Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
FACUTLY OF SCIENCE
Department of Plant and Environmental Sciences
John R Porter
Section for Crop Sciences, Research group ...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 2
IPCC AR5 was Hell!
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 3
Goals
 Agriculture and GHG emissions
 The KPI framework
 Result...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 4
Sources of GHG emissions from human activities
CBO (2012): www.cbo...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 5
Four objectives of the study
Main Objective:
To develop an identit...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 6
The KPI (Kaya-Porter identity)
 An idea based on the Kaya identit...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 7
App. 1/3
Agriculture
Energyuse
Energy-based emissions
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 8
The Kaya Identity – UNFCC/IPCC
= GHG
GHG
ENERGY
ENERGY
GDP
GDP
POP...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 9
App. 1/3
Agriculture
Energyuse
Global GHG emissions by sector
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 10
JRP’s Simple Land-Use Identity Equation (KPI)
× = GHG
YIELD
AREA
...
KSLA – FACCE Seminar
Slide 11
The KPI
Area
Area
DM
DM
E
E
E
E
GHG
E
GHG
E
GHG
GHG out
out
out
out
in
in
energy
out
soil
ou...
KSLA – FACCE Seminar
Slide 12
Decomposition analysis of emission categories
- using the KPI
c
c
c
c
outc
outc
inc
inc
inEc...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 13
Many many factors
and equations
Input data* OutputCalculations
Ar...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 14
Methods briefly
Global + 9 world regions
Period = 1970 to 2007 ...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 15
Production and GHG emissions in different regions
CEA
SSEA
MENA
E...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 16
GHG emissions by region
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 17
GHG emissions from crop production
- sub-Saharan Africa vs. Europ...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 18
GHG/livestock
GHG/livestock
areaSSA 4580 0,93
MENA 1604 0,76
SSEA...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 19
Global GHG emissions by source; 1970 - 2007
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 20
c
c
c
c
outc
outc
inc
inc
E
outc
soil
outc
LUC
crop area
area
DM
...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 21
Emissions per produced livestock
l
outloutl
outl
inl
inloutl
efmn...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 22
Future GHG emissions?!
FAOSTAT
FAOSTAT, 2013
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 23
Future GHG emissions from global agriculture
- 2007-2050
Past emi...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 24
Connecting wealth, population, consumption
and GHG emissions
popu...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 25
Conclusions
• The KPI enables new insights to the climate efficie...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 26
Sustainable Intensification
What does it mean? More for less?
Int...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 27
From
To
THE DEVELOPMENT OF CROP IDEO-SYSTEMS
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 28
Biomass
N UptakeN Input
N Available
A back of the envelope idea…....
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 29
Identities in food production
Interactions?
Porter and Christense...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 30
Deconstructing RUE
<Udfyld sidefod-oplysninger her>
RUE = Biomass...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 31
Porter et al., 2015
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 32
Papers
Bennetzen EH et al. 2012. Eur. J. Agron., 41, 66-72.
Porte...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 33
Suggestions
Use of identities in conjunction with modelling and
e...
Eskild H. Bennetzen, PhD defence, May 4th, 2016
Slide 34
Thanks
Eskild Bennetzen - KU
Pete Smith - Aberdeen University
Joh...
Upcoming SlideShare
Loading in …5
×

Webinar LAMNET 1 Kaya-Porter Identity

575 views

Published on

A novel inventory framework to estimate and analyze greenhouse gas emissions from agricultural crop production and livestock systems, by deconstructing emission into its relevant elements, as a basis for policy-making on climate change mitigation. Presented by its author, John R Porter

Published in: Education
  • Be the first to comment

  • Be the first to like this

Webinar LAMNET 1 Kaya-Porter Identity

  1. 1. FACUTLY OF SCIENCE Department of Plant and Environmental Sciences John R Porter Section for Crop Sciences, Research group for Climate & Food Security Greenhouse gas emissions from agricultural production - developing and applying the KPI framework
  2. 2. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 2 IPCC AR5 was Hell!
  3. 3. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 3 Goals  Agriculture and GHG emissions  The KPI framework  Results: - Danish crop production - Global agriculture, past and future - Regional agriculture Sustainable intensification  Conclusions and discussions
  4. 4. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 4 Sources of GHG emissions from human activities CBO (2012): www.cbo.gov IPCC conclusion = Now ~24 % of emissions
  5. 5. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 5 Four objectives of the study Main Objective: To develop an identity framework (the KPI) for analysing GHG emissions from agricultural production Objective 2: Analyse past GHG emissions from Danish crop production using the KPI Objective 3: Analyse past agricultural production and GHG emissions from world regions using the KPI Objective 4: Analyse past global agricultural production and GHG emissions using the KPI and build BAU storylines for future emissions
  6. 6. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 6 The KPI (Kaya-Porter identity)  An idea based on the Kaya identity – analysing emissions from energy-use  For the first time an identity framework has been build for analysing GHG emissions from agricultural production  It has had several formulations along the way  Two different formulations  Initial formulation for Danish case study  ”Final” formulation for global/regional study
  7. 7. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 7 App. 1/3 Agriculture Energyuse Energy-based emissions
  8. 8. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 8 The Kaya Identity – UNFCC/IPCC = GHG GHG ENERGY ENERGY GDP GDP POPUL ATION × Fuels Sectors × Services × POPUL ATION
  9. 9. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 9 App. 1/3 Agriculture Energyuse Global GHG emissions by sector
  10. 10. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 10 JRP’s Simple Land-Use Identity Equation (KPI) × = GHG YIELD AREA ENERGY YIELD GHG ENERGY x × AREA Production Technology Fuels Porter 2009. Land use emissions
  11. 11. KSLA – FACCE Seminar Slide 11 The KPI Area Area DM DM E E E E GHG E GHG E GHG GHG out out out out in in energy out soil out LUC crop                Bennetzen et al. in prep. Land-use change Emissions from soils Carbon intensity of the energy Energy use efficiency Cultivated area Total emissions Yield (productivity) Deconstruction Management ‘handles’
  12. 12. KSLA – FACCE Seminar Slide 12 Decomposition analysis of emission categories - using the KPI c c c c outc outc inc inc inEc outc soil outc LUC crop area area DM DM E E E E GHG E GHG E GHG GHG                   ; ; ; ; ; ;; L Loutl outl inl inl E outl efmn outl fodder livestock area area DM DM E E E E GHG E GHG E GHG GHG LL inL                   ; ; ; ;;; ; Fodder Enteric fermentation & manure
  13. 13. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 13 Many many factors and equations Input data* OutputCalculations Area DMout/Area Eout/Dmout Ein/Eout GHG/Ein GHGsoil/Eout GHGLUC/Eout Total GHG emissions Modified from Bennetzen et al. 2012 *) 40 variables. At national, regional or global levels from mainly FAOstat and UN Energy Database The KPI – basic calculations Area Production Fertilizer use Energy use Land-use change Fodder
  14. 14. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 14 Methods briefly Global + 9 world regions Period = 1970 to 2007 (+global future; 2007 - 2050) Data available from international databases (e.g. FAOSTAT) Emissions from soils and livestock estimated via IPCC inventory tier 1 GHG emissions from land-use change (Houghton 2008, http://cdiac.ornl.gov) Energy includes: directly consumed by the sector (incl. labour and draught animals) Indirectly consumed from manufacture of fertilizers Manufacture of machinery and building
  15. 15. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 15 Production and GHG emissions in different regions CEA SSEA MENA EUR EER NA CSA SSA OCE
  16. 16. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 16 GHG emissions by region
  17. 17. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 17 GHG emissions from crop production - sub-Saharan Africa vs. Europe GHG/crop GHG/croparea CSA 362 14,97 SSA 309 4,79 SSEA 245 9,64 World 166 5,41 MENA 124 2,25 OCE 69 0,89 CEA 68 3,91 EER 63 1,03 EUR 38 1,62 NA 34 1,11 Global share SSA EUR % of crop area 13 3 % of crop production 6 8 % of GHG emissions from crops 11 1
  18. 18. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 18 GHG/livestock GHG/livestock areaSSA 4580 0,93 MENA 1604 0,76 SSEA 1532 9,15 CSA 1448 1,57 World 1110 1,51 OCE 1105 0,36 EUR 741 5,25 EER 614 1,26 NA 632 1,33 CEA 591 0,90 Global share SSA EUR % of livestock area 21 3 % of livestock production 3 17 % of GHG emissions from livestock 13 11 GHG emissions from livestock production - sub-Saharan Africa vs. Europe
  19. 19. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 19 Global GHG emissions by source; 1970 - 2007
  20. 20. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 20 c c c c outc outc inc inc E outc soil outc LUC crop area area DM DM E E E E GHG E GHG E GHG GHG c                   ; ; ; ;;; Emissions per produced crop
  21. 21. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 21 Emissions per produced livestock l outloutl outl inl inloutl efmn outl fodder livestock area area DM DM E E E E GHG E GHG E GHG GHG outloutl inl                   ;; ; ;; ; ; ;;;
  22. 22. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 22 Future GHG emissions?! FAOSTAT FAOSTAT, 2013
  23. 23. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 23 Future GHG emissions from global agriculture - 2007-2050 Past emissions Future emissions ?
  24. 24. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 24 Connecting wealth, population, consumption and GHG emissions population capita GDP GDP energy energy GHG GHGkaya  area area drymatter drymatter energy energy GHG GHGKPI  population capita calorie calorie totalprot totalprot animalprot Demand  . . . . . animalpop animalpop energy energy GHG GHG bio bio bio 
  25. 25. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 25 Conclusions • The KPI enables new insights to the climate efficiency of agricultural production • Emissions are being decoupled from production • In Denmark • In world regions • Globally • Only emissions from energy-use have increased per unit product • Past gains in efficiency adds to the discussion on future trajectories for emissions • Vast diffences in climate efficiency between world regions • More intensively managed industrialised systems are generally more climate efficient • Land-use change is highly critical • Nutrient- and energy-use are critical
  26. 26. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 26 Sustainable Intensification What does it mean? More for less? Intensification of what? What’s the metric? System level? What’s the boundary? Production and/or consumption?
  27. 27. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 27 From To THE DEVELOPMENT OF CROP IDEO-SYSTEMS
  28. 28. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 28 Biomass N UptakeN Input N Available A back of the envelope idea….. Porter, 2013
  29. 29. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 29 Identities in food production Interactions? Porter and Christensen 2012
  30. 30. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 30 Deconstructing RUE <Udfyld sidefod-oplysninger her> RUE = Biomass/Absorbed radiation Ξ Biomass/CO2 flux x CO2 flux/Temperature x Temperature/Incident radiation x Incident radiation/Absorbed radiation Porter and Christensen 2012
  31. 31. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 31 Porter et al., 2015
  32. 32. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 32 Papers Bennetzen EH et al. 2012. Eur. J. Agron., 41, 66-72. Porter JR et al. 2013. Plant Cell Envt, 36, 1919-1925. Bennetzen EH et al., 2016 Global Change Biol., 22, 763–781. Bennetzen EH et al., 2016 Global Envt Change, 37, 43–55.
  33. 33. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 33 Suggestions Use of identities in conjunction with modelling and experimental studies – model validation More experimental validation of models Exploration of the effect of data uncertainty on the accuracy and precision of models and their projections
  34. 34. Eskild H. Bennetzen, PhD defence, May 4th, 2016 Slide 34 Thanks Eskild Bennetzen - KU Pete Smith - Aberdeen University John Ingram - Oxford University IPCC WG2 Chapter 7 colleagues Climate and Food Security Group - KU

×