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.

在一年的实验中，我们验证了一个模型，该模型可预测正在生长的植物中的干物质（DM）产量，而无需对三个番茄品种进行破坏性取样。为了改善DM，我们对温室中的温度和CO ₂浓度以及番茄植株的叶面积指数（LAI）进行了管理，以满足根据模型预测确定的目标。在该模型中，通过无损手动测量叶片的宽度和长度以及叶片的数量，可以获得叶片的面积，从而获得了截获的光线。光利用效率表示为白天CO ₂的函数浓度。尽管模型通常成功预测了两个品种的LAI，但观察到的LAI与第三个品种的预测值不同。然而，根据光合有效辐射，温度，CO ₂和人工测量叶片，在所有三个品种中都可以高精度预测DM的产生。在三个采样时间，所有品种的预测总DM均在观察到的DM±标准差范围内。通过根据在模型上进行的模拟确定的目标控制温度，白天的CO ₂和LAI，我们能够将产量提高到每年> 50 kg·m ^-2。因此，该模型对于提高番茄产量很有用。