Preprints
https://doi.org/10.5194/gmd-2024-105
https://doi.org/10.5194/gmd-2024-105
Submitted as: model description paper
 | 
11 Jul 2024
Submitted as: model description paper |  | 11 Jul 2024
Status: this preprint is currently under review for the journal GMD.

A modeling System for Identification of Maize Ideotypes, optimal sowing dates and nitrogen fertilization under climate change – PREPCLIM-v1

Mihaela Caian, Catalin Lazar, Petru Neague, Antoanela Dobre, Vlad Amihaesei, Zenaida Chitu, Adrian Irasoc, Andreea Popescu, and George Cizmas

Abstract. The impact of climate change on crops and agricultural yield is an actual threat while being a challenging issue due to the high complexity of factors that intervene at the local scale of the crop. Assessing it, requires the use of coupled models climate-phenology, meanwhile methods to identify management and genotypes suitable for local future conditions, in order to sustain adaptation strategies. We present the implementation and use of a new integrated climate-phenology adaptation support modeling system based on regional CORDEX climate models and the CERES Maize model from DSSAT platform, with new modules for optimal management and genotype identification using a hybrid method: deterministic modeling and -ML/ genetic algorithms. It was run as a regional pilot over Romania, operating in real-time in interaction with users, performing agro-climate projections (combination of fertilization, sowing date, soil) and providing best crop management simulated under climate change projections. Multi-model ensemble simulations for two climate scenarios RCP4.5 and RCP8.5 and twelve management scenarios show new results for the region.

For the actual genotype we find a projected mean decrease in yield in both climate scenarios for all sowing dates and fertilization levels tested, response shown to be sensitive to initial soil parameters. This response was linked to two factors: a shorter growing season by up to 10 % and a loss of fertilization efficiency in a warmer climate. A warning points to results showing a narrowing of agro-management opportunities for crop yield but in opposite it is shown a significant role of optimal genotype-range identification that may provide crop solutions under climate change even in extreme years. Identifying best genotype under warmer climate along sets of six cross-parameter simulations show systematic lower values of the maximal yields, but emphasizes genotype windows of increases in the intermediate yield values in scenarios compared to actual climate. The highest harvest sensitivity to genotype is shown to be to changes in the thermal time to juvenil respectively to maturity stage under warmer climate. The results sustain using a deterministic coupled modeling system combined with data-driven modeling for identifying optimal adaptation including fertilization paths that contribute to climate change mitigation.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Mihaela Caian, Catalin Lazar, Petru Neague, Antoanela Dobre, Vlad Amihaesei, Zenaida Chitu, Adrian Irasoc, Andreea Popescu, and George Cizmas

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'No compliance with the policy of the journal', Juan Antonio Añel, 16 Jul 2024
    • AC1: 'Reply on CEC1', Mihaela Caian, 31 Jul 2024
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 31 Jul 2024
        • CC1: 'Reply on CEC2', Catalin Lazar, 31 Jul 2024
          • CEC3: 'Reply on CC1 - compliance solved', Juan Antonio Añel, 31 Jul 2024
  • RC1: 'Comment on gmd-2024-105', Anonymous Referee #1, 23 Oct 2024
    • CC2: 'Reply on RC1', Catalin Lazar, 23 Oct 2024
    • AC2: 'Reply on RC1', Mihaela Caian, 18 Nov 2024
  • RC2: 'Comment on gmd-2024-105', Anonymous Referee #2, 30 Oct 2024
    • CC3: 'Reply on RC2', Catalin Lazar, 30 Oct 2024
    • AC3: 'Reply on RC2', Mihaela Caian, 18 Nov 2024
Mihaela Caian, Catalin Lazar, Petru Neague, Antoanela Dobre, Vlad Amihaesei, Zenaida Chitu, Adrian Irasoc, Andreea Popescu, and George Cizmas
Mihaela Caian, Catalin Lazar, Petru Neague, Antoanela Dobre, Vlad Amihaesei, Zenaida Chitu, Adrian Irasoc, Andreea Popescu, and George Cizmas

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Short summary
We present the implementation and use of a new integrated climate-phenology adaptation modeling system for climate change using CORDEX scenarios and DSSAT crop model with new developed modules for optimal agro-management and genotype identification under future climate. Optimisation is a hybrid deterministic /ML genetic algorithms method. The system is user-interactive in real time, has been implemented and tested for South Romania, is applicable for Southern-Europe and extendable for Europe.