# Rooting for food security in Sub-Saharan Africa

Nicolas Guilpart, Patricio Grassini, Justin van Wart, Haishun Yang, Martin

Klass van Ittersum, Lenny G. J. van Bussel, Joost Wolf, Lieven Claessens,

Johan G. B. Leenaars, Kenneth G. Cassman

► To cite this version:

Nicolas Guilpart, Patricio Grassini, Justin van Wart, Haishun Yang, Martin Klass van Ittersum, et al.. Rooting for food security in Sub-Saharan Africa. Environmental Research Letters, 2017, 12 (11), pp.1-8. 10.1088/1748-9326/aa9003. hal-02627913

HAL Id: hal-02627913

https://hal.inrae.fr/hal-02627913

Submitted on 26 May 2020

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
LETTER • OPEN ACCESS

# Rooting for food security in Sub-Saharan Africa

To cite this article: Nicolas Guilpart et al 2017 *Environ. Res. Lett.* **12** 114036

View the [article online](#) for updates and enhancements.

## Related content

- Robust features of future climate change impacts on sorghum yields in West Africa
  B Sultan, K Guan, M Kouressy et al.

- Integrated crop water management might sustainably halve the global food gap
  J Jägermeyr, D Gerten, S Schaphoff et al.

- Impacts of 1.5 versus 2.0 °C on cereal yields in the West African Sudan Savanna
  Babacar Faye, Heidi Webber, Jesse B Naab et al.
Environmental Research Letters

<figure><img src="image_2.png" /></figure>

CrossMark

LETTER

Rooting for food security in Sub-Saharan Africa

OPEN ACCESS

RECEIVED

3 July 2017

REVISED

31 August 2017

ACCEPTED FOR PUBLICATION

29 September 2017

PUBLISHED

17 November 2017

Original content from
this work may be used
under the terms of the
Creative Commons
Attribution 3.0 licence.

Any further distribution
of this work must
maintain attribution to
the author(s) and the
title of the work, journal
citation and DOI.

<figure><img src="image_3.png" /></figure>

Nicolas Guilpart<sup>1,2</sup> ●, Patricio Grassini<sup>1</sup>, Justin van Wart<sup>1</sup>, Haishun Yang<sup>1</sup>, Martin K van Ittersum<sup>2</sup>, Lenny G J van Bussel<sup>3,7</sup>, Joost Wolf<sup>3</sup>, Lieven Claessens<sup>4,5,8</sup>, Johan G B Leenaars<sup>6</sup> and Kenneth G Cassman<sup>1,9</sup>

E-mail: kcassman1@unl.edu

Keywords: food security, sub-saharan Africa, yield gap, maize, soil depth, water requirements

Supplementary material for this article is available online

Abstract

There is a persistent narrative about the potential of Sub-Saharan Africa (SSA) to be a ‘grain breadbasket’ because of large gaps between current low yields and yield potential with good management, and vast land resources with adequate rainfall. However, rigorous evaluation of the extent to which soils can support high, stable yields has been limited by lack of data on rootable soil depth of sufficient quality and spatial resolution. Here we use location-specific climate data, a robust spatial upscaling approach, and crop simulation to assess sensitivity of rainfed maize yields to root-zone water holding capacity. We find that SSA could produce a modest maize surplus but only if rootable soil depths are comparable to that of other major breadbaskets, such as the US Corn Belt and South American Pampas, which is unlikely based on currently available information. Otherwise, producing surplus grain for export will depend on expansion of crop area with the challenge of directing this expansion to regions where soil depth and rainfall are supportive of high and consistent yields, and where negative impacts on biodiversity are minimal.

Introduction

This paper provides a quantitative assessment of the
degree to which major agricultural countries in west and
east Sub-Saharan Africa (SSA) can join the ranks of the
world most productive crop producing regions. Previ-
ous studies [1–4] and popular magazines like National
Geographic [5] suggest that SSA, and especially the vast
Guinea Savannah zone, could become a future world
breadbasket. Breadbaskets are regions that produce a
large and stable surplus of one or more major food crops
(typically cereals and oilseeds) that not only meet local
demand but also can make substantial contributions to
food supply in other regions. By this definition, there
are only a few major breadbaskets in the world: the US
Corn Belt, Brazilian Cerrados, and Argentinean pam-
pas for rainfed maize and soybean [6, 7], the lowland

irrigated river basins and deltas of south and south-east
Asia for rice [8], and the North China Plains, Indo-
Gangetic Plains, central North American Plains, coastal
belt of east, south, and western Australia, and central
and northwest Europe for wheat [8, 9]. The potential
for SSA to be another major breadbasket is suggested by
the fact that most of the existing SSA cereal crop land
(where nearly all grain is produced under rainfed—
rather than irrigated—conditions) receives abundant
precipitation (> 900 mm per year), equal to or greater
than all existing breadbaskets except for the humid
tropical lowland rice areas in Asia.

The degree to which crop water requirements
are satisfied, however, also depends on evaporative
demand (called reference evapotranspiration) and soil
capacity to store water, which may be very different in
SSA than in other breadbaskets. For example, US Corn

<footnote n="1">¹ Department of Agronomy and Horticulture, University of Nebraska-Lincoln, PO Box 830915, Lincoln, NE 68583-0915, United States of America</footnote>

<footnote n="2">² AgroParisTech, UMR Agronomie 211 INRA AgroParisTech Université Paris-Saclay, F-78850 Thiverval-Grignon, France</footnote>

<footnote n="3">³ Plant Production Systems, Wageningen University & Research, PO Box 430, 6700 AK, Wageningen, The Netherlands</footnote>

<footnote n="4">⁴ International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), PO Box 39063, 00623 Nairobi, Kenya</footnote>

<footnote n="5">⁵ Soil Geography and Landscape Group, Wageningen University & Research, PO Box 47, 6700 AA Wageningen, The Netherlands</footnote>

<footnote n="6">⁶ ISRIC-World Soil Information, PO Box 353, 6700 AJ, Wageningen, The Netherlands</footnote>

<footnote n="7">⁷ Environmental Systems Analysis Group, Wageningen University & Research, PO Box 47, 6700 AA, Wageningen, The Netherlands</footnote>

<footnote n="8">⁸ International Institute of Tropical Agriculture (IITA), PO Box 10, Duluti, Arusha, Tanzania</footnote>

<footnote n="9">⁹ Author to whom any correspondence should be addressed.</footnote>
<figure><img src="image_4.png" /><figcaption>**Table 1.** Comparison of annual precipitation, annual reference evapotranspiration, and soil plant-available water-holding capacity in the US Corn Belt and Sub-Saharan Africa (SSA). West SSA: Burkina Faso, Ghana, Mali, Nigeria. East SSA: Ethiopia, Kenya, Tanzania, Uganda, Zambia. Sources: *US Corn Belt* [14] and *Global Yield Gap Atlas* (www.yieldgap.org) for SSA. For SSA, reported values refer to all locations selected following the Global Yield Gap Atlas protocols in the nine countries. A depth of 150 cm is specified for the US Corn Belt, which corresponds to the depth to which maize roots can grow in the absence of any soil physical or chemical constraint limiting root growth [15]. The range in values for water holding capacity reflects variation in soil texture to that depth. In contrast, with less reliable data on rootable soil depth for SSA, the range in values reflects differences in both rootable soil depth from 50 to 150 cm and the plant-available soil water holding capacity to that depth [16].</figcaption></figure>

<table><thead><tr><th>Parameter<sup>a</sup></th><th>US CornBelt</th><th>West SSA</th><th>East SSA</th></tr></thead><tbody><tr><td>Annual precipitation</td><td>600–1000</td><td>900–1400</td><td>900–1400</td></tr><tr><td>Plant-available water holding capacity in the root zone</td><td>200–300</td><td>20–140</td><td>25–130</td></tr><tr><td>Annual reference evapotranspiration</td><td>600–1000</td><td>2200–2900</td><td>2100–2400</td></tr></tbody></table>

Belt soils are deep and of recent origin, and young, laid down during the past 20 000 years, whereas soils in the Guinea Savannah region of SSA are much older and weathered from parent material dating back to the Precambrian era at least 540 million years ago [10]. Given the same soil particle size distribution (called soil texture), weathered tropical soils have water retention properties very different from younger temperate soils, behaving like a clay soil at high soil water content but resembling a sandy soil at low soil water content [11, 12]. Moreover, as described by Nye and Greenland in their seminal book on agricultural soils of west Africa [13]: ‘over vast stretches of savannah, the rooting zone is often restricted by the presence of an indurated iron-oxide-cemented pan, often referred to as laterite, above the rotting rock’. As a consequence, despite greater precipitation, agricultural soils in SSA typically have much smaller capacity to store water in the rootable soil depth than US Corn Belt soils. Furthermore, evaporative demand as quantified by reference evapotranspiration is substantially larger in SSA, because of mostly tropical and sub-tropical climates (table 1).

The capacity of soils to store water determines the size of the water reservoir, which can support crop growth during periods of water deficit when rainfall does not meet evapotranspiration demand [17, 18]. The size of this reservoir depends on the depth to which roots can grow, as determined by soil physical and chemical properties, and on soil water holding capacity in that root zone, which is largely determined by soil texture. The degree to which soils can buffer against transient water deficits is of particular importance because plants are especially sensitive to short-term water deficits during specific growth stages, such as the period between silking and pollen shed in maize, called the anthesis-silking interval [19–21]. In addition to supporting higher average rainfed yield potential (the yield when crop growth is only limited by water supply), large soil water holding capacity in the root zone also confers greater yield stability due to smaller inter-annual yield variation arising from year-to-year variation in rainfall amount and timing. This is of great importance because yield stability has a large influence on farm income and the degree of risk associated with investments in inputs such as improved seed, fertilizer, and pest control measures. At regional and

national scales, yield stability has a large influence on the potential to be self-sufficient in grain production or to be a dependable grain exporter.

Despite the importance of root zone water holding capacity in determining yield potential of rainfed crops and inter-annual yield variation [22], most previous estimates of SSA crop production potential have relied only on aboveground water balance (i.e. annual precipitation versus evapotranspiration) without considering root zone water holding capacity [1, 3]. In large part this omission reflects the paucity of data on rootable soil depth in agricultural soils of SSA [16]. The objective of this paper is to quantify the effect of uncertainty in rootable soil depth on crop production potential in SSA and the impact of rooting depth on capacity of SSA soils to support high and stable crop yields. To this end, we perform a sensitivity analysis of rainfed maize yield potential for major crop-producing countries of west and east Africa in relation to rootable soil depth to assess the likelihood that SSA could become a future maize breadbasket. Our analysis utilizes recent climate data and extends to 2050, a period in which the magnitude of climate change is projected to be modest compared to subsequent change to 2100 [23]. Likewise, the impact of climate change on SSA crop yields is thought to be mostly negative [24], so that achieving breadbasket status would be more difficult under climate change than as benchmarked in our study.

## Methods

Maize production potential on existing maize land was evaluated in nine countries of SSA (west: Burkina Faso, Ghana, Mali and Nigeria, and east: Ethiopia, Kenya, Tanzania, Uganda, and Zambia) following protocols of the Global Yield Gap Atlas (www.yieldgap.org) established for estimating rainfed yield potential of major cereal crops [25, 26]. These nine countries represent 70% and 65% of total population in West Africa and East Africa, respectively, accounting for about the same proportions of total maize production area [27], and they contain most of the Guinea Savannah zone within their borders [1]. We focus on maize because of its increasing importance as a staple food crop in this region. For example, in the 2000–2013 period: (i) maize

<footnote>a All values are expressed in mm of water.</footnote>
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 1.** Effect of rootable soil depth on (a) frequency distribution of water-limited yield potential and (b) inter-annual variation expressed as the temporal coefficient of variation (CV). The arrows and black bold numbers indicate the range in the 50th percentile (50% of maize area) across different rootable soil depths. For each rootable soil depth, the frequency distribution includes 5815 simulated yields across 105 locations, 1–3 crop sequences, and 3–5 soil types at each location over, at least, 15 recent years.</figcaption></figure>

production area increased by 75% in West Africa and 50% in East Africa [27] and this trend is expected to continue [6], (ii) maize accounted for 8% (West Africa) and 24% (East Africa) of dietary calories [27], and (iii) maize use for livestock feed is expected to increase more than four-fold by 2050 compared to 2005 [28]. Likewise, the SSA population will more than double during the same time period [29], driving a dramatic increase in food demand [30].

Rainfed yield potential was simulated with a well-validated biophysical crop model [17, 31, 32] at 105 locations in major maize producing regions of these nine countries (figure S1) using local weather, soil, and cropping system data as the basis for simulations. This model has been rigorously evaluated on its ability to reproduce measured maize yields, across a diverse range of production environments where yields ranged from complete failure up to 18 t ha⁻¹ [17, 31, 32]. At least 15 recent years of weather data were utilized for simulations at each location to allow estimation of yield stability, as quantified by the temporal coefficient of variation for yield, due to normal inter-annual variation in weather. Taken together, these 105 locations are representative of 70% of total maize area within the nine countries (table S1) as delineated by climate zones within the geospatial framework of the Global Yield Gap Atlas [26]. Separate simulations were performed at each location for: (i) the dominant maize-based crop sequences (typically one or two), and (ii) the dominant soil types on which maize is grown (typically 3–5). Aggregate estimates for each location were based on weighting for proportion of maize production area with each crop sequence and soil type [26, 33]. Location-specific results were then aggregated to climate zone and national spatial scales following an upscaling approach based on the current distribution of harvested maize area [26, 33]. National maize production capacity was assumed to be 80% of rainfed yield potential [14, 34, 35] on existing maize production area. Details about methods and sources of data are described

in the supplementary materials methods section available at stacks.iop.org/ERL/12/114036/mmedia.

Separate sets of simulations were performed with soil depth specified at 50, 75, 100 and 150 cm across all locations, and with a recently completed map of root zone plant available water holding capacity for SSA, which represents the best available data on rootable soil depth in SSA [16]. This recent soil depth database has a number of weaknesses, however, due to lack of adequate underpinning data for key soil properties at sufficient spatial resolution, which makes the estimated rooting depths highly uncertain. Thus, the analysis presented here is crucial for assessing the importance of soil depth in estimating rainfed yield potential and its stability at national to regional spatial scales.

## Results and discussion

### Soil root-zone depth and maize yield potential

Results from this sensitivity analysis showed that median rainfed yield potential with 50 cm rooting depth was only 60% of the yield potential with 150 cm depth (figure 1(a)). Even with a relatively deep soil profile of 100 cm, median yield decreased by 15% compared to the typical rooting depth of 150 cm found in most of the US Corn Belt, Argentinean pampas, and northwest Europe wheat belt. Most notable, however, is the sensitivity of maize yield stability to rootable soil depth in these SSA countries. Across all soil depths, the median coefficient of variation ranged from 32% to 62% (figure 1(b)). This degree of inter-annual yield variation is comparable to that of harsh, rainfed environments as found, for example, in the driest areas of the western US Corn Belt (figure 2), where 50% of maize area is irrigated to provide stability to annual grain production [36]. In contrast, regions considered breadbaskets for rainfed maize, such as the central US Corn Belt states of Illinois and Iowa, and the Argentinean Pampas, have coefficients of variation in yield less than 25%. Even
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 2.** Relationship between simulated rainfed maize yield potential and its variability over years (temporal CV) in Sub-Saharan Africa and comparison with rainfed maize yield potential and its stability (i) in two current maize breadbaskets: Argentina and the US Corn Belt, and (ii) in the driest rainfed areas of the western US Corn Belt. Each data point represents a climate zone of the Global Yield Gap Atlas (see table S2 for a list of these climate zones, and figure S2 for a map of their locations). White circles: driest areas of the western US Corn Belt. Yellow circles: favorable rainfed areas of the central US Corn Belt. Yellow triangles: favorable rainfed areas of the Argentinean pampas. All yields are simulated rainfed maize yield potential. Current world breadbaskets included in the figure all have average rainfed maize yield potential >11 Mg ha⁻¹ and a CV <25%. Source: Global Yield Gap Atlas (www.yieldgap.org).</figcaption></figure>

with the deepest soil profile evaluated (150 cm), only 30% of maize production area in the nine SSA countries have yield and yield stability comparable to those of regions considered breadbaskets for rainfed maize (figure 2), and this proportion decreased to 15% with a rooting depth of 100 cm.

Rootable soil depth has a large influence on the simulated maize yields in SSA through the probability of water stress occurring during the sensitive anthesis-silking interval (figure 3(a)), which is a critical stage for yield formation in maize [19–21]. Indeed, water stress intensity around flowering explained ca. 60% of variation in rainfed yield potential across the 105 SSA locations and the four rootable soil depths (figure 3(b)). Average water stress index (WSI<fnref n="10" />) during this period more than doubled when soil depth decreased from 150 cm (WSI = 0.14) to 50 cm (WSI = 0.30), highlighting the importance of water storage capacity in the root zone to mitigate yield loss in the tropical wet-dry climates that exist in much of SSA with relatively large total annual rainfall.

## The next breadbasket?

Aggregated for all nine countries, total annual maize production capacity decreased from 96 Mt to 76 Mt (21% reduction) when rootable soil depth decreased from 150 cm to 75 cm (figure 4, table S3). This reduction represents maize supply for 214 million people

and is comparable to the difference in projected total annual maize demand in 2050 for the high (95 Mt) and low (78 Mt) fertility variants of the United Nations (U.N.) population scenarios [29] and per capita maize demand projections from the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) [37] (table S4). Using the best available data on soil depth in SSA [16], total annual maize production capacity is estimated to be 6% greater than projected 2050 maize demand of 86 Mt based on the medium U.N. population projection, and suggests an average rootable soil depth, weighted by current maize production area, of about 120 cm. If, however, average rooting depth is 100 cm or 75 cm, then production capacity falls to 97% and 88% of projected demand, respectively (figure 4). And while the 150 cm rooting depth scenario projects an annual surplus of 10 Mt, the magnitude of this projected surplus is considerably smaller than current annual exports of 40 and 16 Mt from the US and Argentina, respectively (5 yr average, 2009–2013). The high degree of sensitivity in SSA maize production capacity to rootable soil depth highlights the need for accurate maps of rootable soil depth of sufficient spatial resolution to evaluate food security scenarios with an acceptable degree of accuracy [38].

<footnote n="10">¹⁰ WSI = 1-(AT/PT), where AT and PT are the simulated water-limited and non-water limited transpiration, respectively.</footnote>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

**Figure 3.** Relationship between rootable soil depth, water stress intensity around flowering (± 10 d), and rainfed yield potential. (a) Effect of rootable soil depth on probability of water stress around flowering. Water stress intensity reflects the water stress index (WSI) as simulated by the Hybrid-Maize model [31,32], which ranges from 0 (no stress) to 1 (maximum stress), indicating the degree to which daily crop water requirements are met by the available water supply from plant-available water reserves in the rootable soil volume and rainfall during the ±10 day period centered on flowering. (b) Relationship between rainfed yield potential and water stress intensity around flowering for different rootable soil depths. Each data point corresponds to the mean rainfed yield potential for a specific location. Grey shadow represents the 95% confidence interval of the linear regression.

<figure><img src="image_11.png" /></figure>

**Figure 4.** Effect of rootable soil depth on attainable total annual maize production on existing maize area in nine countries in Sub-Saharan Africa, which are indicated in dark grey in the maps. Estimates of attainable total maize production are based on 80% of simulated rainfed yield potential for major maize producing climate zones in each country and current maize harvested area. Note that circles areas are not strictly proportional to maize production totals. *Projection of total maize demand in 2050 is based on per capita maize demand projections from the IMPACT model [37] and the United Nations population projections [29] using the medium (in bold) or the low and high (values in parenthesis) fertility variants.
<figure><img src="image_12.png" /></figure>

Given these projections, and the goal of food self-
sufficiency for SSA as proposed by the World Bank
[39], the analysis presented here confirms the need
for substantial acceleration in yield gains and some
expansion of both rainfed and irrigated production area
to meet cereal demand by mid-century [30]. Annual
gain in maize yield of 133 kg ha⁻¹ year⁻¹ is required
to support a yield increase from 1.7 Mg ha⁻¹ in 2015
to 6.3 Mg ha⁻¹ in 2050, which is 80% of the average
rainfed yield potential with 100 cm rootable soil depth
in the studied countries. Although such rapid rates of
gain were achieved for some cereal crops in several
countries during the green revolution in the last half
of the 20th century [35], it would require more than a
four-fold acceleration in the current (2004–2014) rate
of maize yield gain in many of these nine SSA coun-
tries, with the exception of Ethiopia that achieved an
annual gain in maize yield of 160 kg ha⁻¹ yr⁻¹ dur-
ing this recent 10 yr period [27]. And it is notable
these high rates of yield increase occurred in favor-
able environments for rainfed crop production over
a period of several decades (e.g. Argentina and cen-
tral US Corn Belt). Thus, the challenge for SSA is to
increase yields at annual rates similar to those achieved
in environments with favorable climate and deep soils,
but with the disadvantage of having harsher climatic
conditions and reduced water storage capacity in the
root zone.

The need for accurate data on root-zone depth

While estimates of grain production capacity are nec-
essary to evaluate food security scenarios for SSA, they
are not sufficient and must be augmented by analysis of
economic, social, environmental and policy constraints
for effective prioritization of research and develop-
ment investments. The analysis presented here focuses
on rainfed yield potential, and thus on water as the
main limiting factor to crop yields. It assumes that
other limiting factors can be overcome with currently
available technologies, such as fertilizers to overcome
soil fertility constraints, which are recognized as one
of the main factors currently limiting crop yields in
SSA [40, 41], and adoption of improved crop vari-
eties [6]. But overcoming these constraints will still
come up short with respect to attaining food self-
sufficiency if rootable soil depth is less than 100 cm.
Moreover, even with soil depth greater than 100 cm,
the high degree of yield instability adds substantial risk
to investment in these critical inputs. Therefore, esti-
mation of rainfed yield potential and its stability based
on realistic assumptions about rootable soil depth is
crucial to assess whether SSA can become a bread-
basket, self-sufficient, or a major importer of grain,
and also to identify regions with greatest opportuni-
ties for crop intensification. Such insight would better
inform policies and priorities for investment in agri-
cultural research to support agricultural development
and to minimize potential negative impact on habitat
to maintain biodiversity. It would also help identify

current rainfed crop production areas that would ben-
efit most from the development of irrigation, assuming
a sustainable supply of irrigation water is available.
With concerns about climate change and increasing
variability in rainfall amount and timing [42], irrigated
crop production is likely to play an increasingly impor-
tant role, which would contribute to higher and more
stable crop yields [38, 43].

To this end, a key question is how to design a soil
sampling plan for SSA to collect data in areas most rele-
vant to current and future crop production, and where
data are lacking or most uncertain. Global databases
and spatial frameworks such as those developed in the
Global Yield Gap Atlas [25, 26] and AfSIS [16] pro-
vide a point of departure to design such a cost-efficient
sampling plan by identifying the smallest number of
locations required to achieve adequate spatial resolu-
tion to cover current SSA crop production area. As
shown by previous studies, one cannot assume that
other underpinning data used in our analysis, in addi-
tion to soil depth, are free of uncertainty (e.g. see
[44] regarding weather data). Hence, while we rec-
ognize there is room and need for improvement of
the underpinning data [25], the results from our study
utilize the best available data and provide strong evi-
dence that rootable soil depth is a major determinant
of cereal production capacity in SSA and stability of
that production over time due to year-to-year variation
in weather.

Acknowledgments

Support for this research was provided by the Bill and
Melinda Gates Foundation and the Daugherty Water
for Food Global Institute at University of Nebraska-
Lincoln (UNL). We thank other members of the Global
Yield Gap Atlas team who have been involved in
the project: H de Groot and H Boogaard (Wagenin-
gen Environmental Research). We thank the country
agronomists that contributed to the Global Yield
Gap Atlas for their respective countries including: Dr
Korodjouma Ouattara (Institut de l'Environnement et
de Recherches agricoles, Burkina Faso), Dr Mamoutou
Kouressy (Institute of Rural Economy, Mali), Dr
Abdullahi Bala (Federal University of Technology,
Minna, Nigeria), Dr Samuel Adjei-Nsiah (University
of Ghana, Ghana), Dr Kindie Tesfaye (CIMMYT,
Ethiopia), Dr Ochieng Adimo (Jomo Kenyatta Univer-
sity of Agriculture and Technology, Kenya), Dr Joachim
Makoi (Ministry of Agriculture Food Security and
Cooperatives, Tanzania), Dr Kayuki Kaizzi (National
Agriculture Research Laboratories, Uganda), and Dr
Regis Chikowo (University of Zimbabwe, Zimbabwe).

ORCID iDs

Nicolas Guilpart https://orcid.org/0000-0003-3804-0211
<figure><img src="image_13.png" /></figure>

References

[1] Morris M L, Binswanger-Mikhize H P and Byerlee D 2009
*Awakening Africa's Sleeping Giant: Prospects for Commercial
Agriculture in the Guinea Savanah Zone and Beyond* (World
Bank Publications)

[2] Sanchez P A 2010 Tripling crop yields in tropical Africa Nat. Geosci. 3 299–300

[3] Mueller N D et al 2013 Closing yield gaps through nutrient and water management Nature 490 254–7

[4] Nkonya E, Johnson T, Kwon H Y and Kato E 2016
Economics of land degradation in Sub-Saharan Africa
Economics of Land Degradation and Improvement—A Global
Assessment for Sustainable Development (Springer) pp 215–59
(https://doi.org/10.1007/978-3-319-19168-3)

[5] Bourne J K Jr 2014 The next breadbasket Nat. Geogr. 226
46–77

[6] Shiferaw B, Prasanna B M, Hellin J and Bänziger M 2011
Crops that feed the world 6. Past successes and future
challenges to the role played by maize in global food security
Food Sec. 3 307–27

[7] Hartman G L, West E D and Herman T K 2011 Crops that feed the world 2. Soybean-worldwide production, use, and constraints caused by pathogens and pests Food Sec. 3 5–17

[8] Cassman K G 1999 Ecological intensification of cereal
production systems: yield potential, soil quality, and precision
agriculture Proc. Natl Acad. Sci. USA 96 5952–9

[9] Boogaard H, Wolf J, Supit I, Niemeyer S and Van Ittersum M K 2013 A regional implementation of WOFOST for calculating yield gaps of autumn-sown wheat across the European Union Field Crops Res. 143 130–42

[10] Sanchez P A and Buol S W 1975 Soils of the tropics and the
world food crisis Science 188 598–603

[11] Sharma M L and Uehara G 1968 Influence of soil structure in
water relations in a low humic latossol: I. Water retention Soi
Sci. Soc. Am. J. 32 765–70

[12] Sanchez P A 1976 Properties and management of soils in the tropics (New York: Wiley)

[13] Nye P and Greenland D 1960 *The Soil Under Shifting Cultivation* (Harpenden: Commonwealth Bureau of Soils)

[14] Van Wart J, Kersebaum K C, Peng S, Milner M and Cassman K G 2013 Estimating crop yield potential at regional to national scales Field Crops Res. 143 34–43

[15] Dardanelli J L, Bachmeier O A, Sereno R and Gil R 1997
Rooting depth and soil water extraction patterns of different
crops in a silty loam haplustoll Field Crops Res. 54 29–38

[16] Leenaars J G B et al 2015 Root Zone Plant-Available Water Holding Capacity of Sub-Saharan Africa soils, version 1.0. Gridded functional soil information (dataset RZ-PAWHC SSA v. 1.0). *ISRIC Report* 2015/02. Collaboration project of Africa Soil Information Service (AfSIS) and Global Yield Gap and Water Productivity Atlas (GYGA). *ISRIC–World Soil Information, Wageningen, the Netherlands* (www.isric.org/documents/document-type/isric-report-201502-root-zone-plant-available-water-holding-capacity-sub)

[17] Grassini P, Yang H and Cassman K G 2009 Limits to maize
productivity in Western Corn-Belt: a simulation analysis for
fully irrigated and rainfed conditions Agric. Forest Meteorol.
149 1254–65

[18] Passioura J and Angus J 2010 Improving productivity of crops
in water-limited environments Adv. Agron. 106 37–76

[19] Otegui M E and Bonhomme R 1998 Grain yield components
in maize I. Ear growth and kernel set Field Crops Res. 56
247–56

[20] Hall A J, Vilella F, Trapani N and Chimenti C 1982 The effects
of water stress and genotype on the dynamics of
pollen-shedding and silking in maize Field Crops Res. 5 349–63

[21] Andrade F H et al 1999 Kernel number determination in maize Crop Sci. 39 453–9

[22] Van Ittersum M K et al 2013 Yield gap analysis with local to global relevance–a review Field Crops Res. 143 4–17

[23] Meyer L A 2014 Climate change 2014: synthesis report
*Contribution of Working Groups I, II and III to the Fifth*

Assessment Report of the Intergovernmental Panel on Climate
Change Core Writing Team ed R K Pachauri and L A Meyer
(Geneva: IPCC) pp 151

[24] Schlenker W and Lobell D B 2010 Robust negative impacts of climate change on African agriculture Environ. Res. Lett. 5 014010

[25] Grassini P et al 2015 How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis Field Crops Res. 177 49–63

[26] Van Bussel L G J et al 2015 From field to atlas: upscaling of location-specific yield gap estimates Field Crops Res. 177 98–108

[27] Food and Agriculture Organization of the United Nations
FAOSTAT Database Collections (http://faostat.fao.org/
default.aspx) (Accessed: March 2016)

[28] The International Model for Policy Analysis of Agricultural
Commodities and Trade (IMPACT) (http://impact-
model.ifpri.org/#scenario/SSP2_NoCC/map/qlxagg/agg-
subcontinent/maiz) (Accessed: March 2016)

[29] United Nations–Department of Economic and Social Affairs
World Population Prospects, the 2015 Revision (http://esa.un.org/wpp/) (Accessed: March 2016)

[30] Van Ittersum M K et al 2016 Can sub-saharan Africa feed
itself? Proc. Natl Acad Sci. 113 14964–9

[31] Yang H et al 2004 Hybrid-Maize—a maize simulation model that combines two crop modeling approaches Field Crops Res. 87 131–54

[32] Yang H, Grassini P, Cassman K G, Aiken R M and Coyne P I 2017 Improvements to the Hybrid-Maize model for simulating maize in harsh rainfed environments Field Crops Res. 204 180–90

[33] Van Wart J et al 2013 Use of agro-climatic zones to upscale simulated crop yield potential Field Crops Res. 143 44–55

[34] Cassman K G, Dobermann A, Walters D T and Yang H 2003
Meeting cereal demand while protecting natural resources and
improving environmental quality Ann. Rev. Environ. Res. 28
315–58

[35] Grassini P, Eskridge K M and Cassman K G 2013
Distinguishing between yield advances and yield plateaus
in historical crop production trends Nat. Commun. 4
2918

[36] Grassini P, Specht J, Tollenaar M, Ciampitti I and Cassman K G 2014 High-yield maize–soybean cropping systems in the US corn belt Crop Physiology—Applications for Genetic Improvement and Agronomy (Netherlands: Elsevier)

[37] Robinson S et al 2015 *The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT)* (Washington, DC: IFPRI Discussion paper, International Food Policy Research Institute)

[38] Cassman K G and Grassini P 2013 Can there be a green
revolution in Sub-Saharan Africa without large expansion of
irrigated crop production? Glob. Food Secur. 2 203–9

[39] Deininger K and Byerlee D 2011 *Rising Global Interest in Farmland: Can It Yield Sustainable and Equitable Benefits?* (Washington, DC: World Bank Publications)

[40] Sanchez P A 2002 Soil fertility and hunger in Africa Science 295 2019–20

[41] Gonzalez-Dugo V, Durand J-L and Gastal F 2010 Water
deficit and nitrogen nutrition of crops. A review Agron. Sust.
Dev. 30 529–44

[42] Niang I, Ruppel O C, Abdrabo M A, Essel A, Lennard C, Padgham J and Urquhart P 2014 Africa. in: Climate change 2014: impacts, adaptation, and vulnerability. Part B: regional aspects Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change ed V R Barros et al (Cambridge: Cambridge University Press) pp 1199–265

[43] Xie H, You L, Wielgosz B and Ringler C 2014 Estimating the potential for expanding smallholder irrigation in Sub-Saharan Africa Agric. Water Manage. 131 183–93

[44] van Wart J, Grassini P and Cassman K G 2013 Impact of derived global weather data on simulated crop yields Glob. Change Biol. 19 3822–34