Simulation of alfalfa yield with AquaCrop
The AquaCrop simulation model, originally designed for annual crops, is expanded here to simulate alfalfa, a perennial forage crop. A new routine was added to the model to mimic the assimilate partitioning between above and below-ground plant parts to account for the utilization of reserves in Spring and for their storage in the Fall. The simulation of the transfer of assimilates requires only three extra crop parameters which makes the model also easy to calibrate. To evaluate the model, yield data collected in Louvain-La-Neuve (Belgium), Isparta (Turkey), and Ottawa (Canada) for different alfalfa cultivars, various years and field and irrigation management strategies were used. To assess the accuracy and robustness of the simple assimilate remobilization process, simulations were run for the three different environments with a common set of crop parameters which were parameterized. The dispersion between the observed and simulated cumulative dry above-ground biomass during the growing cycle was small (r² = 0.97; nRMSE = 11%; Nash-Sutcliffe model EF = 0.97), and a systematic over- or underestimation by the model was not observed (Willmott's index of agreement, d = 0.99). When evaluating the goodness of fit of the 81 individual harvest events, the results were still very satisfactory although the nRMSE doubled. The simulations indicated that the AquaCrop model adapted to perennial crops and with a novel storage-remobilization routine, could predict well alfalfa yields in various climates and environments, with and without water and fertility stress, and for three different alfalfa cultivars.
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Language: | English |
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Elsevier
2023-06-30
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Subjects: | Transfer of assimilates, Alfalfa, AquaCrop, Crop simulation models, Length of growing cycle, Natural self-thinning, Perennial forage crops, |
Online Access: | http://hdl.handle.net/10261/349320 https://api.elsevier.com/content/abstract/scopus_id/85154028875 |
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dig-ias-es-10261-3493202024-05-17T20:58:52Z Simulation of alfalfa yield with AquaCrop Raes, Dirk Fereres Castiel, Elías García Vila, Margarita Curnel, Yannick Knoded, David Çelik, Sema Kale Ucar, Yusuf Türk, Mevlüt Wellens, Joost The Scientific and Technological Research Council of Turkey Transfer of assimilates Alfalfa AquaCrop Crop simulation models Length of growing cycle Natural self-thinning Perennial forage crops The AquaCrop simulation model, originally designed for annual crops, is expanded here to simulate alfalfa, a perennial forage crop. A new routine was added to the model to mimic the assimilate partitioning between above and below-ground plant parts to account for the utilization of reserves in Spring and for their storage in the Fall. The simulation of the transfer of assimilates requires only three extra crop parameters which makes the model also easy to calibrate. To evaluate the model, yield data collected in Louvain-La-Neuve (Belgium), Isparta (Turkey), and Ottawa (Canada) for different alfalfa cultivars, various years and field and irrigation management strategies were used. To assess the accuracy and robustness of the simple assimilate remobilization process, simulations were run for the three different environments with a common set of crop parameters which were parameterized. The dispersion between the observed and simulated cumulative dry above-ground biomass during the growing cycle was small (r² = 0.97; nRMSE = 11%; Nash-Sutcliffe model EF = 0.97), and a systematic over- or underestimation by the model was not observed (Willmott's index of agreement, d = 0.99). When evaluating the goodness of fit of the 81 individual harvest events, the results were still very satisfactory although the nRMSE doubled. The simulations indicated that the AquaCrop model adapted to perennial crops and with a novel storage-remobilization routine, could predict well alfalfa yields in various climates and environments, with and without water and fertility stress, and for three different alfalfa cultivars. The Isparta research was supported by TUBITAK project 215O326 (Y. Ucar, M.Turk, and S.Kale Celik). Peer reviewed 2024-03-05T12:41:02Z 2024-03-05T12:41:02Z 2023-06-30 artículo http://purl.org/coar/resource_type/c_6501 Agricultural Water Management 284: 108341 (2023) 0378-3774 http://hdl.handle.net/10261/349320 10.1016/j.agwat.2023.108341 2-s2.0-85154028875 https://api.elsevier.com/content/abstract/scopus_id/85154028875 en Publisher's version The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.agwat.2023.108341 https://doi.org/10.1016/j.agwat.2023.108341 Sí open application/pdf Elsevier |
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Transfer of assimilates Alfalfa AquaCrop Crop simulation models Length of growing cycle Natural self-thinning Perennial forage crops Transfer of assimilates Alfalfa AquaCrop Crop simulation models Length of growing cycle Natural self-thinning Perennial forage crops |
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Transfer of assimilates Alfalfa AquaCrop Crop simulation models Length of growing cycle Natural self-thinning Perennial forage crops Transfer of assimilates Alfalfa AquaCrop Crop simulation models Length of growing cycle Natural self-thinning Perennial forage crops Raes, Dirk Fereres Castiel, Elías García Vila, Margarita Curnel, Yannick Knoded, David Çelik, Sema Kale Ucar, Yusuf Türk, Mevlüt Wellens, Joost Simulation of alfalfa yield with AquaCrop |
description |
The AquaCrop simulation model, originally designed for annual crops, is expanded here to simulate alfalfa, a perennial forage crop. A new routine was added to the model to mimic the assimilate partitioning between above and below-ground plant parts to account for the utilization of reserves in Spring and for their storage in the Fall. The simulation of the transfer of assimilates requires only three extra crop parameters which makes the model also easy to calibrate. To evaluate the model, yield data collected in Louvain-La-Neuve (Belgium), Isparta (Turkey), and Ottawa (Canada) for different alfalfa cultivars, various years and field and irrigation management strategies were used. To assess the accuracy and robustness of the simple assimilate remobilization process, simulations were run for the three different environments with a common set of crop parameters which were parameterized. The dispersion between the observed and simulated cumulative dry above-ground biomass during the growing cycle was small (r² = 0.97; nRMSE = 11%; Nash-Sutcliffe model EF = 0.97), and a systematic over- or underestimation by the model was not observed (Willmott's index of agreement, d = 0.99). When evaluating the goodness of fit of the 81 individual harvest events, the results were still very satisfactory although the nRMSE doubled. The simulations indicated that the AquaCrop model adapted to perennial crops and with a novel storage-remobilization routine, could predict well alfalfa yields in various climates and environments, with and without water and fertility stress, and for three different alfalfa cultivars. |
author2 |
The Scientific and Technological Research Council of Turkey |
author_facet |
The Scientific and Technological Research Council of Turkey Raes, Dirk Fereres Castiel, Elías García Vila, Margarita Curnel, Yannick Knoded, David Çelik, Sema Kale Ucar, Yusuf Türk, Mevlüt Wellens, Joost |
format |
artículo |
topic_facet |
Transfer of assimilates Alfalfa AquaCrop Crop simulation models Length of growing cycle Natural self-thinning Perennial forage crops |
author |
Raes, Dirk Fereres Castiel, Elías García Vila, Margarita Curnel, Yannick Knoded, David Çelik, Sema Kale Ucar, Yusuf Türk, Mevlüt Wellens, Joost |
author_sort |
Raes, Dirk |
title |
Simulation of alfalfa yield with AquaCrop |
title_short |
Simulation of alfalfa yield with AquaCrop |
title_full |
Simulation of alfalfa yield with AquaCrop |
title_fullStr |
Simulation of alfalfa yield with AquaCrop |
title_full_unstemmed |
Simulation of alfalfa yield with AquaCrop |
title_sort |
simulation of alfalfa yield with aquacrop |
publisher |
Elsevier |
publishDate |
2023-06-30 |
url |
http://hdl.handle.net/10261/349320 https://api.elsevier.com/content/abstract/scopus_id/85154028875 |
work_keys_str_mv |
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1802820016569057280 |