Daily soil carbon dioxide flux under different tillage conditions

Törő, Ágnes; Ragán, Péter; Rátonyi, Tamás; Kith, Károly; Harsányi, Endre

doi:10.34101/actaagrar/2/3693

Cited by 3 publications

(3 citation statements)

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“…Greenhouse gas emissions, including CO 2 from Conv tillage are usually reported as higher than emissions from Cons agricultural practices with minimal tillage [59][60][61][62][63][64][65]. Data from the present study were consistent with the observations from the previous findings mentioned above.…”

Section: Discussionsupporting

confidence: 92%

Comparison of Carbon Dioxide (CO2) Fluxes between Conventional and Conserved Irrigated Rice Paddy Fields in Myanmar

Min

Rulı́k

2020

Sustainability

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Rice (Oryza sativa. L.), a major food crop widely grown in Myanmar, is the most prominent cause of greenhouse gas (GHG) emissions in agriculture. Moreover, as a result of modification in agricultural management practices (such as soil tillage), the soil organic matter is exposed to more oxidizing conditions, releasing CO2 into the environment, contributing to global warming. Therefore, we studied the effects of both conventional and conservation soil tillage management practices on CO2 fluxes on an experimental rice paddy field in Myanmar. Total CO2 emissions during the night from paddies farmed under conventional practices were significantly higher than those from paddies farmed under conservation practices; however, no net CO2 flux differences were found between practices. Total net CO2 fluxes ranged from −59 to 1614 mg CO2 m−2 h−1 in conventional practices and from −282 to 1082 mg CO2 m−2 h−1 in conservation practices, respectively. Significantly higher rice biomass and grain yields were observed in conventional practices when compared to those in conservation practices, causing a significant rise in both CO2 uptake and emissions during the day and night, respectively. In addition, the results of this study revealed that CO2 emissions in rice fields could be much higher than expected, requiring further study to elucidate key factors driving the dynamics of CO2 in rice paddy systems.

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Section: Discussionsupporting

confidence: 92%

Comparison of Carbon Dioxide (CO2) Fluxes between Conventional and Conserved Irrigated Rice Paddy Fields in Myanmar

Min

Rulı́k

2020

Sustainability

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“…In the continental experiment (i.e., Debrecen), the digital meter Testo 535 (TESTO; 0560 5350) was used for capturing and recording CO 2 emission. This devise measures CO 2 concentration via infrared absorption with 1 ppm of CO 2 measuring resolution (Törő et al, 2019). Five chambers (118 × 250 mm) were placed in each subplot, then CO 2 emission was measured and recorded, giving a total of 25 chambers for each location (2 tillage systems × 3 replicate× 5 chambers = 30 chambers).…”

Section: Methodsmentioning

confidence: 99%

“…Five chambers (118 × 250 mm) were placed in each subplot, then CO 2 emission was measured and recorded, giving a total of 25 chambers for each location (2 tillage systems × 3 replicate× 5 chambers = 30 chambers). At the end of each monitoring day, the average of five chambers from each subplot was calculated and recorded as a representative value (Törő et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Soil carbon dioxide emissions from maize (Zea mays L.) fields as influenced by tillage management and climate*

Mohammed

Mirzaei

Törő

et al. 2021

Irrigation and Drainage

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Emissions of CO2 from the soil are the second‐largest component of the global carbon cycle, which has altered the climate and led to climate change. The main aim of this research was to evaluate the direct impact of climate and soil management systems on soil carbon emissions. Thus, CO2 emissions were measured from maize fields located in two different climate regions (continental and semi‐arid). The experimental design involved two different soil management systems (conventional tillage [CT], non‐tillage [NT]) from two different sites (Debrecen [Hungary], Karaj [Iran]). The results showed that total CO2 emission from the cultivated system (CT) was higher than that from the non‐cultivated (NT) one, regardless of the climate region. However, CO2 emissions from agricultural soil in a humid region are significantly different (p < .05) from semi‐arid regions, which clearly emphasizes the role of climate conditions in the CO2 emission processes. However, the general linear model reveals that all studied variables (soil management systems, date of measurement, soil temperature, soil water content) had a significant (p < .05) effect on soil carbon emission, where the explained variance was 0.866. The findings of this research stress the importance of NT in CO2 mitigations on the farm scale. However, the output could help to draw up mitigation strategies to minimize the total greenhouse gas emissions from agricultural soil in both countries.

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Assessment of Advanced Machine and Deep Learning Approaches for Predicting CO2 Emissions from Agricultural Lands: Insights Across Diverse Agroclimatic Zones

Harsányi,

Mirzaei,

Arshad

et al. 2024

Earth Syst Environ

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Prediction of carbon dioxide (CO2) emissions from agricultural soil is vital for efficient and strategic mitigating practices and achieving climate smart agriculture. This study aimed to evaluate the ability of two machine learning algorithms [gradient boosting regression (GBR), support vector regression (SVR)], and two deep learning algorithms [feedforward neural network (FNN) and convolutional neural network (CNN)] in predicting CO2 emissions from Maize fields in two agroclimatic regions i.e., continental (Debrecen-Hungary), and semi-arid (Karaj-Iran). This research developed three scenarios for predicting CO2. Each scenario is developed by a combination between input variables [i.e., soil temperature (Δ), soil moisture (θ), date of measurement (SD), soil management (SM)] [i.e., SC1: (SM + Δ + θ), SC2: (SM + Δ), SC3: (SM + θ)]. Results showed that the average CO2 emission from Debrecen was 138.78 ± 72.04 ppm (n = 36), while the average from Karaj was 478.98 ± 174.22 ppm (n = 36). Performance evaluation results of train set revealed that high prediction accuracy is achieved by GBR in SC1 with the highest R2 = 0.8778, and lowest root mean squared error (RMSE) = 72.05, followed by GBR in SC3. Overall, the performance MDLM is ranked as GBR > FNN > CNN > SVR. In testing phase, the highest prediction accuracy was achieved by FNN in SC1 with R2 = 0.918, and RMSE = 67.75, followed by FNN in SC3, and GBR in SC1 (R2 = 0.887, RMSE = 79.881). The performance of MDLM ranked as FNN > GRB > CNN > SVR. The findings of the research provide insights into agricultural management strategies, enabling stakeholders to work towards a more sustainable and climate-resilient future in agriculture.

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Daily soil carbon dioxide flux under different tillage conditions

Cited by 3 publications

References 0 publications

Comparison of Carbon Dioxide (CO2) Fluxes between Conventional and Conserved Irrigated Rice Paddy Fields in Myanmar

Comparison of Carbon Dioxide (CO2) Fluxes between Conventional and Conserved Irrigated Rice Paddy Fields in Myanmar

Soil carbon dioxide emissions from maize (Zea mays L.) fields as influenced by tillage management and climate*

Assessment of Advanced Machine and Deep Learning Approaches for Predicting CO2 Emissions from Agricultural Lands: Insights Across Diverse Agroclimatic Zones

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