TURION: A physiological crop model for yield prediction of asparagus using sentinel-1 data

Abstract

In Peru, asparagus is an important crop for the export market. Forecasting the yields is key in planning ahead sales to exporters. Farmers currently apply an empirical method by counting the number of mature buds per crown per metre and making linear regressions with the previous harvests. There was no simulation model so far for a continuous cycling crop grown in Peru. Therefore, this research describes TURION, a mechanistic crop model coded in Python which includes 27 physiological parameters, some crop variables based on literature and field data. Growth rate, thermal time per phenological stages, biomass partition, stem diameter variations, spear volume and leaf area index (LAI) across the crop cycle were determined for model parameterisation. TURION includes three sub-models: (1) spears production and its root carbohydrates (CHO) depletion, (2) stems establishment and its root CHO depletion and (3) replenish CHO storage in roots by photosynthesis, LAI and CHO translocation. This model predicted: yield, numbers of spears, biomass of spears/stems, and root CHO changes brix% values. Predictions provided outputs at plant level. This model was validated on crops ranging from 3 to 12 years post-establishment, for 75 commercial harvests reported between July 2018 and May 2020 over 38 different plots. Results showed a relative root mean square error (rRMSE) of 16.72 % for final yield, 13.46 % for CHO at the end of harvest and 9.79 % CHO at the end of the crop cycle.