Holger Meinke and colleagues DPI - APSRU

Transcription

1 From Decision to Discussion Support Decision Making Under Uncertainty Data < Information < Knowledge < Wisdom Holger Meinke and colleagues DPI - APSRU

2 A perfect forecast is also perfectly useless unless it changes a decision

3 Systems Research at APSRU Overview ρ Setting the scene ρ Cropping system context - NE Australia ρ Background on APSRU ρ APSRU s research approach and outcomes: systems research philosophy key result areas for ; research issue examples core technologies

4 The triple bottom line Using the best science has to offer we need to improve the performance of agricultural systems in terms of ρ economic performance ρ environmental impact ρ and social consequences effective integration (of data, information or knowledge) requires an issue focus rather than a methodology or discipline focus

5 Current practices: low profitability, high environmental impact Future practices: high profitability, low environmental impact

6 Technologies must match the socio-economic system ==> need for rural sociology Threshing finger millet, India Threshing wheat, Australia

7 Context The NE Australian cropping system Characterised by high climate variability cost-price squeeze erodible soils declining fertility policy of self-reliance a wide range of crop options water main determinant of crop yield soil water storage by fallowing fixed rotations vs opportunity cropping some ability to forecast seasons concerns over salt and pesticides

8 APSRU Background Established 1990 to capture synergies between DPI and CSIRO in development and application of agricultural models initially 15 staff co-located in Toowoomba - regional focus joint venture arrangement with 5-year horizon external review and renewal process 1995 and 2000 now 70 staff from 4 organisations (DPI, NR&M, CSIRO, UQ) national focus with international linkages development of cropping system modelling platform (APSIM) as core technology

9 APSRU s Systems Research Philosophy - Integrated Systems Approach - Practice -relevance- Research -understanding- Modelling -integration- To benefit subtropical Australia, and the nation, through innovative agricultural systems R&D in rural industries

10 What is a systems approach? ρ network of interacting elements receiving certain inputs and producing certain outputs where the systems dynamics are implicit ρ can be applied to the full range of agricultural and natural ecosystems and their associated business and government systems, climate or economic systems.

11 An effective systems approach... ρ clearly defines the boundaries of the system ρ explicitly addresses the scale of the system (what is internal / external to the system?) and ρ seeks and utilises understanding of system composition and dynamics derived from relevant research to enable prediction of the responses or behaviour.

12 For any well defined issue, an effective systems approach......will include all relevant agricultural and natural ecosystems components and their associated business and government structures, eg. a crop or field and its management a farm and its management the national wheat crop and its marketing the national drought policy and its development a catchment and its management a species population and its management

13 APSRU s Key Result Areas (KRA) KRA 1: Sustainable Agricultural Systems: Agricultural sustainability positively enhanced via innovative research in rural business systems KRA 2: Sustainable Landscapes: New approaches towards integrating and balancing environmental, social and economic factors that can contribute to more sustainable management of landscapes. KRA 3: Improved Crop Design For Productivity And Sustainability: Enhanced efficiency and effectiveness in plant breeding and crop improvement programs KRA 4: Modelling Tools and Methods: Research communities and industry benefiting from and utilizing a range of quality software tools KRA 5: APSRU Communication And Collaboration: APSRU functioning as a high achieving research team through coordination of its activities and broad collaboration with its stakeholders.

14 KRA I - Sustainable Agricultural Systems Risk Management ρ Issue - how to manage risks generated by climate variability ρ Research questions - can agricultural systems be managed better by improved analysis of risks? - can decision makers value simulation as a tool in this process? - can seasonal climate forecasts be used to advantage?

15 Several conditions must be met before a seasonal forecast can result in improved outcomes. A forecast must be ρ skilful, ρ probabilistic (ie. honest ), ρ relevant (neither trivial nor obvious; timely), ρ of value, ρ and the information content must be applied.

16 Skill vs Value ρ Clearly define what we mean by skill ρ Move towards a consensus view why we need to measure skill and how to do it ρ Put skill into perspective together with other essential attributes of effective forecasting systems

17 Some facts ρ Skill does not linearly translate from one forecast quantity to the next (eg. rainfall not equal production) ρ Skill is an essential but not a sufficient attribute of a effective forecasting system ρ Skill must be seen in context with the decisions based on the forecast

18 Reference distributions SHIFT Forecast distribution Forecast distribution Reference distributions DISPERSION

19 Quality Measures LEPS BS BSP LEPSP KWP ICCP PEX QKL Revised LEPS score Brier score Brier score (adapted) Revised LEPS score (adapted) Kruskall-Wallis test p- value Intra Class Correlation Probability of exceedence Kullback-Leibler Divergence measure (KL1 + KL2) LRP KL1 SAVGD AMD VR KL2 RR IQR Log rank test p-value Measure of shift in QKL Squared Average Deviation Absolute Median Difference Variance Ratio Measure of variance in QKL 80% Range Ratio Inter Quartile Range Ratio Traditional measures (4) Other (7) Shift (3) Dispersion (4) ***All quality measures were derived using cross validation

20 Comparison of Forecast Quality Measures Wheat 1 Q4 Q No 0.5single quality measure is adequate!!! Q3 Q2 Dispersion Shift Symbols: -shift, - dispersion, - traditional measures and -QKL

21 To effectively manage risk in a variable climate requires ρ a sound scientific understanding of the causes of climatic variability, the consequences for production and our ability to predict future variability, ρ knowledge and appreciation of how this ability to forecast production risks can influence and change management decisions and ρ ability to implement this knowledge to achieve improved outcomes.

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23 Name and/or Type of Climate Phenomena Frequency (years) Madden-Julian Oscillation, intraseasonal (MJO) SOI phases based on El Niño Southern Oscillation (ENSO), seasonal to interannual Quasi- biennial Oscillation (eg. NAO) 1 2 Antarctic Circumpolar Wave (AWC), interannual 3 5 Latitude of Sub-tropical ridge, interannual to decadal?? 11 Interdecadal Pacific Oscillation (IPO) 13+ Decadal Pacific Oscillation (DPO) Multidecadal Rainfall Variability Interhemispheric Thermal Contrast (secular climate signal) Climate change???

24 Agricultural Systems and Climate Variability Decision Type (eg. only) Logistics (eg. scheduling of planting / harvest operations) Tactical crop management (eg. fertiliser / pesticide use) Crop type (eg. wheat or chickpeas) Crop sequence (eg. long or short fallows) Crop rotations (eg. winter or summer crops) Crop industry (eg. grain or cotton, phase farming) Agricultural industry (eg. crops or pastures) Landuse (eg. agriculture or natural systems) Landuse and adaptation of current systems Frequency (years) Intraseasonal (> 0.2) Intraseasonal ( ) Seasonal ( ) Interannual ( ) Annual / biennial (1 2) Decadal (~ 10) Interdecadal (10 20) Multidecadal (20 +) Climate change

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27 June to Oct Rainfall (mm) Yield (t/ha) Dalby Rainfall Median June to Oct Rainfall (190) Models are needed to objectively Consistently negative April/May SOI Phase assess the impact of climate Dalby Wheat Yields variability, seasonal climate Median Wheat Yield forecasting and management on agricultural systems Quantifying climate variability: the importance of agricultural simulation models

28 Dalby Rainfall Median June to Oct Rainfall (190) Dalby Rainfall 500 Me dian June to Oct Ra infa ll (19 0 mm) Consistently negative April/May SOI Phase Rapidly rising April/May SOI Phase Dalby Wheat Yields Median Wheat Yield Yield (t/ha) June to Oct Rainfall (mm) June to Oct Rainfall (mm) Dalby Wheat Yields Median Wheat Yield Yield (t/ha) Skill does not linearly translate from one forecast quantity to the next

29 Biological Modules Manager E nvironmenta l Modules Crop Crop A BCrop C Pasture Pasture A Pasture B C A P S I M Soilwat or Swim SoilN or O ther N module Surface Residue Erosion Erosion A B Economics Climate Cropping Systems Modelling Seasonal Climate Forecasting Workshops Better on-farm risk management

30 Whopper Cropper - Crop Management Discussion Support - An APSIM derived tool: Analysis of risky crop management decision options Designed with public and private advisors to suit their needs Generates analyses for input to discussion on management issues Data base of APSIM simulations with simple presentation graphics Analyses of range of crop management issues Analyses of range of soil conditions Analyses of utility of seasonal climate forecasts

31 Whopper Cropper -big decisions made easy Exploring "What if?" questions: Which crop to sow? When to sow? How much N to apply? Which variety to sow? What density? Analysis of different starting conditions and seasonal forecasts

32 Graphical outputs 5000 Yield (kg/ha) Over Time Name Applied NO3=50 kg per ha Yield (kg/ha) Yield (kg/ha) kg per ha 25 k g per ha 50 k g per ha 75 k g per ha 100 kg per ha 150 kg per ha Year Applied NO3 90 Applied NO3=50 kg per ha;soi Phase=Negative Applied NO3=50 kg per ha;soi Phase=Positive Probability GM ($/ha)

33 Sorghum yield time series Yield (kg/ha) Over Time Name Applied NO3=50 kg per ha Yield (kg/ha) Year

34 Managing Water & N in a Variable Climate Maturity * density * SOI (Emerald) Moderate depth vertisol, full profile, Nov planting 7000 Box plot of Yield (kg per ha) 6000 Yield (kg per ha) Late Late maturity High density Early maturity Low density Early Variety

35 Managing Water & N in a Variable Climate Maturity * density * SOI (Emerald) Moderate depth vertisol, full profile, Nov planting SOI phase consistently negative SOI phase consistently positive Box plot of Yield (kg per ha) 7000 Box plot of Yield (kg per ha) Yield (kg per ha) Yie ld (kg per ha) Late maturity High density La te Ne gative SOI Phase,Variety Early maturity Low density Early Ne gative 0 Late Positive Late maturity High density SOI Phase,V ariety Early Positive Early maturity Low density ρ Basis of simulation-aided discussion - process not tool ρ Private and public advisors being trained

36 NT Legend: 0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% % No data Em e r al d# WA Rom a # Dalby # SA G o ond i wi n di # NSW VIC Probability of exceeding median shire wheat yield SOI phase consistently negative, May/June TAS

37 Commodity forecasting

38 What type of model do we need? Depends on the issue and the clients (customer focus) Needs to deliver agreed outcomes (achievable) Needs to capture consequences of key drivers (relevant) 21 st Century Transport might not be the best example.

39 Modelling as a communication tool ρthe language of climatologists Decadal climate variability is evident in the first EOF of near-global SSTs and Pressure Field data, band filtered for various frequencies. 3 2 Decadal Variability - EOF scores years years

40 Modelling as a communication tool ρthe language of farmers The last few years were bad - first the drought and then too much rain. What can I do to better cope with such a variable climate? 3 2 Decadal Variability - EOF scores years years

41 Modelling as a communication tool ρthe language of crop physiologists 1600 Crop transpiration demand (T d ) is determined by the amount of intercepted radiation and VPD T d = TE c / VPD * (RUE * I) (TE c_wheat = 4.7 g m -2 mm -1 kpa) TDM (g m -2 ) Cumulative intercepted PAR (MJ m -2 ) Decadal Variability - EOF scores years years

42 Modelling as a communication tool ρ The language of computer programmers! potential biomass production based on intercepted radiation call dm_plt_tot_pot ()! demand for soil water. new subroutine sw_demand uses! dm_plt_tot_act_dlt to determine demand for soil water based on! a tec of 4.7 call sw_demand (c_demand_switch) 3 2 Decadal Variability - EOF scores years years

43 Modelling as a communication tool ρthe language of cropping systems scientists Changing between dryland cotton and intensive cereal rotations based on the long-term climate outlook could lift your average profits by 50%. This would also reduce your runoff and erosion potential. 5 4 Median wheat yields and standard deviations by April/May SOI phase (eg: 1998) Yield (t / ha) 3 2 (eg: 1997) Std Dev Mean Median 1 0 Con neg Con pos Rap fall Rap rise Near '0' SOI Phases years years Decadal Variability - EOF scores

44 Modelling as a communication tool The aim is to provide knowledge and wisdom systems rather than information systems 5 Medi an wheat yi elds and stand ard d eviation s by April /M ay SOI ph ase 4 (eg: 1998) Yield (t / ha) (eg: 1997) Std Dev Mean Median 0 Con neg Con pos Rap fall Rap r ise Near '0' SOI Phases 3 Decadal Variability - EOF scores years years

45 Vision without action is just talk. Action without vision is just activity. Vision combined with action can change the world.

46 KRA I - Sustainable Agricultural Systems ρ System design for sustainability - paddocks over long term - soil health; solute and pesticide movement ρ Risk management - paddocks over short term - production tactics; climate risk ρ Whole farm management - business unit focus - enterprise mix; spatial aspects

47 KRA I - Sustainable Agricultural Systems System design for sustainability ρ Issue - dryland salinity from rising water table ρ Research question - can drainage and solute movement be reduced and profitability retained by re-design of the cropping system?

48 Research on dryland salinity Problem Introduction of Lucerne? Simulation analysis Change cropping intensity? Field experimentation

49 Evapotranspiration: wheat / lucerne Rain / Evaporation (mm) Fallow Fallow 94 Lucerne 93 Wheat Lucerne Rain Measured ET Predicted ET Apr Sep Apr Sep Apr Sep- 95 Date 31-Mar Sep Mar Sep Mar Sep- 98

50 Drainage below the root zone for four simulated crop rotations on the eastern Darling Downs Drainage (mm) 3000 Wheat zero till Wheat / bare fallow Sunflower zero till Years Wheat / Sunflower

51 KRA II - Sustainable Landscapes ρ Vegetation and water management - regional salinity and water resource management - links to landscape modelling ρ Regional scale operations - industry and supply chain links - commodity forecasting; finance; insurance

52 Pesticide movement studies Endosulfan in cotton fields Soil concentration 400 (g/ha) 200 High risk periods Measured Simulated Sprays 0 1-Nov-94 1-Dec Dec Jan-95 1-Mar Mar-95 Date

53 Pesticide movement studies Measured movement according to management practice 25 Site: Emerald Annual endosulfan transport (g/ha) % of applied 0.4% 5 0.1% 0.1% 0 Conventional Improved irrigation 0.03% Dryland Stubble retained Reduced sprays

54 Land management in a catchment context ρ Issue - connecting studies on salt and pesticide movement at field/farm level to catchment level

55 KRA III - Organism Design ρ Matching organisms to environment - Genotype x Management x Environment optimisation (G x M x E) - physiological dissection and modelling of trait variation - links to plant breeding and gene technologies

56 Environment Characterisation Studies - Crop View of Abiotic Stress - Relative transpiration patterns through the season Stress index (water supply/ demand) Mild terminal stress Severe terminal stress Mid-season stress Thermal time from emergence ( Cd) Clustered stress indices to produce three environment types from 547 simulations

57 Environment Characterisation Studies - Crop View of Abiotic Stress - Relative frequency of environment types 100% % Frequency of Drought 50% Environment Type Yield (t/ha) Overall ratios of 37:35:28 M-T:S-T:M-S 25% 3.5 0% Start year of 12 year sequence 3.0 Mild terminal Severe terminal Mid-season Yield Changes in frequencies effect yield likelihood and cause G x E Important in weighting representativeness of selection environments

58 Trait Simulation Studies ρ Estimate value of trait in particular environments ρ Examine breeding strategies by connecting to models of breeding systems to simulate trajectories on adaptation landscape

59 KRA IV - Modelling Tools & Approaches ρ Software engineering APSIM derived products eg Whopper Cropper, DamEasy methodology development - landscape linkages; gene function linkages; optimisation

60 Agricultural Production Systems Simulator (APSIM) Simulates ρ yield of crops and pastures ρ key soil processes (water, N, carbon) ρ surface residue dynamics & erosion ρ range of management options ρ crop rotations + fallowing ρ short or long term effects ρ BUT, not pests nor diseases

61 APSIM aims to deliver. ρ A solid platform for modelling system s performance over time ρ Equal emphasis on crop and soil dimensions ρ Capability to deal comprehensively with management matters ρ A new emphasis on better software engineering process to achieve the above

62 Key features of agricultural systems Transpiration Evaporation Crop production Weeds Soil and residue management Soil water Crop management Soil nutrients Runoff, erosion, chemicals T I M E Drainage

63 What type of model do we need? Depends on the issue and the clients (customer focus) Needs to deliver agreed outcomes (achievable) Needs to capture consequences of key drivers (relevant) 21 st Century Transport might not be the best example.

64 Dimensions of Systems Models Research via experimentation Participatory Action Research Science Bio-physical Modules Engineering Module interfaces, User interfaces, Utilities Databases etc Applications with / for clients Improved Software Engineering Process Education and teaching

65 Successful systems analytical approaches are based on ρ client involvement relevance ρ good science ρ sound and efficient implementation (software engineering) Manager Report Climate Wheat Maiz Crop C Arbitrator Cowpea Crop Crop B C Surface Residue E N G I N E Soilwater Soil ph Soil N Soil P Erosion