respectively. Application of dispersed peat + nanoparticles of silicate minerals also showed high nitrification inhibition potential. In sand, content of N-NO₃⁻ after 15, 30 and 45 days of incubations were 81.5, 74.3 and 75.1 mg kg⁻¹ and in soil - 87.1, 75.1 and 78.5 mg kg⁻¹ respectively. Under corn in control these values were 99.3, 168.4 and 255.8 mg kg⁻¹ for sand and 125.7, 191.0, 287.1 mg kg⁻¹ for soil. After ATC+hydrophilic silica nanoparticles application: 81.2, 73.7 and 77.6 mg kg⁻¹ in sand and 87.9, 76.4, 82.3 mg kg⁻¹ in soil. After dispersed peat+nanoparticles of silicate minerals application - 72.5, 65.1 and 61.7 mg kg⁻¹ in sand and 74.8, 72.1, 65.7 mg kg⁻¹ in soil.

*Field experiments.* Application of studied nitrification inhibitors caused a significant decrease of N-NO₃⁻ content in soil. Both inhibitors showed comparable results and significantly inhibited nitrification for 6-8 weeks. However even after that period of time - till the end of vegetation period content of nitrates in soil was 15-18% lower, where dispersed peat+nanoparticles was applied. We did not find such an aftereffect after application of ATC+hydrophilic silica nanoparticles, probably because of biodegradation of ATC. Both nitrification inhibitors significantly increased wheat productivity - up to 800 kg ha⁻¹ in comparison to control - 693 kg ha⁻¹.

In our previous studies we showed that peat enriched with nanoparticles of silicate minerals and rocks such as rotten stone, analcime, glauconite and silica was an effective soil amendment and significantly increased productivity of maize, rice, sugar beet, soybean and wheat [2]. We also found that application of this amendment slows down mineralization rate of soil organic matter and organic fertilizers.

Application of organic matter surrounded by silica nanoparticles led to synthesis of polysilicic acids nSiO₂·mH₂O in soil [3]. These acids are poorly soluble in water and form colloidal suspensions, which significantly improve agrophysical soil properties and can be used for soil remediation [4]. We explain slowing down the rates of nitrification by a significant increase of polysilicic acids content in soil.

## Conclusions

This study has shown that a mixture of nanoparticles of silicate minerals with dispersed peat is an effective nitrification inhibitor. Application of this mixture is followed by an important aftereffect - content of N-NO₃⁻ in soil was lower not only during the first 6-8 weeks, but till the end of vegetation period. This fact needs to be tested under different climate conditions and might be a useful tool to reduce N₂O emission.

Efficiency of nanoparticles of silicate minerals in nitrification inhibition depends on content of particulate organic matter in soil. If content of POM is low - dispersed peat is to be added, if high - nanoparticles of silicate minerals may be used without any other components to slow down nitrification rate in soil.

## References

[1] Zaimenko, N. 2008. Scientific principles of structural and functional design of artificial biogeocenosis in the system soil-plant-soil. Kyiv. 303 p. (in Ukrainian)

[2] Zaimenko, N., Didyk, N., Dzyuba, N., Zakrazov, O., Rositska, N., Viter, A. 2014. Enhancement of drought resistance in wheat and corn by nanoparticles of natural mineral analcite. Ecologia Balkanica, 6(1), 1-10 p.

[3] Zaimenko, N., Slyusarenko, O., Bedernichek, T. 2016. Peat enriched with nanoparticles of silicate minerals is an effective soil amendment. Proceedings of the 15th International Peat Congress 2016, Kuching, Sarawak, Malaysia

[4] Telysheva, G., Lebedeva, G., Dizhbite, T., Zaimenko, N., Ammosova, J. 2000. Use of Silicon-containing Lignin Products for in situ Soil Bioremediation. Environmental science and pollution control series. p. 699-728
# NITROGEN BALANCES IN FIELD EXPERIMENTS, FARMS AND REGIONS

Tapio Salo

*Natural Resources Institute Finland, Jokioinen, Finland*

## Objectives

The calculation of nitrogen (N) balances has been identified as one of the priority agri-environmental indicators. N balances can be calculated for whole countries and their regions from the statistical information, but also for individual fields using farm management data. Field experiments can show the relationships between different N rates, yields and N balances in controlled and often more optimal conditions than in practice. In this study, we compared the level of Finnish N balances from regions, farmers' fields and field experiments. The objective is to quantify the differences of N balances from various sources.

## Method

Regional N balances have been calculated in Finland since 1990 [1]. National and regional N balances are calculated annually and the results are reported for Eurostat and Finnish Ministry of Agriculture and Forestry (MAF). N balances from farmers' fields have been collected by various research projects, agricultural companies and Finnish advisory service, Pro Agria. The available field data have been collected in one database during an on-going project funded by MAF, and database includes over 200 000 lines to where N balance can be calculated. Data of nitrogen field experiments with cereals in Finland have been collected and their N balances are determined when sufficient data exist [2]. Part of the data from farmers' field is reported [3] together with some additional field experiment data.

The level and variation of N balances from farmers' fields are compared against N balances in field experiments. Regions are divided according to their number of farm animals and area of grassland, and the size of regional N balances compared to N balances from cereal and grassland fields.

## Results

National N balance has decreased from 90 kg/ha to 50 kg/ha in Finland since 1985. In 1990, regional N balances were approximately 40 kg/ha higher in regions with intensive animal production compared to areas of producing mostly cereals. Nitrogen balances have decreased in all regions, and especially in areas of intensive dairy production. N rates have decreased 30-40 kg/ha and N output in yields has increased approximately 10 kg/ha between 1990 and 2014. N input in manure has been rather constant during the last 25 years. In field data from 1990 to 2014, mineral N rates of spring cereals decreased only 10 kg/ha, mineral N rates of winter cereals 40 kg/ha and mineral N rates of grassland 30 kg/ha. Yield increases resulted also slightly higher N outputs and thus further decreased field N balances.

Field experiments and data from farmers' fields showed linear relationships between N rate and N balance of cereals and other crops. The slope between N rate and N balance in field experiments was 0.50-0.74 and 0.81-0.94 in farmers' fields. Linear regression estimated 27-51 kg/ha N uptake from field experiments without N fertilization, while in farmers' fields the estimated N uptake without N application ranged from 44 to 56 kg/ha.

## Conclusions

N balances calculated either from statistical data or from data collected by farmers have decreased in Finland since 1990. Currently N balances are not decreasing and the expectations of increased production due to climate change and improved cultivars has risen discussion, that N rates should be increased, which can lead also to increased N balances.
References

[1] Salo, T., et al. 2007. Agricultural and Food Science 16:366-375.

[2] Valkama, E., et al.. 2013. Agriculture, Ecosystems and Environment 164: 1-13.

[3] Salo, T., et al. 2013. MTT Report 102. 37 p. http://jukuri.mtt.fi/bitstream/handle/10024/481096/mt-traportti102.pdf
# ESTIMATING THE NITROGEN FERTILIZER REQUIREMENTS OF HIGH-YIELDING HYBRID GRAIN CORN VARIETIES

Josep M Villar¹, Josep Rufat², Hemza Belguerri¹,², Pere Villar³, Miquel Pascual¹

¹University of Lleida, Lleida, Spain, ²IRTA, Lleida, Spain, ³ILERSAP, Lleida, Spain

## Objectives

High-yielding hybrid corn varieties (Zea mays L. ssp. May) are widely cultivated in the Ebro Valley (Spain), where corn growers tend to apply N in large amounts. Current guidelines for grain corn N fertilization indicate that a maximum of 28-30 kg N per ton of grain yield are necessary [1], although the usual recommendations are around 25 kg N t⁻¹. While grain N concentration is not a very useful indicator for characterizing the N status of corn [2], it has been widely used to compute the N fertilizer requirements of corn. In the Ebro Valley, 78% of farmers apply more than 300 kg N ha⁻¹ [3]. The objectives of this field study were to review and improve the guidelines for the use of N fertilizer for high-yielding hybrid grain corn varieties grown in irrigated calcareous soils in the Ebro Valley.

## Method

We used experimental data from eight field experiments (316 experimental plots) carried out over four growing seasons (2012 to 2015). All the trials were done on commercial farms located in the Ebro Valley (North-East Spain) and received either sprinkler (seven fields) or surface (one field) irrigation. The plot area was 9 x 9 m. All the soils were calcareous and had medium textures and distinct organic matter contents (1.8-3%), but the soil depths were different. Eight hybrid cultivars (600-700 FAO cycles and one FAO 280) were planted with a row spacing of 70 cm. The planting dates were between 15 April and 1 May, except for the FAO 280 (on 1 July). N was applied at the V6 stage. The N sources were fertilizers such as urea and calcium ammonium nitrate, some of which included urease or nitrification inhibitors. The treatments also included a non-fertilized plot. The amounts of N applied ranged from 0 to 375 kg N ha⁻¹. Soil samples were taken from all plots before planting and/or applying N fertilizer (at V6 stage) and then at harvest to measure NO₃-N content. N concentration was determined by Kjeldahl digestion. A stalk nitrate test was performed on all the plots at harvest [4]. The experimental design was a randomized complete block with four replications.

## Results

The mean percentage of N in the grain was 1.36% (±0.17, CV 12.5%), while the percentage of protein was 8.3%. The highest grain yields (>16 tha⁻¹) were obtained with grain N contents of between 1.2 and 1.4%. These results were consistent with [5] and [6].

Our results showed that there was no relationship between yield and grain N content. There was also no correlation between applied N and the N content in the grain or stalk. However, a positive correlation was observed between applied N and the concentration of nitrate in the cornstalk at harvest. A linear plus plateau model was adjusted between available N (Soil NO₃-N plus applied N) and grain yield. The critical level of available N at corn stage V6 was 370 kg N ha⁻¹. Above this critical level of available N, no increase in yield was obtained. On average, the harvest index was around 0.5. An appropriate concentration of N in the stalk is around 0.58%. An excess of available N tends to increase the concentration of N in the stalk at physiological maturity, causing a luxury consumption of N. The N fertilizer recovery never exceeded 50%. Under appropriate irrigation management, very high yields of corn can be achieved (>16 t ha⁻¹) in the Ebro Valley when the amount of N applied does not exceed 250 kg N ha⁻¹. When preparing a fertilization plan for corn, the previous crop [7][8], soil organic matter content, nitrate content in irrigation water, and soil nitrate test [9] should all be considered, in addition to the yield goal.

## Conclusions

Our results indicate that the total amount of N fertilizer applied must not exceed 275 kg N ha⁻¹ even when
there is a high yield goal (more than 16 t ha⁻¹). In the Ebro Valley, we recommend that the amount of N fer-
tilizer to be applied to properly managed, irrigated corn (weeds and irrigation) should range from 200 and
275 kg N ha⁻¹ for yields of between 12 and 20 t ha⁻¹.

References

[1] Betrán J. 2010. In: Guía práctica de la fertilización racional de los cultivos en España. 135-141. Ministerio de Medio ambiente y Medio Rural y Marino.

[2] Cerrato and Blackmer. 1990. Agron J. 82:744-749

[3] Lloveras et al. 2012. Vida Rural, February. 24-30.

[4] Wilheim et al. 2005. Agron. J. 97: 1502-1507

[5] Gabriel and Quemada. 2011. Europ J. Agronomy 34, 133-143.

[6] Ciampitti and Vyn. 2012. Field Crops Research 133, 48-67.

[7] Bundy. 2004. Univ. of Wisconsin-Extension. Madison.

[8] Cela et al. 2011. Agron J. 103: 520-528.

[9] Ferrer et al. 2003. Agronomie, 23, 561-570.

Acknowledgements

The authors wish to thank the fertilizer companies and farmers for their cooperation.
# NITROGEN LOSSES FROM AUSTRIAN AGRICULTURAL SOILS - USE OF MODEL RESULTS FOR THE GREENHOUSE GAS INVENTORY

Bettina Schwarz¹³, Barbara Amon¹,², Katrin Sedy³, Gerhard Zethner³, Michael Anderl³, Martina Kasper², Cecilie Foldal², Jasmin Schiefer², Andreas Baumgarten⁴, Georg Dersch⁴, Barbara Kitzler⁵, Sophie Zechmeister-Boltenstern⁰

¹Leibniz Institute for Agricultural Engineering, Potsdam-Bornim, Potsdam, Germany, ²University of Natural Resources and Life Sciences Vienna (BOKU), Department of Forest and Soil Sciences, Institute of Soil Research, Wien, Austria, ³Umweltbundesamt GmbH, Wien, Austria, ⁴Austrian Agency for Health and Food Safety (AGES), Wien, Austria, ⁵Federal Forest Office, Wien, Austria

## Objectives

The project "NitroAustria" aims to broaden the data base for regional nitrous oxide emission factors in Austria from agricultural soils. The findings of model results of LandscapeDNDC [1] in seven regions will be further proofs for the usage of regionally/nationally differentiated emission factors instead of the IPCC default values for calculating direct and indirect N₂O emissions from soil.

A national N₂O inventory based on model and national greenhouse gas inventory results and on a literature review of different N₂O modelling approaches will be conducted. Site specific and regional nitrous oxide emission factors for more targeted mitigation measures on arable land and grassland in Austria will be derived.

The overall aim of NitroAustria is to identify the drivers for nitrous oxide emissions on a regional basis and to reflect the drivers and related amounts of N₂O emissions in the national greenhouse gas inventory.

## Method

As party to the United Nations Framework Convention on Climate Change (UNFCCC) Austria is required to produce and regularly update National Greenhouse Gas Inventories and submit a National Inventory Report (NIR, [2]). For CRF Sector 3 Agriculture, CRF Category 3.D Agricultural soils, the emissions of N₂O that result from anthropogenic N inputs or N mineralization are calculated using the default emission factor of 0.01 kg N₂O-N/kg N applied for inorganic and organic N fertilizers (1 %). Results of the ACRP project "FarmClim" (2012-2015, [3]) highlight that the IPCC default emission factor is not able to reflect region specific N₂O emissions from Austrian arable soils.

In "NitroAustria" the biogeochemical LandscapeDNDC model will be used to calculate N₂O emissions and nitrate leaching for seven regions in Austria representing the diversity of main land uses as well as climatic, soil and management characteristics. These simulated site- and region-specific C and N budgets will be compared with results from other process oriented models based on a literature review and from pre-projects and with the results from the GHG inventory reporting. A national N₂O emission inventory will be simulated using the gathered nitrous oxide data.

Trade off-effects will be explored and suggestions for policy implementation of mitigation measures will be made by means of literature review and expert judgement from the project partners Universität für Bodenkultur Wien, AGES, BfW, KIT and Environment Agency Austria.

## Results

Simulation results of N₂O emissions at site and regional scale will be used to derive region specific emission factors and simulate nitrate leaching rates, which will allow estimating the importance of indirect N₂O emissions [4].

Based on a literature review on background publications of the IPCC default value (empirical regression relationship between fertiliser amount and induced emissions) and other process-oriented nitrous oxide modelling approaches the modelling results will be evaluated for the GHG inventory use. The model outcomes of LandscapeDNDC will be compared and discussed with the NIR conception and extrapolated to simulate a national N₂O emission inventory. Furthermore regional drivers for N₂O emissions will be identified taking
into account different land uses, soil types, climate and agricultural management.

Recommendations will be given on how mitigation measures can be implemented in a policy framework.

Based on the proposed mitigation measures also trade-off effects between different greenhouse gases and other nitrogen emissions (e.g. ammonia, nitrate leaching) will be taken into account. Proposals for implementation of these measures will be elaborated focusing on the applicability for the agricultural sector and their policy frameworks.

The findings of the modelled regions will be further proofs for the usage of regionally/nationally differentiated emission factors for calculating N₂O emissions from agricultural soil. Validated nitrous oxide emission factors for Austrian agricultural managed soils will be evaluated to be integrated in the Austrian GHG emission inventory calculations. Results will be regionally modelled emission factors able to underpin future national deviations from the IPCC default emission factors.

## Conclusions

NitroAustria uses the LandscapeDNDC model to update the N₂O emission factors, the results will be evaluated for their integration into the national emission inventory.

Based on this and on a literature review on background publications of the IPCC default value for N₂O emissions, which is a mean value for global assessments, and results from other process oriented models, a “best available” national nitrous oxide inventory will be simulated. Thereby “mean” emission factors for regions considered with similar conditions for N₂O fluxes can be developed.

The results of the process-oriented model LandscapeDNDC will be utilized in future GHG inventory methodology development, if representativeness for Austria is ensured and model results are validated and published in international or national peer reviewed journals.

## Acknowledgements

This paper is a contribution to NitroAustria, a project funded under the Austrian Climate Research Programme.
# NITROGEN DYNAMICS AND N-CYCLING BACTERIAL COMMUNITIES IN A MEDITERRANEAN OLIVE ORCHARD UNDER A SUSTAINABLE FARMING SYSTEM

Silvia Pascazio¹, Carmine Crecchio¹, Marina Scagliola¹, Alba N. Mininni², Bartolomeo Dichio², Cristos Xiloyannis², Adriano Sofo⁰

¹Department of Soil, Plant and Food Sciences (DiSSPA), Università degli Studi di Bari “Aldo Moro”, Bari, Italy, ²Department of European and Mediterranean Cultures: Architecture, Environment and Cultural Heritage (DiCEM), Università degli Studi della Basilicata, Matera, Italy, ³School of Agricultural, Forestry, Food and Environmental Sciences (SAFE), Università degli Studi della Basilicata, Potenza, Italy

## Objectives

In the recent years, soils has been recognized to play a double role in the entire agro-ecosystem: it is important for a good production as well as for a healthy environment [1, 2]. The conventional, non-sustainable, agronomic practices should evolve in a more sustainable management addressed to ameliorate the ecological networks and nutrient cycling in which soil microorganisms are involved [3, 4, 5].

The main objective of this study was to analyze N dynamics in an olive orchard managed with sustainable agricultural practices. For improving the system and better characterizing the components responsible for the transformation of the different forms of soil N and their availability for plants, a biological and biochemical N indicator was used, and the expression of genes of N-cycling bacterial communities were investigated.

## Method

The trial was in a 2-ha mature olive grove located in Southern Italy managed using sustainable and organic agricultural practices for 15 years. Plants were drip irrigated with urban wastewater (total N = 18.3 mg L⁻¹; with a mean presence of *Escherichia coli*, Enterococci and *Clostridium* spores of 3500, 7000 and 2300 CFU 100 mL⁻¹, respectively) and pruned every year. The soil was permanently covered by spontaneous self-seeding weeds mowed twice a year. Cover crop residues and prunings were shredded and left on the ground as mulch. Two sampling areas were identified: along the row (under the emitters) and along the inter-row (rain-fed). Three soil samplings (composite bulk samples collected from the 0-10 cm soil layer) were performed in a year.

The degree of soil quality was expressed by the ratio Nc/Nk, where Nk is Kjeldahl total soil N, while Nc is a linear function of microbial biomass carbon and N mineralization capacity, combined with three enzyme activities, calculated by the following equation [5]:

$$ \text{Total N} = (0.38 \times 10^{-3}) \text{ microbial biomass C} + (1.4 \times 10^{-3}) \text{ mineralized N} + (13.6 \times 10^{-3}) \text{ phosphomonoesterase} + (8.9 \times 10^{-3}) \beta\text{-glucosidase} + (1.6 \times 10^{-3}) \text{ urease} $$

The expression levels nitrogenase (*nifH*), ammonia monooxygenase (*amoA*), nitrite reductase (*nirK* and *nirS*), and nitrous oxide reductase (*nosZ*) were determined by real-time quantitative PCR, using specific primers and microbial reporter genes (from *Nitrosomonas europaea*, *Azospirillum irakense*, *Azospirillum brasiliense*, *Pseudomonas stutzeri* and *Pseudomonas fluorescens*). Total soil RNA extraction was carried out by RNA PowerSoilTM Total RNA Isolation Kit Sample (Mobio) followed by a DNase treatment. Retrotranscripts were purified by using the Wizard SV Gel and PCR Clean-Up System (Promega).

Microbial biomass C was determined by the fumigation-extraction method [6]. Mineralized N was evaluated as the difference of inorganic N at the beginning and at the end of an incubation period [5]. Inorganic N was determined by the method of Bremner [7]. Urease, phosphomonoesterase, and β-glucosidase activities were measured following the methods described in [7], [8] and [9], respectively.

Together with N analyses, counts of specific N-cycling bacterial groups, soil organic matter, soil moisture and soil physical structure, were monitored.
## Results

In a soil, a large number of physical, chemical and biochemical properties are involved. However, due to the impossibility of considering all of them, it is of key importance to make a selection. In this study, the ratio Nc/Nk exhibited all the attributes of a good soil fertility indicator, as it was sensitive to changes that occur in the soil, capable of reflecting the improvement of soil quality and not too sensitive to environmental and fluctuations. Nc/Nk showed significant differences in the different areas of each orchard (row and inter-row), being generally higher along the row (in the areas wetted by drippers and with high organic matter content) and so indicating a better soil quality. This ratio, together to the microbial biomass carbon, N mineralization capacity and enzyme activities, gave a precise idea on nitrogen soil dynamics (fixation, mineralization, immobilization, organication, nitrification and denitrification) in the different parts of the olive orchard studied. In the areas along the row, a higher bacterial functional activity and diversity was also found, compared to the inter-row areas.

The sequences of DNA with high phylogenetic information content, such as the rRNA genes, has been used for the description of the N microbial networks, increasing our knowledge on the bacterial diversity in soils [10]. Studies based on DNA mainly provide information on the community structure, while the RNA studies, and in particular the analysis of mRNA expression, provide information on the activities of specific populations. Considering the different conditions in the different parts of the orchard (along the row, under the emitters, with high soil moisture; and along the inter-row, where cover crop residues and prunings were shredded) and the seasonal effects (mainly due to rainfall distribution, soil moisture and soil temperature), mRNAs from N-cycling communities were considered for this study. The number of ammonifying bacteria, proteolytic bacteria and nitrogen-fixing Azotobacter in the wetted areas under the drippers were significantly higher than along inter-rows, whereas denitrifying Pseudomonas were not significantly different between the two parts of the orchard. The higher bacterial counts along the row were accompanied by higher expressions of *nifH*, *amoA*, *nirK* and *nirS*.

## Conclusions

This study confirms the need for Mediterranean orchards to encourage farmers to practice soil management based on organic matter inputs associated with zero tillage, in order to improve soil fertility. The increase of knowledge on biochemical processes of the soil microorganism involved in soil N dynamics influencing N availability for plants, can lead to optimize management strategies for a modern and multifunctional concept of agriculture, based on product quality, environmental protection, resource saving and promotion of human health.

[1] Sofo, A., Palese, A.M., Casacchia, T., Dichio, B., Xiloyannis, C. 2012. In: Ahmad, P., Prasad, M.N.V. (eds) Abiotic Stress Responses in Plants. Springer, New York, USA. p. 105-129

[2] Ding, G.C., Piceno, Y.M., Heuer, H., Weinert, N., Dohrmann, A.B., Carrillo, A., Andersen, G.L., Castellanos, T., Tebbe, C.C., Smalla, K. 2013. PLoS ONE 8: e59497

[3] Graf, D.R.H., Jones, C.M., Hallin, S. 2014 PLoS ONE 9: e114118

[4] Gil-Sotres, F., Trasar-Cepeda, C., Leirós, M.C., Seoane, S. 2005. Soil Biology & Biochemistry 37: 877-887

[5] Trasar-Cepeda, C., Leirós, M.C., Gil-Sotres, F., Seoane, S. 1998. Biology and Fertility of Soils 26: 100-106

[6] Vance, E.D., Brookes, P.C., Jenkinson, D.S. 1987. Soil Biology & Biochemistry 19: 703-707

[7] Bremner, J.M., 1995. In: Black, C.A. et al. (eds) Methods of Soil Analysis. ASA, CSSA, SSSA, Madison, WI, USA. p. 1179-1237

[8] Tabatabai, M.A., Bremner, J.M., 1972. Soil Biology & Biochemistry 4: 479-487

[9] Eivazi, F., Tabatabai, M.A., 1977. Soil Biology & Biochemistry 9: 167-172

[10] Eivazi, F., Tabatabai, M.A. 1988. Soil Biology & Biochemistry 20: 601-606
# NITROUS OXIDE AND NITRATE LOSSES – INFLUENCING FACTORS IN WILLOW CROPPING INVESTIGATED BY MODELLING

Hongxing He¹, Per-Erik Jansson², Anna Hedenrud¹, Sophie Rychlik¹, Per Weslien¹, Leif Klemedtsson¹, Åsa Kasimir¹

¹Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden, ²Land and Water Resources Engineering, Royal Institute of Technology, Stockholm, Sweden

## Objectives

Bioenergy crops are expected to play an important role in securing the energy supply and mitigating greenhouse gas emissions. However, to increase the yield, mineral N fertilizer and sewage sludge fertilizer are normally applied which might in turn increase the risk of soil N₂O emissions and also nitrate leaching. This paper thus focuses on the N₂O emissions and soil nitrate leaching and their influencing factors for a conventional Swedish willow bio-energy plantation.

The following research questions were addressed:

1. What influencing factors are found for N₂O emissions and nitrate leaching, respectively?

2. What is the optimum fertilization amount rate to minimize N₂O emissions in relation to gain in biomass?

3. What is the possible impact of drainage on the willow biomass growth and also N losses?

## Method

We calibrated a detailed process-oriented model, CoupModel with a number of measured high resolution data obtained from the Skrehalla field experiment site in Grästorp, Sweden (N58°16′ E12°46′), having willow (*Salix viminalis*). The site has heavy clay soil with pH 5.8 (±0.1). Soil C/N ratio was 12 (± 0.4) for the first 0-50 cm soil layers. The site was drained by a tile pipe drainage system, depth of ~ 1 m and 10 m spacing.

The field experiments started in 2012, the year before the fifth harvest. NH₄NO₃ was added in June 2012, 100 kg N ha⁻¹. Harvesting was performed 29th of March and sewage sludge from a local waste water treatment plant was then added in May, in total 270 (80-460) kg N ha⁻¹.

The current general model structure setups and basic parameterizations were based on previous usage of CoupModel on similar ecosystems. The calibration and uncertainty analysis method used in this study is the Generalized Likelihood Uncertainty Estimation (GLUE). For the GLUE calibration, a number of high resolution data including biotic and abiotic variables were used together with eddy covariance measurements of CO₂ and N₂O fluxes. The application of mineral fertilizer was assumed to directly add ammonia and nitrate to the soil surface N content pool. Sewage sludge was analogously treated as “Faeces” in the model, adding to both soil mineral and organic pools, and assumed to be mixed into the soil surface. The initial conditions of the willow plants are defined by measured plant growth biomass data. Soil physical structure was defined by soil water retention curves derived from pedo-functions from measured soil texture fractions. Where measured data show large variations, coefficients with high uncertainties were included into calibration. Since initial values for total soil organic matter content was unknown, this was subject for calibration. Meteorological data, hourly resolution, was used as model forcing.

## Results

A detailed investigation of the period, June to July 2012 and May to October 2013 shows an emission peak in June 2012 to be closely connected to the soil surface water content variation induced by rainfall soon after mineral fertilizer application. Despite some discrepancies in capturing the measured peaks, the simulated
emissions after commercial fertilizer addition were 0.05 (0.02 to 0.15) g N₂O-N m⁻², similar to what was measured in the year of 2012, 0.035 g N m⁻². The simulated emissions after adding sewage sludge in 2013 were estimated to be 0.2 (0.1 to 0.37) g N₂O-N m⁻² which was slightly higher than measured, 0.17 g N m⁻².

After the mineral fertilizer addition leaching was estimated to 0.66 (0.34 to 1.08) g NO₃⁻-N m⁻² yr⁻¹, where high leaching was found connected to intense rainfall, also being a period with high N₂O emissions. Sewage sludge application resulted in lower nitrate leaching, 0.2 (0.05 to 0.35) g N m⁻² yr⁻¹. Our modeling also describes the leaching of dissolved organic N (DON), which is simulated to be 0.14 (0.13 to 0.15) g DON-N m⁻² for the first year but only 0.02 (0.01 to 0.03) g N m⁻² for the second year. The simulated leaching dynamics was linked to precipitation patterns, where the total precipitation in 2012 was 919 mm yr⁻¹, much higher than the 30 years mean 683 mm yr⁻¹ (data from a nearby SMHI station), while 2013 was dry with only 524 mm yr⁻¹, which explains the small soil nitrate leaching in 2013 despite sludge application. The relatively low N₂O emissions and soil nitrate leaching can also be explained by large plant N uptake, simulated to be 108 (83 to 133) kg N ha⁻¹ in 2012, and 147 (108 to 186) kg N ha⁻¹ in 2013. Therefore the plant nitrogen uptake is the dominating controlling factor in the soil N cycle, leaving relative small amount of available N for N₂O production and nitrate leaching.

## Conclusions

Our modeling indicates that N₂O emissions are mainly produced by the nitrification process, most influenced by soil water content and the amount of fertilizer added. The modelled soil nitrate leaching was negatively influenced by the denitrification and positively by water drainage flow. Model sensitivities indicate that to minimize the N₂O emissions in relation to biomass gain, the application rate of mineral fertilizer should be within the range of 50 to 100 kg N ha⁻¹, sewage sludge within 150 to 300 kg N ha⁻¹. The high sensitivity to drainage suggests that an optimum drainage is of need both to achieve high plant yield and minimize N losses.
# HOW DOES GRAZING ABANDONMENT AFFECT SOIL MICROBIAL ACTIVITY AND AMINO ACID UTILIZATION?

Lur Epelde¹, Anders Lanzén¹, Mikel Anza¹, Enaitz Muguerza¹, Estibaliz Sarrionaindia², Haritz Arriaga¹, Carlos Garbisu¹

¹NEIKER-Tecnalia, Derio, Spain, ²University of the Basque Country, Bilbao, Spain

## Objectives

In mountain pastures of the Basque Country (northern Spain), grazing has been an important economic activity since the Neolithic However, management changes - shorter stays in the mountains, lower stocking rates and less shepherd control - have recently been detected [1]. If these trends continue, a progressive grazing abandonment is expected. On the other hand, soil microbial communities play a key role in many soil processes, providing a direct measure of soil functioning [2]. Nevertheless, the question of whether soil microbial functioning and diversity may be modified by livestock grazing remains poorly explored. The objective of this study was to assess the effect of the abandonment of grazing on the functioning and diversity of soil microbial communities. In addition, the use of amino acids by soil bacterial populations was studied as protein degradation could be a rate-limiting step in the overall N cycle of soils.

## Method

The current study was carried out in the Gorbeia Natural Park (43°N 2.5°W), in the Atlantic region of the Basque Country (northern Spain). The climate is humid temperate, with an annual mean temperature of 10 °C and a mean precipitation of 2,000 mm. This area has traditionally been ranged by mixed and unguarded livestock (sheep, beef cattle and horses). Within the Natural Park, five locations of contrasting characteristics of elevation, vegetation type according to the Habitats Directive (European Commission, 2006), and parent material were chosen: Oderiaga (mountain at 630-720 m, Habitats Directive code 6230a, siliceous), Usote-gieta (mountain, Habitats Directive code 6230c, siliceous), Arimegorta (mountain, Habitats Directive code 6170, siliceous), Ipinaburu (valley at 240-410 m, calcareous) and Urigoiti (valley, siliceous). In spring 2012, four permanent exclusions of 10 x 10 m were placed in the mountain locations (3 locations x 4 exclusion cages) and two in the valley locations (2 locations x 2 exclusion cages). Two years after the establishment of the exclusions, in autumn 2014, topsoil samples from 0-10 cm were collected using a core soil sampler (25 mm diameter). Soil humidity was determined at sampling time. For the measurements of physicochemical parameters, soil samples were air-dried at ambient temperature until constant weight. Total nitrogen (TN), pH, organic matter (OM), Olsen phosphorus (P) and extractable potassium (K) were measured according to standard methods. For biological parameters, soils were stored fresh at 4°C for a maximum of two months until analysis. Basal and substrate-induced respirations (SIR) were measured following ISO 16072 Norm (2002) and ISO 17155 Norm (2002), respectively. Community-level physiological profiles (CLPPs) of culti-vable heterotrophic bacteria were determined with Biolog Ecoplates™ (Insam, 1997). Measurements corre-sponding to an incubation time of 48 h for Biolog Ecoplates™ were chosen to study the utilization of the fol-lowing amino acids: L-arginine, L-asparagine, L-phenylalanine, L-serine and L-threonine. In addition, plant richness was determined by throwing at random a 50x50 cm square (5 times per sampling area). Root depths were measured *in situ*. Soil compaction was measured using a digital penetrometer (Rimik CP40II).

## Results

Soil microbial activity was not significantly affected by grazing abandonment. Mean basal respiration rates were 2.6 (±1.3) and 2.2 (±1.4) µg C g⁻¹ soil h⁻¹ for excluded and grazed soils, respectively. Similarly, substrate-induced respiration averaged 19.6 (±6.4) and 19.4 (±7.3) µg C g⁻¹ soil h⁻¹ for both treatments, respectively. As expected, the OM content of sampled soils was positively related to soil microbial activity, especially the soil basal respiration (R² = 0.87; P < 0.05). After two years of differing management, results demonstrated that grazing abandonment did not have any significant effect on soil OM content at Gorbeia Park (P > 0.05). Amino acid utilization by bacterial populations was not different between excluded and
grazed soils ($P > 0.05$). Soils, in which grazing herbivores were excluded, presented the following absorbance values in the Biolog Ecoplates™: 1.1 for L-arginine, 2.1 for L-asparagine, 0.5 for L-phenylalanine, 1.7 for L-serine and 0.08 for L-threonine. On the other hand, amino acid utilization showed the following mean values for grazed soils: 1.1 for L-arginine, 2.0 for L-asparagine, 0.6 for L-phenylalanine, 1.6 for L-serine and 0.1 for L-threonine. Interestingly, grazed soils showed a higher variability concerning the amino acid utilization by bacterial populations in all the experimental sites. Therefore, the mean coefficient of variation (CV) for all the amino acids accounted for 32.8% in grazed sites whereas CV of soils under exclusion was 18.9%. Results from CLPPs analyses demonstrated that microbial amino acid utilization tended to be higher in valleys compared to mountains for L-arginine (1.4 vs 0.9), L-serine (1.9 vs 1.6) and L-threonine (0.15 vs 0.07). This pattern could be related to the lower OM (5.1% vs 12.8%) and TN content (0.4% vs 0.7%) recorded in the valleys. It is suggested that amino acids would have been utilized more efficiently by microbes under limited C and N conditions [3]. The type of soil, which has been described as a key factor modulating amino acid utilization, did not show here any effect on their use ($P > 0.05$). Soil microbial activity, measured as respiration rates, was not affected by the type of soil ($P > 0.05$). Overall, grazing abandonment reduced significantly soil compaction (mean, 1212 vs 1912 MPa) but did not modify the nutritional status of soils. Mean N, P and K contents were close to 0.6 g kg⁻¹, 16 mg kg⁻¹ and 140 mg kg⁻¹, respectively, for exclusion and grazing treatments. The current animal stocking rate at Gorbeia Park seems not to be enough to modify significantly the soil characteristics. Finally, plant diversity H' index (≈ 2) and plant root depth (≈ 19 cm) were not affected by grazing abandonment.

## Conclusions

Soil microbial activity and microbial amino acid utilization were not affected by grazing abandonment. The microbial efficiency for soil amino acid utilization could be related to C and N status of soils. Grazing abandonment reduced significantly soil compaction but did not change the nutritional status of soils at Gorbeia Park. Plant diversity and plant root depth were not affected by grazing abandonment.

## Acknowledgements

This work has been financially supported by MINECO AGL2013-48361-C2-2-R project. The authors thank Isabel Albizu, Sorkunde Mendarte and Josean Elorrieta for their assistance in plant richness and diversity determinations.

## References

[1] Aldezabal, A., Moragues, L., Odriozola, I., Mijangos, I. 2015. Applied Soil Ecology 96, 251-260.

[2] Garbisu, C., Alkorta, I., Epelde, L., 2011. Applied Soil Ecology 49, 1-4.

[3] Farrell, M., Prendergast-Miller, M., Jones, D.L., Hill, P.W. 2014. Soil Biology and Biochemistry 77, 261-267.
# DYNAMIC OF SOIL O₂ AFFECTS N₂O PRODUCTION PATHWAYS AFTER APPLICATION OF CATTLE SLURRY AND DMPP NITRIFICATION INHIBITOR

Quan Van Nguyen¹,⁴, Di Wu², Xianwang Kong³, Roland Bol², Lars Stoumann Jensen¹, Sander Bruun¹

¹Department of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, Denmark, ²Institute of Bio- and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany, ³Department of Agroecology, Aarhus University, Foulum, Tjele, Denmark, ⁴Department of Environmental Research for Livestock, National Institute of Animal Sciences, ThuyPhuong, TuLiem, Hanoi, Denmark

## Objectives

The application of cattle manure provides nitriogen (N) rich substrates for autotrophic nitrifiers under aerobic conditions, and heterotrophic denitrifiers in low-oxygen or anoxic soil environments. Thus, soil O₂ plays an important role as “a controller” of nitrogen transformation pathways and has significant impacts on N₂O emission [1]. Monitoring spatial and temporal distribution of O₂ in soil can help to understand the mechanisms governing the production of N₂O in the soil. Furthermore, N₂O isotopomer analysis can provide new insights in the pathways leading to N₂O formation [2, 3]. The aims of this study were to 1) gain an understanding how soil O₂ dynamic after surface application of cattle manure affect N₂O formation in soil; 2) understand how these processes are affected by the application of the nitrification inhibitor DMPP.

## Method

Soil sample (4mm) was collected (0-15 cm) from a grassland site, Rollesbroich, Germany (50°37'18"N, 6°18'15"E), and cattle slurry was obtained from a dairy cattle house at AU-Foulum (Tjele, Denmark). The slurry has a dry matter content of 11.5%, pH 7.0 (H₂O, 1:2.5 v/v), total ammonical N of 2.15 g (kg⁻¹ fresh wt). The oxygen planar optode system was described in detail by [1] and [4].

The experiment consisted of three treatments (4 reps) of cattle slurry (CS), cattle slurry with DMPP (CSD-MPP), and a control (CTR). Soil was packed into optode chambers to a depth 4 cm (1 g cm⁻³), and soil water was adjusted twice during the experiment: 85% WFPS (the first 5 days), and 90% WFPS (the last 13 days). Surface application (120 kg NH₄-N ha⁻¹) of cattle slurry was restricted to a 12cm² frame (50% of surface area). For CSDMPP, 0.5 ml DMPP solution (1.495 kg L⁻¹) was mixed into the cattle slurry corresponding to 1.2 kg ha⁻¹ (1% of applied NH₄-N). The incubation was conducted in darkness at room temperature for 18 days. Optode images were taken every 60 minutes during the incubation.

The gas sampling was performed at day 0, 1, 2, 3, 5, 7, 11, 14 and 18 for measurement of CO₂, CH₄ and N₂O by Gas Chromatography (Clarus 580, PerkinElmer, Rodgau, Germany), and on day 0, 1, 3, 5, 7, 11 and 14 for N₂O isotopomer analysis by an isotope ratio mass spectrometer (IsoPrime 100, Elementar Analysensysteme, Hanau, Germany). N₂O isotope signatures were determined and calculated following an approach described in [5]. The source partitioning of N₂O production (i.e nitrification and denitrification) was calculated using a two endmember mixing model.

## Results

The spatial and temporal distribution of soil O₂ was significantly affected by treatments of cattle slurry and DMPP. The greatest effects were observed within the top 1.5 cm soil layer. In this layer, soil O₂ was decreasing rapidly to between 42% and 56% (% air sat.) for CS and CSDMPP, respectively, within the first 48 hours after manure application, while soil O₂ of the CTR remained at 83%. In the deeper layer (1.5-4 cm), negligible changes in O₂ concentration were observed for all treatments. After 5 days, the soil water content was increased and soil O₂ was depleted rapidly in CS (from 56% to 42%) and CSDMPP (from 73% to 63%), but only slowly in CTR (from 83% to 79%) throughout the soil core. The O₂ depletion zone was larger and lasted longer in CS compared to CSDMPP. During the last 7 days, soil O₂ gradually increased in all treatments. Soil O₂ depletion can be due to microbial respiration or nitrification. For the CS and CSDMPP treatments, the lowest O₂ content coincided with peaks of CO₂ evolution, suggesting most soil O₂ was consumed by mi-
crobial respiration.

N₂O emissions were significant higher from CS compared to CSDMPP and CTR. For CS, N₂O flux peaked at day 1 and day 15 with 2 and 1.7 µg N kg⁻¹ soil h⁻¹, whereas it reached at a peak of 1.8 µg N kg⁻¹ soil h⁻¹ at day 1 and then steady decreased for CSDMPP. At the end of the experiment N₂O flux of CS declined to 1 µg N kg⁻¹ soil h⁻¹, and remained much higher than that of CSDMPP. The greatest effect of DMPP was observed between day 5 and 18 (90% WFPS). At this soil moisture content, apparently it likely that DMPP infiltreated better into deeper soil layers (2 – 4 cm) and inhibited nitrification more effectively. Over 18 days incubation, CSDMPP reduced N₂O emission by 60% in comparision with CS treatment. This reduction is lower than observed in field studies [6,7].

N₂O sources partitioning indicated that nitrification was the dominant source of N₂O for both CS (58%) and CSDMPP (65%) in the first 24 h, then bacterial denitrification was gradually taking over when soil O₂ content was signifcantly depleted between 48 and 96 hr. After that the predominance of nitrification took placed relative to detrinitrification and followed the increase of soil O₂ throughout soi columns. Total cumu-lative N₂O source partitioning underlined that denitrification dominated nitrification over a period of 18 days incubation, and contributed to 58.1 % and 57.2% in CS and CSDMPP, respectively.

Conclusions

The results demonstrated that soil O₂ content was significantly reduced by cattle slurry application on the soil surface. The most severe O₂ depletion ocurred within the top 0-1.5 cm. Soil O₂ concentration remained for the CS than CSDMPP treatment, while soil O₂ of CTR treatment remained relatively high. Soil O₂ dynamic influenced significantly sources of N₂O production. Nitrification was the dominant source in the initial 24 hours after slurry application, but after this when soil O₂ decreased denitrification dominated, and as oxygen returned towards the end of the experiment nitrification was again dominating. However, over the whole pe-riod of 18 days incubation, denitrification was the main source of N₂O production from soil. The application of DMPP reduced total N₂O emission by 60% compared to CS treatment without DMPP.

Acknowledgments

The authors would like acknowledge the support of the Innovation Fund Denmark via the project ‘Optimisa-
tion of value chains for biogas production in Denmark (BioChain).

References

[1] K, Bruun S, Larsen M, Glud RN, Jensen LS. Soil Biology and Biochemistry 2015;84:96–106.

[2] Baggs EM. Rapid Commun Mass Spectrom 2008;22:1664–1672.

[3] Well R, Kurganova I, Lopesdegerenyu V, Flessa H. Soil Biology and Biochemistry 2006;38:2923-2933.

[4] Larsen M, Borisov SM, Grunwald B, Klimant I, Glud RN. Limnology and Oceanography: Methods 2011;9:348-360.

[5] Toyoda S, Yoshida N. Anal Chem 1999;71:4711–4718.

[6] Scheer C, Rowlings DW, Firrel M, Deuter P, Morris S, Grace PR. Soil Biology and Biochemistry 2014;77:243-251.

[7] Abalos D, Sanz-Cobena A, Andreu G, Vallejo A. Agriculture, Ecosystems & Environment 2016.
# UK-CHINA VIRTUAL JOINT CENTRE FOR IMPROVED NITROGEN AGRONOMY

Tom Misselbrook¹, Alison Carswell¹, Fusuo Zhang², David Chadwick³, Bridget Emmett⁴, Keith Goulding⁵,
Rob Griffiths⁶, Penny Hirsch⁵, David Jones³, Xuejun Liu², Lin Ma⁷, Tina Roosevelt⁸, Ute Skiba⁹, Lianhai Wu¹,
Mingang Xu¹⁰

¹Rothamsted Research, North wyke, Okehampton, Devon, UK, ²China Agricultural University, Beijing, China, ³Bangor University, Bangor, Wales, UK, ⁴CEH, Bangor, Wales, UK, ⁵Rothamsted Research, Harpenden, Hertfordshire, UK, ⁶CEH, Wallingford, Oxfordshire, UK, ⁷Chinese Academy of Sciences, Beijing, China, ⁸Southampton University, Southampton, Hampshire, UK, ⁹CEH, Edinburgh, Scotland, UK, ¹⁰Chinese Academy of Agricultural Sciences, Beijing, China

## Objectives

The use of synthetic nitrogen (N) fertilisers in agriculture supports c 50% of the current global population, but its inefficient use has led to increasingly serious environmental consequences. The problem is especially acute in fast developing countries such as China, where the subsidised production and use of N fertilisers has succeeded in helping to deliver food security, but at the expense of severe environmental problems. This project will bring together researchers in the UK and China with the specific objectives of: 1) developing novel indicators of nitrogen use efficiency (NUE) incorporating soil health and quality; 2) using these indicators and other emerging knowledge to test and develop farm systems that permit the sustainable intensification of (especially Chinese) agriculture; 3) to translate these developments to Chinese farmers using the proven ‘Science and Technology Backyard’ programme developed by CAU and CAAS that is moving farmers out of poverty and enriching whole communities.

## Method

The UK-China Centre for Improved Nitrogen Agronomy (CINAg) is one of two Newton-funded UK-China Centres on Agricultural N, which will work closely together. The research in CINAg will be delivered through 4 integrated work packages:

WP1 will work towards an improved fundamental understanding of N cycling and improved NUE prediction through development of biological, physical and chemical soil quality indicators. Soil samples will be collected in both the UK and China across a broad range of land uses and N use intensities. Biological measurements will include abundance and activity of key N-cycle genes using qPCR with primers to amplify denitrification genes *nirS*, *nirK*, *nosZ*, the nitrification gene *amoA* from archaea and bacteria and *nifH* for nitrogen fixation, together with community diversity using high throughput sequencing assays for bacteria/archaea and fungal communities. We will also explore the use of protease and chitinase genes (e.g. *apr*, *npr*, *chiA*,) for looking at the upstream controls of mineralization as this represents a major bottleneck in soil N cycling. Measurements of plant performance and climate will support the biological data.

WP2 will focus on harnessing novel N technologies which will be assessed through a series of plot scale experiments. Technologies to be assessed and developed will include ‘smart fertilisers’ (e.g. inhibitors, slow release), novel sensors providing information on soil N supply, novel treatments and practices in the recycling of organic materials and novel crop varieties. Measurements will be made of crop yield, N offtake and quality, soil N transformations and losses to the environment, soil quality indicators as described above and meteorology.

WP3 will assess improved agronomic practices to enhance NUE as identified from WP1/2 at the field scale. Data from these field-scale studies will be used to validate predictions from the 3D-Rhizosphere [1] and SPACSYS [2] models and to feed through to the development of decision support tools.

WP4 is concerned with developing predictive capacity and knowledge exchange. Current limitations to agronomic extension in China and the UK will be examined, focusing on identifying the main barriers preventing the adoption of ‘best practice’ and formulating new ways of promoting NUE on farm.
## Results

The project has only just commenced, so no results are available to date. However, expected deliverables include:

* New soil quality indicators for NUE; a gene-to-landscape understanding of N cycling.

* Enhanced understanding of where and when 'smart fertilisers' will work including predictive models describing the environmental fate of nitrification inhibitors and their efficacy.

* An understanding of spatial variability of key soil quality indicators responsible for NUE in a range of cropping systems, from which spatial optimisation of sensors can be based; transfer of soil nitrate sensor technology to UK and Chinese research groups.

* Identification of the best manure processing technologies to improve NUE during both the processing and land-application stages, improved algorithms to describe mineralisation rates of organic N from acidified and digested slurry 'fractions' to feed into statistical and process-based models such as SPACSYS and guidance on fertiliser replacement rates for different manure 'fractions' following advanced slurry processing techniques.

* Evaluation of theoretical (from modelling) and actual (by experimentation) impact of changes in crop species or trait on N capture and NUE.

* Evaluation of combined technologies for enhancing NUE; validation of mathematical models and creation of new Decision Support Tool Frameworks for better managing inorganic and organic fertilisers and for reducing N losses.

* Shared information on knowledge transfer approaches to improve NUE in UK and China (from Backyard Programs to sophisticated (and improved) DSSs and mobile phone Apps), a critical analysis of the barriers to extension and adoption and the generation of a series of policy and practice briefs targeted to policymakers, industry, regulators and farmers which contains a Road Map for implementing and delivering lasting NUE in agronomic systems.

Through an increase in the skills and knowledge base of Chinese agricultural researchers the VJC will improve China's ability to undertake and disseminate research to maximise impact on the issues of rural and economic growth through the sustainable intensification of agriculture.

## Conclusions

The formation of a UK-China Virtual Joint Centre for Improved Nitrogen Agronomy will accelerate an improved understanding of how NUE can be increased across different cropping systems in the UK and China. The Centre will deliver enhanced outputs and impacts through the sharing of key resources, working on complementary Farm Platforms and networks, sharing experimental methodologies, results, databases and modelling approaches, joint scientific publications and sharing approaches to knowledge exchange. The UK-China Virtual Joint Centre for Improved Nitrogen Agronomy will also be an excellent platform for training young scientists (including Postdoc, PhD and MS students etc.) and conducting integrated research on agricultural nitrogen management in both China and the UK.
# CALIBRATION OF A SIMPLE MECHANISTIC WHEAT MODEL FOR WITHIN-FILED VARIABLE N APPLICATION

Lina Nolin Nyström¹, Bo Stenberg¹, Johanna Wetterlind¹

¹Swedish University of Agricultural Sciences, Skara, Sweden

## Objectives

In precision agriculture (PA) crop management strive for considering spatial and temporal variability in soil and crop factors within a field [1]. One of the issues in PA is N management. At the time for N application, it is difficult to predict the crop N requirement and the soil capacity to release N to the crop. Computer models of agricultural systems can make predictions of the soil-and-crop interactions and give indications of coming soil and crop status.

In this study, the overall objective was to test the potential of a simple and fast-simulating crop model to be used as a decision support for practical site-specific N fertilization in Sweden. More specifically, we tested the model applicability to Swedish climate data and the model potential to predict grain yield with input data from different places in a field that varies largely in both texture and organic matter content.

## Materials and Methods

The mechanistic wheat model Sirius [2] was used in this study. In the model, the physical properties of the soil are described by the hydrological parameters (HP:s); porosity (P), field capacity (FC) and wilting point (WP). Fertilizers and mineralization of soil organic N (SON) are the sources of crop N uptake in the model. Mineralization is restricted to the top 40 cm and depends on the amount of SON, temperature, soil moisture and soil type.

Observed data from a field in the south-west of Sweden (58°06′N, 12°50′E) was used to calibrate model outputs under Swedish conditions. The field varies largely in texture and soil organic matter (SOM). Site-specific data of texture, SOM and soil mineral N for 12 study plots located across the field was used to derive input data to the model. Three N treatments (0, 80 and 200 kg N ha⁻¹) were available at each study plot and grain yield and grain N were analysed at harvest. The 12 study plots showed large variations in grain yield, grain N, protein content and residual soil N.

Four different simulation set-ups were tested during the calibration process with weather and management data kept the same in all simulations. Soil and crop parameter values were modified step by step and every new modification was kept in the next set-up. In set-up 1, site-specific estimated data on HP:s, SON, soil mineral N at time of sowing and percolation coefficient (estimated from clay content) were used as input for every study plot. In set-up 2, modifications were made in the crop parameter values related to thermal time requirements according to the estimates for Swedish conditions. In set-up 3, further adjustments were made in the crop parameters related to grain filling duration and maximum protein concentration, to agree with field observations. In set-up 4, modifications of soil parameters (minimum mineral N content of soil, constant for denitrification pulse and mineralization constant) were made to further improve the agreement of simulated and observed values in the unfertilized treatments.

## Results and discussion

The initial simulation, set-up 1, greatly overestimated grain yield (at most 4300 kg ha⁻¹) and grain N (at most 175 kg N ha⁻¹), especially for plots with high SON. In set-up 2, crop parameters were adjusted to Swedish estimates which improved particularly the grain yield, but also the grain N, agreement to observed data. Still, e.g. in the unfertilized treatments, grain yield and grain N were overestimated by at most 2000 kg ha⁻¹ and 110 kg N ha⁻¹.

In set-up 3, the adjusted maximum protein concentration value did not affect grain yield at all, whereas it
decreased the maximum level of simulated grain N remarkably in the 200 kg N-rate. Grain yield levels were
improved to some extent by increasing the grain filling duration value from 320 to 380 degree days (DD).
Simulated grain yield were overestimated at plots with high SON in the unfertilized treatments and grain N
were overestimated in all treatments. In set-up 4, changing the values of minimum mineral N content of soil
and the constant for denitrification pulse had no impact on the results. However, the mineralization constant
showed large influence on the outputs and it was modified site-specifically to further correct simulated grain
yield and grain N for better agreement with observed data. The effects of changes in mineralization constant
were greatly dependent on the SON-content. For study plots with low SON (≤ 10.0 ton ha⁻¹), constituting
half of the plots, mineralization constant was increased and for the other half of the study plots (SON = 15.7-
31.8 ton ha⁻¹), it was decreased, compared to the default value of mineralization constant. The values of min-
eralization constant correlated well to SON and the correlation may be useful in other simulation studies as
well where SON is known but not the mineralization constant.

The root mean square error (RMSE) of the simulated grain yield was improved considerably using the cali-
brated model set-up 4 (RMSE = 446-757 kg ha⁻¹ or 11-12% of observed mean) compared to the uncalibrated
set-up 1 (RMSE = 1920-2844 kg ha⁻¹ or 30-76% of observed mean). However, although the RMSE for grain
N was improved with set-up 4, grain N was still overestimated and residual soil N underestimated.

Conclusions

In this study, we wanted to test the Sirius model applicability to Swedish conditions and its potential to predict site-specific grain yield within a field. Results showed that the model has potential to be used as decision support for site-specific N fertilization in Sweden, after some adjustments of input data values to current climate and soil conditions.

A model set-up based on site-specific weather, soil hydrologic properties, soil organic N, soil mineral N at sowing and percolation coefficient, gave the right variation in yield, but grain yield (and grain N) levels were highly overestimated. Crop parameters describing thermal time for crop phenology (mainly grain filling duration and phyllochron) showed to be important for maximum yield levels. The input parameter maximum protein concentration did not affect grain yield but governed maximum grain N levels. Adapting the mineralization constant depending on the SOM content instead of using the same static mineralization constant for the whole range of SOM content in the field at the start of the simulation improved the total simulated grain yield at harvest.

In the calibrated set-up, the model generally overestimated grain N levels and underestimated residual N in soil at harvest. So, at least with the current input data used, the simulated N dynamics in soil and crop did not work properly. However, the model gave realistic grain yield levels and variation in grain yield over a field, although not always the right absolute grain yield amount at every place.

Acknowledgements

The authors are grateful to Sören Nyman for information about his field and to the personnel at Lanna field station for weather data supply. STRÅNG data used here are from the Swedish Meteorological and Hydrological Institute (SMHI), and were produced with support from the Swedish Radiation Protection Authority and the Swedish Environmental Agency. This study was financed by Precision Agriculture Sweden (POS) and The Knowledge Foundation.

References

[1] Stafford, J V. (2000). Implementing Precision Agriculture in the 21st Century. *J. agric. Engng Res*, 76, 267-275.

[2] Jamieson P D., Semenov M A., Brooking I R. and Francis G S. (1998). Sirius: a mechanistic model of wheat response to environmental variation. *European Journal of Agronomy* 8, 161-179.