Browse Publications Technical Papers 2002-01-2520
2002-07-15

Crop Models for Varying Environmental Conditions 2002-01-2520

New variable environment Modified Energy Cascade (MEC) crop models were developed for all the Advanced Life Support (ALS) candidate crops and implemented in SIMULINK. The MEC models are based on the Volk, Bugbee, and Wheeler Energy Cascade (EC) model and are derived from more recent Top-Level Energy Cascade (TLEC) models. The MEC models were developed to simulate crop plant responses to day-to-day changes in photosynthetic photon flux, photoperiod, carbon dioxide level, temperature, and relative humidity. The original EC model allowed only changes in light energy and used a less accurate linear approximation.
For constant nominal environmental conditions, the simulation outputs of the new MEC models are very similar to those of earlier EC models that use parameters produced by the TLEC models. There are a few differences. The new MEC models allow setting the time for seed emergence, have more realistic exponential canopy growth, and have corrected harvest dates for potato and tomato.
The new MEC models indicate that the maximum edible biomass per meter squared per day is produced at the maximum allowed carbon dioxide level, the nominal temperatures, and the maximum light input. Reducing the carbon dioxide level from the maximum to the minimum allowed in the model reduces crop production significantly. Increasing temperature decreases production more than it decreases the time to harvest, so productivity in edible biomass per meter squared per day is greater at nominal than maximum temperatures. The productivity in edible biomass per meter squared per day is greatest at the maximum light energy input allowed in the model, but the edible biomass produced per light energy input unit is lower than at nominal light levels. Reducing light levels increases light and power use efficiency. The MEC models suggest we can adjust the light energy day-to-day to accommodate power shortages or use excess power while predicting and controlling edible biomass production.

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