bioRxivPlant BiologyPreprint
Field-based prediction of sugarcane photosynthesis through environmental inputs
Rafael L. AlmeidaiD, Tamires da Silva MartinsiD, José R. Magalhães Filho, Regina Célia de Matos PiresiD, Marcos Guimarães de Andrade LandelliD, Mauro Alexandre XavieriD, Eduardo Caruso MachadoiD, Rafael Vasconcelos RibeiroiD
Abstract
Sugarcane (Saccharum officinarum) is a highly productive C4 crop prevalent in tropical and subtropical areas. However, its photosynthetic efficiency is influenced by environmental factors such as light, moisture and temperature. Understanding these interactions is critical for optimizing yields and addressing climate-related challenges. This study investigated the effects of environmental variables on carbon assimilation in four Brazilian sugarcane varieties (SP79-1011, IAC94-2094, IACSP94-2101 and IACSP95-5000), addressing both optimal and limiting conditions for key parameters. Over a 530-day field experiment, data were collected every 30 days from 7:00 to 17:00, measuring diurnal CO2 assimilation (A), photosynthetically active radiation (PAR), vapor pressure deficit (VPD), and air temperature. Polynomial models and multiple linear regression were used to quantify the contributions of these variables in CO2 uptake, yielding robust model fits (p<0.05, R2 = 0.84-0.99). Herein, optimal photosynthetic performance occurred under PAR at 1800 mol m-2 s-1, VPD at 2.34 kPa, and air temperature close to 32.5{degrees}C. A strong correlation (r = 0.92, p<0.001) between observed and predicted photosynthesis and high model efficacy (R2=0.60, p<0.001) underscored the reliability of the approach, explaining 60% of the observed variation. While the results highlighted the models effectiveness in predicting sugarcane photosynthetic rates under varying diurnal and seasonal conditions, deviations indicated the influence of unmeasured parameters and complex interactions that need further investigation. These findings provide valuable insights to refine sugarcane management practices, enhance yield potential, and improve crop resilience under climate change scenarios.
- organism: plant (other)
- assay: metabolomics
- research focus: metabolism & bioenergetics
- approach: statistical model
- Citations
- 1
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