scholarly journals Modelling the contribution of short-range atmospheric and hydrological transfers to nitrogen fluxes, budgets and indirect emissions in rural landscapes

2012 ◽  
Vol 9 (5) ◽  
pp. 1647-1660 ◽  
Author(s):  
J.-L. Drouet ◽  
S. Duretz ◽  
P. Durand ◽  
P. Cellier
Keyword(s):  
Short Range ◽  
Landscape Scale ◽  
Soil Conditions ◽  
N2o Emissions ◽  
Spatial Interactions ◽  
Rural Landscapes ◽  
Environmental Threats ◽  
Spatially Distributed ◽  
Simulation Results ◽  
Oligotrophic Ecosystems

Abstract. Spatial interactions within a landscape may lead to large inputs of reactive nitrogen (Nr) transferred from cultivated areas and farms to oligotrophic ecosystems and induce environmental threats such as acidification, nitric pollution or eutrophication of protected areas. The paper presents a new methodology to estimate Nr fluxes at the landscape scale by taking into account spatial interactions between landscape elements. This methodology includes estimates of indirect Nr emissions due to short-range atmospheric and hydrological transfers. We used the NitroScape model which integrates processes of Nr transformation and short-range transfer in a dynamic and spatially distributed way to simulate Nr fluxes and budgets at the landscape scale. Four configurations of NitroScape were implemented by taking into account or not the atmospheric, hydrological or both pathways of Nr transfer. We simulated Nr fluxes, especially direct and indirect Nr emissions, within a test landscape including pig farms, croplands and unmanaged ecosystems. Simulation results showed the ability of NitroScape to simulate patterns of Nr emissions and recapture for each landscape element and the whole landscape. NitroScape made it possible to quantify the contribution of both atmospheric and hydrological transfers to Nr fluxes, budgets and indirect Nr emissions. For instance, indirect N2O emissions were estimated at around 21% of the total N2O emissions. They varied within the landscape according to land use, meteorological and soil conditions as well as topography. This first attempt proved that the NitroScape model is a useful tool to estimate the effect of spatial interactions on Nr fluxes and budgets as well as indirect Nr emissions within landscapes. Our approach needs to be further tested by applying NitroScape to several spatial arrangements of agro-ecosystems within the landscape and to real and larger landscapes.

scholarly journals Influence of short-term transfers on nitrogen fluxes, budgets and indirect N<sub>2</sub>O emissions in rural landscapes

2011 ◽  
Vol 8 (4) ◽  
pp. 7593-7622 ◽  
Author(s):  
S. Duretz ◽  
J.-L. Drouet ◽  
P. Durand ◽  
P. Cellier
Keyword(s):  
Landscape Scale ◽  
Soil Conditions ◽  
N2o Emissions ◽  
Spatial Interactions ◽  
Short Term ◽  
Rural Landscapes ◽  
Environmental Threats ◽  
Spatially Distributed ◽  
Simulation Results ◽  
Oligotrophic Ecosystems

Abstract. Spatial interactions at short-term may lead to large inputs of reactive nitrogen (Nr) to oligotrophic ecosystems and induce environmental threats such as additional N2O emissions and global warming. The paper presents a new methodology to estimate Nr fluxes, especially additional N2O emissions, at the landscape scale by taking into account spatial interactions between landscape elements. We used the NitroScape model which integrates processes of Nr transformation and short-term transfer in a dynamic and spatially distributed way to simulate Nr fluxes and budgets at the landscape scale. Four configurations of NitroScape were implemented by taking into account or not the atmospheric, hydrological or both pathways of Nr transfer. We simulated Nr fluxes, especially direct and indirect N2O emissions, within a test landscape including pig farms, croplands and unmanaged ecosystems. Simulation results showed the ability of NitroScape to simulate patterns of Nr losses and recapture for each landscape element and the whole landscape. They made it possible to quantify the contribution of both atmospheric and hydrological transfers in Nr fluxes and budgets. Indirect N2O emissions were estimated at almost 25 % of the total N2O emissions. They varied within the landscape according to land use, meteorological and soil conditions as well as topography. This first attempt has proved that the NitroScape model is a useful tool to estimate the effect of spatial interactions on Nr fluxes and budgets as well as indirect N2O emissions within landscapes. Our approach needs to be further tested by applying NitroScape to several spatial distributions of ecosystems within the landscape and to real and larger landscapes.

scholarly journals Spatial and temporal variability of nitrous oxide emissions in a mixed farming landscape of Denmark

2012 ◽  
Vol 9 (8) ◽  
pp. 2989-3002 ◽  
Author(s):  
K. Schelde ◽  
P. Cellier ◽  
T. Bertolini ◽  
T. Dalgaard ◽  
T. Weidinger ◽  
...  
Keyword(s):  
Land Use ◽  
Nitrous Oxide ◽  
Agricultural Land ◽  
Nitrous Oxide Emissions ◽  
Soil Conditions ◽  
Spatial And Temporal Variability ◽  
N2o Emissions ◽  
N2o Production ◽  
Mixed Farming ◽  
N2o Fluxes

Abstract. Nitrous oxide (N2O) emissions from agricultural land are variable at the landscape scale due to variability in land use, management, soil type, and topography. A field experiment was carried out in a typical mixed farming landscape in Denmark, to investigate the main drivers of variations in N2O emissions, measured using static chambers. Measurements were made over a period of 20 months, and sampling was intensified during two weeks in spring 2009 when chambers were installed at ten locations or fields to cover different crops and topography and slurry was applied to three of the fields. N2O emissions during spring 2009 were relatively low, with maximum values below 20 ng N m−2 s−1. This applied to all land use types including winter grain crops, grasslands, meadows, and wetlands. Slurry application to wheat fields resulted in short-lived two-fold increases in emissions. The moderate N2O fluxes and their moderate response to slurry application were attributed to dry soil conditions due to the absence of rain during the four previous weeks. Cumulative annual emissions from two arable fields that were both fertilized with mineral fertilizer and manure were large (17 kg N2O-N ha−1 yr−1 and 5.5 kg N2O-N ha−1 yr−1) during the previous year when soil water conditions were favourable for N2O production during the first month following fertilizer application. Our findings confirm the importance of weather conditions as well as nitrogen management on N2O fluxes.

scholarly journals Measuring Multifunctional Agricultural Landscapes

Land ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 260
Author(s):  
Bingjie Song ◽  
Guy Robinson ◽  
Douglas Bardsley
Keyword(s):  
Rural Communities ◽  
Production Systems ◽  
Landscape Scale ◽  
Agricultural Landscapes ◽  
Future Research ◽  
The European Union ◽  
Multifunctional Agriculture ◽  
Policy Makers ◽  
Rural Landscapes ◽  
Future Research Agenda

Multifunctional agriculture (MFA) has attracted increased attention from academics and policymakers in recent years. Academic researchers have utilised various approaches to assess and measure the multifunctionality of agriculture and rural landscapes. This paper outlines the nature of MFA and key supporting policies, before reviewing the applied research approaches, drawing primarily from the European Union and China where specific policies on MFA have been implemented to support rural development and promote sustainable rural communities. Four distinct types of valuation of modern MFA are recognised: economic, biophysical, socio-cultural, and holistic. Following a search of both the recent and older MFA literature, evaluations of the strengths and weaknesses of quantitative, qualitative, and mixed methods applications are provided using examples from a range of recent studies. The review illustrates the diversity of approaches to measure MFA. While noting that many studies operate at a landscape scale, the challenge remains that the lack of commonality in the research approaches applied means it is difficult to provide effective comparisons between studies or to compare findings. A future research agenda will need to emphasise the need for more consideration of the roles of MFA research to support decision-makers, especially policy makers, but also farmers who largely make decisions for individual farms but, if considered collectively, can transform production systems at a landscape scale.

scholarly journals Modelling Methane and Nitrous Oxide Emissions from Rice Paddy Wetlands in India Using Artificial Neural Networks (ANNs)

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2169 ◽  
Author(s):  
Tabassum Abbasi ◽  
Tasneem Abbasi ◽  
Chirchom Luithui ◽  
Shahid Abbas Abbasi
Keyword(s):  
Nitrous Oxide ◽  
Paddy Fields ◽  
Anthropogenic Sources ◽  
Nitrous Oxide Emissions ◽  
Soil Conditions ◽  
N2o Emissions ◽  
Ch4 Emissions ◽  
Artificial Neural ◽  
Ch4 And N2o

Paddy fields, which are shallow man-made wetlands, are estimated to be responsible for ~11% of the total methane emissions attributed to anthropogenic sources. The role of water use in driving these emissions, and the apportioning of the emissions to individual countries engaged in paddy cultivation, are aspects that have been mired in controversy and disagreement. This is largely due to the fact that methane (CH4) emissions not only change with the cultivar type but also regions, climate, soil type, soil conditions, manner of irrigation, type and quantity of fertilizer added—to name a few. The factors which can influence these aspects also encompass a wide range, and have origins in causes which can be physical, chemical, biological, and combinations of these. Exceedingly complex feedback mechanisms, exerting different magnitudes and types of influences on CH4 emissions under different conditions, are operative. Similar is the case of nitrous oxide (N2O); indeed, the present level of understanding of the factors which influence the quantum of its emission is still more patchy. This makes it difficult to even understand precisely the role of the myriad factors, less so model them. The challenge is made even more daunting by the fact that accurate and precise data on most of these aspects is lacking. This makes it nearly impossible to develop analytical models linking causes with effects vis a vis CH4 and N2O emissions from paddy fields. For situations like this the bioinspired artificial intelligence technique of artificial neural network (ANN), which can model a phenomenon on the basis of past data and without the explicit understanding of the mechanism phenomena, may prove useful. However, no such model for CH4 or N2O has been developed so far. Hence the present work was undertaken. It describes ANN-based models developed by us to predict CH4 and N2O emissions using soil characteristics, fertilizer inputs, and rice cultivar yield as inputs. Upon testing the predictive ability of the models with sets of data not used in model development, it was seen that there was excellent agreement between model forecasts and experimental findings, leading to correlations coefficients of 0.991 and 0.96, and root mean square error (RMSE) of 11.17 and 261.3, respectively, for CH4 and N2O emissions. Thus, the models can be used to estimate CH4 and N2O emissions from all those continuously flooded paddy wetlands for which data on total organic carbon, soil electrical conductivity, applied nitrogen, phosphorous and potassium, NPK, and grain yield is available.

scholarly journals A new look at an old concept: using <sup>15</sup>N<sub>2</sub>O isotopomers to understand the relationship between soil moisture and N<sub>2</sub>O production pathways

SOIL ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 265-274 ◽  
Author(s):  
Katelyn A. Congreves ◽  
Trang Phan ◽  
Richard E. Farrell
Keyword(s):  
Soil Moisture ◽  
Mitigation Strategies ◽  
Soil Conditions ◽  
Site Preference ◽  
N2o Emissions ◽  
Spectroscopic Techniques ◽  
N2o Production ◽  
Nitrification And Denitrification ◽  
The Relationship ◽  
Source Partitioning

Abstract. Understanding the production pathways of potent greenhouse gases, such as nitrous oxide (N2O), is essential for accurate flux prediction and for developing effective adaptation and mitigation strategies in response to climate change. Yet there remain surprising gaps in our understanding and precise quantification of the underlying production pathways – such as the relationship between soil moisture and N2O production pathways. A powerful, but arguably underutilized, approach for quantifying the relative contribution of nitrification and denitrification to N2O production involves determining 15N2O isotopomers and 15N site preference (SP) via spectroscopic techniques. Using one such technique, we conducted a short-term incubation where N2O production and 15N2O isotopomers were measured 24 h after soil moisture treatments of 40 % to 105 % water-filled pore space (WFPS) were established for each of three soils that differed in nutrient levels, organic matter, and texture. Relatively low N2O fluxes and high SP values indicted nitrification during dry soil conditions, whereas at higher soil moisture, peak N2O emissions coincided with a sharp decline in SP, indicating denitrification. This pattern supports the classic N2O production curves from nitrification and denitrification as inferred by earlier research; however, our isotopomer data enabled the quantification of source partitioning for either pathway. At soil moisture levels < 53 % WFPS, the fraction of N2O attributed to nitrification (FN) predominated but thereafter decreased rapidly with increasing soil moisture (x), according to FN=3.19-0.041x, until a WFPS of 78 % was reached. Simultaneously, from WFPS of 53 % to 78 %, the fraction of N2O that was attributed to denitrification (FD) was modelled as FD=-2.19+0.041x; at moisture levels of > 78 %, denitrification completely dominated. Clearly, the soil moisture level during transition is a key regulator of N2O production pathways. The presented equations may be helpful for other researchers in estimating N2O source partitioning when soil moisture falls within the transition from nitrification to denitrification.

MESOSCOPIC FIELD THEORY OF IONIC SYSTEMS

2005 ◽  
Vol 19 (21) ◽  
pp. 3309-3343 ◽  
Author(s):  
A. CIACH ◽  
G. STELL
Keyword(s):  
Field Theory ◽  
Short Range ◽  
Tricritical Point ◽  
Universality Class ◽  
Point Location ◽  
Generic Models ◽  
Quantitative Level ◽  
Ionic Systems ◽  
Simulation Results ◽  
Good Agreement

A mesoscopic field theory for the primitive model of ionic systems with additional, short-range interactions is presented. Generic models in continuum space and with positions of the ions restricted to lattice sites of various lattices are described in detail. We describe briefly the field-theoretic methods and review the foundations of the mesoscopic description. The types of phase diagrams predicted by our theory for different versions of the model are presented and discussed. They all agree with recent simulations. On the quantitative level our theory yields an RPM tricritical-point location on the sc lattice which is in good agreement with the simulation results. Arguments indicating that the critical point in the RPM belongs to the Ising universality class are given.

scholarly journals Modelling soil bulk density at the landscape scale and its contributions to C stock uncertainty

2013 ◽  
Vol 10 (7) ◽  
pp. 4691-4704 ◽  
Author(s):  
K. P. Taalab ◽  
R. Corstanje ◽  
R. Creamer ◽  
M. J. Whelan
Keyword(s):  
Bulk Density ◽  
Soil Bulk Density ◽  
Landscape Scale ◽  
Sampling Point ◽  
Mean Values ◽  
Landscape Models ◽  
Spatially Distributed ◽  
C Stock ◽  
Stock Estimation ◽  
Landscape Units

Abstract. Soil bulk density (Db) is a major contributor to uncertainties in landscape-scale carbon and nutrient stock estimation. However, it is time consuming to measure and is, therefore, frequently predicted using surrogate variables, such as soil texture. Using this approach is of limited value for estimating landscape-scale inventories, as its accuracy beyond the sampling point at which texture is measured becomes highly uncertain. In this paper, we explore the ability of soil landscape models to predict soil Db using a suite of landscape attributes and derivatives for both topsoil and subsoil. The models were constructed using random forests and artificial neural networks. Using these statistical methods, we have produced a spatially distributed prediction of Db on a 100 m × 100 m grid, which was shown to significantly improve topsoil carbon stock estimation. In comparison to using mean values from point measurements, stratified by soil class, we found that the gridded method predicted Db more accurately, especially for higher and lower values within the range. Within our study area of the Midlands, UK, we found that the gridded prediction of Db produced a stock inventory of over 1 million tonnes of carbon greater than the stratified mean method. Furthermore, the 95% confidence interval associated with total C stock prediction was almost halved by using the gridded method. The gridded approach was particularly useful in improving organic carbon (OC) stock estimation for fine-scale landscape units at which many landscape–atmosphere interaction models operate.

Targeted technologies for nitrous oxide abatement from animal agriculture

2008 ◽  
Vol 48 (2) ◽  
pp. 14 ◽  
Author(s):  
C. A. M. de Klein ◽  
R. J. Eckard
Keyword(s):  
Nitrous Oxide ◽  
Animal Feed ◽  
Ghg Emissions ◽  
Housing System ◽  
Soil Conditions ◽  
N2o Emissions ◽  
Nitrification Inhibitors ◽  
Emission Rates ◽  
Animal Agriculture ◽  
Management Interventions

Nitrous oxide (N2O) emissions account for ~10% of global greenhouse gas (GHG) emissions, with most of these emissions (~90%) deriving from agricultural practices. Animal agriculture potentially contributes up to 50% of total agricultural N2O emissions. In intensive animal agriculture, high N2O emission rates generally coincide with anaerobic soil conditions and high soil NO3–, primarily from animal urine patches. This paper provides an overview of animal, feed-based and soil or management abatement technologies for ruminant animal agriculture targeted at reducing the size of the soil NO3– pool or improving soil aeration. Direct measurements of N2O emissions from potential animal and feed-based intervention technologies are scarce. However, studies have shown that they have the potential to reduce urinary N excretion by 3–60% and thus reduce associated N2O emissions. Research on the effect of soil and water management interventions is generally further advanced and N2O reduction potentials of up to 90% have been measured in some instances. Of the currently available technologies, nitrification inhibitors, managing animal diets and fertiliser management show the best potential for reducing emissions in the short-term. However, strategies should always be evaluated in a whole-system context, to ensure that reductions in one part of the system do not stimulate higher emissions elsewhere. Current technologies reviewed here could deliver up to 50% reduction from an animal housing system, but only up to 15% from a grazing-based system. However, given that enteric methane emissions form the majority of emissions from grazing systems, a 15% abatement of N2O is likely to translate to a 2–4% decrease in total GHG emissions at a farm scale. Clearly, further research is needed to develop technologies for improving N cycling and reducing N2O emissions from grazing-based animal production systems.

scholarly journals Nitrous oxide emissions at the landscape scale: spatial and temporal variability

2011 ◽  
Vol 8 (6) ◽  
pp. 11941-11978 ◽  
Author(s):  
K. Schelde ◽  
P. Cellier ◽  
T. Bertolini ◽  
T. Dalgaard ◽  
T. Weidinger ◽  
...  
Keyword(s):  
Land Use ◽  
Nitrous Oxide ◽  
Agricultural Land ◽  
Mineral Fertilizer ◽  
Landscape Scale ◽  
Nitrous Oxide Emissions ◽  
Spatial And Temporal Variability ◽  
N2o Emissions ◽  
N2o Production ◽  
N2o Fluxes

Abstract. Nitrous oxide (N2O) emissions from agricultural land are variable at the landscape scale due to variability in land use, management, soil type, and topography. A field experiment was carried out in a typical mixed farming landscape in Denmark, to investigate the main drivers of variations in N2O emissions, measured using static chambers. Measurements were done over a period of 20 months, and sampling was intensified during two weeks in spring 2009 when chambers were installed at ten locations or fields to cover different crops and topography and slurry was applied to three of the fields. N2O emissions during the spring 2009 period were relatively low, with maximum values below 20 ng N m−2 s−1. This applied to all land use types including winter grain crops, grassland, meadow, and wetland. Slurry application to wheat fields resulted in short-lived two-fold increases in emissions. The moderate N2O fluxes and their moderate response to slurry application were attributed to dry soil moisture conditions due to the absence of rain during the four previous weeks. Measured cumulated annual emissions from two arable fields that were both fertilized with mineral fertilizer and manure were large (17 kg N2O-N ha−1 yr−1 and 5.5 kg N2O-N ha−1 yr−1, respectively) during the previous year when soil water conditions were favourable for N2O production during the first month following fertilizer application, confirming the importance of the climatic regime on N2O fluxes.

scholarly journals Pengaruh Pengairan Terhadap Hasil Emisi Gas Nitro-Oksida (N2O) pada Padi Sawah

2020 ◽  
Vol 16 (1) ◽  
pp. 46-52
Author(s):  
Indriati Meilina Sari
Keyword(s):  
Rice Field ◽  
Block Design ◽  
Gas Analysis ◽  
Soil Conditions ◽  
N2o Emissions ◽  
Energy Agency ◽  
Central Java ◽  
A Value ◽  
Randomized Block Design ◽  
Total Observation

The aim of the study is the effect of nitrous oxide (N2O) emissions on rice field. The study was conducted at the Experimental Garden of the Faculty of Agriculture, Sriwijaya University, Indralaya Campus. The N2O gas analysis was conducted at the Laboratory of the Indonesian Agricultural Environment Research Institute, Ministry of Agriculture, Central Java. From April to July 2016. This study used a Randomized Block Design (RBD) with irrigation classes, namely 5 cm (A1), intermittent / intermittent (A2) plots, and saturated land (A3), divided into three (3) groups with a total of 18 treatments, so that there were 54 plots in total. Observation of N2O gas was chosen at weeks 2, 4, 6 and 8 after planting using a closed lid for 24 hours. N2O gas flux is calculated based on an equation adopted from the International Atomic Energy Agency (IAEA). The results obtained are the emission of N2O gas produced by maps with air saturation condition of 5 cm from the surface of the land (A3) in the amount of 1564,554 mg N2O / ha / day and the lowest is produced by inundated plots (A1) with a value of 648,996 mg N2O / ha / day. Soil conditions that are inundated during plant growth produce anaerobic soil conditions capable of lifting N2O flux compared to air-saturated conditions.

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