Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy?
Data acquisition for parameterization is one of the most important limitations for the use of potato crop growth models. Non destructive techniques such as remote sensing for gathering required data could circumvent this limitation. Our goal was to analyze the effects of incorporating ground-based s...
| Main Authors: | , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2017
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/78288 |
Similar Items: Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy?
- A regional sahelian grassland model to be coupled with multispectral satellite data. I. Model description and validation
- In-season crop yield forecasting in Africa by coupling remote sensing and crop modeling: A systematic literature review
- A regional sahelian grassland model to be coupled with multispectral satellite data. II. Toward the control of its simulations by remotely sensed indices
- Physiological interpretation of a hyperspectral time series in a citrus orchard
- Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications
- Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat