L. Brilli*1, K. Fuchs2, L. Merbold3, C. Dibari4, G. Argenti4, R. Ferrise4, M.Moriondo1, S. Costafreda-Aumedes4, M. Bindi4.
1 IBIMET-CNR, Via Madonna del Piano 10, 50019 Sesto Fiorentino (Fi), Italy
2 ETHZ, Department of Environmental Systems Science, 8092 Zürich, Switzerland
3 Mazingira Centre, International Livestock Research Institute, 00100 Nairobi, Kenya 4 University of Florence, DiSPAA, Piazzale delle Cascine 18, 50144 Firenze, Italy.
* Lorenzo Brilli, l.brilli@ibimet.cnr.it
Abstract
Grassland systems are one of the main source of greenhouse gases (GHG), particularly nitrous oxide (N2O) and methane
(CH4). This makes grassland systems great contributors to global warming. Management practices, soil types and climatic
conditions are the main drivers influencing the magnitude of GHG emissions, therefore assessing their interaction is essential in order to identify practices that lead to GHG emission reductions. Biogeochemical process-based models are a powerful tools to overcome known constraints of field experiments, i.e. high costs, limited range of practices, etc.. On this basis, the process based model DAYCENT, widely applied worldwide on grassland sites, has been applied for estimating the emission of the major N trace gases (i.e. N2O, NO flux, etc.) and CH4 considering different management options.
Keywords
Grassland modelling, DAYCENT, Mitigation, Nfluxes
Parole chiave
Modellistica dei pascoli, DAYCENT, Mitigazione, Flussi di N
Introduction
Grasslands are known to be one of the main source of greenhouse gases (GHG), particularly nitrous oxide (N2O) and
methane (CH4). These fluxes are closely linked with management practices, soil types and climatic conditions (Soussana et
al., 2004). Understanding the role played by these systems as contributors to global warming has been widely addressed through several experiments (e.g. Allard et al., 2007; Soussana et al., 2007). The scouting of all possible interactions between management practices, soil types and climatic conditions may indeed allow to open new perspective for limiting grasslands contribution to global warming and, at the same time, maintaining a sustainable level of production. This scouting, however, is sometimes limited by constraints, particularly in field experiments and include high costs, defined but static experimental designs and covering only few management practices. These constraints can be overcome using process-based models. Process-based models have been applied widely on several ecosystems worldwide, can reproduce ecosystems soil-plant-atmosphere dynamics, thus providing indication for understanding the role played by grassland systems to global warming (Chen et al., 2008; Seijan et al., 2011). In this work, the DAYCENT model has been applied in a Swiss grassland over a period of three years (2003-2005) in order to assess the effect of different fertilizer types (i.e. ammonium nitrate, urea and manure) and amount (50 kg N ha-1) on the harvested biomass and the relative Nitrogen (N)
content and emission of the major N trace gases (i.e. N2O, NO flux, etc.) and CH4.
Materials and Methods
Study area: The test site is a permanent grassland located in Chamau (Switzerland, 47°12'36.8" N and 8°24'37.6" E) at 393
m asl, and is part of a former ETH Research Station. The typical management of the grassland consists of few days of grazing with sheep per year, and regular (up to 6 times a year) application organic fertilizer, decadal ploughing and more frequent oversowing besides the regular harvests (up to 6 times a year).
Experimental design: Five (5) fertilizer application and five (5) harvest dates to simulate grazing with residues removal per
year- were hypothesized based on typical grassland management. The fertilizer scenarios included: i) AMN, using 50 kg N ha-1 per fertilizer application as ammonium nitrate; ii) URE, using 50 kg N ha-1 as urea; iii) MAN, using 50 kg N ha-1 as manure (C:N=30). Fertilization and harvest dates changed across the years (Tab. 1)
Model description and application: DAYCENT, the daily time step version of the biogeochemical model CENTURY
(Parton et al., 1994, 1998), was designed to simulate soil C dynamics, nutrient flows (N, P, S), and trace gas fluxes (CO2,
different agronomic practices (tillage, mowing, fertilization) into account driving C-dynamics, the effects of elevated CO2
and other consequences of global change on net primary production, transpiration rate, and C:N ratio in biomass. Based on these features, DAYCENT can be considered a tool highly suitable at simulating the GHG mitigation potential of different management activities in grasslands (De Gryze et al., 2010).
Results and Discussion
The DAYCENT model was a priori calibrated over an Swiss grassland within the project “Robust models for assessing the effectiveness of technologies and managements to reduce N2O emissions from grazed pastures” (M4P, 2014-2017), under
the auspices of the Global Research Alliance for Agricultural Greenhouse Gases – Integrative Research Group. Then, the model was applied to evaluate the effect of different type (i.e. ammonium nitrate, urea and manure) and amount (50 kg N ha-1) of fertilizer on the harvested biomass and the relative Nitrogen (N) content (Fig. 1).
Fig.1 – Histograms of total three-years average harvested biomass (kg DM ha-1) and three years harvested biomass N
content (g N m-2) over the period 2003-2005 for the ammonium nitrate, urea and manure scenarios.
Fig.2 - Istogrammi della biomassa media annua (kg DM ha-1) e del contenuto di N (g N m-2) per il periodo 2003-2005 per
gli scenari di fertilizzazione rispettivamente con ammonio nitrato, urea e letame.
In figure 1a no substantial differences in the three-years average harvested biomass have been observed when ammonium nitrate (6272.8 kg DM ha-1) and urea (6320 kg DM ha-1) were applied. By contrast, using manure a decrease of about 17%
has been observed. This more efficient vegetation response using inorganic fertilization may be probably due to the climate conditions of the area. More specifically lower temperature may have decreased the manure decomposition thus providing lower N availability for vegetation. A similar pattern has been observed also for the N content in the harvested biomass (Fig. 1b), where the highest values were found using ammonium nitrate (12.17 g N m-2) and urea (12.25 g N m-2), whilst
the lowest using manure (10.45 g N m-2, i.e. -14.4% on average).
Fig.2 – Histograms of cumulated the major N trace gases (i.e. N2O, NO flux, etc.) and CH4.over the period 2003-2005 for
the ammonium nitrate, urea and manure scenarios.
Fig.2 - Istogrammi del cumulato totale relativo ai gas serra investigati (N2O, NO flux e CH4) nel periodo 2003-2005 per gli
Concerning N2O emissions (Fig. 2a) the highest values were found using urea (0.39 g N m-2) whilst the lowest using
manure (0.19 g N m-2). A similar pattern was observed also for NO flux (Fig.2b), where the highest emissions were
observed using urea (3.8 g N m-2) whilst the lowest using manure (1.7 g N m-2). These results significantly diverge with literature, since manure is well known to increase soil N2O emissions by stimulating nitrification and denitrification
processes. These unexpected results may be due to the low efficiency of DAYCENT in reproducing manure either the effect of low temperature over the alpine grassland. The joint effect of these two modelling limitations may have decreased the simulated nitrification and denitrification processes, thus strongly reduced the N emissions from manure. The ability of a process-based model at correctly reproduce a specific type of fertilizer should be considered fundamental for assessing N trace gas emissions. The impacts of several type of fertilizer on GHG emissions are linked to the type of fertilizer used as well as to a wide number of soil properties including moisture content, texture, pH, source of organic amendments and the C and N contents of amendments. Therefore, limitations at reproducing specific type of fertilizer and/or the joint effect with specific climate conditions (I.e. low temperature) may decrease the suitability of a process based model for investigating on GHG dynamics. Concerning CH4 emissions, no huge differences using all fertilizer types (Fig.2c) were found. The level of
emissions are in line with those reported in the most recent literature (Louro et al. 2016, Jones et al., 2017) that analysed GHG fluxes responses to inorganic and organic fertilization in grazed areas.
Conclusions
Our results show that DAYCENT is a suitable tool to model pasture growth as well as to analyse N content in harvested biomass. Due to its ability at reproducing several management options such as grazing, cutting, fertilization, tillage and irrigation, the model can be used to assess GHG emissions under different scenarios and to improve nitrogen use efficiency. However, limitations due to bias in nitrogen partitioning or nitrification/denitrification rate over specific conditions may reduce the suitability of the model at reproducing specific grassland scenarios (i.e. limitations at representing specific type of fertilizer or geographical areas, etc). Nevertheless, field experiments providing initial soil data and high-frequency GHG flux data should be incentivized since this may allow to test C-N processes of models under different conditions, and subsequently lead to better model performances and/or implementation.
Acknowledgments
This work was developed by the project “Robust models for assessing the effectiveness of technologies and managements to reduce N2O emissions from grazed pastures” (M4P, 2014-2017), under the auspices of the Global Research Alliance for
Agricultural Greenhouse Gases – Integrative Research Group (http://globalresearchalliance.org/research/integrative). Kathrin Fuchs and Lutz Merbold were supported by the Swiss National Science Foundation under the 40FA40_154245 / 1 grant agreement.
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