Modelling nitrogen leaching from overlapping urine patches
Highlights
► Overlapping urine depositions can affect significantly the estimates of N leaching. ► APSIM simulations are used to study the effect of the interval between depositions. ► Overlaps separated by less than 20–75 days can be simulated as a single deposition. ► Overlaps separated by more than 150–180 days can be considered independent. ► These recommendations apply to soils and climates similar to those in New Zealand.
Introduction
Urine deposited by grazing ruminants is a major contributor to the high heterogeneity in soil nitrogen conditions found in pastoral systems (Haynes and Williams, 1993; Bogaert et al., 2000; McGechan and Topp, 2004; Hutchings et al., 2007). The areas affected by urine depositions receive much larger quantities of nitrogen (N) than the remaining area, typically from 500 to 1000 kg N/ha for dairy cows (Haynes and Williams, 1993). Such quantities are far in excess of the pasture's ability to take N up before leaching occurs and thus urine patches are the major sources for N loss in grazing systems (Ball and Ryden, 1984; Di and Cameron, 2002; van Groenigen et al., 2005). The N load and the timing of deposition have been shown to be important factors defining the fate of N in urine patches (Cuttle and Bourne, 1993; Shepherd et al., 2011; Snow et al., 2011).
The heterogeneity arising from the deposition of urine patches complicates the measurement of N loss from a grazed paddock (Lilburne et al., 2012). The high spatial variability makes it difficult and costly to quantify N loss from pastoral systems and means that estimates of N loss from grazed paddocks are often associated with large uncertainties. These large uncertainties are a cause for controversy when defining environmental policies and evaluating the efficacy of mitigation actions. Simulation modelling can be an effective adjunct to experimental methods for monitoring N cycling and estimating N losses. Computer models of various levels of complexity have increasingly been used to estimate N losses worldwide. Most of these models, however, do not account for field heterogeneity. This is mainly because the complexity of the modelling setup required to simulate heterogeneity presents a technical challenge in terms of computing resources (Addiscott, 1995; Hutchings et al., 2007; Wang, 2008; Romera et al., 2012). Furthermore, the importance of heterogeneity and methodologies for the use of information with uncertainty for decision making has not been well established (Beven, 2002; Lowell, 2007).
It has been shown that for estimating N leaching from systems with grazing animals it is necessary to account explicitly for the effects of urine patches (Ryden et al., 1984; Haynes and Williams, 1993; Snow et al., 2009). However, accounting for the heterogeneity created by urine depositions is not trivial. Describing such a system within models places a high demand on computing resources and has been attempted only with considerable simplifications (McGechan and Topp, 2004; Hutchings et al., 2007; Snow et al., 2009), Apart from the variability created by single urine depositions, urine overlaps can further alter the load of N deposited onto the soil and so are potentially important for defining the amount of N leached (Pleasants et al., 2007). Overlapping urine depositions on the same grazing day are likely to be minor because these overlaps typically represent a very small fraction of the grazing area (Shorten and Pleasants, 2007). However, a urine deposition affects the soil and the plants for some length of time, and leaching from a second, overlapping, urine patch deposited some weeks or months after the first deposition will be affected by the first deposition. These temporal overlaps are likely to be more significant than those occurring within the same grazing day because the probability of the overlap and therefore the area affected greatly increases. For example, Pleasants et al. (2007) using a statistical approach estimated that, at a typical 24-h grazing density of 100 cows per ha, less than 1% of the area grazed would be affected by overlapping urine patches. Considering that there would typically be about 14 grazings per year per paddock, and that in winter the animals could be mobbed with stocking densities of up to 1000 cows per ha, the proportion of a paddock that will experience delayed overlaps rises considerably. Over one year, urine depositions affect about a quarter of the area for a typical dairy farm (Pleasants et al., 2007; Moir et al., 2011) and overlaps can represent 20% of this, according to the statistical approach of Pleasants et al. (2007). Accounting for all temporal overlaps would result in a rapid increase in modelling complexity and thus simplifications are needed. While these issues are of most interest for intensively grazed systems where the animals graze the pastures year round, the principles are applicable to grazing situations in general.
The purpose of this study was to investigate options for simplifying the representation of overlapping urine patches in the simulation modelling of grazed systems. While the work here is presented in the context of New Zealand soils and climates, the concepts are generally applicable to any grazed system. We used outputs from the simulation model Agricultural Production Systems Simulator (APSIM) to identify the nature of the interactions between successive urine depositions and how this interaction is affected by the interval between depositions. We hypothesise that when the delay (Td) between overlaps is short, the first deposition can be delayed and aggregated with the second deposition without significant error; we call this the Delayed Representation (DR). At higher values of Td, urine patches become functionally independent because the N from the first urine patch is depleted before the second is deposited and in this case, the urine patches can be considered separately with an Independent Representation (IR). We test these simplifications by comparing simulations employing DR and IR against simulations where the overlapping urine patches are considered explicitly (Explicit Representation, ER) in the same simulation. We analyse whether it is possible to define general rules for simplifying the description of urine patch overlaps using DR and IR.
Section snippets
The APSIM model
The APSIM modelling framework (Keating et al., 2003; Holzworth et al., 2010), version 7.3, was used to simulate N leaching from urine depositions in a pastoral system. The primary modules that are relevant for this work are described briefly below. SWIM (Verburg et al., 1996) models soil water dynamics using the Richards' and convection–dispersion equations with a Freundlich isotherm to simulate solute adsorption onto the soil particles. Soil N (Probert et al., 1998) calculates the C and N,
Impact of urine depositions on pasture and soil
Urine depositions affect both pasture and soil conditions, with the effects depending on many factors including the time of deposition. Fig. 2 shows the effect of single depositions in March (late summer/early autumn), June (mid-winter), September (spring) and December (summer) 1990 for the irrigated Lismore soil in the Lincoln environment with high fertiliser input. There was no increase in pasture growth following the March deposition, compared to the simulations that had not yet received a
Conclusions
Previous experimental (e.g. Ball and Ryden, 1984) and modelling work (e.g. Snow et al., 2009) has shown that the effect of urine patches is important to leaching from grazed systems. However, the inclusion of explicit urine patches in simulation models considerably increases the complexity of the model. This complexity is further aggravated if the overlaps of urine patches must also be considered. Here, we tested a two-stage methodology to simplify the account of two overlapping urine patches.
Acknowledgements
This work was supported by the Ministry for Science and Innovation through the programme “Dairy Systems for Environmental Protection”. The authors wish to thank the anonymous reviewers for their constructive comments that improved the manuscript.
References (56)
Modelling the fate of crop nutrients in the environment: problems of scale and complexity
Eur. J. Agron.
(1995)- et al.
Estimating the size of the inert organic matter pool from total soil organic carbon content for use in the Rothamsted carbon model
Soil Biol. Biochem.
(1998) - et al.
Nutrient cycling and soil fertility in the grazed pasture ecosystem
Adv. Agron.
(1993) - et al.
Simplifying environmental model reuse
Environ. Model. Softw.
(2010) - et al.
Modelling spatial heterogeneity in grazed grassland and its effects on nitrogen cycling and greenhouse gas emissions
Agric. Ecosyst. Environ.
(2007) - et al.
An overview of APSIM, a model designed for farming systems simulation
Eur. J. Agron.
(2003) - et al.
Maximum conductances for evaporation from global vegetation types
Agric. For. Met.
(1995) - et al.
Modelling environmental impacts of deposition of excreted nitrogen by grazing dairy cows
Agric. Ecosyst. Environ.
(2004) - et al.
A stochastic model of urinary nitrogen and water flow in grassland soil in New Zealand
Agric. Ecosyst. Environ.
(2007) - et al.
Evaluation of pedo-transfer functions for unsaturated soil hydraulic conductivity using an independent data set
Geoderma
(2001)
Nitrogen relationships in intensively managed temperate grasslands
Plant Soil
Towards a coherent philosophy for modelling the environment
Proc. R. Soc. Lond. A – Math.
Within-field variability of mineral nitrogen in grassland
Biol. Fert. Soils
A functional evaluation of virtual climate station rainfall data
N. Z. J. Agric. Res.
Describing the Fate of High Dose Nitrogen in Pastoral Soils – Modelling N Leaching Under High N Loads (Urine Patches)
Modelling the effect of a nitrification inhibitor on N leaching from grazed pastures
Proc. N. Z. Grass. Assoc.
Field study of pesticide leaching in an allophanic soil in New Zealand. 2: Comparison of simulations from four leaching models
Aust. J. Soil Res.
Simulating pasture growth rates in Australian and New Zealand grazing systems
Aust. J. Agric. Res.
Uptake and leaching of nitrogen from artificial urine applied to grassland on different dates during a growing season
Plant Soil
Nitrate leaching and pasture production from different nitrogen sources on a shallow stoney soil under flood-irrigated dairy pasture
Aust. J. Soil Res.
Nitrate leaching losses and pasture yields as affected by different rates of animal urine nitrogen returns and application of a nitrification inhibitor – a lysimeter study
Nutr. Cycl. Agroecosyst.
New Zealand Soil Classification
Nitrogen concentration in the urine of cattle, sheep and deer grazing a common ryegrass/cocksfoot/white clover pasture
N. Z. J. Agric. Res.
Nitrogen cycling in low input legume-based agriculture, with emphasis on legume/grass pastures
Plant Soil
Modelling seasonal and geographical pattern of pasture production in New Zealand
N. Z. J. Agric. Res.
Computer-based evaluation of methods to sample nitrate leached from grazed pasture
Soil Use Manag.
At what level will decision-makers be able to use uncertainty information?
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2019, Advances in AgronomyCitation Excerpt :Extensive studies have evaluated N leaching from urine patches, including overlapping patches and uncertainty in soil properties (Cichota et al., 2013; Vogeler et al., 2017b). For example, Cichota et al. (2013), using the APSIM model, studied N leaching from grazing systems in New Zealand and found that when urine was deposited on the same location twice within 20 days, N in the two urine events should be accumulated and treated as a single urine patch. However, when the delay between the two urine events was > 180 days, independent patch simulation is sufficient for N leaching.
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