Greenhouses are playing an increasingly important role in the global food system. Not only do they offer year-round production of fresh food, but they increase annual yields up to ten-fold compared to traditional field production. This increase is partly the result of the optimization of climate factors that are essential for crop growth and development.
Temperature determines the amount of light that can be processed by the plant. The Ratio of Temperature to Radiation (RTR) is a parameter commonly used by growers to adjust their lighting settings in response to temperature. In general, RTR is based on daily averages measured by climate box positioned above the canopy. However, when temperature and light intensity inside a greenhouse varies over in time and in space, a single RTR value may not be representative of the climate perceived by the canopy.
FOTONIQ has been exploring different definitions of RTR. This parameter includes both temporal as well as spatial variation, being calculated using local and instantaneous measurements, instead of a single daily average for the entire greenhouse.
The so-called “RTR+” may offer a more accurate representation of the microclimate at plant level compared to the traditional RTR definition. RTR+ extends the concept of radiation–temperature ratio in multiple dimensions by combining both spatial and temporal enhancements. The spatially extended component (S-RTR+) measures at the plant or organ level, rather than at the canopy level. The temporally extended component (T-RTR+) reflects instantaneous measurements instead of daily averages.
The aim of this project is to demonstrate that S-RTR+ and/or T-RTR+ outperform the conventional RTR, in terms of predictive accuracy and achieved results.
The research questions are intended to be answered by the construction of a statistical model and/or running data/crop growth simulations.
· What are the effects of growing based on S-RTR+ and T-RTR+ on plant processes (e.g. fruiting time, photosynthesis rate)?
· How do optimal settings differ when comparing S-RTR+ and T-RTR+ to conventional RTR?
· What is the impact of S-RTR+ and/or T-RTR+ on resource efficiency (light, water, CO2) in relation to yield change?
· To what extent does the model improve by combining S-RTR+ and T-RTR+ into a single parameter, RTR+?
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