Modeling Seed Longevity and Percentile Prediction: A Sigmoidal Function Approach in Soybean, Maize, and Tomato

This study aims to evaluate the behavior of seed longevity in soybean, maize, and tomato stored under controlled conditions using Logistic and Boltzmann sigmoidal models. Additionally, it seeks to determine the performance of these models in predicting P50, P85, and P25. The models were fitted to the experimental longevity data, and their performance in predicting the percentiles was evaluated. The Logistic model showed better performance in predicting P50 (time for viability to drop to 50%), P85 (time for viability to drop to 85%), and P25 (time for viability to drop to 25%), estimating the parameters more frequently within the experimental range (obtained from the initial viability data). The results of this study suggest that some cultivars exhibited different patterns in deterioration rates, with some showing abrupt declines in viability, highlighting differences in the speed and nature of seed deterioration. The Logistic model proved to be superior, with an accuracy of 83% in estimating the P85 and P25 percentiles, while the Boltzmann model achieved an accuracy of 54%. The tomato cultivar Gaucho showed the greatest loss in germination, reaching P25 quickly, while the soybean cultivar M 7119 IPRO and maize cultivar MAM06 maintained high germination for a longer period. These findings emphasize the importance of using viability percentiles to optimize storage practices, minimize economic losses, and prevent genetic erosion in conservation programs. Modeling seed longevity using sigmoidal models can significantly contribute to determining various viability percentiles, supporting storage practices and providing valuable insights for strategic decision-making in seed management, proving useful in both commercial and species conservation contexts.