Justification for a Global Yield Gap Atlas

Ken Cassman and Martin van Ittersum

University of Nebraska and Wageningen University

• A comprehensive, graphically intuitive, Global Yield

Gap Atlas within 4 years that is publicly available and

widely used as a source of information by policy

makers, researchers, students, and industry

• Transparent, reproducible, robust, comprehensive,

scientifically sound, agronomically relevant protocol

to estimate crop yield gap at field, regional, and

national scales

• Key scientific issues

– Source and type of weather data

– Source and detail of agronomic data requirements

– Crop model selection

– Method of upscaling from point to region, and nation

Vision and Goal

• More rapid economic growth rates in the

world’s most populous countries

• Rapid rise in energy prices that caused

convergence of energy and agriculture

• Stagnating yields of major food crops in

some of the world’s most productive

cropping systems

• None of these trends were clearly

evident or widely recognized until 2007…

Unexpected Mega-Trends

Urban-industrial expansion onto prime farmland at the periphery

of Kunming (+6 million), the capital of Yunnan Province, China,

Photo: K.G. Cassman

Food insecurity: unsustainable crop production on marginal

land by poor farm families without other options

Photo: K.G. Cassman

Clearing virgin rain forest in Brazil

Photo: K.G. Cassman

Decreasing water supply in all major irrigated areas

Yet, irrigated agriculture produces 40% of global food supply

on just 18% of the cropped area.

Year

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Po

pu

lati

on

(x 1

09)

0

1

2

3

4

5

6

7

Rural

Urban

Global rural and urban population trends. Source: UN Population

Division, http://esa.un.org/unup/index.asp, accessed 26 Jan 2010.

70%

30%

Can rainfed agriculture reliably provision an urban world with 6+ billion?

• Business as usual will not meet projected

global food demand in 2050 without large

expansion of crop area

• 60% increase in cereal production needed

by 2050 (40 yr, from 2010) = 1.5% yr-1 of

current average yield, or 1.15% annual

exponential growth rate

• How can we determine if this is possible to

meet food demand without a large

expansion of crop area?

Year

1960 1970 1980 1990 2000 2010

Gra

in y

ield

(t

ha

-1)

1

2

3

4

5

Corn yield

slope = 64 kg ha-1 y

-1

Rice yield

slope = 53 kg ha-1 y

-1

Wheat yield

slope = 40 kg ha-1 y

-1

Global Cereal Yield Trends, 1966-2009

Source: FAOSTAT

corn: 2.8%

rice: 2.9%

wheat: 2.9%

1966

corn: 1.3%

rice: 1.3%

wheat: 1.4%

2009

(~1 bu ac-1 y-1)

Year

1970 1980 1990 2000 2010

Cro

p h

arv

es

ted

are

a (

Mh

a)

400

450

500

550

900

1000

1100

1965-1980

slope = 3.9 Mha y-1

Total cropland area

1965-1980

slope = 6.0 Mha y-1

1980-2002

slope = 1.4 Mha y-1

Rice + wheat + maize area

2002-2009

slope = 10.2 Mha y-1

2003-2009

slope = 7.2 Mha y-1

Global trends in land use for crop production.

Source: FAOSTAT, 1996-2009. Total area includes all majore cereal, oilseed,

pulse, root, fiber, and tuber crops. (Grassini et al.)

Critical need for a Global Yield Gap Atlas

Past yield gap estimates not robust, transparent, reproducible

To help interpret historical yield trends of the major food crops in a given country or region (and yield plateaus)

Estimate global food production capacity on existing farm land, or the additional land requirements due to land use change under different policy scenarios (e.g. impact of biofuel or GMO policies on direct and indirect land use change)

Prioritize research and inform agricultural policies to ensure global food and water security

Identify areas with largest unexploited yield gaps with smallest inter-annual variability; identify constraints; close yield gaps through ecological intensification

Identify where new technologies or technology packages have greatest potential for success and impact (e.g. irrigation)

Hypothetical: Using spatial distribution of maize yield gap in East Africa to locate technology transfer projects that require specific soil type and market access

Soil type Maize yield gaps

Transportation

& demographics

Ethiopia

Kenya Uganda

Tanzania

Yield Potential, Attainable Yield, and the Exploitable Yield Gap

Yield Potential

(Yp)

Attainable

Yield

75-90% Yp

Actual or

Average Yield

determined

by: SR, T,

plant date,

cv maturity

Exploitable yield gap

Yie

ld L

ev

el

(water-limited yield potential for rainfed agriculture, Yw)

When producing crops near the yield potential ceiling, there is a razor-

thin margin for error in managing inputs (too much, too little, too early,

too late), and also in managing pests because of dynamic interactions

between nutrient status and pest incidence and severity

Global Hypothesis: average farm yields plateau

when they reach 75-90% of yield potential in

irrigated systems, or water-limited yield

potential in rainfed systems

What is the cause of yield plateaus for several cereal crops in

some major producing countries: Korea and China for rice, wheat

in northwest Europe and India, maize in China and……..perhaps

also for irrigated maize in the USA.

Cassman et al, 2003, ARER 28: 315-358

Maize

China

Brazil

USA - rainfed

USA - irrigated

0

2

4

6

8

10

12

1960 1970 1980 1990 2000 2010Year

Wheat

China

India

Northwest Europe

0

1

2

3

4

5

6

7

8

1960 1970 1980 1990 2000 2010Year

Rice

India

R. Korea

China

Indonesia

0

1

2

3

4

5

6

7

1960 1970 1980 1990 2000 2010Year

Yie

ld (

Mg

ha

-1)

Yie

ld (

t h

a-1

)

Cassman, 1999. PNAS, 96: 5952-5959

?

Cassman et al., 2003, ARER 28: 315-358

Cassman et al., 2010, Handbook of Climate Change

Grassini et al., 2011. FCR 120:142-152

Requirements for YGA

• Can be performed at different scales (field, AEZ, nation)

• Crop area distribution (irrigated vs rainfed distinct)

• Weather data (actual vs interpolated or modeled)

• Cropping system specified (sowing date or rule for

when sowing occurs; cultivar/hybrid growth duration)

• Robust simulation model (Yp, Yw estimation)

• Actual yields (field, AEZ, subnational, national)

• Upscaling method (global climate zone x soil type

AEZs, weighted by current production area)

• Ground truth validation (Ya, Yp, Yw, crop system, soils)

Source: Ramankutty et al., 2004 All agricultural land (crops and pasture)

Pasture

Cropland Source: Ramankutty et al., 2004

Pasture

Cropland

Major food crops Source: Ramankutty et al., 2004 and Portmann et al., 2010

World area

million km2 billion ha % of total land area

Land area 134.0 13.4

Agricultural land 49.6 5.0 37%

Pasture/fodder crops 35.0 3.5 26%

Cropland 14.6 1.5 11%

Food Crops* 9.5 0.9 7%

Source: FAOSTAT and Portmann et a., 2010 based on the year 2000

*Food crops include wheat, maize, rice, barely, rye, millet, sorghum, soybeans, sunflower, potatoes, cassava, sugar cane, sugar beet, oil palm, rape seed/ canola, groundnuts/peanuts, pulses

Source: Portmann et al., 2010

Simulated yield potential must be based on current agronomic practices within existing cropping systems. In this example, complex cropping systems with maize, rice, and wheat grown in the same year in China

Importance of agronomic context

Geospatial distribution of maize- and soybean-based cropping

systems in South America, some with two crops per year

Source: Portmann et al., 2010

Similar to the complex rice

cropping systems in China,

maize and soybean yield

potential must be estimated

within the confines of

existing cropping systems

with regard to timing of

planting dates and maturity

Crop models - Selection

Crop-specific models preferred, generic models next best

Models should ideally:

operate on daily time steps

flexible in implementing management practices that define Yp and Yw (growth duration, water supply, plant population, etc)

account for fundamental physiological processes

rigorously validated against field data in which crops are grown at Yp and Yw levels

publicly available

published in a peer-reviewed journal

• A comprehensive, graphically intuitive, Global Yield

Gap Atlas within 4 years that is publicly available

and widely used as a source of information by policy

makers, researchers, students, and industry

• Transparent, reproducible, robust, comprehensive,

scientifically sound, agronomically relevant protocol

to estimate crop yield gap at field, regional, and

national scales

• Key scientific issues

– Source and type of weather data

– Source and detail of agronomic data requirements

– Crop model selection

– Method of upscaling from point to region, and nation

Vision and Goal

• Depends on the underpinning time frame

– Short term, probably not so important because size of yield

gap is so large and constraints obvious

– Longer-term, in a world with limited land with suitable soil

quality to support agriculture of moderate to high

production potential, it is crucial to know the productive

capacity of every hectare of current farm land.

• Brazilian cerrado: 38 years ago few would have

predicted the contribution of this region to global

and regional food supply and economic

development; GYGA would have seen this coming!

Is a Yield Gap Atlas relevant for low-

yield agriculture in SSA and S. Asia?

Related projects • AgMIP: Agricultural Model Comparison and

Improvement Project (distributed climate-scenario

simulation)

• Harvest Choice (knowledge products to guide

strategic investments in SSA and SA)

• Global Futures (assess how changes in global

trading, biobased economy and climate change

affect human well-being)

• CCAFS: Climate change, Agriculture and Food

Security (overcome threats to agriculture and food

security in a changing climate)

• aWhere (geo-spatial tools, incl. generation of daily,

gridded weather databases)

Initial roll out of

“Global Yield Gap Atlas”

in mid-July 2012:

http://www.yieldgap.org/

Univ. of Nebraska Wageningen Univ. & Alterra

Global Yield Gap Atlas (GYGA) Coordinating Team

Funding from: Gates Foundation. UNL Water for Food Inst.

Other partners: ICRISAT, IRRI, AfricaRice, and NARES

ICRISAT