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Yields of various crops within selected remote sensing monitoring areas are estimated using crop growth trend models and yield estimation models.
Existing predictive models for yield estimation were reviewed by Crul (1992) and his estimates are also presented in Table 2.9 for comparison.
Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications.
Remotely sensed variables showed to be better predictors for spatial grain yield estimation than soil variables.
A physically-based approach for sediment yield estimation during storms was proposed in this study.
The ability to regionally monitor crop progress and condition through the growing season benefits both crop management and yield estimation.
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Therefore, our approach is of interest for monitoring and yield estimations in vineyards as well as for grapevine breeders.
197Au nano-solution, including 20 nm shaped spherical gold and water, was irradiated under Tehran reactor flux at 2.5E + 13 n/cm2/s for 196,198Au activity and production yield estimations.
The results indicate that the GEP formulas are able to yield estimations with good precision resulting in better predictions (R ≥ 0.96) compared to the regression model.
Good results were obtained for both local biomass (R = 0.98; RRMSE = 14%) and yield (R = 0.81; RRMSE = 8.9%), and for yield estimations at regional scale (R = 0.96; RRMSE = 4.6%).
For biomass yield estimations (g dry weight L−1), the contents of 10 unsampled Bioscreen wells were pooled at t124h and dried to constant mass using a centrifugal evaporator (Heto Maxi Dry Plus).
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