Screening Chickpea Accessions for Aluminium and Manganese Toxicity Tolerance

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Bell, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9087674/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract In acidic soils, toxic levels of plant available aluminium (Al) and manganese (Mn) can severely restrict plant growth. Identification of chickpea accessions tolerant to soil with low pH is required to increase production in acidic environments. This study evaluated Al and Mn toxicity tolerance among 26 chickpea accessions through solution and soil screening conducted in a glasshouse. The accessions were screened in nutrient solutions containing 0, 15, and 60 µM Al concentrations for Al toxicity and 2 and 150 µM Mn for Mn toxicity, with cowpea as the control. Soil experiments used soil pH (H 2 0) levels of 4.5 and 6.5. Root tolerance index (RTI), relative root length (RRL), relative shoot length (RSL), and relative root and shoot weights (RRW, RSW) were used as indices for Al tolerance, while RSL, RSW, and symptom scores were used for Mn tolerance. Indices of 50–70% were considered moderately tolerant, while > 70% indicated tolerance. At 15 µM Al, accessions Saina 1, ICCVs 11514, 11519, 11504, 11316, and 07114 were consistently tolerant to moderately tolerant across all indices, whereas Chania 1, ICCVs 03305, 93954, 07313, 96329, and 97110 were sensitive. At 60 µM Al, most accessions were sensitive. In soil, RRL results correlated significantly (r = 0.67) with those from the hydroponic experiments at 15 µM Al, with Saina 1, ICCV 11504, ICCV 11514, ICCV 07114, ICCV 10515, ICCV 11316, ICCV 00108, K036, Leldet 068, and Chania 2 being moderately tolerant. For Mn toxicity, ICCV 07313, 07101, 97128, 11514, Chania 2, and Saina 1 were tolerant. In the germplasm collection only ICCV 11514 and Saina 1 demonstrated moderate tolerance to both Al and Mn. Future research should validate these findings in a range of acid soils and seasons in field experiments to confirm their suitability for acidic environments. Al toxicity acid soils Mn toxicity Figures Figure 1 Figure 2 Figure 3 Figure 4 2. Introduction Chickpea ( Cicer arietinum L.) cultivation is limited in acid soils. The current recommendation for chickpea production is in soils with a pH (H 2 0) 6.0–7.0 [ 1 ]. In acidic soils, aluminium (Al) and manganese (Mn) toxicities not only reduce crop production but also impair mineral nutrition uptake and nitrogen fixation in legumes [ 2 , 3 ]. While lime application can remediate soil acidity, incorporation is costly and less effective in deeper soil horizons [ 4 ]. Consequently, the use of tolerant accessions represents a practical, alternative solution for expanding chickpea cultivation on acid soils. In Kenya, approximately 13% of cultivated land is acidic [ 5 ]. Although this represents a relatively small percentage of total arable land, they are common in the high-potential areas that serve as the country’s food basket [ 6 ]. Additionally, excessive use of ammonium-based fertilizers and acid rains have exacerbated the problem in previously acid-free soils, and hence their unsuitability for chickpea production [ 7 ]. Soil acidity is a syndrome of problems that include Al toxicity, Mn toxicity, and deficiencies of phosphorus (P), calcium (Ca), magnesium (Mg), and molybdenum (Mo) [ 8 ]. Of these, Al toxicity is the most important constraint to crop production in acid soils [ 9 ]. Aluminium naturally occurs in soil solution in forms that are harmless to plants [ 10 ]. However, when soil solution pH drops below 5.5, non-toxic forms are solubilised into toxic forms, primarily Al 3+ , which interact with plant roots and disrupt cellular processes [ 9 ]. Aluminium toxicity in plants occurs through inhibition of root growth, disrupts cytoplasm Ca homeostasis, induces of oxidative stress, and causes nutrient imbalances in the cell wall and plasma membranes [ 11 ]. Root exposure to Al inhibits growth and, consequently, the plant’s nutrient uptake [ 12 ]. Additionally, the variance in tolerance levels has also been observed among plant species and even among varieties belonging to similar plant species, including chickpeas [ 13 , 14 , 15 ]. Tolerant plant species and accessions cope with high Al levels by either excluding the toxic ion from root tissues, tolerating it within the cell cytoplasm, or combination of both [ 9 , 16 ]. Mechanisms of tolerance among chickpeas are not widely studied despite their importance as a pulse crop. Pang et al . [ 17 ] reported that Al tolerant chickpea accessions exude the carboxylate malonate from their roots under Al stress, enhancing P absorption and promoting root growth under Al stress. However, Yong et al . [ 18 ] determined organic acid excretion to be an insufficient tolerance mechanism due to the low levels of expression observed. Moreover, Sultana et al . [ 19 ] found Al tolerant chickpea accessions accumulated less concentrations of Al and greater nutrient concentrations in their roots and shoots when compared to sensitive accessions. The observations suggest that chickpea Al toxicity tolerance mechanisms may be a combination of exclusion from the roots and tolerance within the cell cytoplasm. Singh and Choudhary [ 20 ] demonstrated Al tolerance among chickpeas to be an inheritable trait, and hence the possibility of breeding tolerant accessions through backcross breeding programs. The genetic diversity present in both domesticated and wild chickpea germplasm offers valuable sources of Al tolerance alongside other stressors [ 21 ]. In addition to Al, Mn is a common constraint to crop production in acid soils. Manganese is essential for plant growth [ 22 ], however, when in excess it can be toxic to plants under certain soil and climatic conditions [ 23 ]. At soil pH (H 2 O) levels below 5.5, the concentration of Mn ions increases and can reach concentrations that are toxic to plants, depending on the genotypic tolerance. Moreover, waterlogging and high temperatures can exacerbate Mn toxicity, even at pH levels as high as 6.0 [ 24 , 25 ]. Excess Mn can alter enzymatic activities in the cell cytoplasm, and disrupt the uptake, use, and redistribution of other nutrients [ 26 ]. In addition, Santos et al . [ 22 ] found excess Mn decreased shoot biomass, reduced stomatal conductance, and lowered CO 2 assimilation. Manganese toxicity also causes degradation of proteins, nucleic acids, and carbohydrates causing cell death [ 27 ]. Additionally, it can reduce photosynthesis by damaging chloroplasts [ 28 ]. Manganese tolerance mechanisms such as compartmentation, detoxification, and sequestration of excess Mn ions, have been reported in crops such as macadamia ( Macadamia integrifolia ) and sunflowers ( Helianthus annuus ) [ 23 ]. In chickpea, Mn tolerance has been attributed to sequestration within shoot tissues [ 29 ]. Under Mn stress, chickpea symptoms include reduced leaf size, yellowing of serrated margins of leaves, and reduced shoot growth [ 29 ]. Additionally, absorption of nutrients such as Ca, Mg, and P is greatly limited under Mn stress [ 20 , 30 ]. However, chickpea response to Mn toxicity and acidic soil conditions varies across different accessions [ 31 , 32 ]. This provides an opportunity for the development of Mn-tolerant accessions. Aluminium and Mn toxicity to plants in acid soil can be overcome by using tolerant accessions. Solution screening is a quick and effective method that discriminates tolerant and sensitive germplasm [ 33 ]. It avoids the variation in soil conditions that limit screening in the field where variability within even small areas in acidic soils make it difficult to accurately rank germplasm [ 34 ]. However, eventually it is essential to establish a positive correlation between results obtained through solution screening and those obtained in the field [ 35 ]. In Kenya, there are no confirmed acid-tolerant chickpea accessions available for use by farmers presently. This is despite the severity of soil acidity in chickpea growing areas [ 36 ]. The lack of acid-tolerant accessions is attributed to limited research [ 37 ]. This study was aimed at screening selected chickpea accessions for tolerance to Al and Mn toxicities, and to identify promising lines for future field trials. The null hypothesis was that there are no distinct differences among chickpea accessions in tolerance to Al and Mn toxicities. 3. Materials and methods This study was conducted between March and June 2022 at the University of Nairobi, Upper Kabete Campus in Kenya. The experiments were conducted in a glasshouse with temperatures averaging 17 ◦ C at night and 25 ◦ C during the day. Twenty-six chickpea accessions (11 Kabuli types and 15 Desi) were selected for use in these experiments (Table 1 ). Six accessions were obtained from farmers in regions identified to have acidic soils (pH (H 2 0) 4.2–5.7) in Kenya including parts of Bomet, Laikipia, Embu, and Machakos Counties. Another 20 accessions, selected due to prior exposure to acid soils, were sourced from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Nairobi. The accessions were screened for tolerance to Mn and Al toxicities hydroponically and in soil experiments with cowpea ( Vigna unguiculata ), used as a low pH-tolerant control. Table 1 Description of chickpea accessions used in the study 1 Source/release name Regen year (year of breeding/introduced) ACCESSION NAME (breeding identifier) Country of origin Basic descriptor/seed color ICRISAT/_ Not released ICCV 11316 India Kabuli/cream 2 ICRISAT/_ Not released ICCV 93954 India Kabuli/cream 3 ICRISAT/_ Not released ICCV 97125 India Desi/brown 4 ICRISAT/_ Not released ICCV 11504 India Desi/brown 5 ICRISAT/_ Not released ICCV 11514 India Desi/brown 6 ICRISAT/_ Not released ICCV 11519 India Desi/brown 7 ICRISAT/_ Not released ICCV 10515 India Desi/brown 8 ICRISAT/Chania 1 Egerton 2011 ICCV 97105 India Desi/brown 9 ICRISAT/Chania 2 Egerton 2014 ICCV 92944 India Desi/brown 10 ICRISAT/Chania 3 Egerton 2014 ICCV 97126 India Desi/brown 11 ICRISAT/_ Not released ICCV 97128 India Desi/brown 12 ICRISAT/_ Not released ICCV 03305 India Kabuli/cream 13 ICRISAT/_ Not released ICCV 07313 India Kabuli/cream 14 ICRISAT/_ Not released ICCV 07114 India Desi/brown 15 ICRISAT/_ Not released ICCV 07101 India Desi/brown 16 ICRISAT/_ Not released ICCV 08107 India Desi/brown 17 ICRISAT/_ Not released ICCV 97110 India Desi/brown 18 ICRISAT/Saina 1 /KALRO/Egerton 2011 ICCV 95423 India Kabuli/cream 19 ICRISAT/_ Not released K036 Kenya Kabuli/cream 20 ICRISAT/Leldet 068 2014 ICCV 00305 India Kabuli/cream 21 Farmer’s field/Ndaria Introduced to farmers by ICRISAT in the 1970s - Kenya. Sourced from Nanyuki (0 o 10’48’’S 36 o 89’67’’E) Kabuli/cream 22 Farmer’s field/Israel Sourced from Israel in 2015 - Israel. Sourced from Athi river (1 o 55’62’’S 37 o 10’13’’E) Kabuli/cream 23 Farmer’s field/ Saina Ndogo Introduced to farmers by ICRISAT in the 1970s - Kenya. Sourced from Embu (1 o 12’02.5’’S 37 o 33’11’’E) Desi/brown 24 Farmer’s field /Saina Kubwa Introduced to farmers by ICRISAT in the 1970s - Kenya. Sourced from Matuu (1 o 20’69.3’’S 37 o 54’90’’E) Kabuli/cream 25 Farmer’s field/Nubirbi 2020 ICCV 96329 Kenya. Sourced from Longisa (0 o 54’37.9’’S 35 o 23’34.7’’E) Kabuli/cream 26 Farmer’s Field/Nubirbi 2010 ICCV 00108 Kenya Desi/brown Source: International Crops Research Institute for the Semi-Arid Tropics 3.1 Experiment 1: Aluminium toxicity screening Seeds were scarified by removing a small portion of the coat using a scalpel and then disinfected using 4% sodium hypochlorite for five minutes followed by thorough rinsing with running tap water. Fifteen sterilized seeds were transferred to glass petri dishes with bottoms covered with paper towels for moisture retention. About 20 mL of tap water was added to each petri dish and then transferred to a growth chamber for pre-germination for four days at 26°C. Three pre-germinated seedlings were transplanted per accession for each treatment. Root and shoot length measurements were recorded prior to transplanting. A split-plot design with three replicates was used with the three Al concentrations (0, 15, 60 µM) as the main plot factor and accessions as the sub-plot factor. Seedlings were inserted into holes in a styrofoam sheet with each seed wrapped in polyurethane foam. The polyurethane material secured seedlings in place without impeding root and shoot growth while the styrofoam material ensured that the seedlings floated on the nutrient solution. The styrofoam sheets with seedlings were then transferred to a glasshouse and placed on the containers (35 cm × 29 cm × 21 cm) with the nutrient solution. Each container accommodated up to 33 seedlings and three containers were used for each treatment in a randomized position. Each container held 10 L of nutrient solution with concentration as follows (µM): KNO 3 , 650; CaCl 2 .2H 2 O, 400; MgCl 2 .6H 2 O, 250; NH 4 NO 3 , 40; H 3 BO 3 , 23; (NH 4 ) 2 SO 4 , 10; Na 2 HPO 4 , 13; MnCl 2 .4H 2 0, 9; ZnSO 4 .7H 2 O, 0.8; CuSO 4 .5H 2 O, 0.3; Na 2 MoO 4 .2H 2 O, 0.1. Iron (20 µM) was supplied as Fe-EDTA prepared from equimolar amounts of FeCl 3 .6H 2 O and Na 2 EDTA (Vance et al ., 2021). The two Al treatments (15 and 60 µM) were supplied as AlCl 3 .6H 2 O and selected based on the recommendations of Vance et al. [ 21 ]. The nutrient solution was constantly aerated by bubbling air through it using a compressor pump. The pH of the solution was maintained at 4.2 throughout the experiment by adding either 0.1 M HCl or 0.1 M NaOH. pH measurements were taken daily and adjusted to 4.2 when necessary. Additionally, the nutrient solution together with their respective treatments were replaced every other day. The experiment was harvested after 10 days. 3.1.1 Plant growth measurements and calculation of indices Harvested seedlings were rinsed using distilled water to wash away surface contaminants. Thereafter, they were separated into roots and shoots and the longest length of root (LLR) was measured using a ruler (mm). The shoot length was also measured from its point of separation from the root to the tip of the longest leaf/branch when straightened. Fresh weights of the samples were recorded. Root tolerance index (RTI) and relative root length (RRL) were calculated. The RTI was calculated by dividing the mean LLR of the treated plants (15 µM and 60 µM) by the mean LLR of the control (0 µM) for each replicate. Relative root length was calculated by dividing the mean root growth (difference in LLR between sowing and harvest) of the treated plants by the mean root growth of the control. Relative shoot lengths (RSL) (mean shoot growth treated/ mean shoot growth control) and relative root dry weights (RRW) (mean root dry weight treated/mean root dry weight control) were also calculated. The chickpea accessions were classified as tolerant, moderately tolerant and sensitive, when the RRL, RTI, RSL, and RRW values were: > 70% = tolerant; less than or equal to 70% and > 50% = Moderately tolerant and less than or equal to 50% = Sensitive. 3.2 Experiment 2: Mn toxicity screening Seed preparation and experimental design were similar to those described above for the Al experiment. The experiment had two Mn treatments, 2 (control) and 150 µM Mn. The optimal concentration range to screen for Mn toxicity tolerance among chickpea accessions is 100 to 200 µM of Mn (Pradeep et al ., 2020). The 2 µM Mn in the control provided sufficient for chickpea growth. The basal nutrient solution had the following concentrations (µM): Ca(NO 3 ) 2 .4H 2 O, 400; K 2 SO 4 , 350; MgSO 4 .7H 2 O, 200; NH 4 NO 3 , 100; NH 4 H 2 PO 4 , 60; H 3 BO 3 ,10; ZnSO 4 .7H 2 O, 0.6; CuSO 4 .H 2 O, 0.2; and Na 2 MoO 4 .2H 2 O, 0.1 (Pradeep et al ., 2020). Iron was added as 20 µM Fe-EDTA prepared from equimolar amounts of FeCl 3 ·6H 2 0 and Na 2 EDTA. Manganese was supplied as MnSO 4 .H 2 O. The pH of the solution was maintained at 5.2 throughout the experiment by adding either 0.1 M HCl or 0.1 M NaOH. pH (H 2 O) measurements were taken daily and adjusted to 5.2 when necessary. The nutrient solution was replaced weekly. When replacing the solution, the styrofoam sheet would be carefully lifted from the container to minimize disturbance to the seedlings. The chickpeas initially grew in the basal nutrient solution treated with 2µM MnSO 4 .H 2 O (control). After 12 days of growth, initial shoot length was measured, and treatments were added. Subsequent nutrient solution changes maintained the treatments and the seedlings were sampled after 28 days of growth. 3.2.1 Plant growth measurements, and symptom scoring The final shoot length and weight were measured at 28 days. The RSL and RSW were calculated. The seedlings were also scored for toxicity symptoms on the 19th and 28th days of the experiment. The scores were taken using a scorecard developed by Pradeep et al . [ 21 ] ranging from 0 (no symptoms) to 5 (very severe). The presence and extent of yellowing and brown necrotic spots on leaves, leaf size, and the overall health of the plant were considered when scoring. 3.3 Experiment 3: Soil screening Soil screening was conducted in a split-plot design with soil pH level as the main plot factor and accessions as the sub-plot factor and replicated thrice. A total of 26 accessions and a control (cowpea) were grown in soils with different pH (H 2 0) of 4.5 and 6.5 (control). Acidic soil with pH (H 2 0) of 4.5 classified as Nitisol was collected from Kericho. Exchangeable Al was extracted with 1 N KCl and its content in the soil determined by atomic absorption spectrophotometry to be 1.5 cmol kg⁻¹ at pH (H 2 0) 4.5 and 0.06 cmol kg⁻¹ at pH (H 2 0) 6.5. Available Mn was extracted with DTPA (diethylenetriaminepentaacetic acid) solution and its content was determined by atomic absorption spectrophotometry to be 33.6 mg Mn/kg at pH (H 2 0) 4.5 and 7.6 mg Mn/kg at pH (H 2 0) 6.5. Approximately 2.5 kg of soil was weighed into 27 pots (25 cm diameter ×30 cm depth), one per accession, ensuring uniform bulk density. Another 27 pots (control) were each supplied with 2.5 kg of the same soil with pH (H 2 0) adjusted to 6.5 using lime at a rate of 4 t/ha as recommended by Kanyanjua et al . [ 5 ]. All pots had four 1 cm drainage holes at the base and were placed on plastic plates. Seed preparation was the same as for the solution screening. Seeds were sown at 1 cm depth in the pots and thinned to three (3) after 4 days of growth. Seed sowing rates were determined through pre-germination tests. Moisture content of 80% of field water holding was maintained in all pots throughout the experiment by pouring water on the plates and allowing the soil in the pots to absorb moisture through capillarity. Pot position in the glasshouse was assigned randomly. 3.4. Plant growth measurements and calculation of indices Data was collected 21 days after sowing. The pots were inverted and contents were gently tapped to minimise disturbance to the plant root structure. The soil was then carefully separated from the roots before the measurements were taken. Longest root length, shoot lengths, and their fresh weights were measured using a ruler (mm) and weighing balance, respectively. The measurements were then used to calculate RTI, RRL, RSL, and RRW. 3.5 Statistical analysis Data obtained from all three experiments were analyzed using R version 4.5.1. Response variables including LLR, shoot length, shoot and root weights and calculated tolerance indices (RTI, RRL, RSL, RSW, and RRW) were analyzed using a linear mixed model (LMM) implemented with the nlme package [ 38 ]. The model included fixed effects for treatment, accession, and their interaction, with random effects for replicate and mainPlotID nested within replicate to account for the split-plot structure. The model was specified as: Y ijk = µ + T i +A j +(T×A) ij +R k +M i(k) +ϵ ijk Where Y ijk is the response, µ is the overall mean, T i is the effect of treatment ( i ), A j is the effect of accession ( j ), (T×A) ij is the treatment × accession interaction, R k is the random effect of replicate ( k ), M i(k) is the random effect of MainPlotID within Replicate, and ϵ ijk is the residual error. Type II Wald chi-square tests were used to assess the significance of fixed effects using the ANOVA function from the car package [ 39 ]. Normality of residuals was evaluated using the Shapiro-Wilk test, Q-Q plots, and histograms. Post-hoc pairwise comparisons for treatments were conducted using the Kenward-Roger degrees-of-freedom method for small-sample accuracy. Pairwise comparisons among accessions within each treatment level were performed using Tukey’s Honestly Significant Difference (HSD) test, implemented via the emmeans package [ 40 ], to control the family-wise error rate across 26 accessions. Symptom scores were analyzed using a cumulative link mixed model (CLMM) to account for the ordinal nature of the response variable and the hierarchical structure of the split-plot experimental design. As the experiment had one treatment level (150 µM Mn), the analysis focused on the effect of accession on symptom score. The CLMM was fitted using the clmm function from the ordinal package [ 41 ]. The model specified symptom score as the ordinal response variable, with accession as a fixed effect and random intercepts for mainPlotID nested within replicate to account for the split-plot structure. The model was formulated as: Symptomscore∼ Accession+(1∣Replicate/MainPlotID) The proportional odds assumption was evaluated using the nominal_test function from the ordinal package to test for non-proportional odds across the accessions. Significance of the fixed effect (accession) was assessed using a Type II Wald chi-square test implemented with the Anova function from the car package [ 41 ]. For significant effects, post-hoc pairwise comparisons among the accessions were conducted using the emmeans package [ 39 ], with Tukey’s adjustment for multiple comparisons to control the family-wise error rate. Statistical significance was evaluated at p = 0.05 across all analyses. Correlation coefficients were computed using the cor() function for Pearson's product-moment correlation (assuming linearity and normality). The analysis was conducted on untransformed data, with results rounded to two decimal places. Coloured correlation matrix tables were generated in Microsoft Excel using a 3-colour scale setting where the lowest value was − 1 and coloured red, the midpoint value was 0 and coloured white, and the highest value was 1 and coloured blue. 4. Results 4.1 Solution screening for tolerance to Al toxicity 4.1.1 Longest root length, RTI, and RRL Both the treatment (p < 0.001) and accessions (p < 0.001) had significant influence on LLR, but their interaction did not. Mean LLR at 0 µM Al was higher by 40.0 mm when compared to 15 µM (p = 0.0039) and by 49.9 mm compared to 60 µM Al (p = 0.0017). Mean LLR at 15 µM was also higher than 60 µM Al by an average of 9.9 mm, but the difference was not significant, suggesting root inhibition at both treatment levels. Cowpeas consistently showed significant differences when compared to most chickpea accessions across all treatments, often with large differences in LLR and highly significant p values (p < 0.0001 in many cases). The effects of treatments (p < 0.001) and accessions (p < 0.001) on RTI were significant but their interaction was not. Relative tolerance index values at 15 µM were significantly higher than at 60 µM Al (p = 0.0336). Root tolerance index values at 15 µM Al ranged between 37.1 and 62.4% with ten accessions moderately tolerant and the rest sensitive. Saina Kubwa and Saina 1 posted the highest RTIs of 62.4% and 61.1%, respectively. Other moderately tolerant accessions included ICCV 08107 (60.4%), ICCV 11514 (56.4%), 11316 (54.6%), 07101 (53.2%), 11504 (52.9%), 11519 (50.6%), 00108 (50.5%), and Saina Ndogo (50.1%). Cowpeas had significantly higher RTI (p < 0.05) compared to most accessions except ICCV 08107 (p = 0.074), Saina 1 (p = 0.0878), and Saina Kubwa (p = 0.1197). At 60 µM Al concentration, RTI values ranged between 22.7 and 50.7%. All the accessions except Saina 1 were sensitive to Al at this treatment level. Saina 1 had the highest RTI at 50.7%. Other accessions with relatively high RTIs included ICCV 07101 (49.5%), Saina Kubwa (49%), and ICCV 08107 (46.4%). Cowpeas had significantly higher RTI compared to most chickpea accessions (p < 0.05). The effects of treatments (p < 0.001) and accessions (p < 0.001) on RRL were significant but their interaction was not. Relative root lengths at 15 µM were significantly higher than at 60 µM Al (p = 0.007). Relative root length ranged between 35.4 and 68.1% at 15 µM Al with eleven accessions moderately tolerant and fifteen sensitive (Fig. 1 ). Among the moderately tolerant accessions, ICCV 11514 posted the highest RRL of 68.1%. Cowpeas had significantly higher RRL compared to most accessions except ICCV 10515, ICCV 11316, ICCV 11504, ICCV 11514, ICCV 11519, K 036, Ndaria, and Saina 1. Differences between the best and worst performing chickpea accessions approached significance. All accessions were sensitive at 60 µM Al, the range in RRL being 2.9 to 35.7%. At this treatment level, cowpeas also had significantly higher RRL compared to all accessions (p < 0.05) except ICCV 10515. 4.1.2 Shoot length and RSL Both treatment (p < 0.001) and accession (p < 0.001) had significant influence on shoot length, however their interaction was not significant. Shoot length was significantly higher at 0 µM as compared to 15 µM (p = 0.0008) and 60 µM Al (p = 0.0004), outperforming them by an average of 35.9 mm and 42.1 mm respectively. Shoots of plants treated with 15 µM Al were on average longer by 6.2 mm when compared to those treated with 60 µM Al, but this difference was not significant. Unlike LLR, cowpeas did not maintain consistently significant difference when compared to the chickpea accessions at 0 µM Al. At 15 µM Al, cowpeas consistently had significantly higher shoot lengths compared to most chickpea accessions. ICCV 10515 also performed better in some comparisons, having significantly higher shoot length than accessions such as Chania 1 (p = 0.0341) and Chania 3 (p = 0.0055). Other accessions generally did not differ significantly. At 60 µM Al, cowpeas again had significantly higher shoot lengths compared to most chickpea accessions. ICCV 07313 stood out in some comparisons, having significantly higher shoot length than some accessions such as ICCV 07114 (p = 0.0160). Other accessions generally showed no significant differences. The effects of treatments (p = 0.02685) and accessions (p < 0.001) on RSL were significant but their interaction was not. Relative shoot lengths at 15 µM were higher than at 60 µM Al but the difference was not significant. Relative shoot length values at 15 µM Al ranged between 33% and 62.7% with fourteen accessions moderately tolerant and the rest being sensitive. Cowpeas had significantly higher RSL compared to most accessions except ICCV 10515, ICCV 11504, K 036 and Saina 1. Most pairwise comparisons among the accessions were not significant (p ≈ 1.0000) with only a few approaching significance. In the 15 µM Al-treated plants, RSL correlated strongly and positively with RRL (r = 0.6), suggesting that the treatment had similar effects on both roots and shoots (Table 2 ). At 60 µM Al, RSL values ranged between 27.1% and 56.1%. Only four accessions were moderately tolerant and included ICCV 11519 (56.1%), Saina 1 (55.8%), ICCV 10515 (53.3%), and K 036 (52.3%). At this treatment level, cowpeas had significantly higher RSL compared to most accessions (p < 0.05 for most comparisons), except ICCV 10515, ICCV 11514, ICCV 11519, K 036, and Saina 1. Most pairwise comparisons among the accessions were not significant (p ≈ 1.0000) with only a few approaching significance. The most sensitive accessions included ICCV 07313 (27.1%), ICCV 96329 (27.1%), and ICCV 97125 (28.6%). Table 2: Correlations among RTI, RRL, RSL, RRW, and RSW at 15 µM and 60 µM Al. 4.1.3 Root weight and RRW Both treatment (p < 0.001) and accession (p < 0.001) significantly influence on root weight, but their interaction did not. Root weights at 0 µM were significantly higher by an average of 0.2076 and 0.2588 g when compared to 15 µM (p = 0.0011) and 60 µM (p = 0.0005) respectively. Root weights at 15 µM and 60 µM Al were not significantly different from each other. When comparing root weight across treatments, cowpeas had significantly higher root weights compared to Chania 2 (p = 0.0488), ICCV 96329 (p = 0.0418), and Saina Ndogo (p = 0.0184), but differences with other accessions were not significant at 0 µM Al. A few comparisons among chickpea accessions showed significance, with the most notable difference observed between ICCV 11316 and Chania 2 (p = 0.0278). Most other chickpea accessions performed similarly at 0 µM Al. For 15 µM Al, cowpeas consistently had significantly higher root weights compared to most chickpea accessions except a few including ICCV 10515, Saina 1, Saina Kubwa (p > 0.05). Chickpea accessions generally did not differ significantly. At 60 µM, cowpeas showed fewer significant differences to the chickpea accessions than at 15 µM Al. Cowpea had significantly higher root weights compared to ICCV 96329 (p = 0.0499), with some other comparisons e.g. vs. Chania 2, ICCV 97125, and K 036 approaching significance. Most chickpea accessions performed similarly at this treatment level. The effects of treatments (p < 0.001) and accessions (p < 0.001) on RRW were significant but their interaction was not (p = 0.9959). Relative root weights at 15 µM were higher than at 60 µM Al (p = 0.0343). Relative root weight values at 15µM Al ranged between 35.6% and 75.4%. Fourteen accessions had RRWs > 50% while the rest were below 50%. Accessions with the highest RRWs included ICCV 11519 (75.4%), Saina 1 (60%), ICCV 11504 (62.3%), and Saina Ndogo (59.7%). ICCV 07313 (36.5%), ICCV 03305 (40.7%), and ICCV 97125 (40.8%) posted the least RRW values. At 60 µM Al, RRWs ranged between 25.8% and 61.3%. RRW at 15µM correlated strongly and positively with RRL (r = 0.52) and RSL (r = 0.67) at the same treatment level. Cowpeas had significantly higher RRW compared to most accessions at 15 µM Al. At 60 µM Al, cowpeas also had higher RRW compared to all chickpea accessions, but most differences were not significant. 4.1.4 Shoot weights and RSW Both treatment (p < 0.001) and accession (p < 0.001) significantly influenced shoot weight, but their interaction did not. Shoot weights at 0 µM were significantly higher than at 15 µM and 60 µM Al by an average of 0.174 g (p = 0.0009) and 0.210 g (p = 0.0004) respectively. Shoot weights differences between 15 µM and 60 µM were not significant. Cowpeas had significantly higher shoot weights compared to all the accessions, with highly significant p-values (< 0.0001) at 0 µM Al whereas chickpea accessions generally performed similarly. At 15 µM and 60 µM Al, cowpeas consistently had significantly higher shoot weights compared to all chickpea accessions (p < 0.0001). The effects of treatments (p < 0.001) and accessions (p < 0.001) on RSW were significant but their interaction was not. Relative shoot weight values 15 µM were significantly higher than at 60 µM Al (p = 0.0465). RSL values at 15 µM Al ranged between 32% and 70.5%. ICCV 11504 (70.5%) ICCV 11519 (60.9%) and Ndaria (58.6%) had the highest RSW values while ICCV 03305 (32%), Chania 3 (37.3%), and ICCV 97125 (39.3%) the lowest. At 60 µM Al, shoot weights markedly dropped, ranging between 29.2 and 63.7%. Cowpeas had significantly higher RSW at both treatment levels compared to nearly all accessions (p < 0.05), except for ICCV 11504. 4.2 Mn toxicity screening 4.2.1 Symptom scores Most chickpea accessions and cowpea (control) displayed very mild to mild symptoms a few days after exposure to 150 µM Mn with marginal leaf chlorosis. After fourteen days, the symptoms had worsened. At this stage, most accessions displayed either mild or moderate symptoms with the marginal leaf chlorosis extending inwards towards the midrib. Reduced leaf sizes and curling were also observed among accessions with more severe symptoms. However, no leaflet droppings were observed except among cowpeas. Accessions (p < 0.01) had a significant effect on symptom scores. After fourteen days of exposure to the treatments, average symptom scores at 150 µM Mn ranged between 1.67 and 4.0 (Fig. 3 ). Three accessions, ICCV 11519, Saina 1, and ICCV 07313 displayed the least symptoms with mean scores of 1.67, 1.67 and 1.77 respectively. Twenty accessions displayed mild symptoms, five moderate, and one - Leldet 068 – showed severe symptoms. Cowpeas, a legume known to be Mn sensitive, were severely affected, posting a mean score of 4.52 with severe necrotic spotting and leaf drops observed. No symptoms were observed among seedlings grown under 2 µM Mn (control treatment). 4.2.2 Shoot lengths, weights, RSL and RSW Both treatment (p < 0.001) and accession (p < 0.001) significantly influenced shoot length, and their interaction was also significant (p < 0.001). Shoot lengths at 2 µM were significantly higher compared to 150 µM Mn (p = 0.0368). Several pairwise comparisons among chickpea accessions were significant. At 2 µM Mn, Chania 1, Chania 2, ICCV 00108, ICCV 08107, ICCV 11504, ICCV 97125, and Saina Kubwa had significantly higher shoot lengths compared to Chania 3, ICCV 07114, ICCV 07313, ICCV 11316, ICCV 97128, Israel, K036, Leldet 068, and Ndaria. At 150 µM Mn, Chania 1, Chania 3, ICCV 00108, ICCV 07101, ICCV 08107, ICCV 11514, and ICCV 97125 had significantly higher shoot lengths compared to ICCV 07114, ICCV 10515, ICCV 11316, Israel, K036, Leldet 068, and Ndaria. Cowpeas had significantly higher shoot lengths compared to all accessions (p < 0.0001) across both treatments. Accessions had significant effects on RSL (p < 0.001). RSL ranged between 30.3% and 91% (Fig. 3 ). Seven accessions were tolerant, thirteen were moderately tolerant, and six were sensitive. ICCV 07313 posted the highest RSL score of 91%. Other tolerant accessions included ICCV 07101 (83.9%), ICCV 97128 (82.9%), ICCV 11514 (79.6%), Chania 2 (73.2%), and Saina 1 (72.2%). ICCV 10515 (30.3%), ICCV 07114 (34.9%), Ndaria (38.6%), Chania 1 (45.2%), and Saina Kubwa (47.7%) were the most sensitive accessions. RSL correlated positively with symptom scores (r = 0.55). Both treatment (p < 0.001) and accession (p < 0.001) significantly influenced chickpea shoot weight, and their interaction was highly significant (p < 0.001). Shoot weights at 2 µM were significantly higher compared to 150 µM Mn (p = 0.0030). Similar to shoot lengths, several shoot weight pairwise comparisons among chickpea accessions were significant. ICCV 08107, ICCV 11504, ICCV 11519, Saina 1, and Saina Kubwa stood out as the best performing accessions at 2 µM Mn. At 150 µM Mn, differences between the accessions were not significant. Cowpeas had significantly higher shoot weights compared to all accessions (p < 0.0001) across both treatments. Accessions had significant effects on RSW (p < 0.001), which ranged between 33.4 and 92.7%. ICCV 00108, ICCV 97110, Saina Ndogo, ICCV 97125, ICCV 11514, and ICCV 97128 posted the highest RSW values while ICCV 07114, Ndaria, Leldet 068, and Saina 1 posted the least. 4.3 Soil screening 4.3.1 Longest root length, RTI and RRL Both treatment (p < 0.001) and accession (p < 0.001) significantly influenced LLR, and their interaction was highly significant (p < 0.001). Chickpeas planted in pH (H 2 0) 6.5 (control) had significantly higher LLR compared to those in pH (H 2 0) 4.5. Several pairwise comparisons among chickpea accessions were significant. In pH (H 2 0) 6.5, accessions ICCV 00108, ICCV 03305, ICCV 07101, and ICCV 07114 had significantly higher LLR relative to most others. In pH (H 2 0) 4.5, accessions ICCV 00108 and ICCV 07114 had significantly high LLR compared to most accessions. Chania 1 and Chania 2 had significantly lower LLR compared to most other accessions across both treatments. The effects of accessions (p < 0.001) on RTI and RRL were significant. Root tolerance index after 14 days of growth ranged between 44.3 and 72.1% (Fig. 4 ). Four accessions were tolerant, 19 were moderately tolerant, and only three were sensitive. ICCV 11514 was the most tolerant accession. Other tolerant accessions included K036, ICCV 10515, and Saina1. Relative root length ranged between 29.8 and 65.3%. Accessions Saina 1 (65.3%), ICCV 11504 (64%), ICCV 11514 (64%), ICCV 07114 (60.8%), ICCV 11316 (60.4%), Chania 2 (60%), ICCV 00108 (58.7%), K036 (57.6%), Leldet 068 (55.9%), and ICCV 10515 (54.5%) were moderately tolerant. Other moderately tolerant included Israel, ICCV 11519, ICCV 08107 and Saina Kubwa. The rest of the accessions were sensitive, with Chania 3 (29.8%), Chania 1 (30.9%), and ICCV 03305 (36.1%) being the most sensitive. Soil RRL results correlated significantly (r = 0.67) with those from the hydroponic experiments at 15 µM Al (Table 3 ). Saina 1, ICCV 11504, ICCV 11514, K036, ICCV 11316, Chania 2, ICCV 07114, and ICCV 00108 emerged as the standout tolerant accessions in both experiments. Table 3: Correlations of RRL, RTI, RSL, and RRW between the soil and Al hydroponic experiments. 4.3.2 Shoot length and RSL Both treatment (p < 0.001) and accession (p < 0.001) significantly influenced shoot length, but their interaction was not significant (p = 0.6728). Chickpeas planted in pH (H 2 0) 6.5 (control) had significantly higher shoot lengths than those in pH (H 2 0) 4.5 (p = 0.0076). In the control, no pairwise comparisons among the accessions were statistically significant (all p-values > 0.05). In pH (H 2 0) 4.5, the only significant pairwise comparison was Chania 3 vs. ICCV 11514 (p = 0.0391), with Chania 3 having significantly lower shoot values. The effects of accessions (p < 0.001) on soil shoot lengths were significant. Relative shoot length values for the accessions ranged between 47.1 and 81.3%. Four accessions were tolerant, including ICCV 11514 (81.3%), ICCV 00108 (80.6%), K036 (81.3%) and ICCV 11316 (75%). Seventeen accessions were moderately tolerant and five sensitive. Chania 3 (47.1%), ICCV 11504 (48.4%), ICCV 07313 (48.5%), Chania 1 (48.5%) and ICCV 97125 (48.9%) were the most sensitive accessions. Tolerance indices from the soil experiments showed weak correlation with those from Mn screening in hydroponic experiments (Table 4 ). Table 4: Correlations among soil (pH 4.5) and Mn tolerance indices 4.3.3 Root weights and RRW Both treatment (p < 0.001) and accession (p < 0.001) significantly influenced root weights, and their interaction highly significant (p < 0.001). Chickpeas in the control experiment had significantly higher root weights than those in pH (H 2 0) 4.5 (p = 0.0100). In the control, many pairwise comparisons among chickpea accessions were significant with Chania 1 and Chania 3 standing out for having significantly higher root weights. In pH (H 2 0) 4.5, fewer pairwise comparisons were significant compared to the control, but there were still notable differences. ICCV 08107, ICCV 11504, and Saina 1 had higher root weights compared to most other accessions whereas ICCV 00108, ICCV 07101, and ICCV 07114 had lower root weights. The effects of accessions (p < 0.001) on RRW were significant. Relative root weights ranged between 38.7 and 69.5%. ICCV 11504 (69.5%), Chania 2 (64.8%), and K036 (64.1%) were the best-performing accessions. 4.3.4 Shoot weights and RSW Both treatment and accession significantly influence shoot weights, and their interactions were highly significant (p < 0.001). Chickpeas planted in the control had significantly higher shoot weights than those in pH (H 2 0) 4.5 (p = 0.0038). Many pairwise comparisons among the accessions were significant under both treatments. In the control, accessions ICCV 97128, Israel, and ICCV 11514 had higher shoot weights compared to most other accessions while ICCV 07313, ICCV 08107, and Israel stood out in pH (H 2 0) 4.5. The effect of accession on RSW was also significant (p < 0.001). Shoot weights were not as affected as the root weights by the soil acidity, with RSW values ranging between 47.5 and 91.1%. ICCV 07313 (91.1%), ICCV 08107 (76.3%), ICCV 93954 (74.1%), Israel (70.7%), and Chania 1 (70.2%) posted the highest RSW values. 5. Discussion 5.1 Al toxicity screening Variation in Al toxicity tolerance was observed among the chickpea accessions screened, likely due to the underlying genetic variability. Similarly, Negusse et al . [ 42 ] reported varied accession response to Al toxicity in a hydroponic assay study to identify acid-tolerant chickpea accessions in Ethiopia. Another study by Vance et al . [ 21 ] found domestic and wild chickpea cultivars had different tolerance levels to soluble Al in low ionic strength solution screening. Genotypic variation in tolerance to Al toxicity has also been reported in other crops such as maize, wheat, sunflowers and barley [ 24 , 43 , 44 , 45 ]. Aluminium treatments selected for use in this experiment were 15 and 60 µM, with the hypothesis that moderately tolerant accessions would be observed at 15 µM, and the highly tolerant at 60 µM [ 21 ]. While there was variation in Al tolerance among the 26 chickpea accessions, none of them matched the high level of tolerance at 60 µM shown by the cowpea, which is known for its high level of Al tolerance [ 46 ]. In this experiment, both root and shoot growth indices were used to determine accession tolerance to Al toxicity. Strong and positive correlations between root and shoot indices were observed (RSL correlated positively with RRL; r = 0.6), while RRW also correlated strongly and positively with RRL (r = 0.52) and RSL (r = 0.66) at 15µM Al. Root measurements are widely used as indicators of plant Al tolerance because root growth inhibition is the major consequence of Al toxicity in plants even upon brief exposures [ 47 , 11 ]. Additionally, some studies have reported root measurements as better indicators of Al tolerance. Vance et al. [ 21 ], for example, determined RRL to be the most accurate discriminator of Al tolerance in chickpea screening. Root length has also been used to discriminate between Al-sensitive and Al-tolerant accessions in Dolichos beans, soya beans, and barley among others [ 48 , 49 , 34 ]. The results of this study, however, signify that both shoot and root indices are adequate indices of Al toxicity among chickpea accessions. Similarly, Kushwaha et al. [ 50 ] utilized root and shoot growth indicators to screen cowpeas for tolerance to Al toxicity. Screening based on shoot growth has advantages over root screening, particularly for soil screening since it avoids the need to recover and wash roots before measurement. The tolerance rankings observed at 15 µM were not maintained at 60 µM. The decrease in tolerance of accessions to an increased Al concentration is attributed to the severely damaged chickpea roots which impedes the absorption of nutrients and water. Inconsistent displays of tolerance in chickpea at different Al concentrations levels were also reported by Vance et al . [ 21 ] when screening wild chickpea accessions for tolerance to Al toxicity. Inconsistent Al tolerance at different concentration levels has also been reported in dolichos beans ( Lablab purpureus ), cowpeas and rice ( Oryza sativa ) [ 48 , 51 , 50 ]. Hence the ranking obtained here are relevant to moderate levels of Al toxicity only and insufficient tolerance was identified in the Kenyan chickpea collection for high levels of Al toxicity in soils. 5.2 Mn toxicity screening Mn toxicity symptoms are attributed to accumulation of Mn ions in plant roots and shoots that reduced nutrient uptake and translocation [ 52 ]. In cowpeas, the symptoms were more severe, and leaf senescence was observed, indicating that the chickpeas exhibited relatively better tolerance to Mn toxicity Similar Mn toxicity symptoms were also reported by Ahlawat [ 53 ] and El-Jaoual and Cox [ 54 ], who observed marginal chlorosis and necrosis of leaves, dark reddish brown leaf spots, reduced root growth, and distorted small-sized leaves in chickpeas. Chlorosis and impaired growth are common Mn toxicity symptoms in other plants including sugarcane ( Saccharum officinarum ) [ 55 ]. Symptom scores correlated weakly with RSL (r = 0.2), which is attributed to the effect of environmental and other external factors that may skew symptom scoring. These findings agree with those of Fernando and Lynch (2015) and Pradeep et al . (2020) who reported that accession genetics and environmental factors such as light intensity and temperature may have an impact on symptom scoring. According to Millaleo et al. [ 56 ] and Alejandro et al. [ 57 ], photosynthesis is an early target of Mn toxicity, which not only decreases the rate of photosynthesis, but also the chlorophyll content of the affected leaves. This, therefore, suggests that exposed plants exhibit symptoms such as leaf chlorosis and appearance of necrotic spots in the short term and reduced shoot growth in the long term. As such, accession symptom scores and corresponding shoot growth may not be closely related [ 29 ]. Therefore, incorporating plant physiological responses related to photosynthesis alongside symptom scoring may enhance the accuracy of screening chickpeas for Mn tolerance. Variation in tolerance to Mn toxicity was observed among the chickpea accessions, likely reflecting the presence of diversity within the collection These results agree with those of Pradeep et al . [ 29 ] who found differing responses to Mn toxicity at different treatment levels among prominent chickpea accessions in Australia. Studies on other crops have also documented genotypic differences in tolerance to Mn. Khabaz-Saberi et al. [ 24 ] reported varying levels of Mn toxicity tolerance among different wheat genotypes. Similarly, Husted et al . [ 58 ] and Muhammad et al. [ 59 ] observed differences in growth among barley genotypes exposed to low Mn concentrations. 5.3 Soil screening In screening for Al toxicity, inconsistencies between results from the two experimental methods, hydroponics screen versus soil screen, were observed. The inconsistencies between soil and hydroponic experiments are attributed to the greater effectiveness of secreted organic acids in decreasing levels of toxic Al 3+ in the soil rhizosphere than in the hydroponic solutions, where organic acids are diluted in solution and discarded in the replacement of solutions every 2 days [ 60 ]. Yong et al . [ 18 ] reported that chickpea and wild Cicer accessions vary in citrate secretion, while malonic acid is excreted in higher concentrations than citrate or malate. Further examination of variation in the type and level of organic acid secretion among the 26 chickpea accessions tested could further explain variations in tolerance to Al and differences between soil and hydroponic studies. Additionally, ion interactions in soil may compound or alleviate Al toxicity due to changes in Al speciation that are particularly sensitive to pH, ionic strength of solution, soluble organic matter, phosphate concentrations, calcium and magnesium concentrations and sulfate concentrations [ 61 ]. Previous studies have reported variable responses to Al toxicity when screening in hydroponic and in soil experiments. Kulkarni et al . [ 62 ] found soil screening to be less efficient than hydroponics when screening lentil germplasms for Al toxicity tolerance, citing major differences between results from the two experiments. Other studies, however, reported some similarities. Baier et al. [ 63 ] found a strong correlation between hydroponic and soil experiments when screening wheat for tolerance to Al toxicity. Similarly, Nava et al . [ 64 ] concluded that results from hydroponic experiments and those from acid soils were similar when screening oat cultivars for tolerance to Al toxicity. While inconsistencies between hydroponic and soil screening results were evident for most of the indices used, an exception was noted where RRL in soil correlated significantly (r = 0.67) with those from the hydroponic experiments at 15µM Al. This consistency is a result of RRL taking into account the mean root growth, unlike other indices such as RTI which was calculated from the root length at harvest and did not take into account root length variations at sowing. Therefore, RRL is a better indicator of accession tolerance to Al toxicity. RTI can be useful in experiments where measurement of initial root length poses a challenge, such as when screening in soil experiments. In a similar study, Vance et al. [ 21 ] determined RRL to be the most accurate discriminator of Al tolerance in chickpea screening. In screening for tolerance to Mn toxicity, RSL measurements from hydroponic and soil experiments correlated weakly and negatively, (r = -0.09) while RSW correlated weakly (r = 0.13), a further indicator of inconsistencies between the two experiments. The weak correlation is attributed to differing levels of Mn ions present in the nutrient solutions and soil used in the experiments. Plants grown hydroponically were treated with 150 µM while soil Mn ion content was 33.6 mg Mn/kg. Other than pH, soil Mn content is also influenced by many factors including redox conditions and moisture levels [ 56 ]. Studies by Porter et al. [ 65 ] and Weil et al. [ 66 ] indicate that both flooding and dry soil exacerbate Mn toxicity, with the least symptoms observed at soil moisture levels near field capacity. Apart from soil properties, soil Mn availability is also controlled by plant characteristics and the interactions between roots and the rhizosphere [ 67 ]. 6. Conclusions and recommendations In hydroponic screening for Al toxicity, accessions Saina 1, ICCV 11519, ICCV 11504, ICCV 11514, ICCV 11316, and ICCV 07114 showed high to moderate tolerance across all indices at 15µM Al. At 60 µM Al, Saina 1 based on RSL; ICCV 11316, ICCV 11514, and ICCV 07114, based on RRW; ICCV 11504 based on RSW; and ICCV 11519 based on RSL, RRW and RSW were moderately tolerant. In screening for tolerance to Mn toxicity, accessions ICCV 07313, ICCV 07101, ICCV 97128, ICCV 11514, Chania 2, and Saina 1 were tolerant based RSL. Symptom scoring highlighted ICCV 11519, Saina 1, and ICCV 07313 as the standout accessions with the least Mn toxicity symptoms. Hydroponic results, however, were largely inconsistent with those obtained from the soil experiments. Despite this observation, soil experiment results showed accessions Saina 1, ICCV 11316, ICCV 11504, ICCV 07114, and ICCV 11514 to be tolerant based on the various indices. Overall, accessions Saina 1 (Kabuli) and ICCV 11514 (Desi) stood out for moderate toxicity tolerance of both Al and Mn based on hydroponic and soil experiments. These accessions emerged as the promising lines for further long-term experiments or field validation. Additionally, further research on the combined effects Al and Mn toxicities on chickpeas in solution and soil and investigation of tolerance mechanism would shed more light on the traits to select chickpea for improved acid soil tolerance. However, there would be merit in further collection of chickpea accessions from elsewhere to increase the level of Al and Mn toxicity tolerance available for breeding of adapted cultivars for acid soils in Kenya and East Africa. Declarations 7.1 Funding Part funding of this study was through the Australian Awards fellowship awarded to the second author for his Postdoctoral studies. 7.2 Conflicts of interest The authors have no competing interests to declare that are relevant to the content of this article. 7.3 Ethics and consent to participate All plant materials were obtained with appropriate permissions. Seed materials from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Nairobi, Kenya, were accessed under institutional guidelines for research use. Permission was also sought when collecting seed material from farmers. No special permits were required for the use of cultivated chickpea ( Cicer arietinum L . ) varieties, as it is not classified as an endangered or protected species. Clinical trial number: not applicable. 7.4 Data availability The authors declare that the data supporting the findings of this study are available within the paper. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request. 7.5 Code availability Not applicable 7.6 Authors' contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Elkanah Kipkoech and Richard Onwonga. The first draft of the manuscript was written by Elkanah Kipkoech and Richard Onwonga. Authors Karuma Anne, Richard W. Bell, Wendy H. Vance, Karthika Pradeep, and Janeth Chepkemoi provided valuable revisions. All authors read and approved the final manuscript. 7.7 Consent to publish declaration Not applicable References Macil, P.J., Ogola, J.B.O., Odhiambo, J.J.O., 2020. Response of soil pH and nodulation of three chickpea genotypes to biochar and rhizobium inoculation. Communications in Soil Science and Plant Analysis. 51(18):2377–2387. https://doi:10.1080/00103624.2020.1836204. Agegnehu, G., Amede, T., Erkossa, T., Yirga, C., Henry, C., Tyler, R., … Sileshi, G. W., 2021. Extent and management of acid soils for sustainable crop production system in the tropical agroecosystems: A review. Acta Agriculturae Scandinavica, Section B — Soil & Plant Science, 71(9):852–869. https://doi.org/10.1080/09064710.2021.1954239. Singh, Z., Singh, G., 2018. Role of Rhizobium in chickpea ( Cicer arietinum ) production - A review. Agricultural Reviews. 39(1):31-9. https://doi.org/10.18805/ag.R-1699. Butterly, C.R., Amado, T.J.C., Tang, C., 2022. Soil acidity and acidification. In: Oliveira, T.S.d., Bell, R.W. (eds) Subsoil Constraints for Crop Production. Springer, Cham. https://doi.org/10.1007/978-3-031-00317-2_3. Kanyanjua, S.M., Ireri, L., Wambua, S., Nandwa, S.M., 2002. Acidic soils in Kenya: Constraints and remedial options. KARI Technical Note No. 11 June 2002. Okalebo, J.R., Otieno, C.O., Nekesa, A.O., Ndungu-Magiroi, K.W., Kifuko-Koech, M.N., 2009. Potential for agricultural lime on improved soil health and agricultural production in Kenya. African Crop Science Conference Proceedings. 9: 339–341. Liu, X., Zhou, J., Li, W., Xu, J., Brookes, P.C., 2014. The combined effects of urea application and simulated acid rain on soil acidification and microbial community structure. Environ Sci Pollut Res. 21:6623–6631. https://doi.org/10.1007/s11356-014-2573-9. Rahman, M.A., Lee, S.-H., Ji, H.C., Kabir, A.H., Jones, C.S., Lee, K.-W., 2018. Importance of mineral nutrition for mitigating aluminum toxicity in plants on acidic soils: Current status and opportunities. International Journal of Molecular Sciences. 19(10):3073. https://doi.org/10.3390/ijms19103073. Gupta, N., Gaurav S.S., Kumar A., 2013. Molecular basis of aluminium toxicity in plants: a review. American Journal of Plant Sciences, 12C:41018. https://DOI:10.4236/ajps.2013.412A3004. Bhalerao, S.A., Prabhu, D.V., 2013. Aluminium toxicity in plants—a review. Journal of Applicable Chemistry, 2(3):447-74. Silva, S., 2012. Aluminium toxicity targets in plants. Journal of Botany. 1:219462. https://doi.org/10.1155/2012/219462. Shetty, R., Prakash, N.B., 2020. Effect of different biochars on acid soil and growth parameters of rice plants under aluminium toxicity. Scientific Reports. 10:12249. https://doi.org/10.1038/s41598-020-69262-x. Ofoe, R., Thomas, R.H., Asiedu, S.K., Wang-Pruski, G., Fofana, B., Abbey, L., 2023. Aluminum in plant: Benefits, toxicity and tolerance mechanisms. Frontiers in Plant Science. 13:1085998. https://doi.org/10.3389/fpls.2022.1085998. Singh, D., Chaturvedi, S.K., 2007. Rapid and effective screening technique for aluminium tolerance in chickpea at seedling stage. In National Symposium on Legumes for Ecological Sustainability: Emerging Challenges and Opportunities held at Indian Institute of Pulses Research Kanpur from 2007 Nov 3:3-5. Wayima, E.F., Ligaba-Osena, A., Dagne, K., Tesfaye, K., Machuka, E.M., Mutiga, S.K., Delhaize, E., 2019. Screening of diverse Ethiopian durum wheat accessions for aluminum tolerance. Agronomy. 9(8):440. https://doi.org/10.3390/agronomy9080440. Sade, H., Meriga, B., Surapu, V., Gadi J., Sunita M.S., Suravajhala P., Kavi Kishor P.B., 2016. Toxicity and tolerance of aluminum in plants: tailoring plants to suit to acid soils. Biometals. 29:187–210. https://doi.org/10.1007/s10534-016-9910-z. Pang, J., Bansal, R., Zhao, H., Bohuon, E., Lambers, H., Ryan, M.H., Ranathunge, K., Siddique, K.H.M., 2018. The carboxylate-releasing phosphorus-mobilizing strategy can be proxied by foliar manganese concentration in a large set of chickpea germplasm under low phosphorus supply. New Phytologist. 219:518-529. https://doi.org/10.1111/nph.15200. Yong, J., Pradeep, K., Vance, W.H., Zhang, X., Weir, B., Wei, H., Deng, Z., Zhang, Y., Xu, X., Zhao, C., Berger, J.D., 2022. Identification of two chickpea multidrug and toxic compound extrusion transporter genes transcriptionally upregulated upon aluminum treatment in root tips. Frontiers in Plant Science.13:909045. https://doi.org/10.3389/fpls.2022.909045. Sultana, S., Bell, R.W., Vance, W.H., 2020. Genotypic variation among chickpea and wild Cicer spp. in nutrient uptake with increasing concentration of solution Al at low pH. Plant Physiology and Biochemistry. 157:390-401. https://doi.org/10.1016/j.plaphy.2020.10.034. Singh, D., Choudhary, A.K., 2010. Inheritance pattern of aluminum tolerance in pea. Plant Breeding. 129:688-692. https://doi.org/10.1111/j.1439-0523.2009.01739.x. Vance, W., Pradeep, K., Strachan, S.R., Diffey, S., Bell, R.W., 2021. Novel sources of tolerance to aluminium toxicity in wild cicer ( Cicer reticulatum and Cicer echinospermum ) collections. Frontiers in Plant Science. 12:678211. https://doi.org/10.3389/fpls.2021.678211. Santos, E.F., Santini, J.M., Paixão, A.P., Júnior, E.F., Lavres, J., Campos, M., Dos Reis, A.R., 2017. Physiological highlights of manganese toxicity symptoms in soybean plants: Mn toxicity responses. Plant Physiology and Biochemistry. 113:6-19. https://doi.org/10.1016/j.plaphy.2017.01.022. Fernando, D.R., Lynch, J.P., 2015. Manganese phytotoxicity: new light on an old problem. Annals of Botany, 116(3):313-9. https://doi.org/10.1093/aob/mcv111. Khabaz-Saberi, H., Rengel, Z., Wilson, R., Setter, T.L., 2010. Variation of tolerance to manganese toxicity in Australian hexaploid wheat. Zeitschrift für Pflanzenernährung und Bodenkunde. 173:103-112. https://doi.org/10.1002/jpln.200900063. Pais, I.P., Moreira, R., Semedo, J.N., Ramalho, J.C., Lidon, F.C., Coutinho, J., Maçãs, B., Scotti-Campos, P., 2023. Wheat crop under waterlogging: Potential soil and plant effects. Plants. 12(1):149. https://doi.org/10.3390/plants12010149. Lavres Junior, J., Reis, A.R., Rossi, M.L., Cabral, C.P., Nogueira, N.D., Malavolta, E., 2010. Changes in the ultrastructure of soybean cultivars in response to manganese supply in solution culture. Scientia Agricola. 67:287-94. https://doi.org/10.1590/S0103-90162010000300006. Fernando, D.R., Marshall, A., Baker, A.J., Mizuno, T., 2013. Microbeam methodologies as powerful tools in manganese hyperaccumulation research: present status and future directions. Frontiers in Plant Science, 4:319. https://doi.org/10.3389/fpls.2013.00319. Weng, X.Y., Zhao, L.L., Zheng, C.J., Zhu, J.W., 2013. Characteristics of the hyperaccumulator plant phytolacca acinosa (phytolaccaceae) in response to excess manganese. Journal of Plant Nutrition. 36(9):1355–1365. https://doi.org/10.1080/01904167.2013.790428. Pradeep, K., Bell, R.W., Vance, W., 2020. Variation of Cicer germplasm to manganese toxicity tolerance. Frontiers in Plant Science. 11:588065. https://doi.org/10.3389/fpls.2020.588065. Blamey, F.P.C., Hernandez-Soriano, M.C., Cheng, M., Tang, C., Paterson, D.J., Lombi, E., Wang, W.H., Scheckel, K.G., Kopittke, P.M., 2015. Synchrotron-based techniques shed light on mechanisms of plant sensitivity and tolerance to high manganese in the root environment. Plant Physiology, 169(3):2006-2020. https://doi.org/10.1104/pp.15.00726. Singh, K., Ocampo, B., 1997. Exploitation of wild Cicer species for yield improvement in chickpea. Theoretical and Applied Genetics. 95:418-23. https://doi.org/10.1007/s001220050578. Ryan, P.R., 2018. Assessing the role of genetics for improving the yield of Australia’s major grain crops on acid soils. Crop and Pasture Science. 69(3):242-64. https://doi.org/10.1071/CP17310. Abate, E., Hussien, S., Laing, M., & Mengistu, F., 2013. Aluminium toxicity tolerance in cereals: Mechanisms, genetic control and breeding methods. Afr J Agric Res, 8(9), 711-722. http://www.academicjournals.org/AJAR. Wang, Jp., Raman, H., Zhang, G.P., Mendham, N., Zhou, M.X., 2006. Aluminium tolerance in barley (Hordeum vulgare L.): Physiological mechanisms, genetics and screening methods. J. Zhejiang Univ. - Sci. B. 7:769–787. https://doi.org/10.1631/jzus.2006.B0769. Choudhary, A.K., Singh, D., Iquebal, M.A., 2011. Selection of pigeonpea genotypes for tolerance to aluminium toxicity. Plant Breeding, 130:492-495. https://doi.org/10.1111/j.1439-0523.2010.01833.x. Kimurto, P.K., Gatongi, I., 2016. An overview of chickpea research and development programs in Kenya: current status, challenges and opportunities. Harnessing Chickpea Value Chain for Nutrition Security and Commercialization of Smallholder Agriculture in Africa. 40. Kimurto, P.K., Towett, B., Mulwa, R.S., Njogu, N., Kosgei, A., Songok, S., Oyier, M., Rutto, D., Korir, P., Rao, G., Silim, S., Kaloki, P., Muruiki, R., Barmao, G., Macharia, J.K., 2014. An overview of chickpea breeding programs in Kenya. Legume Perspectives, 3:55-57. http://oar.icrisat.org/id/eprint/8529. Pinheiro, J., 2025. nlme: Linear and nonlinear mixed effects models. R package version , 3 , p.1. https://cran.r-project.org/web/packages/nlme/nlme.pdf Fox, J., and Weisberg, S., 2025. An R Companion to Applied Regression (3rd ed.). Sage. https://cran.r-project.org/web/packages/car/car.pdf Lenth, R. V., 2025. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.8.9. https://cran.r-project.org/web/packages/emmeans/emmeans.pdfLima, M.L., Copeland, L., 1994. Changes in the ultrastructure of the root tip of wheat following exposure to aluminium. Functional Plant Biology. 21(1):85-94. https://doi.org/10.1071/PP9940085. Christensen, R. H. B., 2025. Ordinal: Regression Models for Ordinal Data. R package version 2023.12-4. https://cran.r-project.org/web/packages/ordinal/ordinal.pdf Negusse, H., Cook, D.R., Haileselassie, T., Tesfaye, K., 2022. Identification of aluminum tolerance in Ethiopian chickpea (Cicer arietinum L.) germplasm. Agronomy. 12(4):948. https://doi.org/10.3390/agronomy12040948. Richard, C., Munyinda, K., Kinkese, T., Osiru, D.S., 2015. Genotypic variation in seedling tolerance to aluminum toxicity in historical maize inbred lines of Zambia. Agronomy. 5(2):200-219. https://doi.org/10.3390/agronomy5020200. Ferreira, J.R., Faria, B.F., Comar, M., Delatorre, C.A., Minella, E., Pereira, J.F., 2017. Is a non-synonymous SNP in the HvAACT1 coding region associated with acidic soil tolerance in barley? Genetics and Molecular Biology, 40:480-90. https://doi.org/10.1590/1678-4685-GMB-2016-0225. Singh, V.K., Chander, S., Sheoran, R.K., Anu, Sheoran, O.P., Garcia-Oliveira, A.L., 2022. Genetic variability for aluminium tolerance in sunflower ( Helianthus annuus L.). Czech J. Genet. Plant Breed. 58:201–209. https://doi.org/10.17221/110/2021-CJGPB. Silva, F. W. R., & Santos, J. Z. L., 2023. Response of cowpea plants submitted to acid conditions: Aluminum and hydrogen stress. Revista Brasileira de Ciência do Solo , 47 , e0220107. Lima ML, Copeland L. Changes in the ultrastructure of the root tip of wheat following exposure to aluminium. Functional Plant Biology. 1994;21(1):85-94. Ansari, M.T., Pandey, A.K., Mailappa, A.S., Singh, S., 2019. Screening of Dolichos bean [ Lablab purpureus (L.) Sweet] genotypes for aluminium tolerance. Legume Research-An International Journal, 42(4):495-9. https://DOI:10.18805/LR-3949. Spehar, C., Galwey, N., 1997. Screening soya beans [Glycine max (L.) Merill] for calcium efficiency by root growth in low-Ca nutrient solution. Euphytica. 94:113–117. https://doi.org/10.1023/A:1002985720197. Kushwaha, J.K., Pandey, A.K., Dubey, R.K., Singh, V., Mailappa, A.S., Singh, S., 2017. Screening of cowpea [Vigna unguiculata (L.) Walp.] for aluminium tolerance in relation to growth, yield and related traits. Legume Research-an International Journal. 40(3):434-8. https://doi.org/10.18805/lr.v0i0.7016. Awasthi, J.P., Saha, B., Regon, P., Sahoo, S., Chowra, U., Pradhan, A., Roy, A., Panda, S.K., 2017. Morpho-physiological analysis of tolerance to aluminum toxicity in rice varieties of North-East India. PloS one, 12(4):e0176357. https://doi.org/10.1371/journal.pone.0176357. Xu, X., Chen, X., Shi, J., Chen, Y., Wu, W., & Perera, A., 2007. Effects of manganese on uptake and translocation of nutrients in a hyperaccumulator. Journal of plant nutrition , 30 (10), 1737-1751. Ahlawat, I.P., 1990. Diagnosis and alleviation of mineral nutrient constraints in chickpea. Chickpea in the Nineties. Eds. HA Vanrheenen and MC Saxena. :93-9. El‐Jaoual, T., Cox, D.A., 1998. Manganese toxicity in plants. Journal of Plant Nutrition, 21(2):353–386. https://doi.org/10.1080/01904169809365409. Huang, Y.L., Yang, S., Long, G.X., Zhao, Z.K., Li, X.F., Gu, M.H., 2016. Manganese toxicity in sugarcane plantlets grown on acidic soils of southern China. PLoS One, 11(3):e0148956. https://doi.org/10.1371/journal.pone.0148956. Millaleo, R., Reyes-Díaz, M., Ivanov, A.G., Mora, M.L., Alberdi, M., 2010. Manganese as essential and toxic element for plants: transport, accumulation and resistance mechanisms. Journal of Soil Science and Plant Nutrition. 10(4):470-81. http://dx.doi.org/10.4067/S0718-95162010000200008. Alejandro, S., Höller, S., Meier, B., Peiter, E., 2020. Manganese in plants: from acquisition to subcellular allocation. Frontiers in Plant Science, 11:300. https://doi.org/10.3389/fpls.2020.00300. Husted, S., Laursen, K.H., Hebbern, C.A., Schmidt, S.B., Pedas, P., Haldrup, A., Jensen, P.E., 2009. Manganese deficiency leads to genotype-specific changes in fluorescence induction kinetics and state transitions. Plant Physiology, 150(2):825-33. https://doi.org/10.1104/pp.108.134601. Muhammad, N., Cai, S., Shah, J.M., Zhang, G., 2016. The combined treatment of Mn and Al alleviates the toxicity of Al or Mn stress alone in barley. Acta Physiologiae Plantarum. 38:277. https://doi.org/10.1007/s11738-016-2296-2. Heim, A., Brunner, I., Frey, B., Frossard, E., & Luster, J., 2001. Root exudation, organic acids, and element distribution in roots of Norway spruce seedlings treated with aluminum in hydroponics. Journal of Plant Nutrition and Soil Science , 164 (5), 519-526. Kopittke, P.M., Blamey, F.P.C., 2016. Theoretical and experimental assessment of nutrient solution composition in short-term studies of aluminium rhizotoxicity. Plant Soil. 406:311–326. https://doi.org/10.1007/s11104-016-2890-5. Kulkarni, V., Sawbridge, T., Kaur, S., Hayden, M., Slater, A.T., Norton, S.L., 2021. New sources of lentil germplasm for aluminium toxicity tolerance identified by high throughput hydroponic screening. Physiology and Molecular Biology of Plants. 27(3):563-576. https://doi.org/10.1007/s12298-021-00954-y. Baier, A.C., Somers, D.J., Gustafson, J.P., 1995. Aluminium tolerance in wheat: correlating hydroponic evaluations with field and soil performances. Plant Breeding, 114:291-296. https://doi.org/10.1111/j.1439-0523.1995.tb01236.x. Nava, I.C., Delatorre, C.A., Pacheco, M.T., Scheeren, P.L., Federizzi, L.C., 2016. Aluminium tolerance of oat cultivars under hydroponic and acid soil conditions. Experimental Agriculture. 52(2):224-36. https://doi.org/10.1017/S0014479715000046. Porter, G. S., Bajita-Locke, J. B., Hue, N. V., & Strand, D., 2004. Manganese solubility and phytotoxicity affected by soil moisture, oxygen levels, and green manure additions. Communications in Soil Science and Plant Analysis , 35 (1-2), 99-116. Weil, R. R., Foy, C. D., & Coradetti, C. A., 1997. Influence of soil moisture regimes on subsequent soil manganese availability and toxicity in two cotton genotypes. Agronomy Journal , 89 (1), 1-8. Godo, G.H., Reisenauer, H.M., 1980. Plant effects on soil manganese availability. Soil Science Society of America Journal, 44:993-995. https://doi.org/10.2136/sssaj1980.03615995004400050024x. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 11 May, 2026 Reviews received at journal 08 May, 2026 Reviews received at journal 01 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 20 Mar, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 19 Mar, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9087674","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630067413,"identity":"77965097-2c1a-4e5d-9b51-e3164e9185df","order_by":0,"name":"Elkanah Kipkoech Langat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie3PMUsDMRTA8Rce3C25m+8o2K9gcSpo/SqGrOri4uDwykG6tLqek9+ic48HToofIJPfIN2KFDGxg116cRTMf0jekB9JAFKpP1iFgGG/AEBaOT9l+e+JoK4NBGME9gjKMMZIPSv42N1OriGfTvn0YzksEYRbXx4mAyy1al/0DciO+Orejox/av24PEyOUJ5wYVaKKuXJ3ApPMiyi5NOT4TvxeG7Po2TgiS4o3CKIYWNVlNRNqUfts1ZGKuoWZLVB0fT+pXp75crdTdRDzuw2W3v2NGs6t+4hAHK3ZWER5nulvvM/ZNc2cjiVSqX+ZV8Rn07MgkMXCwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Nairobi","correspondingAuthor":true,"prefix":"","firstName":"Elkanah","middleName":"Kipkoech","lastName":"Langat","suffix":""},{"id":630067414,"identity":"079272b1-fc37-4907-83e7-3e27174fe042","order_by":1,"name":"Richard Onwonga Ndemo","email":"","orcid":"","institution":"University of Nairobi","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"Onwonga","lastName":"Ndemo","suffix":""},{"id":630067415,"identity":"dcbabfc5-a0af-44d0-9990-3a0377a3f5df","order_by":2,"name":"Anne Karuma","email":"","orcid":"","institution":"University of Nairobi","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Karuma","suffix":""},{"id":630067416,"identity":"d0a22f40-5019-4229-98cd-7782d7ed0986","order_by":3,"name":"Richard W. Bell","email":"","orcid":"","institution":"Murdoch University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"W.","lastName":"Bell","suffix":""},{"id":630067417,"identity":"f82a2ab5-ca44-4fa9-9591-0659d6bfb6d8","order_by":4,"name":"Wendy H. Vance","email":"","orcid":"","institution":"Murdoch University","correspondingAuthor":false,"prefix":"","firstName":"Wendy","middleName":"H.","lastName":"Vance","suffix":""},{"id":630067418,"identity":"baa520cb-2e68-47e0-ba0c-f0545dbb0b93","order_by":5,"name":"Karthika Pradeep","email":"","orcid":"","institution":"Murdoch University","correspondingAuthor":false,"prefix":"","firstName":"Karthika","middleName":"","lastName":"Pradeep","suffix":""},{"id":630067419,"identity":"72143fc0-3f51-4541-aa10-4f4223601bcb","order_by":6,"name":"Janeth Chepkemoi","email":"","orcid":"","institution":"University of Nairobi","correspondingAuthor":false,"prefix":"","firstName":"Janeth","middleName":"","lastName":"Chepkemoi","suffix":""}],"badges":[],"createdAt":"2026-03-10 20:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9087674/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9087674/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108190040,"identity":"83388ad7-2894-4904-a147-d6e2bc60e05c","added_by":"auto","created_at":"2026-04-30 09:47:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":144984,"visible":true,"origin":"","legend":"\u003cp\u003eMean relative root length (%), with error bars showing SE, of 26 chickpea accessions grown at 15 and 60 µM \u0026nbsp;Al.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9087674/v1/1c755cddf266dd5ab7e543d6.jpg"},{"id":108190042,"identity":"15ca0d1c-3795-4613-ba79-662005d87efa","added_by":"auto","created_at":"2026-04-30 09:47:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112642,"visible":true,"origin":"","legend":"\u003cp\u003eSymptom scores with error bars showing SE, of 26 chickpea accessions grown at 150 µM Mn.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9087674/v1/251000132d82ecc506c0b190.jpg"},{"id":108491216,"identity":"214d971d-adc3-4158-8550-82edb575b186","added_by":"auto","created_at":"2026-05-05 09:52:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":152655,"visible":true,"origin":"","legend":"\u003cp\u003eRelative shoot weight and shoot lengths, with error bars showing SE, of 26 chickpea accessions grown at 150 µM Mn.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9087674/v1/33b21c76eb83fa1bcbfacb4b.jpg"},{"id":108190043,"identity":"e16e9d18-a348-448a-bef5-b31eb59ac733","added_by":"auto","created_at":"2026-04-30 09:47:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160243,"visible":true,"origin":"","legend":"\u003cp\u003eRoot relative tolerance indexes (RTI) and relative lengths (RRL), with error bars showing SE, of 26 chickpea accessions grown in acid soils.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9087674/v1/0e64689bd1b844015f2b1c9d.jpg"},{"id":108804929,"identity":"10ce0bf8-ebe1-4dd0-8c2f-d3728937b96d","added_by":"auto","created_at":"2026-05-08 15:24:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1253439,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9087674/v1/fd4db1b3-b963-4296-97bd-65469225138e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eScreening Chickpea Accessions for Aluminium and Manganese Toxicity Tolerance\u003c/p\u003e","fulltext":[{"header":"2. Introduction","content":"\u003cp\u003eChickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.) cultivation is limited in acid soils. The current recommendation for chickpea production is in soils with a pH (H\u003csub\u003e2\u003c/sub\u003e0) 6.0\u0026ndash;7.0 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In acidic soils, aluminium (Al) and manganese (Mn) toxicities not only reduce crop production but also impair mineral nutrition uptake and nitrogen fixation in legumes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While lime application can remediate soil acidity, incorporation is costly and less effective in deeper soil horizons [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Consequently, the use of tolerant accessions represents a practical, alternative solution for expanding chickpea cultivation on acid soils. In Kenya, approximately 13% of cultivated land is acidic [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although this represents a relatively small percentage of total arable land, they are common in the high-potential areas that serve as the country\u0026rsquo;s food basket [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, excessive use of ammonium-based fertilizers and acid rains have exacerbated the problem in previously acid-free soils, and hence their unsuitability for chickpea production [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSoil acidity is a syndrome of problems that include Al toxicity, Mn toxicity, and deficiencies of phosphorus (P), calcium (Ca), magnesium (Mg), and molybdenum (Mo) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Of these, Al toxicity is the most important constraint to crop production in acid soils [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Aluminium naturally occurs in soil solution in forms that are harmless to plants [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, when soil solution pH drops below 5.5, non-toxic forms are solubilised into toxic forms, primarily Al\u003csup\u003e3+\u003c/sup\u003e, which interact with plant roots and disrupt cellular processes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Aluminium toxicity in plants occurs through inhibition of root growth, disrupts cytoplasm Ca homeostasis, induces of oxidative stress, and causes nutrient imbalances in the cell wall and plasma membranes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Root exposure to Al inhibits growth and, consequently, the plant\u0026rsquo;s nutrient uptake [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, the variance in tolerance levels has also been observed among plant species and even among varieties belonging to similar plant species, including chickpeas [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTolerant plant species and accessions cope with high Al levels by either excluding the toxic ion from root tissues, tolerating it within the cell cytoplasm, or combination of both [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Mechanisms of tolerance among chickpeas are not widely studied despite their importance as a pulse crop. Pang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] reported that Al tolerant chickpea accessions exude the carboxylate malonate from their roots under Al stress, enhancing P absorption and promoting root growth under Al stress. However, Yong \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] determined organic acid excretion to be an insufficient tolerance mechanism due to the low levels of expression observed. Moreover, Sultana \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] found Al tolerant chickpea accessions accumulated less concentrations of Al and greater nutrient concentrations in their roots and shoots when compared to sensitive accessions. The observations suggest that chickpea Al toxicity tolerance mechanisms may be a combination of exclusion from the roots and tolerance within the cell cytoplasm. Singh and Choudhary [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] demonstrated Al tolerance among chickpeas to be an inheritable trait, and hence the possibility of breeding tolerant accessions through backcross breeding programs. The genetic diversity present in both domesticated and wild chickpea germplasm offers valuable sources of Al tolerance alongside other stressors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to Al, Mn is a common constraint to crop production in acid soils. Manganese is essential for plant growth [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], however, when in excess it can be toxic to plants under certain soil and climatic conditions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. At soil pH (H\u003csub\u003e2\u003c/sub\u003eO) levels below 5.5, the concentration of Mn ions increases and can reach concentrations that are toxic to plants, depending on the genotypic tolerance. Moreover, waterlogging and high temperatures can exacerbate Mn toxicity, even at pH levels as high as 6.0 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Excess Mn can alter enzymatic activities in the cell cytoplasm, and disrupt the uptake, use, and redistribution of other nutrients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition, Santos \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] found excess Mn decreased shoot biomass, reduced stomatal conductance, and lowered CO\u003csub\u003e2\u003c/sub\u003e assimilation. Manganese toxicity also causes degradation of proteins, nucleic acids, and carbohydrates causing cell death [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, it can reduce photosynthesis by damaging chloroplasts [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eManganese tolerance mechanisms such as compartmentation, detoxification, and sequestration of excess Mn ions, have been reported in crops such as macadamia (\u003cem\u003eMacadamia integrifolia\u003c/em\u003e) and sunflowers (\u003cem\u003eHelianthus annuus\u003c/em\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In chickpea, Mn tolerance has been attributed to sequestration within shoot tissues [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Under Mn stress, chickpea symptoms include reduced leaf size, yellowing of serrated margins of leaves, and reduced shoot growth [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, absorption of nutrients such as Ca, Mg, and P is greatly limited under Mn stress [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, chickpea response to Mn toxicity and acidic soil conditions varies across different accessions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This provides an opportunity for the development of Mn-tolerant accessions.\u003c/p\u003e \u003cp\u003eAluminium and Mn toxicity to plants in acid soil can be overcome by using tolerant accessions. Solution screening is a quick and effective method that discriminates tolerant and sensitive germplasm [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It avoids the variation in soil conditions that limit screening in the field where variability within even small areas in acidic soils make it difficult to accurately rank germplasm [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, eventually it is essential to establish a positive correlation between results obtained through solution screening and those obtained in the field [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Kenya, there are no confirmed acid-tolerant chickpea accessions available for use by farmers presently. This is despite the severity of soil acidity in chickpea growing areas [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The lack of acid-tolerant accessions is attributed to limited research [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This study was aimed at screening selected chickpea accessions for tolerance to Al and Mn toxicities, and to identify promising lines for future field trials. The null hypothesis was that there are no distinct differences among chickpea accessions in tolerance to Al and Mn toxicities.\u003c/p\u003e"},{"header":"3. Materials and methods","content":"\u003cp\u003eThis study was conducted between March and June 2022 at the University of Nairobi, Upper Kabete Campus in Kenya. The experiments were conducted in a glasshouse with temperatures averaging 17 \u003csup\u003e◦\u003c/sup\u003eC at night and 25 \u003csup\u003e◦\u003c/sup\u003eC during the day. Twenty-six chickpea accessions (11 Kabuli types and 15 Desi) were selected for use in these experiments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Six accessions were obtained from farmers in regions identified to have acidic soils (pH (H\u003csub\u003e2\u003c/sub\u003e0) 4.2\u0026ndash;5.7) in Kenya including parts of Bomet, Laikipia, Embu, and Machakos Counties. Another 20 accessions, selected due to prior exposure to acid soils, were sourced from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Nairobi. The accessions were screened for tolerance to Mn and Al toxicities hydroponically and in soil experiments with cowpea (\u003cem\u003eVigna unguiculata\u003c/em\u003e), used as a low pH-tolerant control.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of chickpea accessions used in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource/release name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegen year (year of breeding/introduced)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eACCESSION NAME (breeding identifier)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCountry of origin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBasic descriptor/seed color\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 11316\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKabuli/cream\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 93954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKabuli/cream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 97125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 11504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 11514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 11519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 10515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/Chania 1 Egerton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 97105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/Chania 2 Egerton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 92944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/Chania 3 Egerton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 97126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 97128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 03305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKabuli/cream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 07313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKabuli/cream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 07114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 07101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 08107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 97110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/Saina 1 /KALRO/Egerton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 95423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKabuli/cream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot released\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKabuli/cream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT/Leldet 068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 00305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKabuli/cream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u0026rsquo;s field/Ndaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntroduced to farmers by ICRISAT in the 1970s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKenya.\u003c/p\u003e \u003cp\u003eSourced from Nanyuki (0\u003csup\u003eo\u003c/sup\u003e10\u0026rsquo;48\u0026rsquo;\u0026rsquo;S 36\u003csup\u003eo\u003c/sup\u003e89\u0026rsquo;67\u0026rsquo;\u0026rsquo;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKabuli/cream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u0026rsquo;s field/Israel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSourced from Israel in 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIsrael. Sourced from Athi river (1\u003csup\u003eo\u003c/sup\u003e55\u0026rsquo;62\u0026rsquo;\u0026rsquo;S 37\u003csup\u003eo\u003c/sup\u003e10\u0026rsquo;13\u0026rsquo;\u0026rsquo;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKabuli/cream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u0026rsquo;s field/ Saina Ndogo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntroduced to farmers by ICRISAT in the 1970s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKenya. Sourced from Embu (1\u003csup\u003eo\u003c/sup\u003e12\u0026rsquo;02.5\u0026rsquo;\u0026rsquo;S 37\u003csup\u003eo\u003c/sup\u003e33\u0026rsquo;11\u0026rsquo;\u0026rsquo;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u0026rsquo;s field /Saina Kubwa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntroduced to farmers by ICRISAT in the 1970s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKenya. Sourced from Matuu (1\u003csup\u003eo\u003c/sup\u003e20\u0026rsquo;69.3\u0026rsquo;\u0026rsquo;S 37\u003csup\u003eo\u003c/sup\u003e54\u0026rsquo;90\u0026rsquo;\u0026rsquo;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKabuli/cream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u0026rsquo;s field/Nubirbi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 96329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKenya. Sourced from Longisa (0\u003csup\u003eo\u003c/sup\u003e54\u0026rsquo;37.9\u0026rsquo;\u0026rsquo;S 35\u003csup\u003eo\u003c/sup\u003e23\u0026rsquo;34.7\u0026rsquo;\u0026rsquo;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKabuli/cream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u0026rsquo;s Field/Nubirbi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICCV 00108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesi/brown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: International Crops Research Institute for the Semi-Arid Tropics\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Experiment 1: Aluminium toxicity screening\u003c/h2\u003e \u003cp\u003eSeeds were scarified by removing a small portion of the coat using a scalpel and then disinfected using 4% sodium hypochlorite for five minutes followed by thorough rinsing with running tap water. Fifteen sterilized seeds were transferred to glass petri dishes with bottoms covered with paper towels for moisture retention. About 20 mL of tap water was added to each petri dish and then transferred to a growth chamber for pre-germination for four days at 26\u0026deg;C.\u003c/p\u003e \u003cp\u003eThree pre-germinated seedlings were transplanted per accession for each treatment. Root and shoot length measurements were recorded prior to transplanting. A split-plot design with three replicates was used with the three Al concentrations (0, 15, 60 \u0026micro;M) as the main plot factor and accessions as the sub-plot factor. Seedlings were inserted into holes in a styrofoam sheet with each seed wrapped in polyurethane foam. The polyurethane material secured seedlings in place without impeding root and shoot growth while the styrofoam material ensured that the seedlings floated on the nutrient solution. The styrofoam sheets with seedlings were then transferred to a glasshouse and placed on the containers (35 cm \u0026times; 29 cm \u0026times; 21 cm) with the nutrient solution. Each container accommodated up to 33 seedlings and three containers were used for each treatment in a randomized position.\u003c/p\u003e \u003cp\u003eEach container held 10 L of nutrient solution with concentration as follows (\u0026micro;M): KNO\u003csub\u003e3\u003c/sub\u003e, 650; CaCl\u003csub\u003e2\u003c/sub\u003e.2H\u003csub\u003e2\u003c/sub\u003eO, 400; MgCl\u003csub\u003e2\u003c/sub\u003e.6H\u003csub\u003e2\u003c/sub\u003eO, 250; NH\u003csub\u003e4\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e, 40; H\u003csub\u003e3\u003c/sub\u003eBO\u003csub\u003e3\u003c/sub\u003e, 23; (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, 10; Na\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, 13; MnCl\u003csub\u003e2\u003c/sub\u003e.4H\u003csub\u003e2\u003c/sub\u003e0, 9; ZnSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO, 0.8; CuSO\u003csub\u003e4\u003c/sub\u003e.5H\u003csub\u003e2\u003c/sub\u003eO, 0.3; Na\u003csub\u003e2\u003c/sub\u003eMoO\u003csub\u003e4\u003c/sub\u003e.2H\u003csub\u003e2\u003c/sub\u003eO, 0.1. Iron (20 \u0026micro;M) was supplied as Fe-EDTA prepared from equimolar amounts of FeCl\u003csub\u003e3\u003c/sub\u003e.6H\u003csub\u003e2\u003c/sub\u003eO and Na\u003csub\u003e2\u003c/sub\u003eEDTA (Vance \u003cem\u003eet al\u003c/em\u003e., 2021). The two Al treatments (15 and 60 \u0026micro;M) were supplied as AlCl\u003csub\u003e3\u003c/sub\u003e.6H\u003csub\u003e2\u003c/sub\u003eO and selected based on the recommendations of Vance \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe nutrient solution was constantly aerated by bubbling air through it using a compressor pump. The pH of the solution was maintained at 4.2 throughout the experiment by adding either 0.1 M HCl or 0.1 M NaOH. pH measurements were taken daily and adjusted to 4.2 when necessary. Additionally, the nutrient solution together with their respective treatments were replaced every other day. The experiment was harvested after 10 days.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Plant growth measurements and calculation of indices\u003c/h2\u003e \u003cp\u003eHarvested seedlings were rinsed using distilled water to wash away surface contaminants. Thereafter, they were separated into roots and shoots and the longest length of root (LLR) was measured using a ruler (mm). The shoot length was also measured from its point of separation from the root to the tip of the longest leaf/branch when straightened. Fresh weights of the samples were recorded.\u003c/p\u003e \u003cp\u003eRoot tolerance index (RTI) and relative root length (RRL) were calculated. The RTI was calculated by dividing the mean LLR of the treated plants (15 \u0026micro;M and 60 \u0026micro;M) by the mean LLR of the control (0 \u0026micro;M) for each replicate. Relative root length was calculated by dividing the mean root growth (difference in LLR between sowing and harvest) of the treated plants by the mean root growth of the control. Relative shoot lengths (RSL) (mean shoot growth treated/ mean shoot growth control) and relative root dry weights (RRW) (mean root dry weight treated/mean root dry weight control) were also calculated. The chickpea accessions were classified as tolerant, moderately tolerant and sensitive, when the RRL, RTI, RSL, and RRW values were: \u0026gt; 70% = tolerant; less than or equal to 70% and \u0026gt;\u0026thinsp;50% = Moderately tolerant and less than or equal to 50% = Sensitive.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Experiment 2: Mn toxicity screening\u003c/h2\u003e \u003cp\u003eSeed preparation and experimental design were similar to those described above for the Al experiment. The experiment had two Mn treatments, 2 (control) and 150 \u0026micro;M Mn. The optimal concentration range to screen for Mn toxicity tolerance among chickpea accessions is 100 to 200 \u0026micro;M of Mn (Pradeep \u003cem\u003eet al\u003c/em\u003e., 2020). The 2 \u0026micro;M Mn in the control provided sufficient for chickpea growth. The basal nutrient solution had the following concentrations (\u0026micro;M): Ca(NO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e.4H\u003csub\u003e2\u003c/sub\u003eO, 400; K\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, 350; MgSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO, 200; NH\u003csub\u003e4\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e, 100; NH\u003csub\u003e4\u003c/sub\u003eH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 60; H\u003csub\u003e3\u003c/sub\u003eBO\u003csub\u003e3\u003c/sub\u003e,10; ZnSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO, 0.6; CuSO\u003csub\u003e4\u003c/sub\u003e.H\u003csub\u003e2\u003c/sub\u003eO, 0.2; and Na\u003csub\u003e2\u003c/sub\u003eMoO\u003csub\u003e4\u003c/sub\u003e.2H\u003csub\u003e2\u003c/sub\u003eO, 0.1 (Pradeep \u003cem\u003eet al\u003c/em\u003e., 2020). Iron was added as 20 \u0026micro;M Fe-EDTA prepared from equimolar amounts of FeCl\u003csub\u003e3\u003c/sub\u003e\u0026middot;6H\u003csub\u003e2\u003c/sub\u003e0 and Na\u003csub\u003e2\u003c/sub\u003eEDTA. Manganese was supplied as MnSO\u003csub\u003e4\u003c/sub\u003e.H\u003csub\u003e2\u003c/sub\u003eO.\u003c/p\u003e \u003cp\u003eThe pH of the solution was maintained at 5.2 throughout the experiment by adding either 0.1 M HCl or 0.1 M NaOH. pH (H\u003csub\u003e2\u003c/sub\u003eO) measurements were taken daily and adjusted to 5.2 when necessary. The nutrient solution was replaced weekly. When replacing the solution, the styrofoam sheet would be carefully lifted from the container to minimize disturbance to the seedlings. The chickpeas initially grew in the basal nutrient solution treated with 2\u0026micro;M MnSO\u003csub\u003e4\u003c/sub\u003e.H\u003csub\u003e2\u003c/sub\u003eO (control). After 12 days of growth, initial shoot length was measured, and treatments were added. Subsequent nutrient solution changes maintained the treatments and the seedlings were sampled after 28 days of growth.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Plant growth measurements, and symptom scoring\u003c/h2\u003e \u003cp\u003eThe final shoot length and weight were measured at 28 days. The RSL and RSW were calculated. The seedlings were also scored for toxicity symptoms on the 19th and 28th days of the experiment. The scores were taken using a scorecard developed by Pradeep \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] ranging from 0 (no symptoms) to 5 (very severe). The presence and extent of yellowing and brown necrotic spots on leaves, leaf size, and the overall health of the plant were considered when scoring.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Experiment 3: Soil screening\u003c/h2\u003e \u003cp\u003eSoil screening was conducted in a split-plot design with soil pH level as the main plot factor and accessions as the sub-plot factor and replicated thrice. A total of 26 accessions and a control (cowpea) were grown in soils with different pH (H\u003csub\u003e2\u003c/sub\u003e0) of 4.5 and 6.5 (control). Acidic soil with pH (H\u003csub\u003e2\u003c/sub\u003e0) of 4.5 classified as Nitisol was collected from Kericho. Exchangeable Al was extracted with 1 N KCl and its content in the soil determined by atomic absorption spectrophotometry to be 1.5 cmol kg⁻\u0026sup1; at pH (H\u003csub\u003e2\u003c/sub\u003e0) 4.5 and 0.06 cmol kg⁻\u0026sup1; at pH (H\u003csub\u003e2\u003c/sub\u003e0) 6.5. Available Mn was extracted with DTPA (diethylenetriaminepentaacetic acid) solution and its content was determined by atomic absorption spectrophotometry to be 33.6 mg Mn/kg at pH (H\u003csub\u003e2\u003c/sub\u003e0) 4.5 and 7.6 mg Mn/kg at pH (H\u003csub\u003e2\u003c/sub\u003e0) 6.5. Approximately 2.5 kg of soil was weighed into 27 pots (25 cm diameter \u0026times;30 cm depth), one per accession, ensuring uniform bulk density. Another 27 pots (control) were each supplied with 2.5 kg of the same soil with pH (H\u003csub\u003e2\u003c/sub\u003e0) adjusted to 6.5 using lime at a rate of 4 t/ha as recommended by Kanyanjua \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. All pots had four 1 cm drainage holes at the base and were placed on plastic plates.\u003c/p\u003e \u003cp\u003eSeed preparation was the same as for the solution screening. Seeds were sown at 1 cm depth in the pots and thinned to three (3) after 4 days of growth. Seed sowing rates were determined through pre-germination tests. Moisture content of 80% of field water holding was maintained in all pots throughout the experiment by pouring water on the plates and allowing the soil in the pots to absorb moisture through capillarity. Pot position in the glasshouse was assigned randomly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Plant growth measurements and calculation of indices\u003c/h2\u003e \u003cp\u003eData was collected 21 days after sowing. The pots were inverted and contents were gently tapped to minimise disturbance to the plant root structure. The soil was then carefully separated from the roots before the measurements were taken. Longest root length, shoot lengths, and their fresh weights were measured using a ruler (mm) and weighing balance, respectively. The measurements were then used to calculate RTI, RRL, RSL, and RRW.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eData obtained from all three experiments were analyzed using R version 4.5.1. Response variables including LLR, shoot length, shoot and root weights and calculated tolerance indices (RTI, RRL, RSL, RSW, and RRW) were analyzed using a linear mixed model (LMM) implemented with the \u003cem\u003enlme\u003c/em\u003e package [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The model included fixed effects for treatment, accession, and their interaction, with random effects for replicate and mainPlotID nested within replicate to account for the split-plot structure. The model was specified as:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eY\u003csub\u003eijk\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;T\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+A\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+(T\u0026times;A)\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+R\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+M\u003c/em\u003e\u003csub\u003e\u003cem\u003ei(k)\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+ϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere Y\u003csub\u003eijk\u003c/sub\u003e is the response, \u0026micro; is the overall mean, T\u003csub\u003ei\u003c/sub\u003e is the effect of treatment ( i ), A\u003csub\u003ej\u003c/sub\u003e is the effect of accession ( j ), (T\u0026times;A)\u003csub\u003eij\u003c/sub\u003e is the treatment \u0026times; accession interaction, R\u003csub\u003ek\u003c/sub\u003e is the random effect of replicate ( k ), M \u003csub\u003ei(k)\u003c/sub\u003e is the random effect of MainPlotID within Replicate, and ϵ\u003csub\u003eijk\u003c/sub\u003e is the residual error.\u003c/p\u003e \u003cp\u003eType II Wald chi-square tests were used to assess the significance of fixed effects using the ANOVA function from the \u003cem\u003ecar\u003c/em\u003e package [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Normality of residuals was evaluated using the Shapiro-Wilk test, Q-Q plots, and histograms. Post-hoc pairwise comparisons for treatments were conducted using the Kenward-Roger degrees-of-freedom method for small-sample accuracy. Pairwise comparisons among accessions within each treatment level were performed using Tukey\u0026rsquo;s Honestly Significant Difference (HSD) test, implemented via the \u003cem\u003eemmeans\u003c/em\u003e package [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], to control the family-wise error rate across 26 accessions.\u003c/p\u003e \u003cp\u003eSymptom scores were analyzed using a cumulative link mixed model (CLMM) to account for the ordinal nature of the response variable and the hierarchical structure of the split-plot experimental design. As the experiment had one treatment level (150 \u0026micro;M Mn), the analysis focused on the effect of accession on symptom score. The CLMM was fitted using the \u003cem\u003eclmm\u003c/em\u003e function from the \u003cem\u003eordinal\u003c/em\u003e package [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The model specified symptom score as the ordinal response variable, with accession as a fixed effect and random intercepts for mainPlotID nested within replicate to account for the split-plot structure. The model was formulated as:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSymptomscore\u0026sim;\u003cem\u003eAccession+(1∣Replicate/MainPlotID)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe proportional odds assumption was evaluated using the \u003cem\u003enominal_test\u003c/em\u003e function from the \u003cem\u003eordinal\u003c/em\u003e package to test for non-proportional odds across the accessions. Significance of the fixed effect (accession) was assessed using a Type II Wald chi-square test implemented with the \u003cem\u003eAnova\u003c/em\u003e function from the \u003cem\u003ecar\u003c/em\u003e package [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. For significant effects, post-hoc pairwise comparisons among the accessions were conducted using the \u003cem\u003eemmeans\u003c/em\u003e package [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], with Tukey\u0026rsquo;s adjustment for multiple comparisons to control the family-wise error rate. Statistical significance was evaluated at p\u0026thinsp;=\u0026thinsp;0.05 across all analyses.\u003c/p\u003e \u003cp\u003eCorrelation coefficients were computed using the \u003cem\u003ecor()\u003c/em\u003e function for Pearson's product-moment correlation (assuming linearity and normality). The analysis was conducted on untransformed data, with results rounded to two decimal places. Coloured correlation matrix tables were generated in Microsoft Excel using a 3-colour scale setting where the lowest value was \u0026minus;\u0026thinsp;1 and coloured red, the midpoint value was 0 and coloured white, and the highest value was 1 and coloured blue.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Solution screening for tolerance to Al toxicity\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Longest root length, RTI, and RRL\u003c/h2\u003e \u003cp\u003eBoth the treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had significant influence on LLR, but their interaction did not. Mean LLR at 0 \u0026micro;M Al was higher by 40.0 mm when compared to 15 \u0026micro;M (p\u0026thinsp;=\u0026thinsp;0.0039) and by 49.9 mm compared to 60 \u0026micro;M Al (p\u0026thinsp;=\u0026thinsp;0.0017). Mean LLR at 15 \u0026micro;M was also higher than 60 \u0026micro;M Al by an average of 9.9 mm, but the difference was not significant, suggesting root inhibition at both treatment levels. Cowpeas consistently showed significant differences when compared to most chickpea accessions across all treatments, often with large differences in LLR and highly significant p values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 in many cases).\u003c/p\u003e \u003cp\u003eThe effects of treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on RTI were significant but their interaction was not. Relative tolerance index values at 15 \u0026micro;M were significantly higher than at 60 \u0026micro;M Al (p\u0026thinsp;=\u0026thinsp;0.0336). Root tolerance index values at 15 \u0026micro;M Al ranged between 37.1 and 62.4% with ten accessions moderately tolerant and the rest sensitive. Saina Kubwa and Saina 1 posted the highest RTIs of 62.4% and 61.1%, respectively. Other moderately tolerant accessions included ICCV 08107 (60.4%), ICCV 11514 (56.4%), 11316 (54.6%), 07101 (53.2%), 11504 (52.9%), 11519 (50.6%), 00108 (50.5%), and Saina Ndogo (50.1%). Cowpeas had significantly higher RTI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to most accessions except ICCV 08107 (p\u0026thinsp;=\u0026thinsp;0.074), Saina 1 (p\u0026thinsp;=\u0026thinsp;0.0878), and Saina Kubwa (p\u0026thinsp;=\u0026thinsp;0.1197). At 60 \u0026micro;M Al concentration, RTI values ranged between 22.7 and 50.7%. All the accessions except Saina 1 were sensitive to Al at this treatment level. Saina 1 had the highest RTI at 50.7%. Other accessions with relatively high RTIs included ICCV 07101 (49.5%), Saina Kubwa (49%), and ICCV 08107 (46.4%). Cowpeas had significantly higher RTI compared to most chickpea accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe effects of treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on RRL were significant but their interaction was not. Relative root lengths at 15 \u0026micro;M were significantly higher than at 60 \u0026micro;M Al (p\u0026thinsp;=\u0026thinsp;0.007). Relative root length ranged between 35.4 and 68.1% at 15 \u0026micro;M Al with eleven accessions moderately tolerant and fifteen sensitive (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the moderately tolerant accessions, ICCV 11514 posted the highest RRL of 68.1%. Cowpeas had significantly higher RRL compared to most accessions except ICCV 10515, ICCV 11316, ICCV 11504, ICCV 11514, ICCV 11519, K 036, Ndaria, and Saina 1. Differences between the best and worst performing chickpea accessions approached significance. All accessions were sensitive at 60 \u0026micro;M Al, the range in RRL being 2.9 to 35.7%. At this treatment level, cowpeas also had significantly higher RRL compared to all accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) except ICCV 10515.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Shoot length and RSL\u003c/h2\u003e \u003cp\u003eBoth treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accession (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had significant influence on shoot length, however their interaction was not significant. Shoot length was significantly higher at 0 \u0026micro;M as compared to 15 \u0026micro;M (p\u0026thinsp;=\u0026thinsp;0.0008) and 60 \u0026micro;M Al (p\u0026thinsp;=\u0026thinsp;0.0004), outperforming them by an average of 35.9 mm and 42.1 mm respectively. Shoots of plants treated with 15 \u0026micro;M Al were on average longer by 6.2 mm when compared to those treated with 60 \u0026micro;M Al, but this difference was not significant. Unlike LLR, cowpeas did not maintain consistently significant difference when compared to the chickpea accessions at 0 \u0026micro;M Al.\u003c/p\u003e \u003cp\u003eAt 15 \u0026micro;M Al, cowpeas consistently had significantly higher shoot lengths compared to most chickpea accessions. ICCV 10515 also performed better in some comparisons, having significantly higher shoot length than accessions such as Chania 1 (p\u0026thinsp;=\u0026thinsp;0.0341) and Chania 3 (p\u0026thinsp;=\u0026thinsp;0.0055). Other accessions generally did not differ significantly. At 60 \u0026micro;M Al, cowpeas again had significantly higher shoot lengths compared to most chickpea accessions. ICCV 07313 stood out in some comparisons, having significantly higher shoot length than some accessions such as ICCV 07114 (p\u0026thinsp;=\u0026thinsp;0.0160). Other accessions generally showed no significant differences.\u003c/p\u003e \u003cp\u003eThe effects of treatments (p\u0026thinsp;=\u0026thinsp;0.02685) and accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on RSL were significant but their interaction was not. Relative shoot lengths at 15 \u0026micro;M were higher than at 60 \u0026micro;M Al but the difference was not significant. Relative shoot length values at 15 \u0026micro;M Al ranged between 33% and 62.7% with fourteen accessions moderately tolerant and the rest being sensitive. Cowpeas had significantly higher RSL compared to most accessions except ICCV 10515, ICCV 11504, K 036 and Saina 1. Most pairwise comparisons among the accessions were not significant (p\u0026thinsp;\u0026asymp;\u0026thinsp;1.0000) with only a few approaching significance.\u003c/p\u003e \u003cp\u003eIn the 15 \u0026micro;M Al-treated plants, RSL correlated strongly and positively with RRL (r\u0026thinsp;=\u0026thinsp;0.6), suggesting that the treatment had similar effects on both roots and shoots (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At 60 \u0026micro;M Al, RSL values ranged between 27.1% and 56.1%. Only four accessions were moderately tolerant and included ICCV 11519 (56.1%), Saina 1 (55.8%), ICCV 10515 (53.3%), and K 036 (52.3%). At this treatment level, cowpeas had significantly higher RSL compared to most accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for most comparisons), except ICCV 10515, ICCV 11514, ICCV 11519, K 036, and Saina 1. Most pairwise comparisons among the accessions were not significant (p\u0026thinsp;\u0026asymp;\u0026thinsp;1.0000) with only a few approaching significance. The most sensitive accessions included ICCV 07313 (27.1%), ICCV 96329 (27.1%), and ICCV 97125 (28.6%).\u003c/p\u003e \u003cp\u003eTable 2: Correlations among RTI, RRL, RSL, RRW, and RSW at 15 \u0026micro;M and 60 \u0026micro;M Al.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"724\" height=\"706\"\u003e\u003c/p\u003e\n\u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Root weight and RRW\u003c/h2\u003e \u003cp\u003eBoth treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accession (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly influence on root weight, but their interaction did not. Root weights at 0 \u0026micro;M were significantly higher by an average of 0.2076 and 0.2588 g when compared to 15 \u0026micro;M (p\u0026thinsp;=\u0026thinsp;0.0011) and 60 \u0026micro;M (p\u0026thinsp;=\u0026thinsp;0.0005) respectively. Root weights at 15 \u0026micro;M and 60 \u0026micro;M Al were not significantly different from each other. When comparing root weight across treatments, cowpeas had significantly higher root weights compared to Chania 2 (p\u0026thinsp;=\u0026thinsp;0.0488), ICCV 96329 (p\u0026thinsp;=\u0026thinsp;0.0418), and Saina Ndogo (p\u0026thinsp;=\u0026thinsp;0.0184), but differences with other accessions were not significant at 0 \u0026micro;M Al. A few comparisons among chickpea accessions showed significance, with the most notable difference observed between ICCV 11316 and Chania 2 (p\u0026thinsp;=\u0026thinsp;0.0278). Most other chickpea accessions performed similarly at 0 \u0026micro;M Al.\u003c/p\u003e \u003cp\u003eFor 15 \u0026micro;M Al, cowpeas consistently had significantly higher root weights compared to most chickpea accessions except a few including ICCV 10515, Saina 1, Saina Kubwa (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Chickpea accessions generally did not differ significantly. At 60 \u0026micro;M, cowpeas showed fewer significant differences to the chickpea accessions than at 15 \u0026micro;M Al. Cowpea had significantly higher root weights compared to ICCV 96329 (p\u0026thinsp;=\u0026thinsp;0.0499), with some other comparisons e.g. vs. Chania 2, ICCV 97125, and K 036 approaching significance. Most chickpea accessions performed similarly at this treatment level.\u003c/p\u003e \u003cp\u003eThe effects of treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on RRW were significant but their interaction was not (p\u0026thinsp;=\u0026thinsp;0.9959). Relative root weights at 15 \u0026micro;M were higher than at 60 \u0026micro;M Al (p\u0026thinsp;=\u0026thinsp;0.0343). Relative root weight values at 15\u0026micro;M Al ranged between 35.6% and 75.4%. Fourteen accessions had RRWs\u0026thinsp;\u0026gt;\u0026thinsp;50% while the rest were below 50%. Accessions with the highest RRWs included ICCV 11519 (75.4%), Saina 1 (60%), ICCV 11504 (62.3%), and Saina Ndogo (59.7%). ICCV 07313 (36.5%), ICCV 03305 (40.7%), and ICCV 97125 (40.8%) posted the least RRW values. At 60 \u0026micro;M Al, RRWs ranged between 25.8% and 61.3%. RRW at 15\u0026micro;M correlated strongly and positively with RRL (r\u0026thinsp;=\u0026thinsp;0.52) and RSL (r\u0026thinsp;=\u0026thinsp;0.67) at the same treatment level.\u003c/p\u003e \u003cp\u003eCowpeas had significantly higher RRW compared to most accessions at 15 \u0026micro;M Al. At 60 \u0026micro;M Al, cowpeas also had higher RRW compared to all chickpea accessions, but most differences were not significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.1.4 Shoot weights and RSW\u003c/h2\u003e \u003cp\u003eBoth treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accession (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly influenced shoot weight, but their interaction did not. Shoot weights at 0 \u0026micro;M were significantly higher than at 15 \u0026micro;M and 60 \u0026micro;M Al by an average of 0.174 g (p\u0026thinsp;=\u0026thinsp;0.0009) and 0.210 g (p\u0026thinsp;=\u0026thinsp;0.0004) respectively. Shoot weights differences between 15 \u0026micro;M and 60 \u0026micro;M were not significant. Cowpeas had significantly higher shoot weights compared to all the accessions, with highly significant p-values (\u0026lt;\u0026thinsp;0.0001) at 0 \u0026micro;M Al whereas chickpea accessions generally performed similarly. At 15 \u0026micro;M and 60 \u0026micro;M Al, cowpeas consistently had significantly higher shoot weights compared to all chickpea accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eThe effects of treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on RSW were significant but their interaction was not. Relative shoot weight values 15 \u0026micro;M were significantly higher than at 60 \u0026micro;M Al (p\u0026thinsp;=\u0026thinsp;0.0465). RSL values at 15 \u0026micro;M Al ranged between 32% and 70.5%. ICCV 11504 (70.5%) ICCV 11519 (60.9%) and Ndaria (58.6%) had the highest RSW values while ICCV 03305 (32%), Chania 3 (37.3%), and ICCV 97125 (39.3%) the lowest. At 60 \u0026micro;M Al, shoot weights markedly dropped, ranging between 29.2 and 63.7%. Cowpeas had significantly higher RSW at both treatment levels compared to nearly all accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except for ICCV 11504.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Mn toxicity screening\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Symptom scores\u003c/h2\u003e \u003cp\u003eMost chickpea accessions and cowpea (control) displayed very mild to mild symptoms a few days after exposure to 150 \u0026micro;M Mn with marginal leaf chlorosis. After fourteen days, the symptoms had worsened. At this stage, most accessions displayed either mild or moderate symptoms with the marginal leaf chlorosis extending inwards towards the midrib. Reduced leaf sizes and curling were also observed among accessions with more severe symptoms. However, no leaflet droppings were observed except among cowpeas.\u003c/p\u003e \u003cp\u003eAccessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) had a significant effect on symptom scores. After fourteen days of exposure to the treatments, average symptom scores at 150 \u0026micro;M Mn ranged between 1.67 and 4.0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Three accessions, ICCV 11519, Saina 1, and ICCV 07313 displayed the least symptoms with mean scores of 1.67, 1.67 and 1.77 respectively. Twenty accessions displayed mild symptoms, five moderate, and one - Leldet 068 \u0026ndash; showed severe symptoms. Cowpeas, a legume known to be Mn sensitive, were severely affected, posting a mean score of 4.52 with severe necrotic spotting and leaf drops observed. No symptoms were observed among seedlings grown under 2 \u0026micro;M Mn (control treatment).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Shoot lengths, weights, RSL and RSW\u003c/h2\u003e \u003cp\u003eBoth treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accession (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly influenced shoot length, and their interaction was also significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Shoot lengths at 2 \u0026micro;M were significantly higher compared to 150 \u0026micro;M Mn (p\u0026thinsp;=\u0026thinsp;0.0368). Several pairwise comparisons among chickpea accessions were significant. At 2 \u0026micro;M Mn, Chania 1, Chania 2, ICCV 00108, ICCV 08107, ICCV 11504, ICCV 97125, and Saina Kubwa had significantly higher shoot lengths compared to Chania 3, ICCV 07114, ICCV 07313, ICCV 11316, ICCV 97128, Israel, K036, Leldet 068, and Ndaria. At 150 \u0026micro;M Mn, Chania 1, Chania 3, ICCV 00108, ICCV 07101, ICCV 08107, ICCV 11514, and ICCV 97125 had significantly higher shoot lengths compared to ICCV 07114, ICCV 10515, ICCV 11316, Israel, K036, Leldet 068, and Ndaria. Cowpeas had significantly higher shoot lengths compared to all accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) across both treatments.\u003c/p\u003e \u003cp\u003eAccessions had significant effects on RSL (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RSL ranged between 30.3% and 91% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Seven accessions were tolerant, thirteen were moderately tolerant, and six were sensitive. ICCV 07313 posted the highest RSL score of 91%. Other tolerant accessions included ICCV 07101 (83.9%), ICCV 97128 (82.9%), ICCV 11514 (79.6%), Chania 2 (73.2%), and Saina 1 (72.2%). ICCV 10515 (30.3%), ICCV 07114 (34.9%), Ndaria (38.6%), Chania 1 (45.2%), and Saina Kubwa (47.7%) were the most sensitive accessions. RSL correlated positively with symptom scores (r\u0026thinsp;=\u0026thinsp;0.55).\u003c/p\u003e \u003cp\u003eBoth treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accession (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly influenced chickpea shoot weight, and their interaction was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Shoot weights at 2 \u0026micro;M were significantly higher compared to 150 \u0026micro;M Mn (p\u0026thinsp;=\u0026thinsp;0.0030). Similar to shoot lengths, several shoot weight pairwise comparisons among chickpea accessions were significant. ICCV 08107, ICCV 11504, ICCV 11519, Saina 1, and Saina Kubwa stood out as the best performing accessions at 2 \u0026micro;M Mn. At 150 \u0026micro;M Mn, differences between the accessions were not significant. Cowpeas had significantly higher shoot weights compared to all accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) across both treatments.\u003c/p\u003e \u003cp\u003eAccessions had significant effects on RSW (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which ranged between 33.4 and 92.7%. ICCV 00108, ICCV 97110, Saina Ndogo, ICCV 97125, ICCV 11514, and ICCV 97128 posted the highest RSW values while ICCV 07114, Ndaria, Leldet 068, and Saina 1 posted the least.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Soil screening\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Longest root length, RTI and RRL\u003c/h2\u003e \u003cp\u003eBoth treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accession (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly influenced LLR, and their interaction was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Chickpeas planted in pH (H\u003csub\u003e2\u003c/sub\u003e0) 6.5 (control) had significantly higher LLR compared to those in pH (H\u003csub\u003e2\u003c/sub\u003e0) 4.5. Several pairwise comparisons among chickpea accessions were significant. In pH (H\u003csub\u003e2\u003c/sub\u003e0) 6.5, accessions ICCV 00108, ICCV 03305, ICCV 07101, and ICCV 07114 had significantly higher LLR relative to most others. In pH (H\u003csub\u003e2\u003c/sub\u003e0) 4.5, accessions ICCV 00108 and ICCV 07114 had significantly high LLR compared to most accessions. Chania 1 and Chania 2 had significantly lower LLR compared to most other accessions across both treatments.\u003c/p\u003e \u003cp\u003eThe effects of accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on RTI and RRL were significant. Root tolerance index after 14 days of growth ranged between 44.3 and 72.1% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Four accessions were tolerant, 19 were moderately tolerant, and only three were sensitive. ICCV 11514 was the most tolerant accession. Other tolerant accessions included K036, ICCV 10515, and Saina1.\u003c/p\u003e\u003cp\u003eRelative root length ranged between 29.8 and 65.3%. Accessions Saina 1 (65.3%), ICCV 11504 (64%), ICCV 11514 (64%), ICCV 07114 (60.8%), ICCV 11316 (60.4%), Chania 2 (60%), ICCV 00108 (58.7%), K036 (57.6%), Leldet 068 (55.9%), and ICCV 10515 (54.5%) were moderately tolerant. Other moderately tolerant included Israel, ICCV 11519, ICCV 08107 and Saina Kubwa. The rest of the accessions were sensitive, with Chania 3 (29.8%), Chania 1 (30.9%), and ICCV 03305 (36.1%) being the most sensitive. Soil RRL results correlated significantly (r\u0026thinsp;=\u0026thinsp;0.67) with those from the hydroponic experiments at 15 \u0026micro;M Al (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Saina 1, ICCV 11504, ICCV 11514, K036, ICCV 11316, Chania 2, ICCV 07114, and ICCV 00108 emerged as the standout tolerant accessions in both experiments.\u003c/p\u003e \n\u003cp\u003eTable 3: Correlations of RRL, RTI, RSL, and RRW between the soil and Al hydroponic experiments.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"704\" height=\"373\"\u003e\u003c/p\u003e\n\u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Shoot length and RSL\u003c/h2\u003e \u003cp\u003eBoth treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accession (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly influenced shoot length, but their interaction was not significant (p\u0026thinsp;=\u0026thinsp;0.6728). Chickpeas planted in pH (H\u003csub\u003e2\u003c/sub\u003e0) 6.5 (control) had significantly higher shoot lengths than those in pH (H\u003csub\u003e2\u003c/sub\u003e0) 4.5 (p\u0026thinsp;=\u0026thinsp;0.0076). In the control, no pairwise comparisons among the accessions were statistically significant (all p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In pH (H\u003csub\u003e2\u003c/sub\u003e0) 4.5, the only significant pairwise comparison was Chania 3 vs. ICCV 11514 (p\u0026thinsp;=\u0026thinsp;0.0391), with Chania 3 having significantly lower shoot values.\u003c/p\u003e \u003cp\u003eThe effects of accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on soil shoot lengths were significant. Relative shoot length values for the accessions ranged between 47.1 and 81.3%. Four accessions were tolerant, including ICCV 11514 (81.3%), ICCV 00108 (80.6%), K036 (81.3%) and ICCV 11316 (75%). Seventeen accessions were moderately tolerant and five sensitive. Chania 3 (47.1%), ICCV 11504 (48.4%), ICCV 07313 (48.5%), Chania 1 (48.5%) and ICCV 97125 (48.9%) were the most sensitive accessions. Tolerance indices from the soil experiments showed weak correlation with those from Mn screening in hydroponic experiments (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \n\u003cp\u003eTable 4: Correlations among soil (pH 4.5) and Mn tolerance indices\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" style=\"width: 466px; height: 136.598px;\" width=\"466\" height=\"136.598\"\u003e\u003c/p\u003e\n\u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Root weights and RRW\u003c/h2\u003e \u003cp\u003eBoth treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and accession (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly influenced root weights, and their interaction highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Chickpeas in the control experiment had significantly higher root weights than those in pH (H\u003csub\u003e2\u003c/sub\u003e0) 4.5 (p\u0026thinsp;=\u0026thinsp;0.0100). In the control, many pairwise comparisons among chickpea accessions were significant with Chania 1 and Chania 3 standing out for having significantly higher root weights. In pH (H\u003csub\u003e2\u003c/sub\u003e0) 4.5, fewer pairwise comparisons were significant compared to the control, but there were still notable differences. ICCV 08107, ICCV 11504, and Saina 1 had higher root weights compared to most other accessions whereas ICCV 00108, ICCV 07101, and ICCV 07114 had lower root weights. The effects of accessions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on RRW were significant. Relative root weights ranged between 38.7 and 69.5%. ICCV 11504 (69.5%), Chania 2 (64.8%), and K036 (64.1%) were the best-performing accessions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.3.4 Shoot weights and RSW\u003c/h2\u003e \u003cp\u003eBoth treatment and accession significantly influence shoot weights, and their interactions were highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Chickpeas planted in the control had significantly higher shoot weights than those in pH (H\u003csub\u003e2\u003c/sub\u003e0) 4.5 (p\u0026thinsp;=\u0026thinsp;0.0038). Many pairwise comparisons among the accessions were significant under both treatments. In the control, accessions ICCV 97128, Israel, and ICCV 11514 had higher shoot weights compared to most other accessions while ICCV 07313, ICCV 08107, and Israel stood out in pH (H\u003csub\u003e2\u003c/sub\u003e0) 4.5.\u003c/p\u003e \u003cp\u003eThe effect of accession on RSW was also significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Shoot weights were not as affected as the root weights by the soil acidity, with RSW values ranging between 47.5 and 91.1%. ICCV 07313 (91.1%), ICCV 08107 (76.3%), ICCV 93954 (74.1%), Israel (70.7%), and Chania 1 (70.2%) posted the highest RSW values.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Al toxicity screening\u003c/h2\u003e \u003cp\u003eVariation in Al toxicity tolerance was observed among the chickpea accessions screened, likely due to the underlying genetic variability. Similarly, Negusse \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] reported varied accession response to Al toxicity in a hydroponic assay study to identify acid-tolerant chickpea accessions in Ethiopia. Another study by Vance \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] found domestic and wild chickpea cultivars had different tolerance levels to soluble Al in low ionic strength solution screening. Genotypic variation in tolerance to Al toxicity has also been reported in other crops such as maize, wheat, sunflowers and barley [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Aluminium treatments selected for use in this experiment were 15 and 60 \u0026micro;M, with the hypothesis that moderately tolerant accessions would be observed at 15 \u0026micro;M, and the highly tolerant at 60 \u0026micro;M [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. While there was variation in Al tolerance among the 26 chickpea accessions, none of them matched the high level of tolerance at 60 \u0026micro;M shown by the cowpea, which is known for its high level of Al tolerance [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this experiment, both root and shoot growth indices were used to determine accession tolerance to Al toxicity. Strong and positive correlations between root and shoot indices were observed (RSL correlated positively with RRL; r\u0026thinsp;=\u0026thinsp;0.6), while RRW also correlated strongly and positively with RRL (r\u0026thinsp;=\u0026thinsp;0.52) and RSL (r\u0026thinsp;=\u0026thinsp;0.66) at 15\u0026micro;M Al. Root measurements are widely used as indicators of plant Al tolerance because root growth inhibition is the major consequence of Al toxicity in plants even upon brief exposures [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, some studies have reported root measurements as better indicators of Al tolerance. Vance \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], for example, determined RRL to be the most accurate discriminator of Al tolerance in chickpea screening. Root length has also been used to discriminate between Al-sensitive and Al-tolerant accessions in Dolichos beans, soya beans, and barley among others [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The results of this study, however, signify that both shoot and root indices are adequate indices of Al toxicity among chickpea accessions. Similarly, Kushwaha et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] utilized root and shoot growth indicators to screen cowpeas for tolerance to Al toxicity. Screening based on shoot growth has advantages over root screening, particularly for soil screening since it avoids the need to recover and wash roots before measurement.\u003c/p\u003e \u003cp\u003eThe tolerance rankings observed at 15 \u0026micro;M were not maintained at 60 \u0026micro;M. The decrease in tolerance of accessions to an increased Al concentration is attributed to the severely damaged chickpea roots which impedes the absorption of nutrients and water. Inconsistent displays of tolerance in chickpea at different Al concentrations levels were also reported by Vance \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] when screening wild chickpea accessions for tolerance to Al toxicity. Inconsistent Al tolerance at different concentration levels has also been reported in dolichos beans (\u003cem\u003eLablab purpureus\u003c/em\u003e), cowpeas and rice (\u003cem\u003eOryza sativa\u003c/em\u003e) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Hence the ranking obtained here are relevant to moderate levels of Al toxicity only and insufficient tolerance was identified in the Kenyan chickpea collection for high levels of Al toxicity in soils.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Mn toxicity screening\u003c/h2\u003e \u003cp\u003eMn toxicity symptoms are attributed to accumulation of Mn ions in plant roots and shoots that reduced nutrient uptake and translocation [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In cowpeas, the symptoms were more severe, and leaf senescence was observed, indicating that the chickpeas exhibited relatively better tolerance to Mn toxicity Similar Mn toxicity symptoms were also reported by Ahlawat [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and El-Jaoual and Cox [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], who observed marginal chlorosis and necrosis of leaves, dark reddish brown leaf spots, reduced root growth, and distorted small-sized leaves in chickpeas. Chlorosis and impaired growth are common Mn toxicity symptoms in other plants including sugarcane (\u003cem\u003eSaccharum officinarum\u003c/em\u003e) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSymptom scores correlated weakly with RSL (r\u0026thinsp;=\u0026thinsp;0.2), which is attributed to the effect of environmental and other external factors that may skew symptom scoring. These findings agree with those of Fernando and Lynch (2015) and Pradeep \u003cem\u003eet al\u003c/em\u003e. (2020) who reported that accession genetics and environmental factors such as light intensity and temperature may have an impact on symptom scoring. According to Millaleo \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] and Alejandro \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], photosynthesis is an early target of Mn toxicity, which not only decreases the rate of photosynthesis, but also the chlorophyll content of the affected leaves. This, therefore, suggests that exposed plants exhibit symptoms such as leaf chlorosis and appearance of necrotic spots in the short term and reduced shoot growth in the long term. As such, accession symptom scores and corresponding shoot growth may not be closely related [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, incorporating plant physiological responses related to photosynthesis alongside symptom scoring may enhance the accuracy of screening chickpeas for Mn tolerance.\u003c/p\u003e \u003cp\u003eVariation in tolerance to Mn toxicity was observed among the chickpea accessions, likely reflecting the presence of diversity within the collection These results agree with those of Pradeep \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] who found differing responses to Mn toxicity at different treatment levels among prominent chickpea accessions in Australia. Studies on other crops have also documented genotypic differences in tolerance to Mn. Khabaz-Saberi \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported varying levels of Mn toxicity tolerance among different wheat genotypes. Similarly, Husted \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] and Muhammad \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] observed differences in growth among barley genotypes exposed to low Mn concentrations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Soil screening\u003c/h2\u003e \u003cp\u003eIn screening for Al toxicity, inconsistencies between results from the two experimental methods, hydroponics screen versus soil screen, were observed. The inconsistencies between soil and hydroponic experiments are attributed to the greater effectiveness of secreted organic acids in decreasing levels of toxic Al\u003csup\u003e3+\u003c/sup\u003e in the soil rhizosphere than in the hydroponic solutions, where organic acids are diluted in solution and discarded in the replacement of solutions every 2 days [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Yong \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] reported that chickpea and wild Cicer accessions vary in citrate secretion, while malonic acid is excreted in higher concentrations than citrate or malate. Further examination of variation in the type and level of organic acid secretion among the 26 chickpea accessions tested could further explain variations in tolerance to Al and differences between soil and hydroponic studies. Additionally, ion interactions in soil may compound or alleviate Al toxicity due to changes in Al speciation that are particularly sensitive to pH, ionic strength of solution, soluble organic matter, phosphate concentrations, calcium and magnesium concentrations and sulfate concentrations [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Previous studies have reported variable responses to Al toxicity when screening in hydroponic and in soil experiments. Kulkarni \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] found soil screening to be less efficient than hydroponics when screening lentil germplasms for Al toxicity tolerance, citing major differences between results from the two experiments. Other studies, however, reported some similarities. Baier \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] found a strong correlation between hydroponic and soil experiments when screening wheat for tolerance to Al toxicity. Similarly, Nava \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] concluded that results from hydroponic experiments and those from acid soils were similar when screening oat cultivars for tolerance to Al toxicity.\u003c/p\u003e \u003cp\u003eWhile inconsistencies between hydroponic and soil screening results were evident for most of the indices used, an exception was noted where RRL in soil correlated significantly (r\u0026thinsp;=\u0026thinsp;0.67) with those from the hydroponic experiments at 15\u0026micro;M Al. This consistency is a result of RRL taking into account the mean root growth, unlike other indices such as RTI which was calculated from the root length at harvest and did not take into account root length variations at sowing. Therefore, RRL is a better indicator of accession tolerance to Al toxicity. RTI can be useful in experiments where measurement of initial root length poses a challenge, such as when screening in soil experiments. In a similar study, Vance \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] determined RRL to be the most accurate discriminator of Al tolerance in chickpea screening.\u003c/p\u003e \u003cp\u003eIn screening for tolerance to Mn toxicity, RSL measurements from hydroponic and soil experiments correlated weakly and negatively, (r = -0.09) while RSW correlated weakly (r\u0026thinsp;=\u0026thinsp;0.13), a further indicator of inconsistencies between the two experiments. The weak correlation is attributed to differing levels of Mn ions present in the nutrient solutions and soil used in the experiments. Plants grown hydroponically were treated with 150 \u0026micro;M while soil Mn ion content was 33.6 mg Mn/kg. Other than pH, soil Mn content is also influenced by many factors including redox conditions and moisture levels [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Studies by Porter et al. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] and Weil et al. [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] indicate that both flooding and dry soil exacerbate Mn toxicity, with the least symptoms observed at soil moisture levels near field capacity. Apart from soil properties, soil Mn availability is also controlled by plant characteristics and the interactions between roots and the rhizosphere [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions and recommendations","content":"\u003cp\u003eIn hydroponic screening for Al toxicity, accessions Saina 1, ICCV 11519, ICCV 11504, ICCV 11514, ICCV 11316, and ICCV 07114 showed high to moderate tolerance across all indices at 15\u0026micro;M Al. At 60 \u0026micro;M Al, Saina 1 based on RSL; ICCV 11316, ICCV 11514, and ICCV 07114, based on RRW; ICCV 11504 based on RSW; and ICCV 11519 based on RSL, RRW and RSW were moderately tolerant. In screening for tolerance to Mn toxicity, accessions ICCV 07313, ICCV 07101, ICCV 97128, ICCV 11514, Chania 2, and Saina 1 were tolerant based RSL. Symptom scoring highlighted ICCV 11519, Saina 1, and ICCV 07313 as the standout accessions with the least Mn toxicity symptoms. Hydroponic results, however, were largely inconsistent with those obtained from the soil experiments. Despite this observation, soil experiment results showed accessions Saina 1, ICCV 11316, ICCV 11504, ICCV 07114, and ICCV 11514 to be tolerant based on the various indices. Overall, accessions Saina 1 (Kabuli) and ICCV 11514 (Desi) stood out for moderate toxicity tolerance of both Al and Mn based on hydroponic and soil experiments. These accessions emerged as the promising lines for further long-term experiments or field validation. Additionally, further research on the combined effects Al and Mn toxicities on chickpeas in solution and soil and investigation of tolerance mechanism would shed more light on the traits to select chickpea for improved acid soil tolerance. However, there would be merit in further collection of chickpea accessions from elsewhere to increase the level of Al and Mn toxicity tolerance available for breeding of adapted cultivars for acid soils in Kenya and East Africa.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e7.1 Funding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003ePart funding of this study was through the Australian Awards fellowship awarded to the second author for his Postdoctoral studies.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e7.2 Conflicts of interest\u003c/h2\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003ch2\u003e7.3 Ethics and consent to participate\u003c/h2\u003e\n\u003cp\u003eAll plant materials were obtained with appropriate permissions. Seed materials from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Nairobi, Kenya, were accessed under institutional guidelines for research use. Permission was also sought when collecting seed material from farmers. No special permits were required for the use of cultivated chickpea (\u003cem\u003eCicer arietinum\u0026nbsp;\u003c/em\u003eL\u003cem\u003e.\u003c/em\u003e) varieties, as it is not classified as an endangered or protected species.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003ch2\u003e7.4 Data availability\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the data supporting the findings of this study are available within the paper. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e7.5 Code availability\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003e7.6 Authors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Elkanah Kipkoech and Richard Onwonga. The first draft of the manuscript was written by Elkanah Kipkoech and Richard Onwonga. Authors Karuma Anne, Richard W. Bell, Wendy H. Vance, Karthika Pradeep, and Janeth Chepkemoi provided valuable revisions. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003e7.7 Consent to publish declaration\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMacil, P.J., Ogola, J.B.O., Odhiambo, J.J.O., 2020. Response of soil pH and nodulation of three chickpea genotypes to biochar and rhizobium inoculation. Communications in Soil Science and Plant Analysis. 51(18):2377\u0026ndash;2387. https://doi:10.1080/00103624.2020.1836204.\u003c/li\u003e\n\u003cli\u003eAgegnehu, G., Amede, T., Erkossa, T., Yirga, C., Henry, C., Tyler, R., \u0026hellip; Sileshi, G. W., 2021. Extent and management of acid soils for sustainable crop production system in the tropical agroecosystems: A review. Acta Agriculturae Scandinavica, Section B \u0026mdash; Soil \u0026amp; Plant Science, 71(9):852\u0026ndash;869. https://doi.org/10.1080/09064710.2021.1954239.\u003c/li\u003e\n\u003cli\u003eSingh, Z., Singh, G., 2018. Role of Rhizobium in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e) production - A review. Agricultural Reviews. 39(1):31-9. https://doi.org/10.18805/ag.R-1699.\u003c/li\u003e\n\u003cli\u003eButterly, C.R., Amado, T.J.C., Tang, C., 2022. Soil acidity and acidification. In: Oliveira, T.S.d., Bell, R.W. (eds) Subsoil Constraints for Crop Production. Springer, Cham. https://doi.org/10.1007/978-3-031-00317-2_3.\u003c/li\u003e\n\u003cli\u003eKanyanjua, S.M., Ireri, L., Wambua, S., Nandwa, S.M., 2002. Acidic soils in Kenya: Constraints and remedial options. KARI Technical Note No. 11 June 2002.\u003c/li\u003e\n\u003cli\u003eOkalebo, J.R., Otieno, C.O., Nekesa, A.O., Ndungu-Magiroi, K.W., Kifuko-Koech, M.N., 2009. Potential for agricultural lime on improved soil health and agricultural production in Kenya. African Crop Science Conference Proceedings. 9: 339\u0026ndash;341.\u003c/li\u003e\n\u003cli\u003eLiu, X., Zhou, J., Li, W., Xu, J., Brookes, P.C., 2014. The combined effects of urea application and simulated acid rain on soil acidification and microbial community structure. Environ Sci Pollut Res. 21:6623\u0026ndash;6631. https://doi.org/10.1007/s11356-014-2573-9.\u003c/li\u003e\n\u003cli\u003eRahman, M.A., Lee, S.-H., Ji, H.C., Kabir, A.H., Jones, C.S., Lee, K.-W., 2018. Importance of mineral nutrition for mitigating aluminum toxicity in plants on acidic soils: Current status and opportunities. International Journal of Molecular Sciences. 19(10):3073. https://doi.org/10.3390/ijms19103073.\u003c/li\u003e\n\u003cli\u003eGupta, N., Gaurav S.S., Kumar A., 2013. Molecular basis of aluminium toxicity in plants: a review. American Journal of Plant Sciences, 12C:41018. https://DOI:10.4236/ajps.2013.412A3004.\u003c/li\u003e\n\u003cli\u003eBhalerao, S.A., Prabhu, D.V., 2013. Aluminium toxicity in plants\u0026mdash;a review. Journal of Applicable Chemistry, 2(3):447-74.\u003c/li\u003e\n\u003cli\u003eSilva, S., 2012. Aluminium toxicity targets in plants. Journal of Botany. 1:219462. https://doi.org/10.1155/2012/219462.\u003c/li\u003e\n\u003cli\u003eShetty, R., Prakash, N.B., 2020. Effect of different biochars on acid soil and growth parameters of rice plants under aluminium toxicity. Scientific Reports. 10:12249. https://doi.org/10.1038/s41598-020-69262-x.\u003c/li\u003e\n\u003cli\u003eOfoe, R., Thomas, R.H., Asiedu, S.K., Wang-Pruski, G., Fofana, B., Abbey, L., 2023. Aluminum in plant: Benefits, toxicity and tolerance mechanisms. Frontiers in Plant Science. 13:1085998. https://doi.org/10.3389/fpls.2022.1085998.\u003c/li\u003e\n\u003cli\u003eSingh, D., Chaturvedi, S.K., 2007. Rapid and effective screening technique for aluminium tolerance in chickpea at seedling stage. In National Symposium on Legumes for Ecological Sustainability: Emerging Challenges and Opportunities held at Indian Institute of Pulses Research Kanpur from 2007 Nov 3:3-5.\u003c/li\u003e\n\u003cli\u003eWayima, E.F., Ligaba-Osena, A., Dagne, K., Tesfaye, K., Machuka, E.M., Mutiga, S.K., Delhaize, E., 2019. Screening of diverse Ethiopian durum wheat accessions for aluminum tolerance. Agronomy. 9(8):440. https://doi.org/10.3390/agronomy9080440.\u003c/li\u003e\n\u003cli\u003eSade, H., Meriga, B., Surapu, V., Gadi J., Sunita M.S., Suravajhala P., Kavi Kishor P.B., 2016. Toxicity and tolerance of aluminum in plants: tailoring plants to suit to acid soils. Biometals. 29:187\u0026ndash;210. https://doi.org/10.1007/s10534-016-9910-z.\u003c/li\u003e\n\u003cli\u003ePang, J., Bansal, R., Zhao, H., Bohuon, E., Lambers, H., Ryan, M.H., Ranathunge, K., Siddique, K.H.M., 2018. The carboxylate-releasing phosphorus-mobilizing strategy can be proxied by foliar manganese concentration in a large set of chickpea germplasm under low phosphorus supply. New Phytologist. 219:518-529. https://doi.org/10.1111/nph.15200.\u003c/li\u003e\n\u003cli\u003eYong, J., Pradeep, K., Vance, W.H., Zhang, X., Weir, B., Wei, H., Deng, Z., Zhang, Y., Xu, X., Zhao, C., Berger, J.D., 2022. Identification of two chickpea multidrug and toxic compound extrusion transporter genes transcriptionally upregulated upon aluminum treatment in root tips. Frontiers in Plant Science.13:909045. https://doi.org/10.3389/fpls.2022.909045.\u003c/li\u003e\n\u003cli\u003eSultana, S., Bell, R.W., Vance, W.H., 2020. Genotypic variation among chickpea and wild Cicer spp. in nutrient uptake with increasing concentration of solution Al at low pH. Plant Physiology and Biochemistry. 157:390-401. https://doi.org/10.1016/j.plaphy.2020.10.034.\u003c/li\u003e\n\u003cli\u003eSingh, D., Choudhary, A.K., 2010. Inheritance pattern of aluminum tolerance in pea. Plant Breeding. 129:688-692. https://doi.org/10.1111/j.1439-0523.2009.01739.x.\u003c/li\u003e\n\u003cli\u003eVance, W., Pradeep, K., Strachan, S.R., Diffey, S., Bell, R.W., 2021. Novel sources of tolerance to aluminium toxicity in wild cicer (\u003cem\u003eCicer reticulatum\u003c/em\u003e and \u003cem\u003eCicer echinospermum\u003c/em\u003e) collections. Frontiers in Plant Science. 12:678211. https://doi.org/10.3389/fpls.2021.678211.\u003c/li\u003e\n\u003cli\u003eSantos, E.F., Santini, J.M., Paix\u0026atilde;o, A.P., J\u0026uacute;nior, E.F., Lavres, J., Campos, M., Dos Reis, A.R., 2017. Physiological highlights of manganese toxicity symptoms in soybean plants: Mn toxicity responses. Plant Physiology and Biochemistry. 113:6-19. https://doi.org/10.1016/j.plaphy.2017.01.022.\u003c/li\u003e\n\u003cli\u003eFernando, D.R., Lynch, J.P., 2015. Manganese phytotoxicity: new light on an old problem. Annals of Botany, 116(3):313-9. https://doi.org/10.1093/aob/mcv111.\u003c/li\u003e\n\u003cli\u003eKhabaz-Saberi, H., Rengel, Z., Wilson, R., Setter, T.L., 2010. Variation of tolerance to manganese toxicity in Australian hexaploid wheat. Zeitschrift f\u0026uuml;r Pflanzenern\u0026auml;hrung und Bodenkunde. 173:103-112. https://doi.org/10.1002/jpln.200900063.\u003c/li\u003e\n\u003cli\u003ePais, I.P., Moreira, R., Semedo, J.N., Ramalho, J.C., Lidon, F.C., Coutinho, J., Ma\u0026ccedil;\u0026atilde;s, B., Scotti-Campos, P., 2023. Wheat crop under waterlogging: Potential soil and plant effects. Plants. 12(1):149. https://doi.org/10.3390/plants12010149.\u003c/li\u003e\n\u003cli\u003eLavres Junior, J., Reis, A.R., Rossi, M.L., Cabral, C.P., Nogueira, N.D., Malavolta, E., 2010. Changes in the ultrastructure of soybean cultivars in response to manganese supply in solution culture. Scientia Agricola. 67:287-94. https://doi.org/10.1590/S0103-90162010000300006.\u003c/li\u003e\n\u003cli\u003eFernando, D.R., Marshall, A., Baker, A.J., Mizuno, T., 2013. Microbeam methodologies as powerful tools in manganese hyperaccumulation research: present status and future directions. Frontiers in Plant Science, 4:319. https://doi.org/10.3389/fpls.2013.00319.\u003c/li\u003e\n\u003cli\u003eWeng, X.Y., Zhao, L.L., Zheng, C.J., Zhu, J.W., 2013. Characteristics of the hyperaccumulator plant phytolacca acinosa (phytolaccaceae) in response to excess manganese. Journal of Plant Nutrition. 36(9):1355\u0026ndash;1365. https://doi.org/10.1080/01904167.2013.790428.\u003c/li\u003e\n\u003cli\u003ePradeep, K., Bell, R.W., Vance, W., 2020. Variation of Cicer germplasm to manganese toxicity tolerance. Frontiers in Plant Science. 11:588065. https://doi.org/10.3389/fpls.2020.588065.\u003c/li\u003e\n\u003cli\u003eBlamey, F.P.C., Hernandez-Soriano, M.C., Cheng, M., Tang, C., Paterson, D.J., Lombi, E., Wang, W.H., Scheckel, K.G., Kopittke, P.M., 2015. Synchrotron-based techniques shed light on mechanisms of plant sensitivity and tolerance to high manganese in the root environment. Plant Physiology, 169(3):2006-2020. https://doi.org/10.1104/pp.15.00726.\u003c/li\u003e\n\u003cli\u003eSingh, K., Ocampo, B., 1997. Exploitation of wild Cicer species for yield improvement in chickpea. Theoretical and Applied Genetics. 95:418-23. https://doi.org/10.1007/s001220050578.\u003c/li\u003e\n\u003cli\u003eRyan, P.R., 2018. Assessing the role of genetics for improving the yield of Australia\u0026rsquo;s major grain crops on acid soils. Crop and Pasture Science. 69(3):242-64. https://doi.org/10.1071/CP17310.\u003c/li\u003e\n\u003cli\u003eAbate, E., Hussien, S., Laing, M., \u0026amp; Mengistu, F., 2013. Aluminium toxicity tolerance in cereals: Mechanisms, genetic control and breeding methods. Afr J Agric Res, 8(9), 711-722. http://www.academicjournals.org/AJAR.\u003c/li\u003e\n\u003cli\u003eWang, Jp., Raman, H., Zhang, G.P., Mendham, N., Zhou, M.X., 2006. Aluminium tolerance in barley (Hordeum vulgare L.): Physiological mechanisms, genetics and screening methods. J. Zhejiang Univ. - Sci. B. 7:769\u0026ndash;787. https://doi.org/10.1631/jzus.2006.B0769.\u003c/li\u003e\n\u003cli\u003eChoudhary, A.K., Singh, D., Iquebal, M.A., 2011. Selection of pigeonpea genotypes for tolerance to aluminium toxicity. Plant Breeding, 130:492-495. https://doi.org/10.1111/j.1439-0523.2010.01833.x.\u003c/li\u003e\n\u003cli\u003eKimurto, P.K., Gatongi, I., 2016. An overview of chickpea research and development programs in Kenya: current status, challenges and opportunities. Harnessing Chickpea Value Chain for Nutrition Security and Commercialization of Smallholder Agriculture in Africa. 40.\u003c/li\u003e\n\u003cli\u003eKimurto, P.K., Towett, B., Mulwa, R.S., Njogu, N., Kosgei, A., Songok, S., Oyier, M., Rutto, D., Korir, P., Rao, G., Silim, S., Kaloki, P., Muruiki, R., Barmao, G., Macharia, J.K., 2014. An overview of chickpea breeding programs in Kenya. Legume Perspectives, 3:55-57. http://oar.icrisat.org/id/eprint/8529.\u003c/li\u003e\n\u003cli\u003ePinheiro, J., 2025. nlme: Linear and nonlinear mixed effects models. \u003cem\u003eR package version\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, p.1. https://cran.r-project.org/web/packages/nlme/nlme.pdf\u003c/li\u003e\n\u003cli\u003eFox, J., and Weisberg, S., 2025. An R Companion to Applied Regression (3rd ed.). Sage. https://cran.r-project.org/web/packages/car/car.pdf \u003c/li\u003e\n\u003cli\u003eLenth, R. V., 2025. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.8.9. https://cran.r-project.org/web/packages/emmeans/emmeans.pdfLima, M.L., Copeland, L., 1994. Changes in the ultrastructure of the root tip of wheat following exposure to aluminium. Functional Plant Biology. 21(1):85-94. https://doi.org/10.1071/PP9940085.\u003c/li\u003e\n\u003cli\u003eChristensen, R. H. B., 2025. Ordinal: Regression Models for Ordinal Data. R package version 2023.12-4. https://cran.r-project.org/web/packages/ordinal/ordinal.pdf\u003c/li\u003e\n\u003cli\u003eNegusse, H., Cook, D.R., Haileselassie, T., Tesfaye, K., 2022. Identification of aluminum tolerance in Ethiopian chickpea (Cicer arietinum L.) germplasm. Agronomy. 12(4):948. https://doi.org/10.3390/agronomy12040948.\u003c/li\u003e\n\u003cli\u003eRichard, C., Munyinda, K., Kinkese, T., Osiru, D.S., 2015. Genotypic variation in seedling tolerance to aluminum toxicity in historical maize inbred lines of Zambia. Agronomy. 5(2):200-219. https://doi.org/10.3390/agronomy5020200.\u003c/li\u003e\n\u003cli\u003eFerreira, J.R., Faria, B.F., Comar, M., Delatorre, C.A., Minella, E., Pereira, J.F., 2017. Is a non-synonymous SNP in the HvAACT1 coding region associated with acidic soil tolerance in barley? Genetics and Molecular Biology, 40:480-90. https://doi.org/10.1590/1678-4685-GMB-2016-0225.\u003c/li\u003e\n\u003cli\u003eSingh, V.K., Chander, S., Sheoran, R.K., Anu, Sheoran, O.P., Garcia-Oliveira, A.L., 2022. Genetic variability for aluminium tolerance in sunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e L.). Czech J. Genet. Plant Breed. 58:201\u0026ndash;209. https://doi.org/10.17221/110/2021-CJGPB.\u003c/li\u003e\n\u003cli\u003eSilva, F. W. R., \u0026amp; Santos, J. Z. L., 2023. Response of cowpea plants submitted to acid conditions: Aluminum and hydrogen stress. \u003cem\u003eRevista Brasileira de Ci\u0026ecirc;ncia do Solo\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e, e0220107.\u003c/li\u003e\n\u003cli\u003eLima ML, Copeland L. Changes in the ultrastructure of the root tip of wheat following exposure to aluminium. Functional Plant Biology. 1994;21(1):85-94.\u003c/li\u003e\n\u003cli\u003eAnsari, M.T., Pandey, A.K., Mailappa, A.S., Singh, S., 2019. Screening of Dolichos bean [\u003cem\u003eLablab purpureus\u003c/em\u003e (L.) Sweet] genotypes for aluminium tolerance. Legume Research-An International Journal, 42(4):495-9. https://DOI:10.18805/LR-3949.\u003c/li\u003e\n\u003cli\u003eSpehar, C., Galwey, N., 1997. Screening soya beans [Glycine max (L.) Merill] for calcium efficiency by root growth in low-Ca nutrient solution. Euphytica. 94:113\u0026ndash;117. https://doi.org/10.1023/A:1002985720197.\u003c/li\u003e\n\u003cli\u003eKushwaha, J.K., Pandey, A.K., Dubey, R.K., Singh, V., Mailappa, A.S., Singh, S., 2017. Screening of cowpea [Vigna unguiculata (L.) Walp.] for aluminium tolerance in relation to growth, yield and related traits. Legume Research-an International Journal. 40(3):434-8. https://doi.org/10.18805/lr.v0i0.7016.\u003c/li\u003e\n\u003cli\u003eAwasthi, J.P., Saha, B., Regon, P., Sahoo, S., Chowra, U., Pradhan, A., Roy, A., Panda, S.K., 2017. Morpho-physiological analysis of tolerance to aluminum toxicity in rice varieties of North-East India. PloS one, 12(4):e0176357. https://doi.org/10.1371/journal.pone.0176357.\u003c/li\u003e\n\u003cli\u003eXu, X., Chen, X., Shi, J., Chen, Y., Wu, W., \u0026amp; Perera, A., 2007. Effects of manganese on uptake and translocation of nutrients in a hyperaccumulator. \u003cem\u003eJournal of plant nutrition\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(10), 1737-1751.\u003c/li\u003e\n\u003cli\u003eAhlawat, I.P., 1990. Diagnosis and alleviation of mineral nutrient constraints in chickpea. Chickpea in the Nineties. Eds. HA Vanrheenen and MC Saxena. :93-9.\u003c/li\u003e\n\u003cli\u003eEl‐Jaoual, T., Cox, D.A., 1998. Manganese toxicity in plants. Journal of Plant Nutrition, 21(2):353\u0026ndash;386. https://doi.org/10.1080/01904169809365409.\u003c/li\u003e\n\u003cli\u003eHuang, Y.L., Yang, S., Long, G.X., Zhao, Z.K., Li, X.F., Gu, M.H., 2016. Manganese toxicity in sugarcane plantlets grown on acidic soils of southern China. PLoS One, 11(3):e0148956. https://doi.org/10.1371/journal.pone.0148956.\u003c/li\u003e\n\u003cli\u003eMillaleo, R., Reyes-D\u0026iacute;az, M., Ivanov, A.G., Mora, M.L., Alberdi, M., 2010. Manganese as essential and toxic element for plants: transport, accumulation and resistance mechanisms. Journal of Soil Science and Plant Nutrition. 10(4):470-81. http://dx.doi.org/10.4067/S0718-95162010000200008.\u003c/li\u003e\n\u003cli\u003eAlejandro, S., H\u0026ouml;ller, S., Meier, B., Peiter, E., 2020. Manganese in plants: from acquisition to subcellular allocation. Frontiers in Plant Science, 11:300. https://doi.org/10.3389/fpls.2020.00300.\u003c/li\u003e\n\u003cli\u003eHusted, S., Laursen, K.H., Hebbern, C.A., Schmidt, S.B., Pedas, P., Haldrup, A., Jensen, P.E., 2009. Manganese deficiency leads to genotype-specific changes in fluorescence induction kinetics and state transitions. Plant Physiology, 150(2):825-33. https://doi.org/10.1104/pp.108.134601.\u003c/li\u003e\n\u003cli\u003eMuhammad, N., Cai, S., Shah, J.M., Zhang, G., 2016. The combined treatment of Mn and Al alleviates the toxicity of Al or Mn stress alone in barley. Acta Physiologiae Plantarum. 38:277. https://doi.org/10.1007/s11738-016-2296-2.\u003c/li\u003e\n\u003cli\u003eHeim, A., Brunner, I., Frey, B., Frossard, E., \u0026amp; Luster, J., 2001. Root exudation, organic acids, and element distribution in roots of Norway spruce seedlings treated with aluminum in hydroponics. \u003cem\u003eJournal of Plant Nutrition and Soil Science\u003c/em\u003e, \u003cem\u003e164\u003c/em\u003e(5), 519-526.\u003c/li\u003e\n\u003cli\u003eKopittke, P.M., Blamey, F.P.C., 2016. Theoretical and experimental assessment of nutrient solution composition in short-term studies of aluminium rhizotoxicity. Plant Soil. 406:311\u0026ndash;326. https://doi.org/10.1007/s11104-016-2890-5.\u003c/li\u003e\n\u003cli\u003eKulkarni, V., Sawbridge, T., Kaur, S., Hayden, M., Slater, A.T., Norton, S.L., 2021. New sources of lentil germplasm for aluminium toxicity tolerance identified by high throughput hydroponic screening. Physiology and Molecular Biology of Plants. 27(3):563-576. https://doi.org/10.1007/s12298-021-00954-y.\u003c/li\u003e\n\u003cli\u003eBaier, A.C., Somers, D.J., Gustafson, J.P., 1995. Aluminium tolerance in wheat: correlating hydroponic evaluations with field and soil performances. Plant Breeding, 114:291-296. https://doi.org/10.1111/j.1439-0523.1995.tb01236.x.\u003c/li\u003e\n\u003cli\u003eNava, I.C., Delatorre, C.A., Pacheco, M.T., Scheeren, P.L., Federizzi, L.C., 2016. Aluminium tolerance of oat cultivars under hydroponic and acid soil conditions. Experimental Agriculture. 52(2):224-36. https://doi.org/10.1017/S0014479715000046.\u003c/li\u003e\n\u003cli\u003ePorter, G. S., Bajita-Locke, J. B., Hue, N. V., \u0026amp; Strand, D., 2004. Manganese solubility and phytotoxicity affected by soil moisture, oxygen levels, and green manure additions. \u003cem\u003eCommunications in Soil Science and Plant Analysis\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(1-2), 99-116.\u003c/li\u003e\n\u003cli\u003eWeil, R. R., Foy, C. D., \u0026amp; Coradetti, C. A., 1997. Influence of soil moisture regimes on subsequent soil manganese availability and toxicity in two cotton genotypes. \u003cem\u003eAgronomy Journal\u003c/em\u003e, \u003cem\u003e89\u003c/em\u003e(1), 1-8.\u003c/li\u003e\n\u003cli\u003eGodo, G.H., Reisenauer, H.M., 1980. Plant effects on soil manganese availability. Soil Science Society of America Journal, 44:993-995. https://doi.org/10.2136/sssaj1980.03615995004400050024x.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Agriculture](https://www.springer.com/journal/44279)","snPcode":"44279","submissionUrl":"https://submission.nature.com/new-submission/44279/3","title":"Discover Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Al toxicity, acid soils, Mn toxicity","lastPublishedDoi":"10.21203/rs.3.rs-9087674/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9087674/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn acidic soils, toxic levels of plant available aluminium (Al) and manganese (Mn) can severely restrict plant growth. Identification of chickpea accessions tolerant to soil with low pH is required to increase production in acidic environments. This study evaluated Al and Mn toxicity tolerance among 26 chickpea accessions through solution and soil screening conducted in a glasshouse. The accessions were screened in nutrient solutions containing 0, 15, and 60 \u0026micro;M Al concentrations for Al toxicity and 2 and 150 \u0026micro;M Mn for Mn toxicity, with cowpea as the control. Soil experiments used soil pH (H\u003csub\u003e2\u003c/sub\u003e0) levels of 4.5 and 6.5. Root tolerance index (RTI), relative root length (RRL), relative shoot length (RSL), and relative root and shoot weights (RRW, RSW) were used as indices for Al tolerance, while RSL, RSW, and symptom scores were used for Mn tolerance. Indices of 50\u0026ndash;70% were considered moderately tolerant, while\u0026thinsp;\u0026gt;\u0026thinsp;70% indicated tolerance. At 15 \u0026micro;M Al, accessions Saina 1, ICCVs 11514, 11519, 11504, 11316, and 07114 were consistently tolerant to moderately tolerant across all indices, whereas Chania 1, ICCVs 03305, 93954, 07313, 96329, and 97110 were sensitive. At 60 \u0026micro;M Al, most accessions were sensitive. In soil, RRL results correlated significantly (r\u0026thinsp;=\u0026thinsp;0.67) with those from the hydroponic experiments at 15 \u0026micro;M Al, with Saina 1, ICCV 11504, ICCV 11514, ICCV 07114, ICCV 10515, ICCV 11316, ICCV 00108, K036, Leldet 068, and Chania 2 being moderately tolerant. For Mn toxicity, ICCV 07313, 07101, 97128, 11514, Chania 2, and Saina 1 were tolerant. In the germplasm collection only ICCV 11514 and Saina 1 demonstrated moderate tolerance to both Al and Mn. Future research should validate these findings in a range of acid soils and seasons in field experiments to confirm their suitability for acidic environments.\u003c/p\u003e","manuscriptTitle":"Screening Chickpea Accessions for Aluminium and Manganese Toxicity Tolerance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 09:47:14","doi":"10.21203/rs.3.rs-9087674/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-12T09:09:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T03:01:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T10:55:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T16:31:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136524338930788430595836460578882241488","date":"2026-04-27T06:50:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180560618230949177730816802635710947494","date":"2026-04-27T06:37:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153177408140715571497730353953289591551","date":"2026-04-26T19:25:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311529503228009503994890749738300497948","date":"2026-04-21T10:44:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T16:35:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T15:14:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T17:28:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Agriculture","date":"2026-03-19T16:36:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Agriculture](https://www.springer.com/journal/44279)","snPcode":"44279","submissionUrl":"https://submission.nature.com/new-submission/44279/3","title":"Discover Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f3a24f13-dc11-405b-9142-23efa382b811","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-12T09:09:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T03:01:22+00:00","index":39,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T10:55:04+00:00","index":38,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T16:31:48+00:00","index":37,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T09:28:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 09:47:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9087674","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9087674","identity":"rs-9087674","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}