Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset

Crop improvement aims to produce high yielding genotypes for target environments. Crop models simulate yield formation as the outcome of a series of low-level processes, driven by environmental (E) variables and regulated by genetic (G) traits. There is potential for crop models to aid sugarcane breeding, by identifying desirable genetic traits for target environments. The objective of this study was to evaluate existing concepts of G and E control of plant processes for explaining crop development, growth and yield, using an international growth analysis dataset. Crop development, growth and yield were monitored in the plant and 1st ratoon crops for seven cultivars (N41, R570, CP88-1762, HoCP96-540, Q183, ZN7 and NCo376) grown under well-watered conditions at La Mare (Reunion Island, France), Pongola (South Africa (RSA), Chiredzi (Zimbabwe), and Belle Glade (Florida, USA). Weather data were collected and environmental conditions characterized for each experiment. Derived process-level phenotypic parameters, based on concepts from four sugarcane growth simulation models (DSSATCanegro, Mosicas, APSIM-Sugar and Canesim), were calculated from observations and used to (1) evaluate current understanding of E drivers of sugarcane growth and development processes, and (2) identify and quantify G control at a process level. Final yields showed significant E and GxE variation; dry above-ground biomass and stalk yields were highest in La Mare and lowest in Pongola. Cultivar rankings in stalk dry mass for the common cultivars (N41, R570, CP88-1762) varied significantly between Es. Significant E variation in phenotypic parameters describing germination, tillering and timing of the onset of stalk growth (OSG) revealed shortcomings in the underlying simulation concepts. Significant G variation was found for germination rate, leaf appearance rate and canopy development rate per unit thermal time (TT), and maximum radiation use efficiency, indicating strong G control of the associated underlying processes. Solar radiation was found to influence tillering rate per unit TT, and TT to OSG, challenging the current theory of TT as the sole driver of these processes. By explaining more of the E variation, more stable and accurate G-specific model parameters can be defined and evaluated. This is anticipated to lead to less GxE confounding of modelled processes, and hence crop models that are better-equipped for supporting sugarcane crop improvement.

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Main Authors: Jones, M.R., Singels, A., Chinorumba, S., Patton, A., Poser, Christophe, Singh, M., Martiné, Jean-François, Christina, Mathias, Shine, J., Annandale, J., Hammer, Graeme
Format: article biblioteca
Language:eng
Subjects:F30 - Génétique et amélioration des plantes, F62 - Physiologie végétale - Croissance et développement, Saccharum officinarum, génotype, rendement des cultures, facteur du milieu, tallage, couvert, modèle de simulation, http://aims.fao.org/aos/agrovoc/c_6727, http://aims.fao.org/aos/agrovoc/c_3225, http://aims.fao.org/aos/agrovoc/c_10176, http://aims.fao.org/aos/agrovoc/c_2594, http://aims.fao.org/aos/agrovoc/c_7773, http://aims.fao.org/aos/agrovoc/c_1262, http://aims.fao.org/aos/agrovoc/c_24242, http://aims.fao.org/aos/agrovoc/c_6543, http://aims.fao.org/aos/agrovoc/c_7252, http://aims.fao.org/aos/agrovoc/c_8516, http://aims.fao.org/aos/agrovoc/c_2985, http://aims.fao.org/aos/agrovoc/c_3081,
Online Access:http://agritrop.cirad.fr/594564/
http://agritrop.cirad.fr/594564/1/594564.pdf
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record_format koha
institution CIRAD FR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cirad-fr
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CIRAD Francia
language eng
topic F30 - Génétique et amélioration des plantes
F62 - Physiologie végétale - Croissance et développement
Saccharum officinarum
génotype
rendement des cultures
facteur du milieu
tallage
couvert
modèle de simulation
http://aims.fao.org/aos/agrovoc/c_6727
http://aims.fao.org/aos/agrovoc/c_3225
http://aims.fao.org/aos/agrovoc/c_10176
http://aims.fao.org/aos/agrovoc/c_2594
http://aims.fao.org/aos/agrovoc/c_7773
http://aims.fao.org/aos/agrovoc/c_1262
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_6543
http://aims.fao.org/aos/agrovoc/c_7252
http://aims.fao.org/aos/agrovoc/c_8516
http://aims.fao.org/aos/agrovoc/c_2985
http://aims.fao.org/aos/agrovoc/c_3081
F30 - Génétique et amélioration des plantes
F62 - Physiologie végétale - Croissance et développement
Saccharum officinarum
génotype
rendement des cultures
facteur du milieu
tallage
couvert
modèle de simulation
http://aims.fao.org/aos/agrovoc/c_6727
http://aims.fao.org/aos/agrovoc/c_3225
http://aims.fao.org/aos/agrovoc/c_10176
http://aims.fao.org/aos/agrovoc/c_2594
http://aims.fao.org/aos/agrovoc/c_7773
http://aims.fao.org/aos/agrovoc/c_1262
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_6543
http://aims.fao.org/aos/agrovoc/c_7252
http://aims.fao.org/aos/agrovoc/c_8516
http://aims.fao.org/aos/agrovoc/c_2985
http://aims.fao.org/aos/agrovoc/c_3081
spellingShingle F30 - Génétique et amélioration des plantes
F62 - Physiologie végétale - Croissance et développement
Saccharum officinarum
génotype
rendement des cultures
facteur du milieu
tallage
couvert
modèle de simulation
http://aims.fao.org/aos/agrovoc/c_6727
http://aims.fao.org/aos/agrovoc/c_3225
http://aims.fao.org/aos/agrovoc/c_10176
http://aims.fao.org/aos/agrovoc/c_2594
http://aims.fao.org/aos/agrovoc/c_7773
http://aims.fao.org/aos/agrovoc/c_1262
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_6543
http://aims.fao.org/aos/agrovoc/c_7252
http://aims.fao.org/aos/agrovoc/c_8516
http://aims.fao.org/aos/agrovoc/c_2985
http://aims.fao.org/aos/agrovoc/c_3081
F30 - Génétique et amélioration des plantes
F62 - Physiologie végétale - Croissance et développement
Saccharum officinarum
génotype
rendement des cultures
facteur du milieu
tallage
couvert
modèle de simulation
http://aims.fao.org/aos/agrovoc/c_6727
http://aims.fao.org/aos/agrovoc/c_3225
http://aims.fao.org/aos/agrovoc/c_10176
http://aims.fao.org/aos/agrovoc/c_2594
http://aims.fao.org/aos/agrovoc/c_7773
http://aims.fao.org/aos/agrovoc/c_1262
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_6543
http://aims.fao.org/aos/agrovoc/c_7252
http://aims.fao.org/aos/agrovoc/c_8516
http://aims.fao.org/aos/agrovoc/c_2985
http://aims.fao.org/aos/agrovoc/c_3081
Jones, M.R.
Singels, A.
Chinorumba, S.
Patton, A.
Poser, Christophe
Singh, M.
Martiné, Jean-François
Christina, Mathias
Shine, J.
Annandale, J.
Hammer, Graeme
Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset
description Crop improvement aims to produce high yielding genotypes for target environments. Crop models simulate yield formation as the outcome of a series of low-level processes, driven by environmental (E) variables and regulated by genetic (G) traits. There is potential for crop models to aid sugarcane breeding, by identifying desirable genetic traits for target environments. The objective of this study was to evaluate existing concepts of G and E control of plant processes for explaining crop development, growth and yield, using an international growth analysis dataset. Crop development, growth and yield were monitored in the plant and 1st ratoon crops for seven cultivars (N41, R570, CP88-1762, HoCP96-540, Q183, ZN7 and NCo376) grown under well-watered conditions at La Mare (Reunion Island, France), Pongola (South Africa (RSA), Chiredzi (Zimbabwe), and Belle Glade (Florida, USA). Weather data were collected and environmental conditions characterized for each experiment. Derived process-level phenotypic parameters, based on concepts from four sugarcane growth simulation models (DSSATCanegro, Mosicas, APSIM-Sugar and Canesim), were calculated from observations and used to (1) evaluate current understanding of E drivers of sugarcane growth and development processes, and (2) identify and quantify G control at a process level. Final yields showed significant E and GxE variation; dry above-ground biomass and stalk yields were highest in La Mare and lowest in Pongola. Cultivar rankings in stalk dry mass for the common cultivars (N41, R570, CP88-1762) varied significantly between Es. Significant E variation in phenotypic parameters describing germination, tillering and timing of the onset of stalk growth (OSG) revealed shortcomings in the underlying simulation concepts. Significant G variation was found for germination rate, leaf appearance rate and canopy development rate per unit thermal time (TT), and maximum radiation use efficiency, indicating strong G control of the associated underlying processes. Solar radiation was found to influence tillering rate per unit TT, and TT to OSG, challenging the current theory of TT as the sole driver of these processes. By explaining more of the E variation, more stable and accurate G-specific model parameters can be defined and evaluated. This is anticipated to lead to less GxE confounding of modelled processes, and hence crop models that are better-equipped for supporting sugarcane crop improvement.
format article
topic_facet F30 - Génétique et amélioration des plantes
F62 - Physiologie végétale - Croissance et développement
Saccharum officinarum
génotype
rendement des cultures
facteur du milieu
tallage
couvert
modèle de simulation
http://aims.fao.org/aos/agrovoc/c_6727
http://aims.fao.org/aos/agrovoc/c_3225
http://aims.fao.org/aos/agrovoc/c_10176
http://aims.fao.org/aos/agrovoc/c_2594
http://aims.fao.org/aos/agrovoc/c_7773
http://aims.fao.org/aos/agrovoc/c_1262
http://aims.fao.org/aos/agrovoc/c_24242
http://aims.fao.org/aos/agrovoc/c_6543
http://aims.fao.org/aos/agrovoc/c_7252
http://aims.fao.org/aos/agrovoc/c_8516
http://aims.fao.org/aos/agrovoc/c_2985
http://aims.fao.org/aos/agrovoc/c_3081
author Jones, M.R.
Singels, A.
Chinorumba, S.
Patton, A.
Poser, Christophe
Singh, M.
Martiné, Jean-François
Christina, Mathias
Shine, J.
Annandale, J.
Hammer, Graeme
author_facet Jones, M.R.
Singels, A.
Chinorumba, S.
Patton, A.
Poser, Christophe
Singh, M.
Martiné, Jean-François
Christina, Mathias
Shine, J.
Annandale, J.
Hammer, Graeme
author_sort Jones, M.R.
title Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset
title_short Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset
title_full Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset
title_fullStr Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset
title_full_unstemmed Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset
title_sort exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset
url http://agritrop.cirad.fr/594564/
http://agritrop.cirad.fr/594564/1/594564.pdf
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spelling dig-cirad-fr-5945642024-01-29T02:30:18Z http://agritrop.cirad.fr/594564/ http://agritrop.cirad.fr/594564/ Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset. Jones M.R., Singels A., Chinorumba S., Patton A., Poser Christophe, Singh M., Martiné Jean-François, Christina Mathias, Shine J., Annandale J., Hammer Graeme. 2019. Field Crops Research, 244:107622, 12 p.https://doi.org/10.1016/j.fcr.2019.107622 <https://doi.org/10.1016/j.fcr.2019.107622> Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset Jones, M.R. Singels, A. Chinorumba, S. Patton, A. Poser, Christophe Singh, M. Martiné, Jean-François Christina, Mathias Shine, J. Annandale, J. Hammer, Graeme eng 2019 Field Crops Research F30 - Génétique et amélioration des plantes F62 - Physiologie végétale - Croissance et développement Saccharum officinarum génotype rendement des cultures facteur du milieu tallage couvert modèle de simulation http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_10176 http://aims.fao.org/aos/agrovoc/c_2594 http://aims.fao.org/aos/agrovoc/c_7773 http://aims.fao.org/aos/agrovoc/c_1262 http://aims.fao.org/aos/agrovoc/c_24242 La Réunion Afrique du Sud Zimbabwe Floride France http://aims.fao.org/aos/agrovoc/c_6543 http://aims.fao.org/aos/agrovoc/c_7252 http://aims.fao.org/aos/agrovoc/c_8516 http://aims.fao.org/aos/agrovoc/c_2985 http://aims.fao.org/aos/agrovoc/c_3081 Crop improvement aims to produce high yielding genotypes for target environments. Crop models simulate yield formation as the outcome of a series of low-level processes, driven by environmental (E) variables and regulated by genetic (G) traits. There is potential for crop models to aid sugarcane breeding, by identifying desirable genetic traits for target environments. The objective of this study was to evaluate existing concepts of G and E control of plant processes for explaining crop development, growth and yield, using an international growth analysis dataset. Crop development, growth and yield were monitored in the plant and 1st ratoon crops for seven cultivars (N41, R570, CP88-1762, HoCP96-540, Q183, ZN7 and NCo376) grown under well-watered conditions at La Mare (Reunion Island, France), Pongola (South Africa (RSA), Chiredzi (Zimbabwe), and Belle Glade (Florida, USA). Weather data were collected and environmental conditions characterized for each experiment. Derived process-level phenotypic parameters, based on concepts from four sugarcane growth simulation models (DSSATCanegro, Mosicas, APSIM-Sugar and Canesim), were calculated from observations and used to (1) evaluate current understanding of E drivers of sugarcane growth and development processes, and (2) identify and quantify G control at a process level. Final yields showed significant E and GxE variation; dry above-ground biomass and stalk yields were highest in La Mare and lowest in Pongola. Cultivar rankings in stalk dry mass for the common cultivars (N41, R570, CP88-1762) varied significantly between Es. Significant E variation in phenotypic parameters describing germination, tillering and timing of the onset of stalk growth (OSG) revealed shortcomings in the underlying simulation concepts. Significant G variation was found for germination rate, leaf appearance rate and canopy development rate per unit thermal time (TT), and maximum radiation use efficiency, indicating strong G control of the associated underlying processes. Solar radiation was found to influence tillering rate per unit TT, and TT to OSG, challenging the current theory of TT as the sole driver of these processes. By explaining more of the E variation, more stable and accurate G-specific model parameters can be defined and evaluated. This is anticipated to lead to less GxE confounding of modelled processes, and hence crop models that are better-equipped for supporting sugarcane crop improvement. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/594564/1/594564.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1016/j.fcr.2019.107622 10.1016/j.fcr.2019.107622 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.fcr.2019.107622 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.fcr.2019.107622