Automated Author ProfileSilva, Evandro Henrique Figueiredo Moura Da
Silva, Evandro Henrique Figueiredo Moura Da
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.3 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Abstract: The objective of this work was to evaluate the use of plant height as a calibration variable for improving indirect measurements of the leaf area index (LAI). Three experiments were conducted with different crops - corn (Zea mays), soybean (Glycine max), and sugarcane (Saccharum officinarum) -, to compare the performance of the LAI measured indirectly (LAIind) and corrected by the calibration variable with the LAI measured directly (LAIref). Without the proposed correction, the LAIind tended to be overestimated by 20%, on average, compared with the LAIref, for the three crops. After crop height was used to adjust the LAIind, a strong positive relationship was observed between the LAIref and the corrected LAIind (R2 = 0.96); overestimation was reduced to 4% and the root-mean-square error decreased to 0.35 m2 m-2. The variable canopy height is promising for the correction of the LAI of the soybean, corn, and sugarcane crops.
Authors
- Gonçalves, Alexandre Ortega ;
- Silva, Evandro Henrique Figueiredo Moura Da ;
- Gasparotto, Letícia Gonçalves ;
- Rosa, Juliano Mantelatto ;
- Carmo, Stephanie Do ;
- Izael Martins Fattori Júnior ;
- Marin, Fabio Ricardo
Abstract: The objective of this work was to evaluate the use of plant height as a calibration variable for improving indirect measurements of the leaf area index (LAI). Three experiments were conducted with different crops - corn (Zea mays), soybean (Glycine max), and sugarcane (Saccharum officinarum) -, to compare the performance of the LAI measured indirectly (LAIind) and corrected by the calibration variable with the LAI measured directly (LAIref). Without the proposed correction, the LAIind tended to be overestimated by 20%, on average, compared with the LAIref, for the three crops. After crop height was used to adjust the LAIind, a strong positive relationship was observed between the LAIref and the corrected LAIind (R2 = 0.96); overestimation was reduced to 4% and the root-mean-square error decreased to 0.35 m2 m-2. The variable canopy height is promising for the correction of the LAI of the soybean, corn, and sugarcane crops.
Authors
- Gonçalves, Alexandre Ortega ;
- Silva, Evandro Henrique Figueiredo Moura Da ;
- Gasparotto, Letícia Gonçalves ;
- Rosa, Juliano Mantelatto ;
- Carmo, Stephanie Do ;
- Izael Martins Fattori Júnior ;
- Marin, Fabio Ricardo