aagw2010 june 10 andries potgieter spatial production analysis

8/9/2019 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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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 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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Food Security

source: Lui et al & FAO


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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


7/27 The State of Queensland, Department of Employment, Economic Development and Innovation, 2009


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/


8/27 The State of Queensland, Department of Employment, Economic Development and Innovation, 2009


Hot Spot Analysis- FII = [P PoP*GR]/GR;

P is grain production (yield x area),

PoP is population (>2 &


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Hot Spot Analysis landing areas


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Hot Spot Analysis


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Hot Spot Analysis


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Hot Spot AnalysisPopulation change(people/km2/year) from 2000 to2005. Agricultural land use isoverlayed (hatched).


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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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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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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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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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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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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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Crop InsurancePrimacy Insurance:

New product to hedge farmers riskagainst crop losses due to water stress

within a growing season


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Remote Sensing: Cropped Area (HANTS)

2005 2006


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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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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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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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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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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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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


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).

aagw2010 june 10 andries potgieter spatial production analysis

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