Between 2005 and 2025, research output showed three clear peaks in 2015, 2019, and 2024, each accounting for about 13–14% of total publications, while earlier years (2005–2012) had only a few studies annually. Breeding emerged as the dominant focus with around half of all studies, particularly strong in 2019, followed by disease research (≈39%) which surged in 2024, while climate-related studies were least represented (≈25%) and never exceeded two per year (Figure 2). These patterns suggest that scientific attention has primarily centred on breeding and disease management, with climate aspects related to common beans remaining underexplored, and that periodic surges in output are likely linked to shifts in funding priorities, emerging threats, or policy-driven research agendas.

Studies were categorized by primary climatic stressors (heat, drought, flooding, and variable climatic factors). For each category, frequencies and proportions with 95% confidence intervals were computed as indicated in Table 1.

To identify the most significant common bean diseases reported in Tanzania, a frequency analysis was conducted using data extracted from 103 systematically reviewed studies. Each mention of a specific disease within a study was counted as one occurrence, regardless of whether multiple mentions appeared in the same document. The frequencies were then aggregated across all sources to generate a ranked list of the most frequently cited diseases. A total of 94 distinct diseases were mentioned. The counts were converted into relative frequencies and percentages to assess the prominence of each disease. The cumulative percentage was also calculated to show the proportion of total mentions captured by the leading diseases. The top ten diseases were visualized using horizontal bar charts, highlighting anthracnose (12.8%), common bacterial blight (10.6%), and angular leaf spot (9.6%) as the most dominant, followed by bean common mosaic virus (8.5%), leaf rust (7.5%), and root rots (7.5%). Less frequently reported but noteworthy diseases included haloblight (6.4%), bean common mosaic necrotic virus (3.2%), white mould (2.1%), and ascochyta leaf spot (1.1%) as presented in Figure 3.

Breeding methods used in the reviewed studies indicate a clear dominance of molecular-based approaches. Molecular techniques, including marker-assisted selection (MAS) and quantitative trait loci (QTL) mapping, were the most frequently reported, accounting for 26.3%, highlighting the growing importance of genomic tools in modern plant breeding. This was closely followed by integrated approaches combining conventional pedigree/phenotypic selection with molecular techniques (21.1%) and purely conventional methods (21.1%), showing that breeders still rely heavily on classical approaches (21% vs. 26%), particularly in resource-limited contexts. Screening and evaluation trials were also commonly reported (18.4%), reflecting their role in preliminary identification of resistant or high-performing genotypes before advancing them to molecular characterization. A smaller proportion of studies had no clear methods used (unspecified or other methods) (3 mentions, 7.9%) and participatory/demand-led/adoption-oriented approaches (2 mentions, 5.3%). The least represented were statistical G × E models (AMMI/GGE), reported only once (2.6%), indicating limited use of advanced genotype-by-environment interaction modelling in the reviewed studies as shown in Figure 4.

High temperatures during sensitive stages shorten safe windows and accelerate epidemic development. Climate and field analyses support hot-site screening linked to disease performance rather than temperature alone (Jha et al., 2023; Kadege et al., 2024a). Heat alone is estimated to cause a yield loss of up to 40% by 2050 (Ramirez-villegas and Philip, 2015). Combined stress environments reduce yield stability and heighten vulnerability to opportunistic infections, which argues for joint evaluation of heat with disease pressure (Mazengo and Tryphone, 2019). Elevated temperatures also intensify expression of bacterial blight in common beans, underscoring the need to test resistance under multi-stress conditions (Aman et al., 2023).

Water-deficit stress predisposes beans to root rots and necrotrophs. Multi-environment trials show yield retention in selected drought-tolerant lines, but disease risk still increases when dry spells coincide with disease inoculum presence (Karantin et al., 2019). Genotype × environment studies on Pythium confirm that disease outcomes shift with moisture variability (Binagwa et al., 2019a,b). Rainfall-based risk work links disease surges to sequences of dry–wet events and highlights drainage and sanitation as core packages even in drought-prone zones (Farrow et al., 2011; Namayanja et al., 2014). These findings support integrating drought × pathogen screening into advancement of bean breeding pipelines.

Flooding and waterlogging favor Pythium and allied root rots and raise foliar disease risk during humid periods. Analyses that track consecutive wet days explain stand losses and guide location and drainage actions in lowlands (Farrow et al., 2011). Resistance sources and QTL on Pv07 and Pv08 provide a path to waterlogging and root-rot resilience (Binagwa et al., 2019a,b; Namayanja et al., 2014; Soltani et al., 2018). Reviews and location tests emphasize pairing tolerant genetics with residue management and site-specific drainage to limit epidemic spread after heavy rains (Kadege et al., 2022). To distil these findings, Table 2 summarizes the major climatic factors influencing bean diseases, their impacts, and adaptive solutions.

Outbreak patterns have shifted over the past two decades. Anthracnose, angular leaf spot, leaf rust, and common bacterial blight remain the most frequent problems in major bean-growing zones. Surveys and race studies confirm high anthracnose diversity across Tanzania, with widespread in the Southern Highlands and other production belts (Kadege et al., 2024b; Kazimoto et al., 2022; Masunga et al., 2020). Leaf rust populations show substantial virulence diversity across tropical and subtropical environments, reinforcing the need for broad resistance screening for example, a total of 380 U. appendiculatus isolates were differentiated into 65 virulence phenotypes on bean lines derived for rust resistance genes (Jochua et al., 2008). Field and screenhouse work also links root rots and bacterial diseases to stressed environments, tightening the climate–disease link in lowland and mid-altitude systems (Aman et al., 2023; Binagwa, et al., 2019a).

Geographic risk profiles are broadening. Race surveys and multi-location evaluations report disease pressure beyond historical hotspots, with anthracnose, rust, and root rots now recorded across a wider set of districts than earlier extension reports suggested (Kadege et al., 2024b; Masunga et al., 2020). Regional climate analyses for Tanzania document more frequent extremes and changing rainfall onset/cessation, which aligns with the observed intensification of foliar and root diseases in-season (Jackson et al., 2018; Jha et al., 2023; Luhunga and Songoro, 2020).

With time and climate change, pathogen aggressiveness and host resistance are co-evolving. Recent breeding and Genome-wide association studies (GWAS) outputs identify strong and shifting selection on resistance loci, especially for anthracnose, with donor lines and resistance panels informing pyramiding strategies (Kadege et al., 2024a; Kazimoto et al., 2022; Kuwabo et al., 2023). Release of multiple-disease-resistant materials for Tanzanian conditions (for example, Kikatiti) reflects this response, though on-farm disease pressure still varies with seasonal climate anomalies (Aman et al., 2023; Nchimbi-Msolla et al., 2024).

Rainfall timing and intensity shape disease pressure in beans. Late or erratic onset shifts planting and exposes crops to peak pathogen periods during flowering and pod fill (Jackson et al., 2018; Luhunga and Songoro, 2020). Seasonal drought patterns align with yield losses and higher disease risk in East Africa, reinforcing the need to set planting windows to avoid stress peaks (Jha et al., 2023). Simulated and observed rainfall analyses link root rot risk to consecutive wet days and heavy events, especially in lowlands (Binagwa, et al., 2019a; Farrow et al., 2011; Namayanja et al., 2014).

High humidity and frequent rain increase foliar epidemics. Reports from Tanzania show anthracnose and angular leaf spot intensifying under wet conditions, consistent with multi-location race surveys and reviews (Kadege et al., 2022, 2024a; Masunga et al., 2020). Leaf rust populations display broad virulence, so humid, high-canopy fields require wide-spectrum resistance and vigilant scouting (Jochua et al., 2008).

Dry spells alter virus problems through vector–host dynamics. Regional work documents diverse virus complexes in Tanzanian beans, including Potyvirus and Begomovirus groups; disease expression tracks seasonal stress and vector activity (Mwaipopo et al., 2021). Bacterial blight severity also increases under heat and variable moisture, which complicates calendar-based control (Aman et al., 2023).

Pathogen populations affecting beans in Tanzania are diverse and virulent. Anthracnose surveys and race studies reported wide variation across production zones, with strong disease pressure in the Southern Highlands and northern sites (Kadege et al., 2024a; Masunga et al., 2020). Rust populations also show broad virulence across tropical and subtropical environments, which supports wide-spectrum screening and regional monitoring (Jochua et al., 2008). Viral landscapes in Tanzanian bean systems include Potyvirus and Begomovirus groups, confirming multi-pathogen contexts that vary with season and site (Mwaipopo et al., 2021).

Genetic sources of disease resistance in common beans are thoroughly documented and play a crucial role in breeding programs aimed at combating major bean diseases in Tanzania. For anthracnose, significant progress has been made through the mapping and validation of Co-gene loci in diverse common bean materials. These loci serve as reliable genetic anchors for breeding and selection, ensuring that new bean varieties maintain stable and effective resistance across different environments (Geffroy et al., 2008). In the case of angular leaf spot, detailed phylogenetic research has revealed a distinct Afro-Andean genetic structure within pathogen populations (Serrato-Diaz et al., 2020). This understanding has been instrumental in guiding the deployment of Phg resistance loci and associated molecular marker sets, which are now used to select and breed for durable resistance to angular leaf spot in Tanzanian germplasm (Serrato-Diaz et al., 2020). For common bacterial blight, the identification of quantitative trait loci (QTL) such as SAP6, along with the development of closely linked molecular markers, has greatly improved the effectiveness of selection and evaluation under local Tanzanian conditions (Duncan et al., 2011; Tryphone et al., 2012). Recent efforts have expanded upon these foundational discoveries by screening advanced breeding lines under simultaneous pressure from multiple diseases. These screenings ensure that the resistance traits remain effective and relevant in the locally adapted germplasm, helping to sustain bean production even as disease pressures evolve (Aman et al., 2023).

Breeding progress combines association genetics with multi-environment testing. GWAS on anthracnose resistance identified significant loci in yellow-seeded panels, strengthening marker-assisted selection, and cross-validating field scores (Kuwabo et al., 2023). Evaluations in Tanzania report concurrent gains in disease resistance and yield components, with lines selected across contrasting locations (Kadege et al., 2024a,b). A multi-disease-resistant cultivar (Kikatiti) released for Tanzania shows broad adaptation, reflecting sustained selection across pathogen and site gradients (Nchimbi-Msolla et al., 2024). Introgression efforts add to this pipeline by moving resistance into farmer-preferred backgrounds (Kazimoto et al., 2022).

Integrated disease management is essential for protecting bean crops in Tanzania as climate conditions become increasingly unpredictable. Combining resistant varieties with improved agronomic practices, timely monitoring, and strategic seasonal planning has proven to significantly reduce disease losses. Recent studies from Tanzania and neighbouring regions demonstrate that these approaches are effective across diverse environments and changing climate and disease patterns.

Conventional and participatory breeding have delivered varieties and advanced lines that align well with farmer priorities such as grain type, cooking quality, and marketability, which remains essential for adoption and seed diffusion (Binagwa et al., 2019a; Hillocks et al., 2006; Letaa et al., 2015). However, the market focused selection can miss climate driven disease risk. As rainfall becomes more erratic and wet spells cluster, foliar diseases can surge in humid canopies and root rots can increase in lowlands and poorly drained fields (Jackson et al., 2018; Jha et al., 2023; Luhunga and Songoro, 2020). Under these conditions, a variety that performs well in a typical season may fail in an anomalously wet season if resistance is not durable, or if it was not tested under those wet canopy conditions. Participatory selection remains a major strength, but its adaptive value under climate change depends on where trials are run. Farmer panels should be placed deliberately in drought prone sites, hot sites, and flood risk lowlands so that farmer preferred traits are assessed alongside disease resistance under the climate conditions that drive outbreaks. This aligns with the broader recommendation in the manuscript that resilient variety development must combine farmer preference screening with climate informed trial placement and monitoring.

Tanzanian bean improvement built on station selection and farmer participation. Early releases such as Selian 05, Uyole 04, Lyamungu 90, Jesca, and Kabanima matched grain type, cooking quality, and market traits, which drove uptake across highland and semi-arid zones (Hillocks et al., 2006; Kanyeka et al., 2007). Regional assessments show adoption responds to local preferences, input access, and market signals (Katungi et al., 2009; Letaa et al., 2015). Program records document successive releases and scaling efforts that improved productivity and seed quality (Binagwa et al., 2018; Kalyebara and Buruchara, 2008). Participatory variety selection linked farmer panels with trial advancement, improving placement and shortening feedback loops (Binagwa et al., 2019a,b). However, there is limited efforts breeding for climatic pressure adaptability compared to other market traits. Some of reported efforts includes genotyping for flooding (Soltani et al., 2018) and drought (Sadohara et al., 2024) resistant Tanzanian germplasm. Different breeding activities have been conducted for viral, bacterial and fungal diseases as described in Table 4. However, many breeding targets have historically emphasized market and agronomic traits, and there is still limited routine selection for combined climate stresses and climate-associated disease pressures.

Molecular breeding and marker assisted selection can be presented as an adaptive response as it allows breeders to keep resistance in the background while climate variability increases disease unpredictability. In Tanzania, anthracnose, angular leaf spot, common bacterial blight, rust, and virus diseases remain dominant, and their outbreaks are increasingly shaped by rainfall variability and heat (Aman et al., 2023; Jha et al., 2023; Kadege et al., 2024b; Masunga et al., 2020). Under wetter and more humid periods, anthracnose and related foliar problems intensify, which means that deploying anthracnose resistance loci is not just a genetic achievement, it is a direct adaptation to the rainfall patterns that extend leaf wetness and sustain epidemics (Kadege et al., 2022, 2024b; Masunga et al., 2020). For angular leaf spot and other foliar pathogens that respond strongly to canopy humidity, MAS allows the use of resistant donor sources and the tracking of resistance across multiple genomic regions, including performance across loci on Pv01 to Pv07 in multi-location work (Kadege et al., 2024a,b; Kuwabo et al., 2023). ALS work also confirms Afro-Andean population structure, guiding Phg locus use and marker choice for Tanzanian isolates (Luseko et al., 2016; Okii et al., 2019; Serrato-Diaz et al., 2020). This matters under future climates because rainfall variability can intensify splash dispersal and maintain humid canopy periods, increasing the odds that susceptible varieties experience repeated infection cycles.

For common bacterial blight, the climate link should be made explicit. Bacterial blight severity rises under variable moisture and heat, and elevated temperatures can intensify expression, so resistance screening and deployment should be validated under warm conditions to ensure resistance remains effective when heat waves occur (Aman et al., 2023; Beebe et al., 2016). The CBB, QTL such as SAP6 and linked markers support introgression and field validation in local backgrounds for this disease (Duncan et al., 2011; Tryphone et al., 2012). Virus resistance provides another clear example of breeding as adaptation. Routine marker checks for I and bc-3 help maintain resistance while breeding lines are advanced into new environments where climate variability can shift planting windows and disease exposure patterns (Aman et al., 2023; Mwaipopo et al., 2021; Nchimbi-Msolla et al., 2024). For abiotic stress, a QTN for seed yield on Pv03 that also confers heat tolerance, and additional QTNs on Pv01, Pv05, and Pv10 linked to early maturity and yield under water limitation, can be interpreted as adaptive as they reduce yield instability when drought and heat become more frequent and when stress shortens the safe production window (Sadohara et al., 2024). Flooding tolerance and root rot resilience should also be tied directly to climate drivers. Flooding and waterlogging favour Pythium and allied root rots, and rainfall based triggers using consecutive wet days help explain stand losses in lowlands (Farrow et al., 2011). A QTL region on Pv08 associated with multiple beneficial traits offers a practical marker assisted route for targeting both flooding tolerance and Pythium root rot resistance, which is increasingly relevant where clustered rains and poor drainage create repeated waterlogging events (Farrow et al., 2011; Soltani et al., 2018).

Climate variability increases the likelihood that farmers face multiple diseases within a single season because the same year may include humid periods that favour foliar epidemics and saturated soils that favour root rots. In this context, multi-disease resistance becomes a climate adaptation strategy because it buffers yield when climate anomalies shift which disease dominates in a given location and season (Farrow et al., 2011; Jackson et al., 2018; Soltani et al., 2018). Drought remains the most consistent constraint. Multi-environment trials and index-based selection identify stable performers such as SER16 and BFS60 that retain yield under stress and mature early (Mazengo and Tryphone, 2019). Seasonal drought patterns in East Africa support sowing windows that avoid stress peaks and reduce downstream disease pressure (Jha et al., 2023). Hot-site evaluations show the need for heat screens that preserve foliar disease performance during flowering and pod fill (Jha et al., 2023; Kadege et al., 2024a). Flood-prone lowlands face root-rot surges; QTL on Pv07 and Pv08 and tolerant donors guide selection for waterlogging and Pythium resilience (Binagwa et al., 2019a,b; Namayanja et al., 2014; Soltani et al., 2018). Rainfall-based risk models link clusters of wet days to stand loss, which supports drainage investments and site choice during variety placement (Farrow et al., 2011). In future work, the adaptive value of “climate resilience” breeding will be strongest when drought, heat, and flooding screening are routinely paired with disease pressure so that selected lines are proven under the combined stress profiles that are becoming more common.

Multiple-resistant materials suited to Tanzanian conditions are available, including releases and advanced lines combining resistance to anthracnose, rust, common bacterial blight, and BCMV or Bean mosaic common necrotic virus (BCMNV) with acceptable yield and grain traits (Binagwa et al., 2018; Kadege et al., 2024a). Kikatiti provides a clear example of broad adaptation with stacked resistance in national testing and release (Nchimbi-Msolla et al., 2024). However, race surveys report wide anthracnose diversity and strong virulence variation for rust, which signals that resistance durability cannot be assumed across locations and seasons (Jochua et al., 2008; Masunga et al., 2020). Under increasing climate variability, this diversity becomes more consequential because shifting seasonal conditions can favour different pathogen populations over time. These findings support race-aware screening, rotation and pyramiding of resistance sources, and post-release surveillance in hotspot environments so that breeding remains responsive to evolving disease risks (Jochua et al., 2008; Masunga et al., 2020).

In general, breeding pipelines are transitioning toward integration of farmer-preferred traits with MAS and GWAS outputs, and stability analyses to inform release decisions (Philipo et al., 2021; Tryphone and Bilaro, 2022). Regional networks and institutional reviews also outline practical routes for scaling seed, agronomy, and markets while keeping disease and climate risks in view (Beebe et al., 2010, 2011; Clare et al., 2022; Katungi et al., 2019; Taba-Morales et al., 2020). However, current programs remain limited in their ability to address climate–disease challenges in a systematic way. Key gaps include limited routine screening under combined stress, for example heat or drought paired with pathogen pressure, insufficient evidence on resistance stability under variable moisture and heat across contrasting agro-ecologies, and weak linkage between climate risk indicators, disease surveillance, and advancement or placement decisions (Aman et al., 2023; Farrow et al., 2011; Jha et al., 2023; Tryphone and Bilaro, 2022).

Addressing these gaps requires a conceptual shift toward climate–disease trait bundling, in which breeding targets are explicitly designed to mitigate climate-driven disease pressures rather than treating stresses in isolation. A practical workflow is feasible: first confirm farmer acceptance in target ecologies; then use routine marker confirmation for key resistance stacks such as Co, Phg, SAP6-linked regions, and virus resistance loci; and finally test multi-site stability across drought-prone, heat-prone, and flood-risk environments, with seasonal outlooks guiding trial placement and varietal recommendation domains (Clare et al., 2022; Philipo et al., 2021; Tryphone and Bilaro, 2022). In this framing, loci and QTLs are not reported only as discoveries. Their value is demonstrated when they stabilize yield and resistance in the specific climate anomalies that increasingly drive disease losses in Tanzanian bean systems (Farrow et al., 2011; Sadohara et al., 2024; Soltani et al., 2018).