Noah Braun
Biographic Data
| ID | 7994752 |
|---|---|
| NAME | Noah Braun |
| GIVEN NAMES | Noah |
| FAMILY NAME | Braun |
| SIGNATURE | BRAUN N |
| AFFILIATIONS | North Carolina State University |
| ORCID | 0000-0002-9710-0686 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2017 |
| H-INDEX | 0 |
Comparing and combining process-based crop models and statistical models with some implications for climate change
We compare predictions of a simple process-based crop model (Soltani and Sinclair 2012), a simple statistical model (Schlenker and Roberts 2009), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the…
Greater Sensitivity to Drought Accompanies Maize Yield Increase in the U.S. Midwest
Predicting Responses to Drought The U.S. Corn Belt accounts for a sizeable portion of the world's maize growth. Various influences have increased yields over the years. Lobell et al. (p. 516 ; see the Perspective by Ort and Long ) now show that sensitivity to drought has been increasing as well. It seems that as plants have been bred for increased yield under ideal conditions, the plants become more sensitive to non-ideal conditions. A key factor…
No prominent works on this page.
Greater Sensitivity to Drought Accompanies Maize Yield Increase in the U.S. Midwest
Predicting Responses to Drought The U.S. Corn Belt accounts for a sizeable portion of the world's maize growth. Various influences have increased yields over the years. Lobell et al. (p. 516 ; see the Perspective by Ort and Long ) now show that sensitivity to drought has been increasing as well. It seems that as plants have been bred for increased yield under ideal conditions, the plants become more sensitive to non-ideal conditions. A key factor…
Comparing and combining process-based crop models and statistical models with some implications for climate change
We compare predictions of a simple process-based crop model (Soltani and Sinclair 2012), a simple statistical model (Schlenker and Roberts 2009), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the…
Climate change impacts on agriculture (2 works) · Agricultural risk and resilience (1 works) · Agronomy (1 works) · Biology (1 works) · Calibration (1 works) · Climate change (1 works) · Climate model (1 works) · Computer Science (1 works) · Crop Yield and Soil Fertility (1 works) · Drought Resistance (1 works)