Aagw2010 June 10 Andries Potgieter Spatial Production Analysis
Transcript of Aagw2010 June 10 Andries Potgieter Spatial Production Analysis
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Pathways to Food Security in eastern andsouthern African through more SustainableIntensification of Maize-Legume based Farming
Systems (SIMLESA)
Andries Potgieter, Peter Davis, Daniel Rodriguez
Spatial Production Analysis
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Overview
SIMLESA
Hot Spot Analysis
Water Use Efficiency
Decision Making & Systems ModellingTools
Complex Systems
Regional Commodity Forecasting
InsuranceRemote Sensing
Summary
NDVI AUC 1999,2000,2001Inverse colours (blue low, red high)
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
SIMLESASustainable intensification of maize-legume cropping
systems for food security in eastern and southern Africa(Ethiopia, Kenya, Tanzania, Malawi, Mozambique )ACIAR funded project $20,000 over 4 years
Commissioned Organisation: CIMMYTAustralian Organisation: QLD Government
working together with many others
AimIncrease food security and incomes at household and regional levels andeconomic development in eastern and southern Africa through improvedproductivity from more resilient and sustainable maize-based farming systems.Overall objectiveSustainably increase the productivity of selected maize-legume systems ineastern and southern Africa by 30% from the 2009 average for each targetcountry by the year 2020, and at the same time reduce seasonal down-siderisks by 30%.
SRA Activity 1: Collection and compilation of spatial information- Assist funding bodies and policy makers- To derive maps of food insecurity & yield gaps that will have a high
impact from intervention and investment
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
The objective of this work was to identify hot spots in south easternAfrica where SIMLESA is likely to have the highest impact in terms of
relieving food security and poverty issues
Hot Spot Analysis
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Food Security
source: Lui et al & FAO
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Ethiopia: Population vs Time
0
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
70,000,000
80,000,000
90,000,000
1 2 3 4 5 6 7 8 9 10 11
Year
No.
ofPeop
le
Pop
Increase in population
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Ethiopia: Total (all cereal) Production vs Timey = 4E+08x + 5E+09
R2 = 0.6909
0
2,000,000,000
4,000,000,000
6,000,000,000
8,000,000,000
10,000,000,000
12,000,000,000
1 2 3 4 5 6 7 8 9 10 11
Year
Production(k
g)
Prod Linear (Prod)
Increase in production
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Food Security
Ethiopia: PWoF vs Time
y = 409613x + 4E+07R2 = 0.1615
0
5000000
10000000
15000000
20000000
25000000
30000000
35000000
40000000
45000000
50000000
1 2 3 4 5 6 7 8 9 10 11
Year
NoofPeoplewitho
utFood
PwoF Linear (PwoF)
Food Insecurity!
FII vs Time
FII (FII)
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
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Queensland Primary Industries and Fisheries
Hot Spot AnalysisApproach
- Yield gap (YG): potential for improvement in cropping systems(www.harvestchoice.org)
- Food Insecurity Index (FII, supply & demand): potential impact of
improvement in food security (www.harvestchoice.org)
- Most critical regions(hot spots): the biggest (negative) number of FIIand the highest YG.
Hot Spots = f[FII, YG]
- Areas only suitable for agriculture production (Land use) were used
http://www.harvestchoice.org/http://www.harvestchoice.org/http://www.harvestchoice.org/http://www.harvestchoice.org/ -
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Queensland Primary Industries and Fisheries
Hot Spot Analysis- FII = [P PoP*GR]/GR;
P is grain production (yield x area),
PoP is population (>2 &
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Hot Spot Analysis landing areas
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Hot Spot Analysis
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Hot Spot Analysis
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Hot Spot AnalysisPopulation change(people/km2/year) from 2000 to2005. Agricultural land use isoverlayed (hatched).
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Where is the water not used by the crops?Is it contributing to runoff and erosion?
Water Use Efficiency (crop production / rainfall; 1999-2001)
Questions for modelling
Australian WUE 15kg/ha/mm
Is it contributing to deep drainage? And ifso, how much water is left in depth in thesoil profile after the harvest of maize?
Can that water be harvested by a deeprooted crop?
How water harvesting, conservationagriculture, and the use of N fertilisers canhelp increase water productivity and reduceunproductive losses?
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Food security & household livelihoodsHuman dimensions
e.g. multiple objectives, risk behaviour, aspirations, culture
Uncertainty (unknowns) e.g. markets, policy,climate change
Decision making and systems models
Risks (known unknowns) e.g. Climatevariability
Quantifiable/m
easurable
Complicated system
Co-learning
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Horses for Courses
HowWet
Whopper
Cropper APSIM APSFarm
Generic
N calculator
Fallow efficiency
Water balance
District
Soil water
Crop inputs
In-crop rainfall
SOI phase
Yield
Field
Stored water
Crop inputs
In-crop inputs
Real-time rainfall
SOI phase
Yield
Household
Multiple objectives
Cash
Land
Labour
Water
Nutrients
Crops & livestock
Livelihoods
Complex
ity
Field Farm livelihoodsSoil typeSoil type
Region
Soil water
Crop inputs
In-crop rainfall
ENSO
Yield, WaterStress, Area
DistrictClimate data, CropPhenology
Oz-Wheat &
Remote Sensing
Dynamic modelling
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Farmers are already adapting to change
Determine optimum adaptation strategies through whole
farm models (e.g. APSFarm)
September 2006, Central Queensland Australia
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Decision Making
HOME
Maizefarmers
selected seed
(Pioneer hybrid) Food
Services
Credit?
Beans
1 ha
there is lack of improved seeds and
access to fertilisers and other inputs atthe time they are needed
Nutrients, inputs & crop residues
LabourCash
Resource flows
How much, when and where seeds and fertilisers are going to beneeded this season across the targeted areas?
Can seasonal climate and CROP predictions help answer thisuestions?
Question for modelling (spatial & GIS)
Farm example in Ethiopia dealing with complex systems
1 out of 4 years is areally bad season
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Regional Commodity Forecasting
WA
NT
QLD
SA
NSW
VIC
TAS
ACT
Legend:
0-10%
10-20%
20-30%
30-40%
40-50%
50-60%
60-70%
70-80%
80-90%
90-100%
Oct 2006 - Percentile
WA
NT
QLD
SA
NSW
VIC
TAS
ACT
0 400 800200 Kilometres
Legend:
0-10%
10-20%
20-30%
30-40%
40-50%
50-60%
60-70%
70-80%
80-90%
90-100%
June Forecast 2006
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Crop InsurancePrimacy Insurance:
New product to hedge farmers riskagainst crop losses due to water stress
within a growing season
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Remote Sensing: Cropped Area (HANTS)
2005 2006
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Cropping Intensity and Patterns (curve fitting)Likely cropping
- Start, end & length of season- Canopy vigour- Cropping & land use patterns- Trends in vegetation/agriculture
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Conclusion Successfully derived Landing areas SIMLESA highest impact in termsof relieving food security and poverty
Water Use Efficiency has raised more research questions. Most of thesewill be addressed in the SIMLESA project improved food security
Farming Systems are complex and needs a holistic & participatory decision
making approach
Predictive technologies from other countries (e.g. Australia) could beapplied successfully to farming systems and regions in NE Africa
Insurance industry has taken up such technologies and successfullyimplemented crop insurance product for farmers to hedge their productionrisk
Further research needs to be done on spatial production data for otheryears.
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Asante
thank youPotgieter, AB, Apan, A, Hammer, G & Dunn, P 2010, 'Early-Season Crop Area Estimates for Winter Crops in NE Australia Using MODIS Satellite Imagery ',
ISPRS Journal of Photogrammetry and Remote Sensing. Published.Potgieter, A.B., Apan, A., Hammer, G. and Dunn, P., 2010. Estimating winter crop area across seasons and regions using time-sequential MODIS imagery.
International Journal of Remote Sensing, Accepted.
Potgieter, A, Apan, A, Dunn, P & Hammer, G 2007, 'Estimating Crop Area Using Seasonal Time Series of Enhanced Vegetation Index from MODIS Satellite
Imagery', Australian Journal of Agricultural Research, vol. 58, pp. 316-25.
Potgieter AB, Hammer GL, Doherty A (2006) Oz-Wheat: a regional-scale crop yield simulation model for Australian wheat. Queensland Department of Primary
Industries & Fisheries. Information Series No, QI06033, Brisbane, Australia. (ISSN 0727 - 6273).
Potgieter AB, Hammer GL, deVoil P (2005) A simple regional-scale model for forecasting sorghum yield across North-Eastern Australia. Agriculture and Forest
Meteorology 132, 143-153.
Potgieter, A.B, Hammer, G.L., Meinke, H., Stone, R.C. and Goddard, L., 2005. Three Putative Types of El Nino Revealed by Spatial Variability in Impact on
Australian Wheat Yield. Journal of Climate.
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Hot Spot Analysis
Food insecurity index for all 5
countries. Yellow to red representsareas with most people that are likelyto have a food shortage (per km2).Derived (FII) using gridded data from1999 to 2001 (www.harvestchoice.org).
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Hot Spot AnalysisSimulated yield gap percentage
deviation. Negative values showingthose areas with the largestdifference between maize yieldsassuming high technology inputsand low technology inputs.
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The State of Queensland, Department of Employment, Economic Development and Innovation, 2009
Queensland Primary Industries and Fisheries
Average total crop production 1999-2001 Rainfall 1999 Rainfall 2000
Rainfall 2001
Water Use Efficiency (crop production / rainfall)
Precipitation: ftp://ftp.dwd.de/pub/data/gpcc (Average Precipitation 1999 Jan-Dec - 2001 Jan-Dec, 50km x 50km grid)Production: http://www.harvestchoice.org/ (Average Production 1999 2001, ~9km x 9km grid)
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The State of Queensland Department of Employment Economic Development and Innovation 2009
Queensland Primary Industries and Fisheries
Hot Spot Analysis
Grain requirement
(kg/person/year) for eachcountry (source: Harvest Choice).Grain requirement ranged between115 to 400 kg grain /annum perperson. In case of missing data weused a minimum grain requirement perperson of 190 kg/grain/annumassuming a caloric requirement of
1,900 calories/day and a typical caloriccontent of 3,600 calories per kilogramof grain (Liu et al, 2008).