Modelling carbon dioxide emissions under a maize-soy rotation using machine learning

Abbasi, Naeem Akhtar; Hamrani, Abderrachid; Madramootoo, Chandra A.; Zhang, Tiequan; Tan, C. S.; Goyal, Manish Kumar

doi:10.1016/j.biosystemseng.2021.09.013

Cited by 30 publications

(18 citation statements)

References 86 publications

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“…The cumulative CO 2 and N 2 O emissions during the growing season were estimated by using Fritsch and Carlson's (1980) Piecewise Cubic Hermite Interpolating Polynomial (PCHIP). The PCHIP was performed using MATLAB (MATLAB r2020a) (Abbasi et al., 2021).…”

Section: Methodsmentioning

confidence: 99%

Soil nutrients and plant uptake parameters as related to greenhouse gas emissions

2022

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Fertilizer and water management practices have short-and long-term effects on soil chemical and physical properties and, in turn, greenhouse gas (GHG) emissions. The goal of this 4-yr field study was to establish the relationships between soil properties, agronomic practices, and GHG (CO 2 and N 2 O) emissions under different fertilizer and water table management practices. There were two fertilizer treatments: inorganic fertilizer (IF) and a mix of solid cattle manure and inorganic fertilizer (SCM), combined with tile drainage(DR) and controlled drainage with subirrigation(CDS). The cropping system was a maize (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation. Nitrogen in biomass (BMN) and N in grain (GRN) were measured and used to calculate other plant N parameters. Nitrous oxide and CO 2 fluxes were collected weekly, and their respective cumulative emissions were calculated. The results show that soil organic matter (OM), soil total carbon (C), and soil total nitrogen (N) were greater in SCM than IF by 23.7, 35.2, and 24.4%, respectively. Water table management did not significantly affect soil N and C. Increased CO 2 emissions were witnessed under higher soil OM, soil total C, and total N. Plant N uptake parameters were negatively correlated with N 2 O and CO 2 emissions. Higher plant N uptake can reduce environmental pollution by limiting N 2 O and CO 2 emissions. INTRODUCTIONAgriculture is responsible for 10% of the total greenhouse gas (GHG) emissions in Canada (ECCC, 2020). The use of fertilizer for crop production has increased from 57.6 ( 2002) to 87.6 (2016) kg ha −1 in Canada (World Data Atlas, 2020). Greenhouse gas emissions from agricultural soils have also increased with fertilizer use (Rochette et al., 2008), contributing to climate change.

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Section: Methodsmentioning

confidence: 99%

Soil nutrients and plant uptake parameters as related to greenhouse gas emissions

2022

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“…Therefore, a small error in soil respiration measurements will have a significant impact on the predicted results of the CO 2 concentration in the environment. This further demonstrates that accurate soil respiration monitoring is an important part of the study of global climate change (Forde et al 2019, Abbasi et al 2021, Sanz-Cobena et al 2021.…”

Section: Introductionmentioning

confidence: 65%

Study of a calibration system for soil respiration measurement chambers

Jiang

Yin

et al. 2022

Environ. Res. Commun.

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Purpose: Soil respiration measurement is an important component of the global carbon cycle assessment. To effectively validate the measurement performance of the monitoring instruments and provide accurate carbon flux data, a new flux-monitoring gas-chamber calibration system was investigated. Method: In an environmentally controlled laboratory, a concentration calculation calibration system, mass calculation calibration system, and flow calculation calibration system were used to quantify soil CO2 emissions. The measurement performance of the soil-respiration monitoring gas chamber was investigated, and the strengths and weaknesses of each calibration system were examined. Results: The unsteady-state flow chamber and steady-state chamber measurements had fewer errors and provided better results than the unsteady-state nonflow chamber. The measured values of the closed chamber were low, whereas the measured values of the open chamber were occasionally high and low. For calibration systems, the concentration calculation system is easy to operate; however, the reference flux values are unstable, and the mass calculation system allows for different gas transport mechanisms. However, it is complex to operate and it is difficult to control the air pressure in the diffusion chamber. The calibration process of the flow calculation system was stable and easy to operate; however, the experimental time was long, and the CO2 gas consumption was high. However, for the calibration effect, the optimal calibration system was the flow-meter algorithm. Conclusion: This study proposes a better calibration method for the soil CO2 flux gas chamber, which is conducive to improving the measurement accuracy of the instrument, and provides new ideas for the calibration of other environmental gas monitoring instruments.

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“…Various studies have already shown the effect of the considered predictor variables on CO 2 and N 2 O emissions. For example, Abbasi et al (2021) and Hamrani et al (2020) used time series of soil water content, soil temperature, and other climate variables that are typically used to calculate reference evapotranspiration as variable input to their models. Soil water content (WC soil , volumetric basis) and soil temperature (T soil ) at the 0-to-5-cm depth were measured at each gas sampling event using a calibrated HH2 moisture sensor (Delta-T-Devices) and a thermometer (Acurite Digital Meat Thermometer, 00641W, AcuRite Company).…”

Section: Soil Surface Greenhouse Gas Monitoringmentioning

confidence: 99%

“…Maize ( Zea may s L.) and soybean ( Glycine max L.) are commonly grown in a 2‐yr rotation in more than 70 million hectares of the Midwest Corn Belt region (Balboa et al., 2019). In this area, intensive fertilizer inputs to maize, N 2 ‐fixing soybean, and residual N enhance N 2 O emissions due to the nitrification and denitrification process (Behnke et al., 2018; Millar et al., 2010).…”

Section: Introductionmentioning

confidence: 99%

Prediction of greenhouse gas emissions from agricultural fields with and without cover crops

Kotlar

Singh²

2022

Soil Science Soc of Amer J

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The significant contribution of greenhouse gas (GHG) emissions to global climate change and stratospheric ozone depletion has been calling the attention to assess the effect of agricultural management on them. Although machine learning (ML) methods have been widely used for the quantification of various inherent and dynamic soil properties, the accuracy of these techniques in the prediction of agricultural soil GHG emissions remains unclear. Therefore, this study aims at evaluating the performance of six different ML methods including simple linear regression, Cubist, support vector machines (SVM) with three different kernel functions, and random forest (RF) for the prediction of CO2 and N2O fluxes from plots managed with and without cover cropping. The input parameters include typical meteorological data as well as soil temperature and soil water content. Results show that the daily flux of CO2 is positively correlated with soil temperature and solar radiation and negatively with soil water content, whereas daily N2O flux is positively correlated with soil water content. For plots without cover cropping, Cubist and SVM with polynomial kernel function outperformed in the prediction of the testing dataset with RMSE of 14.18 kg ha–1 d–1 and 11.52 g ha–1 d–1 for daily CO2 and N2O emissions, respectively. Where cover cropping was considered, SVM with radial basis function and Cubist were the best models in the prediction of daily CO2 and N2O fluxes with respective RMSE values of 15.71 kg ha–1 d–1 and 14.12 g ha–1 d–1. Considering the ranking of the models for all scenarios and both GHGs, SVM with nonlinear kernel functions and Cubist method surpassed other ML techniques.

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Modelling carbon dioxide emissions under a maize-soy rotation using machine learning

Cited by 30 publications

References 86 publications

Soil nutrients and plant uptake parameters as related to greenhouse gas emissions

Soil nutrients and plant uptake parameters as related to greenhouse gas emissions

Study of a calibration system for soil respiration measurement chambers

Prediction of greenhouse gas emissions from agricultural fields with and without cover crops

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