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Explicit wheat production model adjusted for semi-arid environments
Year:
2019
Source of publication :
Field Crops Research
Authors :
Bonfil, David J.
;
.
Volume :
231
Co-Authors:

Miller, O., Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel; Helman, D., Department of Geography and the Environment, Bar-Ilan University, Ramat Gan, 5290002, Israel; Svoray, T., Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel, Department of Psychology, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel; Morin, E., Institute of Earth Sciences, Hebrew University of Jerusalem, E. J. Safra Campus, Jerusalem, 9190401, Israel;

Facilitators :
From page:
93
To page:
104
(
Total pages:
12
)
Abstract:

Current literature suggests that wheat production models are limited either to wide-scale or plot-based predictions ignoring pattern of habitat conditions and surficial hydrological processes. We present here a high-spatial resolution (50 m) non-calibrated GIS-based wheat production model for predictions of aboveground wheat biomass (AGB) and grain yield (GY). The model is an integration of three sub-models, each simulating elemental processes relevant for wheat growth dynamics in water-limited environments: (1) HYDRUS-1D, a finite element model that simulates one-dimensional movement of water in the soil profile; (2) a two-dimensional GIS-based surface runoff model; and (3) a one-dimensional process-driven mechanistic wheat growth model. By integrating the three sub-models, we aimed to achieve a more accurate spatially continuous water balance simulation with a better representation of root zone soil water content (SWC) impacts on plant development. High-resolution grid-based rainfall data from a meteorological radar system were used as input to HYDRUS-1D. Twenty-two commercial wheat fields in Israel were used to validate the model in two seasons (2010/11 and 2011/12). Results show that root zone SWC was accurately simulated by HYDRUS-1D in both seasons, particularly at the top 10-cm soil layer. Observed vs simulated AGB and GY were highly correlated with R2 = 0.93 and 0.72 (RMSE = 171 g m−2 and 70 g m−2) having low biases of -41 g m−2 (8%) and 52 g m−2 (10%), respectively. Model sensitivity test showed that HYDRUS-1D was mainly driven by spatial variability in the input soil characteristics while the integrated wheat production model was mostly affected by rainfall spatial variability indicating the importance of using accurate high-resolution rainfall data as model input. Using the integrated model, we predict decreases in AGB and GY of c. 10.5% and c. 12%, respectively, for 1 °C of warming and c. 7.7% and c. 7.3% for 5% reduction in rainfall amount in our study sites. The suggested model could be used by scientists to better understand the causes of spatial and temporal variability in wheat production and the consequences of future scenarios such as climate change. © 2018 Elsevier B.V.

Note:
Related Files :
climate change
GIS
Grain yield
HYDRUS
Triticum aestivum
wheat
Show More
Related Content
More details
DOI :
10.1016/j.fcr.2018.11.011
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
38601
Last updated date:
02/03/2022 17:27
Creation date:
11/12/2018 15:10
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Scientific Publication
Explicit wheat production model adjusted for semi-arid environments
231

Miller, O., Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel; Helman, D., Department of Geography and the Environment, Bar-Ilan University, Ramat Gan, 5290002, Israel; Svoray, T., Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel, Department of Psychology, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel; Morin, E., Institute of Earth Sciences, Hebrew University of Jerusalem, E. J. Safra Campus, Jerusalem, 9190401, Israel;

Explicit wheat production model adjusted for semi-arid environments

Current literature suggests that wheat production models are limited either to wide-scale or plot-based predictions ignoring pattern of habitat conditions and surficial hydrological processes. We present here a high-spatial resolution (50 m) non-calibrated GIS-based wheat production model for predictions of aboveground wheat biomass (AGB) and grain yield (GY). The model is an integration of three sub-models, each simulating elemental processes relevant for wheat growth dynamics in water-limited environments: (1) HYDRUS-1D, a finite element model that simulates one-dimensional movement of water in the soil profile; (2) a two-dimensional GIS-based surface runoff model; and (3) a one-dimensional process-driven mechanistic wheat growth model. By integrating the three sub-models, we aimed to achieve a more accurate spatially continuous water balance simulation with a better representation of root zone soil water content (SWC) impacts on plant development. High-resolution grid-based rainfall data from a meteorological radar system were used as input to HYDRUS-1D. Twenty-two commercial wheat fields in Israel were used to validate the model in two seasons (2010/11 and 2011/12). Results show that root zone SWC was accurately simulated by HYDRUS-1D in both seasons, particularly at the top 10-cm soil layer. Observed vs simulated AGB and GY were highly correlated with R2 = 0.93 and 0.72 (RMSE = 171 g m−2 and 70 g m−2) having low biases of -41 g m−2 (8%) and 52 g m−2 (10%), respectively. Model sensitivity test showed that HYDRUS-1D was mainly driven by spatial variability in the input soil characteristics while the integrated wheat production model was mostly affected by rainfall spatial variability indicating the importance of using accurate high-resolution rainfall data as model input. Using the integrated model, we predict decreases in AGB and GY of c. 10.5% and c. 12%, respectively, for 1 °C of warming and c. 7.7% and c. 7.3% for 5% reduction in rainfall amount in our study sites. The suggested model could be used by scientists to better understand the causes of spatial and temporal variability in wheat production and the consequences of future scenarios such as climate change. © 2018 Elsevier B.V.

Scientific Publication
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