Modelling crop yield and harvest index : the role of carbon assimilation and allocation parameters
(2023) In Modeling Earth Systems and Environment 9(2). p.2617-2635- Abstract
Crop yield improvement during the last decades has relied on increasing the ratio of the economic organ to the total aboveground biomass, known as the harvest index (HI). In most crop models, HI is set as a parameter; this empirical approach does not consider that HI not only depends on plant genotype, but is also affected by the environment. An alternative is to simulate allocation mechanistically, as in the LPJ-GUESS crop model, which simulates HI based on daily growing conditions and the crop development stage. Simulated HI is critical for agricultural research due to its economic importance, but it also can validate the robust representation of production processes. However, there is a challenge to constrain parameter values... (More)
Crop yield improvement during the last decades has relied on increasing the ratio of the economic organ to the total aboveground biomass, known as the harvest index (HI). In most crop models, HI is set as a parameter; this empirical approach does not consider that HI not only depends on plant genotype, but is also affected by the environment. An alternative is to simulate allocation mechanistically, as in the LPJ-GUESS crop model, which simulates HI based on daily growing conditions and the crop development stage. Simulated HI is critical for agricultural research due to its economic importance, but it also can validate the robust representation of production processes. However, there is a challenge to constrain parameter values globally for the allocation processes. Therefore, this paper aims to evaluate the sensitivity of yield and HI of wheat and maize simulated with LPJ-GUESS to eight production allocation-related parameters and identify the most suitable parameter values for global simulations. The nitrogen demand reduction after anthesis, the minimum leaf carbon to nitrogen ratio (C:N) and the range of leaf C:N strongly affected carbon assimilation and yield, while the retranslocation of labile stem carbon to grains and the retranslocation rate of nitrogen and carbon from vegetative organs to grains after anthesis mainly influenced HI. A global database of observed HI for both crops was compiled for reference to constrain simulations before calibrating parameters for yield against reference data. Two high- and low-yielding maize cultivars emerged from the calibration, whilst spring and winter cultivars were found appropriate for wheat. The calibrated version of LPJ-GUESS improved the simulation of yield and HI at the global scale for both crops, providing a basis for future studies exploring crop production under different climate and management scenarios.
(Less)
- author
- Camargo-Alvarez, Hector LU ; Elliott, Robert J.R. ; Olin, Stefan LU ; Wang, Xuhui ; Wang, Chenzhi ; Ray, Deepak K. and Pugh, Thomas A.M. LU
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Calibration, LPJ-GUESS, N concentration, Parameter sensitivity, Retranslocation
- in
- Modeling Earth Systems and Environment
- volume
- 9
- issue
- 2
- pages
- 2617 - 2635
- publisher
- Springer
- external identifiers
-
- scopus:85144840048
- ISSN
- 2363-6203
- DOI
- 10.1007/s40808-022-01625-x
- language
- English
- LU publication?
- yes
- id
- 1f910ad2-61d7-4e64-babb-be12153b07a9
- date added to LUP
- 2023-01-23 12:21:55
- date last changed
- 2023-10-26 14:53:41
@article{1f910ad2-61d7-4e64-babb-be12153b07a9,
abstract = {{<p>Crop yield improvement during the last decades has relied on increasing the ratio of the economic organ to the total aboveground biomass, known as the harvest index (HI). In most crop models, HI is set as a parameter; this empirical approach does not consider that HI not only depends on plant genotype, but is also affected by the environment. An alternative is to simulate allocation mechanistically, as in the LPJ-GUESS crop model, which simulates HI based on daily growing conditions and the crop development stage. Simulated HI is critical for agricultural research due to its economic importance, but it also can validate the robust representation of production processes. However, there is a challenge to constrain parameter values globally for the allocation processes. Therefore, this paper aims to evaluate the sensitivity of yield and HI of wheat and maize simulated with LPJ-GUESS to eight production allocation-related parameters and identify the most suitable parameter values for global simulations. The nitrogen demand reduction after anthesis, the minimum leaf carbon to nitrogen ratio (C:N) and the range of leaf C:N strongly affected carbon assimilation and yield, while the retranslocation of labile stem carbon to grains and the retranslocation rate of nitrogen and carbon from vegetative organs to grains after anthesis mainly influenced HI. A global database of observed HI for both crops was compiled for reference to constrain simulations before calibrating parameters for yield against reference data. Two high- and low-yielding maize cultivars emerged from the calibration, whilst spring and winter cultivars were found appropriate for wheat. The calibrated version of LPJ-GUESS improved the simulation of yield and HI at the global scale for both crops, providing a basis for future studies exploring crop production under different climate and management scenarios.</p>}},
author = {{Camargo-Alvarez, Hector and Elliott, Robert J.R. and Olin, Stefan and Wang, Xuhui and Wang, Chenzhi and Ray, Deepak K. and Pugh, Thomas A.M.}},
issn = {{2363-6203}},
keywords = {{Calibration; LPJ-GUESS; N concentration; Parameter sensitivity; Retranslocation}},
language = {{eng}},
number = {{2}},
pages = {{2617--2635}},
publisher = {{Springer}},
series = {{Modeling Earth Systems and Environment}},
title = {{Modelling crop yield and harvest index : the role of carbon assimilation and allocation parameters}},
url = {{http://dx.doi.org/10.1007/s40808-022-01625-x}},
doi = {{10.1007/s40808-022-01625-x}},
volume = {{9}},
year = {{2023}},
}