Giorgio Ragaglini*, Ricardo Villani, Federico Triana, Iride Volpi, Nicoletta Nassi o Di Nasso, Enrico Bonari, Simona Bosco
Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa
*Giorgio.ragaglini@santannapisa.it
Abstract
In this study, conducted in Tuscany region (Central Italy), site and regional DNDC models were used for simulating the potential effect on N2O emissions in durum wheat cultivation caused by the shift from conventional to low nitrogen
fertilization rates (from 170 to 110 kg N ha-1 year-1). In particular, the site model was used for defining the parameter sets for
running the model and for crop parameter calibrations, while the regional module allowed to estimate emissions at wider scale, for the portions of the study area under durum wheat cultivation, using a regional climate and soil database.
Since the magnitude of N2O flux is strongly influenced by the amount and the distribution of rainfall, three climatic scenarios
were built based on the level of annual rain considering: a) a dry year, b) a wet year, and c) a year with an average precipitation level. Climate scenarios were selected out of a 25-year continuous time-series database of climatic parameters with daily values available for 98 weather stations of the regional agrometeorological network of Tuscany Region.
In the year with the average precipitation level, the regional average of N2O emission reduction was -0.62 kg N ha yr-1 when
shifting from conventional to low nitrogen fertilization. This represents -41% N2O emission saving, for a total reduction of -
70,960 kg N year-1. These values vary from -26% to -45% N
2O emission saving as the climatic scenarios vary from dry to wet
years.
The results highlighted that spatially explicit modelling of the effects on N2O emissions of a single mitigation strategy, may
provide helpful insights for decision making at district and regional scale to mitigate emissions from agriculture.
Keywords: nitrogen; nitrous oxide; GHG soil fluxes; GIS; geostatistics.
Parole chiave: Azoto; protossido d’azoto; flussi di gas serra dal suolo; GIS; geostatistica. Introduction
Agricultural soils emit approximately 10.3–12.8 Tg N2O-N year-1 globally, a harmful greenhouse gas (GHG) and ozone
depleting gas (Butterbach-Bahl et al., 2013). According to estimates, N₂O emissions from agriculture represent about 70% of total emissions of this gas in Italy: some of this comes from manure (20%), but 80% comes from agricultural soils (ISPRA, 2016).
Agricultural practices have been previously reported to influence N2O emissions from soil, thus the challenge is to identify
management practices for N2O mitigation (Snyder et al., 2014).
The evaluation of best management practices is necessary in order to contribute to a widespread adoption of mitigation strategies and to provide to policy makers information at territorial scale concerning the effects of possible and reliable alternatives.
In particular, an improved efficiency in the use of N by crops was often reported to be an effective strategy to mitigate N2O
emissions, since increased N fertilizer rates may increase N2O emissions (Shcherbak et al., 2014) without impacting the yield.
However, the mitigation potential of agricultural management practices is difficult to assess due to the huge background variability in time and space of the N2O flux and its close dependence to meteorological conditions (e.g. air temperature,
rainfall amount and distribution) and soil conditions (e.g. soil temperature, soil water content, oxygen availability) (Skiba and Smith, 2000). Combining direct measurements of N2O emissions from soil with process-based spatially explicit models
can allow the analysis of alternative scenarios of crop cultivation and provide helpful insights for decision making at district level. In such manner, a regional simulation can provide further support in the decision of the management practice to be prioritized for a specific crop.
Among existing GHGs simulation models, the DNDC (Denitrification–Decomposition) (Li et al, 1992) process-based model, has been developed for simulating N2O fluxes from soil under variable conditions. This model can predict C and N
biogeochemistry in agroecosystems at site and regional scales. Four major ecological drivers, namely climate, soil physical properties, vegetation, and anthropogenic activities, drive the entire model (Li et al, 1992).
DNDC has been independently tested worldwide in a wide range of researches carried out during the past decades on several different ecosystems and climate (Smith et al., 1997; Cai et al., 2003; Beheydt et al., 2007; Hastings et al., 2010).
This paper reports the results of a study conducted within the LIFE+ “Improved flux Prototypes for N2O emission reduction
from Agriculture” (IPNOA) project in Tuscany region, central Italy. Direct measurements of soil N2O emissions from durum
wheat under two different N fertilizer rates were used to calibrate the model with field data, and then to up-scale results at regional level. The study was focused on durum wheat, being the most spread crop in the study area, covering 21.4% of the total arable land (ISTAT, 2010).
The aim was to contribute to a possible improvement of the regional GHGs inventory in Tuscany to a Tier 3 approach on durum wheat, modelling a business as usual scenario and a mitigation scenario over tree different climate conditions, concerning two N fertilizer rates.
Materials and Methods
Site-specific model calibration
The calibration of DNDC at field scale was required to define the parameters set for running the model and calibrate specific crop parameters. In this study the DNDC model was firstly calibrated at field scale with data on measured N2O emissions
and grain yield, provided in Bosco et al. (2015), and referred to the 2013-2014 growing season of durum wheat cultivated in the tuscan coastal plains, specifically in San Piero a Grado, Pisa (43° 40’ N, 10° 19’ E and 1 m a.s.l.). In this experiment, measurements were carried out on durum wheat under two tillage intensities and three fertilization rates. N2O fluxes were
monitored twice a month, with samplings intensified immediately after nitrogen fertilization events, when measurements were carried out twice a week for two/three consecutive weeks, giving a total of 29 sampling days per year. The monitoring of N2O emissions was carried out with the closed dynamic chamber (flow-through non-steady state) method, using a portable
instrument developed within the LIFE+IPNOA project and described in Laville et al (2015). The standard set of non-site specific parameters in DNDC were left unchanged while site specific parameterization included specifying the field capacity and wilting point, soil texture, bulk density and total soil C content.
Model running at territorial scale
When the DNDC is used for regional estimates, the model needs spatially and temporally differentiated input data stored in geographical information system type database. Therefore, a georeferenced database containing climate (agrometeorological network of Tuscany Region), land use (Corine Land-Cover 2012), soil texture and soil organic matter content data (1:250.000 soil database of Tuscany Region – LaMMA Consortium) was developed by integrating ArcGIS 10.2 and PostgreSQL software.
Outputs from the model simulations are daily variation of soil temperature, moisture and concentrations of total soil organic carbon, nitrate, nitrite, ammonium, ammonia as well as daily fluxes of trace gases (N2O, CH4 and CO2). We focused on
simulations of N2O emissions from durum wheat cultivation under two different N fertilization levels, considering spatial
variability of climate and soil conditions in Tuscany.
Simulations were performed with a high spatial resolution (100 x100 m) by using regional land use, climate and soil databases on a set of 6 scenarios given by the combination of three annual precipitation patterns per two N fertilization levels: 170 kg N ha-1 (SC1) 110 kg N ha-1 (SC2). Climate data from a 25-year continuous time-series database (years 1990-2014) containing daily values of climatic parameters were analysed in order to identify representative annual precipitation patterns of: i) a DRY year (2007), ii) a WET year (2014), and iii) a year with an average (AVG) precipitation level (1999). The DNDC model was run for each weather station considering the six scenarios and 23 soil classes derived from the regional soil geodatabase considering clay fraction and soil organic matter content. Overall, 138 simulations were obtained and then elaborated through geostatistical interpolation using the ordinary kriging model. Kriging contour maps were merged according to their spatial match to specific areas covered by the corresponding soil type. In this way for each scenario, maps of grain yield and N2O
annual emissions were obtained taking into account both climate spatial variability and the spatial patterns of the soil properties (Fig. 1).
Finally, obtained maps allowed to estimate the N2O emission saving potentially derived by shifting from SC1 to SC2 under
Fig. 1: Scheme of the process based on DNDC model for the spatial simulation of N2O soil emission.
Fig. 1: Schema del processo basato sul modello DNDC per la simulazione a scala territoriale delle emissioni di N2O dal
suolo.
Results and Discussion
Regional simulations allowed to take into account the complex relationships between soil spatial variability, distribution of rain and annual precipitation volume in arable land suited to durum wheat cultivation in Tuscany. The different climate patterns determined an important effect on N2O emissions and thus on potential saving, deriving from the reduction of N
fertilization. On regional basis, the shifting from SC1 to SC2 showed the highest N2O emissions saving in the WET year (-
111,980 kg N year-1,-45%), while in the DRY year, it showed the lowest mitigation potential (-27,500 kg N year-1,-26%). In
the AVG year (Fig. 2) the estimated N2O emission saving at regional level equaled to -41% (-70,962 kg N year-1)
Fig. 2:Maps of annual N2O emissions saving by reducing N-fertilization rate, from SC1 (170 kg N ha-1) to SC2 (110 kg
N ha-1), in durum wheat in the average rainfall scenario (AVG).
Fig. 3:Mappe della diminuzione annuale delle emissioni di N2O attraverso la riduzione della concimazione azotata, da