Food and nutritional security in the context of climate change:

eco-efficiency or agroecology?

Jean-François Soussana,

Scientific Director Environment,

INRA, Paris, France

Session 4. Which model(s) for agriculture?

Two Goals of Our Time

1. Achieving Food and Nutritional Security

– 800 million chronically undernourished, more with micronutrients deficits,

– Far reaching implications of obesity on chronical diseases,

– Food production to increase 50-70% by 2050,

– Adaptation to climate change is critical

2. Avoiding Dangerous Climate Change

- The ’ 2°C railguard ’ requires major emission cuts,

- Agriculture and land use contribute to 24% of GHG emissions...

...and need to be part of the solution

Which model(s) for agriculture?

Outline

Global dimension

• Food supply and demand under climate change by 2050

• GHG emissions and soil carbon sequestration

• Emerging risks of climatic variability

Which agricultural models?

• Eco-efficiency and sustainable intensification

• Agroecology and organic farming

• Climate smart agriculture

The global food system: past and future AgRIPE framework: • Mass balance of supply, demand and GHG emissions • Mathematical identities to capture drivers of Supply and Demand

Years

1960 1970 1980 1990 2000 2010

Da

ta s

tan

da

rdiz

ed

to

19

61

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0Population (P)

Agricultural land (L)

Food affluence (a.)

Technology (t.)

Carbon intensity (g., Direct emissions)

Indirect GHG (Land use change, Pre-chain)

Global food system dynamics over 1961-2011

:

. .

Supply Demand t a P

Supply Demand

L

GHG g g

(Soussana, Ben Ari et al., in prep.)

A more efficient global food system (1961-2011)

(Soussana, Ben Ari et al., in prep.)

• The conversion efficiency into plant and animal food of total raw (arable and grassland) proteins has increased from 12 to 19%,

• The fraction of feed which is edible by humans has increased from 24 to 42% (increased reliance on grains

of livestock systems) • Since the 1990’s, direct GHG emissions per unit food have declined (i.e. lower carbon intensity of agricultural production) at a slow pace (0.75% per year) Note that global grassland and arable soil carbon stock changes since 1961 are not known

Pro

tein

use e

ffic

iency

0.12

0.13

0.14

0.15

0.16

0.17

0.18

0.19

0.20

Fraction human edible livestock feed

Fra

ctio

n h

um

an

ed

ible

liv

esto

ck f

ee

d

0.22

0.24

0.26

0.28

0.30

0.32

0.34

0.36

0.38

0.40

0.42

0.44

Protein conversion efficiency

Years

1960 1970 1980 1990 2000 2010 2020Carb

on inte

nsity (

ton C

O2 e

quiv

ale

nts

ton

-1 p

rote

in)

0

20

40

60

80

100

120

gCH4

gN2O

g Prechain

g. direct

gLUC

g.

Carbon intensity components

SSP1 is the sustainable world with strong development goals that include reducing fossil fuel dependency and rapid technological changes directed towards environmentally friendly processes including yield-enhancing technologies.

SSP2 is the continuation of current trends with some effort to reach development goals and reduction in resource and energy intensity. On the demand side, investments in education in not sufficient to slow rapid population growth. In SSP2 there is only an intermediate success in addressing vulnerability to climate change.

SSP3 is a fragmented world characterized by strongly growing population and important regional differences in wealth with pockets of wealth and regions of high poverty. Unmitigated emissions are high, low adaptative capacity and large number of people vulnerable to climate change. Impact on ecosystems are severe.

Shared socio-economic pathways 1-3

Food protein supply: projections for 2050’s (IIASA, GLOBIOM model & AgRipe with CO2 effect)

Food protein supply

Past SSP1 SSP2 SSP3

Sta

nda

rdiz

ed

da

ta

1.0

1.2

1.4

1.6

1.83.0

3.2

3.4

3.6

Animal protein supply

Plant protein supply

Large variation across Shared Socioecon. Pathways (SSPs) • Plant food supply scales with population • Animal food supply is more variable:

- Less demand of meat under SSP1 - Higher prices under SSP3 (slow

productivity growth) - Largest supply and demand under

middle of the road scenario (SSP2) Climate change impacts are stronger with grain crops than with grasslands (Havlik et al., FAO 2014) Plant proteins supply: -12 to -15% Animal proteins: supply 0 to -10%

Relative effects of climate change on food protein supply

SSP1 SSP2 SSP3

Sta

nd

ard

ize

d d

ata

0.80

0.85

0.90

0.95

1.00

1.05

1.10

Animal proteins

Plant proteins

2050, Land use change emissions (CO2 eq.)

To

ns C

O2 e

qu

iva

len

ts0

1e+9

2e+9

3e+9

No climate change

RCP 8.5

SSP1 SSP2 SSP3

2050, Chronic undernourishment

Ch

ron

ica

lly u

nd

ern

ou

rish

ed

nu

mb

er

0.0

2.0e+8

4.0e+8

6.0e+8

8.0e+8

1.0e+9

1.2e+9

1.4e+9

No climate change

RCP 8.5

SSP1 SSP2 SSP3

Food security and land use change projections for 2050

(IIASA, GLOBIOM model & AgRIPE)

SSP2 and SSP3: ncreased deforestation (LAM and SSA)

SSP2 and SSP3: ncreased undernourishment (SSA, S. Asia) Climate change exacerbates these trends

Global food system and land use emissions may prevent climate stabilization (RCP2.6)

Agriculture and LUC: 2050, % total GHG emissions%

of

tota

l G

HG

em

issio

ns

0

10

20

30

40

50

60

RCP2.5

RCP 8.5

SocioEconomic Pathways SSP1 SSP2 SSP3

(IIASA, GLOBIOM model & AgRIPE)

Biological soil carbon sequestration could smooth the transition to stabilization by 2050

2050, Agricultural and LUC emissions (CO2 eq.)

To

ns C

O2 e

qu

iva

len

ts

0.0

1.0e+9

2.0e+9

3.0e+9

4.0e+9

5.0e+9

6.0e+9

7.0e+9

8.0e+9

9.0e+9

1.0e+10

1.1e+10

No climate change

2050, Potential soil carbon sequestration (CO2 eq.)

Tons C

O2 e

quiv

ale

nts

-8e+9

-6e+9

-4e+9

-2e+9

No climate change

SSP1 SSP2 SSP3 Agriculture Other biomes

This would lead to a massive change in agricultural practices (e.g. agroforestry, continuous soil cover…)

Emissions from agriculture and land use Soil carbon sequestration potential

• Compare past & future distributions from ensembles of global crops models (AgMIP/ISI-MIP) – Extreme (-) percentiles, variance & skewness of distributions

generally getting worse

– Global 1-in-100 year historical event occurs almost 1-in-30 years within only several decades

Non-stationary risk in agriculture (J. Elliott, Chicago University)

12

Reproduced from Extreme weather and resilience of the global food system

Prepared for the UK-US Taskforce on Extreme Weather and Global Food System Resilience

A view from the insurance industry: Lloyd’s emerging risk report - 2015

Experts have developed a worst case scenario of a large ENSO event, combining: - Direct weather impacts on key grain producing regions, - Indirect impacts through crop pathogens (stem rust of wheat), - Consequences for markets and stocks

Eco-efficiency (sustainable intensification):

the standard paradigm

Sustainable intensification (since 1980’s)

Eco-efficiency

Substitution

Reduced emissions per unit product

Specialisation (since 1960’s)

Increased inputs

Simplified systems

Reduced commodity costs increased volumes

Nevertheless, resilience of specialized systems is at risk !

Increased sensitivity to pests and diseases, and to climatic hazards,

Reduced biodiversity and ecosystem services

(apart from production)

Increased GHG emissions per unit land (not necessarily per unit product)

Eco-efficiency (land sparing) paradigm

• Eco-efficiency: the maximization of plant and animal products per unit of inputs or natural resources (e.g. Wilkins, 2008). It would allow: — Environmentally sustainable intensification of agricultural production,

— Land sparing for nature conservation,

— Large production volumes suitable for industries and exports

• In the context of modernized and simplified systems, eco-efficiency can be further developed through: — Genome based plant and animal breeding, advanced phenotyping,

— Precision agriculture and livestock farming,

— ‘Big data’ combining soil, weather, micro-climate, remote sensing, markets, etc… with decision support models.

• Socio-economics: capital intensive systems, with low on-farm labor.

Agroecology (and organic farming): an alternative paradigm

Facilitation, niche complementarity, Root symbioses..

Recoupling

C-N-P cycles (eg. crop-livestock-integration)

Reduced emissions per unit land System

diversification

Heterogeneity in

space & time Balanced ecosystem services

Reduced external inputs

Increased resilience to pest & diseases, and to climatic hazards?

Increased on-farm labor

Increased biodiversity and ecosystem services

Reduced GHG emissions per unit land (not necessarily per unit product)

Functional

diversity

Ecological

infrastructures

Agro-ecology: ecologically grounded production systems fitted to local conditions (e.g. Gliessman et al., 2006)

Agroecology would:

Reduce dependency to external inputs and increase resilience to climatic and sanitary hazards,

Share land between production and other ecosystem services, diversify food products and diets,

Increase or preserve labor in farms (smallholders) and in rural areas.

Agroecology could develop through participatory research supported by advanced knowledge of ecological processes in agriculture and by dedicated technologies (e.g. bio-control, soil biota indicators, etc..) at field and lanscape scales

However, it requires capacity building, dedicated tools and extra-monitoring time, reorganization of up- and downstream industries.

Agroecology (land sharing) paradigm

Hurricane impacts in Central America on monocultures vs. agroecological terraces

After Hurricane Mitch in Central America, Honduran farms under monoculture exhibited higher levels of damage in the form of mudslides (left photo) than neighboring biodiverse farms featuring agroforestry systems, contour farming, cover crops, etc. (right photo)

(after Nicholls, in press, FAO)

Is agroecology providing increased resilience to climatic hazards?

Agroecology comes with systems diversification which reduces the impacts of climatic hazards,

Agroecology can directly protect crops and animals against high temperatures (e.g. agroforestry via shade) and soils from heavy precicipitation (e.g. continuous soil cover)

In ecology, the biodiversity insurance hypothesis states that more biodiverse systems are more resilient to hazards (some demonstrations in literature)

Nevertheless, inputs often buffer variability:

Mineral fertilizers replace soil mineralization at e.g. low soil temperature,

Irrigation buffers climatic variability, etc…

Pests and diseases can be directly controled by pesticides/antibiotics

Transition in adaptation strategies: layering risk

(Cattaneo, OECD, 2011

& Vermeulen et al., 2014, PNAS)

Systemic Incremental Transformative

Transitions in types of adaptation

Ecoefficiency? Agoecology?

Coût (en euros par tonne de CO2e évité) et potentiel d'atténuation annuel en 2030 a l’échelle du territoire métropolitain (en Mt de CO2e évité) des actions instruites.

* * * * * * * *

* * *

* Agroecology option

Of total mitigation potential: • Ecoefficiency: 60% • Agroecology: 59% • Options in common: 19% Costs are spread in a similar way

Shares of ‘ecoefficiency’ and ‘agroecology’ options in the mitigation potential for French agriculture

Climate smart agriculture:

bridging agroecology and ecoefficiency?

• Climate smart agriculture (CSA) has been defined as agriculture that sustainably increases productivity and resilience (adaptation), reduces greenhouse gases (mitigation), and enhances food security and development. FAO (2010) Technical input for the Hague Conference on Agriculture, Food Security and Climate Change.

• A sustainable intensification of agriculture, that would allow closure of yield gaps and increasing biological efficiencies can enhance food security, ecosystem services and contribute to mitigating climate change

• During the third science conference on CSA, it was stated that the concept also applies to the challenges of sustainable food systems and landscapes.

• However, the metrics of CSA and how will riks be layered in different systems is still unclear

Conclusions

Climate change has large implications for agriculture and food systems which question both the eco-efficiency (standard) and the agroecology (alternative) models,

Both of these models can offer solutions that could ultimately contribute to climate smart food systems and landscapes,

Rapid changes will be required to create transitions in both agricultural (e.g. soil carbon sequestration) and food (e.g. diet transitions) systems,

Business-as-usual is not an option as it leads to risks of food system shocks with increasingly apparent geopolitical implications.

Thank you for your attention!

Acknowledgements: - Tamara Ben Ari, Inra - Petr Havlik, IIASA - Pierre Gerber, FAO - Joshua Elliot, Chicago University