temperature and C0 <sub><font size="-2">2</font></sub> concentration) and crop management (plant density and fruit pruning) were analysed and quantified. A simulation model, TOMSIM, was developed, using existing explanatory crop growth models for tomato, and validated.<p>Sink demand, influenced by fruit pruning (retaining two to seven fruits per truss), did not influence dry matter production per unit of intercepted radiation. Measurements in daylit phytotron compartments showed an increase in crop photosynthesis with 17%, when C0 <sub><font size="-2">2</font></sub> concentration increased from 500 to 900 μmol mol <sup><font size="-2">-1</font></SUP>. Based on periodic destructive measurements in 12 experiments, a crop efficiency of 2.5 g dry matter MJ <sup><font size="-2">-1</font></SUP>PAR (photosynthetically active radiation) was observed. At the end of most experiments, 54-60% of total dry matter produced was partitioned into the fruits and the ratio between produced leaf and stem dry weight was 7:3. For late autumn plantings, only 35-38% was partitioned into the fruits, owing to poor fruit set. From first harvest onwards, an (almost) constant partitioning existed: 70% of dry matter was distributed to the fruits, when the number of fruits per truss was seven.<p>Dry matter partitioning was strongly influenced by the number of fruits on the plant. Generative sink strength appeared to be proportional to the number of fruits per truss (two to seven fruits per truss). Temperature (18-24°C) and assimilate supply (varied by plant density, 1.6-3.1 plants m <sup><font size="-2">-2</font></SUP>) had no direct influence on dry matter partitioning. In double-shoot plants, where either all trusses were removed from one shoot or every other truss was removed from both shoots at anthesis, dry matter partitioning was the same, supporting the assumption of one common assimilate pool, i.e. no influence of phloern transport resistance on partitioning.<p>In TOMSIM, potential crop growth rate is simulated, based on leaf photosynthesis rate, crop light interception, maintenance respiration rate (R <sub><font size="-2">m</font></sub> ) and conversion efficiency from carbohydrates to structural dry matter. The canopy is assumed to be composed of leaves with identical photosynthetic and respiratory characteristics. Dry matter partitioning is simulated based on the sink strengths of the plant organs, where sink strength of an organ is described by its potential growth rate. The model was validated thoroughly, both at the level of submodels and as a whole, using independent experimental data as well as measurements on commercially grown crops. In general, model predictions agreed well with the measurements. Simulated crop growth appeared to be very sensitive to LAI simulation, especially at low LAI. At low light intensity and/or high crop biomass, crop growth rate was underestimated, most likely as a result of overestimation of R <sub><font size="-2">m</font></sub> under these conditions. Simulation results improved, when R <sub><font size="-2">m</font></sub> was related to crop metabolic activity, described by crop relative growth rate. The simulation of specific leaf area, flower and/or fruit abortion and R <sub><font size="-2">m</font></sub> requires more investigation. The possibility of generalisation of the model to other (greenhouse) crops, and the possible applications of the model in supporting grower's decisions, in research and in education are discussed.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
Supervisors/Advisors
  • Challa, H., Promotor
Award date15 Mar 1996
Place of PublicationS.l.
Publisher
Print ISBNs9789054854982
Publication statusPublished - 1996

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Keywords

  • growth
  • solanum lycopersicum
  • tomatoes
  • plant physiology
  • plant development
  • flowers
  • fruits
  • plant organs
  • greenhouses
  • climate
  • computer simulation
  • simulation
  • simulation models

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