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«International 17 Workshop th Nitrogen The was jointly organised by Teagasc and AFBI Printed by Print Depot Suggested citation Authors, 2012. Title ...»

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The modelling demonstrates a clear effect of soil texture and rainfall on relative N2O emissions. Hence, due consideration is required of the soil texture–rainfall zone combination when assessing the currently available emission factors from different N sources, and the ‘gap filling’ required by additional experimental measurements.

4. Conclusions As a result of this process, i.e. having taken account of a) the land area under different soil texture–rainfall zones, b) the sensitivity of N2O soil EFs to soil texture and rainfall (via the DNDC modelling), and c) an improved stock-take of existing and planned experiments which will deliver IPCC compliant EFs, we were able to confirm the geographical locations of the proposed sites across England, Northern Ireland, Scotland and Wales.

The choice of N source (urine, dung, livestock manure and fertiliser) to apply at these nine experimental sites needed to reflect the major sources of N2O identified by the current UK N2O inventory, and be representative of the geo-climatic zones. The experimental treatments

at each experimental site were chosen to:

 generate new (gap filling) EFs for the typical range of N sources (fertiliser N type, manure type, urine and dung)  provide additional 12-month N2O flux data sets for a range of soil/climate/N management combinations for model validation and assist future model interpolation  provide an understanding of the relationship between N application rates and N2O EFs  determine the effect of N application timings on the N2O EF  explore mitigation methods which could be included in the new inventory structure (e.g. split doses of mineral N fertiliser and use of nitrification inhibitors)  generate EFs that future proof the improved inventory for potential ammonia emission mitigation, e.g. use of low trajectory slurry application techniques.

    149 Nitrogen Workshop 2012 Model estimation of nitrogen leaching under derogation measures on organic nitrogen fertilization in Lombardia (northern Italy) Perego, A.a, Bernardoni, E.b, Carozzi, M.a, Giussani, A.a, Brenna, S.b, Acutis, M.a a Department of Plant Production, University of Milan, Italy b Lombardia Regional Agency for Agriculture and Forestry, Italy

1. Background & Objectives The derogation approved by the European Commission for the Italian Nitrate Vulnerable Zones (NVZ) located in the Po plain contains, among others, two main measures related to N management: (i) the autumn distribution of manure should be reduced, in order to minimize nitrogen losses, (ii) derogation farms will be required to improve manure management adopting long growing season and high nitrogen uptake cropping systems, including in particular winter and summer herbage, after maize and winter cereal harvest, respectively. The objective of this paper was to evaluate nitrate leaching under 3 alternative scenarios of cropping systems by applying ARMOSA simulation model (Acutis et al., 2007) in the entire plain area of Lombardia region. One of the studied scenarios was defined according to the outline of the derogation decision.

2. Materials & Methods The ARMOSA model ran over 20 years (1988-2007) in 35 simulation units, obtained by dividing Lombardia plain in homogenous districts in terms of pedological, climatic and cropping systems features located in both Nitrate Vulnerable Zones (NVZs, 22 districts) and non-Nitrate Vulnerable Zones (nNVZs, 13 districts). Each district was characterized by (i) two representative soil types, (ii) a 20 years meteorological data set, (iii) crop rotations according to the regional land use analysis, (iv) organic N load, calculated on the basis of livestock density. Three scenarios have been then defined for districts laying in NVZs: (i) an hypothetical scenario with no limitation in organic N application (1), (ii) a scenario compliant with the mandatory threshold of 170 kg organic N ha-1y-1 (2) provided by the Nitrate Directive (676/91/CE), (iii) a scenario in which N organic threshold was enhanced to 250 kg N ha-1y-1 (3) according to the Italian derogation outline. Under 1 scenario organic-N supply was defined on the basis of district load and mineral-N was 100 to180 kg N ha-1yaccording to the crops need. In 2 organic-N was 170 and mineral-N up to 180 kg N ha-1y-1. Under 1 and 2 scenarios, both autumn and spring application of organic-N were simulated. In 3 organic-N was limited to a maximum of 250 kg N ha-1y-1, which was applied only in spring, and mineral N input was up to 100 kg N ha-1y-1. The 5-years rotations were: A (monoculture of FAO 600 maize), B (permanent grass), C (alfalfa -grain maize-winter wheat), D (grain maize-winter wheat), E (grain maize-grass), F (alfalfa-winter wheat), G (alfalfa-winter wheat), H (FAO 500 maize-Italian ryegrass as autumn sown crop), L (grain maize-winter wheat-foxtail millet as summer herbage). The two latter rotations were simulated only under 3 scenario, being defined according to the derogation outline. The model was calibrated for both maize silage and grain crops, Italian ryegrass and winter wheat in monitoring sites (Lombardia plain), whose description is given by Perego et al. (2011).

3. Results & Discussion Mean N leaching amount were 37, 22 and 14 kg N ha-1y-1 under 1, 2 and 3, respectively. ANOVA test confirmed the statistically significance of scenario factor in determining N leaching (p0.0001).





Games-Howell post-hoc test has confirmed that each scenario differed statistically to others (1 vs 2 p0.0001, 1 vs 3 p0.0001, 2 vs 3 p=0.035). On average, N leaching decreased by 27% from 1 to 2, and by 59% from 1 to 3. B (permanent grass) and F (alfalfa-maize-wheat) rotations resulted to be

–  –  –

4. Conclusions The ARMOSA simulation results indicated that the 3 scenario appeared a good solution to face the current concern of N leaching in Lombardia plain, in fully agreement with derogation outline. Grain maize crops, as well as silage maize in a double-cropping systems with Italian ryegrass showed in particular an high N uptake; similarly, summer herbage after winter wheat harvest lowered nitrogen losses even in the case of organic fertilizers application at planting in summer. The increasing organic N supply and proportionally reduced mineral fertilization allowed for similar or even higher nitrogen uptake and lower leaching.

References Acutis M., Brenna S., Pastori M., Basile A., De Mascellis R., Bonfante A., Manna P., Perego A., Fumagalli M., Gusberti D., Velardo M.C., Trevisiol P., Sciaccaluga M., Albani G., Malucelli F., Vingiani S. and Orefice, N. 2007.

Modelling water and nitrogen dynamics Nitrogen in Lombardy – ARMOSA project. Regione Lombardia quaderno della ricerca n. 65, 128 pp.

Perego A., Basile A., Bonfante A., De Mascellis R., Terribile F., Brenna S. and Acutis M. 2011. Nitrate leaching under

maize cropping systems in Po Valley (Italy). Agriculture, Ecosystems and Environment, doi:

10.1016/j.agee.2011.06.014.

Nitrogen Workshop 2012 Modelling the effects of temporal overlap of urine patches on nitrogen leaching Cichota, R.a, Snow, V.O.b a AgResearch, Grasslands Research Centre, Palmerston North, New Zealand b AgResearch, Lincoln Research Centre, Christchurch, New Zealand

1. Background & Objectives In pastoral systems the uneven return of nitrogen (N) via urine is the major source for N leaching losses because the amount of N in urine patches is typically in excess of the plant’s ability to take it up. The amount and timing of deposition are important factors defining the N fate in urine patches (Ledgard, 2001) and must be considered when modelling pastoral systems (Hutchings et al., 2007;

Snow et al., 2009). The overlap of urine depositions is considered a potentially important issue as it can significantly alter the N load in the soil. N losses from spatial overlaps, those occurring in the same grazing day, can be quite large, but the contribution to losses over the whole paddock seems to be small, unless the stocking rate is very high (Pleasants et al., 2007). The likelihood of overlaps increases for patches deposited in subsequent grazings. We call these temporal overlaps. This is when model complexity increases rapidly and simplification is needed. The objective of this work was to investigate the extent to which temporal overlaps affect N leaching from urine patches and to test possible ways to simplify their description in modelling simulations.

2. Materials & Methods Simulations of a ryegrass/white clover sward were constructed using the APSIM model (Keating et al., 2003) and were successfully tested against leaching experiments (e.g. Cichota et al., 2010). The simulations used here describe the overlap of two consecutive urine depositions separated by time lags varying between 1 and 240 days. These used depositions of many years and months and N amounts. Here we present data from simulations with 500 kg N ha-1 depositions, the first occurring either in March (Autumn) or September (Spring). Weather and soils from two locations in New Zealand were used: Ruakura (1164 mm rain/yr) was paired with the Horotiu Silt Loam (well drained allophanic, with 95 mm of plant-available water; PAW) and the Atiamuri Sandy Loam (well drained pumice, PAW=115 mm); and Lincoln (634 mm rain/yr) paired with the Templeton Silt Loam (well drained alluvial, PAW=90 mm) and the Lismore Silt Loam (well drained stony, PAW=65 mm). The simulations were under centre-pivot irrigation and a fertiliser regime of 250 kg N ha-1 yr-1. Two parallel simulations were run, one with the overlap explicitly simulated and the second aggregating the two urine depositions into the time of the second deposition. N leaching was summed for three years after the first urine deposition. The difference between the two simulation runs were used to investigate the effect of aggregating urine deposition over time rather than running the two depositions explicitly.

3. Results & Discussion The temporal overlap of urine depositions clearly increased N leaching as compared to single deposition, but the effect decreased as the time lag between depositions increased (Figure 1).

Location and time of deposition were the most important factors for this variation. The deviation between simulations with explicit and aggregated urine depositions showed wide variation, and generally increased as the lag between depositions increased (Figure 2). It also showed substantially higher deviations when total leaching was low (e.g. Spring). For very short time periods (one to ten days) the error produced by aggregating the depositions was small (10%). The deviations were still relatively small (20%) for lags up to 90 days and therefore might be considered sufficient for

Nitrogen Workshop 2012

granting the simplification. Exceptions like Ruakura-Spring happened when N leaching was low.

For systems with high propensity for leaching (e.g. shallow soils) the aggregation error was small because the deposited N was leached regardless how the overlap was described. For systems where the pasture had high potential to take up the deposited N, the description of urine overlaps should be explicit as the deviation increased sharply with increasing the lag between depositions. The time of urine deposition is therefore the most important factor defining whether aggregation of depositions is possible. The presence or absence of irrigation can also be important as it alters N use efficiency.

–  –  –

4. Conclusion This work highlights the importance of accounting for urine patches in grazing simulations. Overlap of urine depositions in the short-term can be aggregated into a single deposition. Aggregation can result in considerable errors for depositions in different grazings, but might be an alternative when simplification is really needed. Based on the simulations, the time of urine deposition is the most important factor defining whether aggregation can sensibly be used.

Acknowledgements This project was funded by MSI under DairyNZ’s “Dairy Systems for Environmental Protection”.

References Cichota, R., Vogeler, I., Snow, V.O. and Shepherd, M. 2010. Describing the fate of high dose nitrogen in pastoral soils Modelling N leaching under high N loads (urine patches). 19th World Congress of Soil Science, Brisbane, Australia.

Hutchings, N.J., Olesen, J.E., Petersen, B.M. and Berntsen, J. 2007. Modelling spatial heterogeneity in grazed grassland and its effects on nitrogen cycling and greenhouse gas emissions. Agr. Ecosyst. Environ. 121, 153-163.

Keating, B.A., Carberry, P.S., et al. 2003. An overview of APSIM, a model designed for farming systems simulation.

Eur. J. Agron. 18, 267-288.

Ledgard, S.F. 2001. Nitrogen cycling in low input legume-based agriculture, with emphasis on legume/grass pastures.

Plant Soil 228, 43-59.

Pleasants, A.B., Shorten, P.R. and Wake, G.C. 2007. The distribution of urine deposited on a pasture from grazing animals. J. Agric. Sci. 145, 81-86.

Snow, V.O., Johnson, I.R. and Parsons, A.J. 2009. The single heterogeneous paddock approach to modelling the effects of urine patches on production and leaching in grazed pastures. Crop and Pasture Science 60, 691-696.

Nitrogen Workshop 2012

N availability from pre-treated chicken and goat manure in an organic cropping system Willekens, K.a, Reubens, B. a, Vandecasteele, B.a, Beeckman, A.b, Delanote, L.b, De Neve, S.c a Institute for Agricultural and Fisheries Research, (ILVO), Plant Sciences Unit, Crop Husbandry and Environment, Merelbeke, Belgium b Inagro, Department of Organic Farming, Rumbeke-Beitem, Belgium c Ghent University, Faculty of Bioscience Engineering, Department of Soil Management, Ghent, Belgium

1. Background & Objectives Organic farmers are used to applying animal manure from different origin, and the recycling of this manure is needed to close nutrient cycle as much as possible. Improving manure product quality is another possibility to facilitate organic manure to find its way from one organic farm to another.



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