Evaluating the added value of subseasonal weather forecasts for EU national wheat yield forecasts - Maximilian Zachow; Ivana Aleksovska; Riccardo Henin; Harald Kunstmann; Michele Meroni; Lorenzo Seguini; Elena Tarnavsky; Senthold Asseng (Agricultural and Forest Meteorology, 2026)

Oral lipoteichoic and lipoic acids improve insulin resistance and body composition in porphyria mice on a high-carbohydrate diet - Miriam Longo; Teresa Rubio; Araceli Lamelas; Daniel Jericó; Andrea Rodenes-Gavidia; Jordi Cervero; Juan Martínez-Blanch; Empar Chenoll; Patricia Martorell; Erika Paolini; Marica Meroni; José Ignacio Riezu-Boj; et al. (Journal of Physiology and Biochemistry, 2025)
Acute intermittent porphyria (AIP) is a genetic metabolic disorder characterized by neurovisceral attacks. Although high-carbohydrate diets or intravenous glucose administration can help alleviate incipient attacks in patients, these interventions may also promote insulin resistance and increase metabolic risk. This study explored targeted dietary interventions to manage hyperinsulinemia and to enhance glucose uptake in insulin-sensitive organs under high-carbohydrate diet. Body composition and fecal microbiota profile were also investigated in a murine model of the disease. Wild-type and AIP mice ( n = 6/group) were supplemented with tapioca maltodextrin in drinking water for 12 weeks, alongside heat-treated Bifidobacterium animalis subsp. lactis CECT-8145 (BPL1®HT), its by-product lipoteichoic acid (LTA), or the insulin-sensitizing agent α-lipoic acid (α-LA). Liver-targeted therapies, previously assessed in AIP mice, were also included in this study. AIP mice on a high-carbohydrate diet exhibited hyperinsulinemia and tissue-specific differences in glucose uptake compared to wild-type mice. Dysbiosis, marked by reduced fecal Dorea spp. and Adlercreutzia muris , alongside higher abundance of Escherichia coli, was also showed. Supplementation with α-LA and LTA revealed superior ability to improve glucose tolerance test and skeletal muscle glucose uptake, reduce hyperinsulinemia, and enhance body composition by increasing lean mass relative to fat, compared to gene therapy or liver-targeted insulin administration. Notably, LTA restored fecal microbiota profiles resembling those of wild-type mice. In conclusion, supplementation with LTA from BPL1®HT and α-LA may represent promising dietary interventions to manage glucose tolerance, improve insulin sensitivity in muscle and adipose tissues, and potentially ameliorate body composition in AIP patients under a high-carbohydrate diet.

Potential and limitations of machine learning modeling for forecasting Acute Food Insecurity - Melissande Machefer; Anne-Claire Thomas; Michele Meroni; Jose Manuel Veiga Lopez Pena; Michele Ronco; Christina Corbane; Felix Rembold (Global Food Security, 2025)

Is deeper always better? Evaluating deep learning models for yield forecasting with small data - Filip Sabo; Michele Meroni; François Waldner; Felix Rembold (Environmental Monitoring and Assessment, 2023)
Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible deep learning approach to forecast crop yield at the provincial administrative level based on deep 1D and 2D convolutional neural networks using limited data. This approach meets the operational requirements—public and global records of satellite data in an application ready format with near real time updates—and can be transferred to any country with reliable yield statistics. Three-dimensional histograms of normalized difference vegetation index (NDVI) and climate data are used as input to the 2D model, while simple administrative-level time series averages of NDVI and climate data to the 1D model. The best model architecture is automatically identified during efficient and extensive hyperparameter optimization. To demonstrate the relevance of this approach, we hindcast (2002–2018) the yields of Algeria’s three main crops (barley, durum and soft wheat) and contrast the model’s performance with machine learning algorithms and conventional benchmark models used in a previous study. Simple benchmarks such as peak NDVI remained challenging to outperform while machine learning models were superior to deep learning models for all forecasting months and all tested crops. We attribute the poor performance of deep learning to the small size of the dataset available.

A phenology-based method to derive biomass production anomalies for food security monitoring in the Horn of Africa - Michele Meroni; Michel M. Verstraete; Felix Rembold; Ferdinando Urbano; François Kayitakire (International Journal of Remote Sensing, 2014)

Chlorophyll concentration mapping with MIVIS data to assess crown discoloration in the Ticino Park oak forest - Cinzia Panigada; Micol Rossini; Lorenzo Busetto; Michele Meroni; Francesco Fava; Roberto Colombo (International Journal of Remote Sensing, 2010)

Remote sensing of sun‐induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP) - ALEXANDER DAMM; JAN ELBERS; ANDRÉ ERLER; BENIAMINO GIOLI; KARIM HAMDI; RONALD HUTJES; MARTINA KOSVANCOVA; MICHELE MERONI; FRANCO MIGLIETTA; ANDRÉ MOERSCH; JOSE MORENO; ANKE SCHICKLING; et al. (Global Change Biology, 2010)
Terrestrial gross primary production (GPP) is an important parameter to explore and quantify carbon fixation by plant ecosystems at various scales. Remote sensing (RS) offers a unique possibility to investigate GPP in a spatially explicit fashion; however, budgeting of terrestrial carbon cycles based on this approach still remains uncertain. To improve calculations, spatio‐temporal variability of GPP must be investigated in more detail on local and regional scales. The overarching goal of this study is to enhance our knowledge on how environmentally induced changes of photosynthetic light‐use efficiency (LUE) are linked with optical RS parameters. Diurnal courses of sun‐induced fluorescence yield ( F Syield ) and the photochemical reflectance index of corn were derived from high‐resolution spectrometric measurements and their potential as proxies for LUE was investigated. GPP was modeled using Monteith's LUE‐concept and optical‐based GPP and LUE values were compared with synoptically acquired eddy covariance data. It is shown that the diurnal response of complex physiological regulation of photosynthesis can be tracked reliably with the sun‐induced fluorescence. Considering structural and physiological effects, this research shows for the first time that including sun‐induced fluorescence into modeling approaches improves their results in predicting diurnal courses of GPP. Our results support the hypothesis that air‐ or spaceborne quantification of sun‐induced fluorescence yield may become a powerful tool to better understand spatio‐temporal variations of fluorescence yield, photosynthetic efficiency and plant stress on a global scale.