humic material is constant, the manure organic N added to the humic N ($H_N$; kg) can be calculated. The remainder is partitioned to the easily degraded manure N ($E_{manN}$; kg). The N mineralised is partitioned between nitrate leaching ($k_{NO3}$), denitrification ($k_{den}$) and plant available N ($1 - (k_{NO3} + k_{den})$). The flows are calculated for two periods in the year; in and outside the growing season. In the simulations here, 100 kg ha$^{-1}$ of fertiliser or cattle slurry N was applied at the start of the first year. Gaseous N emissions were set to zero. The model was run for 10 years, with harvested N set at 65% of plant-available N in each year. The mineral and organic N still in the soil after 10 years was assumed to be partitioned between harvested material and nitrate leaching in the same proportions as that found in the 10th year.

## RESULTS AND DISCUSSION

The dynamics of the soil OM N pools are shown in Fig 2a. During the year of application, all fertilizer N that enters the soil is incorporated into plant material. Some is harvested and the crop residues are largely decomposed, with the organic N either mineralized or converted into humus N. Some of this N is leached during the first winter and the remainder incorporated into plant material the next season. This is repeated in subsequent years, with the remaining N from the fertilizer mainly being immobilized in humic OM, where it slowly decomposes and is gradually depleted by harvesting and leaching (Fig 2b). When N is applied as manure or digestate, the mineral N

<figure><img src="image_150.png" /><figcaption>Figure 2 The organic N and nitrate leaching dynamics from the same total N added as fertilizer or manure</figcaption></figure>

component behaves as fertilizer N. Most manure OM N enters the labile pool and the rest enters the humic pool. More labile and humic OM is present over winter than when fertilizer is the N source, fueling higher nitrate leaching. Over a 15 year period, 83 and 67% of the N applied in fertilizer and manure was harvested respectively, while 11 and 19% respectively was leached.

## CONCLUSION

This model allows the N dynamics following the application of various N sources to be followed over time. Despite its conceptual simplicity, it appears capable of simulating the major processes fairly realistically.

**Acknowledgements:** This work was supported by the Strategic Research Alliance DNMARK (www.dnmark.org).

## REFERENCES

Cameron, K.C., Di, H.J., Moir, J.L., 2013. Nitrogen losses from the soil/plant system: a review. Annals of Applied Biology 162(2), 145-173.
# NITROGEN SUPPLY BY ROLLER-CRIMPED AGRO-ECOLOGICAL SERVICE CROPS IN ORGANIC CABBAGE PRODUCTION

HEFNER, M.¹, KRISTENSEN, H.L.¹

¹ Department of Food Science, Aarhus University, Denmark

## INTRODUCTION

Agro-ecological service crops (ASC), also known as green manure, catch or cover crops, are widely used in organic agriculture. However, challenges exist in accounting for the nitrogen (N) supply from the ASC to the cash crop, as the amount of N supply depends on the species employed. Legumes have a low C/N ratio and decompose faster than cereals, whereas cereals are better in scavenging soil N than legumes. Growing a mixture of legume/cereal has therefore been proposed as the best strategy (Fageria et al., 2005). Interest in reduced tillage techniques is rising and roller-crimping the ASC provides an alternative to the traditional incorporation of the ASC as green manure. However, N shortage due to plant-soil interactions can pose a challenge in the N management of this reduced tillage system.

## MATERIAL AND METHODS

A field experiment was conducted at the Research Centre Årslev, Denmark (10°27'E, 55°18'N) on a sandy loam (Typic Agrudalf) from October 2016 until November 2017. The experiment was a complete block design with two treatment factors, which were (1) the ASC species and (2) the termination method of the ASCs. The 6 ASC species tested were (1) winter fababean (*Vicia faba* L.), (2) winter pea (*Pisum sativum* L.), (3) winter vetch (*Vicia sativa* L.), a 50/50 mixture of (4) winter rye (*Secale cereal* L.) and winter fababean, (5) winter rye and winter pea and (6) winter rye and winter vetch. The ASC termination treatments were (1) full incorporation by tillage of the ASC as green manure and (2) roller-crimping of the ASC. Tilled bare soil served as the control. The ASC was sown on October 9th, 2016 and terminated on May 30th, 2017. White cabbage (*Brassica oleracea* L. cv. Coronet) was transplanted on June 21st and harvested on November 3rd. Measurements were conducted on ASC and cabbage biomass and N content, and soil mineral N to 2.5 m depth.

## RESULTS AND DISCUSSION

The termination of ASC by full incorporation or roller-crimping did not affect the N uptake of cabbage (data not shown), indicating that N supply for the cabbage was equal for both termination strategies, even though the N released from ASC residues left on the soil surface and from soil N mineralization was expected to be lower in the roller-crimped treatment. The three ASCs of legume/rye mixtures and the pure pea showed the highest ASC N uptake in the spring (Figure 1), whereas N uptake was lower for fababean and lowest for vetch. The low N uptake of vetch was caused by a very low biomass of vetch (data not shown), due to poor overwintering. The soil mineral N content in 0 to 2.5 m depth was highest in the bare soil, followed by the pure legume ASCs of fababean, pea and vetch and it was lowest in the legume/rye ASCs (Table 1). The low soil mineral N content correlated with a higher plant N uptake of the legume/rye ASCs (Figure 1). The N uptake of cabbage at harvest (Figure 1) followed a similar pattern as the soil mineral N in the spring (Table 1), where lowest values were obtained in the treatments with legume/rye ASCs and highest values in the pure legume ASCs and the bare soil control. The low N uptake of cabbage after the legume/rye ASCs may be a result of slower N release from the legume/rye residues left on the soil surface and pre-emptive competition, as rye took up mineral N, which was subsequently not available for the cabbage (Thorup-Kristensen et al., 2003).
<figure><img src="image_151.png" /><figcaption>Figure 1. Plant N uptake (kg N ha⁻¹) by the ASCs at termination (24. May 2017) and white cabbage at harvest (3. November 2017). Bars represent standard deviations, n=3. Different letters indicate significant differences at p<0.05.</figcaption></figure>

<table><caption>Table 1. Soil mineral N (kg ha⁻¹) at planting of cabbage (June 13th, 2017). Standard deviations are shown, n=3. Different letters indicate significant differences between ASC species at p<0.05.</caption><thead><tr><th>Soil depth (m)</th><th>Bare soil</th><th>Bean</th><th>Pea</th><th>Vetch</th><th>Bean/ Rye</th><th>Pea/ Rye</th><th>Vetch/ Rye</th></tr></thead><tbody><tr><td>0-0.25</td><td>45±5.5<sup>a</sup></td><td>25.7±3.6<sup>b</sup></td><td>23.5±2.9<sup>bc</sup></td><td>15.4±5.3<sup>d</sup></td><td>15.7±5.5<sup>cd</sup></td><td>15.7±3.4<sup>cd</sup></td><td>14.2±2.4<sup>d</sup></td></tr><tr><td>0.25-0.5</td><td>38±5.1<sup>a</sup></td><td>12±2.7<sup>bc</sup></td><td>18.7±10.2<sup>b</sup></td><td>10.8±4.8<sup>bc</sup></td><td>4.1±2.4<sup>c</sup></td><td>8.2±5.1<sup>c</sup></td><td>4.2±1.2<sup>c</sup></td></tr><tr><td>0.5-1</td><td>76.7±17.9<sup>a</sup></td><td>50.2±18.7<sup>b</sup></td><td>58.2±9.4<sup>ab</sup></td><td>45.1±14.8<sup>b</sup></td><td>15.3±4.2<sup>c</sup></td><td>11±5.9<sup>c</sup></td><td>8.3±3.3<sup>c</sup></td></tr><tr><td>1-1.5</td><td>72.1±2.9<sup>ab</sup></td><td>76.7±27.1<sup>a</sup></td><td>63.3±7<sup>abc</sup></td><td>70.9±20.7<sup>ab</sup></td><td>34.3±2<sup>cd</sup></td><td>24.2±13.9<sup>d</sup></td><td>38.6±28<sup>bd</sup></td></tr><tr><td>1.5-2</td><td>53.7±5.9<sup>a</sup></td><td>38.6±5.4<sup>ac</sup></td><td>46.8±10.7<sup>ab</sup></td><td>39.6±10.9<sup>ac</sup></td><td>40.9±16.5<sup>ac</sup></td><td>24.4±6.6<sup>c</sup></td><td>30.6±13.6<sup>cd</sup></td></tr><tr><td>2-2.5</td><td>40.2±8.2<sup>a</sup></td><td>19.2±6.8<sup>bc</sup></td><td>29.6±13.6<sup>ab</sup></td><td>18.2±4.1<sup>bc</sup></td><td>21.9±2.9<sup>bc</sup></td><td>16.3±4.9<sup>bc</sup></td><td>12.7±4.3<sup>c</sup></td></tr></tbody></table>

## CONCLUSION

The N uptake of white cabbage was not affected by ASC termination, indicating that roller-crimping did not hamper N supply. The legume/rye ASCs took up more soil mineral N during growth and resulted in lower N uptake by cabbage compared to pure legume ASCs. The highest cabbage N uptake was obtained following pea and fababean ASCs and in the bare soil control, showing the potential of roller-crimping of ASC as a reduced tillage system for production of organic cabbage.

**Acknowledgements:** The project was funded by the ERA-net CORE Organic PLUS, the Green Growth and Development programme (GUDP) under the Danish Ministry of Environment and Food and Aarhus University as part of the project SOILVEG.

## REFERENCES

* Fageria, N. K., Baligar, V. C., and Bailey, B. A. 2005. Role of cover crops in improving soil and row crop productivity. Communications in Soil Science and Plant Analysis 36, 2733-2757.

* Thorup-Kristensen, K., Magid, J., and Jensen, L. S. 2003. Catch crops and green manures as biological tools in nitrogen management in temperate zones. 79, 227-302.
# ACIDIFICATION PRIOR TO DRYING OF DIGESTATE SOLIDS AS A BIO-BASED FERTILISER AFFECTS NITROGEN AND PHOSPHORUS UPTAKE AND FERTILISER VALUE WHEN APPLIED TO MAIZE

LIU, JN.¹, MÜLLER-STÖVER, DS.¹, JENSEN, LS.¹

¹ University of Copenhagen, Department of Plant and Environmental Science, Plant and Soil Science, Denmark

## INTRODUCTION

As one of the most efficient treatment technologies of biodegradable waste, anaerobic digestion could simultaneously achieve environmental pollution control and energy recovery. The process produces biogas (60–70% CH₄), but also a digestate requiring further disposal, like land application or liquid-solid separation and drying of the solid fraction into a stabilized organic fertilizer product. Among waste management technologies, thermal drying is an attractive option since it’s a highly effective way to reduce digestate volume/ weight by evaporating moisture and facilitate the sanitation, storage and transportation of the end product (Pantelopoulos et al., 2016). However, relative high N losses via NH₃ volatilization will occur during the thermal drying process, which reduces the agronomic value of the processed waste as a fertilizer and contributes to eutrophication and acidification of ecosystems. Acidification before thermal drying of digestate solids can largely mitigate NH₃ emissions and therefore increase the N fertilizer value of the final dried product. Concentrated H₂SO₄ is commonly used in Denmark to acidify slurry and also documented to minimise N losses significantly during thermal drying of digestate solids (Pantelopoulos et al., 2016; Liu et al., unpublished). Acidification can also be achieved by using aluminium sulphate through hydrolysing to the aluminium hydroxide precipitate and dilute sulfuric acid solution (Regueiro et al., 2016). Using aluminium sulphate as acidifier has also been shown to significantly reduce NH₃ emissions from digestate solids during the drying process (Liu et al., unpublished)

However, while the N and P fertilizing effect of the liquid fraction of digestate is well documented by previous studies, the behaviour of thermally treated digestate solids in soil and their potential for mineral N and P fertilizer substitution in crop production is so far rarely reported. There are indications that the mineralization rate of the organic nitrogen in dried products could increase (Smith and Durham, 2002) or decrease (Case et al., 2016) on account of drying induced conditioning of the organic matter. Furthermore using aluminium sulphate as acidifier has the risk of reducing P plant availability in the products by the formation of insoluble aluminium phosphate compounds (Smith and Moore, 2001).

Thus, the overall objective of this study was to evaluate the yield response and N and P uptake of maize when fertilized with digestate solids (DS) treated by drying and acidification (using concentrated sulfuric acid or aluminium sulphate as acidifier). It was hypothesized that:

1) Acidification using aluminium sulphate and concentrated sulfuric acid will result in higher retention of NH₄⁺ in dried digestate solids and therefore lead to higher N uptake at the beginning, but the reduced organic N mineralisation will lead to a lower subsequent N uptake rate compared to non-acidified dried products

2) Drying and aluminium sulphate used as acidifier will result in a lower soluble fraction of P in the thermally dried products, leading to a lower P uptake of the plant after soil application, compared with applying the untreated digestate solids

## MATERIAL AND METHODS

### Sampling and treatment of solids producing

Biogas-digestate solids (DS), i.e. the fiber fraction from mechanically (decanter centrifuge) separated biogas plant effluents, were collected freshly from Morsø biogas plant, Denmark. The input to the biogas plant includes mainly cow (70%) but also pig (20%) and chicken manure (8-9%), with some food waste (1-2%) as co-substrate.
Additionally, anaerobically digested sewage sludge solids (SS) were sampled from Bjergmarken WWTP, Roskilde, Denmark. The WWTP process includes biologic and chemical precipitation of P (incl. some AlCl₃ and Al₂(SO₄)₃). The secondary sludge is passed to a thermophilic biogas digester and processed with 15 d hydraulic retention time (HRT). Effluent is treated with flocculants (polymers) and dewatered in a screw decanting centrifuge. After collection, DS and SS were stored at -20 °C until further use. Acidification of DS/SS was performed prior to drying by placing 50 g (w.w) DS/SS in a glass beaker (500 ml) and gradually applying Al₂(SO₄)₃ powder directly or concentrated sulfuric acid, followed by continuous stirring until acid evenly distributed on all surfaces of the solids. Based on earlier trials to maximize ammonium retention, a final pH of 7.5 (DS) or 8.0 (SS) with Al₂(SO₄)₃ powder, 6.5 (DS) or 7.5 (SS) with concentrated sulfuric acid was targeted. Acidification with Al₂(SO₄)₃ was done 24 h and with sulfuric acid immediately prior to drying. Drying was performed in a conductive oven at 130°C under ventilation (525ml min⁻¹, corresponding to a headspace exchange rate of 286 times hour⁻¹) till a final DM content of 85%.

## Experimental setup for the pot trial

Six different solid SS or DS materials (raw, raw acidified with Al₂(SO₄)₃, raw acidified with concentrated H₂SO₄, dried, dried-acidified with Al₂(SO₄)₃, dried-acidified with concentrated H₂SO₄) were mixed into a layer in pots containing 3 kg of a sandy loam soil. Mineral fertilized reference treatments with different NP fertilization rates were set up to evaluate the yield response and N and P uptake of maize.

At harvest, all aboveground biomass above 20 mm from the soil surface was cut, and dry matter was determined after oven-drying at 60 °C for 96 h. The dry biomass was milled and analysed for total N. Total P and other elements were determined by ICP-OES after microwave digestion of biomass samples.

## RESULTS AND DISCUSSION

Acidification significantly increased NH₄⁺-N in the dried DS and SS, from 18% in the non-acidified DS control up to 113% and 134% after DS acidification with concentrated sulfuric acid and Al₂(SO₄)₃, respectively (based on initial NH₄⁺-N in the control) and from 76% to 100% and 112% after SS acidification with concentrated sulfuric acid and Al₂(SO₄)₃, respectively. Moreover, drying decreased Water extractable P (WEP) compared with raw material, by 44% and 36% in dried DS and SS, respectively. Surprisingly, acidification even intensified the decrease of WEP. The maize pot trials are ongoing and will be reported in the presentation.

## REFERENCES

* Case, SDC., Gómez-Muñoz B., Magid J., Jensen LS., 2016. Increasing thermal drying temperature of biosolids reduced nitrogen mineralisation and soil N₂O emissions. Environ. Sci. Pollut. Res., 14: 14383-14392

* Pantelopoulos A., Magid J., Jensen LS., 2016. Thermal drying of the solid fraction from biogas digestate: Effects of acidification, temperature and ventilation on nitrogen content. Waste Manag., 48: 218-226.

* Smith, D., Moore, P., 2001. Effects of alum and aluminum chloride on phosphorus runoff from swine manure. J. Environ. Qual. 30, 992-998.

* Smith SR., Durham E., 2002. Nitrogen release and fertiliser value of thermally-dried biosolids. Water Environ. J., 16: 121-126.

* Regueiro I., Coutinh, J., Gioelli F., Balsari P., Dinuccio E., Fangueiro D., 2016. Acidification of raw and co-digested pig slurries with alum before mechanical separation reduces gaseous emission during storage of solid and liquid fractions. Agric. Ecosyst. Environ., 227: 42-51.
# 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 provived 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_152.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
# YIELD OF WINTER CROPS WITH LEGUMES MONOCROPPED AND INTERCROPPED WITH GRASSES: EFFECTS ON A SUBSEQUENT MAIZE CROP IN A DOUBLE CROPPING SYSTEM

BÁEZ, M.D., GARCÍA, M.I., M. MELLA, GILSANZ, C.

INGACAL-Centro de Investigaciones Agrarias de Mabegondo (CIAM), A Coruña, Spain. E-mail: doloresbaez@ciam.es

## INTRODUCTION

In the temperate humid climate of Galicia (NW Spain), the dominant maize-Italian ryegrass double cropping system can be diversified by introducing other winter crops in the rotation (Botana *et al.*, 2016). Growing legumes such as winter pea or crimson clover can produce high dry matter yields, fix N₂ and positively affect subsequent crops (N'Dayegamiye *et al.*, 2015). The aims of the present study were to evaluate the performance of four winter crops in a maize forage system: monocultures of Italian ryegrass and of crimson clover, a mixture of these species and a triticale-pea mixture.

## MATERIAL AND METHODS

A five-year long field trial was established in the CIAM Research Centre (NW Spain), on a silt loam soil classified as Humic Cambisol. The average annual temperature in the study area is 13.3 °C and the average annual rainfall, 1128 mm (10-years average). A split plot design was used, with 4 replicates of each treatment. The main plots consisted of 4 winter treatments, R: Italian ryegrass (Lolium multiflorum L.), C: crimson clover (Trifolium incarnatum L.), a mixture of these species (R-C), and finally, a mixture of triticale (x Triticosecale Witt.) and pea (Pisum sativum L.)(T-P). The winter crops received a broadcast N fertilization (50 kg N/ha) at sowing, and only R received an additional 60 kg N/ha after the first cut (in March). The winter crops were harvested between the end of April and the middle of May. The maize crop was then planted at the end of May, and subplots were treated with two different rates of N fertilizer (0 and 160 kg N/ha) applied at maize establishment. Samples of winter crops and maize were collected at harvest for determination of dry matter (DM) yield and Kjeldahl N contents and for subsequent calculation of the N uptake by the crops. Samples of all winter crops were obtained, and subsamples of the mixtures were used to determine the proportions of grass and legumes.

## RESULTS AND DISCUSSION

### Dry matter yield of winter crops

The DM yield of winter crops during the growth period is shown in Figure 1. In the monocultures, R produced the highest DM yield, except in the second year. Regarding the mixtures, R-C produced the highest yields. Considering the average values of the five years, C yielded 22% less than R in monoculture, and for the mixtures, T-P yielded 11% less than the R-C mixture. The N uptake in the C monoculture was on average 43.4% higher than in the R monoculture. N extraction was 23.6% higher in T-P than in R, but was not higher in the R-C mixture than in R.

### N uptake of maize (summer crop)

In the maize crop, yield and N uptake were highest when the crop was preceded by C or the T-P mixture, with an increase in DM yield of 58% in treatments that did not receive N and 25% in those fertilized with 160 kg N/ha, relative to the maize crop planted after R. Similar results were observed for N uptake (Table 1): C and the T-P mixture increased the N uptake by 62% in the non-fertilized plots and by 26% in the fertilized plots. The N uptake was very low in the last two years, as a consequence of very dry weather in July.
<figure><img src="image_153.png" /><figcaption>Figure 1. Dry matter yield of winter crops in each year during the field trial</figcaption></figure>

<table><caption>Table 1. N uptake (kg N/ha) by maize crops in each year during the field trial</caption><thead><tr><th rowspan="2">Preceding<br>Winter crop<fnref n="1" /></th><th rowspan="2">Fertilization<br>Maize²</th><th colspan="5">N uptake, kg N/ha</th></tr><tr><th>Year 1</th><th>Year 2</th><th>Year 3</th><th>Year 4</th><th>Year 5</th></tr></thead><tbody><tr><td>R</td><td>0</td><td>37.3</td><td>36.0</td><td>37.9</td><td>29.8</td><td>26.9</td></tr><tr><td>C</td><td>0</td><td>76.4</td><td>46.1</td><td>67.7</td><td>55.2</td><td>40.1</td></tr><tr><td>R-C</td><td>0</td><td>35.9</td><td>38.1</td><td>36.8</td><td>29.2</td><td>22.6</td></tr><tr><td>T-P</td><td>0</td><td>58.3</td><td>50.3</td><td>63.1</td><td>49.0</td><td>38.1</td></tr><tr><td>R</td><td>160</td><td>80.0</td><td>63.8</td><td>124.8</td><td>68.7</td><td>37.0</td></tr><tr><td>C</td><td>160</td><td>93.7</td><td>83.4</td><td>140.9</td><td>97.2</td><td>53.2</td></tr><tr><td>R-C</td><td>160</td><td>76.7</td><td>70.9</td><td>127.3</td><td>75.2</td><td>36.9</td></tr><tr><td>T-P</td><td>160</td><td>91.9</td><td>80.2</td><td>159.2</td><td>93.4</td><td>49.4</td></tr><tr><td colspan="7">Significance³</td></tr><tr><td>WC</td><td></td><td>**</td><td>ns</td><td>*</td><td>**</td><td>*</td></tr><tr><td>FM</td><td></td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>WC*FM</td><td></td><td>*</td><td>ns</td><td>ns</td><td>ns</td><td>ns</td></tr></tbody></table>

## CONCLUSION

The results of a medium-term experiment (five years) with intensive forage rotations based on maize as a summer crop and four winter crops showed that incorporation of monoculture legume crops, such as crimson clover or a pea-triticale mixture, during winter increased dry matter yield and nitrogen uptake in the subsequent maize crop relative to those obtained in the conventional ryegrass-maize rotation.

**Acknowledgements:** "Xunta de Galicia": 10MRU503001PR project and actions: 2015/96, 2016/119. Spanish National Institute for Agricultural and Food Research and Technology under project: RTA2015-00058-C06-01.

## REFERENCES

Botana A., Valladares J., Pereira-Crespo S., Díaz N., Resch C., Fernández-Lorenzo B., Dagnac T., Veiga M., Flores- Calvete G., 2016. Annual legumes mixtures can improve the productivity of the rotation italian ryegrass- forage maize. In: Báez et al (Eds.), Innovación sostenible en Pastos: Hacia una Agricultura de respuesta al cambio climático, Lugo-A Coruña 26-29 April 2016, pp. 81-86.

N'Dayegamiye A., Whalen J.K., Tremblay G., Nyiraneza J., Grenier M., Drapeau A., Bipfubusa M., 2015. The benefits of legume crops on corn and wheat yield, nitrogen nutrition, and soil properties improvement. Agron. J., 107 (5), 1653-1665.

<footnote n="1">¹Winter crop (WC): R: Italian ryegrass, C: Crimson clover, R-C: R and C mixture, T-P: triticale - pea mixture. ²Fertilization Maize (FM): Calcium ammonium nitrate 27%. ³Sig.:*** (p<0,001); ** (p<0,01); * (p<0,05); ns, no sig.</footnote>
# IMPLICATIONS OF THE COVER CROP TERMINATION DATE ON N AND WATER CYCLES

ALONSO-AYUSO, M<fnref n="1" />, QUEMADA, M<fnref n="1" />, VANCLOOSTER², M, RUIZ, M<fnref n="1" />, RODRIGUEZ, A³, GABRIEL, J.L.<fnref n="4" />,

## INTRODUCTION

The cover crop (CC) termination date (TD) is a key management tool to enhance some of CC benefits such as soil water conservation and N recycling, and to minimize risks as the pre-emptive competition of CC with subsequent cash crops. However, the optimum date depends on annual meteorological conditions, and climate variability induces uncertainty in a decision that needs to be taken every year. One of the most important cover crop benefits is reducing nitrate leaching, a major concern for irrigated agricultural systems and highly affected by the termination date. This study aimed to determine the effects of cover crops and their termination date on the water and N balances of an irrigated Mediterranean agroecosystem under present and future climate conditions. Understanding the TD management in different scenarios and climatic conditions would be useful to achieve efficient management strategies.

## MATERIAL AND METHODS

The crop growth, water transport and nitrogen modules of WAVE model were calibrated by inverse calibration and validated with six years of soil water content and soil inorganic N data from two previous field experiments [1,2]. Simulations were performed for current climatic conditions (with a 30-year database) and for climate change projections, combining different strategies: fallow vs. winter CC with different TD (from March 1st to April 14th) and different hypothetical planting dates (HPD; April 15th and May 1st) for the main crop. Besides, other factors were combined: different initial soil water and N content in autumn; and two different planting dates of the subsequent cash crop. The cumulated biomass and CC transpiration, drainage, leaching, and N and water content in the upper soil layer previous to the cash crop planting were the variables evaluated.

## RESULTS AND DISCUSSION

The modelling errors of the observed system properties were usually smaller than the observed field variability, reinforcing the efficiency of the model for simulating the behaviour of this cropping system. The application of a validated model to the 40 agronomical scenarios and the 30-year baseline weather time series confirmed the importance of the termination date of the cover crop to maximize cover crop benefits [3]. Significant differences were found in cover crop outputs (cumulated biomass and transpiration), percolation water and leaching, and in both soil water content and $N_{min}$ at HPD, suggesting an increase in the competition with the following cash crop (Table 1). Moreover, the CC treatments always reduced percolation of water and nitrate leaching compared to fallow treatment (Table 1). However, the simulated reduction was larger when the soil or/and the climatic conditions favoured large leaching periods. This leaching appeared to be very dependent on soil autumn conditions as values was very variable depending on the autumn soil N and water content. The scenario analysis also suggested that high nitrate leaching during the intercropping period could occur, in particular when two or more adverse situations were combined, especially high initial $N_{min}$ and water in autumn with high rainfall in winter. The CC effect on nitrate leaching reduction was mostly produced during the first months of the CC period, because early termination dates and late HPD had a very small effect on the final nitrate leaching. So, if the effect of the termination date on nitrate leaching is small, early cover crop termination could be a valid option, allowing farmers more time for performing the required soil management, but also allowing crop residues to mineralize and the soil to reduce water and nutrient competition.

<footnote n="1">¹ Universidad Politécnica de Madrid, Spain; ² Universidad catholique de Louvain, Belgium; ³ Universidad de Castilla-La Mancha, Spain;</footnote>

<footnote n="4">⁴ INIA (INAGEA-CEIGRAM), Spain,</footnote>
<table><caption>Table1. Effect of termination date (TD) and cash crop sowing date (HPD) on the decrease of N leaching and Nmin/soil water content (SWC) at 20-cm seed bed and at cash crop sowing date under current climatic conditions. The values represent the 10%/90% percentile decrease with average decrease in brackets. The reference is the scenario with early HPD and no CC.</caption><thead><tr><th>TD</th><th>HPD</th><th>Leached N (kg N ha<sup>-1</sup>)</th><th>N<sub>min</sub> (kg N ha<sup>-1</sup>)</th><th>SWC (%)</th></tr></thead><tbody><tr><td>early</td><td>early</td><td>33/99 (57.3)</td><td>2/14 (8.9)</td><td>5.5/3.0 (5.0)</td></tr><tr><td>late</td><td>early</td><td>35/112 (63.0)</td><td>5/22 (15.0)</td><td>12.3/16.8 (14.0)</td></tr><tr><td>early</td><td>late</td><td>31/91 (50.6)</td><td>0/11 (3)</td><td>3.3/3.0 (3.0)</td></tr><tr><td>late</td><td>late</td><td>35/111 (61.7)</td><td>4/20 (13.2)</td><td>8.8/12.0 (10.0)</td></tr></tbody></table>

Based on the results obtained in this study under different climate change scenarios, we conclude that cover crops tend to grow more and faster, mostly because of increasing temperature through a period where temperature is the most limiting factor. This possible biomass increase also affects both nitrate leaching (reducing) and pre-emptive competition with the subsequent cash crop (increasing), but it could also improve the weed suppression capacity and the C sequestration. With respect to the effect on nitrate leaching and pre-emptive competition, the simulated climatic change scenarios tended, in general, to increase the differences between the cover crop and fallow treatment. Therefore, the competition for N with the subsequent cash crop was not so important for most of the management scenarios. Soil water content at HPD was also affected in the climate change scenarios, but differences between fallow and cover crop treatments only increased when the temperature increased. Indeed, rainfall variation equally affects fallow and cover crop treatments. Therefore, under different climate change scenarios, fallow tends to increase pre-emptive competition with the subsequent cash crop and to increase nitrate leaching risk, while cover crops tend to be more resilient, maintaining their pre-emptive competition levels but reducing nitrate leaching with respect to the current situation. Therefore, the use of cover crops was confirmed as a meaningful strategy to climate change adaptation.

## CONCLUSION

The TD delay caused a greater CC biomass that led to a higher resources extraction, which was considered an environmental advantage by reducing the risks of leaching and drainage, but at the same time it constituted a risk by enhancing pre-emptive competition with the main crop, mainly in scenarios in which water and N were limiting soil resources. The cash crop planting date showed to be relevant, because it allowed reducing N and water competition. With climate change projections, the CC efficiency in leaching and drainage reduction was enhanced but at the same time the risk of soil depletion increased. Therefore, the CC termination date was confirmed as a key management tool and showed to have relevant implications with climate change scenario projection.

**Acknowledgements:** Project founded by Spanish Ministry (AGL2017-83283-C2), Comunidad de Madrid (S2013/ABI2717). We would also like to thank the staff from La Chimenea field station (IMIDRA).

## REFERENCES

* Gabriel, J.L., Muñoz-Carpena, R., Quemada, M., 2012. The role of cover crops in irrigated systems: Water balance, nitrate leaching and soil mineral nitrogen accumulation. Agric. Ecosyst. Environ. 155, 50-61.

* Alonso-Ayuso, M., Gabriel, J.L., Quemada, M., 2014. The kill date as a management tool for cover cropping success. Plos One 9, 12

* Alonso-Ayuso M., Quemada M., Vanclooster, M., Ruiz-Ramos, M., Rodríguez, A., Gabriel J.L., 2018. Assessing cover crop management under actual and climate change conditions. Sci. Total Environ. 621, 1330-1341.
# FERTILIZER STRATEGIES TO IMPROVE NUE IN GRAZED DAIRY PASTURES

SMITH, A. P. <fnref n="1" />, CHRISTIE, K. C. ², RAWNSLEY, R. P. ², ECKARD, R. J. <fnref n="1" />

## INTRODUCTION

Since the early 1990s there has been a significant increase in the amount of N fertilizer used by pasture-based dairy farmers in Australia. However, there is a mounting body of evidence that N fertilizer is being used inefficiently at the farm scale to produce pasture dry matter (DM) for grazing pure-grass grasslands (Gourley et al., 2012). This comes at a time when there are drastic imperatives to reduce input costs, and although N use is not regulated in Australia, there is increasing scrutiny of the potential risk for agricultural industries to pollute.

One of the avenues that has been proposed to increase the nitrogen use efficiency (NUE) of pasture (i.e. pasture produced per unit of N fertilizer relative to a zero treatment), is using agro-environmental information. It has been suggested that the use of 'sensors' available to farmers to determine how and when to apply fertilizers is preferable to a recipe-book approach of fixed fertilizer applications. Many farmers are sometimes overwhelmed and unconvinced by the different options, and unsure about how best to modify their fertilizer management. Therefore, they continue to use a flat rate of fertilizer across the year. The value proposition of different approaches is needed for grazed pastures. Therefore, we conducted a modelling study to compare the growth rate response, N loss and NUE of fertilizer strategies which used increasing knowledge of soil N mineralisation and environmental conditions in different ways. The working hypothesis was that applying N fertilizer using strategies based on increasing levels of environmental information and sophistication will improve NUE, reduce N losses and reduce N inputs required to maintain production.

## MATERIAL AND METHODS

This study used DairyMod v 5.7.5 (Johnson 2016) to compare 7 fertilizer strategies using a plot study grazed by dairy cows: (1) *Zero rate* (ZR): no fertilizer applied, (2) *Flat rate* (FR): a rate of 40 kg N/ha (rainfed) or 50 kg N/ha (irrigated) applied after every grazing event, (3) *Seasonally modified* (SM): The N rate varied according to season with a fixed rate across all years, (4) *Precision agriculture - soil* (Ps): N fertilizer was applied at 30 kg/ha when the soil available N concentration fell below 20 mg/kg in the top 15cm, and then was not applied for at least 21 days, (5) *Precision agriculture - plant* (Pp): N fertilizer applied at 30 kg/ha when live leaf N concentration drops below 90% of optimum (i.e. a level at which plant growth potential is 10% limited by N efficiency) and then was not applied for at least 21 days, (6) *Daily - plant* (Dp): as for Pp above except N fertilizer was applied daily, and (7) *Daily - soil* (Ds): as for Ps above except N fertilizer was applied daily. The same treatments were repeated at Terang - rainfed site - and Mt Gambier - an irrigated site. The simulations were conducted from 1997 to 2017.

## RESULTS AND DISCUSSION

There was little difference (18.8±0.9 t/ha/yr; 11.3±0.6) in the long term annual DM production between the strategies on an annual basis or seasonal basis; with both locations showing similar relative differences between the strategies compare to FR. For the rainfed location, summer DM production differed most between the strategies (CV of 9%) and autumn the least (CV of 3%), whereas for the irrigated location winter (CV of 7%) and spring/summer were (4%) across all years and strategies. N fertilizer amounts applied differed between strategies; with the variation being consistently great between locations during autumn, and least during spring. In Terang, the SM strategy required 50% less N fertilizer annually than the FR strategy - at Mt. Gambier this was 30% less. The Dp strategy required the least annual N fertilizer at the irrigated location, while at the rainfed location the SM strategy required the least. Some seasonal differences existed: overall the strategies informed by soil information,

<footnote n="1">¹The University of Melbourne, Australia; ²Tasmanian Institute of Agriculture, Australia</footnote>
required more fertilizer (particularly in autumn) than those based on plant requirements. At the rainfed location in most years (78%) there was a need for fertilizer in summer (mostly in December) using the variable strategies; however, in autumn only one year in 18 justified N fertilizer based on Pp and about a third of the years based on Ps. Therefore, cost savings and realized opportunities occurred with the precision strategies that would not have been possible with other strategies. There were large differences in the total N losses between strategies during spring and the least differences during autumn.

In terms of NUE, all the strategies showed a significant improvement over the FR strategy (Figure 1). The SM led to large improvement in NUE of 5 kg DM/kg N in the case of Terang and 5 kg DM/kg N for Mt. Gambier. The Dp strategy was the most (or equal most) efficient across all locations overall. Across both locations, strategies informed by plant based information (i.e. Pp, Dp) were 16% efficient than based on soil information (i.e. Ps, Ds).

<figure><img src="image_154.png" /><figcaption>Figure 1. Long term annual NUE of 7 different fertilizer strategies at an irrigated and rainfed location (NUE; kg DM*/kg N fertilizer. * indicates that the response is relative to a zero-fertilizer treatment.</figcaption></figure>

## CONCLUSION

Fertilising strategies developed per plant N requirements were the most efficient, however at rainfed locations the high variability of response made it difficult in some years to differentiate between responses. The use of precision fertilizer management strategies has value in terms of reducing fertilizer use and loss during autumn and to a lesser extent summer, with the least value in winter.

**Acknowledgements:** The 'More Profit from Nitrogen: enhancing the nutrient use efficiency of intensive cropping and pasture systems' project was supported by funding from Dairy Australia and the Australian Government Department of Agriculture and Water Resources as part of its Rural R&D for Profit program.

## REFERENCES

- Johnson IR (2016) DairyMod and the SGS Pasture Model: a mathematical description of the biophysical model structure. IMJ Consultants, Dorrigo, NSW, Australia.

- http://imj.com.au/wp-content/uploads/2016/05/DM_SGS_documentation.pdf

- Gourley, C.J.P., Dougherty, W.J., Weaver, D.M., Aarons, S.R., Awty, I.M., Gibson, D.M., Hannah, M.C., Smith, A.P., Peverill, K.I., 2012. Farm-scale nitrogen, phosphorus, potassium and sulfur balances and use efficiencies on Australian dairy farms. Animal Production Science 52, 929-944.
MODELLING THE EFFECT OF WIDE RANGING DRIP IRRIGATION REGIMES AND N-FERTIGATION RATES ON
POTATO GROWTH AND PRODUCTION ON A COARSE SAND

MOTARJEMI, S.K.<fnref n="1" />, ZHOU, Z.², ANDERSEN<fnref n="1" />, M.N, PLAUBORG, F.<fnref n="1" />

INTRODUCTION

To achieve satisfactorily high yields of potato in Denmark, high amounts of nitrogen (N) fertiliser are needed. However, studies have shown that potato production causes a high-risk of N loss by nitrate leaching not only after harvest, but also during the growing season when heavy rainfalls occur (Haverkort and MacKerron, 2000). To mitigate this risk, drip irrigation and N-fertigation offer a more precise allocation of both water and N. Potentially high water and N use efficiencies might be achieved, if predictive tools such as crop models can estimate the need for water and N accurately. Hence, the objective of this study was to set up the Daisy model (Hansen et al., 2012) and to test the model performance using data from a field experiment on drip irrigated and N fertigated potatoes.

MATERIAL AND METHODS

Data from a field experiment with multiple treatments in drip and N fertigated potatoes (Zhou et al., 2016) was
used. However, this paper only allowed presentation of results on crop growth and soil water content from two
of the treatments in 2013 and 2014. The high input treatment (I1N4) was fully irrigated (determined from TDR
measurements) with a N input of 180 kg N ha⁻<fnref n="1" /> applied as split N, 40 kg N (NPK) at planting of the mother tubers
and then seven times 20 kg N ha⁻<fnref n="1" /> every week (NH4NO3 and CaNO3, N ratio 1:2). The low input treatment (ION0)
k had no input of irrigation water and nitrogen. The Daisy model calibration (Heidmann et al., 2008) was improved
as measured leaf area index (LAI) development has been taken into account by calibration on data from 2014. The
need for calibration of effective hydraulic parameters (Djurhuus et al. 1999), especially, the saturated hydraulic
property and the form of the unsaturated hydraulic conductivity curve (the l parameter in Van Genuchten-Mualem
equation) were evaluated by comparing measured and simulated soil water dynamics.

RESULTS AND DISCUSSION

Figure 1a. shows that negative I (-0.58) for the C-horizon drained the soil (0-0.8 m layer) to a too low soil water content, and I was calibrated to 1.18. Figure 1c. shows that the old LAI equation in Daisy was in error, especially later in the growing season. Hence, another LAI equation in Daisy was parameterised by calibration resulting in a more realistic Fm parameter (photosynthetic capacity = 4.0) instead of the unrealistically high 4.8 found by

<table><caption>Table 1. Measured and simulated dry matter yield for tuber (Meas. DM Yield and Daisy DM Yield, respectively). Measured and simulated nitrogen yield for tuber (Meas. N Yield and Daisy N Yield, respectively)</caption>
  <thead>
    <tr>
      <th rowspan="2">Treatment</th>
      <th colspan="2">2014</th>
      <th colspan="2">2013</th>
    </tr>
    <tr>
      <th>ION0</th>
      <th>I1N4</th>
      <th>ION0</th>
      <th>I1N4</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Irrigation (mm)</td>
      <td>0</td>
      <td>92.71</td>
      <td>0</td>
      <td>117.35</td>
    </tr>
    <tr>
      <td>Amount of Nitrogen (kg.ha<sup>-1</sup>)</td>
      <td>0</td>
      <td>180</td>
      <td>0</td>
      <td>180</td>
    </tr>
    <tr>
      <td>Meas. DM Yield (Mg.ha<sup>-1</sup>)</td>
      <td>3.3</td>
      <td>9.8</td>
      <td>3.0</td>
      <td>9.3</td>
    </tr>
    <tr>
      <td>St. Error of The Mean</td>
      <td>0.3</td>
      <td>0.2</td>
      <td>0.6</td>
      <td>0.9</td>
    </tr>
    <tr>
      <td>Daisy DM Yield (Mg.ha<sup>-1</sup>)</td>
      <td>2.8</td>
      <td>10.0</td>
      <td>2.8</td>
      <td>8.8</td>
    </tr>
    <tr>
      <td>Meas. N Yield (kg.ha<sup>-1</sup>)</td>
      <td>34.4</td>
      <td>130.5</td>
      <td>28.3</td>
      <td>129.5</td>
    </tr>
    <tr>
      <td>St. Error of The Mean</td>
      <td>4.2</td>
      <td>2.9</td>
      <td>6.0</td>
      <td>12.7</td>
    </tr>
    <tr>
      <td>Daisy N Yield (kg.ha<sup>-1</sup>)</td>
      <td>22.3</td>
      <td>133.9</td>
      <td>27.4</td>
      <td>124.2</td>
    </tr>
  </tbody>
</table>

<footnote n="1">¹ Department of Agroecology, Aarhus University, Denmark; ² Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, Sweden</footnote>
Heidmann et al. (2008). The improved Daisy model was then tested on data from the I1N4 and ION0 treatment in 2014 (Fig. 1b,d-h) and the final DM and N yield (measured and simulated) from both 2014 and 2013 were compared (Table 1).

<figure><img src="image_155.png" /><figcaption>Figure 1. For I1N4 and ION0, respectively, dynamics during the growing season of observed and simulated: a,b Soil water content. Neg_L indicates neg. I in VG-Mualem before calibration. c,d Leaf area index. OldLAI, curve before calibration. e,f Dry matter yield in top and tuber. g,h Nitrogen yield in top and tuber. Error bars in all plots indicate the standard deviation.</figcaption></figure>

CONCLUSION

The calibration of hydraulic parameters resulted in better correlation between observed and simulated data for N uptake and dry matter production in potato during the season and final dry matter and N yield. With correct optimization of input parameters, Daisy could perform as a strong drip and N-fertigation decision support tool for farmers growing potato on coarse sand in temperate climate.

REFERENCES

Djurhuus, J., Hansen, S., Schelde, K., Jacobsen, O.H., 1999. Modelling the mean nitrate leaching from spatial variable fields using effective parameters. Geoderma. 87, 261–279.

Haverkort, A. J. and MacKerron, D. K. L. (eds.), 2000: Management of nitrogen and water in potato production. Wageningen Pers, Wageningen, The Netherlands, 353 pp., ISBN 90-74134-77-7

Heidmann, T., Tofteng, C., Abrahamsen, P., Plauborg, F., Hansen, S., Battilani, A., Coutinho, A.J., Dolezál, F., Mazurczyk, W., Ruiz, J.D.R., Takác, J., 2008. Calibration procedure for a potato crop growth model using information from across Europe. Ecol. Model. 211, 209–223.

S. Hansen, P. Abrahamsen, C.T. Petersen, M. Styczen, 2012. Daisy: model use, calibration, and validation. Trans. ASABE, 55, pp. 1315-1333.

Zhou, Z., Andersen, M.N., Plauborg, F., 2016. Radiation interception and radiation use efficiency of potato affected by different N fertigation and irrigation regimes. Eur. J. Agron. 81, 129–137.
# NITROGEN INPUT MAPPING AT THE FIELD SCALE USING REMOTELY PILOTED AIRCRAFT SYSTEMS IMAGERY

MAIRE J.¹, ², ³, ⁴, GIBSON-POOLE S.², ³, COWAN N.⁴, RICHARDS K.², SKIBA U.⁴, REES R.M.², REAY D.S.³, LANIGAN G.¹

¹ Teagasc Johnstown Castle, Environmental Research Centre, Co. Wexford, Y35 TC97, Ireland, ² Scotland's Rural College, West Mains Road, Edinburgh, EH9 3JG, Scotland, ³ University of Edinburgh, Alexander Crum Brown Road, Edinburgh, EH9 3FF, Scotland, ⁴ Center of Ecology and Hydrology, Bush Estate, Penicuik EH26 0QB, Scotland

## INTRODUCTION

Knowledge of the spatial heterogeneity of soil nitrogen at the field scale is important when determining nitrogen loss dynamics and the spatial variability of nitrous oxide (N₂O) emissions. Understanding the spatial distribution of key nitrogen inputs such as synthetic fertiliser and the randomly spaced deposition of livestock urine or dung is fundamental in assessing potential nitrogen losses. Typical apportionment values as a percentage of the deposited urinary nitrogen are estimated as: 13% ammonia volatilization; 2% N₂O emission; 20% leaching nitrate; 41% pasture uptake and 26% gross immobilization (Selbie et al. 2015). However, monitoring of urine deposition at the field scale is difficult due to the fact that the urine itself is not visible in the soil. Capturing field scale variability of available nitrogen using traditional methods is labour intensive and requires costly laboratory analyses; however, urine deposition has visible consequences on growth, colour and density of the grass (Moir et al. 2011) so may serve as a useful proxy for urine and dung deposition. The grass growth response depends significantly on soil type, soil moisture content, seasonal climatic conditions and the nitrogen content of the deposition (Clough et al. 2004). The method developed in this project is based on the detection of grass growth response areas from the urine or dung deposition, which are referred to as patches. Urine and dung patch distribution have been studied before from either single short term grazing event or with intensive field work (Li et al. 2012; Misselbrook et al. 2016). The objective of this study was to exploit Remotely Piloted Aircraft System (RPAS) technology to map urine and dung deposition at the field scale without the need for intensive sampling strategies. The resulting nitrogen input was monitored to assess the impact of nitrogen input on grass growth, grass quality and potential nitrogen losses to the environment. The use of RPAS can offer images of the urine and dung patch distribution using an object detection script which is applied to construct a deposition map and to study the overlapping of patches and to monitor the evolution of each patch during the grazing season.

The main aim of this study was to determine the potential of the RPAS method to measure urine and dung patch cover over two different grazing systems (sheep and dairy cows grazed fields) and to create an indicative nitrogen input map for these systems.

## MATERIAL AND METHODS

### Field survey using RPAS

In this project, RPAS was used to identify urine and dung patches in a 2 ha field (Johnstown Castle farm, Ireland) and in a 5 ha field (Easter Bush, Scotland), which had been grazed by dairy cows and sheep respectively. The field was surveyed regularly over the grazing season of 2017 in Ireland and in a single occasion in April 2016 in Scotland. In Scotland, the RPAS was a hexa-copter mount with a twin camera system and was used to identify urine patches in a field which had been grazed by sheep (47 ewes and 90 lambs for seven weeks) three weeks previously. The preliminary results presented in this abstract correspond to this survey only.

### Image object detection script

Urine and dung patch detection on the images collected by the RPAS was completed in a three main steps: 1) stitch the pictures collected to create an unique orthoimage for each survey; 2) run the object detection code to select areas in the orthoimage corresponding to the patches; 3) aggregate data about the patches detected. This method was designed to study coverage, size, colour, and timing of the patches. The object detection code was
developed specially for this purpose in the freely available statistical software R (the R foundation, USA). The image analysis is based on the *Kmeans* clustering method which group together pixels of the images which have similar colour information. For more accuracy, the code was run on vegetation colour indices (such as Normalized Difference Vegetation Index, Red Excess Index and Near Infra-red) were calculated from the images taken by the RPAS.

## RESULTS AND DISCUSSION

The results were summarised to construct a map highlighting the size and colour properties of the urine patches. The imagery of four samples of approximately 50 m² areas within the field were analysed using a custom pixel based model using colour channel thresholding and Kmeans clustering. For a total of 210 m² of grassland, 4.12 % of the total area was considered influenced by urine events, with 82 individual patches. The urine patch coverage has been used for a simple interpolation of N₂O emissions using IPCC Tier 1 emission factor (EF) for urine deposition ((EF = 1 % of nitrogen deposited) and fertiliser Ammonium Nitrate (EF = 2 % of nitrogen amended). In this equation, two urine nitrogen loadings were used, with a minimum value of 500 kg N.ha⁻¹ for the area touched by urine and a maximum value of 1089 kg N.ha⁻¹ as reported by Selbie et al. 2015. The total N₂O emissions for this period were estimated between 1.19 kg N₂O.ha⁻¹ and 1.78 kg N₂O.ha⁻¹ based on the reported emission factors.

## CONCLUSION

Mapping nitrogen deposition at the field scale enable farmers to adapt their practices to improve nutrient management and optimise production with a reduced environmental impact. However, low-cost accessible tools are required to provide farmers with the information required to make these management decisions. RPAS imagery can deliver for a low cost and in a limited time an accurate tool to study and manage field nutrient input; however, in the case of grazed grasslands the challenge is bigger due to the high spatial variability of the terrain. The preliminary results of this study showed a high potential to create precise images of urine and dung patches, which account for much of the spatial variability of nitrogen deposition in grazed pasture. This method shows potential to aid automatic and fast determination of patch cover at the field scale, providing information which is essential for a better spatial modelling of available nitrogen and ultimately reducing nitrogen losses in agriculture.

**Acknowledgements:** The authors gratefully acknowledge the University of Edinburgh farm manager Wim Bosma for allowing access to the Easter Bush study field and to Johnstown Castle technical staff in the management of the livestock throughout the trial. Valuable assistance was also provided by the Biomathematics and Statistics Scotland, BioS. Funding for this study was provided by the Walsh fellowship program by Teagasc, Ireland.

## REFERENCES

* Clough, T.J. et al., 2004. Lime and Soil Moisture Effects on Nitrous Oxide Emissions from a Urine Patch. *Soil Science Society of America Journal*, 68(5), pp.1600–1609.

* Li, F.Y. et al., 2012. Effects of nitrogen load variation in animal urination events on nitrogen leaching from grazed pasture. *Agriculture, Ecosystems and Environment*, 159, pp.81–89.

* Misselbrook, T. et al., 2016. Agriculture, Ecosystems and Environment Automated monitoring of urination events from grazing cattle. "*Agriculture, Ecosystems and Environment*", 230, pp.191–198.

* Moir, J.L. et al., 2011. The spatial coverage of dairy cattle urine patches in an intensively grazed pasture system. *The Journal of Agricultural Science*, 149(2011), pp.473–485.

* Selbie, D.R., Buckthought, L.E. & Shepherd, M.A., 2015. *The Challenge of the Urine Patch for Managing Nitrogen in Grazed Pasture Systems*, Elsevier Ltd.
# MANURE N PLAYS A DUAL ROLE IN C STABILIZATION AND N SUPPLY SOIL FUNCTIONS – EVIDENCE FROM LONG-TERM FIELD STUDIES

CHANTIGNY, M.H., ANGERS, D.A.

¹ Agriculture and Agri-Food Canada, Québec Research and Development Centre, Canada

## INTRODUCTION

The potential contribution of agricultural soils in mitigating climate change through carbon (C) sequestration has recently received much interest. However, sequestering more C requires nitrogen (N) input to meet stoichiometric balances. Considering the general consensus that we must learn to grow more food with lower reactive N inputs, an apparent paradox appears between expected soil functions of mitigating climate change (C storage function) and growing food (N supply function). Recent research suggests that both functions may actually co-exist in integrated crop-livestock systems through efficient use of manure nutrients. For instance, it has recently been suggested that microbial peptidic N plays a central role in C stabilization in mineral-organic associations (MOAs) (Knicker, 2011; Kopittke, 2017). The presence of microbial metabolites in manure could therefore foster C accumulation in soils. In parallel, it is recognized that repeated manure application leads to the accumulation of organic N in soils that is eventually restituted and may become a major source of N for crops (reviewed by Webb et al., 2013). More field-based evidence is however required to determine whether and how livestock manure can actually sustain C storage and N supply soil functions at the same time.

## MATERIAL AND METHODS

A conceptual model was constructed, based on findings in previous long-term field experiments (>15 y), to propose possible C and N flows and mechanisms underlying C and N accumulation and restitution in soils fertilized with or without manure (Fig. 1). The long-term studies used either ¹⁵N-labelling/tracing techniques to determine the soil N supply capacity (e.g., Nyiraneza et al., 2010), or physical fractionation schemes to explore the physical location of accumulated C and N (e.g., Maillard et al., 2015).

## RESULTS AND DISCUSSION

### Soil N supply capacity – Nyiraneza et al., 2010

Results showed that, after 28 y of cropping history, plant N uptake was 50 kg ha⁻¹ greater in arable soils receiving mineral fertilizers plus 20 m³ ha⁻¹ yr⁻¹ dairy manure than in soils with mineral fertilizers only (same amounts of available N). This different increased to 150 kg N ha⁻¹ in cropping systems including a grassland phase, indicating that the legacy effect of past manure applications provided a substantial portion of crop N requirements. Inefficient use of this legacy N is likely to increase emissions of reactive N through leaching and greenhouse gas emissions, stressing the need to better predict its contribution to crop N uptake and adjust fertilizer recommendations accordingly (Webb et al., 2013).

Nyiraneza et al. (2010) also demonstrated that in manured soils, the legacy effect increased disproportionately compared to the gain in soil total N, and that a gain of 20 to 50% in soil total N was associated with a 200 to 400% increase in the soil N supply capacity. This finding suggests that the nature or location of accumulated N is not homogeneous in soil. It is hypothesized that N accumulated in manured soils, and contributing to the legacy effect, may be concentrated in a specific fraction of soil organic matter.

### Physical localization of stored C and N in soils – Maillard et al., 2015

The results showed that more C and N accumulate in soils amended with liquid dairy cattle manure than with mineral fertilizers. This finding calls for a specific role of manure C and N. In agreement with Knicker (2011) and
Kopittke (2017), we suggest that the fraction of manure organic N comprised of microbial metabolites is directly involved in the formation of MOAs (Fig. 1).

Using a soil physical fractionation scheme, Maillard et al. (2015) found that while a major portion of C and N accumulated in fine (silt+clay) MOAs, differences between mineral fertilizer and manure were especially large in the coarse (sand-size) MOAs. Carbon and N found in coarse MOAs could conceptually be seen as more labile than in fine MOAs. We hypothesize that while C and N found in fine MOAs is stable [physically recalcitrant], coarser MOAs represent a smaller but more dynamic portion of soil C and N directly involved in the soil N supply capacity (Fig. 1). By extension, it can be hypothesized that manure N compounds play a dual role depending on their origin (e.g., N in mineral forms, in undigested feed and beddings, or in microbial/digestive tract excreta) and physical location in either fine (C stabilization) or coarse (N supply) MOAs.

<figure><img src="image_156.png" /><figcaption>Figure 1. Conceptual model on possible mechanisms and role of manure N in C Storage and N supply soil functions. Solid lines represent primary paths; dashed lines represent secondary paths.</figcaption></figure>

## CONCLUSION

The results indicate that repeated use of dairy cattle manure favors the accumulation of C and N in soil. We suggest that C and N located in coarse MOAs are more dynamic and directly involved in the legacy effect and soil N restitution to crops, whereas C and N located in fine MOAs are more stable. More work is needed to confirm these hypotheses and to explore the influence of manure type, and climate and soil conditions.

## REFERENCES

* Knicker H., 2011. Soil organic N – An under-rated player for C sequestration in soils? Soil Biol. Biochem. 43, 1118-1129.

* Kopittke P.M., 2017. Nitrogen-rich microbial products provide new organo-mineral interactions for the stabilization of soil organic matter. Global Change Biol. doi:10.1111/gcb.14009.

* Maillard E., Angers D.A., Chantigny M.H., 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.

* Nyiraneza J., Chantigny M.H., N'Dayegamiye A., Laverdière M.R., 2010. Long-term manure application and forages reduce nitrogen fertilizer requirements of silage corn – cereal cropping systems. Agron. J. 102, 1244-1251.

* Webb J., Sorensen P., Velthof G., Amon B., Pinto M., Rohde L., Salomon E., Hutchings N., Burczyk P., Reid J., 2013. An assessment of the variation of manure nitrogen efficiency throughout Europe and an appraisal of means to increase manure-N efficiency. Adv. Agron. 119, 371-442.
# CARBON AND NITROGEN SEQUESTRATION AND NITRATE LEACHING MITIGATION BY COVER CROPS

GARCÍA-GONZÁLEZ, I.¹, HONTORIA, C.¹, GABRIEL, J.L.² ALONSO-AYUSO, M.¹, QUEMADA, M.¹

¹ Universidad Politécnica de Madrid, Spain; 2 INIA-INGEA-CEIGRAM, Spain

## INTRODUCTION

Integrating cover crops (CC) into crop rotations presents an opportunity to increase soil C and N sequestration and provides other important ecosystem services such as erosion control and nitrate leaching mitigation (Lal, 2015). In Mediterranean areas with low biomass production, increasing soil organic carbon (SOC) is particularly challenging. If irrigation is available, the production of biomass can be greatly increased, but the challenge of increasing SOC remains due to rapid mineralization rate. Moreover, in irrigated areas CC have been suggested as an economical approach to maintaining soil and water quality without reducing agricultural production (Gabriel et al., 2012). The objective of this work was to study the effect of replacing the traditional winter fallow by CC in crop rotations of irrigated semi-arid areas on C and N sequestration, and nitrate leaching during a 10-year period.

## MATERIAL AND METHODS

The study was conducted in the central Tajo river basin near Aranjuez (Madrid, Spain) from April 2006 to November 2016. The soil, mapped as *Haplic Calcisol*, was highly calcareous with moderate organic matter content. The field experiment consisted of a 10-year crop rotation, with or without a winter cover crop between consecutive main summer crops (maize or sunflower). The three different winter treatments were barley and vetch CC and fallow, and were replicated four times.

The cumulative biomass produced was calculated by adding the aboveground biomass of main crops at harvest and that of the CC at termination from each year. Cumulative C and N input by main crop residues and CC was also obtained for each plot.

Soil organic nitrogen (SON) and SOC were determined from the 0-5 cm and 5-20 cm horizons in samples collected at the beginning of the experiment and after harvesting the main crop every two years. At the end of the experiment, a trench was dug in each of the fallow and barley plots to obtain a profile of inorganic N distribution according to depth in the 0-4 m soil profile. The N surplus was calculated for each plot as the difference between N inputs and N exported by the crop.

## RESULTS AND DISCUSSION

The main crops produced most of the biomass, and after 2015 cumulative biomass become larger for the vetch than for the fallow treatment. The C input was larger for both CC than for the fallow treatment from March 2007 on, reaching 12 Mg ha⁻¹ in barley and 8 Mg ha⁻¹ in vetch treatments.

Soil organic C content increased with time, being larger for both CC treatments than for the fallow after 2010 (Fig. 1a). Those differences were notable only in the top 0-5 cm soil layer. The increase was equivalent to 10.7 Mg C ha⁻¹ in both CC treatments. The suppression of inverted tillage explains the increased SOC in the top layer of the fallow treatment. A similar behavior was obtained in the SON content (Fig. 1b). In the top 20 cm of soil, the rate of C sequestration in the CC treatments was 420 kg ha⁻¹ year⁻¹ and reduced to 180 kg ha⁻¹ year⁻¹ when the tillage effect was removed. The rate of N retention in the CC treatments was 38 kg N ha⁻¹ year⁻¹ and reduced to 14 kg N ha⁻¹ year⁻¹ when the tillage effect was removed.

The inorganic N content in the top 4 m of the soil profile was larger for the fallow treatment than for the barley CC (Table 1). Differences in the top 1 m were not significant but appeared in the 1 to 4 m depth, showing the
potential of barley CC to mitigate nitrate leaching. The difference in N surplus in the last four years between the barley and the fallow (62 kg N ha⁻¹) was close to the difference in N-NO₃⁻ content in the full 4 m soil profile.

<figure><img src="image_157.png" /><figcaption>Figure 1. Temporal evolution of soil organic carbon (SOC) and soil organic nitrogen (SON) expressed as concentration (%). Significant differences between the treatments at a 0.05 probability level are indicated over each sampling time and depth by using treatment initials (B for barley, V for vetch, F for fallow).</figcaption></figure>

<table><caption>Table 1. Soil inorganic N content distribution according to depth at the end of the experiment (2016) for the barley and fallow treatments. Different letters show significant differences between treatments and depths at a 0.05 probability level.</caption><thead><tr><th rowspan="2">Depth</th><th colspan="2">Soil inorganic N content (kg N ha<sup>-1</sup>)</th></tr><tr><th>Fallow</th><th>Barley</th></tr></thead><tbody><tr><td>0-1 m</td><td>16.7 (6.3)</td><td>17.1 (8.4)</td></tr><tr><td>1-4 m</td><td>83.5 (7.8) a</td><td>38.1 (11.2) b</td></tr><tr><td>0-4 m</td><td>100.2 (5.8) a</td><td>55.2 (15.2) b</td></tr></tbody></table>

## CONCLUSION

Replacing the winter fallow in a crop rotation in an irrigated semi-arid area by a grass or a legume CC promoted C and N sequestration. Compared to the fallow, the soil C stocked in the 0-20 cm top layer increased by 1.6 Mg ha⁻¹ in a ten-year period, and the soil N increased by 0.12 Mg N ha⁻¹. Adding the reduced tillage effect increased the retention rates. The capacity of barley CC to mitigate nitrate leaching was evident from the reduction in the inorganic N content in the soil profile with respect to the fallow, and the N surplus was a good indicator.

**Acknowledgements:** Spanish Ministry of Economy and Competitiveness (AGL201452310R) and Comunidad de Madrid (S2013/ABI2717). Staff of La Chimenea field Station and C. Gómez-Paccard.

## REFERENCES

* Gabriel, J.L., Muñoz-Carpena, R., Quemada, M., 2012. The role of cover crops in irrigated systems: Water balance, nitrate leaching and soil mineral nitrogen accumulation. Agr. Ecosyst. Environ. 155, 50-61

* Lal, R., 2015. Soil carbon sequestration and aggregation by cover cropping. J. Soil Water Conserv. 70, 329-339
# GRAIN YIELD AND NITROGEN USE EFFICIENCY (NUE) RESPONSE IN OLD AND NEW DURUM WHEAT GENOTYPES TO DIFFERENT FERTILIZATION TIMING

GIOACCHINI, P.<fnref n="1" />, PALUMBO, M.², SCIACCA, F.², VIRZÌ, N.², ALLEGRA, M.³, ROCCUZZO, G.³ CIAVATTA, C.<fnref n="1" />

## INTRODUCTION

Management of nitrogen (N) is the key for sustainable and profitable wheat production in low N soils. In order to optimize N fertilization, it is crucial to meet N availability with the period of maximum uptake by the plant, thus increasing N use efficiency of fertilizer and reducing the risk of N losses from the system. Therefore, the time of fertilization can strongly affect crop uptake of N fertilizer (Garrido-Lestache, 2005). The use of ¹⁵N labelled fertilizer allows to follow this pattern in the plant-soil system and to quantify N uptake and distribution of this specific N source in the different organs of the plant. The N use efficiency (NUE) can thus be calculated more accurately. However, different genotypes of wheat can have different needs in terms of N demand, and the optimum fertilization timing might change when several genotypes are compared (Ortiz-Monasterio, 1997). In this study, the effect of three periods of N fertilizer supply was investigated on four genotypes of durum wheat in terms of production of grain, total amount of N in the grain and NUE.

## MATERIAL AND METHODS

The experiment was carried out in the 2013-2014 growing season in Sicily (37°32' N; 14°34' E; 189 m a.s.l.), in a typical area of cereals production characterized by Mediterranean climate with dry spring-summer period. The annual precipitation, about 400 mm, is concentrated in autumn and winter. The experimental design was a "split-plot" with two factors (principal factor: fertilization time; secondary factor: genotypes) with three replicates. A not fertilized control (F1) and three N fertilization timing were compared: F2 - the entire N rate at the beginning of stem elongation; F3 - 67% of N dose at the beginning of stem elongation (BBCH 30) and 33% at early boot stage (BBCH 41); F4 - the entire N rate during the stem elongation (BBCH 33). The total amount of fertilizer N was 90 kg ha⁻<fnref n="1" />, distributed as NH₄NO₃. In 3 m² subplots the fertilizer was labelled in both N atoms at 2.8 atom % of ¹⁵N. The four genotypes used in the experiment have different traits: Aureo is characterized by a high gluten and protein content in the caryopsis; Iride and Simeto are high yielding and widely cultivated in the Mediterranean environment; Timilia is an old Sicilian landrace characterized by high plant and low yield. After harvest, grain yield was assessed, then grains were dried, milled and analysed for N content and isotopic composition by CF-IRMS (Continuous Flow Isotope Ratio Mass Spectrometry DeltaV Advantage Thermo Fisher Scientific) to determine the total amount of N, the N derived from fertilizer (Ndff) and the NUE in the grains. Data for each trait were analyzed by STATISTICA (Statsoft Inc. 1995) and an analysis of variance (ANOVA) was conducted to test significant differences among genotypes and fertilization treatments.

## RESULTS AND DISCUSSION

The four genotypes pointed out different responses to fertilization time in terms of crop yield. The lowest grain yield (about 3 t ha⁻<fnref n="1" />) was obtained with the landrace Timilia. N addition never caused a significant increase of grain production with respect to the control. A similar behavior was observed also for Aureo that showed higher grain yield than Timilia (about 4.5-5 t ha⁻<fnref n="1" />) but no significant increase following the addition of N fertilizer, compared to the control. The highest grain yields were measured for Iride and Simeto and a significant increase in the production compared to the control was obtained with the addition of N fertilizer, in spite of the fertilization time. A similar behavior was observed for the total amount of N in the grains. Since Timilia had the lowest grain yield, this landrace also showed the lowest amount of N in the caryopsis. The other three cultivars had higher N content

<footnote n="1">¹ Department of Agriculture and Food Sciences, University of Bologna, Italy; ² Council for Agricultural Research and Economics, Research centre for cereal and industrial crops - Acireale, Italy; ³ Council for Agricultural Research and Economics, Research Centre for Olive, Citrus and Tree Fruit - Acireale, Italy</footnote>
compared to Timilia, but only Iride and Simeto showed a significantly higher amount of grain N in the fertilized treatments compared to the control.

The use of ¹⁵N-labelled fertilizer allowed to follow N derived from fertilizer in crop yield and to distinguish between the two N sources, soil and fertilizer. In the four genotypes, fertilization time affected Ndff in the grains. This content was lowest in the F2 treatment (about 20%), whereas the F3 and even more the F4 treatments caused a similar and significant increase of Ndff with values double than those of F2 treatment. Therefore, the NUE was also affected by the fertilization time, with the lowest NUE for F2 treatment with values ranging from 15% in Timilia to 27% in Simeto. The other two fertilization treatments strongly improved NUE, with F3 ranging from 35% in Timilia up to 49% in Iride; slightly higher values were measured in all the cultivars with F4 treatment. However, the landrace Timilia showed the lowest NUE in all the treatments compared to the other cultivars.

<figure><img src="image_158.png" /><figcaption>Figure 1. Nitrogen use efficiency (NUE) of the three fertilization treatments measured in the grain yield of the four genotypes. Bars are standard errors (n=3)</figcaption></figure>

## CONCLUSION

Our study shows that the response of grain yield and caryopsis N content to the amount and timing of N fertilization depends on the genotype. In particular old landraces respond less than the new cultivars to N addition. However, the isotopic technique highlighted that the NUE mainly depends on the fertilization time and that the highest NUE can be obtained when the fertilizer is given during the stem elongation phase. A higher NUE also means less N remaining in soil at harvest and reduced risk of N losses from the system.

**Acknowledgements:** The authors are grateful to PON01_01145 "ISCOCEM" Project (Sviluppo tecnologico e innovazione per la sostenibilità e competitività della cerealicoltura meridionale) for the financial support. The authors also acknowledge Stefania Licciardello and Antonio Leonardi for the valuable help provided in managing the field experiments.

## REFERENCES

* Garrido-Lestache E., Lopez-Bellido R., Lopez-Bellido L. 2005. Durum wheat quality under Mediterranean conditions as affected by N rate, timing and splitting, N form and S fertilisation. Eur. J. Agron. 23, 265-278.

* Ortiz-Monasterio J.I., Sayre K.D., Rajaram S., McMahon M. 1997. Genetic progress in wheat yield and nitrogen-use efficiency under four nitrogen rates. Crop Sci. 37, 892-898.
# DECISION RULES FOR ENVIRONMENT-FRIENDLY WHEAT N FERTILIZATION: COMBINING CROP MODEL AND VIABILITY THEORY

JEUFFROY, M.-H.¹, LENOIR, A.¹, RAVIER, C.¹,², SABATIER, R.³, MEYNARD, J.-M.⁴

¹ INRA UMR Agronomie, France; ² Arvalis-Institut-du-végétal, France; ³ INRA UR Ecodéveloppement; ⁴ INRA UMR SADAPT, France

## INTRODUCTION

Current fertilization practices on wheat in France often result in low N use efficiency (NUE) and high N losses to the environment. One of the main reasons is that all existing methods and tools supporting fertilizer-N management aim at maintaining the crop in an optimal crop N nutrition status, i.e. with a Nitrogen Nutrition Index (NNI) not significantly lower than 1, during all the crop cycle. Yet, recent studies showed that some early N deficiencies do not lead to a decrease in yield or grain protein content, and even result in improved NUE (Ravier et al., 2017), and reduced N losses. Moreover, crop N demand and soil N supply, both influencing crop N uptake dynamics, are highly dependent on weather, thus questioning the current recommendations of applying N fertilizer at precise stages. To maximize crop N uptake, N application should be preferred when soil moisture is high enough, or when a rainfall is shortly following fertilizer application. Based on innovative design methods, Ravier et al. (2018) proposed to manage N applications throughout the crop cycle, based on the regular monitoring of the crop NNI (NNI-based method). Therefore, N management decision rules that favour periods of acceptable N deficiency need to be provided, to ensure high N use efficiencies and high crop performance.

## MATERIAL AND METHODS

The method was developed on one case study, in a luvisol in Normandy, and maize as preceding crop. For each of the past 20 years, the optimal days for N application were identified between the end of winter and booting stage: either a positive cumulative P-ETP in the 5 days preceding N application, or cumulative rainfall exceeding 10 mm in the three days following the application. Then, fertilization strategies were defined: each optimal day, five N rates were tested (0, 40, 60, 80, 100 kgN.ha⁻¹). Then, the Azodyn crop model (Jeuffroy & Recous, 1999) was used to simulate NNI dynamics for each N fertilization strategy and each year. Following viability theory (Aubin, 1991), two constraints were defined to sort the simulated strategies: (i) the simulated crop NNI had to be above the minimum NNI path from tillering until flowering (defined by Ravier et al., 2017), (ii) N losses, indirectly assessed as the non-recovered fertilizer N, had to be lower than 20 kgN.ha⁻¹. Then, robustness of each strategy was calculated, as the proportion of years for which there is at least one fertilization strategy that allows the stochastic dynamic system to match both constraints. Finally, a comparison between current recommendations and NNI-based method was implemented, based on Azodyn simulations.

## RESULTS AND DISCUSSION

For early crop stages, robustness of N fertilization strategies decreased with increased N rates, while it increased for later stages (Figure 1). Moreover, at this late stage, the lower the crop NNI, the higher the N rate with the highest robustness. For some conditions (low NNI at late stages), robustness was far from 1 (maximal value): for example, at Z31, for a plant NNI of 0.6, robustness only reached 0.5 (Figure 1), indicating that, for such a NNI level, the fertilizer rates that prevent yield losses often result in N losses outreaching 20 kgN.ha⁻¹.

For each stage and each NNI level, the minimum N rate resulting in the maximum robustness was indicated in the table of recommended decision rules, diffused among farmers (Table 1). Applying these rules in the case study resulted in a lower mean total N rate applied than the current one, estimated by the balance sheet method (130 kgN.ha⁻¹ vs 200 kgN.ha⁻¹). The NNI-based method resulted in a delay, between 30 and 70 days, in the first N application, compared to the current recommendation. This delay allowed to increased NUE, due to a higher crop
growth rate at the application date. Mean yields were not significantly different between the current (8.7 t.ha⁻¹) and the NNI-based (8.4 t.ha⁻¹) methods. The NNI-based method resulted in a strong decrease of simulated N losses (from 55 kg N.ha⁻¹ with current recommendations until 5 kgN.ha⁻¹). Finally, while only 50% of the cases reached 11.5% as grain protein content with the balance-sheet method, this proportion increased to 70% with the NNI-based method.

<figure><img src="image_159.png" /></figure>

<figure><img src="image_160.png" /><figcaption>Figure 1. Robustness of the fertilizer application options (N rate) for different values of NNI (curves) for two stages, Zadoks 25 (Z25) and Zadoks 31 (Z31).</figcaption></figure>

<table><caption>Table 1. N fertilizer rates that yielded maximum robustness while respecting both viability constraints.</caption><thead><tr><th>Growth stages</th><th>Z25</th><th>Z29</th><th>Z31</th><th>Z32</th><th>Z37</th><th>Z40</th></tr></thead><tbody><tr><td>≤ 0.4</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>0.5</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>0.6</td><td>0</td><td>0</td><td>60</td><td></td><td></td><td></td></tr><tr><td>0.7</td><td>0</td><td>0</td><td>60</td><td>100</td><td></td><td></td></tr><tr><td>0.8</td><td>0</td><td>0</td><td>60</td><td>80</td><td>80</td><td>40</td></tr><tr><td>0.9</td><td>0</td><td>0</td><td>40</td><td>60</td><td>40</td><td>40</td></tr><tr><td>1</td><td>0</td><td>0</td><td>40</td><td>40</td><td>40</td><td>40</td></tr><tr><td>> 1.1</td><td>0</td><td>0</td><td>40</td><td>40</td><td>0</td><td>0</td></tr></tbody></table>

## CONCLUSION

Viability theory provides an original and powerful framework, never used before for the development of a decision support system for N fertilizer management based on a soil-crop model. Field experiments are now required to analyze the ability of the NNI-based method to support farmers' change in N fertilizer management, and to confirm the high agronomic and environmental performance of this innovative method.

## REFERENCES

* Aubin J.-P., 1991. Viability Theory. Birkhäuser Boston, Boston.

* Jeuffroy M.-H., Recous S., 1999. Azodyn: a simple model simulating the date of nitrogen deficiency for decision support in wheat fertilization. Eur. J. Agron. 10, 129–144.

* Ravier C., Meynard J.-M., Cohan J.-P., Gate P., Jeuffroy M.-H., 2017. Early nitrogen deficiencies favor high yield, grain protein content and N use efficiency in wheat. Eur. J. Agron., 89, 16–24.

* Ravier C., Jeuffroy M.-H., Gate P., Cohan J-P., Meynard J.-M., 2018. Combining user involvement with innovative design to develop a radical new method for managing N fertilization. Nutr. Cycl. Agroecosyst., 110, 117–134.
# CONSERVING CARBON STOCKS AND MITIGATING NITROUS OXIDE EMISSIONS BY USING A FORAGE CROP ROTATION INSTEAD OF CONTINUOUS MAIZE CULTIVATION

LOGES, R.¹, BUNNE, I., HERMANN, A.¹, TAUBE, F.¹

¹ Grass and Forage Science / Organic Agriculture, CAU Kiel, 24118 Kiel

## INTRODUCTION

The development of mitigation strategies for climate change is one of the major environmental challenges of the 21st century. A major contributor to anthropogenic greenhouse gas (GHG) emissions is agriculture. Particularly livestock farming is responsible for roughly 12-17% of all European (EU-27) GHG emissions, with methane (CH₄) emissions from enteric fermentation from ruminants being the largest contributor, accounting for 36% of these emissions (Bellarby et al., 2013). With emissions from enteric fermentation being difficult to control, two possibilities to offset GHG emissions from ruminant agriculture are increments in carbon sequestration, or reductions from nitrous oxide (N₂O) emissions. Hence, this work compared a perennial system (i.e. grassland, GR), a complex annual system (i.e. crop rotation, CR) and a simple annual system (i.e. continuous maize, MA) for the development in belowground carbon stocks and the N₂O emissions that resulted from these production systems.

## MATERIAL AND METHODS

The field experiment was established in the North of Kiel (N 54°27′55 E 9°57′55; 15 m a.s.l.) in autumn 2010, with measurements being conducted for ANPP and BNPP in two periods, namely between April 2012-March 2013 (P₁) and April 2013-March 2014 (P₁₁). The production systems were maintained after this time, however, to observe the long-term carbon impact by annual soil carbon measurements. Production systems included a perennial grassland (GR), a maize monoculture (MA) and a crop rotation (CR). The crop rotation consisted of grass-clover, followed by maize and winter wheat (with grass clover undersown). Each of the three production systems existed both as unfertilized treatments, as well as fertilized with 240 kg N ha⁻¹ derived from cattle slurry, with the exception of the clover-grass in the crop rotation, which was not fertilized due to the large clover share. Belowground biomass growth was analysed using the ingrowth core method, with a sampling interval of four weeks throughout the growing season and one core for the winter period. Long-term changes in the soil carbon stocks were determined using the C-model of Petersen et al. (2005). Carbon input in annual systems was defined as the amount of roots and stubbles that were incorporated by ploughing, while excluding potential pre-harvest losses of senesced leaves. Since no ploughing was conducted in the grassland system in addition to carbon derived by roots, a shoot turnover (leaf litter and standing dead) of 28% was assumed (from Schuman, et al., 1999). Biomass production is reported as organic matter, due to the large differences in ash content particularly in the belowground biomass.

## RESULTS AND DISCUSSION

The net primary production (NPP) was comparable among production systems with mean values of 13.3 t organic matter (OM) ha⁻¹ in the grassland compared to 12.2 t OM ha⁻¹ for continuous maize and 12.7 t OM ha⁻¹ in the crop rotation. However, the harvestable aboveground biomass was lower (P <0.01) in the grassland with 7.1 t OM ha⁻¹ compared to 9.7 t OM ha⁻¹ and 9.3 t OM ha⁻¹ for continuous maize and the crop rotation, respectively. Resulting from the year round soil cover, together with the reduced requirement for ploughing, with the complex annual system requiring ploughing only every second year due to the winter wheat cultivation with the undersown grass-clover, the effect on the soil carbon stocks increases substantially. As a result, in our model the soil carbon content after 100 years was predicted to increase by 44 t C ha⁻¹ under fertilized grassland cultivation, and by 18 t C ha⁻¹ for the unfertilized grassland, contrary to a depletion of soil carbon stocks by 7 t C ha⁻¹ or 18 t C ha⁻¹ for fertilized and unfertilized maize, respectively. The crop rotation, however, showed increments in soil carbon stocks by 3 t C ha⁻¹, and decreased by 7 t C ha⁻¹ in the fertilized and unfertilized treatments, respectively. However, constant
management practices yield and environmental conditions are assumed, leading to potential errors in the long-term prediction, even though soil carbon measurements showed a good fit to predicted soil carbon stocks (Figure 1).

<figure><img src="image_161.png" /><figcaption>Figure 1: Modelled $C_{soil}$ stocks in soil for the Grassland (GR), continuous maize (MA) and crop rotation (CR), both without (ON) or with fertilization using cattle slurry (N240). Model validation was performed using soil $C$ measurements from 2011-2017.</figcaption></figure>

Biological N fixation was high, independent of the N-fertilization, particularly in the first experimental year, with 202 kg N ha⁻¹ in the grassland, and 234 kg N ha⁻¹ in the grass clover stand of the crop rotation. However, while the grass-clover system even increased the N-fixation to 324 kg N ha⁻¹ in the second experimental year, the grassland reduced its potential to 67 kg N ha⁻¹, due to the decreased clover share. Cumulative nitrous oxide emissions were generally lowest in the grassland, with an average 0.7 and 0.8 kg N₂O ha⁻¹ a⁻¹ for the unfertilized and fertilized grassland, respectively. This compared favorably to both the crop rotation, with 2.6 and 3.9 kg N₂O ha⁻¹ a⁻¹ or the continuous maize, with 3.0 and 3.9 kg N₂O ha⁻¹ a⁻¹, for unfertilized and fertilized treatments, respectively. However, despite the N₂O emissions being equal between the crop rotation and the continuous maize, the crop rotation would have lower overall emissions, due to the biological nitrogen fixation, which results in lower mineral fertilizer inputs, as well as due to the higher carbon inputs.

## CONCLUSION

The grassland resulted in higher carbon inputs and lower nitrous oxide emissions, thus making it the best solution from a climate smart agriculture perspective. The crop rotation, however, managed to obtain comparable harvestable biomass to the continuous maize, while having an improved impact on the soil carbon content relative to the continuous maize and the additional benefit of the biological nitrogen fixation.

**Acknowledgements:** These investigations were supported by the European Commission (Project ID: 289328, Funded under: FP7-KBBE, CANTOGETHER (Crops and ANimals TOGETHER) project).

## REFERENCES

* Bellarby, J., Tirado, R., Leip, A., Weiss, F., Lesschen, J.P., and Smith, P. (2013). Livestock greenhouse gas emissions and mitigation potential in Europe. Global Change Biol 19, 3-18.

* Petersen, B.M., Berntsen, J., Hansen, S., and Jensen, L.S. (2005). CN-SIM—a model for the turnover of soil organic matter. I. Long-term carbon and radiocarbon development. Soil Biology and Biochemistry 37, 359-374.

* Schuman, G.E., Reeder, J.D., Manley, J.T., Hart, R.H., and Manley, W.A. (1999). Impact of grazing management on the carbon and nitrogen balance of a mixed-grass rangeland. Ecological Applications 9, 65-71.
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**
**Posters**
# A SPATIAL FRAMEWORK TO FACILITATE TESTING AND ADOPTION OF NEW TECHNOLOGIES

GRASSINI, P, RATTALINO EDREIRA, JI, CASSMAN, KG

University of Nebraska-Lincoln, NE, USA

## INTRODUCTION

Ensuring adequate food production in systems that protect environmental quality and conserve natural resources requires acceleration in the rate of crop yield gains on existing farmland. Meeting this challenge will be difficult without a robust spatial framework that facilitates rapid evaluation and adoption of currently available and emerging technologies. Here we developed a global spatial framework to delineate 'technology extrapolation domains' (TEDs) based on key climate and soil factors that govern crop yields and stability.

## MATERIALS AND METHODS

The spatial framework we delineated utilizes four biophysical factors to delineate TEDs: (i) annual total growing degree-days, which in large part determines length of time during the year that crop growth is not limited by cold temperature; (ii) aridity index, which largely defines the degree of water limitation in rainfed cropping systems; (iii) annual temperature seasonality, which differentiates between temperate and tropical climates; (iv) and plant-available water holding capacity (PAWHC), which determines the capacity of a soil to store water to support crop growth during rain-free periods. We assessed the robustness of the TEDs by evaluating average yields and temporal yield variability (quantified with the coefficient of variation, CV) of maize across the US Corn Belt under the hypothesis that a robust spatial framework will adequately represent yield differences across a wide range of climate and soil types. Average yield and CV were evaluated across a group of selected counties in two dimensions: (i) a transect with different climate but with similar PAWHC, and (ii) a transect with soils with different PAWHC within the same climate. We demonstrated applications of the TEDs by showing how to maximize coverage of crop production with a minimum number of testing locations for an existing field trial network conducted to evaluate a product that improves nitrogen (N) fertilizer use efficiency of maize.

## RESULTS AND DISCUSSION

Results showed that differences in average yields and CVs varied in a manner consistent with expectations due to climate and soil type. For example, counties with similar PAWHC but located in different climates exhibited significant differences in average yield and CV across two directional transects in the US north-central region. In the NW-SE direction, both average yield and yield stability increased towards the SE due to longer growing season and smaller aridity index (i.e., greater water supply). In the SW-NE direction, yields and yield stability likewise increase due to smaller aridity index. Similarly, counties located in the same climate, but with different PAWHC, exhibited increasing yields and decreasing CVs in TEDs with greater PAWHC. These results suggest that the TED scheme is robust for capturing the influence of key biophysical factors on crop yields and its variability and, by extension, to also capture differences in crop response to management practices that depend on amount and reliability of water supply and length of growing season.

Given the high cost of time and labor to implement replicated field studies in commercial production fields, the TED framework presented here can help (i) optimize the number of environments covered by a field trial to maximize the crop area coverage in unique TEDs for a given number of sites or, alternatively, to reduce the number of sites without sacrificing crop area coverage in unique TEDs, (ii) select specific environments for testing a technology where it is most likely to have the greatest impact based on biophysical attributes of the selected TEDs, (iii) delineate the extrapolation domain for specific field trials, allowing up-scaling of expected impact from trial locations to TEDs in which the trials were conducted, and (iv) facilitate technology transfer across analog TEDs located in different geographic regions. Potential to improve efficiency of a field testing program is illustrated in Figure 1. The curvilinear line represents the crop production area coverage for a given number of field trials if each
site is located in a unique TED, starting from the origin with TEDs that include largest crop production area to
those with smallest area to the right (Fig. 1A). Hence, any set of field experiments can be compared against this
'efficiency frontier' line to identify opportunities for greater coverage of crop area within unique TEDs. To illustrate
this point, we evaluate maize area coverage by a set of 96 field experiments conducted in 2015 and established
in farmer fields to evaluate a product thought to improve nitrogen fertilizer efficiency of maize. The 96 sites were
located within 19 TEDs (Fig. 1B) which accounted for 42% of total US maize area. In contrast, strategic reallocation
of each field experiment in a unique TED with greatest crop area would achieve the same coverage with only 11
field studies (Fig. 1C), or double the coverage if each of the 96 trials would be reallocated in a unique TED (Fig.
1D).

<figure><img src="image_162.png" /><figcaption>**Figure 1.**</figcaption></figure>

Strategic location of field experiments to maximize coverage of unique TEDs based on a network of 96 experiments conducted in 2015 and located in farmers' fields to evaluate a product that improves N fertilizer use efficiency of maize. Each color in the maps represents a unique TED. (A) The efficiency frontier shown by the curvilinear line representing maximum maize crop area coverage for a given number of sites if each experiment was allocated in a unique TED unit, starting from the TED with largest crop area on the left and sequentially smaller crop area to the right. Mapping the 96 sites showed that many were located in the same TED such that only 19 unique TEDs and 42% of total maize area was covered as indicated by the red dot in (A). TEDs in which field trials were located are shown in (B). Strategic placement of field experiments such that each was located in a unique TED reduces the number of trials by 90% to give the same crop area coverage within unique TEDs as shown in (C) and the green dot in (A), while the same number of field experiments would more than double the crop area coverage as shown in (D) and the blue dot in panel (A).

CONCLUSION

Results from evaluation of the TED framework presented here show promise for capturing effects of dominant
climate and soil factors responsible for variation in rainfed crop yields, and for facilitating greater efficiency in
testing of new technologies and gaining adoption of those that improve yields, yield stability, profits and reduce
negative environmental impact through delineating the area where they are likely to work best.

REFERENCES
Rattalino Edreira JI, Cassman KG, Hochman Z, van Ittersum MK, van Bussel L, Claessens L, Grassini P (2018)
Beyond the plot: Technology extrapolation domains for scaling out agronomic science. Environmental
Research Letters 13, 054027. An online version of the TEDs is available at: http://nutrientstar.org/ted-
framework/
SHADE TREE SPECIES IMPACTS ON SOIL FAUNA AND C, N, P CYCLES IN COSTA RICAN ORGANIC AND CONVENTIONAL COFFEE AGROFORESTRY SYSTEMS

SAUVADET, M.¹, VAN DEN MEERSCHE, K.²,³, ALLINNE, C.³,⁴, GAY, F.²,³, VIRGINIO FILHO, E.DE.M,³, CHAUVAT, M.⁵, BECQUER, T.¹, TIXIER, P.⁶, HARMAND, J-M.²,⁷

¹ IRD, UMR Eco&Sols, France; ² CIRAD, UMR Eco&Sols, France; ³ CATIE, Costa Rica; ⁴ CIRAD, UMR System, France; ⁵ Université de Normandie, Ecodiv, France; ⁶ CIRAD, UPR Geco, France; ⁷ ICRAF, Cameroon

INTRODUCTION

Coffee is a major export product for developing countries that provides the livelihoods of tens millions of people (Pendergrast, 2009). Coffee is traditionally grown under shade trees to decrease physiological stress and provide additional services to the farmers as food or material supply (fruit trees, timber trees) (Tscharntke et al., 2011). However, agricultural intensification in Central America led to conversion of shaded to unshaded coffee systems to increase short-term income, but may have direct consequences on soil biological fertility (Perfecto et al., 2007). An important stake nowadays is to find the right compromise between decent yields and synthetic inputs reduction through promotion of soil biological activity.

Our objective was thus to determine how two common shade trees in coffee agrosystems, *Terminalia amazonia* and *Erythrina poeppigiana*, would impact on soil C, N, P cycling in association with soil fauna.

MATERIAL AND METHODS

We worked in an experimental site from Turrialba (Costa Rica), where various management levels and shade tree associations in coffee plantation were continuously compared since 2000 (Haggar et al., 2011). We compared in August 2017 soil fertility from two management levels (organic and conventional, Table 1), both management being declined in three shade trees association: unshaded, shaded by *Terminalia amazonia* (timber tree), shaded by *Erythrina poeppigiana* (N₂-fixing service tree).

<table><caption>Table 1: Organic and conventional system annual management.</caption><thead><tr><th></th><th>Organic</th><th>Conventional</th></tr></thead><tbody><tr><th>Fertilization</th><td>5 t ha<sup>-1</sup> coffee pulp:<br>66 kg N ha<sup>-1</sup>, 2 kg P ha<sup>-1</sup>, 44 kg K ha<sup>-1</sup></td><td>Mineral fertilizers:<br>150 kg N ha<sup>-1</sup>, 10 kg P ha<sup>-1</sup>, 75 kg K ha<sup>-1</sup></td></tr><tr><th>Phytosanitary Control</th><td>Regular mowing</td><td>5 Herbicides applications<br>1-4 fungicides/insecticides as required</td></tr></tbody></table>

Total C, total N, inorganic N and Olsen P contents, along with pH (H₂O) were analyzed on the top soil layer (0-10 cm) in August 2017. A bioassay (biomass production of maize in pots for 45 days) was done from each combination of management and shade trees associations. Soil nematodes and microarthropods communities were extracted, counted, identified and separated into functional groups.

RESULTS AND DISCUSSION

Shade tree impacts on soil fertility depended on tree- species. The association with *Erythrina* increased soil inorganic N content and bioassay (Fig. 1a), but not Olsen P. *Terminalia* impacts depended more on the system management. Moreover, association with shade trees had higher impacts on soil fauna than on soil fertility, with a large increase of its density and diversity compared to the unshaded systems excepted under *Terminalia* in organic system (Fig. 1b). *Erythrina* had higher positive effects on soil fauna (both for density and diversity) than *Terminalia*. Specific relationships between soil community structure and soil fertility were studied in this work and
showed positive correlation of nematodes functional groups with soil inorganic N content, while soil Olsen P was
more linked to detritivores diversity.

<figure><img src="image_163.png" /><figcaption>Figure 1. Shade tree impacts on soil fertility (a) and fauna (b). Non-significant data (P-Value > 0.05) were tested by ANOVAs and are represented with hollow symbols. Fauna diversity was represented with Shannon index calculated at respectively the genera (nematodes) and morphotype (detritivores) levels. Ratios between shaded and unshaded coffee are represented in the Y-axis with a log-2 scale. Detritivores: collembola and oribatid mites.</figcaption></figure>

CONCLUSION

Our results underscore the importance of associated shade trees in soil biological properties and in fertility of
coffee systems. Part of the shade tree impacts on soil fertility were balanced by fertilizers addition in the
conventional system, yet soil fauna mirrored well soil biogeochemical changes in both systems, underlining the
potential of using fauna functional groups as indicators of soil biological fertility.

Acknowledgements: The present work was funded by Agropolis foundation with STRADIV project (n° 1504-003).
The authors thank Victor Hugo Mendez Sanabria, Patricia Leandro, Nancy Rakotondrazafy, Najat Talha, Jean Larvy-
Delarivière and Jean-Marc Souquet for their help with soil sampling and analyses.

REFERENCES

Haggar, J., Barrios, M., Bolaños, M., Merlo, M., Moraga, P., Munguia, R., Ponce, A., Romero, S., Soto, G., Staver, C., Virginio, E. de M.F., 2011. Coffee agroecosystem performance under full sun, shade, conventional and organic management regimes in Central America. Agroforestry Systems 82, 285-301.

Pendergrast, M., 2009. Coffee second only to oil? Is coffee really the second largest commodity? Tea & Coffee Trade Journal.

Perfecto, I., Armbrecht, I., Philpott, S.M., Soto-Pinto, L., Dietsch, T.V., 2007. Shaded coffee and the stability of rainforest margins in northern Latin America. In: Stability of Tropical Rainforest Margins, Environmental Science and Engineering. Springer, Berlin, Heidelberg, pp. 225-261.

Tscharntke, T., Clough, Y., Bhagwat, S.A., Buchori, D., Faust, H., Hertel, D., Hölscher, D., Juhrbandt, J., Kessler, M., Perfecto, I., Scherber, C., Schroth, G., Veldkamp, E., Wanger, T.C., 2011. Multifunctional shade-tree management in tropical agroforestry landscapes – a review. Journal of Applied Ecology 48, 619–629.
# N FERTILIZER VALUE OF LEGUME-BASED CATCH CROPS

DE NOTARIS, C.¹, SØRENSEN, P.¹, RASMUSSEN, J.¹, OLESEN, J. E.¹

¹ Department of Agroecology, Aarhus University, Blichers Allé 20, Tjele, Denmark

## INTRODUCTION

N availability is one of the main factors limiting productivity in organic arable systems (Olesen et al., 2007). Legume-based catch crops (CC) were shown to both increase main crop N yield and reduce N leaching at the crop rotation scale, but management strategies are required to stabilize their growth, such as early undersowing and a reduced competition with the main crop (De Notaris et al., 2018). The present study aims to investigate the effect of main crop manure application and row space, and CC sowing time on N accumulation in undersown CC biomass and their fertilizer value (the amount of mineral N that can be replaced) for the subsequent crop.

## MATERIAL AND METHODS

### N fertilizer replacement value (NFRV) experiment

A CC mixture of red clover, white clover, ryegrass and chicory (commonly used by organic farmers in Denmark) was undersown in organically managed spring wheat in 2015 and 2016 at Foulumgaard Experimental Station (56° 30'N, 9° 34' E), Denmark. Three treatment factors were tested, within a fully randomized factorial design with 4 replicates: main crop manure application (with and without) and row space (12, 18 and 24 cm), and CC sowing (early May, late May and no CC). CC above- and below-ground (20 cm depth) samples were taken in late October to determine dry matter and N content. In March of the following years (2016 and 2017), the plots were harrowed (CC termination) and, 10 days later, CC biomass was incorporated in the soil by ploughing. In April, oat was sown to test the N fertilizer replacement value (NFRV) of the CC. NFRV indicates to how many kg N fertilizer ha⁻¹ corresponded the effect of the CC. Plots that had CC in the previous year did not receive any fertilization. The others were fertilized with mineral fertilizer (NS 27-4) to a rate of 0, 40 or 80 kg N ha⁻¹, to establish oat grain N yield mineral N response curves. Regression coefficients of the response curves (separately for 2016 and 2017, without and with manure) were used to calculate the NFRV of CC (Jensen, 2013).

Statistical analyses were performed using R (R Core Team, 2016). Analysis of variance (ANOVA) tests were performed to assess the effect of main crop manure and row space, and CC sowing time on CC N and NFRV in 2015 and 2016. The assumptions of normality and homoscedasticity were checked with the Shapiro-Wilk test and visual examination of the residuals against fitted values. For all statistical tests α=0.05.

## RESULTS AND DISCUSSION

### N in catch crops biomass and NFRV in the following year

In 2015, N harvested in CC biomass (above- plus below-ground) was significantly affected by manure application (p<0.001) and by the interactions: manure-row space (p<0.01) and manure-sowing time (p<0.05). In particular, manure reduced CC biomass (and thus CC N), but early sowing and increased row space counteracted the reduction (Table 1). The NFRV in the following year (2016), measured on oat grain N yield, was on average 25 kg N ha⁻¹. It was significantly higher in treatments with no manure in 2015 (p<0.01) and early sowing of CC (p<0.01), but no interaction among factors could be detected. In 2016, N accumulated in CC biomass was only significantly affected by manure application (p<0.001). The average NFRV in the following year (2017) was 43 kg N ha⁻¹, in the range of the highest NFRV values in 2016, with no significant difference among treatments.

N accumulated in CC biomass was positively affected by early sowing and an increased row space when the growing conditions for the CC were not favorable, like in 2015. This effect was not visible in 2016, when CC growth
was not limited. N accumulated in CC biomass varied from a minimum of 15 kg N ha⁻¹ in 2015 to a maximum of 133 kg N ha⁻¹ in 2016, whereas NFRV varied from 10 to 50 kg N ha⁻¹. This indicates how the residual effect of CC is not solely determined by N accumulation in their biomass, whose variations were not always reflected in the NFRV.

<table><caption>Table 1. N accumulated in CC biomass (above- plus below-ground; Catch Crop N) and N Fertilizer Replacement Value (NFRV) in the following year (kg N ha⁻¹) by different manure application and row space in the first-year main crop and by different sowing time of the CC. Data shown are average values (n=4). M= with manure; NM= no manure.</caption><thead><tr><th colspan="2">Previous year treatment</th><th rowspan="2">Space (cm)</th><th colspan="2">2015-16</th><th colspan="2">2016-17</th></tr><tr><th>Catch crop sowing</th><th>Manure</th><th>Catch Crop N</th><th>NFRV</th><th>Catch Crop N</th><th>NFRV</th></tr></thead><tbody><tr><td rowspan="6">Early May</td><td rowspan="3">M</td><td>12</td><td>25</td><td>21</td><td>99</td><td>49</td></tr><tr><td>18</td><td>65</td><td>16</td><td>100</td><td>44</td></tr><tr><td>24</td><td>47</td><td>22</td><td>102</td><td>46</td></tr><tr><td rowspan="3">NM</td><td>12</td><td>98</td><td>50</td><td>122</td><td>35</td></tr><tr><td>18</td><td>74</td><td>48</td><td>115</td><td>45</td></tr><tr><td>24</td><td>102</td><td>31</td><td>133</td><td>34</td></tr><tr><td rowspan="6">Late May</td><td rowspan="3">M</td><td>12</td><td>15</td><td>19</td><td>91</td><td>45</td></tr><tr><td>18</td><td>24</td><td>10</td><td>88</td><td>39</td></tr><tr><td>24</td><td>30</td><td>12</td><td>82</td><td>49</td></tr><tr><td rowspan="3">NM</td><td>12</td><td>108</td><td>13</td><td>109</td><td>43</td></tr><tr><td>18</td><td>85</td><td>24</td><td>126</td><td>40</td></tr><tr><td>24</td><td>102</td><td>29</td><td>116</td><td>46</td></tr></tbody></table>

## CONCLUSION

Early CC sowing time and an increased main crop row space can help stabilize CC growth and increase N accumulation, especially when the conditions for the CC are not favorable. Despite the variations in N accumulated in CC biomass, legume-based catch crops represent a valuable source of N for the subsequent crop, with their fertilizer value being less variable than the N accumulated in CC biomass.

**Acknowledgements:** We thank Erling Nielsen for his technical support. The study was part of the RowCrop project funded under Organic RDD2 by the Danish Ministry of Environment and Food.

## REFERENCES

* de Notaris, C., Rasmussen, J., Sørensen, P. & Olesen, J. E. 2018. Nitrogen leaching: A crop rotation perspective on the effect of N surplus, field management and use of catch crops. *Agriculture, Ecosystems & Environment*, 255, 1-11.

* Jensen, L. S. 2013. Animal Manure Fertiliser Value, Crop Utilisation and Soil Quality Impacts. *Animal Manure Recycling*. John Wiley & Sons, Ltd.

* Olesen, J. E., HANSEN, E. M., ASKEGAARD, M. & RASMUSSEN, I. A. 2007. The value of catch crops and organic manures for spring barley in organic arable farming. *Field Crops Research*, 100, 168-178.

* R CORE TEAM 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
# MORE PROFIT FROM NITROGEN IN AUSTRALIAN AGRICULTURE

SMITH, A. P. <fnref n="1" />, WHITE, M. ²

¹ University of Melbourne; ² ICD Project Services, Australia

## INTRODUCTION

Many Australian soils are low in organic matter and nutrient deficiency is widespread therefore regular fertilization is often required to lift the productivity of many crops and pastures. Given the potential risk of reduced yields from insufficient N, coupled with the relatively-low unit cost of N fertilizer, producers in high rainfall or irrigated regions generally err on the side of over-applying N fertiliser. In drier or regions with higher climate variability, the higher production risks mean farmers may tend to under fertilise to manage financial risks. The result is poor nitrogen use efficiency (NUE) being common across agricultural industries.

Over many decades, much research in Australia has been conducted on the N requirements and management for a range of agricultural systems. The potential to improve the NUE of many modern farming systems, declining terms of trade and increasing pressure to reduce agricultural greenhouse gas emissions, means that ongoing N research remains essential. Improving NUE and balancing the productivity and environmental impacts of N use are increasing imperatives for Australian agriculture.

A new national research program in Australia called ‘More Profit from Nitrogen’ (MPfN) draws on a multi-institutional and cross industry unified effort to address these imperatives. MPfN is a proactive industry based initiative to improve NUE to reduce environmental impact and increase the long-term sustainability of Australian farming businesses by increasing yield, quality product and overall profitability. The agricultural industries involved in the MPfN Program share common markets that are increasingly requiring evidence of sustainability credentials of the products they produce.

## MATERIAL AND METHODS

MPfN is an AUD $15.6M, 4-year program jointly funded by the Australian government through its Rural Research and Development for Profit program, each of the participating industry Rural Research & Development Corporations<fnref n="1" />, and the project research partners. This industry-led program aims to bring about increased farm profitability and reduced environmental impact by increasing NUE across the Australia’s four major intensive users of nitrogenous fertilizers: cotton, dairy, sugar and horticulture. This will be achieved by generating a greater knowledge and understanding of

* the interplay of factors to optimise N formulation, rate and timing across industries, farming regions and irrigated/ non-irrigated situations;

* the contribution of mineralisation to a crop or pasture's nitrogen budget; and

* matching the formulation of enhanced efficiency fertilizers (EEF) formulations with crop or pasture's specific N requirements.

<footnote n="1">¹ Cotton Research & Development Corporation, Dairy Australia, Sugar Research Australia and Hort Innovation</footnote>
## RESULTS AND DISCUSSION

Ten research projects are being delivered under the MPfN program through 23 collaborating partners. The ten projects are producing outcomes which will inform new N fertilizer formulations, application and measurement technologies, decision support tools and industry best practice guidelines. A total of 35 field based experiments have been established in the first eighteen months in various climate zones (see Figure 1). This in-field research is supported by laboratory experimentation, analysis, simulation and biophysical modelling.

<figure><img src="image_164.png" /><figcaption>Figure 1. Locations of MPfN study sites based on agricultural industry in the different rainfall regions of Australia.</figcaption></figure>

MPfN is providing a platform for cross industry collaboration on N management, the results of which is fostering unprecedented information and knowledge exchange amongst Australia's leading scientists. It is also extending research outputs to producers, leading to N best management practice adoption.

## CONCLUSION

MPfN is a proactive industry based initiative to improve NUE to reduce environmental impact and increase the long-term sustainability of Australian farming businesses by increasing yield, product quality and overall profitability. Through collaboration, the MPfN program outcomes will provide the agricultural industries involved with much needed evidence of their continuously improving performance in the pursuit of quality food and fibre production with the lowest possible environmental impact. Since commencing in 2016, the MPfN Program has already been produced information and early results which will be of widespread interest.
# BENCHMARKING ON-FARM N FOOTPRINT USING A SIMPLIFIED N-BALANCE APPROACH

TENORIO, F.A.¹, MCLELLAN, E. ², EAGLE, A.², GRASSINI, P.¹

University of Nebraska-Lincoln, Nebraska, USA; ²Environmental Defense Fund, Washington, D.C., USA

## INTRODUCTION

Achieving further increases in grain production will require greater nitrogen (N) uptake by crops. However, N fertilizer application can negatively impact environmental quality if not properly utilized. Meeting both productivity and environmental goals requires fine-tuning the application of external N inputs relative to crop N requirements. A number of studies have shown that magnitude of N losses is associated with the soil N-balance, that is, the difference between N inputs and harvested grain N. In other words, the magnitude of N surplus can be taken as an indicator of potential N losses. The ‘N-balance’ approach offers a practical way to benchmark N input use in farmer fields. Despite these potential applications, the potential of using the N-balance as a metric to benchmark and monitor N footprint across large agricultural areas remains unexploited. The goal of this study was to develop a simple N-balance method for benchmarking on-farm N use and provide proof of concept using irrigated and rainfed maize in Nebraska (USA) as a study case.

## MATERIALS AND METHODS

The framework developed here was intended to be simple, robust, and to only require a few, easily retrievable data inputs. As previously mentioned, estimation of N-balance requires information about amount of N inputs and crop N uptake. In our framework, N inputs were calculated as the sum of N applications from fertilizer and manure. In contrast to N inputs, farmers rarely record crop N uptake. Fortunately, farmers keep yield records; hence, N uptake can be estimated based on yield, assuming a constant maize grain N concentration (assumed to be 1.14% based on literature review). Our approach assumes steady state for indigenous soil N (i.e., no net change over time) and that other external sources of N (e.g., deposition, irrigation water) are relatively small.

This simplified N-balance is conceptually robust as (i) it accounts for major components of the N-balance in agro-ecosystems (i.e., N inputs and crop N uptake), (ii) it requires a small number of data inputs for its calculation, and (iii) a small positive N-balance can be used as an attainable benchmark for farmers seeking to maximize yields and minimize N footprint. Ideally, a small positive N-balance reduces potential N losses, while maximizing yield and maintaining soil quality over time. A negative N-balance (i.e., N deficit) indicates that crop N removal exceeds N replenished through fertilizer and manure inputs; hence, if this trend persists over time, there will be progressive soil mining and declining crop productivity. In contrast, a large positive N-balance (i.e., N surplus) indicates that N inputs exceed crop N removal, leading to high potential N losses. In other words, a large environmental footprint associated with N inputs in agro-ecosystem is expected when too much N input is applied in relation with crop N demand.

We provided proof of concept using maize in eastern Nebraska (USA) as a study case. Farmer-reported data were available from rainfed and irrigated fields sown with maize during 2004-2015; fields were located within the same soil-climate domain. The database contained a total of 2,468 field-year observations and associated data on yield and N fertilizer rates. We calculated N-balance for each field using farmer-reported yield and N fertilizer rate. Partial factor productivity for N fertilizer (PFP<sub>N</sub>) was calculated as the yield-to-N fertilizer ratio. We evaluated the influence of water regime (irrigated versus rainfed), previous crop (maize grown after soybean versus maize after maize), and their interaction on yield, N-balance, PFP<sub>N</sub>, and N-balance per yield using ANOVA. LSD test was used to determine statistically significant differences (α=0.05) between water regimes x previous crop combinations.
RESULTS AND DISCUSSION

Average on-farm irrigated and rainfed maize yield was 12.7 and 8.4 Mg ha⁻¹, respectively (Table 1). These yields corresponded to ca. 85 and 75% of average yield potential estimated for irrigated (14.8 Mg ha⁻¹) and rainfed maize (11.2 Mg ha⁻¹) in eastern Nebraska (www.yieldgap.org). As a result of erratic rainfall, average rainfed yield was highly variable, as indicated by its high inter-annual coefficients of variation (CVs), which was 4x larger than for irrigated maize. Average N fertilizer rate was 50% higher in irrigated versus rainfed fields and 10% higher in fields sown with maize after maize versus maize after soybean (P<0.01) (Table 1). There was a statistically significant interaction between water regime and previous crop on N-balance, with the latter ranging from 58 kg N ha⁻¹ (irrigated maize after maize) to 26 kg N ha⁻¹ (rainfed maize after soybean). Average N-balance was ca. 5x more stable in irrigated versus rainfed maize (CVs= 24-32 versus 106-126%), indicating a strong year x water regime interaction influencing the N-balance (P<0.01). Despite higher N fertilizer rate, irrigated maize after soybean exhibited similar N-balance and PFPN as rainfed crops, with much higher and stable yields and lower N-balance per yield (Table 1).

<table><caption>**Table 1.** Average yield, nitrogen (N) fertilizer, N-balance, partial factor productivity for N fertilizer (PFPN; yield-to-N fertilizer ratio), and N-balance per yield for irrigated and rainfed maize fields sown after maize or soybean. Parenthetic values indicate the inter-annual coefficient of variation (in %). Yields and PFPN were expressed at 15.5% grain moisture content.</caption><thead><tr><th>Water regime</th><th>Previous crop</th><th>Yield (Mg ha<sup>-1</sup>)</th><th>N fertilizer (kg N ha<sup>-1</sup>)</th><th>N-balance (kg N ha<sup>-1</sup>)</th><th>PFP<sub>N</sub> (kg grain kg<sup>-1</sup> N)</th><th>N-balance/yield (kg N Mg<sup>-1</sup> grain)</th></tr></thead><tbody><tr><td rowspan="2">Irrigated</td><td>maize</td><td>12.5 (5)<sup>a</sup></td><td>201 (8)<sup>a</sup></td><td>58 (24)<sup>a</sup></td><td>63 (7)<sup>b</sup></td><td>4.6 (24)<sup>ab</sup></td></tr><tr><td>soybean</td><td>12.8 (7)<sup>a</sup></td><td>182 (8)<sup>b</sup></td><td>36 (32)<sup>b</sup></td><td>71 (7)<sup>a</sup></td><td>2.8 (34)<sup>b</sup></td></tr><tr><td rowspan="2">Rainfed</td><td>maize</td><td>8.7 (24)<sup>b</sup></td><td>130 (17)<sup>c</sup></td><td>31 (126)<sup>b</sup></td><td>69 (36)<sup>ab</sup></td><td>4.8 (149)<sup>ab</sup></td></tr><tr><td>soybean</td><td>8.0 (29)<sup>c</sup></td><td>118 (17)<sup>d</sup></td><td>26 (106)<sup>b</sup></td><td>70 (33)<sup>a</sup></td><td>5.4 (176)<sup>a</sup></td></tr></tbody></table>

*DIFFERENT LETTERS INDICATE STATISTICALLY SIGNIFICANT (P<0.05) DIFFERENCES BETWEEN WATER REGIME X PREVIOUS CROP COMBINATIONS.

Annual regional averages masked variation in yield and N-balance across farmer fields (Figure 1). Based on Grassini et al (2012), we identified fields with large N surplus as those with N-balance ≥50 kg N ha⁻¹. Based on this categorization, ca. 53 and 27% of irrigated and rainfed maize fields exhibited a large N surplus (Figure 1). We found a statistically significant linear relationship between yield and N-balance for all water regime x previous crop combinations (P<0.01). Consistent with this observation, ca. 40% and 70% of irrigated and rainfed maize fields, respectively, exhibited above-average yields with N-balance < 50 kg N ha⁻¹. This finding suggests that achieving high yields with a small N-balance and high PFPN are not conflicting goals. Indeed, the large variation in yield at the same level of N-balance highlights existing room to (i) increase yield with same N fertilizer rate, or (ii) attain same yield with less fertilizer, or (iii) both, by fine tuning crop and fertilizer practices.

<figure><img src="image_165.png" /><figcaption>**Figure 1.** Nitrogen (N) balance in irrigated (left) and rainfed maize fields (right) plotted against grain yield. Each data point represents a field-year case. Data were disaggregated by previous crop: soybean (green) and maize (yellow). Horizontal arrows indicate N-balance = 50 kg N ha⁻¹, which was used as a threshold to identify fields with large N surplus. Vertical arrows indicate average irrigated and rainfed maize yields. Linear regression between yield and N-balance (not shown here) was significant for all water regime x previous crop combinations (P<0.01).</figcaption></figure>
## CONCLUSION

This study presented a robust and generic framework to diagnose on-farm N footprint. When applied to maize fields in eastern NE, the framework showed that a substantial number of fields exhibited a large N surplus. However, there was (i) large yield variation at any level of N-balance and (ii) a negative relationship between N-balance and yield, suggesting substantial room for improving both yields and N-balance. Magnitude of N surplus depended upon water regime and crop sequence. The framework presented here can be easily extended to other cropping systems around the world as long as data on farmer yields and N inputs are available. Despite its simplicity and limitations, the N-balance approach provides farmers and a wide range of stakeholders with a clear indicators about the potential environmental footprint associated with N inputs use in agro-ecosystems.

## REFERENCE

Grassini, P., Cassman, K.G., 2012. High-yield maize with large net energy yield and small global warming intensity. Proc. Natl. Acad. Sci. U.S.A., 109, 1074–9.
# EFFECT OF FEEDING THE GRASS FIBROUS FRACTION OBTAINED FROM BIOREFINERY ON N AND P UTILISATION OF DAIRY COWS

PIJLMAN, J.<fnref n="1" />, KOOPMANS, S.², DE HAAN, G.³, LENSSINCK, F. <fnref n="4" />, VAN HOUWELINGEN, K.M.⁵, SANDERS, J.P.M.²,⁶,DERU, J.G.C. <fnref n="1" />, ERISMAN, J.W.<fnref n="1" />,⁷

## INTRODUCTION

Biomass produced by intensively managed dairy grasslands on peat soils generally contains higher amounts of N and P than biomass from mineral soils, due to the relative high organic matter mineralisation under oxic soil conditions. Consequently, if these higher amounts are not compensated for in the ration by adding lower N and P feed components, dairy cows receive a dietary surplus of N and P which in turn leads to higher on farm N and P surpluses compared to farms on mineral soils (De Visser et al., 2001). Biorefinery can split grass biomass into a fibrous fraction with lower N and P concentrations, and a fraction with higher N and P concentrations. We hypothesised that substituting part of the grass silage by an ensiled grass fibrous fraction would not affect milk production but could improve cow N and P efficiency in a grass based dairy ration with a dietary surplus of N and P, which is a common situation in a dairy farming system on a peat soil.

## MATERIAL AND METHODS

On the 1st of August 2017, one half of a dairy grassland near Lelystad (the Netherlands) was harvested "conventionally" by mowing, tedding, raking and ensiling within a period of about 48h. Between 20 July and 14 August 2017, the other half of the dairy grassland biomass was refined into a N and P concentrated fraction and a fibrous fraction using a mobile installation with a refiner (GRASSA! BV, the Netherlands, 2nd prototype) followed by a screw-press (Keydollar, the Netherlands). The obtained fibrous fraction was directly ensiled. Average N and P concentrations in unrefined ensiled grass and the ensiled fibrous fraction were 37.3 vs. 29.4 and 2.8 vs. 1.9 g kg⁻<fnref n="1" /> dry matter (DM), respectively.

In autumn 2017, a feed experiment was carried out to compare the effect of unrefined ensiled grass to the ensiled fibrous fraction of refined grass on dairy cow performance and N and P balance. The experiment was set up as a 2 x 2 Latin square, consisting of two dietary treatments (unrefined grass silage, UGS and grass fibrous fraction silage, GFS) and two experimental periods of 14 days each. Eight multiparous Holstein Frisian dairy cows were evenly assigned to each dietary treatment based on lactation number and stage. Cows in the UGS treatment received 13.0 kg DM grass silage per day, while cows in the GFS treatment received 8.0 kg DM of grass fibrous fraction silage in exchange for unrefined grass silage. Both diets were equally supplemented with 8.6 kg DM of concentrates. Average crude protein and P contents of the diets were 210 vs. 190 g kg DM⁻<fnref n="1" /> and 3.3 vs. 3.0 g kg DM⁻<fnref n="1" />, respectively for treatments UGS and GFS. Diets were formulated to meet or exceed daily energy, protein and macro-mineral requirements for dairy cows (CVB, 2016).

Cows feed intake and milk production were monitored daily. During the last three days of each experimental period, feed, milk, spot faeces and spot urine samples were collected. Feed and faeces samples were analysed for DM, crude ash, acid-insoluble ash (HCl-ash), N and P content. Milk samples were analysed for protein, fat, N and P content. Fat and protein corrected milk (FPCM) was calculated as 0.337 + 0,116 x fat percentage + 0,06 x protein percentage x kg milk production day⁻<fnref n="1" />. Urine samples were analysed for N, P and creatinine content. Dietary digestibility of N was determined by the HCl-ash in feed and faeces (Sales and Janssens, 2003). Total urine output was estimated assuming a fixed daily urinary creatinine excretion (De Boer et al., 2002). Differences between input

<footnote n="1">¹ Louis Bolk Institute, the Netherlands; ² GRASSA! BV, the Netherlands; ³ Gras- en Groenvoeders Hoogland Marrum B.V, the Netherlands;</footnote>

<footnote n="4">⁴ Veenweiden Innovatiecentrum, the Netherlands; ⁵ Kennis Transfer Centrum Zegveld, the Netherlands; ⁶ Wageningen University and Research, the Netherlands; ⁷ VU University Amsterdam, the Netherlands</footnote>
and output of N and P were assumed to be a result of retention and other losses than accounted for. Data was analysed by ANOVA using R (version 3.4.0, R core team, 2017).

## RESULTS AND DISCUSSION

Cows in the GFS treatment had a significant lower total feed intake (P=0.011) compared to the UGS treatment (Table 1). The difference tended to be greater in the first compared to the second period (P=0.051). Cows in the GFS treatment had a significant lower daily N intake (P<0.001) and difference between N input and output (P=0.021), and tended to excrete less N via urine (P=0.061) compared to the UGS treatment. Furthermore, cows in the GFS treatment had a significant lower daily P intake (P<0.001) and faecal P excretion (P=0.008), and tended to have a lower daily difference between P input and output (P=0.089) compared to the UGS treatment. Finally, the percentage of dietary N and P excreted via milk was significantly higher for cows in the GFS treatment compared to the UGS treatment (P=0.006 and P=0.001, respectively). Since cows fed the GFS diet did not produce less FPCM but had a lower feed intake, it appeared that cows utilised the grass fibrous fraction more efficiently than the unrefined grass silage, although FCE was not significantly higher for the GFS treatment (P=0.304). The significant lower N input and output difference, and tendency towards a significant lower P input and output difference for cows in the GFS treatment may have been a result of a higher daily dietary N and P surpluses, resulting in higher N and P body retentions of cows (Valk and Šebek, 1999).

<table><caption>Table 1. Feed intake, milk production and daily N and P balances of multiparous dairy cows fed a diet based on unrefined grass silage (UGS) versus cows fed a diet of which part of the grass silage is replaced by a grass fibrous fraction (GFS).</caption><thead><tr><th rowspan="2">Parameter</th><th rowspan="2">Unit</th><th colspan="2">Treatment</th><th rowspan="2">Treatment (T)</th><th colspan="2">P-values</th></tr><tr><th>UGS</th><th>GFS</th><th>Period (P)</th><th>T x P</th></tr></thead><tbody><tr><td>Total dry matter feed intake</td><td>kg cow<sup>-1</sup> day<sup>-1</sup></td><td>21.5</td><td>20.0</td><td>0.011</td><td>0.697</td><td>0.051</td></tr><tr><td>Fat and protein corrected milk</td><td>kg cow<sup>-1</sup> day<sup>-1</sup></td><td>30.7</td><td>30.3</td><td>0.801</td><td>0.475</td><td>0.541</td></tr><tr><td>Feed conversion efficiency</td><td>kg feed kg milk<sup>-1</sup></td><td>1.39</td><td>1.49</td><td>0.304</td><td>0.979</td><td>0.320</td></tr><tr><td>N Intake</td><td>g cow<sup>-1</sup> day<sup>-1</sup></td><td>721</td><td>605</td><td>&lt;0.001</td><td>0.026</td><td>0.410</td></tr><tr><td>Faecal N excretion</td><td>g cow<sup>-1</sup> day<sup>-1</sup></td><td>174</td><td>163</td><td>0.311</td><td>0.534</td><td>0.241</td></tr><tr><td>Milk N excretion</td><td>g cow<sup>-1</sup> day<sup>-1</sup></td><td>162</td><td>162</td><td>0.970</td><td>0.846</td><td>0.735</td></tr><tr><td>Urine N excretion</td><td>g cow<sup>-1</sup> day<sup>-1</sup></td><td>267</td><td>227</td><td>0.061</td><td>0.196</td><td>0.566</td></tr><tr><td>N input minus output</td><td>g cow<sup>-1</sup> day<sup>-1</sup></td><td>117</td><td>52</td><td>0.021</td><td>0.742</td><td>0.525</td></tr><tr><td>P Intake</td><td>g cow<sup>-1</sup> day<sup>-1</sup></td><td>71.0</td><td>60.4</td><td>&lt;0.001</td><td>0.145</td><td>0.168</td></tr><tr><td>Faecal P excretion</td><td>g cow<sup>-1</sup> day<sup>-1</sup></td><td>30.6</td><td>23.7</td><td>0.008</td><td>0.992</td><td>0.374</td></tr><tr><td>Milk P excretion</td><td>g cow<sup>-1</sup> day<sup>-1</sup></td><td>28.4</td><td>28.0</td><td>0.783</td><td>0.666</td><td>0.521</td></tr><tr><td>Urine P excretion</td><td>g cow<sup>-1</sup> day<sup>-1</sup></td><td>0.25</td><td>0.24</td><td>0.902</td><td>0.655</td><td>0.975</td></tr><tr><td>P input minus output</td><td>g cow<sup>-1</sup> day<sup>-1</sup></td><td>11.7</td><td>8.5</td><td>0.089</td><td>0.434</td><td>0.389</td></tr><tr><td>Feed-N to milk-N</td><td>%</td><td>22.6</td><td>26.8</td><td>0.006</td><td>0.349</td><td>0.566</td></tr><tr><td>Feed-P to milk-P</td><td>%</td><td>40.0</td><td>46.4</td><td>0.001</td><td>0.429</td><td>0.527</td></tr></tbody></table>

## CONCLUSION

The fibrous fraction of grass obtained from biorefinery, being less concentrated in N and P than unrefined grass silage, improved dairy cow dietary N and P utilisation without affecting milk production. Biorefinery could be used as tool to reduce environmental N and P loads of dairy farms, especially in situations where diets are grass based and grass biomass is relatively rich in N and P, such as in a dairy farming system on peat soils.

## REFERENCES

CVB. 2016. Tabellenboek Veevoeding 2016. Voedernormen Rundvee, Schapen en Geiten en voederwaarden voedermiddelen voor Herkauwers. CVB-reeks nr. 52, Federatie Nederlandse Diervoederketen, the Netherlands.

de Boer, I. J. M., Smits, M. C. J., Mollenhorst, H., van Duinkerken, G., Monteny, G. J. 2002. Prediction of ammonia
emission from dairy barns using feed characteristics part I: Relation between feed characteristics and urinary
urea concentration. Journal of Dairy Science 85, 3382–3388.

de Visser, P. H. B., van Keulen, H., Lantinga, E. A. and Udo, H. M. J. 2001. Efficient resource management in dairy farming on peat and heavy clay soils. Netherlands Journal of Agricultural Science 49, 255 – 276.

Sales, J., Janssens., G. P. J. 2003. Acid-insoluble ash as a marker in digestibility studies: a review. Journal of Animal and Feed Sciences 12, 383–401.

Valk. H., Šebek, L. B. J. 1999. Influence of long-term feeding of limited amounts of phosphorus on dry matter intake, milk production, and body weight of dairy cows. Journal of Dairy Science 82, 2157-2163.
# CROP RESIDUES DECOMPOSITION DYNAMICS IN FARMS PRACTISING CONSERVATION AGRICULTURE IN THE GRAND EST REGION, FRANCE

THIEBEAU, P., RECOUS, S.

FARE Laboratory, INRA, Université de Reims Champagne-Ardenne, Reims, France

## INTRODUCTION

Agriculture can contribute to attenuation of climate change, notably through the carbon sink function of agricultural soils, by promoting practices that increase soil organic matter. The reduction or suppression of tillage in so-called "conservation" farming systems modifies the restitution of crop residues within the agro-systems that apply them: they are no longer incorporated and mixed in the volume of ploughed soil, but kept on the surface in the case of direct sowing (DS), or partially incorporated into the first centimeters of soil in systems of simplified farming techniques (reduce tillage, RT). This plant biomass is thus found in the form of mulch partially in contact with the soil, which reduces its rate of degradation and increases the time required to release nutrients. Models for decomposition of organic matter should be adapted to take into account the characteristics of these residues and the fact that they decompose on the soil surface.

The objective of the work was to characterize and model the dynamics of degradation: loss of dry matter (DM), carbon (C) and nitrogen (N) of mulches of plant residues: maize (Zea mays L.), sunflower (Helianthus annuus L.), rapeseed (Brassica napus L.), winter wheat (Triticum aestivum L.) and winter barley (Hordeum vulgare L.), in agricultural situations practising RT or DS, from harvesting time to their disappearance from the soil surface.

## MATERIAL AND METHODS

This work was carried out with farmers belonging to a network of farmers practising conservation agriculture, in the Grand-Est region of France. The experiments took place over three years (2009 to 2011) during which contrasting climatic situations were encountered. Changes in mulches of residues on the soil have been monitored from crop harvests, according to a mulch removal protocol developed earlier by us (Thiébeau and Recous, 2016), with time intervals of one to several months between 2 samplings, depending on the nature of the residues and the time of year. The C and N concentrations of the remaining residues were determined on biomasses collected over time. The climatic conditions encountered during decomposition kinetics are expressed in number of normalized days (ND) for temperature, i. e. the time is expressed as the sum of the days at a reference temperature of 15°C (ND15), which permits to compare crop residue dynamics for the different sites and the three years.

## RESULTS AND DISCUSSION

Initial situations ranged from 400 to over 1200 g dry DM m⁻². After one year, residue masses were less than 200 g DM m⁻² in all situations. We observed an absence of effect of the type of culture on the kinetics of mass loss. It is therefore possible to adjust these losses of DM (Fig. 1A), C (Fig. 1B) and N (Fig. 1C) of the residues according to a single function, of type:

$$Y = a \times [\text{Exp}(-b \times \Sigma \text{ND15})]$$

with: ΣND15 = sum of the temperatures in normalized days at 15°C

This means that the loss of mass, C and N was independent of the crop under consideration year and geographical location, and depended only on the sum of ND15. These results indicate that the temperature factor was the primary driver of residue decomposition dynamics at the soil surface for the climatic context of our study, driver more important than soil moisture and species-related chemical characteristics. This conclusion would not be valid in areas where it does not rain regularly. This is the case, for example, under tropical climatic conditions where
the rainfall regime controls decomposition even though the temperature varies little or not at all (Vanlauwe et al., 1995).

<figure><img src="image_166.png" /><figcaption>Figure 1. Dynamics of dry matter (A), carbon (B) and nitrogen (C) losses expressed (% initial) over time, expressed as a sum of normalized days (ND) at 15°C.</figcaption></figure>

## CONCLUSION

These results indicate that by using simple adjustments based on a standardized time scale, it is possible to estimate the dry matter, carbon and nitrogen loss dynamics of these crop residues, and therefore for the latter element, indirectly, the supply (mineralisation) of N by soil crop residues. These results can therefore be included in simple terms, based on knowledge of the quantities returned and the average air temperatures, in decision-support tools for the management of organic matter and nutrients in agricultural systems with reduced tillage.

**Acknowledgements:** This work was funded by INRA and the PEPITES project (Ecological Processes and Technical Innovation Process in Conservation Agriculture) by ANR.

## REFERENCES

* Thiébeau P., Recous S., 2016. Une méthode pour quantifier les biomasses de résidus de récolte à la surface des sols après la moisson. Cah. Agric., 25 (4): 45001.

* Thiébeau P., Recous S., 2017. Dynamiques de décomposition des résidus de cultures sur les exploitations pratiquant l'agriculture de conservation en région Grand-Est, France. Cah. Agric., 26 (6): 65001.

* Vanlauwe B, Vanlangenhove G, Merckx R, Vlassak K. 1995. Impact of rainfall regime on the decomposition of leaf litter with contrasting quality under subhumid tropical conditions. Bio. Fertil. Soils, 20 (1): 8-16.
# AN EVALUATION OF NITROGEN BALANCES AT THE WHOLE-FARM AND FIELD SCALE ON 21 IRISH DAIRY FARMS

MURPHY, P.M.<fnref n="1" />, MURPHY, P.N.C.², AND WALL, D.P.<fnref n="1" />

## INTRODUCTION

Ireland's soil fertility: pH, P and K, has been in sharp decline since 2006 (Wall, et al., 2013). As a result, approximately 10% of dairy farm soils currently have the optimum status of pH, P and K for intensive agronomic production. Differences occur between fields in management (fertiliser input and cropping) and biophysical properties (soil drainage class and topography). Such factors make maintaining soil fertility very challenging as resources become more limited. This project investigates the productive and environmental performance of individual fields using field scale nutrient balances. This paper focusses on field scale Nitrogen (N) balances as an indicator of N use sustainability. Additionally, the data collected for calculating nutrient balances were also used to model the environmental performance of these dairy farms with OVERSEER® (Wheeler, et al., 2007).

## MATERIAL AND METHODS

21 farms were selected to represent different management and biophysical conditions across the south and south east of Ireland. Nitrogen balances were calculated at the field scale as follows: Nitrogen Balance = Nitrogen Inputs - Nitrogen Off-takes. Inputs were; fertiliser N, N in manure (Coulter, et al. 2008) and N in concentrate feed (based on protein content). Off-takes were; N in milk (based protein content), N in meat (McDonald, et al., 1995). Herbage samples were also taken to determine the N concentration in the plant, but this was not used in the N balance as the N in herbage which is not exported off the farm (milk and meat), is recycled in manure. Linear regression analysis and analysis of variance (ANOVA) using R (R Core Team, 2013) was carried out to identify the most significant factors (management and biophysical) effecting field nitrogen balance. Experimentally, OVERSEER® was used to model the nutrient flows and estimate nutrient balance and N leaching in an Irish dairy farming system. A methodology was developed to parameterised and calibrate the model to typical Irish farms conditions

## RESULTS AND DISCUSSION

<figure><img src="image_167.png" /><figcaption>Figure 1. Field scale Nitrogen balance values (kg ha⁻<fnref n="1" />) for fields with three different types of drainage class.</figcaption></figure>

Results are based only on fields used for the milking platform i.e. excluding fields used for beef or silage crop production. ANOVA analysis indicates that field scale nitrogen balances were significantly different between soil

<footnote n="1">¹Teagasc, Crops, Environment and Land-use Programme, Johnstown Castle, Wexford, Ireland. ²School of Agriculture and Food, University College Dublin, Ireland.</footnote>
drainage classes ($p < 0.01$) (Fig.1.). Drainage class differences can be expected to be found across fields on every farm. Poorly drained soils are commonly water logged for long periods of time which increases the risk of N being lost in nitrate. Nitrogen inputs however are not amended to suit the different classes; total inputs are less, potentially because of poor trafficability during the growing season. Farm and sward composition (presence of *Trifolium*) also had significant effects, $p < 0.01$ and $p < 0.05$ respectively. Results for this study showed that N surplus was lower on fields when *Trifolium* (clover) was present. The remaining 51% of fields in this study did not contain clover and those fields had higher N balance values. This suggests the presence of clover influenced the N input in those fields. The significant difference between farms may be due to the range in stocking rate intensity and drainage characteristics of the farms selected. In terms of N balance calculations, chemical N input had a significant positive relationship with field scale nitrogen balance surplus($R^2 = 0.40$). Whereas milk exported had a negative relationship ($R^2 = -0.28$). Ways of increasing nitrogen use sustainability include reducing chemical N inputs and increasing exports of N in milk.

For the experiment with OVERSEER®, the model estimated an average 49 kg ha⁻¹ N leached from the root zone on these dairy farming systems per annum. The amount of estimated N that actually reaches a waterway is unknown. Generally, the amount of N lost to water differs greatly depending on input (Ryan, M., 2002) and the ability of the soil to buffer N. This result will be further validated in a follow-up paper.

## CONCLUSION

There are significant differences between fields in both management and biophysical characteristics. Soil drainage can be overlooked; however, it is significantly influencing the nitrogen balance at the field level and ultimately farm level too. Clover presence is a sward composition factor that also influenced nitrogen balance, but the majority of fields are not managed to include clover. Management differences between farms and fields can be influenced by improved extension services. Currently fertiliser N recommendations are not anchored by any tangible field level factor or coefficient value. Field level differences are affecting the nitrogen cycle and so influence the productive and environmental performance of the fields. N recommendations customised to field level differences may improve nitrogen use sustainability. Field scale nutrient balances are an effective indicator of the impacts these factors have on nutrient use efficiency. Increased N use efficiency could be achieved by more targeted distribution of N inside the farm boundary. A fertiliser strategy that builds-in field level biophysical and nutrient requirements factors would be a progressive step towards improving the production, economic and environmental sustainability of Irish dairy systems.

## REFERENCES

* Coulter, S., Stan Lalor, Contributors Stephen Alex, Alastair Black, Andy Boland, Jim Burke, Owen T. Carton et al. "Major and micro nutrient advice for productive agricultural crops." (2008).

* McDonald, P., R. A. Edwards, J. F. D. Greenhalgh, and C. A. Morgan. "Animal Nutrition 5th." *Essex: Pearson Education Publishers* (1995): 49-127.

* R Core Team (2013). R: A language and environment for statistical computing. *R Foundation for Statistical Computing*, Vienna, Austria. URL http://www.R-project.org/.

* Ryan, M. "Nitrogen losses in relation to soil type." In *Proceedings of the Fertilizer Association of Ireland's Winter Scientific Meeting* (2002), no. 39, pp. 3-12.

* Wall, D. P., Philip Jordan, A. R. Melland, P-E. Mellander, S. Mechan, and G. Shortle. "Forecasting the decline of excess soil phosphorus in agricultural catchments." *Soil Use and Management* 29, no. s1 (2013): 147-154.

* Wheeler, D. M., S. F. Ledgard, and R. M. Monaghan. "Role of the Overseer® nutrient budget model in nutrient management plans." *Designing sustainable farms: critical aspects of soil and water management* (2007): 58-62.
# UMT ALTER'N: TO STRENGTHEN THE STRATEGIC FARM ADVISORY FOR CROPPING SYSTEMS BASED ON LEGUME CROPS OR ORGANIC FERTILISERS WITH LOW NITROGEN LOSSES AND LOW DEPENDENCY TO SYNTHETIC FERTILISERS

SCHNEIDER A.<fnref n="1" />, COLNENNE-DAVID C.², CADOUX S. <fnref n="1" />, DROUET J.-L. ³, HOUOT S. ³, JEUFFROY M.-H. ², LE GALL C. <fnref n="1" />, REAU R. ²

## INTRODUCTION

Supported by the ministry in charge of agriculture since 2015, the 5-year programme Alter'N aims to produce resources to support strategic advice for the insertion of legumes (nitrogen fixing crops) and for the valorisation of organic fertilizers in productive crop systems with low nitrogen (N) losses. Its objective is to deliver actionable knowledge and operational resources which will help agricultural and environmental actors to diagnosis, design, assess and manage N-efficient cropping systems including legume crops and/or organic fertilisers.

The priority is to strengthen joint analysis of current state-of-the-art to deliver clear and agreed messages, and to develop specific tools and methods for designing, managing and evaluating crop systems including these "Alter'N" sources in complement (or replacement) of the industrial mineral N, so that they are productive and with low N losses.

## INSTITUTIONS INVOLVED

Based at Grignon, Alter'N involves Terres Inovia and two research units of INRA, UMR-Agronomie and UMR Ecosys. Its consultative Committee ensures interactions with Acta, Arvalis-Institut du Végétal and ITB as well as representatives from RMT « Fertilisation et Environnement », and RMT « Systèmes de Culture innovants ». The programme is developed in close collaboration with other R&D teams, such as the UMR INRA SAS at Rennes, UMR AgroEcologie at Dijon, UMR AGIR at Toulouse, UR AgrolImpact at Laon, LEVA-ESA Angers, as well as collaborations through European and international networks.

<figure><img src="image_168.png" /></figure>

## THEMATIC AXES

The programme of UMT Alter'N includes four thematic axes:

Axis 1. Build an integrative approach of N losses: in order to gain knowledge on N-losses and interactions among the different types of N losses, to develop simulation tools which are adapted to the alternative sources. Current work aims at adapting parameters of existing crop models (CERES-EGC, STICS, Azodyn), and of tools simulating all types of N losses (Syst'N), in order to better reproduce measured dynamics related to these sources and alter'n based systems.

Axis 2. Improve knowledge about specific dynamics of N from legume crops and organic fertilisers: current progress in knowledge are summarized into operational references and complemented with on-going studies.

Axis 3. Test, design and assess cropping systems including N alternative sources, by quantifying N fluxes and losses in a wide range of cropping systems: on the basis of past or current crop systems trials, data of measurements and multi-assessment outputs are discussed and simulations are investigated.

Axis 4: Make diagnosis of N losses, design and manage N-efficient crop systems with the actors from territorial authorities and value chains: collaborations with public or private partners are developed in activities at regional level or value chain scale.

<footnote n="1">¹ Terres Inovia, France; ² INRA Agronomie – AgroParisTech – Université Paris-Saclay, France; ³ INRA Ecosys France</footnote>
UP TO DATE ACTIONS

The main activities developed so far are:

✓ Collaborations among UMT's partners, with exchanges of information (expertise, on-going activities) thanks to regular meetings (at management board or plenary levels) to share information and plan future plans;

✓ Data acquisition and analyses of the dynamics of nitrogen fluxes in cropping systems issued from these specific sources (laboratory and field trials as well as modelling studies);

✓ Data acquisition and analyses of different cropping system experiments with innovative cropping systems managed under some environmental constraints or with multi-performant objectives (SIC, La Cage, Phyto-Sol, SYPPRE, sites from SOERE PRO such as Qualiagro);

✓ Development or contribution of joint projects at regional, French or European levels (ANR-Legitimes, PSDR-PROLEG, Ademe-PROTERR, ADEME-Methapolsol, Casdar-AgroEcosyst'N, Casdar-Outillage, H2020-LegValue, H2020-DiverImpacts, etc.);

✓ Organisation of specific thematic workshops with other groups of experts in nitrogen or cropping systems issues, to share expertise to diversify nitrogen sources and change N management in crop systems;

✓ Collaboration with regional partners to share territorial analyses related to the insertion of such sources in agricultural production.

The major plans for the future are to:

✓ Continue the improvement of simulation tools including nitrogen losses in the different environmental compartments: defining adapted parameters for these « Alter'N » sources and developing simulations of regional crop systems cases in (i) Syst'N tool (Casdar-AgroEcoSyst'N) and (ii) SIMEOS-AMG and STICS models;

✓ Reinforce the joint analyses on grain legume N dynamics and related ecosystem services (level of system productivity, quantity of inputs, level of polluting fluxes) in order to provide operational schemes taking into account the optimal expression of their benefits in the system conception with adapted management;

✓ Formalize the methodologies for territory diagnosis of water quality management and develop the territorial management of organic fertilizers or urban residual materials with regional partners;

✓ Share successful cases of farm systems with organic fertilisation or legume crops and further analyse the organisation of legume value chains in different French and European case studies;

✓ Investigate operational ways for actors to get financial value of the ecosystem services issued from harvested grain legumes into crop successions into operational territorial or value chain projects.

REFERENCES

http://www.terresinovia.fr/umt-altern
# COMPARATIVE ANALYSIS OF SOME ECOSYSTEM SERVICES COMPONENTS LINKED TO NITROGEN FLUXES OF CROPPING SYSTEMS WITH GRAIN LEGUMES

SCHNEIDER A.¹, ODDOS L.¹, PELZER E.², JEUFFROY M.-H.²

¹ Terres Inovia, France; ² INRA, UMR Agronomie, AgroParisTech, Université Paris-Saclay, France

## INTRODUCTION

In the current French arable cropping systems mainly based on cereals and oilseed rape, legume crops provide both a botanic diversification and nitrogen supply, thanks to their ability to fix atmospheric N₂. However, ecosystem services provided by legumes are not well characterised according to each context, preventing them to be fully valued by a large number of farmers. For instance, the effects of legume crops on the performances of following crops rely usually on i) average estimates, ii) only in cases of some couples of species, and iii) based on fragmented data (as synthetized in Jeuffroy *et al.* 2015). And the major factors explaining the variability of these effects are not known. This prevents to forecast the crop system and adapted technical management which would have led to optimal services in a given context, such as enhanced production with lower charges. In order to complement on-going analysis of a series of detailed traits in one location (Guinet 2016), our study aims to contribute of multiplying the trial contexts in order to address the comparative characterisation of some variables and related services provided by grain legume crop to the following (or intercropped) crop.

## MATERIAL AND METHODS

Several trials were carried by partners of the UMT Alter'N. The trials managed by the Technical Institute Terres Inovia aim at quantifying some services of pea, faba bean, lentil crops compared with non-legume crop (wheat, rape) in two locations (« Berry » in the Indre area since 2015, and « Grignon » in the Yvelines area since 2016) in different couple of years. Berry trial is characterized by a superficial calcareous-glay soil and a high organic matter content (3,1%) whereas Grignon trial has a deep silty soil with a rather organic matter level (between 1,3 and 1,9%). The yields of 2016 harvest were exceptionally low for all winter type crops, with strong disease damages, which occurred in May.

Several variables were analyzed: yield (quantity and quality), N fixed (%Ndfa, following 15N enrichment in the case of analytical trials), residual mineral soil nitrogen (at 3 dates), crop and grain nitrogen content, soil biological activity indicator (soil nematofauna), and, in one location, N₂O emissions. Preceding crops on year n include non-legume crops (wheat and oilseed rape in both locations), and legume species (winter pea and pea-wheat intercrop in both locations, winter faba bean and spring lentil only in Berry, spring faba bean and spring pea only in Grignon). Following crops on year n+1 include wheat and oilseed rape without N fertilisation (ON) or with a suboptimal N fertilisation (N1). The objectives of the first stage analysis (on the current set of data) are (i) characterize the service of the nitrogen fixation especially through the quantity of nitrogen issued from N fixation; (ii) compare the potential of modifying the yield of the following (or intercropped) crop.

In addition, links with on-farm situations are initiated thanks to comparison with on-farm observations followed by INRA in Burgundy. In this latter, 46 farming systems have been followed during two years with three series between 2015 and 2018 (5-7 fields per legume species each year). The aim is to quantify, in farm conditions, the variability of performances of legumes (spring pea and alfalfa) and of the following wheat: yield (quantity and quality), N fixed, N balance, residual mineral soil nitrogen.

## RESULTS AND DISCUSSION

At that stage, three series of « preceding crops » (Berry16, Berry17 and Grignon17) and a single series of « following crop » (Berry17) of the analytical experiment are available to analyse variables linked to ecosystem services of harvested grain legumes. The tendencies are summarized here below for two types of services:
## « Symbiotic nitrogen entry » service

Symbiotic fixation rates (%Ndfa) were high in Berry (compared to reference data): about 75-80% for pea and lentil (versus 60-70%) and 90% for the faba bean (versus 70-80%). In Grignon, on the contrary, %Ndfa rates were particularly low for monospecific peas, about 40%, but remained higher for the intercropped pea (75%). There is a strong inter- and intraspecific variability depending on year and location. This variability is confirmed with results from Burgundy on-farm trials, which underline that fixed N rate (and amount) was higher in fields with higher potential. According to a principal component analysis, the dispersal of individuals is mainly explained by the quantity of fixed nitrogen (QNdfa) and the dry biomass (grains + stems) at harvest. Three groups can be distinguished and associated with three different situations Berry16, Berry17 and Grignon17: low biomass and QNdfa not affected by diseases for the first group, high biomass production and high QNdfa for the second one, and high biomass production but lower QNdfa for the third one. No particular relation between the %Ndfa and the residual soil mineral nitrogen content before or after winter was observed in trials and farmers' fields. In farmers' fields of Burgundy, wheat yield was higher after pea when the amount of N in pea straws and the pea yield were higher.

### "nitrogen absorption by the following crop"

The service directly derived from the preceding effect of pea enables the following wheat to absorb from 59%N more than the wheat which follows a cereal and it leads to a rape which has absorbed 38% N more than rape crop which follows wheat. The trends confirm previous results of another trial (2009-10).

<figure><img src="image_169.png" /><figcaption>Figure 1. Quantity of nitrogen absorbed in the following non-fertilized crop (wheat or oilseed rape) according to its preceding crop.</figcaption></figure>

## The « yield level of the following crop » service

Figure 2 shows the results for the case of the year 2017, taken into account the specific conditions for the preceding crop in 2016 campaign marked by a normal growth during most of the crop cycle but strong damages before harvest. It shows that the legume crop leads to higher non fertilized wheat yields compared to a wheat as preceding crop and that the pea effect is significantly higher than the effect of lentil or pea-wheat association. The latter leads to higher yields of non fertilised rape compared with fababean, lentil or wheat, whereas the differences between the other preceding crops effects are not significant. There is a linear relation between the ON wheat yield and the amount of nitrogen (QN) in crop residues of the preceding crops, but not for the ON rape yields.
<figure><img src="image_170.png" /><figcaption>Figure 2. Yields of non-fertilised (ON) wheat or oil seed rape according to its preceding crop in Berry trial for 2017 harvest.</figcaption></figure>

This first set of results will be further complemented (additional couples "year x location", other variables and services). This will be also further compared to results issued from other partners' trials (especially from project ANR-LEGITIMES) and from a meta-analysis (1) to enable deeper analysis on a wider set of data.

REFERENCES

Cernay C., 2016. Identifier des légumineuses à graines productives en Europe par synthèses quantitatives de données à large échelle. Thèse. 322 pages.

Guinet M., 2016. Quantification of nitrogen fluxes and explanatory traits. Legumes for a sustainable World 11-14 October 2016, Troia, Portugal.

Jeuffroy *et al.*, 2015. Performances agronomiques et gestion des légumineuses dans les systèmes de productions végétales, *In*: Les légumineuses pour des systèmes agricoles et alimentaires durables, Schneider et Huyghe, Ed Quae, pp.139-224.

This communication is associated to the working programme of the UMT Alter'N: http://www.terresinovia.fr/umt-altern
# OPTIMISING N-TRANSFER IN OILSEED RAPE BASED CROP ROTATIONS IN NORTHERN GERMANY

ROSE, M.¹, PAHLMANN, I.¹, KAGE, H.¹

¹ Institute of Crop Science and Plant Breeding, Christian-Albrechts-University, Germany

## INTRODUCTION

Especially in regions with high precipitation rates during drainage period and in fertilizer intensive cropping systems, the risk of N losses is very high. In northern Germany, winter oilseed rape (OSR) is an important break crop in cereal crop rotations. After harvest of OSR high contents of soil mineral nitrogen (SMN) are common, typical subsequent crops such as winter wheat (WW) show little N uptake in autumn (Henke et al., 2008). Thus, there is an increased potential of N leaching after OSR and N transfer between the crops is suboptimal. Ecological and economic factors necessitate keeping N in the agroecosystem. There are different ways to decrease N leaching, e.g. optimizing N fertilization or growing catch crops (CC) during the drainage period (Constantin et al., 2010). The latter option often results in a modified crop rotation. A project of the European Innovation Partnership (EIP) compares local and adjusted crop rotations regarding improved N transfer under the environmental conditions of northern Germany.

## MATERIAL AND METHODS

The field trials were established in 2015 at three typical sites in northern Germany. At each site a local common crop rotation was compared to different adjusted crop sequences. Bristle oat (*Avena strigosa*) was used as the catch crop. The experimental design was a split-plot design with four replications. A plot contains one cultivar with five nitrogen fertilization levels (from no fertilizer to oversupply), thus the effect of preceding crop on optimum N rates ($N_{opt}$) and thereby N transfer can be determined.

After harvest and during the drainage period (October to March) SMN contents were observed at intervals of about two weeks to detect SMN dynamics under differing subsequent crops. In December, destructive measurements were carried out to observe N contents in the above-ground biomass. During the vegetation period, N uptake dynamics of subsequent crops were observed non-destructively with a handheld spectrometer system (HandySpec Field®, tec5). For data processing, the statistical environment R (R Core Team, 2016) was used. Statistical significance was evaluated at $P \le 0.05$.

The presented data were collected in 2016/2017 at Hohenschulen Experimental Station of Kiel University. N dynamics under WW are compared to CC following OSR (fertilized with 210 kg N ha⁻¹) and the effect on $N_{opt}$ of silage maize (SM) grown after OSR – CC (adjusted crop rotation) vs. a common sequence WW – bare fallow (BF) is studied.

## RESULTS AND DISCUSSION

Different SMN dynamics after OSR occurred depending on subsequent crop, similar to observations by Kramberger et al. (2009). There is evidence that these differences are partly based on higher N uptake through the grown catch crop. Bristle oat showed significantly higher N contents in above-ground biomass in December (about 100 kg N ha⁻¹) than winter wheat (about 25 kg N ha⁻¹). No differences occurred in N uptake dynamics from sowing to flowering of SM depending on preceding crop but $N_{opt}$ was significantly lower in the adjusted crop rotation; there was no effect on yield (Figure 1). The difference between N uptake of CC and fertilizer N reduction in maize suggests N immobilization through CC residues. Constantin et al. (2010) showed increasing N stocks in soil organic matter through establishing CC.
<figure><img src="image_171.png" /><figcaption>Figure 1. Quadratic-plateau yield response functions of silage maize (SM) deferring on crop rotation. Market prices are assumed to be 0.74 € kg⁻¹ N⁻¹ and 75 € t⁻¹ SM, plot yields are corrected with factor 0.9.</figcaption></figure>

## CONCLUSION

Field trials are still running and additional results will be included in analysis. Preliminary results show potential for improved N transfer and thereby reduced N leaching through adaption of crop rotations. But other studies suggest that establishing CC may not only result in lower optimal N rates of the subsequent crop but also in higher soil organic N stocks (Constantin et al., 2010). The collected data will be implemented in a soil-plant model to calculate N leaching of differing crop rotations.

## REFERENCES

* Constantin J., Mary B., Laurent F., Aubrion G., Fontaine A., Kerveillant P., Beaudoin N., 2010. Effects of catch crops, no till and reduced nitrogen fertilization on nitrogen leaching and balance in three long-term experiments. Agriculture, Ecosystems and Environment, 135, 268-278.

* Henke J., Böttcher U., Neukam D., Sieling K., Kage H., 2008. Evaluation of different agronomic strategies to reduce nitrate leaching after oilseed rape (Brassica napus L.) using a simulation model. Nutr Cycl Agroecosyst, 82, 291-314.

* Kramberger B., Gselman A., Janzekovic M., Kaligaric M., Bracko B., 2009. Effects of cover crops on soil mineral nitrogen and on the yield and nitrogen content of maize. Europ. J. Agronomy, 31, 103-109.

* R Core Team, 2016. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
# TOWARDS NITROGEN SELF SUFFICIENCY IN CROPPING SYSTEM OF CHALK SOILS IN CHAMPAGNE-ARDENNE AND PICARDIE (FRANCE)

CROS, C.¹, DUPARQUE, A.², REAU, R.³

¹Chambre Régionale d'agriculture Grand Est, France; ²AgroTransfert Ressources & Territoires, France; ³INRA UMR Agronomie, France

## INTRODUCTION

The common crop systems on chalky soils of Champagne are capable of high productivity levels, using synthetic agricultural inputs, mainly nitrogen (N) synthetic fertilizer. Over the last 6 years, the average rate of N fertilizer application was 146 kgN/ha/year, which makes the Champagne-Ardenne region the third largest consumer of N fertilizer per hectare in France (UNIFA, 2016). The fluctuation of energy cost and the strengthening of national and European regulations make the dependency on synthetic N fertilizer an issue for the economic health of farms in chalky soils. Moreover, adding fertilizer N to agricultural systems is accompanied by environmental issues due to N losses to the atmosphere and water.

In the Champagne-Ardenne region, the representatives of regional chambers of agriculture and agricultural cooperatives decided to explore alternative practices in order to reduce the rate of synthetic N fertilizers, without decreasing the quantity and the quality of the crop production. Thus, the Auto'N project was launched in 2013 and the objectives were:

- To design alternative crop systems and to evaluate their sustainability;

- To highlight the use and the efficiency of alternative N sources and to promote alternative practices towards the farmers of chalky soils;

- To provide and test a method for advisers to accompany farmers in changing practices.

## MATERIAL AND METHODS

### A novel design method (Reau and al., 2012)

We worked with 7 "pioneers" farmers and their advisers to design and implement alternative crop systems. The method combines "de novo" designs and step-by-step designs (Meynard and al, 2012). "De novo" design consists of creating a new crop system, forgetting the current one, by gathering information inside the "pioneers" group in "design workshops" (Bertet, 2013). Before each workshop, the central farmer assesses his satisfaction for each service linked to N management: crop nutrition, limitation of N losses and carbon storage in soil. This assessment is formalized in a global target, submitted to the group during the workshop. The other farmers, considered as "consultants", are invited to propose all the ideas which would help the central farmer to reach his target. There were 7 design workshops, so that each farmer became the central farmer once. After these workshops, each farmer chooses the practices to test in his farm and designs a crop system with the animator and his adviser. This step requires a thorough analysis of his motivations, through the characterization of the results he observes in his fields.

After the *de novo* design step, a "step by step" design begins based on the yearly results from the fields. Observations and measurements are realized during 5 years to evaluate the services of N in the crop system. Each year, the project animator and the farmer's adviser analyze how the farmer evaluates his results and help him to understand why failures happened, if happened. Then the farmer can decide to change practices in order to avoid other failures and/or to change his expectations on crop results. If all the expected results are obtained, the farmer also can decide to apply this crop system on other fields of his farm. The traceability of practices, decision rules and production performances is used to characterize *a posteriori* the crop systems. These data are also used to improve our knowledge on water and N fluxes in chalky soils.

## RESULTS AND DISCUSSION
De novo design process has been improved over the 7 design workshops in 2015 and 2016. The rules of the workshops imposed to forget the regulation and economic constraints and to adopt a high tolerance for the target and project of the other farmers. Once these rules were accepted, we observed that the farmers focused on an agronomic analysis of their practices and showed their will to understand N fluxes in their fields. The diversity and the large quantity of ideas generated during the workshops showed that the farmers let express their creativity and their expectations. Finally, they chose to explore different ways to reduce N consumption, corresponding to their motivation, their sensibility and their technical and economic constraints.

<table><caption>Table 1. Description of the 7 cropping systems designed and tested by the “pioneer” farmers in 2016</caption><thead><tr><th>Expected services of N (OM = Organic Matter)</th><th>No economic losses because of N shortcut or excess OM storage</th><th>No production losses because of N shortcut Low level of N losses</th><th>No production losses because of N shortcut OM storage</th><th>No economic losses because of N shortcut OM stock maintenance</th><th>No production losses because of N shortcut Low level of NO<sub>3</sub> losses</th><th>No production and economic losses because of N shortcut</th></tr></thead><tbody><tr><td><strong>Synthetic N input</strong></td><td>89 kgN/ha/yr</td><td>124 kgN/ha/yr</td><td>110 kgN/ha/yr</td><td>12 kgN/ha/yr</td><td>103 kgN/ha/yr</td><td>131 kgN/ha/yr</td></tr><tr><td><strong>Practices</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Crops with low needs in N</strong> (W = winter, S = spring)</td><td>Sunflower, S barley (instead of rape and W wheat)</td><td>/</td><td>S barley (instead of W barley)</td><td>Fodder W wheat or S barley (instead of miller wheat)</td><td>S barley (instead of W wheat)</td><td>Sunflower (instead of rape)</td></tr><tr><td><strong>Legume crops</strong> (frequency)</td><td>S pea and clover (17%)</td><td>W pea (12,5%)</td><td>Alfalfa (10,5%)</td><td>Legume seeds (20%)</td><td>Alfalfa (10%)</td><td>Lens, Alfalfa (18%)</td></tr><tr><td><strong>Intercrop cover</strong> (frequency)</td><td>83%</td><td>63%</td><td>42%</td><td>20%</td><td>43%</td><td>41%</td></tr><tr><td><strong>Organic N input</strong></td><td>Compost and manure every 3 years</td><td>Vinasse every 2-3 year</td><td>Vinasse every 3 years</td><td>Digestate every year</td><td>Composted manure every 3 years</td><td>Manure with straw every 6 years</td></tr><tr><td><strong>Efficiency of synthetic N input</strong></td><td>Date of input according to rain forecast</td><td>5 inputs on wheat and pilotage</td><td>Liquid N with sulfur and pilotage</td><td>Burying digestate 24h after input</td><td>Liquid N with sulfur, 4 inputs on wheat</td><td>Pilotage on rape and wheat</td></tr></tbody></table>

## CONCLUSIONS

The main alternative practices designed by “pioneer” farmers were (i) choosing, between crops of similar gross margin, the one with the lowest N need, (ii) using organic N instead of synthetic N for spring crops and (iii) improving management of intercrops to recycle soil mineral N surplus and fix atmospheric N₂ with legumes. These alternative practices lead to a synthetic N input 30% lowest than the average rate of N fertilizer application in Champagne-Ardenne. Two farmers explore additional practices at farm level in order to suppress the use of any synthetic mineral N fertilizer: producing N fertilizer with a methanation unit, or converting the entire farm in organic farming.

## REFERENCES

* Berthet E., 2013. Contribution à une théorie de la conception des agro-écosystèmes : Fonds écologique et inconnu commun. Thèse de Doctorat en sciences de gestion. Paris : Économie, Organisations & Société, p. 248.

* Meynard J-M., Dedieu B. et Bos A.P., 2012. Re-design and co-design of farming systems. An overview of methods and practices, In : Farming Systems Research into the 21st century: The new dynamic. Ika Darnhofer, David Gibon, Benoît Dedieu, Editors, Springer, pp. 407-432.

* Reau R., Monnot L.A., Schaub A., Munier-Jolain N., Pambou I., Bockstaller C., Cariolle M., Chabert A. et Dumans P., 2012. Les ateliers de conception de systèmes de culture pour construire, évaluer et identifier des prototypes prometteurs. Innovations Agronomiques, 20, pp. 5-33.

* UNIFA, 2016. La fertilisation en France. Le marché en chiffres. [http://www.unifa.fr/le-marche-en-chiffres/la-fertilisation-en-france.html]
# N AND S CYCLES IN CRUCIFER-LEGUME COVER CROP MIXTURES

COUËDEL, A.<fnref n="1" />, ALLETTO, L.<fnref n="1" />,², JUSTES, E.<fnref n="1" />,³

## INTRODUCTION

Cover crops grown in rotation with cash crops provide ecosystem services by reducing pollution and anthropogenic inputs. Among cover crop families, crucifers can efficiently prevent nitrate and sulphate leaching by catching residual soil mineral nitrogen (N) and sulphur (S) to enhance N and S catch crop services (Couëdel et al., 2018a, 2018b). However, compared to legume cover crops, crucifers provide less mineralised N to the subsequent main cash crop (N green manure service). Bispecific crucifer-legume cover crop mixtures can be seen as a potential solution to increase biodiversity in cropping systems combining advantages of both species (Couëdel et al., 2018b). Nevertheless crucifers could be a poor companion crop for two main reasons: 1) they strongly compete for light, water and nutrients due to their rapid root and shoot growth and 2) they can have an allelopathic effect on legumes during their growth due to production of glucosinolates, which are exuded by roots in the rhizosphere and transformed into biocides such as isothyocianates (Matthiessen and Kirkegaard, 2006). There is a lack of information on levels of ecosystem services linked to the N and S cycles provided by mixtures compared to sole cover crops. The aim of our study was to assess the trade-offs of bispecific crucifer-legume mixtures in comparison to sole cover crops on N and S cycles.

## MATERIAL AND METHODS

Experiments were conducted at two sites (near Toulouse and Orléans, France) over two years where few cultivars from eight crucifers (rape, white mustard, Indian mustard, Ethiopian mustard, turnip, turnip rape, radish and rocket) and nine legumes (Egyptian clover, crimson clover, common vetch, purple vetch, hairy vetch, pea, soya bean, faba bean, and white lupin) were tested in sole-crop and in 98 bispecific mixtures (substitutive design of 50%-50% density of sole crops). Statistical analysis was performed on four measured variables (shoots+roots): 1) N acquired, 2) S acquired, 3) N mineralised and 4) S mineralised, the N and S mineralization being calculated using respectively C:N and C:S ratios (Eriksen et al., 2004; Justes et al., 2009). Analysis of variance (ANOVA) was used to evaluate effects of site, year and cover crop type (sole crop or mixture) on each variable. Tukey test allowed distinguishing differences among cover crop types for each site-year. Statistical analyses were performed using R software (R Core Team, 2016), and differences among treatments were considered significant at P<0.05.

## RESULTS AND DISCUSSION

Only few differences were obtained between cultivars compared. Consequently results were averaged at the species level and are presented here at the family level for highlighting the main messages.

Crucifer - legume bispecific mixtures provided the same N and S catch crop service (mean soil acquisition of 50 kg N ha⁻<fnref n="1" /> and 12 kg S ha⁻<fnref n="1" />) and significantly increased the N green manure service (mean mineralisation of 22 kg N ha⁻<fnref n="1" />) compared to pure crucifers (mean of 8 kg N ha⁻<fnref n="1" />) (Couëdel et al., 2018a, 2018b). Despite half the density of crucifers, S green manure service was only reduced by 15% in the mixture (mean mineralisation of 5.5 kg S ha⁻<fnref n="1" />) (Couëdel et al., 2018a). On a species basis, despite the high and variable competition for abiotic resources generated by crucifers, no incompatibility of growth was observed in mixtures. Overall crucifers and legumes tested were sufficiently complementary to mutualise multi-ecosystem services by providing level of each service close to that of the best sole cover crop family (Table 1).

<footnote n="1">¹ AGIR, Université de Toulouse, INRA, INPT, INP-EI PURPAN, Castanet-Tolosan, France; ² Chambre Régionale d'Agriculture Occitanie, 31321 Castanet-Tolosan, France; ³ CIRAD, UMR SYSTEM Univ. Montpellier, CIHEAM-IAMM, CIRAD, INRA, Montpellier SupAgro, 34980 Montpellier, France</footnote>
<table><caption>Table 1. Services associated with nitrogen (N) and sulphur (S) cycles for crucifer and legume in sole crop (SC) or in mixture. The service is provided in proportion of the best sole crop family (average of all species tested), which then is considered at 100%.</caption><thead><tr><th>Service</th><th>Mean crucifer SC</th><th>Mean legume SC</th><th>Mean mixtures</th></tr></thead><tbody><tr><td>N catch crop</td><td>100%</td><td>66%</td><td>98%</td></tr><tr><td>N green manure</td><td>18%</td><td>100%</td><td>63%</td></tr><tr><td>S catch crop</td><td>100%</td><td>30%</td><td>99%</td></tr><tr><td>S green manure</td><td>100%</td><td>23%</td><td>85%</td></tr></tbody></table>

## CONCLUSION

Our study confirms for a wide range of cover crop species and on four sites x years that crucifer-legume mixtures tested can provide multi-ecosystem services beyond well-known N management services. Therefore it can be recommended to diversify the species included in cover crops in order to provide a high level of multi-ecosystem services and to secure the success of cover cropping in particular for summer sowing carried out in dry regions or under low rainfall conditions.

**Acknowledgements:** This study was part of the CRUCIAL project, which was financially supported by the French Ministry of Agriculture (CASDAR project no. C-2013-05) and the Occitanie Region (CLE project no. 13053068).

## REFERENCES

* Couëdel, A., Alletto, L., Justes, É., 2018a. Crucifer-legume cover crop mixtures provide effective sulphate catch crop and sulphur green manure services. Plant Soil. doi:10.1016/j.agee.2017.11.017

* Couëdel, A., Alletto, L., Tribouillois, H., Justes, E., 2018b. Cover crop crucifer-legume mixtures provide effective nitrate catch crop and nitrogen green manure ecosystem services. Agric. Ecosyst. Environ. 254, 50–59. doi:10.1016/j.agee.2017.11.017

* Eriksen, J., Thorup-Kristensen, K., Askegaard, M., 2004. Plant availability of catch crop sulfur following spring incorporation. J. Plant Nutr. Soil Sci. 167, 609–615. doi:10.1002/jpln.200420415

* Justes, E., Mary, B., Nicolardot, B., 2009. Quantifying and modelling C and N mineralization kinetics of catch crop residues in soil : parameterization of the residue decomposition module of STICS model for mature and non mature residues. Plant Soil 325, 171–185. doi:10.1007/s11104-009-9966-4

* Matthiessen, J., Kirkegaard, J., 2006. Biofumigation and Enhanced Biodegradation: Opportunity and Challenge in Soilborne Pest and Disease Management. CRC. Crit. Rev. Plant Sci. 25, 235–265. doi:10.1080/07352680600611543
# AUTUMN SOIL MINERAL NITROGEN CONCENTRATION AS POTENTIAL PREDICTOR OF NITROGEN LEACHING

SWARTZ, E. ¹, BØRGESEN, C.D.¹, BLICHER-MATHIESEN, G. ², PIIL, K. ³, VINTHER, F.P. ¹, ØSTERGAARD, H.S. ³

¹ Institute of Agroecology Aarhus University, Denmark; ² Department of Bioscience Aarhus University, Denmark; SEGES, Denmark

## INTRODUCTION

Nitrogen (N) leaching from arable land to ground and surface water has been identified as an environmental problem in Europe (Kronvang et al., 2017). In Denmark, leaching from arable land (54% of the land area) is highly climate dependent and occurs mostly during autumn to early spring, where there is surplus precipitation and low evaporation. Denmark has greatly reduced its N emissions since the late 1980s and is currently transitioning from a national to a targeted regulatory approach (Jabloun et al., 2015; Kronvang et al., 2017). An approach towards targeted regulation of excess N is to consider the soil content of mineralised nitrogen (Nmin) of arable land. This is currently in use in Belgium and Germany for controlling N-inputs in groundwater protection zones. Nmin varies throughout the year; in autumn, it depends on the net mineralization, nitrogen uptake by crops, crop type, soil type and gaseous losses by denitrification.

Since nitrate in soil is highly soluble, it is argued that the residual Nmin (mainly nitrate) in the soil during autumn may be a valuable indicator of the amount of N which can potentially leach during the leaching season in autumn and winter. This project hypothesises that the Nmin in agricultural fields in the autumn can be used as a predictor to quantify the nitrogen that leaches during the leaching season when selected additional parameters are known. It is aimed to develop an empirical model to predict nitrogen leaching from agricultural fields based on Nmin measurements in the autumn and the field management data as supporting input variables ex. soil type, water balance and applied fertiliser. The optimal sample depth and sampling time in autumn to predict leaching will also be investigated. This study is ongoing throughout the duration of this year and it is aimed to present the model and results at the conference.

## MATERIAL AND METHODS

### Experimental sites and treatment

Field management data was collected from eight experiments consisting of 76 fields with different crop sequences in Denmark starting from April 2014 up to March 2017 (three leaching seasons, where a leaching season is from October – March). The experimental sites were located at seven different locations throughout Denmark representing four Danish soil types (JB1, JB3, JB4, JB6) and had an average precipitation of 700mm. Crop sequences consisted of winter cereals (winter barley, wheat, rye), and spring cereals (spring barley, oats) combined with bare soil, volunteers or catch crops during winter. Different fertilisation rates were tested, ranging from 0 to 200% of the recommended N rate were applied.

### Measurements and calculations

Soil mineral content was measured in composite samples taken in a depth of up to one meter. Each drilling was split into four depths (0-25cm, 25-50cm, 50-75cm, 75-100cm) and the samples from each depth was combined to make up four composite soil samples. Sampling occurred over three periods during Autumn; beginning with Round 1 (R1) in early October, Round 2 (R2) in early November and Round 3 (R3) at the end of November. The samples were analyzed for nitrate (NO₃⁻) and ammonium (NH₄⁺). Two monthly soil water samples were taken from ceramic suction cups installed at one-meter depth on each field (four blocks with two suction cups at each place). The soil water was analysed for NO₃⁻ and NH₄⁺.
A water balance and hence the water drained from the soil profile (percolation) was calculated for each experimental plot using the DAISY 5.18 model. The soil water N concentration and percolation was used to calculate the N leaching from October – March for each plot and year.

Statistical and Principle Component Analysis (PCA) were performed in R-Studio (3.4.2) where plots were made using the ggplot and ggbiplot packages.

## RESULTS AND DISCUSSION

Preliminary PCA indicated that soil type and the depth of the sampling was the most important variable influencing the Nmin concentrations. Hereafter, followed the sampling time, fertilization level and sowing time. Higher levels of calculated leaching were observed for similar treatments with sandier soils (JB4) compared to soil with a higher clay content (JB6). Experiments with catch crops showed a clear reduction in excess Nmin in the autumn and calculated seasonal leaching compared to no catch crops during the autumn and winter. For example, Figure 1a illustrates the efficient utilization of the excess Nmin by rye grass (catch crop) compared to the bare soil treatment during autumn following a summer harvest of spring barley. No fertilization treatments were applied to the catch crops, hence, the excess N applied to the spring barley was utilized by the rye grass. Consequently, the calculated leaching (Figure 1b) was also significantly lower with ryegrass compared to the bare soil treatment due to much lower Nmin and thus much lower potentially leachable N present in the soil.

<figure><img src="image_172.png" /><figcaption>Figure 1. Spring barley with ryegrass as a catch crop (triangle) and bare soil (dark dot) as autumn/winter treatment on a JB4 soil in 2016. (a) Sampled Nmin (Round 3 at depth 0-25cm) with increasing fertiliser level and (b) calculated seasonal leaching vs. sampled Nmin (Round 3 at depth 0-25cm).</figcaption></figure>

Early planting (at least 2 weeks prior to normal planting times) of the summer crop also resulted in lower Nmin and consequently lower calculated seasonal N leaching.

## CONCLUSION

The current results clearly indicate that the soil type, use of catch crops and sowing time of winter crops affects the Nmin in the autumn which as a result influences the potential leachable N present in the soil during the leaching season. The next steps in developing an empirical model for predicting seasonal leaching based on Nmin in the autumn will be (i) to establish which Nmin depth best correlates with the calculated leaching; (ii) to quantify the reduction of the different autumn/winter treatments on the calculated leaching. The results also showed that soil type is important for the relationship between Nmin and leaching, therefore (iii) a ratio of the Nmin and normalised leaching should be established for each soil type to be included in the model. Further conclusions of the study will be presented at the conference.

## REFERENCES

Jabloun, M., Schelde, K., Tao, F., Olesen, J.E., 2015. Effect of temperature and precipitation on nitrate leaching
from organic cereal cropping systems in Denmark. Eur. J. Agron. 62, 55–64.

https://doi.org/10.1016/j.eja.2014.09.007

Kronvang, B., Blicher-Mathiesen, G., Windolf, J., 2017. 30 years of nutrient management learnings from Denmark: A successful turnaround and novel ideas for the next generation. (No. 30), Science and Policy: Nutrient Management Challenges for the Next Generation. Massey University, Palmerston North, New Zealand.
# LIVING COVER COMPETITION IN BANANA PLANTATION AND NITROGEN MANAGEMENT AFTER A LEGUME COVER CROP ROTATION

ACHARD, R.<fnref n="1" />, ROSALIE, E. <fnref n="1" />, DOREL M. <fnref n="2" />.

## INTRODUCTION

Intercropping banana with a mown spontaneous cover or sown cover crop represents an alternative to herbicide use for the control of weeds. Intercropping a living cover induces nitrogen competition for the banana during the first crop cycle, then for following cycles the lower growth of the cover and the cycling of nutrient induced negligible competition effects (Achard, 2016). In that case, maintaining crop productivity for the first cycle supposed to compensate the additional nitrogen demand of the living cover by increasing N fertilization. An adaptation of N fertilization program to banana plantation with living covers without increasing N leaching, has been proposed through a modeling approach (Ripoche et al, 2012). The current question is to design herbicide free banana cropping system integrating living cover, in order to compensate the additional nitrogen demand of the living cover and ensuring a sufficient nitrogen availability to maintain banana productivity, without increasing leaching risk. The goal of this study was to evaluate, in the field, the performance of two herbicide free banana management options based on mechanical control of weeds after a previous cover crop, and determine if those options would require an increase of N fertilization and/or would increase nitrogen leaching risk.

## MATERIAL AND METHODS

The study was carried out in Martinique on nitisols, with a mean annual rainfall of 2400mm. Eleven month after sowing *Stylosanthes guianensis*, the cover crop was destroyed by mowing before banana plantation. The field trial compared three management treatments through a randomized block design with three replicates. The first management treatment (MW) combined no tillage, mulched soil with cover crop residues and weed control by mowing. The second treatment (PW) combined a soil tillage with a weed control by mowing and the control treatment (CT) consist in a conventional chemical control of weeds. Banana in vitro derived plantlets were planted with a 2.65 m x 1.5 m spacing, i.e. 4m<fnref n="2" /> per plant. The fertilization corresponded to common producer's practice, and was equal for all treatments: 9 g N per plant at planting and 28 g N per plant at 60 and 120 days after planting (DAP), i.e. a total amount of 110 kg N.ha⁻<fnref n="1" /> during this study. For P and K, the fertilization ensured a non-limiting nutrition of the banana plants. Each experimental plot consisted of six banana plants, surrounded by a border. The weed biomass was evaluated by destructive observations on 0.25 m<fnref n="2" /> area per plot, randomly localized. The weed biomass was negligible on CT. The banana growth was estimated through the pseudostem girth and the leaf emission, the biomass of banana plants was estimated by an allometric relation. The relation between %N content and the banana biomass from Thieuleux (2006) was used.

## RESULTS AND DISCUSSION

### Cover crop production and weed growth

At 30 days after planting (DAP), despite the mulch of *S. guianensis*, MW had significantly higher weed biomass in comparison to PW (130 g/m<fnref n="2" /> vs 50 g/m<fnref n="2" />), meaning that mulching with *S. guianensis* is less efficient than soil tillage for controlling weed emergence. At 100 DAP, just before the first mowing, weed biomass remained higher on MW treatment than on PW with 0.88 and 0.44 kgDM/m<fnref n="2" /> (corresponding to 18 and 9 gN/m<fnref n="2" />), respectively.

### Banana growth response

Banana plants had similar growth response in CT and PW (cf. table1) until 100 DAP. However, at 160 DAP on PW, banana plants had lower growth and leaves emission. As CT and PW managements distinguished only according to their mowing management of weed, the depressive effect of MW reflected the weed competition effect. In

<footnote n="1">¹ GECO, CIRAD UPR GECO, Campus Agroenvironnemental de la Martinique, 97232 Le Lamentin, Martinique, France;</footnote>

<footnote n="2">² CIRAD UPR GECO, Neufchâteau, 97130 Capesterre, Guadeloupe, France.</footnote>