W
W. McNair Bostick
Researcher at University of Florida
Publications - 7
Citations - 234
W. McNair Bostick is an academic researcher from University of Florida. The author has contributed to research in topics: DSSAT & Soil carbon. The author has an hindex of 4, co-authored 7 publications receiving 216 citations.
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Journal ArticleDOI
Testing and Improving Evapotranspiration and Soil Water Balance of the DSSAT Crop Models
TL;DR: In this article, the authors evaluate various potential evapotranspiration (E0) equations and different ways of partitioning E0 between soil evaporation and crop transpiration within the DSSAT models and particularly the CROPGRO faba bean (Vicia faba L.) model.
Journal ArticleDOI
Soil carbon dynamics and crop residue yields of cropping systems in the Northern Guinea Savanna of Burkina Faso
W. McNair Bostick,Vincent Boubié Bado,André Bationo,Cecilia M. Tojo Soler,Gerrit Hoogenboom,James W. Jones +5 more
TL;DR: In this article, an 11-year experiment from the Northern Guinea Savanna of West Africa was analyzed, which consisted of 56 cropping system treatments that combined various crop rotation sequences with various input levels and an additional treatment of native grass fallow.
Journal ArticleDOI
Soil organic carbon dynamics and crop yield for different crop rotations in a degraded ferruginous tropical soil in a semi-arid region: a simulation approach.
TL;DR: There are many opportunities for the application of the cropping system model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT) for carbon sequestration and resource management in Sub-Saharan Africa.
Journal ArticleDOI
A web-based data exchange system for crop model applications
TL;DR: The functional design and implementation of the ICASA Data Exchange, an internet-based system that provides a convenient forum for documenting, archiving, and exchanging cropping system experiment and/or weather data sets, are presented.
Proceedings ArticleDOI
Combining Model Estimates and Measurements Through an Ensemble Kalman Filter to Estimate Carbon Sequestration
W. McNair Bostick,Jawoo Koo,James W. Jones,Arjan J. Gijsman,P. Sibiry Traoré,B. Vincent Bado +5 more
TL;DR: In this article, a data assimilation technique, the Ensemble Kalman Filter (EnKF), was used to improve carbon sequestration estimates, combining measurements, model estimates, and the uncertainties thereof to optimally estimate system variables and parameters.