August 2008 Richard Rawnsley, Danny Donaghy and Mark … · August 2008 Richard Rawnsley ... in...

Investigating the potential of deficit irrigation strategies to improve the efficiency of

water use in irrigated temperate pastures

Final report to DairyTas

August 2008

Richard Rawnsley, Danny Donaghy and Mark Freeman

Tasmanian Institute of Agricultural Research

Table of contents Executive summary............................................................................................................. 1

Pasture performance and water use efficiency ............................................................... 2

Modelling outcomes ........................................................................................................ 2

Communication of results ............................................................................................... 2

Other activities................................................................................................................ 3

Conclusion ...................................................................................................................... 3

Project team ........................................................................................................................ 3

Paper (1st draft) for Australian Journal of Agricultural Research....................................... 4

Abstract ........................................................................................................................... 5

Introduction..................................................................................................................... 6

Materials and methods.................................................................................................... 8

Results and discussion .................................................................................................. 13

Conclusion .................................................................................................................... 17

References ..................................................................................................................... 17

Appendix 1 – PowerPoint slides presented at field day.................................................... 32

1

Executive summary This document is a final report to DairyTas for the project titled ‘Investigating the

potential of deficit irrigation strategies to improve the efficiency of water use in irrigated

temperate pastures’.

The research in this project was successfully completed according to the project schedule

outlined in the DairyTas small grant proposal. The principle aims of the project were to:

Monitor pasture performance and water use efficiency from different deficit

irrigation strategies.

To model the results of the study and extend outcomes to differing soil types and

regions by running model simulations for different dairy regions over a number of

years.

To communicate results of the study through presentations of the findings at field

days and in the Pasture PLU$ newsletter.

This project encompassed a 2.0 ha experimental site located at Elliott research and

demonstration station. The trial design was a randomized complete block design with

four replications. A rainfall deficit (rainfall – potential evapotranspiration) of 20 mm was

used to schedule irrigation at which point 20, 16, 12, 8 or 0 mm of irrigation water was

applied, referred to as treatments 100, 80, 60, 40 and 0%, respectively. The experimental

site was grazed by 60 Holstein Friesian heifers each time the 100% irrigation treatment

regrew between 2.5 and 3.0 leaves/tiller. The difference between pre- and post-grazing

pasture biomass estimates was used to calculate the amount of pasture utilised from each

grazing for each irrigation treatment. The marginal irrigation water use index (MIWUI)

was calculated as marginal production due to irrigation/irrigation water applied.

2

Pasture performance and water use efficiency The cumulative pasture yield was 9.16, 8.92, 7.64, 6.89 and 3.74 t DM/ha for the 100, 80,

60, 40 and 0% irrigation treatments, respectively. The resulting MIWUI (marginal

production due to irrigation) was 1.29, 1.54, 1.55, and 1.87 t DM/ML, for 100, 80, 60 and

40% irrigation treatments, respectively. The study demonstrated that improvements in the

average response to irrigation across the Australian dairy industry of 1 t DM/ML can be

increased by 50 to 90% under a deficit irrigation approach. A deficit irrigation approach

maintains the pasture in a responsive state and makes more efficient use of any summer

rainfall events.

Modelling outcomes The results of this study were modelled through the biophysical model DairyMod and

pasture growth was simulated across all treatments based on summary statistics and

strong agreement between modelled and observed data. Results were then simulated

using forty years of climatic data (June 1967-July 2006) for five differing dairy regions of

Tasmania. The modelling work has shown that removal of edaphic limitations and

maximising pasture growth significantly improves the marginal irrigation response of

dairy pastures. When edaphic limitations are removed, a deficit irrigation approach

between 60 and 80% would result in an average MIWUI of 2.8 t DM/ML. This is a

considerable improvement above the industry average response and these findings are

viewed as being critical to improving the efficiency of irrigation water use within the

Tasmanian dairy industry.

Communication of results Results of the study were communicated in issue 25 of the Pasture PLU$ newsletter

(February 2008). A field day was held on the 7th March 2008. The field day was well

attended by a total of 41 farmers and service providers. Slides from the field day

presentation are given in Appendix 1.

3

Other activities The experimental site provided a medium for the research and demonstration of a number

of other activities. This included Janice Perry undertaking a School of Agricultural

Science Honours study entitled ‘Investigating the effect of deficit irrigation on perennial

ryegrass (Lolium Perenne) morphology and herbage quality’. Result from this Honours

study will be made available to DairyTas following completion in October, 2008. In

addition, the CSIRO Information and Communication Technologies centre demonstrated

and tested the use of their soil moisture sensor network and HydroTas demonstrated the

use of their Adjenti units.

Conclusion The observed experimental data and simulated results from the current study suggest that

the obtainable marginal water use index is much higher than the current industry standard

of 1 t DM/ML. This study has therefore shown that the opportunity exists for irrigated

pasture systems to better manage an increasingly scarce resource while still maintaining

economically feasible yields.

Project team The project supervisor and principal investigator was Richard Rawnsley. Mark Freeman

and Danny Donaghy were also investigators on the project. Technical support was

provided by Peter Chamberlain and Karen Christie. Lesley Irvine assisted in coordinating

grazing for the experimental site. .

4

Paper (1st draft) for Australian Journal of Agricultural Research Deficit strategy increases marginal irrigation response

Potential of deficit irrigation to increase marginal irrigation response of perennial

ryegrass (Lolium perenne L.) on Tasmanian dairy farms.

R.P. RawnsleyA, B.R. CullenB, L.R. TurnerA, D.J. DonaghyA, M. FreemanA, K.M.

ChristieA

ATasmanian Institute of Agricultural Research, University of Tasmania, Burnie, Tas

BFaculty of Land and Food Resources, University of Melbourne, Vic

Keywords: Irrigation, dairy, pastures, modeling

Journal: Australian Journal of Agricultural Research

Keywords: Irrigation, dairy, pastures, modeling

Journal: Australian Journal of Agricultural Research

5

Abstract To date, the aim of irrigation has been to maximise pasture yield by ensuring that plants

do not suffer water stress. Consequently, irrigation of pastures has often been applied

above the rate of plant water requirements, resulting in high inefficiencies. Deficit

irrigation is an approach to minimise these inefficiencies by scheduling irrigation events

when soil moisture reaches pre-determined levels, which minimises the amount of

irrigation water required to gain maximum pasture dry matter (DM) yields. Increasing

water use efficiency by implementing deficit irrigation is recognised as an approach that

could result in a competitive edge for the dairy industry as water resources become more

limiting. An irrigation experiment was undertaken in North West Tasmania, between

October 2007 and April 2008, examining the potential effect of deficit irrigation

treatments on pasture growth and water use efficiency. A rainfall deficit (rainfall –

potentials evapotranspiration) of 20 mm was used to schedule irrigation at which point 20,

16, 12, 8 or 0 mm of irrigation water was applied, referred to as treatments 100, 80, 60,

40 and 0%, respectively. The trial was a randomized complete block design with 4

replications. There was 21 irrigation events, between October and Arpil. The

accumulative pasture yield was 9.16, 8.92, 7.64, 6.89 and 3.74 t DM/ha for the 100, 80,

60, 40 and 0% irrigation treatments, respectively. The resulting marginal irrigation water

use index (marginal production due to irrigation) was 1.29, 1.54, 1.55, and 1.87 t DM/ML,

for 100, 80, 60 and 40% irrigation treatments, respectively. The results of this study were

modeled through the biophysical model DairyMod and pasture growth was simulated

across all treatments based on summary statistics and strong agreement between modeled

and observed data. Results were then simulated using forty years of climatic data (June

6

1967-July 2006) for 5 differing dairy regions of Tasmania. The observed experimental

data and simulated results from the current study suggest that the experimental marginal

water use index is a lot higher than the current industry standard of 1 t DM/ML. This

study has therefore shown that the opportunity exists for irrigated pastoral systems to

better manage an increasingly scarce resource and maintain economically feasible yields.

Introduction Nationally, the dairy industry is the second highest user of water, accounting for 19% of

the total water diverted for agricultural use (ABS, Water account 2004-05). Tasmanian

dairy farmers use approximately 85 GL of water on 19,700 ha with an average yearly

application amount of 4.3 ML/ha (ABS, Water account 2004-05). The efficient use of

irrigation water is viewed as being critical to the sustainability of the industry as there is a

heavy reliance on irrigation in the summer months to provide an adequate feed source to

maintain high levels of annual pasture production.

Perennial ryegrass (Lolium perenne L.) is the predominant pasture species used in

Australia to feed grazing dairy cows and according to Armstrong et al. (2000), such

pastures are relatively inefficient utilisers of available water; reliant on high levels of

moisture to maintain optimal growth rates and persistence. To date, the aim of irrigation

in the dairy industry has been to maximise the yield of perennial ryegrass pastures by

ensuring that plants do not suffer water stress. In practice, this has been achieved by

applying irrigation water above the rate of plant water requirements, to compensate for

any unevenness in the distribution and to ensure that the plants are never “under watered”.

7

However, this has tended to result in high losses of irrigation water through drainage and

run off and a lower level of return of production per unit of water, with the industry

average of 1 t DM/ML often quoted as an average response (Rawnsley et al. 2007a).

However, significant improvements in irrigation responses have been shown through

avoiding over irrigating, improving scheduling, and using appropriate start-up times,

while maintaining adequate available moisture (Rawnsley et al. 2007b).

Deficit irrigation, the deliberate and systematic under-irrigation of crops, has been

suggested as one way to further increase water use efficiency from applied irrigation

water. It is commonly practiced by either irrigating at the same frequency, but applying

less water at each irrigation event, or maintaining the amount of water per irrigation but

increasing the interval between irrigations events (Ganji et al. 2006). Ward et al. (1998)

investigated both deficit irrigation approaches with only one treatment. This study found

that delaying the irrigation interval by 3 to 4 days or applying only 70% of irrigation

requirements reduced the water use efficiency of the pasture from 1.04 t DM/ha/ML for

the full irrigation treatment to 0.57 and 0.82 t DM/ha/ML, respectively. In contrast, a 3

year study by Donaghy et al. (2006) in North West Tasmania in which 50% of irrigation

requirements were applied, found that the water use efficiency of temperate pastures

increased from 1.72 t DM/ML/ha for the full irrigation treatment to 1.91 t DM/ML for the

50% treatment. The contrasting results of these studies highlight the effect of climate on

responses to irrigation. The objective of this study was to quantify the impacts of a deficit

irrigation approach in which the interval remained constant but proportional amounts of

irrigation were applied to pasture in a cool temperate climate. It also aimed to validate the

8

irrigation response using the biophysical DairyMod (Johnson et al. 2003; Johnson et al.

2008) and extrapolate the results to different dairying regions across Tasmania.

Materials and methods Site and soil description

The experiment was undertaken at Elliott Research and Demonstration Station in North

West Tasmania (-41.08 S, 145.77 E, elevation 155.0 m) with a winter dominant rainfall

pattern and a mean annual rainfall of 1202mm. The 2.0 hectare experimental site was on

a deep clay loam ferrosol soil (red mesotrophic haplic ferrosol; Isbell 1996). The soil

fertility of the experimental site prior to commencing was as follows: Olsen phosphorus

(P) – 22.1 mg/kg, Colwell potassium (K) – 134 mg/kg, mono-calcium phosphate

extracted sulphur (S) – 60.7 mg/kg, pH (H20) – 6.2 and electrical conductivity – 0.11

dS/m. Monthly rainfall, evaporation, and the mean monthly maximum and minimum

daily temperatures for the experimental period are given Table 1.

Insert Table 1 near here

The experimental site was an established perennial ryegrass (cv. Impact ) pasture, and

was sprayed with the Agritone® (a.i. MCPA, present as dimethylamine salt, 750g/L) in

August 2007 to elevate influences of broadleaf weeds. A basal dressing of diammonium

phosphate (18% nitrogen, 20% phosphorus, 1.5% sulphur) fertiliser was applied in

September. Nitrogen (N) fertiliser was reapplied to the experimental site, as urea (46% N)

as 46 kg N/ha on the 12th November 2007, 16th January 2008 and 15th February 2008.

9

Experimental design

The experimental design consisted of 5 irrigated treatments arranged as a randomized

block design with 4 replications. Each treatment consisted of 4 K-line pods separated by

10m with controlled pressure at the inlet of each line maintained at 20 psi by a NelsonTM

high flow pressure limiting device. This resulted in a delivery rate of 4 mm/hr, with

distribution uniformity of >80% within a 3 m buffer in a perpendicular direction of each

line. The area of each experimental treatment per replication (from here on referred to as

experimental plots) was 180 m2 (30 m x 6 m).

The 5 irrigation treatments were applications of 20 (100%), 16 (80%), 12 (60%), 8 (40%),

and 0 mm (0%) of irrigation at a rainfall deficit of 20 mm. The rainfall deficit was

calculated as the difference in rainfall minus potential evapotranspiration. The

evapotranspiration was estimated as 0.8 x daily “Class A” pan evaporation, which was

located at the experimental site. Each irrigation or rainfall event that brought the deficit

of the 100% treatment back to zero or above was classed as zero. Figure 1 displays the

irrigation scheduling interval for the experimental period for the 100% irrigation

treatment.

Insert Fig. 1 near here

Sampling

10

The 2.0 ha experimental area was grazed by 60 Holstein Friesian heifers between the 2.5

and 3.0 leaf re-growth stage of the 100% irrigation treatment. A pre experimental

grazing was completed on the 4th October 2007, and a total of 6 grazings were undertaken

between October 2007 and April 2008. Pre grazing and post-grazing herbage mass

estimates were made prior to and following every grazing event using a calibrated rising

plate meter (Earle and McGowan 1979). The difference between the pre and post grazing

estimate was used to estimate amount of pasture utilised from each treatment. Between

40 and 50 rising plate meter height measurements were taken in all treatments and

calibration quadrates (0.25 m2) were cut to ground level with hand shears to allow for

conversion of meter reading to herbage mass.

Three water use indices as defined by (Purcell and Currey 2003) were used to compare

water use efficiency between irrigation treatments. The gross production water use index

(GPWUI) was calculated as the total product/total water applied, the irrigation water use

index (IWUI) was calculated as total product/irrigation water applied and the marginal

irrigation water use index (MIWUI) was calculated as marginal production due to

irrigation/irrigation water applied.

Soil moisture

Daily soil water potential was monitored constantly using WatermarkTM soil sensors.

Three WatermarkTM soil sensors were placed at soil depths of 15, 30 and 45 cm within

each treatment for 1 replication. An AM400 Hansen data logger was used to

11

automatically record readings every 4 hours from the 3 WatermarkTM soil sensors. Soil

moisture data is given in Figure 2.


Statistical analysis

Statistical analyses were performed using the statistical package SPSS (Version 11.5,

SPSS Corporation, Illinois, USA), and least significant difference (LSD), as defined by

Steel and Torrie (1960). Amount of pasture consumed and WUE indices of each

irrigation treatment for the irrigation period were compared between treatments using an

ANOVA randomized block design.

Model validation and extrapolation

DairyMod, a mechanistic biophysical models developed for the Australian dairy industry

(Johnson et al. 2003; Johnson et al. 2008) was used to extrapolate the results of the

current study across years and 5 dairy regions of Tasmania. The model uses daily

weather information and comprises soil water, soil nutrient, pasture growth and animal

production modules. The model is sufficiently versatile to simulate the range of

environments represented by the pastoral regions of Australia (Johnson et al. 2008).

Validation of the model by Cullen et al. (2008) has shown strong agreement between

modeled and actual data across a number of pastoral systems in Australia and New

Zealand.

12

The management parameters used for each site were identical to those applied to the

experimental site. The current study data was modeled using DairyMod with the

following parameters:

Pasture was cut to a residual of 1.5 t DM/ha and defoliation occurred at first day of each

month for the period of 1960 to September 2007. Actual dates of defoliation were used

for the period of October 2007 to April 2008. Nitrogen was applied at a rate of 230 kg

N/ha/year as 5 applications of 46 kg N/ha on the following dates: 18th September, 12th

November, 16th January, 15 February and 17th April. Irrigation was applied when a

rainfall deficit of 20 mm was reached and 0, 8, 12, 16 or 20 mm of irrigation was applied.

Modeled data for the current experimental period was validated against the experimental

data using a range of model evaluation statistics, based on the work of Tedeschi (2006).

These statistics were calculated separately for pasture utilised and irrigation responses

across all treatments. The statistics calculated were: Mean bias, the difference between

measured and simulated mean; r2, coefficient of determination; Mean Prediction Error

(MPE), a measure of general model efficiency expressed as % of mean (Bibby and

Toutenburg 1977); Model Efficiency (MEF), the proportion of variation explained by the

modeled value with a value of 1 indicating a perfect fit; Variance ratio (v), the amount of

variance in the measured and modeled data-sets with a value of 1 indicating the same

amount of variance; Bias correction factor (Cb), which indicates bias from the y=x line

with a value of 1 indicating no bias; and the concordance correlation coefficient (CCC)

which is a simultaneous measure of accuracy and precision with an ideal fit indicated by

a value of 1. Further details of these statistics are available in Tedeschi (2006).

13

Following validation of the data from the experimental site, simulations for 5 dairy

regions in Tasmania (Table 2); were run for a 40 year period (1st July 1966 to 30th June

2007). Site specific climatic data and soil physical properties were used (Table 2). Daily

climate data for each site was obtained from the Bureau of Meteorology SILO database

(Jeffery et al. 2001).


Results and discussion Pasture consumed and water use indices

Total pasture consumed for the experimental period (October to April) differed

significantly (P < 0.05) between irrigation treatments (Table 3). The 100 and 80%

irrigation treatments resulted in a mean pasture consumption of 9.16 and 8.92 t DM/ha,

respectively; significantly (P < 0.05) greater than all other treatments, but not

significantly (P > 0.05) different to each other. The mean pasture consumption for the 0%

irrigation treatment (3.74 tDM/ha) was significantly (P < 0.05) lower than for all other

irrigation treatments, as was the GPWUI (0.91 t DM/ML). The GPWUI of the remaining

irrigation treatments did not significantly differ (P > 0.05) from each other. The mean

GPWUI of the 40, 60, 80 and 100% irrigation treatments was 1.16 t DM/ML. This is

consistent with the recorded GPWUI of perennial ryegrass pasture in other regions of

Australia (Armstrong et al. 2000; Callow and Kenman, 2004; Lawson et al. 2007).

14

The IWUI differed significantly (P < 0.05) between all irrigation treatments, with the

highest IWUI occurring for the 40% irrigation treatment (4.10 t DM/ML) and the lowest

occurring for the 100% irrigation treatment (2.18 t DM/ML). The IWUI is known to vary

considerably from year to year depending on seasonal rainfall, and is often higher in areas

with low irrigation requirements. The inverse relationship between irrigation amount and

IWUI was obvious in the current study, with a 20, 40 and 60% reduction in irrigation

from the 100% treatment resulting in a 22, 36% and 88% increase in the IWUI.

There were no significant (P > 0.05) differences in the MIWUI between the 100%, 80%

or 60% irrigation treatments. The 40% irrigation treatment had a significantly higher

MIWUI than the 100% irrigation treatment, but this was not significantly (P > 0.05)

different to the 80% or 60% irrigation treatment. Although there were few significant

differences in the MIWUI between irrigation treatments there was a significant (P < 0.05)

negative linear relationship between irrigation amount and MIWUI.

Individual average pasture consumption values for all grazing assessments under each

treatment are given in Figure 3. At each grazing assessment the 0% irrigation treatment

had significantly (P < 0.05) less pasture consumed that the irrigated treatments. However,

at grazing assessments in November, January and April there was no significant (P > 0.05)

difference in pasture consumed between the 40, 60, 80 and 100% irrigation treatments.

Irrigation treatment effects were more pronounced in the December, February and March

grazing events. For example, in the February grazing event, significantly more pasture (P

< 0.05) was consumed from the 100% irrigation treatment than the 80% irrigation

15

treatment. Similarly, in the February and March grazing events, significantly more

pasture (P < 0.05) was consumed from the 60% irrigation treatment than the 40%

irrigation treatment.


Model Testing

There was strong agreement between observed versus modeled pasture consumption data

for each of the 5 irrigation treatments across the 6 grazing events (Fig. 4).


Summary statistics between the thirty observed data points and the modeled data

indicated that 93% of the variation can be accounted for by the model with a bias

correction factor of 0.99, indicating little bias between the 1:1 relationship (Table 4).


Model simulations were run for the forty-year period between 1967 and 2006 using the

experimental simulation set up across the 5 dairy regions described in Table 2 (from here

on referred to as the experimental simulations). In addition, the simulations were repeated

using the same set-up, but allowing for N to be applied as needed to maintain adequate

plant available N (from here on referred to as non-limiting N simulations).

16

There were discernible differences in the median MIWUI across the 5 sites under both

simulation set ups (Figure 5) and substantially more year to year variation in the MIWUI

when a deficit irrigation strategy was applied. The highest median MIWUI for the

experimental study was achieved under a 40% irrigation treatment for each of the 5 sites.

This was supported by the experimental simulation, with MIWUI ranging from 1.24 t

DM/ML at Smithton to 1.56 t DM/ML at Deloraine, under the 40% irrigation treatment.

It appears that the 230 kg N/ha applied over the experimental period (in the experimental

study and simulations) limited pasture growth, as there was a substantial increase in

MIWUI when N was non-limiting. In the non-limiting N simulation, the effect of

irrigation treatments was also altered. At all 5 sites the median MIWUI was higher under

the 60% and 80% irrigation treatment than the 40% irrigation treatment, indicating that

when N is non-liming and pasture yield is maximised, a deficit approach that provides

between 60 and 80% of plant water requirements provides the highest MIWUI.


This exercise has highlighted the importance of modeling experimental results as a 40%

deficit irrigation recommendation would not necessarily maximise the MIWUI response

for the 5 dairy regions examined. The results of this study indicated that when all other

edaphic limitations are removed, and N is therefore non-limiting, the MIWUI could be

significantly increased.

17

Conclusion This study has shown that a deficit irrigation strategy in a cool temperate environment

can significantly improve the marginal irrigation response for perennial ryegrass-based

dairy pastures. The study has demonstrated that improvements in the average dairy

industry response of 1 t DM/ML can be increased by 50 to 90% under a deficit irrigation

approach. A deficit irrigation approach maintains the pasture in a responsive state and

makes the use of any summer rainfall events more efficient. In addition, modeling work

undertaken in the study has validated the findings and allowed for the extrapolation of

results across years, differing dairy regions and under differing management scenarios.

The modeling work has shown that removal of edaphic limitations (i.e. N) and maximised

pasture growth significantly improves the marginal irrigation response of dairy pasture.

When edaphic limitations are removed, a deficit irrigation approach between 60 and 80%

would result in a MIWUI of 2.8 t DM/ML for the dairy regions of Tasmania. This is a

considerable improvement above the industry average response and these findings are

viewed as being critical to improving the efficiency of irrigation water use` within the

Tasmanian dairy industry.

References Armstrong D, Knee J, Doyle P, Pritchard K, Gyles O (2000) Water use efficiency on

irrigated dairy farms in northern Victoria and southern New South Wales. Australian

Journal of Experimental Agriculture 40, 643-653.

18

Australian Bureau of Statistic (ABS) (2006) Water Account Australia 2004-05, category

number 4610.0. The Australian Bureau of Statistics. (Commonwealth of Australia:

Canberra)

Bibby J, Toutenburg H (1977) ‘Prediction and improved estimation in linear models.’

(Wiley: Berlin)

Callow MN, Kenma SJ (2004) Evaluation of the water use efficiency of dairy production

using crops and pastures. In 'New directions for a diverse planet: Proceedings for the 4th

International Crop Science Congress'. Brisbane, Australia. (Ed. Tea Fischer). (The

Regional Institute Ltd).

Cotching WE, Cooper J, Sparrow LA, McCorkell BE, Rowley W (2002a) Effects of

agricultural management on dermosols in northern Tasmania. Australian Journal of Soil

Research 40, 65-79.

Cotching WE, Cooper J, Sparrow LA, McCorkell BE, Rowley W, Hawkins K (2002b)

Effects of agricultural management on vertosols in Tasmania. Australian Journal of Soil

Research 40, 1267-1286.

Cotching WE, Hawkins K, Sparrow LA, McCorkell BE, Rowley W (2002c) Crop yields

and soil properties on eroded slopes of red ferrosols in north-west Tasmania. Australian

Journal of Soil Research 40, 625-642.

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Cullen BR, Eckard RJ, Callow MN, Johnson IR, Chapman DF, Rawnsley RP, Garcia SC,

White T, Snow VO (2008) Simulating pasture growth rates in Australian and New

Zealand grazing systems. Australian Journal of Agricultural Research, 59. (Accepted).

Donaghy DJ, Rawnsley RP, Christie KM (2006) Development of a more relevant forage

base for the Tasmanian dairy industry, Final Report to Dairy Australia UT11478,

Tasmania.

Earle DF, McGowan AA (1979) Evaluation and calibration of an automated rising plate

meter for estimating dry matter yield of pasture. Australian Journal of Experimental

Agriculture and Animal Husbandry 19, 337–343.

Ganji A, Ponnambalam K, Khalili D, Karamouz M (2006) A new stochastic optimization

model for deficit irrigation. Irrigation Science 25, 63–73.

Isbell RF (1996) ‘Australian soil and land survey handbook: the Australian soil

classification.’ (CSIRO Publishing: Collingwood, Victoria)

Jeffrey SJ, Carter JO, Moodie KM, Beswick AR (2001) Using spatial interpolation to

construct a comprehensive archive of Australian climate data. Environmental Modelling

and Software 16, 309-330.

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Johnson IR, Chapman DF, Parsons AJ, Eckard RJ, Fulkerson WJ (2003) DairyMod: A

biophysical model of the Australian dairy system. In ‘Proceedings of the Australian

Farming Systems Conference’. Toowoomba, Australia.(www.afsa.asn.au/)

Johnson I R, Chapman DF, Snow VO, Eckard RJ, Parsons AJ, Lambert MG, Cullen BR

(2008) DairyMod and EcoMod: Biophysical pastoral simulation models for Australia and

New Zealand. Australia Journal of Experimental Agriculture 48, 621-631.

Lawson A, Greenwood K, Kelly K (2007) Future farming systems-dairy. Our rural

landscape 2.5. Final report. Kyabram, Department of Primary industries.

Purcell J, Currey A (2003) National program for sustainable irrigation. Gaining

acceptance of water use efficiency framework, terms and definitions. Final Report –Stage

2, NSW, Australia.

Rawnsley RP, Donaghy DJ, Carlson SM, Christie KM (2007a) Responses of Tasmanian

dairy pastures to irrigation. In ‘Proceedings of the Grasslands Society of Southern

Australia 48th Annual Conference’. pp. 90 (Grassland Society of Southern Australia Inc.

Murray Bridge, South Australia).

Rawnsley RP, Donaghy DJ, Stevens DR (2007b) What is limiting production and

consumption of perennial ryegrass in temperate dairy regions of Australia and New

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Zealand? In ‘Proceedings of the 3rd Dairy Science Symposium’. pp 256-276. (The

University of Melbourne: Victoria, Australia)

Steel RGD, Torrie JH (1960) ‘Principles and Procedures of Statistics.’ (McGraw-Hill

Book Company Inc.: New York)

Tedeschi LO (2006) Assessment of the adequacy of mathematical models. Agricultural

Systems 89, 225-247.

Ward G, Burch S, Jacobs J, Ryan M, McKenzie F, Rigby S (1998) Effects of sub-optimal

irrigation practices dairy pasture production in south west Victoria. In ‘Proceedings of the

9th Agronomy Conference’. pp254-257. (Charles Sturt University: Wagga Wagga, NSW).

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Table 1. Monthly climatic information (October 2007 – April 2008) for Elliott

Research and Demonstration Station.

Total monthly

rainfall (mm)

Mean daily

evaporation

(mm/day)

Mean daily

maximum

temperature (oC)

Mean daily

minimum

temperature (oC)

October 2007 83.5 3.01 14.2 6.0

November 2007 24.8 3.96 19.0 9.1

December 2007 142.3 4.54 18.7 10.4

January 2008 6.0 5.97 21.8 11.2

February 2008 50.0 4.75 20.3 10.1

March 2008 41.4 4.42 20.1 10.1

April 2008 45.5 2.24 16.1 7.9

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Table 2. Rainfall and soil water holding capacity of 5 dairy regions simulated using DairyMod. 1

Mean monthly rainfall (mm)C

Site Soil type

Plant available

waterb (mm)

Readily available

waterb (mm)

Mean annual rainfall

(mm/yr) Oct Nov Dec Jan Feb Mar

Elliott aRed Ferrosol 42.0b 20.0b 1191 106 77 76 50 55 57

Scottsdale aBrown Dermosol 45.0 b 21.0 b 1001 87 69 68 61 40 51

Smithton aHemic Organosols 58.5 b 28.0 b 1106 100 82 71 50 50 57

Deloraine aBrown Kurosols 27.0 b 12.0 b 951 94 64 64 51 46 50

Bushy Park aBlack Vertosol 58.0 b 12.0 b 576 58 53 51 41 35 39

aIsbell (1996) 2 bCotching et al. (2002a, 2002b, 2002c). 3 CSource bureau of meteorology - http://www.bom.gov.au 4 1Plant available water taken as amount of water (mm) between field capacity and permanent wiling point in the root zone. 5 2Effective root zone taken as 30 cm for perennial ryegrass 6

24

Table 3. Mean pasture consumption and the GPWUI, IWUI and MIWUI for each of

the irrigation treatments over the experimental period (Oct to April).

Treatment Total pasture

Consumption (t DM/ha)

Irrigation

(t DM/ML)

1GPWUI

(t DM/ML)

2IWUI

(t DM/ML)

3MIWUI

(t DM/ML)

100% 9.16 ± 0.49 4.20 1.10 ± 0.06 2.18 ± 0.12 1.29 ± 0.14

80% 8.92 ± 0.30 3.36 1.19 ± 0.07 2.65 ± 0.16 1.54 ± 0.05

60% 7.64 ± 0.61 2.52 1.15 ± 0.09 3.03 ± 0.24 1.55 ± 0.12

40% 6.89 ± 0.56 1.68 1.19 ± 0.05 4.10 ± 0.18 1.87 ± 0.13

0% 3.74 ± 0.49 0.00 0.91 ± 0.12 N/A N/A

LSD (P =0.05) 0.96 N/A 0.15 0.36 0.35

aRainfall for the experimental period was 412mm

1GPWUI = = total product/total water applied

2IWUI = total product/irrigation water applied

3MIWUI = marginal production due to irrigation/irrigation water applied

25

Table 4. Summary statistics indicating model performance for pasture consumption

for each irrigation treatment and grazing event.

Summary statistic

Measured mean 1.21

Simulated mean 1.29

Mean bias -0.08

r2 0.93

Mean prediction error 18.45%

Modeling efficiency 0.88

Variance ratio 0.87

Bias correction factor 0.99

Concordance correlation coefficient 0.95

26

Fig. 1. Accumulated rainfall deficit (mm) of the 100% irrigation treatment and the daily

irrigation or rainfall event (mm) for the experimental period.

Fig. 2. Daily soil water potential of the 5 irrigation treatments at a depth of 15 cm (a), 30

cm (b) and 45 cm (c).

Fig. 3. Individual average pasture consumption (kg DM/ha) for each grazing event under

each irrigation treatment. Standard error of means shown as error bars.

Fig. 4. Actual ( ) and modeled ( ) pasture consumption for each irrigation treatment

100% (a), 80%(b), 60%(c), 40%(d) and 0%(e) at each of the 6 grazing events.

Fig. 5. Box and whisker plots showing 10th, 25th, 50th, 75th and 90th percentiles of the

marginal irrigation water use index (t DM/ML) for 5 dairy regions, Elliott (a), Scottsdale

(b), Smithton (c), Deloraine (d) and Bushy Park (e) for the period of 1967 to 2006.

Simulation outputs for nitrogen applied at 230 kg/ha/year shown as ( ) and for non-

limiting nitrogen shown as ( ).

27

05

101520253035404550556065

01-Oct

15-Oct

29-Oct

12-Nov

26-Nov

10-Dec

24-Dec

07-Jan

21-Jan

04-Feb

18-Feb

03-Mar

17-Mar

31-Mar

14-Apr

Irrig

atio

n or

rain

fall (

mm

/day

)

-26.0-24.0-22.0-20.0-18.0-16.0-14.0-12.0-10.0-8.0-6.0-4.0-2.00.0

Accu

mul

ated

def

icit

(mm

)

Irrigation Rainfall Accumulated deficit

Fig. 1.

28

0

25

50

75

100

125

150

175

200

01/11/07 22/11/07 13/12/07 03/01/08 24/01/08 14/02/08 06/03/08 27/03/08 17/04/08

Soil w

ater

pot

entia

l (-K

pa)

0

25

50

75

100

125

150

175

200

01/11/07 22/11/07 13/12/07 03/01/08 24/01/08 14/02/08 06/03/08 27/03/08 17/04/08

Soil w

ater

pot

entia

l (-K

pa)

0

25

50

75

100

125

150

175

200

01/11/07 22/11/07 13/12/07 03/01/08 24/01/08 14/02/08 06/03/08 27/03/08 17/04/08

Soil w

ater

pot

entia

l (-K

pa)

0% 40% 60% 80% 100%

Fig. 2.

29

0

500

1000

1500

2000

2500

Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08

Past

ure

cons

umed

(kg

DM

/ha)

0%

40%

60%

80%

100%

Fig. 3.

30

0

0.5

1

1.5

2

2.5


past

ure

cons

umed

(t D

M/h

a)

0

0.5

1

1.5

2

2.5


past

ure

cons

umed

(t D

M/h

a)

0

0.5

1

1.5

2

2.5


past

ure

cons

umed

(t D

M/h

a)

0

0.5

1

1.5

2

2.5


past

ure

cons

umed

(t D

M/h

a)

0

0.5

1

1.5

2

2.5


past

ure

cons

umed

(t D

M/h

a)

Fig. 4.

31

0

0.5

1

1.5

2

2.5

3

3.5

4

40% 60% 80% 100%

Deficit irrigation treatment

MIW

UI (

t DM

/ML)

a

0

0.5

1

1.5

2

2.5

3

3.5

4

40% 60% 80% 100%


MIW

UI (

t DM

/ML)

a

0

0.5

1

1.5

2

2.5

3

3.5

4

40% 60% 80% 100%


MIW

UI (

t DM

/ML)

b

0

0.5

1

1.5

2

2.5

3

3.5

4

40% 60% 80% 100%


MIW

UI (

t DM

/ML)

b

0

0.5

1

1.5

2

2.5

3

3.5

4

40% 60% 80% 100%


MIW

UI (

t DM

/ML)

c

0

0.5

1

1.5

2

2.5

3

3.5

4

40% 60% 80% 100%


MIW

UI (

t DM

/ML)

c

0

0.5

1

1.5

2

2.5

3

3.5

4

40% 60% 80% 100%


MIW

UI (

t DM

/ML)

d

0

0.5

1

1.5

2

2.5

3

3.5

4

40% 60% 80% 100%


MIW

UI (

t DM

/ML)

d

0

0.5

1

1.5

2

2.5

3

3.5

4

40% 60% 80% 100%


MIW

UI (

t DM

/ML)

e

0

0.5

1

1.5

2

2.5

3

3.5

4

40% 60% 80% 100%


MIW

UI (

t DM

/ML)

e

Fig. 5.

32

Appendix 1 – PowerPoint slides presented at field day.

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