Predicting within-field soybean yield variability by coupling Sentinel-2 leaf area index with a crop growth model.
ABSTRACT: Accurate within-field yield estimation is an essential step to conduct yield gap analysis and steer crop management towards more efficient use of resources. This study aims to develop and validate a process-based soybean model and to predict within-field yield variability by coupling leaf...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , |
| التنسيق: | article |
| اللغة: | الإنجليزية |
| منشور في: |
2021
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://ainfo.inia.uy/consulta/busca?b=pc&id=62331&biblioteca=vazio&busca=62331&qFacets=62331 |
| الوسوم: |
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة: Predicting within-field soybean yield variability by coupling Sentinel-2 leaf area index with a crop growth model.
- Efficiency of assimilating leaf area index into a soybean model to assess within-field yield variability.
- Beyond assimilation of leaf area index: Leveraging additional spectral information using machine learning for site-specific soybean yield prediction.
- Predicting spatial and temporal variability in soybean yield using deep learning and open source data.
- Use of fluorescence indices as predictors of crop N status and yield for greenhouse sweet pepper crops.
- Respuesta del rendimiento de soja a la densidad de siembra en ambientes de productividad contrastante. [Response of Soybean Yield to Planting Density in Environments of Contrasting Productivity ].
- Evidence for increasing global wheat yield potential. [Letter].