# Simulation of strawberry yield using dry matter distribution based on the potential growth of the sink–source organs

## TL;DR

This paper presents a model that simulates strawberry yield based on dry matter distribution, showing good accuracy in predicting production under greenhouse conditions.

## Contribution

The model introduces a novel method for simulating dry matter distribution using potential growth of sink-source organs in strawberries.

## Key findings

- Total dry matter production was accurately simulated with RRMSE values of 0.15 and 0.17.
- Cumulative yields matched measured values with RRMSE between 0.11 and 0.15 across two seasons.
- The model overestimated production at the start and end of the harvest period.

## Abstract

Strawberry, a vital crop in horticulture, faces challenges like pest infestations and climate variability that affect stable production. A crop model based on photosynthesis-derived dry matter (DM) production is an effective method to examine the environment–plant growth relationship. The developed model simulates total DM production and yield overtime using greenhouse environment, each inflorescence anthesis dates, leaf area, and physiological parameters as inputs. Total DM production was accurately simulated by inputting leaf area measured by either destructive measurement or web-camera based imaging without destructive measurements (RRMSE = 0.15 and 0.17). Cumulative yields closely matched measured values across two distinct growing seasons (RRMSE = 0.11–0.15). The monthly yield generally aligned with the observed values, except at the beginning and end of the harvest period, where the model tended to overestimate production. These result suggested the process of DM distribution calculation based on the potential growth of the individual leaves and fruit clusters present on that day was effective in capturing the dynamics of DM distribution to the fruit. The model could be applied to strawberry production in greenhouses controlled with optimal ranges for the plant growth. The model’s applicability to diverse greenhouse conditions would be broadened by improving the physiological parameters in future work.