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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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References Andrews, M., Scholefield, D., Abberton, M.T., McKenzie, B.A., Hodge, S. and Raven, J.A. 2007. Use of white clover as an alternative to nitrogen fertilizer for dairy pastures in nitrate vulnarable zones in the UK: productivity, environmental impact and economic consideration. Annals of Applied Biology 151, 11-23 Humphreys, J., O’Connell, K. and Casey, I. A. 2008. Grass and Forage Science 63, 467–480 Humphreys, J., Casey, I. A. and Laidlaw, A. S. 2009. Irish Journal of Agricultural and Food Research 48, 189-207 Schils, R.L.M., Verhagen, A., Aarts, H.F.M., Kuikman, P.J. and Šebek, L.B.J. 2006. Effect of improved nitrogen management on greenhouse gas emissions from intensive dairy systems in the Netherlands. Global Change Biology 12, 382-391.

van Dorland, H.A., Wettstein, H-R, Leuenberger, H. and Kreuzer, M. 2007. Effect of supplementation of fresh and ensiled clovers to ryegrass on nitrogen loss and methane emission of dairy cows. Livestock Science 111, 57-69.

Nitrogen Workshop 2012

Integrated assessment of nutrient management options in the food chain of China L. Maa,d*, F.H. Wanga, W.F. Zhanga, W.Q.Mab, G.L. Velthof c, W. Qind, O. Oenemac,d, F.S. Zhanga a Department of Plant Nutrition, China Agricultural University, Key Laboratory of Plant-Soil Interactions, Ministry of Education, Beijing 100094, P. R. China ; b College of Resources and Environmental Sciences, Agricultural University of Hebei, Baoding, 071001, China; c Alterra, Wageningen University, Wageningen, P.O. Box 47, 6700 AA, the Netherlands; d Department. of Soil Quality, Wageningen University, Wageningen, P.O. Box 47, 6700 AA, The Netherlands *Presenting author: lin.ma@wur.nl

1. Background & Objectives Nitrogen (N) and phosphorus (P) costs of food production have greatly increased in China during the last 30 years (Ma et al., 2012). Forecasts suggest that the food demand is rapidly increasing further during the coming decades, and that the N and P costs of the food produced will also increase further. However, these forecasts are not based on rigorous quantitative assessments, taking into account various possible options for a more sustainable nutrient management in the food chain at national and regional levels. Here, we present the results of a scenario analysis for improving nutrient management for the year 2030.

2. Materials & Methods NUFER is a model developed to calculate the flows, use efficiencies, and emissions of N and P in the food chain of 31 regions in China on an annual basis (Figure 1). It uses a mass balance approach with detailed accounts of the partitioning of N and P inputs and outputs, and of N and P losses via NH3 and N2O emissions, denitrification and N and P leaching, runoff and erosion. The N and P use efficiencies of crop production (NUEc, PUEc), animal production (NUEa, PUEa), and food chain (NUEf, PUEf) were defined by the ratio of N and P output in main products and the total input (Ma et al., 2010). Here, mean results for the whole of China are presented.

Fertilizer and manure (Tg)

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The five scenarios for 2030 were as follows: (i) Business-as-usual (BAU); (ii). Balanced N and P fertilization in crop production (BNFc); (iii) Balanced N and P feeding in animal production

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(BNFa); (iv) Improved manure management (IMM), and (v) Integrated nutrient management (INM = BNFc + BNFa + IMM).

3. Results & Discussion Figure 2 presents the use of fertilizer and manure N and P in crop production in 2005 and in 2030 for the 5 scenarios. Figure 3 shows the losses of N and P from the food chain, and Table 2 shows the NUE and PUE in crop production, animal production and the whole food chain in 2005 and in 2030 for the 5 scenarios, respectively. Increases in N and P fertilizer use and in N and P losses in the BAU scenario were large relative to 2005. Scenarios BNFc, BNFa, IMM and INM were all effective in decreasing N and P losses, and in increasing NUE and PUE, but in different degrees.

leaching, runoff and erosion NOx N2 N2O NH3 N losses (Tg)

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4. Conclusion Implementation of a package of integrated nutrient management measures (INM) would more than nullify the expected increases in estimated losses in the BAU scenario, and would greatly increase NUE and PUE in the whole food chain.

References Ma, L., Ma, W. Q., Velthof, G. L., et al., 2010. Modeling Nutrient Flows in the Food Chain of China. Journal of Environmental Quality 39(4), 1279-1289.

Ma, L., Velthof, G. L., Wang F. H., et al., 2012. Nitrogen and phosphorus use efficiencies and losses in the food chain in China at regional scales in 1980 and 2005. Science of total environment (in press).

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Carbon footprint of Irish milk production Yan, M.-J.a, Humphreys, J.b and Holden, N. M.a a UCD School of Biosystem Engineering, University College Dublin. Belfield, Dublin 4, Dublin, Rep. of Ireland b Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co Cork, Rep. of Ireland

1. Background and objectives There is an established concern about the effect of greenhouse gas (GHG) emissions on global climate change. In Ireland, agriculture is the single largest contributor to overall emissions at 26% (EPA, 2010). A holistic method, Life Cycle Assessment (LCA), can better reveal the environmental impacts of an agricultural system. The LCA interpretation of GHG emissions is also referred to as carbon footprint (CF). CF of milk production has been carried out on commercial farms in Europe either with statistics or with once-off surveys, where the farm-gate turnover was used, and the main theme was to compare production mode (i.e. organic Vs. conventional). However, it can be argued that production mode may not necessarily indicate higher or lower CF and multiple management strategies may need to be adopted by farmers according to their own circumstances. The objective of this paper was to estimate the CF of Irish milk production at commercial dairy farms according to methodology defined by ISO 14040.

2. Materials & Methods The LCA model was developed with MS® EXCEL 2007. The system boundary was set at the farm gate and included relevant pre-farm processes (production and transportation of fertilizer and concentrate feed) and the on-farm production. Soil carbon sequestration, pesticides, medicine, plastic sheets etc. were not included. In order to exclude other enterprises (e.g. cattle rearing) on the commercial farms and only include the “dairy-unit”, a self-defined proportionate rule was performed. This was done by 1) converting all animals into livestock unit (LU) equivalence according to the ratio of nitrogen excretion against a dairy cow as defined in Statutory Instruments No. 610 (2010); 2) assuming the dairy herd consisted of dairy cows + replacement animals + bulls or suckler cows (if any), deriving the proportion factor of dairy herd as dairy LU/total LU; and 3) excluding from the farm GHG inventory the GHG associated with electricity production, which was predominantly used by dairy herd, and multiply the rest with the proportion factor, and then adding up GHG associated with electricity production to derive the dairy unit GHG. Proportionate, economic allocation between milk and meat was performed based on farm sale records.

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Large variation among farm management was found. For example, stocking rate varied from 1.5 to

2.8 LU ha-1, fertilization rate from below 150 to above 250 kg N ha-1. However, the CF of milk among the farms only had a CV of 13%, with an average of 1.23 kg CO2 eq kg ECM-1.

Geographically, 80% of the total GHGs were from on-farm. The single largest contributor was enteric fermentation (43%), followed by excreta deposition and fertilizer spreading (both 11%), manure storage and fertilizer production (both 10%), concentrate feed production (6%), manure spreading (4%), electricity production (3%), field work and transportation (both 1%). These were in general agreement with previous studies on commercial farms with small scale survey (Casey and Holden, 2005a) and national scale statistics and modelling (GGELS, 2010). CF was found to be correlated with milk per cow, economic allocation factor and on-farm diesel use, but not with other parameters such as concentrate per cow, fertilization rate, electricity use per kg milk (Fig 1).

Figure 1. Relationship between milk CF and Left: milk ouptut per cow (r2 = 0.

43, p 0.001), middle: economic allocation factor (r2=0.36, p 0.001); right: on-farm diesel use (r2 = 0.25, p 0.05).

4. Conclusions It was concluded that a combination of multiple strategies would determine CF of milk production on commercial dairy farms, and one of the most important indicators was milk output per cow. The effect of the proportionate rule on CF needs to be further analysed.

References Casey, J.W. and Holden, N.M., 2005a. Analysis of greenhouse gas emissions from the average Irish milk production system. Agricultural Systems 86, 97-114.

EPA (Environmental Protect Agency), 2010. Ireland's national inventory report for 2010. EPA, Johnstown Castle, Wexford, Ireland.

GGELS, 2010. Evaluation of the livestock sector’s contribution to the EU greenhouse gas emissions. Joint Research Centre, European Commission.

Statutory Instruments No. 610, 2010. European Communities (Good agricultural practice for protection of waters) regulations 2010. The Statutory Office, Dublin, Ireland

Nitrogen Workshop 2012

Effect of timing of the first nitrogen fertilizer application on yield of winter wheat in Ireland Efretuei, A.a, Gooding M.a, White E.b, Hackett R.c, and Spink J. c a School of Agriculture Policy and Development, University of Reading, Reading UK, bAFBI Crossnacreevy Plant Testing Station Crossnacreevy, Belfast, UK, cTeagasc Crop Research Centre Oak Park Carlow, Co. Carlow, Rep. of Ireland

1. Background & Objectives To improve the nitrogen use efficiency (NUE) in winter wheat, appropriate fertilizer management practices must be adopted. According to Moll et al. (1982) NUE is the amount of grain produced for each unit of nitrogen (N) available to the crop. Moll et al. (1982) expressed NUE as a product of two components; the nitrogen uptake efficiency which studies how efficiently the plant takes up nitrogen supplied to it, and the nitrogen utilization efficiency which examines how efficiently the plant utilizes or channels the nitrogen taken up for grain production (Moll et al., 1982; Moll et al., 1987). NUE is influenced by the rate and time of application and applying N fertilizers only when required by the plant may improve the nitrogen uptake and utilization efficiency. The objective of this study was to improve the NUE of winter wheat sown in Ireland by estimating the appropriate time for first N application.

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Slurry was applied at 22 m3 ha-1 one day before sowing in autumn 2010. Slurry treatments were introduced to determine whether the effect of N timing was influenced by soil and/or crop N amount at the onset of spring growth. Two seed rates were used to create a high and low tillering pattern (Darwinkel, 1978). The size of each split-plot was 24 x 2.15 m. The variety of wheat used was Cordiale. Grain yield was determined using a combine harvester. Subsamples were taken for moisture content determination and yield expressed at 85% dry matter. Results were analysed using the linear mixed model analysis in GENSTAT version 13 (VSN international Ltd). The model included slurry (S), N timing, (NT), seed rate (SR) and the interactions S x NT, S x SR, NT x SR, and S x NT x SR. Treatment means were separated using the Fischer’s LSD (p0.05).

Nitrogen Workshop 2012

3. Results & Discussion There were no significant two-way or three-way interactions between N timing, seed rate and slurry treatment. N timing and seed rate treatments had a significant effect on yield (p0.001) but slurry application had no significant effect on yield. The lack of a slurry effect on yield might have been due to N losses over the winter period. Compared to all other N timing treatments, applying the first application of N at GS 30 gave a significantly higher yield (p0.05). Grain yields where first N application was at GS 24 or GS 31 were not significantly different. There was a significant reduction in yield where the first N application was delayed until GS 32 compared to applying first N at GS 31(p0.05). There was a further significant decrease in yield when first N application was delayed until GS 37 compare to GS 32 (p0.05). The lowest yield was observed in the control treatment (zero N application). The high seed rate treatment had a significantly higher yield than low seed rate treatment (p0.001).

Yield (t/ha)

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Figure 1. Effect of 1st N application on grain yield showing growth stages for 1st and 2nd split applications respectively and standard error bars.

t/ha=tonnes/hectares. Data are averaged over seed rate and slurry treatments.

4. Conclusions Application of first N fertilizers at GS 30 appears to have a beneficial effect on final grain yield compared to earlier or later timings. Since all treatments received the same amount of fertilizer N, the results indicate that NUE is decreased when the first N application to winter wheat is made earlier or later than GS 30. The results also indicate that the appropriate timing for the first N application to winter wheat is not dependent on plant population density.

Acknowledgements We acknowledge the Teagasc Walsh Fellowship Scheme for financial support.

References Darwinkel, A. 1978. Patterns of tillering and grain production of winter wheat at a wide range of plant densities, Netherlands Journal of Agricultural Science 26, 383-398.

Moll, R.H., Kamprath, E.J. and Jackson, W.A. 1982. Analysis and interpretation of factors which contribute to efficiency of nitrogen utilization, Agronomy Journal 74, 562-564.

Moll, R.H., Kamprath, J.E. and Jackson, W.A. 1987. Development of nitrogen-efficient prolific hybrids of maize, Crop Science 27, 181-186.

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Effect of Organic and Inorganic Nitrogen fertilizer and Plant Densities on Yield and Quality of Sugar beet Eman.I.R.E.a,.Mahmoud, E.A.a, Hassanin, M.A.a a Agronomy Deparment, Faculty of Agricultural, Cairo university.

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