ReviewFrontiers in plant science2026
High-fidelity crop modelling: innovations driving sustainable fruit horticulture.
Review in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective decision support systems rely on a clear understanding of real-world conditions and on developing strategies to manage them. In the current era of severe climate change, crop modelling has emerged as a valuable tool in fruit production. It enables farmers and researchers to mitigate the adverse effects of biotic and abiotic stresses by providing scientifically grounded, practical, and forward-looking solutions. This review aims to evaluate the influence of climate and management practices on fruit quality and to highlight how crop modelling can address challenges related to crop growth and development. It focuses on enabling timely, informed decision-making to improve outcomes. These models help in understanding perennial fruit crops with respect to canopy architecture, plant structure, root and aerial systems, light interception, photosynthesis, respiration, carbon allocation, nutrient uptake, phenology, and pest and disease forecasting. Each model's applications and limitations are considered to provide a comprehensive perspective. They facilitate the interpretation of experimental data and enhance understanding of complex biological systems. Dynamic and quantitative modelling approaches support timely decision-making, improve analysis of agricultural systems, and promote interdisciplinary research, ultimately increasing management efficiency. These technologies enhance decision-making across pre-harvest, harvest, and post-harvest stages, contributing to yield improvement and the wider adoption of precision agriculture, especially in developing countries.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.