Workshop Notes

DAY ONE

Toss Gascoigne Introductions

Short Term Climate Forecasting – Does it Work? Elizabeth Ebert

Can Crop Models Capture the Effects of Water Stress and Water Logging?Dr David White

QUESTIONS

Steve Raine:  Models good in terms of predicting soil moisture…. Effect of excessive water is not particularly well done…. Physiology effects – happen rapidly – cotton if it water logged – start to expect yield decrease.  Sugar cane – effect of water logging surface irrigation vs drip irrigation – don’t seem to be very well catered for in models. David:  General structure – calibration need for specific sites.  Water logging is the new factor that is being focussed on.  Calibration as well as validation at this stage.

Mark Littleboy:  Water logging question.  Ignore lateral movement of water – don’t pick up areas where water is accumulating.  One dimensional single point models.

David:  Models are working well.    Models can be tripped up.  Exclude a variable in particular environment Model will misfire.  Models are being integrated.  Defining processes.  Understanding of processes a long to constructing appropriate model. 

Ted:  Dryland situation rather than irrigated.  Time scale occurs over weeks and not over hours.   Mark agrees.

Water logging very important spatial issue. 

Proof of Concept:  Short Term Forecasting and WUE Peter Timmers

QUESTIONS

Richard Wells:-  Improve irrigation efficiency.  Addressed only half the potential benefits.  25-30 times a year irrigate – prior to irrigating – considering – leading to harvest – potential to impact on pest disease.  Reasonable expectation weather to be fine if it raining – pest disease incidence.  Spray after irrigation rain event – problem of compaction.  Addressing – is great – hasn’t touched the surface.  Value of property impacted by rain event – 22 percent bonus because of increments – if it rained – 40 percent decrease – real money. 

Irrigation in horticulture – not to deal with water but deals with nutrients – not a loss of adoption of models in horticulture – biomass development focus – quality and fairly complex physiological issues.  Importance of irrigation. 

Brett Tucker:  Water table control and salinity.  Risk in watering is a communal increase.  Excessive drainage.  Choices – don’t irrigate for benefit of community – individual – crop.  Social dimension to some of these choices.  Cop the risk if they get it wrong.

David White:  Water to Horticulture crops is great.  Water being the major component.  Quote from paper.

Abawai:  Future directions…. The next step – what is linking his work with farm management models – practicality of irrigation prevent the full expression of the theoretical gain.  The reason being that it takes 7-10 days to go round and water all the blocks on your farm.

David Clark:  Furrow irrigation.  Horticulture context - most not furrow irrigated.  Irrigated 3-4 days.  Advantage of weather forecast – more.  Small rain event coming and delay irrigation – get to that stage different industries would be able to respond to that information.

Most gain – horticulture – where we have least sophisticated models – David White to say something about.  Horticulture models available.

Enormous potential – generic model – pest and disease models – they can be done.  Very good work coming out of Europe.  Kiwis are getting into it then we should be.

David – so many holes in model.  Water logging – avoid.  Drainage event – surface event have far larger …   deep drainage nowhere near as important.

Positive thing (Ted) – identify those sorts of issues – change priorities -.

David:  Drainage – horticulture context.

Steve Raine – deficit becomes smaller irrigation frequency more frequent. Decision to delay irrigation by day would cost you your crop.  Optimisation issues.  Application system in wetted area effect – concern – looking at drainage figures – gains look to be in drainage side – drip irrigation – 1/3 area – overestimated – filled up deficit that is uniform – function of model using – very careful – numbers into practical context – looking at in field scale – uniformity.

Ian:  Issue about drainage component – fairly comfortable with results of PERFECT model.  Confidence in a particular scenario.  Drip irrigated scenario on cotton – function of the total system or management.  Complex.  Second point – scoping study – prioritise where to go next.  Articulate key bits of the system that we need to fine tune.

Richard:  Smaller rainfall events really wasted.

Don Yule:  Crucial concept – water balance – hardest to get across – has done pretty good job – has reduced irrigation – improved efficiency.  Yield? Yield and quality will have effect – runoff and drainage – reduced.  Proof of concept has done a pretty good job.

Ted:  Transpiration for all runs would be similar – yields different.

David:  Is daily timestep enough? 

Alison:  Daily timestep – more complex models – hard to parameterise. 

Neville Moss:  Water use efficiency – if you can’t have on farm storage to collect runoff then you are not going to have high water use efficiency – storage on farm.

National Benchmarking on Water Use Efficiency Brett Tucker

QUESTIONS:

Ian:  Role of new technology vs management and infrastructure.  Variation – policy statement – dollars to support poorest performance or support best performance.

Tendered to always focus on the worst group…. Different examples – Rice industry – 14% any above that not allowed to grow rice.   Dairy – invest plenty of dollars.  Over time – concentrated on making good growers better. 

Ian:  Different Tools.

Brett:  Technology fix was not fix at all.  All programs – changing attitudes and behaviours.  Search our better technologies to improve.  Well run furrow system – changed technology.    Attitude change not technology.  Bigger successes.

What use is it going to be – valid comparison to compare yourself with someone in Griffith.  Better comparison is with neighbour or someone in the district.

Marketing driven incentive in terms of being able to sell your commodity. 

Europe it is now major issue – we are getting environment subsidies through mining our resources.  Trade barrier.  Second hand information.

Policy point of view – have addressed the issues.  Assist the process.  Adjustment pressures are happening. 

$200,000 critical point of farm turnover.  Not enough dollars to sustain yourself.< >  Same amount of money with smaller megalitres.

Why delivery efficiency in pipes low.  (Nick Clarkson).

Great systems in depression – sadly out of date – dragging the averages down.< >  Newer systems SA and Sunraysia nowhere near that figure – 97 and 98.

Benchmarking – cotton industry – concentrating cost management.< >  Can go soft using new products.    People to pick up act and improve things.

Brett: Work best more regional than local scale benchmarking – collecting framework.< >  Question benefit of being able to benchmark between different regions with different benchmarks. 

Delivering RWUE through Industry and Government Partnerships Graham Milligan

Paul Dalton:  Assuming that less demand on the water storage. Catchment scale. 

Pinching from Peter to pay Paul.

Other changes in landscape that alter catchment hydrology – water allocation planning processes – keen to move towards concepts and philosophies WUE all levels is exactly one of those – how that happens on the ground is very interesting.

Frank:  Ongoing modelling. 

Review processes – broad scope of how we look at changing – allocations are.

Ian: Role of science has to play – expectation science to deliver outcomes on farm – major policy decision to happen – downplays degree of risk that farmers are willing to take.   Reflection of how it should happen.

Government wanting to set up a framework – water planning – establishing certainty for peoples rights – fairly solid ground for that.  People on land will take inherently more risks to get things done and make a buck.  Setting up frameworks to allow it to happen.  RWUE provide opportunities for people on ground to make the water go further.  What is the level of risk in them doing that.  Outcome from the science – get out of investment in science – strive for those practical applications on the ground.  Outcome – practical outcomes – economic justifications, increase potential for uptake, people on ground make risk management decisions.

Ian:  Social targets.  Providing opportunities and outcomes which depends on where you are putting benchmarking effort – on ground – not fair call on science.  Science providing the opportunity.

Graham:  Expecting too much from scientist.

Ian:  Rules – more sociological background.

Steve:   Will delivery practical benefit.  Take some time for science – timing is wrong R&D started after adoption.  Policy level – cost benefits – how relates to RWU program.  Social indicators – problem within program – big emphasis – social indicators – how many people in door, etc.  How many extra dollars have you returned.  Spread of operators.  Practices and not make change.  Field officers struggle – need big workshop – need to get across industry – get better with going to a large grower.  Change direction half way thru to make dollar gain.  Horticulture – lot of participation and not big dollar gain.  Forego group activities – focus on small number large producers – then get bigger gain.  

Graham:  Long term outcomes – lot of work developed in consultation with industries.  We can adapt and keep going.  Mid term review of the program.  Four year program – serious think what is happening after June 2001. 

Ian Bell – Awareness moving thru to adoption and dollars. 

Richard Wells:  Farmers adopt technology as well as anybody.   Reality check of where farmers are at the time.  NPIRD prioritising budget – looked at research – that had not being adopted - adoption will happen.

Working with industries – government take hands off role.

Can Rural Water Use Efficiency Deliver the Goods for the Australian Irrigation Industry? Tony Horton

Barry White:  IAA identified climate changes – what in mind. 

Tony:  Board meeting in late May – chair had meeting – strategic issue that had been identified.

Barry:  Survey 2500 farmers – some industries, cotton and sugar –half farmers were aware.  Some industries uptake on climate forecasts.

David:  Climate change – big concern – climate change and climate variability – SOI – more likely to be higher frequency higher magnitude extremability.

REFLECTIONS ON AFTERNOON SESSION Brett Tucker

Implications.

No doubt made significant improvement over time.  Scheduling on times.  Issues – investment point of vie w- what extent make gains in this areas.  Figures may or may not be indicative – gains of half meg to one meg to two megs per hectare.  Adoption forecasting and scheduling.  10meg variation best and worst.  Where are we better off targeting the dollars.  Yes proof of concept – is there.  We can now betterpredict.>  Equate to more investment in this area.   Haven’t talked through these issues yet.

Some of the growers are already at the top of end of where they want to be – next 5-10 percent squeeze out of system.  Filters down to next level of growers.  Continue research better prediction of weather and in better application of that at a farm level but it is just one small component of overall quantum change that needs to take place.

David:  Hypothetically – big figure saving in other areas – complex to adopt – lower level advance may be a lot lower.

Brett:  Fairly complex area to get farmers to adopt.  Trying to convince a drip system properly and also consider 5 day forecast.   Not much adoption of scheduling – flow irrigated systems – flood and 4 or 5 day ordering of water.  Not as good as water on demand.  Benefits of scheduling not as good.

Steve:  Small marginal benefit – potential gains biometric gains sense.  Equally gains of technology in wrong way.   Reduce irrigation volume – more so in production side, quality control, pests and disease, water logging effects, the only gain out of this technology – irrigation WUE – more as a gain as a production gain.  No data yet that demonstrates magnitude of the change.  Far greater than dollar return to improve irrigation scheduling – short term forecast.

Brett:  Practical outcomes to research – good to have research there to know what benchmarks are.  Sets targets unrealistic in short term but keeps pushing boundaries.  Comparative analysis of growers to each other rather than what we know crops can grow on.   Redefine what crop can survive on.  Sets boundaries.  Pushes the thinking a bit.

Brett:   Areas we should go.  Institutional impediments in place as to why this is not adopted as strong as it is.   Debit auditing – major potential problems – easy to let water go past.    Researchable area.

Beth: Limitations to what we will ever be able to do.  Summertime thunderstorms – cannot be predicted.  Predictability limit.  Take e.g. Peters work – five day total.  Used in irrigation decision making.  Water Users – how useful would probabilistic forecast be to you.  Should BOM be doing more research.  We can start to provide in next year or two.  Percentage figure.  Farm Weather probability but based on ensembles on forecast rather than past performance of models.  Is it useful product to you….

Looking to give you a best guess of what the amount would be – less reliable than probability.  Break it down – probability greater than one, five, ten etc….  Publication needs to go along with that.  Coming on line.

Mark Mammino:  False events would mean big financial loss.  Take gamble and nothing happens – astronomical financial.

Beth:  Most seven to ten days forecast.  Rotation as long as yours (Mark) may not be able to help you very much.

Steve:  False alarms – dropped off rapidly after three days.  Peters on five days – big assumption that you can go from 3 to 5 days. 

Toss:  Would a five day forecast be of use to farmers.

Meteograms - $107 per year.   Good value.

Dave Clark:  78% prediction – no notice of 40%.  A lot of notice under 10 percent.< >  Because if it wasn’t going to rain then we can go ahead and irrigate.  Real value – using in practical sense – knowing over four day period if it wasn’t going to rain.

Ted:  Argument to test – historic predictions.  BOM could supply etc Dalby or Grafton.  Ran model – incorporate errors of BOM predictions… Can we get historic forecasts going back a few years.

Beth:  Yes.  Four or five years.  Models have changed so much – pointless to go back.

Ian:  Same model today as five years ago.  Beth:  Has evolved.  Limited with forecasts.  Lack in time series length – lots and lots of samples of different places – statistical reliability.

Beth:>  5 day forecast on own – rain will be 5 days – hard ask.  Getting better withtime.>  Not a good product yet.

Cold fronts – several days.  Most difficulty – convective storms.

Paul Dalton: Models to capture water stress and logging – understand water logging is real issue.  Days over upper level, days even as indicator, days at saturation – we can’t model – this as anindicator.>  Models are giving that good indication.   Water savings – my impressions of industry – that is not high on the priority list.  Productivity gains that they are interested in than saving water.  Downs – Goondiwindi – one year dryland crop that beat irrigated crop.  Making buck, maximising production.  Cheapest was not to irrigate at all.

Worth Government and policy people to consider – discuss science of whole system – effect on water balance.  Not just academic exercise – difficult – one that should be considered in the whole discussion.

Benchmarking – very useful tool on a farmer to farmer basis. Definitely should be built into system of evaluating of how science is serving the practitioner.

Toss:  What do you think needs to be done next.

Paul:  I think what is actually happening – practitioners are using it – whatever forecasting technique is out there – the way the thing tends to work – calibrate your own – get trust and reliability in system – systems improve – people will get more faith.  Ground level – science is building.

Dave:  Your own reliability figures on it.

Ted:  Weather radar giving a few days advance.

Beth:  Good way to observes rain – not good for forecasting from radar.  Hours or a day but no further.

Graham:  Baling hay – you will go a day earlier rather than leave in paddock for another day.

Don:   Model a few days in crop – big issue – irrigation scheduling – stop irrigation – hurt by water stress – if irrigate get cost of water logging associated.  Balance of costs.  How well you can model for few days not whole year.

Barry:  Stressed nutritionally – crops recover from minor stress.

David:>  Respond of behalf of crop modellers – 5 day forecasts – we need to model – cannot analyse systems – if you are not modelling. How crops responding.  Water logging  - because most of crop modelling on dryland area – first look indicates fair bit of success…. Work overseas on horticulture modelling – quality is an issue – quality can be modelled.  

Self Calibrating…

Richard Wells: Look at what is most robust crop.

Can short term climate forecast etc – potentially huge yes.  Really exciting project. 

Is Water use efficiency – broadest context – examples from on the board.  Richard explains off whiteboard.   Defer irrigation – realistic objective to save irrigations.  Real savings in spraying – if you knew for sure if it wasn’t going rain huge savings.   Broad acre guys – limited ability  - saving biggest volumes – cheapest.  

Need to think of forecast – how it is presented.  Focused on rain – frost issues should not be forgotten.  Accuracy and frequency.  Best info at time – forecast you hear at 7 in morning is not prepared for farmer.  Forecast for night before beneficial for farmers.  12 hours more usable for farmers.  How when and updates – greater need for updates – if changing forecasts.

Big gains by optimising how forecast is presented – timing of event – how many mms might fall in that event and we need to know probability – what is going to happen after rain event – more important than knowing when it is going to rain.  Only half the info that you need to know – pushing vigorously for after rain event.  Huge difference – would not spray before event. 

Project is in hindsight – really valuable when event occurs.  Cost of water increasing always – minimise application.

Years less than 100 percent allocation – so intense – get every information they can get.  Forecast right on top of this.

Modelling – individual farmer – pay big bucks to groups and service providers – role for them but not sure for the farmer to interact in the first instance.< >  Only support that he would purchase. 

Toss:  What is the scheduling of forecast – how often does bureau issue forecast. 

Robert:  Only familiar with Victoria.  3 forecasts issued a day …. After 5 and after 10 to eleven.  Twenty past four.  Most reliable likely to be the afternoon.  Based on mornings observations.  Evening – particularly in south – prepared in noon for the following four days – but by time next morning only 3 half day forecast. 

Richard:  Morning forecast then is pretty useless.

What is the ET – if that figure published – would be better off.

Steve:  Not in game to save water – Richard: Optimise your return on your investment.

DAY TWO

REFLECTIONS Ian Gordon

Richard Wells comment:  Better Irrigation management didn’t change the volume of water applied but reduced the amount of deep drainage in the Sunraysia district of 60 percent.

The Farm, a Review of Measurement Methods and Case Studies Paul Dalton

Evaporation from storage and deep drainage – major losses.

Debate on DD estimates using mass balance – 80-100mm per crop seems reasonable.

Perspective from a Practical Irrigator:  Can Irrigation Scheduling Theory be Put into Practice? Ian Hayllor

Cotton main crop.  Best Management practice program – involved in.  Involved RWUE project. 

Maximising return on water – pump, delivered or flows on farm.  Every facet right.  Soil management, crop selection, marketing, dollars per megalitre.  Measure the resource – what’s out there.  We then need to start benchmarking and compare with others in the area.  Identify areas where your operation is not performing the way it should.  Storage is a big concern.  Adopting new practices and some of research from WUE project.  Accurate info – irrigator – but no evidence.  No accurate info to argue case – important project for irrigators.  Local community will support – if we can create jobs that are sustainable in community.  Pure economics to motivate farmers.  Research – very important – ways for improvement – farmer will adopt.  Cotton industry is leader at adopting new technology.  Water supply – WAMP – having some horror reports that we can lose – up to 70 percent water – fighting – hopefully keep close to what we have been using.  Cost of water more expensive.   Charge for meters on farm – fuel cost repairs, maintenance costs – incentive to improve efficiency – 10 percent saving in many areas on the farm.  Crop competitions – well recognised – whole of farm management.  Farm enthusiasm – long term farmers – make sure every chance of taking over profitable farm with sustainable future – working hard to adopt new technology.  Environment is protected – looking after own farm – whole environment is protected so there for children.  Involved in two benchmarking projects – whole of farm – help people adopt best management – gone away from time of yield.  Gross margins and profit at end of day rather than yield.  Industry has to have competence in research – relevant to operations.  Science – has it improved – 20 years ago guesswork – no experience – moved into DPI research program – very helpful – crop factor and evaporation figure – calculate when you should water.  Problem – rainfall efficiency – no figure.  Enviroscan and diviners – used for five years – very good.< >  Info is good to plan farm operations around that.  Improved management of water.< >   Future – 100 ml deficit – works for us – good yields – using similar water to neighbours – reducing deficit 85-90 mls – water on and off quicker.  More air space in the soil – less water logging problem.  Irrigate three times  - 150 ml – 3 crop waterings – 200 mls effective rainfall during season.  Enviroscan - .. cotton – less expression of in guard gene – plant under stress – potential insect problem – genes to control the plant.  Whether to help out plant.  Conducting of demonstration farms is happening in project now – way to do it – lectures don’t do too much for farmers.  Want to see computer models are really proved in the field – very important.  Farmers very practical.  Equipment for research must be reliable – trouble with meters blocking up.  No valuable data gained – has to be proven and not break down.   Field day – wants to have new valuable info to encourage people to come along.  Same old story and people get fed up.< >  Need to get growers out.  New info – will go away enthusiastic – and look at trying it.   Whole system and not just irrigation of paddock – where biggest gains are made.

Seven day climates – more accurate forecast – easy to be irrigator.  Accurate forecasting reduce chance of water logging or moisture stress.  Potential for bigger disaster – hold of and nothing turns up – real danger.  Very expensive if farmer gets it wrong.  Prioritise farm operations with accurate forecast – if we have to spray, fertiliser.  Monitoring equipment – choose right paddocks to water.  Least likely paddock to water log. Lots to do if we can trust the forecast.   Less overland flow type water to worry about.  4-5 inch rainfall event on top of farm – lot of water hanging round – end up in river system.

Weather forecasting – long way from perfect.  Improve marketing opportunities.  Knew in advance seasons forecast would be good.   Disease management, harvesting efficiency, quality, help everybody – know how to crop our farms for the coming season.

7 day forecast day to day management – earlier comments – have run thru.

Scheduling tools – rapidly gaining pace – useful equipment out there.  Very successful project.

Problems of irrigators – lack of water – how you manage your irrigation.   Do we plant an area that we can water fully or hope its going to rain.  Irrigation decision – need to do – important – looking long term forecasts – important as short term.  Gains of 2/3 percent short term – 50 percent in long term.  More work needs to be done on this.  Planning the season.

Fast irrigation system – you can still go slow.  Wait to forecast – can irrigate within four or five days and never get behind. 

Improve WUE efficiency – deeper storage than average.   Would have go to 8 or 9 mm.   A long way to go.  Reduce surface area.  Needs to be worked on over next few months and years.     Fields laser levelled – increasing slopes – good drainage across the farm.   Soil management paid a lot of attention to.  Regular soil tests.  Improve quality of soil – everything that we can do we do.  Whole package of doing everything right.  Monitoring and scheduling equipment to predict irrigations – water quickly – pretty efficiency tailwater and stormwater management system across farm.

Can’t design system for one in five year event – one in three event.

WAMP – biggest concern – 10 year plan.  Do not know what is going happen.   Investing in WUE – serious concern about 10 year plan and what happens at the end of it.  Long term investment – serious questions.

QUESTIONS

Steve Raine:  7 day forecasts – comments yesterday – reliability – 3 to 5 days.< >  How did that influence some of your comments.  Is three to five days still useful.

Most out of the water that you have got. 

Do people take risky choice.  Are people more conservative.

Ian:  Most people took the risk and got caught.  We have larger storages than average.  Have gone that way to protect irrigation system.  Neighbours – some started with no water at all  - percentage pump didn’t pay off because the rain came late.

Long term is much more important than short term.  Use SOI now – negative.

Seasonal outlook (Nick Clarkson).  If SOI stays in negative phase – way down average stream flow.  

Don:  What sort of crop measurements do you take.

Ian:  Purely yield.

Ian:  Plant mapping.  Monitoring crop health and insect damage – happening on regular basis.  Monitor plant height.  Elongation in length – growth shortener on crop to hold height of crop.  Grain crops would not measurement during season – cotton yes.

Delay irrigation by few days if it is getting too long.

More than happy with Enviroscan.  – soil moisture levels – future prediction not whats happened in the past.

Assess value of rainfall problem.

Validation of the PERFECT Water Balance Model Peter Timmers/Mark Littleboy

Steve Raine:  10 year plan – year away – 20 years away.  Infiltration models.

Broaderscale landscape balance.

We are not in business of developing models – using models to make decisions.  It is not ready for us.

Water logging with furrows – landscape – no capability of pick up at the moment. Real gap between very good soil models at 2 models crop physiology models – no answers – is this the direction – relatively easy to do off shelf product.

PERFECT – can answer questions that you proposed about hills and furrows.

Water logging problems in top 20cm.  Cracking clays behave differently in top 5cm.

Steve: My experiences – science want more data than extension people do.  Is there a way in which we can communicate – we can go out as scientists – linkages – one to one basis – good communication with whoever is doing the work in your field.  Yes – most practitioners are more than happy to tell you what we are doing – tag along and get the extra data. Needs to happen on a one to one basis.

Don Yule:  Good opportunities WUE – if we can’t influence – enormous loss of opportunity.

DY: Research people will do what research people do best.

Major progress in grains, proposed project in cotton, attempt by L&W to integrate program…it is still developing.

Bring this up later in the session – this afternoon.

AB: SILO project done – prepare met records for use by modelling – data sets available.

Ian H: What does it mean to the farmer – advantage -.

ML: Total soil water right, how much water in total soil roughly right, fairly good validation, deeper depth developing root growth.  Value of modelling – we can look at what is going to happen over 40 to 100 years.  Met data – long term probabilistic outcome – one in five year, 10 year event – shorter term experimental data – feeding long term climate data into model – long term picture – looks at risk factors, SOI phases –will this effect decision making.

Richard Wells:  Any chance these models going to be user friendly – see the forecast – reasonable realistic prediction.

ML:  Lookup table that is derived from model.  Synthesis of whole range.

RW:  Optimum times to use limited water you have available – Geoff Bamber:  Trial and error – likely stress periods – which are likely to be most successful – as season progresses – rains initially delay irrigation.  SOI to make some judgement about future.  Using model has kept capability of doing thousands of tests to see which options are best.

REFLECTIONS Neville Moss

Attitudes – if it not sold to irrigator – whole scheme is doomed.  How irrigation is used – how effects environment – WAMP working on.  180,000 megalitres water – hopefully increase production by 200 million – growers looks at brochure – top 30 percent better off holding workshop for them.  Their gain is negligible – bottom 40 percent is the one that should be looked at.   How to get this forty percent to kick.

65 percent and 70 percent in Emerald – six or seven times in Emerald (water).  Four weeks quicker than on the Downs for minimum – 5 and 7 megalitres depending on the year.   Irrigation scheduling – our area – soil moisture detection – 10 years ago.  Don’t water anything under nine days – pay more attention to waterscheduling.>  Two days behind and then start watering – no big way of determining of actual loss you have made in return by three days late – what have you got to compare it to.

Short term weather forecasting – how reliable would it have to be – after last two seasons – used three day outlook – should we start water now or not – insect applications come into this as well.  Reliability of short term forecasting could be used – long term probably not so much for us – dryland guys and on Downs – big benefit.  Value of water:  economic loss thru lack of overland flow and lack of rainfall.

QUESTIONS

SR:  RWU – needs to be sold to irrigators – you don’t think it has been sold.  How should we be thinking about selling the program.

Neville:  Like most of these programs – initially – top 30 percent of growers who are keen – how you get the next sixty percent involved in this is without financial gains  - cannot see them really jumping on bandwagon.  Half of sixty percent moving on it.

SR:  Horticulture – real dollar return.  We don’t want to waste time.  Are you having trouble seeing profitability gains.

Neville:  Extension officer missed first season of program.  Behind some of the other groups because of the first season being missed.

Warwick Waters: Extension reflection of speakers this morning.  Two areas – what impact will those issues have and what issues do farmers have to effect those impacts.

Paul highlighted scientist assessment of impact of efficiency of cotton farms – big variation best and worst case – message:  evaporation prevention big impact opportunities there.  Area potential influence by farmers – simple steps – flow rates – key questions of management.  Simple tools – high influence not difficult – have to be shown how they can impact.

Ideas – need to look at assistance approach to handling evaporation – drainage aspect.< >  Main things from Ian’s talk – irrigation needs to be reviewed as part of whole farm.< >  Economics drive change on farm.  Policy observation – lot of economics goes on farm at moment – quality and quantity and timeliness of production.  No economics has nothing to do with natural resource.  More accurate way of paying for primary production.  Fight for amount of water – managing on farm and off farm to get policy in place to manage.

20 years ago progression – guesswork to evapotranspiration – thru to Enviroscan.   Comparing Enviroscan to neutron probes.  Take Enviroscan every time – useful data to manage. Seeing models proven in practice, reliability of equipment, need for info to be fresh and new at field days.  Weather forecasting – how having good long term weather forecasting would have big impact – no big influence over at the moment.

Peter and Marks presentation – one dimensional aspect of models.< >  Distribution uniformity of rainfall on region if we talking about short term climate forecasting – see effect of this.  Data presentation – blames wife for not given Peter the info that he wanted.

Yahya Abawi: Economic value – yes.  More serious question does STC etc …… reserve answer for time being – like to see wider definition of the term.  Based on results that have been presented – improvement of four to 5 percent using perfect 5 day forecast – assuming you make right decision – is far short of what you can gain from other management practices – managing storage – evaporation.  Evaporation on dams can range anything from 35 to 70 percent.  Applying irrigation in different way – DD can be ….. water balance use to improve WUE – can STC etc – productivity – look at value of crop and quality – take in account – answer may be yes.

Paul Dalton presentation – different ranges.  Challenge  model capture this variability.   Modelling – 70 percent of answer can be obtained with 30 percent of effort.  Suggestion – before we move on and look at APSIM model – this exercise – incorporate some of the other factors that is influence decision process – management issues on farm – whole system farm – order water today and it rains by the time it comes to the doorstep – reject.  Depending on licence – not efficient use of water but given price of water is low – much higher price on water – forced to capture water and use effectively later on.

Economics.  Variables need to be incorporated into modelling exercise.  Role of Govt in policy is 10 percent improvement in WUE 10 percent cut in licencing or increase in crop area.  What if you haven’t got more to plant.  Why aim for improvement in WUE.  Role of Govt play encourage people to improve water use.  Height of farm dams – 4.9999 metres deep – not efficient.  

Long term forecasting being more useful than short term – 4 year study on long term in cotton – report out on this – economic benefit of using LT seasonal forecast in decision making.   Farmers see forecast thing as another way of reinforcing their gut feeling.  Making decision on a number of other factors.  SOI is negative and if that trend continues – outlook may be for a dryer season.  If price of cotton rises – you would ignore forecast completely and plant the whole lot.

Driving force is ECONOMIC’s.

QUESTIONS:

Ted:  Height of dams – how much is below and above dam.

RW:  Any saving.  Real cost is not water handling the stuff.  Save on labour.

SESSION 4:  FOCUS ON OUTCOMES

Q1:  Free for all to identify R&DE issues considered important by participants.

Modelling of water logging in soil water balance.

Reduce false alarms, increase reliability.

Get three days out to five days

Probability total rain over five day period, and nature of falls

Transfer one dimensional models to dimension

Evaporation figure

Incorporating short and long term climate forecasting into the farm systems

Improve format of forecasts by BOM talking to irrigators (what are farmers already doing?) “Real World”

Cost of potential loss of water logging and water stress

Effect of water logging on plants

How do farmers adjust to better BOM predictions

Module for community and environment interests

Ways of optimising how  the modellers can access data sets

PRD & RDI – as they become more important, forecasts will increase in importance

What Farmers Want?

Better information to reduce water logging, stress

The expected ET figures 3-4 day out

Like to know probability of event on specific day, and the more notice the better

Better information on post event conditions

Knowing that no rain for two weeks is useful in horticulture

Knowing that no rain for 5-7 days is useful for lucerne

Knowing that no rain for 5-7 days is useful for potato picking and spraying

Knowing (for cotton spraying)

Dairy like to know small event probability

Cotton:  notice of cold front and rain.   Rice:  cold events in January replanting

$$ behind various courses

Q2:  What potential advantages do you see if the Short Term Climate Forecasting Irrigation Scheduling system were to be implemented?

Potential water savings

– will vary between industries and regions

– will vary between different application systems

– average will fluctuate 0 to 10? from year to year for cotton

Putting stochastic error in hindcasting exercise

Skill will increase with more customers – potential has not been scratched

Non irrigation benefits

– Achievable low cost component in best farm systems

–Environmental benefits (salinity, water quality

–More efficient farmng – general community benefits (economic, social and environment)

–Only a matter of time ….

–Spraying, disease ……

Q3:  What further research and development needs to be carried out? What is the best way to progress this idea? Where should this happen?

What’s the key information that they would find now.  What is available now.  Farmers do not have computers (stuff on web).  Very few horticulture crops that have decent models.  Irrigators in horticulture – no uniformity – one dimensional model is huge restriction to quickability and results.  Leading to practical advice.

STWF – no meteorologist on project – modeller – boundaries to resources where would we go forward from here.  How much can he do in two years.  Potential for another position in project for next two years – experimentalist or communicator.  Bunch of clients.

Extension/communicator position?  How science can be linked with other programs

Continued discussions with BOM

Water logging issue

Q4:  Is it possible to confirm the climate scheduling concept with experimentation?  Where?By Whom?  How should farmers be compensated for allowing the system to be tested on their property?  How should the system be implemented?

Ian Hayllor relaxed about compensation

Q5:  Can models help devise new management systems?  How do you extend limited experimental evidence to cover wider areas, and other periods of time?

Yes and with difficulty.

Use demonstration sites next to site of data collection – in dairy pay back to allow shift to flexible systems

JUST DO IT    but do it on farmers farms not on demo farms

Video of case studies

Ted wraps up….

Everyone seems to be interested in news – weather is one of things – innate interest in knowing – important area – how we turn it into position action statement. 

Could go higher in catastrophic situations