Case for support: Integrated assessment of the determinants of the maize yield gap in SubSaharan Africa: towards farm innovation and enabling policies
Introduction.
According to the latest FAO projections, agricultural production in Sub-Saharan Africa (SSA) will
have to triple to fulfil demand by 2050 (Alexandratos and Bruinsma, 2012). Around 80 % of the
projected growth will have to come from intensification, predominantly an increase in yields
through better use of inputs. Yield gap estimations and explanations provide important information
on the scope for production increases on existing agricultural land through better farming systems,
farm management and enabling policies (Lobell et al., 2009; Laborte et al., 2012; van Ittersum et
al., 2013). To identify the required changes in systems, management and policy that allow for
narrowing yield gaps, the analysis of agricultural productivity and its determinants is crucial. Such
analysis should incorporate both information on economic efficiency (e.g. Coelli and Rao, 2005;
Bravo-Ureta et al., 2006; Fuglie, 2008) and the biophysical processes, i.e., crop, environment and
management interactions (Zhengfei et al., 2006).
Recently, a number of studies and initiatives have emerged that measure and explain crop yield
gaps in SSA. Some reveal an uneven distribution of the yield gap across SSA (Licker et al., 2010;
van Dijk et al., 2012; Tittonell and Giller, 2013), suggesting substantial scope for yield
improvements. Several studies have adopted a macro-economic approach to explain these patterns
(Neumann et al., 2010; Baldos and Hertel, 2012), correlating yield gap data with economic and
policy variables. These studies, however, are not able to address the underlying micro-mechanisms,
i.e. how local bio-physical conditions (e.g. differences in radiation, rainfall and soil), farm level
factors (e.g. farm and plot size, age and management) and interactions with institutional factors (e.g.
infrastructure, agricultural support prices and market conditions) relate to realised production and
technical efficiency (Giller et al., 2011; Tittonell and Giller, 2013; Affholder et al., 2013). In order
to increase agricultural productivity, it is important to better understand the biophysical and
socioeconomic factors, and their interactions that prevent closing the yield gap.
Because of its straightforward and powerful implications, the notion of yield gap has been used
increasingly as a framing device for agricultural development policy in SSA. However, in a recent
paper Sumberg (2012), who analysed the use of the yield gap notion in four high profile policy
documents1 concludes that: “…while the yield gap of policy discourse provides a simple and
powerful framing device, it is most often used without the discipline or caveats associated with the
best examples of its use in production ecology and microeconomics. […] In general, the link
between the yield gap and issues addressed by the favoured policy options is lacking or at best
poorly specified” (Sumberg, 2012 p. 510).
This project addresses this limitation by providing an innovative framework that combines and
operationalizes the biophysical and agronomic assessment of yield gaps with micro-level socioeconomic approaches that explain technical efficiency and productivity at the farm and plot level.
This framework will allow an enhanced understanding of the various types of yield gaps, their size
and determinants. The results will be used to derive targeted policy and farming recommendations
that account for the complex environments in which male and female farmers in the SSA-region
operate and incorporate the basic mechanisms that link farm performance to the broader enabling
1
These are: Realizing the Promise and Potential of African Agriculture (2004) - InterAcademy Council, World Development Report
2008 (2007) - World Bank, Agriculture at Crossroads (2009) - International Assessment of Agricultural Science and Technology
(IAAST) and The Future of Food and Farming (2009) – UK Foresight.
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environment. This process will be supported by initiating participatory on-farm demonstration trials
on the one hand and a policy dialogue with stakeholders on the other hand.
Aim and research questions.
The aim of this project is to identify the key bio-physical and farm and crop management factors
that determine the maize yield gap in SSA and how these are related to existing institutional,
infrastructural, socio-economic and policy constraints. The research focuses on the major food crop
in SSA, maize, mainly produced by small scale farmers. Maize is consumed in almost all SubSaharan African countries, accounting for 30-50 % of low-income household expenditure.
Addressing yield performance in maize is therefore valuable from both a food security and poverty
perspective. The project will focus on Ghana and Ethiopia as maize-growing case study countries
where we can build on existing data and local partnerships. We assume that enhanced
understanding for these two countries from West and East Africa will have wider meaning.
The main research questions are:
1. What is a scientifically sound and applicable generic framework linking agronomic, socioeconomic, institutional, infrastructural and policy factors, explaining maize yield gaps in
SSA?
2. What are the main biophysical and farm and crop management factors that help to explain
yield gaps in the case study countries?
3. What are the main infrastructural, institutional, socio-economic and policy factors that
explain farm and crop management and consequently yield gaps?
4. Which policies and farm management options are key for increasing yield performance in
SSA?
Research theme
By analysing the maize yield gap in SSA, the proposal directly relates to the theme 1 “Agriculture
and Growth” in the DFID-ESRC Growth Research Programme and more specifically to the
subtheme: “Raising agricultural productivity in low-income countries”.
Research methods
The innovative part of this project is the use of a framework that integrates agronomic and
economic approaches to assess the yield gap and analyse agricultural performance at the plot and
farm level (de Koeijer et al., 1999; Hoang, 2013). In the literature several definitions are used for
yield gap and its components (De Bie, 2000; Fischer et al., 2009; Lobell et al., 2009; van Ittersum et
al., 2013). In this project we identify three different gaps between four yield estimates using
production ecological concepts (van Ittersum and Rabbinge, 1997) and economic production theory
(Coelli et al., 2005). Figure 1 summarises the current thinking about our framework.
1. Calculated potential yield is the yield of a crop when grown under favourable conditions
without growth limitations from water (in case of rainfed systems water-limited yield
potential is calculated), nutrients, pests, or diseases. For any given site and growing season,
potential yield is determined by growth defining factors: crop genetic characteristics, solar
radiation, temperature and CO2. For water-limited yield potential water supply is important.
2. Best practice yield is the yield that is reached when farms (in a specific agro-ecological
zone) are operating at the technical frontier and are considered to be fully technically
efficient. In this situation farmers are not able to increase output without increasing at least
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one of their inputs. Best practice yield takes the contribution of all inputs jointly into
account.
3. Economic ceiling yield is the optimum (profit maximizing or cost minimizing) yield given
prices paid/received by farmers and taking into account existing institutions. Normally, the
market price of the crop and the costs of essential inputs are such that profit is highest at
input levels that are below what is required to reach (near) potential yield.
4. Actual farmer yield is the observed yield at the farm or plot level in a defined region. Actual
farmer yield is often lower than the economic ceiling yield, best practice yield and
calculated potential yield due to a combination of farm level and environmental factors.
Figure 1: Yield gaps and key determining factors
Yield levels
Calculated
potential*
Modelled
Gap 1
Best
practice
yield
Gap 2
Economic ceiling
Gap 3
yield (given
Actual farmer
current markets
yield
and institutions)
Farm and plot level observations
Factors determining the gap(s)

Access to technology

Socio-economic context: market conditions; support
programs; infrastructure.

Farm-specific factors: farm size, knowledge,
experience, crop and farm management and risk
aversion

Unexpected or random events: extreme weather
events, unanticipated seasonal events but also price
volatility and crisis.
*water-limited yield potential in case of rainfed systems
The analysis consists of three stages. In the first stage crop growth and economic production models
are used to calculate potential, technical efficient (‘best practice’) and economic ceiling yields at the
national and regional level, which are subsequently combined with actual yield data from surveys to
compute the various yield gaps. In the second stage, econometric techniques are used to analyse
variations in the observed yield gaps in space and relate them to plot-level, farm-level and context
determining factors. As Figure 2 indicates the yield performance or yield gap at plot level will be
influenced by decisions taken at the farm-household level. Moreover, the farm or farming system is
itself embedded in a wider socio-economic and biophysical context, which will also potentially
affect the achieved crop yield. In the third stage, a small number of local case studies are organised
at the village level to deepen the analyses of stage 1 and 2 and to thorougly understand the
determinants of yield gaps to allow identification of farm and management innovations and policy
interventions.
Stage 1: National and regional estimation of yield gaps
Initially, the potential and water-limited yield levels will be derived from the Global Yield Gap
Atlas (GYGA) project (van Ittersum et al., 2013 - www.yieldgap.org), which presents consistent
estimates of these yield levels for 10 SSA countries, including Ghana and Ethiopia, with a focus on
most dense crop areas. Potential yield (or water-limited yield under rainfed conditions) is calculated
using crop growth models tested in the region that simulate phenological development, net
assimilation and resource allocation to different organs and under water-limited conditions also soil
moisture contents and evapotranspiration are simulated, all with a daily time step. Nutrientlimitation and growth-reducing factors such as weeds and pests are assumed to be managed
perfectly. These estimations will be used as starting point and simulations at higher resolution will
be added where needed based on the purpose of the present project.
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Figure 2: Context, farm and plot levels
Best practice yield will be estimated by applying frontier analysis (Coelli et al., 2005) at the plot
level (Neumann et al., 2010). Here, a frontier production function, which defines the outer boundary
of input-output combinations for any set of observations of farms in a specific region, is
constructed. A plot located in a specific region is said to be technically efficient if it is producing at
the best practice frontier for that region. All plots operating below the frontier are considered
technically inefficient because their output falls short of what could have been produced, given the
inputs used. In order to anticipate measurement errors and other stochastic factors (e.g. extreme
weather events or unexpected pests), stochastic frontier analysis is used to estimate best practice
yield (by climate or agro-ecological zone).2 The production frontier or production technology
specification will be chosen in such a way that it allows for the various possibilities of substitution
of inputs and the limitations to this (for example because of essentiality (non-substitutability) of
inputs3) and will be parsimonious (to save degrees of freedom and allow efficient estimation). Apart
from best practice yield, stochastic frontier analysis can also be used to determine the economic
ceiling yield and (given available price information) also to determine allocative efficiency (Alene
and Hassan, 2006; Bravo-Ureta and Evenson, 1994). Finally, the four yield levels will be combined
to quantify the different yield gaps at the farm and plot level (Figure 1).
Stage 2: Explaining yield gaps at the national and regional level
In the second stage, regression analysis is applied to assess the impact of the biophysical and socioeconomic context as well as farm-level factors on the yield gap (Figure 2). Production ecology (van
Ittersum and Rabbinge, 1997) is used to analyse specific biophysical factors and management
practices such as the use of fertilizers and crop protection methods. This requires detailed
information on soils, management and crop responses. The various (plot level) yield gap measures
(𝑦𝑖ℎ𝑣 ) are regressed on a number of explanatory factors, including farm/household-specific level
variables and measures that describe the socio-economic, institutional, infrastructural and political
environment. This analysis will provide an insight into the main determinants and constraints of
various yield gap measures and agricultural performance at the farm/household level, while
controlling for bio-physical conditions. The following function is used (also see Huang et al., 2006):
𝑦𝑖ℎ𝑣 = 𝛼0 + ∑ 𝛽𝑋𝑖ℎ𝑣 + ∑ 𝛿𝐻ℎ𝑣 + 𝜃𝑉𝑣 + 𝜀𝑖ℎ
(1)
where yih is the yield gap of the ith plot of the hth household of the vth village. The term Xih denotes
plot-specific characteristics, including, soil quality, topography, and other growth factors. The term,
Hhi denotes a large number of household determinants, including education, off-farm employment,
2
We will also explore the use of parametric (DEA) and semi-parametric approaches to estimate the frontier.
See the literature on the Von Liebig and other crop production functions (e.g. de Wit, 1992; Paris, 1992) and Van Ittersum and
Rabbinge (1997) for further details on limits to input substitutability.
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household composition and allocation of inputs (labour, capital, fertilizer and pesticides). Vv
represents a group of broader socio-economic variables that are only known at “village” or
community level such as distance to roads, village size, rich versus poor areas and prices. Vv also
comprises relevant regional, institutional and policy variables (e.g. availability of micro credit and
extension services). β,δ and θ are vectors of parameters that capture the effect of the plot, household
village and region-specific variables on the yield gap.
Estimating plot level functions optimizes the use of available data and creates a direct link
between our measure of potential yield and actual yield. Furthermore, it allows for the use of
advanced econometric techniques that correct for endogeneity issues (see Jansen et al., 2006;
Dorosh et al., 2012 for the use of instrumental variables in a similar context) and omitted variable
bias (see for example Huang et al., 2006 who use a fixed effect framework). The approach also
makes it possible to explicitly deal with multiple levels (i.e. plot, farm and village context) in the
regression analysis (see Reidsma et al., 2007).
Stage 3: Local explanation of yield gaps
To improve understanding of explanatory factors and to allow for farm and policy
recommendations and interventions, two maize producing regions (i.e. several villages within a
province) will be selected in both Ghana and Ethiopia for further research. Selection criteria of the
regions within those countries include relevancy from a national food security perspective,
sufficient variation in biophysical characteristics and yield potential and available data (part of the
household survey that is used for stages 1 and 2). In each case study region, two to three villages
will be selected that are representative of the agro-ecological conditions of the area, but may vary in
terms of market access and infrastructural setting. Based on a farm typology, at least three
households will be sampled per type and per village to collect detailed information on plot-level
crop production, soil characteristics, cultivation history and agronomic management including input
use, planting date, land preparation and weeding calendars (see Data Management Plan). The case
studies will be organised in cooperation with the University of Ghana and the Centro Internacional
de Mejoramiento de Maíz y Trigo (CIMMYT) in Ethiopia.
Where possible the collected data will be linked and compared with information from the
GYGA. Potential and water-limited yields will be simulated using the detailed soil characteristics
and similar to the approach in stage 2, regression analysis will be used to examine the relationship
between the yield gap, farm type, farm management and other explanatory factors. We will also
explore the use of regression tree analysis (Tittonell et al., 2008), which is used in production
ecology.
The results from the analysis will provide further insights on within-village and within-farm
variability in yield gaps, and its determinants, including farm-level practices (e.g. management),
contextual variables (e.g. access to roads, irrigation and extension services) and bio-physical
factors (soil, slope and rainfall). Through linkage to local and national partners we aim to identify
feasible farm interventions to narrow yield gaps, which will be tested in on-farm demonstration
trials (four in total, one in each of the case study regions). Ideally, the project will initiate such
demonstrations as a concrete deliverable and on-the-ground impact. Secondly, we hypothesize that
different policy and institutional interventions are needed to tackle specific causes of yield gaps
and their explanatory factors. This may vary from teaching and extension towards infrastructure or
economic incentives. We target a dialogue with our local and national partners in which discussions
on policy implications to narrow yield gaps will be initiated (see Pathways to Impact).
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Data
Information on yield potentials and actual yields will be taken from the Global Yield Gap Atlas
(GYGA) that is currently being developed by University of Nebraska, Wageningen University and
many partners from SSA, and co-funded by the Bill and Melinda Gates Foundation.4 GYGA is an
international project that aims to present consistent estimates of potential and water-limited yield
levels, actual yields and yield gaps using a standard protocol combined with a bottom-up approach
based on field-survey data and robust crop simulation models. The first results will be published in
2013, including detailed yield gap maps will for maize, rice, wheat, sorghum, millet, and soybean in
10 SSA countries, including Ethiopia and Ghana.
The farm/household level data and plot level information for Ethiopia will be taken from the
World Bank’s Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMSISA).5 The LSMS-ISA are nationally representative panel household surveys with a strong focus on
agriculture. The surveys cover a wide range of agricultural and socio-economic household and
village level indicators including geo-referenced plot level data that can be linked with the GYGA
data. For Ghana, use will be made of the EGC-ISSER Ghana panel survey that is expected to
become available in 2013 (request for access under review).6 The design and the number of
indicators of the survey is very similar to that of the LSMS-ISA.
In addition, a survey in each of the case-study regions will be organised to collect additional
and detailed information on plot-level crop production and management that is not provided in the
LSMS-ISA and EGC-ISSER databases, in particular soil characteristics, cultivation history and
agronomic management including input use, planting date, land preparation and weeding calendars.
Also complementary information at the farm level (e.g. education, gender and market access) will
be collected to explain the yield gap. Data will be collected such that disaggregation is possible
across gender, age, education and spatial geography. Based on a farm typology, households will be
sampled in a stratified random way so that at least three farms are randomly selected from each type
in each study village.
Outputs
Scientific output. At least four scientific manuscripts will be prepared to be submitted scientific
conferences and academic journals. Due to its interdisciplinary approach, we aim to publish in both
agricultural and development economics journals and agronomic journals. We aspire that our work
will demonstrate and stimulate cooperation and exchange of ideas between economic and
agronomic researchers. Papers will be presented during annual GYGA meetings and international
conferences.
Policy briefs and leaflets. Policy briefs will be prepared to inform the two policy roundtables in
Ghana and Ethiopia and leaflets will be made to support the on-farm demonstration trails in both
countries (see Pathways to Impact).
Data and results. Publications will be posted on a website for further dissemination. Results of
national and more detailed level analyses of yield gap determinants and proposed innovations and
interventions will be made available as web-based maps. We will also make available underpinning
data. In the first year of the project, we will explore whether these results and data will be published
on the GYGA website or whether we will open an additional website linked to the GYGA site (see
Data Management Plan).
4
www.yieldgap.org [Accessed October 3, 2013].
5
http://web.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/EXTLSMS/EXTSURAGRI/0,,contentMDK:2280238
3~pagePK:64168427~piPK:64168435~theSitePK:7420261,00.html [Accessed October 3, 2013]
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www.econ.yale.edu/~egcenter/egc_isser_overview.html [Accessed October 3, 2013].
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