Effect of Potential Evapotranspiration Estimates on Crop Model Simulations

1985 ◽  
Vol 28 (2) ◽  
pp. 471-475 ◽  
Author(s):  
W. A. Dugas ◽  
C. G. Ainsworth
Keyword(s):  
Potential Evapotranspiration ◽  
Crop Model ◽  
Model Simulations

scholarly journals Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs

2007 ◽  
Vol 34 ◽  
pp. 211-222 ◽  
Author(s):  
GA Baigorria ◽  
JW Jones ◽  
D Shin ◽  
A Mishra ◽  
JJ O’Brien
Keyword(s):  
Circulation Model ◽  
Crop Model ◽  
Model Simulations ◽  
Regional Circulation

Marker-based crop model-assisted ideotype design to improve avoidance of abiotic stress in bread wheat

2020 ◽  
Author(s):  
Matthieu Bogard ◽  
Delphine Hourcade ◽  
Benoit Piquemal ◽  
David Gouache ◽  
Jean-Charles Deswartes ◽  
...  
Keyword(s):  
Climate Change ◽  
Heat Stress ◽  
Stem Elongation ◽  
Heading Date ◽  
Crop Model ◽  
Weather Data ◽  
Prediction Errors ◽  
Model Simulations ◽  
Stress Avoidance ◽  
Climate Analysis

Abstract Wheat phenology allows escape from seasonal abiotic stresses including frosts and high temperatures, the latter being forecast to increase with climate change. The use of marker-based crop models to identify ideotypes has been proposed to select genotypes adapted to specific weather and management conditions and anticipate climate change. In this study, a marker-based crop model for wheat phenology was calibrated and tested. Climate analysis of 30 years of historical weather data in 72 locations representing the main wheat production areas in France was performed. We carried out marker-based crop model simulations for 1019 wheat cultivars and three sowing dates, which allowed calculation of genotypic stress avoidance frequencies of frost and heat stress and identification of ideotypes. The phenology marker-based crop model allowed prediction of large genotypic variations for the beginning of stem elongation (GS30) and heading date (GS55). Prediction accuracy was assessed using untested genotypes and environments, and showed median genotype prediction errors of 8.5 and 4.2 days for GS30 and GS55, respectively. Climate analysis allowed the definition of a low risk period for each location based on the distribution of the last frost and first heat days. Clustering of locations showed three groups with contrasting levels of frost and heat risks. Marker-based crop model simulations showed the need to optimize the genotype depending on sowing date, particularly in high risk environments. An empirical validation of the approach showed that it holds good promises to improve frost and heat stress avoidance.

scholarly journals Assessing uncertainty and complexity in regional-scale crop model simulations

2017 ◽  
Vol 88 ◽  
pp. 84-95 ◽  
Author(s):  
Julian Ramirez-Villegas ◽  
Ann-Kristin Koehler ◽  
Andrew J. Challinor
Keyword(s):  
Regional Scale ◽  
Crop Model ◽  
Model Simulations

scholarly journals Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates

2016 ◽  
Author(s):  
Christian Folberth ◽  
Joshua Elliott ◽  
Christoph Müller ◽  
Juraj Balkovic ◽  
James Chryssanthacopoulos ◽  
...  
Keyword(s):  
Input Data ◽  
Crop Yields ◽  
Potential Evapotranspiration ◽  
Wheat Yield ◽  
Crop Management ◽  
Crop Model ◽  
Physical Models ◽  
Scale Model ◽  
Field Scale ◽  
Absolute Yield

Abstract. Global gridded crop models (GGCMs) combine field-scale agronomic models or sets of plant growth algorithms with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different bio-physical models, setups, and input data. While algorithms have been in the focus of recent GGCM comparisons, this study investigates differences in maize and wheat yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison (GGCMI) project. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, geographic distribution of cultivars, and selection of subroutines e.g. for the estimation of potential evapotranspiration or soil erosion. The analyses reveal long-term trends and inter-annual yield variability in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. Absolute yield levels as well depend not only on nutrient supply but also on the parameterization and distribution of crop cultivars. All GGCMs show an intermediate performance in reproducing reported absolute yield levels or inter-annual dynamics. Our findings suggest that studies focusing on the evaluation of differences in bio-physical routines may require further harmonization of input data and management assumptions in order to eliminate background noise resulting from differences in model setups. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for bracketing such uncertainties as long as comprehensive global datasets taking into account regional differences in crop management, cultivar distributions and coefficients for parameterizing agro-environmental processes are lacking. Finally, we recommend improvements in the documentation of setups and input data of GGCMs in order to allow for sound interpretability, comparability and reproducibility of published results.

The effect of aerosols on fog lifetime: observational evidence and model simulations

2020 ◽  
Author(s):  
Shuqi Yan ◽  
Bin Zhu ◽  
Tong Zhu ◽  
Chune Shi ◽  
Duanyang Liu ◽  
...  
Keyword(s):  
Observational Evidence ◽  
Model Simulations

Modelling the Bioclimatic Niche of a Cohort of Selected Mite Species (Acari, Acariformes) Associated with the Infestation of Stored Products

2019 ◽  
Vol 53 (5) ◽  
pp. 399-416
Author(s):  
V. M. Tytar ◽  
Ya. R. Oksentyuk
Keyword(s):  
Potential Evapotranspiration ◽  
Species Distribution Modeling ◽  
Mite Species ◽  
Extreme Weather Events ◽  
Stored Products ◽  
Weather Events ◽  
Moisture Regimes ◽  
Bioclimatic Variables ◽  
Contemporary Climate ◽  
Future Management

Abstract In this study an attempt is made to highlight important variables shaping the current bioclimatic niche of a number of mite species associated with the infestation of stored products by employing a species distribution modeling (SDM) approach. Using the ENVIREM dataset of bioclimatic variables, performance of the most robust models was mostly influenced by: 1) indices based on potential evapotranspiration, which characterize ambient energy and are mostly correlated with temperature variables, moisture regimes, and 2) strong fluctuations in temperature reflecting the severity of climate and/or extreme weather events. Although the considered mite species occupy man-made ecosystems, they remain more or less affected by the surrounding bioclimatic environment and therefore could be subjected to contemporary climate change. In this respect investigations are needed to see how this will affect future management targets concerning the safety of food storages.

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