Field trial reveals shading impacts on yield and morpho-physiology in wheat, suggesting genetic variability in shade tolerance.
BACKGROUND Solar radiation is a primary constraint in silvoarable agroforestry, with yield losses near the trees well documented in temperate climates. However, genetic variability for shade tolerance remains largely unexplored. This 2‐year field trial investigated the impact of artificial shading – using nets that reduced photosynthetically active radiation (PAR) by moderate (−30%) and severe (−50%) levels relative to full sun – on the morpho‐physiology and yield of common wheat. During the first growing season, three varieties were evaluated: the modern variety, Bologna, and two old, tall varieties, Piave and Terminillo . In the second season, only Bologna was retested under the same shading treatments, to further assess its performance. RESULTS Shading treatments delayed crop phenology from heading onward. They were associated with a prolonged canopy stay‐green, with effects accentuated by increasing shading severity. In the first year, moderate shading increased Bologna 's grain yield by 8% ( P ≤ 0.05) and raised protein content by 0.7%. In the second year, drier conditions and reduced radiation from March–June – causing more severe limitation of PAR, the amount of sunlight available for photosynthesis – led to a 40% yield loss. Old varieties suffered yield reductions due to lodging (−43% in Terminillo, −66% in Piave ), yet showed notable increases in mineral content (up to +48% Mg in Piave ) and grain protein (+1.7% in Terminillo and +3.4% in Piave ). Under shaded conditions, increased gliadin content led to enhanced gluten accumulation, potentially improving dough extensibility. CONCLUSIONS Although nets can mimic shading in wheat effectively, these results highlight the need to extend varietal screening to identify suitable genotypes and key morpho‐physiological traits to optimize wheat cultivation in shaded environments such as agroforestry systems, with further validation in specific emerging models.
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Panozzo et al. (2025) studied this question.
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