Abstract
Agrivoltaic (APV) systems offer a promising approach to co-locating food and energy production; however, their effects on crop performance remain insufficiently quantified, particularly for shade-sensitive crops such as potato (Solanum tuberosum L.).\r\nThis study reports a four-year field experiment (2021–2024) conducted in Northern Italy under a REM Tec Agrovoltaico® dual-axis sun-tracking APV system. The objective was to quantify potato yield response and tuber characteristics under contrasting array setups and associated shading patterns, including the adoption of an anti-tracking (AT) management strategy during critical growth stages.\r\nExperimental treatments included a full-light control (FL), standard sun-tracking (ST1), intensified shading (ST2), and a dynamic anti-tracking strategy applied during the tuber initiation phase in 2024 in combination with ST1 (ST1+AT).\r\nYield response was strongly dependent on both shading intensity and timing. The standard sun-tracking configuration (ST1), characterized by a low mean seasonal shade depth (∼13 %), resulted in limited yield penalties (–12 % on average), whereas the higher-shade configuration (ST2) caused yield reductions exceeding 30 %.\r\nThe AT management during early tuber development partially mitigated yield losses, indicating that dynamic light management can represent an effective strategy to balance agricultural productivity and energy generation in APV systems. Weibull-based modelling of tuber size distribution (TSD) revealed a consistent shift toward smaller tubers under increasing shade, while tuber dry matter content (DMC) remained relatively stable.
| Original language | English |
|---|---|
| Pages (from-to) | 1-30 |
| Number of pages | 30 |
| Journal | Smart Agricultural Technology |
| Volume | 14 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 2 Zero Hunger
All Science Journal Classification (ASJC) codes
- Computer Science (miscellaneous)
- General Agricultural and Biological Sciences
- Artificial Intelligence
Keywords
- agrivoltaico
- agrivoltaics
- crop modelling
- modelli colturali
- patata
- potato
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