What is he best known for?
The fields of study he is best known for:
- Ecology
- Ecosystem
- Climate change
Danilo Dragoni mainly focuses on Ecosystem, Eddy covariance, Phenology, Ecology and Deciduous.
Danilo Dragoni works in the field of Ecosystem, focusing on Carbon cycle in particular.
His Eddy covariance research incorporates themes from Terrestrial ecosystem, Biome and Stomatal conductance.
His research in Deciduous intersects with topics in Climate change and Biosphere.
He combines subjects such as Vegetation and Growing season with his study of Climate change.
In his work, Atmospheric sciences is strongly intertwined with Photosynthesis, which is a subfield of Carbon dioxide in Earth's atmosphere.
His most cited work include:
- Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise (625 citations)
- Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis (429 citations)
- A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis (235 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Eddy covariance, Ecosystem, Deciduous, Atmospheric sciences and Ecology.
His Eddy covariance research includes elements of Transpiration, Terrestrial ecosystem, Leaf area index and Stomatal conductance.
His studies in Ecosystem integrate themes in fields like Productivity, Canopy and Biosphere.
His Deciduous research is multidisciplinary, relying on both Primary production, Climate change, Phenology, FluxNet and Evergreen.
Within one scientific family, Danilo Dragoni focuses on topics pertaining to Water content under Phenology, and may sometimes address concerns connected to Carbon sink.
The concepts of his Atmospheric sciences study are interwoven with issues in Photosynthesis, Water-use efficiency, Turbulent flux and Meteorology.
He most often published in these fields:
- Eddy covariance (34.33%)
- Ecosystem (32.84%)
- Deciduous (31.34%)
What were the highlights of his more recent work (between 2013-2019)?
- Ecology (23.88%)
- Ecosystem (32.84%)
- Water use (5.97%)
In recent papers he was focusing on the following fields of study:
Danilo Dragoni focuses on Ecology, Ecosystem, Water use, Atmospheric sciences and Canopy.
Many of his studies on Ecology apply to Agronomy as well.
The various areas that he examines in his Ecosystem study include Ozone, Atmospheric chemistry and Deciduous.
His Deciduous study incorporates themes from Sink, Phenology, Temperate forest, Carbon sink and Water content.
His Atmospheric sciences research integrates issues from Ecosystem carbon, Tropospheric ozone, Meteorology and Water-use efficiency.
The Canopy study combines topics in areas such as Eddy covariance, Net ecosystem exchange, Vapour Pressure Deficit, Stomatal conductance and Leaf water.
Between 2013 and 2019, his most popular works were:
- The role of isohydric and anisohydric species in determining ecosystem-scale response to severe drought. (126 citations)
- Chronic water stress reduces tree growth and the carbon sink of deciduous hardwood forests. (109 citations)
- Mycorrhizal type determines the magnitude and direction of root-induced changes in decomposition in a temperate forest. (94 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Ecosystem
- Statistics
His primary areas of investigation include Ecosystem, Ecology, Agronomy, Eddy covariance and Canopy.
His biological study spans a wide range of topics, including Phenology, Deciduous and Water content.
Danilo Dragoni merges many fields, such as Ecology and Wood production, in his writings.
Danilo Dragoni has included themes like Soil water, Soil carbon, Soil organic matter, Botany and Nutrient in his Agronomy study.
In general Eddy covariance study, his work on Net ecosystem exchange often relates to the realm of Water regulation, thereby connecting several areas of interest.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
