Achieving optimal yield and quality at harvest depends on the grower’s ability to avoid abiotic stresses (water, light, and temperature). This task has usually been satisfied through the implementation of adequate horticultural practices. In the context of clean energy transition and global climate change, growers nowadays have the possibility to grow their crops under solar panels, which modify the micro-environment of the crops. Being able to anticipate the behavior of plants under these new micro-environmental conditions would help growers adapt their horticultural practices. For electricity producers, in the context of dynamic agrivoltaic systems, anticipating the crop status is useful to choose a solar panels steering policy that maximizes electricity production while ensuring favorable environmental conditions for the crop to grow. To help electricity producers and growers estimate a crop status under panels, we developed a decision support system (DSS) called crop_sim. As of now, it can be used to monitor two types of perennial crops: grapevines and apple trees. crop_sim produces three indicators of the crop status: predawn water potential, canopy temperature and carbon production. Besides providing information on the crop status, the DSS incorporates an expert system which indicates the best time and the amount of irrigation to maintain a desired water status under the new micro-environmental conditions.
This paper first focuses on the description of crop_sim and the usefulness of the three indicators. Then, a case study is presented. Our results show that, in a mature vineyard, with a typical panel steering policy conservative on crop yield, growers could save 13% of water compared to an open-field reference.
Experimental data pertaining to apple trees, grapevines, tomatoes, and maize are being collected. They will be used to adapt the model to tomato and maize, evaluate it and make it robust enough to bring to market. Further improvements of the crop_sim model may be required to finely reproduce observations in the field. A full validation of the model is expected when all data from the experiments will be available. The DSS will evolve depending on the requirements of the agrivoltaics community and may incorporate additional plant indicators and new expert system rules.