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 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 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.

Efficacy and Rate of Release of EPTC and Butylate from Starch Encapsulated Formulations Under Greenhouse Conditions

Weed Science ◽  
1978 ◽  
Vol 26 (6) ◽  
pp. 679-686 ◽  
Author(s):  
M. M. Schreiber ◽  
B. S. Shasha ◽  
M. A. Ross ◽  
P. L. Orwick ◽  
D. W. Edgecomb
Keyword(s):  
Controlled Release ◽  
Slow Release ◽  
Residual Activity ◽  
Biological Evaluation ◽  
Soil Conditions ◽  
Short Term ◽  
Rapid Release ◽  
Initial Release ◽  
Wet Chemical ◽  
Selection Of

Four starch-encapsulated formulations of EPTC(S-ethyl dipropylthiocarbamate) and of butylate(S-ethyl diisobutylthiocarbamate) were prepared and evaluated by comparison with their respective emulsifiable concentrate formulations for their slow-release capabilities and efficacies. Chemical and biological evaluation indicated that difference in controlled-release could be achieved by the selection of the starch xanthate and oxidant used in the formualtion process. EPTC and butylate released slower when formulated as starch-encapsulated granules than when formulated as emulsifiable concentrates under soil conditions that favored rapid release. The initial release was adequate for weed control and slow enough for desired residual activity. Repeated seeding and harvesting the treated soils and bioassays of treated soils generally produced release rate anticipated from short term dry and wet chemical tests.

Knowledge transfer from simulation to reality via Long Short-Term Memory networks:  Estimating groundwater table depth anomalies over Europe

2021 ◽  
Author(s):  
Yueling Ma ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Short Term Memory ◽  
Scientific Data ◽  
Groundwater Table ◽  
Short Term ◽  
Term Memory ◽  
Continental Scale ◽  
Groundwater Table Depth ◽  
Long Short Term Memory ◽  
Simulation Results

&lt;p&gt;Near real-time groundwater table depth measurements are scarce over Europe, leading to challenges in monitoring groundwater resources at the continental scale. In this study, we leveraged knowledge learned from simulation results by Long Short-Term Memory (LSTM) networks to estimate monthly groundwater table depth anomaly (&lt;em&gt;wtd&lt;sub&gt;a&lt;/sub&gt;&lt;/em&gt;) data over Europe. The LSTM networks were trained, validated, and tested at individual pixels on anomaly data derived from daily integrated hydrologic simulation results over Europe from 1996 to 2016, with a spatial resolution of 0.11&amp;#176; (Furusho-Percot et al., 2019), to predict monthly &lt;em&gt;wtd&lt;sub&gt;a&lt;/sub&gt;&lt;/em&gt; based on monthly precipitation anomalies (&lt;em&gt;pr&lt;sub&gt;a&lt;/sub&gt;&lt;/em&gt;) and soil moisture anomalies (&lt;em&gt;&amp;#952;&lt;sub&gt;a&lt;/sub&gt;&lt;/em&gt;). Without additional training, we directly fed the networks with averaged monthly &lt;em&gt;pr&lt;sub&gt;a&lt;/sub&gt;&lt;/em&gt; and &lt;em&gt;&amp;#952;&lt;sub&gt;a&lt;/sub&gt;&lt;/em&gt; data from 1996 to 2016 obtained from commonly available observational datasets and reanalysis products, and compared the network outputs with available borehole &lt;em&gt;in situ&lt;/em&gt; measured &lt;em&gt;wtd&lt;sub&gt;a&lt;/sub&gt;&lt;/em&gt;. The LSTM network estimates show good agreement with the &lt;em&gt;in situ&lt;/em&gt; observations, resulting in Pearson correlation coefficients of regional averaged &lt;em&gt;wtd&lt;sub&gt;a&lt;/sub&gt;&lt;/em&gt; data in seven PRUDENCE regions ranging from 42% to 76%, which are ~ 10% higher than the original simulation results except for the Iberian Peninsula. Our study demonstrates the potential of LSTM networks to transfer knowledge from simulation to reality for the estimation of &lt;em&gt;wtd&lt;sub&gt;a&lt;/sub&gt;&lt;/em&gt; over Europe. The proposed method can be used to provide spatiotemporally continuous information at large spatial scales in case of sparse ground-based observations, which is common for groundwater table depth measurements. Moreover, the results highlight the advantage of combining physically-based models with machine learning techniques in data processing.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Reference:&lt;/p&gt;&lt;p&gt;Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J. and Kollet, S. (2019). Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation. Scientific Data, 6(1).&lt;/p&gt;

Full year of continuous N2O flux measurements in a drained agricultural peatland in northern Finland

2021 ◽  
Author(s):  
Stephanie Gerin ◽  
Tuomas Laurila ◽  
Liisa Kulmala ◽  
Juha-Pekka Tuovinen ◽  
Henriikka Vekuri ◽  
...  
Keyword(s):  
Seasonal Pattern ◽  
N2o Emission ◽  
Soil Freezing ◽  
Spatial And Temporal Variability ◽  
N2o Emissions ◽  
Short Term ◽  
Meadow Fescue ◽  
Boreal Peatlands ◽  
Annual Budget ◽  
Data Points

&lt;p&gt;Pristine boreal peatlands are often considered neutral or even small sinks for nitrous oxide (N2O). However, drained peatlands are a significant source of N2O. In these managed sites, oxygen becomes more available, increasing denitrification and therefore N2O release into the atmosphere. N2O emissions do not typically follow a strong seasonal pattern like carbon dioxide but instead, have high spatial and temporal variability. Short-term N2O peak emissions can be observed after various meteorological or soil management events throughout the year, for example after soil freezing or thawing, or fertilization. However, it is not well known how exactly those events trigger the N2O emission peaks. Therefore, N2O annual budget based on punctual chamber measurement can introduce large uncertainties. That is why it is important to measure N2O emissions with a continuous method to better understand the controlling factors and to estimate the annual budgets more accurately.&lt;/p&gt;&lt;p&gt;For the first time in the boreal region of Europe, N2O emissions were continuously observed during a full year in a drained agricultural peatland with the eddy covariance (EC) technique. The study site is a managed peatland in northern Finland, in Ruukki (Latitude: 64.684010; Longitude: 25.106473), with a peat depth between 10 and 90 cm. It is currently managed as a grass field, composed of a mixture of timothy and meadow fescue. We will show a first overview of the N2O fluxes measured since November 2019 with the EC technique. We will present how various meteorological and management events can explain some short-term variations. Then, we will compare the N2O annual budget estimated from the EC measurements to the IPCC emission factor and to different estimates achieved using several sets of non-continuous data points, representing manual chamber measurements with varying frequency.&lt;/p&gt;

scholarly journals Diurnal fluxes of CO2 and N2O from cattle-impacted soil and implications for emission estimates

2010 ◽  
Vol 56 (No. 10) ◽  
pp. 451-457 ◽  
Author(s):  
M. Šimek ◽  
P. Brůček ◽  
J. Hynšt
Keyword(s):  
Spatial Heterogeneity ◽  
Time Course ◽  
Diurnal Changes ◽  
Short Term ◽  
Gas Emissions ◽  
Gas Fluxes ◽  
Spatially Distributed ◽  
The Relationship ◽  
Overwintering Area

Short-term diurnal changes in emissions of CO<sub>2</sub> and N<sub>2</sub>O were determined in a cattle overwintering area during three specific periods of the year. Production of both N<sub>2</sub>O and CO<sub>2</sub>, as determined with gas chambers buried in soil and spatially distributed changed rapidly, and the general course of fluxes of the two gases was different. CO<sub>2 </sub>emissions were basically controlled by temperature, and most gas chambers showed the same trends in CO<sub>2</sub> flux, indicating low spatial heterogeneity. In contrast, N<sub>2</sub>O emissions were much more spatially heterogeneous and each chamber had its own time course of emission; therefore, the relationship between flux and temperature was more complicated for N<sub>2</sub>O than CO<sub>2</sub>. For estimating gas emissions over long periods, we strongly recommend the use of frequent emission measurements during periods of high gas fluxes. &nbsp;

scholarly journals Effects of air pollution on children from a socioecological perspective

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Jong In Kim ◽  
Gukbin Kim ◽  
Yeonja Choi
Keyword(s):  
Air Pollution ◽  
Life Expectancy ◽  
Natural Resource ◽  
Solid Fuel ◽  
Resource Depletion ◽  
Short Term ◽  
Environmental Threats ◽  
Country Level ◽  
Per Capita

Abstract Background Country-level inequality in life expectancy (ILE) and deaths of children under age five due to air pollution (DCAP) can be influenced by country-level income per capita, solid fuel, electrification, and natural resource depletion. The ILE and DCAP in the short-term are useful indicators that can help in developing ways to reduce environmental threats. This study confirms evidence for ILE and DCAP as the effects of environmental threats by country-level income, energy, and natural resource levels from a socioecological approach. Methods This study based on life expectancy and children data on 164 countries acquired from the United Nations Development Programme. We obtained the country-level socioecological data from the United Nations and the World Bank database. We assessed the associations between ILE, DCAP, and the country-level indicators applying correlations coefficient and the regression models. Results These study findings showed considerable correlations between ILE and country-level socioecological indicators: gross national income per capita (GNI), non-solid fuel (NSF), electrification rate (ER), and natural resource depletion (NRD). The DCAP in short-term predictors were low NSF and low ER (R2 = 0.552), and ILE predictors were low GNI, NSF, and ER and higher NRD (R2 = 0.816). Thus, the countries with higher incomes and electrification rates and more sustainable natural resources had lower expected DCAP in the short-term and ILE in the long-term. Conclusions Based on our results, we confirmed that country-level income, energy, and natural resource indicators had important effects on ILE in long-term and DCAP in short-term. We recommend that countries consider targeting high standards of living and national incomes, access to non-solid fuel and electricity as energy sources, and sustainable natural resources to reduce ILE and DCAP in short-term.

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.

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