Agrivoltaics (AVs), the co-location of photovoltaic panels and agricultural production, is increasingly promoted as a strategy to enhance land-use efficiency and support renewable energy transitions. While numerous studies emphasize potential synergies, growing evidence indicates that AV systems also entail significant biophysical, ecological and socio-economic trade-offs. This review synthesizes published literature on the negative impacts and management challenges associated with agrivoltaics across diverse crops, climates and institutional contexts. A structured literature analysis was conducted, integrating findings from experimental field studies, ecological assessments, economic evaluations and policy analyses. The reviewed evidence demonstrates that panel-induced shading and altered microclimatic conditions frequently reduce photosynthetically active radiation, modify soil temperature and moisture regimes, and impair photosynthetic efficiency, yield stability, and quality in light-demanding crops. Open-field AV installations further alter understory vegetation, pollinator activity and soil arthropod communities, leading to functional biodiversity losses beneath panel-covered areas. Economic and institutional analyses reveal high investment costs, regulatory ambiguity and land-tenure constraints that disproportionately transfer agronomic and financial risks to farmers, while land-use conflicts may reduce food production and contribute to indirect land-use change. Overall, open-field AV outcomes are strongly context- and design-dependent. The review highlights the need for long-term, integrative assessments and governance frameworks that explicitly address trade-offs to ensure that AVs contribute to sustainable land-use transitions rather than undermining agricultural and ecological functions.

Prays oleae Bernard, 1788, or the olive moth, is a significant pest in Croatian olive groves. This study aims to develop a functional model based on an artificial neural network to detect olive moths in real time. This study was conducted in two different orchards in Zadar County, Croatia, in the periods from April to September 2022 and from May to July 2023. Moth samples were collected by placing traps with adhesive pads in these orchards. Photos of the pads were taken every week and were later annotated and used to develop the dataset for the artificial neural network. This study primarily focused on the average precision parameter to evaluate the model’s detection capabilities. The average AP value for all classes was 0.48, while the average AP value for the Olive_trap_moth class, which detected adult P. oleae, was 0.59. The model showed the best results at an IoU threshold of 50%, achieving an AP50 value of 0.75. The AP75 value was 0.56 at an IoU = 75%. The mean average precision (mAP) was 0.48. This model is a promising tool for P. oleae detection; however, further research is advised.

The olive fruit fly (Bactrocera oleae Rossi, 1790) is the most damaging pest of olive cultivation, threatening both yield and quality of olive oil across the Mediterranean. Early and accurate detection of adult flies is a cornerstone of integrated pest management, yet conventional monitoring methods based on manual trap inspection remain slow and labor-intensive. In this study, we developed and validated a convolutional neural network (CNN) model trained exclusively on a real-field dataset of 4,278 images collected in olive orchards in Zadar County, Croatia. The model was designed to distinguish B. oleae adults from other insects captured on adhesive traps. It achieved a mean average precision (mAP) of 0.74, with particularly strong performance for the olive fly class (AP = 0.81) and high accuracy at Intersection over Union (IoU) = 50% (AP50 = 0.95). Our results demonstrate that CNN-based detection models trained on field data can provide fast, reliable, and scalable pest monitoring solutions. This approach holds promise for reducing the reliance on manual inspections and supports the development of more sustainable and precise pest management strategies in olive production.

Agrivoltaika-AV (eng. agrivoltaics) ili agrosolarni sustavi, odnosno integracija solarnih panela u poljoprivredi, već se smatra inovativnim pristupom koji omogućuje dvostruko iskorištavanje zemljišta. Donosi istovremeno koristi za proizvodnju hrane i dobivanje električne energije iz obnovljivih izvora. Iako se primjenjuje u različitim sektorima biljne proizvodnje, voćarstvo se ističe kao pogodno područje zbog višegodišnjeg karaktera nasada i osjetljivosti pojedinih voćnih vrsta na mikroklimatske promjene uslijed promjena klime. Ovaj rad donosi pregled dosadašnjih znanstvenih saznanja o primjeni AV u voćarstvu, s naglaskom na utjecaj na mikroklimu ispod panela i ekološko fiziološki odgovor biljke, prinos i kvalitetu plodova, te održivost gospodarstva. Analizirani su povoljni i nepovoljni učinci na različite voćne vrste te su razmotreni ključni tehnički i dizajnerski aspekti implementacije. Također je istaknut potencijal AV na smanjenje potrošnje vode, zaštitu od temperaturnih stresova i povećanje energetske neovisnosti, te su navedeni primjeri eksperimentalnih projekata u Hrvatskoj i svijetu.