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

• Published in: bioRxiv
• Main Authors: Severini, Alan David, Álvarez-Prado, Santiago, Otegui, Maria Elena, Kavanová, Monika, Vega, Claudia, Zuil, Sebastián, Ceretta, Sergio, Martín, Moisés Acreche, Amarilla, Fidencia, Mariano Andrés Cicchino, Fernández Long, María Elena, Aníbal Omar Crespo, Román Serrago, Miralles, Daniel Julio
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 23.11.2023
• Subjects: Developmental stages; Environmental factors; Flowering; Genotypes; Maturity; Phenology; Photoperiods; Soybeans
• DOI: 10.1101/2023.09.18.558336

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 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.Competing Interest StatementThe authors have declared no competing interest.Footnotes* - More appropriate abstract - The photoperiod threshold is properly called optimum photoperiod (not critical photoperiod) - Large variance-covariance matrices are now presented in the last section