Mapping global cropland potential to deliver iron and zinc where they are most needed

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Here we present a spatial decision framework that links a Deficiency-Weighted Nutrient Score (DWNS) for 37 crops with Global Agro-Ecological Zones (GAEZ) attainable yields to identify crop–region opportunities to boost iron and zinc supply. Four crops (soybean, cowpea, pearl millet, and common beans) consistently rank highest across deficiency hotspots. Under rain-fed, intermediate-input assumptions, attainable nutrient yields (mg ha⁻¹) in Eastern Africa, Southern/Western/Central Africa, Southern Asia, and the Caribbean could meet the daily iron requirements of ~7,000–20,000 people per hectare, depending on crop and region. We distinguish between potential nutrient supply and actual dietary intake, highlighting the economic and behavioural factors that determine whether production translates into impact. This framework complements existing economic and demand-side approaches, enabling governments and development agencies to direct investments toward nutrient-dense crops and value chains that are regionally viable, advancing SDG 2 while recognising real-world feasibility constraints. Earth and environmental sciences/Environmental social sciences/Sustainability Scientific community and society/Agriculture Scientific community and society/Developing world Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Micronutrient deficiencies remain one of the most pressing global public health and food security challenges. More than two billion people suffer from inadequate intakes of essential micronutrients such as iron, zinc, vitamin A, and folate – a condition often termed “hidden hunger” 1 . Vulnerable groups, including children, adolescent girls, women of reproductive age (15–49 years), and pregnant or lactating women, are disproportionately affected due to higher nutrient requirements and frequent dietary insufficiencies 2,3 . These deficiencies, largely driven by inadequate dietary quality, lead to serious consequences such as anaemia, impaired neurocognitive development, and elevated morbidity and mortality 3–5 . Progress has been made through supplementation, industrial fortification, and diet diversification programs 2,6 , yet global food systems still fall short of meeting population requirements for many micronutrients 1,7 . For instance, even under optimistic scenarios, the global food system in 2018 provided only ~64% of required calcium and 69% of vitamin E 7 , underscoring persistent gaps in micronutrient supply. This imbalance reflects deeper structural issues in agricultural production and diets 6,8 . Energy-rich but micronutrient-poor staples (e.g. maize, wheat, rice) dominate global crop output due to their high yields and market value, while nutrient-dense crops like pulses, legumes, and millets are underrepresented in both production and consumption 2,9,10 . In many regions, traditional diets that once included legumes and diverse grains have shifted towards mostly refined cereals, exacerbating micronutrient deficiencies 8 . Addressing these nutrient gaps requires a fundamental reorientation of how we prioritize crops and allocate land 11 . Emerging strategies in nutrition-sensitive agriculture aim to realign agricultural investments with human nutrient requirements 12,13 , yet practical frameworks to guide crops and land-use decisions for nutrition outcomes remain limited. Two analytical tools with untapped potential to inform such strategies are nutrient profiling and land suitability analysis 14,15 . Nutrient profiling ranks foods by their contribution to diet quality; for example, the Nutrient Rich Foods index scores foods based on concentrations of vitamins, minerals, and other nutrients 9,16,17 and has been used to shape dietary guidelines and policy 18 . Separately, land suitability models like the Food and Agriculture Organization/International Institute for Applied Systems Analysis (FAO–IIASA) Global Agro-Ecological Zones (GAEZ) platform identify where crops can be optimally produced given local climate, soil, and terrain conditions 15,19 . While both frameworks are well established, they have seldom been combined to target micronutrient deficiencies – a gap that leaves agricultural policy disconnected from nutrition goals. Integrating these approaches could enable food system planners to prioritize investments in crops that are not only agronomically suitable but also most likely to alleviate nutrient shortfalls. Here, we bridge this gap by linking crop nutrient profiles with spatial land productivity data to inform crop prioritization for micronutrient interventions. We introduce a Deficiency-Weighted Nutrient Score (DWNS) that ranks crops by their nutrient density and the severity of region-specific micronutrient deficiencies (focused on iron and zinc). We then couple DWNS with high-resolution GAEZ suitability and attainable yield data to identify where these nutrient-rich crops could be cultivated most effectively at scale. Finally, we use clustering analysis to delineate global regions where high nutrient needs and crop suitability converge, yielding a geographically explicit framework to guide nutrition-sensitive agriculture. In doing so, we provide a data-driven tool for policymakers and development agencies to guide nutrition-sensitive agricultural investments and integrated food systems planning. This integrative approach offers clear guidance on crop selection and land allocation to mitigate micronutrient deficiencies. Importantly, the framework is meant to inform prioritization of agricultural interventions rather than replace economic, policy, or behavioral strategies needed to translate production gains into improved consumption. By aligning agro-ecological potential with nutritional needs, our study demonstrates how agricultural development can be strategically leveraged to combat hidden hunger, while acknowledging the complementary measures required for success. Results Nutrient density of selected crops : The iron and zinc composition of the 37 food crops varied widely, underscoring the importance of crop choice for nutrition. Detailed values and percent contributions to daily requirements are provided in Supplementary Data 1a. In summary, a few crops are outliers in their micronutrient density. High-performing crops, such as soybeans, cocoa, cowpea, pearl millet, and common beans (Phaseolus vulgaris), deliver a large fraction of daily iron and zinc requirements per 100 g serving (Figure 1a). For example, 100 g of cocoa powder provides ~62% of a woman’s daily iron requirement and 67% of zinc, owing to cocoa’s exceptional mineral content. Soybean (raw, whole) provides ~70% of daily iron needs and 48% of zinc per 100 g. In contrast, many widely grown staples like cassava, polished rice, or banana are poor sources of these micronutrients, each providing less than 5% of daily iron or zinc requirements per 100 g. This analysis confirms that focusing on nutrient-dense crops (especially certain legumes and millets) could substantially improve the micronutrient output of farming systems relative to common staples. Deficiency-Weighted Nutrient Scores (DWNS) : When we incorporate regional micronutrient deficiency data, the top-ranking crops become even more distinct. Figure 1b shows the DWNS for iron and zinc combined, for each crop averaged across all subregions. After weighting for deficiency prevalence, soybean, cowpea, pearl millet, and common beans emerge as the highest scoring crops globally (each with a combined DWNS around 50–65%). These crops not only have high iron and zinc composition, but they are also particularly relevant to regions suffering from anemia and zinc deficiency. For instance, soybeans had one of the highest unweighted nutrient scores (due to high iron content), and deficiency-weighting preserved soybean’s high rank because many regions with iron deficiency (e.g. South Asia, Africa) could grow soy. Cocoa, while very nutrient-dense, saw its weighted score moderate slightly (to ~64%) because cocoa-producing regions like West Africa do have high anemia rates but cocoa is not typically consumed in large quantities as a staple food. Overall, our deficiency-weighting approach identifies crops that are both micronutrient-rich and aligned with global public health needs, ensuring that a score reflects potential impact (nutrients delivered where people lack them) rather than just nutrients per gram. These scores indicate that in regions with high iron and zinc deficiency, a single serving of these foods could provide roughly half or more of population’s daily requirement for those nutrients. Priority crops for suitability analysis : Based on the DWNS ranking, we prioritized four crops – soybean, cowpea, pearl millet, and common beans, for detailed land suitability and yield potential analysis. These stood out as offering the greatest potential to supply iron and zinc in areas of need. This selection was not only driven by their DWNS values, but also by practical considerations: cowpea, pearl millet, and common beans are traditionally grown by smallholders in many high-deficiency regions and can be utilized as food with minimal processing 26–28 . Soybean, while often cultivated as an industrial or feed crop, was included due to its exceptional per-hectare nutrient yield; we acknowledge that soybeans typically require processing (e.g. for oil, soy flour, or fermented foods) and targeted support to be adopted at smallholder level, and we return to these challenges in the discussion. By focusing on these four crops, we concentrate on interventions that could feasibly enhance iron and zinc intake in vulnerable regions. Geographic potential for iron and zinc yield: Using GAEZ data, we mapped and quantified the attainable iron and zinc output (in mg per hectare) for the priority crops across global regions. Figure 2a summarizes these results, and Supplementary Figures 2b–e provides crop-specific maps. There is pronounced regional heterogeneity. For example, cowpea shows highest potential micronutrient yields in the Caribbean (≈235,800 mg iron/ha and 161,400 mg zinc/ha, under rain-fed average attainable yields) and Eastern and Middle Africa (≈175,000–164,000 mg iron/ha). Regions with cooler climates like Northern Europe or Central Asia have negligible cowpea potential due to agronomic unsuitability. Soybean displays a different pattern: the highest iron and zinc yields are attainable in Eastern Europe (up to 436,000 mg iron and 135,000 mg zinc per ha, reflecting very high yield potential in favorable temperate zones), followed by the Caribbean (~419,000 mg Fe and 130,000 mg Zn/ha) and Middle Africa (~344,000 mg Fe/ha). Soybean’s potential is low in regions such as Central Asia and much of Micronesia where climate or soils are limiting. Common beans (Phaseolus) reach their peak iron yield potential in Western Europe (~261,000 mg Fe/ha), the Caribbean (~245,000 mg Fe/ha), and Eastern Africa (~189,000 mg Fe/ha), with similar relative rankings for zinc. Pearl millet, being a crop adapted to arid environments, has its highest iron and zinc yields in Eastern Africa (~152,800 mg Fe and 59,200 mg Zn/ha) and parts of Western Africa and the Caribbean, whereas humid or high-latitude regions (e.g. Europe, Eastern Asia) show near-zero potential for millet. These findings illustrate that each crop has a distinct geography of maximum impact. Notably, sub-Saharan Africa, South Asia, and the Caribbean appear repeatedly as high-potential areas for one or more of the prioritized crops, aligning with the heavy micronutrient burden in those regions. Spatial overlap of need and potential (cluster analysis): By combining the nutritional-need and yield-potential datasets, we identified six “crop–region typologies” through clustering (Fig. 3a–c). Detailed summary and variable statistics are provided in Supplementary Tables S1a and S1b, while Supplementary Figures 3d and 3e show the standardized yield-deficiency distributions and the Pseudo-F statistic used to determine the optimal number of clusters. These typologies group countries with similar deficiency rates and crop potentials, even if they are geographically distant (Fig. 3a). For instance, one cluster (Cluster 2) includes Eastern Africa and the Caribbean, two distant regions that both exhibit high iron/zinc deficiency prevalence and strong yield prospects for the selected crops. Another cluster (Cluster 6) spans much of Southern, Western, and Middle Africa as well as Southern Asia, areas all characterized by high micronutrient deficiencies and moderate-to-high suitability for the priority crops. In contrast, a cluster comprising parts of Europe and Central Asia shows high yield potential (especially for soybeans and beans) but low current deficiency burdens. These data-driven clusters provide insight into where similar strategies might be applied. However, for ease of communication we also interpret the results in conventional regional terms. In Eastern Africa and the Caribbean (cluster 2), for example, our analysis suggests that promoting cowpea and pearl millet could be particularly effective, as both crops thrive agro-ecologically and match local nutritional needs. In Western, Middle, and Southern Africa plus South Asia (cluster 6), soybean and millet stand out as promising options to boost iron and zinc availability. Meanwhile, Eastern Europe (cluster 5), with its high yield capacity for soy and beans but relatively lower deficiency levels, might serve as a surplus production zone or exporter of nutrient-rich crops given appropriate policies (we discuss this below). Overall, the clustering highlights that regions like sub-Saharan Africa and South Asia would benefit most from nutrition-driven crop shifts, whereas some high-production regions could leverage their capacity to support global micronutrient distribution. Current food use versus potential: We compared the identified production potential with present-day consumption patterns (using FAO food balance data for 2022) to gauge how large a shift would be needed. In many regions, the current per capita consumption of these nutrient-dense crops is very low, revealing a substantial gap between what is agronomically possible and what is actually contributing to diets (Fig. 4). For instance, pearl millet, despite its importance in parts of Western Africa, remains a minor part of diets elsewhere. Our analysis shows pearl millet contributes at most ~2–3% of per capita food supply (by weight) in its strongholds and far less in most regions, even though its yield potential is high in several of those regions. Common beans are relatively widely eaten (e.g. contributing ~2.3% of diet by weight in Eastern Africa), which corresponds to Eastern Africa also having good yield potential for beans. Soybean stands out: in regions like South-eastern Asia and the Americas, it contributes up to ~1.6–1.9% of diets, but in many high-deficiency areas, direct soybean contribution is negligible. In Eastern and Western Europe, for example, we found a striking mismatch, these regions have among the highest potential yields for soybeans and common beans, yet almost none of that potential is used for human food, as diets rely on other foods and much of the soybean grown is for animal feed or export. This mismatch between food supply and potential suggests a large untapped opportunity but also indicates the presence of non-agronomic barriers to utilization. Discussion and Conclusion Our study provides a novel approach to align agriculture with nutritional goals by integrating nutrient profiling and land suitability analysis in a geographically specific way. We extend established concepts like nutrient density indices 14,17,30 , by incorporating a public health dimension, weighted by deficiency prevalence and overlaying the results with high-resolution agro-ecological data. The outcome is a decision-support framework indicating not just which crops are nutritious, but where they could be prioritized to address micronutrient shortfalls most effectively. This approach offers a new perspective on the intersection of nutrition and agronomy, identifying regions where shifting or diversifying production toward nutrient-rich crops could yield major public health benefits. It is intended as a tool to guide policymakers and planners in making nutrition-sensitive agricultural decisions, complementing existing interventions in public health. Consistent with prior research on nutrition-sensitive agriculture, our analysis highlights a familiar cast of crops known for high micronutrient content and adaptability to marginal environments. Pearl millet, cowpea, soybean, and common beans have all been emphasized in the literature as promising crops to improve dietary quality in low-income settings 28,31–33 . We confirmed that these crops, despite being underutilized in many regions, have exceptional potential to supply iron and zinc. At the same time, our results reiterate that staple root and tuber crops (cassava, yam, potato) and even many cereals, while critical for energy supply, contribute relatively little to micronutrient intake 9 . This dichotomy supports calls to better integrate pulses, legumes, and millets into farming systems and diets as a strategy to combat hidden hunger. The geographic analysis provides actionable insights. For example, soybean stands out as a crop with extremely high iron yield per hectare in several regions. In Eastern Europe, an average hectare of soy could meet the iron requirements of ~19,500 people annually (and zinc for ~13,300 people), and similarly a hectare in the Caribbean could support ~18,700 people for iron (~12,800 for zinc) based on attainable yields. While Eastern Europe does not have a high deficiency burden, regions in sub-Saharan Africa and South Asia would greatly benefit from increased soybean cultivation if the production could be channeled into local diets. In cluster 6 (encompassing much of Africa and Southern Asia) and cluster 2 (Eastern Africa and Caribbean), our data indicate that soybean could substantially bolster iron and zinc availability. For instance, attainable soybean yields in Middle Africa could provide enough iron for ~15,300 people per hectare, and in Southern Asia for ~7,700 people per hectare, which is impactful given the prevalence of anemia. Notably, these findings align with nutritional studies showing that soybean can be an effective source of bioavailable iron when consumed 34 , especially if traditional preparation or fermentation methods are used to enhance iron absorption 35 . Soybean exemplifies the opportunity and challenge identified in this work: it offers immense potential nutritional gains, but realizing these gains will require innovations to incorporate soy into local food cultures and value chains, as we discuss further below. Our regional results also suggest complementarities among crops. We observed that soybean and common beans often share high-yield potential in the same areas (Fig. 3c), particularly in parts of Eastern Africa and the Caribbean (clusters 2 and 6). Common beans demonstrated the single highest per-hectare iron supply among all crops in some instances (e.g. Eastern Africa), indicating that where climate allows, it is a central for nutrition. These two legumes could be deployed in rotation systems to sustain soil fertility, both fix atmospheric nitrogen via rhizobia, benefiting subsequent crops 36,37 . While intercropping soybean and common bean together is generally not practiced (since they occupy a similar niche), rotating them or intercropping each with cereals could maximize land use efficiency and nutrient output 38 . Their nitrogen-fixing ability also offers economic and environmental benefits by reducing fertilizer needs and greenhouse emissions 36,37 . Likewise, cowpea and pearl millet show synergy (Fig 3c), both are well-suited to arid, low-fertility conditions and have been successfully grown in intercrops, where cowpea’s ground cover and millet’s height make for complementary resource use 39,40 . In Eastern Africa and the Caribbean (cluster 2), our data suggest cowpea and millet each could provide on the order of 100,000+ mg of iron per hectare; combined in farming systems, they could improve resilience and yield stability 40,41 . These agronomic considerations reinforce that introducing nutrient-rich crops need not come at the expense of system productivity, in fact, it can enhance it when done thoughtfully. However, translating the potential supply of nutrients into actual nutritional outcomes is far from straightforward. Our findings reveal large gaps between where a crop could be grown for nutrition and how much it is currently contributing to diets (Fig. 4). Several high-potential regions (e.g. parts of Latin America, or Europe in the case of soybean/beans) do not presently use these crops for food to any significant extent. This highlights that simply identifying agro-ecological “hotspots” for nutrient-rich crops is not enough – there are economic, cultural, and policy hurdles that determine whether those crops will be planted by farmers and eaten by consumers 11,42,43 . For example, in Eastern and Western Europe, despite suitable land for soybean and bean cultivation, the actual supply of these crops for human food is negligible. Farmers there may prefer to grow higher-profit crops or use soy for livestock feed, and consumers obtain their nutrients through other foods. This suggests barriers like market incentives, pricing, dietary preferences, lack of processing facilities, or competing land uses that can prevent a nutritionally optimal allocation of land 32,44 . On the other hand, we found that in some regions such as Western Africa or Southern America, current food use of certain crops (e.g. millets, soybeans) is more in line with yield potential, reflecting successful traditional integration of those crops into diets 45 . These examples highlight that context matters, local food culture and market infrastructure can either facilitate or impede the adoption of nutrient-dense crops. Crucially, increasing production of nutritious crops does not guarantee improved consumption. Even when yields are boosted, the food might not reach those who need it. In practice, much of the world’s production of nutrient-rich crops is diverted. A striking example is soybean: globally, only about 6% of soybean harvest is used directly as human food, with most going to animal feed, biofuel, and industrial uses 46 . This disconnects between production potential and human nutrition means that without deliberate interventions, simply growing more soybeans (or other nutritious crops) in a high-deficiency region might have little impact on local micronutrient intake. Income and consumer demand also play a major role as well 47,48 . In some low-income communities, pulses and coarse grains are perceived as “foods of the poor,” 49 and as incomes rise, diets often shift towards meat, dairy, and refined staples, potentially reducing demand for traditional legumes 50,51 . Changing such perceptions requires nutrition education and behavior change initiatives so that the value of these foods is recognized and they remain desirable. Conversely, boosting supply without building demand could lead to excess production that farmers cannot profitably sell, undermining the intervention. To bridge the gap between potential production and actual nutrition outcomes, a suite of economic and policy measures must accompany agronomic recommendations. Farmers need incentives and support to grow these priority crops: for instance, price support or subsidies can make nutrient-rich crops competitive with cash crops, and crop insurance or input support can reduce the risk of switching to a less familiar crop 52,53 . Market development is equally important – investments in storage, processing, and distribution infrastructure (such as mills for millet flour, or facilities to process soybeans into food products like tofu, soymilk, or fortified blends) can create value chains that bring these crops from farm to fork. In regions where large-scale soybean exporters set global prices, local smallholder farmers may struggle to find a profitable niche for food-grade soybeans 44 . Targeted public procurement (e.g. including cowpeas or millet in school feeding programs or food aid baskets) can secure a market and stimulate production. Trade policies might also be aligned with nutritional goals: for example, countries with surplus capacity in iron-rich crops could receive trade preferences or support to export to countries with deficits, as long as those exports are actually used for food. Our analysis identified several such possibilities e.g., Caribbean and parts of Africa have overlapping potential that could be leveraged through regional trade in legumes and grains if properly structured (cluster 2 and 6 interplay). Consumer-focused policies like nutrition education campaigns and social marketing can raise awareness and acceptance of these foods, increasing demand. In summary, realizing the promise of the DWNS–GAEZ framework will require connecting it to broader food system actions: aligning extension services, subsidies, market incentives, and educational programs to create an enabling environment for these crops to flourish from field to plate. Agronomic practices and biofortification are additional critical pieces of the puzzle 38 . Beyond choosing which crops to grow, how those crops are grown can influence their nutritional payoff. Agronomic biofortification – the use of fertilizers or soil amendments containing micronutrients – has shown success in increasing the zinc and iron content of staple crops. For instance, applying zinc-enriched fertilizer can substantially raise zinc concentrations in wheat grain, improving dietary zinc intake without changing the crop 38 . Similarly, improving soil health and organic matter can enhance plants’ uptake of minerals from the soil. These practices could complement our approach by ensuring that crops grown in targeted regions express their full nutrient potential. Meanwhile, plant breeding and genetic biofortification programs are already focusing on the very crops we identified. Organizations like HarvestPlus and ICRISAT have developed high-iron and high-zinc varieties of common beans, pearl millet, and other staples 31,33 . For example, iron-biofortified pearl millet bred by ICRISAT and partners contains significantly more iron and zinc and has proven effective to improve iron status in human trials. Integrating these improved cultivars into the suitable areas highlighted by our analysis could amplify impact – farmers could grow not just any cowpea or millet, but the nutritionally supercharged versions in the environments where they thrive best. By acknowledging and incorporating these parallel strategies, soil management to enhance micronutrient uptake, and crop improvement to raise inherent nutrient content – we paint a more holistic picture of how micronutrient supply can be increased. In practice, a nutrition-sensitive agriculture program could combine our spatial targeting (to know where to act) with biofortified seeds and appropriate agronomic techniques (to maximize impact), underpinned by the policy supports discussed earlier. Our analysis has several limitations that must temper the conclusions. First, we focused on iron and zinc deficiencies; while these are among the most widespread micronutrient deficiencies, others like vitamin A, iodine, or folate were beyond our scope. A multi-nutrient extension of this framework could yield different crop priorities (for example, orange-fleshed sweet potato for vitamin A, or leafy vegetables for folate) and should be explored in future research. Second, we limited our crop list to 37 major crops, which means some regionally important but globally minor crops were omitted. Underutilized traditional foods (indigenous grains, leafy greens, etc.) might have high nutritional value and local suitability 10,54 ; excluding them could bias our findings towards well-known commodities. Relatedly, our nutrient composition data came largely from global databases and may not reflect local varietal differences – nutrient levels can vary by cultivar and soil, so on-the-ground impact might differ from our estimates. Third, our yield estimates are based on GAEZ attainable yields with intermediate inputs and do not account for climate change, land degradation, or other dynamic factors; nor do they guarantee that farmers can achieve those yields without capacity building and investment. We also did not factor in post-harvest losses or supply chain inefficiencies – our nutrient yield figures assume that what is grown is fully harvested, stored, and made available for consumption, which in reality is often not the case (especially for perishable or pest-prone crops). Additionally, our cluster analysis produced groupings that are analytically useful but not immediately aligned with political boundaries; implementing policies based on these may require translating our findings into existing administrative regions or economic communities. Importantly, we did not incorporate economic feasibility into the DWNS–GAEZ model. A crop might be biophysically ideal for a region, but if it is not profitable or if it requires more labor or different skills, farmers may not adopt it. Factors like relative prices, input costs, and farmer risk tolerance are critical to real-world outcomes and were beyond the scope of our study. Future work could integrate an economic optimization or cost-benefit layer to identify “low-hanging fruit” interventions that are both nutritionally and economically advantageous. Finally, livestock systems were not considered in this framework, even though animal-source foods remain critical providers of bioavailable iron, zinc and vitamin B₁₂ in many regions 9 . Integrating livestock suitability and feed-crop dynamics with this crop-based approach would provide a more complete picture of potential nutrient supply and trade-offs across food systems. Despite these limitations, which largely reflect the availability and resolution of global datasets rather than shortcomings of the framework itself, the insights gained have meaningful policy implications. The results reinforce the concept that agricultural policy and nutritional outcomes are deeply intertwined, spatially targeting crop production for nutrient needs can be a viable strategy to support public health objectives, but it must be embedded in a broader systems approach 11 . This perspective directly supports Sustainable Development Goal 2 (Zero Hunger), particularly Target 2.1 on universal access to safe and nutritious food and Target 2.4 on sustainable and resilient agricultural systems. By identifying opportunities for countries and regions to better nourish their populations through what they grow, this framework provides an evidence base to integrate nutrition objectives into agricultural and land-use planning. For instance, governments in East Africa or South Asia could use these findings to justify programs that promote legumes and millets, backed by extension training and input support, in areas shown to have both high malnutrition and high suitability. Similarly, international development agencies could target biofortified seed distribution or micronutrient fertiliser initiatives to mapped hotspot regions, maximising the nutritional return on investment. At the same time, our findings make clear that there is no single blueprint for success. Interventions must be adapted to local conditions, considering environmental sustainability, cultural acceptance and economic feasibility. We caution that boosting the supply of micronutrients through agriculture is a necessary but not sufficient condition to eliminate hidden hunger. It should go hand in hand with efforts to improve dietary diversity, nutrition education, and food affordability, ensuring that increased availability actually leads to increased intake. In conclusion, our DWNS–GAEZ framework offers a starting point for an integrated food systems planning approach, one that explicitly meshes agricultural development with nutritional well-being. By using data to direct attention and resources to where they can have the biggest impact, and by coupling those insights with supportive policies and programs, stakeholders can make tangible progress toward eradicating micronutrient deficiencies in an equitable and sustainable way. Changing what we grow and where can improve what people eat, but only if we simultaneously address the economic and behavioral drivers of food choice. Such an integrated strategy is essential to achieve global nutrition targets and ensuring that agricultural gains translate into healthier populations. Methods Crop selection and nutrient profiling We obtained crop productivity and environmental data from the Global Agro-Ecological Zones v4 (GAEZ v4) platform 15 which provides global assessments of land suitability and attainable yield for a wide range of crops. GAEZ evaluates biophysical production potential by integrating detailed climatic, soil, and terrain data with crop growth requirements and management assumptions. From GAEZ database, we selected a subset of 37 major food crops representing cereals, legumes/pulses, roots and tubers, oilseeds, and vegetables, excluding crops mainly used for non-food purposes and those with minimal relevance to human diets, to concentrate the analysis on food security and nutrition outcomes. For each crops, we compiled nutrient profile data on iron and zinc composition (mg per 100 g, in the raw, edible portion) from the USDA FoodData Central database 21 , supplemented by other published sources for a few crops (e.g. pearl millet from 22 ; foxtail millet from 23 ). These values represent typical cultivar averages. Where multiple entries or varieties were available for a given crop, a representative value was chosen (for example, a common market variety or mean of several entries). No adjustments were made for cultivar- or region-specific differences in nutrient composition, which we acknowledge as a limitation given the natural variability in crop nutrient profiles. To put these nutrient values in context, we used Harmonized Average Requirement (H-AR) values for women of reproductive age (WRA, 15–49 years) from 20 as daily population reference intakes. The H-ARs account for average bioavailability in typical diets. For iron, we assumed 5% absorption for plant (non-heme) iron, reflecting high phytate diets, and for zinc we used requirements for unrefined, high-phytate diets. These conservative absorption assumptions mean that our analysis inherently considers the lower bioavailability of iron and zinc from plant sources when evaluating how much of a requirement a food can meet. For each crop we computed its nutrient contribution, the percentage of a WRA daily iron or zinc requirement that 100 g (raw, edible portion) provides, using H-ARs. We then applied a deficiency weighting using regional prevalence of iron-deficiency anaemia in WRA 24 and zinc deficiency 25 , so nutrients count more where deficiencies are more widespread, based on available data from 184 countries. This produced a Deficiency-Weighted Nutrient Score (DWNS) for each crop and region, capped at 100% to prevent overweighting of any single food. Regional DWNS values were then averaged across subregions to obtain a global ranking, with regional results retained for mapping and clustering (Supplementary Data 1). Land suitability and attainable yield analysis After identifying nutrient-dense crops through DWNS, we evaluated the suitability of current croplands worldwide for cultivating these crops. We used GAEZ v4 Theme 4 (“Suitability and Attainable Yield”) data, which provides crop-specific estimates of agronomically attainable yield and suitability class for each 5-arc-minute grid cell (~9 x 9 km at the equator) under various input and climate scenarios 15 . To reflect realistic farming conditions in many low- and middle-income regions, we focused on rain-fed production with intermediate inputs (sometimes termed “average inputs”). This means we considered yields achievable with modest levels of inputs and management, for example, using improved seed varieties and some fertilizer and pest control, but not the maximum technologically possible yields – and without irrigation infrastructure (rain-fed conditions). This scenario reflects typical smallholder farming systems and provides an attainable yield estimate rather than an idealized maximum. We extracted the attainable yield (in tonnes/ha) and suitability index for each prioritized crop on currently cultivated land areas (as defined by GAEZ’s cropland mask) and harmonized regional boundaries using Global Administrative Areas (GADM v4.1) shapefiles 55 for consistent regional summaries. The resulting gridded yield surfaces were aggregated to country and regional levels using area-weighted averages and totals for subsequent analysis. Spatial clustering of high-potential regions To identify global patterns and group regions with similar nutrient–production profiles, we conducted an unsupervised clustering analysis combining micronutrient deficiency prevalence with crop yield potential. The analysis covered 184 countries aggregated into subregions (those with data for both deficiencies). Features included (i) the prevalence of iron and zinc deficiencies and (ii) the average attainable nutrient yield (mg Fe ha⁻¹ and mg Zn ha⁻¹) for each of the top nutrient-dense crops. K-means clustering was first applied to partition subregions into groups sharing comparable nutrient-need and production-potential characteristics 56,57 . After testing multiple cluster solutions, six clusters were selected as providing the best balance between granularity and interpretability, based on Calinski–Harabasz and silhouette metrics (Supplementary Figure 3e). Hierarchical agglomerative clustering (Ward’s method, Euclidean distance) was then applied to the K-means output to visualise relationships among clusters 58 . Each cluster was characterised by its dominant subregions and crop potential. Because the clustering was data-driven, some clusters encompass non-contiguous regions that nonetheless share similar profiles (for example, Eastern Africa and the Caribbean were grouped together due to comparable high deficiency rates and crop yield potentials). For interpretability, we describe clusters in terms of their constituent sub-continents, but we recognize that these groupings do not always align with traditional political or agro-ecological zones. Comparison of agronomic potential with current food supply To assess whether regions are realizing their agronomic potential for nutrient-dense crops, we compared attainable yield potential from GAEZ with actual food supply of the same crops. Regional food supply data (g cap⁻¹ d⁻¹) were obtained from the FAO Food Balance Sheets 29 , which account for post-harvest losses, feed use, processing, and other diversions from the food system. This metric represents food available for human consumption, not actual intake. For each crop and region, we expressed attainable yield as a percentage of the highest observed regional average yield worldwide and paired with corresponding per-capita food supply. The comparison identifies cases where dietary availability is low despite high agro-ecological suitability, or conversely, where both are aligned. The resulting indicator supports cross-regional interpretation of the gap between production potential and realized food supply (visualized in Fig. 4). Declarations Resource availability Lead contact Further information and requests for resources or data should be directed to the lead contact Ejovi Abafe ( [email protected] ). Materials availability This study did not generate new physical materials. Data availability All data used in this study are from publicly available sources or published literature as cited. Global cropland suitability and average yield projections were obtained from the Global Agro-Ecological Zones (GAEZ) v4 database (https://gaez.fao.org/) and nutrient composition data were sourced from USDA FoodData Central (https://fdc.nal.usda.gov/). Harmonized nutrient requirement values were taken from Allen et al. 2020 (https://doi.org/10.1093/advances/nmz096). All other data supporting the findings of this study, including supplementary tables and region-level projections, are available within the supplementary information files or from the corresponding author upon reasonable request. Code availability Global cropland suitability and average yield projections were processed and reclassified in ArcGIS Pro (version 3.4.0, Esri). 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Dissimilarity-Based Cluster Analysis of Educational Data: A Comparative Tutorial Using R. in Learning Analytics Methods and Tutorials: A Practical Guide Using R (eds Saqr, M. & López-Pernas, S.) 231–283 (Springer Nature Switzerland, Cham, 2024). doi:10.1007/978-3-031-54464-4_8. Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryDataMappingglobalcroplandpotential.xlsx Supplementary Dataset Number SupplementaryinformationMappingglobalcroplandpotential.docx Mapping global cropland potential to deliver iron and zinc where they are most needed Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":133942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea | Nutrient contributions (per 100 g) of 37 crops to iron (x-axis) and zinc (y-axis) Harmonized Average Requirements (H-ARs) for women of reproductive age (WRA)\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003eBubble size = combined nutrients %. Values use raw edible portion; bioavailability handled via H-ARs; processing losses not applied. Nutrient reference values are based on H-ARs for WRA \u003csup\u003e20\u003c/sup\u003e. Nutrient composition data are sourced from USDA FoodData Central \u003csup\u003e21\u003c/sup\u003e, except pearl millet \u003csup\u003e22\u003c/sup\u003e and foxtail millet \u003csup\u003e23\u003c/sup\u003e. Crop data are from \u003csup\u003e15\u003c/sup\u003e, reclassified and processed by the authors (see also Supplementary Data 1a for Nutrient Composition and Contribution).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb | Deficiency-weighted nutrient score (DWNS) by crop, averaging 22 subregions. Points reflect iron- and zinc-weighted contributions (size = combined DWNS). \u003c/strong\u003eCrops in the upper-right with larger bubbles (soybean, cowpea, common beans, pearl millet) rank highest once public-health need is applied.\u003cstrong\u003e \u003c/strong\u003eCrops with a combined score \u0026gt;1% are labelled. Nutrient reference values are based on Harmonized Average Requirements (H-ARs) for women of reproductive age \u003csup\u003e20\u003c/sup\u003e. Iron and Zinc data are from \u003csup\u003e24,25\u003c/sup\u003e and crop data \u003csup\u003e15\u003c/sup\u003e, reclassified and processed by the authors (see also Supplementary Data 1a for nutrient-weighted contribution and Supplementary Data 1b for combined nutrient scores across subregions).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7983932/v1/ad81dbd0a643118c648015ec.png"},{"id":96430664,"identity":"7314d9b7-97e4-4fd6-a312-25aaf980873a","added_by":"auto","created_at":"2025-11-21 03:54:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":144349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional attainable iron and zinc yields for nutrient-dense crops.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAttainable iron (mg ha⁻¹) and zinc (mg ha⁻¹) under rain-fed, intermediate inputs for soybean, cowpea, pearl millet (PM), and common beans (PB) on current cropland\u003cstrong\u003e. \u003c/strong\u003eDarker indicates higher potential and shows where nutrient-dense crops could supply the largest per-hectare micronutrient output.\u003cstrong\u003e \u003c/strong\u003eNutrient yield (mg/ha) was calculated as the product of crop yield (tons/ha), a conversion factor of 10⁶ (to convert tonnes to grams), and crop-specific nutrient concentrations (mg/g). Nutrient composition data are from \u003csup\u003e21\u003c/sup\u003e, except for pearl millet \u003csup\u003e22\u003c/sup\u003e. Crop suitability and yield data are from \u003csup\u003e15\u003c/sup\u003e, reclassified and processed by the authors (see also Supplementary Figures 2b–e for corresponding maps).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7983932/v1/5834c97065e4d79168d98208.png"},{"id":96430663,"identity":"bb8b5509-e2f6-4dc6-bb24-21a00d043f9d","added_by":"auto","created_at":"2025-11-21 03:54:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":274124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal crop–region typologies based on micronutrient deficiencies and attainable nutrient yields.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Six data-driven clusters identified from deficiency prevalence (iron, zinc) and average attainable nutrient yields (mg ha⁻¹) for cowpea, pearl millet, soybean, and common bean. Colours denote clusters; non-contiguous regions may group when profiles match. \u003cstrong\u003eb–c, \u003c/strong\u003eCluster characteristics and validation. \u003cem\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/em\u003e shows standardized (z-score) distributions of micronutrient deficiencies and crop yield potentials (tonnes ha⁻¹) for cowpea (Cpea), pearl millet (PM), soybean (Soy), and phaseolus bean (PB) across clusters, with box plots indicating variability and overlaid lines the mean standardized values. \u003cem\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/em\u003e presents the corresponding hierarchical clustering dendrogram, where proximity along the tree indicates similarity between clusters. Colour shading distinguishes six major clusters: (1) Northern America, Europe, Eastern Asia, Australia/New Zealand; (2) Eastern Africa, Caribbean; (3) South/Central America, Southeast Asia; (4) Northern Africa, Western/Central Asia; (5) Eastern/Western Europe; (6) Southern Asia, Southern/Western/Central Africa. Iron and zinc data \u003csup\u003e24,25\u003c/sup\u003e and crop data \u003csup\u003e15\u003c/sup\u003e, reclassified and processed by the authors (see also Supplementary Data 2–6).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7983932/v1/bbadb1b003e4222d7f53f20b.png"},{"id":96454018,"identity":"8753dc79-842a-4b96-bd32-17cca16b6598","added_by":"auto","created_at":"2025-11-21 10:02:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":121080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional food supply and attainable yield potential for nutrient-dense crops.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each region and crop: left tile = current food supply (g cap⁻¹ d⁻¹, FAOSTAT 2022); right tile = attainable yield potential (% of max regional average).\u003cstrong\u003e \u003c/strong\u003eMisalignments highlight non-agronomic constraints where use is low, but potential is high. Food supply values reflect national-level availability from FAO food balance sheets of each crop in the regional food system in 2022, not actual intake, and include both direct consumption and processed forms (e.g., soy oil or cowpea flour) \u003csup\u003e29\u003c/sup\u003e, while yield potential was derived from crop suitability estimates \u003csup\u003e15\u003c/sup\u003e, reclassified and processed by the authors (see also Supplementary Data 7).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7983932/v1/b611cd6c14c7755fea6fdc1e.png"},{"id":107438881,"identity":"acc72a9d-ceea-4047-bdb0-e1202b3f732d","added_by":"auto","created_at":"2026-04-21 13:43:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1309517,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7983932/v1/30ce3ba3-4404-4ca8-9485-b89667853efb.pdf"},{"id":96430662,"identity":"fdcb2cfa-cbdf-4a1a-90de-2948375eae0d","added_by":"auto","created_at":"2025-11-21 03:54:12","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48765,"visible":true,"origin":"","legend":"Supplementary Dataset Number","description":"","filename":"SupplementaryDataMappingglobalcroplandpotential.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7983932/v1/c3a7ee612bf1db153d7fc682.xlsx"},{"id":96454016,"identity":"2b4dde9f-6007-4a60-babe-d8928cfec704","added_by":"auto","created_at":"2025-11-21 10:02:14","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":311349,"visible":true,"origin":"","legend":"Mapping global cropland potential to deliver iron and zinc where they are most needed","description":"","filename":"SupplementaryinformationMappingglobalcroplandpotential.docx","url":"https://assets-eu.researchsquare.com/files/rs-7983932/v1/d73fc28d3d0ae3cb6d0536a5.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Mapping global cropland potential to deliver iron and zinc where they are most needed","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicronutrient deficiencies remain one of the most pressing global public health and food security challenges. More than two billion people suffer from inadequate intakes of essential micronutrients such as iron, zinc, vitamin A, and folate \u0026ndash; a condition often termed \u0026ldquo;hidden hunger\u0026rdquo;\u0026nbsp;\u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;Vulnerable groups, including children, adolescent girls, women of reproductive age (15\u0026ndash;49 years), and pregnant or lactating women, are disproportionately affected due to higher nutrient requirements and frequent dietary insufficiencies\u0026nbsp;\u003csup\u003e2,3\u003c/sup\u003e. These deficiencies, largely driven by inadequate dietary quality, lead to serious consequences such as anaemia, impaired neurocognitive development, and elevated morbidity and mortality\u0026nbsp;\u003csup\u003e3\u0026ndash;5\u003c/sup\u003e.\u0026nbsp;Progress has been made through supplementation, industrial fortification, and diet diversification programs\u0026nbsp;\u003csup\u003e2,6\u003c/sup\u003e, yet global food systems still fall short of meeting population requirements for many micronutrients\u0026nbsp;\u003csup\u003e1,7\u003c/sup\u003e.\u0026nbsp;For instance, even under optimistic scenarios, the global food system in 2018 provided only ~64% of required calcium and 69% of vitamin E\u0026nbsp;\u003csup\u003e7\u003c/sup\u003e, underscoring persistent gaps in micronutrient supply.\u003c/p\u003e\n\u003cp\u003eThis imbalance reflects deeper structural issues in agricultural production and diets\u0026nbsp;\u003csup\u003e6,8\u003c/sup\u003e. Energy-rich but micronutrient-poor staples (e.g. maize, wheat, rice) dominate global crop output due to their high yields and market value, while nutrient-dense crops like pulses, legumes, and millets are underrepresented in both production and consumption \u003csup\u003e2,9,10\u003c/sup\u003e.\u0026nbsp;In many regions, traditional diets that once included legumes and diverse grains have shifted towards mostly refined cereals, exacerbating micronutrient deficiencies\u0026nbsp;\u003csup\u003e8\u003c/sup\u003e.\u0026nbsp;Addressing these nutrient gaps requires a fundamental reorientation of how we prioritize crops and allocate land\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e. Emerging strategies in nutrition-sensitive agriculture aim to realign agricultural investments with human nutrient requirements\u0026nbsp;\u003csup\u003e12,13\u003c/sup\u003e, yet practical frameworks to guide crops and land-use decisions for nutrition outcomes remain limited.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo analytical tools with untapped potential to inform such strategies are nutrient profiling and land suitability analysis \u003csup\u003e14,15\u003c/sup\u003e. Nutrient profiling ranks foods by their contribution to diet quality; for example, the Nutrient Rich Foods index scores foods based on concentrations of vitamins, minerals, and other nutrients \u003csup\u003e9,16,17\u003c/sup\u003e and has been used to shape dietary guidelines and policy \u003csup\u003e18\u003c/sup\u003e.\u0026nbsp;Separately, land suitability models like the Food and Agriculture Organization/International Institute for Applied Systems Analysis (FAO\u0026ndash;IIASA) Global Agro-Ecological Zones (GAEZ) platform identify where crops can be optimally produced given local climate, soil, and terrain conditions\u0026nbsp;\u003csup\u003e15,19\u003c/sup\u003e.\u0026nbsp;While both frameworks are well established, they have seldom been combined to target micronutrient deficiencies \u0026ndash; a gap that leaves agricultural policy disconnected from nutrition goals. Integrating these approaches could enable food system planners to prioritize investments in crops that are not only agronomically suitable but also most likely to alleviate nutrient shortfalls.\u003c/p\u003e\n\u003cp\u003eHere, we bridge this gap by linking crop nutrient profiles with spatial land productivity data to inform crop prioritization for micronutrient interventions. We introduce a Deficiency-Weighted Nutrient Score (DWNS) that ranks crops by their nutrient density and the severity of region-specific micronutrient deficiencies (focused on iron and zinc). We then couple DWNS with high-resolution GAEZ suitability and attainable yield data to identify where these nutrient-rich crops could be cultivated most effectively at scale. Finally, we use clustering analysis to delineate global regions where high nutrient needs and crop suitability converge, yielding a geographically explicit framework to guide nutrition-sensitive agriculture. In doing so, we provide a data-driven tool for policymakers and development agencies to guide nutrition-sensitive agricultural investments and integrated food systems planning. This integrative approach offers clear guidance on crop selection and land allocation to mitigate micronutrient deficiencies. Importantly, the framework is meant to inform prioritization of agricultural interventions rather than replace economic, policy, or behavioral strategies needed to translate production gains into improved consumption. By aligning agro-ecological potential with nutritional needs, our study demonstrates how agricultural development can be strategically leveraged to combat hidden hunger, while acknowledging the complementary measures required for success.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eNutrient density of selected crops\u003c/strong\u003e: The iron and zinc composition of the 37 food crops varied widely, underscoring the importance of crop choice for nutrition. Detailed values and percent contributions to daily requirements are provided in Supplementary Data 1a. In summary, a few crops are outliers in their micronutrient density. High-performing crops, such as soybeans, cocoa, cowpea, pearl millet, and common beans (Phaseolus vulgaris), deliver a large fraction of daily iron and zinc requirements per 100 g serving (Figure 1a). For example, 100 g of cocoa powder provides ~62% of a woman\u0026rsquo;s daily iron requirement and 67% of zinc, owing to cocoa\u0026rsquo;s exceptional mineral content. Soybean (raw, whole) provides ~70% of daily iron needs and 48% of zinc per 100 g. In contrast, many widely grown staples like cassava, polished rice, or banana are poor sources of these micronutrients, each providing less than 5% of daily iron or zinc requirements per 100 g. This analysis confirms that focusing on nutrient-dense crops (especially certain legumes and millets) could substantially improve the micronutrient output of farming systems relative to common staples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeficiency-Weighted Nutrient Scores (DWNS)\u003c/strong\u003e: When we incorporate regional micronutrient deficiency data, the top-ranking crops become even more distinct. Figure 1b shows the DWNS for iron and zinc combined, for each crop averaged across all subregions. After weighting for deficiency prevalence, soybean, cowpea, pearl millet, and common beans emerge as the highest scoring crops globally (each with a combined DWNS around 50\u0026ndash;65%). These crops not only have high iron and zinc composition, but they are also particularly relevant to regions suffering from anemia and zinc deficiency. For instance, soybeans had one of the highest unweighted nutrient scores (due to high iron content), and deficiency-weighting preserved soybean\u0026rsquo;s high rank because many regions with iron deficiency (e.g. South Asia, Africa) could grow soy. Cocoa, while very nutrient-dense, saw its weighted score moderate slightly (to ~64%) because cocoa-producing regions like West Africa do have high anemia rates but cocoa is not typically consumed in large quantities as a staple food. Overall, our deficiency-weighting approach identifies crops that are both micronutrient-rich and aligned with global public health needs, ensuring that a score reflects potential impact (nutrients delivered where people lack them) rather than just nutrients per gram. These scores indicate that in regions with high iron and zinc deficiency, a single serving of these foods could provide roughly half or more of population\u0026rsquo;s daily requirement for those nutrients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePriority crops for suitability analysis\u003c/strong\u003e: Based on the DWNS ranking, we prioritized four crops \u0026ndash; soybean, cowpea, pearl millet, and common beans, for detailed land suitability and yield potential analysis. These stood out as offering the greatest potential to supply iron and zinc in areas of need. This selection was not only driven by their DWNS values, but also by practical considerations: cowpea, pearl millet, and common beans are traditionally grown by smallholders in many high-deficiency regions and can be utilized as food with minimal processing \u003csup\u003e26\u0026ndash;28\u003c/sup\u003e. Soybean, while often cultivated as an industrial or feed crop, was included due to its exceptional per-hectare nutrient yield; we acknowledge that soybeans typically require processing (e.g. for oil, soy flour, or fermented foods) and targeted support to be adopted at smallholder level, and we return to these challenges in the discussion. By focusing on these four crops, we concentrate on interventions that could feasibly enhance iron and zinc intake in vulnerable regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeographic potential for iron and zinc yield:\u003c/strong\u003e Using GAEZ data, we mapped and quantified the attainable iron and zinc output (in mg per hectare) for the priority crops across global regions. Figure 2a summarizes these results, and Supplementary Figures 2b\u0026ndash;e provides crop-specific maps. There is pronounced regional heterogeneity. For example, cowpea shows highest potential micronutrient yields in the Caribbean (\u0026asymp;235,800 mg iron/ha and 161,400 mg zinc/ha, under rain-fed average attainable yields) and Eastern and Middle Africa (\u0026asymp;175,000\u0026ndash;164,000 mg iron/ha). Regions with cooler climates like Northern Europe or Central Asia have negligible cowpea potential due to agronomic unsuitability. Soybean displays a different pattern: the highest iron and zinc yields are attainable in Eastern Europe (up to 436,000 mg iron and 135,000 mg zinc per ha, reflecting very high yield potential in favorable temperate zones), followed by the Caribbean (~419,000 mg Fe and 130,000 mg Zn/ha) and Middle Africa (~344,000 mg Fe/ha). Soybean\u0026rsquo;s potential is low in regions such as Central Asia and much of Micronesia where climate or soils are limiting. Common beans (Phaseolus) reach their peak iron yield potential in Western Europe (~261,000 mg Fe/ha), the Caribbean (~245,000 mg Fe/ha), and Eastern Africa (~189,000 mg Fe/ha), with similar relative rankings for zinc. Pearl millet, being a crop adapted to arid environments, has its highest iron and zinc yields in Eastern Africa (~152,800 mg Fe and 59,200 mg Zn/ha) and parts of Western Africa and the Caribbean, whereas humid or high-latitude regions (e.g. Europe, Eastern Asia) show near-zero potential for millet. These findings illustrate that each crop has a distinct geography of maximum impact. Notably, sub-Saharan Africa, South Asia, and the Caribbean appear repeatedly as high-potential areas for one or more of the prioritized crops, aligning with the heavy micronutrient burden in those regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial overlap of need and potential (cluster analysis):\u0026nbsp;\u003c/strong\u003eBy combining the nutritional-need and yield-potential datasets, we identified six \u0026ldquo;crop\u0026ndash;region typologies\u0026rdquo; through clustering (Fig. 3a\u0026ndash;c). Detailed summary and variable statistics are provided in Supplementary Tables S1a and S1b, while Supplementary Figures 3d and 3e show the standardized yield-deficiency distributions and the Pseudo-F statistic used to determine the optimal number of clusters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese typologies group countries with similar deficiency rates and crop potentials, even if they are geographically distant (Fig. 3a). For instance, one cluster (Cluster 2) includes Eastern Africa and the Caribbean, two distant regions that both exhibit high iron/zinc deficiency prevalence and strong yield prospects for the selected crops. Another cluster (Cluster 6) spans much of Southern, Western, and Middle Africa as well as Southern Asia, areas all characterized by high micronutrient deficiencies and moderate-to-high suitability for the priority crops. In contrast, a cluster comprising parts of Europe and Central Asia shows high yield potential (especially for soybeans and beans) but low current deficiency burdens. These data-driven clusters provide insight into where similar strategies might be applied. However, for ease of communication we also interpret the results in conventional regional terms. In Eastern Africa and the Caribbean (cluster 2), for example, our analysis suggests that promoting cowpea and pearl millet could be particularly effective, as both crops thrive agro-ecologically and match local nutritional needs. In Western, Middle, and Southern Africa plus South Asia (cluster 6), soybean and millet stand out as promising options to boost iron and zinc availability. Meanwhile, Eastern Europe (cluster 5), with its high yield capacity for soy and beans but relatively lower deficiency levels, might serve as a surplus production zone or exporter of nutrient-rich crops given appropriate policies (we discuss this below). Overall, the clustering highlights that regions like sub-Saharan Africa and South Asia would benefit most from nutrition-driven crop shifts, whereas some high-production regions could leverage their capacity to support global micronutrient distribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent food use versus potential:\u0026nbsp;\u003c/strong\u003eWe compared the identified production potential with present-day consumption patterns (using FAO food balance data for 2022) to gauge how large a shift would be needed. In many regions, the current per capita consumption of these nutrient-dense crops is very low, revealing a substantial gap between what is agronomically possible and what is actually contributing to diets (Fig. 4). For instance, pearl millet, despite its importance in parts of Western Africa, remains a minor part of diets elsewhere. Our analysis shows pearl millet contributes at most ~2\u0026ndash;3% of per capita food supply (by weight) in its strongholds and far less in most regions, even though its yield potential is high in several of those regions. Common beans are relatively widely eaten (e.g. contributing ~2.3% of diet by weight in Eastern Africa), which corresponds to Eastern Africa also having good yield potential for beans. Soybean stands out: in regions like South-eastern Asia and the Americas, it contributes up to ~1.6\u0026ndash;1.9% of diets, but in many high-deficiency areas, direct soybean contribution is negligible. In Eastern and Western Europe, for example, we found a striking mismatch, these regions have among the highest potential yields for soybeans and common beans, yet almost none of that potential is used for human food, as diets rely on other foods and much of the soybean grown is for animal feed or export. This mismatch between food supply and potential suggests a large untapped opportunity but also indicates the presence of non-agronomic barriers to utilization.\u003c/p\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eOur study provides a novel approach to align agriculture with nutritional goals by integrating nutrient profiling and land suitability analysis in a geographically specific way. We extend established concepts like nutrient density indices \u003csup\u003e14,17,30\u003c/sup\u003e, by incorporating a public health dimension, weighted by deficiency prevalence and overlaying the results with high-resolution agro-ecological data. The outcome is a decision-support framework indicating not just which crops are nutritious, but where they could be prioritized to address micronutrient shortfalls most effectively. This approach offers a new perspective on the intersection of nutrition and agronomy, identifying regions where shifting or diversifying production toward nutrient-rich crops could yield major public health benefits. It is intended as a tool to guide policymakers and planners in making nutrition-sensitive agricultural decisions, complementing existing interventions in public health.\u003c/p\u003e\n\u003cp\u003eConsistent with prior research on nutrition-sensitive agriculture, our analysis highlights a familiar cast of crops known for high micronutrient content and adaptability to marginal environments. Pearl millet, cowpea, soybean, and common beans have all been emphasized in the literature as promising crops to improve dietary quality in low-income settings \u003csup\u003e28,31\u0026ndash;33\u003c/sup\u003e.\u0026nbsp;We confirmed that these crops, despite being underutilized in many regions, have exceptional potential to supply iron and zinc. At the same time, our results reiterate that staple root and tuber crops (cassava, yam, potato) and even many cereals, while critical for energy supply, contribute relatively little to micronutrient intake\u0026nbsp;\u003csup\u003e9\u003c/sup\u003e.\u0026nbsp;This dichotomy supports calls to better integrate pulses, legumes, and millets into farming systems and diets as a strategy to combat hidden hunger.\u003c/p\u003e\n\u003cp\u003eThe geographic analysis provides actionable insights. For example, soybean stands out as a crop with extremely high iron yield per hectare in several regions. In Eastern Europe, an average hectare of soy could meet the iron requirements of ~19,500 people annually (and zinc for ~13,300 people), and similarly a hectare in the Caribbean could support ~18,700 people for iron (~12,800 for zinc) based on attainable yields. While Eastern Europe does not have a high deficiency burden, regions in sub-Saharan Africa and South Asia would greatly benefit from increased soybean cultivation if the production could be channeled into local diets. In cluster 6 (encompassing much of Africa and Southern Asia) and cluster 2 (Eastern Africa and Caribbean), our data indicate that soybean could substantially bolster iron and zinc availability. For instance, attainable soybean yields in Middle Africa could provide enough iron for ~15,300 people per hectare, and in Southern Asia for ~7,700 people per hectare, which is impactful given the prevalence of anemia. Notably, these findings align with nutritional studies showing that soybean can be an effective source of bioavailable iron when consumed \u003csup\u003e34\u003c/sup\u003e, especially if traditional preparation or fermentation methods are used to enhance iron absorption \u003csup\u003e35\u003c/sup\u003e. Soybean exemplifies the opportunity and challenge identified in this work: it offers immense potential nutritional gains, but realizing these gains will require innovations to incorporate soy into local food cultures and value chains, as we discuss further below.\u003c/p\u003e\n\u003cp\u003eOur regional results also suggest complementarities among crops. We observed that soybean and common beans often share high-yield potential in the same areas (Fig. 3c), particularly in parts of Eastern Africa and the Caribbean (clusters 2 and 6). Common beans demonstrated the single highest per-hectare iron supply among all crops in some instances (e.g. Eastern Africa), indicating that where climate allows, it is a central for nutrition. These two legumes could be deployed in rotation systems to sustain soil fertility, both fix atmospheric nitrogen via rhizobia, benefiting subsequent crops \u003csup\u003e36,37\u003c/sup\u003e. While intercropping soybean and common bean together is generally not practiced (since they occupy a similar niche), rotating them or intercropping each with cereals could maximize land use efficiency and nutrient output \u003csup\u003e38\u003c/sup\u003e. Their nitrogen-fixing ability also offers economic and environmental benefits by reducing fertilizer needs and greenhouse emissions \u003csup\u003e36,37\u003c/sup\u003e. Likewise, cowpea and pearl millet show synergy (Fig 3c), both are well-suited to arid, low-fertility conditions and have been successfully grown in intercrops, where cowpea\u0026rsquo;s ground cover and millet\u0026rsquo;s height make for complementary resource use \u003csup\u003e39,40\u003c/sup\u003e. In Eastern Africa and the Caribbean (cluster 2), our data suggest cowpea and millet each could provide on the order of 100,000+ mg of iron per hectare; combined in farming systems, they could improve resilience and yield stability \u003csup\u003e40,41\u003c/sup\u003e. These agronomic considerations reinforce that introducing nutrient-rich crops need not come at the expense of system productivity, in fact, it can enhance it when done thoughtfully.\u003c/p\u003e\n\u003cp\u003eHowever, translating the potential supply of nutrients into actual nutritional outcomes is far from straightforward. Our findings reveal large gaps between where a crop could be grown for nutrition and how much it is currently contributing to diets (Fig. 4). Several high-potential regions (e.g. parts of Latin America, or Europe in the case of soybean/beans) do not presently use these crops for food to any significant extent. This highlights that simply identifying agro-ecological \u0026ldquo;hotspots\u0026rdquo; for nutrient-rich crops is not enough \u0026ndash; there are economic, cultural, and policy hurdles that determine whether those crops will be planted by farmers and eaten by consumers \u003csup\u003e11,42,43\u003c/sup\u003e. For example, in Eastern and Western Europe, despite suitable land for soybean and bean cultivation, the actual supply of these crops for human food is negligible. Farmers there may prefer to grow higher-profit crops or use soy for livestock feed, and consumers obtain their nutrients through other foods. This suggests barriers like market incentives, pricing, dietary preferences, lack of processing facilities, or competing land uses that can prevent a nutritionally optimal allocation of land \u003csup\u003e32,44\u003c/sup\u003e. On the other hand, we found that in some regions such as Western Africa or Southern America, current food use of certain crops (e.g. millets, soybeans) is more in line with yield potential, reflecting successful traditional integration of those crops into diets \u003csup\u003e45\u003c/sup\u003e. These examples highlight that context matters, local food culture and market infrastructure can either facilitate or impede the adoption of nutrient-dense crops.\u003c/p\u003e\n\u003cp\u003eCrucially, increasing production of nutritious crops does not guarantee improved consumption. Even when yields are boosted, the food might not reach those who need it. In practice, much of the world\u0026rsquo;s production of nutrient-rich crops is diverted. A striking example is soybean: globally, only about 6% of soybean harvest is used directly as human food, with most going to animal feed, biofuel, and industrial uses \u003csup\u003e46\u003c/sup\u003e. This disconnects between production potential and human nutrition means that without deliberate interventions, simply growing more soybeans (or other nutritious crops) in a high-deficiency region might have little impact on local micronutrient intake. Income and consumer demand also play a major role as well \u003csup\u003e47,48\u003c/sup\u003e. In some low-income communities, pulses and coarse grains are perceived as \u0026ldquo;foods of the poor,\u0026rdquo; \u003csup\u003e49\u003c/sup\u003e and as incomes rise, diets often shift towards meat, dairy, and refined staples, potentially reducing demand for traditional legumes \u003csup\u003e50,51\u003c/sup\u003e. Changing such perceptions requires nutrition education and behavior change initiatives so that the value of these foods is recognized and they remain desirable. Conversely, boosting supply without building demand could lead to excess production that farmers cannot profitably sell, undermining the intervention.\u003c/p\u003e\n\u003cp\u003eTo bridge the gap between potential production and actual nutrition outcomes, a suite of economic and policy measures must accompany agronomic recommendations. Farmers need incentives and support to grow these priority crops: for instance, price support or subsidies can make nutrient-rich crops competitive with cash crops, and crop insurance or input support can reduce the risk of switching to a less familiar crop \u003csup\u003e52,53\u003c/sup\u003e. Market development is equally important \u0026ndash; investments in storage, processing, and distribution infrastructure (such as mills for millet flour, or facilities to process soybeans into food products like tofu, soymilk, or fortified blends) can create value chains that bring these crops from farm to fork. In regions where large-scale soybean exporters set global prices, local smallholder farmers may struggle to find a profitable niche for food-grade soybeans \u003csup\u003e44\u003c/sup\u003e. Targeted public procurement (e.g. including cowpeas or millet in school feeding programs or food aid baskets) can secure a market and stimulate production. Trade policies might also be aligned with nutritional goals: for example, countries with surplus capacity in iron-rich crops could receive trade preferences or support to export to countries with deficits, as long as those exports are actually used for food. Our analysis identified several such possibilities e.g., Caribbean and parts of Africa have overlapping potential that could be leveraged through regional trade in legumes and grains if properly structured (cluster 2 and 6 interplay). Consumer-focused policies like nutrition education campaigns and social marketing can raise awareness and acceptance of these foods, increasing demand. In summary, realizing the promise of the DWNS\u0026ndash;GAEZ framework will require connecting it to broader food system actions: aligning extension services, subsidies, market incentives, and educational programs to create an enabling environment for these crops to flourish from field to plate.\u003c/p\u003e\n\u003cp\u003eAgronomic practices and biofortification are additional critical pieces of the puzzle \u003csup\u003e38\u003c/sup\u003e. Beyond choosing which crops to grow, how those crops are grown can influence their nutritional payoff. Agronomic biofortification \u0026ndash; the use of fertilizers or soil amendments containing micronutrients \u0026ndash; has shown success in increasing the zinc and iron content of staple crops. For instance, applying zinc-enriched fertilizer can substantially raise zinc concentrations in wheat grain, improving dietary zinc intake without changing the crop \u003csup\u003e38\u003c/sup\u003e. Similarly, improving soil health and organic matter can enhance plants\u0026rsquo; uptake of minerals from the soil. These practices could complement our approach by ensuring that crops grown in targeted regions express their full nutrient potential. Meanwhile, plant breeding and genetic biofortification programs are already focusing on the very crops we identified. Organizations like HarvestPlus and ICRISAT have developed high-iron and high-zinc varieties of common beans, pearl millet, and other staples \u003csup\u003e31,33\u003c/sup\u003e. For example, iron-biofortified pearl millet bred by ICRISAT and partners contains significantly more iron and zinc and has proven effective to improve iron status in human trials. Integrating these improved cultivars into the suitable areas highlighted by our analysis could amplify impact \u0026ndash; farmers could grow not just any cowpea or millet, but the nutritionally supercharged versions in the environments where they thrive best. By acknowledging and incorporating these parallel strategies, soil management to enhance micronutrient uptake, and crop improvement to raise inherent nutrient content \u0026ndash; we paint a more holistic picture of how micronutrient supply can be increased. In practice, a nutrition-sensitive agriculture program could combine our spatial targeting (to know where to act) with biofortified seeds and appropriate agronomic techniques (to maximize impact), underpinned by the policy supports discussed earlier.\u003c/p\u003e\n\u003cp\u003eOur analysis has several limitations that must temper the conclusions. First, we focused on iron and zinc deficiencies; while these are among the most widespread micronutrient deficiencies, others like vitamin A, iodine, or folate were beyond our scope. A multi-nutrient extension of this framework could yield different crop priorities (for example, orange-fleshed sweet potato for vitamin A, or leafy vegetables for folate) and should be explored in future research. Second, we limited our crop list to 37 major crops, which means some regionally important but globally minor crops were omitted. Underutilized traditional foods (indigenous grains, leafy greens, etc.) might have high nutritional value and local suitability \u003csup\u003e10,54\u003c/sup\u003e; excluding them could bias our findings towards well-known commodities. Relatedly, our nutrient composition data came largely from global databases and may not reflect local varietal differences \u0026ndash; nutrient levels can vary by cultivar and soil, so on-the-ground impact might differ from our estimates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThird, our yield estimates are based on GAEZ attainable yields with intermediate inputs and do not account for climate change, land degradation, or other dynamic factors; nor do they guarantee that farmers can achieve those yields without capacity building and investment. We also did not factor in post-harvest losses or supply chain inefficiencies \u0026ndash; our nutrient yield figures assume that what is grown is fully harvested, stored, and made available for consumption, which in reality is often not the case (especially for perishable or pest-prone crops). Additionally, our cluster analysis produced groupings that are analytically useful but not immediately aligned with political boundaries; implementing policies based on these may require translating our findings into existing administrative regions or economic communities. Importantly, we did not incorporate economic feasibility into the DWNS\u0026ndash;GAEZ model. A crop might be biophysically ideal for a region, but if it is not profitable or if it requires more labor or different skills, farmers may not adopt it. Factors like relative prices, input costs, and farmer risk tolerance are critical to real-world outcomes and were beyond the scope of our study. Future work could integrate an economic optimization or cost-benefit layer to identify \u0026ldquo;low-hanging fruit\u0026rdquo; interventions that are both nutritionally and economically advantageous.\u0026nbsp;Finally, livestock systems were not considered in this framework, even though animal-source foods remain critical providers of bioavailable iron, zinc and vitamin B₁₂ in many regions \u003csup\u003e9\u003c/sup\u003e. Integrating livestock suitability and feed-crop dynamics with this crop-based approach would provide a more complete picture of potential nutrient supply and trade-offs across food systems.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, which largely reflect the availability and resolution of global datasets rather than shortcomings of the framework itself, the insights gained have meaningful policy implications. The results reinforce the concept that agricultural policy and nutritional outcomes are deeply intertwined, spatially targeting crop production for nutrient needs can be a viable strategy to support public health objectives, but it must be embedded in a broader systems approach \u003csup\u003e11\u003c/sup\u003e. This perspective directly supports Sustainable Development Goal 2 (Zero Hunger), particularly Target 2.1 on universal access to safe and nutritious food and Target 2.4 on sustainable and resilient agricultural systems. By identifying opportunities for countries and regions to better nourish their populations through what they grow, this framework provides an evidence base to integrate nutrition objectives into agricultural and land-use planning. For instance, governments in East Africa or South Asia could use these findings to justify programs that promote legumes and millets, backed by extension training and input support, in areas shown to have both high malnutrition and high suitability. Similarly, international development agencies could target biofortified seed distribution or micronutrient fertiliser initiatives to mapped hotspot regions, maximising the nutritional return on investment.\u003c/p\u003e\n\u003cp\u003eAt the same time, our findings make clear that there is no single blueprint for success. Interventions must be adapted to local conditions, considering environmental sustainability, cultural acceptance and economic feasibility. We caution that boosting the supply of micronutrients through agriculture is a necessary but not sufficient condition to eliminate hidden hunger. It should go hand in hand with efforts to improve dietary diversity, nutrition education, and food affordability, ensuring that increased availability actually leads to increased intake. In conclusion, our DWNS\u0026ndash;GAEZ framework offers a starting point for an integrated food systems planning approach, one that explicitly meshes agricultural development with nutritional well-being. By using data to direct attention and resources to where they can have the biggest impact, and by coupling those insights with supportive policies and programs, stakeholders can make tangible progress toward eradicating micronutrient deficiencies in an equitable and sustainable way. Changing what we grow and where can improve what people eat, but only if we simultaneously address the economic and behavioral drivers of food choice. Such an integrated strategy is essential to achieve global nutrition targets and ensuring that agricultural gains translate into healthier populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eCrop selection and nutrient profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained crop productivity and environmental data from the Global Agro-Ecological Zones v4 (GAEZ v4) platform \u003csup\u003e15\u003c/sup\u003e which provides global assessments of land suitability and attainable yield for a wide range of crops. GAEZ evaluates biophysical production potential by integrating detailed climatic, soil, and terrain data with crop growth requirements and management assumptions. From GAEZ database, we selected a subset of 37 major food crops representing cereals, legumes/pulses, roots and tubers, oilseeds, and vegetables, excluding crops mainly used for non-food purposes and those with minimal relevance to human diets, to concentrate the analysis on food security and nutrition outcomes.\u003c/p\u003e\n\u003cp\u003eFor each crops, we compiled nutrient profile data on iron and zinc composition (mg per 100 g, in the raw, edible portion) from the USDA FoodData Central database \u003csup\u003e21\u003c/sup\u003e, supplemented by other published sources for a few crops (e.g. pearl millet from \u003csup\u003e22\u003c/sup\u003e; foxtail millet from \u003csup\u003e23\u003c/sup\u003e). These values represent typical cultivar averages. Where multiple entries or varieties were available for a given crop, a representative value was chosen (for example, a common market variety or mean of several entries). No adjustments were made for cultivar- or region-specific differences in nutrient composition, which we acknowledge as a limitation given the natural variability in crop nutrient profiles. To put these nutrient values in context, we used Harmonized Average Requirement (H-AR) values for women of reproductive age (WRA, 15\u0026ndash;49 years) from \u003csup\u003e20\u003c/sup\u003e as daily population reference intakes. The H-ARs account for average bioavailability in typical diets. For iron, we assumed 5% absorption for plant (non-heme) iron, reflecting high phytate diets, and for zinc we used requirements for unrefined, high-phytate diets. These conservative absorption assumptions mean that our analysis inherently considers the lower bioavailability of iron and zinc from plant sources when evaluating how much of a requirement a food can meet.\u003c/p\u003e\n\u003cp\u003eFor each crop we computed its nutrient contribution, the percentage of a WRA daily iron or zinc requirement that 100 g (raw, edible portion) provides, using H-ARs. We then applied a deficiency weighting using regional prevalence of iron-deficiency anaemia in WRA \u003csup\u003e24\u003c/sup\u003e and zinc deficiency \u003csup\u003e25\u003c/sup\u003e, so nutrients count more where deficiencies are more widespread, based on available data from 184 countries. This produced a Deficiency-Weighted Nutrient Score (DWNS) for each crop and region, capped at 100% to prevent overweighting of any single food. Regional DWNS values were then averaged across subregions to obtain a global ranking, with regional results retained for mapping and clustering (Supplementary Data 1).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1763696805.png\" width=\"747\" height=\"696\"\u003e\u003cstrong\u003eLand suitability and attainable yield analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter identifying nutrient-dense crops through DWNS, we evaluated the suitability of current croplands worldwide for cultivating these crops. We used GAEZ v4 Theme 4 (\u0026ldquo;Suitability and Attainable Yield\u0026rdquo;) data, which provides crop-specific estimates of agronomically attainable yield and suitability class for each 5-arc-minute grid cell (~9 x 9 km at the equator) under various input and climate scenarios \u003csup\u003e15\u003c/sup\u003e. To reflect realistic farming conditions in many low- and middle-income regions, we focused on rain-fed production with intermediate inputs (sometimes termed \u0026ldquo;average inputs\u0026rdquo;). This means we considered yields achievable with modest levels of inputs and management, for example, using improved seed varieties and some fertilizer and pest control, but not the maximum technologically possible yields \u0026ndash; and without irrigation infrastructure (rain-fed conditions). This scenario reflects typical smallholder farming systems and provides an attainable yield estimate rather than an idealized maximum. We extracted the attainable yield (in tonnes/ha) and suitability index for each prioritized crop on currently cultivated land areas (as defined by GAEZ\u0026rsquo;s cropland mask) and harmonized regional boundaries using Global Administrative Areas (GADM v4.1) shapefiles \u003csup\u003e55\u003c/sup\u003e for consistent regional summaries. The resulting gridded yield surfaces were aggregated to country and regional levels using area-weighted averages and totals for subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial clustering of high-potential regions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify global patterns and group regions with similar nutrient\u0026ndash;production profiles, we conducted an unsupervised clustering analysis combining micronutrient deficiency prevalence with crop yield potential. The analysis covered 184 countries aggregated into subregions (those with data for both deficiencies). Features included (i) the prevalence of iron and zinc deficiencies and (ii) the average attainable nutrient yield (mg Fe ha⁻\u0026sup1; and mg Zn ha⁻\u0026sup1;) for each of the top nutrient-dense crops.\u0026nbsp;K-means clustering was first applied to partition subregions into groups sharing comparable nutrient-need and production-potential characteristics \u003csup\u003e56,57\u003c/sup\u003e. After testing multiple cluster solutions, six clusters were selected as providing the best balance between granularity and interpretability, based on Calinski\u0026ndash;Harabasz and silhouette metrics (Supplementary Figure 3e). Hierarchical agglomerative clustering (Ward\u0026rsquo;s method, Euclidean distance) was then applied to the K-means output to visualise relationships among clusters \u003csup\u003e58\u003c/sup\u003e. Each cluster was characterised by its dominant subregions and crop potential. Because the clustering was data-driven, some clusters encompass non-contiguous regions that nonetheless share similar profiles (for example, Eastern Africa and the Caribbean were grouped together due to comparable high deficiency rates and crop yield potentials). For interpretability, we describe clusters in terms of their constituent sub-continents, but we recognize that these groupings do not always align with traditional political or agro-ecological zones.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of agronomic potential with current food supply\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess whether regions are realizing their agronomic potential for nutrient-dense crops, we compared attainable yield potential from GAEZ with actual food supply of the same crops. Regional food supply data (g cap⁻\u0026sup1; d⁻\u0026sup1;) were obtained from the FAO Food Balance Sheets \u003csup\u003e29\u003c/sup\u003e, which account for post-harvest losses, feed use, processing, and other diversions from the food system. This metric represents food available for human consumption, not actual intake. For each crop and region, we expressed attainable yield as a percentage of the highest observed regional average yield worldwide and paired with corresponding per-capita food supply. The comparison identifies cases where dietary availability is low despite high agro-ecological suitability, or conversely, where both are aligned. The resulting indicator supports cross-regional interpretation of the gap between production potential and realized food supply (visualized in Fig. 4).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eResource availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLead contact\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther information and requests for resources or data should be directed to the lead contact Ejovi Abafe ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study did not generate new physical materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are from publicly available sources or published literature as cited. Global cropland suitability and average yield projections were obtained from the Global Agro-Ecological Zones (GAEZ) v4 database (https://gaez.fao.org/) and nutrient composition data were sourced from USDA FoodData Central (https://fdc.nal.usda.gov/). Harmonized nutrient requirement values were taken from Allen et al. 2020 (https://doi.org/10.1093/advances/nmz096). All other data supporting the findings of this study, including supplementary tables and region-level projections, are available within the supplementary information files or from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobal cropland suitability and average yield projections were processed and reclassified in ArcGIS Pro (version 3.4.0, Esri). Data analysis and processing were conducted in Microsoft Excel 2024 and R (version 4.4.2; RStudio). Custom R scripts used for data visualization are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEAA, NWS, TMRM, and WCM conceptualized the study and developed its methodology.\u003cbr\u003e\u0026nbsp;EAA calibrated the model, analyzed the data, compiled the outputs, and drafted the manuscript.\u003cbr\u003e\u0026nbsp;NWS, TMRM, and WCM critically reviewed and revised the manuscript. All authors reviewed and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no competing interests exist\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePassarelli, S. \u003cem\u003eet al.\u003c/em\u003e Global estimation of dietary micronutrient inadequacies: a modelling analysis. \u003cem\u003eThe Lancet Global Health\u003c/em\u003e S2214109X24002766 (2024) doi:10.1016/S2214-109X(24)00276-6.\u003c/li\u003e\n\u003cli\u003eFAO; IFAD; UNICEF; WFP; WHO. \u003cem\u003eThe State of Food Security and Nutrition in the World 2024\u003c/em\u003e. (FAO; IFAD; UNICEF; WFP; WHO;, Rome, Italy, 2024). doi:10.4060/cd1254en.\u003c/li\u003e\n\u003cli\u003eStevens, G. 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Here we present a spatial decision framework that links a Deficiency-Weighted Nutrient Score (DWNS) for 37 crops with Global Agro-Ecological Zones (GAEZ) attainable yields to identify crop–region opportunities to boost iron and zinc supply. Four crops (soybean, cowpea, pearl millet, and common beans) consistently rank highest across deficiency hotspots. Under rain-fed, intermediate-input assumptions, attainable nutrient yields (mg ha⁻¹) in Eastern Africa, Southern/Western/Central Africa, Southern Asia, and the Caribbean could meet the daily iron requirements of ~7,000–20,000 people per hectare, depending on crop and region. We distinguish between potential nutrient supply and actual dietary intake, highlighting the economic and behavioural factors that determine whether production translates into impact. This framework complements existing economic and demand-side approaches, enabling governments and development agencies to direct investments toward nutrient-dense crops and value chains that are regionally viable, advancing SDG 2 while recognising real-world feasibility constraints.","manuscriptTitle":"Mapping global cropland potential to deliver iron and zinc where they are most needed","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 03:54:07","doi":"10.21203/rs.3.rs-7983932/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50558684-a384-40f2-a291-3c76710f3081","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58276398,"name":"Earth and environmental sciences/Environmental social sciences/Sustainability"},{"id":58276399,"name":"Scientific community and society/Agriculture"},{"id":58276400,"name":"Scientific community and society/Developing world"}],"tags":[],"updatedAt":"2026-04-21T13:43:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 03:54:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7983932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7983932","identity":"rs-7983932","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}