metadata: authors: 0: author: Srivastava,Bhavya
1: author: Edochie,Ifeanyi Nzegwu
2: author: Goyal,Aparajita
3: author: Dabalen,Andrew L.
count: Africa
docna: 0: docna: Agriculture Production Potential of
            Groundwater Irrigation in Sub-Saharan Africa
docty: Policy Research Working Paper
abstracts: cdata!: Sub-Saharan Africa&#x2019;s low
            agricultural productivity exacerbates rural poverty. An
            important investment, the sustainable use of groundwater for
            irrigation, has the potential to increase agricultural
            productivity, but the region has been much slower to adopt
            this irrigation method compared to other regions, despite
            abundant reserves. This study uses a simulation-driven
            approach to examine the benefits of sustainably utilizing
            groundwater for irrigation. By mapping data from 291,798
            global agro-ecological zones to 8,099 groundwater grids, the
            study models the production gains from groundwater
            irrigation for rain-fed croplands. Simulation results
            indicate that groundwater access could increase output by
            27.97 to 129.42 percent, contingent on crop and model
            conditions. This research facilitates the assessment of the
            transformative potential of groundwater irrigation and
            identifies areas in Sub-Saharan Africa where investments can
            yield significant returns without depleting the groundwater table.
display_title: Agriculture Production Potential of
            Groundwater Irrigation in Sub-Saharan Africa
listing_relative_url: /research/2024/08/34382181/agriculture-production-potential-groundwater-irrigation-sub-saharan-africa
topicv3: Environmental Sustainability m1325984 1786,Crop Production m1326627 174,Agricultural Policies m1326884 170,Groundwater Irrigation m1504478 3679
publishtoextweb_dt: 2024-08-27T00:00:00Z
dois: 10.1596/1813-9450-10885
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 58
author_keys: 0: author_key: 2212323
1: author_key: 1862778
2: author_key: 784032
3: author_key: 238140
model_extractions: Policy Research Working Paper 10885 Agriculture Production Potential of Groundwater Irrigation in Sub-Saharan Africa _Bhavya Srivastava Ifeanyi N. Edochie Aparajita Goyal Andrew Dabalen_ Africa Region Office of the Chief Economist August 2024
Policy Research Working Paper 10885

# **Abstract**

Sub-Saharan Africa’s low agricultural productivity exacerbates rural poverty. An important investment, the sustainable use of groundwater for irrigation, has the potential to increase agricultural productivity, but the region has been much slower to adopt this irrigation method compared to other regions, despite abundant reserves. This study uses a simulation-driven approach to examine the benefits of sustainably utilizing groundwater for irrigation. By mapping data from 291,798 global agro-ecological zones to 8,099 groundwater grids, the study models the production gains from groundwater irrigation for rain-fed croplands. Simulation results indicate that groundwater access could increase output by 27.97 to 129.42 percent, contingent on crop and model conditions. This research facilitates the assessment of the transformative potential of groundwater irrigation and identifies areas in Sub-Saharan Africa where investments can yield significant returns without depleting the groundwater table.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bs1088@georgetown.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Agriculture Production Potential of Groundwater Irrigation in Sub-Saharan Africa<sup>*</sup>

Bhavya Ifeanyi N. Aparajita Andrew Srivastava Edochie Goyal Dabalen

> * Corresponding author: Bhavya Srivastava, email: bs1088@georgetown.edu. Bhavya Srivastava, Department of Economics, Georgetown University, email: bs1088@georgetown.edu; Ifeanyi N. Edochie, Poverty and Equity Global Practice, World Bank, email: iedochie@worldbank.org; Aparajita Goyal, Africa Chief Economist’s Office, World Bank, email: agoyal3@worldbank.org; Andrew Dabalen, Africa Chief Economist’s Office, World Bank, email: adabalen@worldbank.org. The authors thank Garance Genicot, Vivek Grewal, Reshmaan Hussam, Aude-Sophie Rodella, Lutz Sager, Amal Talbi, Marcus Wijnen, and Esha Zaveri for their valuable comments and feedback. The authors also thank Ashok Anom Dule, Agartha Adubofuor, and Diana Jaganjac for excellent research assistance. Funding from the Africa Chief Economist Office, World Bank is gratefully acknowledged. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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# **1 Introduction**

Pervasive rural poverty in Sub-Saharan Africa is rooted in inadequate agricultural productivity growth (Suri & Udry, 2022). While many developing countries in other regions of the world have effectively raised their agricultural productivity, Sub-Saharan Africa still lags behind. However, enhancing agricultural productivity in Sub-Saharan Africa would not only elevate the incomes of farm households, comprising more than half of the region’s population but also reduce food costs for non-farming populations. This possibility of reduced food costs is especially important because food security remains an ongoing problem for 30% of the region’s population (Pfister, Bayer, Koehler, & Hellweg, 2011). Consequently, greater food security would spur broader economic growth by stimulating demand for non-agricultural goods and services, and high productivity would liberate resources such as labor for expansion toward other economic sectors. Therefore, improving agricultural productivity in Sub-Saharan Africa remains a crucial strategy for alleviating poverty, fostering inclusive growth, and facilitating structural transformation within the region (Goyal & Nash, 2017).

One untapped source of potential agricultural productivity and production growth is the sustainable use of groundwater for irrigation. Groundwater irrigation accounts for 1% of irrigated agricultural land in Africa, compared with 14% in Asia (Altchenko & Villholth, 2015). Although international policymakers may be wary of promoting groundwater utilization, sustainable and moderate usage of Sub-Saharan Africa’s vast groundwater resources could significantly enhance agricultural productivity.

In this paper, we are interested in measuring the potential of groundwater irrigation for agricultural productivity and production gains. To do so, we first provide an overview of Africa’s groundwater endowment and its relative abundance relative to other parts of the world. We then proceed to study the foregone agricultural productivity of African farmers who rely on rain rather than irrigation for their croplands.

The existing literature from Sub-Saharan Africa (henceforth referred to as SSA) documents a substantial increase in crop yields and farm income attributable to the adoption of irrigation technology. Canal irrigation increases farm profits in Rwanda by 43%–62% (Jones, Kondylis, Loeser, & Magruder, 2022). Gravity irrigation through rivers, reservoirs, and dams has also been thoroughly studied. In Ghana, research shows this form of irrigation increases crop yields of vegetables and cereals by 3.5–28.6 tons per hectare (Namara et al., 2011) and crop revenues per hectare per season by 300–1700 USD (Acheampong, Ozor, & Sekyi-Annan, 2014). In Malawi, researchers note a 350% increase in annual Maize production (Concern Universal, 2012) and an increase in agricultural production of 1.25 tons per household Dillon (2011). Further, this irrigation form has an internal rate of return of 14% for vegetable farming in SSA as a whole (Inocencio et al., 2007). More modern technologies such as drip and sprinkler irrigation are associated with a 193–200 kg per hectare increase in Maize yields in Rwanda (Ngango & Hong, 2021).<sup>1</sup>

> 1Refer to Table A2 for a comprehensive summary of the literature on the effects of irrigation on agricultural outcomes in Sub Saharan African countries.

2
However, the literature studying the impacts of groundwater irrigation on agricultural outcomes is scant. In one study in Mali, Burney, Woltering, Burke, Naylor, and Pasternak (2010) find the introduction of solar-powered irrigation pumps is associated with a 1.9 ton increase in the production of crops, which results in a 17% reduction in food insecurity in treatment villages. The scarcity of research is attributable to low rates of groundwaterirrigation adoption in the region. We contribute to filling this gap in the literature by simulating the potential gains in agricultural production through groundwater irrigation across the region.

This study makes three important contributions.

First, we create a novel database for SSA that combines agricultural data with a granularity of 291,798 grids with aquifer data composed of 8,099 grids. This approach allows us to combine information on agricultural production, productivity, and climatic and soil conditions with information on groundwater availability for each agricultural grid.

Second, we provide quantitative evidence on agricultural productivity gains associated with groundwater access. Our simulation framework estimates the gains in agricultural production through groundwater irrigation on existing rain-fed croplands. We reach these estimates by determining the proportion of existing crop water deficits in agricultural grids that can be mitigated through groundwater irrigation. We find the median gains in production range between 27.97% and 129.42% depending on the crop of interest and methodology used to predict crop yield gains.

Third, this is the first study to incorporate sustainability conditions on groundwater extraction. We do so by simulating production gains only in agriculture grids with a sufficiently low ratio of groundwater extraction to recharge. Furthermore, our analysis is confined to calculating gains solely from renewable groundwater sources within the region.

The remainder of this paper is organized as follows. Section 2 provides a brief background on the need for groundwater irrigation in SSA. Section 3 details the data used for the simulation. Section 4 provides statistics on groundwater availability in the region and the current state of agriculture. Section 5 describes the methodology used for the simulation. Section 6 presents and discusses the results. Section 7 conducts several robustness checks. Section 8 discusses groundwater access costs, and section 9 concludes.

# **2 Background**

The majority of the population in SSA resides in rural areas, where the primary source of livelihood is rain-fed agriculture. Extreme weather conditions such as droughts, high temperatures, or delayed rainfall can impose severe hardships, especially for the rural population in agriculture-dependent areas (Khan, Kuate, Pongou, & Zhang, 2024). These hardships are further exacerbated by the lack of adaptive measures to climate change.

However, SSA harbors a largely untapped reserve of groundwater aquifers, which could mitigate the effects of adverse rainfall events such as droughts. A report suggests 3
countries in this region could supply 130 liters of drinking water per person per day from groundwater aquifers, without exceeding 25% of the long-term average recharge, with most using less than 10% (WaterAid, 2022).

In places experiencing groundwater stress, access to shallow groundwater aquifers could enable farmers to irrigate their fields for up to two drought years. Nonetheless, the extraction rate is only 1%–25% of the existing renewable groundwater resources (WaterAid, 2022). Therefore, unlike in India, where groundwater irrigation led to a significant decline in resources, tapping into groundwater could be a sustainable choice for SSA, provided that measures are taken early in the process to prevent its over-exploitation.

Although water scarcity is a growing issue in SSA, our analysis shows a large, mostly untapped abundance of groundwater resources. The main challenge lies in a lack of knowledge and initiative to utilize these groundwater resources, as well as a policy framework for preventing over-extraction. The cost of accessing groundwater may also present a barrier for Africans, given the region’s average GDP per capita of 1700 USD.

To address this issue, our analysis focuses on local shallow groundwater aquifers, which are abundant and relatively easier to access, due to their proximity to the earth’s surface. SSA has the highest share of local shallow aquifers (out of its total endowment) globally, comprising 60.77%, closely followed by South Asia at 54.54%.

# **2.1 Groundwater Aquifers**

Groundwater is an integral part of meeting the global demand for water. Estimates suggest nearly 50% of drinking, 40% of irrigation, and over 30% of industrial global water demands are currently sourced from groundwater (UNESCO and UN-Water, 2022); (Margat & van der Gun, 2013).

Aquifers, or groundwater aquifers, store precipitation that seeps through the soil. The aquifer itself is the rock or sediment that collects and holds groundwater in empty spaces underground. Some of these aquifers are easy to access, whereas others lie under rocks that are harder to cut through.

Following the definitions of the Water Global Practice at the World Bank, we broadly have four aquifer types that we use in our analysis. These categories are based on various geological and hydrogeological characteristics of aquifers. The four categories of aquifers (or aquifer systems) are complex aquifers, karstic aquifers, major alluvial aquifers, and shallow/local aquifers.

A complex aquifer consists of several interconnected layers of permeable and impermeable materials. These types of aquifers can have a high storage capacity and can provide a reliable source of water for agriculture. However, the complexity of the aquifer can make efficiently managing and extracting water difficult. A karstic aquifer is formed in soluble rock such as limestone or dolomite and can have many cracks, fissures, and caves that allow the rapid movement of water. These types of aquifers can be suitable for agriculture because they can provide a steady supply of water. However, the rapid movement of water through the aquifer can also lead to contamination from pollutants. An alluvial aquifer 4
is formed from the accumulation of sediment deposited by rivers or streams. These types of aquifers can have a high storage capacity and can provide a reliable source of water for irrigation and crop production. They are often found in floodplains or along the course of major rivers.

The local shallow aquifer is typically found near the surface<sup>2</sup> (Figure 2b) and can be replenished relatively quickly by local precipitation or surface-water sources (Figure 2a). These aquifers may be suitable for small-scale irrigation and crop production. Because around 70% of African farmers practice subsistence agriculture, their abundance (Figure 3) and relatively lower costs of extraction make these aquifers most suited for farming. Therefore, we will restrict our analysis to local shallow aquifers.

# **3 Data**

## **3.1 Groundwater Data**

We employ a novel aquifer-typology dataset compiled by Rodella, Zaveri, and Bertone (2022). It consists of a spatially exhaustive list of 8,099 aquifer grids in SSA, along with information on the surface area they cover, the volume of water they hold, and their aquifer type. This dataset is up to date as of 2018.

This dataset contains information on the four classes of aquifers discussed in the Background section. The data are collected at the grid level (0.5 degree fishnet). For each grid, the data contain information on what proportion of the grid is major alluvial, complex, karstic, and local shallow, as well as the percentage of the grid cell that is not covered by the data for typology classification. This dataset does not contain information on replenishment (or recharge) rates of aquifers.

In this analysis, we combine the global aquifer dataset provided by Rodella et al. (2022) to the official World Bank administrative boundaries (with a special focus on SSA). The groundwater data contain the geographic centroids of each aquifer. We assign each aquifer to the country containing its centroid. To ascertain the exact shape, we use Rodella et al. (2022) to obtain the shapefiles of the major aquifers. For aquifers whose shapefiles we were unable to find, we construct circular buffers around these centroids such that the area of the circle matches the surface area of the aquifer.

We estimate aquifer statistics including the area of land covered by the aquifers, the volume of water in absolute terms, per capita, and as a proportion of the entire land area of the country to get a sense of which countries have the richest resources of shallow ground water.

We complement the Rodella et al. (2022) dataset with data from the Africa Groundwater Atlas, a dataset curated by the British Geological Survey. The Africa Groundwater

> 2In SSA, 70% of aquifers lie at a depth of 50 meters or less, 20% lie at a depth less than 25 meters, and 30% lie at a depth of less than 7 meters. This finding suggests groundwater extraction using dug wells is possible in the region, allowing for access to groundwater at lower costs.

5
Atlas provides aquifer productivity<sup>3</sup> and aquifer-type information from 51 African countries. Additionally, it has information on the recharge rates of groundwater, as well as groundwater depth maps across the African continent.

We merge the groundwater datasets with the Global Agro-Ecological Zones (GAEZ) dataset to impute water-supply information at the GAEZ grid level.

# **3.2 Agricultural Productivity**

We use the GAEZ dataset to measure agricultural productivity in SSA. The GAEZ dataset is curated by FAO and IIASA (2020) and provides information on agricultural productivity and estimates production gaps across the world for the years 2000 and 2010 at a granular level of approximately a 9x9 kilometer (km) grid. For our analysis, we consider the data for 2010 only, because they are the latest data.<sup>4</sup> The data are also disaggregated by crop type and irrigation type: rain-fed or irrigated using water from sources such as rivers, lakes, and/or groundwater. We have data on 291,798 GAEZ grids, and our subsequent analysis is conducted at the GAEZ grid level.

In addition to productivity data, the GAEZ dataset offers insights into agro-climatic conditions such as soil conditions, temperature, precipitation during the agricultural growing season, water availability, and land type in the area. We focused our study on croplands to determine whether shifting toward groundwater irrigation could lead to significant gains in agricultural productivity. To answer this question, we examined yield and potential yield at the crop–grid cell–year level to identify productivity gaps by irrigation type.

We restrict our analysis to cereals, namely, barley, millet, maize, rice, sorghum, and wheat. We focus on cereals for our analysis, because they are the primary source of energy for most households and the demand for cereals is expected to grow in the coming decades (OECD, 2016).

Table 1 provides descriptive statistics for the GAEZ dataset, showing that despite the average harvested area of rain-fed croplands being higher than that of irrigated croplands (100 hectares per grid cell versus 20 hectares per grid cell), the mean production and yield of irrigated croplands are relatively higher. This finding suggests production and productivity gains are possible through irrigation, which is also reflected in larger production gaps for rain-fed croplands than for irrigated croplands.

> 3Aquifer productivity, measured in liters per second, refers to the ability of an aquifer to sustain a certain level of groundwater extraction through a well or a bore. It can be thought of as the water yielded from a well or bore that sits on a certain aquifer.

> 4Note the irrigation landscapes of 2000 and 2010 are very similar for SSA, and thus, we opt to work with cross-sectional data for the latest year, which has a slightly higher rate of irrigation.

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## **3.3 Other Data**

### **3.3.1 Night Lights Data**

We employ night light luminosity (NTL) data constructed by Elvidge, Zhizhin, Ghosh, Hsu, and Taneja (2021) as a proxy for electrification. More specifically, we use the average VIIRS (Visible Infrared Imaging Radiometer Suite) annual cloud-free composite VNL version 2 for the year 2015. The resolution of these data is 15 arc seconds (approximately 0.0327 km around the Equator). Thus, after extracting it for SSA, we aggregate the NTL measure at the level of each GAEZ grid.

### **3.3.2 Water-Usage Data**

Large variations exist in water-usage patterns within countries. We employ annual countrylevel freshwater-withdrawals data for agriculture, industry, and municipal (i.e., domestic) use.<sup>5</sup> Annual freshwater withdrawals refer to total water withdrawals, not including water loss due to evaporation from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Using this data, we calibrate country-level thresholds needed for crop irrigation purposes. By subtracting the freshwater needed for sanitation and industrial production purposes from the total freshwater consumption of the country, the thresholds represent the share of freshwater that is available for irrigation purposes. The agricultural water withdrawals is collected at the country level. As a result, we are unable to account for within-country variation in access to water for sanitation.

### **3.3.3 TerraClimate Data**

To incorporate climatic factors into our yield estimations, we utilize the dataset curated by Abatzoglou, Dobrowski, Parks, and Hegewisch (2018). This dataset provides monthly climatic information at a spatial resolution of approximately 4 km (1/24th degree). For each of the GAEZ grids, we extract data on mean monthly temperature, total monthly precipitation, and monthly Palmer Drought Severity Index (PDSI) for 2010. We then calculate the quarterly averages of the temperature and PDSI, as well as the quarterly sums of the total precipitation. Additionally, we consider the squared values of the mean quarterly temperature and the total quarterly precipitation.

# **4 Descriptive Statistics**

## **4.1 Groundwater Access and Availability in SSA**

The first exercise we undertake is to map the existing groundwater availability in SSA.

> 5For access to the data, refer to: https://ourworldindata.org/water-use-stress#freshwater -withdrawals-by-country.

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As discussed earlier, our aquifer dataset consists of four types of aquifers. In our simulations, we focus on groundwater supply through local shallow aquifers, due to their lower costs and relatively higher recharge rates than other aquifer types.

In Table 2, we present the per-capita concentration of local shallow water aquifers in the African context compared with other aquifer types. SSA has the highest share of local shallow aquifers among all regions worldwide (see Figure 1).

Furthermore, we observe that grids predominantly composed of local shallow aquifers are distributed across the region (see Figure 3). Comparing these areas with recharge rates (see Figure 2a) reveals that regions with a high share of local shallow aquifers also coincide with high recharge rates, supporting the earlier observation of sustainable recharge in these aquifers. Similarly, comparing groundwater depths (see Figure 2b), we find that regions with low groundwater depth align with areas predominantly containing local shallow aquifers.

One potential concern regarding local shallow aquifers is their lower productivity relative to major alluvial aquifers, which may make them unsuitable for irrigation purposes. Therefore, we map aquifer productivity across SSA (see Figure 2c) and find only a few regions have aquifer productivity below low to moderate levels. This observation suggests the aquifers in the region can, at the very least, support small-scale irrigation.

Given the availability of local shallow aquifers and their ease of access, the possibility of using them sustainably for irrigation presents an opportunity for African farmers.

## **4.2 Agriculture in Sub-Saharan Africa**

This paper aims to explore the potential increase in agricultural production and productivity that can be achieved through groundwater irrigation. Currently, only 6.4% of the GAEZ grids have irrigated croplands. However, by examining the descriptive statistics presented in Figure 4, we can observe that although the harvest area of irrigated grids has remained constant over the years, the productivity, measured in terms of crop yields, of these irrigated croplands is nearly three times higher, on average, than that of rain-fed croplands.

To conduct our analysis, we focus on cereals in the year 2010. Figure 5 illustrates the distribution of existing rain-fed croplands in green, irrigated croplands in blue, and regions where cereals are not grown in grey. Our analysis primarily investigates the potential increase in production that can be achieved by implementing groundwater irrigation in the existing croplands.

# **5 Methodology**

Given the minimal adoption of groundwater irrigation in SSA, we undertake a simulation approach to our analysis, where we quantify the potential gains in agricultural production and productivity achievable through access to groundwater irrigation.

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To do so, we analyze GAEZ grids that are (partially or fully) rain-fed croplands. Our objective is to assess whether groundwater irrigation can compensate for crop water deficits experienced within these GAEZ grids for rain-fed cultivation. Thus, note that in this paper, we do not shift the cropping landscape, but just change the irrigation landscape of SSA. We simulate the production gains by assuming that with adequate water supply, the yield of rain-fed croplands would match that of irrigated croplands either within the same grid, or of irrigated croplands from grids that are either neighbors or have similar geographic and climatic conditions. In other words, we assume that holding geographic and demographic factors constant, the gap in yields of rainfed and irrigated croplands is explained by water use.

We restrict our sample to the year 2010 because the data from that year are the latest made available to us.

# **5.1 Yield Difference**

We start by estimating the yield difference (YLD) for crop _c_ in grid _g_ between irrigated and rain-fed croplands. In other words, this estimation represents the change in productivity that is attributable to irrigating the land. We follow two methods to arrive at our yield-difference measure: (1) using a simple difference between crop yields of irrigated croplands and crop yields of rain-fed croplands, and (2) employing a Structural Ricardian model using Heckman selection (SRHS) to obtain predicted differences in yields by allowing irrigation choice to be endogenous.

## **5.1.1 Simple Difference (SD)**

In this approach, we consider the difference in crop yields of irrigated and rain-fed croplands. This approach is valid under the assumption that irrigated and rain-fed croplands are comparable within the same GAEZ grid cell given that the same controls apply, enabling a ceteris paribus evaluation of productivity gains.<sup>6</sup> Two cases are possible under the SD approach.

1. The GAEZ grid _g_ has both rain-fed and irrigated croplands for crop _c_ . In this case, the yield difference is calculated following equation (1).

Here, _Ycg_<sup>_D_and</sup><sup>_Ycg_are the crop yields of the grids that only consist of rain-fed crop-</sup> lands and the grids that adopted some level of irrigation practices, respectively.

2. The grid cell exclusively comprises rain-fed croplands for crop _c_ . In this case, the 6Note we cannot spatially identify which portion of the grid is rain-fed versus irrigated; this means all the information that we have on controls, such as soil conditions, other inputs in agricultural production, and so on, is at the GAEZ grid level.

9
yield difference is calculated following equation (2).

Here, _Y_<sup> ̄</sup> refers to the mean yield of irrigated croplands that are either the immediate neighbors, neighbors within 25 kms, or the national average.

Because, for a majority of the grid cells, we had to use equation (2), we have difficulty justifying the validity of the assumption regarding ceteris paribus comparisons of rain-fed and irrigated croplands. Thus, we turn to the second method.

# **5.1.2 Structural Ricardian Model Using Heckman Selection**

Another way to estimate yield differences is to run regression models of irrigation choice on agricultural productivity. However, evidence suggests irrigation choice is non-random and endogenous, and therefore, running a simple ordinary least squares model with controls would result in biased estimates. As Table 3 shows, grids with some form of irrigation differ from those comprising only rain-fed croplands. Notable distinctions include higher crop water deficits in irrigated croplands and their prevalence in rain-deficient terrains at higher elevations than rain-fed croplands. Therefore, farmers from specific regions and with specific characteristics may choose to follow irrigation practices for their croplands, necessitating tackling this issue to ensure unbiased estimates.

A Heckman selection model has been used in the literature to address the selection into the choice to irrigate. Mendelsohn, Nordhaus, and Shaw (1994) were the first to apply a Structural Ricardian model to study the effects of climate change on agricultural revenue. This model, keeping factor prices fixed, estimates the impacts of climate and other inputs on net agricultural revenue. To address the irrigation choice being endogenous, Kurukulasuriya, Kala, and Mendelsohn (2011) extended the model to account for Heckman selection. We follow Kurukulasuriya et al. (2011) to estimate irrigation adoption (I equals 1 when the grid has any irrigation and 0 when it only has rain-fed croplands) for each crop separately in the first stage, as specified in equation (3).

Here, **X** includes climatic factors as well other factors, such as the slope of the terrain and soil moisture. We also control for the country and first administrative division that the grid lies in. To avoid arbitrary selection of controls, we apply a LASSO model for variable selection from a set of candidate indicators. Whereas most of our controls are at the grid level, we include the harvest area of irrigated croplands at the crop-grid level.

In the second stage, we estimate conditional yield functions for grids that were irrigated versus not. For all available exogenous variables **Z** and **Z**<sup>_D_</sup> (where **Z** , **Z**<sup>_D_</sup> _⊂_ **X** ), we estimate equations (4) and (5). Additionally, note that **Z** and **Z**<sup>_D_</sup> include the inverse mills ratio.

10
_Yg_ = **_Zg_** _∗ γ_ + _μ_ (2 _,g_ ) if _Ig_ = 1 (4) Here, _I_ is the latent variable determining the irrigation status of the grid, and as before, _Yg_<sup>_D_</sup> and _Yg_ are the crop yields of the grids that only comprise of rain-fed croplands and the grids that adopted some level of irrigation practices, respectively. We allow the error terms to be correlated, such that _μ_ (1 _,g_ ) and _μ_ (2 _,g_ ) and _μ_ (1 _,g_ ) and _μ_ (3 _,g_ ) are jointly normally distributed independently of **X** and **Z** with an expectation of 0. This approach is consistent with the model proposed by Heckman (1979).

Put simply, this model first predicts the exogenous factors that influence irrigation adoption, and then partitions the data, for each crop, into grids that had any irrigation adoption ( _I_ = 1) and grids that did not have any irrigation adoption ( _I_ = 0). For both, we separately estimate the yield gains given the existing set of climatic controls, while allowing for the errors in the first stage and each equation of the second stage to be correlated, because climatic controls drive the selection into irrigation choice, which is then associated with crop yield.

Note that unlike the model proposed by Kurukulasuriya et al. (2011), our model is at the grid level instead of the farm level. The primary limitation of this exercise is that in our data, irrigation adoption is a fraction rather than a dummy; however, we restrict our irrigation adoption variable to either take the value of 0 or 1. Thus, despite having more information on the level (or share) of irrigation adoption within the grid, our model uses a more simplified approach to follow the Heckman framework. While using farm-level data, Kurukulasuriya et al. (2011) have a binary irrigation-adoption variable and therefore are not forced to restrict their data in any way. We run our model for each of the crops separately.

Because we are interested in using these estimates to calculate yield differences between irrigated and rain-fed croplands, we use the explained part of the model to obtain predicted conditional crop yield values and then compute the mean difference (YLD) at the “Administrative Level 1”,<sup>7</sup> as shown in equation (6).

> 7Administrative Level 1 refers to the largest subnational administrative unit of a country. It usually comprises of states or provinces, depending on the country.

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Here, _a_ ( _g_ ) represents the ”Administrative Level 1” that the grid is mapped to. _Y_ ˆ and _Y_ ˆ _D_ are the admin 1 level averages of predicted _Y_<sup>ˆ</sup> and _Y_<sup>ˆ</sup><sup>_D_</sup> , respectively.

# **5.2 Simulating Production Gains**

We then use the YLD obtained from each of the two methods described above to simulate production gains.

However, we only calculate these production gains under three key conditions. First, the mean aquifer productivity or yield (= _η_ ) for the GAEZ grid must be at or above the low to moderate threshold. Adequate aquifer productivity is essential for fulfilling irrigation needs, and this condition ensures its consideration. Second, the ratio of groundwater extraction to recharge, _ν_ , must remain below 70%.<sup>8</sup> This condition is crucial to ensure gains are computed only for regions with sustainable access to groundwater, addressing concerns regarding current or future over-exploitation of this resource. To set the threshold, we rely on the guidelines set in Chapter 22.2.1 by the Ground Water Resource Estimation Committee (2015), Government of India. Third, to account for ease in accessibility of groundwater, we consider aquifers that have a depth of 7 meters or less ( _ζ_ ). For more details on the parameters used, refer to Appendix 9. We summarize all the parameters used in Table A1.

We calculate gains in production using equation (7).

Here, _QGA_ is the production gain for crop _c_ in GAEZ grid cell _g_ . _HAR_ is the harvest area of rain-fed croplands, and _Y LD_ refers to the yield difference between irrigated croplands and rain-fed croplands. _Share of Irrigable Area_ denotes the fraction of the rain-fed area within the grid where the crop’s water demand can be sufficiently met with the crop’s water supply.<sup>9</sup> It equals one if the crop’s water supply is greater than or equal to the crop’s water demand; otherwise, it is set as the crop’s water demand divided by the crop’s water supply. This variable can be thought of as a discount on the achievable production gain, where we scale down the gain in production to consider only the share of the grid where groundwater availability can fill in existing crop water deficits or the extent to which the existing crop water deficits can be filled using groundwater.

## **5.2.1 Estimating Water Supply**

We overlay water-volume gridded data with gridded agricultural data (referred to as GAEZ grids). Subsequently, we estimate the volume of local shallow groundwater in each GAEZ grid _g_ using equation (8).

> 8To compute this ratio, we divided the total water use by the mean recharge rate of each grid. For each country, we divided total water use across grids within the country weighted by the GAEZ grid’s area and multiplied by, _φ_ , the share of water use that is attributable to groundwater use (=67.5%) (Rodella et al., 2022)).

> 9In the water-supply calculations, we only consider groundwater availability after accounting for sanitation needs.

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Here, _V LS_ refers to the volume of local shallow groundwater in m<sup>3</sup> that is available in GAEZ grid cell _g_ . _q_ ranges from 1 to _N_ , representing the set of aquifer grids intersecting with GAEZ grid _g_ . _V GW_ is the total volume of groundwater across all aquifer types available in aquifer grid _q_ . We multiply _V GW_ by the share of the aquifer area that intersects with GAEZ grid _g_ and the share of groundwater that is attributable to local shallow aquifers, _PoLS_ . We then multiply the total volume of local shallow water by (1 - _SDU_ ), where _SDU_ is the share of groundwater needed for sanitation and drinking usage.

To estimate a crop’s potential share of local shallow groundwater, we multiply _V LS_ by the share of the crop’s harvest area relative to the grid’s total area.

## **5.2.2 Estimating Water Demand**

To estimate the water demand for each crop in each grid, we sum the evapotranspiration of the crop and the crop’s water deficit during the growing season. For grids where either of these variables is missing, we impute the crop water demand or crop’s water requirement based on the mean crop water demand of immediate neighbors, neighbors within 25 kms of the grid’s center, or the national average crop water requirement for that crop.

# **5.3 Measuring Income Gains**

Understanding the income potential of the simulated gains in production is important for agricultural policy engagements with governments in Africa. To estimate income gains, we simply apply producer prices to GAEZ grids with estimated production gains and then compare the total gains to the 1.90 USD poverty line. We employ a back-of-the-envelope approach as described below.

We first source geo-located food product price data (Development Data Group, 2021) via the World Bank Micro-data Library API. Our API query returns local currency (nominal) prices for all African countries with production gains for each crop and each market between 2007 and 2015 ( _πcm_<sup>_nom_).Thisallowsustoincreasethenumberofmarket-level</sup> prices available within each country and smooth over sudden price shocks. We apply the corresponding national monthly consumer price index (CPI) and PPP conversion factors from the World Bank’s Poverty and Inequality Platform (PIP) (Aron et al., 2023) to convert to real monthly average prices in 2010 USD for each crop in each market.

We then perform a spatial merge of the grids with production gains to the nearest market from the food producer price database. Consequently, the prices that apply to specific crop production gains in each grid are those from the nearest markets in the food 13
price database. Coincidentally, the size of the GAEZ grids and the sparsity of the market geo-location data prevent us from finding multiple markets within any grids.

Finally, we compute expected per capita income gains by taking the product of crop prices and the simulated production gains for each crop-grid. We aggregate this for all grids ( _G_ ) within a country to estimate an expected change in income for individuals at the poverty line. That is, for a representative farmer at the 1.90 USD extreme poverty line (2011 ppp) within a specific country, we compute the expected percentage income growth ( _E_ ( _θ_ )) from all crops combined.

Here, _AEPL_ represents the annualized extreme poverty line (i.e., 1.90 * 365 USD), and therefore _E_ ( _δθc_ ) represents the expected change in income for farmers around the poverty line from adopting groundwater irrigation to reduce the crop water deficit.

# **6 Results**

Table 4 presents the simulation results we obtained using the two different approaches— SD and SRHS—when all three conditions are met. Each row in the tables corresponds to a unique combination of model and crop choice.

The mean gains in production are much higher than the median gains, suggesting the distribution of production gains is right-skewed. Therefore, we use median production gains to discuss our results.

Using the two approaches, our simulated median gains in production for different crops range between 29.57% and 33.28% for barley, 76.35% for millet, 107.96 and 123.26% for maize, 108.09% and 114.64% for rice, 89.49% and 129.42% for sorghum, and 27.97% and 53.08% for wheat. Refer to the map of production gains in Figure 6 and Figure 7 for the SD and SRHS approaches, respectively.

Note we were not able to run simulations for millet using the SRHS approach, due to very few grids being irrigated. With the exception of barley, the simulated gains using SRHS are higher than when using the SD approach. These results suggest large production gains can be realized through groundwater irrigation in SSA, especially in the production of maize and rice. For a country-wise overview of our results, refer to the median production gains by country in Figure 9 and Figure 11 for maize and rice, respectively. For the results using the SRHS approach, refer to Figure 8 and Figure 10 for maize and rice, respectively.

Furthermore, note that 24.91% of all GAEZ grids are existing croplands. Among these croplands, 21.96% satisfy all three conditions considered in our simulations. Since we do lose a substantial portion of grids by imposing a depth constraint of 7m, we check for 14
the robustness of our results by considering a depth of 50m. This increases the fraction of croplands satisfying our conditions to 62.05%; the high fraction of croplands satisfying our conditions provides further evidence for the feasibility and potential for sustainable groundwater irrigation in Sub-Saharan Africa. Our results remain consistent with this change in the depth constraint (refer to Table A3. Refer to the map of production gains in Figure A1 and Figure A2 for the SD and SRHS approaches, respectively. Figure A4 and Figure A6 plot the country-wise gains for maize and rice, respectively, using the SD approach. For the SRHS approach results, refer to Figure A3 and Figure A5.

Our findings align with those in existing literature. For the Indian context, Duflo and Pande (2007) found surface water irrigation boosts cereal crop production by 34%-53%. Similarly, in Rwanda, Jones et al. (2022) observed a 43%-62% increase in annual cash profits. Dyer and Shapiro (2023) also reported a 41% increase in net revenues from fruits and vegetables through irrigation pumps in Kenya. Our results for most of the crops fall within the range of gains reported in these studies, but vary by crop type.

## **6.1 Income Gains**

Groundwater irrigation is one of several input factors that are determinants of agricultural productivity. Small to modest simulated income growth should be expected from filling these crop water deficits. Our modelling approach does not account for the possibility of water transportation arrangements from areas with low water deficits to areas with large water deficits. Consequently, we simulate expected income growth (over the annualized 1.90 USD International Poverty Line) ranging from 0% to 3.1% across African countries with simulated productivity gains.

The gains appear to be highest in the Sahel countries with Togo (3.1%), Benin (2.4%) Guinea (2.4%), and Nigeria (1.0%). These results are driven by large deposits of available groundwater in areas where crop water deficits exist and are thereby correlated with pre-existing low levels of crop production. It should also be noted that because we are unable to estimate the number of farmers in these specific areas, we use World Bank national estimates of number of people in agriculture. Hence, our results are likely quite conservative in terms of per-farmer earnings gains corresponding to filling in existing crop water deficits.

# **7 Robustness Checks**

## **7.1 Considering a Different Combination of Conditions**

We test the robustness of our results by relaxing each of the three conditions. Table 5 and Table 6 present the simulation results we obtained using SD and SRHS, respectively. Each row in the tables corresponds to a unique combination of conditions that were imposed while running the simulations. The first row for each crop corresponds to the results in Table 4 where all three conditions hold.

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Using the SD approach, our simulated median gains in production for different crops range between 29.57% and 47.96% for barley, 71.87% and 76.35% for millet, 89.38% and 107.99% for maize, 105.64% and 108.09% for rice, 59.99% and 89.49% for sorghum, and 27.97% and 38.96% for wheat. The corresponding gains using the SRHS approach are between 33.28% and 41.87% for barley, 106.64% and 123.26% for maize, 112.56% and 114.64% for rice, 93.38% and 129.42% for sorghum, and 53.08% and 60.11% for wheat. Our results are more or less consistent despite considering a combination of conditions imposed.

# **7.2 Considering a Different Dataset for Water Supply**

Our groundwater supply measure comes from Rodella et al. (2022). We test if our results remain robust to using a different dataset for water supply, especially because groundwater volume computations tend to be probabilistic in nature. We use the Groundwater Storage Map in the digital groundwater maps of Africa<sup>10</sup> collected by MacDonald, Bonsor, ́O Dochartaigh, and Taylor (2012) to obtain a measure of groundwater storage.

In our main simulations, we rely on the volumes of groundwater associated with the local shallow groundwater aquifer classification. However, the classification system we used—including the “local shallow aquifer” classification—is not consistent with hydrogeological classifications of groundwater aquifers. Thus, we consider the subset of aquifers that closely resemble the characteristics of local shallow aquifers. These aquifers include the following:

- Unconsolidated Sedimentary (U): These aquifers consist of loose sediments, such as sand and gravel, which allow water to flow more easily.

- Consolidated Sedimentary Fracture (CSF): These aquifers are composed of consolidated sedimentary rocks that have fractures or cracks, which enhance water storage and flow.

- Consolidated Sedimentary Intergranular (CSI): These aquifers are made up of consolidated sedimentary rocks with interconnected pore spaces, allowing water to move through the rock matrix.

- Consolidated Sedimentary Intergranular / Fracture (CSIF): These aquifers are a combination of consolidated sedimentary rocks with both intergranular pore spaces and fractures, providing multiple pathways for water movement.

- Basement (B): These aquifers are located in the underlying bedrock, typically consisting of hard and fractured rocks that can store and transmit water.

These hydrogeological aquifer types are considered closest to the definition of local shallow aquifers because they share similar characteristics, such as their geological composition and hydrogeological properties, which make them suitable for groundwater storage and extraction in the context of the study.

> 10See https://www2.bgs.ac.uk/groundwater/international/africangroundwater/ mapsDownload.html.

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To construct this measure of water supply, we first consider the modal aquifer type associated with each BGS storage grid. We then overlap the GAEZ grids with BGS storage grids. Because each grid can be mapped to multiple aquifers, we sum the volume for the BGS storage grids mapped to each GAEZ grid that has a (modal) aquifer type that is either U, CSF, CSI, CSIF, or B.

Our results using this measure of water supply can be found in Table 8 and Table 9 for the SD and SRHS approaches, respectively.

Using the SD approach, our simulated median gains in production for different crops range between 85.47% and 92.95% for barley, 59.57% and 75.11% for millet, 112.38% and 125.64% for maize, 124.41% to 127.22% for rice, 88.25% and 100.49% for sorghum, and 45.92% and 53.93% for wheat. The corresponding gains using the SRHS approach are between 52.87% and 68.80% for barley, 148.02% and 158.14% for maize, 126.88% and 129.00% for rice, 148.48% and 170.86% for sorghum, and 71.02% and 80.75% for wheat. These results are much higher than those found using the data from Rodella et al. (2022). We believe these differences arise due to the mapping of local shallow aquifers not being perfect between the two datasets. However, because our main results are on the conservative side, we believe we can safely say large gains in production are possible through groundwater irrigation.

# **8 Discussion on Costs**

We have so far discussed the potential for increased production through groundwater irrigation. However, its high initial cost can be a barrier to adoption, which might explain the low prevalence of this method among farmers who are credit constrained.

The cost of accessing groundwater is twofold: the fixed cost of groundwater exploration and well construction, and the variable cost of regular operation and maintenance. Fixed costs cover the detection of groundwater resources, water-quality testing, transportation of materials, pump purchase and drilling costs. Variable costs refer to the frequent operational and maintenance costs of running groundwater pumps. Given the substantial investment, some of the benefits previously discussed may not be realized. One approach to mitigating the fixed costs of groundwater irrigation involves the implementation of treadle pumps, capable of accessing groundwater up to 7 meters deep (Burney, Naylor, & Postel, 2013).

In Sub-Saharan Africa (SSA), Burney et al. (2013) note that treadle pumps are priced between USD 100 and USD 300, significantly lower than earlier estimates by Xenarios and Pavelic (2013). Furthermore, Giordano, de Fraiture, Weight, and van der Bliek (2012) suggest that introducing treadle pumps in SSA could yield annual net revenues of up to USD 22 billion and benefit 185 million individuals. Therefore, to consider gains that can be cost effective, our analysis focuses on local shallow aquifers that are less than 7 meters deep and therefore easier and cheaper to access. A recent study found irrigation pumps for pumping from surface water sources pay for themselves within three years in 17
Kenya (Dyer & Shapiro, 2023). Generalizing this finding to the context of groundwater irrigation pumps, the study suggests that although high costs may dissuade farmers from using groundwater irrigation, interventions providing information about the costs and benefits of these technologies could enhance productivity through increased adoption. It must be noted, however, that the costs substantially increase beyond 8 m groundwater depth (Sekhri, 2014). Research on groundwater costs across 10 SSA countries indicates the total cost for a pump with a depth of 40-100 meters ranges between USD 6,000 and USD 23,200, on average (Xenarios & Pavelic, 2013). With the expansion of input markets in SSA, these costs could have been substantially reduced over the past decade, and that is why we present some estimates on gains from groundwater access up to 50 m deep in our Appendix.

Another constraint to groundwater access stems from the lack of access to the electricity grid. A proposed solution that reduces the variable cost of electricity is the provision of photovoltaic- (or solar-) powered drip irrigation pumps. Although these pumps may help reduce the variable cost, regulation of their use is crucial to prevent over-exploitation of groundwater resources. Finally, subsidizing the fixed costs of groundwater irrigation technologies (while regulating sustainable use) is another avenue of inquiry for policymakers and future research.

# **9 Conclusions**

Rural poverty in Sub-Saharan Africa is closely linked to the lack of substantial growth in agricultural productivity. Enhancing agriculture productivity could stimulate wider economic growth, promote inclusive development, structural transformation, and poverty reduction. Agricultural productivity growth worldwide has been driven by investments in high return rural public goods, improved policies, and institutions. However, Sub-Saharan Africa significantly under-invests in these areas. This paper explores one untapped growth source—the sustainable use of groundwater for irrigation. We measure potential agricultural production gains in Sub-Saharan Africa through groundwater irrigation.

To do so, we construct a novel dataset by merging gridded agricultural data with local shallow aquifer data and their corresponding details. We then use our dataset to simulate the gains in production that can be achieved if we use groundwater irrigation to fill in the existing crop water deficits that persist in the region’s rain-fed croplands. While running our simulations, we impose conditions during our simulations to only compute gains in regions with sufficient renewable groundwater. We also limit our analysis to aquifers suitable for small-scale irrigation and accessible at low depths to ensure cost effectiveness.

Our findings show the median production gains range from 27.97% to 129.42%, depending on the crop type and the method used to predict crop yield gains. The most pronounced gains are for maize and rice crops. Our results remain consistent, even with changes in the method and dataset used to measure groundwater availability. Most importantly, our analysis enables policymakers to delineate regions likely to experience agricul18
tural production gains through groundwater irrigation. Knowledge of where agricultural production gains can be realized could facilitate strategic investment decisions regarding the deployment of irrigation technology in specific regions.

One limitation of our study is that we were unable to causally estimate production gains due to the low rates of groundwater irrigation adoption in the region, as captured in household surveys, which would have helped us obtain more precise estimates of the net benefits of groundwater irrigation. We leave this exercise for future research work.

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24
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25
# **Tables**

Table 1: Summary Statistics for Sub-Saharan Africa

|Variable|Irrigated<br>Cropland|Rain-fed<br>Cropland|Difference|p-value|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Harvested Area (1000 Ha)|0.00|0.09|-0.08|0.00***|
|Production (1000 T)|0.57|0.23|0.33|0.00***|
|Yield (T/Ha)|14.70|5.00|9.70|0.00***|
|Production Gap(1000 T)|0.57|0.63|-0.06|0.00***|

Notes. In this table we compare agricultural outcomes across rain-fed croplands and irrigated croplands. Columns (1) and (2) compute the averages of the outcomes for rain-fed- and irrigated- croplands, respectively. Column (3) indicates the difference in their means, whereas column (4) denotes whether these mean differences are statistically significant. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

Table 2: Aquifer Types Across SSA

|Aquifer<br>Type|Share of Area<br>(%)|Volume<br>(Billion Liters)<br>V|olume Per Capita<br>Per Day<br>(Liters)|
|---|---|---|---|
|(1)|(2)|(3)|(4)|
|Local Shallow|60.77|1698302|4530|
|Karstic|4.97|138873|370|
|Major Alluvial|8.16|228088|608|
|Complex|26.10|729357|1945|

Notes. This table presents descriptive statistics on the four aquifer types in Sub-Saharan Africa. Column (1) specifies the aquifer type. Column (2) is the mean share of the aquifer grid area that is covered by the aquifer type. Column (3) presents the corresponding total groundwater volume. Column (4) presents the total groundwater volume available per person per day. To compute the volume per capita per day, we divided the volume by the total population of the country that each groundwater aquifer grid lies in and then summed up the volume per-capita measure. Subsequently, we divided the measure by 365 days to obtain an estimate of groundwater availability per person per day. We use the World Bank’s curated groundwater aquifer dataset to construct this table.

26
Table 3: T-Test Results and Mean Differences

||Only|Any|||
|---|---|---|---|---|
|Variable|Rainfed<br>(1)|Irrigated<br>(2)|Difference<br>(3)|p-value<br>(4)|
|Growing Season: Longest Consecutive Dry Days|85.718|90.157|+|0.000***|
|<br>Growing Cycle: Crop Water Defcit (mm)|33.741|54.269|+|0.000***|
|Terrain Median Altitude|733.693|834.656|+|0.000***|
|Terrain Median Slope|4.101|4.678|+|0.000***|
|Nutrient Availability, Low Inputs|7.523|8.314|+|0.000***|
|Nutrient Retention Capacity|8.823|9.408|+|0.000***|
|<br>Water Collection Sites|1.27|1.74|+|0.000***|
|No of Growing Season Days|197.36|155.923|-|0.000***|
|<br>No. of Rain Days|158.776|114.575|-|0.000***|
|<br>Grid Suitability: MS-VS|8179.999|5450.374|-|0.000***|
|Crop Actual Evapotranspiration (mm)|259.967|216.966|-|0.000***|
|<br>Growing Cycle: Accumulated Temp (C / Day)|3183.938|3164.676|-|0.000***|
|Moisture Constraint in Growth|9158.173|6337.78|-|0.000***|
|Temp Winter|31.59|30.287|-|0.000***|
|Temp Spring|32.04|30.884|-|0.000***|
|<br>Temp Fall|28.711|27.075|-|0.000***|
|Temp Summer|31.73|28.919|-|0.000***|
|<br>Prec Winter|969.649|929.788|-|0.000***|
|Prec Spring|1057.05|898.431|-|0.000***|
|Prec Fall|1276.33|736.766|-|0.000***|
|Prec Summer|1028.897|645.531|-|0.000***|
|PDSI Spring|0.165|0.311|+|0.000***|
|<br>PDSI Fall|-0.425|-0.29|+|0.000***|
|PDSI Summer|-0.223|0.248|+|0.000***|
|PDSI Winter|0.215|0.116|-|0.000***|
|Rooting Condition, High Inputs|9.105|8.704|-|0.000***|
|<br>Rooting Condition, Low Inputs|9.107|8.706|-|0.000***|
|Root Oxygen Availability, High Inputs|10.017|10.017|+|0.794|
|<br>Root Oxygen Availability, Low Inputs|9.391|9.389|-|0.539|
|<br>Presence of Salinity and Sodicity, High Inputs|9.884|9.817|-|0.000***|
|Presence of Salinity and Sodicity, Low Inputs|9.886|9.82|-|0.000***|
|<br>Presence of Lime and Gypsum, High Inputs|9.993|9.956|-|0.000***|
|<br>Presence of Lime and Gypsum, Low Inputs|9.995|9.958|-|0.000***|
|<br>Workability, High Inputs|9.149|8.775|-|0.000***|
|<br>Workability, Low Inputs|8.704|8.115|-|0.000***|
|<br>Annual P-PET Ratio|69.273|50.969|-|0.000***|
|**N**|**905150**|**84259**|||

Notes. In this table we compare agro-climatic factors and conditions across croplands that were solely rain-fed and croplands that practiced any level of irrigation. Columns (1) and (2) compute the averages of the outcomes for rainfed- and irrigated- croplands, respectively. Column (3) indicates the sign difference in their means, whereas column (4) denotes whether these mean differences are statistically significant. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

27
Table 4: Simulation Results Using World Bank Measure of GW Volume

|Model|Crop<br><br>Sa<br>Co|Grids<br>tisfying<br>nstraints<br>(%)|Grids with<br>Production<br>Gains (%)|Median<br>Gains in<br>Production<br>(%)|Mean<br>Gains in<br>Production<br>(%)|
|---|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|(6)|
|SD|Barley|5_._47|2_._75|29_._57|101_._40|
|SD|Millet|5_._47|4_._31|76_._35|102_._88|
|SD|Maize|5_._47|43_._45|107_._96|184_._96|
|SD|Wetland Rice|5_._47|41_._41|108_._09|126_._08|
|SD|Sorghum|5_._47|18_._22|89_._49|112_._43|
|SD|Wheat|5_._47|8_._05|27_._97|46_._87|
|SRHS|Barley|5_._47|4_._68|33_._28|65_._23|
|SRHS|Millet|5_._47|0_._00|||
|SRHS|Maize|5_._47|57_._93|123_._26|221_._50|
|SRHS|Wetland Rice|5_._47|44_._04|114_._64|135_._14|
|SRHS|Sorghum|5_._47|40_._36|129_._42|242_._49|
|SRHS|Wheat|5_._47|14_._02|53_._08|102_._22|

Notes. In this table we present the results of the SD approach using the groundwater supply or volume measure using the aquifer dataset constructed by World Bank. Each row represents one simulation. Column (1) specifies the choice of model. Column (2) specifies the crop for which the analysis was run. Column (3) presents the fraction of all croplands where the constraints were met. Column (4) presents the share of croplands where we see production gains, as compared to the all the croplands where the constraints were met. Columns (5) and (6) present the median and mean of the simulated gains in production. All the results presented imposed constraints on groundwater depth _≤_ 7m, an extraction recharge ratio _≤_ 0.7, and aquifer productivity _≥_ 0.05.

28
Table 5: Simulation Results Using SD Approach and World Bank Measure of GW Volume

||GW<br>Depth|Extraction<br>Recharge<br>Ratio|Aquifer<br>Productivity|Grids<br>Satisfying<br>Constraints|Grids with<br>Production|Median<br>Gains in<br>Production|Mean<br>Gains in<br>Production|
|---|---|---|---|---|---|---|---|
|Crop|Constraint|Constraint|Constraint|(%)|Gains (%)|(%)|(%)|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Barley|7_._00|0_._70|0_._05|5_._47|2_._75|29_._57|101_._40|
|Barley|7_._00|0_._70||5_._47|2_._75|29_._57|101_._40|
|Barley|7_._00||0_._05|5_._48|2_._74|29_._57|101_._40|
|Barley||0_._70|0_._05|20_._98|2_._94|47_._91|98_._42|
|Barley|7_._00|||5_._48|2_._74|29_._57|101_._40|
|Barley||0_._70||20_._98|2_._95|47_._96|98_._47|
|Barley|||0_._05|24_._91|2_._48|47_._91|98_._42|
|Barley||||24_._91|2_._48|47_._96|98_._47|
|Millet|7_._00|0_._70|0_._05|5_._47|4_._31|76_._35|102_._88|
|Millet|7_._00|0_._70||5_._47|4_._31|76_._35|102_._88|
|Millet|7_._00||0_._05|5_._48|4_._31|76_._35|102_._88|
|Millet||0_._70|0_._05|20_._98|7_._04|71_._87|105_._05|
|Millet|7_._00|||5_._48|4_._31|76_._35|102_._88|
|Millet||0_._70||20_._98|7_._04|71_._87|105_._05|
|Millet|||0_._05|24_._91|5_._93|71_._87|105_._05|
|Millet||||24_._91|5_._93|71_._87|105_._05|
|Maize|7_._00|0_._70|0_._05|5_._47|43_._45|107_._96|184_._96|
|Maize|7_._00|0_._70||5_._47|43_._45|107_._96|184_._96|
|Maize|7_._00||0_._05|5_._48|43_._39|107_._99|185_._02|
|Maize||0_._70|0_._05|20_._98|30_._56|89_._39|158_._64|
|Maize|7_._00|||5_._48|43_._39|107_._99|185_._02|
|Maize||0_._70||20_._98|30_._56|89_._38|158_._63|
|Maize|||0_._05|24_._91|25_._75|89_._50|158_._90|
|Maize||||24_._91|25_._75|89_._49|158_._89|
|Wetland Rice|7_._00|0_._70|0_._05|5_._47|41_._41|108_._09|126_._08|
|Wetland Rice|7_._00|0_._70||5_._47|41_._41|108_._09|126_._08|
|Wetland Rice|7_._00||0_._05|5_._48|41_._34|108_._09|126_._08|
|Wetland Rice||0_._70|0_._05|20_._98|24_._76|105_._64|133_._09|
|Wetland Rice|7_._00|||5_._48|41_._34|108_._09|126_._08|
|Wetland Rice||0_._70||20_._98|24_._76|105_._64|133_._09|
|Wetland Rice|||0_._05|24_._91|20_._85|105_._64|133_._09|
|Wetland Rice||||24_._91|20_._85|105_._64|133_._09|
|Sorghum|7_._00|0_._70|0_._05|5_._47|18_._22|89_._49|112_._43|
|Sorghum|7_._00|0_._70||5_._47|18_._22|89_._49|112_._43|
|Sorghum|7_._00||0_._05|5_._48|18_._24|88_._61|112_._10|
|Sorghum||0_._70|0_._05|20_._98|15_._72|59_._99|103_._19|
|Sorghum|7_._00|||5_._48|18_._24|88_._61|112_._10|
|Sorghum||0_._70||20_._98|15_._72|59_._99|103_._19|
|Sorghum|||0_._05|24_._91|14_._08|63_._90|107_._25|
|Sorghum||||24_._91|14_._08|63_._90|107_._24|
|Wheat|7_._00|0_._70|0_._05|5_._47|8_._05|27_._97|46_._87|
|Wheat|7_._00|0_._70||5_._47|8_._05|27_._97|46_._87|
|Wheat|7_._00||0_._05|5_._48|8_._14|28_._24|48_._22|
|Wheat||0_._70|0_._05|20_._98|8_._83|36_._39|61_._23|
|Wheat|7_._00|||5_._48|8_._14|28_._24|48_._22|
|Wheat||0_._70||20_._98|8_._83|36_._39|61_._24|
|Wheat|||0_._05|24_._91|7_._81|38_._93|69_._25|
|Wheat||||24_._91|7_._81|38_._96|69_._26|

Notes. In this table we present the results of the SD approach using the groundwater supply or volume measure using the aquifer dataset constructed by World Bank. Each row represents one simulation. Column (1) specifies the crop for which the analysis was run. Columns (2)-(4) specify whether or not constraints were imposed; if yes, then what the thresholds were. Column (5) presents the fraction of all croplands where the constraints were met. Column (6) presents the share of croplands where we see production gains, as compared to the all the croplands where the constraints were met. Columns (7) and (8) present the median and mean of the simulated gains in production.

29
Table 6: Simulation Results Using SRHS Approach and World Bank Measure of GW Volume

||GW<br>Depth|Extraction<br>Recharge<br>Ratio|Aquifer<br>Productivity|Grids<br>Satisfying<br>Constraints|Grids with<br>Production|Median<br>Gains in<br>Production|Mean<br>Gains in<br>Production|
|---|---|---|---|---|---|---|---|
|Crop|Constraint|Constraint|Constraint|(%)|Gains (%)|(%)|(%)|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Barley<br>|7_._00<br>|0_._70<br>|0_._05|5_._47<br>|4_._68<br>|33_._28|65_._23|
|Barley|7_._00|0_._70||5_._47|4_._68|33_._28|65_._23|
|Barley|7_._00||0_._05|5_._48|4_._67|33_._28|65_._23|
|Barley||0_._70|0_._05|20_._98|4_._33|41_._85|68_._83|
|Barley|7_._00|||5_._48|4_._67|33_._28|65_._23|
|Barley||0_._70||20_._98|4_._33|41_._87|68_._89|
|Barley|||0_._05|24_._91|3_._64|41_._85|68_._83|
|Barley||||24_._91|3_._65|41_._87|68_._89|
|Millet|7_._00|0_._70|0_._05|5_._47|0_._00|||
|Millet|7_._00|0_._70||5_._47|0_._00|||
|Millet|7_._00||0_._05|5_._48|0_._00|||
|Millet||0_._70|0_._05|20_._98|0_._00|||
|Millet|7_._00|||5_._48|0_._00|||
|Millet||0_._70||20_._98|0_._00|||
|Millet|||0_._05|24_._91|0_._00|||
|Millet||||24_._91|0_._00|||
|Maize|7_._00|0_._70|0_._05|5_._47|57_._93|123_._26|221_._50|
|Maize|7_._00|0_._70||5_._47|57_._93|123_._26|221_._50|
|Maize|7_._00||0_._05|5_._48|57_._84|123_._26|221_._52|
|Maize||0_._70|0_._05|20_._98|42_._72|106_._65|246_._21|
|Maize|7_._00|||5_._48|57_._84|123_._26|221_._52|
|Maize||0_._70||20_._98|42_._73|106_._64|246_._20|
|Maize|||0_._05|24_._91|35_._99|106_._67|246_._28|
|Maize||||24_._91|35_._99|106_._67|246_._26|
|Wetland Rice|7_._00|0_._70|0_._05|5_._47|44_._04|114_._64|135_._14|
|Wetland Rice|7_._00|0_._70||5_._47|44_._04|114_._64|135_._14|
|Wetland Rice|7_._00||0_._05|5_._48|43_._96|114_._64|135_._14|
|Wetland Rice||0_._70|0_._05|20_._98|26_._44|112_._56|136_._85|
|Wetland Rice|7_._00|||5_._48|43_._96|114_._64|135_._14|
|Wetland Rice||0_._70||20_._98|26_._44|112_._56|136_._85|
|Wetland Rice|||0_._05|24_._91|22_._27|112_._56|136_._85|
|Wetland Rice<br>||||24_._91|22_._27|112_._56|136_._85|
|Sorghum|7_._00|0_._70|0_._05|5_._47|40_._36|129_._42|242_._49|
|Sorghum|7_._00|0_._70||5_._47|40_._36|129_._42|242_._49|
|Sorghum|7_._00||0_._05|5_._48|40_._35|129_._23|242_._05|
|Sorghum||0_._70|0_._05|20_._98|37_._39|93_._76|195_._35|
|Sorghum|7_._00|||5_._48|40_._35|129_._23|242_._05|
|Sorghum||0_._70||20_._98|37_._39|93_._74|195_._34|
|Sorghum|||0_._05|24_._91|32_._33|93_._39|194_._80|
|Sorghum||||24_._91|32_._33|93_._38|194_._80|
|Wheat|7_._00|0_._70|0_._05|5_._47|14_._02|53_._08|102_._22|
|Wheat|7_._00|0_._70||5_._47|14_._02|53_._08|102_._22|
|Wheat|7_._00||0_._05|5_._48|14_._09|53_._80|102_._49|
|Wheat||0_._70|0_._05|20_._98|12_._51|57_._32|91_._78|
|Wheat|7_._00|||5_._48|14_._09|53_._80|102_._49|
|Wheat||0_._70||20_._98|12_._51|57_._37|91_._79|
|Wheat|||0_._05|24_._91|10_._91|60_._10|94_._95|
|Wheat||||24_._91|10_._91|60_._11|94_._96|

Notes. In this table we present the results of the SRHS approach using the groundwater supply or volume measure using the aquifer dataset constructed by World Bank. Each row represents one simulation. Column (1) specifies the crop for which the analysis was run. Columns (2)-(4) specify whether or not constraints were imposed; if yes, then what the thresholds were. Column (5) presents the fraction of all croplands where the constraints were met. Column (6) presents the share of croplands where we see production gains, as compared to the all the croplands where the constraints were met. Columns (7) and (8) present the median and mean of the simulated gains in production.

30
Table 8: Simulation Results Using SD Approach and BGS Measure of GW Volume

||GW<br>Depth|Extraction<br>Recharge<br>Ratio|Aquifer<br>Productivity|Grids<br>Satisfying<br>Constraints|Grids with<br>Production|Median<br>Gains in<br>Production|Mean<br>Gains in<br>Production|
|---|---|---|---|---|---|---|---|
|Crop|Constraint|Constraint|Constraint|(%)|Gains (%)|(%)|(%)|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Barley|7_._00|0_._70|0_._05|5_._47|3_._20|85_._47|158_._42|
|Barley|7_._00|0_._70||5_._47|3_._20|85_._47|158_._42|
|Barley|7_._00||0_._05|5_._48|3_._19|85_._47|158_._42|
|Barley||0_._70|0_._05|20_._98|3_._34|92_._72|146_._09|
|Barley|7_._00|||5_._48|3_._19|85_._47|158_._42|
|Barley||0_._70||20_._98|3_._34|92_._95|146_._11|
|Barley|||0_._05|24_._91|2_._81|92_._72|146_._09|
|Barley||||24_._91|2_._81|92_._95|146_._11|
|Millet|7_._00|0_._70|0_._05|5_._47|4_._01|59_._57|89_._75|
|Millet|7_._00|0_._70||5_._47|4_._01|59_._57|89_._75|
|Millet|7_._00||0_._05|5_._48|4_._00|59_._57|89_._75|
|Millet||0_._70|0_._05|20_._98|7_._13|75_._11|114_._85|
|Millet|7_._00|||5_._48|4_._00|59_._57|89_._75|
|Millet||0_._70||20_._98|7_._13|75_._11|114_._85|
|Millet|||0_._05|24_._91|6_._00|75_._11|114_._85|
|Millet||||24_._91|6_._00|75_._11|114_._85|
|Maize|7_._00|0_._70|0_._05|5_._47|48_._27|125_._63|245_._42|
|Maize|7_._00|0_._70||5_._47|48_._27|125_._63|245_._42|
|Maize|7_._00||0_._05|5_._48|48_._19|125_._64|245_._48|
|Maize||0_._70|0_._05|20_._98|33_._76|112_._38|223_._85|
|Maize|7_._00|||5_._48|48_._19|125_._64|245_._48|
|Maize||0_._70||20_._98|33_._76|112_._38|223_._83|
|Maize|||0_._05|24_._91|28_._44|112_._42|224_._09|
|Maize||||24_._91|28_._44|112_._40|224_._07|
|Wetland Rice|7_._00|0_._70|0_._05|5_._47|45_._43|127_._22|146_._03|
|Wetland Rice|7_._00|0_._70||5_._47|45_._43|127_._22|146_._03|
|Wetland Rice|7_._00||0_._05|5_._48|45_._35|127_._22|146_._03|
|Wetland Rice||0_._70|0_._05|20_._98|26_._71|124_._41|150_._98|
|Wetland Rice|7_._00|||5_._48|45_._35|127_._22|146_._03|
|Wetland Rice||0_._70||20_._98|26_._71|124_._41|150_._98|
|Wetland Rice|||0_._05|24_._91|22_._49|124_._41|150_._98|
|Wetland Rice||||24_._91|22_._49|124_._41|150_._98|
|Sorghum|7_._00|0_._70|0_._05|5_._47|18_._40|100_._18|115_._91|
|Sorghum|7_._00|0_._70||5_._47|18_._40|100_._18|115_._91|
|Sorghum|7_._00||0_._05|5_._48|18_._48|100_._49|116_._65|
|Sorghum||0_._70|0_._05|20_._98|16_._03|88_._25|128_._10|
|Sorghum|7_._00|||5_._48|18_._48|100_._49|116_._65|
|Sorghum||0_._70||20_._98|16_._03|88_._25|128_._09|
|Sorghum|||0_._05|24_._91|14_._43|96_._28|144_._78|
|Sorghum||||24_._91|14_._43|96_._28|144_._77|
|Wheat|7_._00|0_._70|0_._05|5_._47|8_._46|45_._92|66_._40|
|Wheat|7_._00|0_._70||5_._47|8_._46|45_._92|66_._40|
|Wheat|7_._00||0_._05|5_._48|8_._54|46_._09|67_._56|
|Wheat||0_._70|0_._05|20_._98|9_._23|50_._89|80_._35|
|Wheat|7_._00|||5_._48|8_._54|46_._09|67_._56|
|Wheat||0_._70||20_._98|9_._23|50_._93|80_._35|
|Wheat|||0_._05|24_._91|8_._14|53_._92|87_._59|
|Wheat||||24_._91|8_._15|53_._93|87_._59|

Notes. In this table we present the results of the SD approach using the groundwater supply or volume measure using the aquifer dataset constructed by BGS. Each row represents one simulation. Column (1) specifies the crop for which the analysis was run. Columns (2)-(4) specify whether or not constraints were imposed; if yes, then what the thresholds were. Column (5) presents the fraction of all croplands where the constraints were met. Column (6) presents the share of croplands where we see production gains, as compared to the all the croplands where the constraints were met. Columns (7) and (8) present the median and mean of the simulated gains in production.

31
Table 9: Simulation Results Using SRHS Approach and BGS Measure of GW Volume

||GW<br>Depth|Extraction<br>Recharge<br>Ratio|Aquifer<br>Productivity|Grids<br>Satisfying<br>Constraints|Grids with<br>Production|Median<br>Gains in<br>Production|Mean<br>Gains in<br>Production|
|---|---|---|---|---|---|---|---|
|Crop|Constraint|Constraint|Constraint|(%)|Gains (%)|(%)|(%)|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Barley|7_._00|0_._70|0_._05|5_._47|5_._05|52_._87|100_._25|
|Barley|7_._00|0_._70||5_._47|5_._05|52_._87|100_._25|
|Barley|7_._00||0_._05|5_._48|5_._04|52_._87|100_._25|
|Barley||0_._70|0_._05|20_._98|4_._68|68_._80|101_._21|
|Barley|7_._00|||5_._48|5_._04|52_._87|100_._25|
|Barley||0_._70||20_._98|4_._68|68_._80|101_._26|
|Barley|||0_._05|24_._91|3_._94|68_._80|101_._21|
|Barley||||24_._91|3_._94|68_._80|101_._26|
|Millet|7_._00|0_._70|0_._05|5_._47|0_._00|||
|Millet|7_._00|0_._70||5_._47|0_._00|||
|Millet|7_._00||0_._05|5_._48|0_._00|||
|Millet||0_._70|0_._05|20_._98|0_._00|||
|Millet|7_._00|||5_._48|0_._00|||
|Millet||0_._70||20_._98|0_._00|||
|Millet|||0_._05|24_._91|0_._00|||
|Millet||||24_._91|0_._00|||
|Maize|7_._00|0_._70|0_._05|5_._47|65_._07|158_._09|286_._43|
|Maize|7_._00|0_._70||5_._47|65_._07|158_._09|286_._43|
|Maize|7_._00||0_._05|5_._48|64_._97|158_._14|286_._44|
|Maize||0_._70|0_._05|20_._98|48_._09|148_._02|358_._88|
|Maize|7_._00|||5_._48|64_._97|158_._14|286_._44|
|Maize||0_._70||20_._98|48_._09|148_._02|358_._85|
|Maize|||0_._05|24_._91|40_._51|148_._09|358_._91|
|Maize||||24_._91|40_._51|148_._07|358_._89|
|Wetland Rice|7_._00|0_._70|0_._05|5_._47|48_._72|129_._00|155_._34|
|Wetland Rice|7_._00|0_._70||5_._47|48_._72|129_._00|155_._34|
|Wetland Rice|7_._00||0_._05|5_._48|48_._65|129_._00|155_._34|
|Wetland Rice||0_._70|0_._05|20_._98|28_._81|126_._88|156_._57|
|Wetland Rice|7_._00|||5_._48|48_._65|129_._00|155_._34|
|Wetland Rice||0_._70||20_._98|28_._80|126_._88|156_._57|
|Wetland Rice|||0_._05|24_._91|24_._26|126_._88|156_._57|
|Wetland Rice||||24_._91|24_._25|126_._88|156_._57|
|Sorghum|7_._00|0_._70|0_._05|5_._47|43_._03|148_._48|291_._29|
|Sorghum|7_._00|0_._70||5_._47|43_._03|148_._48|291_._29|
|Sorghum|7_._00||0_._05|5_._48|43_._07|148_._61|291_._26|
|Sorghum||0_._70|0_._05|20_._98|43_._18|167_._81|309_._64|
|Sorghum|7_._00|||5_._48|43_._07|148_._61|291_._26|
|Sorghum||0_._70||20_._98|43_._18|167_._80|309_._62|
|Sorghum|||0_._05|24_._91|37_._30|170_._86|312_._03|
|Sorghum||||24_._91|37_._30|170_._86|312_._02|
|Wheat|7_._00|0_._70|0_._05|5_._47|14_._29|71_._02|113_._35|
|Wheat|7_._00|0_._70||5_._47|14_._29|71_._02|113_._35|
|Wheat|7_._00||0_._05|5_._48|14_._36|71_._60|113_._55|
|Wheat||0_._70|0_._05|20_._98|12_._84|77_._83|105_._62|
|Wheat|7_._00|||5_._48|14_._36|71_._60|113_._55|
|Wheat||0_._70||20_._98|12_._84|77_._88|105_._63|
|Wheat|||0_._05|24_._91|11_._19|80_._74|108_._35|
|Wheat||||24_._91|11_._19|80_._75|108_._36|

Notes. In this table we present the results of the SRHS approach using the groundwater supply or volume measure using the aquifer dataset constructed by BGS. Each row represents one simulation. Column (1) specifies the crop for which the analysis was run. Columns (2)-(4) specify whether or not constraints were imposed; if yes, then what the thresholds were. Column (5) presents the fraction of all croplands where the constraints were met. Column (6) presents the share of croplands where we see production gains, as compared to the all the croplands where the constraints were met. Columns (7) and (8) present the median and mean of the simulated gains in production.

32
Table 7: Percentage Income Gains Per Capita of 1.90 USD International Poverty Line

||Country|ISO-3 Code|Gains(%)|
|---|---|---|---|
|1|Togo|TGO|3.20|
|2|Benin|BEN|2.42|
|3|Guinea|GIN|2.39|
|4|Sierra Leone|SLE|1.91|
|5|Cameroon|CMR|0.92|
|6|Nigeria|NGA|0.83|
|7|Liberia|LBR|0.61|
|8|Tanzania|TZA|0.32|
|9|Kenya|KEN|0.24|
|10|Uganda|UGA|0.15|
|11|Cˆote d’Ivoire|CIV|0.13|
|12|Lesotho|LSO|0.10|
|13|Central African Republic|CAF|0.05|
|14|<br>Guinea-Bissau|GNB|0.04|
|15|Zambia|ZMB|0.02|
|16|Angola|AGO|0.02|
|17|Ethiopia|ETH|0.02|
|18|Malawi|MWI|0.01|
|19|Congo, Democratic Republic of|COD|0.01|
|20|Mali|MLI|0.01|
|21|Rwanda|RWA|0.01|
|22|Gabon|GAB|0.01|
|23|Chad|TCD|0.01|
|24|Mozambique|MOZ|0.00|
|25|Burundi|BDI|0.00|
|26|Congo, Rep. of|COG|0.00|
|27|<br>Zimbabwe|ZWE|0.00|
|28|Botswana|BWA|0.00|
|29|Senegal|SEN|0.00|
|30|Burkina Faso|BFA|0.00|
|31|Mauritania|MRT|0.00|
|32|Niger|NER|0.00|
|33|Sudan|SDN|0.00|
|34|Eritrea|ERI|0.00|
|35|South Africa|ZAF|0.00|
|36|eSwatini|SWZ|0.00|
|37|South Sudan|SSD|0.00|

Notes. In this table we compute the expected percentage income growth from all six cereals in our analysis. Income growth is defined as an expected change in income, defined as the monetary value of the simulated production gains, for individuals or farmers at the poverty line. We use the results from the SRHS model (when all three constraints are imposed) for computing the monetary value of production gains.

33
<!-- Start of picture text --> Aquifer Type Percentages by Region<br>60%<br>40%<br>=<br>oaD<br>Bs]J®<br>2<br>oO<br>a<br>20%<br>0%<br>Ro 2 s e cs e<br>oyo RGNe ©% so SS° ss<br>eSaeEa Ss&2&&Ss cSéo2raia RFSs&©$ gS °SSefy @&<br>© RS &<br>Na »<br>Region<br>HH Local Shallow Mill Karstic ll Major Alluvial fll Complex<br><!-- End of picture text -->
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<!-- Start of picture text --> Maize Millet Sorghum<br>Production Gain (%) EE Production Gain (%) Production Gain (%)<br>0 7580 ) 1050 ) 3150<br>Rice Wheat Barley<br>Production Gain (%)) — Production Gain (%) — Production Gain (%)) ell<br><!-- End of picture text -->
<!-- Start of picture text --> Maize Millet Sorghum<br>Production Gain (%) EE Production Gain (%) Production Gain (%)<br>0 7580 ) 1050 ) 3150<br>Rice Wheat Barley<br>Production Gain (%)) — Production Gain (%) — Production Gain (%)) ell<br><!-- End of picture text -->
<!-- Start of picture text --> South Sudan<br>Ethiopia<br>eSwatini<br>South Africa |<br>Botswana |<br>Kenya U<br>Eritrea i<br>Cameroon i<br>Mozambique |<br>Burkina Faso mz<br>Zimbabwe |<br>Senegal f<br>— |<br>Malawi<br>Burundi<br>£ Nigeria TT<br>5 CoteMauritaniad'ivoire PY<br>8 Zambia<br>Somia<br>Rwanda<br>Usanda<br>Gabon<br>Gira<br>Togo<br>Lesotho<br>Central African Republic NN<br>bea<br>III<br>Congo, Rep. of |<br>Guinea-Bissau<br>Congo, Democratic Republic of Intl<br>Angora<br>0 100 200 300 400<br>Median Production Gain (%) of Maize<br><!-- End of picture text -->
<!-- Start of picture text --> eSwatini<br>Eritrea |<br>South Africa |<br>Mozambique i<br>Burkina Faso i<br>Cameroon |<br>Zimbabwe |<br>S Mauritania as<br>fs)<br>0 100 200 300<br>Median Production Gain (%) of Maize<br><!-- End of picture text -->
<!-- Start of picture text --> Senegal | |<br>Kenya a<br>South Sudan a<br>Guinea-Bissau hs<br>Cameroon PY<br>Central African Republic PO<br>Congo, Democratic Republic of PO<br>0 100 200 300<br>Median Production Gain (%) of Rice<br><!-- End of picture text -->
<!-- Start of picture text --> Kenya wi<br>Senegal |<br>Angola yy<br>South Sudan hay<br>Guinea-Bissau Ps<br>0 50 100 150 200<br>Median Production Gain (%) of Rice<br><!-- End of picture text -->