A model for simulating the timelines of field operations at a European scale for use in complex dynamic models

Hutchings, Nicholas J.; Reinds, G.J.; Leip, Adrian; Wattenbach, Martin; Bieńkowski, Jerzy; Dalgaard, Tommy; Dragosits, Ulrike; Drouet, Jean‐Louis; Durand, Patrick; Maury, Olivier; Vries, Wim de

doi:10.5194/bg-9-4487-2012

Cited by 28 publications

(37 citation statements)

References 12 publications

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“…Also, global data sets on crop rotations (Kollas et al, 2015), the timing of field operations (Hutchings et al, 2012), and crop residue management, as well as livestock management systems would be an asset, as the interaction of different cropping systems and natural vegetation is now further increased via the nitrogen cycle.…”

Section: Discussionmentioning

confidence: 99%

Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

et al. 2018

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Abstract. The well-established dynamical global vegetation, hydrology, and crop growth model LPJmL is extended with a terrestrial nitrogen cycle to account for nutrient limitations. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included in the model. All new model features are described in full detail and the results of a global simulation of the historic past ) are presented for evaluation of the model performance. We find that the implementation of nitrogen limitation significantly improves the simulation of global patterns of crop productivity. Regional differences in crop productivity, which had to be calibrated via a scaling of the maximum leaf area index, can now largely be reproduced by the model, except for regions where fertilizer inputs and climate conditions are not the yield-limiting factors. Furthermore, it can be shown that land use has a strong influence on nitrogen losses, increasing leaching by 93 %.

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Section: Discussionmentioning

confidence: 99%

Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

et al. 2018

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“…Fixed and oversimplified temporal profiles (monthly, daily, or hourly resolved) are often used (Van Pul et al, 2009). In this section, we outline how we explicitly described the temporal allocation of ammonia emissions from manure and fertilizer application, grazing, animal housing and manure storage based on the concepts of Skjøth et al (2004), Gyldenkaerne et al (2005) and Hutchings et al (2012).…”

Section: Temporal Allocatormentioning

confidence: 99%

“…Emissions from some activities are short term and highly variable, such as manure and fertilizer application, while some other activities contribute long term and less variable emissions, such as animal housing and manure storage. Many factors influence the variability of agricultural ammonia emissions (Battye et al, 2003;Dennis et al, 2010;Hutchings et al, 2012;Pinder et al, 2004Pinder et al, , 2006, including: https://doi.org/10.5194/acp-2019-979 Preprint. Discussion started: 2 March 2020 c Author(s) 2020.…”

Section: Introductionmentioning

confidence: 99%

Modeling Atmospheric Ammonia using Agricultural Emissions with Improved Spatial Variability and Temporal Dynamics

Ge¹,

Schaap²,

Kranenburg³

et al. 2020

Preprint

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Abstract. Ammonia emissions to the atmosphere have increased substantially in Europe since 1960, largely due to the intensification of agriculture as illustrated by enhanced livestock and increasing use of fertilizers. These associated emissions of reactive nitrogen, particulate matter and acid deposition have contributed to negative societal impacts on human health and terrestrial ecosystems. Due to the limited availability of measurements, emission inventories are used to assess large-scale ammonia emissions from agriculture, creating gridded annual emission maps as well as emission time profiles, both globally and regionally. The modeled emissions are in turn used in chemistry transport models to obtain ammonia concentrations and depositions. However, current emission inventories usually have relatively low spatial resolutions and coarse categorizations that do not distinguish between fertilization on various crops, grazing, animal housing, and manure storage in its spatial allocation. Furthermore, in assessing the seasonal variation of ammonia emissions, they do not take into account local climatology and agricultural management, which limits the capability to reproduce observed spatial and seasonal variations in the ammonia concentrations. This paper describes a novel ammonia emission model that quantifies agricultural emissions with improved spatial details and temporal dynamics over the year of 2010, in Germany and Benelux. The spatial allocation was achieved by embedding the agricultural emission model INTEGRATOR into MACC-III, thus accounting for differences in manure and fertilizer application on croplands and grassland, grazing, animal houses and manure storage systems. The more detailed temporal distribution comes from the integration of the TIMELINES model, which provided predictions of the timing of key agricultural operations including the day of fertilization across Europe. The emission estimates and time profiles were imported into LOTOS-EUROS to obtain surface concentrations and total columns for validation. The comparison of surface concentration time series between modeled output and in-situ measurements illustrated that the updated model has been improved significantly with respect to the temporal variation of ammonia emission, and its performance was more stable and robust. The comparison between ammonia total columns from remote sensing and simulations showed that there is an overestimation in Southern Germany and underestimation in Northern Germany, which suggested that updating ammonia emission fractions and accounting for manure transport is the direction for further improvement.

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“…Senthilkumar et al (2015) highlighted the link between sowing, harvesting dates and water scarcity. Hutchings et al (2012) demonstrated the importance of the timing of field operations for complex dynamic carbon and nitrogen models. However, the timing of operations is often derived from rough regional estimates, or from farmers' enquiries further extrapolated to cover the whole catchment.…”

Section: Crop Sowing Datementioning

confidence: 99%

On the Use of Hydrological Models and Satellite Data to Study the Water Budget of River Basins Affected by Human Activities: Examples from the Garonne Basin of France

et al. 2016

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A model for simulating the timelines of field operations at a European scale for use in complex dynamic models

Cited by 28 publications

References 12 publications

Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

Modeling Atmospheric Ammonia using Agricultural Emissions with Improved Spatial Variability and Temporal Dynamics

On the Use of Hydrological Models and Satellite Data to Study the Water Budget of River Basins Affected by Human Activities: Examples from the Garonne Basin of France

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