megan sheahan and christopher b. barrett cornell university

Understanding the existing agricultural input landscape in sub-Saharan Africa: Recent field, household, and community-level evidence

Megan Sheahan and Christopher B. BarrettCornell UniversityPresented at the International Livestock Research Institute, Nairobi, Kenya, 10 April 2014

A G R I C U L T U R E

I N A F R I C AT E L L I N G F A C T SF R O M M Y T H S

Broader Motivation: LSMS-ISA data

Current challenges

The world in which African farmers operate has changed: •High and more volatile food price environment•Africa is growing and urbanizing quickly•Production environment changes due to climate change and soil erosion

Concurrent renewed investment in agricultural sector

However, knowledge base is grounded in “old ideas” about African agriculture or inappropriate data Salient case studies, purposively selected samples, agricultural statistics of unknown quality


An opportunity!

• Collecting household survey data with focus on agriculture in 8 SSA countries Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, Uganda

• Improving methodologies in data collection, producing best practice guidelines and research

• Documenting and disseminating micro data for policy research

• Building capacity in national institutions


Survey features

• Nationally representative (rural and urban, and various administrative levels)

• 4 I’s Integrated: multi-topic and geo-

referenced (link with eco-systems)Individual: gender/plotInter-temporal: panels with trackingInformation technology:

concurrent data entry (CAPI, GPS)• Open data access policy

http://www.worldbank.org/lsms-isa

http://www.worldbank.org/lsms-isa


Survey instruments

Household• Individual-level data on demographics, education, health, labor & anthro• Housing, durable assets• Food & non-food consumption• Income• Food security• Non-Farm enterprises• Subjective welfare

Agriculture• Plot-level data on (i) Land Areas, (ii) Labor & non-labor inputs, (iii) Crop cultivation & production• Crop sales & utilization• Farm implements• Extension services• Livestock• Fisheries

Community• Demographics• Services• Facilities• Infrastructure• Governance• Organizations & groups• Market prices


Data collection schedule for panel rounds

Burkina 2014/15

Ethiopia 2011/12 2013/14

Malawi 2010/11 2013/14

Mali 2014/15

Niger 2011/12 2014/15

Nigeria 2010/11 2012/13

Tanzania 2008/09 2010/11 2012/13

Uganda 2009/10 2010/11 2011/12 2013/14

Broader Motivation: “Myths and Facts” Project

Project objectives

• Provide a solid, updated, and bottom-up picture of Africa’s agriculture and farmers’ livelihoods

• Create a harmonized and easy-to-use database of core agricultural variables for tabulation and regional cross-country benchmarking

• Build a community of practice– Partnering institutions: World Bank, African Development Bank, Cornell University,

Food and Agriculture Organization, Maastricht School of Management, Trento University, University of Pretoria, Yale University

– Mentorship program for young African scholars from US and African institutions

A G R I C U L T U R E

I N A F R I C AT E L L I N G F A C T SF R O M M Y T H SProject led by Luc Christiaensen

[email protected]


Common wisdoms revisited

1) Use of modern inputs remains dismally low

2) Land, labor and capital markets remain largely incomplete

3) Agricultural labor productivity is low

4) Land is abundant and land markets are poorly developed

5) Rural entrepreneurs largely operate in survival mode.

6) Extension services are poor7) Agroforestry is gaining traction8) African agriculture is intensifying

9) Women perform the bulk of

Africa’s agricultural tasks10) Seasonality continues to permeate rural livelihoods11) Smallholder market

participation remains limited12) Post harvest losses are large13) Droughts dominate Africa’s risk

environment14) African farmers are increasingly

diversifying their incomes15) Agricultural commercialization

and diversification improves nutritional outcomes


Common wisdoms revisited

1) Use of modern inputs remains dismally low

Motivation

Why is it important to explore input use?• Increase in agricultural productivity necessary for agricultural transformation

and poverty reduction • Expanded use of modern inputs, embodying improved technologies, is often

seen as a prerequisite to increasing agricultural productivity• Common wisdoms (“stylized facts”):

– African farmers use few modern inputs – Input provision systems remain poor

• Those stylized facts have helped spur the new government input subsidy paradigm in SSA, although little cross-country, nationally representative, and recent evidence exists to support those stylized facts

• Use LSMS-ISA data to describe “input landscape” related to fertilizer, modern seed varieties, agro-chemicals (pesticides, herbicides), irrigation, mechanized inputs (animal traction, farm machinery)

Structure of Paper

Meticulously assembled data set but simple descriptive methodology

• From where do common conceptions on input use currently come? –Macro-statistics: FAOStat, World Bank’s World Development Indicators,

CGIAR’s Diffusion and Impact of Improved Varieties in Africa project–Micro-statistics: Literature review of studies on input use from

household level data with large samples by country and input

• With the newest available round of LSMS-ISA data in each country:1. Who uses modern inputs and in what amounts? 2. What is the input provisioning situation?3. What is the main source of variation in binary input use decision?

• 10 most striking and important findings presented here

Sample and data considerations

Households that cultivate at least one field in main ag season

Sample includes over 22,000 households and 62,000 plotsacross 6 countries

Country Year Season # hh # plotsEthiopia 2011/12 - 2,852 23,051Malawi 2010/11 Rainy 10,086 18,598Niger 2011/12 Rainy 2,208 6,109Nigeria 2010/11 - 2,939 5,546Tanzania 2010/11 Long rainy 2,372 4,794

Uganda 2010/11 First 1,934 3,349

(1) Input use is not uniformly low, especially with respect to percentage of cultivating households using inputs but also rates of use.

Most true of inorganic fertilizer and agro-chemical use

• Relatively high shares of households use inorganic fertilizer, with 3 of 6 countries > 40 percent

• Where > 30 percent of households use agro-chemicals, any implications for human health?

• Uganda has lowest input use prevalence of 6 included countries

• Near-perfect match with macro-stats on inorganic fertilizer use rates across four countries Ethiopia, Niger, Tanzania, Uganda

• Largest discrepancies in 2 of 3 countries with fertilizer subsidy programs Malawi and Nigeria

EthiopiaMalawi

NigerNigeria

TanzaniaUganda

LSMS-ISA avgSSA avg

OECD0

20

40

60

80

100

120

25

56

2

64

81

262333

16 7

212 13

121Inorganic fertilizer application (nutrients)

Micro data (LSMS-ISA, 2009-2011) Macro data (World Bank, 2010)

kg/ha

Ethiopia Malawi Niger Nigeria Tanzania Uganda 0

102030405060708090

31

3 8

33

13 11

56

77

17

41

17

3

Share of cultivating households (%) using input on fields

any agro-chemical inorganic fertilizer

(2) The incidence of irrigation and mechanization are really quite small.

Micro-statistics similar to macro-statistics

Mechanization is proceeding slowly• Traction animal ownership above 20 percent

in all countries except Malawi• 1-2 percent of households own a tractor• 1/4 of households in Nigeria used a

mechanized input or animal power on their plots during main ag season

EthiopiaMalawi

NigerNigeria

TanzaniaUganda

LSMS-ISA average0123456789

10

1

0.2

1

32

4

2

9

0.4

7

44 4

5

Water control is limited

% of all cultivated land under irrigation by smallholders % of households with at least some irrigation on farm

(3) Huge amount of variation within countries in the prevalence of input use and intensity.

Example from Ethiopia

• Most input use appears to be driven by certain regions and zones within countries

(4) Input use is as high on maize dominated plots as it is on average at the household level.

Cash crops not driving input use?

Ethiopia

Malawi

Niger

Nigeria

Tanzania

Uganda

-10 10 30 50 70 90 110 130 150

88

135

1

123

15

3

45

146

5

128

16

1

Inorganic fertilizer use (kg/ha)

Householdsmaize plots*

*Niger: millet/sorghum/millet/cowpea instead of maize (too few)

• Commercially purchased maize seeds are used by 25-40 percent of maize cultivating households

(5) Consistent negative relationships between farm and plot sizes and input use intensity.

Example from Nigeria

-50

0

50

100

150

200

Ave

rage

inor

gani

c fe

rtiliz

er u

se (k

g/ha

)

0 1 2 3 4Total hectares of land under cultivation

95% CI lpoly smooth

kernel = epanechnikov, degree = 1, bandwidth = .34, pwidth = .51

Local polynomial smooth

Nigeria – household level

0

50

100

150

200

kg/h

a of

inor

gani

c fe

rtiliz

er a

pplie

d to

fiel

d

0 .5 1 1.5plot size in hectares

95% CI lpoly smooth

kernel = epanechnikov, degree = 1, bandwidth = .2, pwidth = .3

Local polynomial smooth

Nigeria – plot level

• Local linear non-parametric regressions of unconditional inorganic fertilizer use rates• Shape differs by country, especially where ranges in size vary substantially relatively

flat for Malawi and different pattern for Ethiopia and Uganda• Negative relationship is even more pronounced at the plot level in all cases except

Ethiopia important policy implications!

(6) Little variation in input use when households and plots are split by soil quality and erosion status, both farmer-perceived and geo-referenced.

Moreover, few farmers consider their plots of ‘poor’ quality

• Regression analysis reveals that ‘average’ and ‘poor’ plots significantly are more likely to receive inorganic fertilizer treatments than those categorized as ‘good’

• Knowledge gap among farmers? Weak evidence against ‘poor but efficient’ claim?• Implications for extension programs and the need to invest in simple soil quality tests

(7) Surprisingly low correlation between the joint use of commonly ‘paired’ inputs.

Especially apparent when moving from household to field level

Ethiopia – household level Ethiopia – field level

• Show correlation between two-way input use in paper• Can investigate three-way input use (fertilizer, seed, irrigation) for Ethiopia and Niger• Farmers may use >1 modern input on farm, but appear to be diversifying within farm

rather than reaping output gains by pairing inputs together • Synergies from pairing inputs still yet to be exploited

(8) Fertilizer subsidies are not as universal as often believed or reported in government statistics.

3 of 6 current LSMS-ISA countries have government fertilizer subsidy programs

More Less About the same Not applicable0

5

10

15

20

25

30

35

40

45

30

19

29

22

39

14

26

21

Percent of households in Malawi by perception of current input market accessibility

relative to five years ago

Fertilizer Improved maize seed

• Mixed reviews by households on input market accessibility changes over time in Malawi, where fertilizer subsidies are most pervasive

• > 50 percent of households receive a government fertilizer subsidy only in Malawi

• Relatively low fertilizer subsidy occurrence in Nigeria alongside high rates of fertilizer use

• Estimates of fertilizer subsidy coverage in LSMS-ISA data fall short of other estimates using government data – Could be issue of LSMS-ISA data collection

timing or sluggish/failed distribution of planned vouchers by subsidy program implementers

(9) There is very low incidence of credit use for purchasing modern inputs.

Statistics should capture both informal and formal credit types

• < 1 percent of cultivating households used credit to purchase improved seed varieties, inorganic fertilizer, and agro-chemicals – True of all countries except

Ethiopia, where the government issues input credit

• (1) Country-averaged input-output price ratios imply that fertilizer is a good investment at aggregate levels + (2) Wealthier households more likely to use fertilizer – No or under-use may signal cash

flow constraints, which could be aided by expanding credit options

• Policy implication addressing rural financial market failures may be key for expanding input use

(10) Over half of the variation in inorganic fertilizer and agro-chemical use comes from the country level.

Suggests that policy and institutional environment are very important

• Ultimately interested to learn where most of the variation in input use comes from– Biophysical, infrastructure, market, socio-

economic, or policy-specific variables?• Binary use at household level (avoids bias

from survey design)– Also reported at field level under a number

of different specifications (fewer field level characteristics match across surveys)

• R2 decomposition using Shapley-Owen values

• > 50 percent of variation in fertilizer use can be explained by country level!

• Suggests that geography, policy, and institutional environment are important for ushering a Green Revolution in Africa

Conclusions

Main take-away messages

• Input use is not always low Much more heterogeneity in input use between and within countries than commonly assumed

- Varies by country, input, crop, and a large number of important covariates - Micro-level statistics allow us to investigate this variation more fully

• Scope for improvement remains- Synergies in effectively combining inputs appropriately yet to be exploited - Policy and institutions seem important for encouraging yield-enhancing input use

•10 findings presented here are only a small subset of what can be gleaned about input use from the LSMS-ISA surveys

•Exploiting newly emerging panel data will allow us to provide greater nuance for guiding more intelligent policy design

Thank you!

Contact: [email protected]

megan sheahan and christopher b. barrett cornell university

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