Using a three-dimensional canopy photosynthesis model validated in real greenhouse conditions, they found that enhancing parameters such as light quantum efficiency under limiting light (κ₂LL) and maximum electron transport rate (Jmax), together with optimized row spacing, offered the greatest potential for yield gains.
Canopy photosynthesis—the cumulative process of leaves capturing light energy—directly underpins crop yields. While field crops have been extensively studied, vegetable crops grown in greenhouses face unique challenges due to their deep canopies, multi-season growth cycles, and diverse climatic settings. Past models often relied on simplified assumptions, overlooking seasonal and regional variation. This left a knowledge gap in identifying which environmental or plant traits limit photosynthesis and how to manipulate them to achieve higher productivity. Based on these challenges, more detailed research was needed to clarify limitations and test enhancement strategies
A study (DOI: 10.1016/j.plaphe.2025.100069) published in Plant Phenomics on 7 June 2025 by Jianming Li’s team, Northwest A & F University, provides a blueprint for breeding high-efficiency tomato cultivars and refining cultivation practices to secure vegetable production under climate change.
In this study, researchers developed a three-dimensional canopy photosynthesis model that combined greenhouse structural measurements, detailed digitization of tomato plants, and physiological data including gas exchange, chlorophyll content, and nitrogen allocation. The model featured three modules: a structural reconstruction module to simulate plant architecture, a radiation module using Monte Carlo ray tracing to estimate light absorption, and a photosynthesis module extending the Farquhar–von Caemmerer–Berry framework to include stomatal conductance, transpiration, and energy balance. To test performance, a two-year trial was conducted in two contrasting regions of Shaanxi Province, China—one with high solar radiation (HR) and one with low radiation (LR)—where tomatoes (Solanum lycopersicum L. cv. ‘Jinpeng’) were grown across spring–summer and autumn–winter seasons. The model was calibrated using extensive field measurements of fluorescence, nitrogen content, and biomass, and validation confirmed high consistency between simulations and observations (R² > 0.99 for biomass and ~0.9 for photosynthesis). With this platform, the researchers quantified the contributions of light environment (LE), canopy structure (CS), photosynthetic physiology (PP), and air environment (AE) to seasonal differences in accumulated canopy photosynthesis (ACP). Results revealed that ACP was consistently higher in HR than LR, with seasonal ACP 30.5% greater in spring and 56.1% greater in autumn. Factor decomposition showed that in spring, LE (58.7%) and PP (19.6%) were dominant drivers, while in autumn CS (34.7%), PP (24.2%), and LE (18.8%) explained most variance. Limitation analysis indicated that light was the main constraint in LR during spring, whereas biochemical capacity was the key limitation in autumn. Scenario simulations suggested that enhancing κ₂LL, Jmax, and mesophyll conductance (gm) could raise ACP by up to 16%, while adopting uniform row spacing provided further gains of 7–9%. By contrast, increasing greenhouse film haze offered only minor improvements of less than 2.5%.
This study highlights how combining physiological breeding targets with agronomic adjustments can raise tomato productivity sustainably. The identification of κ₂LL and Jmax as key traits for high-efficiency breeding points to molecular targets for future crop improvement. Meanwhile, fine-tuning plant arrangements within existing densities provides immediate management strategies for growers. As global climate change alters light regimes, these insights will support resilient vegetable production systems. By quantifying where limitations occur and how to overcome them, the research lays a foundation for optimizing greenhouse tomato yield through breeding and cultivation innovations.
###
References
DOI
10.1016/j.plaphe.2025.100069
Original URL
https://doi.org/10.1016/j.plaphe.2025.100069
Funding information
This work was funded by China Agriculture Research System (CARS-23-C-05) and National Natural Science Foundation of China (32472816).
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.