Prediction and Improvement of Yield and Dry Matter Production Based on Modeling and Non-destructive Measurement in Year-round Greenhouse Tomatoes

We validated a model for predicting dry matter (DM) production in growing plants without the need for destructive sampling with three tomato cultivars in a one-year experiment. In an attempt to improve DM, we managed the temperature and CO2 concentration in the greenhouse as well as the leaf area index (LAI) of the tomato plants to meet targets determined based on model predictions. In the model, leaf area and thus the intercepted light is obtained by non-destructive, manual measurements of leaf width and length and the number of leaves. Light-use efficiency is expressed as a function of daytime CO2 concentration. Although the model generally successfully predicted LAI in two of the cultivars, the observed LAI differed from the predicted value in the third cultivar. DM production, however, was predicted with high accuracy in all three cultivars from photosynthetically active radiation, temperature, CO2, and manual measurements of leaves; the predicted total DM in all cultivars at three sampling times fell within the range of observed DM ± standard deviation. By controlling temperature, daytime CO2, and LAI according to targets determined by simulations run on the model, we were able to improve yield to > 50 kg·m−2 per year. Therefore, the model was useful for improving tomato yield.