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Purpose This study aims to evaluate the impact of different organic amendments (digestate from a biogas plant and spent
mushroom substrate) and environmental factors (temperature, rainfall, soil temperature, soil moisture, and organic matter
content) on CO2
flux from agricultural soils. The research seeks to determine how these variables influence CO2
emissions,
providing insights into optimizing soil management practices to mitigate climate change.
Materials and methods The study was conducted between 11 May and 22 September 2022, assessing CO2
flux from agricultural
soils treated with organic amendments. The amendments included digestate and spent mushroom substrate, compared
against control soils without additives. CO2
flux was measured and analyzed concerning environmental variables, including
temperature, rainfall, soil temperature, moisture, and organic matter content. Predictive models, including an artificial neural
network (ANN) with Bayesian regularization and an ElasticNet regression model, were developed to predict CO2
flux
based on the collected data. The performance of the models was evaluated using metrics such as R-value, MSE, and RMSE.
Results and discussion Cumulative CO2
flux varied across treatments, with the control soil showing the lowest emissions
(3911 mg CO2
m⁻²), while soils with digestate and spent mushroom substrate showed higher emissions (5757.87 mg CO2
m⁻² and 6150.07 mg CO2
m⁻², respectively). The spent mushroom substrate, which had the highest organic matter content,
resulted in the highest mean CO2
flux of 255.28 mg CO2
m⁻². The study found that organic amendments significantly affected
CO2
flux, with environmental factors like water-filled pore space, air and soil temperature, and rainfall also playing crucial
roles. The ANN model outperformed the ElasticNet model, achieving an R-value of 0.99023, an MSE of 33.2040, and an
RMSE of 5.7623, indicating its superior predictive capability.
Conclusions The integration of organic amendments and environmental factors into an ANN model provides a robust and
accurate method for predicting CO2
flux in agricultural soils. This enhanced predictive capability is essential for optimizing
soil management practices aimed at reducing CO2
emissions, thereby contributing to climate change mitigation in agricultural
settings. The study concludes that the integration of organic amendments and environmental conditions into the
ANN model provides a robust and accurate method for predicting CO2
flux in organic waste treated soils. This advance in
predictive capability is crucial for optimizing soil fertilization practices to mitigate CO2
flux in agricultural environments.
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