Field-based prediction of sugarcane photosynthesis through environmental inputs
Almeida, R. L.; Martins, T. S.; Magalhaes, J. R.; Pires, R. C. M.; Landell, M. G. A.; Xavier, M. A.; Machado, E. C.; Ribeiro, R.
Show 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.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Transpiration efficiency variations in the pearl millet reference collection PMiGAP 96%
- Environment of origin and domestication affect morphological, physiological, and agronomic response to water deficit in chile pepper (Capsicum sp.) 95%
- Evaluation of Conditional Treatment Effect of Salt Stress on Tomato Sugar Content Using Causal Machine Learning: A Pilot Study 94%
Similar papers in this journal
- Mesophyll conductance in two cultivars of wheat (Triticum aestivum) grown in glacial to super-elevated 96%
- Combining modelling and experimental approaches to assess the feasibility of developing rice-oil palm agroforestry system 96%
- Role of Triose Phosphate Utilization in photosynthetic response of rice to variable carbon dioxide levels and plant source-sink relations 94%
Similar papers in this journal
- An integrative process-based model for biomass and yield estimation of hardneck garlic (Allium sativum) 96%
- Trends in stomatal density and size in maize hybrids representing 100 years of long-term breeding for yield 94%
- Phenotypic Variation from Waterlogging in Multiple Perennial Ryegrass Varieties under Climate Change Conditions 94%
Similar papers in this journal
- Citrus photosynthesis and morphology acclimate to phloem-affecting huanglongbing disease at the leaf and shoot levels 95%
- Photosynthetic adjustments maintain lettuce growth under dynamically changing lighting in controlled indoor farming setups 94%
- Gene co-expression networks highlight key nodes associated with Ammonium nitrate in sugarcane 94%
Similar papers in this journal
- Raspberry plant stress detection using hyperspectral imaging 94%
- Crop modeling defines opportunities and challenges for drought escape, water capture, and yield increase using chilling-tolerant sorghum 93%
- A metabolomics study of ascorbic acid-induced in situ freezing tolerance in spinach (Spinacia oleracea L.) 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.