Assessment of current and future spatial suitability of market-demanded old and new common bean varieties in Tanzania

Climate change is projected to reduce land suitable for common bean cultivation in Sub-Saharan Africa. This poses serious risks to food security and livelihoods, especially as most farmers continue to grow older varieties under changing conditions. This study evaluated current and future suitability of common bean varieties in Tanzania by comparing older (>10 years) and newly released, market-preferred cultivars under a CMIP6 climate scenario. Suitability was analyzed for near-current (1970–2000) and future (2021–2040) periods using the Targeting Tool Kit and land suitability models in R. Model outputs were validated with 98.7% accuracy. By 2040, highly suitable areas are projected to decline by 4.9%, while low-suitability areas may expand by 31.2%. Older varieties (JESCA, UYOLE 03) experience the greatest reductions (–5.7%, –5.0%), whereas newer varieties show greater resilience. TARIBEAN7 shows no clear spatial advantage over Lyamungu 90. Although JESCA may decline under future climates, its strong environmental predictability makes it a valuable reference variety for breeding. Altitude, rainfall, and soil properties remain dominant factors shaping suitability. Findings highlight the need for climate-smart and area -targeted breeding, broader seed dissemination, and improved soil management to sustain production and resilience.

1 Introduction

Common bean demand in Sub-Saharan Africa (SSA) is projected to increase in the 21st century due to its nutritional and economic benefits (Jha et al., 2023). However, climate variability is projected to affect its adaptation suitability due to fluctuations in environmental-related factors (Smith et al., 2022; Bibi and Rahman, 2023). Several studies (Lobell et al., 2008; Thornton et al., 2009, 2011; Ramirez-Villegas and Thornton, 2015) have projected a decline in its suitability, growth, and yield. Economically vulnerable tropical and subtropical nations, which lack robust adaptation resources, are anticipated to bear the brunt of these impacts (Iturbide Martínez de Albéniz et al., 2020; Gbode et al., 2025). Projections indicate a dramatic decline in common bean cultivation areas, with over 50% of suitable regions expected to become less viable (Hummel et al., 2018). According to Thornton et al. (2009), bean yields in Tanzania are projected to decline by 50–70% under a high-emission scenario by 2050. Different bean varieties have varying levels of tolerance to these stresses. Drought-tolerant varieties can maintain yield and quality even with limited water (Polania et al., 2016). Similarly, heat-tolerant varieties flower and set pods even when temperatures are high (Batool et al., 2023), while excessive moisture tolerance also varies among bean genotypes, with some lines showing better survival under excess water conditions (Soltani et al., 2018). Varieties developed and released under past environmental conditions such as higher soil fertility may no longer perform well in the same locations where soil nutrients have been depleted (Thornton et al., 2009; Atlin et al., 2017). The situation is more complex in areas where rainfall and temperature patterns have shifted, but the varieties in use have remained the same.

Common bean (Phaseolus vulgaris L.) is third, after maize and rice (National Bureau of Statistics (NBS), 2024) as the most cultivated crop in Tanzania due to its nutritional, economic, and environmental benefits. The crop is widely grown in regions situated in the Northern zone, Southeastern zone, Lake zone and Western zone (Ndimbo et al., 2022). The crop is often intercropped with many crops such as maize (Nassary et al., 2020; Nassary, 2025) and banana and coffee (Beebe et al., 2012). The top four major bean market classes exist in Tanzania, and these include Yellow, Red-mottled, Kablanketi/Purple and Sugars (Ochieng et al., 2023). Tanzania, being the top bean producer in Africa, exports over 40% of its produce to the international markets (Birachi et al., 2021). Due to the market reliability of some varieties, most farmers tend to hold onto old varieties (over ten years of age) whose adaptation in the current state and future climatic conditions remains in question.

Over the past 10 years (2014-2024), Tanzania Agricultural Research Institute (TARI) has released twenty-five bean varieties that are climate-smart, market and farmer-demanded. However, various challenges continue to limit the broad adoption of these new varieties. A study conducted in southern Tanzania by Letaa et al. (2015) revealed that 51% of the bean varieties cultivated by farmers were released in the 1990s. This continued use was largely attributed to their taste, established market, and disease resistance (Letaa et al., 2015). According to Atlin et al., 2017, varietal turnover averages 10–15 years in SSA; far longer than the 3–5 years recommended for effective climate response. Lower varietal age indicates faster adoption of improved varieties, with breeding benefits typically realized within 10–15 years of release (Singh et al., 2019).

The Limited involvement of seed companies in common bean seed multiplication, due to the self-pollination nature, is another challenge that limits variety replacement. As a result, farmers tend to buy grains from markets, sort and replant the good ones as potential seeds (Kessy et al., 2020), and sometimes grain traders sort and sell to farmers (Ochieng et al., 2023). Nevertheless, adaptation of newly released genotypes will vary significantly under different environmental conditions. Some older varieties continue to outperform newer ones despite changing climatic conditions. Adaptation in common bean is strongly shaped by environmental factors. Optimal growth occurs at temperatures between 14–30°C, while daytime temperatures above 30°C or nighttime temperatures above 20°C impair flowering, pod formation, and pollen viability (Beebe et al., 2011). Adequate rainfall ranges from 300–600 mm; excessive moisture above thresholds can cause waterlogging, nutrient loss, and higher disease pressure, ultimately reducing yields (Katungi et al., 2009; Beebe et al., 2013). Altitude preferences vary by gene pool, with Andean types thriving at 1,400–2,800 m and Mesoamerican types at 400–2,000 m (Beebe et al., 2011). Growth cycles typically span 60–120 days (Mamo et al., 2023). The common bean performs best in slightly acidic to neutral soils (pH 5.5–6.5), with topsoil organic carbon levels > 2% supporting optimal fertility and crop performance in tropical systems (Jaleta et al., 2025; Katungi et al., 2010).

Geographic Information Systems (GIS) for Land Suitability Climate Modelling is used to determine how suitable a particular area of land is for growing crops under current and future climate conditions. The analysis offers a cost-effective approach for assessing extensive areas and predicting outcomes in a controlled virtual environment. This aids in making informed decisions and strategizing, especially in dynamic systems like climate change (Alimagham et al., 2025). Traditional land suitability approaches focus mainly on biophysical characteristics, whereas modern methods integrate both biophysical and socio-economic factors (Mugiyo et al., 2021).

The present study conducts a 20-year land suitability climate assessment (2021–2040) for bush bean varieties that currently drive the grain market. It focuses on comparing older varieties that were released more than 10 years ago with newer varieties released within the past decade. The analysis evaluates the potential of newer varieties to replace the older ones under future climate conditions.

Several studies have previously examined climate adaptation in common bean in the region. However, unlike Jha et al. (2023); Farrow and Muthoni Andriatsitohaina (2020); Taba-Morales et al. (2020)—which primarily assessed broad agroecological suitability or climate-driven crop shifts at national or regional scales— this study specifically evaluates the comparative suitability of older versus newly released common bean varieties under both current conditions and Coupled Model Intercomparison Project Phase 6. (CMIP6)-projected climates in Tanzania. Furthermore, while earlier work mainly focused on general crop–climate interactions, the present analysis incorporates variety-level characteristics within a multi-criteria decision analysis (MCDA) framework, allowing a more detailed assessment of the potential performance of individual bean varieties across future climate scenarios.

The justification for the two-decade analysis arises from the observation that a substantial proportion of farmers continue to cultivate varieties that are over 15 years old, rather than adopting newer cultivars released within the past 3–5 years (Letaa et al., 2015; Atlin et al., 2017). Identifying current and future suitability zones will generate valuable data for agricultural planning, research and decision-making, supporting researchers, policymakers, and extension services in implementing targeted interventions such as seed distribution and resource allocation. As noted by Atlin et al. (2017), breeding and seed system efforts targeting smallholder farmers should focus on ensuring access to varieties released within the past 10 years. Hence, a 20-year timeframe provides sufficient opportunity for breeders to develop, test, and disseminate improved varieties to replace older ones.

2 Materials and methods

2.1 Study area

The study was conducted in Tanzania (1°–12° S, 29°–41° E), East Africa, encompassing diverse agroecological zones ranging from coastal lowlands to high-altitude highlands. Annual rainfall ranges from 500 to 1,500 mm, and altitude strongly influences temperature and cropping systems. Common bean (Phaseolus vulgaris L.) is predominantly cultivated in mid- and high-altitude zones. Tanzania’s climatic heterogeneity and vulnerability to climate change make it an ideal location for assessing current and future varietal suitability.

2.2 Bean variety selection

Varieties were selected based on market relevance in Tanzania, focusing on Red-Mottled, Kablanketi/Purple, and Sugar bean market classes (Birachi et al., 2021; Ochieng et al., 2023). The Yellow bean class was excluded due to the absence of newly released replacement varieties. Six varieties were analyzed: Lyamungu 90 and TARIBEAN 7 (Red-Mottled), JESCA and UYOLE 18 (Kablanketi/Purple), and TARIBEAN 6 and UYOLE 03 (Sugar). Varieties were classified as older (>10 years since release) or newer (≤10 years) (Table 1).

Table 1

Table 1. Common bean variety-specific adaptation thresholds.

2.3 Variety adaptation thresholds

Variety-specific biophysical thresholds were derived from the Tanzania Agricultural Research Institute (TARI) catalogue (Ndimbo et al., 2022) and variety release dossiers submitted to the Tanzania Official Seed Certification Institute (TOSCI) https://www.tosci.go.tz/. These thresholds defined the suitability ranges for each environmental criterion and formed the basis for standardization within the suitability analysis (Table 1).

2.4 Environmental data

Near-current climate data (1970–2000) for mean annual temperature and precipitation were obtained from WorldClim v2.1 at 1-km resolution (Fick and Hijmans, 2017). Elevation data were sourced from SRTM-DEM (30 m; USGS). Soil organic carbon and soil pH were obtained from ISDA soil (30 m). Length of growing period (LGP) data were obtained from ISDA soil at 10-km resolution. Protected areas were extracted from the World Database on Protected Areas and excluded from all suitability outputs (Table 2).

Table 2

Table 2. Climate modeling data sources.

2.5 Future climate projections

Future climate suitability was assessed using the IPSL-CM6A-LR model from the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Boucher et al., 2020). The Shared Socio-economic Pathway SSP3-7.0 was selected due to its demonstrated ability to reproduce historical temperature and precipitation patterns in East Africa and its extensive application in agricultural impact studies (Chemura et al., 2024; Omay, 2024). A single global climate model (GCM), IPSL-CM6A-LR, was used to ensure methodological consistency and facilitate robust comparisons at the variety level. Because the study focuses on relative differences in suitability between older and newly released bean varieties, rather than absolute projections or uncertainty quantification, maintaining a consistent climate forcing was essential to isolate varietal responses within the MCDA framework (Malczewski, 1999; Araújo and New, 2007).

2.6 Data processing

All spatial layers were projected to WGS 84 and resampled to a common 30-m resolution using bilinear interpolation for continuous variables and nearest-neighbour resampling for categorical data. CMIP6 projections employed a delta-change bias-correction method to preserve projected climate signals while aligning them with the observed baseline climatology.

2.7 Suitability analysis

A Multi-Criteria Decision Analysis (MCDA) framework based on weighted linear combination was used to integrate environmental variables (Food and Agriculture Organization, 1976; Malczewski, 1999). Variables included precipitation, temperature, elevation, soil pH, soil organic carbon, and LGP. Each criterion was standardized to a 0–1 scale using variety-specific thresholds. The Land Suitability Index (LSI) was calculated as:

$$LSI(x,y)=\sum _{i=1}^n{W}_i\times S_i(x,y)$$

where $W_i$ is the weight of criterion $i$, $S_i(x,y)$ is the standardized suitability score, and $n$ is the number of criteria. Suitability classes were defined as: Highly Suitable (LSI ≥ 0.9), Suitable (0.7–0.9), Moderately Suitable (0.5–0.7), Marginally Suitable (0.3–0.5), and Not Suitable (<0.3). Protected areas were masked from the final outputs.

2.8 Software and tools

Data preprocessing and analysis were conducted in R statistical package 4.5.1 using the terra, stats, and corrplot packages. Suitability modelling was implemented using the Targeting Tools used by (Nguru and Mwongera, 2023) Kit (https://targetingtools.ciat.cgiar.org), and final map layouts were produced in ArcGIS Pro (Esri, Redlands, CA, USA).

2.9 Model evaluation

This study applied an ensemble validation approach, whereby multiple variety-specific suitability model outputs were integrated into a single consensus suitability surface and subsequently evaluated against observed data. The consensus map was generated using the median of the individual suitability layers to reduce the influence of extreme predictions and enhance robustness (Araújo and New, 2007; Marmion et al., 2009). Model performance under current conditions was assessed using 1,681 independent georeferenced bean demonstration plot locations collected between 2015 and 2023. As future suitability cannot be directly observed, validation was restricted to current conditions only.

3 Results

3.1 Current and future bean suitability

In this analysis, the darker greens denote higher suitability, while lighter shades and white areas indicate lower or no suitability.

3.1.1 Current and future suitability for Lyamungu 90 and TARIBEAN7

Current and future suitability patterns for Lyamungu 90 (‘a’ and ‘b’) and TARIBEAN7 (‘c’ and ‘d’) are largely similar, with most of Tanzania remaining moderately to highly suitable for production. Some areas show declining suitability under future climate scenarios, while others remain stable or show improvement. As shown in Figures 1A, B, high suitability is concentrated in the western, central, and north-eastern regions, whereas the eastern coastal areas exhibit mostly marginal to moderate suitability. Both varieties perform best in high-elevation areas with slightly higher rainfall and longer growing seasons Figure 1.

Figure 1

Figure 1. Spatial suitability of common bean varieties Lyamungu 90 and TARI BEAN 7 under current and future climate conditions. (A, C) show current suitability for Lyamungu 90 and TARI BEAN 7, respectively, while panels and while (B, D) show projected future suitability for the same varieties.

Pairwise future suitability comparison: Both varieties exhibit similar patterns of increase and decrease across the different suitability classes. Highly suitable areas are projected to decline by 4.7% for both varieties in the future, while marginally suitable areas are expected to expand by 30.9%. This indicates that TARIBEAN7 (2024), a newer variety, does not demonstrate a clear comparative advantage over the older variety Lyamungu 90 (1990) in terms of future climate adaptability.

3.1.2 Current and future suitability for JESCA and UYOLE 18

Overall, significant parts of the country show suitable to high suitability adaptation of the two bean varieties. A Spatial shift from the highly suitable category is observed in the JESCA variety to the suitable category from the current map (a) to the future suitability map (b) (Figure 2). This can be easily noted at the convergence boundaries of Singida, Simiyu and Tabora regions, as indicated in a red circle. However, JESCA is among the few bean varieties that show broader adaptation in the country under both current and future suitability even in the central part of Tanzania, which is known to be drier and less popular in bean production.

Figure 2

Figure 2. Spatial suitability of common bean varieties JESCA and UYOLE 18 under current and future climate conditions. (A, C) show current suitability for JESCA and UYOLE 18, respectively, while (B, D) show projected future suitability for the same varieties.

On UYOLE 18 (Figure 2) - map (c) and (d)- a spatial shift from highly suitable to suitable in the near future suitability seems to expand in western and in the border of Shinyanga, Simiyu and Tabora regions. Transition from low suitable to highly suitable is noted in the Northern region of Arusha, Manyara and the Southwestern regions of Njombe and Ruvuma. This suggests that the newer variety, UYOLE 18, may have a marginally better comparative advantage under future conditions in maintaining some of its high suitability zones, although both varieties face substantial challenges.

Pairwise future suitability comparison: In central Tanzania, JESCA and UYOLE18 exhibit differing suitability levels, with JESCA showing “highly suitable” zones (darker greens on suitability maps) compared to UYOLE18’s “high” classification. This discrepancy arises from local rainfall patterns: most of the region receives 0–600 mm annually, with limited areas reaching up to 900 mm, as shown in Supplementary Figure 2. JESCA thrives in moderate rainfall (400–1000 mm), aligning well with central Tanzania’s conditions, while UYOLE18 performs optimally in higher rainfall (800–1200 mm), making it less suited to the area’s predominant climate. JESCA’s adaptability to lower and moderate rainfall gives it a slight advantage in this region, reflecting the importance of matching crop varieties to localized environmental conditions. Even though JESCA variety may have a broader adaptation, it exhibits a slightly higher decrease of 5.7% in area of “Highly Suitable” in the future suitability compared to UYOLE18, at 4.7%. Meanwhile, low suitability areas are expected to increase by 30.6% and 30.9% for JESCA and UYOLE 18, respectively. This suggests that an old variety, JESCA (1997), is likely to face more challenges in future climates than a newer variety, UYOLE 18 (2024) Figure 3.

Figure 3

Figure 3. Spatial suitability of common bean varieties TARIBEAN6 and UYOLE 03 under current and future climate conditions. (A, C) show current suitability for TARIBEAN6 and UYOLE 03, respectively, while (B, D) show projected future suitability for the same varieties.

3.1.3 UYOLE 03 and TARIBEAN6 suitability

Overall, large parts of the country show moderate to high suitability, with highly suitable areas covering the western part of Tanzania. From current to future scenarios, some areas shift toward lower suitability. TARIBEAN6 (a), Overall, large parts of the country show high suitable and suitable areas covering a significant part of Tanzania. Similar suitability is also observed in the counterpart variety of UYOLE 03 (maps c and d) in the current and future (Figure 3).

The high suitability area of UYOLE 03 under the current state is expected to decrease (c), while an increase in suitable areas (d) is predicted to happen in the Rukwa region, as well as along the convergence borders of Shinyanga, Singida, Simiyu, and Tabora regions in the future (in red circle). The opposite is noted in the western part of the Njombe region, where some areas will transition from suitable to highly suitable. The unsuitable location is also likely to expand in the Kagera region near Lake Victoria while the opposite is expected in central Tanzania, with a decrease in the unsuitable locations (Figure 3).

Pairwise future suitability comparison: Statistically highly suitable areas are expected to drop in the future by 4.8% for TARIBEAN6 and 5.0% for UYOLE 03. Low suitability areas are expected to increase by 31.1% and 32.7% for TARIBEAN6 and UYOLE 03 respectively, as shown in Figure 4. This suggests that while both varieties face suitability losses, the newer variety, TARIBEAN6 might retain more moderately suitable areas than UYOLE 03, potentially making it a better option under future climate suitability. TARIBEAN6, released in 2024, being a potential replacement of UYOLE 03, released in 2003, is likely to have a slightly higher adaptation comparative advantage in the future climate suitability (Figure 4).

Figure 4

Figure 4. Pairwise comparison of predicted variety suitability area changes in (%) from current to future climatic conditions.

Overall, from near current to future highly suitable areas are projected to decline by -4.9%, in contrast low and medium suitability zones, show modest gains of 31.2% and 15.5% respectively (Figure 5).

Figure 5

Figure 5. Total suitability area changes in (%) from current (1970-2000) to future (2021-2040) climatic conditions.

3.2 Regression analysis

3.2.1 Current conditions

Altitude emerges as a consistently strong and positive driver across all varieties. Precipitation (PPT) exhibits a variety-specific response: it is weakly negative and marginally significant for JESCA, but significantly positive for the remaining varieties. Soil organic carbon (OC) and soil pH are consistently negative and highly significant across all varieties, while the length of the growing period (LGP) shows a significant negative effect only for JESCA (Table 3).

Table 3

Table 3. Regression analysis summary for current conditions.

3.2.2 Future projections

Altitude remains a consistently positive and highly significant driver for all varieties, while Precipitation (PPT) remains positive and significant to all varieties except for JESCA. Soil organic carbon (SOC) and soil pH remain strongly negative and highly significant across all varieties. In contrast to current conditions, mean temperature (Tmean) becomes statistically significant for JESCA and UYOLE 03, with negative coefficients (Table 4).

Table 4

Table 4. Regression analysis summary for future conditions.

4 Model evaluation

Since future suitability cannot be directly validated, model performance was assessed using known bean production sites to validate the current suitability map, following a similar approach to Zabel et al. (2025). To achieve this, a single consensus suitability map was generated by merging six variety-specific rasters into a median surface, where each pixel represents the median value across all varieties. This approach reduces the influence of extreme outliers and enhances robustness, as demonstrated in ensemble mapping studies (Araújo and New, 2007; Marmion et al., 2009).

A total of 1,681 georeferenced demonstration plot points collected between 2015 and 2023, representing major bean production zones, were overlaid on the current suitability map. Duplicate coordinates were removed to retain unique locations. The proportion of plots within the “Highly Suitable” and “Suitable” categories was then calculated, with over 98.7% of sites falling in these classes, indicating that the model accurately captured the environmental conditions favourable for bean production (Figure 6).

Figure 6

Figure 6. Accuracy assessment of the suitability map.

5 Discussion

5.1 Current and future bean suitability of old and new variety releases

Projected climate suitability models indicate a consistent decline in highly suitable areas across all evaluated common bean varieties, irrespective of their release year. Although TARIBEAN7 is a newer release and a replacement variety offering anthracnose resistance superior to Lyamungu 90 (Kadege et al., 2024), it does not show a clear adaptive advantage over Lyamungu 90 under future climate scenarios. The results indicate that both varieties are equally vulnerable to projected climatic changes. On average, high-suitability areas are expected to decrease by 4.9%, while marginally suitable zones expand by 31.2% (Figure 5).

Recently released varieties, such as TARIBEAN6 (2024) and UYOLE 18 (2018), demonstrate modest comparative advantages in maintaining adaptability, reflecting incremental improvements in resilience resulting from ongoing breeding efforts. In contrast, older varieties, such as JESCA (2007) and UYOLE 03 (2003), are projected to experience the most significant reductions in suitable areas, at 5.7% and 5.0%, respectively (Figure 5). The contraction of highly suitable zones, combined with the expansion of marginally suitable areas, suggests that a larger proportion of bean production will occur in less favorable environments by 2040. Continuous breeding and releasing climate -smartvarieties matched to specific locations will be key.

The most affected regions include north-western e.g Kagera western e.g, Rukwa, the north-central corridor (Shinyanga, Singida, Simiyu, and Tabora), and southern Ruvuma. The vulnerability of older varieties highlights that not all will remain reliable under future climatic conditions, emphasizing the importance of careful variety selection. To sustain or enhance production, adoption of newer, climate-resilient varieties will be essential. Areas such as the Njombe region will see an increase in suitability for almost all varieties.

Meanwhile, newly released bean varieties TARIBEAN6 (2024), and UYOLE 18 (2018) demonstrate consistently strong adaptability to future climate conditions and outperform their older counterparts. This highlights the need for researchers to prioritize and scale climate-resilient varieties in breeding programs. At the same time, farmers are encouraged to adopt newer varieties, such as like TARIBEAN6 and UYOLE 18, to sustain productivity under changing climates. For markets, these varieties support stable supply, price stability, and expanded trade opportunities.

5.2 Climate variety interaction

Regression analysis under current conditions identified altitude as a consistently strong and positive determinant of suitability for all varieties. Thornton et al. (2009), also noted that lowland areas may experience yield losses exceeding 20% by 2050, whereas highland regions could have more potential for common bean production in the future.

Precipitation exhibited a variety-specific effect: significantly positive for most varieties but weakly negative for JESCA. Data in this study shows that precipitation across Tanzania is projected to increase by 17–50 mm during the period 2021–2040 (Supplementary Figure 2). Precipitation intensity has also been projected for East Africa (Kalemera et al., 2025; Kotikot et al., 2024; Jha et al., 2023). Therefore, excessive moisture tolerant varieties will be key in areas with moisture levels beyond threshold. Notably, mean temperature emerges as a significant negative driver for JESCA and UYOLE 03 only, reflecting potential vulnerability to warming trends. These findings indicate that future climatic conditions may differentially affect varieties, emphasizing the need for targeted adaptation strategies that account for both spatial and varietal sensitivities. However, temperature has been indicated as one of the serious threats to common bean adaptation and yield production by previous studies (Thornton et al., 2009; Farrow and Muthoni Andriatsitohaina (2020); Jha et al., 2023); therefore, its influence may extend beyond few varieties. It is important to note that this study only used single climate scenario, which might have contributed to its underestimation of strong influence of possible future conditions. According to Gbode et al. (2025), global temperature trajectories under the IPSL-CM6A-LR model remain relatively consistent across all suitability categories up to around 2040, with steady warming observed during this period. This implies that the strongest temperature influences are expected after 2040, which poses a limit for this study as it focuses on the period 2021–2040.

Both soil organic carbon (SOC) (p < 0.001) and soil pH (p < 0.01–0.001) maintained strong negative effects across all models and scenarios, particularly soil pH, indicating that acidic soils (low pH) reduce bean suitability (Tables 3, 4). A large portion of Tanzania’s agricultural land, particularly in the southern highlands, western, and northern zones, is moderately to strongly acidic. Soil acidity is a major limitation on nutrient uptake and nitrogen fixation. According to Beebe et al. (2012), (2014), extreme or imbalanced soil pH will continue to constrain bean suitability by impairing nutrient uptake, with more than 23% of bean production in Eastern Africa occurring in areas where pH ≤ 5.0. Therefore, improving soil management and breeding varieties tolerant to poor soil with particularly low pH could significantly enhance bean productivity.

5.2 Study limitation

Users should note that crop suitability assessments come with certain limitations. This relied on a single downscaled model, which may introduce bias and limit the representation of different climate futures. Secondly, the analysis focuses on a relatively short projection period (2021–2040), which may not fully capture the long-term impacts of climate change. Third, the study primarily considers biophysical factors such as rainfall, temperature, and elevation, while other important determinants of bean adaptation, including pests and diseases, management practices, and socio-economic conditions, were not fully integrated, which may affect the accuracy of the suitability assessments. However, the study provides valuable insights into the environmental adaptation of both old and newly released common bean varieties, as well as their current status and projected future performance in Tanzania.

6 Conclusion and recommendations

This study evaluated the adaptation of established market-preferred and newly released common bean varieties in Tanzania under current and projected climate conditions. Results highlight complex interactions among climatic variables, environmental factors, and varietal traits that shape suitability patterns. Model validation showed high accuracy, with over 98.7% of known bean-growing sites classified as highly suitable. Overall, climate change is projected to reduce optimal growing areas by 4.9% while expanding less favourable (marginally suitable) production zones by 31.2%, (Figure 5) with older varieties facing greater impacts than newer counterparts. Sustaining common bean productivity in the face of climate change will require coordinated efforts among researchers, farmers, and grain traders. Researchers play a key role in developing and promoting climate-resilient varieties by integrating traits that address moisture, heat, drought, and poor soil tolerance, particularly soil pH and by conducting on-farm testing to match varieties to local conditions and market demands. Policymakers and extension services should support farmers’ access to improved seeds, as well as timely climate and market information, to enhance productivity and sustainability. Future studies should simulate varietal performance under diverse climate scenarios, extend projection periods to capture long-term temperature effects, and incorporate non-climatic factors such as pests, diseases, and socio-economic dynamics for a comprehensive assessment of adaptation needs.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Author contributions

SK: Conceptualization, Validation, Writing – review & editing, Investigation, Software, Methodology, Writing – original draft. PV: Visualization, Supervision, Conceptualization, Writing – review & editing, Investigation, Validation. EM: Writing – review & editing, Supervision, Methodology, Validation, Visualization, Conceptualization. TA: Writing – review & editing, Methodology, Supervision, Validation, Investigation, Conceptualization. JI: Methodology, Visualization, Writing – review & editing, Investigation, Conceptualization, Validation, Supervision. JO: Visualization, Investigation, Validation, Supervision, Writing – review & editing, Conceptualization, Methodology. JR: Writing – review & editing, Supervision, Conceptualization, Investigation, Visualization, Validation.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Alliance of Bioversity International and CIAT, and the Gates Foundation (GF), United States of America, through the ACCELERATE and Genomic Selection (GS) projects implemented in Tanzania. Additional support was provided by Global Affairs Canada through the BRAINS project.

Acknowledgments

The authors acknowledge the support of staff from the Nelson Mandela African Institution of Science and Technology and the Alliance of Biodiversity International and CIAT for their valuable contributions and assistance in copy-editing this manuscript.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fagro.2025.1723541/full#supplementary-material

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