Controlled environment agriculture (CEA) is a fast-growing sector of the horticultural marketplace in mid-western states like Iowa. Supporting this expanding industry necessitates empirically supported management strategies for the input-intensive nature of its production systems. One of the most significant opportunities for improving production efficiency is optimizing energy consumption. This thesis aims to accomplish that goal through two overarching objectives: (1) improving the corpus of temperature physiology for specialty leafy green crops; and (2) creating a new experimental framework and a decision support tool for the production of commonly grown leafy greens and culinary herbs.

These objectives were furthered over the course of two experiments. In our first experiment, changes to specialty leafy green growth, development, and gas exchange were modeled against average daily temperature (ADT). The results provided evidence for the establishment of cardinal temperatures and temperature classifications for arugula, kale, swiss chard, and pac choi. The statistical analysis of fresh mass and node appearance indicated that these responses followed an asymmetrical parabolic trend from which a linear range of effective production temperatures could be formed. These linear ranges are the values of ADT within which the commercial production of these species should be maintained.

The second experiment explored the manipulation of diurnal temperature difference (DIF) on lettuce and basil. Plants were grown between -10 and 15 °C of DIF at 20 and 25 °C ADT. The resulting values for yield were incorporated into greenhouse heating simulation software, forming the season-specific temperature recommendations and fuel consumption estimates. Almost universally, maintaining a 5 °C DIF resulted in optimized heating efficiency.