Crop model ideotyping for agricultural diversification.

Area of Science:

• Agricultural Science
• Computational Biology
• Agronomy

Background:

• Crop diversification relies on modeling neglected or underutilized crops.
• Limited data and funding hinder calibration for these crops, impacting policy interest.
• Existing crop models require extensive, resource-intensive calibration for specific varieties.

Purpose of the Study:

• To develop and validate an ideotyping technique for approximating crop modeling parameters for underutilized crops.
• To address the lack of calibration data for minor crops by leveraging knowledge from well-studied crops.
• To encourage the use of advanced modeling techniques for less-known crops and their varieties.

Main Methods:

• Developed an ideotyping technique to approximate crop model parameters using data from related, well-calibrated crops.
• Utilized the AquaCrop model to estimate growth coefficients for hemp (Cannabis sativa L.) based on a sugar cane model.
• Employed a sequential procedure to approximate phenological stages, ensuring model performance in calibration and validation.

Main Results:

• Successfully tested the ideotyping method for hemp (Cannabis sativa L.) using an established crop model.
• The approach effectively approximated growth coefficients and phenological stages for hemp.
• Demonstrated the feasibility of transferring knowledge between crop models for underutilized species.

Conclusions:

• The developed ideotyping technique offers a viable solution for modeling underutilized crops with limited data.
• This method can reduce the resource demands of crop model calibration, facilitating research and policy support.
• Further development of such techniques is essential for advancing the modeling of minor crops and promoting agricultural diversity.