IFEVA   02662
INSTITUTO DE INVESTIGACIONES FISIOLOGICAS Y ECOLOGICAS VINCULADAS A LA AGRICULTURA
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Environmental control of dormancy in weed seed banks: Predicting temporal patterns of weed emergence
Autor/es:
DIEGO BATLLA
Lugar:
Caete
Reunión:
Congreso; V Conference of Seed Ecology; 2016
Institución organizadora:
Seed Science Society
Resumen:
Understanding seed dormancy sufficiently to predict temporal patterns of seedling emergence from soil seed-banks has long been a goal to both seed ecologists and agronomists. On one hand, timing of emergence can have critical consequences for plant fitness and population dynamics. Therefore, forecasting temporal patterns of emergence under different climatic scenarios is relevant for understanding how weed populations will respond to future climate change. On the other hand, because plants are more vulnerable in the seedling stage, the possibility of predicting seedling emergence patterns is instrumental for improving the efficacy of weed control methods. In order to predict dormancy changes of buried seeds, and consequently temporal patterns of emergence, we should: 1-have a clear notion of which and how environmental factors affect the dormancy level of the seed-bank, 2-quantify the effect of those factors on seed-bank dormancy level and 3-include the developed quantitative relationships into a coherent modeling framework. In this work, I present an attempt to conceptualize the effect of the environment in the seed-bank dormancy level, distinguishing between factors that modulate dormancy level of buried seeds (as for example soil temperature) and those that usually terminates dormancy (as for example light and alternating temperatures). Based on this conceptual framework I show approaches that can be used to establish functional relationship between dormancy level regulating factors and changes in the dormancy status of the seed-bank. Finally, I present examples of how these approaches can be included into a coherent modelling framework to forecast temporal patterns of weed emergence under different environmental scenarios.