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Research Paper: SEdStructures and Environment

Reducing variability in estimating wastewater compositionin dairy farms during milking operations

Soledad Gutierrez*, Noel Cabrera, Alejandra Benıtez, Enzo Melani

Departamento de Ingenierıa de Reactores, Instituto de Ingenierıa Quımica, Facultad de Ingenierıa-Universidad de la Republica Oriental del

Uruguay, Montevideo 11300, Uruguay

a r t i c l e i n f o

Article history:

Received 14 May 2008

Received in revised form

13 May 2009

Accepted 9 June 2009

Published online 3 July 2009

* Corresponding author.E-mail address: soledadg@fing.edu.uy (S.

1537-5110/$ – see front matter ª 2009 IAgrEdoi:10.1016/j.biosystemseng.2009.06.004

The estimated waste on dairy farms has been studied from a nutrient management

perspective in order to handle waste in an environmentally-friendly way. For pasture-based

dairy operations, estimating the amount of manure collected in the milking area is critical for

designing management systems. With this aim, the ‘‘average stay time’’ concept was

developed as a relevant parameter for the cattle on the milking area. It is possible to estimate

the average milking time based on individual milking time, number of cows, number of

milking units and batches used to divide the cattle. This time varied, from 0.7 to 3.6 h d�1 in

the farms studied. The waste under local operating conditions was characterized with

experimental data from five farms. An adjusted estimate for solids and nutrients was

generated by normalizing the stay time. Mean values per cow per day for 2 h of average stay

time were 590 g total solids (TS), 24.6 g total Kjeldahl nitrogen (TKN), 400 g Chemical Oxygen

Demand (COD) and 2.6 g total phosphorous (TP). Comparing data obtained using a standard

estimate with this developed estimate, a reduction in standard deviation from 41 to 16% in

TKN and 46 to 10% TS was obtained. The adjusted variables were tested from over a year on

another farm, showing a significant improvement for estimates.

ª 2009 IAgrE. Published by Elsevier Ltd. All rights reserved.

1. Introduction environmental point of view it is necessary to correctly esti-

Uruguay has 5000 dairy farms and one dairy cow per four

people. Therefore, dairy farm production represents a serious

environmental problem in terms of potential water contami-

nation and soil degradation (Gutierrez and Cabrera, 2005;

DIEA, 2009).

Estimating animal waste production on dairy farms has

been studied worldwide from a nutrient management

perspective in order to handle local waste in an environ-

mentally friendly way. It has also been evaluated to estimate

greenhouse gas emissions. For nutrient management it is

paramount to develop nutrient management plans and to

design storage facilities (Nennich et al., 2005). From the

Gutierrez).. Published by Elsevier Ltd

mate waste generated in order to design treatment, storage

and disposal systems, as well as to estimate greenhouse gas

emissions (Dong et al., 2006).

To aid nutrient management planning, authorities from

milk-producing countries have proposed methods to estimate

rates of manure excretion in line with their specific produc-

tion conditions. For example, in the USA there is a standard

for design purposes about manure production and charac-

teristics (ASAE, 2003) and the Agricultural Waste Management

Field Handbook (USDA, 1992).

Also for designing environmental pollution control

systems there are estimates of the composition of wastewa-

ters generated while washing the facilities, as well as of the

. All rights reserved.

Nomenclature

Ai adjusted estimate of waste component i

(g cow�1 d�1)

Aie experimental adjusted value of i (g cow�1 d�1)

Ci concentration of waste component i in

wastewater (mg l�1)

COD chemical oxygen demand

gj number of cows in set j

nDC number of dairy cows

n number of milking units

NSi national standard estimate of waste component i

(g cow�1 d�1)

NAi national adjusted estimate of waste component i

(g cow�1 d�1)

Si standard estimate of waste component i

(g cow�1 d�1)

Sie experimental standard value of waste component

i (g cow�1 d�1)

t average milking time (h d�1)

ta average milking time (min milking�1)

ti idle time (min)

tij idle time for set j (min)

tm mean individual milking time (min)

tu total milking time, from the moment the first cow

enters the milking area until the last one leaves

(h milking�1)

TKN total Kjeldahl nitrogen

TP total phosphorous

TS total solids

VSS volatile suspended solids

b i o s y s t e m s e n g i n e e r i n g 1 0 3 ( 2 0 0 9 ) 4 9 7 – 5 0 3498

solid residues generated and stored. In New Zealand author-

ities have issued a manual (Dairying and the Environment

Committee, 2006) which defines parameters in order to design

management systems for dairy farms. For our region, work

has been done (Nossetti et al., 2002; Charlon and Taverna,

2004; Salazar et al., 2007), however, design parameters are still

not available. Argentinean Institute of Agricultural Tech-

nology (INTA) has issued a manual of experimental data of

collected solids characteristics during milking (Taverna et al.,

1999). Table 1 shows data from these sources.

The daily excreted manure and its nutrient content depend

on animal weight and feeding (Van Horn, 1998). According to

Van Horn, there is little difference expected between species

when animals consume diets of similar nutrient composition

and digestibility. It is then expected that differences in manure

amount and composition will be experienced between US

herds (animals of 650–700 kg live weight and fed in an intensive

production system), and those in New Zealand or South

America (500–550 kg live weight with pasture-based feeding).

Uruguay has a traditional pasture-based milk production,

originally feeding the cattle by grazing on natural pastures.

Table 1 – Reference parameters for dairy farm wastecollection in milking centers from different regions

TS TKN TP Conditions

g cow�1 d�1

New Zealanda 550 22 2.5 Reference parameters for design

purposes based on 10–20% of total

daily manure excreted.

Argentinab 363 * * Experimental data (number of

samples occasion not described).

USAc 742 44.5 4.5 Based in reference parameters for

450 kg live weight. Calculated

assuming 15% of total daily manure

collected.

*means data not available.

a Dairying and the Environment Committee (2006), Chapter 1,

Table 1.6-1, and Chapter 2, Table 2.2-1.

b Taverna et al. (1999).

c USDA (1992), Tables 4–6.

Due to the increasing worldwide demand for dairy products,

farmers currently provide feeding based on prairies, forage,

silage, etc., in addition to traditional natural pastures. The

cattle are mostly Holando breeds of 500 kg live weight. Due to

farm management, the herd is usually under-fed in compar-

ison with New Zealand practices. Therefore, less individual

milk production is obtained, although live weight is compa-

rable. Owing to the differences between production regimes, it

is necessary to evaluate national conditions related to excreta

amount and composition in order to establish national

parameters for design purposes.

For pasture-based cattle operations, as well as the vari-

ability in manure composition, the uncertainty relating to the

time the cattle stay in waste collection areas must be added,

and this depends greatly on the type of operation. This is

generally time required for milking operations, which varies

widely. Collected waste is usually estimated as a percentage of

total daily excreta. However, it is preferable to use experi-

mental farm data when available, as the amount collected is

very variable. For example, New Zealand (Dairying and the

Environment Committee, 2006) design parameters are based

on 10–20% of daily total excreta, while USDA (1992) assumes

that 15% residue is collected during milking.

In Uruguay, waste collection in dairy farms mostly derives

from the milking operations. Naturally, the amount of residue

collected depends on the length of time cattle stay in the

milking area, which under our conditions can vary between

40 min and 5 or 6 h. This broad time range is generally the

consequence of increases in herd size while milking facilities

have retained the same dimensions that were adequate for

the original herd. Presupposing that cattle produce waste

proportionally to length of their stay in the area, this result in

a variation of 10 times in the amount of residue found. This

poses a serious problem since traditional estimates regarding

expected amount of residues for example 15% of the daily

waste, or 1 kg cow�1 d�1 suggested by the Uruguayan envi-

ronmental authorities (DINAMA, 2004) can over or under

estimate actual waste amount, causing serious flaws in design

of treatment and storage systems. Estimating the mean time

that cows spend in the area could be useful to reduce design

parameter variability for adequate management systems, as

Each batch comprises 2n cows. The first batch spends a total time of ti+2tm minutes in the waste collection area.

Fig. 1 – Collection area (in grey): holding pen D dairy parlour during milking. As an example, a dairy parlour with four

milking units (n [ 4) is presented. Eight (2n) cows per batch stay in the dairy parlour.

b i o s y s t e m s e n g i n e e r i n g 1 0 3 ( 2 0 0 9 ) 4 9 7 – 5 0 3 499

well as better estimates of greenhouse gas emission

reduction.

1.1. Waste characteristics

Existing literature provides several methods of varying

complexity for calculating composition and amount of

excreta. However, under current local conditions, data such as

intake characteristics and individual live weight are difficult

to gather, hence requiring simpler estimations.

Such simple estimation (which we will refer to as the

standard value, Si) for waste composition can be obtained

using a reference value for each milking cow per day for each

component of the wastewater: total solids (TS), total phos-

phorus (TP), total Kjeldhal nitrogen (TKN), volatile suspended

solids (VSS), and chemical oxygen demand (COD) giving the

standard values STS, STP, STKN, SVSS, SCOD.

From Table 1, for example, the estimation from New Zea-

land, is that 550 gTS cow�1 d�1, 22 gTKN cow�1 d�1, and

2.5 gTP cow�1 d�1 are collected at milking facilities. These

estimates do not take into account average stay time in the

collection area; they only consider the number of dairy cows.

Table 2 – Experimental conditions in dairy farms

Dairy farm Farm 1 Farm 2

Sampling occasion 1 2 1 2

nDC 120 120 107 10

tu (h milking�1) 2.75 2.3 2 2

tm (min) 8 8 7 8

Milk production (l cow�1 d�1) 17 17.5 20 21

# of herded sets 2 2 1 1

g1¼ # cows 1st set 70 80 107 10

g2¼ # cows 2nd set 50 40 – –

g3¼ # cows 3rd set – – – –

ti1 (1st set), (min) 20 10 10 10

ti2 (2nd set), (min) 10 10 – –

ti3 (3rd set), (min) – – – –

n 8 8 11 11

Wash water (l cow�1 d�1) 40 36 26 38

Washing time (min milking�1). 60 55 23 34

t (h d�1) 1.82 1.72 1.7 1.

To take into account the stay time in the estimate, we can use

standard average stay time, e.g., 2 h d�1. The adjusted value

ATS refers to the amount of solids weighted by 2 h by day as

average time spent by a cow in the collection area (2 h is the

most common spending time for Uruguayan farms). This

value is calculated by Eq. (1)

ATS ¼ STS2

t(1)

In the same way, we can calculate for the remaining

parameters ATP, ATN, AVSS, ACOD, etc.

2. Objective

The goal of this paper is to define relevant parameters for

design purposes under local conditions. This requires an

analytical expression to calculate the average stay time for

cattle in the milking facilities using easily collected experi-

mental data. The aim is to assess the relevance of this factor in

the solids, organic matter and nutrient content estimates for

the waste collected at the milking facilities such that it can be

used for designing waste and nutrient management systems.

Farm 3 Farm 4 Farm 5

1 2 1 2 1 2

5 240 235 55 55 135 133

1.5 2.15 1 1.25 1.75 1.5

8 9 6 6 8 8

15 13 16 16 16.3 15

2 2 3 3 1 1

5 210 220 20 20 135 133

30 15 20 20 – –

– – 15 15 – –

5 5 0 0 10 10

0 0 0 0 – –

– – 0 0 – –

12 12 3 3 6 6

76 50 22 22 30 26

55 35 12 12 44 37

88 2.5 3.04 0.76 0.76 3.6 3.56

Table 3 – Mean values for samples per dairy farm,standard estimation (g cowL1 dL1)

STSe STKNe STPe SCODe SVSSe

Farm 1 480 4.4 2.1 390 200

Farm 2 840 23 3.2 380 280

Farm 3 220 33 3.5 600 340

Farm 4 700 9.8 0.9 160 100

Farm 5 700 37 3.8 520 190

NSTS NSTKN NSTP NSCOD NSVSS

Mean value 580 23 2.7 410 220

Standard deviation 41% 46% 44% 40% 41%

b i o s y s t e m s e n g i n e e r i n g 1 0 3 ( 2 0 0 9 ) 4 9 7 – 5 0 3500

For this purpose, we will present a validation of the estimate

under Uruguayan conditions.

3. Mathematical modeling

First, the expression for the average stay time in the milking

area is determined. Animals are confined together mostly

during milking operations and hence waste accumulation

occurs during this time. For local farms, milking operations

are usually organized into one or more sets of animals. For

a single set of nDC cows, the entire set is herded together to the

holding pen.

1. They are kept in the temporary empty area while the

milking operations are prepared, using the drinking or

feeding troughs for an idle time (ti min). During this time,

the entire herd is kept in holding pen. The idle time ti can be

minimal or purposefully extended to enable feeding or

drinking.

2. Then 2n cows enter the dairy parlour, where n represents

the number of milking units (Fig. 1).

In this way, nDC/2n batches are milked. If each cow takes an

average of tm min to milk, and each milking unit milks two

cows per batch it is then possible to estimate the time the

cattle spends in the collection area according to:

ð1Þ1st 2n cows spend t1 min : t1 ¼ ti þ 2tm (2)

ð2Þ2nd 2n cows spend : t2 ¼ ti þ 4tm (3)

ð3Þ3rd 2n cows spend : t3 ¼ ti þ 6tm (4)

In this way the last batch is number nDC/(2n):

Table 4 – Experimental values corrected by 2 h dL1 of average

ATSe ATKNe

Farm 1 550 28

Farm 2 760 25

Farm 3 610 24

Farm 4 570 26

Farm 5 390 21

NATS NATN

Mean value 590 25

Standard deviation 16% 10%

Last nDC=ð2nÞ cows spend : tu ¼ ti þnDC

ntm (5)

Average time spent by a cow in the milking area during one

milking operation ta is the weighed sum of individual times

Eq. (6):

ta ¼2nt1 þ 2nt2 þ 2nt3 þ.þ 2ntu

nDC(6)

Substituting Eqs. 2–5 in Eq. (6) and regrouping terms results

in Eq. (7):

ta ¼ ti þ2nnDC

tm

�2þ 4þ 6þ.þ nDC

n

�(7)

The term in brackets on the right side of Eq. (7) is a finite

arithmetic series. Substituting its summarized formula and

multiplying by 2 as the milking operation is performed twice

adayweobtaintheaveragedailyofmilkingtimeperday(t)where

the factor 60 min h�1 is included to express t in h d�1 Eq. (8).

t ¼�

ti þ2nnDC

tmnDC

2n

�nDC

2nþ 1�� 2

60¼�

ti þ tm

�nDC

2nþ 1�� 2

60(8)

If the cattle are not herded together but divided instead into

separate sets, the average stay time decreases. The average

time for each set is calculated in the same way considering the

number of cows in the batch instead of the total number of

dairy cows nDC. The average time for the entire herd is

obtained by weighing the number of cows in each set. For

example, for three sets of g1, g2 and g3 cows, respectively, Eq.

(9) (expressed in h d�1) is obtained:

t ¼��

ti1 þ tm1

�g1

2nþ 1�� g1

nDCþ�

ti2 þ tm2

�g2

2nþ 1�� g2

nDC

þ�

ti3 þ tm3

�g3

2nþ 1�� g3

nDC

�260

ð9Þ

where gj stand for number of cows per set, tij is the idle time

for set j, and tmj is average milking time for set j.

In this way, based on Eq. (8) or (9), and using data known to

the milking operator, it is possible to estimate the average

time an animal is kept in the waste collection area.

4. Methods

4.1. Generating national estimates

Five dairy farms distributed throughout the local dairy basin

were selected, in order to cover a broad range of

stay time, adjusted estimates (g cowL1 dL1)

ATPe ACODe AVSSe t

2.4 440 230 1.8

3.6 430 320 1.8

2.5 430 240 2.8

2.5 430 260 0.8

2.1 290 110 3.6

NATP NACOD NAVSS

2.6 400 230

21% 16% 34%

Table 5 – Experimental values for Farme

Sampling occasion 1 2 3 4 5 6 7 8 9 10 11 12

nDC 264 232 218 150 150 193 259 276 257 143 202 218

Wash water (l cow�1 d�1) 60 53 57 55 55 55 59 59 58 55 55 59

t (h d�1) 3.1 3.1 3.1 3.0 3.0 2.8 3.0 3.2 3.0 2.9 2.8 3.0

Milk production (l cow�1 d�1) 18 14 13 12 16 22 20 19 19 13 20 22

CTKN (mg l�1) 500 ND 330 870 1180 1058 1170 1130 710 390 ND 800

CTP (mg l�1) 105 40 90 65 110 150 ND ND ND 60 55 225

CCOD (mg l�1) 14250 12500 6850 14600 18100 21750 20450 17800 8900 7000 2650 7100

CVSS (mg l�1) 5000 7400 6800 11700 ND 12600 10800 8750 4200 2850 1550 6500

ND: not determined.

b i o s y s t e m s e n g i n e e r i n g 1 0 3 ( 2 0 0 9 ) 4 9 7 – 5 0 3 501

infrastructural and operational characteristics. In all of them

cleaning procedures consist of water-washing all the waste,

including solid and liquid excretions as the main polluting

agents. This is the cleaning method commonly used in the

country. Total water consumption was measured for each

milking operation sampled. Total milking time, individual

milking time, idle time, number of batch and number of

milking units in each parlour were surveyed. Dairy farms’

waste was analyzed in terms of the relevant variables (TS,

TKN, TP, VSS, COD). The average value of each parameter was

obtained. These are considered as national characteristics

NSTS, NSTKN, etc.

4.2. Sample collection

Each farm was measured on two occasions, covering the two

milking schedules (morning and afternoon). Composite

wastewater samples were collected manually for the entire

washing. Two litre samples were taken every 2 min and

collected in a 100 l tank. Then, one l of tank content was

transferred after gentle mixing to a bottle and transported on

ice and stored at 4 �C. Each sample was preserved by adding

H2SO4 (36 N) to decrease pH below 2. Analyses were performed

within 1–3 days of sampling.

Table 6 – Experimental and estimated Nitrogen, Phosphorus, V

TKN TP

Exp. NSTKN NATKN Exp. NSTP NATP

1 30 23 38 6.3 2.7 4.0

2 ND * * 2.0 2.7 4.0

3 19 23 38 5.0 2.7 4.0

4 48 23 37 3.6 2.7 3.9

5 65 23 37 5.9 2.7 3.9

6 58 23 34 8.5 2.7 3.7

7 69 23 37 ND * *

8 67 23 39 ND * *

9 41 23 37 ND * *

10 21 23 36 3.2 2.7 3.8

11 ND * * 3.1 2.7 3.7

12 47 23 37 13 2.7 3.9

Mean 46 23 37 5.6 2.7 3.9

ND: not determined, Exp: experimental values in Farme.*not presented a

4.3. Assessing the model in a dairy farm

Waste of a farm was analyzed on 12 specific days covering all

seasons (over more than 1 year). The farm where assessment

was performed (Farme) is an experimental dairy farm. It was

selected because it has the advantage of registering the

information in an organized and accessible way as well as

measuring water consumption, which is rather uncommon in

privately-owned dairy farms. Models were tested for both

standard and adjusted estimators for the obtained samples.

This farm has 16 milking machines and 300 cows at maximum

production and 120 at minimum. The herd is managed in up to

four sets at maximum production, while a single set is used at

minimum production. This determines that the seasonal

variation of the average stay time in the milking area is not too

great for different periods of the year. Variations in amount of

nutrients and solids are expected because of differences

between feed and milk production during the year.

4.4. Analytical methods

The following methods described in APHA (1995) were

employed: 5220-D COD (close reflux. colorimetric method),

2540-B total solids (TS), 2540-E VSS, and 4500-P-B,C TP (color-

imetric method). Total Kjeldahl nitrogen (TKN) were

SS and COD for Farme (g cowL1 dL1)

VSS COD

Exp. NSVS NAVSS Exp. NSCOD NACOD

300 220 360 860 410 630

390 220 360 660 410 630

390 220 360 390 410 630

640 220 350 800 410 610

ND * * 1000 410 610

690 220 320 1200 410 570

640 220 350 1200 410 610

520 220 370 1050 410 650

240 220 350 520 410 610

160 220 340 390 410 590

90 220 320 140 410 570

380 220 350 420 410 610

400 220 350 720 410 610

s experimental data is not available.

Table 7 – Difference between experimental and estimatedvalues for Farme (relative estimation error)

Estimation

b i o s y s t e m s e n g i n e e r i n g 1 0 3 ( 2 0 0 9 ) 4 9 7 – 5 0 3502

determined by Kjeldahl method using an automatic digestion

system (Foss Tecator Digestion System 2508 Auto) and

programmable distillation systems with titration (Foss Teca-

tor Distillation Unit 2100).

Standard (%) Adjusted (%)

TKN 51 20

TP 52 31

VSS 45 14

COD 43 16

Mean 48 20

Standard deviation 4 8

5. Results and discussion

5.1. Waste characteristics

Table 2 shows the different characteristics of surveyed farms,

where 1 and 2 represent two sampling occasions. Both average

milking time t and total milking time tu are highly variable,

wastewaters for each farm were analyzed during two milking

operations. STSe is the measured contribution per dairy cow

per day to TS.

STKNe, STPe, SVSSe, and SCODe stand for total Kjheldal nitrogen,

TP, VSS and COD gathered experimentally. Values for both

milking instances were averaged, since often morningwashing

operations include pen and parlour while on the afternoon

only parlour is washed. Hence, morning wash gathers part of

the solids generated during the afternoon.

The mean value of measured variables between farms

(Table 3) are taken as National Standard Estimates (NS).

Results are comparable with those reported in Dairying

and the Environment Committee (2006) (Table 1), but solids

content is greater than that specified for the region by INTA

(1999). US values are greater probably due to the more inten-

sive feeding regime.

It is possible to generate a similar table using average

milking time for each case, obtaining adjusted variables (A)

according to Eq. (1). The mean experimental adjusted values

ATKNe, ATPe, AVSSe, and ACODe for an individual farm and mean

values between farms taken as National Adjusted Estimates

(NA) are shown in Table 4.

It can be seen that including average milking time signifi-

cantly reduces standard deviations of all variables (from 41 to

16% for instance for TS). This observation shows that average

stay time significantly affects the amount of collected waste.

However, this does not occur if total milking time is used

instead of average time (data not shown). Adjusted values

presented in Table 4 are adequate reference design parame-

ters for local conditions referred to 2 h d�1 as the mean

milking time.

5.2. Model assessment

Table 5 shows sampling conditions and results on Farme.

Experimental values were compared with NS estimates

from Table 3, and NA estimates (from Table 4). Results are

shown in Table 6 (TKN, TP, VSS and COD).

Relative inaccuracy for adjusted and standard estimates

related to experimental mean values is presented in Table 7.

In order to compare methods, a statistical Student’s t test

was done over data in Table 7. Adjusted parameters signifi-

cantly improve estimation of all variables analyzed

(tcalculated¼ 6.26 compared to tcritical¼ 1.94 for 95% of signifi-

cance level). Even though in this case a simple estimation was

performed, and hence more prone to errors, it is possible to

adjust more complex estimation by the average stay time,

adjusting for example those estimates that consider average

live weight, milk production and dietary intake.

6. Conclusions

A parameter representing cattle’s average stay time in milking

facilities was successfully introduced and an analytical

development based on experimental data easily obtained on-

site was proposed. Design parameters were obtained for dairy

farm in Uruguayan conditions. Values of 590, 25,

2.6 g cow�1 d�1 of TS, TKN and TP are collected for 2 h d�1 of

average stay time. Standard deviation of TS and nitrogen

decreased from 41 to 16% and from 46 to 10%, respectively,

when taking into account average stay time, by comparing

experimental data from five dairy farms. This enabled

a decrease between estimated and experimental mean values

from 51 to 20% for TN and from 45 to 14% for TS in an

experimental dairy Farm.

Acknowledgements

This research was supported by INIA-FPTA138 program and

Ministerio de Ganaderıa Agricultura y Pesca-Uruguay.

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