Cargando…
Modeling Flood-Induced Stress in Soybeans
Despite the detrimental impact that excess moisture can have on soybean (Glycine max [L.] Merr) yields, most of today's crop models do not capture soybean's dynamic responses to waterlogged conditions. In light of this, we synthesized literature data and used the APSIM software to enhance...
Autores principales: | Pasley, Heather R., Huber, Isaiah, Castellano, Michael J., Archontoulis, Sotirios V. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7028700/ https://www.ncbi.nlm.nih.gov/pubmed/32117398 http://dx.doi.org/10.3389/fpls.2020.00062 |
Ejemplares similares
-
County-scale crop yield prediction by integrating crop simulation with machine learning models
por: Sajid, Saiara Samira, et al.
Publicado: (2022) -
Revisiting Biological Nitrogen Fixation Dynamics in Soybeans
por: Ciampitti, Ignacio A., et al.
Publicado: (2021) -
Quantitative proteomics reveals the effect of protein glycosylation in soybean root under flooding stress
por: Mustafa, Ghazala, et al.
Publicado: (2014) -
The intervention of classical and molecular breeding approaches to enhance flooding stress tolerance in soybean – An review
por: Yijun, Guan, et al.
Publicado: (2022) -
Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N(2)O Emissions, and Seed Yield
por: Elli, Elvis F., et al.
Publicado: (2022)