# ESTIMATION OF DELAYED EFFECT OF SOME ORGANIC MATTER TYPES ON SUGARCANE

FEVRIER A.<fnref n="1" />, PAILLAT J.<fnref n="1" />, MARION D.<fnref n="1" />,²

## INTRODUCTION

Sugarcane production is mainly dependent on mineral fertilization in Reunion Island. At the same time, the territory is producing more and more organic residues which can be use as organic fertilizers for sugarcane. However, Reunion, is in a sub-tropical area, with great pedo-climatic diversity, and there is little information available on the dynamics of this organic matter in this context. TERO project aims to study the nitrogen efficiency of diverse organic matters from Reunion Island by estimating N Apparent Use Efficiency (AUC) and performing foliar diagnosis for different organic materials, and distinguishing their direct and delayed effects.

## MATERIAL AND METHODS

TERO Project is located at several sites, and is a long term experiment (4 trials) on Reunion Island which started in 2014. The first results come from one location, La Mare, Sainte-Marie. 6 organic matter types are tested with 3 repetitions. 3 modalities of organic matter were applied:

<table><caption>Table 4: Organic matter modalities</caption><thead><tr><td>Modalities</td><td>Treatments</td><td>Application</td></tr></thead><tbody><tr><td>1</td><td>75 % of nitrogen requirement (soil analysis and Serdaf* recommendation each year)</td><td>Each 2 or 3 years</td></tr><tr><td>2</td><td>37,5 % of nitrogen requirement (soil analysis and Serdaf* recommendation each year)</td><td>Each 2 or 3 years</td></tr><tr><td>3</td><td>75 % of nitrogen requirement (soil analysis and Serdaf* recommendation for the planting year)</td><td>Every year</td></tr></tbody></table>

### Organic matter modalities

Pork manure is applied every year. Filter mud, green waste compost, are applied every three years, poultry litter, Camp Pierrot compost (mixed with poultry litter and pig manure) and pellets of sewage sludge are applied every two years.

### Mineral modalities

5 mineral modalities are used to determine a nitrogen response curve. The need of sugarcane (x) is determined by Serdaf. The mineral modalities are 0x; 0,5x; 0,75x; 0,9x and 1,5x.

This mineral modalities are used to calculate AUC of urea and organic matter.

### Nitrogen measurement

Using the NIRS method, the nitrogen rate is determined in the leaf at 6, 8 and 10 months after planting or harvesting and the nitrogen content in entire aerial part sugarcane is determined by chemical analysis at harvest.

## RESULTS AND DISCUSSION

<footnote n="1">¹ eRcane, Réunion Island; ² CIRAD, Réunion Island</footnote>

<footnote>*Serdaf (expert system for fertilization) is a tool for fertilization advice in sugarcane crop at Réunion Island.</footnote>
After 2 growing cycles, at the first location of the project, the first delayed effects are observed in two types of observations, the foliar diagnosis and the AUC.

## Foliar diagnosis shows a delayed effect similar to Apparent Use Efficiency

Extreme mineral fertilization are included in the tables of interpretation of leaf diagnosis (Fillols and Chabalier, 2007). Therefore the values of zero and 150% of fertilization are used as reference for the results.

As expected, organic matter applied every two years has a low delayed effect. Only sewage sludge of the first modality has a better FD than the modality without mineral fertilization. The response of AUC is nul for all modalities apart from the first modality of sewage sludge, with 0.05 (NS) AUC, the year without input, and 0,14 the year with application. More than 50% of N is in the form of NH₄⁺ for Camp Pierrot compost, 33% for poultry litter, while only 10% in pellet sewage sludge, which can explain why a delayed effect was observed for this product.

For organic matter applied every three years, the foliar diagnosis suggests a stronger delayed effect than AUC. Filter mud (modality 1) has a similar rate of nitrogen uptake as mineral modality with 150% of nitrogen requirement, at 6 months (respectively 15,8% and 15,9%) and 8 months (respectively 13,4% et 13,7%) of analysis. At 10 months the rate is slightly lower for filter mud (respectively 11,2% and 12,6%). However, AUC is 0 for the first modality and 0,08 for the second modality. This result is the outcome of an underestimation of equivalence factors. Organic nitrogen is dominant in filter mud, which is why the expected delayed effect is observed.

<figure><img src="image_125.png" /><figcaption>Fig1: Nitrogen rate obtained by FD in first ratoon for filter mud</figcaption></figure>

Composts are more stable than raw organic matter and have a good amending value and a lower fertilizer value (Flavel et al 2006, Houot et al 2016). Nitrogen content in compost is in the organic form (90%), so mineral nitrogen content depends on mineralization rate. FD and AUC show this indirect effect of fertilization. At 6 and 8 months, the nitrogen rate is similar between green waste compost (modality 1) and the mineral modality with 150% of nitrogen requirement. At 10 months, the nitrogen rate decreases between these two extremes. At harvest, the apparent utilization coefficient is 0.03. This includes compost application of the previous year, which is half of the AUC of the current year of application.

<figure><img src="image_126.png" /></figure>
Fig 2: Nitrogen rate obtained by FD in first ratoon for green waste compost (GWC)

CONCLUSION

After 2 years of fertilization, the first delayed effects are shown by foliar diagnosis, especially with organic matter containing more organic nitrogen, like filter mud and green waste compost.

REFERENCES

Fillols E, Chabalier P.F, 2007. Guide de la fertilisation de la canne ç sucre à la Réunion. CIRAD, CTICS. 166p.

Flavel T.C, Murphy D.V, 2006, Carbon and Nitrogen Mineralization Rates after Application of Organic Amendments to soil. Journal of environmental quality 35 : 183-193.

Houot S., Pons M.-N., Pradel M, Tibi A, 2016. Recyclage de déchets organiques en agriculture. Edition Quae. 199p
# A NOVEL PLATFORM TO PROVIDE SERVICES IN THE MONITORING OF GREENHOUSE GASES FOR AGRICULTURAL SYSTEMS

DECUQ, C.¹, GENERMONT, S.¹, LOUBET, B.¹, CELLIER, C¹. GABRIELLE, B. ¹

¹ UMR EcoSys;INRA; AgroParisTech, Université Paris-Saclay, France

## INTRODUCTION

Mitigating future climate changes, likely to result in increasing damages from droughts, tornados, typhoons or extreme precipitation events warrants a detailed knowledge of the emissions of greenhouse gases (GHG) originating from various economic sectors. Assessing the contribution of agriculture to climate change is one of the key questions that environmental scientists have to address in order to identify possible measures to reduce the burden of agriculture on global warming. Solutions to reduce these emissions exist but are less well quantified than in other sectors due to the large variability of GHG exchanges over time and space. Building on its 20 years of experience in quantifying and predicting biosphere-atmosphere exchanges of GHGs, the ECOSYS research unit (INRA and AgroParisTech) is now able to propose a commercial offer for the environmental assessment of agricultural systems, directed at academic research, agricultural extension services, agro-chemical companies or government agencies overseeing agriculture and environmental management. Applications of the novel platform of INRA Transfer at ECOSYS include the evaluation of, among others: (a) the GHG balance of agricultural practices (b) the potential solutions for reducing GHG emissions (c) the potential emissions from chemical or organic fertilizers and their dynamics. This paper details the types of measurements and monitoring that this platform can deliver, and showcases some of the results obtained in past programmes, and their use for evaluation and decision-making processes.

## MATERIAL AND METHODS

### Measurement of GHG concentrations and fluxes

At the core of its new offer of services, the ECOSYS Research Unit in Grignon (40 kms West of Paris) set up a GHG analysis platform to quantify GHG concentrations in air samples such as N₂O, CH₄, CO₂ and SF₆ by gas chromatography. The preparation of sampling vials, a critical step in the process of field air sampling is included in the services provided by the platform. Emissions of GHG from cropping systems may be directly measured in field trials using static chambers (Rochette et al., 2008). The surface area covered by the chamber was 0.25 m² while the chamber height above the soil surface was 0.2 m. Air is sampled from the chambers' headspace using sampling vials put into vacuum beforehand. GHG concentrations are quantified by gas chromatography equipped with double detection: an Electron Capture Detector (ECD) and a flame ionization detector (FID). The simultaneous measurement of N₂O, CH₄, CO₂ is an original feature of the analysis chain, which makes it possible to detect leaks in chambers. Gas concentrations were used to calculate fluxes making a linear regression of GHG concentrations as a function of the different sampling times. Measurement steps from the «sample tube preparation» to « GHG concentration determination» are quality-insured. The "ECOSYS GHG analysis platform" has validated analysis methods following french normalisation (NF V03-110, 2010). The uncertainties of measurements on each gas concentration are determined.

### Two cropping systems examples

To illustrate the use of this platform for scientific research, we report on a PhD project aiming at assessing the potential of cropping systems design to mitigate GHG emissions (Goglio et al., 2013). N₂O emissions were monitored on two cropping systems set up in Grignon: (a) a reference system (referred to as PHEP), aiming at reaching altogether High Environmental Performances and Productivity, while being representative of arable systems in the region, and (b) a cropping system aiming at halving greenhouse gas emissions compared with the reference system (referred to "low emissions system GHG"). This system seeks to increase C sequestration in the
soil and decrease N2O emissions. GHG emissions were measured every week after fertilizer N inputs, also depending on rainfall events, and every month otherwise. An agro-ecosystem model was used to simulate the emissions and interpolate them over time. (Gabrielle et al., 2006)

## RESULTS AND DISCUSSION

<figure><img src="image_127.png" /><figcaption>Figure 1. Simulated and observed N₂O emissions for the (a) low emissions system and (b) reference cropping system (Vertical lines: date of fertiliser application, error bars: 95 % confidence interval for observed data). (Goglio et al., 2013)</figcaption></figure>

Overall, N₂O emissions were of relatively low magnitude over the 2 years of measurement, being under 10 g N₂O-N ha⁻¹ day⁻¹. Spikes above this level were recorded following the harvest of faba bean in the fall of 2011, with similar ranges across the two cropping systems. The model could capture the variations of N₂O emissions whether on a seasonal or daily basis, with a mean deviation under 1 g N₂O-N ha⁻¹ day⁻¹ and a root mean squared error around 5 g N₂O-N ha⁻¹ day⁻¹. The average cumulative N₂O emissions from the 50%GHG system was 28.5% lower than the reference PHEP system. This ratio is lower than the 50% abatement target assigned to the former system, but does not factor in the differences in soil C sequestration potentials between these systems, which was later confirmed. When taking each crop separately, the highest emissions occurred with faba beans in the reference system (amounting to 1500 g N₂O-N ha⁻¹), while the least-emitting crop was winter wheat in the 50% GHG system (totalling 291 g N₂O-N ha⁻¹; Goglio et al., 2013).

## CONCLUSION

The novel service platform set up by ECOSYS is now ready to deliver high-quality measurements of GHG exchanges for a wide range of agricultural systems, and potential applications. It is expected to facilitate GHG monitoring and assessments in various contexts and its services may be readily customized according to the user's needs and objectives.

**Acknowledgements:** The authors are grateful to Pietro Goglio (Cranfield U., UK). Financial support from INRA, Agroparitech, FIRE, and Terres Inovia is also acknowledged. Romain Cresson (INRA Transfert) is the manager of the commercial unit and leads the transfer process.

## REFERENCES

* Rochette P, Eriksen-Hamel NS (2008) Chamber Measurements of Soil Nitrous Oxide Flux: Are 24 Absolute Values Reliable? Soil Science Society of America Journal 72: 331–342. 25 doi:10.2136/sssaj2007.0215

* Goglio, P., Colnenne-David, C., Laville, P., Doré, T., Gabrielle, B., 2013. 29 % N₂O emission reduction from a modelled low-greenhouse gas cropping system during 2009-2011. Environmental Chemistry Letters, 11, 143-149.

* Gabrielle, B.; Laville, P.; Hénault, C.; Nicoullaud, B. & Germon, J. C. Simulation of nitrous oxide emissions from wheat-cropped soils using CERES Nutrient Cycling Agroecosys., 2006, 74, 133-146
# COMPARING AMMONIA VOLATILISATION OF LIVESTOCK EFFLUENTS HAVING UNDERGONE DIFFERENT TREATMENTS IN FIELD CONDITIONS

VOYLOKOV, P.¹, LOUBET, B.¹, HOUOT, S. ¹, SAVOIE, A.², GENERMONT, S.¹

¹ UMR EcoSys, INRA, France ² INRA UEPAO, FRANCE

## INTRODUCTION

The study presented here is part of the MetaMetha project, designed to evaluate the impact of including an anaerobic digestion step in manure treatment on C and N cycles in agriculture [see Savoie et al., this workshop]. The series of experiments undertaken in the spring and summer of 2017 sought to compare nitrogen use efficiency NUE for a range of treated and raw digestates and non-digested manure and slurry as well as conventional mineral fertiliser applied to crops. NUE is diminished both due to volatilisation as NH3 and N2O or leaching which have adverse effects on the environment and also constitute a loss of fertiliser and potentially yield for the farmers.

A novel method of gaseous emission measurement based on the inversion of the FIDES model (Loubet et al, 2017) [see Loubet et al., in the side event] and the use of passive ammonia samplers (ALPHA, Tang et al., 2001) was implemented to quantify the ammonia emission fluxes. This method indeed is convenient for this sort of experiment where numerous treatments are applied to small parcels. The experimental site was over-equipped in the objective to test assumptions made for the calculations, and to undertake an analysis of the method performed on this set of data.

## MATERIAL AND METHODS

The experiment took place from March 2017 to August 2017 on 5 experimental plots located in Nouzilly near a biogas plant (INRA, Centre-Val de Loire region, France). There were three fertiliser application events (2017/03/22, 2017/04/19 and 2017/08/02). During the first two events, four treatments were applied to parcels of about 1800 m² each, with an additional parcel serving as a control with no treatments. The treatments were bovine slurry, a mineral fertiliser, a raw digestate obtained after the anaerobic digestion of various organic products including pig, cow and horse effluents and the separated liquid fraction of the same digestate. During the last application event, the treatments were a bovine manure, the solid fraction of the same digestate and the raw digestate, likewise with a control treatment.

The parcels were divided into three sections serving as replicates and each section contained a mast with passive sensors (ALPHA badges) attached at different heights. During the first experiment, a setup of 1 to 3 sensor heights was tested. During the remaining two, each section had sensors at two heights. Samplers measured integrated concentrations over periods ranging from several hours just after application to several days at the end of the volatilisation event. The badges were analysed using the FloRRia analyser. Raw micrometeorological data collected using a Gill sonic anemometer, was processed using LabView and EddyPro software in order to obtain the necessary parameters used to calculate the transfer resistance of each sensor/parcel using the FIDES model. This model is based on a semi-analytical solution of the advection-diffusion equation in the surface layer. The sources are subdivided into grid cells each contributing to the observed concentration at the measurement heights. These transfer resistances were used along with concentrations in order to calculate instantaneous ammonia flux from each parcel by least square inference (QR decomposition). Using this data, a sensitivity analysis to several FIDES parameters was performed. The resolution (mesh size) was tested as well as choice of the lateral dispersion function sigma Y (according to Sutton, BNL or Briggs), minimum Monin-Obukhov length, minimum roughness length, and the number of sensors used.
## RESULTS AND DISCUSSION

Raw digestate tended to have the highest ammonia losses followed by liquid digestate, and slurry for the first two experiments. During the third stage, solid manure, solid digestate and raw digestate had significantly higher emissions than the control.

<figure><img src="image_128.png" /></figure>

The sensitivity analysis has not yet been fully performed. The following ANOVA was performed on the results of the first period. These results were obtained, using 0.03 and 0.05 m as minimum roughness length, 1, 2 and 5 as min MO length, 0.5, 1, 2 and 5 m as mesh size and using sigma Y calculations according to Sutton, BNL and Briggs. The mesh size is of a particular interest as it would be helpful to know what the best resolution is given the parcel surface area. The minimum roughness length appears to be significant as well.

<table><caption>Analysis of Variance Table</caption><thead><tr><th></th><th>Df</th><th>Sum Sq</th><th>Mean Sq</th><th>Response:</th><th>Emissions kg ha<sup>-1</sup></th></tr></thead><tbody><tr><td>Mesh</td><td>3</td><td>1.9</td><td>0.65</td><td>6.3897</td><td>0.0002999</td></tr><tr><td>Sigma</td><td>3</td><td>0.1</td><td>0.03</td><td>0.2584</td><td>0.8553095</td></tr><tr><td>Lmin</td><td>2</td><td>0</td><td>0.01</td><td>0.1309</td><td>0.8772953</td></tr><tr><td>z0min</td><td>1</td><td>0.9</td><td>0.9</td><td>8.8334</td><td>0.0031108</td></tr><tr><td>Treatment</td><td>4</td><td>8808.9</td><td>2202.22</td><td>21702.8178</td><td>&lt;2.2E-16</td></tr><tr><td>Residuals</td><td>466</td><td>47.3</td><td>0.1</td><td></td><td></td></tr></tbody></table>

## CONCLUSION

This method is well suited for the comparison of different treatments in the field and its deployment should help evaluate ammonia volatilization after slurry and manure as well as mineral fertilizer applications in various French agricultural contexts and elaborating strategies of reduction. Concerning the effect of the treatment process before application, treated manure gave higher ammonia emissions due to greater ammonia content. The N losses due to ammonia volatilization will be compiled in the global N budget calculations of the MetaMetha project.

**Acknowledgements:** The MetaMetha project, funded by Centre Val de Loire(conv. n° 2016 – 00108447). The authors warmly acknowledge intern A. Lemekhova, and the lab technicians B. Esnault, C. Decuq, E. Vachet and Y. Delaunay.

## REFERENCES

* Loubet, B., Carozzi, M., Voylokov, P., Cohan, J.-P., Trochard, R., and Génermont, S.: Evaluation of a new inference method for estimating ammonia volatilisation from multiple agronomic plots, Biogeosciences Discuss., https://doi.org/10.5194/bg-2017-424, in review, 2017.

* Y.S. Tang, J.N. Cape, and M.A. Sutton, “Development and Types of Passive Samplers for Monitoring Atmospheric NO2 and NH3 Concentrations,” TheScientificWorldJOURNAL, vol. 1, pp. 513-529, 2001.
EFFECT OF UREASE AND NITRIFICATION INHIBITORS ON NITRIC AND NITROUS OXIDE EMISSIONS, AND YIELD
IN AN IRRIGATED MAIZE CROP

RECIO, J.<fnref n="1" />,², MONTOYA, M.<fnref n="1" />,², GUARDIA, G.<fnref n="1" />,², GINES, C.<fnref n="1" />,², SANZ-COBEÑA, A.<fnref n="1" />,² VALLEJO, A.<fnref n="1" />,², ÁLVAREZ, JM.<fnref n="1" />,²

INTRODUCTION

Reducing nitrogen (N) losses from agriculture is pivotal to lessen the environmental impacts associated to the application of organic and synthetic fertilizers. The use of nitrification (NI) and urease inhibitors (UI) has demonstrated the potential to decrease N losses to the atmosphere or groundwater. Apart from NIs commonly used, such as dicyandiamide (DCD) or 3,4-dimethylpyrazole phosphate (DMPP), there are still few studies to use the new NI 3,4-dimethylpyrazole succinic acid (DMPSA). On the other hand, UIs have demonstrated their efficiency to mitigate ammonia (NH₃) and nitrous oxide (N₂O). However, little is known about the effect of these inhibitors over both N₂O and nitric oxides (NO) fluxes.

MATERIAL AND METHODS

A field experiment was carried out at the field station “Center”, situated near Torrejon de Ardoz (Madrid, Spain), in order to compare the N₂O and NO emissions from different fertilizer treatments. Fifteen plots (81 m²; 9 m x 9 m) sown with maize (Zea Mays L. FAO class 600) were arranged in a three-replicated randomized block design. Fertilizers were provide by EuroChem Agro® and were applied by hand at top-dressing (29th June 2017) with a rate of 200 kg total N ha⁻<fnref n="1" /> for all treatments. The different fertilizer treatments were: 1) Urea (Urea), 2) Urea with DMPSA (U+NI); 3) Urea with the UI N-(n-butyl) thiophosphoric triamide (NBPT) (U+UI); 4) Urea + NBPT + DMPSA (U+2I); 5) Control with no N fertilization (Control)

N₂O sampling and analyses

During the first 45 days after fertilization (DAF), samples of gases were taken 2-3 times per week considering it is the most critical period of high gas emissions. Afterwards, the frequency of sampling was decreased progressively, increasing after rainfall events. The N₂O fluxes were measured using the closed chamber technique (Sanz-Cobena et al., 2012) and their concentrations were quantified by gas chromatography.

NO sampling and analyses

Nitric oxides were measured with an entirely automated laboratory measuring system over the cropping season of maize from June 29th to August 8th, 2017. The completely automated laboratory measuring system was designed and constructed following the method proposed by Butterbach-Bahl et al. (1997) with some modifications. The experimental set-up, schematically consisted of 16 flow dynamic chambers, a pump and a chemiluminescence NO-NO2-NOX analyser (model 42i Thermo Environmental Instruments Inc., USA). The opening time of the valves, the sequence of closure and re-opening chambers and data collection were controlled by a PC, running a specially developed computer program (SIMdas, DNOTA ®, Spain). A full cycle takes 3 h to complete, resulting eight measurements per day and plot.

Statistical methods

The statistical analysis of data was performed by using the R software. Analyses of variance were performed for NO and N₂O cumulative emissions and grain yield among all treatments. Data distribution normality and variance uniformity were previously assessed. Averages were separated by LSD test at P< 0.05.

RESULTS AND DISCUSSION

<footnote n="1">¹ ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Spain; ² Research Center for the Management of Environmental and Agricultural Risks (CEIGRAM), Universidad Politécnica de Madrid, Spain</footnote>
Cumulative N₂O and NO emissions (Figure. 1) were significantly higher in urea treatment than in the rest of fertilized N treatments. Total NO emission decreased in the order: Urea > U+UI ≥ U+2I ≥ U+NI ≥ Control (P < 0.05). The effectiveness of NBPT abating NO losses (more than 40%) was close to has been supported by Sanz-Cobena et al. (2012) in an irrigated maize crop (an abatement of 67%). The DMPSA (U+NI) alone performed slightly better potential mitigation than DMPSA+NBPT (U+2I). Cumulative N₂O emissions decreased in order: Urea > U+UI ≥ U+NI ≈ U+2I ≥ Control (P<0.05). In this case, DMPSA significantly decreased (more than 50%) cumulative N₂O fluxes at the end of the experimental period. Guardia et al. (2018) reported similar efficacy of DMPSA in cumulative N₂O emissions in maize, under similar soil and environmental conditions. Treatment of UI alone (U+UI) significantly mitigated the cumulative N₂O emissions in comparison of urea.

Grain and biomass yield were significantly increased by the application of N fertilized treatments with respect to Control. Interestingly, the U+NI treatment produced an increase (not significantly) of grain and biomass yield in relation to Urea treatment. In addition, statistically similar grain and biomass yield was observed in treatments based on UI and U+2I treatment compared to urea and U+NI.

<figure><img src="image_129.png" /><figcaption>Figure 6: a) Total cumulative N₂O-N and NO emissions in maize crop. b) Maize yield. Vertical bars indicate standard error and letters above bars indicate significant differences by applying the LSD test at P < 0.05</figcaption></figure>

CONCLUSION

The use of DMPSA, with or without NBPT, led to highest mitigation rates of no, n2o and yield-scaled n2o emissions. Despite the double inhibitor was not as effective as DMPSA in abatement nitric oxide emissions, the potential mitigation of this treatment (U+2i) is to mitigate not only N₂O and no compared with urea alone, but also the reduction of ammonia volatilization and indirect N₂O emissions.

REFERENCES

Butterbach-Bahl, K., Gasche, R., Breuer, L., Papen, H., 1997. Fluxes of NO and N₂O from temperate forest soils: impact of forest type, N deposition and of liming on the NO and N₂O emissions. Nutr. Cycl. Agroecosystems 48, 79–90.

Guardia, G., Vallejo, A., Cardenas, L.M., Dixon, E.R., García-Marco, S., 2018. Fate of 15 N-labelled ammonium nitrate with or without the new nitrification inhibitor DMPSA in an irrigated maize crop. Soil Biol. Biochem. 116, 193–202.

Sanz-Cobena, A., Sánchez-Martín, L., García-Torres, L., Vallejo, A., 2012. Gaseous emissions of N₂O and NO and NO3⁻ leaching from urea applied with urease and nitrification inhibitors to a maize (Zea mays) crop. Agric. Ecosyst. Environ. 149, 64–73.
# HIGHER NITROGEN USE EFFICIENCY UNDER REDUCED TILLAGE

ERNFORS, M.¹, JENSEN, ES.².

¹,² Department of Biosystems and technology, Swedish university of agricultural sciences, Sweden

## INTRODUCTION

Reduced tillage practices increase the carbon (C) content in the upper soil layer (Haddaway et al. 2017). Since C and nitrogen (N) cycles are closely coupled, an altered distribution of C may also affect the N turnover in the soil. It has been hypothesised that the higher soil organic matter content in the surface layers of reduced tillage soils could cause a higher initial immobilization of N fertilizer in the spring, restricting N availability for the crops, but also decreasing the risk of losses of N through leaching and denitrification. This hypothesis was confirmed by Couto-Vázquez and González-Prieto (2016), after 14-15 years of reduced tillage, but not by Giacomini et al. (2010), after 5 years of reduced tillage, or by Thomsen and Christensen (2007) in a study with less than 10 years of reduced tillage at most of the sites used. The hypothesis of the present study was that changes in N mineralization-immobilization turnover due to reduced tillage would be detectable in an 18-year-old field experiment, where an altered organic matter distribution has had time to develop.

## MATERIAL AND METHODS

The study was carried out within a long-term cropping systems field experiment at the SITES Lönnstorp Research Station in Sweden. The experiment had been running for 18 years at the start of the experiment and included conventionally tilled plots (plowed) and plots with reduced tillage (shallow non-inverting tillage). There were also some differences between the treatments in the six-year crop rotation. A one-year grass-clover ley and a faba bean crop in the reduced tillage system corresponded to cereal crops in the conventional system. There was also an oil radish cover crop before the sugar beet crop in the reduced tillage system. The tillage treatment plots contained response plots, with several nitrogen fertilization levels, that had been in place since the start of the experiment. Two of the levels in the N fertilisation sequence were used: one step above and one step below standard rates. N loss and plant N recovery were determined by applying an isotopically labelled fertilizer solution (5 at% ¹⁵NO₃·¹⁵NH₄) in 2012 and measuring the recovery of the isotope in crop and soil in 2012, 2013 and 2014. Data was analyzed by two-way ANOVA, using SPSS software.

## RESULTS AND DISCUSSION

The crops in the reduced tillage treatment assimilated significantly more of the N fertilizer added in spring 2012, in the first year, compared to the same crops in the conventional tillage treatment (50% and 38%, respectively). In 2013, an additional 2 and 5% of the N fertilizer added in 2012 were recovered in the crop, in the reduced tillage and conventional tillage treatments, respectively. In 2014, 1% of the N from 2012 was recovered in each of the two treatments. Preliminary results show a higher total (soil + plant) recovery of labelled N over the period 2012-2014, in the reduced tillage treatment, and thus lower losses of fertilizer N. The higher crop uptake of fertilizer N in the reduced tillage system was associated with a higher total N uptake (Figure 1). This may have been either a cause or an effect of lower N losses. A seemingly more efficient use of N and a lower fertilizer N loss in the reduced tillage treatment point to positive effects on N cycling by reduced tillage. However, labelled N was added only in one year and variation between years can be large.

The design of the field experiment as a systems experiment, with differences in more than one factor between the two treatments, also calls for a more cautious interpretation than in traditional factorial experiments. Additional analyses of collected samples will be carried out and final results will be presented at the workshop.
<figure><img src="image_130.png" /><figcaption>Figure 1. Crop N uptake from soil and fertilizer sources, in the same year as ¹⁵N labelled fertilizer addition. RT = reduced tillage, CT = conventional tillage. Low N = below standard, High N = above standard.</figcaption></figure>

## CONCLUSION

Reduced tillage may lead to a more efficient use of fertilizer N and a lower fertilizer N loss, compared to conventional tillage.

**Acknowledgements:** This study was made possible by the Swedish Infrastructure for Ecosystem Science (SITES), specifically the SITES Lönnstorp Research Station. The work was funded by FORMAS.

## REFERENCES

* Haddaway N.R., Hedlund K., Jackson L.E., Kätterer T., Lugato E., Thomsen I.K., Jorgensen H.B., Isberg P., 2017. How does tillage intensity affect soil organic carbon? A systematic review. Environ. Evid., 6: 30.

* Couto-Vázquez A. and González-Prieto S.J., 2016. Fate of ¹⁵N-fertilizers in the soil-plant system of a forage rotation under conservation and plough tillage. Soil Tillage Res., 161: 10-18.

* Giacomini S.J., Machet J.M., Boizard H., Recous S., 2010. Dynamics and recovery of fertilizer ¹⁵N in soil and winter wheat crop under minimum versus conventional tillage. Soil Tillage Res., 108: 51-58.

* Thomsen I.K., and Christensen B.T., 2007. Fertilizer ¹⁵N recovery in cereal crops and soil under shallow tillage. Soil Tillage Res., 97: 117-121.
# N FERTILIZER FORMS AFFECT NITROUS OXIDE EMISSIONS IN A FERTIGATED POTATO SYSTEM

LEBENDER, U.<fnref n="1" />, TIERLING, J.<fnref n="1" />, BRUECK, H.<fnref n="1" />

## INTRODUCTION

Nitrogen fertilizers are supposed to be a major source of nitrous oxide (N₂O) emissions from arable soils. The N₂O emitted originates mainly from microbial nitrogen pathways, the balance of which is affected by the application of N fertilizers (Robertson and Grace, 2004) or by irrigation regime, aside from natural factors. Agricultural practices that increase water and N use efficiency (NUE) are considered as potential N₂O mitigation options (Smith et al., 2008). Drip fertigation is considered as one of the best management practices regarding water and NUE. Urea and calcium nitrate are very water-soluble, and thus their use in fertigation is widespread. Experiments in such systems comparing the effect of different N fertilizer types on N₂O emissions within the same study are scarce.

## MATERIAL AND METHODS

The study was carried out on a sandy soil in North-West Germany (51.832° N, 7.290° E). Potato was planted at a density of 3,9 tubers m⁻². Three treatments were examined: (1) control without fertilizer application, (2) calcium nitrate (CN) and (3) urea, each at a total application rate of 250 kg N ha⁻<fnref n="1" />. Fertigation started around tuber bulking stage until mid of flowering stage with a total of 6 fertigation events. Static gas chambers were used including PVC collars. The collars were installed permanently in the ridge allowing the subsurface fertigation dripline to pass through. N₂O emissions were measured with the closed chamber method on 39 occasions during the experimental period of 72 days. Emission rates were estimated with linear regression (de Klein and Harvey, 2015). Cumulative emissions were calculated with the trapezoidal integration method (de Klein and Harvey, 2015). Treatments were compared with ANOVA and Tukey's post-hoc tests. All calculations were performed with R 3.1.1 (R core team, 2015).

## RESULTS AND DISCUSSION

Total N₂O emissions from the urea treatment were almost twice those of the CN treatment (Table 1), which is similar to results found by Abalos et al. (2014) in a fertigated melon system.

<table><caption>Table 1. Cumulative N₂O emissions (gN ha⁻<fnref n="1" />), emission factors EF (in % of Total fertilizer applied) for various Nitrogen fertilizer forms.</caption><thead><tr><th>Treatment</th><th>Emission period</th><th>N amount (kg N ha<sup>-1</sup>)</th><th>Cumul. N<sub>2</sub>O (gN ha<sup>-1</sup>)</th><th>EF (%)</th></tr></thead><tbody><tr><td rowspan="3">control</td><td>Pre-fertigation period</td><td>0</td><td>211.5</td><td></td></tr><tr><td>Fertigation period</td><td>0</td><td>178.5</td><td></td></tr><tr><td>Total experimental period</td><td>0</td><td>390.0</td><td></td></tr><tr><td rowspan="3">CN</td><td>Pre-fertigation period</td><td>150</td><td>213.8</td><td>0.00</td></tr><tr><td>Fertigation period</td><td>100</td><td>79.4</td><td>-0.10</td></tr><tr><td>Total experimental period</td><td>250</td><td>293.2</td><td>-0.07</td></tr><tr><td rowspan="3">urea</td><td>Pre-fertigation period</td><td>150</td><td>390.9</td><td>0.12</td></tr><tr><td>Fertigation period</td><td>100</td><td>257.3</td><td>0.08</td></tr><tr><td>Total experimental period</td><td>250</td><td>652.2</td><td>0.10</td></tr></tbody></table>

<footnote n="1">¹ Research Centre Hanninghof, Yara GmbH & Co. KG, Germany</footnote>
<figure><img src="image_131.png" /><figcaption>Figure 1: Cumulative daily nitrous oxide emission rates after the application of Nitrogen fertilizer either as calcium nitrate (CN) or urea in ridged potato cropping system. Plots show mean values ± se (n=4).</figcaption></figure>

While almost no additional N₂O was detected in the CN compared to the control treatment, N₂O emissions were observed in the urea treatment during the whole measurement period (Fig. 1).

The injection of nitrogen to the soil during the fertigation events induced further N₂O emissions in the urea treatment while the CN treatment did not react at all (Fig.1), implying that nitrification was the prevailing N₂O producing process within the fertigated potato ridge. Comparing the pre-fertigation period with the fertigation period, broadcast fertilizer application resulted in 85% higher emissions compared to the fertigation period. This indicates that fertigation can serve as a mitigation option under arid to semiarid conditions.

## CONCLUSION

We conclude that in arid and semiarid climates fertigation of row crops like potato might be a useful strategy to mitigate N₂O emissions. Under non-denitrifying conditions the nitrification of fertilizers can play a major role regarding N₂O emissions, therefore the choice of N fertilizer form is highly important especially in consideration of climate change mitigation.

## REFERENCES

* Robertson, G.P., Grace, P.R., 2004. Greenhouse gas fluxes in tropical and temperate agriculture: the need for a full-cost accounting of global warming potentials. Environ. Dev. Sustainability 6, pp. 51–63.

* Smith, P., Martino, D., Cai, Z., Gwary, D., Janzen, H., Kumar, P., McCarl, B., Ogle, S., O'Mara, F., Rice, C., Scholes, B., Sirotenko, O., 2007. Agriculture. In climate change 2007: mitigation. In: Metz, O.R.D., Bosch, R. (Eds.), Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK.

* de Klein, C. A. M., Harvey, M., 2015. Nitrous Oxide Chamber Methodology Guidelines. Ministry for Primary Industries, Wellington, New Zealand.

* R Core Team, 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/

* Abalos, D., Sanchez-Martin, L., Garcia-Torres, L., van Groenigen, J. W., Vallejo, A., 2014. Management of irrigation frequency and nitrogen fertilization to mitigate GHG and NO emissions from drip-fertigated crops. Science of the Total Environment 490, pp. 880-888.
# CONTRIBUTION OF ESCHERICHIA COLI FOR REDUCING GREENHOUSE GAS AND AMMONIA EMISSIONS FROM TREATED CATTLE-SLURRY LIQUID FRACTION

MIRANDA, C.<fnref n="1" />, SOARES, A.S.<fnref n="1" />, TEIXEIRA, C.A.<fnref n="1" />, COELHO A.C.², TRINDADE, H.<fnref n="1" />

## INTRODUCTION

Slurry management poses serious environmental problems in dairy farming activities. On the one hand, slurry is a common organic fertilizer applied to grassland due to its rich composition in organic matter and nutrients, namely nitrogen. On the other hand, its degradation leads to greenhouse gas (GHG) emissions such as methane (CH₄), carbon dioxide (CO₂) and nitrous oxide (N₂O) and ammonia (NH₃) (Fangueiro et al., 2008). In addition, the direct deposition of cattle slurry to land causes pathogens transport from crop land to streams resulting in increased water contamination. Many pathogens like *Escherichia coli* (*E. coli*), *Salmonella* sp. and *Listeria monocytogenes*, which persist in soil, slurry, and water are a serious human health issue (Biswas et al., 2016). Although several studies have been performed to reduce gaseous emissions and mitigate pathogens from animal waste, it is unknown the impact of these pathogens on gaseous emissions during the management of slurry. This study was conducted to evaluate the influence of the *E. coli* presence in dairy slurry liquid fraction during storage conditions on GHG and NH₃ emissions.

## MATERIAL AND METHODS

The cattle slurry was obtained from a dairy farm in north of Portugal and subjected to mechanical separation by sieving, generating a solid and a liquid fraction (LF). The LF was stored in kilner jars filled with 1L of effluent and subjected to 5 different treatments in quadruplicate, including the application of biochar (4-5%), sulphuric acid (to pH=5.5) or beneficial microorganisms (Bm - bio buster®) and two controls. In the treatment with additives and in one of the two controls used, *E. coli* was inoculated (10⁹ cfu/mL). During 90 days of storage at 20°C, samplings were carried out at different time intervals for gas measurements. For each measurement of GHG emissions, the jar was hermetically sealed by replacing the lid and the first gas sample was immediately taken and the followings after 10 min and 20 min of closure. Samples were flushed through evacuated 20 mL gas vials and analysed by gas chromatography (Fangueiro et al., 2015). To evaluate NH₃ emissions, ammonia fluxes were measured using acid traps containing 50 mL of 0.02M orthophosphoric acid. Acid solutions collected were analyzed for total ammonium N content by automated segmented-flow spectrophotometry (Houba et al., 1995). Data was statistically analysed for GHG and NH₃ fluxes and cumulative emissions, using the software STATISTIX 10 (Tallahassee, FL).

## RESULTS AND DISCUSSION

During the 3 months of slurry-storage, the cumulative emissions showed that most of the additives contributed to the reduction of GHG (CH₄ and CO₂: p<0.01) and NH₃ (p=0.04) emissions. Comparing the results of the treatments with the slurry control without addition of *E. coli*, there was a decrease in NH₃, N₂O and CO₂ emissions for all treatments (Figure 1). In LF with just the addition of *E. coli* a reduction of NH₃ cumulative emissions of ca. 50% was observed compared to the acidified treatment (*E. coli* + acid). Fangueiro et al. (2015) concluded that decreasing the slurry pH by acid addition is a solution to minimise NH₃ emissions. Although the treatment with acidification showed a marked decrease of CH₄ emissions, the treatments with added biochar showed a marked increase of emissions (109%). In contrast, the treatments with no acidification showed the greatest contribution to the reduction of N₂O emissions, including the LF with added *E. coli* (-47%). A similar effect has been reported in previous studies, where the addition of biochar resulted in a reduction in N₂O emissions and the inhibition of the microbial activity at low pH values reduced the CH₄ emissions (Fangueiro et al., 2015; Brennan et al., 2015). To the best of our knowledge, no study has reported the impact of *E. coli* on GHG and NH₃ emissions from slurry.

<footnote n="1">¹ Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Universidade de Trás-os-Montes e Alto Douro (UTAD), Portugal; ² The Animal and Veterinary Research Centre (CECAV), UTAD, Vila Real, Portugal.</footnote>
<figure><img src="image_132.png" /></figure>

Figure 1. Effect on NH₃ and GHG emissions resulting from the single addition to cattle slurry liquid fraction of E. coli or its addition in combination with biochar, sulphuric acid and Bm, in comparison to the control (not treated). Bars with different letters are significantly different from each other at p<0.05, using the Tukey HSD mean separation test.

## CONCLUSION

Addition of *E. coli* to LF contributed to the reduction of cumulative emissions of NH₃ and N₂O. The combination of sulphuric acid and *E. coli* enhanced even more the effect in reducing NH₃ and GHG emissions, namely CH₄ and CO₂. In contrast, the addition of biochar to cattle slurry LF contributed to increased CH₄ emissions.

**Acknowledgements:** This research was financed by Project I&D INTERACT (Integrative Research in Environment, Agro-Chain and Technology), NORTE-01-0145-FEDER-000017, co-financed by the ERDF through NORTE 2020 (Regional Operational Programme North 2014-2020) and supported by FEDER/COMPETE/POCI, under Project POCI-01-0145-FEDER-006958 and FCT, under the project UID/AGR/04033/2013.

## REFERENCES

* Brennan R.B., Healy M.G., Fenton O., Lanigan G.J., 2015. The effect of chemical amendments used for phosphorus abatement on greenhouse gas and ammonia emissions from dairy cattle slurry: synergies and pollution swapping. PLoS ONE, 10(6), e0111965.

* Biswas S., Pandey P.K., Farver T.B., 2016. Assessing the impacts of temperature and storage on Escherichia coli, Salmonella, and L. monocytogenes decay in dairy manure. Bioprocess Biosyst Eng., 39(6), 901-13.

* Fangueiro D., Coutinho J., Chadwick D., Moreira N., Trindade H., 2008. Effect of cattle slurry separation on greenhouse gas and ammonia emissions during storage. J. Environ. Qual., 37(6), 2322-31.

* Fangueiro D., Pereira J., Bichana A., Surgy S., Cabral F., Coutinho J., 2015. Effects of cattle-slurry treatment by acidification and separation on nitrogen dynamics and global warming potential after surface application to an acidic soil. J. Environ. Manage., 162, 1-8.

* Houba V.J.G., Van der Lee J.J., Novozamsky I., 1995. Soil Analysis Procedures e Other Procedures, Part 5B, Sixth ed. Wageningen Agricultural University, The Netherlands.
# CHANGES OF PHOSPHORUS AVAILABILITY IN SOILS UNDER DIFFERENT NITROGEN FERTILIZATION LEVELS IN A LONG-TERM FIELD EXPERIMENT

ČERMÁK, P., MÜHLBACHOVÁ, G., KÁŠ, M., MARKOVÁ, K., PECHOVÁ, M.

Crop Research Institute, Drnovská 507/73, Prague 6 – Ruzyně, CZ-161 06, Czech Republic

## INTRODUCTION

Field experiments represent the important tool for studying the long-term effectivity of different management systems in agriculture and they contribute to better understanding of nutrient processes and interactions in soils. The mineral and organic fertilization improves the nutrient availability in soils and subsequently also the crop yields and their quality. Long-term fertilization with simultaneous use of mineral and organic fertilizers can affect the crop yields more than only mineral fertilization (Lin et al. 2015).

Nitrogen and phosphorus are the most important nutrients in soils affecting significantly crop yields. Higher inputs of nitrogen increase crop yields. However, the nutrient uptake by crops can cause the disbalance among nutrients in soils if they are not sufficiently saturated with their adequate amounts.

## MATERIAL AND METHODS

### Field trial

The permanent organic nitrogen long-term field experiment with organic, mineral and combined organic and mineral fertilization was founded in the year 1984 in Lukavec u Pacova (region Vysočina; altitude 620 m., cambisol, loamy-sandy soil, average precipitations and temperature – 633 mm; 8.7° C, pH ($CaCl_2$) 6.2, available nutrients according to Mehlich 3: 126 mg P/kg, K 217 mg K/kg, 125 mg Mg/kg, 1624 mg Ca/kg) with the following crop rotation: potatoes – winter wheat and spring barley. The experiment was divided into three basic blocks: 1) mineral fertilization; 2) fertilization with farmyard manure and combinations with mineral fertilization; 3) straw and combinations with mineral fertilization. The farmyard manure (FYM) was added in dose of 30 t/ha each third year to potatoes, the straw remained in the relevant plots after cereals harvest. Each of basic combinations was fertilized with 0 - 40 - 80 -120 - 160 kg N/ha and with exception of 0 N treatments also with 35 P kg/ha and 83 K kg/ha (PK treatments). Annual dose of nutrients applied as FYM was 43 kg N/ha and 17 kg P/ha. Each treatment had three replications. Treatments with mineral and organic fertilization were concentrated in blocs shifted for every replication in order to change its vertical and horizontal position.

### Harvest and analytical procedures

The plant and soil samples were taken immediately before harvest the 6th August 2016. 2 m of the wheat row were taken from opposite parts of each plot. The straw and grain were weighed separately. The total bioavailable phosphorus content in soils was determined by Mehlich 3 extract (Mehlich, 1984). The phosphorus content in plants was determined by digestion in concentrated $HNO_3$, 30% $H_2O_2$ by use of microwave Milestone 1200 (Connecticut, USA). All soil and plant extracts were analysed by ICP-OES Thermo Jarrel Ash (Nebrasca, USA).

## RESULTS AND DISCUSSION

**Winter wheat yield:** The medium winter wheat yields according to ANOVA test increased significantly ($p \le 0.001$) as shows Table 1. The yields of the wheat grain and straw corresponded well to nitrogen supply and the highest yields were generally obtained under the highest dose 160 kg N ha$^{-1}$. It was possible to observe such a diluting effect in P concentrations in wheat grain in treatments with only mineral fertilizers whereas the FYM increased the P concentrations in plant tissues. The treatment with straw did not show such clear results, but as showed also the results, the P input was lower in the straw than in FYM.

**P-content in the soil:** The P content in soils increased after P fertilization with mineral and particularly with FYM containing also considerable amount of phosphorus. However, increasing nitrogen doses caused a decrease in P
contents in soils available for plants. Particularly, the FYM treatments caused the highest increase of P contents but also more rapid decrease at highest N treatments. Bhattacharyya et al. 2015 considered the interactions between N and P in soils to be the single most important nutrient interaction. In fact, the comparison between P-input in fertilizers and P-uptake by plants showed that with exception of control soils and mineral treatment with 160 kg N ha⁻¹, no negative balance was observed. However, the observed decrease of P content in soils following the N fertilization showed that adequate nitrogen fertilization of soils is indispensable for maintaining the right phosphorus supply in agricultural soils.

<table><caption>Table1. Winter wheat yields, phosphorus content and uptake in long-term field experiment</caption><thead><tr><th rowspan="2">Treatment</th><th>yield-grain</th><th>P content</th><th>P uptake</th><th>yield-straw</th><th>P content</th><th>P uptake</th><th>P uptake - total</th></tr><tr><th>t ha<sup>-1</sup></th><th>grain mg kg<sup>-1</sup></th><th>grain kg ha<sup>-1</sup></th><th>t ha<sup>-1</sup></th><th>straw mg kg<sup>-1</sup></th><th>straw kg ha<sup>-1</sup></th><th>kg ha<sup>-1</sup></th></tr></thead><tbody><tr><td>0</td><td>2.84a</td><td>3793ef</td><td>10.8ab</td><td>3.74a</td><td>978cd</td><td>3.66ab</td><td>14.4ab</td></tr><tr><td>PK</td><td>2.29a</td><td>3693e</td><td>8.5a</td><td>3.33a</td><td>1048cd</td><td>3.49a</td><td>12.0a</td></tr><tr><td>N 40 PK</td><td>4.63b</td><td>3459c</td><td>16.0d</td><td>5.41c</td><td>825ab</td><td>4.46c</td><td>20.5c</td></tr><tr><td>N 80 PK</td><td>6.67d</td><td>3226a</td><td>21.5e</td><td>7.26e</td><td>750a</td><td>5.45d</td><td>26.9d</td></tr><tr><td>N 120 PK</td><td>7.55e</td><td>3434c</td><td>25.9e</td><td>8.04e</td><td>853b</td><td>6.86e</td><td>32.8e</td></tr><tr><td>N 160 PK</td><td>8.64f</td><td>3494cd</td><td>30.2f</td><td>8.94f</td><td>883c</td><td>7.89f</td><td>38.1fg</td></tr><tr><td>0</td><td>4.04b</td><td>3388b</td><td>13.7c</td><td>4.77b</td><td>978cd</td><td>4.66c</td><td>18.3b</td></tr><tr><td>PK</td><td>4.41b</td><td>3370b</td><td>14.8c</td><td>5.37c</td><td>875b</td><td>4.70c</td><td>19.5bc</td></tr><tr><td>N 40 PK</td><td>5.70c</td><td>3365b</td><td>19.2de</td><td>6.33d</td><td>817ab</td><td>5.17cd</td><td>24.3d</td></tr><tr><td>N 80 PK</td><td>7.35e</td><td>3420c</td><td>25.1e</td><td>7.86e</td><td>884c</td><td>6.95e</td><td>32.1e</td></tr><tr><td>N 120 PK</td><td>8.10f</td><td>3479cd</td><td>28.2f</td><td>8.53ef</td><td>1143d</td><td>9.75h</td><td>37.9fg</td></tr><tr><td>N 160 PK</td><td>8.81fg</td><td>3646c</td><td>32.1f</td><td>9.18f</td><td>968cd</td><td>8.89g</td><td>41.0g</td></tr><tr><td>0</td><td>4.11b</td><td>3370ab</td><td>13.9c</td><td>5.16c</td><td>894c</td><td>4.61c</td><td>18.5b</td></tr><tr><td>PK</td><td>3.90b</td><td>3597d</td><td>14.0c</td><td>4.78b</td><td>814ab</td><td>3.89ab</td><td>17.9b</td></tr><tr><td>N 40 PK</td><td>6.18cd</td><td>3445c</td><td>21.3e</td><td>6.85d</td><td>745a</td><td>5.10cd</td><td>26.4d</td></tr><tr><td>N 80 PK</td><td>7.55e</td><td>3513d</td><td>26.5e</td><td>8.04e</td><td>805ab</td><td>6.47de</td><td>33.0e</td></tr><tr><td>N 120 PK</td><td>8.12f</td><td>3393b</td><td>27.6ef</td><td>8.70f</td><td>903c</td><td>7.86f</td><td>35.4ef</td></tr><tr><td>N 160 PK</td><td>7.92ef</td><td>3473cd</td><td>27.5ef</td><td>8.39ef</td><td>905c</td><td>7.59f</td><td>35.1ef</td></tr></tbody></table>

<figure><img src="image_133.png" /><figcaption>Figure 1. Phosphorus content in soils from long-term field experiment with mineral and organic fertilization</figcaption></figure>

**Acknowledgement:** The research was carried out with support of NAZV ČR no: QJ1530171

REFERENCES:

Bhattacharyya P., Nayak A.K., Shahid M., Tripathi R., Mohanty S., Kumar A., Raja R., Panda B.B., Lal B, Gautam P., Swain C.K., Roy K. S., Dash P.K. (2015): The Crop Journal, 3: 387-395

Lin Z., Chang X., Wang D., Zhao G., Zhao B (2015): Field Crops Research, 174: 55-60

Mehlich A. (1984): Communications in Soil Science and Plant Analysis, 15: 1409-1416.
# CHANGES OF SULPHUR AVAILABILITY IN SOILS UNDER DIFFERENT NITROGEN FERTILIZATION LEVELS IN A LONG-TERM FIELD EXPERIMENT

MÜHLBACHOVÁ, G., ČERMÁK, P., KÁŠ, M., MARKOVÁ, K., PECHOVÁ, M.

Crop Research Institute, Drnovská 507/73, Prague 6 – Ruzyně, CZ-161 06, Czech Republic

## INTRODUCTION

Sulphur represents one of the most important nutrients necessary for plants. The deficiency of sulphur has been observed in agricultural soils for several decades and is related to environmental legislations in the Czech republic limiting sulphur pollution by power plants. Consequently, sulphur has become the most limiting element before nitrogen for yields (Matula, 2007). Significant amounts of sulfur in the soil is contained in the soil organic matter, which can be available for plants upon mineralization (Eriksen et al., 1998). Sulphur is absorbed by plants usually as SO₄²⁻ anion. The SO₄²⁻ anion is highly mobile in the soil solution, because it is repelled from soil particles exposing negative charges and therefore they can be easily leached into lower layers of the soils.

The aim of the research was to evaluate the uptake of sulphur by winter wheat and the soil sulphur stocks under increasing nitrogen doses in a long-term field experiment.

## MATERIAL AND METHODS

### Field trial

The field trial is described by Mühlbachová et al. (2018). Briefly, a long-term field experiment with organic, mineral and organo-mineral fertilization began in 1984 on Cambisol soil in highlands at Lukavec u Pacova. The crop rotation was potatoes - winter wheat and spring barley. The mineral fertilization, farmyard manure (FYM - 30 t/ha) and straw (remaining postharvest residues) represented three basic combinations of fertilization. The FYM was added each third year to potatoes (annually 43 kg N ha⁻¹ and 17 kg P ha⁻¹). Each of basic combinations was fertilized with 0 - 40 - 80 -120 - 160 kg N/ha and with exception of 0 N treatments also with 35 P kg/ha and 83 K kg/ha (PK treatments). No systematic treatment with sulphur was carried out. Each treatment had three replications.

### Harvest and analytical procedures

The winter wheat (2 m rows from opposite sides of each plot) and soil samples were taken immediately before harvest the 6th August 2016. The total bioavailable sulphur content in soils was determined by Mehlich 3 method (Mehlich, 1984). The plants were digested in concentrated HNO₃, 30% H₂O₂ using the microwave Milestone 1200 (Connecticut, USA) and the soil and plant extracts were determined by ICP-OES Thermo Jarrel Ash (Nebrasca, USA).

## RESULTS AND DISCUSSION

The average of wheat yields increased significantly (p ≤ 0.001) according to ANOVA test, as shown in Table 1. The yields of the wheat grain and straw responded to nitrogen supply and the best yields were generally obtained under the maximum dose of 160 kg N ha⁻¹. The S concentrations in wheat grain and straw under mineral and organic fertilization tended to increase according to the nitrogen dose. Similarly, the sulphur uptake by grain and straw increased significantly. The maximum sulphur uptake by winter wheat reached 18-19 kg S ha⁻¹ for higher N doses probably due to synergic effect of higher yields and S concentrations in plants.
Concerning soils, the sulphur content increased according to nitrogen fertilization up to 120 kg ha⁻¹ for all three fertilization systems (mineral, FYM and straw). The highest nitrogen dose (160 kg N ha⁻¹) decreased the sulphur content in soil. This decrease is not possible to explain by simple S uptake by plants as only in straw treatment the decrease of S-uptake was noted at 160 kg N ha⁻¹. However, Matula (2007) showed that nitrogen fertilization decreased production of sulphates from the mineralization of organic matter and in soils with higher immobilization tendency, the sulphur immobilization was even higher. The interactions between nitrogen and
sulphur reported also Salvagiotti and Mirales (2008) who described positive interactions between these two nutrients and similarly to Matula (2007) reported higher nitrogen use efficiency under sulphur fertilization. The soils our field experiment were not systematically fertilized with sulphur and it could be one of reasons for the decrease of the sulphur content in soils under the highest N rate (Fig. 1).

<table><caption>Table1. Winter wheat yields, sulphur content and uptake in long-term field experiment</caption><thead><tr><th>Treatment</th><th>yield-grain t ha<sup>-1</sup></th><th>S content mg kg<sup>-1</sup></th><th>S uptake kg ha<sup>-1</sup></th><th>yield-straw t ha<sup>-1</sup></th><th>S content mg kg<sup>-1</sup></th><th>S uptake kg ha<sup>-1</sup></th><th>S uptake total kg ha<sup>-1</sup></th></tr></thead><tbody><tr><td rowspan="6">Mineral</td><td>0</td><td>2.84a</td><td>961.4a</td><td>2.73a</td><td>3.74a</td><td>576.9ab</td><td>2.16a</td><td>4.89a</td></tr><tr><td>PK</td><td>2.29a</td><td>950.0a</td><td>2.18a</td><td>3.33a</td><td>684.7c</td><td>2.28a</td><td>4.46a</td></tr><tr><td>N 40 PK</td><td>4.63b</td><td>958.8a</td><td>4.44bc</td><td>5.41c</td><td>534.3a</td><td>2.89b</td><td>7.33c</td></tr><tr><td>N 80 PK</td><td>6.67d</td><td>967.6ab</td><td>6.45d</td><td>7.26e</td><td>626.3bc</td><td>4.55d</td><td>11.00f</td></tr><tr><td>N 120 PK</td><td>7.55e</td><td>1153.1c</td><td>8.70f</td><td>8.04e</td><td>984.4e</td><td>7.92f</td><td>16.62h</td></tr><tr><td>N 160 PK</td><td>8.64f</td><td>1296.6d</td><td>11.20h</td><td>8.94f</td><td>844.3e</td><td>7.55f</td><td>18.75i</td></tr><tr><td rowspan="6">FYM</td><td>0</td><td>4.04b</td><td>963.1a</td><td>3.89b</td><td>4.77b</td><td>538.9a</td><td>2.57ab</td><td>6.46b</td></tr><tr><td>PK</td><td>4.41b</td><td>960.7a</td><td>4.23bc</td><td>5.37c</td><td>520.2a</td><td>2.79ab</td><td>7.03c</td></tr><tr><td>N 40 PK</td><td>5.70c</td><td>1008.8b</td><td>5.75c</td><td>6.33d</td><td>556.4ab</td><td>3.52c</td><td>9.27d</td></tr><tr><td>N 80 PK</td><td>7.35e</td><td>1053.8c</td><td>7.74e</td><td>7.86e</td><td>712.3d</td><td>5.60e</td><td>13.34g</td></tr><tr><td>N 120 PK</td><td>8.10f</td><td>1144.9c</td><td>9.27fg</td><td>8.53ef</td><td>973.8e</td><td>8.30g</td><td>17.58hi</td></tr><tr><td>N 160 PK</td><td>8.81fg</td><td>1293.7d</td><td>11.40h</td><td>9.18f</td><td>840.1e</td><td>7.71f</td><td>19.11i</td></tr><tr><td rowspan="6">Straw</td><td>0</td><td>4.11b</td><td>990.1b</td><td>4.07b</td><td>5.16c</td><td>499.7a</td><td>2.58ab</td><td>6.65b</td></tr><tr><td>PK</td><td>3.90b</td><td>1005.3b</td><td>3.92b</td><td>4.78b</td><td>509.8a</td><td>2.44ab</td><td>6.35b</td></tr><tr><td>N 40 PK</td><td>6.18cd</td><td>1035.7b</td><td>6.40d</td><td>6.85d</td><td>557.8ab</td><td>3.82c</td><td>10.22e</td></tr><tr><td>N 80 PK</td><td>7.55e</td><td>1145.3c</td><td>8.65f</td><td>8.04e</td><td>678.5c</td><td>5.45e</td><td>14.10g</td></tr><tr><td>N 120 PK</td><td>8.12f</td><td>1191.4c</td><td>9.68g</td><td>8.70f</td><td>975.2e</td><td>8.49g</td><td>18.16i</td></tr><tr><td>N 160 PK</td><td>7.92ef</td><td>1275.9d</td><td>10.11g</td><td>8.39ef</td><td>776.2d</td><td>6.51ef</td><td>16.62h</td></tr></tbody></table>

<figure><img src="image_134.png" /><figcaption>Figure 1. Sulphur content in soils from long-term field experiment with mineral and organic fertilization</figcaption></figure>

**Acknowledgement:** The research was carried out with support of NAZV ČR no: QJ1530171

**REFERENCES:**

Eriksen J., Murphy M., Schnug E. (1998): Sulphur in ecosystems (ed- E. Schnug). Kluwer Academic Publishers, Dordrecht, Boston, London: 39-73

Matula J: (2007): Certified methodology, Crop Research Institute, ISBN 978-80-87011-15-7. 39 p.

Salvagiotti F. and Mirales D. (2008): European Journal of Agronomy, 28: 282-290
# EFFECTS OF SUPPRESSION OF HERBICIDES ON VINEYARD NITROGEN STATUS, AND SOIL MICROBIAL AND CHEMICAL INDICATORS

DUROCHER E.<fnref n="1" />, LANGENFELD, A.², LEY L³, KLEIN C.<fnref n="1" />, THIOLLET-SCHOLTUS M.⁴, NASSR, N.²

## INTRODUCTION

In the North Eastern vineyard, cover cropping is widely used by winegrowers to limit soil erosion and use of herbicides. However, weeds under vine rows are still controlled in a chemical way, in order to reduce the competition with the vine (Wilmes, 2014). A project named "soil management in viticulture" began in 2014 to study the effects of two soil management strategies on agronomical and environmental indices. Mechanical weeding and cover cropping are expected to modify nitrogen dynamics in grapevine soil (Celette et al., 2009).

## MATERIAL AND METHODS

### Study site and experimental design

The experimental plot is located in Alsace region (France), in the vineyard catchment basin of Hohrain, near Rouffach city. This plot belongs to the "Domaine de l'Ecole", and was planted in 1998 with Riesling grape variety on a brown calcareous soil. Two different strategies of soil management, covering in an area of 0.15 ha each, were studied. Each strategies is described in Table 1 and were set up in 2014. The first type of soil management, named "classical system" (CS), is the most widespread one in the Alsace region. The second strategy, named "innovative system" (IS), consisted of cover cropping with spontaneous flora one inter-row up to half of the vine-stock and tillage on the second half of the vine-stock and the second inter-row (Table 1). Fungal protection with only organic pesticides was the same for both systems. An organic fertilizer (50 N ha⁻<fnref n="1" />) was applied at IS in May 2016 and in January 2017 with organic amendment.

### Measurements

Several measurements were carried out to follow nitrogen levels in soil and in grapevine throughout the year, from 2014 to 2016. Nitrogen status of grapevine was estimated with three indices: Nitrogen Balance Index, calculated from fluorescence measurements of leaves at veraison, wood weight to estimate vine vigour, and yeast available nitrogen in must.

<table><caption>Table 5: Two soil management strategies applied in the experimental plot</caption><thead><tr><th></th><th>Inter-row 1</th><th>Row</th><th>Inter-row 2</th></tr></thead><tbody><tr><th>Classical system (CS)</th><td>Cover cropping (spontaneous flora)</td><td>Chemical weeding</td><td>Tillage</td></tr><tr><th>Innovative system (IS)</th><td>Cover-cropping (spontaneous flora)</td><td>Tillage</td><td></td></tr></tbody></table>

Potential mineralization of nitrogen was measured in soil samples from each system from 2014 to 2016. The microbiological analysis consists in measuring biological and chemical indicators to assess nitrogen dynamics in soil, i.e. nitrogen mineralization (ISO 14238), microbial biomass (16S and 18S qPCR) and activity (Biolog® Ecoplates). Statistical analyses (using Statgraphics) were performed to determine the relationship between biological indicators and nitrogen mineralization regarding soil management strategies.

## RESULTS AND DISCUSSION

<footnote n="1">¹ EPLEFPA les Sillons de Haute Alsace, France ; ² RITTMO Agroenvironnement, France ; ³ INRA SEAV, France ; ⁴ INRA UR-0055 ASTER, France</footnote>
On the agronomic point of view, a drop in grapevine's yield was observed in the IS the first year of experimentation (2014), this effect worsened the second year (2015; Fig. 1). This was attributed to a nitrogen stress, confirmed by the Nitrogen Balance Index gap observed in 2014 and 2015. Organic fertilization in the IS allowed maintaining a constant yield in 2016. In 2017, the difference between the two systems was not observed due to a drop of yield in the CS. The gap between the two systems was visible in the first year of the experimentation on other **nitrogen status indices**. Pruning wood weights and especially yeast available nitrogen are affected by the presence of cover crop in the IS. However, the impact on the components of the yield, which are built on two years, was most accentuated in 2015. Interestingly, fertilization applied in May 2016 in the IS allowed to recover nitrogen status indices close to the CS (Fig. 1) in 2016, but was not beneficial for the yield.

<figure><img src="image_135.png" /><figcaption>Figure 7: Yield and nitrogen status indices of grapevine of innovative system, as a percentage of classical system from 2014 to 2016.</figcaption></figure>

Samples taken at bud break in 2016 showed an **increase of microbial activity and abundance**, but this effect was not significant. A seasonal effect was observed on the microbial parameter, which could cover the effect of practices. Taken independently, the chemical and microbial parameters did not show any evident effect of management practices on the results obtained. However, when these parameters were combined in a multivariate analysis, classical and innovative systems tend to separate.

## CONCLUSION

Transition towards non chemical weed control method induced significant reduction of yield of grapevines and nitrogen status indices, but can be contained by organic fertilisation. However, we could not differentiate effects of chemical or mechanical weeding on microbiological parameters under the row at 30-cm soil depth. Further analyses are needed to detect potential effects on microbial communities, which can impact biogeochemical cycling of nitrogen in the soil (Steenwerth and Belina, 2008). This project will be pursued in the next years to explore long-term effects of the change of soil management strategy.

**Acknowledgements:** This project was funded by Agence de l'Eau Rhin-Meuse and Comité Interprofessionnel des Vins d'Alsace. We thank the Domaine de l'Ecole for provision of the experimental plot and the experimental unit SEAV (INRA) for participation in the monitoring of the experiment.

## REFERENCES

- Celette F., Findeling A., Gary C., 2009. Competition for nitrogen in an unfertilized intercropping system: The case of an association of grapevine and grass cover in a Mediterranean climate. European Journal of Agronomy, 30, 41-51.

- Steenwerth K., Belina K.M., 2008. Cover crops and cultivation: Impacts on soil N dynamics and microbiological
function in a Mediterranean vineyard agroecosystem. Applied Soil Ecology, 40, 370-380.

Wilmes C., 2014. Un désherbage mécanique sur vigne qui progresse au delà des producteurs bio. Agreste.

Consultable : http://agreste.agriculture.gouv.fr/IMG/pdf/R4214A15.pdf [Consulté le 19 juillet 2017].
# LEGUME NUTRIENTS MANAGEMENT FOR SUSTAINING CROP PRODUCTIVITY AND SOIL BIODIVERSITY

TOLEIKIENĖ, M., ARLAUSKIENĖ, A., KADŽIULIENĖ, Ž., SUPRONIENĖ, S.

Lithuanian Research Centre for Agriculture and Forestry, Lithuania

## INTRODUCTION

The production of organic plant-based fertilizers from local resources contribute to sustainability. The management of plant derived fertilizers not only compensates plant nutrient demand, but also increases the soil functioning. Organic fertilizers could be used to balance basic nutrients and C-N-P-K ratio. Selecting appropriate plant species can control nitrogen depletion and prevent nutrients losses or excess. Plant-based fertilizers are oftenly produced from legumes, which contain average N, P content and comparable small K content. Innovative practices involves mixing of plants with different chemical composition, green manure composting and ensiling. Resulting organic matter determines a wider C:N ratio, which limits mineralization and induces humification processes after insertion of green manure into soil. This demonstrates high potential to maintain the close nutrient cycles in organic plant-based farm. This farming method is based on living agro-ecological systems and cycles, where biodiversity plays an important role. However, researchers argue efficiency of organic farming and different management regimes are questioned. One of the main questions in recent literature and this research is - 'could plant-based organic farming significantly increase biodiversity and maintain sufficient crop productivity?'

## MATERIAL AND METHODS

This study aims to differently prepare green manures, used as fertilizers in organic farming. Organic fertilizers were produced in 2015 summer and inserted in 2016 spring, excluding second treatment. Six fertilization treatments were chosen: first- control with no fertilizers (C); second - green clovers mass cut and ploughed in the fall of 2015 (RC); third - red clover ground mass (N50) fermented under anaerobic conditions (FerRC); fourth - fermented pea and spring wheat mass (N25) manure (FerP+W); fifth – composted red clover and straw (N50) manure (ComRC+S), sixth - granulated cattle (N50) manure (GCM) for comparison.

Field experiments were conducted in 2015 - 2017 cropping system. Trial was carried at the Lithuanian Centre for Agriculture and Forestry, in two locations of Lithuania: Dotnuva and Joniškėlis. Site was managed organically. Crop rotation: 2015 - winter wheat, 2016 - spring wheat with organic manure, 2017 - spring barley with clovers. Mineralization, crops productivity, soil chemical composition, biological activity were investigated during experiment. The variation in the concentration of carbon (C), nitrogen (N), phosphorus (P) and potassium (K) in soil are determined by the Diameter method, Kjeldahl method, final photometric determination and atomic absorption method. Mineralisation was assessed by litter-bag method. Microorganisms, earthworms and insects were observed as indicators of biodiversity content. Ecological groups, species, quantity, quality, activity were identified three times in agricultural vegetation. Colony-forming units (CFUs) in appropriate dilutions of the samples with soil bacteria were determined on PCA agar (Oxoid). Microbiological diversity assessed by CLPP - Community Level Physiological Profiling method, using Biolog EcoPlates (BIOLOG Inc, Hayward, CA, USA). Average well-color development (AWCD), richness (R), Shannon diversity index (H), Simpson index (D) were calculated.

## RESULTS AND DISCUSSION

During the Silage and Compost Experiment the influence to organic matter chemical structure and N losses were studied. N content in mulch decreases by 20.4–29.7%. Contrary to organic substances, N content was 26% lower after ensiling compared with the data before ensiling. Larger amount of N was accumulated in RC, ComRC+S and GCM. These fertilizers contained larger amount of P and K, too. Organic fertilizers from exhibiting the lowest
mineralization intensity C:N to highest were as follows: FerP+W < FerRC < GCM < ComRC+S < RC. Highest C:P ratio (1:200-210) was in RC, FerRC and ComRC+S, the lowest C:P (1:76-81) in ComRC+S and GCM.

<figure><img src="image_136.png" /><figcaption>Figure 1. Nutrients N, P, K, demand for spring crops to produce 1000 kg grain yield.</figcaption></figure>

All produced organic manures could maintain at least 1000 kg grain yield productivity, according to chemical composition and nutrients N, P, K ratio (Figure 1). However, not all fertilizers could produce at least 2000 kg grain yield just by itself, without increasing fertilizers rate. In 2016, grain yield of spring wheat increased significantly (28%) using RC inserted in 2015 autumn. Grain yield increased by 17% and 10%, fertilizing in the 2016 spring by GCM and ComRC+S respectively. The grain yield was reduced by the addition of a fermented wheat-peas mixture. In 2017, grain yield increased by 12% in plots fertilized by RC and FerRC, 10% in ComRC+S and GCM.

Ecological environment in fertilised plots was suitable for different organisms activity increase. Not all fertilisers had the same effect on treated biodiversity indexes. Higher earthworms activity was measured whatever the manure process. Most abundant earthworm species were red earthworm (Lumbricus rubellus Hofm.) and field worm (Allolobophora calignosa Sav.). Significant increase in earthworm amount were observed using ensiled legume biomass fertilizers. Surprisingly, insects quantity was lowest there GCM was applied, all green manure showed good insects activity. Biggest amount traced there FerP+W was applied. Carabidae family was most abundant. All organic manures increased microbial functional diversity in organic farming system. RC and FerRC had higher effect in first vegetation season. FerP+W and ComRC+S - on second year. Best not interrupted effect in both year observed applying GCM. Rates of microbial activity were significantly affected by seasonal conditions and chemical compounds of each manure. Weeds were significantly least abundant in control treatment where no fertilisers were applied.

## CONCLUSION

The highest grain yield was produced using ploughing of fresh red clovers mass in the autumn. The effect was slightly better compared to granulated cattle manure. Despite nutrients conservation while ensiling, it had lower positive effect to grain yield. Nevertheless, we recommend to consider ensilage practise usage for better soil biological activity in organically managed agroecosystems.

**Acknowledgements:** We would like to thank to all technicians in LAMMC that made the best efforts to sample and measure huge amount of data for this work.
# TOWARDS A BETTER UNDERSTANDING OF C AND N CYCLING IN AGRICULTURAL SOILS

SAMSON, M-E.¹,²,³, CHANTIGNY, M.², ANGERS, D.² MENASSERI-AUBRY, S.³ VANASSE, A.¹

¹ Laval University, Canada; ² Agriculture and Agrifood Canada, Canada; ³ Agrocampus-Ouest, Britanny

## INTRODUCTION

Carbon sequestration in agricultural soils could help mitigate climate change, improve soil physical properties and provide nutrients and energy to soil biota through organic matter stabilization (Pautisan et al., 2016). However, in our quest to find strategies to store carbon in agricultural soils, the fact that organic matter decomposition is also a central source of nitrogen for plant growth is generally overlooked. This leads to an important dilemma: is it possible to simultaneously accumulate organic matter and profit from its decay? New theoretical models of the soil organic matter cycle have identified nitrogen as a major player in carbon sequestration (Knicker, 2011). Bimüller et al. (2014) have also suggested a decoupling of carbon and nitrogen cycles in different soil organic matter fractions. Coarse-sized organo-mineral complexes were identified as a fraction that could play an important role in nitrogen mineralization while it presents a relatively low rate of carbon mineralization (Bimüller et al., 2014; Whalen et al., 2000). Agricultural practices such as organic fertilization could promote accumulation of organic matter in this specific fraction (Maillard et al., 2015) and contribute to the release of nitrogen in soil without impacting carbon stocks. The impact of tillage, fertilization and crop residues management on carbon and nitrogen cycle is often studied separately. The objective of this project is to deepen our understanding of the mechanisms involved in carbon storage and nitrogen mineralization soil services considering multiple agricultural practices and their interactions.

## MATERIAL AND METHODS

### Experiment

This project capitalizes on a factorial experimental platform set up in 2009 at Laval University experimental farm (Québec, Canada) under a wheat-maize-soybean rotation. On two contrasted soil types (silty clay vs sandy loam) are compared: 1) soil tillage regimes (moldboard plow [MB] vs minimum till [MT]), 2) nitrogen sources (complete mineral fertilizer [NPK], liquid swine manure [LSM], liquid dairy cattle manure [LDM], solid poultry manure [SPM] and a mineral fertilizer without nitrogen [PK]) and 3) residue management (returned to the soil [RR] vs harvested [RH]). Crop yield and surface (0-8 cm) soil quality parameters (bulk density, humidity, aggregate stability, microbial biomass carbon and nitrogen, particulate organic matter, etc.) were evaluated annually from 2009 to 2017. Every three years, total nitrogen and carbon were measured in four soil layers (0-15, 15-30, 30-45 and 45-60 cm) to estimate soil carbon and nitrogen stocks. In 2017, soil samples were submitted to a particle size and density fractionation (Maillard et al., 2015) and total nitrogen and carbon content of each organic matter fraction was analyzed. An incubation experiment will soon be performed to compare carbon and nitrogen mineralization dynamics in the bulk soil and in the soil fractions. Ultimately, the data will be used to evaluate strengths and weaknesses of the STICS model for simulating C and N fluxes. Statistical analysis was performed with the mixed procedure of SAS (SAS Institute Inc., Cary, NC, USA). A combined factorial ANOVA model was used with repeated measures to consider the interactions between soil type and treatments as well as the evolution over time. Variance-covariance matrix was chosen to minimize the Akaike criterion and the Kenward-Roger method was used to calculate the degrees of freedom. The results of these statistical tests are reported for a significance level of 0.05.

### Statistical analysis

Statistical analysis was performed with the mixed procedure of SAS (SAS Institute Inc., Cary, NC, USA). A combined factorial ANOVA model was used with repeated measures to consider the interactions between soil type and treatments as well as the evolution over time. Variance-covariance matrix was chosen to minimize the Akaike
criterion and the Kenward-Roger method was used to calculate the degrees of freedom. The results of these
statistical tests are reported for a significance level of 0.05.

RESULTS AND DISCUSSION

After eight years of treatments, we observed an important interaction between soil tillage regime and residue management on multiple soil quality parameters. Aggregate mean weight diameter, particulate organic matter and microbial biomass were all significantly greater under MT than MB when crop residues were returned to the soil. In the MB treatments, these parameters had lower values and the effect of residue management was less important. Nitrogen sources had no effect on total carbon in the surface soil layer, but organic fertilizers resulted in greater microbial biomass C and N than mineral fertilizers, and the microbial biomass was also smaller with NPK than PK. Moreover, the microbial biomass C to microbial biomass N ratio was narrower in the treatments with organic amendments compared to NPK and PK treatments suggesting accumulation of relatively labile organic matter in soil amended with organic fertilizers. These results are in line with those of Maillard et al. (2015) who found enrichment with low carbon/nitrogen ratio organic matter in coarse-sized organo-mineral complexes following a recurrent application of liquid swine manure.

CONCLUSION

Preliminary results from our experiment suggest that the nitrogen source has an important effect on soil organic matter quality. They also highlight the impact of interactions among agricultural practices on soil carbon and nitrogen cycling. The soil fractionation data and soil carbon and nitrogen stocks will be analyzed soon and should help better understand carbon and nitrogen storage and remineralization dynamics in functionally different soil organic matter fractions as influenced by tillage, nitrogen source and crop residue management. Ultimately, these results will be compared to the ones simulated by the STICS model to validate the consideration of interactions among agricultural practices and carbon and nitrogen coupling when modeling carbon and nitrogen fluxes in agrosystems.

REFERENCES

Bimüller, C., Mueller, C.W., von Lützow, Kreyling, O., Kölbl, A., Haug, S., Schloter, M. et Kögel-Knaber, I. 2014. Decoupled carbon and nitrogen mineralization in soil particle size fractions of a forest topsoil. Soil biol. biochem. 78: 263-273.

Knicker, H. 2011. Soil organic N – An under-rated player for C sequestration in soils? Soil boil. and biochem. 43: 1118-1129.

Maillard, E., Angers, D.A., Chantigny, M., Bittman, S., Rochette, P., Lévesque, G., Hunt, D., Parent, L.E. 2015. Carbon accumulates in organo-mineral complexes after long-term liquid dairy manure application. Agric. ecosyst. environ. 202: 108-119.

Paustian, K., Lehmann, J., Ogle, S., Reay, D., Robertson, G.P., et Smith, P. 2016. Climate-smart soils. Nat. 532: 49-57.

Whalen, J.K., Bottomley, P.J. et Myrold. D.D. 2000. Carbon and nitrogen mineralization from light and heavy fraction addition to soil. Soil Biol. Biochem. 32: 1345-1352.
# UREA DEEP PLACEMENT FOR TRANSPLANTED RICE: COMPLEMENTARITY OF ¹⁵N-LABELLING AND AMMONIACAL N DIFFUSION STUDIES TO UNDESTAND THE FERTILIZER-N EFFICIENCY

GAUDIN, R.¹, D'ONOFRIO, G.²

¹ UMR System, France; ² retired, Italy

## INTRODUCTION

Urea deep placement is considered as one of the best way to avoid N losses in paddy soils. The urea N and the ammoniacal N deriving very quickly from it are weakly affected by the classical mechanisms of runoff, ammonia volatilization and nitrification-denitrification because of the low diffusion of ammonium from the anaerobic layer of the soil to the aerobic layer. The levels of ammoniacal N attained at the site of placement are in relation with the mass of urea supergranule (USG) used (Gaudin and Dupuy, 1999). If this mass is too high, some unbalance in the source-sink relationship can occur (Gaudin and D'Onofrio, 2015). This phenomenon is a hot topic in the context of paddy rice for ensuring the best N recovery from fertilizer-N. To insure a good balance together with an important level of N fertilization essential for high yield, some researchers are now proposing to use a single USG per transplant (two- or three-strand transplantation) at 12 cm depth (Liu et al., 2017; Wu et al., 2017). Here we discuss the results obtained in studies using ¹⁵N labelling of urea or soil solution sampling associated with a model of ammonia diffusion. The ¹⁵N experiment aimed at specifying the kinetics at which the nitrogen passes from the fertilizer to the grain and straw. The following question is investigated: Should the gain of information obtained with the soil solution measurement be sufficient to generalize this approach and use it to study other geometries either of nitrogen application or/and of fertilizer (capsules, etc.)?

## MATERIAL AND METHODS

A field located near the Onibe bridge, 5 km west from Arivonimamo (Madagascar) has been selected for the ¹⁵N experiment and four years later for the soil solution monitoring in the vicinity of the USG. The mass of USG was 2 g. The soil solution sampler was 15 mm long and 13 mm in diameter. The soil solution was drawn from the soil by vacuum application and collected in penicillin bottles. Ammonia and urea were measured by colorimetry: indophenol blue method and diacetylmonoxim-thiosemicarbazide method respectively. The results of the two experiments have been analyzed separately (Dupuy et al., 1990; Gaudin and Dupuy, 1999). Here we are examining the complementarity of the two kinetics, the first one for the labelling experiment, the second one for ammonia disappearance from the soil solution.

## RESULTS AND DISCUSSION

The recovery of nitrogen was high, attaining 60.7% at 73 DAT (days after transplanting). It was 41.8% at 52 DAT and only 9.8% at 32 DAT. As shown on Fig. 1, the disappearance of ammoniacal N from the soil solution followed a first order kinetics between 10 mM and 0.05 mM ammoniacal N. The time at which this disappearance occurs is well adjusted on the kinetics of labelling suggesting that the assimilation of ammoniacal N was rapid and mobilized all the carbon resources (photosynthates) at disposal at this time (Gaudin and D'Onofrio, 2015).
<figure><img src="image_137.png" /></figure>

Figure 1. Comparison between the dynamic of the Real Utilization Coefficient obtained by a ¹⁵N labelling experiment and the time course of ammoniacal N concentration in the placement site of one 2 g USG (only one of six replications displayed). PER-0 and PER-21, prilled urea applied at transplanting or 21 days later, SGU-O and SGU-21, 2 g urea supergranules deep-placed at 10 cm between four transplants at transplanting or 21 days later.

## CONCLUSION

Soil solution sampling is a simple technique to be used in paddy soils. In this context, the monitoring of ammoniacal N after deep placement of fertilizer-N could be used to test multiple possibilities in new geometries of fertilizer application or of the fertilizer-N itself. Deep line application or open capsules are interesting options.

## REFERENCES

* Dupuy J., Gaudin R., D'Onofrio G., 1990. Etude à l'aide d'urée enrichie en ¹⁵N de la fertilization azotée du riz inondé à Madagascar. III- Cinétique de prélèvement de l'azote apporté sous forme de supergranules d'urée ou de perlurée au repiquage du riz inondé ou 21 jours après. Agro. Trop. 45, 21-30.

* Gaudin R., Dupuy J., 1999. Ammoniacal nutrition of transplanted rice fertilized with large urea granules. Agron. J. 91, 33-36.

* Gaudin R., D'Onofrio J., 2015. Is the source-sink relationship in transplanted rice receiving deep-placed urea supergranules in transplanted rice dependent upon the geometry of transplanting? Paddy Water Environ. 13, 433-442.

* Liu X., Wang H., Zhou J., Chen Z., Lu D., Zhu D., Deng P., 2017. Effect of nitrogen root zone fertilization on rice yield, uptake and utilization of macronutrient in lower reaches of Yangtze River, China. Paddy Water Environ. 15, 625-638.

* Wu M., Li G., Li W., Liu J., Liu M., Jiang C., Li Z., 2017. Nitrogen fertilizer deep placement for increased grain yield and nitrogen recovery efficiency in rice grown in subtropical China. Front. Plant Sci. 8, 1227.
# A COMPREHENSIVE ANALYSIS OF N-LOSSES FROM URINE PATCHES IN DAIRY PASTURE SYSTEMS

CAROZZI, M.¹, BRETSCHER D.¹, VOGLMEIER K. ¹, AMMANN, C.¹

¹ Agroscope, Climate and Agriculture Group, Zurich, Switzerland

## INTRODUCTION

Urination by grazing animals causes a mosaic of isolated patch characterised by high N concentration. In the case of dairy cow, 200 to 2000 kg N ha⁻¹ can be supplied over small soil surfaces ranging from 0.15 to 0.5 m², and which can affect soil processes up to double of their surface (Selbie et al., 2014). These loads largely exceed plant uptake capacity causing significant N losses through NO₃ leaching, gaseous emissions including N₂O, NOx and NH₃, and water runoff. Process-based models revealed suitable tools to interpret and analyse the biogeochemical processes occurring in the soil, plant, animal and the releases of reactive N forms towards the environment. Conversely, the large amount of interacting process involved in the N-cycle means that the dataset required to comprehensively validate these models would be prohibitively large.

The majority of pasture models simulating animal excreta as a uniform broad application at paddock scale, without considering the local effects introduced by these point sources (Giltrap et al., 2015). This simplification does not consider the transformations and the losses occurring within these temporary hotspots for N, often misinterpreting the processes themselves, especially for by-products such as N₂O with large greenhouse potentials. In other hands, considering and simulating all the spatial heterogeneity within a paddock would come with remarkable computing expenses affected by some degree of redundancy and upscaling issues. The aim of this work is to assess via simulation modelling the transformation and the fate of the N applied from urine patches in dairy pasture systems based-model, to address current gaps in the discretisation of N releases. Fertilisation input as NH₄, plant uptake, gaseous emissions as NH₃ and N₂O, NO₃ leaching to groundwater, as well as N-fixation were reported in this analysis.

## MATERIAL AND METHODS

The analysis was carried out by using a representative model, PaSim (Riedo et al., 1998). PaSim is a biogeochemical model able to interpret the dynamics and the interactions of grasslands and breed herbivorous at paddock scale. The model consists of sub-models for vegetation, grazing animals, microclimate, soil biology, soil physics and management. It requires easy-to-obtain input parameters and can simulate a series of management practices (e.g. cutting, fertilisation, number and characteristics of grazers) at daily time step. This model has been applied at the scale of urine-patch assessing the effect of a single urine application over a year.

The application time coinciding with the first grazing event of March referring to a typical rotational system in Switzerland (Posieux, Swiss Plateau). N releases were assessed varying the patch size, from a representative surface of 0.2 m², to a very small surface of 0.05 m², to a surface of 1000 m² to be close enough to the paddock scale. Urine was simulated as a distribution of ammoniacal N to the soil surface at a rate of 20 g N patch⁻¹, together with an additional 2 litres rain, while cow grazing actions were mimicked with cuts. During the simulated year, management events as mowing and fertiliser applications were also reproduced, although the effect of trampling on grass was not evaluated.

## RESULTS AND DISCUSSION

Increasing patch size towards the paddock scale (1000 m²), N losses reducing exponentially and coinciding with the scenario with no application of urine (Figure 1). NO₃ leaching represents the highest N loss from the system, 40 times higher in the representative patch of 0.2 m² with respect to the paddock scale, and over 200 times higher in the 0.05 m² patch. This fact is closely related to the amount of water distributed per surface unit per patch. The emission factor of N₂O, or the ratio between the N₂O emitted to the total N distributed, was 1.0% at paddock
scale, 4.8% for the smallest patch, and 7.5% for the patch of 0.2 m², in reason to the higher nitrate content in the first soil profile. The ratio between NH₃ volatilised and NH₄-N applied was greater at large-scale than in the two small patches, due instead to the lower amount of N infiltrated along the profile.

<figure><img src="image_138.png" /><figcaption>Figure 1. Allocations between the N pools after the application of urine patches for the month of March and over a year. Results are reported in logarithmic scale as the difference with a no-application scenario.</figcaption></figure>

Grass biomass production in both 0.05 and 0.2 m² patches had a +10% increase compared to the paddock scale, with a decrease in N-fixation (-25%) and a 30% higher uptake of N. Varying the date of application to warmer and drier periods, greater N-fixation, higher NH₃ and lower NO₃ losses were observed. Finally, the model exhibit a very high capacity to stock NO₃ and NH₄ into the soil profile, standing several months after the distribution, highlighting some issue probably related to the water and NO₃ retention through soil layers.

## CONCLUSION

Urine patches have large impact on N losses in air and groundwater because of the high amount of N supplied to the soil, even if in small area. The allocation of these amounts have to be better discretised in the N-pools of the biogeochemical models, which might give a proper estimate of the losses compared to measures, although frequently produced via wrong processes.

## REFERENCES

* Giltrap DL, Vogeler I, Cichota R, Luo J, van der Weerden TJ, de Klein CAM, 2015. Comparison between APSIM and NZ-DNDC models when describing N-dynamics under urine patches, New Zealand J. of Agric. Res., 58:2, 131-155

* Selbie D., Buckthought L.E., Shepherd M.A. 2015. The Challenge of the Urine Patch for Managing Nitrogen in Grazed Pasture Systems. Advances in Agronomy 129:229-292.

* Riedo, M., et al. (1998) A Pasture Simulation Model for dry matter production, and fluxes of 786 carbon, nitrogen, water and energy. Ecological Modelling 105, 141-183.
# INCREASING NUTRIENT USE EFFICIENCY BY USING CONTROLLED RELEASE FERTILIZERS FITTED TO THE CROP NEEDS

CLEMENS, R.<fnref n="1" />, TERRONES, C. <fnref n="1" />, PARDOSSI, A.², INCROCCI, L.²

## INTRODUCTION

Sustainable agriculture demands efficient fertilization practices in order to reduce nitrogen (N) losses. N Losses by leaching, volatilization or denitrification can dramatically reduce the efficiency of the applied nitrogen, which is no longer acceptable. For this reason, it is urgent to find solutions which improve the Nitrogen Use Efficiency (NUE) while reducing Nitrogen losses.

Controlled release (coated) fertilizers are relatively new in the agricultural market and offer a predictable and consistent release of nutrients in a specific period of time. Since the amount of N released daily is small and goes in line with plant uptake requirements, this sort of fertilizers could be an interesting tool to reduce nutrient losses and optimize NUE (Diara et al., 2014; Terlingen et al., 2016).

## MATERIAL AND METHODS

Specific leaching studies have been executed in the period 2015-2016 in cooperation with the University of Pisa. In order to overcome the complications caused by the un-controlled rainfall typical on an open-field system, the experiments were conducted in a greenhouse of the University of Pisa. For this purpose, a lysimeter system was used, built using big containers (180 liters volume), filled with sandy soil. The main goal of these trials was to quantify the reduction of N leaching in a tomato crop (F 1 "Optima") when using N-Coated fertilizer (Agrocote, made using the E-Max technology) as compared to ammonium sulfate traditionally used for base fertilization. The experiment was designed in order to apply 10 g N, 1.2g P and 12 g K for each tomato plant during the whole growing cycle, using different N sources as base fertilization:

i) Control (grower's practice) : 2.5g N as Ammonium Sulphate

ii) E-Max 50% : 5.0 g N as Agrocote Max N

iii) E-Max 75% : 7.5 g N as Agrocote Max N

The remaining N was applied via fertigation. Besides N leaching, other parameters such as yield and N uptake were measured in this study. In order to calculate NUE indicators, a zero fertilizer treatment was included (data not shown). All the drainage leaked from the lysimeter was collected and analyzed for its N content. Data on fruit quality and quantity were also recorded.

## RESULTS AND DISCUSSION

As seen on Table 1, the use of E-Max coated N significantly reduced the amount of N leached. This was found despite the fact that the E-Max treatments had a higher fraction of the total N added as base fertilizer. This reduction in leaching was even more evident when a higher percentage of the total N was applied as E-Max coated N (E-Max 75%). In respect to yield and N uptake by the fruits, the use of E-Max coated N had a clear positive effect. The highest yields and N uptake rates were obtained when a higher percentage of the total N was supplied as E-Max coated.

<footnote n="1">¹ ICL Specialty Fertilizers, Netherlands; ² Department of Agriculture, Food and Environment, Pisa University, Italy;</footnote>
<table><caption>Table 1. Nitrogen source influence on different parameters. Different letters identify significant differences (P<0.95), according to Tukey test.</caption><thead><tr><th>Parameter</th><th colspan="2">Control</th><th colspan="2">E-Max 50%</th><th colspan="2">E-Max 75%</th></tr></thead><tbody><tr><td>N leached (kg/Ha)</td><td>46</td><td>a</td><td>28.4</td><td>b</td><td>20</td><td>c</td></tr><tr><td>Total Yield (t/Ha)</td><td>42.1</td><td>b</td><td>47</td><td>ab</td><td>49.5</td><td>a</td></tr><tr><td>Total N in the fruits (Kg/Ha)</td><td>67.3</td><td>b</td><td>75.5</td><td>ab</td><td>84.3</td><td>a</td></tr></tbody></table>

There was a significant improvement in both NUE indicators considered for this study (Figure 1), where the efficiency rate was positively correlated with the amount of N supplied via E-Max coated N.

<figure><img src="image_139.png" /><figcaption>Figure 1. Nitrogen source influence on NUE parameters. Different letters identify significant differences (P<0.95), according to Tukey test.</figcaption></figure>

## CONCLUSION

The use of coated Nitrogen as base fertilizer had a clear effect in reducing nitrogen losses, increasing yields and improving the NUE. The effects were more pronounced when the relative amount of E-Max coated N increased, which proves that this technology is a promising tool to improve fertilization efficiency.

## REFERENCES

* Terlingen, J.G.A., Radersma, S., Out, G.J.J., Hernandez-Martinez, J., Raemakers-Franken, P.C. (2016) Current Developments in Controlled Release Fertilizers. Proceedings / International Fertiliser Society: 781 (ISSN 1466-1314)).

* Diara, C., Incrocci, L., Incrocci, G., Carmassi, G., Pardossi, A., Cozzi, P., Fibbi, F., Marzialetti, P. and Clemens, R. (2014). Reduction of Nutrient Run-off by the Use of Coated Slow-Release fertilizers on Two Container-Grown Nursery Crops. Acta Hortic. 1034, 85-91.
# POLYSULPHATE - A NEW MULTI NUTRIENT FERTILIZER WITH SULPHUR, POTASSIUM, MAGNESIUM AND CALCIUM - FOR BETTER NITROGEN USE EFFICIENCY

IMAS, P.¹

¹ Chief Agronomist, ICL Fertilizers, Israel

Polysulphate is a new multi-nutrient fertilizer, available in its natural state, and mined in the UK. It has four nutrients Polysulphate is the trade mark of the mineral 'Polyhalite', which is one of several evaporate minerals containing potassium. Polyhalite (dehydrate) is a single crystal complex with 2 molecules of water of crystallization. It is not a mixture of salts. The chemical formula is K₂Ca₂Mg(SO₄)₄•2(H₂O). Polysulphate contains sulphur (S, 48% SO₃ as sulphate), potassium (K, 14% K₂O as sulphate of potassium) magnesium (Mg, 6% MgO as magnesium sulphate) and calcium (Ca, 17% CaO as calcium sulphate). It is a water-soluble material therefore its nutrients are readily available for plant uptake. Polysulphate comes from the polyhalite layer of bedrock, over 1000m below the North Sea off the North Yorkshire coast in the UK.

Being nitrogen (N) free, farmers can separate S and K from N application thus giving full flexibility with the N source and dose. Polysulphate can be applied before planting, while N can be applied after germination at the right time for the crop, in the right form, and in right weather conditions avoiding N overdosing or leaching. Higher N use efficiency can be achieved without waste and unnecessary cost to the farmer or the environment.

It is well understood that a balanced supply of available N and S is essential for the synthesis of true proteins from the precursor amino acids. A lack of S limits the production of two of the amino acids which are required constituents of all proteins – cysteine and methionine. The deposition of S from industry to agricultural (and other) land was historically well in excess of crop and animal requirements. Thus, S was not discussed as a crop nutrient.

However, nowadays the S emissions had declined sharply and the need for S fertilizer is recognized and applied by many growers. The majority of the fertilizer S requirement to date has been met using ammonium sulphate. Containing both N and S, this is a useful product for use on crops which require both nutrients, but not for legumes which, because of their Rhizobial associations, do not normally receive any N fertilizer.

Polysulphate, having a high S content and lacking N but with the additional benefit of three other essential nutrients – K, Mg and Ca – is thus a useful fertilizer for legumes. In a Lucerne trial in Scotland, the N:S ratio in the Polysulphate treatment was improved to the 12:1 target value required for optimal digestibility, while at the same time the crude protein content improved by about 10%.

<figure><img src="image_140.png" /><figcaption>Figure 1. Beneficial effects of an application of 96 kg SO₃/ha from Polysulphate on the N:S ratio of Lucerne.</figcaption></figure>
In wheat trials in USA, application of Polysulphate resulted in smaller N:S ratios in grains inferring better baking quality for optimum dough and bread making properties. Additionally, an S deficiency or an N surplus may lead to asparagine and glutamine accumulations. During the baking process, free asparagine may promote acrylamide synthesis, a compound considered to be a neurotoxin and potential carcinogen. Thus, Polysulphate not only increases wheat yield but also promotes a more nutritional processed product.

## CONCLUSION

Polysulphate is a nitrogen free fertilizer which provides four nutrients in one application, avoiding N overdosing or leaching. An increased nitrogen use efficiency can be achieved along with better quality of grain proteins and improved baking quality. In addition, it delivers comprehensive nutrition for nitrogen-fixing legumes, where no nitrogen fertilization is needed.

## REFERENCES

- Barbarick K. 1991. Polyhalite application to sorghum-sudangrass and leaching in soil columns. Soil Sci. 151(2):159-166.

- Dugast P. 2015. Use of polyhalite as a source of sulfur for oilseed rape and winter wheat in France. International Potash Institute (e-ifc) 43:21-26.

- Tiwari D., Pandey S., Katiyar N. 2015. Effects of polyhalite as a fertilizer on yield and quality of the oilseed crops mustard and sesame. International Potash Institute (e-ifc) 42:13-20.

- Vale F. 2016. Calcium and magnesium movement in soil profile with polyhalite as potassium fertilizer for soybean crop. Proceedings of FERTBIO 2016, Goiana, Brazil, October 16-20, 2016.

- Yermiyahu U., Zipori I., Faingold I., Yusopov L., Faust N., Bar-Tal A. 2017. Polyhalite as a multi nutrient fertilizer – potassium, magnesium, calcium and sulfate. Israel Journal of Plant Sciences, 64: 145-156.
# IMPACT OF ANAEROBIC DIGESTION ON N BALANCE IN A CROP SUCCESSION FERTILIZED WITH TREATED OR UNTREATED MANURES: FIRST RESULTS

SAVOIE A.¹, PASQUIER C. ², AYZAC A. ², VOYLOKOV P. ³, LEMEKHOVA A. ³, GENERMONT S. ³, LOUBET B. ³, HENAULT C.², HOUOT S.³

¹ INRA UEPAO, 37380 NOUZILLY, FRANCE; 2 INRA UR SOLS, 45000 ORLEANS, FRANCE; 3 INRA Ecosys, 78850 THIVERVAL-GRIGNON, FRANCE;

## INTRODUCTION

Anaerobic digestion has been encouraged in France since 2013 when the Ministry of Agriculture implemented the EMAA plan (Energie Méthanisation Autonomie Azote). Anaerobic digestion has been quickly developing, mainly on farms with animal breeding. Mesophilic and liquid anaerobic digestion is the most frequent process used on farms, often followed by a post-treatment through separation of liquid from solid phases. Thus, when the farmers used to spread solid farmyard manure and/or liquid slurry in their fertilisation strategies, they now have to use either raw liquid digestate directly coming out of the digester or separated solid and liquid phases when phase separation is present. The MetaMetha project has been started to evaluate the impact of including a digestion step in manure treatment on C and N behaviour when treated or untreated manure were used for crop fertilisation in a field experiment comparing the different fertilisation strategies. The results of the first crop of the field experiment will be presented.

## MATERIAL AND METHODS

The field experiment which will last for 3 years is located in Nouzilly (INRA, Centre-Val de Loire region, France) where anaerobic digestion has been implemented since 2014 (animal manures, sewage sludge, industrial wastes, harvest residues). The anaerobic digestion includes 2 successive reactors of 1600 and 700 m³ (70 and 28 days of residence time, respectively). After anaerobic digestion, raw digestate is processed to achieve a solid-liquid separation using a separator based on screw press. The crop succession will be wheat in 2017, rapeseed in 2018 and wheat in 2019. The field experiment includes 5 large plots (24 x 75 m: 1800 m²) comparing different fertilisation strategies: (A) untreated slurry in March and April (2017/03/22, 2017/04/19) on wheat, then farmyard manure in summer (2017/08/02) after wheat harvest and before rapeseed seedling; (B) mineral N (UAN) in March and April, nothing during summer; (C) liquid phase applied in March and April, then solid phase in summer; (D) control without N application; (E) raw digestate for all applications. The field experiment is presented in Figure 1.

<figure><img src="image_141.png" /><figcaption>Figure 1: Design of the field experiment: (A) liquid slurry; (B) mineral fertilizer; (C) liquid phase of digestate; (D) control without N; (E) raw digestate. The static chambers used for N₂O emission and the passive sensors for NH₃ measurements are represented.</figcaption></figure>

The objective was initially to apply similar amount of available N in the different treatments at each application. Ammonia volatilization was assessed immediately after each application using passive sensors (ALPHA) then using the FIDES model (Loubet et al., 2017). The N₂O emissions were assessed using static chambers and regular
measurement of N₂O emission. The crop yields and N exported in grains and crop residues were also measured. Finally, mineral N stocks in soils were measured in February before the first application, after crop harvest in summer then before the drainage period in late autumn. All data were then used to compare the N efficiency of the different fertilisation strategies and the environmental impacts through gas emissions in the different treatments. Detailed results of ammonia volatilization are presented in a parallel paper proposed by Voylokov et al.

## RESULTS AND DISCUSSION

The mineral and total N applied in each treatment and corresponding to the two applications are presented in Table 1. The slurry was more diluted than expected and the corresponding N applied was low. On the other hand, total N concentration was higher than expected in the raw digestate leading to high total N applied in this treatment.

<table><caption>Table 1: Total and mineral N applied in the different treatments. Total N losses through NH₃ and N₂O emission. Total N exported by crops and corresponding apparent N use (¹ based on rapid hydrolysis of urea).</caption><thead><tr><th rowspan="2">Treatment</th><th>Total N applied</th><th>N-NH<sub>4</sub> applied</th><th colspan="2">N-NH<sub>3</sub> emission</th><th>N<sub>2</sub>O emission</th><th>Net N<sub>2</sub>O emission</th><th>N crop</th><th>Apparent N use</th></tr><tr><th>kg N ha<sup>-1</sup></th><th></th><th>kg N ha<sup>-1</sup></th><th>% N-NH<sub>4</sub> applied</th><th>g ha<sup>-1</sup></th><th>g 100kg<sup>-1</sup>N</th><th>kg N ha<sup>-1</sup></th><th>% N applied</th></tr></thead><tbody><tr><td>A: slurry</td><td>100</td><td>69</td><td>2.2</td><td>15.7</td><td>95</td><td>66</td><td>139</td><td>46</td></tr><tr><td>B: mineral N</td><td>129</td><td>97<sup>1</sup></td><td>-3.3</td><td>4.8</td><td>203</td><td>134</td><td>165</td><td>52</td></tr><tr><td>C: liquid digestate</td><td>260</td><td>136</td><td>13.9</td><td>16.5</td><td>275</td><td>95</td><td>193</td><td>34</td></tr><tr><td>D: control</td><td>0</td><td>0</td><td>-8.6</td><td>-</td><td>30</td><td></td><td>81</td><td></td></tr><tr><td>E: raw digestate</td><td>298</td><td>150</td><td>39.7</td><td>32.3</td><td>250</td><td>74</td><td>168</td><td>23</td></tr></tbody></table>

All measured fluxes are also presented in Table 1. Ammonia deposit was observed in the control treatment due to animal breeding in the vicinity. In all the fertilized treatment NH₃ emission occurred (net from the control). The lowest was observed with mineral N application (4.8% of mineral N emitted). Ammonia emission was larger with the raw digestate (32% of NH₃ lost) than with liquid phase or slurry (15 to 17% of NH₃ lost). The N₂O emission was much lower than ammonia emission as classically observed. The emission factors varied between 0.06 and 0.13% of the total N applied. The apparent use of the N applied was similar in the slurry and mineral N treatment. It was lower with the raw digestate than with the liquid phase. The applied doses larger than expected may explained these low apparent N recoveries. However, with similar amounts of NH₄ applied, volatilization was largest with the raw digestate compared to liquid digestate, which may be explained by their different viscosity.

## CONCLUSION

The first results tended to show that phase separation make possible a better valorisation of N by crops with lower ammonia emission. N₂O emission factors were lower than the 1% classically used in the environmental assessment of crop fertilisation. Similar measurements will be realized after each application of organic and mineral fertilizers during 3 years and will allow to calculate the N balance of all the fertilisation strategies.

**Acknowledgements:** The MétaMétha program is financed by the Centre-Val de Loire region (convention n° 2016 – 00108447). The authors acknowledged Baptiste Hénault, Céline Decuq, Eric Vachet and Yolaine Delaunay for their help with the analysis.

## REFERENCES

Loubet, B., Carozzi, M., Voylokov, P., Cohan, J.-P., Trochard, R., and Génermont, S.: Evaluation of a new inference method for estimating ammonia volatilisation from multiple agronomic plots, Biogeosciences Discuss., https://doi.org/10.5194/bg-2017-424, in review, 2017.
20th Nitrogen Workshop

Coupling C-N-P-S cycles

June 25-27, 2018

Le Couvent des Jacobins, Rennes – France

**Session IV: Studies and mitigation options at the farm level –**
**Oral presentations**
# KEYNOTE PRESENTATION : SOURCES OF NITROGEN IN CEREAL PRODUCTION AND MITIGATION OPTIONS

Ladha, J.K.

University of California, Davis, USA

## INTRODUCTION

Only about half of the amount of synthetic N applied is actually used by a crop whereas the other half is lost to the environment, greatly increasing environmental problems (Galloway et al 2003). Unlike nonreactive gaseous N₂, reactive N has magnified the adverse effects because the same atom of N can cause multiple effects in the atmosphere, in terrestrial ecosystems, in freshwater and marine systems, and on human health. Nonetheless in addition to about half of the synthetic N, the crop receives an equal amount of N from the other sources, often assumed to be become available following net N mineralization of soil organic matter (SOM). Numerous short and long-term research trials carried out across the world during last five decades suggested that on an average, the three cereals obtain 48% of its N from synthetic fertilizer and 52% from SOM. If SOM continuously provides 52% of the total N in the crop, the soil N reserve would have been depleted to alarming low level. In contrary, a recent meta analysis of change in SOM-N in 114 cereal-based long-term experiments conducted globally does not show a large decline in SOM-N (Ladha et al., 2011). Instead, soils cropped to cereals have reached more or less a steady state with respect to total SOM-N suggesting that in addition to synthetic N fertilizer, there is another external source(s) of N input in cereal systems. This begs the questions what is the source(s) and magnitude of this source of unaccounted N? Moreover, is the unaccounted pool of N augmented by the application of synthetic N? These questions can only be resolved by constructing a total global N balance for cereals which are key to (1) increasing our understanding of N transformation and N transfers, and (2) quantifying the size of various N reservoirs that are ultimately needed to conserve N in various transformations and biological processes of the system. Addressing these questions are also critical to developing mitigation options to manage N judiciously. This presentation will (1) quantify the various sources of nitrogen (2) evaluate the methods used to determine NUE, (3) determine the key factors that control NUE, and (5) analyze various strategies available to improve the use efficiency at farm level.

## MATERIAL AND METHODS

The N budget was developed by quantifying the inputs and outputs and the change in soil N (δN = final minus initial) over a period of years. Inputs of N included those from fertilizer (FN), manure (MN), residues of crops (RN), atmospheric deposition (DN), seed (SN), and biological fixation (BNF). Outputs include N harvested by the crop (CN) and losses (LN) from various sources through processes such as volatilization, leaching, denitrification, and soil erosion. Thus,

$$N_{budget} = (FN + MN + RN + DN + SN + BNF) - (CN + LN) \pm \partial N$$

In this equation, all the parameters were estimated except BNF, which is uncertain and difficult to measure. We therefore calculated the contribution of BNF using the following equation:

$$BNF = (CN + LN) - (FN + MN + RN + DN + SN) \pm \partial N$$

Estimations of nitrogen harvested by the crops, fertilizer-N input, crop-N derived from fertilizer, loss of fertilizer-N, and the change in total soil-N (30-cm soil depth) are described by Ladha et al., (2017). The SAS mixed procedure with the Tukey-Kramer test was used to determine the variances and to compare the means for the variables. Un-weighted meta-analysis using Meta-Win software (Rosenberg et al 1997) was used to determine the mean and 95% confidence limit.

## RESULTS AND DISCUSSION
A top-down global N budget for maize, rice, and wheat constructed for a 50-year period (1961 to 2010) by integrating global quantities of various sources and sinks of N, which were easier to estimate, rather than assessing N losses, which are highly location and management specific (Ladha et al 2016). A total of 1551 Tg of N were harvested by these cereals during the period, of which 48% was derived from fertilizer-N source and 4% was contributed through net soil depletion (Figure). The remaining 48% (737 Tg) of crop N harvest had sources other than fertilizer- or soil-N, corresponding to 29, 38, and 25 kg ha⁻¹ yr⁻¹ for maize, rice, and wheat, respectively. The major source of this N is apparently the non-symbiotic N₂ fixation, contributing 25% of total N in the crop, which is equal to 13, 22, and 13 kg ha⁻¹ yr⁻¹ for maize, rice, and wheat, respectively (Ladha et al 2016). Other non-fertilizer and non-soil sources include manure (14%) and atmospheric deposition (6%), while crop residues and seed contribute marginally (2 and 1%, respectively) to the crop N. This finding highlights the need to consider all the sources of N (synthetic, SON, manure/residue, deposition and non-symbiotic BNF) when designing strategies to improve N use efficiency.

<figure><img src="image_142.png" /></figure>

Figure. Global estimates of sources of N in crop harvest of maize, rice, and wheat production systems: total (Tg) for 50 years (1961-2010). Source Ladha et al (2016).

REFERENCES

Galloway J. N. et al. (2004) Nitrogen cycles: past, present, and future. *Biogeochem.* **70**, 153-226.

Greenland DJ. & Watanabe I. (1982) The continuing nitrogen enigma. *Wither Soil Research, Transactions of the 12th International Congress Soil Science*. (New Delhi, IndiA).

Ladha JK et al. (2011) Role of nitrogen fertilization in sustaining organic matter in cultivated soils. *J. Environ. Qual.* **40**, 1756-1766.

Ladha JK et al. (2016) Global nitrogen budget in cereals: A 50-year assessment for maize, rice and wheat.
Scientific Reports 6:19355/DOI:1038/srep19355, 1-9.

Rosenberg, M. S., Adams, D. C. & Gurevitch, J. MetaWin. Statistical software for meta-analysis (Version 2.1) (1997).
# DO AGRICULTURAL PRACTICES IMPACT CARBON, NITROGEN AND PHOSPHORUS STOICHIOMETRY IN PLANTS AND SOILS ON THE LONG-TERM?

FERCHAUD, F.<fnref n="1" />, MARY, B.<fnref n="1" />, KEUPER, F.<fnref n="1" />, MOLLIER, A.², DENOROY, P.², MOREL, C.², GALLET-BUDYNEK, A.², HOUOT, S.³, JOUANY, C.⁴, HEDDE, M.⁵, HINSINGER, P.⁵, JOURDAN, C.⁵, BERTRAND, I.⁵

## INTRODUCTION

Carbon (C), nitrogen (N) and phosphorus (P) cycles are intimately linked in ecosystems through key processes such as primary production and litter decomposition. Ecological stoichiometry has become a common approach for exploring relationships between biogeochemical cycles and ecosystems functions in ecological science (Sterner and Elser, 2002). In agronomy, the concept of stoichiometry is far less utilized, probably because the addition of fertilizer reduced biotic interactions between the C, N and P cycles. Surprisingly, little is known about the long-term impact of agricultural practices on soil stoichiometry. Within the context of agro-ecology, however, alternative agricultural practices aim to increase nutrient recycling from plant residues, soil organic matter and inorganic reserves (e.g. legacy P), while reducing tillage or mineral fertilizer input. The success of such practices relies on the increase of soil biotic interactions and may impact C storage in soils on the long term, if soil organic matter stoichiometry is constrained (Bertrand *et al.*, submitted).

We aimed at determining the long-term impacts of alternative agricultural practices on plant and soil stoichiometry. To do so, we compiled and completed a dataset of long-term (8-49 yr) field experiments in France in which P or N fertilization rates or tillage intensity was strongly reduced.

## MATERIAL AND METHODS

Data were collected from a set of seven long-term experiments (Table 1). The dataset was completed when needed by re-analysing stored soil and/or plant samples (shoot) for their C, N or P content. Different forms of soil P were investigated: total, organic and Olsen-extractable P. A database was built and statistical analyses of C:N:P ratios were performed intra and inter-sites (analyses of variance).

<table><caption>Table 1: Inventory of the main experimental sites, their duration and the type of treatments</caption><thead><tr><td>Sites and Location</td><td>Duration</td><td>Agricultural practices tested</td></tr></thead><tbody><tr><td>Auzeville (near Toulouse)</td><td>1969-2018</td><td>0, 11, 22 and 33 kg P ha<sup>-1</sup> yr<sup>-1</sup> applied as superphosphate</td></tr><tr><td>Mant (south of Bordeaux)</td><td>1975-1991</td><td>0, 27, 79 kg P ha<sup>-1</sup> yr<sup>-1</sup> applied as superphosphate on continuous irrigated maize</td></tr><tr><td>Carcares Sainte-Croix (south of Bordeaux)</td><td>1972-2004</td><td>0, 44, 96 kg P ha<sup>-1</sup> yr<sup>-1</sup> applied as superphosphate on continuous irrigated maize</td></tr><tr><td>La Cage (Versailles)</td><td>1998-2018</td><td>4 cropping systems: conventional, low input, conservation agriculture, organic farming</td></tr><tr><td>SOERE QualiAgro (Grignon)</td><td>1998-2018</td><td>Organic fertilizers (4 types) x N fertilization (2 rates)</td></tr><tr><td>SOERE ACBB (Estrées-Mons)</td><td>2010-2018</td><td>Reduced tillage, crop residue removal and reduced N fertilization</td></tr><tr><td>Biomass & Environment (Estrées-Mons)</td><td>2006-2018</td><td>Crop type (perennials vs. annuals) x N fertilization (2 rates)</td></tr></tbody></table>

## RESULTS AND DISCUSSION

The main factor explaining differences in plant stoichiometric ratios (C:N, C:P, N:P) was the type of plant species. Due to their storage capacity and allocation strategy, autotrophs like plants had higher stoichiometric flexibility than is commonly observed for heterotrophs like soil microorganisms. However, agricultural practices affected the C, N and P contents of the plants and their ratios within plant species. For instance, organic farming in La Cage

<footnote n="1">¹UR AgroImpact, Laon, 2UMR ISPA, Bordeaux, ²UMR ECOSYS, Grignon, ³UMR AGIR, Toulouse, ⁵UMR Eco&Sols, Montpellier</footnote>
produced wheat with higher C:N and lower N:P ratios than the conventional system (30.7 vs. 24.4 and 2.5 vs. 3.4 respectively).

The agricultural soils studied presented C:N and C:P ratios ranging from 8 to 14.5 and from 15 to 28 respectively, and N:P ratios ranging from 1.5 to 2.8 (total P). The site effect was significant on the soil CNP contents and ratios (one-way ANOVA, *P* < 0.05). Interestingly, whereas the soil C:N ratios were constrained and not influenced by the different agricultural practices, in agreement with Zinn *et al*. (2018), the C:P and N:P were more flexible. E.g., at the Mant and Carcares Sainte-Croix sites we observed stable C:N ratios, whatever the treatment and duration of the experiments, while flexible C:P and N:P ratios were observed in response to both treatment and time (Figure 1).

<figure><img src="image_143.png" /><figcaption>Figure 1: Initial and final C:N, C:P and N:P ratios at Mant (dark symbols, 17 years) and Carcares Sainte-Croix (light symbols, 22 years) in the 0-25 cm soil layer for three levels of P fertilization (P0: circles; P1: squares; P3: triangles)</figcaption></figure>

## CONCLUSION

Overall, plant stoichiometry was less constrained than soil stoichiometry in all studied agricultural systems. Specifically, soil C:N ratios were highly constrained and not influenced by long-term agricultural practices. This stability confirms the strong association between C and N in soil organic matter and the statement by Van Groenigen *et al*. (2017) that storing C in soils will increase N-requirements. Interestingly, however, this was not the case for P, and we show here for that agricultural practices can influence soil C:P and N:P ratios.

**Acknowledgements:** The authors would like to thank the department Environment and Agronomy of INRA for its financial support.

## REFERENCES

* Bertrand, I., Viaud, V., Daufresne, T., Pellerin, S., Recous, S. Storing C while reducing nutrient inputs: Could stoichiometry limit the efficiency of agroecological practices? *Submitted*.

* Li, X., Wang, H., Gan, S.H., Jiang, D.Q., Tian, G.M., & Zhang, Z.J. (2013). Eco-Stoichiometric Alterations in Paddy Soil Ecosystem Driven by Phosphorus Application. *Plos One*, 8.

* Sterner, R.W. & Elser J.J. (2002) *Ecological Stoichiometry: Biology of Elements from Molecules to the Biosphere*. Princeton, NJ, USA: Princeton University Press, https://doi.org/10.1093/plankt/25.9.1183

* Van Groenigen, J.W., Van Kessel, C., Hungate, B.A., Oenema, O., Powlson, D.S., & Van Groenigen, K.J. (2017). Sequestering Soil Organic Carbon: A Nitrogen Dilemma. *Environmental Science and Technology*, 51, 4738–4739.

* Zinn, Y.L., Marrenjo, G.J., & Silva, C.A. (2018). Soil C:N ratios are unresponsive to land use change in Brazil: A comparative analysis. *Agriculture, Ecosystems and Environment*, 255, 62–72.
# IDENTIFYING NEMO: A MODEL-BASED METHODOLOGY TO IDENTIFY STRATEGIC N APPLICATION RATES FOR RAINFED CROP

PATTEY, E.¹, MESBAH, M.², JEGO, G.³

¹ Agriculture and Agri-Food Canada, 1Ottawa, ON, Canada; ² Charlottetown, PEI Canada; ³ Quebec, QC, Canada

## INTRODUCTION

Identifying the right amount of nitrogen (N) fertilizer for crop production is critical for limiting N loss to the environment (Galloway et al., 2003). Applying N rates higher than the crop needs contributes to the release of reactive N in the environment through nitrate leaching, ammonia volatilization and nitrous oxide emissions. Because the optimum amount of N (N<sub>opt</sub>) applied varies not only with soil properties but also with climatic conditions, finding the appropriate N recommendation is particularly challenging for rainfed crops with high N demand such as corn (Zea Mays L.). Developing a methodology to account for daily and interannual climate variations in the determination of N<sub>opt</sub> is a key step towards delivering an integrated decision support system for N rate recommendations that mitigate reactive N losses. Modeling is envisioned as a way to test yield response to N rates with small increments (e.g., 10 kg N ha<sup>-1</sup>) for several climatic years (e.g., 30-60 years) and for given soil properties (dominant soil classes for the region of interest). Therefore, adapting and verifying a crop model for the region of interest is important before yields predicted by the selected model can be used to study N<sub>opt</sub>. The STICS crop model (Brisson et al., 2003) met these requirements (Jégo et al. 2011, 2015) and was selected to introduce the methodology and a case study of corn production in eastern Canada. Our objectives were (1) to propose a new yield function and evaluate its performance compared to existing yield functions; (2) to propose a methodology, called Identifying NEMO, to derive N<sub>opt</sub> based on the crop ecophysiology; (3) to derive recommended N rates as a function of expected yield probability, and N excess or deficit by region.

## MATERIAL AND METHODS

A slope-based method is proposed to identify, for a given region and soil, the optimum N use efficiency (NUE<sub>opt</sub>) and to determine the corresponding N<sub>opt</sub> for each growing season. A case study was carried out in five 40 x 40 km regions (ON: Windsor, London, Ottawa; QC: Saint-Hubert, Quebec City) of the Mixedwood Plains ecozone of Canada, which contributes more than 90% of the corn production in Canada. At each location, 50+ years of daily climatic data were available. The three most dominant and contrasting soil textures were selected in each region by overlaying the corn cultivated areas issued from the 2013 space-based crop identification map to the soil map from the Canadian Soil Information System, as soil parameter input to the model. The simulations over the long daily climatic series using the STICS model to predict corn yields were carried out with region-specific seeding dates and corn cultivars. For each climatic year, soil and region, yields were predicted for N rates varying between 50 and 200 kg N ha<sup>-1</sup> by step of 10 kg N ha<sup>-1</sup> except for clay and silty clay soil, for which the range was between 100 and 250 kg N ha<sup>-1</sup>. The case study involved more than 14800 simulations. The performance of the following yield functions was evaluated: the linear-plateau (L-P), which accounts for the fact that yield reaches plateau; Mistcherlich Baule (MB) functions, which accounts for the nonlinearity associated with yield response to N rates, both are among the most commonly used functions, and a new combination, the Mistcherlich Baule-Plateau (MB-P) function.

## RESULTS AND DISCUSSION

The choice of a proper yield function is very important as misspecification of the function could lead to misleading results. To evaluate the yield functions performance, they were fitted to the predicted yield data for each year. The three yield functions perform very well at high and medium yield years, whereas the performance of the nonlinear functions (MB, and MB-P) deteriorates at lower yield level when the response of yield to N becomes almost linear. However, when the slope of the functions at fine resolution N rate intervals were compared to the
central finite difference values for predicting NUE, the MB-P function showed a slightly better performance and was thus selected for identifying N<sub>opt</sub> (Mesbah et al. 2017).

The proposed method is similar to the economic approaches as it analyzes the slope of yield function to N rate. However, the optimum slope and corresponding N rate is defined based on the crop ecophysiological performance. This NUE<sub>opt</sub> is identified by evaluating the linearity of the relationship between yield and N<sub>opt</sub>, and the reduction in yield compared to maximum achievable yield for a given soil and various growing seasons. The steps taken to determine the NUE<sub>opt</sub> are as follows (Mesbah et al. 2017):

1) The selected crop model is used to simulate yield response to various N rates using fine increments in the interval of interest (i.e., 10 kg N ha<sup>-1</sup>) for a long time series of climatic data and relevant soil properties.

2) A regression is performed to fit the MB-P function, to the data of fine resolution N rates and corresponding dry yield predicted by the crop model for each growing season and soil properties.

3) In order to identify the NUE<sub>opt</sub>, a series of yield-N<sub>opt</sub> data are extracted over a range of NUE values. This yield-N<sub>opt</sub> dataset is unique for each NUE, and is generated using the fitted MB-P functions and the selected NUE.

4) For a given NUE, a linear regression is performed to evaluate the linearity between yield and N<sub>opt</sub> data. NUE<sub>opt</sub> is reached when a linear relationship is obtained.

5) For the given NUE, the ratios of yield at N<sub>opt</sub> to maximum yield as well as the yield reduction (i.e., the difference between maximum yield and yield at N<sub>opt</sub>) are calculated for each growing season. The mean values for high yield years, and low yield years are then calculated separately and used to study the relationship between NUE and reduction in yield.

6) Steps 4 to 5 are repeated for all NUEs in the predefined NUE range.

7) A tradeoff plot is then constructed to show how yield reduction (calculated in step 5) and R<sup>2</sup> and RMSE of linear fit (step 3) change by NUEs. The tradeoff plot can then be used to decide on an NUE that meets two criteria: (1) it leads to a good linear relationship between N<sub>opt</sub> and dry yield, and (2) it leads to marginal reduction in maximum achievable yield.

For an expected level of yield and a selected NUE, the corresponding linear relationship between yield and N<sub>opt</sub> is used to derive a recommended level of N rate. For the given expected yield, the distribution N<sub>opt</sub> and yield predicted by the crop model is also used to calculate the chance of achieving an expected yield, as well as the N excess or deficit for the region of interest.

## CONCLUSION

The performance of the proposed methodology, Identifying NEMO, was examined via a case study of multiple regions along with the Mixedwood Plains ecozone. Our results indicated that this methodology could provide valuable information on beneficial N management practices under various climatic conditions and soil textures.

## REFERENCES

* Brisson N., Gary C., Justes E., Roche R., 2003. An overview of the crop model STICS. Eur. J. Agron. 18, 309–332.

* Galloway J.N., Aber J.D., Erisman J.W., Seitzinger S.P., Howarth R.W., Cowling E.B., Cosby B.J., 2003. The nitrogen cascade. Bioscience 53, 341–356.

* Jégo G., Pattey E., Bourgeois G., Drury C.F., Tremblay N., 2011. Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada. Agron. Sustain. Dev. 31, 557–570.

* Jégo G., Pattey E., Mesbah M., Liu J., Duchesne I., 2015. Impact of the spatial resolution of climatic data and soil physical properties on regional corn yield predictions using the STICS crop model. Int. J. Appl. Earth. Obs. Geoinf. 41, 11–22.

* Mesbah M., Pattey E., Jégo G., 2017. A model-based methodology to derive optimum nitrogen rates for rainfed crops - a case study for corn using STICS in Canada. Comput. Electron. Agric., 142 (Part B), 572-584.
# AMMONIA EMISSION FROM ANIMAL MANURE AND MINERAL FERTILIZER MEASURED WITH A CHEAP, RELIABLE & SIMPLE TO USE METHOD

SOMMER, S.G.<fnref n="1" />, KURE, J.L.<fnref n="1" />, KRABBEN, J.<fnref n="1" />, DUELUND L.<fnref n="1" />, PEDERSEN, S.V.<fnref n="1" />

## INTRODUCTION

Emission of ammonia (NH₃) from agriculture is a loss to the farmer, detrimental to the environment (Sutton et al., 2011) and a risk to health. Manure or fertilizers applied in the field are one of the main sources of NH₃ in the atmosphere, and in Europe, agriculture must reduce the emission to the levels given by the NEC Directive. The acceptance of new low emission technologies to manage manure and mineral fertilizer as so-called best available technologies relies on valid estimates of the reduction efficiency. Ammonia volatilization from plots or fields are measured using micrometeorological techniques or portable wind tunnels, and the atmospheric NH₃ concentration is measured with methods, that capture or absorb NH₃ in acids and the amount absorbed is determined with the Berthelot colouring reaction.

There is a need to measure NH₃ emission from manure or fertilizers applied to fields with different soil characteristics and crop cover, and at varying climate conditions. This may be impeded by costs related to existing measuring methods; therefore, there is a need for cheap and reliable measuring technologies that can be used by technicians with no experience in measuring NH₃ emission. We propose that a specialized center could provide the anemometers, NH₃ samplers and instruction to users. Passive ALPHA samplers is proposed as the NH₃ measuring technique; these can be prepared for measurements, be stored for long periods in closed PVC bags without being polluted and be send by post. The ALPHA method was developed for long-term exposure (weeks), and exposure periods are from 4 – 24 h in the experiments measuring NH₃ emission from field plots. The intention of this study is to adapt and test the samplers for measuring NH₃ emission from relative small field plots amended with manure or mineral fertilizers.

## MATERIAL AND METHODS

We examined the effect of oxalic and citric acid on the precision and reproducibility of measurements with a NH₄-electrode and compared these with measurements using the Berthelot colouring method. The tests was carried out using standards containing a solution of ammonium chloride (NH₄Cl) and oxalic or citric acid mimicking eluates from ALPHA samplers. A series of test with the electrode was carried out to provide instruction about use, and information about detection limit, precision and reproducibility. Precision, reproducibility and detection limit of measurements of NH₃ gas concentration was were determined using samplers exposed in a chamber through which air with NH₃ was pumped, and by measuring NH₃ concentration/flux with samplers exposed in the field and compared this to concentration/flux measured with Leuning flux samplers. Data are being analysed at the moment of submission this abstract and will be presented at the workshop, a few introductory results are presented here.

## RESULTS AND DISCUSSION

The absorbance (Berthelot colouring) was affected by the acids in solution with TAN, and reduced the reproducibility of the measurements compared to measuring TAN dissolved in water (Fig. 1A). This was not the case when measuring the signal (charge) of TAN in solutions with and without addition of acids using the electrode, indicating that the acids do not affect the ion specific electrode, if the amount of OH⁻ added to the solution is high enough to release NH₃. Further, samples from field experiments indicated that the CV of

<footnote n="1">¹ University of Southern Denmark (SDU), Faculty of Engineering, Department of Chemical Engineering, Biotechnology and Environmental Technology, Campusvej 55, 5230 Odense M, Denmark</footnote>
<figure><img src="image_144.png" /><figcaption>Figure 1. Reproducibility of the analysis of NH4+ in eluate from absorbers as affected by acids in eluate (TAN 50 µM and acid 101 mM). A: absorbance (Berthelot color reaction) and B: charge (ammonium electrode).</figcaption></figure>

the mean varied from 4 to 15% (number of replicates was 4), which is similar to that for gas washing bottles used to measure NH₃ concentrations in air above plots amended manure or fertilizers.

Emission is calculated by knowing NH₃ concentration and wind speed using the bLS method or by measuring the NH₃ flux at the zinst height (H<sub>ZINST</sub>, m) and multiplying this flux with a constant (K<sub>ZINST</sub>), which has been given for larger plots by Wilson et al. (1982). We have calculated the H<sub>ZINST</sub> and K<sub>ZINST</sub> for small plots (Table 1).

<table><caption>Table 1. The height H<sub>ZINST</sub> at which normalized horizontal flux deviate little at different atmospheric stabilities. The height is calculated at Monin Obukov length 10 m, -10 m and 100000 m (∞ m) using the Windtrax model.</caption><thead><tr><th rowspan="2">Surface characteristic</th><th rowspan="2">Surface roughness (Z<sub>0</sub>, cm)<sup>a</sup></th><th colspan="2">Radius 5 m</th><th colspan="2">Radius 20 m</th></tr><tr><th>H<sub>ZINST</sub> (m)</th><th>K<sub>ZINST</sub></th><th>H<sub>ZINST</sub> (m)</th><th>K<sub>ZINST</sub></th></tr></thead><tbody><tr><td>Harrowed – no obstacles</td><td>0.5<sup>b</sup></td><td>No result</td><td>No result</td><td>0.9</td><td>9.5</td></tr><tr><td>Newly plowed, Grass 3-5 cm Wheat stubble field (5-10 cm stubble)</td><td>2</td><td>0.5</td><td>4.9</td><td>1.1</td><td>8.6</td></tr></tbody></table>
<table><tr><td>Feed lots</td><td>3.6 (2-6)</td><td>0.6</td><td>4.3</td><td>1.5</td><td>6.4</td></tr><tr><td>Wheat 5-10 cm</td><td>5<sup>b</sup></td><td>1</td><td>2.1</td><td>2</td><td>4.3</td></tr><tr><td>Wheat 0.5-1 m</td><td>10<sup>b</sup></td><td>No result</td><td>No result</td><td>2</td><td>5</td></tr></table>

## CONCLUSION

The diffusion samplers developed for measuring NH₃ concentrations over long time intervals provide precise and reproducible measurements for shorter term measuring intervals (>6 h) and for plots with a fetch of 5 m. This measuring technique can reduce cost for measuring emissions and the use of small plot provide opportunities for replicated measurements of a treatment in the same field.

## REFERENCES

* Sutton, M. A., Oenema, O., Erisman, J. W., Leip, A., van Grinsven, H. & Winiwarter, W. 2011. Too much of a good thing. Nature - Comment 472, 159–161.

* Wilson, J.D., G.W. Thurtell, G.E. Kidd, and E.G. Beauchamp. 1982. Estimation of the rate of gaseous mass transfer from a surface source plot to the atmosphere. Atmos. Environ. 16:1861–1867.
NITROGEN USE EFFICIENCY AS AN INDICATOR OF FARM PERFORMANCE

QUEMADA, M.¹, BRENTRUP, F.², RUTKOWSKA, L.³, STOUMANN, L.⁴, SCHULMAN, M.⁵, OENEMA, O.⁶

¹ Universidad Politécnica de Madrid, Spain; ² Yara International, Research Centre Hanninghof, Germany, ³ Institute of Soil Science and Plant Cultivation, Poland, ⁴ University of Copenhagen, Denmark; ⁵ COPA COCEGA, EU; ⁶ Wageningen University, The Netherlands

INTRODUCTION

Nitrogen management in farms aims at improving agronomic objectives and minimizing environmental impacts simultaneously. But proper management is not easy because the N cycle is complex and the risk of losses from the farm to the environment high. Therefore, indicators for the performance of farms can play a relevant role in improving N management. Nitrogen use efficiency (NUE) is commonly used to assess the relative conversion of N inputs into agricultural products [1], but there is not systematic monitoring of NUE at farm level and hence little is known about differences in NUE between farms and regions. The objective of this study was to apply N performance indicators to different farms across Europe, and to understand the factors that contribute to differences in NUE and derive possible target values.

MATERIAL AND METHODS

Farm level data were collected through surveys in Mediterranean, Central Europe, Scandinavia and Western Europe. The data were analysed to calculate three related indicators (NUE, N output and N surplus) according to the procedure described by the EU Nitrogen Expert Panel. Briefly, N input and N output data were collected from each farm, and a farm N balance was estimated according to Table 1. In the case that animal manure was imported to the farm and other manure exported, only the net manure N input was reported as input.

For each farm, NUE was calculated as the ratio of outputs over inputs. The N output (kg N ha⁻¹) was estimated from the amount of products harvested times the mean N content of the particular products, and is an indicator of farm productivity. The N surplus (kg N ha⁻¹) was calculated as the difference between the inputs and the outputs, and is a commonly used indicator for assessing the potential N losses to the environment.

A simple software tool was developed in Microsoft Excel to process the collected information in a uniform way. Results of the NUE, N output and N surplus of the different farm types and regions were statistically analysed using multiple regression analysis. Finally, farm data were mapped into a two-dimensional framework of N output versus N input suggested by the EU N Panel that combines the N performance indicators.

<table><caption>Table 1. Input and output items considered for the farm N balance and the calculation of NUE, N output and N surplus.</caption><thead><tr><td>Nitrogen input items</td><td>Nitrogen output items</td></tr></thead><tbody><tr><td>Mineral fertilizers</td><td>Crop products</td></tr><tr><td>Feed and fodder (net)</td><td>Animals (net)</td></tr><tr><td>Biological nitrogen fixation</td><td>Animal products (milk, egg, wool, meat, ....)</td></tr><tr><td>Atmospheric N deposition</td><td>Orchard trees (net)</td></tr><tr><td>Compost and sewage sludge</td><td></td></tr><tr><td>Seed and planting material</td><td></td></tr><tr><td>Bedding material (straw, saw dust)</td><td></td></tr><tr><td>Animal manure (net)</td><td></td></tr><tr><td>Irrigation water</td><td></td></tr></tbody></table>

RESULTS AND DISCUSSION
Results show a remarkable variation in NUE and N surplus, which were mainly related to differences in farming system and management. Nitrogen output differed between regions, mainly due to differences in climate and farming systems. Arable farms had higher NUE and N output and lower N surpluses than dairy farms.

Combining NUE with indicators of productivity and environmental losses provided a reliable assessment of N performance at a farm level, as shown by arable farms from Mediterranean Europe (Fig. 1). A high NUE does not necessarily lead to a low risk of environmental impact, consequently in some highly intensive systems a large N surplus was observed. Low NUE was often related to large N losses but it may also indicate that farm productivity (N output) was compromised by other factors.

<figure><img src="image_145.png" /><figcaption>Figure 1. Example of annual N input and N output values of 64 irrigated arable farms in Central Spain mapped onto the framework proposed by the EU Nitrogen Expert Panel.</figcaption></figure>

Some arable farms with NUE > 1 showed a risk of soil mining. Changes in soil organic N are common following conversion of permanent grassland to arable land, changes in cultivation practices (tillage, manure application) and modifications in weather conditions. Given the relevance of soil organic N and its implications on soil C, it is recommended that reports on NUE at farm level include a discussion about potential changes in soil N stock.

## CONCLUSIONS

Optimum targets for N performance would be those aiming for high NUE while minimising N surplus without compromising productivity. Differences in NUE and N surplus between farms in Europe are mainly related to differences in farming systems and management, and less to differences in regions. Farm system specific target values for NUE, farm specific guidance, and possible linkages with the Common Agricultural Policy may create the necessary incentives to further enhance NUE at farm level.

**Acknowledgements:** Fertilizers Europe for supporting the European N Expert Panel activities.

## REFERENCES

* EU Nitrogen Expert Panel, 2015. Nitrogen Use Efficiency (NUE) - an indicator for the utilization of nitrogen in agriculture and food systems. Wageningen University, The Netherlands, 47 p.
# AQUAPONICS SYSTEM, A SOLUTION TO LIMIT NUTRIENT RELEASE BY FISH FARMING ?

JAEGER, C.¹, FOUCARD, P.², TOCQUEVILLE, A.², NAHON, S.³, AUBIN, J.¹

¹ INRA UMR SAS, France; 2 ITAVI aquaculture department, France, 3 INRA UMR NuMéa

## INTRODUCTION

In terrestrial agriculture, the use of manure as fertilizer is a sustainable way to provide nutrient to plants. Similarly, aquaponics is an integrated aquaculture system in which fish farming and soilless culture are combined to recycle nutrient. As dissolved nutrient generally released in wastewater from fish culture is recovered into edible plants thus Aquaponics System (AS) can be considered as a sustainable production system (Graber & Junge, 2009). AS is historically set up as a Recirculating Aquaculture System (RAS) for fish rearing connected to a Hydroponic System (HS) for crop cultivation, sparing use of water (Love et al., 2014). The aim of this study was to assess AS efficiency in fish waste treatment, through a nutrient budget of total Nitrogen (TN) and total Phosphorus (TP), in an operational farm.

## MATERIAL AND METHODS

The experiment lasted 52 days inside a single greenhouse. A total of 1001 common carp (Cyprinus carpio) (mean weight = 647 g) were stocked to reach a rearing fish density in accordance with usual commercial fish farming densities, and fed with commercial pelleted feed (32% crude protein, 9% crude fat and 1% phosphorus of the fresh weight). Pelleted feed was delivered at a mean ration of 1.1 kg feed/100 kg fish biomass. For the HS compartment, four culture areas of 4.8 m² each were composed of bed raft (volume = 550 l) covered by floating rafts (extruded polystyrene foam) set at a density of 16 seedlings/m². Lettuce oak leaf and Batavia were set up in equal numbers at a four leaves stage. Water was continuously running through the bed raft (under the floating rafts) at a flow rate of 1 m³/h, then went back to a common tank. From this common tank, water was distributed between the HS, the RAS and the discharge wastewater. In the RAS, water from the 4 fish rearing tanks (3.7 m³ each) was continuously running through the entire system. A rotary drum filter with a mesh of 60 µm catched the solid particles from water and sent them into a settling tank to concentrate them into sludge. Sludge was then transferred to a second tank in which flocculating agent was added, then sent into a water permeable geotextile bag. At the exit of the drum filter, filtered water entered into the common tank in which new water was added at a 6% water exchange rate per day. In the RAS, water was pumped from this common tank through a UV filter until it reached a moving bed biofilm reactor (MBBR) and finally closed the loop by return flow to the rearing tanks.

Total Nitrogen (TN) and Total Phosphorus (TP) concentrations were analysed, at the beginning and at the end of the experiment, in lettuce, sediment, leachate from sludge and water. Water samples were collected from the tap water, the common tank and the discharge waste water. TN and TP concentrations were measured using spectrophotometry, (ISO 5663:1984 and ISO 6878:2004 respectively). In pelleted feed and lettuce, TN content was determined using an Elementar Vario Pyrocube elemental analyzer (Elementar, Langenselbold, Germany). TP content was determined by the molybdate-blue/ascorbic acid method at 820 nm after mineralization and acid digestion (AFNOR, 1992). In fish, TN and TP contents were estimated according to literature available (Schreckenbach, Knosche, & Ebert, 2001). Volumes and quantities of all these compartments were recorded in order to calculate a nutrient budget for N and P.

## RESULTS AND DISCUSSION

Pelleted feed and stocked fish (tab. 1) were the most important inputs in N and P, equivalent to 92.5% and 96.9% of the TN and TP input respectively. N was mainly released from AS through the outlet water whereas P was mainly released through the sediment. The N budget was not well balanced as 24.5% of TN input was not recovered. N gaseous emissions might explain this result (Ru et al., 2017). The N retention rate in fish and lettuce biomass gain represented 18.7% and 0.6% of N delivered by the formulated feed respectively. The P retention rate in fish and
lettuce biomass gain represented 19.9% and 0.7% of TP delivered by the formulated feed respectively. As P is a conservative element and according to uncertainties of measurement and sampling, we consider that the nutrient budget for this element is well balanced. Indeed, 6.6% of P input was not recovered.

<table><caption>Table 1: quantities of nitrogen and phosphorus, inputs and outputs, in the different compartments of the aquaponics system used for nutrient balance calculation of nitrogen and phosphorus.</caption><thead><tr><th rowspan="2">Compartments</th><th colspan="2">Nutrient (g)</th></tr><tr><th>Nitrogen</th><th>Phosphorus</th></tr></thead><tbody><tr><td><strong>Inputs</strong></td><td></td><td></td></tr><tr><td>Feed</td><td>25 047</td><td>4 403</td></tr><tr><td>Stocked fish</td><td>16 613</td><td>3 108</td></tr><tr><td>Circulating water at beginning</td><td>2 592</td><td>213</td></tr><tr><td>Inlet water</td><td>496</td><td>5</td></tr><tr><td>Lettuce seedlings</td><td>21</td><td>2</td></tr><tr><td><strong>Outputs</strong></td><td></td><td></td></tr><tr><td>Harvested fish</td><td>21 292</td><td>3 984</td></tr><tr><td>Outlet water</td><td>7 549</td><td>975</td></tr><tr><td>Circulating water at end</td><td>2 427</td><td>324</td></tr><tr><td>Sediment</td><td>2 076</td><td>1 741</td></tr><tr><td>Harvested lettuce</td><td>174</td><td>32</td></tr><tr><td>Leachate</td><td>254</td><td>166</td></tr><tr><td>Unaccounted: Input-Output</td><td>10 997</td><td>509</td></tr></tbody></table>

## Conclusion

Considering the very low N and P uptake by plants, we can conclude that AS ability to recover nutrients in plant biomass is limited. Nevertheless, it seems necessary to reconsider the design of the AS in order to maximize the bioremediation potential through optimization of the plants/fish ratio.

**Acknowledgements:** The present work was performed within the French project APIVA (AquaPonie Innovation Végétale et Aquaculture). We thank the RATHO team, for providing the space in their horticultural greenhouse; Philippine Sotteau and Manon Lorriaux for their involvement in the data collection; and A. surget (INRA, NuMeA) for her collaboration in Phosphorus analyzes.

## REFERENCES

* Graber, A., & Junge, R. (2009). Aquaponic Systems: Nutrient recycling from fish wastewater by vegetable production. *Desalination*, 246(1-3), 147-156.

* Love, D. C., Fry, J. P., Genello, L., Hill, E. S., Frederick, J. A., Li, X. M., & Semmens, K. (2014). An International Survey of Aquaponics Practitioners. *Plos One*, 9(7).

* Ru, D. Y., Liu, J. K., Hu, Z., Zou, Y. N., Jiang, L. P., Cheng, X. D., & Lv, Z. T. (2017). Improvement of aquaponic performance through micro- and macro-nutrient addition. *Environmental Science and Pollution Research*, 24(19).

* Schreckenbach, K., Knosche, R., & Ebert, K. (2001). Nutrient and energy content of freshwater fishes. *Journal of Applied Ichthyology*, 17(3), 142-144.
# A COMPARATIVE NITROGEN BALANCE OF NOVEL CROPPING SYSTEMS FOR FEEDSTOCK PRODUCTION TO FUTURE BIOREFINERIES: THE ROLE OF PERENNIAL GRASSES AND GRASS-LEGUMES

MANEVSKI, K., LÆRKE, P.E., JØRGENSEN, U.

Arhus University Centre for Circular Bioeconomy (http://cbio.au.dk/en/)

## INTRODUCTION

Amid the “bio-based” reindustrialisation of the EU, agricultural lands are subject to changes for a variety of economic and environmental reasons. Understanding the effects of new cropping systems on soil nitrogen (N; and carbon, C) dynamics is crucial to evaluate crop production and quality, as well as environmental impacts, and these are yet to be studied. This work reports the first N balances for novel cropping systems optimised for high production to target biomass supply to future biorefineries, under north-European pedo-climatic settings.

## MATERIAL AND METHODS

In a three-years study (2013-2015), two types of novel systems, i) an optimised rotation with annual crops (maize, beet, hemp/oat, triticale as main crops, and winter rye and oilseed rape as second crops) and ii) perennial grasses: highly fertilised (festulolium, reed canarygrass, tall fescue and cocksfoot), low-fertilised (miscanthus) and unfertilised (grass-legume mixtures), were compared with traditional systems (continuous maize or triticale, and a cereal crop rotation) on sandy loam and coarse sand soil in Denmark. Harvested biomass N, soil nitrate dynamics and model-supported nitrate leaching, atmospheric N fixation and apparent soil N balances (inputs minus outputs) of the systems were compared (Manevski et al., 2018).

## RESULTS AND DISCUSSION

Relative to continuous maize, the most production-competitive traditional system, fertilised grasses doubled biomass N, and unfertilised grass-legume mixtures yielded similar biomass N, whereas nitrate leaching was reduced by 70-80% (Fig. 1). These perennial systems have been documented to enhance radiation utilisation and biomass production (Manevski et al., 2017), perform well in life cycle assessments (Parajuli et al., 2017) and have higher contents of protein (with better quality, e.g., high lysine and methionine contents) than traditional crops (Solati et al., 2018), making them particularly interesting to substitute the environmentally costly imports of soya bean protein. The optimised crop rotation supplied 70% more biomass N with 40% less nitrate leaching on coarse sandy soil, whereas on sandy loam soil it yielded about 10% less biomass N but leaching was halved, relative to continuous maize. Double-cropping (main and second crop), as well as planting dates, irrigation and N management, are crucial to increase radiation interception, N uptake and high yields (Manevski et al., 2017).

The soil N balance indicated soil N mining for festulolium and continuous maize on sandy loam soil (Fig. 1); on coarse sand soil, lower yield and larger N leaching indicated overfertilisation or unsynchronised irrigation. Inclusion of soil layers down to 1 m will clarify if and how much soil C and N have accumulated beneath the perennial systems based on 5 years (2012-2017), and may explain the apparent discrepancy between the N balance results (soil N mining; Fig. 1) and the soil C/N change (no apparent change for some systems; Table 1)

## CONCLUSION

The results may support management of economic and sustainable feedstock production for biorefineries where biomass N can be produced and refined to valuable products. While additional soil C and N data will come, the experiment continues on sandy loam soil site, in addition to a clayey soil site established in 2015 in Denmark.
<figure><img src="image_146.png" /><figcaption>Figure 1. Nitrogen (N) inputs, outputs and balance for the studied systems in Denmark. Festulolium and cocksfoot are proxy for fertilised perennial grasses at their respective sites. Grass-legume mix on sandy loam soil is proxy for coarse sand soil. All fertiliser is mineral (N-P-K). Detailed description of all systems can be found in Manevski et al. (2017; 2018).</figcaption></figure>

<table><caption>Table 1. Topsoil (0-20 cm) carbon (C) and nitrogen (N) amounts (mean, n=4, std. dev. in brackets) for selected study systems. Results are only for sandy loam soil site. Festulolium is a proxy for fertilised perennial grasses.</caption><thead><tr><th rowspan="2">System</th><th rowspan="2">Crop</th><th colspan="2">C/N</th><th colspan="2">C (ton ha<sup>-1</sup>)</th><th colspan="2">N (ton ha<sup>-1</sup>)</th></tr><tr><th>2012</th><th>2017</th><th>2012</th><th>2017</th><th>2012</th><th>2017</th></tr></thead><tbody><tr><td rowspan="3">Perennial (novel)</td><td>Festulolium</td><td>12.3 (0.2)</td><td>12.4 (0.6)</td><td>43.7 (2.2)</td><td>44.8 (2.7)</td><td>3.6 (0.2)</td><td>3.6 (0.3)</td></tr><tr><td>Miscanthus × giganteus</td><td>12.2 (0.4)</td><td>12.5 (0.5)</td><td>41.9 (2.0)</td><td>41.4 (3.8)</td><td>3.4 (0.3)</td><td>3.3 (0.3)</td></tr><tr><td>Grass-legume mixture</td><td>12.5 (0.3)</td><td>12.1 (0.7)</td><td>40.0 (3.2)</td><td>43.5 (2.9)</td><td>3.2 (0.2)</td><td>3.6 (0.3)</td></tr><tr><td>Annual (novel)</td><td>Optimised rotation</td><td>12.0 (0.1)</td><td>11.7 (0.6)</td><td>42.9 (2.3)</td><td>40.3 (2.0)</td><td>3.6 (0.2)</td><td>3.5 (0.2)</td></tr><tr><td rowspan="3">Annual (traditional)</td><td>Continuous maize</td><td>12.7 (0.3)</td><td>12.0 (0.3)</td><td>43.3 (3.2)</td><td>40.8 (3.4)</td><td>3.4 (0.3)</td><td>3.4 (0.3)</td></tr><tr><td>Continuous triticale</td><td>12.4 (0.5)</td><td>11.3 (0.7)</td><td>42.2 (3.3)</td><td>40.8 (2.6)</td><td>3.4 (0.4)</td><td>3.6 (0.3)</td></tr><tr><td>Grain rotation</td><td>12.3 (0.6)</td><td>11.9 (0.5)</td><td>41.0 (2.7)</td><td>41.5 (2.7)</td><td>3.3 (0.3)</td><td>3.5 (0.3)</td></tr></tbody></table>

**Acknowledgements:** This study was funded by BioValue - Strategic Platform for Innovation and Research on value added products from biomass (http://biovalue.dk), which is co-funded by Innovation Fund Denmark.

REFERENCES

Manevski K., Lærke P.E., Olesen J.E., Jørgensen U., 2018. Nitrogen balances of innovative cropping systems for feedstock production to future biorefineries. Sci. Total Environ. 633, 372-390.

Manevski K., Lærke P.E., Jiao X., Santhome S., Jørgensen U., 2017. Biomass productivity and radiation utilisation of innovative cropping systems for biorefinery. Agric. For. Meteorol. 233, 250-264.

Parajuli R., Knudsen M.T., Birkved M., Djomo S.N., Corona A., Dalgaard T., 2017. Environmental impacts of producing bioethanol and biobased lactic acid from standalone and integrated biorefineries using a consequential and an attributional life cycle assessment approach. Sci. Total Environ. 598, 497-512.

Solati Z., Manevski K., Jørgensen U., Labouriau R., Shahbazi S., Lærke P.E., 2018. Crude protein yield and theoretical extractable true protein of potential biorefinery feedstocks. Ind Crops Prod. 115, 214-226.
MULTI-MODEL ASSESSMENT OF MITIGATION OPTIONS FOR GHG EMISSIONS IN CROPLANDS

CAROZZI, M.¹, EHRHARDT, F.², BRILLI, L.³, BELLOCCHI, G.⁴, BHATIA, A.⁵, DE ANTONI MIGLIORATI, M.⁶, DOLTRA, J.⁷, DORICH, C.⁸, DORO, L.⁹, FITTON, N.¹⁰, GRACE, P.⁶, GRANT, B.¹¹, GIACOMINI, S.J.¹², LÉONARD, J.¹³, LOUBET, B.¹⁴, MASSAD, R.S.¹⁴, MULA, L.⁹, PATTEY, E.¹¹, SHARP, J.¹⁵, SMITH, P.¹⁰, SMITH, W.¹¹, ZHANG, Q.¹⁶, RECOUS, S.¹⁷

¹Agroscope, Zurich, Switzerland; ²INRA, Paris, France; ³University of Florence, Florence, Italy; ⁴INRA, UREP, Clermont-Ferrand, France; ⁵Indian Agricultural Research Institute, New Delhi, India; ⁶Queensland University of Technology, Brisbane, Australia; ⁷Cantabrian Agricultural Research and Training Centre, Muriedas, Spain; ⁸Natural Resource Ecology Laboratory Colorado State University, Fort Collins, USA; ⁹University of Sassari, Sassari, Italy; ¹⁰University of Aberdeen, Aberdeen, UK; ¹¹Agriculture and Agri-Food Canada, Ottawa, Canada; ¹²Universidade Federal de Santa Maria, Santa Maria, Brazil; ¹³INRA, Agrolmpact, Laon, France; ¹⁴INRA, ECOSYS, Grignon, France; ¹⁵Plant and Food Research, Auckland, New Zealand; ¹⁶Chinese Academy of Sciences, Beijing, China; ¹⁷INRA, FARE, Reims, France

INTRODUCTION

The need to mitigate climate change requires the abatement of greenhouse gas (GHG) emissions and the sequestration of organic carbon (C) in cropland and grassland soils. Simulation models represent often the only possible way of extrapolating from current knowledge in both time and space, thus facilitating the construction and analysis of future scenarios. In this context, much attention is nowadays given to the assessment of uncertainties in modelling outcomes, considering that evidence provided by models can provide a solid basis for policy decisions. The IPCC revised guidelines for agricultural GHG emissions encourage the use of carefully calibrated and verified simulation models (Tier 3) to go beyond default values and country-specific emission factors (Tier 1 and Tier 2, respectively) to provide country scale estimates of emissions.

The JPI-FACCE funded CN-MIP project (C and N Models Inter-comparison and Improvement to assess management options for GHG mitigation in agrosystems worldwide), started in 2014, have the specific objective to evaluate, improve and compare the capacity of a variety of crop and grassland models (Brilli et al., 2017) to simulate the impact of combined agronomic practices in a set of cropland and grassland sites worldwide (Ehrhardt et al., 2018). Notwithstanding the mitigation options were well identified for these different systems, their degree of inclusion in the model applications is still limited, and there is a need to assess the uncertainty associated with the simultaneous simulation of production and emissions outputs. In this context, the specific objective of this work was to assess response of the ensemble of models, and associated uncertainty, for a set of management options for GHG attenuation in croplands situations, i.e., nitrogen (N) fertilisation regimes, irrigation amount and management of crop residues.

MATERIAL AND METHODS

During a first step of the project (Ehrhardt et al., 2018), current biogeochemical models were assessed and calibrated through a blind test on five long-term field experiments with rotations of annual crops (including wheat, soybean, canola, maize, triticale, rice, oat) and assessing outputs as GHG emissions, C stocks and biomass production. Models calibration procedure consisted of five stages, gradually providing all the observed outputs to the modellers. The calibrated models were then used to evaluate the effect of mitigation options on GHG emissions and biomass production. A set of options with gradual intensities were tested with respect to site-specific N fertilisation regimes (from maximum to the minimum doses ordinarily used per crop), irrigation amounts (from +50 to -50% of the baseline values) and management of crop residues (exported or returned in the field), while keeping the parameter settings resulting from site-specific calibration. To do so, a simulation protocol provided to the modellers a set of maximum 60 scenarios per site, combining three strategies of management. The experimental data and simulated practices were applied to multiple years rotations from five sites (India, France, Australia, Canada and Brazil). A set of 13 process-based models contributed to the simulations: APSIM 7.6, Agro-C 1.0, CERES-EGC, DailyDayCent, DAYCENT (v4.5.2013, v4.5.2010, v4.5.2006), DNDC CAN, DSSAT GHG, EPIC 0810, FASSET v2.5, INFOCROP v2.1 and STICS v.831. Multi-model ensemble medians are used to interpret the individual responses and improve the accuracy of the assessment.
## RESULTS AND DISCUSSION

This overall assessment suggests that a reduction of N inputs toward minimum site/crop-specific requirements is able to significantly reduce GHG emissions while maintaining satisfying crop productivity. From the five sites N₂O emissions can be mitigated up to 45% (~0.3 kg N₂O-N ha⁻¹ y⁻¹) when the fertilisation is reduced to 80% from the site-specific maximum levels, while a reduction of 5% in the aboveground biomass production (AGB) is projected (~0.6 t DM ha⁻¹ y⁻¹). Irrigation management produces site-specific effects on N₂O emissions. High N doses combined with high irrigation volumes (up to +50% from the baseline) produces only a slight increase in N₂O (+2-3%) accompanied by an equally increase in AGB. Reducing irrigation volumes down to -50% from the baseline and under low N doses causes a reduction of up to 60% in N₂O emissions respect to the high N input, though there is an 18% reduction in AGB. No significant effects were observed from the crop residues management, whether exported or returned in the field, in combination with the other two production factors, N and water.

Although the response of the ensemble of models displays a degree of variability for each N dose (Figure 1), a proportional reduction of the N₂O/AGB ratio is observed for each of the five sites with decreasing doses of N. This means that a reduction of N fertilisation is accompanied by more than a proportional decline in the emitted N₂O with respect to the reduction of the biomass produced. This ratio highlights a contrasting variation of uncertainties among sites, with a strong dependency to N dose in some of them.

<figure><img src="image_147.png" /><figcaption>Figure 1. N₂O to above ground biomass (AGB) ratio in the five contrasted pedoclimatic sites (C1 to C5) with N input reduction from maximum (left values on x axis) to minimum (right values). Boxplots report the response of the model ensemble. Black lines show multi-model median, boxes are 25th and 75th percentile, whiskers 10th and 90th percentile, circles indicate outliers.</figcaption></figure>

## CONCLUSION

This work demonstrates the capability of using multi-model ensemble to quantify the impact of mitigation options on N₂O emissions, such as reductions in fertiliser rates, and provides an estimate of uncertainties associated with the assessment.

## REFERENCES

* Brilli L., et al., 2017. Review and analysis of strengths and weaknesses of agro-ecosystem models in representing C and N fluxes. Sci Total Environ. 598, 445-470.

* Ehrhardt F., Soussana J-F., Bellocchi G., et al. 2018. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N₂O emissions. Glob. Change Biol.; 24(2):e603–e616.
20th Nitrogen Workshop

Coupling C-N-P-S cycles

June 25-27, 2018

Le Couvent des Jacobins, Rennes - France

**Session IV: Studies and mitigation options at the farm level - Highlighted posters**
# FINETUNING ABATEMENT OF AMMONIA EMISSIONS FROM LIVESTOCK HOUSING ACCORDING TO IMPACT ON PROTECTED HABITATS

de Pue, D.¹, Bral, A.¹, Buysse, J.¹

¹Department of Agricultural Economics, Faculty of Bioscience Engineering, Ghent University, Belgium

## INTRODUCTION

94% of the ammonia (NH₃) emission in Europe are caused by agricultural activities. Much of this ammonia pollutes nature areas through atmospheric dispersion, causing eutrophication and acidification of terrestrial ecosystems (Erisman et al., 2013). This process poses a major threat to biodiversity and conservation goals in Natura 2000 sites, the EU conservation network aimed at assuring the long-term survival of Europe's most valuable species and habitats. In Flanders, this problem is controlled by a spatially differentiated licensing policy. A Significance Score is calculated for each new environmental permit application involving reactive nitrogen emissions. This score is a measure of the impact on the location where the damage caused by the new project is presumed to be highest. The Significance Score is based on the critical load of the protected habitats that are impacted by the emission source, with the critical load defined as a level of deposition below which no significant harmful effects on the environment are expected according to current knowledge (Ferm, 1998). Depending on the Significance Score, projects are subdivided in significance classes. Above 5%, authorization is only possible if an 'appropriate assessment' rules out significant deterioration of the protected sites. This type of policy is only partially effective, because the biggest share of ammonia deposition is attributable to sources with a Significance Score below 5% (Cools et al., 2015). The Significance Score reflects the impact in the habitat that is most affected by the ammonia emissions. However, one could also choose for an indicator that aggregates the impact on all the habitats that are affected by the source. Such an Aggregate Deposition Score is a better reflection of the total damage caused by airborne ammonia emissions in nearby Natura 2000 sites (De Pue et al., 2017). To reduce their contribution to critical deposition in protected habitats, farmers could apply ammonia-emission abatement techniques and measures. The goal of our research was twofold. Using an integrated policy assessment, we evaluated the efficiency and effectiveness of the current Flemish ammonia policy that is based on Significance Scores. Secondly, we modelled spatially optimal situations, whereby the mixed integer linear programming framework allowed to identify the optimal stable type and ammonia abatement measure depending on farm type, farm gross margin and, crucially, the location.

## MATERIAL AND METHODS

### Data sources and processing

The simulations are performed on a dataset of all livestock facilities in Flanders, as provided to us by the Flemish Land Agency. The data consist of anonymous exploitation and stable numbers, maximally permitted animal numbers per stable and the animal housing systems for the year 2015. The location of the exploitations are only known up to the level of the municipality where the exploitation is located. For our spatially-explicit model, more precise location characteristics (X- and Y-coordinates) were needed. Therefore, for all municipalities in Flanders, we randomly assigned all exploitations within the municipality to pairs of coordinates from a map with all livestock facilities in that municipality. Next, for all these locations, the Significance Score and Aggregate Deposition Scores were calculated, with the methodology described by De Pue et al. (2017). Then, the exploitations were classified in different farm types, according to the animals present. Exploitations can consist of multiple farm types (eg Dairy and Pig Fattening), each of them modelled as independent units. All animals are characterized by an emission factor that depends on both the type of animals as the type of housing system and the presence of additional emission abatement measures. For the allocation of gross margins to all exploitations, we made use of accounting data provided to us by the Flemish Department of Agriculture and Fisheries. The data consist of farm type specific average gross margins for the years 2009-2015, along with their standard deviations. We use these data to acquire
a gross margin per farm type per exploitations, by sampling from a normal distribution. Yearly costs of stable types and abatement measures (operational costs and capital costs) were obtained from KWIN (www.kwin.nl).

## Model description

We implemented a Mixed Integer Linear Programming (MIP) model in GAMS (General Algebraic Modeling System). The emission of a stable depends on the type of stable and the presence of additional abatement measures, two discrete variables in our model. In all models, the overall benefit is calculated by summing up the gross revenue for all farms, minus the cost of emission abatement for all farms. The overall ammonia emission is also calculated, as well as the total impact on protected habitats (the total Aggregate Deposition Scores) of all farms combined. Furthermore, no farm can have more animals than the maximum number of animals for which the farm has an environmental permit. We discern the following 5 scenario's in our model (Table 1). **FC (Full capacity)**: This scenario assumes the maximum number of animals allowed in every single farm, with every stable built according to the present stable type. In other words, we don't allow the model to choose low ammonia emission stables or other abatement measures. This scenario can be considered to be 'worst case'. **CP1 (Current Policy 1)**: In this scenario, the overall economic benefit is maximized, provided that no single exploitation can have a significance score higher than 5%. Just as in FC, we don't allow the model to choose new stable types. The model can only reduce animal numbers in order to comply with the 5% Significance Score rule. **CP2 (Current Policy 2)**: This scenario is the similar to Ref, with the only difference that in Sc1, the model can choose emission abatement techniques and low ammonia emission stables. **SO1 (Spatial Optimization 1)**: In this scenario, the overall impact is minimized, while constraining the result as such that the overall economic benefit should be the same as in CP2. There are no individual impact constraints imposed on the exploitations. **SO2 (Spatial Optimization 2)**: In this scenario, the overall benefit is maximized, while the overall impact cannot be higher than the impact obtained in CP2. Just as in SO1, no individual impact constraints are imposed.

<table><caption>Table 6: Scenario description</caption><thead><tr><td>Name</td><td>Description</td></tr></thead><tbody><tr><td>FC</td><td>Full Capacity, no stable choice or additional emission abatement</td></tr><tr><td>CP1</td><td>Current Policy (SS &lt; 5%), no stable choice or additional emission abatement</td></tr><tr><td>CP2</td><td>Current Policy (SS &lt; 5%), including stable choice and additional emission abatement</td></tr><tr><td>SO1</td><td>Spatially optimal, minimal impact with benefit in same range as CP2, no individual impact constraints. Rabbits, other poultry, ostriches and horses are fixed to maximum.</td></tr><tr><td>SO2</td><td>Spatially optimal, maximal benefit, impact similar as in CP2, no individual impact constraints. Rabbits, other poultry, ostriches and horses are fixed to maximum.</td></tr></tbody></table>

## RESULTS AND DISCUSSION

Overall results of the 5 model scenarios are displayed in table 2. The current policy of restricting the individual significance score to 5% (Ref) results in a decrease of total impact of 26.1% compared to FC, with a relatively modest decrease in total economic benefit (-5.4%). Allowing the switch to other stable types and the adoption of
emission abatement measures (CP1), the impact decreases with 2.3% compared to Ref, while the total benefit increases slightly (+1.0%). However, in the spatial optimal scenario of SO1, the impact decreases with 40.2% compared to CP2, with equal total benefit, highlighting room for improvement of the effectiveness of the spatially-differentiated policy (obtaining the best environmental quality at the same cost). The room for improving the efficiency (obtaining the same environmental quality at lower cost, or higher total benefit) is more limited: in SO2, the benefit increases with 3.4% compared to Sc1, with equal impact as in CP2.

<table><caption>Table 7: General results of model simulations</caption><thead><tr><th></th><th>FC</th><th>CP1</th><th>CP2</th><th>SO1</th><th>SO2</th></tr></thead><tbody><tr><td>Total NH<sub>3</sub> emission (kton/yr)</td><td>35.7</td><td>33.3</td><td>32.7</td><td>24.1</td><td>31.1</td></tr><tr><td>Total Impact (Σ ADS)</td><td>28976</td><td>21416</td><td>20924</td><td>12510</td><td>20924</td></tr><tr><td>Number of closed stables<br>(max 44540)</td><td>0</td><td>698</td><td>644</td><td>4156</td><td>2121</td></tr><tr><td>Number of closed exploitations<br>(max 23408)</td><td>0</td><td>117</td><td>117</td><td>1710</td><td>918</td></tr><tr><td>Total benefit (billion €)</td><td>1.230</td><td>1.164</td><td>1.183</td><td>1.178</td><td>1.224</td></tr></tbody></table>

Figure 1 shows the choice of emission abatement technique for all exploitations with the farm type Pig Fattening, in the most strict scenario (SO1). The choice is plotted in function of income and impact quantiles (figure 1A) and mapped (figure 1B). Figure 1A only shows the stables that are operated at full capacity in scenario 2. Stables with a very high impact and a very low income (lower right corner) are missing from the plot, because these are either empty or operated below full capacity. For Pig Fattening stables, no stables stick to the low emission variant, because LAES-type V_4_6 has no additional cost compared to the non-LAES-type, while having a lower emission factor. The graph shows a clear pattern from left to right. The stables within the 75% lowest impact adopt stable type V_4_6 (Manure pits with water- and manure ditches, with curved pit walls and grid with elevated manure throughput). The stables with the 25% highest impact have to adopt an additional measure (PAS_V, balance balls in the manure pit), while some of the farms even have to install chemical air scrubbers (S_2). When we look at the distribution of emission abatement techniques on the map of Flanders, we see a clear difference between the West and the East of the region. In the west, with relatively few protected habitats, the model advises to adopt V_4_6 without additional measures, except for locations in the immediate vicinity of Natura 2000 sites. To the contrary, in the East, the model advises to adopt the additional PAS_V measure next to stable type V_4_6. Similar patterns were obtained for other farm types (Dairy, Pig Rearing, Laying Hens, Broilers, etc.)
<figure><img src="image_148.png" /></figure>

**Figure 1:** Choice of emission abatement technique for Fattening Pig Stables in Flanders, SO1. (A): Plotted in function of income quantile (based on gross margin) and impact quantile (based on Aggregate Deposition Score). (B): Plotted on the map of Flanders, showing the locations of protected habitats. V_4_6: Manure pits with water- and manure ditches, with curved pit walls and grid with elevated manure throughput. PAS_V: Balance balls in the manure pit. S_2: Chemical air scrubber.

## CONCLUSION

Our simulations indicate that there is substantial room for improvement in terms of environmental effectiveness of the Flemish ammonia policy, by reducing the overall impact on protected habitats without reducing the total economic benefit. Secondly, the MIP modelling framework allowed to identify the optimal stable types and ammonia abatement measures for all livestock exploitations in Flanders. Both conclusions help policy makers to improve the spatially-differentiated policy: the first by letting go or loosening the restriction by Significance Score, the second by differentiating advice and/or subsidies to farmers in the direction of the desired emission abatement techniques.

**Acknowledgements:** We would like to thank the Flemish Land Agency (VLM) and the Department of Agriculture and Fisheries for supplying us with data on livestock exploitations in Flanders.

## REFERENCES

Cools, N. et al. (2015) Atmosferische stikstofdepositie en Natura 2000 instandhoudingsdoelstellingen in Vlaanderen. Verkennende gewestelijke ruimtelijke analyse van de ecologische impact, van sectorbijdragen en van de bijdrage. Brussel.

Erisman, J. W. et al. (2013) ‘Consequences of human modification of the global nitrogen cycle.’, Philosophical
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10.1016/j.atmosenv.2017.07.020.
# NITRATE LEACHING RISK ASSESSMENT AFTER INCORPORATION OF FERTILIZED CATCH CROPS

DE WAELE, J.<fnref n="1" />, RADAM, E.D. ², DE VLIEGHER, A. ³, VANDECASTEELE, B. ³, DE NEVE, S.<fnref n="1" />

## INTRODUCTION

Stimulating farmers to grow catch crops by increasing N input limits is one of the recurrent measures to reduce the contribution of agriculture to elevated nitrate concentrations in groundwater and surface waters. In some cases, farmers are even allowed to apply a limited N dose to the catch crops themselves, particularly after early harvested preceding crops such as winter cereals. This fertilization stimulates catch crop growth and therefore enhances the yield-related effects of growing catch crops. Uptake of fertilized N by the catch crop is assumed to prevent additional nitrate leaching during autumn and winter. After incorporation of the catch crop in spring, its easily decomposable N is mineralized and is expected to become available for the following crop. However, incorporation of catch crop biomass with a high C:N ratio may initially induce N immobilization, resulting in lower N availability for the following crop. Furthermore, remineralisation of immobilized N might be too late to be still taken up by the crop. For higher catch crop yields due to fertilization, the effect of N immobilization might be more pronounced. Our main research questions for this study were: (1) Are crop yields reduced after incorporation of catch crops with high C:N ratios? (2) Could fertilization of catch crops with high C:N ratios lead to additional nitrate leaching one year after application?

## MATERIAL AND METHODS

### Incubation experiment

Aboveground biomass of white mustard (WM), Italian ryegrass (IR), black oat (BO) and a grass-clover mixture (GC) was collected on a field in Belgium in January 2012. Fresh yields were weighed and subsamples were taken to determine dry matter (DM) yield and C and N content. Subsamples were first dried at 60°C for 3 days. C and N content were then determined with a CN analyzer. Residual moisture content was determined by drying for 12 hours at 105°C in order to obtain DM yield. Catch crops were cut in small pieces of about 0.5 cm² and incorporated according to their yields in a sandy loam and a silt loam soil. The soil was brought at a bulk density of 1.4 g cm⁻³ and maintained at a water filled pore space of 50%. Samples were incubated at 15°C for 98 days and every 2 weeks, some samples were destructed. Soil mineral N content (SMN) was determined with a continuous flow analyzer after extraction in a 1:5 soil:solution ratio with 1 M KCl. Microbial biomass carbon (MBC) was determined at day 0, 28 and 98 according to the chloroform fumigation method (Voroney et al., 1993).

### Field experiment

Maize (Zea mays) was sown on a sandy loam soil in Belgium on May 1st 2013 and received a pig slurry fertilization of 133 kg total N ha⁻<fnref n="1" /> topped up with 30 kg N ha⁻<fnref n="1" /> of NH₄NO₃. The strip-plot experimental design with four replications consisted of two catch crop species incorporated to the soil in early spring: WM and BO, which had both demonstrated N immobilization in the incubation experiment. These catch crops were sown after winter barley on two different dates: August 1st and 30th 2012. Catch crops had received a pig slurry application at sowing at rates of 57 and 114 kg total N ha⁻<fnref n="1" />. Fallow treatments and non-fertilized catch crops were included. DM yield and C and N content of aboveground parts of WM and BO were determined as described for the incubation. Maize development was assessed 64 days after sowing by measuring maize plant height and the number of fully developed leaves for ten randomly chosen plants per plot. Fully developed leaves were determined according to the leaf collar method (Abendroth et al., 2011). The plant height was measured from the base up to where the last fully developed leaf was connected to the stem. Maize grain yield was determined 300 days after sowing by harvesting 4 randomly chosen rows per plot, corresponding to an area of 4.5 m². Maize grain yield was only

<footnote n="1">¹ Ghent University, Department of Environment, Belgium, ² Philippine Rice Research Institute, Philippines, ³ Research Institute for Agriculture, Fisheries and Food (ILVO), Plant Sciences Unit, Belgium</footnote>
determined on plots incorporated with early sown catch crops and on fallow plots. Soil samples were taken before catch crop incorporation in April, in July, in September and in November at 0-30, 30-60 and 60-90 cm and analyzed for SMN. Microbial biomass carbon (MBC) was determined on the 0-30 cm soil samples in September. Both SMN and MBC were determined as described for the incubation.

# RESULTS AND DISCUSSION

## Incubation experiment

The relative amount of N mineralized after 3 months was in the order GC > IR > WM > BO and was negatively correlated with the C:N ratio of the catch crops (respectively 17, 20, 34 and 40). N mineralization was for each catch crop higher on sandy loam than on silt loam. N immobilization was prominent and long lasting for BO, which was supported by a significantly higher MBC than for the bare soil treatment throughout the incubation period.

## Field experiment

C:N ratio of catch crops in spring 2013 differed from the incubated samples collected in 2012: it was 33 to 42 for WM and 17 to 20 for BO, indicating that N immobilization under maize would probably be more prominent on plots incorporated with WM than on plots incorporated with BO.

Before incorporation in April, SMN over the total sampling depth was significantly higher on plots under WM and BO than on fallow plots and it was positively correlated with fertilization. These effects on SMN were clearly observed at all depths under late sown catch crops, while under early sown catch crops, they were only prominent in the 60-90 cm layer. Total SMN was each time higher under late sown catch crops than under early sown catch crops.

In July, no significant differences were observed in SMN. The number of maize leaves ranged from 5 to 6 and was equal for all treatments. However, maize plants were significantly taller on plots incorporated with BO (13.7 cm) compared to plots incorporated with WM (12.4 cm) and fallow plots (12.5 cm), confirming that N immobilization may have been stronger on plots incorporated with WM. There was no effect of catch crop fertilization or sowing date.

In September, total SMN was equal for all treatments. Only in the 60-90 cm layer we did observe a significantly higher SMN on fallow plots than on plots incorporated with catch crops, while on fallow plots SMN was also positively correlated with fertilization. At the onset of autumn, this indicates a higher nitrate leaching potential on fallow plots. MBC in the 0-30 cm layer was significantly higher on plots incorporated with WM (59 mg kg⁻¹ dry soil) and BO (56 mg kg⁻¹ dry soil) than on fallow plots (51 mg kg⁻¹ dry soil), but only for late sown catch crops. Maize development was not assessed in September.

In November, no significant differences were observed in SMN. Maize grain yields were only significantly different between plots incorporated with fertilized BO (10.2 Mg DM ha⁻¹) and fallow plots that had received the highest fertilization dose (11.5 Mg DM ha⁻¹). This result was contradicting with the results for plant height in July.

# CONCLUSION

An incubation experiment demonstrated N immobilization for WM and BO incorporated in soil, but this was less clear in the field. Compared to fallow soil, incorporation of BO had a beneficial effect on the maize plant height in early summer, even though this was not reflected in the final yield. As incorporation of BO and WM resulted only in a small increase in MBC (5-8 mg C kg⁻¹ dry soil) in the late summer, and not in a consistent reduction in final maize grain yields, we believe N immobilization did not last long enough to limit N uptake by the maize. It is therefore unlikely that additional nitrate leaching would occur in autumn due to incorporation of fertilized catch crops, under the condition that spring fertilization of the main crop is reduced for the expected N release from
the catch crop. Growing catch crops seemed furthermore promising in reducing the risk of nitrate leaching even one year after sowing them.

REFERENCES

Abendroth, L.J., Elmore, R.W., Boyer, M.J. and Marlay, S.K., 2011. Corn Growth and Development. PMR 1009. Iowa State University Extension, Ames, pp. 760-788.

Voroney, R.P., Winter, J.P., Beyaert, R.P., 1993. Soil microbial biomass C and N, in: Carter, M.R. (Ed.), Soil Sampling and Methods of Analysis. Lewis, Boca Raton, pp. 277-286.
NITROUS OXIDE-NITROGEN EMISSION FACTORS FOR A BIOFUEL CROP

CANTARELLA, H.<fnref n="1" />, CARVALHO, B.G.O.<fnref n="1" />, LOURENÇO, K.<fnref n="1" />, CARVALHO, J.L.N.², GONZAGA, L.C.<fnref n="1" />; KURAMAE, E.³, SOARES, J.R.⁴; CARMO, J.B.⁵

INTRODUCTION

Ethanol sustainability indicators are important, as this fuel replaces other fuels of fossil origin. Nitrogen fertilizers account for more than 20% of the energy spent to produce sugarcane in the field (Boddey et al., 2008) and for about 40% of the greenhouse gases (GHG), mostly as nitrous oxide (N₂O), emitted to produce ethanol (Lisboa et al., 2011), depending on the N₂O-N emission factor considered. Approximately 10 Mha of sugarcane is grown in Brazil, half of which is used for ethanol production, which comprises 40% of the fuel used in light vehicles in that country. However, very little published information was available on GHGs emission for sugarcane cultivation in that country, which is the largest producer of sugarcane and ethanol from sugarcane in the world. The objective of this study was to obtain solid estimates of N₂O emission for Brazilian sugarcane production, of interest for calculations of the GHGs balance of ethanol.

MATERIAL AND METHODS

A survey of the international literature was used to gather information on N₂O emission from N fertilizer and from important by-products of the sugarcane and ethanol production, recycled in the field as part of the fertilization of this crop. In addition, we added data from recent research, still unpublished, produced by our group, to draw a map as complete as possible of emissions associated with sugarcane production under field conditions in Brazil. We collected data from experiments in which mineral N fertilizers were applied to a variety of fields representative of the most relevant growing areas. In addition, we obtained data about N₂O emission factors of by-products such as filter cake, the residue of sugarcane filtration, and vinasse, the residue of ethanol distillation. The former is applied at rates between 5 and 40 t ha⁻<fnref n="1" /> and the latter, between 50 and 100 m³ ha⁻<fnref n="1" />.

RESULTS AND DISCUSSION

The N₂O EF found in most field experiments (mean value 0.52%) were below the 1% value, used as default value by the IPCC (Table 1) and far below the figures reported in studies conducted in other regions of the world (Lisboa et al., 2011). These low EF values are usually credited to the good drainage properties of the deep Oxisols, where sugarcane is commonly cultivated in Brazil. The low N₂O emissions were found in different soil types, management conditions, and N sources. Emissions are lower with ammonium nitrate (NA) than with urea (UR) (Table 1). A very low EF was found for calcium nitrate; the authors attributed that to the prevalence of nitrification over denitrification as the main cause of N₂O emission in that study (Soares et al., 2016). There is few information about N₂O EF for sugarcane residues in the literature and the data available is all from recent publications. The results of our survey were highly variable but, in general, the values were relatively low, except for vinasse (EF 1.72%) (Table 2). However, the rates of N applied as vinasse are usually below 50 to 60 kg N ha⁻<fnref n="1" />.

CONCLUSION

The relatively low N₂O EF values for sugarcane found in a wide variety of field experiments contribute to decrease the GHGs emissions when ethanol replaces fossil fuels.

<footnote n="1">¹ Agronomic Institute of Campinas, Brazil; ² Bioethanol Sci. and Tech. Laboratory, Brazil; ³ Netherlands Institute of Ecology, the Netherlands; ⁴ University of Campinas, Brazil; ⁵ São Carlos Federal University, Brazil</footnote>
<table><caption>Table 1. Data from experiments in Brazil for N₂O emission factors (EF) of different N sources applied to sugarcane. SA: ammonium sulphate; NA: ammonium nitrate; UR: urea, CAN: calcium ammonium nitrate.</caption><thead><tr><th>Source</th><th>N<sub>2</sub>O-N EF (% of N)</th><th>Observation</th><th>Reference</th></tr></thead><tbody><tr><td>SA</td><td>0.27 – 0.36</td><td>Clay soil</td><td>Vargas et al. (2014)</td></tr><tr><td>NA</td><td>0.55 – 0.74</td><td>Clay soil</td><td>Vargas et al. (2014)</td></tr><tr><td>SA</td><td>0.07 – 0.10</td><td>Medium textured</td><td>Vargas et al. (2014)</td></tr><tr><td>NA</td><td>0.19 – 0.25</td><td>Medium textured</td><td>Vargas et al. (2014)</td></tr><tr><td>UR</td><td>0.69; 0.75, 1.68</td><td>Bare soil, ratoon</td><td>Soares et al. (2015; 2016)</td></tr><tr><td>Calcium nitrate</td><td>0.04</td><td>Bare soil, ratoon</td><td>Soares et al. (2016)</td></tr><tr><td>UR</td><td>1.11</td><td>Plant cane cycle</td><td>Carmo et al. (2013)</td></tr><tr><td>NA</td><td>0.68 – 0.96 (x = 0.80)</td><td>Ratoon, straw</td><td>Carmo et al. (2013)</td></tr><tr><td>NA</td><td>0,21</td><td>Bare soil</td><td>Pitombo et al. (2016)</td></tr><tr><td>NA</td><td>1,06</td><td>Straw</td><td>Pitombo et al (2016)</td></tr><tr><td>NA</td><td>0.07 – 0.12 (x=0.10)</td><td>Wet season; straw</td><td>Lourenço et al (submitted)</td></tr><tr><td>NA</td><td>0.51</td><td>Dry season, straw</td><td>Lourenço et al (submitted)</td></tr><tr><td>UR</td><td>0.3 – 2.3 (x = 1.1)</td><td>Plant</td><td>Soares et al (submitted)</td></tr><tr><td>CAN</td><td>0.1 – 0.7 (x = 0.4)</td><td>Plant</td><td>Soares et al (submitted)</td></tr><tr><td>UR</td><td>0.9 – 1.4 (x = 1.1)</td><td>Ratoon, straw</td><td>Soares et al (submitted)</td></tr><tr><td>CAN</td><td>0.1 – 0.7 (x = 0.4)</td><td>Ratoon, straw</td><td>Soares et al. (submitted)</td></tr><tr><td>NA</td><td>0.24 – 0.44 (x = 0.34)</td><td>80 and 120 kg/ha</td><td>Siqueira Neto et al (2015)</td></tr><tr><td>UR</td><td>0.52 – 0.83 (x = 0.68)</td><td>80 and 120 kg/ha</td><td>Siqueira Neto et al (2015)</td></tr><tr><td>UR</td><td>0.20</td><td>Ratoon, straw</td><td>Paredes et al. (2014)</td></tr><tr><td>SA</td><td>0.58</td><td>Ratoon, straw</td><td>Paredes et al. (2015)</td></tr><tr><td>NA</td><td>0.05 – 0.16</td><td>Straw</td><td>Silva et al. (2017)</td></tr><tr><td>AS</td><td>0.14 – 0.81 (x= 0.50)</td><td>Dry season, straw</td><td>Gonzaga et al (submitted)</td></tr><tr><td>AS</td><td>0.12 – 1.44 (x = 0.75)</td><td>Wet season, straw</td><td>Gonzaga et al (submitted)</td></tr><tr><td>N = 23</td><td>X = 0.52</td><td></td><td></td></tr></tbody></table>

<table><caption>Table 2. N₂O emission factor of by-products of sugar and ethanol production, applied to sugarcane fields.</caption><thead><tr><th>By-product</th><th>N<sub>2</sub>O-N EF (% of applied N)</th><th>Reference</th></tr></thead><tbody><tr><td>Filter cake</td><td>0.10 – 0.17 (0.13)</td><td>Siqueira Neto et al (2015)</td></tr><tr><td>Vinasse</td><td>1.86 – 2,75<br>0.01 – 0.79 (0,30)<br>0.86 – 1.84 (1.35) Dry season<br>1.15 – 4.59<br>1.04-2.20<br>2.50<br>0.54 – 0.77 (0.65)<br>0.44 – 0.78 (0.56)<br><b>X = 1,72</b></td><td>Pitombo et al (2016)<br>Lourenço et al. (submitted)<br>Lourenço et al. (submitted)<br>Silva et al. (2017)<br>Paredes et al. (2015)<br>Paredes et al. (2014)<br>Siqueira Neto et al. (2015)<br>Oliveira et al. (2013)</td></tr><tr><td>Concentrated vinasse</td><td>0.18 – 0.34 (0.27) Wet season<br>0.43 – 0.56 (0.50) Dry season<br>1.36 – 1.68<br><b>X = 0,95</b></td><td>Lourenço et al. (submitted)<br>Lourenço et al. (submitted)<br>Pitombo et al. (2016)</td></tr></tbody></table>

REFERENCES

Boddey, R. M., Soares, L. H. B., Alves, B. J. R., and Urquiaga, S. (2008). Bio-ethanol production in Brazil. *In "Renewable Energy Systems: Environmental and Energetic Issues"* (D. Pimentel, ed.), pp. 321-356. Springer, New York.

Lisboa, C. C., Butterbach-Bahl, K., Mauder, M., and Kiese, R. (2011). Bioethanol production from sugarcane and emissions of greenhouse gases - known and unknowns. *Global Change Biology Bioenergy* **3**, 277-292.

Soares, J. R., Cassman, N. A., Kielak, A. M., Pijl, A., Carmo, J. B., Lourenço, K. S., Laanbroek, H. J., Cantarella, H., and Kuramae, E. E. (2016). Nitrous oxide emission related to ammonia-oxidizing bacteria and mitigation options from N fertilization in a tropical soil. *Scientific Reports* **6**, 30349.
# ILLUSTRATIVE MODELLING OF NITRATE LEACHING FROM FERTILISER AND MANURE NITROGEN APPLICATIONS

HUTCHINGS, N.J.¹, SØRENSEN, P.¹

¹ Dept of Agroecology, Aarhus University, Denmark

## INTRODUCTION

Fertiliser and manure can contribute to nitrate leaching (Cameron et al., 2013) but whereas all fertiliser nitrogen (N) is immediately available to crops, the manure organic N must first be mineralised. If this occurs outside the growing season, this can increase nitrate leaching. Here we use a simple model to illustrate the main processes and how they contribute to differences between nitrate leaching from fertiliser, manure and digestate.

## MATERIAL AND METHODS

The soil model contains three types of organic matter (OM); crop residues, manure OM and humus. The OM is described in terms of C and N (Fig 1). The OM from crop residues is easily degradable ($E_{crop}$; kg) whereas that in manure is partitioned between easily degradable ($E_{man}$; kg) and the more resistant humus ($H$; kg). Easily degradable OM in crop residues and manure decay at different rates ($k_{C1}$ and $k_{C2}$ year$^{-1}$, respectively). However, the same proportion ($h$) of the C in both types of OM is converted to humic OM, when easily degradable OM is decomposed. The C:N of the humic OM ($c_{hHUM}$) is constant, so a given amount of humic C will always bind the same amount of N. The N in degraded OM not bound in the new humic material is mineralised and added to the mineral N ($Min_N$; kg). The humic OM will itself be decomposed, albeit at a much slower rate ($k_{C3}$; year$^{-1}$), and all associated N is mineralised.

<figure><img src="image_149.png" /><figcaption>Figure 1 Schematic of the C and N flows in the model</figcaption></figure>

A proportion ($k_{NH3}$) of the mineral N input in fertiliser or manure is lost as $NH_3$ and the remainder is considered plant available ($P_{availN}$; kg). A proportion ($k_{harv}$) of $P_{availN}$ is removed by harvesting and the remainder is partitioned to crop residues ($E_{cropC}$; kg). The associated addition of C to the crop residue C ($E_{cropC}$; kg) is calculated using an assumed C:N ratio ($c_{nCrop}$). A proportion ($p_{HUM}$) of the C in the manure organic matter is added directly to the humic C ($H_c$; kg). The remaining manure C is added to the easily degraded manure C ($E_{manc}$; kg). Since the C:N ratio of the