holger meinke and colleagues dpi - apsrujhansen/meinke25july.pdf · holger meinke and colleagues...

Holger Meinke and colleagues

DPI - APSRU

Holger Meinke and colleagues

DPI - APSRU

From Decision to Discussion Support –Decision Making Under Uncertainty

Data < Information < Knowledge < Wisdom Data < Information < Knowledge < Wisdom

A perfect forecast is also perfectly useless unless it changes a

decision

A perfect forecast is also perfectly useless unless it changes a

decision

Systems Research at APSRUOverview

ρ Setting the sceneρ Cropping system context - NE Australiaρ Background on APSRU - 1990-2000ρ APSRU’s research approach and outcomes:

• systems research philosophy• key result areas for 2001-2005; research issue examples• core technologies

The ‘triple bottom line’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 performanceρ environmental impactρ and social consequences

effective integration (of data, information or knowledge) requires an issue focus rather than a methodology or

discipline focus

Current practices:low profitability, highenvironmental impact

Current practices:Current practices:low profitability, highlow profitability, highenvironmental impactenvironmental impact

Future practices:high profitability, low environmental impactFuture practices:Future practices:high profitability, low environmental impacthigh profitability, low environmental impact

Technologies must match the socio-economic system ==> need

for rural sociology

Technologies must match the socio-economic system ==> need

for rural sociology

Threshing finger millet, India

Threshing wheat, Australia

ContextThe 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

APSRU Background 1990-2000APSRU Background 1990-2000Established 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

Practice -relevance-

Research-understanding-

Modelling-integration-

APSRU’s Systems Research Philosophy- Integrated Systems Approach -

To benefit subtropical Australia, and the nation, through innovative agricultural systems R&D in rural industries

What is a systems approach?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.

An effective systems approach...An effective systems approach...ρ clearly defines the boundaries of the systemρ explicitly addresses the scale of the system

(what is internal / external to the system?) andρ seeks and utilises understanding of system

composition and dynamics derived from relevant research to enable prediction of the responses or behaviour.

...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

For any well defined issue, an effective systems approach...For any well defined issue, an effective systems approach...

APSRU’s Key Result Areas (KRA)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 cropimprovement 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.

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?

Risk ManagementRisk ManagementKRA I - Sustainable Agricultural SystemsKRA I - Sustainable Agricultural Systems

Several conditions must be met before a seasonal forecast can result in improved

outcomes. A forecast must be

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.

‘Skill’ vs ‘Value’‘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

Some factsSome 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

DISPERSION

Reference distributions

Forecast distribution

Reference distributions

Forecast distribution

LEPSBS

KL2RRIQR

Quality Measures

Shift (3)Dispersion (4)

Traditional measures (4)Other (7)

Measure of shift in QKL

Intra Class Correlation

Squared Average Deviation

Kruskall-Wallis test p- value

Absolute Median Difference

Revised LEPS score (adapted)

Probability of exceedence

Kullback-Leibler Divergence measure (KL1 + KL2)

Log rank test p-value

Variance Ratio

Revised LEPS scoreBrier score

Measure of variance in QKL80% Range RatioInter Quartile Range Ratio

Brier score (adapted)

***All quality measures were derived using cross validation

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Comparison of Forecast Quality Measures –Wheat

Comparison of Forecast Quality Measures Comparison of Forecast Quality Measures ––WheatWheat

No single quality measure is adequate!!!

Symbols: �- shift, - dispersion, - traditional measures and - QKL

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.

Name and/or Type of Climate Phenomena Frequency (years)

Madden-Julian Oscillation, intraseasonal (MJO) 0.1 – 0.2 SOI phases based on El Niño – Southern Oscillation (ENSO), seasonal to interannual

0.5 – 7

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) 13 – 18 Multidecadal Rainfall Variability 18 – 39 Interhemispheric Thermal Contrast (secular climate signal) 50 – 80 Climate change ???

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 (0.2 – 0.5) Seasonal (0.5 – 1.0)Interannual (0.5 – 2.0)Annual / biennial (1 – 2) Decadal (~ 10)Interdecadal (10 – 20)Multidecadal (20 +)Climate change

Dalby Rainfall

Consistently negative April/May SOI Phase

Median June to Oct Rainfall (190)

Dalby Wheat Yields

0123456789

Median Wheat Yield

Quantifying climate variability: the importance of agricultural simulation models

Models are needed to objectively assess the impact of climate variability, seasonal climate

forecasting and management on agricultural systems

Dalby Rainfall

Consistently negative April/May SOI Phase

Median June to Oct Rainfall (190)

Dalby Wheat Yields

0123456789

Median Wheat Yield

Dalby Rainfall

Rapidly rising April/May SOI Phase

all (m

m) Median June to Oct Rainfall (190 mm)

Dalby Wheat Yields

0123456789

Median Wheat Yield

‘Skill’ does not linearly translate from one forecast quantity to the next

Better on-farmrisk management

Cropping SystemsModelling

Seasonal ClimateForecasting

M anager

BiologicalM odules

S urface Residue

E nvironmentalM odules

E rosionB

E rosionA

O ther N moduleorS oilN

P astureC

P astureB

P astureA

S wimorS oilwat

E conomics Climate

Workshops

• Analysis of risky crop management decision options• Designed with public and private advisors to suit their needs

An APSIM derived tool:

Whopper Cropper- Crop Management Discussion Support -

� 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

� 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

Exploring "What if?" questions:

Whopper Cropper -big decisions made easy

Graphical outputs

Applied NO3

0 kg per ha 25 kg per ha 50 kg per ha 75 kg per ha 100 kg per ha 150 kg per ha

Applied NO3=50 kg per ha;SOI Phase=NegativeApplied NO3=50 kg per ha;SOI Phase=Positive

GM ($/ha)

350300250200150100500-50-100-150

3010.924NameApplied NO3=50 kg per haYield (kg/ha) Over Time

19901980197019601950194019301920191019001890

Sorghum yield – time seriesSorghum yield – time series3010.924NameApplied NO3=50 kg per haYield (kg/ha) Over Time

19901980197019601950194019301920191019001890

Maturity * density * SOI (Emerald)

Moderate depth vertisol, full profile, Nov planting

Managing Water & N in a Variable Climate

Box plot of Yield (kg per ha)

Variety

Late maturityHigh density

Early maturityLow density

SOI phase consistently negative SOI phase consistently positiveBox plot of Yield (kg per ha)

SOI Phase,Variety

Box p lot of Yi eld (kg pe r ha)

SOI Phase,V ariety

ρ Basis of simulation-aided discussion - process not toolρ Private and public advisors being trained

Maturity * density * SOI (Emerald)

Moderate depth vertisol, full profile, Nov planting

Managing Water & N in a Variable Climate

R om a

D al by

Em e r al d

G o ond i wi n di

Legend:0-10%10-20%20-30%30-40%40-50%50-60%60-70%70-80%80-90%90-100%N o data

Probability of exceeding median shire wheat yieldSOI phase “consistently negative”, May/June

Commodity forecasting

What type of model do we need?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)

‘21st Century Transport’ might not be the best example….

Modelling as a communication toolModelling as a communication toolρThe language of climatologistsDecadal climate variability is evident in the first EOF of near-global SSTs and Pressure Field data, band filtered for various frequencies.

Decadal Variability - EOF scores

1860 1885 1910 1935 1960 1985

11-13 years 15-20 years

Modelling as a communication toolModelling as a communication toolρThe language of farmersThe 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?

1860 1885 1910 1935 1960 1985

Modelling as a communication toolModelling as a communication toolρThe language of crop physiologistsCrop transpiration demand (Td) is determined by the amount of intercepted radiation and VPD

Td = TEc / VPD * (RUE * I)(TEc_Wheat = 4.7 g m-2 mm-1 kPa)

0 200 400 600 800

Cumulative intercepted PAR (MJ m-2)

1860 1885 1910 1935 1960 1985

Modelling as a communication toolModelling as a communication toolρ 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)

1860 1885 1910 1935 1960 1985

Modelling as a communication toolModelling as a communication toolρThe language of cropping systems scientistsChanging 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.

1860 1885 1910 1935 1960 1985

Median wheat yields and standard deviations by April/May SOI phase

Con neg Con pos Rap fall Rap rise Near '0'

)(eg: 1997)

(eg: 1998)

SOI Phases

Std Dev

MeanMedian

Modelling as a communication toolModelling as a communication tool

1860 1885 1910 1935 1960 1985

Medi an wheat yi elds and stand ard d eviation s by April /M ay SOI ph ase

Con neg Con pos Rap fall Rap r ise Near '0 '

(eg: 1997)

(eg: 1998)

SOI Phases

Std DevMeanMedian

The aim is to provide knowledge and wisdom systems rather than information systems

Vision without action is just talk.Vision without action is just talk.

Action without vision is just activity.

Vision combined with action can change the world.

KRA I - Sustainable Agricultural SystemsKRA I - Sustainable Agricultural Systemsρ 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

System design for sustainabilitySystem design for sustainability

ρ Issue - dryland salinity from rising water tableρ Research question

- can drainage and solute movement be reduced and profitability retained by re-design of the cropping system?

KRA I - Sustainable Agricultural SystemsKRA I - Sustainable Agricultural Systems

Problem

Simulation analysis

Introduction of Lucerne?

Field experimentation

Research on dryland salinityResearch on dryland salinity

Change cropping intensity?

01-Apr-93

30-Sep-93

01-Apr-94

30-Sep-94

01-Apr-95

30-Sep-95

31-Mar-96

29-Sep-96

31-Mar-97

29-Sep-97

31-Mar-98

29-Sep-98

RainMeasured ETPredicted ET

Evapotranspiration: wheat / lucerneEvapotranspiration: wheat / lucerne

93 Wheat

Fallow

94 Lucerne

Fallow

95-97 Lucerne

Drainage below the root zonefor four simulated crop rotations on the eastern Darling Downs

Wheat zero till

Sunflower zero till

Wheat / Sunflower

Wheat / bare fallow

20 40 60

Drainage (mm)

KRA II - Sustainable LandscapesKRA II - Sustainable Landscapesρ 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

Pesticide movement studiesPesticide movement studies

1-Nov-94 1-Dec-94 31-Dec-94 30-Jan-95 1-Mar-95 31-Mar-95Date

Soil concentration

(g/ha)

MeasuredSimulatedSprays

High risk periods

Endosulfan in cotton fields

Conventional Improvedirrigation

Dryland Stubbleretained

Reducedsprays

Annual endosulfan transport

(g/ha)

Site: Emerald

0.3%of applied

0.03%0.1%

Pesticide movement studiesPesticide movement studiesMeasured movement according to management practice

Land management in a catchment contextLand management in a catchment context

ρ Issue - connecting studies on salt and pesticide movement at field/farm level to catchment level

KRA III - Organism DesignKRA III - Organism Designρ 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

Environment Characterisation Studies- Crop View of Abiotic Stress -

Relative transpiration patterns through the season

0 400 800 1200

Thermal time from emergence (°Cd)

Stress index(water supply/

demand)

Mild terminal stressSevere terminal stressMid-season stress

Clustered stress indices to produce three environment types from 547 simulations

Environment Characterisation Studies- Crop View of Abiotic Stress -

Relative frequency of environment types

1891 1903 1915 1927 1939 1951 1963 1975 1987Start year of 12 year sequence

Frequency of Drought

Environment Type

Yield(t/ha)

Mild terminal Severe terminal Mid-season Yield

Overall ratios of37:35:28

M-T:S-T:M-S

Changes in frequencies effect yield likelihood and cause G x EImportant in weighting representativeness of selection environments

Trait Simulation StudiesTrait Simulation Studies

ρ Estimate value of trait in particular environmentsρ Examine breeding strategies by connecting to models

of breeding systems to simulate trajectories on adaptation landscape

KRA IV - Modelling Tools & ApproachesKRA IV - Modelling Tools & Approachesρ Software engineering

• APSIM• derived products eg Whopper Cropper, DamEasy• methodology development - landscape linkages; gene

function linkages; optimisation

Agricultural Production Systems Simulator (APSIM)

Simulatesρ yield of crops and pastures ρ key soil processes (water, N, carbon)ρ surface residue dynamics & erosionρ range of management options ρ crop rotations + fallowingρ short or long term effectsρ BUT, not pests nor diseases

APSIM aims to deliver ….ρ A solid platform for modelling system’s performance over

timeρ Equal emphasis on crop and soil dimensions ρ Capability to deal comprehensively with management

mattersρ A new emphasis on better software engineering process to

achieve the above

Key features of agricultural systems

Runoff, erosion,chemicals

Crop production

Soil and residue management

Drainage

Evaporation

Soil water

Transpiration

T I M E

Soil nutrients

Crop managementWeeds

What type of model do we need?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)

‘21st Century Transport’ might not be the best example….

Dimensions of Systems Models

ParticipatoryAction

ResearchApplications with / for clients

ScienceBio-physical

ModulesEngineering

Module interfaces,User interfaces, Utilities

Databases etc

Researchvia

experimentation

ImprovedSoftware

EngineeringProcess

Education and teaching

Successful systems analytical approaches are based on

ρ client involvement relevanceρ good scienceρ sound and efficient implementation (software

engineering) Manager

Report

Soil pH

Soilwater

Soil N

Erosion

Surface Residue

ENGINE

Crop CMaizWheat

Crop CCrop BCowpea

Soil P

Arbitrator

Climate

holger meinke and colleagues dpi - apsrujhansen/meinke25july.pdf · holger meinke and colleagues...

Documents

ΤΟ ΓΡΑΜΜΑ Ρ, ρ - ΦΥΛΛΑ ΕΡΓΑΣΙΑΣ

primitive bio-chemistry to molecular biology astr 3300 guest...

software development 2d1385 karl meinke nada kth...

robert j. meinke department of botany and plant …...3 the...

spectral transforms spectral vegetation...

lab 1 measurements · 2019. 9. 29. · lab 1...

holger diaz gobernador

holger schlarb - indico.desy.de

ρ ρ ρ crystallin (eq. 4.6) - ì ±ê· ê´ ë í...

prem bindraban, erwin bulte, ken giller, holger meinke

brockmeyer holger - ohrakupunktur

ubuntu holger maldonado

software engineering dd1363 mvk-08 karl meinke nada...

discriminação de áreas de soja por meio de …ivp 1 v 2 a...

holger meinke and roger c. stone oof...climate variability...

agenda - foreststreesagroforestry.org · holger meinke,...

2.imp. sprachen 1 - uni-ulm.de€¦ · code (x := y) ρ =...

the education university of hong kong (eduhk) · 2000. 12....

krapu meinke kramer friedman voda1

presentación de powerpoint · 1389 cm-1 ρ nh + ν cn 1315...