Biology subjects

Severini, A. D.

Publications and source records attributed to Severini, A. D..

3 recordsLinked to original sources

bioRxiv2026

Evaluating crop models for future climate scenarios: wheat yield predictions using APSIM and STICS under combined CO2, warming, and water deficit conditions

Crop models are essential for predicting climate change impacts on agriculture, yet their validation under multi-stress conditions remains limited. This study evaluated two widely-used wheat models, APSIM and STICS, using data from three Free-Air CO2 Enrichment (FACE) experiments (USA, Germany, Australia) combining elevated CO2 (eCO2), water deficit, and warming. Environmental characterisation using simulation-based stress indices revealed that intended "controls" frequently experienced hidden heat and water stress, meaning models were calibrated on crops already undergoing physiological adjustments. Evaluation of simulated yield and components revealed a clear hierarchy in prediction errors (RRMSE): unlimited conditions (3-9%) < single stress (4-27%, with a need to improve response to heat stress) < combined stress (17-123%). Elevated CO2 generally increased prediction uncertainty for crops experiencing water stress. Our results suggest that current stress functions from the models fail to capture the synergistic coupling between drought and heat stress. This highlights the urgent need for more mechanistic modelling to improve the reliability of climate change impact assessments.

plant biology↗

bioRxiv2025

Spatial probabilistic patterns of joint heat and water stress of chickpea in Australia

Quantitative characterisation of the crop environment for breeding and agronomic applications is commonly based on the cluster analysis of isolated climate factors, such as drought and temperature. The focus on individual climate factors fails to account for both the correlations between climate factors in space and time, and the contemporary models of dynamic biological systems where multiple interacting factors simultaneously shape the crop phenotype. With a focus on chickpea in Australia, our aims are to (1) assess the associations between climate factors and actual yield measured in 578 location -years; associations are investigated on a biologically relevant developmental scale; (2) establish the spatial, probabilistic patterns of multivariate environment types at regional and continental scales; and (3) evaluate the shifts in the frequency of environment types with climate change. We identified a syndrome of hot, dry and high vapour pressure deficit with three intensities of stress classified as low, medium and high. Measured yield, in the range from failed crop to 4 t ha-1, aligned more strongly with this multivariate syndrome than with isolated climate factors, with stronger associations at regional than continental scale. The frequency of stressful environment types increased with realised climate change and was most severe in the chickpea growing heartland of central Queensland; our models show that increased frequency of stress could be partially mitigated with earlier flowering varieties. Our findings will inform chickpea agronomy, phenotyping and breeding.

physiology↗

bioRxiv2023

CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone

Accurate prediction of phenology is the most critical aspect for the development of models aimed at estimating seed yield, particularly in species that exhibit variable sensitivity to environmental factors throughout the cycle and among genotypes. With this purpose, we evaluated the phenology of 34 soybean varieties in 43 field experiments located in Argentina, Uruguay, and Paraguay. Experiments covered a broad range of maturity groups (2.2-6.8), sowing dates (from spring to summer), and latitude range (24.9-35.6 {degrees}S), thus ensuring a wide range of thermo-photoperiodic conditions during the growing season. Based on the observed data, daily time-step models were developed and tested, first for each genotype, and then across maturity groups. We identified base temperatures specific for different developmental stages and an extra parameter for calculating the photoperiod effect after the R1 stage (flowering). Also, an optimum photoperiod length for each maturity group was found. Model selection showed that the determinants of phenology across maturity groups were mainly affecting the duration of vegetative and early reproductive stages. Even so, early phases of development were better predicted than later ones, particularly in locations with cool growing seasons, where the model tended to overestimate their duration. Overall, we developed a soybean phenology model that, owing to its process-based nature, is able to simulate phenology well across widespread locations and sowing dates yet, because of its simplicity, can be adopted by a wide audience. The final model was made available at http://cronosoja.agro.uba.ar.

plant biology↗