Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Kultūraugu ražas prognozēšana× | Augu augšanas simulācija× | |
|---|---|---|
| Nozare | Agronomija | Agronomija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2015 | 2003 |
| Autors≠ | Agronomic research institutions (CIMMYT, ICRISAT, IRRI) | John W. Jones, Gerrit Hoogenboom et al. |
| Tips≠ | Analytical and predictive pipeline | Computational pipeline |
| Pirmavots≠ | Lobell, D. B., Thau, D., Seifert, C., Engle, E., & Shadow, B. (2015). A regional crop yield forecasting system for Sub-Saharan Africa. Global Food Security, 5, 6-15. link ↗ | Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Basso, B., Hunt, L. A., ... & Winter, S. R. (2003). The DSSAT cropping system model. European journal of agronomy, 18(3-4), 235-265. DOI ↗ |
| Citi nosaukumi≠ | Yield forecasting, Harvest prediction, Yield monitoring | Crop phenological model, Growth stage simulation |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Crop Yield Estimation is an analytical and predictive pipeline for forecasting final crop yield before harvest or monitoring yield accumulation during the growing season. Developed by agronomic research centers (CIMMYT, ICRISAT, IRRI), this method combines field observations, environmental data, and statistical models to predict grain or biomass output, informing harvest planning, market decisions, and performance evaluation. | Crop Growth Simulation is a computational pipeline for predicting daily or seasonal crop development, biomass accumulation, and yield under varying environmental conditions. Developed by Jones and colleagues in the DSSAT framework, this method integrates agronomic knowledge with process-based modeling to enable decision support in field management. |
| ScholarGateDatu kopa ↗ |
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