Submitted as: model evaluation paper
05 May 2023
Submitted as: model evaluation paper |  | 05 May 2023
Status: a revised version of this preprint was accepted for the journal GMD.

Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)

Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock

Abstract. Farmers around the world time the planting of their crops to optimize growing season conditions and choose varieties that grow slowly enough to take advantage of the entire growing season while minimizing the risk of late-season kill. As climate changes, these strategies will be an important component of agricultural adaptation. Thus, it is critical that the global models used to project crop productivity under future conditions are able to realistically simulate growing season timing. This is especially important for climate- and hydrosphere-coupled crop models, where the intra-annual timing of crop growth and management affects regional weather and water availability. We have improved the crop module of the Community Land Model (CLM) to allow the use of externally-specified crop planting dates and maturity requirements. In this way, CLM can use alternative algorithms for future crop calendars that are potentially more accurate and/or flexible than the built-in methods.

Using observation-derived planting and maturity inputs reduces bias in the mean simulated global yield of sugarcane and cotton but increases bias for corn, wheat, and especially rice. These inputs also reduce simulated global irrigation demand by 15 %, much of which is associated with particular regions of corn and rice cultivation. Finally, we discuss how our results suggest areas for improvement in CLM and, potentially, similar crop models.

Sam S. Rabin et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-66', Anonymous Referee #1, 02 Jun 2023
  • RC2: 'Comment on gmd-2023-66', Anonymous Referee #2, 16 Aug 2023
  • AC1: 'Reply to reviewer comments', Sam Rabin, 13 Sep 2023

Sam S. Rabin et al.

Model code and software

CLM code used for 1850–1957 period and GDD-Generating run Sam S. Rabin, CTSM team, and contributors

CLM code used for spinup CTSM team and contributors

CLM code used for experimental runs Sam S. Rabin, CTSM team, and contributors

Sam S. Rabin et al.


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Short summary
Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.