water use in global dairy farming systems and lessons for breeding policies for dairy production

Water use in Global Dairy Farming

Systems and lessons for breeding

policies for dairy production Results of a research project in

collaboration with IFCN-Dairy

N.Sultana, K. J.Peters Humboldt Universität zu Berlin

[email protected]

Importance of water in animal agriculture

Agriculture: uses 85% of the present global freshwater consumption, of which Æ 29% by Livestock (Mekonnen and Hoekstra, 2012)

Æ 75% for Irrigation (Shilklomanov, 2000)

2. Increase food production, agricultural pollution

1. Human population. 65 % increase (3.7 mrd) by 2050 (Wallace, 2000)

Future challenges

Importance of water in animal agriculture

4. Climate change impact on rainfall distribution pattern

• 19 to 35% decrease in water availability for agriculture

• Increase water scarcity for human population from 7% to 67%

3. Urbanization and industrial, increase in water use and pollution

WSI = Water Scarcity Index (Pfister et al. 2009. Assessing the environmental impacts of freshwater consumption in LCA. Environ. Sci. Technol. 43 (11), 40984104)

Water Stress Index0 <= 0.2

0.2 <= 0.40.6<= 0.70.7 <= 0.1

ÎLow ÎModerate ÎSevere ÎExtreme

National Water Scarcity Index (WSI)

Water scarcity measured :

Total annual freshwater withdrawals / hydrological

availability.

WSI indicates the portion of CWU depriving other users of

freshwater.

Holistic view of current water scarcity by region

1. Around 1.2 mrd people live in areas of physical scarcity and 500 million people are close to it

2. Another 1.6 mrd people face economic water shortage (where countries lack the necessary infrastructure to take water from rivers and aquifers)

3. Though planet water does not change freshwater is distributed unevenly and too much of it is wasted, polluted and unsustainably

managed.

Sources: Human Development Report 2006. UNDP, 2006 Coping with water scarcity. Challenge of the twenty-first century. UN-Water, FAO, 2007.

Effects of current water scarcity

Holistic view of water scarcity problem by regions

Source: IWMI = International Water Management Institute, 2007.

Economic water scarcity: • <25% of water withdrawn from rivers for human purposes but not enough water infrastructure to

make water available for use

Physical water scarcity: • >75% of river flows are withdrawn for agriculture, industry and domestic purposes.

Water scarcity measures: freshwater available for human requirements Æimplies that dry areas are not Necessarily water scarce).

Water availability and dairying

Dairy production highly depenend on water in its various forms Important to know the water demand of a dairy system

USA

Ethiopia Argentina

China

Bangladesh

India

India

Milk production 2011in mill tons ECM

EU-27

15384

34

30

10

21

42

13832

11

Milk volumes cows & buffalo milk –standardized to 4% fat and 3,3% protein

Status of current milk production

Milk production in mill. tonnes

Milk production 2011 = IFCN ( International Farm Comparison Network)

Milk delivered to processor Milk not delivered to processor

Water footprint definition

• A water footprint is measured in terms of the volume of water consumed, evaporated and polluted.

• Three corresponding categories (Water Footprint Network) Blue Water Footprint: The amount of surface water and

groundwater required (evaporated or used directly) to make a product. Green Water Footprint: The amount of rainwater required (evaporated or used directly) to make a product. Grey Water Footprint: The amount of freshwater required to mix and dilute pollutants enough to maintain water quality according to certain standards as a result of making a product.

Consumptive Water Use • Measures Green and blue water

• removed from a local hydrological system • without return to a water system (e.g. water used in

manufacturing and agriculture) • Indirectly includes grey water

Water footprint methods

Is a incomplete Water Foot print


The Water Footprint Network (WFN) method – accounts for the virtual water and is an indicator of direct

and indirect Water Use Volume (green, blue, grey) However – Simple combination of hypothetical pollution volume (grey)

with water consumption (blue) is not meaningful – Inclusion of green water in the WF is misleading, since it does

not fully affect the water cycle and is rather an indicator of land use

Pfister, St. and Ridoutt, B.R. 2013, Environmental Science & Technology 48 (1):4-4


The LCA - Water use impact (ISO 14046,2010, standard approach)

– Accounts for blue water grey water and its water scarcity related impacts of

pollutants expressed as water equivalent along the whole LC (H2Oe)

Pfister, St. and Ridoutt, B.R. 2013, Environmental Science & Technology 48 (1):4-4

• Types of water consideration (e.g. rainfall, stored water in surface

and ground, polluted water)

• Concept of water use in farming systems

• Defining goals and interpretation problem 2. Lack of consistent approach

e.g. Classical or volumetric Impact assessment based approach ÎInternational Standard Method which is under Development (ISO, 14046, 2013)

Methodological challenges in water research

Materials and methods

1. Application of consumptive water use (CWU) and its drivers

2. Application of Water use impact 3. Evaluating differences between Consumptive water use and Water use impact (WF)

Application of different WF methods in diverse dairy systems

Steps in our study to measure water use

3. Comparison of Water use assessment method

IFCN: International Farm Comparison Network method. TIPI-CAL: Technology Impact and Policy Impact Calculation

9 Represent the most common farming system within the regions

9 Average management & performance & high proportion of milk in the region

1. Selection of typical farm within the IFCN-Dairy Net

¾ Typical farm data are collected at farm level

2. System boundary

Drinking and servicing

water

Concentrate, by-products

and roughage

Fuel, Electricity

Fertilizer, pesticides

External inputs Internal farm inputs

Total feed and

fodder

Water for feed

mixing

Buildings and dairy

implements

Co-products:

beef and

manure

Heifers

Dairy cows

Functional unit: 1 kg

energy corrected

milk (ECM)

Farm grown feed (main product and by-products)

2. System boundary (Cradle –to Farm Gate)

ECM = Energy Corrected Milk which is standardized by 4% fat and 3.3% protein


Application of Consumptive Water use (CWU) method (as in Hemme et al, 2010)

960 typical farms from 60 dairy regions of 49 countries and

6 selected dairy systems

Application and comparison of CWU (WFN, 2010) and LCA-based water use impact (WF) (after Ridoutt and Pfister, 2010)

912 typical farming systems from 12 geographical regions

Comparison of Water use assessment methods


0

1000

2000

3000

4000

5000

6000

NO-2

0CH

-23

FI-25

AT-2

2DE

-31S

DE-9

5NDE

-85E

NL-7

6BE

-40N

LU-5

1FR

-39M

CFR

-50W

ES-5

0NW

IT-1

54UK

-146

NW IE-4

8DK

-128

SE-5

5PL

-15

CZ-4

25RS

-2UA

-150

BY-1

BY-6

08RU

-106

3CA

-58

US-8

0WI

US-3

50W

IUS

-66N

YAU

-275

WA

NZ-3

48M

X-15

AR-1

70UY

-119

PY-4

5CL

-47

BR-2

0SBR

-120

PR PE-7

TN-4

DZ-6

MA-

3NEG

-2UG

-3NG

-5CM

-35

ZA-4

22AM

-10A

IL-67

JO-7

5IR

-90

IN-2

WIN

-13W

IN-2

SPK

-5BD

-2ID

-3NG

ID-3

JACN

-17B

ECN

-6IM

CWU

(L/k

g ECM

)

S. America Africa Asia

C. and E. Europe

Western Europe Regions

*Typical farms

N. A

mer

ica

Oce

ania

CWU for feed CWU for other inputs

Mean (St. Dev.)

1771 (±1035)

62 (±45)

Min (Max.) 706 (5400) 31 (304)

*Typical farm code DE-95N: DE=Germany, 95=95 cows and N=North

Application of Consumptive Water use (CWU) method in dairy farms

= CWU for feed

= CWU for other inputs

Relation between consumptive water use and milk yield (kg ECM/cow/year)

y = -0.1168x + 1849.7 R² = 0.68

0

500

1000

1500

2000

0 5000 10000 15000

CW

U (L

H20

/kg

ECM

)

Milk yield

Europe

y = -0.2038x + 3777.1 R² = 0.31

0

1000

2000

3000

4000

5000

6000

0 10000 20000

CW

U (L

H20

/kg

ECM

)

Milk yield

Asia and Africa y = -0.1601x + 2466.4

R² = 0.65

0

500

1000

1500

2000

2500

3000

0 5000 10000 15000

CW

U (L

H20

/kg

ECM

)

Milk yield

USA and Oceania

Major results

Production system Intensive Grazing Small-scale Variable Unit DE-95N US-350WI NZ-348 BR-20SC EG-2 BD-2

Breed HF HF HF CB EB Local

Farm land ha 90 270 130 18 0 0

Grazing hrs./day 0 0 12 12 0 0

Climate Mild with no dry season

Humid, severe winter

Mild, no dry season

Mild with dry winter

Desert area

Monsoon

Rainfall mm/m2 850 860 1250 1300 250 1800

T. (Mean) (°C) 12 15 15

27 32 28

Consumptive water use in selected dairy systems Background information

HF = Holstein Friesian; CB = Crossbred; EB: Egyptial Buffaloes

75%

80%

85%

90%

95%

100%

DE-

95N

US-

350W

I

NZ-

348

BR-2

0SC

EG-2

BD-2

Intensive Grazing Small-scale

0

500

1000

1500

2000

2500

3000

3500

4000

DE-

95N

US-

350W

I

NZ-

348

BR-2

0SC

EG-2

BD-2

CW

U (

L H

20/k

g EC

M)

Feed production & mixing Drnking Servicing Farm manufacturing inputs Capital goods


Consumptive water use in selected dairy systems

CWU = Consumptive water use

FEED

Pasture based

Concentrate, by-product + crop residues

Maize + concentrate

based

Major results

Drinking

Conclusion on consumptive water use • The world average CWU Æ1833 L/kg ECM (range: 739 to 5622),

with large inter- and intra-regional differences

• Feed is the highest single input to CWU Æ96-99% water

• Lower CWU associated with high productivity and farm based feeding systems

• Rather high CWU in pasture based systems • Highest CWU associated with low productivity and higher

concentrate feeding

Comparison of CWU and LCA-based water use impact (WF)

1. Volume of water use based on volumetric approach (CWU) 2. Water use impact assessment including water scarcity with

Life cycle assessment (LCA) approach

Blue and grey water volumes

0

250

500

750

1000

US

-350

WI

DE

-95N

CN

-17B

E

JO-7

5

NZ-

348

BR

-25S

E

AR

-170

ZA-4

22

EG

-5

IN-2

S

MX

-15

BD

-2

L H

2O/k

g E

CM


Blue water Grey water

Major Results

Major Results

H2Oe = Water equivalent; WSI = Water Scarcity Index

WF (H2Oe) =

Water use impact (WF) based on LCA method

a) Blue & grey water volumes considering water scarcity

0

200

400

600

800

1000

1200

1400

1600

US

-350

WI

DE

-95N

C

N-1

7BE

JO

-75

NZ-

348

BR

-25S

E

AR

-170

ZA

-422

E

G-5

IN

-2S

M

X-15

B

D-2

L H

2Oe/

kg E

CM


0,00

0,20

0,40

0,60

0,80

1,00

US

-350

WI

DE

-95N

C

N-1

7BE

JO

-75

NZ-

348

BR

-25S

E

AR

-170

ZA

-422

E

G-5

IN

-2S

M

X-15

B

D-2

m³/m³

National WSI Local WSI


b) Water scarcity of production area

Consumptive water use

• The world average CWU Æ1833 L/kg ECM with huge variability (ranging from 739 to 5622)

• Feed is the main contributer Æ more than 96% of total CWU

• Lower CWU associated with high productivity and farm based feeding systems Water use impact (WF)

• Lower WF associated with pasture based system where water scarcity is low

• Higher WF associated with land less system based on external concentrate supply, and where water scarcity is higher

¾ Planning of dairy production system should include assessment of water foot print and water returns

Home messages

Method perspective

• The summation of water volumes is not a comprehensive tool for assessing water productivity

• Water use impact assessment considering degradative water use and water scarcity is a more appropriate tool for assessing impact of water use

Reasons of WF variation

• Due to interaction effects among the regional water scarcity where production occurs, with amount of degraded water, feeding system and feed efficiency

¾ Dairying in areas with high concentrate feed input in water scarce region is a hotspot of adding to water problem

Home messages (cont.)

Translation of these findings into dairy planning

1. Assessment of water availability and water scarcity 2. Assessment of the appropriate feeding system for a

dairy production system pasture, forage, crop-residues, agro-industrial by-products, LCA grain concentrate

LCA WF Lower larger

3. Assessment of appropriate performance and production efficiency level 4. Define breeding policy

Thank you so far!

and now we need to decide if we can spare time to consider

breeding option for smallholders in Ethiopia

The case of Dairying in Ethiopia Diverse dairy production systems: 1. Commercial Peri-urban dairy systems partly with own Value

Chain (liquid milk and processed products)

2. Semi-commercial Peri-urban and Rural mixed farming systems with linkage to milk collection systems (liquid milk , but also butter and trad. cheese)

3. Extensive Rural mixed farming systems (Trad. Butter and trad. cheese)

4. 99.2 % of the 27 mill. cows are indigenous breeds with a low milk yield, few selected indigenous dairy breeds 129 thousand are cross (0.61 %) and exotic breeds (0.11%); 32 thousand cows with small holders.

Commercial Peri-urban dairy systems � Purebred and grade dairy cows, medium high yield � Modern dairy production and processing technics � Agro-industrial by-products and concentrates � Mais silage, Hay � AI service with own technicians

Semi-commercial systems � crossbred cows of different grade, medium yield � Crop-residues, grazing, hay and agro-industrial by-

products � AI service only in well organized Dairy coops,

otherwise village bull service

Extensive small scale mixed farming systems -Indigenous cows or low grade crossbreds, low yield -Crop-residues, hay, grazing, small amount of by- products -AI service not available, -only NM with available bulls

Agro-ecological breeding policy

,Yilma zelalem,,G.B., Emannuelle aYilmand S., Ameha. 2011. A Review of the Ethiopian Dairy Sector. Ed. Rudolf Fombad, Food and Agriculture Organization of the United Nations, Sub Regional Office for Eastern Africa (FAO/SFE), Addis Ababa, Ethiopia, pp 81. The NEXT STAGE IN DAIRY DEVELOPMENTFOR ETHIOPIA, Dairy Value Chains, End Markets and Food Security, USAID/ Land O+Lakes, 2010

• Absence of effective breeding policies and programs to assure optimum performance levels and efficiencies • AI service has been inefficient for different reasons in rural areas

• Bilateral projects through EDDP link up to World Wide Sires, for AI use in commercial peri-urban dairies, through private enterprises (ALPPIS)

• Chance of forming Dairy Farmer and Cattle Breeder Associations


Yilma zelalem,,G.B., Emannuelle aYilmand S., Ameha. 2011. A Review of the Ethiopian Dairy Sector. Ed. Rudolf Fombad, Food and Agriculture Organization of the United Nations, Sub Regional Office for Eastern Africa (FAO/SFE), Addis Ababa, Ethiopia, pp 81. The NEXT STAGE IN DAIRY DEVELOPMENTFOR ETHIOPIA, Dairy Value Chains, End Markets and Food Security, USAID/ Land O+Lakes, 2010

Attempts to improve dairy merit of national herd include:

• Importation of purebred dairy cows • Production and distribution of Crossbred cows on Government farms • Importation of crossbred cows from Kenya • AI-Center with Purebred, crossbreds and local bulls

• Distribution of imported semen form high yielding breeds • Distribution of crossbred bulls


Options: 1. The intensive commercial dairy sector (ICDS) exotic semen through private sector AI services and purchase of breeding bulls from within the ICDS 3. Less intensive semi commercial and rural dairies obtain crossbred bulls of various grade and sources (appropriateness and supply sustainability?)

Yilma zelalem,,G.B., Emannuelle aYilmand S., Ameha. 2011.FAO, Sub Regional Office for Eastern Africa (FAO/SFE), Addis Ababa, Ethiopia, pp 81.


Supply of breeding bulls for the rural sector – Link up with existing community actions – Crossbred bulls (?) from commercial dairy farmers in and around Addis Ababa, Asella Livestock Farm, Wolaita Jersey Bull Ranch and DDE – 75 % crossbreed bulls distributed to individual farmers through various agencies – Farmers established breeding bull stations

Constraint: Replacement of bulls was and is linked to a

functional supply chain (sustainability?)

A new scheme for Breeding bull provision

Suggestion of a young sire programme to provide crossbred bulls for rural smallholder dairy farmers

1. Concept for application acrosss the highland dairy shed

2. Action domain Rural administrative Community with

established farmer interaction

Evaluation of bulls on the basis of their ancestors’ performances, eg. bull mothers

- future option also on maternal / paternal halfsisters


Definition: Young sire programme

Features: - short generation intervals (minimum 3-4 years) - low accuracies → relatively high genetic response per year - simple, least expensive breeding scheme

- comprises about 200 farmers

- formation of village service co-operatives (e.g. purchase of agricultural inputs, milk collecting, marketing)

- implementation of village bull service


Rural administrative community e.g. Selale

• Crossbred cow population in a PA –200 small holder

- 2 crossbred cows per farm → 400 crossbred cows

4. A new scheme for Breeding bull provision

Determination of number of replacement bulls for rural community

• Number of replacment bulls needed per year

- Mating ratio: 1 : 40 → 10 bulls for service in Useful life of a bull: 3 years → 4 bulls

5. Model calculation for a Young sire scheme

Establishment of local open nuclei based on cow performance

- Second step:

→ start of a farmer based recording system with community verification

Identification of superior cows to breed bull calves:

- First step (no recording)

→farmer identification of best performaning cows

(e.g. milk yield history, field day comparison)



Minimum nucleus size within a PA:

- 14-28 superior cows (7-14% of cow population)

→ no scope for performance selection



Selection intensities for different nucleus sizes

Nucleus size 50 100 150

Expected proportion of bulls selected, % 28-56 14-28 9-19

Selection intensity i 1.16-0.69 1.60-1.16 1.80-1.42

6. Conclusions

• Agro-ecological planning including water

conditions essential for securing efficiency • Rural smallholder need increased dairy

performance for efficient use of resources/water • Community based breeding scheme best suited

to secure operational sustainability • Young Sire program with open nucleus breeding

scheme could lead to sutainable performance with best efficiency

6. Conclusions

• It pre-supposes an active participation of the farmers and respective vocational training, • Calls for extended scientific engagement of higher learning institutes interested in R 4 D and aquainted with participartory research methods

Excellent field lab for College / University students