Phillip D. Alderman
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View article: Gridded drought response assessment of winter wheat in Oklahoma using big data and AquaCrop-OS
Gridded drought response assessment of winter wheat in Oklahoma using big data and AquaCrop-OS Open
Winter wheat is the most dominant crop in Oklahoma and critically important to the economy of agricultural industry in this state and the region. However, weather anomalies such as droughts, which are frequent in Oklahoma, pose serious thr…
View article: Winter Wheat Experiments to Optimize Sowing Dates and Densities in a High-Yielding Environment in New Zealand: Field Experiments and AgMIP-Wheat Multi-Model Simulations
Winter Wheat Experiments to Optimize Sowing Dates and Densities in a High-Yielding Environment in New Zealand: Field Experiments and AgMIP-Wheat Multi-Model Simulations Open
This paper describes the data set that was used to test the accuracy of twenty-nine crop models in simulating the effect of changing sowing dates and sowing densities on wheat productivity for a high-yielding environment in New Zealand. Th…
View article: A parsimonious Bayesian crop growth model for water-limited winter wheat
A parsimonious Bayesian crop growth model for water-limited winter wheat Open
Dynamic crop models are widely used to simulate crop production, but are often complex and thus face parameter non-identifiability issues. In this study, we demonstrate a simple dynamic model for crop growth within a Bayesian hierarchical …
View article: A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations
A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations Open
Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop gr…
View article: Integrating genomic prediction and genotype specific parameter estimation in ecophysiological models: overview and perspectives
Integrating genomic prediction and genotype specific parameter estimation in ecophysiological models: overview and perspectives Open
The Genome-to-Phenome (G2P) problem is one of the highest-priority challenges in applied biology. Ecophysiological crop models (ECM) and genomic prediction (GP) models are quantitative algorithms, which, when given information on a genotyp…
View article: Evidence for increasing global wheat yield potential
Evidence for increasing global wheat yield potential Open
Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of …
View article: Sustaining productivity gains in the face of climate change: A research agenda for US wheat
Sustaining productivity gains in the face of climate change: A research agenda for US wheat Open
Wheat is a globally important crop and one of the “big three” US field crops. But unlike the other two (maize and soybean), in the United States its development is commercially unattractive, and so its breeding takes place primarily in pub…
View article: Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment
Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment Open
Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet bee…
View article: A hierarchical Bayesian approach to dynamic ordinary differential equations modeling for repeated measures data on wheat growth
A hierarchical Bayesian approach to dynamic ordinary differential equations modeling for repeated measures data on wheat growth Open
Experimental data collected on growth and development of plants over a growing season are typically analyzed using a linear mixed model, analogous to a hierarchical linear model in a Bayesian setting. Alternative modeling approaches for re…
View article: Data from the AgMIP-Wheat high-yielding traits experiment for modeling potential production of wheat: field experiments and multi-model simulations
Data from the AgMIP-Wheat high-yielding traits experiment for modeling potential production of wheat: field experiments and multi-model simulations Open
The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing…
View article: Data from the winter wheat potential yield experiment in New Zealand and response to variable sowing dates and densities: field experiments and AgMIP-Wheat multi-model simulations
Data from the winter wheat potential yield experiment in New Zealand and response to variable sowing dates and densities: field experiments and AgMIP-Wheat multi-model simulations Open
The dataset contains 6 growing seasons of a local winter wheat cultivar ‘Wakanui’ at two farms located in the Canterbury Region of New Zealand. The data of the experiment was used in the AgMIP-Wheat Phase 4 project to evaluate the performa…
View article: Ecophysiological modeling of yield and yield components in winter wheat using hierarchical Bayesian analysis
Ecophysiological modeling of yield and yield components in winter wheat using hierarchical Bayesian analysis Open
Yield components are widely recognized as drivers of wheat ( Triticum aestivum L.) yield across environments and genotypes. In this study, we used a hierarchical Bayesian approach to model wheat grain yield in Oklahoma on an eco‐physiologi…
View article: Parallel gridded simulation framework for DSSAT-CSM (version 4.7.5.21) using MPI and NetCDF
Parallel gridded simulation framework for DSSAT-CSM (version 4.7.5.21) using MPI and NetCDF Open
The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used crop modeling system that has been integrated into large-scale modeling frameworks. Existing frameworks generate spatially explicit…
View article: Reply on AC3
Reply on AC3 Open
Abstract. The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used crop modeling system that has been integrated into large-scale modeling frameworks. Existing frameworks generate spatiall…
View article: Reply on RC4
Reply on RC4 Open
Abstract. The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used crop modeling system that has been integrated into large-scale modeling frameworks. Existing frameworks generate spatiall…
View article: Reply on RC3
Reply on RC3 Open
Abstract. The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used crop modeling system that has been integrated into large-scale modeling frameworks. Existing frameworks generate spatiall…
View article: Reply on AC2
Reply on AC2 Open
Abstract. The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used crop modeling system that has been integrated into large-scale modeling frameworks. Existing frameworks generate spatiall…
View article: Comment on gmd-2021-183
Comment on gmd-2021-183 Open
Abstract. The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used crop modeling system that has been integrated into large-scale modeling frameworks. Existing frameworks generate spatiall…
View article: Comment on gmd-2021-183
Comment on gmd-2021-183 Open
Abstract. The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used crop modeling system that has been integrated into large-scale modeling frameworks. Existing frameworks generate spatiall…
View article: Comment on gmd-2021-183
Comment on gmd-2021-183 Open
Abstract. The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used crop modeling system that has been integrated into large-scale modeling frameworks. Existing frameworks generate spatiall…
View article: Parallel gridded simulation framework for DSSAT-CSM (version 4.7.5.21) using MPI and NetCDF
Parallel gridded simulation framework for DSSAT-CSM (version 4.7.5.21) using MPI and NetCDF Open
The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used crop modeling system that has been integrated into large-scale modeling frameworks. Existing frameworks generate spatially-explicit…
View article: The CROPGRO Perennial Forage Model Simulates Productivity and Re-Growth of Tropical Perennial Grasses
The CROPGRO Perennial Forage Model Simulates Productivity and Re-Growth of Tropical Perennial Grasses Open
This paper introduces the CROPGRO Perennial Forage model (CROPGRO-PFM) and describes its ability to simulate regrowth dynamics and herbage production of Brachiaria and Panicum as affected by harvest management and weather. The model simula…
View article: A comprehensive R interface for the DSSAT Cropping Systems Model
A comprehensive R interface for the DSSAT Cropping Systems Model Open
The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used modeling system. The DSSAT R package was developed to provide tools that would facilitate preparing required model inputs, executin…
View article: Morpho-Physiological Characterization of Winter Wheat “Buster” Population during the Vegetative Stage under Heat Stress
Morpho-Physiological Characterization of Winter Wheat “Buster” Population during the Vegetative Stage under Heat Stress Open
Phenotypic assessment of breeding population is important to identify robust lines for incorporating into future breeding programs. The objective of this study was to identify potential lines from a wheat (Triticum aestivum L.) population,…
View article: Comparison of Penman–Monteith and Priestley‐Taylor Evapotranspiration Methods for Crop Modeling in Oklahoma
Comparison of Penman–Monteith and Priestley‐Taylor Evapotranspiration Methods for Crop Modeling in Oklahoma Open
Core Ideas The FAO‐56 estimates higher ET than Priestley‐Taylor under dry and windy conditions Our simulations show higher ET estimates with FAO‐56 than Priestley‐Taylor during winter. The rainfed simulations show a similar seasonal ET for…