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Estimating nitrogen excretion and deposition by lactating cows in grazed dairy systems.


Global N use continues to increase to meet the food demands of an expanding world population. For example, recent reports show that synthetic N fertiliser use has grown from approximately 10 million metric tons (Mt) in 1960 to 112 Mt in 2015 (Glibert et al. 2006; FAO 2016), with urea (which also has non-fertiliser uses in ruminant feed additives) comprising over 70% of all N fertilisers (Glibert et al. 2006). The extensive fertiliser and non-fertiliser uses of N can lead to many environmental and human health effects (Erisman et al. 2013), including degraded air and water quality leading to respiratory and other illnesses, loss of biodiversity, ozone depletion and climate change.

Similarly, the dairy industry worldwide has depended on greater N inputs, mostly in fertiliser and feed, to increase milk production; leading to associated farm N surpluses (Neetson etal. 2003; Kristensen et al. 2005; Nielsen and Kristensen 2005; Hristov et al. 2006; Nevens et al. 2006; Fanguciro et al. 2008; Treacy et al. 2008; Kobayashi et al. 2010; Gourley et al. 2012; Mihailescu etal. 2014). For many confinement systems, feed N inputs far surpassed fertiliser N use with, on average, only 27% of inputs exported in product from farms (Table 1).

Even for dairy systems that rely on grazed pasture, feed and fertiliser N inputs are often high (Ledgard et al. 1997; Beukes et al. 2012; Stott and Gourley 2016). Thus, although access to pasture has historically been an economic advantage, purchased feed has become one of the largest cost items for Australian dairy farmers (Dairy Australia 2010). In a recent survey of feeding systems, 96% of Australian dairy herds grazed pasture, but only 4% did not receive any purchased supplements or concentrates (Dairy Australia 2015). Between 1990 and 2012, average feed concentrate N inputs rose from 24 to 81 kg [ha.sup.-1] and fertiliser N use increased from 18 to 70 kg [ha.sup.-1] for fertilised areas of Australian farms, associated with average N surpluses increasing from 54 to 158 kg [ha.sup.-1] (Stott and Gourley 2016). The effect of these inputs is that for dairy farms across seven climatic regions in Australia (Gourley et al. 2012), N surpluses and farm N use efficiencies (NUE) ranged from 47 to 601 kg N [ha.sup.-1] (mean 208 kg N [ha.sup.-1]) and from 14% to 50% (mean 28%) respectively (Table 1). Although lower than the findings for Australian farms, a mean surplus of approximately 155 kg N Ha-1 was estimated for more than 3200 New Zealand grazing dairy farms, where the high use of feed and fertilisers contributed to the surpluses (Beukes et al. 2012; Scarsbrook and Melland 2015).

Nitrogen surpluses are associated with leaching losses, denitrification and volatilisation of N in both grazing and confinement dairy systems (Bos et al. 2005; Beukes et al. 2012), with animal excreta considered the major contributor of N pollutants. For example, in New Zealand grazing systems cow urine is recognised as the predominant source of leached nitrate-N (Ledgard etal. 2000). Ammonia (N[H.sub.3]) emissions from dairy manure increased in housed dairy systems as the percentage of dietary crude protein (CP) grew (Swensson 2003), driven, in part, by the relationship between N surplus and manure production (Borsting et al. 2003) and between dietary CP and manure or excreted N (Yan et al. 2006; Powell and Rotz 2015). With levels of CP in grazed pasture of up to 35% (Rugoho et al. 2016), and the reported relationship between dietary N and N[H.sub.3] emission, volatilisation losses are also likely to be significant for grazing systems. Further, Laubach et al. (2013) showed that in pasture-based systems more N[H.sub.3] is emitted from deposited urine than dung. This importance of urine in N[H.sub.3] emissions and N03 leaching losses is most likely due to the relatively greater increase in urine N excretion when intakes exceed 400 g N [cow.sup.-1] [day.sup.-1] (Castillo et al. 2000; Kebreab et al. 2001).

In traditional grazing systems, cows spend most of their time in paddocks, with the remainder spent walking to and from the dairy shed (milking parlour). Thus, most of their excreta is directly deposited in paddocks. Aside from deposition in laneway and tracks, excreta is otherwise collected from the dairy shed and the associated concreted yards where animals are held before or after milking. This collected and stored excreta (i.e. effluent) is reapplied to pasture as a source of nutrients and water. However, as the industry has intensified, with larger herds and greater use of supplementary imported feeds, cows spend increasing amounts of time away from pasture. Recently, Aarons et al. (2017) documented greater time spent by lactating animals in feedpads and holding areas on Australian grazing system farms. These changes in placement of grazing dairy cattle have also been observed elsewhere. For example, although almost 80% of Danish dairy farmers graze their cows, as herd sizes increase, the use of grazing decreases (Kristensen et al. 2005). Similarly, Sheppard et al. (2011) reported that most of the herds on smaller Canadian farms (i.e. less than 43 cows) spent most of their day outdoors, with as much as 12 h spent in exercise and standing yards. This change in management of lactating cows on Australian dairy farms has been compounded by poor excreta collection practices as approximately half the holding areas and feedpads on these farms were not concreted (Aarons et al. 2017); thereby limiting collection of excreta for re-use. Similarly for Canadian farms, collection of manure occurred less frequently where cows spent more time in standing and exercise yards (Sheppard et al. 2011).

Improving management of excreted nutrients in grazing system farms, and especially where cows spend a large proportion of their time, will depend on better quantification of excreta returns. Estimating excreted nutrients is relatively simple in confinement-based systems where dietary intakes are more easily determined. In addition, in those systems, relationships predicting N excretion have been developed by various scientists to assist with nutrient management (Castillo et al. 2000; Nennich et al. 2005; Yan et al. 2006). The aim of the present study was to estimate N excretion by lactating dairy cows in diverse grazing systems on Australian dairy farms. Nitrogen excretion was then compared with N inputs on these farms. The data from these farms were used to develop relationships to predict N excretion, which were then compared with the predictions in the literature. The potential for using these relationships to improve N management on grazing system farms is also discussed.

Materials and methods

Study design

The 43 commercial dairy farms in the present study were located in the eight major dairy regions in Australia, across cool and warm temperate, as well as subtropical and tropical regions (Table 2). These farms were selected to represent the range of dairy production systems, including organic and conventional farms, irrigated and rain-fed systems, total dependence on rotationally grazed paddocks and use of concreted feedpads (for more details, see Gourley et al. 2012). A semistructured questionnaire was used by trained interviewers to collect herd data from the farmers at their farms on five occasions every 3 months from late January 2008 until February 2009.

Estimating N intake and excretion on commercial dairy farms

The 'Feed Standards' approach for estimating annual dry matter intake by dairy herds in Australian grazing systems (Heard et al. 2011) was modified to calculate daily dry matter (DM) and nutrient intake by these herds. The input data (i.e. lactating herd size, liveweight, stage of lactation, lactation number, milk production, types and amount of purchased supplements and fodder (home grown and purchased) provided, terrain traversed) were obtained at each interview. The total metabolisable energy (ME) requirements for the 'average lactating cow' for each herd at each interview was estimated from the energy requirement for milk production, maintenance, pregnancy, grazing and activity (Freer et al. 2007; Heard et al. 2011). The ME supplied in supplements fed to the cows was subtracted from the total energy requirements of the herd to give an estimate of the ME gained from pasture. Using N content and ME laboratory analytical data for the diet components, pasture DM intake (DM1) and total N intake (i.e. supplements and pasture) were calculated. Daily N excretion (g [cow.sup.-1] [day.sup.-1]) was calculated by subtracting the N produced in milk by each lactating cow that day from the daily N intake, assuming that liveweight change each day was minimal. Animal feed NUE was calculated as the percentage of total N intake secreted as milk N.

Deposition of N on grazing system dairy farms

The amount of N deposited by lactating herds around dairy farms was estimated by identifying the locations the cows visited on each interview date (as reported by Aarons et al. 2017) and apportioning daily and annual N loads (kg) based on the proportion of each day the animals spent there. Briefly, the farmers were asked to describe the areas their lactating herds visited, and the average time the cows spent in these areas was calculated. These areas included grazed paddocks, feedpads, laneways, holding areas, the yards and the dairy shed where the cows were milked. Feedpads on these farms were used to provide the herd with supplements or fodder and were not always concreted. Cows were placed for extended periods in holding areas, generally paddocks or parts of paddocks, for a variety of reasons. These holding areas were never concreted. The annual N loads deposited by the lactating cows were estimated by weighting the five calculated daily N excretion rates based on the number of days in each quarter that the herds received a particular diet and then standardising to a 305-day lactation.

Nitrogen imports

Calculated N excretion was compared with annual external N inputs to each farm, which were estimated as described and reported by Gourley et al. (2012). Briefly, farm data were collected during the interviews that described all imports of supplement (fodder, concentrates etc.), animals, irrigation water and fertiliser, as well as exports of milk and livestock. Inputs from biological N fixation were assessed using the clover contents of pastures, whereas atmospheric N deposition was based on previously published data that considered distance from the coast and industry emissions (Gourley et al. 2012).

Sample collection and data analysis

Samples of all feeds and pasture were collected on the day of each interview, immediately placed on ice and promptly returned to the laboratory. Grab samples of grazed pasture and fodder crops were collected from paddocks representative of the cows' diet on the day of the interview. Samples of supplements fed were collected from all sources, including hay, silage and concentrates. Samples of bulk tank milk, representative of all milkings for the day, were collected after the vat contents were thoroughly agitated, then stored frozen until analysis. Pasture and feed samples were subsampled for DM estimation (dried at 105[degrees]C for 48 h) and for chemical analysis (dried at 65[degrees]C for 72 h, ground to <2 mm). All samples were analysed at Weston Technologies (Sydney, NSW, Australia) for N concentration in feed (CP/6.25) and in milk (CP/6.38) as given in AOAC (2000) and ME was estimated based on the methods given by Rugoho et al. (2016). Irrigation water was analysed at the Monash Water Centre (Melbourne, Vic., Australia; Greenberg et al. 2005).

Summary statistical analyses (i.e. mean [+ or -] s.d., range) and regression parameters for relationships between N intake and milk N secreted, cxcrcted N and animal feed NUE were computed using Genstat Release 17 (VSN International, Wood Lane, Hemel Hempstead, UK). Graphs were prepared using Microsoft (Armonk, NY, USA) Excel.

Results and Discussion

The area of land the lactating cows routinely visited (i.e. contact land) varied widely for the farms in the present study (Table 2). The lactating herd sizes ranged from 100 to 1263, with the smallest contact land stocking rates in Queensland and the highest in South Australia. These farms were located in diverse climatic zones, from the wet humid tropics in the north (Queensland) to temperate regions with no dry summer in the south (Tasmania), and temperate zones with dry and warm summers in Western Australia (Stern et al. 2000). As well as covering a geographic area larger than Europe, the climatic zones for the farms in the present study are comparable to many regions worldwide according to the Koppen Classification (Table 2). Irrigation of paddocks to increase pasture growth varied on these farms, although drier than usual conditions over the study period would have increased the number of paddocks irrigated.

The use of imported feed (supplements, hay, silage) on these farms also ranged widely. The proportion of imported feed that comprised the ME requirements of the lactating herds on these grazing system farms averaged 34% and was as much as 66%. These data demonstrate that Australian farmers manage very diverse systems and, despite the grazing base of these farms, the use of supplementary feeds had grown, most likely to increase milk production and to manage seasonal variability in pasture growth (Bargo et al. 2003; Wales et al. 2006; Thorrold and Doyle 2007).

The mean daily ME (Table 3) expended by each cow on maintenance and grazing did not vary much (CV 6%) for these farms or interview dates, unlike that required for pregnancy, walking and milk production (CV 68%, 46% and 25% respectively). The variability in ME required for pregnancy was due to differences in calving dates and stage of lactation for the herds at the different interview dates. On some farms at certain visits, all the cows in the herd were in early lactation (and not pregnant), whereas on other farms the cows were at different days in milk. The ME for walking was affected by the terrain and size of individual farms, both of which varied somewhat. Total ME requirements (Table 3) were supplied by a variety of grazed fodder and a range of supplementary feeds, as described by Rugoho et al. (2016). For example, grazed herbage frequently consisted of perennial or annual pastures such as ryegrass, lucerne or kikuyu. On some farms, grazed pasture mixtures included other grasses (sorghum) or legumes (white or subclover) or herbs (chicory), and cows sometimes grazed crops such as millet or turnips. Although the lactating herds on these farms grazed daily, the herds received, on average, 52% (median 51%) of their energy requirements from a variety of supplementary feeds across the five interview dates (data not shown). The supplements fed differed between farms and interview dates and were classified as concentrates (e.g. pellets), grain, mixed grains, by-products, minerals, total mixed rations, organic feeds, silage and hay (Rugoho et al. 2016).

Despite supplements providing equivalent dietary energy requirements, mean grazed N intake was 1.5-fold the supplementary N intake (Table 4). Mean N intakes for the present study were greater than those reported by Kebreab et al. (2001) for cows in five N balance studies (545 vs 437 g N [cow.sup.-1] [day.sup.-1] respectively), but were similar to feeding study data from 554 cows or cow-periods reported by Nennich et al. (2005; 608 g N [cow.sup.-1] [day.sup.-1]). The range in total N intake (268-983 g N [cow.sup.-1] [day.sup.-1]) by cows on the 43 study farms (Fig. la) corresponded to the range reported for four Australian farms by Powell et al. (2012; 203-1233 g N [cow.sup.-1] [day.sup.-1]), but was 1.3-fold higher than data from 581 cows fed 91 diets (Castillo et al. 2000; -200-750 g N [cow.sup.-1] [day.sup.-1]) and was also greater than data from animal feeding trials (Kebreab et al. 2001 ; 289-628 g N [cow.sup.-1] [day.sup.-1]). The grazing system study of Powell et al. (2012) was conducted in southern Victoria, with data collected only during two seasons on four farms, whereas the six cows in the N balance study of Kebreab et al. (2001) were housed.

Calculated N excretion for the farms in the present study was almost fourfold that produced in milk, with associated low animal NUEs (Table 4). Milk output reported by Kebreab et al. (2001) was similar to that reported for the herds in the present study, but the NUE in their study was higher. The mean animal NUE reported in the present study (Table 4) is at the lower end of the ranges reported by Powell et al. (2010) from five different studies, although none reported efficiencies as low as the minimum (11%) calculated for the grazing system cows in the present study. In contrast, in an analysis of data from 564 lactating cows in Ireland, Yan et al. (2006) reported slightly greater mean NUE with a wider range (7-41 %) than in the present study. Because the mean N intake in the Australian grazing systems was greater than the recommended level of 400 g N [cow.sup.-1] [day.sup.-1] (Kebreab et al. 2001), urinary N is likely to be the major route of N excretion. Management and recovery of urine N excretions is most likely to be important for minimising N losses from these systems because gaseous emissions of ammonia (Bussink and Oenema 1998; Kebreab et al. 2001) and nitrous oxide, as well as leaching losses of nitrate, arc strongly associated with urine excretion and deposition (Selbie et al. 2015).

Various predictive relationships for N excretion were developed to assist with management of N excretion (Fig. 1 b). Strong positive relationships between excreted N ([N.sub.Excr]; Eqn 1) or milk N secretion ([N.sub.Milk]; Eqn 2) and intake ([N.sub.In]) were observed for these data, whereas a curvilinear relationship was calculated for the relationship between the efficiency of conversion of dietary N to milk (NUE) and N intake (Eqn 3), as follows:

([N.sub.Excr] (g [cow.sup.-1] [day.sup.-1]) = 0.84[N.sub.ln] (g [cow.sup.-1] [day.sup.-1]) -23.6 ([R.sup.2] = 0.97) (1)

([N.sub.Milk] (g [cow.sup.-1] [day.sup.-1]) = 0.16[N.sub.ln] (g [cow.sup.-1] [day.sup.-1]) +23.6 ([R.sup.2] = 0.52) (2)

NUE = 23.6/[N.sub.ln] (g [cow.sup.-1] [day.sup.-1]) + 0.16 ([R.sup.2] = 0.08) (3)

For the purposes of comparison with the predictions reported in the literature (Yan et al 2006), a linear relationship between NUE and N intake was also developed:

NUE = -0.00009[N.sub.ln] (g [cow.sup.-1] [day.sup.-1]) + 0.259 ([R.sup.2] = 0.08) (4)

These relationships (i.e. Eqns 1, 2 and 4) were similar to those developed by Kebreab etal. (2001 ; see Eqns 5-7), Castillo et al. (2000; Eqns 8, 9) and Yan et al. (2006; Eqn 10) with the parameter acronyms standardised for all equations below:

([N.sub.Excr] (g [cow.sup.-1] [day.sup.-1]) = 0.62[N.sub.ln] (g [cow.sup.-1] [day.sup.-1]) +30 ([R.sup.2] = 0.78) (5)

([N.sub.Milk] (g [cow.sup.-1] [day.sup.-1]) = 0.19[N.sub.ln] (g [cow.sup.-1] [day.sup.-1]) +38.2 ([R.sup.2] = 0.3) (6)

([N.sub.Milk] (g [cow.sup.-1] [day.sup.-1]) = 0.17[N.sub.ln] (g [cow.sup.-1] [day.sup.-1]) +38.8 ([R.sup.2] = 0.9) (7)

([N.sub.Milk] (g [cow.sup.-1] [day.sup.-1]) = 0.17[N.sub.ln] (g [cow.sup.-1] [day.sup.-1]) +41 ([R.sup.2] = 0.42 (8)

NUE = -0.0002[N.sub.ln] (g [cow.sup.-1] [day.sup.-1]) +0.36 ([R.sup.2] = 0.21) (9)

([N.sub.Excr] (g [cow.sup.-1] [day.sup.-1]) = 0.722[N.sub.ln] (g [cow.sup.-1] [day.sup.-1]) ([R.sup.2] = 0.90) (10)

Yan et al. (2006) observed that N intake is the best predictor of excreted N, although diet and animal factors (e.g. feed type, CP concentration, breed, stage of lactation and milk yield) affected the relationship, particularly the size of the constant. Castillo et al. (2000) also suggested that the milk yield of cows could affect the relationships between N intake and excretion. Thus, the generally weaker relationships (i.e. Eqns 2--4) for the animals in the present study can be explained by the wide range of diets, breeds and milk production on these farms. The very high [R.sup.2] for Eqn 1 is to be expected due to the method used to calculate excreted N (N intake minus milk N) on the commercial farms. However, despite these considerations, the similarity of our regression relationships to those in the literature provides confidence in the method used to estimate N excretion in the present study.

The amount of N excreted throughout lactation was calculated by weighting the daily excretion rate (g [cow.sup.-1] [day.sup.-1]) with the number of days for the year each farmer used each diet over a season and corrected for the average length of lactation of 305 days. On average, the N excreted during the lactation period was 69% of total N imports (i.e. feeds, fertiliser, livestock, N fixation, irrigation water and atmospheric deposition), indicating the importance of N excretion by lactating dairy cattle on these farms (Table 5). The largest total imports per farm were onto the South Australian, Tasmanian and Western Australian enterprises, although mean fertiliser N inputs were relatively greater in the latter two states than in South Australia. Consequently, there was a strong relationship between excreted N and total N imports (Fig. 2a). Gustafson et al. (2007) and Kobayashi et al. (2010) similarly observed that N flows in excreta were approximately 60% of nutrient inputs. A bivariate regression of excreted N on fertiliser and feed imports showed partial effects with strong relationships between excreted N adjusted for fertiliser and feed N imports (Fig. 2b), as well as excreted N adjusted for feed and fertiliser N imports (Fig. 2c). The relationship was improved when Farm 4 was excluded, because N inputs on that farm were strongly affected by the high clover content of the pasture and low N fertiliser use. An estimated 104 t N was biologically fixed on Farm 4, compared with a median (90th percentile) of 3.1 t (14.8 t) for all farms. However, only 2.7 t non-urea N fertiliser (median for all farms 15.8 t) and 25 t imported feed (median for all farms 14 t) was brought onto that farm during the study year.

When calculated N excretion was applied to the locations on the farms where the cows were placed, not surprisingly N deposition was greatest in grazed paddocks where the animals spent most time (data not shown). However, because the range of time spent grazing varied between farms, N loads deposited on paddocks ranged considerably, depending on how farmers managed their grazing paddocks. Paddocks where the lactating herd were held overnight were located, on average, 118m closer to the dairy shed (Aarons et al. 2017) and were estimated to have greater loads deposited over the lactation than paddocks primarily grazed during the day (Fig. 3). Feedpads and holding areas (located within 100 m of the dairy shed) received similar N loads to those collected in the dairy shed and yards. Although excreta deposited in the dairy shed and yards were always collected and reused, of the 22 farms with feedpads and/or holding areas only 11 collected deposited excreta from feedpads (Aarons et al. 2017). Thus, half the collectable N excreted is potentially lost from these grazing system farms. Sheppard et al. (2011) reported similar low rates of collection of manure from standing yards in their study and suggested that the low frequency of cleaning would be associated with losses of NH3. Misselbrook et al. (2006) observed that concreted feeding yards were a greater source of NH3 emissions than dairy collecting yards, despite being a similar area and being used for the same number of cows. The difference in emissions was most likely affected by the less frequent cleaning of the feeding yards, as well as by the relatively greater NH3 emissions from hard surfaces than from pasture (Gilhespy et al. 2006).

The mean (median) application rate of N fertiliser to all paddocks was 141 (116) kg N [ha.sup.-1], less than the estimated annual excreted N loading rates to paddocks where cows were placed overnight (201 (146) kg N [ha.sup.-1]), but similar for paddocks grazed during the day (136 (114) kg N [ha.sup.-1] Fig. 4). Because fertiliser N was typically applied uniformly to all paddocks, these data indicate that, on average, paddocks visited overnight received (in excreta and fertiliser) more than twice the rate of N applied by the farmer as fertiliser. Moreover, the applications to these 'night' paddocks of fertiliser and directly deposited N do not account for the N collected from the dairy shed and yards. This effluent was most frequently applied to paddocks close to the dairy shed on these farms.

Farm-gate N balances are a useful metric (Oenema et al. 2003) and arc considered a goal-oriented indicator (Fangueiro et al. 2008) of the potential environmental effect of farms due to the strong links between farm surpluses and N leaching and gaseous losses. As a result, whole-farm balances are frequently used for dairy production systems in Europe and New Zealand and for large (>500 animal) operations in certain states in the US (Gourley and Weaver 2012) to target management changes to reduce N surpluses. Despite the positive N balances reported on Australian grazing system farms (Bennett et al. 2011; Gourley et al. 2012), farm-gate N balances are typically not used for Australian farms or required for many smaller farms elsewhere (Gourley and Weaver 2012; Stott and Gourley 2016).

With N flows in excreta equivalent to a significant proportion of nutrient inputs on dairy farms, excreted N is an important means-oriented indicator that is responsive to farmer practices (Schroder et al. 2003) and can therefore indicate relevant management changes that could minimise nutrient losses at the farm scale. For example, although N leaching losses increase with greater rates of N fertiliser application, urine can contribute up to twice the per-hectare paddock losses (Silva et al. 1999). Thus, techniques that enable estimation of excreted N could assist in improving N management on grazing system dairy farms and minimising nutrient losses. However, in addition to N applied in fertiliser, only N collected from concreted dairy sheds and yards (i.e. effluent) is calculated in the current dairy industry recommended nutrient budget tool (Dairy Australia 2016), while the remaining excreted N largely ignored.

Further information combining excreted N and spatial and temporal variation in animal densities within farms would lead to improved environmental outcomes at a farm and within-farm scale, measured, for example, as reduced leaching and volatilisation losses (Ledgard et al. 2000; Misselbrook et al. 2006). The data reported herein show that as cows spend increasing time in areas of dairy farms that are not grazed (i.e. not pasture) and are not concreted, the importance of estimating N excretion also grows. Routine placement of animals in these areas will lead to nutrient accumulation and the potential for N losses from these hot spot locations. With grazing management also used in many countries, such as Denmark (Kristensen et al. 2005) and Canada (Sheppard et al. 2011), as well as in parts of the US (Parsons et al. 2004), the time cows spend in exercise yards and holding areas needs to be accounted for and managed. Excreted N loads can be combined with estimates of locations visited by the lactating herd and time spent in these locations to ensure improved N management is targeted to nutrient accumulation zones.

Predictive relationships, developed to enable ready estimation of N excretion, are useful for improving nutrient management, such as developing manure treatment and collection systems (Nennich et al. 2005). Typically, few farmers or their advisors test manure before land application, with approximately 10% of Canadian (Sheppard et al. 2011) and 14% of US farmers (Nennich et al. 2005) reported to test manure. The high variability in manure (Dou et al. 2001) and effluent (Jacobs and Ward 2007) nutrient concentrations was thought to be a factor in the low prevalence of manure testing. Regardless, approaches that enable better estimation of nutrient contents will improve within-farm use of manure. The regression relationships identified for the grazing system farms in the present study can be further developed and refined for different breeds, pasture and feed systems used and take seasonal effects into consideration. When these relationships are applied in conjunction with estimates of the time cows spend in different locations, nutrient accumulation zones can be targeted to minimise potential losses.


Quantifying within-farm N flows in excreta on grazing system dairy farms will provide means-oriented indicators for improving nutrient management. Using dietary and lactation information readily sourced on commercial dairy farms, the method demonstrated in the present study enables the quantification of N loads excreted by dairy cattle. The high mean N intakes by these grazing lactating cattle indicate that urine is likely to be the chief route for N excretion, with further research to estimate urinary N excretion warranted The relationships predicting excreted N from N intakes for these grazing systems are comparable to those developed for confinement-based dairy farms, and the similarity of these relationships indicate that the dietary N estimates were realistic and that the predictions could be used for quantifying N excretion.

It is important to account for the considerable spatial variability in N deposition likely to occur on dairy farms where the cows graze pasture, as well as the substantial N loads that are deposited in holding areas and standing yards, because these are likely to contribute to nutrient pollution. Calculation and apportioning of N loads within farms will ensure that fertiliser N applications to paddocks are more strategic and N losses minimised in grazing system farms.


This paper is a more detailed version of papers presented at the International Nitrogen Initiative 2016 conference (04-08 Dec 2016, Melbourne, Vic., Australia). The authors gratefully acknowledge the farmers who allowed us access to their farms and willingly supplied farm data. Data collection was undertaken by a team of staff across the country who visited farms on at least five occasions to collect the necessary data and samples. The authors also thank the anonymous reviewers whose editorial suggestions greatly improved this paper. This research was supported by Dairy Australia (DAV12307) and the Victorian Department of Economic Development, Jobs, Transport and Resources (MIS06854).


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Sharon R. Aarons (A, C), Cameron P. Gourley (A), J. Mark Powell (B), and Murray C. Hannah (A)

(A) Agriculture Victoria Research, Department of Economic Development, Jobs, Transport and Resources, Ellinbank Dairy Centre, 1301 Hazeldean Road, Ellinbank, Victoria 3821, Australia.

(B) USDA Agricultural Research Service, US Dairy Forage Research Center, 1925 Linden Drive, Madison, WI 53706, USA.

(C) Corresponding author. Email:

Received 20 January 2017, accepted 5 July 2017, published online 21 August 2017

Caption: Fig. 1. Relationships between (a) excreted N, N secreted in milk or animal feed N use efficiency (NUE) and N intake for the 43 dairy farms in the present study and (b) the relationships plotted for comparison with those in the literature. Kebreab et al. (2001), Castillo et al. (2000), Yan et al. (2006).

Caption: Fig. 2. Relationship between (a) annual N excreted and annual total N imports, (b) annual excreted N adjusted for fertiliser and annual total feed N imports and (c) annual excreted N adjusted for feed imports and annual total fertiliser imports. The relationships given are for all farms ([??]), or where Farm 4 data are excluded ([??]).

Caption: Fig. 3. Boxplots of N loads deposited over a 305-day lactation at the dairy shed and yards, within 100 m of the dairy shed and in paddocks grazed overnight or during the day by the lactating herds on 42 dairy farms. Boxes show the interquartile range, with the median value indicated by the horizontal line. Whiskers indicate minimum and maximum values.

Caption: Fig. 4. Boxplots of the loading rate of N excreted by the lactating herds to paddocks grazed overnight or during the day, over a 305-day lactation, as well as the fertiliser N application rate on 42 dairy farms. Boxes show the interquartile range, with the median value indicated by the horizontal line. Whiskers indicate minimum and maximum values.
Table I. N imports in feed (concentrate) and fertilisers, farm N
balance/surplus and farm N use efficiencies (NUE) as reported in the
literature for dairy farms globally

Data are presented as the mean [+ or -] s.d. n.d., no data supplied;
n, number of farms

Input                                 Amount (A)        surplus (A)

Kobayashi et al. (2010)
  Purchased feed
    (kg N [ha.sup.-1]
    [year.sup.-1])                 394 [+ or -] 67        378 (B)
  Chemical fertiliser
    (kg N [ha.sup.-1]
    [year.sup.-1])                 109 [+ or -] 20
Nielsen and Kristensen (2005)
  Concentrate (kg N [ha.sup.-1])   112 [+ or -] 49    175 [+ or -] 39
  Fertiliser (kg N[ha.sup.-1])      86 [+ or -] 24
Nielsen and Kristensen (2005)
  Concentrate (kg N [ha.sup.-1])    31 [+ or -] 15    106 [+ or -] 13
  Fertiliser (kg N [ha.sup.-1])      0 [+ or -] 0
Hristov et al. (2006)
  Feeds (t N [year.sup.-1])              461          314 [+ or -] 218
  Fertiliser (t N [year.sup.-1])          7
Fangueiro et al. (2008)

  Concentrates (kg N [ha.sup.-1]
    [year.sup.-1])                 402 [+ or -] 100   413 [+ or -] 129
  Mineral fertilisers
    (kg N [ha.sup.-1]
    [year.sup.-1])                 185 [+ or -] 94
Fangueiro et al. (2008)
  Concentrates (kg N [ha.sup.-1]
    [year.sup.-1])                 574 [+ or -] 92    548 [+ or -] 102
  Mineral fertilisers
    (kg N [ha.sup.-1]
    [year.sup.-1])                 198 [+ or -] 62
Fangueiro et al. (2008)
  Concentrates (kg N [ha.sup.-1]
    [year.sup.-1])                 778 [+ or -] 114   609 [+ or -] 115
  Mineral fertilisers
    (kg N [ha.sup.-1]
    [year.sup.-1])                 225 [+ or -] 56
Grignani et al. (2003) (C)               n.d.             338 (B)
Jarvis et al. (2003) (C)                 n.d.             280 (B)
Le Gall et al. (2003) (C) and
  Chatellier and Pflimlin
  (2006) (C)                             n.d.             217 (B)
Humphreys et al. (2003) (C)              n.d.             290 (B)
Verbruggen et al. (2003) (C)             n.d.             238 (B)
Nevens et al. (2006)
  Concentrate use
    (kg N [ha.sup.-1])                  78 (B)            163 (B)
  Mineral fertiliser use
    (kg N [ha.sup.-1])                  87 (B)
Nevens et al. (2006)
  Concentrate use
    (kg N [ha.sup.-1])                  96 (B)            250 (B)
  Mineral fertiliser use
    (kg N [ha.sup.-1])                 139 (B)
Treacy et al. (2008)
  Concentrate (kg N [ha.sup.-1])       43.8 (B)           244 (B)
  Fertiliser (kg N [ha.sup.-1])        239 (B)
Mihailescu et al. (2014)
  Concentrate feeds
    (kg N [ha.sup.-1])                  27 (B)            175 (B)
  Chemical fertilisers
    (kg N [ha.sup.-1])                 186 (B)
Gourley et al. (2012) (D)
  Concentrates and grain
    (kg N [ha.sup.-1])                  53 (B)            193 (B)
  Fertiliser (kg N [ha.sup.-1])        105 (B)

Input                                  NUE (%)

Kobayashi et al. (2010)
  Purchased feed
    (kg N [ha.sup.-1]
    [year.sup.-1])                 25 [+ or -] 1.5
  Chemical fertiliser
    (kg N [ha.sup.-1]
Nielsen and Kristensen (2005)
  Concentrate (kg N [ha.sup.-1])   24[+ or -] 3.9
  Fertiliser (kg N[ha.sup.-1])
Nielsen and Kristensen (2005)
  Concentrate (kg N [ha.sup.-1])   26 [+ or -] 3.2
  Fertiliser (kg N [ha.sup.-1])
Hristov et al. (2006)
  Feeds (t N [year.sup.-1])        41 [+ or -] 14
  Fertiliser (t N [year.sup.-1])
Fangueiro et al. (2008)

  Concentrates (kg N [ha.sup.-1]
    [year.sup.-1])                 34 [+ or -] 14
  Mineral fertilisers
    (kg N [ha.sup.-1]
Fangueiro et al. (2008)
  Concentrates (kg N [ha.sup.-1]
    [year.sup.-1])                 33 [+ or -] 10
  Mineral fertilisers
    (kg N [ha.sup.-1]
Fangueiro et al. (2008)
  Concentrates (kg N [ha.sup.-1]
    [year.sup.-1])                  42 [+ or -] 9
  Mineral fertilisers
    (kg N [ha.sup.-1]
Grignani et al. (2003) (C)             27 (B)
Jarvis et al. (2003) (C)               19 (B)
Le Gall et al. (2003) (C) and
  Chatellier and Pflimlin
  (2006) (C)                           21 (B)
Humphreys et al. (2003) (C)            21 (B)
Verbruggen et al. (2003) (C)           22 (B)
Nevens et al. (2006)
  Concentrate use
    (kg N [ha.sup.-1])                 38 (B)
  Mineral fertiliser use
    (kg N [ha.sup.-1])
Nevens et al. (2006)
  Concentrate use
    (kg N [ha.sup.-1])                 22 (B)
  Mineral fertiliser use
    (kg N [ha.sup.-1])
Treacy et al. (2008)
  Concentrate (kg N [ha.sup.-1])       20 (B)
  Fertiliser (kg N [ha.sup.-1])
Mihailescu et al. (2014)
  Concentrate feeds
    (kg N [ha.sup.-1])                 23 (B)
  Chemical fertilisers
    (kg N [ha.sup.-1])
Gourley et al. (2012) (D)
  Concentrates and grain
    (kg N [ha.sup.-1])                 25 (B)
  Fertiliser (kg N [ha.sup.-1])

Input                              Study details

Kobayashi et al. (2010)            n = 1, 5 years
  Purchased feed
    (kg N [ha.sup.-1]
  Chemical fertiliser
    (kg N [ha.sup.-1]
Nielsen and Kristensen (2005)      n = 25, conventional farms, 7 years
  Concentrate (kg N [ha.sup.-1])
  Fertiliser (kg N[ha.sup.-1])
Nielsen and Kristensen (2005)      n = 13, organic farms
  Concentrate (kg N [ha.sup.-1])
  Fertiliser (kg N [ha.sup.-1])
Hristov et al. (2006)              n = 6, 1 year
  Feeds (t N [year.sup.-1])
  Fertiliser (t N [year.sup.-1])
Fangueiro et al. (2008)            n = 8, medium-intensity farms,
                                   1 year
  Concentrates (kg N [ha.sup.-1]
  Mineral fertilisers
    (kg N [ha.sup.-1]
Fangueiro et al. (2008)            n=7, intensive farms. 1 year
  Concentrates (kg N [ha.sup.-1]
  Mineral fertilisers
    (kg N [ha.sup.-1]
Fangueiro et al. (2008)            n = 5, very intensive farms, 1 year
  Concentrates (kg N [ha.sup.-1]
  Mineral fertilisers
    (kg N [ha.sup.-1]
Grignani et al. (2003) (C)
Jarvis et al. (2003) (C)
Le Gall et al. (2003) (C) and
  Chatellier and Pflimlin
  (2006) (C)
Humphreys et al. (2003) (C)
Verbruggen et al. (2003) (C)
Nevens et al. (2006)               n = 18, progressive farms, 2 years
  Concentrate use
    (kg N [ha.sup.-1])
  Mineral fertiliser use
    (kg N [ha.sup.-1])
Nevens et al. (2006)               n = 115, all farms, 2 years
  Concentrate use
    (kg N [ha.sup.-1])
  Mineral fertiliser use
    (kg N [ha.sup.-1])
Treacy et al. (2008)               n = 21, averaged data from 4 years
  Concentrate (kg N [ha.sup.-1])
  Fertiliser (kg N [ha.sup.-1])
Mihailescu et al. (2014)           n = 21, averaged data from 3 years
  Concentrate feeds
    (kg N [ha.sup.-1])
  Chemical fertilisers
    (kg N [ha.sup.-1])
Gourley et al. (2012) (D)          n = 43, 1 year
  Concentrates and grain
    (kg N [ha.sup.-1])
  Fertiliser (kg N [ha.sup.-1])

(A) Data are reported as kg [ha.sup.-1], unless specified otherwise.

(B) No data supplied regarding s.d. values.

(C) As reported by Fangueiro et al. (2008).

(D) Median N values are reported.

Table 2. Characteristics of the 43 commercial dairy farms in the
present study that were selected to represent the diversity of
Australian grazing systems

Data are presented as mean values, with the range in parentheses.
Qld, Queensland; NSW, New South Wales; Vic., Victoria; SA, South
Australia; WA, Western Australia; Tas., Tasmania; ME, metabolisable

No. farms   State    Climatic    No. conventional/      Contact
                    zones (A)      organic farms     land (B) (ha)

4           Qld     1, 2                3/1          186 (126-256)
10          NSW     3, 4, 5, 6          9/1          167 (40-361)
17          Vic.    4, 6               16/1          167 (58-460)
3           SA      5                   3/0          317(307-338)
5           WA      5, 6                4/1          226 (89-371)
4           Tas.    7                   4/0          247 (125-365)

No. farms       ECM (C)         Herd size (D)    Stocking rate (E)
            (kg [cow.sup.-1])                    (cows [ha.sup.-1])

4              20 (11-25)       158 (100-234)      0.9 (0.5-1.3)
10             23 (14-34)        201 (51-440)      1.5 (0.5-2.5)
17             24 (11-36)       286 (119-603)      2.0 (1.0-3.3)
3              23 (15-30)       580 (163-1263)     1.7 (0.5-3.7)
5              23 (15-32)        285(106-531)      1.2 (0.4-1.6)
4               17 (9-27)       517 (330-731)      2.2 (2.0-2.6)

No. farms     % Farm         Feed ME
            irrigated    imported (F) (%)

4           23 (0-62)       42 (10-66)
10          35 (0-84)       40 (15-61)
17          28 (0-95)       35 (14-61)
3           55(24-91)       50 (42-61)
5           33 (0-57)       29 (27-32)
4           60 (44-90)      3 (15-21)

(A) Climatic zones as defined by the Australian Bureau of
Meteorology (see http://www/yourhome/gov/
au/introduction/australian-climate-zones and http://www/
Climate-Zone-Map-Australia-Wide, accessed 9 May 2017).

(B) Contact land is the land area on each farm that the lactating
herd regularly visits as distinct from the area of the home farm or
all the land the fanner uses as part of their production system (for
further details, see Gourley et al 2012). (C) Energy/corrected milk
yield (ECM) calculated as (milk yield (L) x 1.0295 x (376 x fat (%)
+ 209 x protein (%) + 948)/3138); modified from Auldist et al.

(D) On farms in each region.

(E) Calculated on the basis of the herd size and the contact land
area that the lactating cows regularly visit.

(F) Feed ME requirements imported onto farms as a percentage of
total ME requirements.

Table 3. Summary statistics for metabolisable energy (ME) required
for maintenance, grazing, pregnancy, to produce milk and for
walking, and ME supplied in supplements or grazed pasture fed to the
lactating herds on the 43 farms in the present study

                                              ME (MJ [cow.sup.-1]
          Maintenance   Grazing   Pregnancy     Milk production

Minimum       47           5          0               47
Mean          55           5         1.7              124
Median        53           5         1.5              125
Maximum       66           7         4.8              203
s.d.          3.2         0.3       1.17             31.0

          Activity   Supplement fed   Pasture intake

Minimum      4             13              0.4
Mean         9            104               98
Median       8             97               97
Maximum      24           251              236
s.d.        4.1           52.2             45.4

Table 4. Summary statistics for milk N secreted, N intake in
supplements and in pasture, N excreted and animal feed N use
efficiency (FNUE) for 43 commercial dairy farms in Australia in the
present study

          Milk N secreted    N intake (g    Grazed
          (g [cow.sup.-1     [cow.sup.-1
           [day.sup.-1])    [day.sup.-1])

Minimum          48                6.8         1.2
Mean            112              226         336
Median          110              197         320
Maximum         190              667         787
s.d.             29.0            138.3       165.9

            N excreted     FNUE
          (g [cow.sup.-1   (%)

Minimum        199          11
Mean           433          21
Median         429          21
Maximum        793          39
s.d.           110.3         4.3

Table 5. Mean (minimum-maximum) N excreted by each herd over a
305-day lactation on the farms in each state, the total N imported
onto each farm and N imported as feed and as fertiliser over the

N imports included feed, fertiliser, biological N fixation,
atmospheric deposition, animals and other imports, such as bedding
(for details, see Gourley et al. 2012)

State               Excreted N (t)      Total

Queensland            15 (10-21)      35 (16-55)
New South Wales       26 (7-48)       38 (9-80)
Victoria              32 (9-86)       47 (11-179)
South Australia       72 (17-155)    114 (34-245)
Western Australia     38 (13-74)      70 (21-130)
Tasmania              51 (25-115)     81 (37-135)

                    N imported onto
State               farms (t) Feed    Fertiliser

Queensland             11 (1-16)      16 (0-30)
New South Wales        19 (4-49)      13 (0-30)
Victoria               20 (4-87)      22 (0-154)
South Australia        71 (15-167)    24 (10-16)
Western Australia      14 (6-20)      41 (0-93)
Tasmania               13 (4-25)      39 (3-93)
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Author:Aarons, Sharon R.; Gourley, Cameron P.; Powell, J. Mark; Hannah, Murray C.
Publication:Soil Research
Article Type:Report
Date:Aug 1, 2017
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