Ashwan Reddy

TL;DR: In this paper, the authors evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the AgMIP.

TL;DR: In this article, the authors compared aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.

TL;DR: The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface and is aimed at promoting further analyses and understanding of cropmodel performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks.

TL;DR: This paper used multi-temporal LiDAR to obtain pre-and post-fire elevations and estimate soil carbon loss caused by the 2011 Lateral West fire in the Great Dismal Swamp National Wildlife Refuge, VA, USA.

TL;DR: In this article, a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY) was developed for wheat and maize at global scale, with large spatial differences driven more by patterns of precipitation than that of evaporative demand.