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Article

Exploitation of Chickpea Landraces for Drought and Heat Stress Adapted Varieties

by
Avraam Koskosidis
1,† and
Dimitrios N. Vlachostergios
2,*
1
Laboratory of Genetics and Plant Breeding, School of Agricultural Science, University of Thessaly, Fytokou Str., 38446 Volos, Greece
2
Institute of Industrial and Forage Crops, Hellenic Agricultural Organization—DEMETER, 41335 Larissa, Greece
*
Author to whom correspondence should be addressed.
†
Current address: School of Biology and Environmental Science, University College Dublin, D04V1W8 Dublin, Ireland.
Agronomy 2025, 15(12), 2909; https://doi.org/10.3390/agronomy15122909
Submission received: 17 November 2025 / Revised: 15 December 2025 / Accepted: 15 December 2025 / Published: 17 December 2025
(This article belongs to the Special Issue New Strategies for Forage Breeding and Cultivation Under Challenging Conditions)
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Abstract

Unpredictable climate fluctuations are a major constraint for chickpea production in the Mediterranean region, increasing the frequency of drought and temperature extremes. Landraces consist of locally adapted genotypes, offering valuable genetic variability. In this context, 12 chickpea landraces and 2 commercial varieties were tested. The breeding scheme consisted of two cycles of single-plant selection for high yield at nil-competition, followed by a 2-year evaluation under farming density in replicated trials. Selection cycles and evaluation were conducted under two different sowing dates, one normal and one nearly 30 days later (off-season), to implement the breeding method under extreme drought and heat stress conditions during yield’s critical stages. Among Improved Lines (ILs) developed under normal conditions, those from landraces 7 and 14 yielded 34% and 31% higher than the controls’ mean, while ILs from landraces 7, 9, and 12 developed under stress showed 11%, 8%, and 11% higher yield than the controls. Furthermore, ILs 7, 9, and 12 expressed the highest tolerance based on drought and heat stress indices and are considered as promising genetic material. Overall, the breeding scheme is suggested as effective for exploiting the natural genetic diversity of chickpea landraces towards the development of high-yielding and tolerant lines.

1. Introduction

Chickpea (Cicer arietinum L.) ranks as the second most important legume crop globally after common bean (Phaseolus vulgaris L.) [1,2], serving as an economically significant, protein-rich food source. India dominates global chickpea production, accounting for approximately 75% of total output [3,4,5]. However, chickpea cultivation faces substantial challenges due to various abiotic stresses, including drought and temperature extremes [1,6,7,8,9,10]. Among these, unpredictable climate fluctuations represent the most critical constraint as they increase the frequency of drought episodes and temperature extremes—both high (>30 °C) and low (<15 °C)—which significantly reduce grain yield [11,12].
Climate change impacts are particularly pronounced in the Mediterranean Basin, where observed warming rates exceed global averages. Basin-wide annual mean temperatures are currently 1.4 °C above late 19th-century levels, with summer months showing the greatest increase [13]. Future projections indicate that warming in the Mediterranean region will surpass global rates by approximately 25%, with summer warming occurring at a pace 40% higher than the global mean [14]. Regional daytime maxima are expected to rise by 2.2 °C [15]. Furthermore, a global temperature increase of 2 °C is likely to coincide with a 10–15% reduction in summer precipitation across Southern France, Northwestern Spain, and the Balkans, and up to 30% in Turkey and Portugal [16]. Scenarios predicting 2–4 °C warming by the 2080s for Southern Europe suggest widespread precipitation declines of up to 30%, particularly during spring and summer, and the near elimination of frost seasons in the Balkans [17]. For each 1 °C increase in global temperature, mean rainfall across much of the region is projected to decrease by approximately 4% [14]. In Greece, heat stress events are expected to become more frequent and prolonged, with the mean Universal Thermal Climate Index (UTCI) rising by 1.2–1.6 °C. Annual “very strong heat stress” days are projected to increase by 2.9–3.7%, while summer values may exceed 9.5%. Similarly, “strong heat stress” days are expected to rise by 11.9–16.9 days annually, accompanied by a 10–20% increase in warm days and nights [18].
Drought stress poses a severe threat to agriculture under climate change, compounded by the growing global population [19,20]. Extreme drought conditions negatively affect plant growth, physiology, and reproduction, ultimately reducing crop yields [21,22]. Globally, drought stress can reduce chickpea yield by 45–50% [23,24]. In addition, high-temperature stress during reproductive development—common in warmer regions and late-sown environments—causes floral bud, flower, and pod abortion, leading to a smaller seed size and lower yield [25,26]. Temperatures exceeding 35 °C during the reproductive stage represent a major constraint for chickpea productivity [27,28,29], while even moderate increases above 30 °C can reduce grain weight and number [30]. Yield losses have been reported for temperature increases as small as 1 °C beyond the threshold of 15 °C [31], with some genotypes experiencing up to 100% yield reduction under severe heat stress [32]. Consequently, developing high-yielding and stable chickpea varieties capable of withstanding drought and heat stress is imperative.
Regionally adapted crop varieties, commonly referred to as landraces, are genetically diverse populations that originated through initial domestication, followed by dispersal and selection for adaptation to local environmental and cultivation conditions [33,34]. Numerous studies have highlighted the value of landraces as a critical genetic reservoir for breeding programs aimed at improving crop resilience to climate change [35,36,37]. Lazaridi et al. (2024) [38] emphasized the potential of landraces in developing superior genotypes and climate-resilient crops. Breeding strategies that exploit the inherent variability within landraces can broaden the genetic base of commercial crops and facilitate the development of modern varieties with durable tolerance to major abiotic stresses [35,39,40]. Utilizing landrace genetic diversity offers a short-term pathway for isolating single-plant progenies, ultimately leading to pure-line varieties [41,42,43,44]. Identifying promising landraces as initial breeding material is therefore a crucial step toward achieving progress through selection [45]. Given the economic and nutritional importance of chickpea worldwide, the development of new genotypes tolerant to drought and heat stress remains a top priority [46,47].
Drought tolerance was defined by Hall (1993) [48] as the relative yield of a genotype compared to others under identical drought stress conditions. Conversely, drought susceptibility is often quantified by yield reduction under stress [49]. Several indices have been proposed to evaluate drought tolerance based on grain yield performance across contrasting environments. Rosielle and Hamblin (1981) [50] introduced stress tolerance (TOL), calculated as the difference in yield between stress (Ys) and non-stress (Yp) environments, and mean productivity (MP), defined as the average yield across both conditions. Fernandez (1992) [51] proposed the stress tolerance index (STI), which identifies genotypes with high yield potential under both stress and non-stress conditions. Geometric mean productivity (GMP) is another widely used metric, particularly in breeding programs where drought severity varies across years [52]. Optimal selection criteria should distinguish genotypes exhibiting consistent superiority in both environments from those performing well only under specific conditions. Fernandez (1992) [51] suggested that selection based on STI and GMP is most effective for identifying genotypes with enhanced stress tolerance and yield potential.
The objectives of this study were (i) to assess the genetic variability of chickpea landraces and estimate the yield potential of 14 genotypes (including landraces and advanced breeding lines) compared with two commercial varieties; and (ii) to implement off-season selection under nil-competition conditions and evaluate selected genotypes as an alternative breeding strategy for developing varieties adapted to dry and hot environments. Additionally, this study examined the impact of drought and heat stress on chickpea yield during crucial phenological stages, namely flowering and pod filling.

2. Materials and Methods

2.1. Field Experimentation

Single-plant selection was carried out at the experimental farm of the Institute of Industrial and Forage Crops (IIFC), Larissa, Greece (39°36′ N, 22°25′ E), over four consecutive growing seasons (2017–2020). The initial genetic material comprised nine kabuli chickpea landraces maintained by IIFC, three landraces obtained from ICARDA, one old local variety, one advanced breeding line, and two commercial varieties developed by IIFC. The two commercial varieties were included as official controls, as they are routinely used by the Department of Cultivated Plant Variety Research for DUS (Distinctness, Uniformity, and Stability) and VCUS (Value for Cultivation, Use, and Sustainability) testing. Both varieties are registered in the Greek National Catalog, having successfully passed DUS and VCU evaluations, which involve two years of testing across four locations against leading commercial cultivars. In terms of agronomic traits, these control varieties exhibit medium earliness and are considered drought tolerant. Both controls demonstrate stable and high yield performance under Mediterranean conditions. Additionally, cultivar C1 is resistant to Ascochyta blight (Ascochyta rabiei), whereas cultivar C2 is tolerant.
Fertilization was applied with phosphate (60 kg ha−1) prior to chickpea seeding, while a visual inspection was conducted for the presence of nodules, confirming abundant root colonization. No insecticides or fungicides were applied for the control of pests and diseases.
The code names of the 16 genotypes, as well as their type and origin, are presented in Table 1.
In the first year (2017) (Figure 1), 70 single plants from each of the 13 initial genotypes were evaluated using the R-13 honeycomb design [53] at an ultra-low density of 1.15 plants m−2 (i.e., plants spaced 100 × 100 cm apart), ensuring conditions of nil-competition among plants [54]. Two sowing dates were applied, i) normal sowing (24 February 2017), aligned with typical climatic conditions in Greece and ii) off-season sowing (24 March 2017), intended to expose plants to maximum drought and heat stress during flowering and pod-filling—critical stages for chickpea yield. Late planting exposes the chickpea crop to terminal drought and heat because, as the season progresses, conserved soil moisture recedes and the temperature increases [55]. For each experiment 910 single plants were grown, thus corresponding to a total of 1820 single plant positions. Plants were individually harvested and their grain yield was adjusted at 13% moisture. Genotypes were ranked according to their Line Crop Yield Potential (LCYP) [56] and the higher yielding single plant was selected within each entry on the basis of their absolute yield (x), forming the progeny line (PL). The LCYP index is used to provide a general assessment of the landrace potential in terms of yield and variability within each entry and is not a criterion for selecting an individual plant. LCYP was calculated by the formula LCYP = ( x ¯ / x t ¯ ) 2 × ( x ¯ / s ) 2 . The first fraction, ( x ¯ / x t ¯ ) 2 , represents the coefficient of the line yield, where x t ¯ is the line mean yield per plant and x ¯ the overall experimental yield per plant. The second fraction, ( x ¯ / s ) 2 , represents the coefficient of homeostasis of the line, where s is the standard deviation of x ¯ . For homogeneous lines this can be used to estimate their vulnerability to spatial heterogeneity, since it is essentially the inverse value of the coefficient of variation (CV) [43].
In the second year (2018) (Figure 1), 70 single plants from each of the 13 selected plants (from 10 landraces, one local variety, and two control varieties) from each sowing period (26 in total), plus two additional Greek landraces (from Lemnos and Kastoria) and one advanced line from Canada, were sown under ultra-low density (1.15 plants m−2) using the R-16 honeycomb design [53]. Two sowing dates were again applied: normal sowing (16 March 2018), consistent with local practices; and off-season sowing (4 April 2018), simulating high-temperature and drier conditions. For each experiment 1120 single plants were grown, thus corresponding in a total of 2240 single plant positions. Plants were individually harvested, and their grain yield was adjusted at 13% moisture; the five highest-yielding plants, within each genotype, were selected and an equal seed quantity was mixed, forming the Improved Lines (ILs) for next year’s experiments.
The 14 ILs and the two controls were evaluated following the Randomized Complete Block Design (RCBD) under farmer’s density (45–50 plants/m2) for two consecutive years (2019 and 2020) (Figure 1). In total, two RCBD experiments were established, one at the normal sowing period (28 February 2019; 3 March 2020) and one at the off-season sowing (1 April 2019; 5 April 2020). Each RCBD experiment consisted of three replications and each replication consisted of 16 plots (48 plots in total). Each plot consisted of three rows, each of 2 m length and 25 cm distance between rows. Furthermore, the 14 ILs were compared with the mean yield of the two control varieties (Cmean).

2.2. Climatic Parameters

2.2.1. First Year

During the growing stage of anthesis the difference in mean temperature between the two sowing periods was 0.5 °C, with the mean temperature at the normal sowing period being 21.2 °C, whereas the mean temperature at the off-season sowing period was 21.7 °C. The precipitation at the normal sowing period of anthesis was 36.8 mm, whereas at the off-season sowing it reached 117.8 mm (Table 2).
During the pod-filling stage the difference in mean temperature between the two sowing periods was 3 °C. The mean temperature at the normal sowing period, as well as at the off-season period was 23 °C and 26 °C, respectively. It is worth mentioning that during the off-season period, the daily mean temperature was higher than 30 °C for 14 days (Table 2). The precipitation level at the normal sowing period was 127.6 mm and at the off-season sowing was 16.4 mm.

2.2.2. 2nd Year

During the growing stage of anthesis the difference in the mean temperature between the two sowing periods was 1.4 °C. At the normal sowing period the mean temperature was 24.9 °C and the maximum temperature was 32.5 °C, whereas, during the same stage, at the off-sowing period the mean temperature was 26.3 °C and the maximum temperature was 37.8 °C. The precipitation level at the normal sowing period was 4.6 mm, whereas at the off-season sowing it was 36 mm.
During the pod-filling stage the difference in the mean temperature between the two sowing periods was 0.5 °C. At the normal sowing period the mean temperature was 24.5 °C and the maximum temperature was 37.8 °C. During the off-season period the mean temperature was 25 °C, while the maximum temperature was 39.9 °C. It should be mentioned that at the off-season period the daily mean temperature was higher than 30 °C for 8 days (Table 2). The precipitation level at the normal sowing period was 101.2 mm. During the off-season sowing the water scarcity was extremely intense since there was almost no rain at all (0.8 mm).

2.2.3. 3rd and 4th Year

During the anthesis growing stage the difference in the mean temperature between the two sowing periods was 3 °C for the 3rd year and 2.3 °C for the 4th year. Regarding the 3rd year of trials, at the normal sowing period the mean temperature was 19.5 °C, mean maximum temperature was 26.6 °C, and precipitation was 31.4 mm. At the off-season sowing the mean temperature was 22.5 °C, daily mean temperature was 30 °C for 2 days, and the precipitation level was only 11.4 mm, 20 mm less than at normal sowing (Table 2). Regarding the 4th year of trial, at the normal sowing period mean temperature was 20.2 °C, with the precipitation level reaching up to 4 mm. At the off-season sowing mean temperature remained the same as the previous year and the precipitation was 45.4 mm, 41.4 mm more than at normal sowing period (Table 2).
During the pod-filling stage, for the 3rd year of trials, at the normal sowing period mean temperature was 25.4 °C, maximum temperature was 38.2 °C, and precipitation level was 22.4 mm. During the off-season sowing period, the mean temperature was 27.8 °C and precipitation measured 13.2 mm, which is 9.2 mm less than at the normal sowing period (Table 2). The difference in the mean temperature between the two sowing periods was 2.4 °C. It should be emphasized that, at the off-season sowing period, the daily mean temperature overcame the crucial temperature of 30 °C for 11 days (Table 2). For the same growing stage, during the 4th year of trials the difference in the mean temperature between the sowing periods was 2.8 °C, with the mean temperature at the normal sowing period and the off-season sowing period being 23.3 °C and 26.1 °C and the precipitation level being 34.7 mm and 18.8 mm, respectively. The daily mean temperature was higher than 30 °C for 6 days (Table 2).

2.2.4. Stress Indices

Drought and heat tolerance/susceptibility indices were calculated for each genotype using the following equations:
  • Mean Productivity   ( MP ) = Ys + Yp 2 [51]
  • Geometric Mean Productivity   ( GMP ) = ( Yp × Ys ) [52]
  • Stress Tolerance Index   ( STI ) = Ys × Yp Yp − 2 [51]
  • In the formulas Ys represents the grain yield of the genotype under stress conditions (late sowing), Yp represents the grain yield of the genotype under control conditions (normal sowing), and Yp− represents the mean yields of the genotypes under control conditions (normal sowing).

2.2.5. Statistical Analyses

The statistical analysis of the first two years’ data was conducted using Honeycomb v.4 (JMP 8 add-in program) statistical program, where all the necessary components for the use of the abovementioned equations were calculated [57,58]. For yield performance under farmer density, a combined ANOVA across the third and fourth years was performed. LSD post hoc tests were applied to compare means averaged over two years and three replicates, as no significant Genotype × Year interaction was detected (P = 0.301). Cluster analysis was performed by using Ward’s method to explore relationships between genotypes. Principal component analysis (PCA) with varimax rotation was performed to explore relationships between traits. All statistical analyses and visualization were performed using SPSS statistical software v. 20 and R Studio v. 4.4.1.

3. Results

3.1. Single-Plant Selection

In the first year, the average yield under normal sowing was 47.85 g, compared to 32.76 g under off-season sowing (Table 3), representing a 46.06% higher yield for normal sowing. The number of plants emerging in the normal period was lower due to lower temperatures and infection of Delia sp.
At both sowing periods, L7 recorded the highest mean yield (Table 4). The mean yield was 62.43 g for the normal sowing period followed by L11 (52.25 g). At the off-season period L7 had a mean yield of 40.59 g followed by L5 (37.70 g). Regarding the index LCYP, at the normal sowing period L10 had the highest value (2.177) followed by L11 (1.885). During the off-season sowing period L7 had the highest LCYP value (3.341) among landraces. Regarding selected plants’ yield, the highest value was recorded for L7 with 252.55 g and the lowest for LV (entry 2) with 80.90 g under normal conditions, while for stress conditions the highest yield among the selected plants was 130.96 g from L5 and the lowest was 61.84 g from entry 2. Entry 2 showed significant earliness in anthesis; however, it was highly susceptible to Aschochyta blight and thus had very low yield.
Data from the 2nd year of experimentation showed that mean yield of all PLs and controls at normal sowing was 29.79 g, whereas at the off-season sowing was 14.54 g, corresponding to 51.2% difference (Table 5). At normal sowing PLs no 3 (41.29 g), 8 (38.12 g), and 5 (37.61 g) gave the highest mean yields under normal conditions and PLs no 12 (20.73 g), 13 (20.07 g), and 8 (19.42 g) under stress conditions.
At normal conditions the mean yield of the five selected plants from PLs ranged from 31.92 g to 72.53 g, while the controls yielded 60.67 g (C1) and 75.56 g (C2). PLs no 5 and 14 yielded more than 70 g (72.53 g and 72.48 g correspondingly) while PLs 8 (69.92 g), 12 (66.78 g), 7 (65.91 g), and 3 (65.77 g) exceeded 65 g. Mean yield of selected plants was 93.6% higher than the mean yield of the experiment, while selected plants from PL 7 yielded 149.0% higher than the initial PL 7, and selected plants from PL 14 yielded 116.9% higher that initial PL14. At the stress conditions the mean yield of the five selected plants from PLs ranged from 20.01 g to 43.88 g, whereas the controls yielded 30.78 g (C1) and 24.92 g (C2). PLs no 7 (43.88 g), 8 (42.02 g), 12 (41.25 g), 13 (40.25 g), and 9 (37.52 g) ranked in the top. Mean yield of selected plants was 119.6% higher than the mean yield of the experiment. Selected plants from PL 7 had the highest difference (142.7%) from the initial entry.

3.2. ILs Evaluation at Farming Conditions

The Analysis of Variance and the Explained variation in Sum Squares (EES%) presented in Table 6 revealed the statistically significant effect that Year, Sowing Date, Genotype, and Sowing Date X Genotype combination had on yield. Sowing Date and Genotype accounted for most of the yield variation, with ESS% of 41.96 and 33.41, respectively, while the effect of Year was 9.18%, followed by the interaction of Sowing Date X Genotype (6.72%).
As shown in Figure 2, during the normal sowing period, ILs 7 and 14 outperformed all other ILs as well as the two control varieties. In the off-season sowing period, ILs 12, 7, and 9 achieved the highest mean yields, whereas the remaining ILs and the two controls recorded significantly lower mean yields.
During the normal sowing period, the mean yield of the two control varieties was 74.64 g and seven ILs (viz: 7, 14, 1, 3, 9, 12, 8) overpassed this mean from 45% to 9% (Figure 3). It is worth mentioning that IL-7 and IL-14 had 45% and 38% higher yield than the controls, respectively. On the other hand, during off-season sowing period, the mean yield of the two controls was 64.56 g and most of the ILs had lower yield, ranging from 4% to 30%, compared to the mean yield of the controls. Exceptions to the previous observation were ILs 7, 9, and 12 which had 13%, 11%, and 14% higher yield than the mean yield of the controls, respectively.
In Figure 4, the ranking alterations of each genotype, based on their yield, between the two sowing dates (normal and off-season) are presented. IL7 and C2 showed a non-cross-over pattern as they ranked almost at the same order in both normal and stress conditions, showing a potentially wide adaptation capacity. On the other hand, genotypes like IL1, IL3, IL9, IL12 or C1 indicated a typical cross-over interaction with significant alterations in the ranking order between stress and normal conditions, showing a more specific adaptability.
Based on genotypes’ yield during the two sowing periods, stress tolerance/susceptibility indices were calculated (Table 7). Evaluation using MP, GMP, and STI identified genotypes with higher stress tolerance. ILs 7, 12, and 9 exhibited the highest values for these indices, indicating strong tolerance to drought and heat stress. Conversely, ILs 11 and 2 recorded the lowest values and were classified as highly susceptible under heat and drought conditions.
Based on the PCA-Biplot analysis, the ILs were divided into five different clusters (Figure 5). Cluster 1 (purple) included ILs 9,12, 7, and C2, while IL14 is not grouped with another IL and formed Cluster 2 (green). Cluster 3 (orange) included ILs 1, 3, 5, 6, 8, and 10. The last cluster (Cluster 4) included ILs 2,11, and C1 (blue). Finally, ILs 4 and 13 formed Cluster 5 (red). C-2 and the ILs 7, 9, and 12 (purple) can be characterized as tolerant to the drought and heat stress conditions. The abovementioned genotypes recorded high yield during normal sowing period, which was retained during off-season sowing too. On the other hand, lines 1, 3, 5, 6, 8, and 10 (orange) may be characterized as the most susceptible to these stress conditions.

4. Discussion

Considerable efforts have been directed toward developing chickpea cultivars tolerant to various abiotic stresses [8]. Chickpea, however, remains highly vulnerable to the combined effects of heat and drought stress. Numerous studies [59,60] have highlighted the significant additive impact of these stresses on chickpea growth, yield, and physiological responses, emphasizing the need for further research to identify potentially tolerant genotypes. Climatic variability, characterized by increased frequency of drought and temperature extremes—both high (>30 °C) and low (<15 °C) [11,12,47]—creates stress conditions during critical reproductive and pod-filling stages, ultimately reducing grain yield by as much as 40–45% [12,47].
The exploitation of the existing genetic variability is crucial to identify genetic material with greater tolerance and increased yield under such harsh, environmental conditions [61]. Conventional breeding of chickpea is mostly focused on intraspecific hybridization and rarely in mutation breeding or gene introduction through interspecific hybridization [5,62]. The exploitation of the variability of local populations is less frequently used because of the disastrous effect of genetic erosion [63] and the subsequent reduction in the number of available local landraces [64]. Landraces, especially those growing in isolated dry areas, such as small islands or isolated mountainous areas, constitute valuable genetic material that has not been sufficiently explored despite the valuable characteristics that are useful for mitigating the effects of climatic fluctuations. Greece, with its characteristic geographical relief, favors the development of local varieties in isolated areas with intense dry-thermal conditions [38]. Such areas are the small Aegean islands and some mountainous areas that are geographically isolated, and the cultivation of local varieties has been a key element for the maintenance of the population [65]. The genetic material studied originated from the islands Sifnos (L7), Mytilene (L8), and Lemnos (L12), whereas L1 originated from the mountainous area of Evros and L13 originated from the lakeside and mountainous area of Kastoria. Additionally, three landraces originated from ICARDA, which represents an area with extreme drought and high temperatures. Furthermore, all landraces tested were kabuli-type chickpeas. This type was chosen because kabuli genotypes have greater commercial value due to seed color and bigger seed size [66] even though desi chickpeas are considered to be more tolerant to drought and heat stress [32]. Exploiting existing genetic variability is essential for identifying germplasm with enhanced tolerance and higher yield under harsh environmental conditions [61]. Conventional chickpea breeding has primarily relied on intraspecific hybridization, with limited application of mutation breeding or interspecific hybridization [5,62]. However, the underutilization of local population variability—largely due to genetic erosion [63] and the consequent decline in landrace availability [64]—poses a major challenge. This highlights the urgent need for conservation strategies to safeguard remaining landraces, which represent irreplaceable reservoirs of adaptive traits. Landraces, particularly those cultivated in isolated dry regions such as small islands or remote mountainous areas, offer unique genetic resources that can be harnessed to develop climate-resilient cultivars. Greece’s distinctive geography fosters such diversity, with local varieties thriving in areas characterized by intense dry-thermal conditions [38]. Examples include small Aegean islands and mountainous regions where traditional cultivation has sustained populations for generations [65]. The genetic material examined in this study originated from Sifnos (L7), Mytilene (L8), and Lemnos (L12), while L1 was collected from the mountainous area of Evros and L13 from the lakeside and mountainous region of Kastoria. Additionally, three landraces were sourced from ICARDA, representing environments with extreme drought and high temperatures. All tested landraces were kabuli-type chickpeas, selected for their higher commercial value due to seed color and larger size [66], despite desi chickpeas being generally more tolerant to drought and heat stress [32]. Integrating these landraces into breeding programs not only enhances genetic diversity but also ensures the long-term sustainability of chickpea production under increasingly variable climatic conditions.
The breeding scheme was designed to exploit the genetic variability of local populations to develop chickpea varieties with enhanced resistance to high temperatures and drought. Research was conducted under both stress and normal conditions to ensure robust evaluation. The scheme comprised two phases: the selection phase (Years 1 and 2) and the evaluation phase (Years 3 and 4), implemented across two sowing periods. The first was the normal sowing period, aligned with Greece’s climatic conditions and standard cultivation practices, while the second was an off-season stress period, where sowing was delayed by approximately 30 days to impose heat and drought stress.
The key point of the project was the implementation of selection and evaluation during the off-season sowing period, when the critical stages of anthesis and pod filling coincided with higher temperatures and drought conditions [5,32,67]. It should be mentioned that off-season sowing is an effective and reliable method for selecting plants with tolerance to high temperatures, but it is not always as effective for selecting drought resistance genotypes. This is because drought stress cannot always be consistently induced by delaying sowing, as it depends on unpredictable rainfall patterns. To overcome this obstacle Gaur et al. (2019) [5] suggested evaluation at multiple locations and over years. In our experiment, during the selection phase (2017–2018), the mean temperature difference between normal and off-season sowing ranged from 0.5 °C to 1.4 °C at anthesis and from 0.5 °C to 3 °C during pod filling. During the off-season sowing period, mean daily temperature exceeded 30 °C for 14 days in 2017 and 8 days in 2018. During anthesis, precipitation in the off-season sowing period was sufficiently high to prevent drought stress and surpassed that of the normal sowing period. In contrast, rainfall during pod filling was very low under off-season conditions, resulting in terminal drought, whereas precipitation was substantially higher in the normal sowing period for both years. Notably, in 2018, total rainfall during the off-season pod-filling stage was almost negligible (0.8 mm). In the evaluation phase (2019–2020), the difference in mean temperature between normal and off-season sowing ranged from 2.3 °C to 3.0 °C during anthesis and from 2.4 °C to 2.8 °C during pod filling. Mean daily temperature exceeded 30 °C for 2 days during anthesis and for 6 or 11 days during pod filling in the off-season sowing period. Precipitation was generally higher during anthesis and pod filling in the normal sowing period in 2019, except in 2020 when anthesis precipitation was greater in the off-season period. Rainfall during pod filling was consistently higher under normal sowing conditions. Overall, under the climatic conditions of Greece, shifting the sowing date as a strategy to induce heat and drought stress within a breeding program proved both feasible and more effective during the pod-filling stage than during anthesis. At anthesis, stress is more accurately characterized as terminal heat combined with variable water limitation.

4.1. Selection Phase

Selecting superior genotypes at the individual plant level within segregating populations or landraces remains one of the most challenging aspects of plant breeding [68]. The negative correlation between competitive ability and productivity forms the basis for single-plant selection under nil-competition conditions [69,70]. In this context, Sakai (1961) [71] and Tokatlidis (2022) [72] identified competition as the primary factor limiting the efficiency of superior plant identification. Implementing a nil-competition regime maximizes the phenotypic expression of genetic differences among individual plants, thereby, facilitating the selection of desirable genotypes.
In the present experiments, single-plant selection at nil-competition was applied for the first two years with the aim of rapid development of Improved Lines with heat and drought resistance characteristics. The aim of the first year was to evaluate the entries at the single plant level, to assess their yield potential, and to gain an insight into the variability that exists within them. Finally, the most productive plant was selected to form a PL. During the first year L7 stood out among other landraces for its high yield potential, as it recorded the highest mean yield in both normal and stress conditions and had the highest value of LCYP index under stress conditions. The second year, the PLs were evaluated and the five high-yielding single plants per PL were selected, bulked, and formed the ILs. PL7 confirmed the profile of a promising genetic material under stress conditions expressed with the highest average yield of selected plants that formed the IL7, corresponding to 142.7% higher yield than the initial entry. Of particular interest was L12, which showed remarkable characteristics in off-sowing period, as it had the highest average yield and LCYP index, and a very high average yield of selected plants (99% more than the original entry). It should be noted that, during the normal sowing period, the prevalence of low temperatures, along with the impact of the insect Delia sp. and Ascochyta blight, likely influenced the final ranking of the genotypes. Additionally, the landraces introduced in the second year underwent only one year of selection, which should be considered, although plants selected from L12 showed promising performance.

4.2. Evaluation Phase

Drought and heat stress conditions negatively impacted the final yield of all ILs, consistent with numerous previous studies [23,24,73]. Wang et al. (2006) [27] reported that heat stress during pod development reduced seed yield by more than 53%, while Canci and Toker (2009) [32] reported yield losses of up to 100% in susceptible genotypes. Chickpea germplasm exhibits significant variability in drought tolerance, expressed through mechanisms such as drought escape or drought avoidance [74]. Gaur et al. (2019) [5] identified early-maturing genotypes capable of drought escape. However, in the present study, all tested genotypes (except LP2) were classified as medium-early, and therefore no clear drought escape or avoidance pattern was detected. The evaluation was based on yield performance, which incorporated several secondary traits, and their tolerance was largely attributed to unidentified mechanisms. However, since phenology was not recorded, we cannot entirely rule out a drought escape or avoidance strategy associated with earliness. During the evaluation phase, the ILs mean yield, in the off-season sowing period, was reduced by 28.0%. ANOVA showed that Sowing period followed by Genotype were the main sources of variation, as they explained 75.37% of the total variation. It is worth mentioning that genotypic effect was 3 times the year effect, underscoring the critical role of genetic variability within the tested material [46,75]. The 2-year mean yield of the ILs developed under normal conditions was 78.23 g instead of 74.64 g of the two controls and the mean yield of the ILs developed under stress was 55.40 g instead of 64.56 g of the controls. Mean yield of the ILs developed under heat and drought stress was 28% lower than those developed under normal season. Among the ILs developed under normal conditions, those originating from landraces no 7 and 14 had 34% and 31% higher yield than the average of the controls, respectively. ILs originating from landraces no 7, 9, and 12 when developed under stress conditions, indicated 11%, 8%, and 11 higher yield than the mean yield of the controls, respectively. Furthermore, ILs 7, 9, 12 along with the commercial variety C2 formed a distinct group (Figure 4) that expressed the highest tolerance according to drought and heat stress indices.

4.3. Selection Efficiency (SE)

Selection Efficiency (SE) of a breeding scheme is usually expressed as the selection response or genetic gain (i.e., yield difference in the ILs regarding the initial landrace) [76,77]. In this study, the term ‘Selection Efficiency’ does not denote a measurement of genetic gain; rather, it serves as a descriptive indicator of the success of single-plant selection within landraces, in relation to the agronomic value of the ILs. Accordingly, SE was calculated as the percentage of the five highest-yielding ILs under farming density that originated from the top five selected PLs. Thus, according to Table 4, under normal conditions SE was 80% as four out of five top yielders originated from the top five selected PLs, while SE was 60% at stress conditions as three out of the five top yielders were among the top five selected PLs. In a similar work on a lentil breeding program under organic farming, Vlachostergios et al. (2011) [78] found SE from 34 to 74%. An alternative approach to measuring SE involves comparing the yield of each IL with that of the two commercial varieties used as official controls by the Department of Cultivated Plant Variety Research for DUS (Distinctness, Uniformity, and Stability) and VCUS (Value for Cultivation, Use, and Sustainability) tests during new variety registration. This comparison underscores the genetic potential of ILs derived from local landraces for possible commercial exploitation. According to current legislation [79], a new variety must exceed the average seed yield of the two controls to qualify for inclusion in the national variety catalog, highlighting the practical significance of superior performance under both normal and stress conditions. Indeed, under normal sowing conditions, ILs 1, 3, 7, 8, 9, 12, and 14, and under stress conditions, ILs 7, 9, and 12 exceeded the mean yield of the two controls. Furthermore, the ILs were characterized for their type of adaptability, based on the ranking order between normal vs. stress period (Figure 4). According to this order, IL7 showed a broad adaptability pattern. On the other hand, IL9 and IL12 tend to be closer to a specific pattern of adaptability, however, further experimentation is required to draw more secure conclusions.

5. Conclusions

Local chickpea landraces represent a scarce plant genetic resource; however, they constitute a valuable gene pool that can be exploited for developing varieties adapted to drought and heat stress conditions. In the present study, Improved Lines (ILs) were developed from local landraces originating from two small Aegean islands characterized by intense dry-thermal conditions and spatial isolation, demonstrating tolerance to drought and heat stress. This finding highlights the importance of local genetic material maintained and cultivated in isolated areas, which serve as biodiversity hotspots for local varieties.
The proposed breeding scheme comprised two biennial phases—selection and evaluation—and was based on shifting the sowing date to impose drought and heat stress, combined with single-plant selection under nil-competition. The results indicate that the proposed scheme could be adopted by breeders working with the natural genetic variability of chickpea landraces under field conditions.
ILs 7, 9, and 12 were identified as promising genetic materials for cultivation under dry and hot conditions, based on drought and heat stress indices and their superior yield performance compared to commercial varieties used as controls. Notably, selection within Landrace 7 led to the development of IL7, a high-yielding line suitable for cultivation under both normal and stress conditions.
The breeding method could be further enhanced by integrating molecular techniques to identify genetic markers associated with drought and heat tolerance and related agronomic traits, thereby facilitating more effective breeding programs [80]. The genetic loci underlying tolerance to combined stress are likely distinct from those conferring tolerance to drought or heat stress alone [81]. Therefore, efforts should focus on identifying and deploying genes that govern combined drought and heat tolerance in chickpea.

Author Contributions

Conceptualization, D.N.V.; methodology, D.N.V.; validation, A.K. and D.N.V.; formal analysis, A.K.; investigation, A.K.; resources, D.N.V.; data curation, A.K. and D.N.V.; writing—original draft preparation, A.K.; writing—review and editing, D.N.V.; visualization, A.K.; supervision, D.N.V.; project administration, D.N.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
GMPGeometric Mean Productivity
ILImproved Line
PLParental Line
LCYPLine Crop Yield Potential
MPMean Productivity
STIStress Tolerance Index
Ypthe grain yield of cultivar under non-stress condition
Yp−the mean yields of all genotypes under non-stress conditions
Ysthe grain yield of genotype under stress
Ys−the mean yields of all genotypes under stress

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Figure 1. Flow diagram of the breeding scheme.
Figure 1. Flow diagram of the breeding scheme.
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Figure 2. Mean yield (g/plot) of the 14 ILs (Improved Lines), the two commercial varieties (C1 and C2), and the mean of the two controls (Cmean) at farming density at the normal sowing period (red) and the off-season sowing period (blue). Means with a letter in common are not significantly different at the 5% level according to LSD (p ≤ 0.05).
Figure 2. Mean yield (g/plot) of the 14 ILs (Improved Lines), the two commercial varieties (C1 and C2), and the mean of the two controls (Cmean) at farming density at the normal sowing period (red) and the off-season sowing period (blue). Means with a letter in common are not significantly different at the 5% level according to LSD (p ≤ 0.05).
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Figure 3. Difference (%) between the ILs’s yield and the mean yield of the two control varieties under two sowing periods.
Figure 3. Difference (%) between the ILs’s yield and the mean yield of the two control varieties under two sowing periods.
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Figure 4. Ranking alterations of genotypes between normal and off-season sowing.
Figure 4. Ranking alterations of genotypes between normal and off-season sowing.
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Figure 5. PCA-Biplot analysis of the ILs and controls based on the drought and heat stress indices.
Figure 5. PCA-Biplot analysis of the ILs and controls based on the drought and heat stress indices.
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Table 1. Entry number, Genotypes (code name), type, size (1000 seeds weight), origin. Genotypes 12–14 were inserted in the experiment in the second year (2018).
Table 1. Entry number, Genotypes (code name), type, size (1000 seeds weight), origin. Genotypes 12–14 were inserted in the experiment in the second year (2018).
EntryGenotype
(Code Name)		TypeSize (1000 SW)Origin
C1M-15370Variety (Control 1)350Greece (IIFC)
C2E-202Variety (Control 2)462Greece (IIFC)
1L1Landrace460Greece (IIFC)
2LVOld Local variety670Mexico
3L3Landrace419Greece (IIFC)
4L4Landrace395ICARDA
5L5Landrace421Greece (IIFC)
6L6Landrace381ICARDA
7L7Landrace445Greece, island Sifnos (IIFC)
8L8Landrace484Greece, island Mytilene
9L9Landrace348ICARDA
10L10Landrace168Greece (IIFC)
11L11Landrace165Greece (IIFC)
12L12Landrace370Greece, island Lemnos (IIFC)
13L13Landrace434Greece, lakeside area Kastoria
14ALAdvanced Line445Canada
Table 2. Number of days with mean temperature > 30 °C and difference in precipitation level between normal vs. off-season sowing period per phenological stage for the period 2017–2020.
Table 2. Number of days with mean temperature > 30 °C and difference in precipitation level between normal vs. off-season sowing period per phenological stage for the period 2017–2020.
YearDays with Mean Temperature > 30 °CPrecipitation Difference (mm)
Normal PeriodOff-SeasonNormal Period—Off-Season
APFAPFAPF
201703314−90.8+111.2
20180138−96.6+100.4
201902211+20.0+9.2
20200126−41.4+15.9
A: Anthesis; PF: Pod Filling.
Table 3. Number of harvested plants (n), mean yield per plant (g), minimum and maximum yield (g), and coefficient of variance (CV%) of the genotypes evaluated at the two sowing periods during the 1st (2017) and 2nd (2018) year of experimentation.
Table 3. Number of harvested plants (n), mean yield per plant (g), minimum and maximum yield (g), and coefficient of variance (CV%) of the genotypes evaluated at the two sowing periods during the 1st (2017) and 2nd (2018) year of experimentation.
1st Year
nMeanMinMaxCV%
Normal sowing66747.850.00252.5082.2
Off-season sowing79032.760.00130.9672.7
2nd Year
nMeanMinMaxCV%
Normal sowing74229.790.0092.5369.6
Off-season sowing81314.540.0059.0071.3
Table 4. Mean yield (g), Line Crop Yield Potential (LCYP) of the genotypes and selected plants’ yield (g) within each entry at the two sowing periods.
Table 4. Mean yield (g), Line Crop Yield Potential (LCYP) of the genotypes and selected plants’ yield (g) within each entry at the two sowing periods.
Normal SowingOff-Season Sowing
EntryMean YieldLCYPYield of Selected PlantMean YieldLCYPYield of Selected Plant
C147.131.476166.6838.343.83591.26
C247.860.961123.5627.580.85899.06
151.511.037205.5931.422.12581.16
245.911.38680.9031.633.31661.84
345.490.96187.5536.912.284125.90
448.711.552138.5635.872.56096.10
545.160.803162.5637.702.126130.96
634.470.449116.8720.890.70165.02
762.431.816252.5540.593.341105.68
841.390.935130.3631.381.57998.82
949.610.942106.8833.291.977111.52
1050.172.177116.8029.552.09382.22
1152.251.885153.6530.772.47870.28
Mean47.851.26141.7332.762.2593.83
Table 5. Mean yield (g) and Line Crop Yield Potential (LCYP) of the PLs and two controls, Mean yield (g) of the five selected plants within each PL and difference (%) between mean yield of five selected plants with the mean yield of each initial PL at the two sowing periods.
Table 5. Mean yield (g) and Line Crop Yield Potential (LCYP) of the PLs and two controls, Mean yield (g) of the five selected plants within each PL and difference (%) between mean yield of five selected plants with the mean yield of each initial PL at the two sowing periods.
Normal Sowing PeriodOff-Season Sowing Period
Entry
(PLs)		MeanLCYPMean Yield of Five Selected PlantsDifference from PL (%)MeanLCYPMean Yield of Five Selected PlantsDifference from PL (%)
C130.481.87160.6799.113.410.85830.78128.0
C237.955.01575.5699.010.933.83524.92129.5
123.690.95851.95119.310.092.12522.37121.7
224.111.59743.4180.011.703.31628.20141.0
341.295.64965.7759.312.772.28428.14120.4
426.321.70451.4595.515.402.56034.86126.4
537.614.29972.5392.815.202.12632.48113.7
626.021.90148.2485.411.400.70125.16120.7
726.471.19465.91149.018.083.34143.88142.7
838.123.59969.9283.419.421.57942.02116.4
926.901.40855.15105.017.331.97737.52116.5
1023.281.01831.9237.110.222.09320.0195.8
1121.990.91439.1978.211.542.47822.1792.1
1231.803.00966.78110.020.736.51741.2599.0
1327.141.99051.6490.320.075.43840.25100.5
1433.412.42972.48116.914.330.96036.72156.2
Mean29.792.4157.6693.614.542.6431.92119.6
Table 6. Analysis of Variance and Explained variation in sum of squares (ESS%) for yield across Years, Genotypes (ILs) and Sowing Dates (SD).
Table 6. Analysis of Variance and Explained variation in sum of squares (ESS%) for yield across Years, Genotypes (ILs) and Sowing Dates (SD).
Source of VariancedfMSFpΕSS (%)
Year117.6364.06*9.18
SD180.56292.81*41.96
Y × SD16.2822.82*3.27
G154.2815.54*33.41
Y × G150.321.17ns2.52
SD × G150.863.13*6.72
Y × SD × G150.381.37ns2.94
Error1280.28
* significance at p < 0.05; ns: non-significant.
Table 7. Drought and heat stress tolerance/susceptibility indices.
Table 7. Drought and heat stress tolerance/susceptibility indices.
GenotypeMPGMPSTI
C-159.2459.240.58
C-279.9679.311.04
IL-165.6762.310.64
IL-251.0951.020.43
IL-366.4963.860.67
IL-466.6366.350.73
IL-561.0759.870.59
IL-659.6558.230.56
IL-790.8689.121.31
IL-865.3463.300.66
IL-977.2677.030.98
IL-1059.7758.940.57
IL-1147.8547.770.38
IL-1278.1778.031.01
IL-1367.1666.950.74
IL-1478.5674.540.92
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Koskosidis, A.; Vlachostergios, D.N. Exploitation of Chickpea Landraces for Drought and Heat Stress Adapted Varieties. Agronomy 2025, 15, 2909. https://doi.org/10.3390/agronomy15122909

AMA Style

Koskosidis A, Vlachostergios DN. Exploitation of Chickpea Landraces for Drought and Heat Stress Adapted Varieties. Agronomy. 2025; 15(12):2909. https://doi.org/10.3390/agronomy15122909

Chicago/Turabian Style

Koskosidis, Avraam, and Dimitrios N. Vlachostergios. 2025. "Exploitation of Chickpea Landraces for Drought and Heat Stress Adapted Varieties" Agronomy 15, no. 12: 2909. https://doi.org/10.3390/agronomy15122909

APA Style

Koskosidis, A., & Vlachostergios, D. N. (2025). Exploitation of Chickpea Landraces for Drought and Heat Stress Adapted Varieties. Agronomy, 15(12), 2909. https://doi.org/10.3390/agronomy15122909

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