Refining the STICS model to predict iceberg lettuce yields based on temperature-dependent photosynthate partitioning
Journal
Ecological Modelling
Journal Volume
521
ISSN
0304-3800
Date Issued
2026-11
Author(s)
Abstract
Process-based crop models are valuable tools for quantifying crop responses to environmental drivers. This study coupled the STICS crop model with dry matter partitioning equation (DMPE) to simulate biomass accumulation and yield formation in iceberg lettuce (Lactuca sativa L.). Model development and evaluation were based on data collected from 14 field experiments conducted at 10 sites during three consecutive cropping seasons from October 2017 to April 2018. Measurements included leaf area, shoot fresh and dry weights, and harvested head yield, along with site-specific weather and soil data. Leaf area index and shoot dry weight dynamics were well described by logistic growth functions, reaching maximum values of 5.17–6.95 cm² cm⁻² and 33.8–47.8 g plant⁻¹, respectively. Biomass partitioning to heads was successfully represented by a logistic DMPE as a function of growing degree days (r² = 0.944), incorporating the combined effects of temperature and radiation. The coupled STICS–DMPE model accurately simulated head dry weight, with a mean error of −0.88 g plant⁻¹, a root mean square error of 2.99 g plant⁻¹, and a model efficiency of 0.76. These results indicate that coupling dry matter partitioning functions with STICS improves the prediction of lettuce head yield and provides a practical approach for assessing crop production under varying environmental conditions. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Subjects
Agricultural resilience
Crop model
Growing degree days
Lactuca sativa
Model coupling
Publisher
Elsevier BV
Description
Article number 111736
Type
journal article