Solar irrigation potential in Sub-Saharan Africa: a crop-specific techno-economic analysis

PAPER • OPEN ACCESS Solar irrigation potential in Sub-Saharan Africa: a crop-specific techno-economic analysis To cite this article: Fhazhil Wamalwa et al 2024 Environ. Res.: Food Syst. 1 025001 View the article online for updates and enhancements. You may also like Nanocomposites based on tubular and onion nanostructures of molybdenum and tungsten disulfides: inorganic design, functional properties and applications Alexander Yu Polyakov, Alla Zak, Reshef Tenne et al. - Model of soil moistening during wide-row crops drip strip irrigation M N Lytov - Impact of the Saline Irrigation Water on Crop Production Ibtisam Raheem Karim and Mohammed Abid Jameel - This content was downloaded from IP address 78.134.45.125 on 17/11/2025 at 18:10 Environ. Res.: Food Syst. 1(2024) 025001 https://doi.org/10.1088/2976-601X/ad5e82 OPEN ACCESS RECEIVED 27 February 2024 REVISED 8 May 2024 ACCEPTED FOR PUBLICATION 3 July 2024 PUBLISHED 12 July 2024 Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. PAPER Solar irrigation potential in Sub-Saharan Africa: a crop-specific techno-economic analysis Fhazhil Wamalwa1,∗, Lefu Maqelepo1, Nathan Williams1,2and Giacomo Falchetta3,4 1Golisano Institute for Sustainability, Rochester Institute of Technology, Rochester, NY 14623, United States of America 2Kigali Collaborative Research Centre (KCRC), Kigali, Rwanda 3IIASA-International Institute for Applied Systems Analysis, Laxenburg, Austria 4Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Venice, Italy ∗Author to whom any correspondence should be addressed. E-mail: [email protected]du Keywords: solar irrigation, smallholder agriculture, Sub-Saharan Africa, food-energy nexus, food security, agricultural profitability Supplementary material for this article is available online Abstract In this study, we introduce an integrated modeling framework that combines a hydrologic model, a biophysical crop model, and a techno-economic model to assess solar irrigation potential in Sub-Saharan Africa (SSA) based on seven commonly grown food crops-maize, wheat, sorghum, potato, cassava, tomato, and onion. The study involves determining the irrigation requirements, location-specific capital investment costs, crop-specific profitability, and the cropland area under various cost scenarios (low and high) and soil fertility (low, moderate, near-optimal, and optimal) scenarios. Our research reveals considerable potential for solar irrigation, with profitability and viable cropland areas that vary according to crop type, irrigation system cost scenarios, and soil fertility levels. Our assessment shows that approximately 9.34 million ha of SSA’s current rainfed cropland are hydrologically and economically feasible for solar irrigation. Specifically, maize and onion display the lowest and highest viability, spanning 1–4 million ha and 29–33 million ha, respectively, under optimal soil fertility conditions. In terms of profitability, maize and onion rank as the least and most economically viable crops for solar irrigation, yielding average annual returns of $50-$125/ha and $933-$1450/ha, respectively, under optimal soil fertility conditions. The lower and upper bounds of profitability and cropland range correspond to high-cost and low-cost scenarios, respectively. Furthermore, our study reveals distinct regional differences in the economic feasibility of solar irrigation. Eastern Africa is more economically favorable for maize, sorghum, tomato, and cassava. Central Africa stands out for onion cultivation, whereas West and Southern Africa are more profitable for potato and wheat, respectively. To realize the irrigation benefits highlighted, an energy input of 940-2,168 kWh/ha/yr is necessary, varying by crop and geographic sub-region of the SSA sub-continent. Our model and its results highlights the importance of selecting the right crops, applying fertilizers at the appropriate rates, and considering regional factors to maximize the benefits of solar irrigation in SSA. These insights are crucial for strategic planning and investment in the region’s agricultural sector. 1. Introduction Sub-Saharan Africa (SSA) is currently the most food-insecure region in the world, with a high dependence on food imports to plug demand deficits [1]. Over 69% of the region’s food is produced by smallholders who account for 80% of the farmlands that are responsible for 90% of the region’s food output [2–4]. This is despite their reliance on traditional farming methods characterized by low mechanization [1], minimal fertilizer usage [5], and high dependence on natural rainfall–only about 5% of the region’s arable land was under irrigation as of 2010 [6])– leading to huge yield gaps [7]. On the other hand, the SSA region’s annual © 2024 The Author(s). Published by IOP Publishing Ltd Environ. Res.: Food Syst. 1(2024) 025001 F Wamalwa et al population growth rate -approximated at 2.4% [8]-is the steepest in the world. This combination of agricultural under-performance and a high population growth rate threatens to exacerbate the current food insecurity problem, undermine the region’s effort to alleviate poverty [1] and destabilize its socioeconomic systems [1,9]. A study by the Food and Agricultural Organisation (FAO) of the United Nations (UN) projected a 60% increase in food production (with 2006/2007 as the base year) for the world to feed its growing population in year 2050 [10]. Production pressure is even higher for SSA sub-continent considering that the region’s population is projected to exceed 2 billion by 2050 [11], which is more than twice the population of the base year (2020). The projected climate-induced rainfall variability threatens the region’s already under-performing agricultural sector [9,12,13], calling for the need to re-appraise the current food production practices in order to improve productivity and enhance adaptation. One of the measures with potential to close the current agricultural yield gaps is irrigation [7,13–17]. Studies have shown that irrigation can potentially double the productivity of field crops [16] by facilitating additional cropping seasons, and enabling farming of high-value crops that require more consistent water supplies [14,18]. Moreover, irrigation serves as a safeguard against climate-induced droughts, a threat projected to intensify in the future [12,15]. However, despite its promising potential and demonstrated success in other developing regions of the world, like South Asia [19,20] and the Middle East and North Africa (MENA) [21], irrigation adoption in SSA has been slow due to region-specific barriers including high capital costs beyond smallholder farmers’ affordability [22,23], inadequate market linkages [24,25], nascent policies and regulatory frameworks [25,26], and limited access to affordable irrigation energy. On average, irrigation energy costs can account for up to 33% of irrigation crop production costs [27], emphasizing the importance of affordable energy in reducing overall irrigation farming expenses. As of 2022, only about 45% of SSA’s population had access to electricity [28], primarily concentrated in urban areas, while agriculture is predominantly a rural activity. In the absence of reliable grid power, diesel engines and solar photovoltaic (PV) systems have emerged as alternative sources of motive power for irrigation. Solar PV-powered irrigation-henceforth solar irrigationhas demonstrated viability in rural areas with high diesel fuel costs [29,30], offering promise in enhancing food security and alleviating rural poverty, particularly among smallholder farmers [31]. Nevertheless, the aforementioned barriers significantly impede its wider adoption, underscoring the necessity for research to inform evidence-based policy interventions. Some of the needed studies include spatial and economic assessments to delineate profitable locations for solar irrigation across the SSA region for each important food crop. Such insights are crucial for decision-making processes aimed at maximizing the benefits of irrigation by identifying suitable locations and crops for irrigation investment. The current literature includes studies on irrigation potential in SSA, examined at both country-level [32–35] and continental scales [36–39]. These range from geographic information system (GIS)-based environmental suitability assessments [32,33] to identify viable locations for solar irrigation, to comprehensive integrated modeling, combining GIS-environmental suitability, hydrological, crop simulation, and economic benefits-costs analyses [34,35,37,40,41]. However, only a limited number of these previous studies have considered solar irrigation potential including assessing and delineating suitable cropland locations for solar irrigation [32,33] and economic benefits thereof [36,40,41]. For example, Xie et al [36] perform a comparative analysis of solar versus diesel-powered irrigation and spatially delineate cropland clusters (at 1 km resolution) where solar is more cost-effective over diesel-powered irrigation. Meanwhile, Wamalwa et al’s [40] study on the economic feasibility of solar irrigation in Kenya highlight the critical roles of crop type and groundwater depth on its feasibility. Their findings underscore that horticultural crops are generally more economically profitable compared to cereals and tubers. In a more recent study, Falchetta et al [41] present one of the most comprehensive assessment of the economic feasibility and nutritional impacts of solar irrigation in SSA. Their study also assessed the implications of climate change on solar irrigation potential and the electricity access implications from excess solar PV system capacity. However, Falchetta et al’s study [41], does not provide a detailed crop-specific irrigation viability analyses, particularly lacking is the precise delineation of profitable irrigable areas for the 19 crops considered in the study. This omission highlights the need for further studies to explicitly delineate the current cropland clusters that are viable for solar irrigation of each of the important food crops grown in SSA. The output of such a study would be helpful to local farmers, rural development agencies, and policy makers, among others, in making informed decisions about solar irrigation potential in SSA. By viable locations, we refer to cropland clusters that are both hydrologically and techno-economically feasible to solar-irrigate. In this study, we present a crop-specific scenario assessment of the economic feasibility of solar irrigation, focusing on seven food crops commonly grown in SSA (viz: maize, wheat, sorghum, cassava, potato, onion, and tomato), employing various assumptions. By crop-specific scenario assessment, we mean an independent evaluation of solar irrigation feasibility for each of the seven crops, aiming to identify opportunities for crop substitution that can maximize irrigation benefits. The choice of the crops considered in this study is based on their economic and nutritional importance in the SSA region. Maize, wheat, and sorghum are the 2 Environ. Res.: Food Syst. 1(2024) 025001 F Wamalwa et al primary cereals cultivated in the region, whereas cassava and potato are the prevalent tuber crops. Onion and tomato are grown as horticultural crops, serving both as food and a source of income. Together, these seven crops occupy 50 (approximately 84 out of 168 million hectares) of the cropland allocated for food production in SSA. The distribution of this cropland by crop type is detailed in figure SI 3 in the supplement. Moreover, Maize, along with cassava, wheat, rice, and palm oils-though the latter two are not included in this study-contribute up to 54% of the daily caloric intake in the region [42]. As a principal staple, maize is cultivated on over 27 million hectares within SSA and represents 19.7% of the daily caloric consumption in Eastern and Southern Africa [43]. Wheat and cassava contribute 8.3% and 10.5%, respectively, to the daily per capita calorie consumption [42]. Our study aim is to delineate locations–spatially and economically—where it is viable to solar-irrigate each of the seven crops, including their associated profitability. We argue in this study that certain crops currently grown on the SSA’s largely rainfed cropland may not optimally contribute to alleviating the region’s economic poverty and the current food insecurity. Therefore, given the options, farmers might benefit from rationally and strategically switching crops to maximize irrigation benefits. Many farmers in SSA face challenges in accessing farm inputs, particularly fertilizers, due to their high costs. It is estimated that 10% of farmers in SSA grow crops without any artificial fertilizer application and half of those who use fertilizers depend on low-value organic options, which contribute to sub-optimal crop yields [44]. To account for these disparities in fertilizer use rates in our model, we incorporate a scenario-based sensitivity analysis of soil fertility. The primary research questions addressed in this study are: (i) Where in the current rainfed cropland of SSA is it feasible to solar-irrigate commonly cultivated food crops?; (ii) What is the profitability of solar irrigating commonly cultivated crops in SSA’s rainfed cropland?; (iii) What area of SSA’s rainfed cropland is viable for solar irrigation of the region’s commonly grown food crops?; and (iv) What is the impact of fertilizer application rates on irrigation feasibility in SSA? In addressing these questions, our study makes several contributions to the existing literature: (a) development of a detailed hydrological and techno-economic framework for assessing solar irrigation feasibility in SSA based on attainable yield simulations by a biophysical crop model; (b) evaluation and quantification of the annual irrigation profitability of each crop, delineating geographic areas where solar irrigation is viable; and (c) assessment and quantification of the impacts of fertilizer application rates on irrigation feasibility in SSA. The remainder of this paper is organized as follows: section 2presents the methods, including the modeling framework, data sources, key model assumptions, and economic model formulation; section 4 presents the study’s results; section 5discusses the results and provides recommendations; while section 6 concludes the paper. 2. Model, data and methods In this section, we present the methods, modeling framework, data sources, and assumptions underpinning the proposed study. The methods include techniques for estimating crop yield gains due to irrigation, assessing land suitability for irrigation based on groundwater recharge, and evaluating the technical and economic feasibility of solar irrigation across the 10 km gridded pixels of SSA’s rainfed cropland. 2.1. The modeling framework and data inputs Figure 1shows the schematic layout of our modeling framework. As shown, the model comprises of four modules: (i) the crop module for estimating the irrigation water requirement (Wirr); (ii) the pump module for estimating the irrigation energy demand (Eirr) with groundwater depth and Wirr as inputs; (iii) the solar pump sizing module for sizing the solar irrigation system based on the peak daily Eirr and site-specific solar radiation data; and (iv) the economic module for estimating the metric of interest, which in this case is the NPV of irrigation. The irrigation module in figure 1is a simplified model based on the FAO’s Penman–Monteith equation [45]; a detailed account is given in section SI 1 in the supplement. For irrigation yield gain, we rely on attainable yield simulated from the FAO’s Aquacrop model [46]; A detailed account on AquaCrop model simulation and irrigation yield gain results is available in section SI 3 of the supplement. Comprehensive modeling, including crop file calibrations and the setup of simulation projects, is based on the methods developed by Izar-Tenorio et al [47]. The main datasets used in our study include (i) the current rainfed cropland data of SSA; (ii) crop producer (farm-gate) prices; and (iii) irrigation infrastructure costs. The first dataset–rainfed cropland–is obtained from the International Food Policy Research Institute’s (IFPRI’s) SPAM2017 v2.1 data product for the SSA region [48]. In this study, we have limited our analysis to the current rainfed cropland by assuming that it (rainfed cropland) meets arability conditions. We have excluded the current irrigated cropland based on the assumption that it is already economically viable to irrigate there. For crop prices, we rely on producer prices from Food and Agricultural Organisation Statistics (FAOSTAT) [49]. We also use the FAOSTAT 3 Environ. Res.: Food Syst. 1(2024) 025001 F Wamalwa et al Figure 1. Schematic layout of the model and the modeling process. Agro-climatic inputs include wind speed, solar irradiation, precipitation, and temperature (min, mean max). SIP =solar irrigation pump. national yield averages to validate the yield estimates from crop simulation model; a detailed account on irrigation yield validation is given in section SI 3.1 in the supplement. The available country-level crop prices for the SSA region are fragmented with missing values. Some countries do not have continuous data for the period of interest (from 2001 to 2020) for the crops considered in this paper. To address the data gaps’ issue, we impute missing values with sub-regional averages. We use annual producer prices at the country level as representative prices in each cropland cluster (10 km gridded cells) within a given country. In the same vein, we use country-level costs–imputed with regional averages–for borehole drilling and development costs obtained from previous studies [27,50]. We acknowledge limitations of these course cost estimates occasioned by data scarcity, necessitating the need for cost-centered sensitivity analysis of the results obtained from the model. For solar irrigation system costs, we rely on the break-even cost of solar PV over diesel-powered irrigation, drawing from the work of Xie et al [36],thus excluding cropland clusters where diesel-powered irrigation is cost-competitive, as in Falchetta et al [41]. For discount rates, we adopt the country-level weighted average cost of capital (WACC) for solar home systems (SHSs) in Agutu et al [51]. This choice is predicated on the comparable deployment scales of SHSs and solar irrigation systems, particularly for smallholder applications, and our analysis focus on solar irrigation feasibility at the 1-hectare level. 2.2. General model assumptions In this sub-section, we outline the fundamental assumptions that form the basis of our model and the modeling process. Firstly, although various crops are cultivated on the current rainfed croplands of SSA, our study focuses exclusively on the seven commonly cultivated food crops in the region: maize, wheat, sorghum, potato, cassava, tomato, and onion. While multiple crops could potentially be grown simultaneously on each of the 10 km gridded cropland clusters (spatial analysis resolution considered in this paper), our assessment independently evaluates the performance of each of the seven crops. This approach allows us to compare 4 Environ. Res.: Food Syst. 1(2024) 025001 F Wamalwa et al Table 1. Criteria and input data used in formulating cropland inclusion constraints. Data category inclusion criteria Data source Groundwater yield >0.1l s−1British Geological Survey [52] Daily solar insolation >0.5 kWh m−2WorldClim [59] Irrigation yield gain >0 From model simulation Groundwater recharge >0 British Geological Survey (BGS) [60] their economic potential for solar irrigation, providing valuable insights for determining the most economically viable crops to cultivate in each gridded cropland cluster. Secondly, concerning irrigation water sources, our analysis is confined to groundwater, despite the economic viability of surface irrigation where accessible. This delimitation is made to simplify the modeling complexity associated with costing irrigation water conveyance systems. The costing of such systems involves intricate considerations of sizing and routing water lines, which cannot be accurately estimated without ground-truth data on cropland demarcations and routing constraints. Additionally, we restrict our analysis to groundwater due to its natural abundance in Africa and its ease of access [52]. Groundwater can be accessed virtually from anywhere that is technically and economically feasible to drill and develop, reducing the costs associated with extensive water reticulation systems [53]. On irrigation infrastructure costs, we assume 10 ha of cropland per groundwater well, following precedents in literature [27,54]. We scale the drilling and development costs of a borehole to a hectare level. The third assumption pertains to the study’s inclusion criteria for cropland clusters. Table 1outlines a select set of technical and environmental constraints considered in our model, restricting our analysis to locations that are hydrologically and technically feasible for solar irrigation. For example, we exclude all cropland cells within agro-ecological zones with insufficient groundwater recharge to mitigate environmental concerns related to over-pumping; See section 2.4 for the recharge model development. The fourth assumption pertains to the choice of irrigation technologies. Although various irrigation systems, including gravity-fed, manual, and pressurized systems (such as sprinkler and drip), are practiced in SSA, our analysis in this paper is limited to the latter systems due to their high irrigation water application efficiencies from a sustainability standpoint. Both sprinkler and drip irrigation can be applied to the common row crops considered in this study, with differences in efficiencies, head requirements, and costs [55]. Drip irrigation is the most efficient (up to 85%) compared to sprinkler irrigation (up to 70%) [55]. We adopt sprinkler irrigation for dense cropping cultivation (common for cereal crops like maize, wheat, and sorghum) and tuber crops (potato and cassava). Drip irrigation is selected for high-value horticultural crops–onions and tomatoes in this case–due to their high vulnerability to fungal and bacterial diseases when water is logged on their leaves [56]. In addition to irrigation efficiencies, our model also considers crop water withdrawal efficiencies as detailed in [27,41]; the applicable values are provided in table SI 1 in the supplement. Lastly, in calculating the net present value (NPV) of irrigation-a profitability metric in this paper-we assume a 15% income loss for the farmer in post-harvest crop handling, consistent with established studies [57,58]. 2.3. Irrigation energy requirement The irrigation water requirement, Wirr, is typically estimated based on crop evapotranspiration, which is the combined water loss due to transpiration and evaporation from plant and soil surfaces. This lost water must be supplied through either natural precipitation or artificial irrigation to facilitate effective crop growth. In this study, we develop a simplified model to estimate crop Wirr, from which the groundwater-fed irrigation energy requirement, Eirr, is derived. A brief explanation is provided here, with a detailed account in section SI 1 of the supplement. The daily Eirr for groundwater-fed irrigation is a function of the daily Wirr and the total dynamic head of the pumping system (TDH) expressed as follows: Eirr (kWh) = 0.00272 (kWhm−3.m)×Wirr (m3)×TDH (m) ηps ,(1) where 0.00 272(kWh m−3.m) is the pumping energy intensity of water and ηps is the efficiency of the pumping system. TDH is the algebraic sum of the elevation head (Hevel), operating pressure head of the pump (Hpres) and frictional head loss (Hfloss) expressed as follows: TDH =Helev (m) + Hpres (m) + Hfloss (m).(2) 5 Environ. Res.: Food Syst. 1(2024) 025001 F Wamalwa et al The Helev is composed of the depth-to-groundwater table (DTW) at rest and the drawdown (Hs) induced by pumping. In this paper, we estimate drawdown using the Theis analytical solution for a single-well model, as adopted by Xie et al [36], a detailed account is given by equation (9) in the supplement. 2.4. Groundwater recharge model To assess the environmental sustainability of solar irrigation, we have developed a simplified groundwater availability assessment model. This model aims to mitigate environmental fallout dye to over-pumping in agro-ecological zones with insufficient groundwater recharge. We utilize long-term average annual groundwater recharge data from the British Geological Survey (BGS) [60] to calculate the net renewable water availability after irrigation withdrawals for each of the seven crops considered, across all 10 km gridded pixels. A cropland cell is deemed unfeasible for solar irrigation development if the net renewable water recharge after irrigation withdrawals is negative. In this simplified model, we assume that each gridded cropland cell is homogeneous and independent of adjacent cells, implying no lateral groundwater or irrigation water flows among gridded cropland cells. This assumption allows for the independent estimation of net groundwater recharge for each cell as follows: Wavail,t={(Rt−WC,t); if Rt⩾WC,t 0;otherwise (3) where Wavail,trepresents the annual renewable water availability, Rtdenotes the long-term groundwater recharge estimated by BGS [60], and WC,tis the annual crop water requirement unmet by natural precipitation and available soil water content. In this study, the 10 km gridded cropland pixel is treated as a recharge surface, and WC,tis confined to the cropland cover within each pixel. Thus, the annual groundwater recharge (in m3) is calculated across the entire 10 km gridded pixel, while the evapotranspirative demand of the crop met by irrigation (WC,t) is computed over the cropland cover within the pixel. It is important to note that while the recharge amount Rtfrom GBS is an initial determinant of the feasibility for sustainable irrigation, it does not incorporate the additional water requirements (WC,t) of potentially irrigated crops not currently grown. Therefore, a comprehensive assessment of sustainability must take into consideration WC,tfor crops that might be introduced through irrigation. 2.5. Economic feasibility of irrigation In this study, we employ a benefit-cost analysis to determine the economic feasibility of irrigation. That is, besides environmental sustainability, a cropland pixel is deemed economically feasible to irrigate if its NPV of cashflows associated with irrigation is positive. That is, the net present revenue (NPR) from irrigation yield gain of a given crop is greater than the net present cost (NPC) of the irrigation system. In general, we express the irrigation NPV as follows: NPV = n ∑ t=1(Cp,t×∆Yt (1+r)t)−(C0+ n ∑ t=1 Ctrans,t+Com,t (1+r)t),(4) where ∆Yt,Cp,t,Ctrans,t, and C0are, respectively, the irrigation yield gain in year t, the annual crop price in $/ton, transport cost of delivering crops to markets (see section 4.3 in the supplement for a detailed account), and the initial capital cost comprising of the investment costs for the pressurized irrigation system, well drilling and development costs, water conveyance system, irrigation pump, water storage, and the associated installation and commissioning costs estimated from prior studies [61,62]. Com,tis the annual operating and maintenance costs of the irrigation system and the borehole, including replacement costs. In computing irrigation profitability ($/ha), we assume that labor, agro-chemicals, seeds, and farm preparation costs for rainfed and irrigated production are comparable and as a result, they can be ignored in the model for estimating the NPV due to extra yield from irrigation as in [27]. The key cost data and their related assumptions used in the economic module to estimate irrigation NPV are given in table SI 5 in the supplement. 3. Sensitivity analysis To account for the inherent uncertainties in our model, which stem from factors such as system costs (shown in table SI 5 in the supplement), uncertainties in groundwater depth as reported by Bonsor and MacDonald [63], uncertainties in applicable discount rates (shown in table SI 6 in the supplement), and the uncertainties in fertilizer application rates due to farmers’ difficulties in obtaining agricultural inputs, we have integrated a cost-based sensitivity analysis into our research study. We delineate two cost scenarios: ‘low-cost’ and 6 Environ. Res.: Food Syst. 1(2024) 025001 F Wamalwa et al ‘high-cost’ based on the technology costs distributions (shown in table SI 5 in the supplement), national variations in the WACC, and discrepancies in groundwater levels [52]. The ‘low-cost’ scenario combines the lower bounds of irrigation infrastructure costs and the lower bounds of the national-level WACC values (given in table SI 6 in the Supplement). In contrast, the ‘high-cost’ scenario combines the upper bounds of system cost estimates and WACC value ranges. In the general sense, the ‘low-cost’ scenario represents an optimistic case, with cost variables set at their lowest, whereas the ‘high-cost’ scenario represents a cost-intensive situation, with all cost variables at their maximum. It is important to highlight the uncertainty of groundwater depth in SSA and its implications for solar irrigation development. According to Bonsor and MacDonald [63], groundwater depth in SSA, at 2.5 arc-min (about 5 km at the equator) resolution, is categorized into various depths as follows: ‘very shallow’ (0–7 m), ‘shallow’ (7–25 m), ‘shallow to medium’ (25–50 m), ‘medium’ (50–100 m),‘deep’ (100–250 m) and ‘very deep’ (>250 m). Another major source of uncertainties in our model is fertilizer application rates. 10% of the farmers in SSA region grow crops without any artificial fertilizer application while 50% of those who use fertilizers rely on low-value organic fertilizers with significant impacts on crop yields [44]. This is largely due to the high cost of inorganic fertilizers beyond the SSA’s smallholder farmers’ affordability. We integrate these uncertainties in fertilizer usage on irrigation feasibility by considering four fertilizer application levels: optimal (100f), near-optimal (85f), moderate (50f), and low (25f), as in [27,47]. While higher fertilizer application rates naturally lead to increased production costs, our paper does not incorporate the incremental cost of fertilizer inputs into the analysis. This exclusion is due to the difficulties in obtaining accurate, localized fertilizer pricing, particularly within the SSA context. Consequently, while this assumption may compromise the precision of our irrigation profitability estimates-especially at the reduced fertility levels which imply reduced production costs-the overall trends are expected to remain valid. Declining soil fertility correlates with a reduction in yield, which, in turn, reduces the irrigation profit margins, in line with findings from [47,64]. 4. Results and discussion 4.1. Preliminary geospatial analysis Figure 2shows spatial mapping of the current rainfed cropland, solar insolation, average annual rainfall, and groundwater attributes (productivity, depth, and recharge), all delimited to the cropland, at 10 km resolution. In generating the maps shown in the figure, we have excluded the cropland pixels that are not feasible for solar irrigation based on the study inclusion/exclusion constraints listed in table 1. The groundwater recharge map for the SSA sub-continent is derived from the long-term average annual recharge data from the BGS [60]. As illustrated in the figure, all mapped features and attributes demonstrate significant spatial variability. Notably, the density of rainfed cropland is highest in Central Ethiopia and West Africa, particularly in Nigeria, Ghana, and Ivory Coast. Meanwhile, Central and West African regions experience the highest rainfall and groundwater recharge. These spatial variations in groundwater attributes (depth, yield, and recharge) and climate attributes (rainfall and solar insolation) have significant implications for solar irrigation feasibility, which is further explored in the subsequent sub-sections of this paper. 4.2. Irrigation requirement, system capacity, and costs Irrigation requirements (Wirr and Eirr) as well as the associated system capacity and capital costs, depend on several factors such as crop type and location-specific soil and climate attributes, primarily temperature and precipitation. In figure 3, we spatially map out the average annual (20-year average) Eirr, per-hectare solar PV system size, and the associated capital costs for solar irrigation system based on tomato crop; most irrigation-intensive crop among the seven crops considered in our study. The sub-regional average Wirr and Eirr for each of the seven crops are presented in figure 4. The 10km gridded simulations results, including irrigation requirements and increase in yield due to irrigation are available in a Zenodo repository (Wamalwa et al 2024). The results highlight regional disparities in irrigation requirements, with some agro-ecological zones requiring significantly high Eirr, irrigation system sizes and the associated capital costs than others. For instance, the Central and West African regions require lower Eirr and lower capital costs compared to the Sahel region and Southwest Africa. These differences are explained by the spatial disparities in SSA’s climatic conditions and groundwater attributes (mainly depth), as illustrated in figure 2. The solar pump capacities correlate with Eirr, indicating that regions with higher Eirr necessitate larger solar pumps. In general, the interaction among these three variables, as depicted in figure 3, influences the solar irrigation feasibility within a given agro-ecological zone. Regions with high irrigation requirements-such as the Sahel, Somalia, and Southwest Africa-have steep initial capital costs, which can impact the long-term 7 Environ. Res.: Food Syst. 1(2024) 025001 F Wamalwa et al Figure 2. Rainfed cropland (a); long term average solar insolation (b); average annual rainfall (c); average groundwater aquifer productivity (d); average groundwater aquifer depth (e); and long term average annual groundwater recharge (f). Figure 3. Average annual per hectare irrigation water requirement (a); solar pump per hectare system capacity (b); per hectare solar irrigation system’s capital cost (CAPEX) (c). economic viability of solar irrigation. Conversely, regions characterized by low irrigation requirements may find solar irrigation development unviable due to insufficient agronomic returns. Figure 4illustrates how irrigation requirements vary with crop types and regional agro-climatic conditions. Notably, tomato and maize crops have the highest and lowest irrigation requirement, respectively, across the four SSA sub-regions. 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