Climate change is reshaping agroecosystems by altering temperature and precipitation regimes, increasing the frequency of extreme events, and intensifying plant stress conditions. In maize agroecosystems these shifts not only affect plant morphophysiological traits and yield formation, but also influence plant-microbiome interactions, thereby increasing the risk of grain contamination by mycotoxins (i.e., aflatoxins and fumonisins) and potentially altering the structure and function of the rhizosphere microbiome. This PhD Thesis adopts an integrated agroecological framework to investigate how environmental drivers operating at different phenological stages shape maize functional traits, modulate grain mycotoxin accumulation, and influence the associated rhizobiome, while simultaneously exploring sustainable mitigation and monitoring strategies. First, the antifungal potential of bio-based extracts derived from agricultural and marine by-products was evaluated as an environmentally friendly alternative to synthetic pesticides. In vitro assays demonstrated that extracts from the green macroalga Ulva sp. markedly inhibited the growth and conidial germination of Aspergillus flavus, a major maize pathogen responsible for aflatoxins production. The high efficacy of Ulva extract highlights the potential of circular economy-derived bio-extracts as sustainable tools for mycotoxin management. Second, statistical models integrating climatic variables, soil properties, and plant functional traits were developed to predict aflatoxin and fumonisin occurrence in maize grain. Results showed that temperature and rainfall patterns exerted stage-dependent effects on mycotoxin accumulation, interacting with plant morphophysiological responses. These findings underscore the importance of considering phenological timing and trait-mediated mechanisms when modelling climate-driven mycotoxin risk. Third, multi-temporal UAV-based multispectral imagery was employed to monitor maize canopy dynamics throughout the growing season. Multispectral images provided non-destructive proxies of plant physiological status, improving the interpretation of crop-environment interactions and supporting early stress detection. Finally, the environmental modulation of the maize-associated rhizobiome was investigated, revealing that climate may directly alter rhizobiome composition rather than acting through climate-driven shifts in plant phenology. Overall, this research demonstrates that integrating climate-aware statistical modelling, high-resolution remote sensing, and sustainable bio-based solutions provides a comprehensive strategy to enhance risk prediction, early stress monitoring, and preventive management of mycotoxin contamination in maize agroecosystems under climate change.
Climate change is reshaping agroecosystems by altering temperature and precipitation regimes, increasing the frequency of extreme events, and intensifying plant stress conditions. In maize agroecosystems these shifts not only affect plant morphophysiological traits and yield formation, but also influence plant-microbiome interactions, thereby increasing the risk of grain contamination by mycotoxins (i.e., aflatoxins and fumonisins) and potentially altering the structure and function of the rhizosphere microbiome. This PhD Thesis adopts an integrated agroecological framework to investigate how environmental drivers operating at different phenological stages shape maize functional traits, modulate grain mycotoxin accumulation, and influence the associated rhizobiome, while simultaneously exploring sustainable mitigation and monitoring strategies. First, the antifungal potential of bio-based extracts derived from agricultural and marine by-products was evaluated as an environmentally friendly alternative to synthetic pesticides. In vitro assays demonstrated that extracts from the green macroalga Ulva sp. markedly inhibited the growth and conidial germination of Aspergillus flavus, a major maize pathogen responsible for aflatoxins production. The high efficacy of Ulva extract highlights the potential of circular economy-derived bio-extracts as sustainable tools for mycotoxin management. Second, statistical models integrating climatic variables, soil properties, and plant functional traits were developed to predict aflatoxin and fumonisin occurrence in maize grain. Results showed that temperature and rainfall patterns exerted stage-dependent effects on mycotoxin accumulation, interacting with plant morphophysiological responses. These findings underscore the importance of considering phenological timing and trait-mediated mechanisms when modelling climate-driven mycotoxin risk. Third, multi-temporal UAV-based multispectral imagery was employed to monitor maize canopy dynamics throughout the growing season. Multispectral images provided non-destructive proxies of plant physiological status, improving the interpretation of crop-environment interactions and supporting early stress detection. Finally, the environmental modulation of the maize-associated rhizobiome was investigated, revealing that climate may directly alter rhizobiome composition rather than acting through climate-driven shifts in plant phenology. Overall, this research demonstrates that integrating climate-aware statistical modelling, high-resolution remote sensing, and sustainable bio-based solutions provides a comprehensive strategy to enhance risk prediction, early stress monitoring, and preventive management of mycotoxin contamination in maize agroecosystems under climate change.
Ecophysiological Response of Maize (Zea mays L.) to Water Stress: Remote Sensing and Upscaling Techniques for a More Efficient Management of Water Resources in Agriculture / Giacomo Boscarol , 2026 Jul 01. 38. ciclo, Anno Accademico 2024/2025.
Ecophysiological Response of Maize (Zea mays L.) to Water Stress: Remote Sensing and Upscaling Techniques for a More Efficient Management of Water Resources in Agriculture
BOSCAROL, GIACOMO
2026-07-01
Abstract
Climate change is reshaping agroecosystems by altering temperature and precipitation regimes, increasing the frequency of extreme events, and intensifying plant stress conditions. In maize agroecosystems these shifts not only affect plant morphophysiological traits and yield formation, but also influence plant-microbiome interactions, thereby increasing the risk of grain contamination by mycotoxins (i.e., aflatoxins and fumonisins) and potentially altering the structure and function of the rhizosphere microbiome. This PhD Thesis adopts an integrated agroecological framework to investigate how environmental drivers operating at different phenological stages shape maize functional traits, modulate grain mycotoxin accumulation, and influence the associated rhizobiome, while simultaneously exploring sustainable mitigation and monitoring strategies. First, the antifungal potential of bio-based extracts derived from agricultural and marine by-products was evaluated as an environmentally friendly alternative to synthetic pesticides. In vitro assays demonstrated that extracts from the green macroalga Ulva sp. markedly inhibited the growth and conidial germination of Aspergillus flavus, a major maize pathogen responsible for aflatoxins production. The high efficacy of Ulva extract highlights the potential of circular economy-derived bio-extracts as sustainable tools for mycotoxin management. Second, statistical models integrating climatic variables, soil properties, and plant functional traits were developed to predict aflatoxin and fumonisin occurrence in maize grain. Results showed that temperature and rainfall patterns exerted stage-dependent effects on mycotoxin accumulation, interacting with plant morphophysiological responses. These findings underscore the importance of considering phenological timing and trait-mediated mechanisms when modelling climate-driven mycotoxin risk. Third, multi-temporal UAV-based multispectral imagery was employed to monitor maize canopy dynamics throughout the growing season. Multispectral images provided non-destructive proxies of plant physiological status, improving the interpretation of crop-environment interactions and supporting early stress detection. Finally, the environmental modulation of the maize-associated rhizobiome was investigated, revealing that climate may directly alter rhizobiome composition rather than acting through climate-driven shifts in plant phenology. Overall, this research demonstrates that integrating climate-aware statistical modelling, high-resolution remote sensing, and sustainable bio-based solutions provides a comprehensive strategy to enhance risk prediction, early stress monitoring, and preventive management of mycotoxin contamination in maize agroecosystems under climate change.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
