I nvited Lecture 1 Best practices on the application of clim ate inform ation in the agricultural sector. Dr Roger Stone and Dr Holger Meinke . Queensland Government; the University of Southern Queensland. I nternational W orkshop on the Applications of Advanced Clim ate I nform ation in the Asia-Pacific Region
Outline of presentation: •The problem faced by agricultural systems. •The need to link to decision making and management. •The need to understand value chains in agricultural production. •The need for simulation modelling to provide scenarios. •How to apply seasonal forecast systems to achieve the best results – use of integrated systems – process models, ‘agroclimatic’ system, farm-scale production, shire-scale forecasts. •Participative approaches and interdisciplinary approaches. •Climate change issues? •Conclusions
20 2.5 10 2.0 heat yield (tonnes/hectare) OI Annual S 0 1.5 W -10 1.0 -20 0.5 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 Year Climate impacts: relationship between annual variation in the SOI and annual Australian wheat yield (N Nicholls). * To achieve best practice need to modify actions ahead of likely impacts.
To have value climate information needs to link to management decisions
Relationships between Spanner Crab Catch and Nino3 SST – but what are the implications for fish management?
The Complexity of Agricultural Systems, , Climate Climate The Complexity of Agricultural Systems Variability, and Management Decisions , and Management Decisions Variability Decision Type (eg. only) Frequency (years) Decision Type (eg. only) Frequency (years) Logistics (eg. scheduling of planting / harvest operations) Intraseasonal (> 0.2) Logistics (eg. scheduling of planting / harvest operations) Intraseasonal (> 0.2) Tactical crop management (eg. fertiliser / pesticide use) Intraseasonal (0.2 – 0.5) Tactical crop management (eg. fertiliser / pesticide use) Intraseasonal (0.2 – 0.5) Crop type (eg. wheat or chickpeas) Seasonal (0.5 – 1.0) Crop type (eg. wheat or chickpeas) Seasonal (0.5 – 1.0) Crop sequence (eg. long or short fallows) Interannual (0.5 – 2.0) Crop sequence (eg. long or short fallows) Interannual (0.5 – 2.0) Crop rotations (eg. winter or summer crops) Annual / biennial (1 – 2) Crop rotations (eg. winter or summer crops) Annual / biennial (1 – 2) Crop industry (eg. grain or cotton, phase farming) Decadal (~ 10) Crop industry (eg. grain or cotton, phase farming) Decadal (~ 10) Agricultural industry (eg. crops or pastures) Interdecadal (10 – 20) Agricultural industry (eg. crops or pastures) Interdecadal (10 – 20) Landuse (eg. agriculture or natural systems) Multidecadal (20 +) Landuse (eg. agriculture or natural systems) Multidecadal (20 +) Landuse and adaptation of current systems Climate change Landuse and adaptation of current systems Climate change
General climate forecast outputs: prepared in a variety of ways
The value of climate information and seasonal climate forecasts to users will depend not only on climate forecast accuracy but also on the management options available to the user to take advantage of the forecasts (Nicholls, 1991).
To achieve best practice, seasonal To achieve best practice, seasonal forecasting must be able to be linked forecasting must be able to be linked to key management decisions to key management decisions How much Nitrogen to apply given current low soil moisture levels and low probability of sufficient in- crop rainfall? Which variety to plant given low rainfall probability values and high risk of damaging frost and anthesis?
To achieve best practice, seasonal forecasting systems To achieve best practice, seasonal forecasting systems must consider scale issues - - linking to decision making. linking to decision making. must consider scale issues Industry Business and Resource Managers Business and Resource Managers Government Government d d e e t t e e Information Axis Information Axis g g • Crop size • Crop size • Water • Irrigation • Improved Planning • Land & r r a a Forecast allocation • Fertilisation Forecast for wet weather Water T T • Early Season • Planning • fallow practice • Early Season disruption – season Resource and policy • land prep Supply Supply start and finish Management • Supply Patterns • Supply Patterns associated • planting • Crop size forecast • Environmenta - Shipping - Shipping with • weed manag. • CCS, fibre levels l Management - Global Supply exceptional • pest manag. - Global Supply • Civil works Events schedule l l a a r r e e n n C l i m a t e forecast information e C l i m a t e forecast information e G G Farm Harvest, Transport, Mill Catchment Marketing Policy Farm Harvest, Transport, Mill Catchment Marketing Policy Industry Scale Axis Industry Scale Axis
To achieve best practice there is need to consider To achieve best practice there is need to consider the whole value chain in agricultural production the whole value chain in agricultural production Understanding issues across the whole value chain Understanding issues across the whole value chain The Cane Sugarcane Harvest & Raw Sugar Marketing & Plant Production Transport Milling Shipping • • Best use of scarce/costly • Better scheduling Best use of scarce/costly • • Improved planning • Better scheduling Improved planning water resources water resources of mill operations for wet weather of mill operations for wet weather • Better decisions on - crop estimates crop estimates • Better decisions on - disruption disruption - early season early season - farm operations farm operations • Best cane supply • Best cane supply cane supply cane supply arrangements arrangements - crush start and - crush start and finish times finish times • • Better marketing decisions based Better marketing decisions based on likely sugar quality on likely sugar quality • More effective forward selling • More effective forward selling based on likely crop size based on likely crop size Everingham et al , 2002 • Improved efficiency of sugar Improved efficiency of sugar • shipments based on supply shipments based on supply pattern during harvest season pattern during harvest season
The Key Linking Role of Modelling The Key Linking Role of Modelling � Simulate management scenarios using analogue years Simulate management scenarios using analogue years � � Evaluate Evaluate outcomes outcomes/ / risks relevant to decisions risks relevant to decisions � Agricultural Production Systems Simulator (APSIM) simulates Agricultural Production Systems Simulator (APSIM) simulates � yield of crops and pastures yield of crops and pastures � � key soil processes (water, N, key soil processes (water, N, � carbon) carbon) � surface residue dynamics & surface residue dynamics & � erosion erosion � range of management options range of management options � � crop rotations + fallowing crop rotations + fallowing � � short or long term effects short or long term effects �
APSI M: precise daily tim e step m odel that m athem atically reproduces the physical processes taking place in a cropping system
Median wheat yields and standard deviations by April/May SOI phase 5 Example for winter wheat: Dubbo, Australia – this information valuable for nitrogen application decisions 4 Yield (t / ha) 3 2 1 0 Con neg Con pos Rap fall Rap rise Near '0'
2.0 1.5 Yield ( t / ha) 1.0 0.5 0.0 Cons. Neg Con. Pos Rap. Ris Near zero SOI Phases APSIM Model output used to establish better cropping systems: Example for the farmers in Pakistan in a given climate. Simulated wheat yields based on June/ July SOI phase
The value of a whole-farm systems approach 3000000 APSFARM simulation Annual operating return ($/farm 2500000 2000000 1500000 1000000 Present farm management 500000 SOI driven area planted 0 3-Nov-88 18-Mar-90 22-Jun-87 31-Jul-91 26-Apr-94 7-Feb-86 12-Dec-92 8-Sep-95 -500000 Rodriguez et al., 2006
July 2001 July 2002 Legend: Legend: 0-10% 0-10% 10-20% 10-20% 20-30% 20-30% 30-40% 30-40% 40-50% 40-50% 50-60% 50-60% 60-70% 60-70% 70-80% NT 70-80% 80-90% 80-90% NT 90-100% 90-100% No data No data Emerald # WA WA R oma # D alby # SA Goon di w i nd i # SA NSW NSW VIC VIC (a) (b) TAS TAS Forecasting agricultural commodities: Probabilities of exceeding long-term median wheat yields for every wheat producing shire (= district) - example for Australia issued in July 2001 and July 2002, respectively. (Grain trading issues).
Case study example from RSA: An integrated Case study example from RSA: An integrated climate- -farming/cropping systems forecast farming/cropping systems forecast climate Probability (%) of exceeding maize yields of 2.5 t/ha Probability (%) of exceeding maize yields of 2.5 t/ha Planting date: 1 November Planting date: 1 November Planting date: 1 November Planting date: 1 November (Cons – –ve SOI phase) ve SOI phase) (Cons +ve SOI phase) (Cons (Cons +ve SOI phase) (Potgieter, 1999)
Best practice for graziers – use of pasture grow th m odels.
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