Ten striking facts about agricultural
input use in Sub-Saharan Africa
Megan Sheahan and Christopher B. Barrett
Presentation for the workshop on Structural Transformation in African
Agriculture and Rural Spaces (STAARS)
African Development Bank Headquarters, Tunis, Tunisia, November 11-12, 2014
A summary of work prepared under the “Myths and Facts” project
Introduction
Improved agricultural productivity is a primary
pathway by which societies can begin down the path
of economic transformation and growth and out of
subsistence level poverty.
Expanded use of modern agricultural inputs, embodying improved
technologies, is often seen as a prerequisite to increasing agricultural
productivity.
Asia and Latin America enjoyed tremendous increases in agricultural
productivity through rapid and widespread uptake of yield-enhancing
modern agricultural inputs.
Benefits accrued to both producers and consumers, helping stimulate
historically unprecedented economic growth and poverty reduction in east
and southeast Asia.
Introduction
What about Sub-Saharan Africa?
Prevailing wisdom = “African farmers use few modern inputs”
Well-perpetuated claim grounded in:
• Macro-statistics (e.g., FAOStat and World Bank’s Development
Indicators)
• Studies derived from micro-data with small or purposively chosen
samples
• Case studies with limited statistical underpinnings
• Data collected 10-20 years ago
Major changes in SSA in last 10-20 years:
• High and volatile food prices
• Urbanization and growth of a middle class
• Increased investments in agricultural sector (including fertilizer
subsidy programs)
• New technologies available to farmers (cell phones)
• Changing bio-physical environment (climate change, soil erosion)
Introduction
It’s time to update our understanding of the
agricultural input landscape in Sub-Saharan Africa.
1.
2.
3.
4.
5.
6.
7.
Large cross section of SSA’s
population
Cross-country comparable
Strong focus on agricultural data
collection
Plot, household, and community
level information
Nationally-representative statistics
as well as within-country (and
even within-household) analysis
Statistics derived from farmers’
accounts
Coupled with growing collection of
geo-referenced data sets
Living Standards Measurement Study Integrated Surveys on Agriculture
Burkina Faso Ethiopia Malawi Mali Niger Nigeria Tanzania Uganda
Introduction
We use one cross section of LSMS-ISA data collected between 2010 and
2012 in each of six countries (Niger, Nigeria, Ethiopia, Malawi, Tanzania,
Uganda), including over 22,000 cultivating households and 62,000
agricultural plots
• Objective: update the basic facts on agricultural input use in SSA
through descriptive statistics
• Not our objective: uncover casual pathways for these conditions
Focus on fertilizer, improved seed varieties, agro-chemicals (pesticides,
herbicides, fungicides), irrigation, and mechanization
Huge number of descriptive statistics included in Sheahan and Barrett
(2014) World Bank Policy Research Working Paper No. 7014
10 most important facts presented here…
Sample selection and
variable creation
Sample: any sampled household cultivating at least one agricultural plot
in the main growing season (mostly rural but not exclusively)
Country
Year
Season
# hh
# plots
Ethiopia
2011/12
-
2,852
23,051
Malawi
2010/11
Rainy
10,086
18,598
Niger
2011/12
Rainy
2,208
6,109
Nigeria
2010/11
-
2,939
5,546
Tanzania
2010/11
Long rainy
2,372
4,794
Uganda
2010/11
First
1,934
3,349
Variable creation:
• Variables created and data “cleaned” using the same rules across all
data sets and countries
• Use of imputed plot size values to limit known reporting bias
• Household sampling weights as well as calculated plot level weights
1 of 10 “striking” facts
Modern input use may be relatively low in aggregate, but is
not uniformly low across these six countries, especially for
inorganic fertilizer and agro-chemicals.
• Average inorganic fertilizer use rates >
widely quoted 13 kg/ha statistic in 3 of 6
countries, simple six country average
nutrient application rate of 26 kg/ha
Share of cultivating households (%) using
input on fields
100
77
80
40
• Application rates are highest in Malawi
and Nigeria, both with government input
subsidy programs, and Ethiopia
56
60
33
31
41
17
20
3
13
8
17
11
3
0
Ethiopia
Malawi
Niger
any agro-chemical
Nigeria
Tanzania Uganda
inorganic fertilizer
121
Inorganic fertilizer application
(nutrients in kg/ha)
120
100
80
• Relatively high shares of households
use inorganic fertilizer, with 3 of 6
countries > 40 percent
• Where > 30 percent of households
use agro-chemicals on plots (others
used in storage), any implications for
human health?
64
56
60
40
33
26
25 23
20
0
2 1
6
8 7
Micro data (LSMS-ISA, 2009-2011)
12
13
1 2
Macro data (World Bank, 2010)
2 of 10 “striking” facts
The incidence of irrigation and mechanization, however,
remains quite small.
Water control is limited
10
Mechanization is proceeding slowly
9
9
Ownership or rental:
•
8
7
7
–
Traction animal ownership >20
percent in all countries except Malawi
–
1-2 percent of households own a
tractor, not many more rent
–
32 percent of households own and 12
percent of households rent some type
of farm equipment that could be used
for mechanization
6
5
4
3
2
1
0
5
4
4
4
4
3
1
0.4
1
2
2
0.2
Use:
•
% of all cultivated land under irrigation by smallholders
–
~50 percent of households in Nigeria
used a mechanized input or animal
power on their plots
–
>50 percent of households in Ethiopia
used oxen to prepare their plots
% of households with at least some irrigation on farm
• 5 percent of households use some form of
irrigation, covering only about 2 percent of
land under cultivation
3 of 10 “striking” facts
Considerable variation exists within countries in the
prevalence of input use and of input use intensity conditional
on input use.
Agro-chemicals
Inorganic fertilizer
Suggests need for research to understand drivers of within-country
agricultural input use variation.
4 of 10 “striking” facts
There is surprisingly low correlation between the use of
commonly “paired” modern inputs at the household- and,
especially, the plot-level.
Ethiopia: household level
Ethiopia: plot level
Raises important questions about prospective untapped productivity
gains from coordinated modern inputs use.
5 of 10 “striking” facts
Input intensification is happening for maize in particular.
Plots with mostly maize are among those
most likely to receive a modern input and
with the highest application amounts,
including agro-chemicals
Related: plots that include a major cash
crop (<25 percent of all plots) are generally
no more likely to receive modern
agricultural inputs
25-40 percent of maize cultivating
households purchased new maize seed
~25 percent of maize cultivating
households in Ethiopia and >50 percent in
Malawi used an improved variety
6 of 10 “striking” facts
An inverse relationship consistently exists between farm or
plot size and input use intensity.
Nigeria: farm level
Nigeria: plot level
Local polynomial smooth
kg/ha of inorganic fertilizer applied to field
Local polynomial smooth
200
150
100
50
0
-50
0
200
150
100
50
0
1
2
3
Total hectares of land under cultivation
95% CI
lpoly smooth
kernel = epanechnikov, degree = 1, bandwidth = .34, pwidth = .51
4
0
.5
1
1.5
plot size in hectares
95% CI
lpoly smooth
kernel = epanechnikov, degree = 1, bandwidth = .2, pwidth = .3
In most cases, this relationship is more pronounced at the plot level, therefore
inter-household variation cannot explain relationship.
Suggests need to better understand intra-household agricultural input
allocation decisions.
7 of 10 “striking” facts
Farmers do not significantly vary input application rates
according to perceived soil quality.
Simple descriptive statistics: farmers do not
appear to adjust input application rates to
accommodate their perceptions of plot soil quality
(Malawi, Tanzania, Uganda)
“Within household” regression analysis: plots
deemed “average” or “poor” quality are more
likely to receive inorganic fertilizer applications,
however only explains a tiny amount of variation
Farmers do not make different input use decisions
across eroded and non-eroded plots (Niger,
Uganda, Malawi, Tanzania), including with
respect to organic fertilizer
Suggests a need for extension programming
around soil fertility and input use and the need to
invest in inexpensive soil quality tests
8 of 10 “striking” facts
Few households use credit to purchase modern inputs.
In all countries except Ethiopia, less than one percent of cultivating households
used credit— either formal or informal—to purchase improved seed varieties,
inorganic fertilizer, or agro-chemicals.
In Ethiopia, where there exist widespread input credit guarantee schemes operated
by cooperatives, nearly 25 percent of cultivating households claimed to receive
some type of “credit service,” although we cannot be sure whether this is for
agriculture or other household purchases.
Reinforces widespread perceptions of the weakness of agricultural input credit
markets in the region.
Much scope remains for deepening rural financial markets, despite recent advances
in money transfer systems based on mobile phone platforms, the proliferation of
microfinance institutions, etc.
9 of 10 “striking” facts
Gender differences in input use exist at the farm
and plot level.
• Male headed households are more likely use
modern inputs across almost all countries and
input types
• Plots managed or owned by men (88 percent of all
plots), are more likely to receive inorganic
fertilizer and in higher amounts; almost always
holds when controlling for gender of household
head
• Male headed households are more likely use modern
inputs across almost all countries and input types
• Related to work on “gender gap” in ag input
productivity
• Plots managed or owned by men (88 percent of all plots),
are more likely to receive inorganic fertilizer and in
higher amounts; almost always holds when controlling
for gender of household head
• Related to work on “gender gap” in ag input productivity
10 of 10 “striking” facts
National-level factors explain nearly half of the farm-level
variation in inorganic fertilizer and agro-chemical use.
Variation in household-level inorganic fertilizer use
Categories of variables
Bio-physical variables: rain, soil,
elevation, maximum greenness, agroecological zones
Socio-economic variables:
consumption level, sex of household
head, household size and dependency
ratio
Farm operation characteristic
variables: farm size, number of crops,
type of crops
Market and accessibility variables:
distance to market and road, prices of
fertilizer and main grain
Country dummy variables
Shapley value
24
4
16
11
45
• Ultimately interested to learn where
most of the variation in input use comes
from: biophysical, infrastructure, market, socioeconomic, or policy-specific variables?
• Binary use at household level (avoids
bias from survey design)
• R2 decomposition using Shapley-Owen
values
• 45 percent of variation in inorganic
fertilizer use can be explained by
country level
• Similar for agro-chemical use (43
percent)
Suggests the policy and operating environments facilitated by
governments and regional processes (e.g., CAADP) are critically important
for ushering in a Green Revolution in Sub-Saharan Africa.
Conclusions
Modern agricultural input use
in Sub-Saharan Africa is far more nuanced and
varied than current claims suggest.
Confirmed longstanding conjectures:
•
•
•
Irrigation and mechanization remain limited
Women farmers use fewer inputs than men
Agricultural input credit use is virtually non-existent
New findings that suggest more policy-relevant research opportunities:
•
•
•
•
•
•
•
More agro-chemical use by smallholder farmers than commonly thought
Huge amount of across and within country variation in fertilizer and agro-chemical
use
Input use is no higher on cash crops; maize is receiving a fair amount of input use
Very little pairing of inputs with bio-physical complementarities at the plot level
Little correlation between farmer-perceived plot quality and input use
Input use intensity if more related to plot than farm size
Country-level factors explain large amount of variation in household fertilizer and
agro-chemical use