# NITROGEN MINERALIZATION FROM CLOVER LEAVES: EFFECT OF SOIL TYPE AND LOW TEMPERATURE

Marina A. Bleken¹, Sissel Hansen³, Randi B. Frøseth¹,²

¹NMBU, Ås, Norway, ²NIBIO, Tingvoll, Norway, ³NORSØK, Tingvoll, Norway

## Objectives

The period following the incorporation of N-rich leys into soil is crucial both for the N supply to the following crop and for the risk of N leaching. Soil-crop models used for exploring the possibility of improving the nitrogen recovery, generally assume that there is a stoichiometric relationship between carbon and nitrogen mineralization. Our aim was to study how temperature and soil type affect the net N mineralization relative to C mineralization of plant litter incorporated in soil. This knowledge is instrumental for improving the modelling of nitrogen dynamics after ploughing. Our hypotheses were:

1. Rapid N mineralization from N-rich plant material occurs even at 0 °C.

2. The ratio of net mineralized N to mineralized C from N-rich plant material is larger at lower than at higher temperature.

3. The ratio of net mineralized N to mineralized C from N-rich plant material is not affected by soil type

## Method

A silty clay loam (27% clay, 3% sand) and a sandy loam (6% clay, 51% sand) from two fields with equivalent weather and cultivation history, were incubated at constant 0, 4, 8.5 and 15 °C for 80 days, alone or with incorporated dried red clover leaves (4.8% N. 4 g leaves dry matter kg⁻¹ dry soil). The leaves were labelled with ¹³C (2.29 atom percent). The soils had been sieved through a 2 mm mesh while moist and pre-incubated in the dark for 4 ½ months at about 15 °C under aerobic and moist conditions. The soils were further moistened to 75% of the pore volume at field bulk density and kept for three days at the final temperature before incorporation of the clover leaves.

The net N mineralization was studied in soil samples equivalent to 50 g dry soil placed in 200 ml jars with the lids left loose, to allow some aeration. At each sampling date, replicates were sampled destructively as the whole sample was extracted with KCl, and analysed for NH₄-N and NO₃-N content. The C mineralization was measured in gas tight 1 l chambers containing soil equivalent to 400 g dry soil. A NaOH solution in a plastic tube placed in the middle of the chamber was used to trap the CO₂. The atmosphere of the chamber was monitored and oxygen was added when needed. Sampling was performed after 24 hours, and on days 3, 8, 15, 30, 52 and 80. Within each temperature, three replicates were used for CO₂ analyses and four replicates for mineral N analyses.

The C mineralization rate *k* was estimated, assuming first order decay, as $k = - \ln([C]/[C_0])/t$ for the period when the slope of the decay curve was close to a straight line (R² ≥ 0.98), where $C_0$ is the initial C amount in the substrate, and *C* is the remaining substrate at sampling time *t*.

For N a similar analytical approach was not possible.

## Results

Carbon and N mineralization occurred even at 0 °C. The rate of C mineralization increased with temperature, as expected. For the soil organic matter and for the initial decomposition of leaves the effect of temperature was well described by Arrhenius-like equations. Soil type affected the rate of soil organic C mineralization, which was twice as fast in the sandy loam as in the clay loam. Contrary to our expectation, soil type affected also the C mineralization of the incorporated leaves, which was faster in the sandy loam.
During the 80 days incubation period the total N mineralization from soil organic matter alone was negligible. We can rule any important loss by denitrification (methods and data not shown). Of the N added with clover leaves, only 13-22% was mineralized, and about half of this was mineralized already during the first few (3 to 8) days. Particularly the mineralization during the first three days was most rapid and it was not affected by temperature. Subsequently, this short but rapid mineralization period was followed by a phase of slow net N mineralization in the sandy loam, and net N immobilization in the silty clay. During this phase, the effect of temperature was weak and irregular in the sandy loam, while immobilization increased with temperature in the silty clay. Only from the sampling on day 52, the net N mineralization from clover leaves increased over all the temperature range of the experiment.

The ratio of inorganic N to mineralized C from clover leaves varied during the experiment: It was remarkably high in the first sampling, then decreased and reached a minimum on day 30. During these 30 days, there was a marked and well-delineated effect of temperature on the N/C ratio. Thus, the N mineralization relative to C mineralization was stronger at 4 °C, and especially at 0 °C, than at higher temperatures. Thereafter, at the last two sampling dates (day 52 and 80) the N/C ratio converged and remained approximately constant (about 0.053), unaffected by temperature and soil type. Thus, only during the last 30 days of incubation there was a clear correlation between net N and C mineralization from the leaves, while in the first 30 days of incubation the ratio of net mineralized N to mineralized C from clover leaves was large at lower temperature, as hypothesized. It was not clear from the data whether soil type modified the N/C ratio in the early period.

These results can be explained by a stronger inhibition of the microbial growth compared to gross mineralization of the plant residue as temperature decreases and approaches 0 °C, leaving a larger share of the gross mineralized N available in the soil solution.

## Conclusions

The experiment confirmed that N mineralization of N-rich plant material occurs even at 0 °C, and that the ratio of net mineralized N to mineralized C is larger at lower than at higher temperature.

The results of this study cannot be properly predicted by decomposition models that do not account for the little correspondence between net N and C mineralization the first month after incorporation of N-rich plant residue. The modelling could probably be improved by modifying the response of the microbial growth to temperature. For prediction in a relatively cold environment in transition to warmer temperature, as e.g. after spring incorporation of a ley before sowing a new crop, an erroneous modulation of the temperature effect relative to a parameterization at 15 or even 20 °C is likely to result in an overestimation of N mineralization, particularly on clayey soils. For prediction of ley mineralization after late autumn ploughing, it is likely to underestimate the N mineralization during the first part of the winter. As nitrification was delayed at the lower temperatures, and ammonium is less prone to leaching than nitrate is, it is not sure whether this model inadequacy would result in an underestimation of the risk of leaching shortly after ploughing in late autumn.

More knowledge of how low temperature affects soil biology, and especially microbial growth, is needed for improving prediction of the net N mineralization of newly incorporated plant material.

## Acknowledgement

Support from the Norwegian Research Council (NRC), project 184970 and 225330/E40, is acknowledged.
EFFICIENCY OF CONTROLLED DRAINAGE AND LAND DRAINAGE SYSTEM IN
IMPROVING WATER SAVING AND REDUCING NON-POINT SOURCE POLLUTION IN A
FIVE-YEAR TRIAL

Massimo Tolomio, Nicola Dal Ferro, Maurizio Borin

University of Padova, Department of Agronomy, Food, Natural resources and Environment (DAFNAE), Pa-dova, north-eastern Italy, Italy

**Objectives**

Water management strategies are key issues in modern agriculture, especially in the context of increasing water scarcity and environmental concerns related to nonpoint-source pollution. Water outflows management has been adopted worldwide in order to improve water and nitrogen use efficiency, although its effectiveness is strongly dependent on the local pedo-climatic conditions. As a result, in this work we evaluated the performances of controlled drainage systems for continuous regulation of the water table during a 5-year monitoring period. The aim was to control excessive drainage and improve water quality in a shallow groundwater system in north-eastern Italy.

Method

The field trial is located at the experimental farm “L. Toniolo” of the University of Padova (Legnaro, north-eastern Italy). The experimental field was set up in 1996 and organised into 12 plots (0.3-0.5 ha each) covering about a 6-ha area. The layout is a split-plot design with three replicates, in which the two factors are: water table management (free drainage, FD vs. controlled drainage, CD), and land drainage system (pipe sub-surface drainage, P vs. open ditches, O). However, to limit lateral seepage all controlled drainage plots are located in the northern part of the field and isolated from free drainage by a PVC film buried up to a depth of 1.5 m in the soil. Downstream of each plot, a 1.2 m-deep delivery ditch collects surface and sub-surface drainage water: the total outflow volume is then measured by a turbine flow meter. Each controlled drainage plot (CD) has a riser installed to avoid complete water outflow and control water table level. Each year, all plots were cultivated, tilled and fertilized the same way (with a mean of 208 kg N ha⁻¹ y⁻¹ and 76 kg P ha⁻¹ y⁻¹).

Inflow and outflow water volumes for each plot were measured in order to determine the yearly water bal-
ance, organized by hydrological years October-September. Water table depth was weekly monitored while
a meteorological station, located in the experimental site, allowed hourly rainfall data collection. Finally,
outflow water collected during the monitoring period was analyzed in terms of N-NO₃ and total N. P-PO₄
and total P concentrations were determined too, in order to outline a more comprehensive framework of the
environmental risk related to nutrient losses from different drainage systems (especially as regards eutrophi-
cation).

Results

Annual rainfall was higher during hydrological years 2009 and 2010 (1077 mm and 1037 mm respectively), while 2011 showed the lowest value (760 mm). Controlled drainage systems (CD) showed low drainage volumes, ranging from a minimum of 10 mm in 2011 to a maximum of 57 mm in 2010 (corresponding to 3% and 16% of the rain fallen during the monitoring period). By contrast, during the same years free drainage (FD) drained respectively 224 mm and 261 mm (59% and 73% of rainfall during the same timeframe), emphasizing a significant difference in terms of outflow water volumes. Moreover, subsurface pipe with free drainage (FD-P) had the highest outflow rates, since ranging from a minimum of 41% of total rainfall in 2009 to a maximum of 91% in 2010. This was due to the intensity and distribution of rainfall events, which started in early autumn and continued throughout winter and spring, when bare soil minimized evapotranspiration.

Overall, N-NO₃ concentrations showed the highest values in FD-P system, with a median of 20.7 mg l⁻¹ and the 95th percentile at 53.4 mg l⁻¹, that significantly differed from open ditches systems (O). Total N followed
a similar trend, with highest values for FD-P (median value of 24.0 mg l⁻¹ and 95th percentile at 60.0 mg l⁻¹). By contrast, the highest P-PO₄ concentrations were found in CD-O, with a median of 0.19 mg l⁻¹ and the 95th percentile at 0.73 mg l⁻¹, suggesting an opposite behaviour than for N dynamics. Nutrients content in discharge water varied during the year: N concentrations sharply increased in autumn (November), reaching a peak in December-January (with 57.6 mg l⁻¹ and 43.3 mg l⁻¹ as median values for total N), then slowly decreased until May. By contrast, P concentrations were somewhat anticipated during the hydrological period, especially in open ditches systems (with a peak of 0.85 mg l⁻¹ median value of total P, for FD-O in September), likely due to P movement with soil particles as a result of water runoff caused by intense rainfall events.

Totally, subsurface pipe with free drainage (FD-P) showed the highest losses per year both for nitrogen and phosphorus (a mean of 103.8 kg ha⁻¹ of total N and 0.31 kg ha⁻¹ of total phosphorus), suggesting a major impact on the environment. Indeed, FD-P was characterised by the highest outflow rates, and by the highest N concentrations too. Conversely controlled drainage, due to limited water discharge, gave the best performance in reducing nitrogen and phosphorus pollution.

## Conclusions

Controlling water outflow volumes in the field proved to be the best strategy to reduce nutrients pollution and improve agro-systems quality. During the 5-years monitoring period, although characterised by different hydrological conditions, controlled drainage systems (CD) showed the best performance by reducing both nutrient concentrations and total losses. Indeed, avoiding a complete outflow improved soil moisture conditions as well as nutrients availability for the crop. In contrast, critical conditions were observed by combining subsurface pipe with free drainage (FD-P). Indeed FD-P was the less efficient management system both in terms of water conservation and nutrients leaching (N and P), leading to increased risk of surface water pollution and eutrophication. Water saving through appropriate water management systems (i.e., controlled drainage) is suggested as a winning strategy to improve agro-systems efficiency in shallow groundwater conditions.
GRAZING BEHAVIOUR, URINE COMPOSITION AND SOIL PROPERTIES ARE KEY DRIVERS
OF NITROUS OXIDE EMISSIONS FROM LIVESTOCK URINE IN THE UPLANDS (UPLANDS-
N2O)

Laura Cardenas¹, Heiko Balzter³, Jon Holmberg², David Jones², Andrew King⁴, Lucy Lush⁴, Karina Marsden², Humberto Perotto⁵, Mick Whellan³, Rory Wilson⁴, David Chadwick²

¹Rothamsted Research, Okehampton, Devon, UK, ²Bangor University, Bangor, Wales, UK, ³University of Leicester, Leicester, Leicestershire, UK, ⁴University of Swansea, Wales, UK, ⁵Texas A&M University, Kingsville, USA

Objectives

Agriculture contributes ~7% of the UK's total Greenhouse gas (GHG) emissions[1]. Nitrous oxide emissions from UK agricultural soils have large uncertainty, particularly for grazed grassland. The current applied default methodology assumes emission factors (EFs) for grazing sheep of 1% of the excreted N deposited to land. Grazing is known to result in soil compaction, and heterogeneous distribution of nutrients due to excreta deposition especially in lowland intensive systems. Effects of grazing on upland extensive systems (no fertiliser addition, low stocking density) have been less studied. Additional factors such as the spatial distribution of palatable plant species may affect sheep movement and the quality of their excreta with potential impacts on soil nutrients and N₂O emissions. We aim to improve our understanding of the spatial and temporal interactions between grazing behaviour, forage selection, urine composition and edaphic conditions to improve the accuracy of N₂O emission estimates from extensive, grazed grassland systems.

Method

The field study will be carried out on a semi-improved enclosed hill land (no fertiliser or lime applications since 1985) at ca. 250 metres above sea level (year 1), and an unimproved area at higher elevation (“heft”, year 2) at ca. 850 metres above sea level at the Bangor University Experimental Station. We will track sheep movement and monitor different behaviours (including grazing and urination) in three 12-week campaigns at each site, via the use of animal-attached high resolution GPS and accelerometer sensors. We will also measure spatial and temporal variability in soil properties such as moisture content, soil mineral N, bulk density, pH, total N and C, and plant species distribution and palatability at each site. We will collect urine from sheep feeding on representative diets and subsequently apply this urine to areas where animals have been excluded for a few weeks before hand. We will measure N₂O emissions from these urine patches for a full year as a requirement for IPCC emission factor determination, using a combination of static[2] and automated flux measurement chambers to determine the spatial and temporal variation in N₂O emissions.

In addition, soil and urine will be collected from each of the two sites and used to explore and rank the fac-
tors controlling N₂O fluxes in a controlled laboratory environment where NO, N₂O and N₂ will be simultane-
ously quantified [3]. We will adopt the Taguchi methodology [4] to statistically represent the combinations of
different controlling factors (including urine N concentration, soil compaction, %WFPS, temperature). The
relationships between N₂O fluxes and the N₂O:N₂ mole fraction with these controlling factors will be used to
parameterise the integrative model described below.

The results from the field measurements will be used to develop a model of the spatial/temporal variability of individual animals and herd dynamics; combining model predictions with knowledge of diet composition (chemical and plant species) from different vegetation communities, urine-N composition, and the spatial & temporal patterns of soil physico-chemical properties and subsequent N₂O emissions.

Results

The project has only just started, so in the first few months we are developing and testing the field pens for
urine collection. Six animals will be housed on slatted flooring, with collection trays beneath for urine col-
lection. The volume of each urination event will be recorded and urine will be stored for metabolomic anal-
ysis and for later use in field experiments. The tracking and behaviour sensors are being optimised for fitting
to sheep and will be tested before the field experiment starts. We are also developing protocols for urine
collection, soil sampling for parameters such as mineral N and moisture; and the emissions of N₂O. The first
intensive campaign will start in the spring 2016, which will provide the first dataset for model testing. The
results from the first campaign will provide data to finalise the parameters to study in the laboratory experi-
mentation.

Conclusions

We believe that a more spatially-explicit approach to estimating N₂O emissions, which takes account of animal behaviour, forage selection (and its effect on urine deposition), topography, soil type and soil conditions, will provide improved insights into the spatial and temporal variability of N₂O emissions and new estimates of emissions from extensive grazing systems for inventory purposes. The improved process-level understanding will also lead to the development of better-informed mitigation strategies for such systems.

References

[1] Brown (2012). UK Greenhouse Gas Inventory, 1990 to 2010: Annual Report for submission under the Framework Convention on Climate Change. AEAT/ENV/R/3264.[2] Chadwick, D.R., L. Cardenas, Misselbrook, T.H., Smith, K.A., Rees, R.M., Watson, C.J., McGeough, K.L., Williams, J.R., Cloy, J.M., Thorman, R.E., Dhanoa, M.S., 2014. European Journal of Soil Science, 65(2): 295-307. [3] Loick, N., Dixon, L., Abalos, D., Vallejo, A., Matthews, G.P., McGeough, K.L., Well, R., Watson, C.J., Laughlin, R.J., Cardenas, L.M. Denitrification as a Source of Nitric Oxide Emissions from a UK Grassland Soil. SBB in press. [4] Garcia-Marco, S., Ravella, S.R., Chadwick, D., Vallejo, A., Gregory, A.S., Cardenas, L.M., 2014. Ranking factors affecting emissions of GHG from incubated agricultural soils. European Journal of Soil Science, 65: 573-583.
FERTILIZATION & ENVIRONMENT JOINT TECHNOLOGICAL NETWORK – CONCEPTS,
PROJECTS AND TOOLS USED AROUND FERTILIZATION AND MANAGEMENT OF
BIOGEOCHEMICAL CYCLES

Sylvie Recous¹, Mathilde Heurtaux², Pascal Dubrulle¹, Nathalie Damay³, François Laurent⁴, Cecile Le Gall⁵,
Jean-Marie Paillat⁶, Bernard Verbeque⁷

¹INRA, Reims, France, ²ACTA, Paris, France, ³LDAR, Laon, France, ⁴Arvalis-Institut du Vegetal, Boigneville, France, ⁵Terres Inovia, Thiverval-Grignon, France, ⁶CIRAD, Montpellier, France, ⁷Chambre d'Agriculture Loiret, Orleans, France

Objectives

Joint technological networks (RMT) are partnership-devices introduced by the French Ministry of Agricul-
ture in 2007 to break down barriers, encourage networking of actors in research, training and development
and foster innovation in the fields of agriculture and agro-food. The RMT Fertilization & Environment
(F&E) aims to collect, develop and build synergies between existing technical and scientific expertise in the
research, educational and agricultural development systems, in order to provide the stakeholders (farmers,
agricultural extension agents, trainers, resource and territory managers, government) references, methods
and tools for the sustainable management of biogeochemical cycles and soil fertility in the major cropping
systems present on the French territory (Metropolitan and Overseas). The network was created in 2007.

Method

The joint technological network RMT Fertilization & Environment brings together 33 partners from re-
search institutes, higher education and technical schools of agriculture and agricultural development bodies
from France, Belgium and Switzerland around a work program. The members of the RMT F&E carry out as
partners various research and development projects that focus on three thematic priorities: crop fertilization,
organic waste products recycling, and control of biogeochemical cycles. While combining plant production
and environmental protection, these priorities are part of the principles of agroecology at different spatial
and temporal scales, i.e. sparing use and equitable distribution of resources, reduction of inputs, recycling of
organic products, ecological intensification, developing and preserving the ecosystem services provided by
agriculture and soil.

To carry out its program of activities and achieve the expected results, the coordination team of the RMT F & E organizes the work in four areas, defined by the type of production they generate:

1. Prospective and scientific monitoring, European and international strategy

2. Coordination and sharing around the acquisition of scientific and technical references and appropria-
tion of new paradigms

3. Development and improvement of decision support tools for actors, particularly for fertilization

4. Transfer, dissemination, teaching and training; public policy support.

The activity of the RMT develops through joint meetings, workshops, R & D projects applied to compet-
itive calls, participation in scientific and technical events at national and European levels in which the net-
work members communicate their results.

Results

The results of RMT F & E are of various kinds, depending on the type of activity and the composition of the
partnership that produced them:
* State of the art, studies and prospective analyzes, formulating new research questions

* Knowledge, references and common databases, including results from the joint projects

* Conceptual frameworks, methods, flow charts

* Decision support and diagnostic tools (specifications, algorithms, prototypes and marketable tools, computer and agronomic manuals)

* Evaluation of agricultural and environmental policy tools

* Educational tools (including tutorials) for teaching and development, training

* Scientific and technical publications (articles, posters, book chapters, internet pages)

* Scientific seminars, technical conferences.

Among notable productions RMT F & E, we can cite:

- Tools software: Régifert®, diagnostic software and prescription for the elements P, K, Mg, Zn, Mn, B, organic C and acid-base status of the soil; Syst'N®, tool for estimating N losses (NO3, NH3, N2O) diagnostics for the management of N at the scale of crops rotation; AzoFert®, software for fertilizer N recommendation for annual crops, with two variants under development, one suitable for the fertilization of fruit trees and vines (N-Perennes), the other suitable for educational purposes to promote learning of the N dynamics and the N balance method (N'EDU).

- A collective book «Fertilization and environment: What avenues for decision support?» (February 2014, co-published Quae ACTA), results of a prospective reflection on the changing context of fertilization in the coming 5-10 years, and future needs for references, tools and methods for managing the biogeochemical cycles and rational fertilization.

- Scientific and technical support to national public policies particularly with the Nitrates Directive

- Numerous projects on animal manures and organic wastes recycling in agriculture to improve the knowledge on the characterization of organic matters composition, potential mineralization, to improve substitution of mineral fertilizers by organic fertilizers, identification of agricultural practices and techniques that reduce losses of N by nitrate leaching, ammonia volatilization and nitrous oxide emission.

## Conclusions

The RMT F & E promotes (i) the sharing of financial and human resources, knowledge, tools and references, avoiding dispersion and duplication, (ii) the development of scientific and technical consensus among its members and beyond, and (iii) the acquisition of a common vision of the major issues related to the management of biogeochemical cycles of main elements in agriculture.

For more information:

http://www.rmt-fertilisationetenvironnement.org

Contacts: mathilde.heurtaux@acta.asso.fr; sylvie.recous@reims.inra.fr
MECHANISTIC INSIGHTS INTO THE EFFECTS OF NITROGEN FERTILIZER APPLICATION ON N₂O-EMISSION PATHWAYS IN ACIDIC SOIL OF A TEA PLANTATION

Yi Cheng¹, Jing Wang¹, Jinbo Zhang¹, Christoph Muller¹, Shenqiang Wang¹

¹Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China, ²School of Geography Science, Nanjing Normal University, Nanjing, China, ³Department of Plant Ecology, Justus-Liebig University Giesen, Giessen, Germany

**Objectives**

Long-term nitrogen (N) fertilization has been shown to stimulate N₂O emissions from acidic soil in tea plantations. However, the potential mechanism behind this stimulation remains unclear. We aimed to investigate the effects of 6 years of fertilizer application on N₂O emission pathways and the N₂O emission ratio from heterotrophic and autotrophic nitrification in tea plantation.

**Method**

We performed a ¹⁵N-tracing experiment under 40% and 60% water-holding capacity (WHC) to investigate the effects of 6 years of fertilizer application on N₂O-emission pathways and emission ratios from heterotrophic and autotrophic nitrification in soil from tea plantations.

**Results**

6 years of fertilizer application stimulated both heterotrophic and autotrophic nitrification, particularly under conditions of higher soil moisture. Autotrophic nitrification was the predominant pathway for N₂O emission in tea soils, being responsible for 66.7–75.9% and 50.4–56.9% of N₂O emission in unfertilized and fertilized soils, respectively. Fertilizer application significantly increased the contribution of denitrification to N₂O emission (10.5–35.7%), independent of soil moisture conditions, which could be due to a fertilizer-induced reduction in soil pH. Fertilizer application and a subsequent reduction in pH resulted in a 3–4 and 8–9 fold increase in the ratio of N₂O emissions from heterotrophic nitrification and autotrophic nitrification, respectively.

**Conclusions**

The increase in N₂O emission following N fertilizer application was attributed to increased heterotrophic and autotrophic nitrification rates and an increased ratio of N₂O emission from heterotrophic and autotrophic nitrification. Our results suggest that pH was a critical factor regulating the ratio of N₂O emission from heterotrophic and autotrophic nitrification and thus controlling N₂O emission from the tea soils studied.
# NITRIFICATION INHIBITORS: NITROGEN USE EFFICIENCY AND RESIDUAL EFFECT

Maria Alonso-Ayuso, Jose L. Gabriel, Miguel Quemada

Universidad Politécnica de Madrid, Agriculture production Department, Madrid, Spain

## Objectives

The application of nitrification inhibitors (NI) is a strategy to increase the efficiency of nitrogen (N) in farming systems [1]. They could have a cumulative effect through N immobilization by microorganisms and ammonium fixation in the soil [2]. Therefore, NI could build up a pool of nutrients and increase the N supply capacity over time and reduce N losses from the cropping system.

During two seasons, a maize crop was established and fertilized with ammonium sulfate nitrate, with and without DMPP, with the objective of determining the effect of NI fertilizers on grain yield, N content and NUE, compared to conventional fertilizers. A non-fertilized sunflower crop was planted in the same plots in order to study the cumulative residual effect after the two previous years with different fertilizer applications. Laboratory determinations were performed in order to elucidate and locate the sources of residual N.

## Method

The study was located in Aranjuez (Spain). The soil had a silty clay loam texture and pH~8 and the climate in the area is semiarid Mediterranean. The experiment design was factorial with two factors – fertilizer type and rate - and two levels. The fertilizers were ammonium sulphate nitrate (ASN); and ASN + the nitrification inhibitor DMPP 3, 4-dimethylpyrazole phosphate (ENTEC). The rates were 170 kg N ha⁻¹, the recommended rate in the region based in previous research, and a lower rate, 130 kg N ha⁻¹. A non-fertilized control was included. Each treatment had three replications randomly distributed.

The maize was sown during 2013 and 2014 in April and harvested in October. Sunflower was grown in the same plots in 2015 from April to September. The grain yield, grain quality and N content of the maize were assessed at harvest. The last season, anticipating the bird damage in sunflower before harvest, a sampling was performed at the end of the full flowering stage when N uptake had mostly occurred, and the crop biomass and N content were determined. Each season, the crop nutritional state was assessed with SPAD readings at different stages. At crop sowing and harvest the soil inorganic N content (Nmin) was determined to 1 m depth.

Two component of the N use efficiency were calculated: the agronomic efficiency, that refers to the kg of crop yield increase obtained per kg of N applied and was calculated for 2013 and 2014 seasons. The second component was the recovery efficiency that refers to the kg of crop N uptake per kg of N applied, calculated in 2013, 2014 and for the whole experimental period (2013 to 2015).

In 2015, eleven months after the fertilizer application, an aerobic incubation was carried out to determine the N mineralization potential in samples from the 0-20 cm soil upper layer. The non-exchangeable ammonium (NH₄⁺) was extracted and determined by the potassium hypobromite-dry soil combustion method in samples from the 0-20, 20-40 and 40-60 cm soil layers. As well, the ¹⁵N was determined by ratio mass spectrophotometry.

## Results

Grain yield and N content were affected by treatments but differences were significant the second year. Treatments fertilized with the recommended rate or the reduced with ENTEC had higher yield (10.8 Mg ha⁻¹) than the reduced rate with the conventional fertilizer (7.6 Mg ha⁻¹) and the control (5 Mg ha⁻¹). The crop N content was higher for ENTEC treatments (196 kg N ha⁻¹) than for the control (69 kg N ha⁻¹). SPAD readings were affected by treatments and sampling time and supported the yield results.
Differences in NUE were observed also by the second year of fertilizer application. The ENTEC with the lower rate achieved the greatest agronomy efficiency (49 kg grain produced per kg N applied), and no differences were found between fertilizer treatments with the recommended rate (33 kg grain kg N applied⁻¹). The recovery efficiency was greater for the ENTEC treatments compared to the ASN-130. The recovery efficiencies for these NI fertilizers were higher than those reported in the literature (up to 70% of N recovery) [3], suggesting a residual effect. No differences between treatments in the upper 0-40 cm layer were found in the soil Nₘₜₗ in 2014 samplings, indicating that the residual effect was not coming from the soil inorganic N content. In 2015, at the full flowering stage in the non-fertilized sunflower, ENTEC-170 had the greatest N content (100 kg N ha⁻¹). Treatments ENTEC-130 and ASN-170 formed a subgroup below and no differences were found between them (76 kg N ha⁻¹). Again, crop results were supported by SPAD results: at the heading stage, ENTEC treatments had greater readings. At harvest, in September 2015, no differences were found in the total Nₘₜₗ content in the profile between treatments, indicating that the sunflower was able to scavenge the N. For the whole period (2013 to 2015) similar results than the observed in 2014 were reported: the N recovery was higher for ENTEC treatments compared to ASN-130, and values were still higher than those observed in the literature. One year after fertilizer application, the aerobic incubation showed that the N mineralization rate was higher for ENTEC-170 (71 kg N ha⁻¹) than for ASN-170 and the control (51 kg N ha⁻¹), suggesting that at that time the residual effect due to the NI was still present. The average NH₄⁺ (120 mg N kg⁻¹) was in the range reported in the literature for medium textured soils [4]. However, the content was low to detect differences between treatments. The q¹⁵N in samples from the 40-60 cm soil layer was higher in ENTEC and ASN-170 than in the other treatments. The NH₄⁺ found and the isotopic imprint suggested that fixation and defixation processes were occurring in the soil.

## Conclusions

The fertilizers with nitrification inhibitors increased the efficiency of fertilizer N in a three year crop rotation, with respect to conventional ASN. In the following year after application, the ENTEC fertilizer rate was reduced 23% from the recommended rate in the region (from 170 to 130 kg N ha⁻¹) without decreasing grain yield or quality of maize. In addition, a non-fertilized sunflower planted after the maize was able to scavenge more N in treatments previously treated with ENTEC than with traditional fertilizers. After NI fertilizer application, N was conserved in non-ready soil available forms during at least 1 year and subsequently released to the following crop, thereby mitigating N losses. The aerobic incubation and the non-exchangeable ammonium extraction showed that this cumulative residual effect was produced by biotic and abiotic processes. In soils prone to retain NH₄⁺, crop rotations using NI should be designed to profit from the residual effect of this fertilizer technology.

## Acknowledgements

Spanish Ministry (AGL2014-52310-R; IJCI-2014-20175), Comunidad de Madrid (S2013/ABI-2717) and EuroChem Agro. The funders had no role in the data collection and analysis or preparation of the manuscript.

## References

[1] Quemada, M. et al. 2013. Agr. Ecosyst. Environ. 174, 1-10

[2] Ma, Q. et al. 2015. Soil Biol. Biochem. 89, 72-81

[3] Gabriel, J.L., Quemada, M. 2011. Eur. J. Agron. 34, 133-143

[4] Nieder, R. et al. 2011. Biol. Fert. Soils 47, 1-14
# FEATURES IN VILLAGE-SCALE NITROGEN FLOW IN THE LOWER REACH OF YANGTZE RIVER, CHINA

Yanhua Wang¹,³, Zucong Cai¹,³, Jixiang Zhang², Hao Yang¹,³

¹School of Geography Science, Nanjing Normal University, Nanjing, China, ²College of Economics and Management, Nanjing University of Aeronautics & Astronautics, Nanjing, China, ³Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China

## Objectives

Nitrogen limits primary productivity in the agro-ecosystems and results in eutrophication and haze. To overcome this limitation and maintain environmental security, densely populated agricultural regions in developing nations now use synthetic nitrogen fertilizers to boost yields. However, nitrogen saturation of aquatic and terrestrial ecosystems were observed here and there. Increased gradually reactive nitrogen emitted to the atmosphere. To assess the fate of reactive nitrogen cascade in the village scale of the lower reach of Yangtze River, material flow analysis method and the principle of conservation of mass was used in the rural area of Changshu City, Jiangsu Province, China.

## Method

### 2.1. Description of study site

Changshu City (31°31′58.2″N、120°41′48.3″E) is located in the lower reach of Yangtze River, China. Most villages and farmlands are near to the rivers. In the past century, due to the excessive population growth and the increasing demands for cultivable lands, the water coverage had been greatly reduced. For the cropping system, the paddy rice-wheat was dominant. The cropping varieties incremented cropping intensity and were more dependent on chemical fertilizer. The number of farm animals had also increased. The excreta were sold for the farmers as manure in their cultivated lands.

### 2.2. Crop Production-Livestock Breeding System

The nitrogen flow in the Crop Production-Livestock Breeding System can be analyzed by the flow route as follows.

#### (1) Crop production subsystem

For the Crop production subsystem, nitrogen input items include nitrogen derived from chemical fertilizer, excreta, seed, straw returned to field, biological fixation, atmospheric deposition and irrigation water. While, total output items include nitrogen derived from crop yield, ammonia emission, emissions of nitrogen oxides, nitrous oxide and di-nitrogen from nitrification and denitrification processes, losses of runoff and leaching and soil accumulation.

#### 2) Livestock breeding subsystem

For the Livestock breeding subsystem, nitrogen input items include nitrogen derived from fodder, kitchen waste, animal by-products and import fodder. While, total output items include nitrogen derived from meat, egg, milk, bone, by-products and excreta.

### 2.3. Data sources

Basic data (e.g. population, fertilizer use, crop yields, cultivated areas and animal numbers) were derived from governmental statistical yearbooks and bulletins (Changshu Statistical Bureau, 2000-2013). Crops category included wheat, paddy and vegetables. Farm animals were aggregated into seven animal categories (pig, dairy cattle, sheep, laying hen, broilers, duck and goose). Data about nitrogen content in crops and animal products, nitrogen excretion values and the partitioning of animal products into edible food were ob-
tained from literatures (Chinese Statistical Bureau, 2000-2013). For nitrogen loss parameters (e.g. ammonia emission, emissions of nitrogen oxides, nitrous oxide and di-nitrogen from nitrification and denitrification processes and losses via leaching, runoff and discharge) were obtained from literatures (IPCC, 1997; Ti et al., 2011; Ma et al., 2012) and surveys.

# Results

## 3.1 Nitrogen flux

During year 2000~2012, the nitrogen flux of Crop production subsystem in the rural areas of Changshu City decreased from 18640 to 14230 kg km⁻². Urbanization development was a great influence. Since 21st century, urbanization rate increased from 36% to 52.8% resulting in the improvement of agricultural intensification and changes of nitrogen flow pattern. For the Livestock breeding subsystem, the nitrogen input flux was within the range of 4250~5090 kg km⁻². The amount of animal fodder was insufficient. 52.9%~67.8% of fodder were imported from outside. With the expansion of livestock farming and enhanced intensification level, animal products-derived nitrogen increased from 350 to 490 kg km⁻². Meanwhile, a large number of manure from animals discharged freely, resulting in environmental pollution.

## 3.2 Nitrogen use efficiency

A low nitrogen flow efficiency of the Crop Production-Livestock Breeding System was found from 2000 to 2012. Results showed that the utilization efficiency of nitrogen (32.5%~37.5%) was higher than that of nitrogen recycle (18.9%~21.2%) in the Crop production subsystem. For the Livestock breeding subsystem, the utilization efficiency of nitrogen (8.1%~11.4%) was lower than that of nitrogen recycle (17.9%~18.3%). In the rural areas of Changshu City, fertilizer nitrogen applied to each crop (wheat 21000, rice 32700, vegetables 38700 kg km⁻², respectively) was far beyond the nitrogen demand of crop growth, restricting the utilization efficient of fertilizer nitrogen. As chemical fertilizer accounted for more than 70% of nitrogen input for the local farmland, its low utilization efficient caused low utilization efficient of nitrogen recycle.

## 3.3 Effects of nitrogen application on environmental loads

The study area is in the water network region of Taihu watershed. The rivers and ponds are near to the rural household and farmlands. Our results showed that 54.5% of total nitrogen losses from the Crop Production-Livestock Breeding System entered into the water body. These may contribute to the higher nitrogen load of Taihu Lake. It was reported that 34% of non-point pollution of Taihu Lake derived from cultivated lands. During year 2000~2012, the Crop production subsystem and Livestock breeding subsystem contributed 42.6%±3.0% and 32.9%±2.9%, respectively to the environmental nitrogen load. Taking year 2012 as an example, chemical fertilizer application was 310.8 kg hm⁻², 1.5 times than the national level. The significant correlation (R²= 0.96, p < 0.01) between water nitrogen load and fertilizer application to the cultivated lands was obtained, suggesting that excessive nitrogen application restricted the nitrogen utilization efficient and enhanced water nitrogen load. With the increased population growth, urbanization and economic growth, large numbers of waste discharge-derived nitrogen from people and animals aggravated the environmental nitrogen load. For year 2012, excreta-derived nitrogen in the study area was 61.1 kg hm⁻², 3.2 times than the national level.

# Conclusions

To confront the severe eutrophication of aquatic ecosystem and reduce non-point source pollution radically in the lower leaches of Yangtze River, China, the nitrogen flux, efficiency and environmental load in the village-scale were studied. Results showed that both amount of nitrogen input in crop production and livestock breeding were high. However, the nitrogen use efficiency was low in both subsystems. Most of the nitrogen inputs were lost to the environment, which was 280 t to atmosphere, 565 t to water body and 192 t stored in soil. As for nitrogen load of atmosphere and water body, crop production was the key factor. It was concluded that the demand for reasonable nitrogen management measures, such as balancing fertilization, increasing
crop and animal production, and improving manure management was urgent.

## Acknowledgements

This work was supported by the “973” project of the Ministry of Science and Technology of China (Grant Nos. 2014CB953801).

## References

[1] IPCC, 1997. Intergovernmental Panel on Climate Change: Greenhouse Gas Inventory Reference Manual [M]. London: Cambridge University Press.

[2] Ti C.P., Xia Y.Q., Pan J.J., et al, 2011. Nitrogen budget and surface water nitrogen load in Changshu: a case study in the Taihu Lake region of China. Nutrient Cycling in Agroecosystems, 91(1): 55-66.

[3] Ma L., Velthof G.L., Wang F.H., et al, 2012. Nitrogen and phosphorus use efficiencies and losses in the food chain in China at regional scales in 1980 and 2005. Science of the Total Environment, 434: 51-61.
# INTEGRAL ASSESSMENT OF FARM RESULTS BY BENCHMARKING

Co Daatselaar, Mark Dolman, Marga Hoogeveen

LEI Wageningen UR, The Hague, The Netherlands

## Objectives

Agricultural policies, and so also minerals policies, need information about the performances in agriculture. In EU-member states participating farms provide this information to the Farm Accountancy Data Networks (FADN). In the Netherlands the FADN has been extended with the Dutch Minerals Policy Monitoring Programme (LMM) to collect even more detailed information on environmental issues such as nutrients and to link this information to quality of groundwater and surface water.

All in all about 1600 agricultural and horticultural holdings voluntarily participate in the FADN and/or LMM. To keep participation in the FADN/LMM attractive the farmers receive a report on their integrative performance (people, planet, profit) with both an internal and external comparison. For a good external comparison a sound benchmark is necessary [1]. Objective of this abstract is to create a sound benchmark for individual farms as well as for groups of farms explained by an example.

## Method

A group of dairy farms in the Netherlands aims at improvement of their internal nutrient cycle (INC). They claim a lower environmental impact per kg of milk production for which reason nine of these farms were already more intensively researched including participation in both FADN and LMM. Data on economic and environmental performance as well as on structure and management were derived from FADN and LMM for the years 2008 and 2009: two years were chosen to stabilize yearly fluctuations. Not only data of the INC-farms were derived but also of 228 other dairy farms, available for both years.

Statistical matching [2] was applied to create the benchmark for the nine INC-farms. For each INC-farm, farms of the other 228 dairy farms were selected with the nearest neighbour method which resembled the INC-farm as closely as possible. Variables, which were used to decide whether a farm resembles an INC-farm (called imputation variables), have been determined by expert judgement. Based on the imputation variables the distance between an INC-farm and another farm is calculated. The farm with the smallest distance to the INC-farm is regarded as the farm that resembles the INC-farm most closely and gets the highest weight. In this case, where a group of farms is benchmarked, 3 or 4 most similar farms (with a total weight of 1) for each farm in the group would already satisfy to avoid underestimation. However for an individual farm the minimum is set at 10 to mitigate the influence of an individual observation and to ensure the privacy of the farms that are chosen for the benchmark.

To present the results of the benchmark especially bar charts and radar plots are suitable. When applying a bar chart the values for the benchmark group are set at zero and the values for the farm (or group of farms) to be benchmarked are shown as deviation from zero. A favourable deviation can be presented in green and an unfavourable deviation in red. A radar plot offers the possibility to give more weight to some indicators compared to other indicators.

## Results

Experts considered soil type, groundwater level, milk production per farm, milk production per ha, nitrogen delivery capacity of the soil and distance in km as most appropriate imputation variables. After application of the statistical matching the averages of the INC-farms on the imputation variables were compared with the averages of the farms, chosen by the statistical matching, on the same variables. No significant differences were found and for all imputation variables the differences were smaller than 5% except for the groundwater level (8%): this indicates a very good matching.

Each INC-farm was connected with one most similar farm, a second best one and so on, up to 10. The aver-
age contribution of the most similar ones in the weight was 32% but even the numbers 10 contributed nearly 5% in the weight so their inclusion was useful.

A two sample T-test assuming unequal variance was conducted on the performance indicators to test for differences between the INC-farms and the farms in the benchmark. Concerning the economic performance the INC-farms performed significantly better on revenues for agri-environmental measures, fertilizer costs and crop protection costs but worse on costs for buildings and quota. All in all the farm income per 100 kg milk was significantly higher on the INC-farms. They also had a lower cost price compared to their benchmark but this difference was not significant. The environmental performance focused on nitrogen in kg per ha. The INC-farms had a lower surplus on the nitrogen surplus balance but the difference was not significant. Because of the method of manure application, used by the INC-farms, the emission of ammonia was significantly higher which caused a significantly lower nitrogen soil surplus. Besides the INC-farms used significantly less crop protection chemicals.

Several times the results of this study have been presented. The bar charts with green (favourable) and red (unfavourable) proved to be very clarifying. The radar plots were very useful for a first quick view to determine where to zoom in further. Partly due to this positive feedback the benchmarking as presented above has been introduced in the report for the participants in FADN and LMM to provide a better comparison compared to the former group comparison. In the former group comparison only the farm size was taken into account to detect comparable farms. Many participants in the FADN and LMM independently and continually mention that the benchmarking is the most useful part of the report they receive on their performance. So any improvement in the benchmarking is much appreciated. They highly value the described bar charts and radar plots because these figures deliver a fast and good integral overview concerning total farm performance.

## Conclusions

To create a sound benchmark for a useful external comparison statistical matching turns out to be a good method. Application of this method to compare the results of dairy farms focussing on their internal nutrient cycle with the results of comparable farms showed excellent similarity between both groups on structure indicators such as size, soil type and intensity. Subsequently differences in farm performance between the groups were not caused by structure indicators.

This is not only important for (groups of) farmers themselves but also for policy makers. They can better zoom in on (improvement of) management because farm structure doesn't influence the external comparison anymore. Visualization in bar charts and radar plots is very helpful for a quick integrated overview, both in comparison of groups and comparison of an individual farm with a comparable group of farms.

## References

[1] Dolman, M.A., Sonneveld, M.P.W, Mollenhorst, H., Boer, I.J.M. de. 2014. Journal of Cleaner Production 73, 245-252

[2] Vrolijk, H.C.J., Dol, W., Kuhlman, T. 2005. Integration of small area estimation and mapping techniques - Tool for Regional Studies. LEI, The Hague, Report 8.05.01
# CALCULATING NITROGEN FOOTPRINTS ALONG THE CHAIN FROM AGRICULTURE TILL RESIDUE WHEREABOUTS ON THE EXAMPLE OF FOOD PROVIDED AT A CONFERENCE

Claudia Garcia Ramirez¹, Adrian Leip², Ina Körner¹

¹Hamburg University of Technology, Hamburg, Germany, ²European Commission, Joint Research Centre (JRC), Ispra, Italy

## Objectives

The Nitrogen (N) chain with human food consumption as core starts in agriculture, since plants need to be fertilized and animals fed. A large part of the N gets lost until food reaches the consumer. Further losses occur with food wastages. The human N intake equalizes the releases from the body, whereas most is contained in excrements. Depending on the chosen disposal, treatment or reuse pathway, waste and excrement N may become an environmental problem, wasted or a valuable resource.

The N footprints along the food chain were calculated for the various stages: from agriculture to consumer (production, processing and transport of ingredients), and from the utilization by consumer (food preparation, cooking, and consumption) to the final whereabouts of food preparation residues, food left-overs, and excrements (generation, collection, transport, disposal, treatment, reuse). The evaluation was carried out for the food provided at the RAMIRAN2015 conference.

## Method

RAMIRAN2015, a 3-days conference, with 229 participants caused an N-footprint due to food provided in 5 coffee breaks (fruit salads, mueslis, croissants, cakes, nuts, drinks), 2 lunch breaks (8 options for main dishes [1], 2 options for soup [1], salad, dessert, soft drink), 1 conference dinner (2 starter and main course choices, dessert buffet, drinks). The N footprints from agriculture to consumer for the food and drinks provided, and from the consumer to final whereabouts for residues were calculated as follows:

1) Data collection regarding type, amount and composition of food and drinks from:

a. Conference organizer; providing companies and default recipes.

b. Cafeteria; a detailed list of ingredients in each lunch meal and each soup was provided.

c. Dinner restaurant; an approximate list of ingredients for starters and main courses was provided; default recipes for desserts.

2) Calculating the N footprints of food and drinks from agriculture to consumer [2]:

a. Linking ingredients with USDA nutrient database; quantification of N in ingredients.

b. Grouping ingredients to primary food groups of 'JRC tool' based on CAPRI factors; calculating ingredient footprints by using footprint factors.

c. Calculating footprints of food and drinks based on provided portions.

3) Data collection for food preparation residues, food left-overs and toilet residues (literature, cafeteria, own measurements).

4) Data collection and chain design for pathways from kitchen resp. toilet over treatment up to product application resp. N losses into environment (literature, treatment companies).

5) Calculating N-footprint for steps from consumer to final whereabouts based on collected data for:
a. Toilet waste from conference location and restaurant to central wastewater treatment facility (WWTP Köhlbrandthöft) in Hamburg for N-Elimination.

b. Food preparation residues and food left-overs from lunch breaks to Parumer biogas plant with digestate application in agriculture.

c. Food left-overs from coffee breaks partial to anaerobic digestion and composting in Bützberg with compost application in agriculture and to N-Elimination in WWTP Köhlbrandthöft.

d. Food preparation residues and food leftovers from gala dinner to Stellinger-Moor biogas plant with partial digestate application in agriculture (65%) and with N-Elimination in WWTP Köhlbrandthöft (35%).

## Results

The results are focused on reactive N (Nr) only. Releases of inactive N are not considered here.

### (1) From agriculture to consumer

**Lunches:** The main dishes had following properties: quantity: 230-810 g, energy: 401-1676 kcal, N intake: 0.64-7.52 g [1]. Additionally 1 soup per participant and day was assumed. The Nr losses ranged between 1.8-13.5 g for main dishes, 0.4 resp. 2.3 g for soups (average Nr: 6.3 g per main dish, 1.5 g per soup, in total 3500 g Nr for 2 lunches).

**Coffee breaks:** 3 fruit salads (41.0 kg, 58.5 g Nr), 3 mueslis (40.2 kg; 675 g Nr), ham and cheese croissants (together 37.1 kg; 539 + 520 g Nr), 2 cake types (13.4 kg, 354 g Nr), nuts (3 kg, 3.6 g Nr) and drinks (coffee, tea, juices, water, in total 329 kg, 987 g Nr - mainly from orange juice) were provided (in total 3100 g for 5 breaks).

**Gala Dinner:** Vegan (38 portions) and mixed menu (152 portions) properties were as follows: salad and "Grilled tofu on barley risotto with glazed vegetables" - 0.30 + 2.6 g Nr; salad with salmon and "Breast of corn-fed chicken on mashed potato-beetroot with glazed vegetables and creamed morels" - 0.66 + 11.6 g Nr. Desserts were estimated with 654 g Nr in total (3.4 g per participant) and white wine with 301 g Nr (1.6 g Nr for 0.5 L wine per person), in total 2900 g for the dinner.

### (2) From consumer to final whereabouts

**Excreta:** 4.9 kg N was diluted in blackwater (assumptions: N intake 5.7 kg by meals and drinks, 86 % of it segregated with excrement's. About 81 % of excrement N is removed via nitrification/ denitrification in the WWTP as N₂. The rest is mainly contained in the effluent and released into a river; minor fractions in sewage sludge go through anaerobic digestion, centrifugation, drying, and incineration. The non-nitrified/denitrified N was accounted for as Nr (900 g).

**Food residues:** 8.2 kg lunch preparation residues and food leftovers (cafeteria information); 18 kg coffee and some fruit residues in coffee breaks (own observations); 84 kg conference dinner residues (estimated from biobin pictures) were produced. Each residue took a different pathway (see methods). Following Nr releases were calculated: 30 g by lunch break residues, <10 g by coffee resp. by fruit residues from coffee breaks, 210 + 40 g by dinner residues (distinguished after digestate whereabouts: portions applied in agriculture + portions treated in WWTP; calculated with: 26 % food waste dry matter; 3.3 % N in DM; 1.3 coffee % DM, 2.5 % N in DM). Food waste residues in total did emit 300 g Nr.

## Conclusions

More than 10 kg Nr were emitted - from agriculture to consumer almost 40 g per participant (18 g in 2 lunch
breaks, 16.1 g in 5 coffee breaks, 15.5 g for gala dinner). In residue pathways, 2/3 of Nr was accounted to excrements and 1/3 to food residues. It makes 9.5 kg Nr up to the consumer and 1.2 kg Nr up to residue whereabouts. The core of the chain is protein intake (suggested for adult woman 7; man 9 gN/d). But eating has also cultural and social dimensions. Wishes of people have to be considered (questionnaire result - mixed food preferred by most). No specific ingredient should be banned, but all ingredients should be handled with care. At conferences food quantities, energy contents and footprints could be declared on menus with considerable effort to support food choice and raise awareness. If choosing a lunch dish with average quantity instead of high at RAMIRAN, Nr would have been 40 % lower per dish. Lists with Nr rich “hidden” (e.g. butter) and “luxury” ingredients (e.g. beef) could be supplemented. It could help to adjust cooking behaviors and meal selections. If at RAMIRAN coffee breaks fruits would have been served instead of mueslis, the N footprint of coffee breaks could have been reduced for about 20 %.

## References

[1] Leip, A., et al. 2015. In: Körner, I. (ed.) Proceedings of the 16th RAMIRAN Conference, Hamburg, Germany, 26-30. www.ramiran2015.de

[2] Leip, A. et al. 2014. J. Agric. Sci. 152. 20.
Laurène Casal¹, Patrick Durand¹, François Laurent², Philippe Leterme¹, Françoise Vertès¹, Jordy Salm-
on-Monviola¹, Nouraya Akkal¹, Cyril Benhamou³, Jean-Louis Drouet³, Sylvain Ferrant⁴, Sabine Sauvage⁵,
Anne Probst⁵

¹INRA UMR1069, Rennes, France, ²Arvalis institut du végétal, Boigneville, France, ³INRA UMR1402, Thiv-
erval-Grignon, France, ⁴Centre National d'Etudes Spatiales CESBIO, Toulouse, France, ⁵CNRS ECOLAB
université de Toulouse, toulouse, France

**Objectives**

In rural landscapes, the nitrogen cascade [1] -i.e., the way reactive nitrogen transforms and transfers into, out of and within the agro-ecosystem- depends on farm management and landscape structure. Most often, mitigation measures are conceived for the field or farm level and focus on reducing one type of N emission (i.e., NO3 leaching or NH3 emission). Designing mitigation strategies at the landscape level to improve overall N use and reduce all types of undesirable emissions is a major challenge [2]. As a part of the ESCAPADE project (ANR-12-AGRO-0003), agri-environmental scenarios are built in contrasted landscapes and their effects on N emissions to air and water are assessed using integrated models. The main objectives are (1) to evaluate the relative efficiency and complementarity of field-oriented and landscape-oriented measures to reduce N emissions (2) to discuss the risks of pollution transfer between N species and between water and air.

Method

Study sites are headwater catchments (around 4 km²) located in Brittany (Western France) and in Gascogne (South-West of France). They are contrasted in terms of agriculture type (respectively mix farming with high livestock density and cereal cropping), soil, climate (warmer and dryer in Gascogne) and landscape structures. Surveys were carried out to describe exhaustively agricultural practices. Measurements and monitoring of some major components of the nitrogen cascade such as discharge, NO3 concentration, NH3 and N2O emissions, are conducted on both sites. The approach is to simulate a set of scenarios aimed at mitigating the nitrogen cascade by: i) optimizing agricultural practices (fertilization adjustment, cover crops, organic manure management...) ii) landscape structure management according to two strategies: land sharing (i.e., diffuse arrangement of ecological structures (hedgerows, vegetated filter strips) in the landscape) and land sparing (large patches of natural vegetation or different production systems located in strategic locations). Two integrated and spatially distributed models are used complementarily. TNT2 is a spatially distributed agro-hydrological model focusing on nitrate transfer, hedgerows and riparian zone functioning [3, 4]. NitroScape is a model designed to quantify the contribution of both atmospheric and hydrological transfers to Nr fluxes, budgets and indirect Nr emissions [5].

Results

Four different scenarios are being simulated for each site. The scenarios will be designed with local stake-
holders and assessed both in terms of overall N emission reduction, agriculture N use efficiency, N retention
by landscape and allocation between NO3, N2O and NH3 emissions. The differences between the sites will
be analyzed to discuss the relative effect of anthropogenic vs environmental context. This will be interpreted
in terms of recommendations for designing context-specific mitigation strategies.

References

[1] Galloway, J. N. et al., 2003. BioScience 53, 341-356.

[2] Cellier, P. et al., 2011. In: Sutton, M. A. et al., (eds.) The European Nitrogen Assessment Sources, Effects and Policy Perspectives. Cambridge University Press, 229-248.

[3] Beaujouan, V. et al., 2002. Hydrological Processes 16, 493-507.

[4] Oehler, F. et al., 2009. Science of the Total Environment 407, 1726-1737.

[5] Drouet, J. L. et al., 2012. Biogeosciences 9, 1647-1660.
# UTILIZATION AND DISTRIBUTION OF ¹⁵N FROM FERTILIZERS TO COMPONENTS OF CEREAL - PEA MIXTURES

Agnieszka Rutkowska, Dorota Pikuła

Institute of Soil Science and Plant Cultivation-State Research Institute, Puławy, Poland

## Objectives

In the last ten years, in Poland, there has been a significant genetical progress in plant breeding, in particular, of field pea, resulted in higher yield of seeds, greater concentration of protein and favorable amino acid composition. Nevertheless, one of the fundamental problems of legumes growing is strong reaction on deficit of precipitation in the phases of critical demand for water. Intercropping of cereals and grain legumes is a practice intended to stabilize yields and make better use of plant growth resources. Fertilization of the mixtures in order to provide the cereal component with nitrogen is the essential, because higher doses of nitrogen under legumes are not economically justifiable as they depress atmospheric N fixation by legumes and prolong the vegetation period. Pot experiment was conducted to evaluate utilization of nitrogen ¹⁵N from fertilizers applied to field pea mixtures with wheat, barley and oats and its distribution to plant organs.

## Method

Two-factorial pot experiments were carried on in 2010 and 2011 at the greenhouse in Experimental Station in Puławy, belonging to the Institute of Soil Science and Plant Cultivation, Poland. The Mitscherlich's pots were filled with 6,0 kg sandy loam soil mixed with 1,0 kg sand, supplemented with optimal rates of macro – and micro – nutrients. The experiment was set up in a completely randomized block design with three replications. Nitrogen fertilizers in the form of ¹⁵NH₄¹⁵NO₃ (10 at% excess) was applied in the rates: 0,3, 0,6, 0,9, and 1,2 g N per pot, accordingly to cereal percentage in the mixtures: 33% (2 cereal plant + 4 pea plants), 57% (4 cereal plants + 3 pea plants), 75% (6 cereal plants + 2 pea plants), and 88% (8 cereal plants + 1 pea plant). Total amount of fertilizers was subdivided into 0.3 g N/pot doses of which the first was applied before sowing and subsequent rates after every 10 days. At maturity, aboveground biomass was harvested, separated into grain, rachis and glumes or pods, and straw (shoots and leaves). Roots were separated from soil. Plant organs were dried at 60°C and the dry matter was evaluated. Total nitrogen concentration in the plant samples, as well as ¹⁵N analysis were performed by the means of Elemental Analyzer – Isotope Ratio Mass Spectrometer (EA-IRMS) in Department of Applied Analytical and Physical Chemistry, Isotope Bioscience Laboratory, Gent University in Belgium. Both, seed yields of cereal-pea mixtures and protein yields were evaluated. Total nitrogen uptake by the mixtures and by the cereal- and pea-components were calculated. Distribution of nitrogen from fertilizers and its allocation to particular plant organs were determined. Nitrogen recovery fraction (¹⁵NRF) was calculated for each component of the mixtures regardless on seedling rate.

## Results

The mean yields of pea with wheat reached 60 g, with barley 43,2 g, and with oats 53,9 g per pot. Yields of wheat – pea and oats – pea mixtures raised together with the increase of the percentage of cereals in the pot, and with barley was affected by a larger share of pea plants. As a literature shows, various species of cereals differ in the degree of dominance in mixed crops with pea. The most competitive is oats and wheat, and the less competitive is barley. Thus in the stand with barley, the proportion of pea seeds in the mixture was about 30% greater than its seedling rate.

There was a slightly increase in protein yield as the pea proportion raised in the pot. Protein yield of pea mixture with wheat reached 8,7 g per pot, and with oats and barley -7,8 g on the average.

Total nitrogen uptake was calculated as the sum of N uptake by cereal and pea components of the mixtures. The mixtures took up from 1,52 to 1,61 g N/pot, and nitrogen rate did not affect significantly this parameter. The greatest quantity of N accumulated the mixture with wheat and the smallest one – with oats. On the contrary, ¹⁵N uptake was strongly related to the quantity of fertilizers applied to the pot. In the pot of 0,3 g
N nitrogen uptake reached 0,28 g on the average, which is to 92% of ¹⁵N applied in fertilizers, and in the pot 1,2 g N – 0,88 g which is to 73%.

Of the total pool of nitrogen, the percentage of ¹⁵N derived from fertilizers (%Ndff) was significantly higher in cereals as compared with pea. Regardless of the N dose and cereal component, pea stored the greatest quantity of ¹⁵N in straw (stalks with leaves – 29% and roots – 28%), and only 16% in seeds. On the contrary, cereals translocated nitrogen from fertilizers mainly to ears, to grain - 57%, and to glumes with rachis – 47% on the average. Roots accumulated relatively large amount of ¹⁵N - 46% of the total N. The percentage of ¹⁵N found in wheat biomass amounted to 55%, in barley - 61%, and in oats – 64% of the total N. Irrespective of mineral N availability, pea plants accumulated in grain and straw from 15% to 19% of ¹⁵N. ¹⁵N recovery fraction (¹⁵NRF) calculated for the whole cereal – pea mixtures was very high and reached 90%-96% for the lowest N rate to 73%-75% for the highest one. Pea component utilized 45% of nitrogen from the rate of 0,3 g N/pot on the average, and from the rate of 1,2 g N/pot about 9%.

## Conclusions

1. Yields of pea mixtures with wheat and oats was determined by the percentage of cereal in the mixture, and with barley – by pea percentage.

2. Regardless of N dose, pea accumulated N from fertilizers mainly in straw and roots, and cereals in ears

3. The pea component utilized more N from fertilizers from the lowest N rate and cereals at the highest rate.
TEMPORAL FATE OF ¹⁵N FROM LEAF-LABELED WHITE AND RED CLOVER IN MIXTURE WITH GRASS

Jim Rasmussen¹,², Gedrime Kusliene¹, Thomas Kätterer³

¹Department of Agroecology, Aarhus University, Tjele, Denmark, ²Department of Soil and Environment, Swedish University of Agricultural Sciences, Uppsala, Sweden, ³Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden

**Objectives**

Efficient utilization of nutrients in agricultural systems is essential in order to achieve a sustainable production of food. Increasing the N supply through biological nitrogen (N) fixation, e.g. from an increased proportion of legumes like white and red clover in forage production will decrease the demands for mineral N fertilization. In order to evaluate the impact of legume-derived N input to cropping systems in terms of efficiency we need, however, an improved understanding of how legume-fixed N cycles in the plant-soil system. Therefore, we need adequate field methods to determine the fate of legume N. The aim of the present study was to investigate the temporal fate of ¹⁵N-urea added to white and red clover via the leaf-feeding method and trace ¹⁵N from the clovers in soil and companion grass.

**Method**

Experiments were conducted in mixtures of ryegrass (*Lolium Perenne* L.) and white (*Trifolium repens* L.) or red clover (*Trifolium pretense* L.) on a sandy soil at Krusenberg estate, Uppsala, Sweden. The mixtures were established in May 2011 with five replicates in a randomized block design. The experiments were carried out from May to October 2012. Leaf-labeling of red and white clover with ¹⁵N-urea [1] was done twice, in spring and autumn, during the growing season in 2012, with subsequent samplings lasting for two months after the labeling events. The ¹⁵N-urea was applied to six randomly selected red clover or white clover plants within each block during four days, either from May 8th to 11th (spring), or from September 9th to 13th (autumn).

Sampling of soil and labeled plants was done at day 1, 4, 7, 14, 28 and 56 after termination of the labeling periods. Sampling was done by inserting a steel cylinder (ø 15 cm) around the labeled plant to a depth of 15 cm. The excavated cores were separated to recover intact plants including their roots and stolons (white clover) connected to the shoots. After gently washing, the roots, shoots and stolons were separated and the root system of the clovers plants was divided into nodules, primary and secondary roots. Roots from the grass were kept as one pool. The soil was sieved through a 2-mm sieve to remove roots in order to obtain an almost “root-free” soil.

All samples were ground to a fine powder in a ball mill and analyzed for ¹⁵N and total N at UC Davis stable Isotope facility. The ¹⁵N enrichment (atom% ¹⁵N excess) was calculated as the difference in atom% ¹⁵N between labeled plants or soil and unlabeled control samples taken prior to the start of the experiments.

The rhizodeposition (% N derived from rhizodeposition - NdfR) was estimated by the Janzen and Bruinsma equation [2] and N transfer from donating red or white clover to companion ryegrass was estimated on the basis of ¹⁵N-yield of donor and receiver plant tissues using a modified version of the equation by Høgh-Jensen and Schjoerring [3].

**Results**

In this experiment we wanted to follow the temporal change in tracer presence in specific pools: plant shoot and root tissues, and in soil. Hence, we sampled only root tissues attached to the shoot of the labeled plant and we removed roots from the soil by sieving to obtain an almost “root-free soil”. Thus, we did not aim at recording the full mass balance of the added tracers.

Red clover in mixture with ryegrass yielded significantly (P<0.001) more dry matter than the white clover/
grass mixture especially in respect to greater leaf and primary root biomass. The dry matter yields of both clovers were significantly (P<0.001) greater in spring than in autumn. Likewise ryegrass yielded significantly (P<0.001) more dry matter in spring than autumn, and there was no significant difference in dry matter yield of ryegrass growing in mixture with red or white clover.

All sampled plant tissues and soil were enriched in ¹⁵N already at day 1 after the end of labeling for both clover species for both the spring and autumn labeling. Red and white clovers shoot enrichments showed decreasing trends with time, whereas grass shoots after day 1 had a stable enrichment over the following two months. The ¹⁵N enrichment of the root tissues of both clover species and their companion grasses were surprisingly stable with time, especially after the spring labeling. We estimated N transfer both on a leaf-to-leaf basis and on a whole plant basis, and found that these two estimates were quite similar. The grass shoot ¹⁵N enrichments were fairly stable and dry matter yield only increased slightly with time in both spring and autumn, therefore the changes in estimated N transfer reflects the changes in ¹⁵N yield in red and white clover, respectively. The estimated N transfer for red clover was stable with time and corresponded to 2-5% of N in red clover in both spring and autumn. White clover on the other hand showed increasing N transfer with time, increasing from 5 to 20% of N in white clover in both spring and autumn. These findings indicated that N transfer was a two-step process, consisting of a fast initial period (a few days) dominated by root exudation of ¹⁵N, and a subsequent slower period (weeks to months) with deposition of ¹⁵N from plant tissue; in case of white clover especially ¹⁵N from shoots.

The estimated rhizodeposition corresponded to 1-5% of soil N with the initial deposition of ¹⁵N until day 1 after labeling constituting the majority of the ¹⁵N recovered in the soil pool. This underlines that using the Janzen and Bruinsma equenation to estimate rhizodeposition can involve significant bias [4].

## Conclusions

The study showed that red and white clover deposition and transfer of N occurs in two phases after leaf-labelling with ¹⁵N-urea, where the first short-term phase is dominated by root exudation and the second longer-term phase of N deposition dominated by the turnover of plant shoot and root tissues; with shoot tissues being particularly important for white clover.

## Acknowledgement

The study was financially supported by the European Union through the FP7-People Marie Curie program (project no. PIEF-GA-2009-252830) and the Danish Council for Independent Research (Technology and Production Sciences) (Grant no. 1335-00760B).

## References

[1] J. Rasmussen, et. al., 2013, Soil Biology & Biochemistry 57, 654-662.

[2] H. H. Janzen, Y. Bruinsma, 1989, Soil Biology & Biochemistry 21, 189-196.

[3] H. Høgh-Jensen, J. K. Schjoerring, 2000, Plant and Soil 227, 171-183.

[4] J. Rasmussen, 2011, Soil Biology & Biochemistry 43, 2213-2214.
# RECYCLING OF BIOGAS DIGESTATE IN AGRONOMIC PLANT PRODUCTION - NITROGEN FERTILIZER VALUE AND POSSIBLE NITRATE LEACHING.

Trine Sogn, Susanne Eich-Greatorex, Ivan Dragicevic

Norwegian University of Life Sciences (NMBU), Ås, Norway

## Objectives

The main objectives of this study are to explore the N fertilizer value, and the possible negative N leaching aspects of bio gas digestates when utilized in agronomic plant production.

## Method

A pot experiment was carried out in a greenhouse with wheat (*Triticum aestivum* L.) as a test crop. The pots had a soil volume of approximately 6.7 L. Three different soil types, i.e. sand, silt and loam, were used as growth media. Texture, carbon and nutrient content in the different soils were determined. The treatments consisted of a control without any fertilization, mineral fertilization, fresh manure, and four digestate treatments: one with digested manure, one based on whey co-digested with manure, one with whey and fish ensilage co-digested with manure, and two based on source-separated organic household waste. The amounts of manure and digestate were adjusted based on their inorganic N content, to correspond to the N applied as mineral N fertilizer, i.e. 400 mg mineral N/pot. Manure and digestates were analysed for the essential macro-nutrients by ICP spectroscopy. Although the organic fertilizers varied in element composition, the experiment was balanced only with respect to mineral N (sum of ammonium (NH₄⁺) and nitrate (NO₃⁻)).

In order to analyse the risk of N leaching, the pots with soil were leached with 1 L distilled water just after harvest, and the leachate was analysed for NH₄⁺ and NO₃⁻ concentration by flow injection analysis.

In addition, a soil leaching experiment without plants, with the same soils and treatments as used in the pot experiment, was carried out in the laboratory. Plexiglass columns of 24 mm in diameter and 30 cm in length were filled with 15 cm of the three soils. Prior to leaching, the soil columns were saturated with distilled water and then regularly irrigated with distilled water at a flow rate of 1.3 ml h⁻¹ corresponding to 500 mm of precipitation during a 7 days period. The leachates were collected at the base of the column every day during the seven days. The NO₃⁻ and NH₄⁺ concentrations were analyzed as described above.

Each treatment was replicated 3 times for each soil in both experiments. Treatment and soil effects were tested statistically by analysis of variance and differences were considered significant at p<0.05.

## Results

In the sand, there was a positive effect of digestate and manure application on biomass production compared to mineral N-fertilizer, and the digestate based on whey and fish ensilage gave the highest biomass production. The digestate based on whey and fish ensilage had the lowest NH₄⁺ concentration, but the highest total N content among the organic fertilizers. Since the digestate with the lowest NH₄⁺ concentration gave the highest biomass production, some of the organic N might have been mineralized during the growth period. Due to the low NH₄⁺ concentration the largest amount of digestate and thereby the highest amounts of phosphorus, potassium and magnesium were applied in this treatment. All the treatments included sufficient concentrations of the essential plant nutrients, and no deficiency symptoms were seen in any of the treatments. However, the positive effect on biomass growth might have been an effect of the high input of other macronutrients than N. No positive effect of organic fertilizers on biomass production was recorded in the loam and the silt soils. Among the organic fertilizers treatments, the lowest biomass production was observed in the loam. In the control without nutrients and the mineral fertilizer treatment, the biomass growth in sand and loam was similar and lower than in silt, while a clear positive effect of organic fertilizers were seen in sand.

Leaching after harvest showed that mineral fertilizer treatments in sand and loam resulted in elevated con-
centrations of mineral N in leachate. In the mineral fertilizer treatment the biomass growth in sand and loam was lower than in silt. The mineral fertilizer was apparently not fully utilized by the plants in these soils. In the loam, the low biomass production was probably caused by compactness. Compactness may restrict root growth and thus the ability of plant roots to reach mineral N during vegetative development when plant N demand is at its highest. Organic fertilizers were expected to have a positive effect on compactness and oxygen availability, but positive effects on biomass growth in the loam was not seen in this study. In the sand, a large part of the $NH_4^+$ given as mineral fertilizer may have been transported towards the bottom of the pot before an effective root system was developed. The amount of $NO_3^-$ leached from the sand receiving manure or digestate was lower than in sand not receiving any fertilizer. Thus, in addition to the positive effect on biomass growth, organic fertilizer application seemed to have an inhibitory effect on $NO_3^-$ leaching in sand. Nitrate leaching was particularly low from sand treated with digestate based on whey and fish ensilage.

The soil column leaching experiment also showed that organic fertilizer application may reduce $NO_3^-$ leaching in sand.

## Conclusions

Digestates generally had an N fertilizing effect similar to manure and similar or better than mineral N fertilizer. In the sand, the organic fertilizers had a positive effect on biomass production, particularly the digestate based on whey and fish ensilage. In the loam and silt, digestate application had the same effect on biomass production as manure and mineral N fertilizer.

In the sand, organic fertilizers reduced the $NO_3^-$ leaching. In the experiment with leaching from the pots after harvest, $NO_3^-$ leaching in sand was not only lower than in the mineral fertilizer treatment, but also lower than in the zero application treatment, indicating an effect of organic matter on sand $NO_3^-$ leaching. In the loam and silt the leaching of $NO_3^-$ were independent of treatments.
# DO CATCH CROPS IMPROVE NITROGEN USE EFFICIENCY OF SILAGE MAIZE?

Martin Komainda, Friedhelm Taube, Christof Kluß, Antje Herrmann

Institute of Crop Science and Plant Breeding; Grass and Forage Science/Organic Agriculture; Kiel University, Kiel, Schleswig-holstein, Germany

## Objectives

Silage maize production in north-western Europe is concentrated on coarse-textured soils and constrained by imbalances between nitrogen (N) fertilisation, N mineralisation and N uptake, leading to reduced apparent N recovery in the harvested biomass and N losses via denitrification and leaching [1]. Resilient crop production aims at maximum resource use efficiency via crop rotations to enhance nutrient recycling. For continuous silage maize, winter catch crops (CC) may enhance N recycling, if timely sowing allows rapid dry matter (DM) and N accumulation. However, this often is limited by unfavourable climatic conditions after maize harvest and mainly depends on harvest date. The present study analyses DM yields, mineral N fertiliser uptake and N use efficiency of silage maize harvested on successive harvest dates as well as the effect of the N accumulation of a rye CC (Secale cereale L.) on N use efficiency of a succeeding silage maize.

## Method

The present study is based on a field experiment conducted between April 2012 and October 2014 at the experimental farm ‘Ostenfeld’, in northern Germany on a silty-sandy soil. The experimental setup comprised a main crop maize (MM), followed by winter catch crops (CC) and a subsequent maize (SM), organised in a 4-factorial randomised block design with three replications. Treatments comprised two years (each cycle on new field), two maize harvest/CC sowing dates (10 September (hd1) early maize cv. Suleyka and 30 September (hd3) mid-early cv. Ronaldinio), maize phase (MM and SM) and two CC treatments (rye cv. Protector and bare fallow (BF)). N fertiliser was applied shortly after maize sowing as calcium-ammonium-nitrate (CAN) to MM and SM (180 and 140 kg N ha⁻¹), taking soil mineral N into consideration but not to unfertilized control. The plots were harvested by plot harvester and the N yield was determined (NIRS).

Above- (AGN) and belowground (BGN) N uptake of rye was determined in spring by cutting plants to ground level and applying the auger technique (depth: 30 cm).

Mineral fertiliser uptake efficiency of rye was quantified by 15N isotope-technique applied to MM on microplots (1.5 m²) as CAN mixed with labelled ammonium-nitrate (15NH415NO3, 2 at% enrichment). Labelled fertiliser N uptake (Ndff) represents the quotient of the differences between 15N concentration in labelled and unlabelled plants and fertilizer and unlabelled plants. The 15N uptake efficiency (FNUE15) was calculated as quotient between Ndff and applied 15N fertiliser amount [2].

Nitrogen uptake efficiency (NuptE) of MM and SM was calculated as the quotient of N yield and N fertilisation according to [3] and apparent N recovery (ANR) as the quotient of the difference between N yield of fertilised and unfertilized control and applied N amount. Analyses of variance were conducted using ‘R’ software. For MM yield and NuptE, year, harvest date and fertilisation were assumed fixed, while for SM year, harvest date and CC were included. For N yield, Ndff and FNUE15 of rye, year and plant part were considered fixed. Block was generally assumed random. Multiple comparisons of means were conducted by linear contrasts.

## Results

The DM yield of MM was affected significantly by the interaction of year × harvest date (p≤0.01) and by N fertilisation (p≤0.01). In both years, delayed harvest (hd3: 15.6 t DM ha⁻¹) resulted in up to 25% higher yield. The N yield was affected by year (p≤0.05) as well as N fertilisation (p≤0.001), but not by harvest date, with a significantly lower N yield in 2012 compared to 2013 (147 vs. 182 kg N ha⁻¹). As expected, N fertilisation increased DM and N yield by 42% and 60%, respectively [4, 5]. NuptE and ANR were unaffected by year and harvest date and averaged 1.5 kg N kg⁻¹ N fertiliser and 92%, respectively. The N uptake of rye in spring was influenced by year (p≤0.001) and plant part (p≤0.001), resulting in significantly lower BGN compared to AGN (20 vs. 31 kg N ha⁻¹), without any sowing date effect. Due to contrasting climatic conditions during winter, the N accumulation was lower in spring 2013 compared to 2014, 27 vs. 74 kg N ha⁻¹,
respectively. Ndff and FNUE15 of rye were both affected significantly by year (p≤0.05) and sowing date (p≤0.05), with Ndff and FNUE15 of AGN and BGN being significantly lower in 2013 than 2014 (0.7-1.2 vs. 3.8-4.7 kg N ha⁻¹) and (0.5-0.8 vs. 2.5-3%), respectively, without any differences between sowing dates. These FNUE15 values are slightly lower compared to [6] reporting 4.1% for Italian ryegrass sown after winter wheat.

For both, DM (20.1 t DM ha⁻¹) and N yield (211 kg N ha⁻¹) of SM ANOVA did not show any influence of the tested factors. The NuptE of SM, however, revealed a significant interaction of year × CC (p≤0.01), with a significantly higher NuptE in 2014 compared to 2013 (2.5 vs. 1.7 kg N kg⁻¹ N fertiliser) averaged over CC treatments and a lower NuptE for rye than BF in 2014 (2.3 vs. 2.7 kg N kg⁻¹ N fertiliser). When considering the N accumulation of rye as N input to SM, the NuptE showed the same interaction of year × CC (p≤0.001), but with a significant difference between rye and BF in both years (average: 1.3 vs. 2.2 kg N kg⁻¹ N supply). This was explained by a lower soil mineral N in spring for rye than BF and consequently a higher N supply (BF: 98, rye: 160 kg N ha⁻¹). Consequently, the ANR, with or without additional N supply of rye, revealed an interaction of year × CC species (p≤0.05), with BF outperforming rye in 2014 (124 vs. 83%). If adding the N supply of rye the difference increased to 124 vs. 64%.

## Conclusions

Delaying the harvest of MM resulted in the expected increase of DM and N yield, whereas no reduction of N accumulation of rye CC was observed. The uptake of residual fertiliser N by rye was low, indicating that the majority of N uptake originated from mineralisation. Thus, N mineralised during late autumn/winter is largely protected against losses, which is beneficial for the environment. NuptE of SM being lower for rye than BF emphasises the importance of a better estimate of CC N release to the succeeding maize with respect to total amount and timing to optimise the N fertiliser amount.

## References

[1] Velthof, G.L. et al., 2014. Science of the Total Environment 468-469, 1225-1233.

[2] Nannen, D.U. et al., 2011. Nutrient Cycling in Agroecosystems 89, 269-280.

[3] Moll, R.H. et al., 1981. Journal Series NC Agric. Res. Service No. 6842.

[4] Masters, M.A. et al., 2016. Agriculture, Ecosystems and Environment 216, 51-60.

[5] Merbach, W. et al., 2013. Archives of Agronomy 59, 1069-1071.

[6] Martinez, J., Guiraud, G. 1990. Journal of Soil Science 41, 5-16.
N₂O FLUXES AND MINERAL N DYNAMICS FOLLOWING GRASSLAND RENEWAL
TECHNIQUES AND GRASSLAND CONVERSION TO ARABLE LAND

Caroline Buchen¹, Matthias Benke³, Heinz Flessa¹, Roland Fuß¹, Andreas Gensior¹, Mirjam Helfrich¹, Manfred Kayser², Reinhard Well¹

¹Thünen Institute of Climate-Smart Agriculture, Braunschweig, Germany, ²Department of Crop Sciences - Grassland Science, Georg-August-University Göttingen, Vechta, Germany, ³Faculty of Grassland and Fodder Crops, Lower Saxony Chamber of Agriculture, Oldenburg, Germany

**Objectives**

Grassland renewal and grassland conversion to arable land are common agricultural practices on intensive-
ly used grassland sites in order to eliminate sward and soil disturbances and establish high-quality grass
types. It is known that renovation can cause a flush of soil organic nitrogen mineralization due to soil distur-
bance during associated soil tillage [1;2] and decomposition of stubbles and roots from the old grass sward.
Knowledge about enhanced nitrous oxide emissions (N₂O) and N leaching losses associated with renovation
practices is scarce, although global climate change mitigation and water protection policies force countries
to deal with this problem.

The aim of our study is to determine the impact of grassland renewal techniques and conversion of grassland to arable land on N₂O fluxes and mineral N dynamics at two sites with differing soil texture and to evaluate the effect on yield and N use efficiency.

Method

We set up a randomized field block trial near Oldenburg (Lower-Saxony, Germany), using two different
soil types with varying C-contents and different drainage regimes (Histic Gleysol and Plaggic Anthrosol)
with the following treatments: renovation through (i) reseeding, (ii) chemical killing and grass seeding, (iii)
chemical killing, sward destruction by rotovating, ploughing and grass seeding. Reference treatments were
(iv) conversion to arable land for maize cropping and (v) permanent grassland. N₂O fluxes were measured
weekly using closed chambers. The relationship between N₂O fluxes and explanatory/controlling variables
was investigated using generalized additive models (GAM). Potential N-losses via NO₃⁻ leaching were quant-
tified by measuring weekly soil mineral N in the top soil (0 to 30 cm) and in depth profiles (0 to 90 cm) in
spring and autumn. N removal by harvested biomass was quantified by determining its dry matter and ana-
lysing the N content. To identify processes of N₂O production and consumption, stable isotope analyses of
emitted N₂O, soil water and soil NO₃⁻ were carried out on selected dates and supported by a ¹⁵N tracing ex-
periment to determine N losses via N₂.

Results

Following grassland renewal, N₂O fluxes from the Histic Gleysol (high organic matter content and mostly high moisture) increased to > 1 kg N ha⁻¹ day⁻¹, whereas N₂O fluxes from the Plaggic Anthrosol (with sandy texture, exhibiting low organic matter content and mostly low moisture) were on a significantly lower level, ranging between 0 and 0.05 kg N ha⁻¹ day⁻¹. In contrast to the site specific variation between the two soils, the mean annual N₂O emissions were not significantly different between the investigated grassland renewal techniques and continuous grassland. This is in contrast to other studies, who found large N₂O emissions following grassland renewal [1;2;3;4;5]. Moreover grassland conversion for maize cropping did not increase N₂O fluxes on the Histic Gleysol, but did result in large N losses via N₂ (52.46±31.47 kg N ha⁻¹) within the first 6 weeks. This means gaseous N losses via denitrification were highly relevant on this site. In contrast, N₂O fluxes from the Plaggic Anthrosol showed distinct N₂O emissions of 2.93±0.35 kg N ha⁻¹ within the first maize cultivation period. Whereas gaseous losses from the Plaggic Anthrosol were relatively low, greater mineralization/nitrification, indicated a higher risk for leaching of excess mineral N following conversion/renewal. This was confirmed by potential N-leaching losses of 100 kg N ha⁻¹ in the Chemical (ii) and Mechanical (iii) treatments over the first winter. Large leaching losses in the first year after grassland renewal have also been reported [1;6]. Moreover, the supposed yield effect could not be stated either, as dry matter yield
and N plant content did not differ significantly between the different treatments.

## Conclusions

Overall, the different grassland renewal techniques and grassland conversion for maize cropping had a lower impact on N₂O emissions at the two investigated sites, than previously expected, but site-specific effects have to be considered. Since high denitrification losses via N₂ were observed in the Histic Gleysol, this might have masked the presumed increase of mineralization due to grassland ploughing. Moreover, we could confirm the negative impact of grassland ploughing during fall for the N budget of grassland systems since potential N-losses via N-leaching substantially increased, especially in the Plaggic Anthrosol.

## Acknowledgements

This study was funded by the DFG Research Training Group 1397.

## References

[1] Velthof, G. et al., 2010. *Nutrient Cycling in Agroecosystems* 86(3): 401-412.

[2] Davies, M. et al., 2001. *Biology and Fertility of Soils* 33, 423-434.

[3] MacDonald, J. et al., 2011. *Soil and Tillage Research* 111, 123-132.

[4] Merbold, L. et al., 2014. *Global Change Biology* 20, 1913-1928.

[5] Mori, A. and Hojito, M., 2007. *Soil Science and Plant Nutrition* 53, 812-818.

[6] Seidel, K. et al., 2009. *Journal of Plant Nutrition and Soil Science* 172, 512-519.
# EARLY SOWING ENHANCES GRAIN YIELDS AND N USE EFFICIENCY IN WINTER WHEAT

Alfonso Suarez-Tapia, Jim Rasmussen, Bent Tolstrup Christensen, Ingrid Kaag Thomsen

Aarhus University, Aarhus, Denmark

## Objectives

Increasing the Nitrogen Use Efficiency (NUE) in agricultural systems is essential to provide high yields while reducing pollution. Designing an N efficient cropping system requires adjustment of agronomic components such as: crop rotation, variety selection, sowing time, tillage, N fertilizer sources, and time of fertilizer application. A field experiment with winter wheat was conducted to evaluate if early sowing can enhance crop yields, reduce soil mineral N (SMN) during autumn and winter, and increase crop N uptake. The objectives were to: (i) test if sowing one month earlier than normal will increase NUE, and (ii) determine variety effect on winter wheat yield and NUE.

## Method

The experiment was embedded in The Askov Long-Term Experiment on Animal Manure and Mineral Fertilizers with a history of 120 years of continuous application of either animal manure (AM) or mineral fertilizer (NPK). A split-split-plot design was used with three replicates. The main plot treatments were the long-term nutrient treatments: Unmanured, 1 AM, 1 ½ AM, 1 NPK, 1 ½ NPK; where 1 AM and 1 NPK in winter wheat are equivalent to 150 kg N ha⁻¹, 30 kg P ha⁻¹ and 120 kg K ha⁻¹. In the autumn 2014, each main plot was divided into four sub-plots with two sowing times (early sowing: August 20, and normal sowing: September 18) and two winter wheat varieties Hereford (Sygenta, Switzerland) and Mariboss (Nordic seeds, Denmark).

Soil mineral nitrogen (N-NO₃⁻ and N-NH₄⁺) was determined on four occasions: before early sowing (August 18), after normal sowing (September 17), before entering winter (November 20), and in early spring (March 18). The samples collected in the field were frozen (-18 °C). Upon analysis soil was left overnight at 4°C and sieved (2 mm). A 15 g subsample was used to determine mineral N by extraction with 1 M KCl (1:2 w/w), shaking for 1 hour and filtering through a 0.7 µm glass fiber filter (Toyo Roshi Kaisha, Ltd, Japan). The filtrates were analyzed colorimetrically for N-NO₃⁻ and N-NH₄⁺ using a Bran+Luebbe Autoanalyzer 3 (Seal Analytical, Nordestedt, Germany). The values were expressed as mg N kg⁻¹ dry soil.

N uptake was determined in six cuts of plant samples that were taken every month from October 2014 until March 2015 (October 25, 2014; November 20, 2014; December 8, 2014; January 6, 2015; February 16, 2015; March 17, 2015). Every sample was taken from 0.9 m of row. Samples were dried, milled and analyzed for total N. The amount of ash was discounted from the dry matter content. The values were expressed as kg N ha⁻¹. On August 20, 2015 grain yield was determined using a combine harvester. Grain was dried for 80 °C minimum 18 hours. Grain yields are reported at 85% dry matter.

## Results

NUE can be defined as the relation between unit of food or feed produced per unit of N fertilizer applied [1] hence, a higher biomass or grain yield with the same or lower N application rates represents a higher efficiency. We investigated the potential of early sowing to increase yields and N uptake while reducing SMN in the autumn-winter period. In general, it was a good season for wheat in terms of precipitation but there were low temperatures especially at the end of the season that delayed harvesting. Early sown wheat had 1.1 Mg ha⁻¹ higher grain yield compared with normal sown wheat (9 vs 7.9 Mg ha⁻¹). Myrbeck et al (2012) also reported a yield increase in favor of early sown wheat in Southern Sweden in a study conducted from 2007 to 2010. [2].

The two varieties tested also showed significant differences. Hereford had higher yield than Mariboss (p<0.001). On average, Hereford provided 0.7 Mg ha⁻¹ more grain than Mariboss (8.8 vs 8.1 Mg ha⁻¹). The difference between Hereford and Mariboss was related to long-term treatments since Hereford is more pro-
ductive under 1 ½ NPK and 1 NPK compared to Mariboss. Rasmussen et al. (2015) found higher yield for Hereford compared with the modern varieties Cordiale, Genius and JB Asano. The authors attributed the higher yield of Hereford with a deep root system that can reach up to 1.7 m which was around 0.3 m deeper than other varieties. The authors found significantly less SMN under this variety compared to other varieties [3].

N uptake in the wheat during autumn and winter showed a significant difference between early sowing and normal sowing (20 vs 8 kg N ha⁻¹). There was a significant interaction for the sowing time and time of plant sampling. The greatest difference between early and normal sowing was found in December and January with around 16 kg N ha⁻¹. The N uptake depended as well on variety and time of cut. In general, Hereford had higher uptake than Mariboss (14 vs 11 kg N ha⁻¹) (p<0.001). The Agronomic efficiency (AE) as a measure of NUE was determined for grain yield. The results show that NPK had a higher AE compared with AM and Hereford had a higher AE compared to Mariboss.

SMN differed depending on the sowing time and the time of sampling. SMN showed the greatest difference at the September sampling with 2 mg N kg⁻¹ of dry soil less for early than for normal sown wheat: representing around 6 kg N ha⁻¹ (assuming a 1.5 g cm⁻³ bulk density). Hence, the greater N uptake and lower SMN in autumn could reduce the risk of N leaching under the early sown wheat.

## Conclusions

This experiment showed higher grain yield of early than of normal sown winter wheat. Early sown wheat had higher N uptake during autumn and winter compared to normal sown wheat. The higher yield in early sowing can be explained in part by a higher N uptake during autumn and early spring and provided a significant decrease in SMN under normal sowing at the September sampling time. Hereford was a better variety for the year 2014/2015 with 0.7 Mg ha⁻¹ more grain than Mariboss.

There were significant differences on N uptake due to sowing time. On average, early sowing had a significant higher uptake than normal sowing followed by a significant decrease in soil mineral N.

## Acknowledgments

We gratefully acknowledge the contribution of the technical staff at Askov Experimental Station for managing the field experiments and at Foulum for chemical analyses. This research is funded in part by Aarhus University and the Secretary for Science, Innovation and Technology from Ecuador.

## References

[1] Dobermann, A. 2005. Agronomy & Horticulture Faculty Publications. Paper 316.

[2] Myrbeck, Å. Et al., 2012. Soil and Tillage Research 120: 25-31.

[3] Rasmussen, I.S. et al., 2015. European Journal of Agronomy 68: 38-49.
# EFFECT OF APPLICATION AND NITRIFICATION INHIBITORS ON TRACE GAS FLUXES FROM A MAIZE FIELD AFTER CATTLE SLURRY AMENDMENT

Christina Herr¹, Thomas Mannheim², Torsten Müller¹, Reiner Ruser¹

¹University of Hohenheim, Stuttgart, Germany, ²EuroChem Agro, Mannheim, Germany

## Objectives

Efficient use of organic N-fertilizers is a main global challenge. Strategies reducing gaseous N losses after application of organic fertilizers are still required.

Injection or incorporation of slurry efficiently reduces ammonia losses and thereby increases N efficiency in agricultural systems. On the other hand, slurry injection was shown to enhance N₂O emissions due to the creation of anaerobic conditions favoring denitrification in the injection slot [2].

The use of nitrification inhibitors (NI) is a technique which can improve N fertilizer use efficiency by retarding the oxidation of ammonium.

The main aim of our study was to test the applicability of two NIs to reduce N₂O emissions from a maize field by desynchronizing carbon availability in the injection slot and NO₃⁻ release from nitrification of the slurry ammonium. We further tested the effect of different application techniques on N₂O and CH₄ fluxes after slurry amendment.

## Method

The study was conducted on the University Hohenheim research farm Heidfeldhof, 13 km south of Stuttgart (410 m asl). The mean annual precipitation in this region is 686 mm and the mean annual temperature 8.8°C. Soil type was a Haplic Luvisol derived from periglacial loess. The soil texture of the stone-free top soil consisted of 2% sand, 68% silt, and 30% clay [3].

We established a fully randomized block experiment with ten treatments and four replicates. Here we present results of the following treatments: (1) unfertilized control, (2) mineral N, (3) cattle slurry surface applied and incorporated immediately after application, (4) cattle slurry injected, (5) cattle slurry injected with NI 3,4-dimethylepyrazole phosphate (DMPP), and (6) cattle slurry injected with NI dicyandiamide (DCD).

N-fertilizers were applied in two doses. The first dose of 133 kg N ha⁻¹ was applied on 10th May. Injection depth of the slurry was 10 cm. The second dose of 37 kg N ha⁻¹ was applied on 12th June. The second dose of treatments 3-6 was surface applied with a trail shoe in order to avoid damages of the growing maize plants. NIs were only applied with the first N dose in treatments 5 and 6. Maize (variety 'Amadeo') was sown mid May 2015 (12.700 plant ha⁻¹) and harvested at the end of August.

N₂O and CH₄ trace gas fluxes were measured between April and December 2015 at least once a week using a closed chamber method. Chambers design, gas sampling procedure, and determination of the gas concentrations in the samples was described in detail by [3]. Gas flux rates were calculated using an R-script (Roland Fuss, 2015, Thünen Institut Braunschweig).

Simultaneously to the gas sampling we measured the soil temperature and took soil samples and determined mineral N and soil moisture to parameterize the trace gas fluxes.

## Results

The N₂O emissions during our investigation did not differ significantly between the unfertilized treatment, the slurry surface application with subsequent incorporation, and the mineral N treatment (2.53 to 3.58 kg N²O-N ha⁻¹).

In contrast, slurry injection strongly increased the N₂O emissions during this period. N₂O emission from the slurry injection treatment without NI was 14.06 kg N₂O-N ha⁻¹. The addition of both NIs did not significantly reduce the N₂O emissions compared to the injection treatment without NI. With 14.58 kg N₂O-N ha⁻¹, the
treatment with DCD showed the highest emission in this investigation within the whole experiment. Application of DMPP reduced the mean N₂O emission by 18%.

In the period between the two slurry applications, NIs significantly reduced the cumulative N₂O emission (DMPP: 25%; DCD: 19%). The magnitude of reduction was in agreement with the values reported by [4]. As compared to the DMPP treatment, the higher N₂O emission in the DCD treatment might have been a result of different chemical and physical NI characteristics. Similar results have been reported by [5]. when testing DCD and DMPP with mineral N-fertilizers, they found a faster mineralization of DCD and thus a lower N₂O reduction after N-fertilization.

After the second fertilization, which did not differ among the slurry treatments (all surface applied and all without NI), the N₂O fluxes from the NI treatments increased stronger than in the injection treatment without NI. This can be attributed to the higher NO₃⁻ contents in the treatments with NIs during this period, which was probably a result of the delay of nitrification.

A stepwise regression exhibited a strong positive correlation between N₂O flux rates and soil water content, NO₃⁻ availability and CO₂ emissions which clearly indicates (heterotrophic) denitrification as the main process for N₂O production.

Soil generally served as a net CH₄ sink. The CH₄ flux uptake in the control, surface applied and incorporated, and the mineral N treatment varied between -0,35 and -0,58 kg CH₄-C ha⁻¹. This indicates that the effect of volatilization of dissolved CH₄ produced during slurry storage was negligible.

In contrast, slurry injection stimulated CH₄ production due to the creation of anaerobic conditions as reported by [2]. Highest emission was measured in the injection treatment without NI (6,81 kg CH₄-C ha⁻¹). Application of NIs significantly reduced CH₄ emission (DMPP: 42%; DCD: 47%). The reason for this reduction remains unclear and will be focus of further research.

## Conclusions

Organic fertilizers like slurry are important nutrient sources and the efficient and environmental friendly use of these nutrients must be a main aim in agricultural production.

There is no doubt, that ammonia volatilization can be avoided best when slurry is incorporated into soil. On the other hand it has been shown frequently, that slurry injection can increase N₂O emissions. In our study, nitrification inhibitors decreased the N₂O emission directly after slurry injection and tended to decrease the cumulative N₂O emission covering an 8 months measurement period.

Significantly lower N₂O emissions were measured, when slurry was applied on the soil surface and subsequently incorporated. However, this application technique needs a further individual operation and it holds the risk of increasing ammonia losses as long as the slurry is not incorporated. Since ammonia losses also contribute to climate change by fertilization of natural ecosystems, further investigations on slurry application have to include N₂O and NH₃ flux measurements.

## References

[1] Flessa, H., Beese, F. 1999. Journal of Environmental Quality, 262-268.

[2] Flessa, H. et al., 1995. J. Geophys. Res., 23115 - 23124.

[3] Pfab, H. et al., 2011. J. Plant Nutr. Soil Sci., 545-553.

[4] Ruser, R., Schulz, R. 2015. J. Plant Nutr. Soil Sci., 171-188.

[5] Weiske, A. et al., 2001. Nutr. Cycl. Agroecosyst., 57-64.
# N₂O EMISSIONS AFTER OILSEED RAPE RESIDUE INCORPORATION AS AFFECTED BY SOIL TILLAGE

Katharina Kesenheimer¹, Hemant Pandeya¹, Franz Buegger², Torsten Müller¹, Reiner Ruser¹

¹Universität Hohenheim, Fertilization and Soil Matter Dynamics (340i), Stuttgart, Germany, ²Helmholtz Zentrum, Institute Biochemical Plant Pathology, München, Germany

## Objectives

Oilseed rape (Brassica napus L.) is a prominent oilseed crop in the northern part of Europe. Production of oilseed rape has increased in Germany during the last decades due to its economic and ecological importance towards biodiesel production. Winter oilseed rape is known for its elevated nitrogen demand during early growth stages and a low nitrogen use efficiency resulting in high N surpluses and possible gaseous or leaching losses [1]. Furthermore the crop residues of oilseed rape are suspected to cause high N₂O emissions in the following crop. In this experiment, the fate of ¹⁵N labelled rape residue was studied during autumn under field conditions. The main objective of this study was to quantify the effect of post-harvest rape crop residues incorporation at two tillage depths on N₂O emissions and on the direct contribution of the residue bound N to the N₂O fluxes.

## Method

### Preparation of ¹⁵N labelled crop residues

Winter oilseed rape (var. Visby) was planted at BBCH 3 into planting pots. ¹⁵N labelled potassium nitrate (60 atom % ¹⁵N) was used as N-fertilizer. Similar to the field experiment, the fertilizer amount used for labelling was 180 kg N ha⁻¹. The final average ¹⁵N abundance of the crop residue (CR) was 14.42 atom%.

### Field experiment

This study was conducted on a Luvisol at the research station Ihinger Hof, South Germany as part of a block experiment (Split-Plot-Design, n = 4). Before the application of the crop residues, rapeseed was grown and harvested on 23. July 2014. The ¹⁵N labeled crop residues were applied end of July and incorporated at the end of September. The total crop residue application rate per mini plot was 455 g (C:N 49,7; total N-amount 121,5 kg N ha⁻¹). In the period between application and incorporation, the residues were covered with a plastic net. On 13. October, winter wheat (var. Julius) was sown.

Four treatments were investigated: (1) Shallow tillage with CR (15 cm), (2) shallow tillage without CR, (3) conventional tillage with CR (30 cm), and (4) conventional tillage without CR. Mini plots (0.3 m²) were established in the regular experimental plots. The mini plots were cleared stubble free and roots of the rape were removed in all treatments in the first 30 cm. Labelled crop residues were mulched on the surface of the +CR treatments, control plots without any crop residues were established too. According to the regular tillage praxis, tillage in the mini plots was simulated.

Trace gas measurement started one week after field preparation. Trace gas fluxes of N₂O, ¹⁵N-N₂O, and CO₂ were measured at least once a week with additional event driven samplings. Closed chambers used for flux measurements and a similar GC-configuration used for the determination of the trace gas concentrations in the gas samples as described in detail earlier [2]. ¹⁵N abundance in N₂O was analyzed using a GC-IRMS.

## Results

Following harvest of the rape crop and several weeks without rainfall, N₂O fluxes increased after heavy rainfall events. N₂O emission in the period between application and incorporation of the crop residues was 960 and 878 g N₂O-N ha⁻¹ 61 days⁻¹ for the +CR and the -CR plots, respectively. This difference was statistically significant for the first three weeks after application (p< 0.05).
Such a rise in the N₂O fluxes after rewetting of dry soil has often been reported [3]. Stimulated N₂O emissions after crop residue application has also been frequently observed [4]. The residues provide easily available labile carbon as a nutrient substrate which in turn can increase the microbial activity in the soil. Rapid oxygen consumption by microbes during turnover of easily available C can result in a strong decrease of the redox potential [5] and thus favoring conditions for denitrification [6].

After this initial pulse, N₂O fluxes from the +CR treatment declined below the fluxes measured on the -CR plots. We attributed this phenomenon to the broad C-to-N-ratio of the crop residues (52) and to the resulting immobilization of nitrate [7] acting as a source for N₂O production in soil.

Immobilization may also be the reason for the low portion of crop residue bound N to the total N₂O emission between application and incorporation (6.5 %).

After tillage, N₂O fluxes declined strongly. The cumulative N₂O emission between tillage and the end of our measurements varied between 132 and 271 g N₂O-N ha⁻¹ 62 day⁻¹. This decline was attributed to aeration of the soil due to tillage and to the exceptional low rainfall in this period.

In the period after tillage, N₂O emissions from the shallow treatments were higher than the emissions from the conventional tillage treatment with a deeper incorporation. This could be a result of increased C-turnover in the topsoil of then shallow treatment due to the higher concentration of easily available C.

The direct share of crop residue bound N to N₂O emissions was again low. It accounted for only 2.0 % in the conventional tillage treatment and for 1.8 % in the shallow tillage treatment.

## Conclusions

Increase in greenhouse gas fluxes are often observed after crop residue application during incubation or field trials. However in our field study, postharvest N₂O emission from winter rape crop residues was low and not significant higher than treatments without crop residues. Due to the high C-to-N ratio present in the crop residues, there was an immobilization of mineral N. Therefore the N losses through postharvest crop residue application play just a minor role in the total reactive N losses during rapeseed cultivation. Consequently, the N-fertilizer not taken up by the plants or N released in the field by humus is of greater importance. Although for short period, significantly higher N₂O emissions from shallow tillage as compared with conventional tillage were observed. These additional N₂O emissions should be considered while accounting for greenhouse gas emissions.

## Acknowledgement

The project is funded by the Agency for Renewable Resources (FNR) due to a decision of the German Federal Parliament with funds of the Federal Ministry of Food and Agriculture.

## References

[1] Rathke et al., 2006. Agriculture, Ecosystems & Environment, 117, 80–108.

[2] Flessa et al., 1995. Journal of Geophysical Research, 100, 23115–23124.

[3] Ruser et al., 2005. Soil Biol. Biochem., 38, 263-274.

[4] Baggs et al., 2003. Plant and Soil, 254, 361–370.

[5] Flessa and Beese, 1995. Soil Science Society of America Journal, 59, 1044–1051.

[6] Miller et al., 2008. Soil Biol. Biochem., 40, 2553-2562.

[7] Chen et al., 2013. Global Change Biology, 19, 2956–2964.
LAND USE RELATED INTERVENTIONS ON THE NITROGEN CYCLE - A CASE STUDY
OF THE UPPER ENNS VALLEY, A LONG-TERM SOCIO-ECONOMIC AND ECOSYSTEM
RESEARCH REGION IN AUSTRIA

Andrea Schroeck¹, Veronika Gaube², Edwin Haas³, Wilfried Winiwarter⁴

¹Institute of Systems Sciences, Innovation and Sustainability Research (ISIS), Graz, Austria, ²Institute of So-
cial Ecology (SEC), Vienna, Austria, ³Institute of Meteorology and Climate Research (IMK), Karlsruhe, Ger-
many, ⁴International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

Objectives

In this paper we investigate i) the flows of reactive N under present conditions and ii) potential alternative opportunities aimed to improve nitrogen use efficiency. Natural conditions, such as climate and soil, are typ-ically characterized by high spatial variability and uncertainties in their future fate. Many techniques to esti-mate environmental consequences are proposed, but too often, cross-links and trade-offs are ignored.

In this paper, we apply a biophysical soil model in order to i) assess future scenarios under different condi-
tions of land use and ii) to provide stocks and biosphere-atmosphere-hydrosphere fluxes of N to an external
decision-making module performed by an autonomous agent approach.

We select a landscape level for a well-investigated study region, which is part of an Austrian Long-Term Socio-economic and Ecosystem Research (LTSER) region. Interventions aimed at optimizing nitrogen use efficiency and closing nutrient cycles are tested, and land use related consequences are identified.

Method

The study region, the Upper Enns Valley, covers about 23 000 ha grassland and alpine pastures and about 8 000 ha arable land. Forest (about 76 000 ha) has not been dealt with in this study. The most recent version of the DeNitrification-DeComposition (DNDC) model, LandscapeDNDC, Version 0.36.4 [1] a process model for simulation of biosphere-atmosphere-hydrosphere exchange processes is used to estimate land-use related N emissions in the selected study region. For a comprehensive regional emission inventory, Homogeneous Spatial Mapping Units (HSMUs) are identified ranging between 1 and 1400 ha. Spatial explicit data is pro-cessed with ArcGIS 10.2. In the following, datasets used to build the HSMUs are described.

Area-specific accounts of crops and animal husbandry have been obtained from a data base generated by a
European integrated administration and control system (IACS). The system has been established to provide
background information on agricultural subsidies. In Austria, this system is known under the acronym IN-
VECOS. Spatial land-use categorization splits grassland areas into i) alpine forage areas, ii) permanent and
common pastures, iii) hay meadows cut once or twice a year and iv) hay meadows with three- or more cuts.
For arable land, typical regional crop rotations are considered for the simulation.

Soil data was retrieved from the digital soil map of Austria (http://www.bodenkarte.at/) developed by the Austrian Federal Research Centre for Forests (BFW). The most crucial soil parameters to N emissions and plant growth i) texture, ii) soil pH and iii) humus content, were intersected to differentiate between relevant variations in soil type.

Information on temperature and precipitation is available on a daily base, on a 10 x 10 km grid. The re-
gionalized and bias-corrected climate scenario datasets have been developed within a national project
(reclip:century - see http://www.klimawandelanpassung.at/ms/klimawandelanpassung/de/kwa_news/
kwa_forschung/kwa_reclip/). Slope and aspect are also considered relevant topics to further separate distinct
HSMUs, for which we used a freely available digital surface model of Austria (http://www.oe3d.at/).

Results

Model results allow to assess the flows of nitrate and nitrous oxide emissions as well as the flows of ammo-
nia, nitrogen oxide and molecular dinitrogen. Thus a full nitrogen balance of the respective agricultural good under selected conditions can be calculated. Nitrogen-use efficiency in terms of emissions per produced unit of agricultural good is established and management strategies with at least the lowest environmental costs compared to agricultural productivity are identified.

The scenario analysis of the arable region show emission factors and yield-based emission factors depend-
ing on i) different crop rotations, ii) the rate of slurry application, iii) the integration of legumes, such as soy
beans, into the crop rotation, and iv) the use of slow-release fertilisers.

The grassland scenario analysis shows differences of emission release concerning i) changes in animal stocking rates, ii) inorganic N fertiliser addition, iii) slurry application rates, iv) changing areas of extensive and intensive grasslands, and v) the conversion of grassland into forest.

About 91 % of the reported arable land and about 62 % of reported grassland is represented by the model results. Soil information is the limiting factor, where for 97 % of arable land and for 74 % of grassland adequate soil information is available. Soil types representing less than 1 % of the considered agricultural area are neglected. For grassland, 1459 ha of the area is excluded. This concerns areas where grassland management is not specified, as well as areas, where biomass is not grazed or harvested for animal husbandry or nutritional purposes (e.g. christmas tree plantations, or land which is no longer used for production purposes, but maintained in good agricultural and environmental conditions).

Since multiple processes and drivers are involved, especially N2O emissions are highly variable and often
associated with “hotspots” (high emissions from small areas) and “hot moments” (high emissions for brief
periods) and often discussed as a non-linear response function. The range of emission factors, as well as
hotspots and hot moments of N emissions in the examined study region will be presented and discussed in
terms of prevention and mitigation options.

Conclusions

The current study demonstrates that local natural conditions determine whether a management change may have stronger effects to yields than to emissions, or whether the opposite reaction occurs. This is still very relevant as emissions per produced unit (as emission footprints) deserve consideration. However, management activities highly depend on human decision. In a further step, motivations and willingness of farmers for management change is explored by the ALISEN (Analysing LInkages of SocioEcological Nitrogen flows) project, aiming to couple an agent-based modeling to the presented stock and flow modeling approach - able to assess impacts of changing management on the N cycle quantitatively.

Acknowledgement

Part of this research work is carried out with support of the project ALISEN (Analysing LInkages of Socio-Ecological Nitrogen flows), funded by the Austrian Science Foundation (FWF), project ID V243.

References

[1] Haas, E. et al., 2012. Landscape Ecology, 28, 615-636.
# SOIL NITROUS OXIDE (N₂O) EMISSIONS IN SPRINKLER CORN (ZEA MAYS L.): INFLUENCE OF IRRIGATION TIME AND FREQUENCY.

Samuel Franco-Luesma¹, Jorge Álvaro-Fuentes¹, José Luis Arrúe¹, Carlos Cantero-Martínez², José Cavero¹
¹Estación Experimental de Aula Dei (EEAD-CSIC), Zaragoza, Spain, ²Universidad de Lleida, Lleida, Spain

## Objectives

Agricultural soils are the primary anthropogenic source of N₂O [1]. Likewise, N₂O has 265 times the global warming potential of CO₂ [2] and it is mainly produced via microbial transformations of inorganic N through nitrification and denitrification processes. The understanding of the relationships between soil N₂O emissions and soil management practices, (e.g., tillage, irrigation and fertilization) are necessary to develop best agricultural practices for reducing N₂O emissions. Several studies have assessed the effects of tillage and fertilization on N₂O emission [3], but just few of them have been conducted to evaluate the influence of irrigation management. The objective of this study was to assess the impact of irrigation management on soil N₂O emissions. In particular, the effect of irrigation frequency (i.e., high frequency vs. low frequency) and the time of irrigation (i.e., daytime vs. nighttime) on soil N₂O emissions were compared during the growing season of a continuous corn crop.

## Method

The field experiment was conducted at the experimental farm of the Estación Experimental de Aula Dei in Zaragoza (NE Spain). The experimental layout was a randomized factorial design with three replicates per treatment. The plot size was 18 m x 18 m.

Two factors were compared: (i) irrigation frequency with two levels: high frequency (H), consisting of daily irrigation, and low frequency (L), with irrigation twice a week; and (ii) irrigation time with two levels: daytime (D) with irrigation starting after 10:00 GTM; and nighttime (N) with irrigation starting after 22:00 GTM. The combination of the two factors resulted in the following four treatments: DH (Daytime, High frequency), DL (Daytime, Low frequency), NH (Nighttime, High frequency), NL (Nighttime, Low frequency). All received the same tillage operations, (i.e., subsoiling and disking) and two fertilizer applications: 800 kg ha⁻¹ NPK (8-15-15) on 9 April 2015 and 100 kg ha⁻¹ N-32% solution on 15 June 2015. Irrigation was applied with sprinklers according to the crop water requirement. Each irrigation event lasted for a minimum of 1 hour (5 mm) and a total of 604 mm of irrigation water was applied at all plots during the growing season.

From April to September 2015, N₂O emissions were measured weekly till August, and every three weeks from late August to harvest (9 October 2015). The sampling frequency was increased following the N fertilizer applications. Gas samples were collected by placing PVC chambers of 32.4 L over permanent collars and collecting gas samples from the chamber headspace through a rubber septum at regular intervals of 0, 20 and 40 min after deployment. Concentration of N₂O in the gas samples was determined using a gas chromatograph (Agilent Technologies 7890B GC System). For each N₂O sampling date, moisture content (water-filled pore space, WFPS) at 5 cm depth was also measured. Harvest was done mechanically for each plot using a combine (New Holland CX8040) and taking by hand a subsample to determine the total biomass and yield components.

## Results

Seasonal N₂O emissions kept steady and low before N fertilizer applications. However, 24 hours after N-32% solution application, N₂O emissions increased from average values near 0 to values about 200 g N₂O-N ha⁻¹ day⁻¹, thereafter N₂O emissions decreased quickly during the rest of the sampling period. The highest peak of N₂O emission occurred on 16 June 2015, the day after N application with the irrigation water. This peak event represented approximately 25% - 30% of the total N₂O emissions measured throughout the entire growing season.

In seven sampling dates, nighttime irrigation resulted in higher N₂O emissions compared to daytime irriga-
tion. Furthermore, the seasonal cumulative N₂O emission was also significantly greater in nighttime irrigation (3.42 kg N₂O-N ha⁻¹) compared to daytime irrigation (1.97 kg N₂O-N ha⁻¹). However, the frequency of irrigation did not affect soil N₂O emissions in any sampling date. At the same time, the seasonal cumulative N₂O emission was similar between low frequency irrigation (2.40 kg N₂O-N ha⁻¹) and high frequency irrigation (2.98 kg N₂O-N ha⁻¹). In only two sampling dates (28 July 2015 and 8 September 2015) there was an interaction between the two factors with greater soil N₂O emissions in the nighttime-high frequency treatment compared to the other three treatments.

Differences in soil N₂O emissions observed could be related with the different soil water content among treatments [4, 5]. Thus, the WFPS (0-5 cm soil depth) in the nighttime irrigation plots was significantly higher than in the daytime irrigation plots.

Corn grain yield was affected by the interaction of the irrigation time and frequency. The daytime-high frequency showed significantly lower grain yield compared to the other three treatments.

Yield-scaled emissions (expressed as N₂O-N emitted per tonne of grain yield) were in the range of 146 to 270 g N₂O-N Mg⁻¹ grain, similar to the values reported in similar experiments [6, 7]. Yield-scaled emissions were significantly affected by the time of irrigation with greater values in the nighttime treatment (226 g N₂O-N Mg⁻¹ grain) compared to the daytime treatment (150 g N₂O-N Mg⁻¹ grain). The frequency of irrigation did not affect yield-scaled emissions.

## Conclusions

During the growing season, soil N₂O emissions were affected by sprinkler irrigation management. Nighttime irrigation resulted in greater N₂O emission than daytime irrigation due to differences in soil water content between treatments. Although irrigation frequency did not affect N₂O emissions, it affected grain yield. Yield scaled emissions of N₂O were greater in nighttime compared to daytime irrigation, even though the highest corn yield obtained in nighttime irrigation. This research suggested the relevance of irrigation management to control soil N₂O emissions.

## Acknowledgements

The field and laboratory technicians Elena Paracuellos and César Romano are gratefully acknowledged. This research was supported by the Ministry of Economy and Competitiveness of Spain (grant AGL2013-49062-C4-4-R).

## References

[1] Bouwman, A., et al. 2001. Food and Agriculture Organization of the United Nations, Rome, Italy

[2] Deng, Q., et al. 2015. Plos One 10, 14.

[3] Linquist, B., et al. 2012. Global Change Biology 18, 194-209.

[4] Abalos, D., et al. 2014. Science of the Total Environment 490, 880-888.

[5] Aguilera, E., et al. 2013. Agriculture Ecosystems & Environment 164, 32-52.

[6] Halvorson, A.D., et al. 2010. Journal of Environmental Quality 39, 1554-1562.

[7] Venterea, R.T., et al. 2011. Journal of Environmental Quality 40, 1521-1531.
# QUANTIFICATION OF N-RHIZODEPOSITION OF PEAS UNDER FIELD CONDITIONS

Anke Hupe¹, Rainer Georg Joergensen¹, Hannes Schulz³, Franziska Naether³, Florian Wichern²

¹University of Kassel, Department of Soil Biology and Plant Nutrition, Witzenhausen, Germany, ²Rhine-Waal
University of Applied Sciences, Faculty of Life Sciences, Kleve, Germany, ³University of Kassel, Department
of Organic Farming and Cropping, Witzenhausen, Germany

## Objectives

Quantification of the C- and N-rhizodeposition under field conditions is difficult as labelling plants with stable or radioactive isotopes and separating labelled and unlabelled roots is difficult and often creates unnatural conditions. As a consequence experiments are often done under controlled conditions. To estimate a realistic amount of C- and N-rhizodeposition, experiments have to be conducted under field conditions without influencing the root system or the water and nutrient dynamics. The objective of the presented study was to quantify the C- and N-rhizodeposition of peas over time, its spatial distribution and its transfer into different soil compartments under field conditions. Special emphasis was put on estimating the microbial biomass C and N derived from rhizodeposition.

## Method

In April 2013, peas (*Pisum sativum* L. "Santana") were manually sown into 40 microplots (0.5 m x 0.38 m) in the field. Per microplot, 12 pea plants were cultivated with a distance of 12.5 cm between the seedlings and between the rows, to reach an optimal seed density (64 plants m⁻²). In 20 microplots, all plants were labelled at 3 leaves unfolded, 41 days after sowing (DAS), with 0.5 ml labelling solution of 2% ¹³C-glucose (99 atom%) and 0.5% ¹⁵N urea (95 atom%) using the cotton wick method [3]. Every two days, the solution uptake was documented. If plants had taken up the solution completely, 0.5 ml solution was refilled by pipette. In case of a contamination of the labelling system (in some cases fungi formation could be observed), a new labelling system was installed. Frequent application of labelling solution aims at mimicking continuous labelling, which is necessary to achieve label homogeneity, a prerequisite for quantification of rhizodeposition. To investigate the development of rhizodeposition over time, plants were harvested at four dates depending on plant development (beginning flowering, end of flowering; green ripe and dry ripe). At each harvest date, plants of 5 labelled and 5 non labelled microplots were separated into shoots, grain and roots. For analyzing the ¹³C/¹²C and ¹⁴N/¹⁵N isotope ratio, soil samples were taken in 0-30, 30-60 and 60-90 cm depth, evenly distributed in each microplot. In order to calculate the complete root biomass of one pea plant, a second soil sampling always took place in three defined sectors of the microplots (directly on one plant; between two plants in the row; between two rows). The microbial biomass N (Nₘic) was calculated according to [1]. Total N derived from rhizodeposition (NdfR) and NdfR in microbial biomass and in mineral N were calculated with an isotope mass balance approach [2].

## Results

It was found that the same amount of N is released by rhizodeposition throughout plant development, as no significant differences could be observed between the four harvest dates (between 20 and 35 mg NdfR plant-1). The amounts of NdfR in the microbial biomass and in the inorganic N pool show a similar pattern. Between 15 and 33% of NdfR were recovered in the microbial biomass and between 8 and 22% in the inorganic N pool of the soil. The results show that pea plants release the largest amount of N by rhizodeposition until the beginning of flowering. After that, possible plant uptake of NdfR might be masked by the additional release of NdfR from root turnover. At the end of flowering, 68 days after sowing, there was significantly more NdfR in the topsoil (30-60) compared to the subsoil (60-90 cm). At dry ripe, 103 days after sowing, the amount of NdfR was significantly lower in 30-60 cm depth, compared to 0-30 and 60-90 cm depth. As already mentioned, for the calculation of rhizodeposition a continuous labelling is important. Our approach of multiple pulse labelling plants using the cotton wick method resulted in homogeneous enrichment of all plant parts and rhizodeposits with ¹⁵N showing the reliability of the methodological approach. When comparing this field study to a former pot experiment with the same harvest dates, the same labelling method and the same soil, differences in the below ground N-to- above ground N ratio (BGN/AGN) are noticeable.
At the beginning of flowering and at dry ripe the plants in the pot experiment have a significantly lower BGN/AGN-ratio. In the pot experiment, the amount of NdfR in mg g-1 plant dry matter was always significantly higher compared to the field study. These differences between pot and field imply possible problems by transferring results (like the amount of NdfR as a percentage of root-N) from greenhouse studies to field conditions. Of course, in many cases, a pot experiment under controlled conditions is easier to perform than a field experiment. In case of N-rhizodeposition, a different BGN-AGN-ratio, because of the restricted root growth in pots or plant growing under optimal abiotic conditions, can influence the amount of NdfR (exudation and root turnover).

## Conclusions

Rhizodeposition of peas is an important pool of available N for soil microorganisms and thus influencing inorganic N status as indicated by our results. However, about half of the N released from roots is not found in these pools, indicating that rhizodeposits consist of substantial particular amounts, such as root residues or root fragments, which take longer to be mineralized. Moreover, our study clearly shows that estimation of NdfR under field conditions is possible, even though laborious and expensive. However, to estimate a realistic amount of N-rhizodeposition, experiments have to be conducted under field conditions, as BGN/AGN and NdfR/BGN ratios differ between field and pot conditions.

## Acknowledgements

We would like to thank Lydia Hans, Gabriele Dormann, Tino Hupe and all the student assistants for their tireless help in field and laboratory!

## References

[1] Brookes, P. C. et al., 1985. Soil biology and biochemistry 17, 837-842

[2] Hupe, A. et al., 2016. Biochemistry 96, 137-144

[3] Russell, C.A., Fillery, I.R.P. 1996. Crop and Pasture Science 47, 1047-1059
# REMOTELY SENSING-BASED ANNUAL NITROGEN BALANCE FORECAST AT FIELD SCALE FOR WINTER WHEAT, POTATO AND MAIZE CROPS IN BELGIUM

Jean-Pierre Goffart¹, Yannick Curnel¹, Isabelle Piccard², Dong Qinghan², Amaury Le Clef¹, Anne Gobin², Viviane Planchon¹, Cindy Delloye³, Pierre Defourny³

¹Centre wallon de Recherches agronomiques (CRA-W), Gembloux, Belgium, ²Vlaamse Instituut voor Technologische Onderzoek (VITO), Mol, Belgium, ³Université catholique de Louvain, Earth and Life Institute (UCL-ELI), Louvain-la-Neuve, Belgium

## Objectives

The establishment of a field-specific provisional annual nitrogen balance for N fertilization advice, at the beginning of the growing season is based on information of different farmers' fields [1]. Required information such as crop rotation, annual farming practices and field delineation is often inaccurate or missing. An objective assessment of most of the required information by remote sensing (RS) could be a solution to face this uncertainty which hampers the nitrogen fertilization advice quality and relevancy.

The objective of this study, which is part of a global research project called “BELCAM” (http://maps.elie.ucl.ac.be/belcam/) started in 2015, is to evaluate the potential of satellite imagery to assess inputs for the nitrogen balance method at the field scale, i.e. mapping of crop type sequence along the year and parcel zoning capturing local heterogeneity. This abstract summarizes the results of the first year of the project.

## Method

The N-balance sheet methods applied in Belgium are on the one hand the “Livre Blanc Method” (Ulg, Gembloux ABT) for winter wheat and on the other hand for potato and maize the Azobil/Azofert method developed by INRA (Laon, France) and adapted to the Belgian conditions, in Wallonia [2].

During the first year of the project (2015), the remotely-sensed information at parcel level was the current main crop identification and the limits of the different parcels, the type of the winter soil cover (species identification) and a qualitative assessment of their biomass production. For each variable, “field” data were collected to calibrate/validate the algorithms/methods used to derive the variable.

The new generation of high resolution optical satellites (e.g. Sentinel-2) allows to collect information over large area on a regular basis and to monitor crops at intra-field scale which opens the door to a modulation of fertilizer applications per homogeneous area (identified by remote sensing) within heterogeneous fields.

As Sentinel 2a satellite images were not available before August 2015, DMC/Deimos and RapidEye high resolution satellite images were acquired in 2015 over Belgium. UAV (eBee) images (RGB, RedEdge and Multispec4C cameras) were acquired for some specific fields. Orthophotographs and digital terrain models were generated for two winter wheat and three potato fields in the Gembloux area at several moments during the growing season. The RGB images were used to evaluate field heterogeneity.

## Results

A crop type map was generated over Belgium for the 2015 summer season based on a DMC/Deimos images time series acquired between mid-March and mid-July, in order to obtain “early crop type estimates”. The IACS dataset derived from farmers’ declarations, available for Flanders and provided by the Flemish government was used as training data set. An overall accuracy of 73% was obtained. Winter crops were classified accurately while summer crops (maize, potato, sugar beet) were often mixed. Possible improvements include per-field post-classification and the use of images in August-September. These improvements should result in an increase of accuracy of at least 10%. Additional improvements of the classification accuracies are expected by the use of the red-edge bands of Sentinel-2

A map was also generated for 2015 autumn cover crops based on RapidEye images acquired in November
2015. A supervised Maximum Likelihood classification was performed. A field survey was organized to collect ground truth information: 220 parcels were surveyed and 15 different crop / land cover types were identified, the dominant classes being winter cereals, mustard and bare soils. The overall accuracy of the obtained autumn cover map was about 77%. Overall, winter cereals were well classified, the presence of mustard was slightly overestimated.

Parcel zoning aims to capture local heterogeneity within a parcel. This heterogeneity may for instance be induced by differences in mineral composition of the soil, soil moisture, organic matter content or soil texture. Local differences in plant growth may be indicative for differences in soil characteristics. Remote sensing derived vegetation indices were used to reveal soil heterogeneity from RapidEye fAPAR images acquired at different moments during the season (13 May, 30 June, 17 July, 31 August, 11 September and 1 November 2015). Tests were performed using blue, green, red, red edge and NIR reflectances separately and using derived vegetation indices. For two winter wheat specific fields, hundreds of soil and biomass measurements were collected. Once processed, these data will be used as reference for determining the optimal settings of the segmentation algorithm. The results were evaluated with field observation, for selected winter wheat, maize and potato fields. For a few fields UAV images and/or yield measurements were available for comparison. First results will be presented during the workshop.

## Conclusions

The results of the first year of the project are promising for the development of a remote sensing-based annual nitrogen balance forecast at field scale. The potential to obtain early identification of the previous crop through remote sensing within a field have been demonstrated; the identification of autumn cover crops is also encouraging. Zonation of a heterogeneous field seems realistic but needs further analysis.

## Acknowledgements

This project is supported by BELSPO, the Belgian Scientific Policy, within the Stereo III program.

## References

[1] Goffart, J.P. & Olivier, M. 2004. In Haverkort A.J., MacKerron DKL (eds). Decision support systems in potato production: Bringing models to practice, Wageningen, The Netherlands, pp. 68-83.

[2] Abras M. et al., 2013. Biotechnol. Agron. Soc. Environ., 2013, 17(S1), 215-220.
# POTENTIAL FOR MITIGATING ATMOSPHERIC AMMONIA IN CANADA

Shabtai Bittman¹, Steven Sheppard², Derek Hunt¹

¹Agriculture and AgriFood Canada, Agassiz, BC, Canada, ²ECOMatters Inc., Pinawa, MB, Canada

## Objectives

Ammonia is the only atmospheric pollutant emitted mainly by agriculture. Atmospheric ammonia impacts the environment and human health. Both ambient and deposited ammonia can damage plants, degrade biodiversity in pristine environments, degrade marine waters and acidify soils. Ammonia reacts quickly with acid gases producing fine particulates (PM₂.₅) that damage human health and can remain airborne and transported for long distances, impacting ecosystems far from the emission sources.

Ammonia volatilization is the largest loss pathway for reactive N from Canadian farms and is also a significant loss of an essential plant nutrient. Many technologies can limit the loss of ammonia from farms, however, the impact of these mitigation methods must be assessed at the level of the whole systems. Emission inventories help to assess the impact of mitigation methods and can help inform policy. The goal of this paper is to assess the potential for mitigating atmospheric ammonia in Canada.

## Method

This paper is based mainly on a number of previously published studies conducted in Canada. Detailed farm surveys quantified farm practices that relate to ammonia emissions across agricultural sectors and eco-regions in 2005 [1, 2] and 2011 [3]. Ammonia emissions in Canada were computed with emission inventory models based on the farm practices data from the surveys, emission factors adapted to Canadian farms and conditions, and official farm statistics such as animal numbers and fertilizer consumption. Costs and efficacy of abatement measures were based on the UNECE Guidance Document [4], and data on food consumption, food waste and food exports were obtained from official Statistics Canada reports [5]. Deposition of ammonia to the Canadian landscape as well as atmospheric transport into and out of the country was calculated for the year 2002 using AURAMS (A Unified Regional Air-quality Modelling System) model [6-8].

## Results

Canada emitted 408 kt of ammonia N from agricultural sources in 2005, which was about 85% of emission from all sources. Of the agricultural sources, 55% was related to food consumed domestically and the remainder 45% to agricultural exports which included large amounts of grains, beef and pigs [5]. On the basis of consumable protein produced, beef production in Canada was associated with most ammonia loss (1.3 kg NH₃-N kg⁻¹), followed by pigs (0.43 kg NH₃-N kg⁻¹), milk, poultry meat and eggs (0.21 - 0.15 kg NH₃-N kg⁻¹). Plant based foods emitted much less than animals based foods with vegetables > cereals > pulses. Therefore, replacing animal protein with pulses in the Canadian diet will significantly reduce ammonia emissions. Since cattle and pigs are value-added products, reducing these exports in favour of more grain exports would harm the agricultural economy.

Largest sectoral emissions were beef (170 ktNH₃-N), crop fertilizers (100 ktNH₃-N), and pigs (70 ktNH₃-N), so targeting these sectors can yield greater impact than targeting dairy (48 ktNH₃-N) or poultry (21 ktNH₃-N). Generally, housing (153 ktNH₃-N) and landspreading (110 ktNH₃-N) of manure were the greatest loss pathways and so offer the greatest potential for mitigation.

It is worth noting that Canadian producers widely employ several practices for economic or social reasons that coincidentally mitigate ammonia. Examples are pasturing of breeding cows, multiple feeding phases for pigs and poultry, injection of mineral N fertilizer, and in some regions, low emission spreading of pig and to a lesser extent cattle slurry. Winter grazing has greatly expanded in recent years to save money, but the impact on emissions was not incorporated into the above estimates. Successful adoption of these practices without regulations or incentives in unsupported sectors (beef, pigs and grain) is instructive.
We considered the potential of low-cost methods for mitigating national emissions in Canada. The overall mitigation was determined in the context of the manure chain and farming sectors. Methods included reducing protein in feed to UNECE target levels, hastening incorporation of solid manure to <4 hours, reducing splash plate application of slurries in favour of low-emission surface-banding and injection methods, increasing winter grazing of beef cattle, covering pig slurry storages and some inexpensive housing changes. Considering only the measures with fairly firm costs (low emission slurry application and slurry storage covers), it was possible to reduce national emissions by 16 ktNH₃-N at a cost of $13m or 0.80 per kg of abated N (net fertilizer value). When considering all the putative low cost methods, it was possible to reduce emissions by 79 ktNH₃-N or 26% of livestock emissions (19% of all agricultural emissions). The will to implement mitigation policies depends on understanding the benefits to the overall community.

## Conclusions

The impact of mitigation must be considered holistically. Currently there is an atmospheric import of about 500 kt of ammonia [6], mostly to southern Ontario, so mitigating ammonia emissions in that region would have little impact. In contrast there is excellent evidence for the Lower Fraser Valley (LFV) of British Columbia were local agricultural emissions greatly influence ambient ammonia concentrations; this was demonstrated by seasonal changes and during a period when much of the regional poultry flock was culled. It is also necessary to understand the harm reduction associated with abatement. For example, in the LFV there is an abundance of ammonia so abatement of 30% will have little effect on PM₂.₅, unlike regions like southwester Ontario and Southeastern Quebec where ammonia is the limiting reactant in PM₂.₅ formation. Note that this relationship changes strongly with season. The study in the LFV suggested that focussing mitigation on periods with poor air quality would be easier to accomplish and more effective than year-round mitigation. More collaborative research is needed among agricultural, environmenta, and health scientists to lessen harm from ammonia in Canada.

## References

[1] Sheppard, S.C. and S. Bittman. 2013. Agric. Ecosys. Environ. 171: 90-102.

[2] Sheppard, S.C. and S. Bittman. 2013. Anim. Feed Sci. Technol. 166-167: 688-698.

[3] Sheppard, S.C. et al. 2015. Can. J. Anim. Sci. 95: 305-321.

[4] Bittman, S., et al. 2015. Atmos. Environ. 113: 108-117

[5] Sheppard, S.C. and S. Bittman 2014. Atmos. Environ. 103: 43-52.

[6] Clair T. et al. 2014, Global Biogeochem. Cycles, 28:1-15.
# USING BAYESIAN HIERARCHICAL MODELS TO BETTER UNDERSTAND AGRICULTURAL NITRATE CONTRIBUTION TO RIVER WATER

Yongqiu Xia¹,², Donald E Weller², Xiaoyuan Yan¹

¹Institute of Soil Science, Chinese Academy of Sciences, Nanjing, Jiangsu province, China, ²Smithsonian Environmental Research Center, Edgewater, Maryland, USA

## Objectives

Understanding agricultural nitrate contribution to river water is critical to water quality assessment and management. Export coefficient models, which assign a calibrated export coefficient (the rate of pollutant loading per unit of land area) for each modeled land use, are attractive tools for predicting nitrogen exports because they can flexibly incorporate processes and have minimal data requirements. However, export coefficient models typically do not account for the extensive spatial and temporal variability in land characteristics, weather, and management practices. This study seeks to better quantify agricultural nitrate contribution to river water by addressing structural and parameter uncertainty of an export coefficient model.

## Method

Confidence in model predictions depends on uncertainties in model structure and parameters. Compared to the base export coefficient model, we investigated three strategies that apply Bayesian hierarchical methods in export coefficient modeling to accommodate spatiotemporal variability in nitrogen sources and sinks and to quantify uncertainty in coefficients and predictions. The first approach assumes that errors arise mainly from unspecified sources or sinks, and it uses additive structural and non-structural error terms in space and time to address uncertainty. The second approach assumes that errors come mainly from using static export coefficients and instead estimates coefficients that vary independently in time and space. The third approach extends the second one by assuming that the temporal and spatial structures of the parameter series are similar, and then using dynamic parameter estimation techniques to accommodate the autocorrelation of parameters. We compared the three strategies in a case study of nitrate export from nine watersheds in the Coastal Plain physiographic province of the Chesapeake Bay drainage.

## Results

All three strategies adapted a published export coefficient model that quantifies nitrate contribution of crop land and nitrate removal. The base model is effective choice for modeling the effects of cropland and riparian buffers on nitrate concentrations. However, the model does not capture spatial variation in model parameters among watersheds or temporal variation among weeks of observation. We expect important spatial and temporal variations because nitrate concentration is strongly affected by spatial heterogeneity of soil types, terrestrial storages, and land use and by the timing of precipitation, evapotranspiration, and agricultural activities. Compared to the base model, model 1 achieved higher model accuracy by adding spatial and temporal variability terms to accommodate structural error. It was not the best method in this study, probably because structural error is not the main cause of uncertainty in the base model. The strong performance of model 2 suggests that parameter uncertainty is the most important source of uncertainty of the base model. Model 2 performed well by representing spatial and temporal variations in parameters as the main source of uncertainty, as in other models of non-point source pollution. However, according to recent inferences, the modeling efficiency of this model would get slow with model runs by assuming complete independence of each parameter distribution. The third approach had the best fit and most reasonable complexity of the three strategies. The Gaussian first-order random walk model well represents correlations among the error terms of spatial parameters, and its application is conceptually similar to hierarchical dynamic linear model (DLM) and the Kalman filter. The DLM model with a discount factor explicitly recognizes temporal correlation within the time series of parameters. Gaussian first-order random walk models and data driven priors have been used previously to characterize ecological process. Our study is the first study to combine the random walk and DLM to simultaneously characterize spatial and temporal parameter variability in a water quality model. We compared our model 3 results with published results from original model in the Coastal Plain province. We found similar strong effects cropland and riparian buffers on stream nitrate concentra-
tions. Our study improved understanding beyond the previous study by quantifying spatial and temporal
variations in nitrate sources and sinks. Consistent with theory and previous measurements, the variations of
cropland nitrate exports were positively related to stream flow and watershed average slope, while instream
nitrate retention was positively correlated with nitrate concentration.

Conclusions

We better quantified agricultural nitrate contribution to river water by addressing parameter uncertainty of an export coefficient model. We confirmed previous findings that cropland is the largest land use source of ex- ported nitrate, and that nitrate removal by riparian buffers can greatly reduce watershed nitrate export. How- ever, we also demonstrated that nitrate sources and sinks vary strongly in space and time, and we verified that Bayesian approaches can deal effectively with that variation. The methods developed here can be readi- ly applied in other watersheds where pollutants have impacts that vary spatially and temporally.
# SENSITIVITY ANALYSIS OF NITIRSOIL MODEL OUTPUTS: SOIL MINERAL NITROGEN, NITROGEN LEACHING LOSSES AND NITROGEN CROP UPTAKE

Fernando Visconti, José Miguel de Paz

Instituto Valenciano Investigaciones Agrarias, Moncada-Valencia, Spain

## Objectives

The NITIRSOIL is a one-dimensional transient-state model with monthly time step for predicting main nitrogen transformations and transport in soils cultivated with irrigated crops [1]. A global sensitivity analysis (SA) was carried out to know how the uncertainty in each input factor of the NITIRSOIL model propagates to each of its main outputs, namely end season soil mineral nitrogen (Nmin), cumulative nitrogen leaching losses (Nleach), and cumulative nitrogen crop uptake (Nupt). The SA was done for the cropping of globe artichoke (*Cynara cardunculus* var. *scolymus*) under Mediterranean climate in the Valencian Community (E Spain). Following the SA, the coefficients of the nitrogen dilution curve (C₁, C₂) were calibrated for this crop with data from three plots and two cropping seasons each. Finally, the NITIRSOIL model was validated for globe artichoke by comparison of predictions and measurements of Nmin, Nleach and Nupt using data from a different plot and two seasons.

## Method

The probability density functions (normal, log-normal or uniform), distribution parameters (mean and standard deviation), and variation ranges (maximum and minimum) of 48 input factors to the NITIRSOIL model were assessed. Depending on the class (crop, soil, weather, irrigation, or management) and type (parameter or variable) of the input factors, the assessment of their variability characteristics was based either on published data, e.g. for soil coefficients, or on available information from the Valencian Community, e.g. for soil, weather, irrigation and management factors. These variability characteristics were used to generate 10,000 independent random samples of the set of 48 input factors by means of the SIMLAB 2.2 software [2]. Note that covariances among input factors were considered null for the development of this SA. Next, the NITIRSOIL model was run on each one of these 10,000 samples and the corresponding Nmin, Nleach and Nupt outputs were recorded. The multiple linear regression models (MLRM) for explaining the variation in each output due to the 48 input factors was assessed by means of the R software [2]. The standardized regression coefficient of each input factor (βᵢ) was squared and divided by the summation of all 48 in each MLRM (Σβᵢ²) in order to obtain the sensitivity measures of the SA, i.e., the percent of variance in each output explained by each input.

For calibration and validation four commercial plots located in different places in the Valencia surroundings were cropped to globe artichoke, during two years in a row each. In each plot four different levels of nitrogen mineral fertilization by means of NH₄NO₃ were tested. The experimental plots were characterised for soil properties, and then monitored during the cropping seasons for irrigation management, weather conditions (rainfall, evapotranspiration, temperature), crop development and nitrogen contents in stems and fruits, nitrogen content in soil at various depths, and nitrate in irrigation and rainfall waters. On basis crop development and nitrogen contents, the parameters C₁ and C₂ of the nitrogen dilution curve were calibrated in three plots, and the NITIRSOIL model validated for the prediction of Nmin, Nleach and Nupt in the remaining plot.

## Results

The MLRM could explain 76, 69 and 55% of output variance for, respectively, Nmin, Nleach and Nupt. Accordingly, the model could be considered linear enough for Nmin and Nleach and thus, the SA could be based on the squared standardized regression coefficients (βᵢ²). With over 50% of explained variance, the most influential input factors on both Nmin and Nleach are the nitrate content of the irrigation water (NO₃irrig) and then, the nitrogen fraction in the soil organic matter of fast turnover rate (N_no_pool). Specifically for Nmin, the input factors in order of explained variance are: NO₃irrig (39%), N_no_pool (23%), the soil organic matter in the arable layer (SOMsup) (13%), the fractional nitrogen uptake (fNU) (7%), the annu-
al fertilizer amount (6%), the soil organic matter mineralization coefficients (Komm) (5%), the nitrogen dilution curve coefficients ($C_1$ and $C_2$) (2%), the desnitrification coefficient (1%), and other factors (3%). For Nleach the same input factors arise, however, for Nleach the important input factors related to the soil water dynamics such as annual rainfall, irrigation and evapotranspiration (ET0ann) and the nitrogen leaching coefficient (Kleach) logically intersperse throughout the list giving rise to the following percentages of explained variance from more to less: $NO_3$irrig (38%), N_no_pool (15%), annual rainfall (10%), SOMup (8%), fNU (8%), annual irrigation (8%), Komm (3%), ET0ann (2%), Kleach (1%), the bulk density in the arable layer (BDsup) (1%) and the rest of input factors (5%). For Nupt the MLRM could not explain a high enough amount of variance, what reveals the existence of higher order effects, i.e. important interactions between crop and management factors. In any case, fNU (68%) and $C_1$ and $C_2$ (14%) were the most influential factors, followed by $NO_3$irrig (4%), harvest index (4%), dry matter index (3%), SOMsup (3%), N_no_pool (2%), annual fertilizer amount (1%), Komm (1%) and others (1%). The coefficients of the nitrogen dilution curve ($C_1$, $C_2$) were thus revealed as very important for Nupt, important for Nmin and barely important for Nleach. Their calibration gave values of $C_1$ = 3.7 ± 0.9 and $C_2$ = 0.41 ± 0.14 at 95% confidence. Following the validation, Nmin, Nleach and Nupt were predicted with RMSE of respectively, 29, 33 and 235 kg/ha and index of agreement of 0.89, 0.67 and 0.99. Thus, Nmin and Nupt were reasonably well predicted while Nleach was remarkably overestimated. This can be a result of the use of a default value for the nitrogen leaching coefficient, which must be correctly estimated according to its importance as revealed by the SA.

## Conclusions

NITIRSOIL is a simple one-dimensional monthly transient-state model for simulating nitrogen transformations and transport through the following processes: mineralization, nitrification, denitrification, volatilization, nitrogen uptake and nitrate leaching. Since SAs are essential in model communication to inform users about the importance input factors have on outputs and thus, about model suitabiltity to specific simulation goals, SAs are required by different authorities like the US EPA, and the European Commission for model use acceptability. Through the SA of NITIRSOIL we identified on which model parameters calibration efforts must be focussed and similarly, on which model variables analytical determinations should more accurately be done. The relevant NITIRSOIL model parameters are those related to the fractional nitrogen uptake throughout the cropping season, the nitrogen dilution curve, the soil organic matter mineralization coefficients, and the nitrogen leaching factor. Amongst the variables, we found mainly the nitrate content in the irrigation water. However, even more important than stressing the need to correctly determine $NO_3$irrig, is to stress the importance the nitrogen content in the organic matter fraction of fast turnover rate has. Accordingly, N_no_pool should be carefully determined for modelling in NITIRSOIL, thus avoiding the use of default values. Similarly the soil organic matter mineralization coefficients, and nitrogen leaching factor must be correctly calibrated.

## References

[1] de Paz, J.M. et al., 2012. In: Richards, K.G. et al. (eds) Proceedings of the 17th Nitrogen Worskhop. Wexford, Ireland. p. 443-444.

[2] Saltelli, A. et al., 2004. Sensitivity Analysis in Practice. Willey: London.
# EFFECT OF DIFFERENT N-MANAGEMENT MEASURES TO REDUCE N SURPLUSES ON N₂O EMISSIONS FROM VEGETABLE PRODUCTION

Reiner Ruser, Perik Seiz, Rudolf Schulz, Torsten Müller

University of Hohenheim, Institute of Crop Science, Stuttgart, Germany

## Objectives

Vegetable production is partly associated with elevated N-fertilization and thus results in high N surpluses since vegetables such as cauliflower and broccoli are harvested in the vegetative growth stage. Different N-management measures like i.e. the reduction of N-fertilization (either as a general reduction or with the help of optical sensors to determine plant demand), nitrification inhibitors, cultivation of catch crops, or the removal of crop residues before winter have been proposed to reduce these N surpluses.

Some of these measures were tested on their applicability to reduce nitrate leaching [1] and [2], whereas the effect on the release of the climate relevant trace gas N₂O from horticultural systems is only rarely investigated. Main aim of our investigation was therefore to test some of these N-management measures for their potential to reduce N₂O emissions under field conditions.

## Method

The study was conducted on block experiment with four replicates on the University Hohenheim research farm Heidfeldhof, near Stuttgart, south Germany. Soil type was a silty Haplic Luvisol derived from periglacial loess. A closer description of soil characteristics was published earlier [3].

In the first experimental year, crisphead lettuce and broccoli were grown, in the second year cauliflower followed by broccoli. Ammonium sulfate nitrate was used throughout the whole experiment as N-fertilizer.

We tested the following treatments:

1. Unfertilized control

2. High N: N-fertilization according to demand without consideration of mineral N and with a safety margin. This approach is common in horticultural practice

3. KNS: Reduction of common N-fertilization rates in horticulture according to the KNS system (German target value system developed for N-fertilization consultation). Except for treatment 1), 2), and 4) the N-fertilizer amount proposed by the KNS system was used for all other treatments reported here

4. SPAD: Reduction of N-fertilization while monitoring chlorophyll contents after a first basal N application with the option for further top dressings in case of plant brightening

5. NI: Use of a nitrification inhibitor (NI: 3,4-dimethylpyrazole phosphate)

6. Integration of green rye as a winter catch crop

7. Immobilization of soil nitrate before winter through straw addition

8. Crop residue removal

Trace gas fluxes were measured at least weekly over the entire two year experimental phase using the closed chamber method. Chamber type, GC-configuration and calculation of the gas flux rates were described elsewhere [4].

Simple N-balances were calculated for each treatment as N-input (N-fertilization + straw-N in treatment 7) - N-removal (N in marketable yield + N removed with the residues in treatment 8).
## Results

$N_2O$ flux rates showed a high temporal variation with high $N_2O$ fluxes after N-fertilization and rainfall or irrigation, after soil tillage and after harvest of the last crop over winter. In both years highest flux rates were measured outside the cropping season. We found strong correlations between the $N_2O$ fluxes, $CO_2$ fluxes, soil moisture and nitrate contents of the topsoil indicating that denitrification was the major $N_2O$ source.

$N_2O$ emissions over the entire experiment varied between 12.8 and 81.5 kg $N_2O-N$ ha⁻¹ 2 yr⁻¹ in the unfertilized control and the High-N treatment. Reduction of N-fertilizer amounts (High-N) to the amount recommended by the KNS system significantly reduced $N_2O$ emissions by 37% (51.3 kg $N_2O-N$ ha⁻¹ 2 yr⁻¹) without any decrease in marketable yield or visible quality characteristics of the vegetables. A similar $N_2O$ reduction for the KNS system at the same study site was found in an earlier study and was explained with the lower nitrate contents in the top soil [3].

As compared to the KNS treatment, N-fertilization in the SPAD treatment was reduced by 36% and the $N_2O$ emission by 51% but yield of some vegetable crops was reduced.

The use of a NI did not reduce the emissions when compared to the treatment KNS without a NI. The statistically not significant lower emission during the cropping season in the NI treatment was nullified by the high $N_2O$ emissions during the winter seasons. The reason for the inefficient NI remains unclear. In an earlier study, $N_2O$ reduction of approximately 45% was found during the cultivation of vegetables at the same site and the same NI [3].

Implementation of green rye as a winter cover crop resulted in lower nitrate contents of the top soil and thus reduced the $N_2O$ emission by 41% as compared to the KNS treatment with black fallow over winter.

Straw application reduced the mean $N_2O$ emission from the KNS treatment (34%). However, this reduction was not statistically significant. This measure significantly reduced nitrate contents over winter but, as observed in other studies, the major problem of this measure was the assessment of the release of the immobilized N.

Although removal of the crop residues required a higher N-fertilizer amount than the KNS treatment (690 vs. 625 kg N ha⁻¹ 2 yr⁻¹), the N-balance for this measure was negative and successful in $N_2O$ reduction. With 74% lower emissions, crop residue removal was the most effective among the N-management measures tested. The $N_2O$ emission was statistically not different from the emission of the unfertilized control.

We found a strong positive correlation between the N-balance and the $N_2O$ emission during the two experimental years. It accounted for 90% of the variability of the $N_2O$ emissions.

## Conclusions

Our data clearly indicate that N-management measures have a high potential for the reduction of $N_2O$ emissions from vegetable fields. A high $N_2O$ mitigation can be achieved by reduction of N-fertilizer amounts according to the KNS system without any decrease in vegetable yield.

The highest $N_2O$ reduction was realized when crop residues were removed, i.e. for composting. On the other hand, this measure removes organic C for humus reproduction. Therefore long-term studies which focus on the effect of crop residue removal on changes of the $C_{org}$ stocks in vegetable production systems seem worthwhile.

Based on the strong correlation between N-balance and $N_2O$ emissions, the most important conclusion of our results is that every measure which reduces N surpluses also (not always statistically significant) reduces $N_2O$ emissions from vegetable production.
Acknowledgement

This study was founded by the German Federal Office for Agriculture and Food (BLE).

Referensers

[1] De Ruijter, F.J. et al., 2010. Nutr. Cycl. Agroecosyst. 86, 241-253

[2] Wiesler, F. et al., 2008. Agrarspectrum 41, 95-108

[3] Pfab, H. et al., 2012. Agric. Ecosyst. Environ. 150, 91-101

[4] Flessa, H. et al., 1995. J. Geophys. Res. 100, 23115 - 23124
# SOIL N AVAILABILITY PATTERN ON HORTICULTURAL LAND

Koen Willekens¹, Bart Vandecasteele¹, Stefaan De Neve²

¹Institute for Agricultural and Fisheries Research, Merelbeke, Belgium, ²Ghent University, Faculty of Biosci-ence Engineering, Ghent, Belgium

## Objectives

Soil organic N, which forms the main part of the soil N stock, is made available to plants as a result of de-composition of soil organic matter (SOM) by soil microbiota. This N release process is referred to as N mineralization. On the other hand, soil mineral N (Nmin) is consumed by soil microbiota and converted in organic forms, i.e., N immobilization. When N losses are not considered, Nmin availability is the net result of N mineralization, N immobilization and crop N uptake. We hypothesized that the balance between min-eralization and immobilization is different in a bare or limited rooted soil compared to an intensively rooted soil. Our objectives were (i) a better understanding of these N availability inducing processes and (ii) based on this new knowledge, making suggestions for optimization of Nmin based fertilization advice systems for field vegetable cropping systems.

## Method

N availability due to mineralization-immobilization was studied in field vegetable cropping systems. It con-cerned two multi-year soil management trials, one under conventional (SMTco) and the other under organic cultivation (SMTorg), and a field survey (FS; 2009) on conventionally cultivated horticultural land, with leek (Allium porrum) as test crop. In SMTco, the crops were broccoli (Brassica oleracea, var. Italica Group) (2009) and leek (2011), and management factors were tillage practice and compost application [2]. In SM-Torg, test crops were leek (2012) and celeriac (Apium graveolens, var. rapaceum) (2013), and management factors were tillage practice (2012&2013), grass-clover termination strategy (2012), cut-and-carry fertiliza-tion (ensilaged grass-clover) and compost application (2013).

In order to assess net N mineralization from SOM, balances of plant available N were calculated. A balance result was obtained by subtracting N supply from N recovery items and represents the “apparent” net N mineralization (ANM) from SOM, inclusive of the organic fraction of recently applied organic material [1]. ANM is the net result of soil N mineralization and immobilization, but “apparent” as it may also include N losses occurring during the growing season. The amount of recovered N is the sum of aboveground crop N uptake and residual Nmin in either the 0-60 cm (SMTorg) or 0-90 cm soil profile (FS & SMTco). The N supply is the sum of the mineral N input and the initial Nmin in the soil profile. Soil of three subsequent soil layers was sampled for determination of Nmin in the soil profile, initially just before fertilization and tillage (s1), intermediately under a young crop (s2) and finally at crop harvest (s3). ANM was calculated for dif-ferent time spans in the cultivation period. Besides an overall balance for the s1-s3 period, partial balances were calculated for the s1-s2 and s2-s3 periods. Both base and top mineral dressing were registered. Inter-mediate and final crop N uptake were known by determination of dry matter yield and crop N content. In FS and in case of the broccoli crop in SMTco, intermediate N uptake was estimated based on crop N uptake curves.

## Results

In FS, ANM<sub>s1-s3</sub> varied from a few tens to a few hundred kg N ha<sup>-1</sup>. Remarkably, ANM<sub>s2-s3</sub> was mostly nega-tive indicating apparent net N immobilization in the second half of the growing season. Planting date signifi-cantly affected mineralization-immobilization processes. All but two early planted fields showed ANM<sub>s1-s2</sub> values larger than 100 kg N ha<sup>-1</sup> followed by large negative ANM<sub>s2-s3</sub> values. This phenomenon was observed to a lesser extent in late planted fields. Given a growing season with no excess precipitation, N leaching losses were likely to be non-significant.

For both broccoli and leek in SMTco, the decrease in soil profile mineral N in the s1-s3 period was larger than the crop N uptake in that period. This resulted in negative ANM<sub>s2-s3</sub> values, on average -66.7 and -20.9
kg N ha⁻¹ in respectively the broccoli and leek growing season. Part of the applied or available Nmin was either lost, immobilized, or both. Only in the leek growing season, we may assume some leaching losses between s2 and s3 as this period spanned the colder months of September and October. Therefore, N mineralization and immobilization apparently were in balance in the s2-s3 time span. In contrast, leaching losses were less probable under broccoli in the s2-s3 time span, because that period comprised the warmer months of June and July. The daily ANM<sub>s1-s2</sub> for broccoli in 2009 was half of the daily ANM<sub>s1-s2</sub> for leek in 2011. In the leek growing season, ANM<sub>s1-s3</sub> was significantly higher under conventional tillage (CT: 112.4 kg N ha⁻¹) compared to reduced tillage (RT: 96.4 kg N ha⁻¹). Neither ANM<sub>s1-s2</sub> nor ANM<sub>s2-s3</sub> were affected by any of the soil management factors. By contrast, partial balance results in the SMTorg trial were clearly affected by soil management factors in both the leek and celeriac growing season. ANM<sub>s1-s2</sub> and ANM<sub>s1-s3</sub> under the leek crop in 2012 were significantly lower in case of removal of a full-grown cut of the preceding grass-clover cover crop compared to both other termination strategies, i.e. early termination after one time mulching and late termination after three times mulching. ANM<sub>s2-s3</sub> was negative by on average -12.5 kg N ha⁻¹. In the celeriac growing season (2013), ANM<sub>s1-s2</sub> was significantly higher under CT compared to RT and significantly higher for the highest dose of cut-and-carry fertilizer compared to the intermediate and zero dose. And, in analogy with FS, a higher ANM<sub>s1-s2</sub> corresponded with a lower ANM<sub>s2-s3</sub> (more negative; on average -64.4 kg N ha⁻¹).

Partial balances showed that N mineralization prevailed over N immobilization in the first half of the growing season, whereas in the second half of the growing season - under a well-established crop - N immobilization prevailed over N mineralization.

## Conclusions

The balance results of both the field survey and the multi-year field trials suggest that N availability in field vegetable cropping systems follows a cyclic rather than a linear pattern. In the course of the growing season, apparently, a transition occurs from a bare soil stage where N release from SOM prevails to a 'plant-soil system' where immobilization prevails. We may assume that under a well-established crop, with a root system that extensively occupies the soil volume, soil microbial life is strongly activated by root exudates.

This understanding of the N availability pattern might contribute to the amelioration of Nmin based fertilizer recommendation systems, in which soil testing for plant N availability assessment should be based on soil sampling both before and after full establishment of the crop, rather than by a single soil sampling during the youth stage of the crop. Both mineral N measurements and N mineralization tests might suit for soil sampled before crop establishment or during the youth stage of the crop. However, we presume that another type of soil test should be developed for estimation of potentially plant available N under a well-established crop.

[1] Feller, C., Fink, M. 2002. Acta Hortic. 571, 195-201.

[2] Willekens, K. et al., 2014. Sci. Hortic.-Amsterdam 178, 79-86.
# N YIELD-SCALED EMISSIONS AND GREENHOUSE GAS INTENSITY FROM FORAGE MAIZE IN RELATION TO ADDITION OF 3,4 DIMETHYLPYRAZOLE PHOSPHATE TO CATTLE SLURRY AND MINERAL FERTILIZERS

Claudia Gilsanz Rey, Dolores Báez Bernal, María Isabel García Pomar

Mabegondo Agricultural Research Centre (CIAM-INGACAL), Xunta de Galicia, Abegondo, Spain

## Objectives

Maize, which is one of the main crops in Galicia, covers around 68, 909 ha of land in the region. Nitrogen (N) based fertilizers are applied to enhance crop yields and can significantly affect greenhouse gas (GHG) emissions. In a previous study carried out in Galicia, where the climate is characterized by high rainfall (>1000 mm) and mild temperatures, high emissions of nitrous oxide (N₂O) were observed in relation to the fertilizers typically applied to maize crops by local farmers [1]. The present research focuses on developing new fertilization strategies to produce lower GHG emissions and higher crop yields. Use of nitrification inhibitors such as 3,4 dimethylpyrazole phosphate (DMPP) is a promising strategy [2].

The aim of this study was to investigate how the addition of DMPP to cattle slurry and mineral fertilizer affects GHG emissions and crop yields under the Galician climate.

## Method

The study was carried out during spring and summer (in 2013 and 2014) at the CIAM Research Centre (NW Spain), on a silt loam soil classified as Humic Cambisol. The soil was characterized as follows: pH 5.9; organic matter (OM) content of 49.6 g kg⁻¹ (on a dry matter [DM] basis); total N content, 1.8 g kg⁻¹ DM and C/N ratio 10.6. Fertilizers were applied twice: at the end of May (before sowing) and mid July, as top dressing. Maize (cv LG 33.85) was sown at a density of 98000 plants per ha. The trial was conducted as a completely randomized block design with four replicates and eight treatments. In three treatments, a single dose (200 kg of total N ha⁻¹) of different N fertilizers was applied before sowing: in T1, cattle slurry (CS) was injected into the soil; in T2, CS was applied with DMPP; and in T3, Entec (ammonium sulphate nitrate established with DMPP) was added. In four treatments, the N fertilizer was split in two doses (100 kg of total N ha⁻¹ each): in T4, only calcium ammonium nitrate (CAN) was added; in T5, CS was combined with CAN; in T6, CS and Entec were combined; and in T7, CS was applied with DMPP and combined with Entec. A control treatment without N was included (T8). To monitor GHG, two closed chambers (diameter 25cm, height 36cm, depth in the soil 3cm) were installed in each plot. Samples collected 40 minutes after closure of the chambers were analyzed by gas chromatography (Agilent 7890A). Soil samples were analyzed to determine Water Filled Pore Space (WFPS), and meteorological station data were used to determine the influence of weather on N₂O fluxes.

Maize was harvested on 1 October and sub-samples were collected to determine DM yield and N uptake. For comparison of gaseous emissions, the following were calculated for each treatment expressed as percentages: i) N yield-scaled emissions, calculated by dividing the net N₂O cumulative emission by the total N uptake for the maize; and ii) greenhouse gas intensity (GHI), calculated as the global warming potential (GWP) divided by the yield.

## Results

Cumulative N₂O emissions during the maize growing season ranged from 0.78 (control) to 4.04 (CS) in 2013 and from 0.54 (control) to 2.08 (CS) kg N₂O-N ha⁻¹ in 2014. Total rainfall during the crop season in 2013 was 36% lower than in 2014. The WFPS values were markedly different at the beginning of each growing season: approximately 55% in 2013 and 70% in 2014. This was decisive in increasing cumulative N₂O emissions in 2013 because the high WFPS values in 2014 promoted conversion of most of the N₂O to N₂ via denitrification. Comparison of total cumulative N₂O emissions did not reveal any differences between mineral fertilization treatments (T3 and T4) or combinations with CS (T5, T6 and T7). Significantly higher cumulative emissions were only found for the single dose CS (without DMPP) (T1) relative to the mineral
treatments (T3 and T4).

The highest emission fluxes occurred after the first application of fertilizer in both years, coinciding with the highest WFPS values (around 60-70%). In plots to which the fertilizer was applied as CS in a single dose (T1 and T2), the combination of DMPP and CS caused a decrease in the emission of N₂O: to 45% in 2013 and 54% in 2014. When only half of the N was applied as CS (T5, T6 and T7), the inhibitory effect of DMPP was 47% in 2013 and 65% in 2014. High WFPS values suggested that N₂O emissions may have been due to nitrification and denitrification.

During the period between the second application of fertilizer and harvesting, a global decrease in N₂O emissions occurred with all the treatments, and cumulative emissions were not significantly different from those associated with the control treatment. In treatments T4, T5, T6 and T7, application of CAN or Entec was not followed by distinct emission peaks. The low fluxes may be attributable to the WFPS values (close to 30%) in both years during this period. These values indicated nitrification as the major process underlying the emissions.

Crop yield was highest in treatments with including CS in a single dose, with or without DMPP. No signifi-
cant differences between treatments were observed in factors such as N yield scaled emissions or GHI in the
first year. In the second year, N yield scaled emissions were significantly higher in plots fertilized with CS
(T1), and for T5 and T6 were higher but not significantly, relative to treatments with mineral N (T3 and T4).
However, this difference disappeared when CS was applied with DMPP (T2 and T7).

Conclusions

We observed large differences in N₂O losses between the two maize cropping seasons. These were probably mainly due to the differences in WFPS values after the first fertilizer application. The highest cumulative N₂O emissions for the crop growing season were observed after application of cattle slurry in May. Use of the inhibitor not decrease yield obtained after CS application and caused a decrease in the cumulative N₂O and N yield scaled emissions to values similar to those obtained after application of mineral fertilizer or CS plus mineral N. The application of a single dose of mineral fertilizer as Entec yielded the same results as application of CAN in two doses (May and July), thus reducing the field effort required.

Acknowledgements

We are grateful to the ‘Xunta de Galicia, Consellería de Medio Rural e do Mar’ for funding through the FEADER 2012/23 project.We thank EuroChem Agro for supplying DMPP, and the INIA for a grant awarded to Claudia Gilsanz. We also thank E. Rodríguez, M.J. Bermúdez, M.J. Casal and P. Rodríguez for help with laboratory analyses.

References

[1] Louro, A. et al., 2015. Geoderma Regional 5, 54-63.

[2] Huérfano, X. et al., 2015. Europ. J. Agronomy 64, 47-57
# NITROGEN-FERTILIZATION EFFECT ON N₂O EMISSION FROM A TYPICAL RUBBER PLANTATION

Wen-Jun Zhou<fnref n="1" />,², Hong-Li Ji<fnref n="1" />,<fnref n="3" />, Jing Zhu⁴, Yi-Ping Zhang<fnref n="1" />,², Li-Qing Sha<fnref n="1" />,², Jun-Hui Zhang<fnref n="5" />, Xun-Hua Zheng⁶

## Objectives

Transformation of primary tropical rainforests to rubber (*Hevea brasiliensis*) plantations has been widely occurring in the tropics. This landuse change could result in lots of environmental impacts, which should be paid more attention to. In Xishuangbanna, Southwest China, rubber plantations cover an area of about 4.7 × 10⁵ ha (24.6% of the local landscape), which represents about half the tropical rainforest. However, little attention has been paid on the GHG emissions from the rubber plantation. Hitherto, the report on the fertilizer effect on the N₂O emission dynamics, mechanism and the global warming potentially (GWP) were lacked. Thus, effects of fertilization on nitrous oxide (N₂O) emission and the contribution to GWP are not clear. To meet this requirement, we carried out 2-year field measurements of N₂O fluxes from fertilized and unfertilized fields of a typical rubber plantation in Xishuangbanna, southwest China.

## Method

### Site description

The study manure rubber plantation (21° 55'30"N, 101° 15'59"E; elevation 580 m), transplanted from tropical rainforest, was planted in 1993 in Xishuangbanna, Yunnan Province, Southwest China. The annual average temperature is 21.7 °C, the mean annual precipitation is 1557 mm.

### Experimental design

Taking into account the slope gradient, slope direction, and buffer zone, there were terraces (NN), fertilizer trenches (NNt), narrow slopes, and wide slope (NN+) treatment in the NF plots, and terraces (UN), dry season fertilizer trenches (UNts), rainy season fertilizer trenches (UNta), narrow slopes, and wide slope (UN+) treatments in the F plots. In line with local farming practices, 1 kg of mineral fertilizer (supplied by the Hubei Sanning Chemical Co. Ltd, China) containing 15% N, 15% P, and 15% K (comprising (NH₄)₂CO, NH₄H₂PO₄, and KCl) was applied to each rubber tree per year; this amounted to an application rate of 75 kg N ha⁻<fnref n="1" /> yr⁻<fnref n="1" />.

### Measurements

We measured the N₂O fluxes simultaneously in all the F and NF treatments for two years from April 1, 2012 to March 30, 2014 using a static opaque chamber technique.

Soil volumetric water content (0-12 cm) was determined with time-domain reflectometry (TDR100, Campbell Scientific, USA). Soil temperature (0-10 cm) and the air temperature were recorded with a needle thermometer. Rainfall was recorded half-hourly by an automatic recorder attached to the top of the eddy flux tower.

Throughout the soil N₂O flux observation period, soil samples were collected for analysis of NH₄⁺-N and NO₃⁻-N, microbial biomass N (MBN), microbial biomass C (MBC), dissolved organic carbon (DOC), and total dissolved nitrogen (TDN). Mineral N was the sum of NH₄⁺-N and NO₃⁻-N, and DON was the difference

<footnote n="1">¹Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, China, ²Xishuangbanna Station for Tropical Rain Forest Ecosystem Studies, Chinese Ecosystem Research Net, Mengla, China,</footnote>

<footnote n="3">³University of Chinese Academy of Sciences, Beijing, China, ⁴Guangxi Normal University, Guilin, China,</footnote>

<footnote n="5">⁵Institute of Applied Ecology, the Chinese Academy of Science, Shenyang, China, ⁶State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China</footnote>
between TDN and mineral N.

## Results

Fertilizer made a noticeable contribution to N₂O emissions in the rubber plantation. The average N₂O emissions from UNts (0.55 ± 0.092 mg N m⁻² h⁻¹) and UNta (0.58 ± 0.11 mg N m⁻² h⁻¹) were 39 and 41 times greater, respectively, than the lowest N₂O emissions from NNt (0.014 ± 0.0016 mg N m⁻² h⁻¹). This shows that the fertilizer trenches were N₂O emission hot spots in the rubber plantation. The area-weighted N₂O flux from the fertilized rubber plantation showed a bimodal pattern. The area-weighted N₂O flux from the F area (0.046 ± 0.032 mg N m⁻² h⁻¹) was significantly higher than that from the NF area (0.029 ± 0.026 mg N m⁻² h⁻¹) in the rubber plantation.

The rubber plantation fertilization EF (1.96%) was within the range of global agricultural systems (1.25 ± 1.0%), but higher than the default factor (1.0%) reported by the IPCC.

In this study, with the exception of the UNta treatment, the N₂O flux correlated with soil temperature and soil moisture. Seasonal variations in the N₂O fluxes from UNta did not correlate well with soil temperature shows that the timing of the fertilizer application influences the relationship between soil temperature and N₂O flux.

The regression relationship between the area-weighted mean N₂O flux and soil water and moisture in the NF and F plots also indicates that soil temperature and moisture predominantly explain the variation in N₂O. Furthermore, fertilizer applications reduced the sensitivity of N₂O emissions to temperature and moisture variations because of variations in the N input to the substrate.

In this study, variations in the N and C fractions had very little influence on temporal variations in N₂O fluxes in any of the treatments.

The significant correlation between the area-weighted mean N₂O fluxes and NH₄⁺-N, and the high percentage of spatial variation in the N₂O fluxes that was explained by NH₄⁺-N suggests that the N₂O emission processes were controlled mainly by nitrification in the rubber plantation.

N₂O from the fertilized rubber plantation contributes 1227.6 kg C ha⁻¹ yr⁻¹ to the 100-year GWP, which accounts for 6.0% of the rubber plantation net ecosystem exchange (NEE) (9.04 t C ha⁻¹ yr⁻¹).

Using the land use ratio, we calculated that for the GWP, the rubber plantation N₂O will offset 17.1% of the NEE of the primary tropical rainforest in Xishuangbanna, Southwest China. This indicates that as the area of the rubber plantation increases, the local tropical rainforest carbon sink will decrease because of the contribution of fertilizer in the rubber plantation, and there will be positive greenhouse effect feedbacks to the local climate, with particular influences on precipitation and temperature

## Conclusions

By comparison study in fertilizer and no-fertilizer rubber plantation, our study showed that chemical fertilizer increase rubber plantation N₂O flux and change the seasonal dynamics of N₂O. The changing of tropical rainforests to fertilized rubber plantations may intensify climate warming by enhancing the regional N₂O emission.

This study only investigated one stage of the life cycle of a mature rubber plantation, so we have insufficient knowledge about the greenhouse gas emissions over the whole life of a rubber plantation. Future studies should investigate the whole life cycle of the rubber plantation to obtain more accurate information about the N₂O feedback from fertilized rubber plantations to the local GWP, and the accumulated greenhouse gas feedback to local climate change.
Acknowledgement

We would also like to acknowledge the staff and technicians of the Xishuangbanna Station for Tropical Rain Forest Ecosystems, and the Public Technology Service Center of XTBG, CAS, for their support with field measurements and soil analyses. This study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant nos XDA05020302 and XDA05050601), Natural Science Foundation of Yunnan province (2015FB188), and CAS 135 project (XTBG-T03), and the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (no. KFJ-EW-STS-084).
POTENTIAL INHIBITOR EFFECT OF HIPPURIC ACID ON NITROUS OXIDE EMISSIONS
FROM GRASSLAND ON A HEAVY CLAY SOIL

Verónica S. Ciganda¹, María López-Aizpún², Miguel Angel Repullo³, Tim J. Clough⁴, Laura Cárdenas⁵

¹National Institute for Agricultural Research (INIA), La Estanzuela, Colonia, Uruguay, ²Universidad de Navarra, Dept. of Chemistry and Soil Science, Pamplona, Spain, ³Institute of Agricultural Research and Training (IFAPA), Cordoba, Spain, ⁴Lincoln University, Lincoln, Canterbury, New Zealand, ⁵Rothamsted Research, North Wyke, Okehampton, Devon, UK

**Objectives**

Of the total agricultural land under permanent grassland in Britain, approximately 50% occurs on soils with a shallow impermeable substrate. This type of heavy soil frequently occurs in western Britain where high levels of rainfall can lead to seasonal water logging where drainage has not been installed [1] greatly reducing the soil aerobic microbial activity and favoring the occurrence of anaerobic processes. Our research aimed to quantify the nitrous oxide (N₂O) emissions from cattle urine patches in a permanent grassland under heavy clay soil in the southwest of the UK, and to study the effectiveness of the hippuric acid (HA) content in artificial urine on reducing N₂O emissions under high water filled pore space (WFPS) soil conditions (> 85%).

**Method**

A field experiment was carried out during autumn 2015 on a permanent grassland comprised mainly with ryegrass (Lolium hybridicum) and white clover (Trifolium repens L.) at Rothamsted Research, North Wyke, Devon, UK (50:46:10N, 3:54:05W) on a clayey typical non-calcareous pelosol soil [2]. A randomized complete block experimental design with four treatments replicated three times was used. Each replicate plot included a gas sampling area with five static chambers installed (i.e., 60 chambers in total), and a soil sampling area of 1 m². Chambers comprised white polyvinyl chloride (PVC) open ended boxes with a volume of 0.032 m³. The lid was fitted with a sampling port and a three-way valve for gas sampling. Treatments were applied on September 30th 2015 and consisted of a control (C) and artificial urine [3] with three different hippuric acid contents: 37 mM (HA1), 55.8 mM (HA2), and 90 mM (HA3). Urine was applied with a measuring cylinder at a rate of 5 L/m² (532 kg N ha⁻¹) inside each chamber using a watering can over the soil sampling area. N₂O was measured one day before treatment application and on 22 occasions after the application date during a total period of 79 days. Gas samples were taken between 11:00 and 14:00, four times a week for the first two weeks, twice weekly for the next five weeks and weekly thereafter. Atmospheric samples were collected each sampling day to provide background values. Chamber lids were placed on the chambers sequentially across the paddocks and after 40 min a gas sample was collected from each closed chamber via the sampling port using a plastic 50 mL syringe [4]. Samples were then placed in pre-evacuated 22 mL headspace vials using a hypodermic needle. Vial N₂O concentration was determined by gas chromatography. The N₂O flux data showed a skewed distribution so a log transformation as ln(flux+1) was applied. A one way analysis of variance (ANOVA) was performed on the transformed data and a pairwise comparison of treatments means was done for each sampling date. Soil samples were taken weekly from the soil sampling area to determine WFPS.

**Results**

The WFPS averaged 97.9% among all treatment replicate plots, with a range of 85-112.5 %, which assured a natural and high soil water saturation level during the entire experimental period.

N₂O daily fluxes showed no significant differences between control and HA treatments during the first 20 days of the experiment with values lower than 20 g of N₂O-N ha⁻¹ d⁻¹. During the entire sampling period, fluxes from HA treatments did not show significant differences among them, but did differ from control (p<0.01) five times during the 30-days “emissive period” of the experiment (sampling days 20 to 50) when high emission peaks were measured. The highest peak appeared 22 days after treatment application (> 40 g of N₂O-N ha⁻¹ d⁻¹). During the last period of the experiment (sampling days 57 to 79), fluxes remained low
and with no differences between HA treatments and control.

Over the study period, cumulative emissions from HA1, HA2 and HA3 were 696, 790 and 627 g N₂O-N ha⁻¹, respectively, and were significantly higher than the emissions from the control (p<0.05), a total of 4.09 g N₂O-N ha⁻¹. Based on previous studies [5, 6] carried out on a silt loam soil and on a loam soil, we have ratified the ‘no effect’ of HA on the N₂O emissions on our heavy clay soil. Whereas the mitigation effect of HA has been proved in incubation experiments [7, 8] no effect has been demonstrated in situ.

Clough et al. [5] study under low WFPS (18-51%) and Krol et al. [6] under higher WFPS (60-80%), both carried out in the autumn, agree with our results which was carried out under much higher soil moisture levels. The current study addresses the knowledge gap of N₂O mitigation potential under favourable environmental conditions for N₂O loss i.e. >85% WFPS in a high clay soil and confirms that it is not a viable route for mitigating emissions at the end of the grazing season.

## Conclusions

N₂O emissions from artificial urine in grasslands on heavy clayey soils under high water content conditions (WFPS >85%) were not affected by the addition of hippuric acid. Whereas the mitigation effect of HA has been proved in incubation experiments [7, 8, 9] we have ratified the no effect in situ.

## References

[1] Granger, S.J. et al., 2010. Rapid Communications in Mass Spectrometry 5, 475-482.

[2] Avery, B.W., 1980. Soil Survey Technical Monograph No. 14, Harpenden, UK.

[3] Doak, B.W., 1952. Journal of Agricultural Science 42, 162-171.

[4] Chadwick, D.R. et al., 2014. European Journal of Soil Science, 65(2): 295-307.

[5] Clough, T.J., et al., 2009. Soil Biology and Biochemistry 41, 2222-2229.

[6] Krol, D.J. et al., 2015. Science of the Total Environment 551, 362-368.

[7] Bertram, J.E. et al., 2009. Global Change Biology 15, 2067-2077.

[8] Van Groenigen, J.W. et al., 2006. Soil Biology and Biochemistry 38, 2499-2502.

[9] Kool, D.M. et al., 2006. Soil Biology and Biochemistry 38, 1021-1027.