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Modelling the surface of a paddock affected by urine deposition by dairy cows during grazing
C.A. Paillette, Luc Delaby, Laurence Shalloo, D. Hennessy, D. O’Connor
To cite this version:
C.A. Paillette, Luc Delaby, Laurence Shalloo, D. Hennessy, D. O’Connor. Modelling the surface of a paddock affected by urine deposition by dairy cows during grazing. Agricultural Research Forum 2013, 2013, Tullamore, Ireland. Agricultural Research Forum, 2013, Proceedings of the Agricultural Research Forum. �hal-01210847�
Modelling the surface of a paddock affected by urine deposition by dairy cows during grazing
C.A. Paillette1,2, L. Delaby3, L. Shalloo1, D. O’Connor2 and D. Hennessy1
1Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork; 2Cork Institute of Technology, Bishopstown, Cork; 3INRA, AgroCampus Ouest, UMR 1348, PEGASE, F-35590 Saint-Gilles, France.
Introduction
Nitrogen (N) fertilizer is highly effective at increasing grass growth (Murphy, 1977) and hence farm productivity, but it also contributes to nitrate (NO3-) leaching to groundwater (Decau and Delaby, 1997). In grazed grass swards, urine deposition from grazing animals augments the N added through fertilization, increasing N availability for grass growth, but also the losses of N (leaching, volatilization of ammonia (NH3)) on a small portion of a paddock surface (urine patch).
Traditional soil models (STICS (Brisson et al., 2003);
SOIL-N (Jansson, 1991)) dilute urine patches across the whole paddock. This study models the heterogeneity of urine deposition on a paddock in order to avoid diluting the effects of urinary N deposition.
Materials and Methods
In the present study the calculation of the surface area affected by urine deposition during any grazing day depends on the number and type of animals grazing.
The effects of variables such as diet or weather on the number of daily urinations per cow or on the surface area affected by urine were not investigated in this study. Each animal type has a specific number of urine depositions per day, which affect a specific area of the paddock. It is assumed in this model that within any given grazing event urine deposition will be random and without overlap. Overlap can occur between grazing events. Figure 1 illustrates the areas influenced by urine deposition over three grazing rotations (not to scale).
Each surface has homogeneous soil N concentration.
Fig. 1. Schematic representation of area covered by urine during three successive grazing events (areas not to scale).
Each urine deposition is randomly located. The probability of overlap of depositions from two grazing events depends on the size of the first affected surface.
The larger the surface, the higher the probability of overlap. This model uses a mean number of urinations on the paddock per cow per day of 10 (Decau and Delaby, 1997; Dennis et al., 2011). The surface affected by each urine deposition varies in the model (as in the
literature), from 2 m² (Decau and Delaby, 1997) to 0.81 m² (Dennis et al., 2011).
The model was evaluated by comparing the predictions of the model with the area affected by urine as reported by Dennis et al. (2011). The assumptions used for the comparison were area affected by urine deposition of 0.81 m², 7.5 urine depositions cow-1 day-1 at a stocking rate of 2.5 cow ha-1.
Results and Discussion
A simulation was run over a 160 day period. The surface area affected by urine deposition was set at 2 m². There were three grazing rotations with numbers of days of animal residence in the paddock of 4, 6 and 4 days for the 1st, 2nd and 3rd rotations, respectively. The stocking rate was 2,5 cow ha-1 year-1. At the end of the 160 days, 33% of the paddock area was unaffected by urine deposition, 8.24% was affected only in first rotation, 18.76% was affected during second rotation only, 8% was affected during the first and second rotations, 12.88% was affected during the third rotation only, 3.88% was affected during first and third rotations, 7.36% was affected during second and third rotations, and 7.88% was affected during all three rotations.
The same model, using an area of 0.81 m² affected by urine deposition as reported by Dennis et al. (2011) (less diffusion of urine in the soil) and 7.5 urine depositions cow-1 day-1 at a stocking rate of 2.5 cow/ha, predicts a cumulative affected surface of 24%, while Dennis et al. (2011) measured a value of 23.7%.
Conclusion
The model allows the partitioning of a grazed paddock into several surfaces depending on the quantity of urinary N received throughout a year. Therefore, a more precise account can be made of the total paddock surface area influenced by urinary N per year allowing more accurate modelling of N within a paddock.
References
Bergström, L., Johnsson, H. & Torstensson, G. (1991).
Nutr Cycl Agroecosys; 27: 181-188
Brisson,N., Gary, C., Justes, E., Roche,R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., Bussière, F., Cabidoche, Y.M., Cellier, P., Debaeke, P., Gaudillère, J.P., Hénault, C., Maraux, F., Seguin, B., Sinoquet, H. (2003). Eur J Agron; 18 : 309- 332 Decau, M.L. & Delaby, L. (1997). Fourrages; 151 : 297-311.
Dennis, S.J., Moir, J.L., Cameron, K.C., Di, H.J., Hennessy, D. & Richards, K.G. (2011) Irish J. of Agri.
and Food Res., 50: 149-160.
Jansson, P.E. (1991). Science, S.U.o.A.
Murphy, W.E. (1977). Proc. International meeting on animal production from temperate grassland; 1: 116- 120.
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