Estimation of diurnal greenhouse gas (GHG) emissions from unfertilized coffee soils using recurrent neural networks (RNN). A case study for Chirinos, San Ignacio Province, Cajamarca, Peru

Authors

  • Wendy Laurent Díaz Saavedra Institute of Data Science, National University of Jaen, Jaen 06800, Peru
  • Jorge Antonio Fernandez Jibaja Institute of Data Science, National University of Jaen, Jaen 06800, Peru
  • Jhon Franklin Oblitas Troyes Institute of Data Science, National University of Jaen, Jaen 06800, Peru
  • Jose Manuel Palomino Ojeda Institute of Data Science, National University of Jaen, Jaen 06800, Peru
  • Manuel Emilio Milla Pino Institute of Data Science, National University of Jaen, Jaen 06800, Peru
  • Annick Estefany Huaccha Castillo Institute of Data Science, National University of Jaen, Jaen 06800, Peru
  • Ruben Eusebio Acosta Jacinto National Institute for Telecommunications Research and Training, National University of Engineering, Rimac 15333, Peru
  • Guillermo Guardia Vázquez Departamento de Química y Tecnología de Alimentos, ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Article ID: 544
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DOI:

https://doi.org/10.18686/cest544

Keywords:

Climate change , Gases , Soil , Air pollution , Artificial intelligence , climate change; gases; soil; air pollution; artificial intelligence

Abstract

Global warming, driven by rising greenhouse gas (GHG) concentrations, has agriculture as a major source of emissions. In coffee plantations, low sampling frequency and the absence of diurnal baselines introduce bias in emission estimates. The objective of this research was to estimate diurnal CO₂, N₂O, and CH₄ emissions from unfertilized coffee soils using recurrent neural networks (RNN). Gas fluxes were measured with a closed dynamic chamber (CDC) at 20-minute intervals between 8:00 and 18:00 over 22 days. For the estimation of GHG emissions, climatic data measured through a meteorological station were used, in addition to environmental parameters incorporated in the CDC. Five RNN models composed of two hidden layers of 20, 25, and 50 neurons were developed, trained, and validated for each GHG. Results indicate that N₂O contributed most to total emissions (734,689 ppm CO₂-eq), with CO₂ (237,579 ppm CO₂-eq) and CH₄ (215,426 ppm CO₂-eq) contributing less. Model performance was strong, with R² values of 0.98 (CO₂), 0.96 (N₂O), and 0.94 (CH₄). It is concluded that the RNNs proved to be reliable models for predicting GHG emissions in unfertilized coffee soils, with this study presenting a replicable framework with the potential to improve temporal estimation and reduce uncertainty in GHG inventories.

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Published

2025-12-10

How to Cite

Díaz Saavedra, W. L., Fernandez Jibaja, J. A., Oblitas Troyes, J. F., Palomino Ojeda, J. M., Milla Pino, M. E., Huaccha Castillo, A. E., Acosta Jacinto, R. E., & Guardia Vázquez, G. (2025). Estimation of diurnal greenhouse gas (GHG) emissions from unfertilized coffee soils using recurrent neural networks (RNN). A case study for Chirinos, San Ignacio Province, Cajamarca, Peru. Clean Energy Science and Technology, 3(4), 544. https://doi.org/10.18686/cest544

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Vol. 3 No. 4 (2025)

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Article

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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