Measuring spatial and temporal variation in lactating dairy cow placement on diverse grazing system farms

Agriculture, Ecosystems and Environment 248 (2017) 175–189

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Measuring spatial and temporal variation in lactating dairy cow placement on diverse grazing system farms

MARK

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S.R. Aarons , C.J.P. Gourley, M.C. Hannah Agriculture Victoria Research, Department of Economic Development, Jobs, Transport and Resources, 1301 Hazeldean Road, Ellinbank, Victoria 3821, Australia

A R T I C L E I N F O

A B S T R A C T

Keywords: Heterogeneity Nutrient distribution Feedpads Holding areas Standing yards Questionnaire Nutrient use efficiency

The locations dairy cows visit on grazing system farms can have important implications for nutrient management. Excreted nutrients can be directly returned to pasture paddocks, or returned to places where these nutrients can either be collected for reuse, or are not collectable and therefore lost. Previous research has shown that nutrient accumulation was related to the time cows spend in places on farms, but there is little literature describing the spatial or temporal variation in the locations lactating dairy cows visit on grazing system farms. We developed a methodology to quantify the time lactating cows spend in different places on 43 representative grazing system farms to better understand the potential for excreta deposition and collection. These farms were diverse, with the herds visiting a wide variety of places where they spent differing lengths of time. The lactating dairy herds spent the majority of their time on pasture paddocks (74.2%; 0 to 97.6%), and only 10% (1 to 25%) of their time in locations where nutrients are routinely collected for storage and re-use, (i.e. the dairy shed (milking parlour) and associated yards). However, the paddocks where the cows were placed were not uniformly distributed around the farm, with significantly more time spent in paddocks overnight, that were located an average of 118 m closer to the dairy shed than paddocks the cows visited during the day. Feedpads and holding areas were the other places on farms that cows often visited, and where they spent 9.5% and 18.2% of their time respectively. However, only half of the feedpads in this study were concreted to facilitate collection of excreted nutrients for redistribution. Using excretion rates from the literature and data collected in this study, an estimated 1.0, 7.3 and 5.1 t of P, N and K respectively would be deposited within 100 m of the dairy shed by a 326 cow herd over a 300 day lactation on a grazed dairy farm. Up to 50% of the nutrients deposited near the dairy shed are potentially not collected and recycled. These results suggest that quantifying animal numbers and the time they spend in various locations are essential to determine nutrient deposition within dairy grazing systems and to improve farm nutrient use efficiencies.

1. Introduction Improving nutrient management is a key requirement of dairy production systems globally, particularly in light of the on-going increase in fertiliser and feed nutrient importation onto farms and the surpluses and positive nutrient balances associated with greater milk production (Neeteson et al., 2003; Kristensen et al., 2005; Fangueiro et al., 2008; Kobayashi et al., 2010; Gourley et al., 2012b; Oenema et al., 2014). Consequently, a wide variety of nutrient management systems have been developed, ranging from simple input-output farm gate balances to more detailed assessment of internal transformations, storages, and distribution of nutrients within the farm (Öborn et al., 2003; Gourley et al., 2007). An essential component of these tools is the quantification and management of dairy cow excreta. In the United States for example, concentrated animal feeding operations are

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required to develop nutrient management plans, such as the Manure Management Planner (MMP, 2014), which details the collection, storage and application of manure (Nennich et al., 2005). Nutrient accounting systems such as MINAS were created in response to the European Union Nitrates Directive, for use in the Netherlands to improve management of animal manure (Oenema et al., 2006). Nutrient management plans are also required of New Zealand grazing system dairy farms located in catchments that are particularly sensitive to nutrient losses (Monaghan et al., 2007). It is expected that by ensuring that excreted nutrients are accounted for and used to produce fodder, farmers could reduce the use of imported fertiliser nutrients and minimise environmental impacts of nutrient loss in both grazing and confinement systems (Nennich et al., 2005; Oenema et al., 2006; Monaghan et al., 2007). In the largely confinement production systems in Europe and the US

Corresponding author. E-mail addresses: [email protected] (S.R. Aarons), [email protected] (C.J.P. Gourley), [email protected] (M.C. Hannah).

http://dx.doi.org/10.1016/j.agee.2017.07.010 Received 27 March 2017; Received in revised form 27 June 2017; Accepted 7 July 2017 Available online 17 August 2017 0167-8809/ Crown Copyright © 2017 Published by Elsevier B.V. All rights reserved.

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after milking. Cows may also have been confined in other locations (holding areas and feedpads) on the farms for feeding or other reasons. While feedpads may be concreted, holding areas were most commonly a paddock or a part of a paddock where cows were held. Paddocks that the cows were collected from in the morning or returned to at night were categorised in this study as ‘night’ paddocks, as distinct from ‘day’ paddocks where the cows grazed during the day.

dairy cows are housed in barns and other structures for much of the year, while grazing system dairy cows spend most of their time away from facilities where excreted nutrients can be collected for re-use. For example, in a study comparing manure management practices in contrasting dairy production systems, Gourley et al. (2012a) reported that between 51 and 89% of estimated manure N was deposited by cows to pastures in grazing systems, while less than 43% of excreted N was deposited on pasture in confinement systems. However, in a survey of Canadian dairy farms lactating cows spent on average 19% of their time on pasture in summer, although in some regions and on smaller farms as much as 17 h per day was recorded (Sheppard et al., 2011). The return of excreta by animals to pasture can lead to considerable between paddock spatial heterogeneity in soil nutrient levels observed in grazing system dairy farms (Gourley, 2004; Lawrie et al., 2004; Gourley et al., 2007, 2015; Fu et al., 2010; Aarons et al., 2015). Fu et al. (2010) observed higher soil P levels near the farmyard and where slurry had been applied. Likewise paddocks closer to the farmyard and dairy shed had significantly greater soil P and K levels (Aarons et al., 2015) while those regularly receiving effluent had higher K levels (Gourley et al., 2015). By contrast, paddocks frequently harvested for fodder (eg silage removal and fewer visits by animals) had lower soil K (McCormick et al., 2009; Gourley et al., 2015). These data indicate that soil nutrient levels could be influenced by daily farm management decisions that determine which pastures dairy cows graze, which pastures are used for fodder production, and the location and time animals spend in places around farms. For example, dairy cows can be placed closer to the dairy shed at night to minimise the travel time for the morning milking (Wardrop, 1953). Herds can be held in standing/exercise yards for up to 12 h on grazing system farms (Sheppard et al., 2011). By contrast certain paddocks can be routinely withheld from grazing for silage or hay production (McCormick et al., 2009). Despite this, little research is reported documenting the locations that cows visit and the time spent there on grazing system farms. Spatial and temporal analysis of cow locations has typically focussed on measuring within paddock variability in excreta deposition (e.g. White et al., 2001; Hirata et al., 2011; Moir et al., 2011). In addition, these studies have not quantified the locations of commercial dairy cows to better understand within farm factors influencing herd placement decisions. The objective of this study was to quantify the spatial and temporal variability in where dairy cows spend time in grazed production systems to better understand within farm nutrient flows. We aimed to identify the typical places visited by lactating cows on commercial dairy farms and the time the herds spent in these locations. We then examined the farm management and environmental factors associated with differences in the distribution of time spent in these places. Finally, we used data from the literature to estimate nutrient loads deposited, to quantify the flows of excreted nutrients through the herds around these farms and the potential for collection of excreta, then conclude by describing the implications of these returns.

2.2. Spatial and temporal data collection The participating farms were visited on five occasions (summer; February/March 2008, autumn; May/June 2008, winter; August 2008, spring; November 2008 and summer; February/March 2009) and the farmers interviewed by trained interviewers using a structured questionnaire. An initial meeting was also held with the farmers where they were informed of the questions to be asked at each interview, to ensure they were prepared to provide the required information. At each interview, the farmers were asked to identify the places on the farm the lactating dairy cows had been placed over the preceding 24 h, and the time animals spent in those places. The interview dates were selected to represent each season and the farmers indicated that their management of the cows was typical for each season. To calculate the areas of places visited and the distances walked along laneways, farms were mapped in detail (ArcView GIS 3.3, Environmental Systems Research Institute) using schematics provided by the farmers and aerial photographs that were available (see Gourley et al., 2015 for more details). All paddocks, buildings and other infrastructure, ponds and dams, laneways, and areas not used as part of the production system (such as woodlands and water courses) were identified and dimensions assigned. Distances to the dairy shed were measured from the gate entrance of each paddock. 2.3. Data and statistical analysis After reviewing the dairy cow data recorded at all farm interviews (2964 records), the places visited were categorised as one of seven locations (‘night’ or ‘day’ paddocks, laneways, dairy shed, yards, feedpads, holding areas). The time (in hours) each cow spent in each location over 24 h was used to calculate the percentage of time spent there for analysis of time in locations. The structure of the data necessitated a number of manipulations for data and statistical analysis. While dairy sheds, yards and laneways were identified on all farms at all interviews, only some farms had feedpads and/or holding areas, and paddocks were not visited on some farms on at least one interview date. The unbalanced design where all factor combinations were not present at all interview visits resulted in a total of 1273 ‘location’ records for each herd on each farm at each visit, or 1673 records when the seven locations are included for all farms. For further analysis, the locations ‘night’ and ‘day’ paddocks were pooled into a single class (paddock) to give six management units (paddocks, laneways, dairy shed, yards, feedpads, holding areas) and the time spent in these management units were then compared. Grouping the locations into the six management units resulted in 1464 or 1162 ‘management unit’ records depending on whether all factor combinations were present or not, respectively. The non-normal distribution of data required logarithmic or angular transformation for statistical analysis, while the repeated measures nature of the data collection (ie data collection on a number of occasions on each farm) was suited to REML (Residual Maximum Likelihood) analysis. After preliminary graphical analysis (R program, Version 2.11.0 R Foundation for Statistical Computing) the data were statistically analysed using Genstat Release 17.1; VSN International Ltd, 2014 to summarise the data (mean, median, ranges) and to investigate the effects of fixed and random terms. The time in hours, spent in the seven location categories, the distance of the locations from the dairy shed, as well as the area of these locations were compared by REML mixed

2. Material and methods 2.1. Grazing system farms Data quantifying the spatial and temporal variability in the location of cows were collected on 43 commercial dairy farms representative of Australian grazing systems (Gourley et al., 2012b). The participating dairy farms were located in temperate, Mediterranean, sub-tropical and tropical environments across the major dairy regions (Fig. 1 and Table 1), and reflected the relative proportion of the national industry in each region (Gourley et al., 2012b). Most of these pasture-based grazing system farms were managed conventionally, with four organic dairy farms also included in the study. In all study farms the cows were not housed and grazed year-round, generally travelling each day along laneways between paddocks and to and from the dairy shed where they were milked. Cows were often held in concreted dairy yards before or 176

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Fig. 1. Map showing the location of the participating grazing system dairy farms across the range of edaphic zones in Australia. The colours of symbols represent different regions and the shapes distinguish different farms.

model analysis; where in the fixed model ‘location’ was crossed with interview visit, while in the random model location was crossed with interview visit, all within farm. Thus for time (or distance) yflv

attributes analysed consisted of farm characteristics averaged over the year as well as those specific to the occasion of each interview. These variates included stocking rate, milk production on the day of the interview as well for the year, percentage of the farm irrigated, percentage of the herd’s annual metabolisable energy (ME) requirements that was imported onto the farm, and percentage of the herd’s DM intake that consisted of supplements on the day of each interview. Any differences in where farmers placed dairy cows could have been in response to pasture availability. Pasture growth rate (PGR) corresponding to each interview date at a farm was calculated using growth rate data from the Pastures from Space website (Pastures from space; CSIRO, 2012), as well as for the four weeks leading up to and including the week of the interview. The REML analyses were performed on data for each management unit individually, and consisted of analysis of the effect of the variate (fixed term) on percentage of time, with random terms for Farm split for interview visit.

yflv = μ + pl + iv + pilv + bf + bpfl + bilv + εflv where in the fixed effects μ is the overall mean pl is the main effect of location l iv is the main effect of the survey interview visit v, and pilv is their interaction, and in the random effects bf is the effect of farm f bpfl is the effect of location l within farm f bifv is the effect of survey interview visit v within farm f εflv is the random error term for farm f, location l and survey interview visit v The data were log transformed in response to the unequal variance observed among residuals. The same model was used for analysis of the percentage of time cows spent in the six management units with a factor for management unit taking the place of location. In addition, the management unit records were summarised to give the mean percentage of time in each management unit for all farms (ie from 1464 to 258 records) for pairwise comparison by ANOVA where the treatments (management units) were blocked by Farm. Associations between a number of attributes (Table 2) with where, and for how long, farmers placed their cows in management units were analysed by linear mixed model, also using REML analysis. The

Yfv = μ + βx + bf + εfv where in the fixed effects μ is the overall mean βh is a slope coefficient for variable x, and in the random effects. bf is the effect of farm f εfhv is the random error term for farm f, at survey interview visit v To account for the large geographic spread of the farms and the likely variations in temperature, rainfall and day length experienced by farms at each interview, the effect of climatic factors on how farmers manage cows was further investigated. 177

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Table 1 Characteristics of the 43 grazed dairy farms for which information was recorded about the places cows frequented and the time they spent there. Farm State/ Regiona

Dairy Regionb

Climatic zones Farm Mgtc Contact Landd (ha)

Herd size Stocking Ratee (cows ha−1)

1 2 3 4

Tas Tas Tas Tas

DairyTas DairyTas DairyTas DairyTas

Tas Tas Tas Tas

Conv Conv Conv Conv

306 125 192 365

622 330 386 731

2.0 2.6 2.0 2.0

7 6 5 8

N Qld N Qld SE Qld SE Qld

Subtropical Dairy Subtropical Dairy Subtropical Dairy Subtropical Dairy

N QLD N QLD SubT SubT

Org Conv Conv Conv

126 256 227 134

100 234 117 180

0.8 0.9 0.5 1.3

9

Dairy NSW

SubT

Conv

171

238

1.4

Dairy NSW

SubT

Conv

98

211

2.2

F

Dairy NSW Dairy NSW Dairy NSW

NCst NSW NCst NSW CstNSW

Conv Org Conv

40 162 66

101 180 127

2.5 1.1 1.9

Dairy NSW

CstNSW

Conv

221

251

Dairy NSW Dairy NSW

NSW NSW

Conv Conv

186 53

21 24

Far N Cst NSW Far N Cst NSW NSW NSW Far N Cst NSW Far N Cst NSW NSW Hunter Valley NSW NSW NSW

Dairy NSW Dairy NSW

NIR_CNSW NIR_CNSW

Conv Conv

32 33 34 10 11 12 13 14 15 16 17 18 19 28 29 30 31

NIR Vic NIR Vic NIR Vic Gipps Vic Gipps Vic Gipps Vic Gipps Vic Gipps Vic Gipps Vic Gipps Vic Gipps Vic Gipps Vic Gipps Vic SW Vic SW Vic SW Vic SW Vic

Murray Dairy Murray Dairy Murray Dairy GippsDairy GippsDairy GippsDairy GippsDairy GippsDairy GippsDairy GippsDairy GippsDairy GippsDairy GippsDairy WestVic Dairy WestVic Dairy WestVic Dairy WestVic Dairy

NIR_CNSW NIR_CNSW NIR_CNSW Gipps Gipps Gipps Gipps Gipps Gipps Gipps Gipps Gipps Gipps SW Vic SW Vic SW Vic SW Vic

36 37 38

SA SA SA

DairySA DairySA DairySA

39 40 41 42 43

WA WA WA WA WA

Western Western Western Western Western

44 26 27 20 22 23 25

Dairy Dairy Dairy Dairy Dairy

Feedpad & /or holding areasf

Concreted areasg

Irrigationh (%) Feed ME Importedi (%) 44 58 49 90

F

N

F/H F/H

N FY/HN

(M) (M) (M) (H)

3 (L) 21 (L) 18 (L) 16 (L)

12 (L) (N) 19 (L) 62 (M)

10 52 66 42

54 (M)

15 (L)

N

N

48 (H)

F/H F/H

FY/HN FY/HN

12 (L) 39 (M) 84 (H)

nd 29 (M) 43 (M)

1.1

H

N

28 (M)

50 (H)

440 51

2.4 1.0

F/H H

FY/HN N

21 (L) 70 (H)

29 (M) 32 (M)

361 315

254 155

0.7 0.5

F/H H

N N

22 (L) 17 (L)

61 (H) 51 (H)

Conv Conv Conv Conv Conv Conv Conv Org Conv Conv Conv Conv Conv Conv Conv Conv Conv

236 102 72 127 364 115 168 127 93 65 112 72 58 460 430 143 100

571 162 228 421 603 119 201 131 198 172 318 214 167 540 550 139 133

2.4 1.6 3.2 3.3 1.7 1.0 1.2 1.0 2.1 2.6 2.6 3.0 2.9 1.2 1.3 1.0 1.3

F F F F

N N N Y

H F/H F/H H

N N FY (70%)

F

N

F

Y

37 (M) 86 (H) 78 (H) N N N 4 (L) N 24 (L) 52 (M) 95 (H) N 95 (H) N N N N

61 45 52 42 22 14 40 26 20 33 34 40 29 42 nd 26 32

SA SA SA

Conv Conv Conv

307 307 338

1263 163 315

3.7 0.5 0.9

F F

Y Y

91 (H) 50 (M) 24 (L)

61 (H) 46 (H) 45 (M)

WA WA WA WA WA

Conv Conv Org Conv Conv

371 186 209 89 275

531 231 106 116 441

1.4 1.2 0.4 1.3 1.6

F

N

F

Y

33 57 N 36 40

27 28 32 31 27

(M) (M) (M) (M)

(L) (H) (H) (M)

(H) (M) (H) (M) (L) (L) (M) (L) (L) (M) (M) (M) (M) (M) (L) (M)

(M) (M) (M) (M) (M)

a States (Tasmania; Tas, South Australia; SA, Western Australia; WA) and regions within Victoria (Gippsland-Gipps Vic, South West Victoria-SW Vic, Northern Irrigation Region-NIR Vic), within New South Wales (NSW, Far North Coast; Far N Cst NSW, Hunter Valley NSW) and within Queensland (North Queensland; N Qld, South East Queensland; SE Qld) where the dairy farms were located. b Eight dairy regions within which the study farms were located. The number of farms selected in each regions was based on their proportional contribution to the national dairy industry. c Organic versus conventionally managed farms. d Applies to the land area on each farm that the lactating herd regularly contacts as distinct from the home farm area or all land that the farmer uses as part of their production system (see Gourley et al., 2012 for further details). e Stocking rate applied to the contact land area that the lactating herd regularly visits. f Presence of feedpad (F) and/or holding areas (H). g Farms where feedpad and/or holding areas were concreted (Y) or not (N). h Ranges in percentage of the farm that is irrigated, where N = no irrigation, L = < 24%, M = from > 24 to < 66%, H = > 66% irrigation. i Ranges in feed metabolisable energy requirements (ME) as a percentage of total ME requirements that is imported on to farms, where L = < 26.5%, M = from > 26.5 to < 45.2%,L = > 45.2% feed imported; nd – no data.

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differed from this pattern. Sometimes the lactating herd did not visit any paddocks (for example Farm 32), and milking once or three times daily was also observed (Farms 1 and 29 respectively). Within farms, where more than one herd was present, these may have been managed differently (for example herds 1 and 2 on Farms 3 and 29), while the same herd on a farm often visited different sequences of locations at different interviews (Farm 14). Holding areas where supplementary feed was provided were also used, with some herds spending more time there than grazing paddocks (Farms 8 and 21). Some farms had both feedpads and holding areas (Table 1) but these were not always used at all times (for example Farm 14, Table 3). Likewise more than one feedpad or holding area were often present on farms. In addition to visiting a variety of places on these grazing system farms, the time the herds spent in these places ranged widely (Fig. 2); except for the dairy shed (Fig. 2a). The paddocks on the farms (categorised according to whether the cows visited them during the day between milking, or were placed there overnight), were visited by the cows for between 2 and 19 h (night paddocks) and between 1 and 17 h (day paddocks). Lactating cows spent significantly (P < 0.001; Fig. 2b) more time in the overnight paddocks (mean of 10.8 h) than in ‘day’ paddocks (6.8 h). The mean time spent in holding areas and feedpads was greater when considered for only those farms with these areas (Fig. 2c). The mean travel distance to ‘day’ paddocks was significantly greater (P < 0.001) than the distance to ‘night’ paddocks, with the latter a mean of 118 m closer to the dairy shed than the ‘day’ paddocks. By contrast, the mean travel distances to feedpads and holding areas were less than 100 m (median ≤ 40 m) from the dairy shed (Fig. 3). The mean areas of ‘day’ and ‘night’ paddocks were similar, and both of these locations were significantly (P < 0.001; data not presented) larger than other locations visited by dairy cows on farms.

Table 2 Summary statistics for herd, milk production, farm management and pasture data for the farms for the study year (annual data) and for the interview dates, that were used in statistical analysis of percent time cows spent in locations and management units on dairy farms.

Farm annual data Areab (ha) Herd Ave. annual herd size Ave. stocking ratec (cows ha−1) Milk production Annual total (kL) Per cow (L cow−1) Per ha (L ha−1) Management Irrigationd (%) Feed importe (%) Interview date data Herd Herd size Ave. stocking rate all herds (cows ha−1) Stocking rate each herd (cows ha−1) Milk production Annualf (L cow–1 day−1) Interviewg (L cow−1 day−1) Supplement DM fedh (%) Pasture Ave. PGRi (kg ha−1 day−1)

Min

Mean

Median

Max

Std Deva

40

194

168

460

113.8

51 0.4

326 1.7

221 1.4

1609 3.7

300.1 0.84

373 3198 2948

2229 7320 12388

1668 7448 10866

11247 10445 36637

1972.1 1649.6 7382.1

0 3.1

34 35

28 31.9

95 65.5

31.8 14.99

2 0.06

246 1.64

196 1.4

1300 4.4

180.6 0.893

0.05

1.39

1.18

4.23

0.825

16.2 14.6 0

22.1 22 49.7

22.3 22.1 47.5

27.1 30.6 100

3.72 4.55 25.4

0

21.5

12.4

153

15.9

a

Standard deviation. Applies to the area (ha) on each farm that the lactating herd regularly visits. Stocking rate applied to the land that the lactating herd regularly contacts as distinct from the home farm area or all the land that the farmer uses as part of his production system (Gourley et al., 2012). d Percentage of the farm irrigated. e Feed metabolisable energy requirements (ME) as a percentage of total ME requirements that is imported on to farm. f Milk production averaged across all interview dates for the year for each farm. g Milk production for each herd at each interview date on each farm. h Percentage of the herd's dietary DM fed as supplements. i Average pasture growth rate. b c

3.2. Cow walking speeds The mean walking speeds (m sec−1) were calculated for the herds using the distance walked and time in laneways data for 42 of the 43 farms for which data (n = 235) were available. Walking speeds ranged from 0.12–3.13 m sec−1, (standard deviation 0.489), with a mean of 0.72 m sec−1, and the average (minimum to maximum) distance the herds walked was 644 m (10 to 2685 m; Fig. 4 and Supplementary Table 1). Using one way analysis of variance, walking speeds were statistically significantly different (P < 0.001) between farms and herds, but not between interviews. While the cows from the bigger farms, that had larger herds, travelled more quickly along laneways, greater access to fodder or supplements was not related to walking speed.

To that end, the effect of season or region (Table 1) on the percentage of time spent in each management unit was investigated, as well as the effect of these factors on the impact of the variates on percentage of time in each management unit. Categorical data (eg presence and absence of irrigation) as well as continuous variables grouped into low, medium and high ranges (where low and high represented the lower and upper quartiles of the data) were also used in the statistical analysis. Graphs were prepared using R Studio (Version 0.99.903).

3.3. Time in management units When the percentage of time spent in ‘night’ and ‘day’ paddocks were pooled for REML analysis, cows spent significantly (P < 0.001) more of their time (mean; minimum to maximum) in paddocks (74.2%; 0–97.6%) than in other management units, despite the variety of places cows visited on these farms (Fig. 5 and Table 4). The smallest percentage of time was spent in dairy sheds (1.7%; 0.6–3.1%) which was significantly less than that in all other locations except feedpads. The mean percentage of time cows spent in feedpads was similar to that spent in laneways and holding areas, but significantly less than that spent in yards. Percentage of time in holding areas was similar to that in laneways and yards. The mean percentage of time spent in feedpads (4.4%; 0–77.7%) and holding areas (5.4%; 0–76.4%) was low when averaged over all farms, as these management units are not present on all farms. Of the 43 farms, 20 had feedpads, 13 identified holding areas, with 8 farms having both (Table 1). When the data were analysed taking into account only those farms where feedpads and/or holding areas were present, the mean percent time cows spent in these areas were 9.5 and 18.2% respectively (data not presented).

3. Results 3.1. Places visited by lactating herds The number of individual places the cows visited on dairy farms over the 24 h prior to each interview ranged from six (i.e. Farms 1, 9 and 19, data not presented) up to as many as 19, although many of these places were visited more than once in a day (Table 3, see graphical figure). On many of these grazing system farms, herd management resulted in the cows moving between eleven different places over a day. Thus, a herd travelled from a paddock, in which they had spent the night, along a laneway to the yards where they waited to be milked in the dairy shed. Leaving the dairy shed, cows walked along laneways to a paddock where they remained during the day, after which they again traversed laneways back to the yards and dairy shed for the evening milking. The herd then walked along laneways to a paddock where they would stay for the night. However, almost half the farms 179

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Table 3 Examples of the variety of times spent in paddocks, laneways, yards, dairy sheds, holding areas and feedpads over 24 h by herds on ten of the 43 study dairy farms, in different regions at different interview dates (season, year). Farm

Region

Seasona

Herd

Cow Nob

Paddock

Laneway

Yards

Dairy shed

3 3 8 8 14 14 29 29 32 36 36 39 40 21 44

Tas Tas Qld Qld Vic Vic Vic Vic Vic SA SA WA WA NSW NSW

Au 08 Au 08 Au 08 Wi 08 Au 08 Sp 08 Sp 08 Sp 08 Su 09 Su 09 Sp 08 Wi 08 Wi 08 Su 09 Su 09

1 2 1 1 1 1 1 2 1 1 2 1 1 1 1

150 190 170 175 134 144 315 315 623 600 600 352 172 249 200

21.08 21.13 14.00 11.21 18.54 21.13 17.21 13.21

2.25 1.33 0.42 0.05 1.25 1.38 1.17 1.00 0.67 0.71 0.67 3.04 0.96 0.96 0.84

0.33 1.04 2.04 2.54 1.25 0.96 1.88 1.38 4.24 2.67 2.67 2.08 1.58 3.79 2.17

0.33 0.67 0.33 0.33 0.42 0.54 0.46 0.50 0.45 0.58 0.29 0.46 0.50 0.46 0.37

a b

17.42 17.71 17.71 20.96 3.50 18.92

Holding Area

Feedpad

6.13 9.86 1.71

1.00 0.83 3.25 7.92 18.64 2.63 2.67 0.50

15.29 1.71

Season; Au - autumn, Wi - winter, Sp - spring, Su - summer. Number of cows in the herd.

time, with significant (P ≤ 0.001) state effects observed. On the other hand, significant regional (P ≤ 0.024) effects on the relationship between all other farm attributes and time spent in paddocks were recorded. Percentage of time spent in holding areas was significantly related to herd size (P = 0.025), farm stocking rate (P = 0.009), per hectare milk production (P = 0.01), percent irrigation (P = 0.028), herd size (P = 0.017) and farm (0.007 ≤ P < 0.038) and herd (P = 0.005) stocking rates, when regional factors were included in the analysis. While significant (0.011 ≤ P < 0.035) regional pasture growth rate effects on percentage of time spent in holding areas were observed, none of the pasture growth rate measures were significant. Despite the generally significant (0.001 > P ≤ 0.044) relationship of most farm attributes on percentage of time spent in yards when season and region were included in the analysis, only interview herd stocking rates had significant (0.001 > P ≤ 0.042) season and regional effects. Neither farm area, farm stocking rate, percentage of the farm irrigated nor percentage of DM fed as supplements on the interview date were significantly related to percentage of time spent in the yards. While PGR estimated at the time of the interviews generally appeared to be positively related to time spent in paddocks, negative relationships were observed for the sub-tropical regions (Queensland and Far North Coast NSW). However, the opposite was observed for PGR calculated for the four weeks leading up to the interview, except for farms located in NSW, South Australia and Western Australia. A generally positive relationship between percentage of time spent in paddocks and percentage of the farm irrigated was observed for most state/regions and climatic regions investigated, with only a negative relationship observed for Queensland, South Australia and Tasmania. By contrast, percentage of time in holding areas was negatively related to percentage of the farm irrigated for Coastal NSW, Gippsland, North Coast NSW and Western Australia climatic zones. Percentage of time spent in paddocks, feedpads or holding areas was not significantly influenced by percentage of the farm irrigated when the analyses accounted for the presence or absence of irrigation or the range in percent irrigation (Table 1) on these dairy farms. Percentage of time in paddocks generally decreased for all state/ regions except North Queensland, South East Queensland and Western Australia as percentage of ME requirements imported as feed onto the farm over the year increased. Similarly, percentage of time in paddocks decreased as percentage of DM fed as supplements on the interview date increased, except for Far North Coast NSW, North Queensland, South Australia and Tasmania. Percentage of time spent in holding areas appeared to increase as percentage of feed ME imported increased, in contrast to time in paddocks. Neither the effect of percentage of DM fed as supplements nor percentage of ME requirements

3.4. Farm attributes impacting time spent in management units A number of annual farm characteristics, as well as farmer management practices related to the date of the interview, were associated with the percentage of time cows spent in management units on dairy farms (Table 5, see Supplementary Table 2 for complete data). For instance, percentage of time spent in the dairy shed was positively (0.018 ≤ P < 0.045) related to annual and interview date farm characteristics of herd size and milk production, but negatively related to farmer use of supplementary feed. Stronger positive relationships with farm milk production and interview date herd size and milk production were observed for the percentage of time spent in yards (0.001 > P ≤ 0.018). However, while ME imported onto the farm for the year was positively (P = 0.001) related to percentage of time in yards, percentage of DM fed as supplements on the day of the interview was not associated with time spent there. Percentage of time spent in feedpads was only positively (P < 0.001) related to supplement DM fed at each interview date, but time in holding areas was positively related to both feed ME imported onto the farm (P = 0.001) as well as to supplement DM fed (P = 0.02). By contrast, the percentage of time spent in paddocks decreased as the percentage of ME imported (P < 0.001) and the percentage of DM fed as supplements increased (P = 0.016). Per cow milk production, either annual or for the interview dates, was negatively (P < 0.001) related to percentage of time spent in paddocks in contrast to time in yards, dairy shed or holding areas. Only time spent in yards (P < 0.025) and laneways (P = 0.024) were positively related to pasture growth rate measures (interview date, average of preceding four weeks, indexed against maximum PGR). Neither farm area, stocking rate, nor percentage of the farm irrigated was related to percentage of time cows spent in any management unit (Supplementary Table 2). 3.5. Factors affecting time spent in management units The effects of a number of factors on the time cows spent in management units were further explored in conjunction with the data in Supplementary Table 2. Season was only significant (0.001 > P≤ 0.009) for percentage of time spent in yards or the dairy shed, while dairy regions were only significant for time in holding areas (P = 0.041). State and climatic zones were significant for percentage of time spent in paddocks (P = 0.006, P = 0.035) and holding areas (P = 0.024). In general, these factors did not change the farm attributes that significantly affected the percent time spent in dairy sheds, feedpads, or laneways. By contrast, farm area (P = 0.038) and interview date milk production (P = 0.014) were significantly related to paddock 180

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Fig. 2. Bean plots of time (hr) dairy cows spent (a) in laneways, yards and dairy shed, showing the mean time (red line) and overall mean time for these locations (dotted line) and the outline of the violin plot within the dairy shed bean; and (b) in night or day paddocks and (c) in feedpads and holding areas for all farms for only those farms (Some farms) where these locations were present. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

4. Discussion

imported as feed on time spent in paddocks and holding areas were affected when the analyses included the calculated ranges in percentage of supplement DM fed or in percentage of feed ME imported onto farms.

Most studies in the literature investigating the locations where dairy cows spend time are focussed on understanding within paddock spatial 181

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Fig. 3. Bean plots of the distance (m) from the dairy shed of the locations visited by dairy cows on all grazing system farms; and for feedpads and holding areas for only those farms (Some farms) where these locations were present. Mean for each location (red lines) and overall mean (dotted lines) are shown. Note the difference in scale for each plot and the log scale used for the left plot. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4. Bean plots of the laneway distances (km) travelled by the lactating herd and the walking speeds (m sec−1) of the cows on all grazing system farms. The mean for each plot (red lines) and all data points are shown. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 5. Bean plots of the proportion of time dairy cows spent in a) paddocks, laneway, yards, dairy shed, and b) feedpad, holding areas on grazed dairy farms, as well as for feedpads and holding areas for only those farms (Some farms) where these locations were present. The mean for each location (red) as well as the overall mean (dotted line) for the feedpad and holding areas are shown. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

grazing animals and were typically applied to small groups of research animals. Fewer studies (e.g. Wardrop, 1953; White et al., 2001) have monitored dairy cow movement in different parts of dairy farms, and these were largely focussed on quantifying urination and defecation

patterns, often linked to defaecation, urination or grazing activities. Global positioning systems (e.g. Oudshoorn et al., 2008; Moir et al., 2011) or observational methods (such as White et al., 2001; Hirata et al., 2011) have been used to quantify the locations and activities of 183

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teat cup and all teat cups being removed. Mein and Thompson (1993) recorded mean milking times of 6.9 and 9.8 min for cows that were milked three times a day and twice daily milking respectively. Faster work routine times were recorded by Armstrong and Quick (1986) with times generally less than 1 min per cow. The time cows spent in the dairy shed in this study were based on estimates of the time taken for the animal to move into and out of the dairy shed, attaching and detaching teat cups, as well as washing teats. Consequently the mean times in this study would be greater than the times reported above for milking (teat cups on and off) only. To further validate the methodology, information about the time cows entered and left laneways was used in conjunction with GIS data for the farm, to calculate cow walking speeds. The mean walking speeds (0.72 m sec−1) in this study are similar to those reported in the literature. For example, mean walking rates of 0.81 to 0.85 m sec−1 for cows walking on surfaces either covered with aggregate or uncovered (Phillips and Morris, 2001) and between 0.79 and 0.8 m sec−1 for dairy herds in New Zealand suffering different prevalence of lameness (Chesterton et al., 1989) have been reported. Cow walking speeds of up to 1.4 m sec−1 for cows with and without lesions (Chapinal et al., 2009) have been observed, while Darold et al. (2003) give average cow walking speeds for Australian dairy herds of 0.56 to 0.83 m sec−1 and up to 1.25 m sec−1 on good laneway systems. The data from the literature indicate that the methodology used in this study provides a reasonable estimate of cow travel times, with differences in mean cow walking speed between these farms likely to be due to lane surfaces and animal foot health as well as disparities in farm management (Chesterton et al., 1989). Depending on the farm cows walked unsupervised along laneways from the paddocks to the dairy shed, while on others cows were herded using vehicles. These practices also varied between interview dates for farms. Although the interview teams differed between regions, the same team interviewed all farmers in a

Table 4 Non-parametric P-values for pairwise comparisons of time spent by dairy cows in management units on grazed dairy farms.

Dairy shed Feedpad Holding Area Laneway Paddocks

Feedpad

Holding

Laneway

Paddocks

Yards

ns(0.071)

0.034 ns

< 0.001 ns ns

< 0.001 < 0.001 < 0.001 < 0.001

< 0.001 < 0.001 ns < 0.001 < 0.001

Probabilities determined from 4999 random permutations.

events. More rarely has the time herds spend in locations on commercial farms been reported (Sheppard et al., 2011). 4.1. Estimating time cows spend in places on farms In this study we developed a methodology based on interviewing farmers about the places within dairy farms that were visited by lactating herds over a 24 h period and the time the animals entered and left those places. In developing the method it was important to calculate the average time the animals spent in a place, rather than the total time for the herd. This approach was needed to overcome the tendency for farmers to give, for example, times of two hours in the dairy shed, rather than a more accurate estimate of 10 or 15 min per cow. A similar method was used by Gutiérrez et al. (2009) to estimate milking time for grazing system herds in Uruguay. In our study the mean total time a cow spent in the dairy shed was 25 min over 24 h, which, for twice daily milking, equates to 12.5 min per milking. This time is greater than milking times reported by others where cows were timed during milking. For example, Touchberry and Markos (1970) measured a mean of 5.19 min between attaching the last

Table 5 Statistical F-test (and effects) output from the REMLa analysis of percent time cows spent in six management units on dairy farms versus herd, milk production, farm management and pasture variates and environmental factors for the farms for the year or from each interview date. Paddocks (n = 209)

Laneways (n = 211)

Yards (n = 211)

Dairy (n = 211)

Feedpads (n = 98)

Holding areas (n = 68)

ns

ns

ns; 0.052 (3.18E04)

0.045 (1.02E-04)

ns

ns

ns < 0.001 (−1.72E04) ns

ns ns

0.003 (0) < 0.001 (7.82E-05)

0.029 (0) ns

ns ns

ns ns

ns

0.018 (1.19E-05)

ns

ns

ns

< 0.001 (−2.17E02)

ns

0.001 (7.63E-03)

ns

ns

0.001 (8.10E-02)

ns

0.002 (7.14E-04)

< 0.001 (6.44E-04)

ns

ns

Ave. stocking rate all herds (cows ha−1) Stocking rate each herd (cows ha−1) Milk production (L cow–1 day−1) Annualc

ns

ns

0.018 (9.16E-02)

ns; 0.061 (9.32E05) ns

ns

ns; 0.052 (-8.28E-01)

ns

0.006 (1.37E-01)

0.004 (1.17E-01)

ns

ns

ns

< 0.001 (-6.94E-02)

ns

< 0.001 (2.96E-02)

ns

0.015 (2.626E-01)

Interviewd Management Supplement DM fede (%)

ns

ns

0.004 (1.51E-02)

0.018 (3.24E-03)

ns; 0.058 (8.28E02) ns

0.016 (-3.21E-03)

0.032 (-2.55E-03)

ns

0.018 (-5.93E-04)

< 0.001 (1.89E02)

0.02 (1.40E-02)

Farm annual data Herd Ave. annual herd size Milk production Annual total (kL) Per cow (L cow−1) Per ha (L ha−1) Management Feed ME importb (%) Interview date data Herd Herd size

a b c d e

In the REML analyses the data were blocked for interview dates within farms, and each management unit was analysed separately. Feed metabolisable energy requirements (ME) as a percentage of total ME requirements that is imported on to farm. Milk production averaged across all interview dates for the year for each farm. Milk production for each herd at each interview date on each farm. Percentage of the herd's dietary DM fed as supplements.

184

ns

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time in standing yards (i.e. holding areas) on grazing system farms (Sheppard et al., 2011).

particular region; thus management rather than the interviewer contributed to variations in cow walking speed. The positive relationship between farm area and herd size on walking speeds also supports the influences farm practices had on mean walk speed. Farmers whose cows have greater distances to travel may have been more inclined to encourage their movement from one location to the other, rather than allowing the cows to move at their leisure. Herd differences observed in walking speed are explained by the placement of second herds typically in paddocks adjacent to the dairy shed, with shorter travel distances and walk times. On dairy farms with more than one herd, the second herd often consisted of a much smaller group that had been segregated for specific purposes, such as calving or health management. Only one farm had three herds with the third herd consisting of 40 cows kept close to the dairy shed.

4.3. Farm management practices The disproportionate use of certain paddocks and the high use of feedpads and holding areas could be associated with farm practices that influence where farmers place cows and the duration the cows are held in those locations. Not surprisingly the time spent in dairy sheds and yards was positively related to milk production and herd size, with time spent in these places significantly affected by season; most likely due to the seasonal nature of milk production nationally. Greater use of purchased feed (either percentage of total farm ME imported, or percentage DM fed as supplements at each visit) was associated with decreased use by farmers of paddocks and increased use of holding areas, feedpads, and yards; locations where purchased feed is provided. Although purchased hay and silage can be fed on paddocks, it appears that holding areas (usually heavily stocked parts of paddocks) were more frequently used in this study. The positive relationship between time spent in yards and use of purchased feed could be associated with cows being held there before being moved to holding areas. The relationships between time spent in paddocks and holding areas and use of irrigation in some regions could be due to the more consistent availability of pasture negating the requirement to feed animals in places other than paddocks. However, no relationship between percent of farms irrigated and time spent on feedpads was observed for any region, although in initial analyses a negative relationship was observed when the repeated measures structure was not used (ie data were not blocked for farm split for interview visit). Sheppard et al. (2011) also reported regional, farm size and seasonal influences on where cows spent time. Not unexpectedly cows spent virtually all day in barns on most Canadian farms in winter, while slightly less than half were housed in summer. On larger farms cows rarely grazed pasture compared with smaller farms, but there were regional differences in the use of housing on large and small farms. We expected that PGR (as an indication of pasture availability) would influence farmers’ selection of pasture versus purchased supplementary feed and therefore the time cows spend in paddocks in comparison to feedpads and holding areas. Surprisingly, no clear relationship was observed for these management units for any of the PGR measures analysed, possibly due to the range of climatic (interview date, season, region) influences as farms in this study were located across temperate to tropical climatic zones. The relationships indicated by these analyses of, for example, a decrease in time in paddocks and increases in time in yards and holding areas in association with increases in imported feed suggests that farm management practices could influence nutrient distribution around farms for the range of grazing systems managed in Australia. Moreover, when the data were analysed without blocking for farm highly significant relationships were observed (P ≤ 0.001, data not presented), suggesting farm management practices influence the locations where farmers place their herds. The design of this study however does not allow a thorough analysis of these relationships. Thus, differences in placement of animals in management units, particularly feedpads and holding areas, as well as in use of paddocks, dairy sheds, laneways and yards on some occasions led to differences in management unit records. In addition, the unbalanced design of this survey (conducted to quantify cow movement on a representative range of grazing systems), makes it difficult to ascribe management practice impacts on percentage of time cows spent around farms. This study indicates that future research, specifically designed to investigate these relationships, is warranted. Greater understanding of the influence of farm practices on herd movements will lead to development of interventions to improve nutrient management on farms.

4.2. Where lactating herds spend time The grazing systems compared in this study were typical of the breadth of farm management practices across the Australian dairy industry (Gourley et al., 2012b), and accordingly these data are considered representative of the types of grazing cow management practiced nationally. In addition, the data collected from each farm in this study were described by the farmers as representative of their herd management for the season represented by the interview and thus provides a useful snapshot of dairy cow movement averaged over a year for these systems. The perception that lactating dairy herds are mostly confined to a single paddock between milkings, and that the latter occurs only twice each day understates the complexities of grazed dairy systems. The only constant on all farms was the requirement to move cows to and from the dairy shed and associated yards for milking. However, where cows spent time between milkings varied greatly; often more than one herd were milked; and on some farms cows were moved frequently to manage feed availability or for other purposes. Thus, farmers utilised a range of locations to feed and manage their cows and made use of various combinations of these locations at different times throughout the year and for different herds. When cows grazed paddocks, these were also not uniformly visited on these dairy farms. Cows can spend more time in paddocks grazed between the evening and morning (eg from 4 pm to 6 am) milking. However, these ‘night' paddocks were not evenly distributed around the farm, with paddocks where cows were placed overnight significantly closer to the dairy shed (presumably for convenience) than daytime paddocks. Wardrop (1953) reported greater defecations and urinations in ‘night’ compared with ‘day’ paddocks and concluded that the transfer of nutrients to ‘night’ paddocks would be a likely consequence of this management. Thus the uneven distribution both of time spent in ‘night’ paddocks and the location of these paddocks relative to the dairy shed are likely to be factors contributing to the observed accumulation of nutrients close to dairy shed on grazed dairy farms (Fu et al., 2010; Gourley et al., 2015). Despite the different locations on grazing dairy farms, on average cows spent almost three quarters of their time on paddocks (combined ‘night’ and ‘day’), only about 10% of their time in the dairy shed and yards, and a similar amount in feedpad and holding areas. However the time in feedpads and holding areas more than doubled when analysed for only those farms with these management units, while the time in paddocks decreased to between 62 and 67%. This lower dependence on grazing pasture paddocks has also been reported for irrigated dairy farms in northern Victoria where purchased fodder crops are used to supplement home-grown forage (Wales et al., 2006). In year-round grazing systems in North Carolina, USA, White et al. (2001) observed that lactating Holstein and Jersey dairy cows only spent approximately 3.4% of their time being milked (in the milking parlour and associated yards), 86% of their time grazing pasture and 7.3% in feeding areas. In some regions of Canada, lactating cows spent on average up to 50% of 185

186

0.270

0.192

142

Wilkerson et al. (1997)

Yan et al. (2006)

205, 220, 177, 150

Wyatt (2004) Wyatt (2004) Wyatt (2004) et al. (1997)

Weiss and Weiss and Weiss and Wilkerson

Valk et al. (2002) Wattiaux and Karg (2004)

Valk et al. (2002)

209

0.208

0.272

211, 251, 206, 247 56 27 47

43.7, 25.6, 39.2, 21.3 18.0,

15.3, 14.2; 32.8, 23.5; 30.6, 22.4

29.1, 26.5; 40.7, 28.7, 22.8; 35.7, 21.1 28.1, 21.1; 36.5, 27.9, 21.9; 35.1,

0.1054

0.074

68.3

Nennich et al. (2005) Nielsen et al. (2001) Ohlsson and Kristensen (1998) Valk et al. (2002)

0.2165

331, 342

0.0739

0.2223

181, 193

184.3, 176.4, 172.0, 167.9 161 213

0.223

0.3434

0.2003

152

146

208.3, 178.4, 214.9, 192.8 162 243

118

161

37.3, 37.2, 45.3, 46.5, 46.6, 86.1, 87.0, 100.5, 83.1, 87.1, 107.6, 103.9, 114.5, 115.2, 121.1 47.2, 41.1, 46.4, 39.5

Multiparous and past peak lactation, averaging 156 DIM; Ohio, USA; Holstein; g day−1

166

Multiparous dairy cows; Netherlands; Holstein-Friesian; g day−1 48 multiparous cows during week 13 and 14 of the lactation; Wisconsin, USA; Holstein; g day−1 54 Holstein cows; USA; g day−1 46 Jersey cows; USA; g day−1 Lactating Holstein cows in their second or greater lactation; USA; g day−1 The group of cows that produced > 20 kg/d of milk during the balance trial averaged 29 kg/d of milk and represented a herd that produced 9000 kg of milk annually; Maryland, USA; Holstein; kg day−1 The group of cows that produced < 20 kg/d of milk during the balance trial averaged 14 kg/d of milk and represented a herd that produced 4000 kg of milk annually; Maryland, USA; Holstein; kg day−1 564 lactating dairy cows (535 Holstein-Friesian and 29 Norwegian breed) with milk yields during digestibility measurements ranging from 6.1 to 49.1 kg/d; Ireland; HolsteinFriesian & Norwegian; g day−1

Multiparous dairy cows; Netherlands; Holstein-Friesian; g day−1

Multiparous dairy cows; Netherlands; Holstein-Friesian; g day−1

Multiparous Jersey (n = 8); cows removed twice daily for milking (0600 and 1800 h); USA; g day−1 Multiparous Holstein (n = 8); cows removed twice daily for milking (0600 and 1800 h); USA; g day−1 Twelve multiparous lactating Holstein; Florida, USA; Holstein; g day−1 30 multiparous and 6 primiparous Holstein-Friesian dairy cows fed the concentrate portion of their diet twice daily at each milking; Ireland; Holstein-Friesian; g day−1 Estimated in new ASAE tables. Lactating cow excretion estimates are based on a 625-kg cow producing 40 kg of milk per day with intakes of 25 kg of DM, 4.38 kg of CP, 0.095 kg of P, and 0.325 kg of K per day; USA; kg day−1 The LACT dataset (554 cows or cow-periods from Latin square experiments) included multiparous lactating Holstein cows; USA; kg day−1 Data on P and K intake and excretion for lactating animals were only available for a subset of animals in the LACT data set. The MINERAL data set (85 cowperiods) included cows for which excretion of faeces and urine were known. K total includes 0.1505 urine K; USA; kg day−1 15 early lactation cows; USA; kg day−1 As reported in Borsting et al., 2003; Denmark; kg cow−1 As reported by Swensson 2003; Denmark; g day−1

Thirty-six Holstein cows (27 multiparous) (early- and midlactation); Virginia, USA; g day−1

Early or mid-lactation cows multiparous Holstein-Friesian dairy cows in early or midlactation; Reading, UK; g day−1 Cows with low, medium and high P intake at weeks 3, 5, 7, 9, 11 of lactation; Virginia, USA; Holstein; g day−1

Multiparous and past peak lactation, averaging 156 DIM; Ohio, USA; Jersey; g day−1

As reported in Borsting et al., 2003; Denmark kg cow year−1 Multiparous and past peak lactation, averaging 156 DIM; Ohio, USA; Holstein & Jersey; g day−1

Comments; Location; Breed; Units

65 93, 101, 190, 185

Urinary

K

62 178, 184, 198, 197 218

Faecal

P

Nennich et al. (2005)

0.5732 116–130 283.58

0.491

Nennich et al. (2005)

Nennich et al. (2005)

355, 467, 340

392.6, 354.9, 386.9, 360.8

127

Total

N

Morse et al. (1992) Mulligan et al. (2004)

Knowlton et al. (2010) Knowlton et al. (2010)

Knowlton et al. (2001)

Knowlton and Herbein (2002)

Borsting et al. (2001) Kauffman and St. Pierre (2001) Kauffman and St. Pierre (2001) Kauffman and St. Pierre (2001) Kebreab et al. (2001)

Citation

Table 6 Data from reference literature used to calculate mean and ranges of nitrogen (N), phosphorus (P), and potassium (K) excreted by dairy cattle. Note difference in units for some sources.

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Table 7 Mean (range) of annual nutrient deposition (t) by a 326 lactating herd to management units on a dairy farm over a 300 day lactation.

Pa

Paddocks

Laneway

Dairy shed

Yards

Feedpad

Holding Area

3.64 (1.03–8.79)

0.28 (0.08–0.69)

0.08 (0.02–0.20)

0.42 (0.12–1.01)

0.22 (0.06–0.52)

0.26 (0.07–0.64)

N

26.83 (11.83–41.60)

2.09 (0.92–3.25)

0.63 (0.28–0.97)

3.07 (1.36–4.77)

1.60 (0.70–2.48)

1.94 (0.86–3.01)

K

18.55 (14.54–24.92)

1.45 (1.13–1.94)

0.43 (0.34–0.58)

2.12 (1.67–2.85)

1.10 (0.87–1.48)

1.34 (1.05–1.80)

a Nutrient deposition is calculated using the average excretion rates from the literature (Table 6) in conjunction with the time the lactating cows spent in each management unit on each farm at each interview date.

excreted there are likely to contribute to the accumulation of nutrients close to the dairy shed observed on these (Gourley et al., 2012a, 2015) and other dairy farms (Fu et al., 2010). Further, effluent collected from the dairy shed and yards is often returned to nearby paddocks, exacerbating the high nutrient loads in areas close to the dairy shed. Night paddocks, also closer to the dairy shed, likewise were calculated to receive on average 2.2, 16.1 and 11.1 t P, N and K. Losses (including from laneways) are estimated to be 1.5, 11.2, 7.8 t of P, N and K respectively over the lactation, where feedpads and holding areas are not concreted (data not presented). The estimated N losses amount to a larger proportion of excreted nutrients than reported for N losses on the year round grazing farms studied by Gourley et al. (2012a), reflecting the wider range of grazing systems investigated in this study. Calculations of nutrient depositions highlight the role of holding areas and non-concreted feedpads in nutrient losses from the production system, and the importance of better quantification of the locations cows visit and the factors affecting the deposition of excreted nutrients in these places. If time in yards is increased in response to importation of feed and use of supplements, then the amount of captured excreta will increase and disposal of these nutrients will need to be managed (Gutiérrez et al., 2009). If, on the other hand, time in unproductive areas with no excreta collection (eg holding areas and non-concreted feedpads in this study) increase, then alternative management strategies will need to be developed so that farmers can get benefit from these nutrients while minimising negative environmental impacts. The calculations in this study assume an even diurnal distribution of excretion events and nutrient amounts. White et al. (2001) reported a significant correlation between time spent in locations on dairy farms and the percentage of excretions that occurred in those areas. Similarly Wardrop (1953) observed that on average about half the defecations and urinations occurred over night while 24 and 28% respectively were deposited on day paddocks. In contrast, Hirata et al. (2010) observed that cow and heifer urinations and defecations occurred more frequently and in greater amounts during the day than overnight, while the amount of faeces produced per faecal output was greater at night than during the day. Further quantification of the diurnal variation in excretion events and in their nutrient content are required for grazing systems, to allow accurate estimation of nutrient loading rates in different parts of dairy farms. The nutrient accumulation evident in areas where animals congregate or are placed for extended periods has implications for on-farm nutrient use efficiency. In addition to recommended approaches to minimise nutrient intake and improve nutrient conversion efficiency for dairy cows (Kebreab et al., 2001; Jonker et al., 2002; Wu et al., 2003), recycling and deposition of nutrients by cows around dairy farms are important components of improving nutrient management in grazing systems. Kobayashi et al. (2010) proposed increasing the rate of cycling and thus the farm cycling index to improve nutrient use efficiency in dairy production. Likewise, Gourley et al. (2007) recommended quantifying nutrient pools, transformations and flows in Australian grazed dairy systems to support nutrient management decisions. Information about the locations dairy cows visit on grazing farms, the time spent there and the farm management and environmental factors that influence farmers’ use of these areas will greatly contribute to improving

Table 8 Estimated annual nutrient deposition (t) by a 326 cow herd within 100 m of the dairy shed, to paddocks, and lost on laneways on a dairy farm over a 300 day lactation.

P N K

Close to dairy shed (< 100 m)

Paddocks

Laneways

Total

Captured

Not captured

Night

Day

Lost

0.99 7.30 5.05

0.50 3.69 2.55

0.49 3.61 2.49

2.18 16.06 11.10

1.45 10.72 7.41

0.29 2.12 1.46

4.4. Implications for nutrient deposition and management Nutrient management approaches that account for excreta returns (Nennich et al., 2005; Oenema et al., 2006; Monaghan et al., 2007) to all locations on farms could be a sound approach to ensure that Australian dairy farmers make best use of nutrients recycled by their herds (Gourley et al., 2007). In New Zealand and the USA waste collection is based on estimates of 10 to 20% and 15% (respectively) of excreta deposited in milking areas (Gutiérrez et al., 2009); a calculated 144 to 288 and 216 min (respectively) spent in places where excreta can be collected. These researchers developed a methodology for Uruguayan pasture based dairy systems to estimate waste collection from lactating cows (Gutiérrez et al., 2009), and calculated milking times of between 42 and 216 min per day based on the average time cows spent in ‘holding pens’ (yards) and being milked. Dairy cows in this study spent an average (minimum to maximum) of 147 (27 to 360) minutes in the dairy shed and yards. While the average times spent during milking are similar, the range in the data for both systems suggest that more accurate estimates of time spent by cows in the dairy shed and yards for each farm will enable better quantification of excreted nutrients for disposal. Information about time spent by cows in locations around grazed dairy farms, in conjunction with excretion rates (eg kg P cow day−1) can be used to calculate nutrient loads deposited in these places. Based on data from the literature (Table 6), P, N and K excretion rates of 50, 370 and 256 g cow per day respectively were used to calculate nutrient loads deposited to management units on an ‘average’ grazing system dairy farm over a 300 day lactation by the mean number of cows in this study (Table 7). Twenty percent of P, N and K excreted (1.0, 7.3 and 5.1 t respectively) were returned within 100 m of the dairy shed, with approximately half collected for re-use (Table 8). For only those farms with feedpads and/or holding areas, the excreted nutrients deposited close to the dairy shed increases to about 2, 13 and 9 t P, N, K respectively. Approximately 70% of these deposited nutrients is not captured if the feedpads and holding areas are not concreted to facilitate collection of excreta. Of the 20 farms in this study with feedpads only half of these were concreted from which excreta could be scraped for re-use, making the amounts that could be captured from these facilities an estimated 0.8, 5.8 and 4.0 t P, N and K respectively (data not presented). Sheppard et al. (2011) likewise report the infrequent cleaning of standing yards on Canadian dairy farms and suggest the potential for contribution of these areas to ammonia losses. Non-concreted feedpad and holding areas and the nutrient loads 187

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within farm nutrient cycling and therefore nutrient use efficiency. However, the between farm variability in herd placement means that interventions will need to be farm specific and thus tools are required that assist farmers to better record herd locations and duration in these places. 5. Conclusions This research shows considerable spatial and temporal heterogeneity in where dairy cows spend time on grazing system farms, with considerable amounts of time spent in areas where excreted nutrients are not typically managed. The consequence of the variability in where cows visit and the length of time they spend in these locations is that excretal return and therefore the deposited nutrient loads will vary. The heterogeneity in animal placement also appears to be related to animal management activities such as provision of supplementary feed. These data indicate that dairy farmers can get greater value from feed nutrients by managing stock placement such that animals spend less time where nutrients are not collected or where they are accumulating. In addition, ensuring excreta is collected from all areas where animals spend large amounts of time and that the collected nutrients are better distributed on grazed paddocks about the farm will improve the efficiency of use of excreted feed nutrients. Interventions are required that incorporate the between farm variability in herd management, acknowledge the range of places herds visit and the management of excreta in these places, and allow for improved estimation of nutrient loads deposited by grazing animals. Research is required a) to assess relationships between farm management practices and animal movement and b) to estimate nutrient excretion by quantifying nutrient intakes associated with the feed types and feed systems used throughout the year on farms. In this way, nutrient management programs can be developed that target herd management practices and are more spatially explicit for grazing system farms. Acknowledgements We would like to thank the 43 farmers who participated in this research and willingly provided information about their herd management practices on the five separate occasions. We also thank the eight regional survey teams who collected the lactating herd location and time (Cows in Space and Time) data for each farm, and especially Lianne Dorling for painstakingly entering and validating the data. We are grateful to Paul Durling for creating the digital maps that were used in this study. Finally we gratefully acknowledge the suggestions of anonymous reviewers that improved this manuscript. This project was funded by Dairy Australia (DAV 12307) and the Victorian Department of Economic Development, Jobs, Transport and Resources (MIS06854). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.agee.2017.07.010. References Aarons, S.R., Gourley, C.J.P., Hannah, M.C., 2015. Between and within paddock soil chemical variability and forage production gradients in grazed dairy pastures. Nutr. Cycl. Agroecosyst. 102, 411–430. Armstrong, D.V., Quick, A.J., 1986. Time and motion to measure milking parlor performance. J. Dairy Sci. 69, 1169–1177. Børsting, C.F., Kristensen, T., Aaes, O., 2000. Kvøg, ab dyr. In: Poulsen, H.D., Børsting, C.F., Rom, H.B., Sommer, S.G. (Eds.), Kvølstof, fosfor og kalium i husdyrgædning—normtal. DJF Rapport, Nr . 36. Danmarks JordbrugsForskning, Husdyrbrug, pp. 42–58. Børsting, C.F., Kristensen, T., Misciattelli, L., Hvelplund, T., Weisbjerg, M.R., 2003. Reducing nitrogen surplus from dairy farms. Effects of feeding and management. Livest. Prod. Sci. 83, 165–178. Chapinal, N., de Passillé, A.M., Weary, D.M., von Keyserlingk, M.A.G., Rushen, J., 2009. Using gait score, walking speed, and lying behavior to detect hoof lesions in dairy

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