• Energy Research

AbstractThis study summarizes a large diverse dataset of methane (CH4) fluxes measured from agricultural sites across the British Isles. A total of 53,976 manual static chamber measurements from 27 different sites were investigated to determine the magnitude of CH4 fluxes from a variety of agricultural fields across the UK and Ireland. Our study shows that contrary to some studies, agricultural soils (both arable and grassland) are small net emitters of CH4 rather than sinks. Mean fluxes measured from arable and grassland sites (excluding fertiliser and tillage events) were 0.11 ± 0.06 and 0.19 ± 0.09 nmol m−2 s−1, respectively, and were not found to be significantly different (Welch t‐test, p = 0.17). Using the values reported in this study, we estimate that an annual emission of 0.16 and 0.09 Mt of CO2‐eq is expected from arable and grassland agricultural soils in the UK and Ireland (comparable to 0.3 and 0.7% of the current annual CH4 emission inventories, respectively). Where CH4 uptake occurs in soils, it is negligible compared to expected emissions of the application of animal manures and tillage events, which were both found to significantly increase CH4 emissions in the immediate few days to months after events. Our study highlights that there are significant differences in CH4 uptake and emissions between sites, and that these differences are partially the result of the moisture content of the soil (i.e., the aerobic status of the soil). We expect uptake of CH4 to be more prevalent in drier soils where volumetric water content does not exceed 35% and emissions to be exponentially greater where agricultural fields become waterlogged.Highlights This study investigated 53,976 CH4 flux measurements from 27 sites across the UK Our study shows both arable and grassland soils are small net emitters of CH4 We estimate annual CH4 emissions of 0.16 Mt of CO2‐eq from agricultural soils in the UK We estimate annual CH4 emissions of 0.09 Mt of CO2‐eq from agricultural soils in Ireland

AbstractThis study summarizes a large diverse dataset of methane (CH4) fluxes measured from agricultural sites across the British Isles. A total of 53,976 manual static chamber measurements from 27 different sites were investigated to determine the magnitude of CH4 fluxes from a variety of agricultural fields across the UK and Ireland. Our study shows that contrary to some studies, agricultural soils (both arable and grassland) are small net emitters of CH4 rather than sinks. Mean fluxes measured from arable and grassland sites (excluding fertiliser and tillage events) were 0.11 ± 0.06 and 0.19 ± 0.09 nmol m−2 s−1, respectively, and were not found to be significantly different (Welch t‐test, p = 0.17). Using the values reported in this study, we estimate that an annual emission of 0.16 and 0.09 Mt of CO2‐eq is expected from arable and grassland agricultural soils in the UK and Ireland (comparable to 0.3 and 0.7% of the current annual CH4 emission inventories, respectively). Where CH4 uptake occurs in soils, it is negligible compared to expected emissions of the application of animal manures and tillage events, which were both found to significantly increase CH4 emissions in the immediate few days to months after events. Our study highlights that there are significant differences in CH4 uptake and emissions between sites, and that these differences are partially the result of the moisture content of the soil (i.e., the aerobic status of the soil). We expect uptake of CH4 to be more prevalent in drier soils where volumetric water content does not exceed 35% and emissions to be exponentially greater where agricultural fields become waterlogged.Highlights This study investigated 53,976 CH4 flux measurements from 27 sites across the UK Our study shows both arable and grassland soils are small net emitters of CH4 We estimate annual CH4 emissions of 0.16 Mt of CO2‐eq from agricultural soils in the UK We estimate annual CH4 emissions of 0.09 Mt of CO2‐eq from agricultural soils in Ireland

In this study, we present the first long-term N2O eddy covariance dataset measured from a working farm. The eddy covariance method was used over a four year period to measure fluxes of the greenhouse gas nitrous oxide (N2O) from an intensively managed grazed grassland, to which regular applications of ammonium nitrate or urea fertilisers were spread, for two years each at the field site. The mean emission factors (EFs) reported for ammonium nitrate and urea fertiliser applications in this study over a period of 30 days after fertilisation, were 0.90 and 1.73% of the nitrogen applied, respectively, with EFs of individual events ranging between 0.13 and 5.71%. Our study accurately quantifies emission factors for multiple events and showing unambiguously that large-scale variability is real. EFs do indeed vary from one fertiliser event to another, even at the same site with the same fertiliser type under similar environmental conditions. This makes distinguishing EFs between different fertiliser types for the purposes of developing emission mitigation policy very difficult.

In this study, we present the first long-term N2O eddy covariance dataset measured from a working farm. The eddy covariance method was used over a four year period to measure fluxes of the greenhouse gas nitrous oxide (N2O) from an intensively managed grazed grassland, to which regular applications of ammonium nitrate or urea fertilisers were spread, for two years each at the field site. The mean emission factors (EFs) reported for ammonium nitrate and urea fertiliser applications in this study over a period of 30 days after fertilisation, were 0.90 and 1.73% of the nitrogen applied, respectively, with EFs of individual events ranging between 0.13 and 5.71%. Our study accurately quantifies emission factors for multiple events and showing unambiguously that large-scale variability is real. EFs do indeed vary from one fertiliser event to another, even at the same site with the same fertiliser type under similar environmental conditions. This makes distinguishing EFs between different fertiliser types for the purposes of developing emission mitigation policy very difficult.

AbstractBackgroundThe evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data.ResultsOur results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: theR2value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that theR2values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF.ConclusionML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems.

AbstractBackgroundThe evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data.ResultsOur results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: theR2value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that theR2values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF.ConclusionML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems.