adaptation to land constraints by derek headey

• Special issue for a special region:1. Only Africa will see rapid rural population growth2. Assumed to be land abundant – but very diverse3. Limited growth in yields (until recently)4. Limited growth (potential?) for modern inputs5. Limited prospects for non-farm jobs6. Very complex and diverse land tenure systems7. Major commercial interest in African farming land8. Land a fundamental input, but surprisingly little

research or policy emphasis on land in recent years…

Special Issue about a special issue:

• Several case studies designed to understand responses to land constraints in diverse contexts

• Several cross-country papers aimed at gauging land availability, trends in farm sizes, responses to constraints, commercial farmland investments

• Several papers focused on land tenure and land governance

• A synthesis paper focusing on the forest rather than the trees

15 articles on a broad range of land-related issues:

5

Adaptation to land constraints: Is Africa different?

Derek HeadeyInternational Food Policy Research Institute (IFPRI)

Thom JayneMichigan State University (MSU)

Jordan ChamberlinMichigan State University (MSU)

Over 200 years ago, Malthus argued that population growth cyclically outstrips agricultural productivity

In much of the world, economic history has not been kind to Malthus, because of “induced innovations”

Malthus himself acknowledge fertility checksEster Boserup (1965) focused on induced innovations

in farming techniques under emerging land constraintsGreen Revolution showed that smallholders can adopt

modern technologies to accelerate innovationMigration and rural non-farm sectors have become

important outlets for rural population pressures

1. Introduction

At the same time, if any continent has been kind to Malthus’ gloomy predictions, it is sub-Saharan Africa

So is Africa different?Framework based on decomposing farm income:

+Growth in rural population is the sum of fertility & net migration:

1. Introduction

h𝑆 𝑟𝑖𝑛𝑘𝑖𝑛𝑔 𝑓𝑎𝑟𝑚𝑠𝑖𝑧𝑒𝑠

Africa is typically thought of as land abundant, but this neglects the heterogeneity within Africa

At least two Africa’s – high density & low density

2. Land expansion

Region Period Hectares per agric. worker (FAO)

Hectares per holding(censuses)

Africa - high densityb

(n=5)1970s 0.84 1.992000s 0.58 1.23

Africa - low densityb

(n=11)1970s 1.65 2.652000s 1.37 2.82

South Asia 1970s 0.78 2.01(n=5) 2000s 0.55 1.19

China & S.E. Asia 1970s 0.80 2.08(n=4) 2000s 0.68 1.58

What can we say prospectively about land expansion? We revisit the Deininger-Byerlee (2011) analysis

FAO-IIASA estimates of yield potential for 9 crops Define low density areas as “unused” Separate forest and non-forest land Separate out very remote areas

Their analysis focused on long run land potential We focus on economic potential based on estimating

profitability as a function of realizable yields and location We focus on potential for smallholder expansion;

largeholders are a different kettle of fish

2. Land expansion

F1. Potential cropland under different assumptions

Deininger-Byerlee*

Revenue>$0

Revenue>$0 & yields=45%

Revenue>$250 & yields=45%

0 20,000 40,000 60,000 80,000 100,000 120,000

West Africa Southern Africa East/Central Africa

Potentially arable cropland (millions of Ha)

Notes: These estimates apply to non-forested area

The unused land is in a handful of countries In the Deininger-Byerlee approach, 50% of

potentially arable cropland is in the two Congos, Madagascar, Central African Republic

Tightening the criteria pushes this to 70%, then 86% Obvious institutional constraints to migration in and

across ethnically diverse countries Sizeable costs to land preparation in some contexts Focus groups in country studies emphasized malaria,

tsetse fly and isolation as most important constraints

2. Land expansion

3. Agricultural intensification In the framework above, the most welfare-relevant

indicator of intensification is just output per hectare Boserup focused more on cropping intensity, and the

ag-econ profession & CGIAR focus on yields But switching to high value crops is obviously also a

potentially important adaptation, especially in SSA.

AFG

ALB

DZAAGO

ARG

ARM

AZE

BGD

BLR

BEN

BTN

BOLBIHBWA

BRA

BGR

BFA

BDIKHMCMR

CAFTCD

CHL

CHNCOL

COMZARCOG

CRI

CIV

DOM

ECU

EGY

SLV

ERI ETH

FJI

GAB

GMB

GEO

GHA

GTM

GINGNB

GUY HTI

HND INDIDNIRN

IRQ

JAM

JOR

KAZ

KENPRKKGZ LAO

LVA

LBN

LSO

LBRLBY

LTU

MKD

MDG MWI

MYS

MLI

MRTMEX

MDAMNGMNE

MAR

MOZ

MMR

NAM

NPL

NIC

NER

NGA

PAK

PANPRY

PER

PHL

ROM

RUS

RWA

SEN

SRB

SLE

SOM

ZAF

LKA

SDN

SWZ

SYR

TJK

TZA

THA

TMPTGO

TUN

TUR

TKM

UGAUKR

URY

UZBVEN

VNM

ZMBZWE

02

00

04

00

06

00

0A

gricu

ltura

l ou

tput p

er

he

catr

e (

20

05

int. d

olla

rs)

0 200 400 600 800Agricultural population density (person per sq km)

3. Agricultural intensification

Ag output per hectare


AFG

ALB

AGO

ARG

ARMAZE

BGD

BLR

BEN

BTN

BOL

BIH

BRA

BGR

BFA

BDI

KHM

CMR

CAF

CHL

CHN

COL

COM

ZARCOG

CRI

CIV

DOM

ECU

EGY

SLV

ETH

FJI

GAB

GMB

GEO

GHA

GTMGINGNB

GUY

HTI

HND

IND

IDN

IRN

IRQJAM

JOR

KEN

PRK

KGZ

LAO

LVA

LBN

LSO

LBRLTUMKD

MDG

MWI

MYS

MEX

MDA

MARMOZ

MMR

NPL

NIC

NGA

PAKPAN

PRY

PER PHL

ROMRUS

RWASEN

SRB

SLE

ZAF

LKA

SWZ

SYR

TJK

TZA

THA

TMP

TGO

TUR

TKM

UGA

UKR

URY

UZB

VEN

VNM

ZMB

ZWE

05

00

100

01

50

0C

ere

al o

utp

ut pe

r h

ect

are

($

/ha)


Cereal output per hectare


AFG

ALB

DZA

AGO

ARGARMAZE

BGD

BLR

BEN

BTN

BOLBIH

BWA

BRA

BGR

BFA

BDI

KHM

CMR

CAF

TCD

CHL

CHN

COL

COM

ZAR

COG

CRICIVDOM

ECU

EGY

SLV

ERI

ETH

FJIGAB

GMB

GEO

GHA

GTM

GIN

GNBGUY

HTI

HND

IND

IDN

IRNIRQ

JAM

JOR

KAZ

KEN

PRKKGZ

LAO

LVA

LBN

LSO

LBR

LBY

LTU

MKD

MDG MWI

MYS

MLIMRT

MEXMDA

MNG

MNE

MAR

MOZ

MMR

NAM

NPL

NIC

NER

NGA

PAK

PAN

PRY

PER

PHL

ROM

RUS

RWA

SEN

SRB

SLESOM

ZAF

LKASDN

SWZ

SYR

TJKTZA

THA

TMP

TGO

TUN

TURTKM

UGA

UKR

URY

UZBVEN

VNM

ZMB

ZWE

05

01

00

150

Cere

als

cro

pp

ing in

tensi

ty (

%)


Cropping intensity in non-Africa sample is heavily explained by irrigation:R-sq = 0.56


AFG

ALB

AGO

ARG

ARM

AZEBGD

BLR

BEN

BTNBOLBIH

BRA

BGR

BFA

BDI

KHM

CMRCAF

CHL

CHN

COL

COMZARCOG

CRI

CIV

DOM

ECU

EGY

SLV

ETH

FJI

GAB

GMB

GEO

GHA

GTM

GINGNBGUY

HTI

HND

INDIDN

IRN

IRQ

JAM

JOR

KEN PRK

KGZ

LAOLVA

LBN

LSO

LBR

LTU

MKD

MDG MWI

MYS

MEX

MDA

MAR

MOZ

MMR

NPL

NICNGA

PAKPAN

PRY

PER PHL

ROM

RUS

RWA

SEN

SRB

SLE

ZAFLKA

SWZ

SYR

TJK

TZA

THA

TMPTGO

TUR

TKM

UGAUKR

URY

UZBVEN

VNM

ZMBZWE

01

00

02

00

03

00

04

00

05

00

0N

on

-sta

ple

s o

utp

ut (%

tota

l cro

p o

utp

ut)


Non-cereal output per hectare

Regression No. R1 R2 R3 R4

Dep. var.Agric. output

per haCereal output

per haCereal crop

intensityNon-cereal

output per haPopulation density 0.33*** 0.18*** 0.20*** 0.28***Density*Africa -0.11** -0.23*** -0.01 -0.01No. Obs 243 243 243 243R-square 0.8 0.74 0.67 0.79

T4. Intensification responses in Africa and other LDCs

Regression No. R5 R6 R7 R8

Dep. var.Fertilizers per hectare

Cattle/oxen per hectare

Irrigation per hectare

Capital per hectare

Population density 0.76*** 0.42*** 0.59*** 0.24***

Density*Africa -0.32** 0.15* -0.47*** -0.10***

Road density -0.08 0.31*** 0.04 0.07**

No. Obs 0.73 0.77 0.92 0.77

R-square 0.69 0.74 0.91 0.73

Stylized facts Potential explanationsLow productivity of cereals sector

Low fertilizer application

Agronomic constraints (e.g. low soil fertility); Poor management practices, low human capital; High transport costs (see regression 1 in Table 4);Low rates of subsidization (structural adjustment)

Low adoption of improved varieties

More varied agroecological conditions and crop mix;Lower returns because of limited use of other inputs (e.g. irrigation); Lower investment in R&D

Low use of plough/ tractors

Tsetse fly in humid tropics; Feed/land constraints in some densely populated areas

Low rates of irrigation

Hydrological constraints; High costs of implementation and maintenance; Poor access to markets limits benefits

Noncereals

High non-cereal output per hectare

Agroecological suitability; Colonial introduction of cash crops; Non-perishable cash crops not limited by poor infrastructure and isolation

T7. Explanations of Africa’s intensification trajectory

3. Reducing rural fertility rates Potentially rational response to land constraints, but

not really examined beforeWe ask 2 questions1) Do land constraints reduce achieved fertility rates?2) Do land constraints reduce desired fertility rates? A difference between desired & achieved fertility

rates is termed the “unmet need for contraception”. Suggests scope for policy interventions

02

46

8R

ura

l fe

rtili

ty r

ates

(#

child

ren

)

0 500 1000 1500Rural population density (person per sq km)

Non-Africa gradient

African gradient

Figure 3. Rural fertility rates and rural population density

3. Reducing rural fertility rates

ALBARMARMARMAZE BGDBGDBGD BGD

BGD

BEN

BEN

BEN

BOLBOLBOLBOLBOL

BWA

BRA

BRA

BFABFABFA

BDI

BDI

KHM

KHMKHM

CMR

CMRCMR

CAF

TCD

TCD

COLCOL

COLCOL

COL

COL

COM

ZAR

COGCIV

CIV

DOM

DOM

DOMDOMDOMDOMECU

ECU

SLV

SLV

ERIERI

ETH

ETH

ETH

GAB

GHA

GHA

GHAGHAGHA

GTMGTMGTMGTM

GINGIN

GUY

HTIHTIHTIHND

INDIND

IND

IDNIDNIDNIDN

IDNIDN

KAZKAZ

KENKENKEN

KEN

KENKGZ

LSO

LBR

LBR

MDG

MDG

MDG

MDGMWI

MWIMWIMWI

MLIMLI

MLIMLI

MRT

MEX

MOZ

MOZ

NAMNAM

NAM

NPL

NPLNPL

NPL

NICNIC

NERNER

NER

NGA

NGA

NGANGA

PAK PAKPRY

PRY

PERPERPERPERPERPER

PHLPHLPHL

PHL

RWA

RWA

RWARWA

RWA

SEN

SENSEN

SEN

SEN

SLE

LKA

SDN

SWZ

TZA

TZA

TZATZATZA

THA

TMPTGO

TGO

TURTUR

TKM

UKR

UZB

VNM VNM

ZMBZMB

ZMB

ZMB

ZWE

ZWE

ZWE

ZWE

ZWE

EGYEGYEGYEGYEGY EGY

JOR

JOR

JORJOR

JOR

MAR

MAR

MARTUN

02

46

81

0D

esir

ed fe

rtili

ty (

# c

hild

ren)

0 500 1000 1500Rural population density (person per sq km)

Full sample gradient

African sample gradient

Figure 4. Desired rural fertility & population density

F5. Unmet contraception needs (%) and rural population density in Africa

BEN

BEN

BEN

BFA

BFA

CMR

CMRCMR

TCD

COM

ZAR

COG

CIVERI

ERI

ETH

ETH

GAB

GHAGHA

GHA

GHA

GIN

GIN

KEN

KEN

KEN

LSO

LBR

MDG

MDG

MWI

MWI

MWIMLI

MLI

MOZ

MOZ

NAMNAM

NER

NERNER

NGA

NGANGA

NGA

RWA

RWARWA

SEN

SEN

SLE

TZA

TZA

TZA

TZA

TGO

ZMB

ZMB

ZMB

15

20

25

30

35

40

Unm

et c

ontr

ace

ptio

n n

eeds

(%

wo

men

)

0 100 200 300 400Rural population density (person per sq km)

Sources

Regression number 1 2 3 4

Dependent variable Actual fertility Actual fertility Desired fertility

Desired fertility

Model Linear Log-log Linear Log-log

b/se b/se b/se b/se

Pop density (per 100 m2)

-0.14*** -0.09*** -0.11*** 0.00

Density*Africa 0.05 0.09*** -0.34*** -0.07***

Female sec. education (%)

-0.02*** -0.05*** -0.01** -0.08***

Ag. output per worker, log -0.58*** -0.13*** 0.01 0.06***

Africa dummy 1.25*** -0.15 2.13*** 0.67***

Number of observations

165 165 164 164

R-square

0.75 0.76 0.77 0.81

Table 8. Elasticities between rural fertility indicators & rural population density

4. Nonfarm diversificationDo high density rural areas see more rural non-farm

employment because of agglomeration economies?Do high density rural areas see more migration to

cities?Do high density countries send more migrants

overseas?

High density Africa Low density Africa Other LDCs

Country W M Country W M Country W M

Benin 50.4 23.7 Burkina Faso 12.9 8.1 BGD 53.4 44.5

Congo (DRC) 14.0 23.5 Chad 13.7 9.6 Bolivia 71.4 25.9

Ethiopia 34.3 9.7 Cote d'Ivoire 31.7 22.1 Cambodia 36.0

Kenya 47.1 37.3 Ghana 50.1 26.6 Egypt 69.4

Madagascar 17.8 15.3 Mali 44.6 16.0 Guatemala 79.1

Malawi 41.5 36.0 Mozambique 5.2 23.0 Haiti 24.0 19.0

Nigeria 65.5 37.0 Niger 60.2 35.8 India 22.4

Rwanda 7.3 14.2 Senegal 63.7 37.1 Indonesia 59.2 39.5Sierra Leone 25.2 20.1 Tanzania 7.2 10.5 Nepal 90.5 34.2Uganda 15.5 20.3 Zambia 30.1 19.5 Philippines 16.2 42.6

Table 9. Estimates of rural nonfarm employment shares for men and women in the 2000s

Regression No. R1 R2 R3 R4 R5 R6

Sample Women Women Women Men Men Men

Population density 0.47 0.09 0.15 -0.33 -0.32 -0.31

Density*Africa -0.19** -0.22** -0.15* 0.03 -0.02 -0.02

Africa dummy -0.25 0.1 0.04 -0.43 0.09 0.09

Sec. educ. by gender 0.03 0.11 0.35*** 0.35***

Road density 0.14* 0.15** 0.17* 0.17*

Electricity 0.20** -0.07 0.09 0.09Ag. Output/worker, log 0.46*** 0.01

No. Obs. 162 122 95 74 74 74R-square 0.2 0.53 0.24 0.55 0.55 0.55

Table 11. Elasticities between RNF employment indicators and rural population density for women and men

Estimator OLS Robust OLS RobustStructure Levels (logs) First difference Levels (logs) First differenceDensity variable Agricultural Agricultural Rural Rural

Population density 0.25*** 0.97** 0.31*** 1.17***Population density*Africa 0.05 -0.94 0.04 -1.22**Total population -0.24*** -1.31** -0.23*** -0.82Lagged remittances -0.21*** -0.24***Lagged population density 0.06 0.06West Africa dummy -0.67* -0.49 Central Africa dummy -1.55*** -1.40*** East Africa dummy -0.90** -0.74* Southern Africa dummy 0.14 0.24 1977-87 dummy 0.15 0.12 1987-97 dummy 0.33* -0.09 0.28* -0.061997-2007 dummy 0.79*** 0.19 0.72*** 0.24*

Number of observations

231 147 231 159

R-square

0.39 147 0.4 0.22

Table 11. Estimating elasticities between national remittance earnings (% GDP) and population density

Adaptation 1 – Land expansionAfrica has land, but heavily clustered in a few

countries, facing major medium-term constraintsAdaptation 2 – Agricultural IntensificationAfrica has intensified agriculture, but largely

through high value non-perishable cropsMuch less historical success with cereals, and much

less potential given limited potential for irrigationShould we shift emphasis of research and development

strategies from cereals to HVCs?

5. Conclusions

Adaptation 3 – Reducing fertility ratesDesired reductions in fertility not met by access to

contraceptive technologies (broadly defined)Ethiopia, Malawi & Rwanda are investing in family

planning, but Uganda resistant, Kenya stallingAsian experience suggests FP yields high returns

5. Conclusions

19601963

19661969

19721975

19781981

19841987

19901993

19961999

20022005

20080.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Bangladesh 2.3

Countries split

Pakistan

3.5

F1. Fertility trends in Pakistan and BangladeshFe

rtilit

y: c

hild

ren

per w

omen

Adaptation 4 – Nonfarm diversificationNonfarm sector doesn’t just grow without engines

like education, infrastructure, agricultureBoom in overseas migration and remittances is new,

and unexpectedly important for many large, high-density countries

20 years ago, Bangladesh was regarded as too big to benefit from remittances

Now around 20% of rural income is from remittancesWhy isn’t Africa earning more remittances?

5. Conclusions

What are the policy implications of this work?Much we already know, but the common thread is

how land constraints permeate so many other important issues: Technological priorities of different farming

systemsMigration, rural non-farm job creationFamily planning: focus on land-constrained

areas?Suggests that land issues need much more attention

in the policy discourse

5. Conclusions