Next Article in Journal
The Economic and Environmental Aspects of Miscanthus × giganteus Phytomanagement Applied to Non-Agricultural Land
Previous Article in Journal
Visible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling
Previous Article in Special Issue
Development of Superabsorbent Polymer (SAP) Seed Coating Technology to Enhance Germination and Stand Establishment in Red Clover Cover Crop
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 14
Issue 4
10.3390/agronomy14040790
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Lentil Cultivar Evaluation in Diverse Organic Mediterranean Environments

by
Dimitrios Baxevanos
1,*,
Anastasia Kargiotidou
1,
Christos Noulas
1,
Antigoni-Maria Kouderi
2,
Maria Aggelakoudi
3,
Christos Petsoulas
1,
Evangelia Tigka
1,
Athanasios Mavromatis
2,
Ioannis Tokatlidis
4,
Dimitrios Beslemes
1,5 and
Dimitrios N. Vlachostergios
1,*
1
Institute of Industrial and Forage Crops, Hellenic Agricultural Organization—DEMETER, 41335 Larissa, Greece
2
Laboratory of Genetics & Plant Breeding, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
3
Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, 68200 Orestiada, Greece
4
Department of Molecular Biology and Genetics, Democritus University of Thrace, 68100 Alexandroupolis, Greece
5
Alfa Seeds SA, 10th kil. Mesorhaxis-Ag. Georgiou, 41335 Larissa, Greece
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(4), 790; https://doi.org/10.3390/agronomy14040790
Submission received: 17 March 2024 / Revised: 2 April 2024 / Accepted: 7 April 2024 / Published: 11 April 2024
(This article belongs to the Special Issue Modern Seed Technologies for Developing Dynamic Agriculture)
Browse Figures
Versions Notes

Abstract

:
Lentil (Lens culinaris Medik.) production faces challenges due to shifting environmental conditions, potentially leading to a transition towards cooler or highland Mediterranean environments. This study assessed the responses of five lentil genotypes across five diverse locations (L1–L5) managed under organic cropping systems over two seasons, focusing on key parameters including seed yield (SY), crude protein (CP), cooking time (CT), seed loss percentage (SL), and yield loss per hectare (YL) caused by bruchid (Bruchus sp.). Excessive seasonal rainfall (500 mm), low winter temperatures (−17.9 °C), bruchid SL, and spring sowing were identified as crucial, particularly in challenging environments like highlands. Genotype selection was highlighted as essential for balancing yield and stability, with the small-seeded cultivar ‘Dimitra’ demonstrating lower YL due to bruchid. Additionally, increased CP was noted in response to heightened bruchid infestations. Specific recommendations were proposed for different environments: In productive lowland areas with low bruchid pressure and high CTs (L1), prioritizing cultivars like ‘Samos’, ‘Dimitra’, and ‘Thessalia’ enhances quality. Locations with high bruchid populations (L4) were not favored organic production but can serve as genetic resistance screening sites. High-elevation environments (L3, L5) proved significantly less productive, underscoring the requirement for earlier and winter-hardy cultivars. These insights guide lentil cultivation, emphasizing the need for tailored breeding strategies adaptable to changing environments.

1. Introduction

Lentil (Lens culinaris Medik.) is a globally cultivated ancient pulse that is significant in organic agriculture due to its alignment with sustainability and regenerative farming principles. Rich in protein, minerals, dietary fibers, folate, and vitamins, lentils are vital for human and animal nutrition, promoting food security and health [1,2,3]. They are a key component of the Mediterranean diet, known for its health benefits and association with reduced risks for chronic diseases and cancer [4,5].
Organic lentil farmers face a significant challenge posed by seed bruchids, small beetles of the subfamily Bruchinae whose larvae develop within a single seed. Major species such as Bruchus lentis Frölich, Bruchus signaticornis Gyllenhal, and Bruchus ervi Frölich are widely recognized as field pests in lentil cultivation across Europe, Africa, and Asia [6]. The infestation and development process of bruchids in the field has been detailed by Stevenson et al. [7]. Upon hatching, young larvae enter developing seeds, consuming part of the cotyledons over six weeks before exiting through an exit hole in the pod. The economic damage caused by emerging bruchid adults can be significant regarding seed weight loss or quality degradation [8]. Organic farmers employ integrated pest management strategies such as crop rotation and interplanting with less attractive crops to disrupt the pests’ life cycle naturally, aligning with organic farming principles [6].
Untreated fungus disease damage in lentil crops can result in substantial yield losses, reaching as high as 70% [9,10,11]. Natural pesticides show promise in mitigating the chemical control’s adverse effects [12]. Additionally, introducing resistant or nearly resistant lentil cultivars, a form of host-plant resistance, is a cost-effective strategy [10,13,14]. However, developing lentil varieties specifically tailored for organic cultivation is still pending [15]. Consequently, organic farmers often rely on conventional breeding varieties, which may lack traits optimized for organic farming but still demonstrate broad adaptability [15,16].
The cooking time of lentils is crucial, affecting nutrient digestibility and consumer satisfaction. Longer cooking times are inconvenient and costly in terms of electricity or fuel for processors and consumers alike [17]. Different lentil cultivars can have varying cooking times due to differences in seed size, seed coat anatomy, and cotyledon color; green-seeded lentils tend to boil faster than red-seeded varieties [18,19].
Lentil production in Greece fails to meet consumption demands, leading to reliance on imports due to factors like drought-induced yield declines. In 2020, lentil cultivation expanded to 11,500 hectares, with a total production of 13,538 tons. However, drought caused the average yield of lentils to decline to 1.17 t h−1 [20]. Lentil is a versatile crop, thriving in various climates and soils without requiring nitrogen fertilizers due to its ability to fix atmospheric nitrogen (N2) [21,22]. However, the success of lentil cultivation in Greece is influenced by a combination of edaphic (soil-related) and climatic conditions [23]. Given the projections of more frequent drought events and higher temperatures in the Mediterranean region due to climate change, there is a pressing need to explore strategies to adapt lentil cultivation to these changing conditions [24]. One such approach could be relocating winter legume production, including lentils, to higher-elevation plains where cooler temperatures and potentially higher water availability during spring may mitigate some of the impacts of climate change [25]. However, the transition to high-elevation plains poses its challenges, such as the harsh winter conditions that can significantly affect yields. Research indicates that yields of spring-sown lentils in winter frost-prone regions may be significantly lower (50–100%) than traditional autumn-sown lentils [26]. Additionally, the winter survival of lentils in such environments is influenced by various factors beyond frost tolerance, including waterlogging, root diseases, and ascochyta blight (Ascochyta lentis) [27]. The longstanding cultivation of lentils in the traditional lentil regions makes these regions possibly unsuitable for organic lentils due to high bruchid populations [28]. Overall, bridging the gap between environmental factors, lentil genetics, and organic farming practices is crucial for optimizing lentil cultivation in Mediterranean regions.
This study tested five lentil cultivars across five locations in Greece with diverse pedoclimatic conditions. The objectives of the study included: (i) Enhancing sustainable lentil cultivation in organic farming by studying genotype responses. (ii) Recommending ideal production sites to optimize yields and quality while reducing environmental risks. (iii) Offering insights for future breeding to enhance lentil resilience in regions facing similar climate challenges.

2. Materials and Methods

2.1. Locations and Experimental Management

The lentil cultivar trials were evaluated during the growing seasons 2018/2019 (referred to as 2019) and 2019/2020 (referred to as 2020). These assessments were conducted at five different locations distributed across Greece, identified by codes L1 to L5, as depicted in Figure 1. The locations are Orestiada (L1) in the region of Thrace (41°30′14″ N, 26°32′99″ E), Thessaloniki (L2) in the region of Central Macedonia (40°32′69″ N, 22°59′83″ E), Anatoliko (L3) in the region of Western Macedonia (40°33′09″ Ν, 21°44′46″ E), Larissa (L4) in Thessaly Central Greece (39°36′81″ N, 22°25′94″ E) and Domokos (L5) in the region of Sterea (39°1′13″ N, 22°19′74″ E).
The locations varied in terms of soil and weather conditions. Details are provided in Table 1. The soils of the tested locations are classified in the orders of Fluvisols, Luvisols, and Vertisols according to FAO soil system classification [29]. Briefly, Fluvisols are mainly young soils with little or no pedogenetic horizon development and are mainly found in alluvial deposits. Annual and woody crops are grown on these lands. Luvisols are developed on alluvial deposits with clayey horizons. They are productive soils for a wide range of crops. Vertisols are considered productive soils; however, due to high content of expanding clay minerals they must be properly managed to avoid constraints in productivity. Soils were fine-textured but varied across the locations: L1 is silty clay loam (SiCL), while L2 and L3 have a clay loam (CL) and L4 and L5 have a clayey (C) texture [30]. The soil pH was neutral or slightly alkaline, ranging from 7.0 to 8.0 [31]. The electrical conductivity (EC) was low (<1.00 mS cm−1, 25 °C) [32]. The soil organic matter (SOM) content was either medium (1.8–2.0%) or low (<1.5%) [33], and the calcium carbonate equivalent (CaCO3) ranged from 1.2% to 4.5% [34]. Soils were either deficient (<10.0 mg P kg−1 soil, L1, L2, L4) or moderately sufficient (10–20 mg kg−1 soil, L3, L5) in available phosphorus (POlsen) [35], with medium or mostly high cation exchange capacity (CEC > 25 cmolc kg−1). The soil-extractable zinc measured by DTPA in locations L2, L3, and L4 was low, while Cu, Fe, and Mn were found to be high in the soil across all sites [36].
The climate in Greece is typically semiarid Mediterranean. According to the Köppen-Geiger climate classification [37,38], three types of climates are found in the study locations: L1 (Orestiada) and L5 (Domokos) feature a hot-summer Mediterranean climate (Csa), locations L2 (Thessaloniki) and L3 (Anatoliko) a humid subtropical climate (Cfa), while there is a combination of BSk (arid, steppe, cold)/Csa in L4 (Larissa). Regional climate variations are influenced by altitude.
In each location and trial, the daily mean air temperature and monthly precipitation during the growing seasons were recorded via a wireless automatic weather station (Pessl iMetos OEM Model-1, Weiz, Austria). In the 2019 growing season (November to July), the mean precipitation (PrS) for the five sites was 474.1 mm, ranging from 367.2 mm (L1) to 566.1 mm (L5), whereas the 2020 growing season, which was in general wetter, the mean precipitation for the five locations was 500.6 mm and ranged from 402.5 mm (L3) to 684.7 mm (L5) (Table 1). Accordingly, the precipitation between April and May (PrA-M) (early reproductive till pod filling growth stages) was much lower in 2019 (PrA-M = 104.9 mm, mean of the five locations) compared to 2020 (PrA-M = 159.0 mm). Among the five locations, the southernmost locations had either the highest season (November to July) precipitation (PrS) (L5) or the lowest precipitation between April and May (PrA-M) (L4). Season (November to July) mean temperature was slightly higher in 2020 compared to 2019 but without substantial differences. The highest season mean temperatures (Ts, °C) were recorded in L2 and L4 and the lowest in the location with the highest altitude (L3). The daily recorded minimum temperatures (TDmin, °C) were recorded in January 2019 at the locations with the highest elevations: TDmin = −17.9 °C in L3 (624 m) and TDmin = −12.8 °C in L5 (570 m) (Table 1).
The experimental arrangement consisted of a randomized complete blocks design (RCBD) with four replications. Each plot comprised seven (7) plant rows, each 4 m long, with a row spacing of 0.25 m. The number of seeds sown was 225 seeds m−2, corresponding to approximately 180 plants m−2 uniformly distributed. Sowing occurred during the last week of November in 2018 and 2019 in all locations, except for location L3 (Anatoliko), where sowing occurred at the end of February due to a high incidence of frost.
The trials were carried out in organically cultivated environments relying on natural precipitation. No chemical fertilizers, pesticides, or other agrochemicals had been used for at least the last 5 years preceding the experiments. In Larissa (L4), the trials were conducted in a field where lentils and other pulses are frequently grown organically and that is considered highly infested by Bruchus sp. In the other locations, the trials were conducted in fields where lentils were occasionally included in rotational schemes with cereals and no agrochemicals had been applied for the past five years. In all locations, the preceding crop was cereal and weed control was achieved through deep plowing in summer and manual weed control. No pesticides were used to control Bruchus sp.

2.2. Genetic Materials

Four green-seeded cultivars, namely ‘Samos’ (G1), ‘Dimitra’ (G2), ‘Thessalia’ (G3), and ‘Elpida’ (G4), property of Institute of Industrial and Forage Crops Larissa Greece (IIFC), along with one red-seeded local population, ‘03-24L’ (G5), which was introduced from ICARDA and subsequently improved by IIFC [39], were evaluated. The cultivars were selected based on their high market share, high yield potential and stability, diversification in flowering date, maturity earliness, and seed size (Table 2).

2.3. Yield, Yield Components, and Seed Bruchid Infection

Plant height (PH) and number of pods per plant (PP) were assessed on 10 randomly selected plants from the three inner plant rows in each plot. The extended BBCH scale was utilized to describe the phenological development of 50% of the plants in each plot [40]. PH was recorded after flowering was completed (BBCH growth stages 69–71), while the PP was determined at physiological maturity (BBCH stage 89). Upon reaching harvest maturity, plots underwent manual harvesting and threshing using a laboratory thresher (Wintersteiger LD350, Wintersteiger Holding AG, Ried im Innkreis, Austria) to evaluate seed yield (SY) and 1000-seed weight (1000 SW). SY was determined on a plot basis (3 m2 per plot) and was then converted to t ha−1 after normalizing the seed weight to 13% seed moisture.
To determine the seed loss percentage (SL) due to bruchid infestation, two random samples of 300 seeds per plot were examined. Seeds were placed in plastic boxes and observed at room temperature until emergence holes appeared, indicating infestation. Infestation levels were assessed by counting emergence holes and examining seeds for adult bruchids [41]. SL per plot was calculated as the mean percentage of infected seeds from the two samples. To further assess the economic implications, the yield loss per hectare (YL) was estimated based on the 1000 SW per plot.

2.4. Cooking Time and Protein

The cooking time (CT) for each cultivar was determined using the tactile method as detailed by Taiwo et al. [42], albeit with several adjustments. In this procedure, five grams of lentil seeds were placed into 100 mL beakers, which were then covered with aluminum foil. Next, 50 mL of boiling distilled water, maintaining a ratio of 1:10 (seed to water), was added to each beaker. Subsequently, the beakers were positioned on a hotplate to initiate cooking. Approximately 20 min later, the softness of the cooked seeds was assessed every 30 s until they achieved a uniform, transparent consistency devoid of any opaque core. This determination was made by sandwiching the cooked seeds between two glass slides and ensuring there was no lateral movement. The optimal cooking time (CT) in minutes was then calculated by averaging the results from triplicate measurements.
Additionally, the seed crude protein percentage (CP) was assessed for each seed sample on a 0% moisture basis. This process included finely grinding a sample from each plot using the Kjeldahl method. The protein percentage was determined by multiplying the total nitrogen (N) content by 6.25 (CP = total N × 6.25).

2.5. Statistical Analysis

A combined ANOVA for balanced RCBD experiments was conducted, where genotypes (G) and locations (L) were treated as fixed effects and Y was considered a random effect [43]. The treatment sum of squares (SSTRMT) was divided into cultivars (SSG), locations (SSL), years (SSY), and the two- and three-way components, expressed as a percentage of the sum of squares of the SSTRMT.
Levene’s test was employed to assess the equality of variances and residual plots were examined to identify any outliers. Trial means were compared using the LSD test at a significance level of p ≤ 0.05. Additionally, Pearson correlation coefficients (r) were calculated to identify significant correlations, using the statistical software JMP 11.0.0 [44].
The multivariate GGE biplot model was employed for: (i) grouping the environments based on the best cultivars by the ‘which-won-where’ pattern and (ii) ranking genotypes for mean performance and stability. The combinations of location and year (e.g., L1 × 2019) were utilized to reveal GEI crossover interactions or to rank genotypes [45]. The GGE biplot analysis was performed using the R package GGEBiplotGUI version 1.0-9 [46].

3. Results

3.1. Locations, Yields, and Agronomic Traits

Genotype (G), location (L), and the two-way interactions (Y × L, G × L) were found to be significant for SY, PP, 1000 SW, and PH (Table 3). Additionally, the G × Y and the three-way interaction (Y × L × G) were significant for SY and 1000 SW but not for PP and PH. The contribution of the L effect was the highest for SY and PP, accounting for over 67% of the variation. Parameters such as PP, 1000 SW, and PH were predominantly genetically controlled, with G accounting for over 9% of the variation, particularly for 1000 SW (73.81%) compared to SY (1.49%). The G × L contribution to the variation was much higher in comparison to G × Y for all the above traits. Due to the SSG×L/SSG×Y ratio for SY being 3.2-fold higher, the GGE biplot analysis based on the G × L interaction was utilized to assess the cultivars’ adaptation across the growing seasons (Figure 2).
L1 followed by L2 were the most productive in terms of mean SY, with SYs of 2.00 t ha−1 and 1.63 t ha−1, respectively (Table 4). The high-elevation sites L3 (624 m asl) and L5 (500 m) and L4 had low yields (0.53–0.90 t ha−1). Accordingly, site L1 had the highest 1000 SW (47.05 g), followed by L2 (44.67 g). The site L3 had a high PP (40.90 pods/plant) and 1000 SW (44.65 g), despite it being low yielding. The tallest plants were recorded in location L2, with a mean plant height of 39.73 cm, whereas the shortest plants were measured in the late-sowed site L3 (32.18 cm, Table 4).
Regarding the differences among the growing seasons, the less humid 2019 was more productive by 15.8% in terms of mean SY compared to 2020 (1.26 and 1.06 t ha−1, respectively). Additionally, the PP was higher by 12.7% in 2019 but the 1000 SW was lower by 4.6% in 2019 compared to 2020. There was a small difference in PH between the two growing seasons (Table 4).
The average environmental temperature T (°C) and precipitation after anthesis (PrA-M mm) were related to SY positively in 2019, but in 2020, these relationships weakened or disappeared. In particular, the SY of each location was related positively to T and PrA-M in 2019 (r = 0.72 and r = 0.90 p < 0.01, respectively), whereas it was not related or only weakly related in 2020 (r = 0.06 and 0.35, respectively.
Among the genotypes, G1 and G3 were the most productive in terms of mean seed yield (SY), with G1 producing 1.29 t ha−1 followed by G3 with 1.20 t ha−1. Both G1 and G3 also had the highest PP of 35.10 pods plant−1. Genotype 4 had the highest 1000 SW of 57.85 g and G3 and G1 were the tallest, at 37.24 cm and 37.20 cm, respectively (Table 4).
The possible existence of different mega-environments was investigated using a GGE biplot, incorporating SY data from both 2019 and 2020. The biplot revealed crossover G × L interactions, suggesting the potential existence of different mega-environments (Figure 2). Notably, G1 consistently performed well in site L3 and G2 in site L4 across both growing seasons, but the grouping pattern of the other sites varied between years. For instance, in L5, G1 excelled in 2019, while G2 did so in 2020. Similarly, in site L2, G1 dominated in 2020, whereas G5 did so in 2019.
The biplot for ranking genotypes for mean performance highlighted that the mean yield order was G1 > G3 > G2 > G4 > G5. The performance of G2, followed by G5 and G3, was the most variable (least stable), whereas the performance of G4, followed by G1, was highly stable (Figure 3).
The comparisons among the L and growing seasons indicated the statistical differences (Table 5). In L4 during both growing seasons, the small-seed genotype G2 exhibited the highest mean SY at 0.92 t ha−1, followed by the red-cotyledon genotype G5 at 0.81 t ha−1. In the short-season site L3, the G1 genotype had a significantly higher yield at 0.73 t ha−1. There were no significant differences in SY observed in L5.

3.2. Seed Loss Percentage and Yield Loss

The primary source of variation for seed loss percentage (SL) and yield loss (YL) attributed to bruchid larvae was identified as the location, accounting for more than 89% of the treatment variation. While significant differences were observed among the genotypes, it is noted that genotype alone explained a relatively small portion of the variation (0.89–1.50%). However, the interactions between genotype and location (G × L) were found to be higher than between genotype and year (G × Y) (Table 3).
In both years of experimentation, L4 consistently exhibited the highest level of infestation by bruchid larvae, with SL values of 32.00% and 41.80% in 2019 and 2020, respectively. Correspondingly, the YL of L4 ranged from 0.47 to 0.55 t ha−1. Following L4, L2 exhibited the second-highest level of infestation, with a mean SL of 10.37% (Table 5). Among the genotypes, G3 demonstrated the highest damage in terms of mean SL and YL, with mean values of 13.60% and 0.19 t ha−1, respectively (Table 6). Conversely, the genotype with small seed size, G2 (1000 SW = 32.98 g), exhibited the lowest YL of 0.12 t ha-1, representing a 36% decrease compared to the medium-seeded genotype G3 (1000 SW = 45.69 g) and a 29% decrease compared to the large-seeded genotype G4 (1000 SW = 57.85 g).

3.3. Protein and Cooking Time

Year (Y), G, and L, as well as their two-way (Y × L, G × Y, G × L) and three-way (Y × L × G) interactions, were highly significant (p ≤ 0.01) for both CP and CT (Table 3). Crude protein was primarily affected by the L effect (67.53%) and G explained the 5.38% of the variation; the interaction G × L was also more influential than the interaction G × Y. Cooking time was mainly genetically controlled, as genotype explained 55.58% of the variation in CT (Table 3).
The two locations with the highest Infestation with bruchids, L4 and L2, demonstrated the highest CP values (28.04% and 27.99%, respectively). Specifically, the highest CP amount was measured in most infested L4, followed by the moderately infested site L2. The mean CP of the non-infested sites was 24.04%.
Similar mean CTs of 26 min were measured in the growing seasons. The difference in mean cooking times (CTs) among the genotypes was relatively small, 3.3 min. The large-seed-size genotype G4 and the red-cotyledon genotype G5 had the longest cooking times, with durations of 25.51 min and 27.49 min, respectively.

3.4. Correlations among Traits

Based on the small number of mean genotypic values (n = 10), weak and insignificant relationships were found among SY and PP, PH, 1000 SW, and CT (r = 0.2, 0.24, 0.19, and −0.20, respectively). However, the most worth mentioning was the moderate correlation between SL or YL and CP, r = 0.59 and r = 0.53 (p < 0.01), respectively (Table S1).

4. Discussion

Analyzing the effects of genotypes, environments, and their interaction (GEI) is crucial for stakeholders such as local organic farmers, seed companies, and breeding programs. Effective interpretation of GEI requires gathering extensive information on critical factors influencing genotype responses, as these interactions are often influenced by environmental, biotic, or abiotic stresses [47,48,49].
Data analysis has shown a huge environmental effect on SY attributed to the L effect (71.61%), whereas the Y effect was small [19]. The significant impact of the location effect on legumes is often cited as a primary factor contributing to the limited adaptation of legume cultivation [49]. In this study, the impact of L followed by the G × L on SY can primarily be attributed to variations in pedoclimatic conditions, biotic stresses such as bruchid infestations, and sowing time.
The climate data provided interesting insights into the relationship between seasonal temperature (Ts), precipitation April–May (PrA-M), and SY. In 2019, medium to strong relationships were observed between Ts and SY (r = 0.72) and between PrA-M and SY (r = 0.90). However, in 2020, this pattern was not repeated or was weak. Generally, 80% of the range in lentil SY in the Mediterranean climate may be explained by the difference in seasonal precipitation, as the greatest precipitation falls in winter, whereas in the period from anthesis to pod filling the plants are subjected to water and temperature stresses [27,50]. The significant relationship between rainfall and yield potential is aligned with our study’s observations in 2019; however, the response observed in 2020 differed, indicating that factors other than precipitation may have influenced yield potential during that growing season. Possible explanations for the discrepancy in yield response between 2019 and 2020 include variations in water availability, with lentils potentially meeting their water requirements earlier in 2020 due to excessive precipitation, leading to yield reductions. Lentil is well-suited to low-to-medium rainfall regions typically receiving 300–400 mm annually. Yield (SY) can reach greater than 1.0 t ha−1 and up to 2.5 t ha−1 if sown early [51,52,53].
In our case, the PrS was more than sufficient in 2020 (500.6 mm) in comparison to 2019 (474.1 mm). During the early reproductive till pod filling growth stages the PrA-M was much higher in 2020 (159.0 mm) in comparison to 2019 (104.9 mm). Lentil plants require ample moisture, particularly during anthesis. However, during the ripening stage, excessive soil moisture can harm plants [54,55]. Excessive rainfall indeed poses a risk to lentil crops, potentially leading to lodging when biomass surpasses a certain threshold, which can result in substantial yield losses [27,56]. Moreover, excessive wet conditions during spring can exacerbate pest problems like aphids [57], numerous aphid-transmitted viruses [11], and foliar disease outbreaks [9]. The observation regarding the low yield achieved in the most humid site (L5) is intriguing and supports the notion that excessive moisture may harm lentil yields. Excessive moisture coupled with the heavy-textured, high-clay-content Vertisol in L5 may have led to poor soil drainage conditions (Vertisols retain water due to their high clay contents), which can negatively affect crop growth. Well-drained soils (e.g., sandy loam-SL) to medium-textured soils (loamy-L, silty loam-SiL, silty-Si) are considered more favorable for the cultivation of lentils. Heavy soils together with a lack of proper management may cause a reduction in yields. Furthermore, Vertisols (high content of expanding clay minerals) are sensitive to changes in rainfall patterns, especially during the critical stages of crop growth, and this limitation can further constrain production.
The low yield observed at the high-altitude (624 m asl) site L3 appears to be primarily attributed to late sowing, which was necessitated by the low temperatures during the typical winter period in the region (daily record minimum in January −17.9 °C and −9.4 °C, in 2019 and 2020, respectively). Despite starting the season with promising indicators such as high PP, the final yield was compromised, likely due to low PH, reduced biomass production, a delay in maturity, and subsequent water stress. Adjusting the sowing time is crucial for maximizing lentil yields, especially in regions with challenging climatic conditions such as high altitudes. Timely sowing ensures proper crop development and minimizes the risk of yield losses due to factors like late water stress [58,59]. Understanding the mechanisms through which lentils combat drought stress is crucial for devising effective cultivation strategies, especially in challenging environments like the Mediterranean. Ludlow [60] outlined various strategies including drought escape, avoidance, and tolerance. In Mediterranean regions, the predominant strategy often involves drought escape, characterized by vigorous growth in winter followed by rapid senescence induced by high temperatures and drought stress in late spring or early summer [51]. Furthermore, utilizing earlier flowering cultivars with improved agronomic traits can enhance productivity in short-season environments [52]. Supplemental irrigation during critical stages such as flowering and early pod filling can help alleviate drought stress in spring-sown lentil crops [51]. However, for Mediterranean conditions, this solution may not be sustainable.
Early sowing in or before winter has the potential to increase water use efficiency, total biological nitrogen fixation, biomass, seed yield, and production by more than 50% [26,61]. However, transitioning lentil cultivation to high-elevation plains, particularly when planted in autumn, presents challenges, primarily due to frost damage. Studies have shown that exposure to temperatures as low as −15 °C for 3 h can distinguish frost-hardy lentil genotypes, but even the hardiest lines may suffer 75% kill at lower temperatures like −18 °C and −20 °C [61]. The observed low yields at the high-elevation site L5, which has low temperatures (−12.8 °C to −10.5 °C), highlight the importance of addressing frost tolerance. Our results show non-significant SY differentiation among cultivars in this site. Moreover, factors beyond frost tolerance, such as waterlogging, root diseases, and ascochyta blight, could also influence winter survival and overall yield [27].
High levels of bruchid infestation at site L4 (Larissa) emphasize the significant impact of this biotic stressor on legume cultivation in the region. The longstanding lentil cultivation in the Larissa region since 1933 has led to prolonged exposure to bruchid infestations, increasing the area’s vulnerability to this pest over time [28].
The previous discussion has outlined the most critical abiotic–biotic factors that could explain the differential response of genotypes. The GGE biplots revealed an inconsistent grouping pattern across locations and variability in genotype performance. This suggests that the region cannot be divided into distinct mega-environments, indicating the importance of selecting cultivars that perform well across a range of conditions [62]. The comparison of biplots for mean yield and stability identifies G1 as the top-performing genotype, excelling in both yield and stability. Following closely is G3, which demonstrates high yield but lower stability. These findings suggest that while G3 may offer high yield potential, it may be more susceptible to environmental variability compared to G1. This is in agreement with another study under conventional production systems, confirming genotype G1 as the most productive [19]. The absence of varieties specifically developed for organic cultivation, as highlighted by Vlachostergios et al. [15], presents a challenge for organic farmers who seek cultivars optimized for their production practices. Selecting conventionally bred cultivars with broad adaptability is essential as it allows them to perform reasonably well under organic farming conditions [15,16].
The key agronomic traits PP, PH, and 1000 SW were primarily determined by genetics rather than environmental factors, which is consistent with previous research [19,28]. While previous studies in conventional cropping systems within the region have demonstrated strong positive correlations between SY and PP and PH and a negative correlation between SY and 1000 SW [19], this study revealed a weak correlation between these traits and SY. This inconsistency may be attributed to the higher dependency of yields in organic systems to infestations such as bruchids or foliar diseases and low soil fertility, leading to varied responses across locations.
Research conducted in Greece during 2006–2007 for comparing bruchid infestation levels between organic and conventional lentils revealed that the mean YL was 8.4-fold higher under organic farming. Early flowering and small seed size were traits associated with low YL [31]. However, the early flowering scenario was not realized, as the earliest genotype, G4, demonstrated a high YL, possibly attributed to its large seed size. Conversely, the genotype with a small seed size, G2, exhibited the lowest YL, representing a 29% decrease compared to the large-seeded G4 and a 36% decrease compared to the medium-seeded genotype G3. The lower YL of G2 is primarily attributed to its low 1000 SW, as the SL was approximately the same as the medium- and large-seeded genotypes, indicating similar infestation levels. This aligns with the understanding that smaller seed sizes are often associated with reduced bruchid damage. Additionally, insights from research on vetch species (Vicia sativa) and faba beans (Vicia faba L.) shed further light on the relationship between plant traits and bruchid infestations. Genotypes with large seeds, many seeds per pod, fewer pods, and fewer branches per plant were found to be more susceptible to bruchid damage. This susceptibility may be attributed to factors such as easier access for female bruchids to lay eggs on a limited number of pods and the availability of more protein content in large seeds [41,63]. Certainly, the ability of certain cultivars, like G3, to maintain high yields despite facing significant yield loss (YL) from bruchids underscores the intricate interplay of multiple factors, including genotypic yield potential and resilience to pests [14,28,64,65], Therefore, while small seed size may play a role in reducing susceptibility to bruchid damage, it is just one aspect of a broader spectrum of factors influencing overall crop performance.
The previous discussion highlighted the importance of considering the cultivation history of specific locations, such as L4, when making decisions about agricultural practices like organic lentil farming. While locations with a history of lentil cultivation may not be ideal for organic lentil farming due to potential pest pressures, they can serve as valuable sites for screening and testing tolerant cultivars to pests such as bruchids [28]. Conversely, high-yielding locations such as L1, characterized by a low YL, are considered ideal for organic farming. Choosing such locations for organic lentil farming can help mitigate the risk of pest-related losses and maximize the success of organic cultivation practices.
Most of the researchers agree that lentil seed CP varies little among locations and there is low G × L [66,67,68]. This supports the hypothesis that the nitrogen-fixing ability of lentil and other legumes by Rhizobium bacteria makes their CP relatively stable across environments [69]. Recent comparisons between organic and conventional environments have shown higher CP in organic farming (organic: 32.0%, conventional: 27.5%), arguing that the organic environments potentially lead to higher CP due to lower yields [21]. Most researchers have also reported a high negative relationship between CP and SY [68,70]. However, our data show variations in CP levels across sites with different levels of bruchid infestation. Specifically, locations with higher levels of bruchid infestation tend to have higher CP levels. This trend is particularly evident in site L4, which had the highest bruchid infestation levels and consequently the highest CP levels (28.04%). Site L2 also showed a similar pattern with moderately high bruchid infestation. Although there are no specific reports on the relationship between bruchid infestation and CP in lentils, studies on other leguminous crops have demonstrated a positive correlation between infected seeds and CP.
The study by Nikolova et al. [71] demonstrated that spring pea seeds (Pisum sativum) damaged by Bruchus pisorum L. had increased CP compared to healthy seeds on the same site. Similarly, Zubareva et al. [72] advocated that B. pisorum L. damage led to increased CP in spring peas. This could be attributed to the plant’s defense mechanisms, wherein plants produce defense-related proteins at high concentrations in response to stressors such as insect infestation [73].
Cooking time is a crucial factor for evaluating pulse cooking quality and is considered highly heritable [74,75]. The current study found that the genotype was the primary factor contributing to CT. Despite variations in growing seasons, the average CT remained consistent at around 26 min. This suggests that environmental factors related to growing seasons did not significantly affect CT in the trials. Similar values were also measured under a conventional cropping system where the ranking of the genotypes in the organic system was pretty much like the one measured in the conventional system [19]. A CT of 30 min is considered acceptable for commercial purposes and cultivars below this threshold are classified as having a short CT [23]. While there were significant differences in CTs among varieties, the range of mean CTs was relatively narrow, at 3.3 min. This variation is considered acceptable by market standards, likely due to the pre-selection of varieties with short CTs before commercial release. The shortest CTs were measured in the small-seeded G2 and in the medium-seeded G1 and G2. These findings support previous results by Iliadis [23], who characterized G1 and G2 as fast cooking, and Theologidou et al. [18], who characterized G1 and G3 as fast cooking. The large-seeded G4 and the red-lentil G5 had the longest in CTs. Cultivar G5 was also evaluated by Theologidou et al. [18], who also found longer CTs for this cultivar. Red cultivars usually require longer CTs and, for this reason, are consumed dehulled to reduce their CTs to as short as 15 min [75,76]. The longer CT of the large-seeded G4 agreed with previous findings that large-seeded lentils have longer CTs than small-seeded ones [74].
Regarding the effect of location on CT, the longest CT was recorded for L1 (29.61 min), whereas the other locations had similar CTs ranging from 24.91 to 26.14 min. However, locations like L1 were expected to have long CTs because of their low precipitation [23]. Finally, a specific pattern linking the environmental conditions to CT was not identified. This lack of a clear pattern may be due to the particular set of lentil cultivars studied. Drawing from the research by Iliadis [23], it suggests that the unfavorable effects of certain soils on CT can be mitigated by using short CT varieties. This finding aligns with the situation discussed, where certain cultivars exhibited shorter CTs despite potentially adverse environmental conditions.

5. Conclusions

Based on the comprehensive analysis of organic lentil cultivation, several specific scenarios for selecting suitable cultivars tailored to different environments were identified. These scenarios likely consider various factors such as the pedoclimatic conditions, pest pressures, and CTs. Here are some potential scenarios:
  (i)
High-yielding organic environments with long CTs (L1): In such environments, cultivars with short CTs (such as G1, G2, or G3) are recommended for maximizing quality.
 (ii)
Regions with a high bruchid populations (L4): In areas where there is a significant presence of bruchids, organic production might not be economically viable due to pest pressure. Conventional production methods might be more suitable in such cases. These areas can serve as screening sites for identifying genetic resistance to bruchids, with attention given to cultivars like G2, which are known for their lower YL.
(iii)
High-elevation environments for spring planting (L3): In such areas, where productivity is significantly lower, there is a need for earlier-maturing cultivars and supplemental irrigation during critical stages like flowering and early pod filling. However, it is noted that spring crops may not be sustainable for Mediterranean conditions, indicating the need for alternative approaches.
(iv)
High elevation for autumn planting (L5): Similar to spring planting in high-elevation environments, low productivity is observed here as well. To address this, planting winter-hardy cultivars at optimal dates with a good agronomic package is advised.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14040790/s1, Table S1. Correlation coefficients among seed yield in kg ha−1 (SY), seed loss percentage (SL), yield loss in t ha−1 (YL), number of pods per plant (PP), plant height in cm (PH), thousand seed weight in g (1000 SW), cooking time in min. (CT) and crude protein percentage (CP).

Author Contributions

Conceptualization, D.N.V., I.T. and A.M.; methodology, D.N.V., D.B. (Dimitrios Baxevanos) and C.N.; validation, D.B. (Dimitrios Baxevanos) and A.K.; formal analysis, D.B. (Dimitrios Baxevanos); investigation, A.K., C.P., A.-M.K., M.A., E.T. and D.B. (Dimitrios Beslemes); data curation, D.B. (Dimitrios Baxevanos), A.K. and C.N.; writing—original draft preparation, D.B. (Dimitrios Baxevanos); writing—review and editing, D.N.V., D.B. (Dimitrios Baxevanos), A.K. and C.N.; visualization, D.B. (Dimitrios Baxevanos); supervision, D.N.V.; project administration, D.N.V., I.T., A.M. and D.B. (Dimitrios Beslemes); funding acquisition, D.N.V., I.T. and A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE-INNOVATE (Project code: T1EDK-04633).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.

Acknowledgments

We thank the staff of the Institute of Industrial and Forage Crops, the Farm of Aristotle University of Thessaloniki, and the Department of Agricultural Development of Democritus University of Thrace for their help during the course of the experimentation. The authors also thank the field crew for conducting the sampling and cultivation works.

Conflicts of Interest

Author Dimitrios Beslemes was employed by the company Alfa Seeds SA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Thavarajah, D.; Thavarajah, P.; Wejesuriya, A.; Rutzke, M.; Glahn, R.P.; Combs, G.F., Jr.; Vandenberg, A. The potential of lentil (Lens culinaris L.) as a whole food for increased selenium, iron, and zinc intake: Preliminary results from a 3 year study. Euphytica 2011, 180, 123–128. [Google Scholar] [CrossRef]
  2. Mudryj, A.N.; Yu, N.; Aukema, H.M. Nutritional and health benefits of pulses. Appl. Physiol. Nutr. Metab. 2014, 39, 1197–1204. [Google Scholar] [CrossRef]
  3. Vasconcelos, M.W.; Balázs, B.; Kelemen, E.; Squire, G.R.; Iannetta, P.P.M. Editorial: Transitions to sustainable food and feed systems. Front. Plant Sci. 2019, 10, 1283. [Google Scholar] [CrossRef] [PubMed]
  4. Kafatos, A.; Verhagen, H.; Moschandreas, J.; Apostolaki, I.; Van Westerop, J.J.M. Mediterranean diet of Crete: Food and nutrient content. J. Am. Diet. Assoc. 2000, 100, 1487–1493. [Google Scholar] [CrossRef] [PubMed]
  5. Kwan, H.Y.; Chao, X.; Su, T.; Fu, X.; Kai Wing Tse, A.; Fong, W.F.; Yu, Z.L. The anticancer and antiobesity effects of Mediterranean diet. Crit. Rev. Food Sci. Nutr. 2017, 57, 82–94. [Google Scholar] [CrossRef] [PubMed]
  6. Pandey, S.P.; Yadav, C.R.; Sah, K.; Pande, S.; Joshi, P.K. Legumes in Nepal. In Legumes in Rice and Wheat Cropping Systems of the Indo-Gangetic Plain—Constraints and Opportunities; Johansen, C., Duxbury, J.M., Virmani, S.M., Gowda, C.L., Pande, S., Joshi, P.K., Eds.; International Crops Research Institute for the Semi-Arid Tropics: Patancheru, India; Cornell University: Ithaca, NY, USA, 2000; pp. 71–97. [Google Scholar]
  7. Stevenson, P.C.; Dhillon, M.K.; Sharma, H.C.; El Bouhssini, M. Insect pests of lentil and their management. In Lentil: An Ancient Crop for Modern Times; Yadav, S.S., McNeil, D., Stevenson, P.C., Eds.; Springer: Dordrecht, The Netherlands, 2007; pp. 342–343. [Google Scholar]
  8. Segers, A.; Caparros Megido, R.; Lognay, G.; Francis, F. Overview of Bruchus rufimanus Boheman 1833 (Coleoptera: Chrysomelidae): Biology, chemical ecology and semiochemical opportunities in integrated pest management programs. Crop Prot. 2021, 140, 105411. [Google Scholar] [CrossRef]
  9. Erskine, W.; Sarker, A. Lentil: The Bangladesh breakthrough. Caravan 1997, 6, 8–9. [Google Scholar]
  10. Muehkbauer, F.J.; Kaiser, W.J. Using host plant resistance to manage biotic stresses in cool season food legumes. Euphytica 1994, 73, 233–246. [Google Scholar]
  11. Kumari, S.G.; Larsen, R.; Makkouk, K.M.; Bashir, M. Virus diseases and their control. In The Lentil: Botany, Production and Uses; Erskine, W., Muehlbauer, F.J., Sarker, A., Eds.; CAB International: Oxfordshire, UK, 2009; pp. 306–325. [Google Scholar]
  12. Saeidi, K.; Shahab-Ghayoor, H. Evaluation of botanical insecticides against lentil weevil, Bruchus lentis Frolich (Coleoptera: Chrysomelidae: Bruchinae) under laboratory conditions. Agric. Commun. 2015, 3, 23–27. [Google Scholar]
  13. Hussain, S.; Ali, A.; Zahid, M.A.; Iqbal, S.M. Multiple disease resistance in lentil germplasm from Pakistan. Pak. J. Bot. 2008, 40, 897–903. [Google Scholar]
  14. Laserna-Ruiz, I.; De-Los-Mozos-Pascual, M.; Santana-Méridas, O.; Sánchez-Vioque, R.; Rodríguez-Conde, M.F. Screening and selection of lentil (Lens Miller) germplasm resistant to seed bruchids (Bruchus spp.). Euphytica 2012, 188, 153–162. [Google Scholar] [CrossRef]
  15. Vlachostergios, D.; Lithourgidis, A.; Roupakias, D. Adaptability to organic farming of lentil (Lens culinaris Medik.) varieties developed from conventional breeding programs. J. Agric. Sci. 2011, 149, 85–93. [Google Scholar] [CrossRef]
  16. Vlachostergios, D.N.; Roupakias, D.G. Response to conventional and organic environment of thirty-six lentil (Lens culinaris Medik) varieties. Euphytica 2008, 163, 449–457. [Google Scholar] [CrossRef]
  17. Joshi, M.; Timilsena, Y.; Adhikari, B. Global production, processing and utilization of lentil: A review. J. Integr. Agric. 2017, 16, 2898–2913. [Google Scholar] [CrossRef]
  18. Theologidou, G.S.; Lazaridou, A.; Zori’c, L.; Tsialtas, I.T. Cooking Quality of Lentils Produced under Mediterranean Conditions. Crop Sci. 2018, 58, 2121–2130. [Google Scholar] [CrossRef]
  19. Vlachostergios, D.N.; Noulas, C.; Kargiotidou, A.; Baxevanos, D.; Tigka, E.; Pankou, C.; Kostoula, S.; Beslemes, D.; Irakli, M.; Tziouvalekas, M.; et al. Identification of the Optimum Environments for the High Yield and Quality Traits of Lentil Genotypes Evaluated in Multi-Location Trials. Sustainability 2021, 13, 8247. [Google Scholar] [CrossRef]
  20. Hellenic Statistical Authority. Available online: https://www.statistics.gr/el/statistics/-/publication/SPG06/-2019 (accessed on 6 April 2020).
  21. Tziouvalekas, M.; Tigka, E.; Kargiotidou, A.; Beslemes, D.; Irakli, M.; Pankou, C.; Arabatzi, P.; Aggelakoudi, M.; Tokatlidis, I.; Mavromatis, A.; et al. Seed Yield, Crude Protein and Mineral Nutrients of Lentil Genotypes Evaluated across Diverse Environments under Organic and Conventional Farming. Plants 2022, 11, 3328. [Google Scholar] [CrossRef] [PubMed]
  22. Watson, C.A.; Reckling, M.; Preissel, S.; Bachinger, J.; Bergkvist, G.; Kuhlman, T.; Lindström, K.; Nemecek, T.; Topp, C.F.E.; Vanhatalo, A.; et al. Grain Legume Production and Use in European Agricultural Systems. Adv. Agron. 2017, 144, 235–303. [Google Scholar]
  23. Iliadis, C. Influence of genotype and soil type on cooking time in lentil (Lens culinaris Medikus). Int. J. Food Sci. Technol. 2003, 38, 89–93. [Google Scholar] [CrossRef]
  24. Giannakopoulos, C.; Kostopoulou, E.; Varotsos, K.V.; Tziotziou, K.; Plitharas, A. An integrated assessment of climate change impacts for Greece in the near future. Reg. Environ. Change 2011, 11, 829–843. [Google Scholar] [CrossRef]
  25. Ergon, Å.; Seddaiu, G.; Korhonen, P.; Virkajärvi, P.; Bellocchi, G.; Jørgensen, M.; Østrem, L.; Reheul, D.; Volaire, F. How can forage production in Nordic and Mediterranean Europe adapt to the challenges and opportunities arising from climate change? Eur. J. Agron. 2018, 92, 97–106. [Google Scholar] [CrossRef]
  26. Sakar, D.; Durutan, N.; Meyveci, K. World Crops: Cool Season Food Legumes; Summerfield, R.J., Ed.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1988; pp. 137–146. [Google Scholar]
  27. Erskine, W.; Tufail, M.; Russell, A.; Tyagi, M.C.; Rahman, M.M.; Saxena, M.C. Current and future strategies in breeding lentil for resistance to biotic and abiotic stresses. Euphytica 1994, 73, 127–135. [Google Scholar] [CrossRef]
  28. Vlachostergios, D.N.; Lithourgidis, A.; Baxevanos, D.V.; Mavromatis, A.G.; Noulas, C.S.; Roupakias, D.G. Evaluation of lentil varieties and farming system effect on seed damage and yield loss due to bruchid (Bruchus spp.) infestation. Crop Pasture Sci. 2018, 69, 387–394. [Google Scholar] [CrossRef]
  29. WRB (World Reference Base for Soil Resources). World Reference Base for Soil Resources 2014, Update 2015 International Soil Classification System for Naming Soils and Creating Legends for Soil Maps; World Soil Resources Reports No. 106; International Union of Soil Sciences; FAO: Rome, Italy, 2015. [Google Scholar]
  30. Gee, G.W.; Bauder, J.W. Particle-size analysis. In Methods of Soil Analysis: Part. 1–Physical and Mineralogical Methods; Soil Society of America: Madison, WI, USA, 1986; pp. 383–411. [Google Scholar]
  31. McLean, E.O. Soil pH and lime requirement. In Methods of Soil Analysis: Part 2—Chemical and Microbiological Properties; American Society Agronomy: Madison, WI, USA, 1982; pp. 199–224. [Google Scholar]
  32. Bower, C.A.; Wilcox, L.V. Soluble salts. In Methods of Soil Chemical Analysis: Part 2—Chemical and Microbiological Properties; American Society Agronomy: Madison, WI, USA, 1965; pp. 933–951. [Google Scholar]
  33. Nelson, D.W.; Sommers, L.E. Total carbon, organic carbon and organic matter. In Methods of Soil Analysis: Part 2—Chemical and Microbiological Properties; American Society Agronomy: Madison, WI, USA, 1982; pp. 539–579. [Google Scholar]
  34. Allison, L.E.; Moodie, C.D. Carbonates. In Methods of Soil Analysis: Part 2—Chemical and Microbiological Properties; American Society Agronomy: Madison, WI, USA, 1965; pp. 1389–1396. [Google Scholar]
  35. Olsen, S.R.; Dean, L.A. Phosphorus. In Methods of Soil Chemical Analysis: Part 2—Chemical and Microbiological Properties; American Society Agronomy: Madison, WI, USA, 1965; pp. 1035–1049. [Google Scholar]
  36. Lindsay, W.L.; Norvell, W.A. Development of a DTPA soil test for zinc, iron, manganese and copper. Soil Sci. Soc. Am. J. 1978, 42, 421–428. [Google Scholar] [CrossRef]
  37. Peel, M.C.; Finlayson, B.L.; McMahon, T.A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 2007, 11, 1633–1644. [Google Scholar] [CrossRef]
  38. Beck, H.E.; Zimmermann, N.E.; McVicar, T.R.; Vergopolan, N.; Berg, A.; Wood, E.F. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 2018, 5, 180214. [Google Scholar] [CrossRef] [PubMed]
  39. Tokatlidis, I.; Vlachostergios, D. Sustainable Stewardship of the Landrace Diversity. Diversity 2016, 8, 29. [Google Scholar] [CrossRef]
  40. Meier, U. Growth Stages of Mono-and Dicotyledonous Plants. In BBCH Monograph, 2nd ed.; Blackwell Science: Berlin, Germany, 2001; p. 158. [Google Scholar]
  41. Tsialtas, I.T.; Irakli, M.; Lazaridou, A. Traits related to bruchid resistance and its parasitoid in vetch seeds. Euphytica 2018, 214, 238. [Google Scholar] [CrossRef]
  42. Taiwo, A.; Akanbi, O.C.; Ajibola, O.O. The effects of soaking and cooking time on the cooking properties of two cowpea varieties. J. Food Eng. 1997, 33, 337–346. [Google Scholar] [CrossRef]
  43. McIntosh, M.S. Analysis of combined experiments. Agro. J. 1983, 75, 153–155. [Google Scholar] [CrossRef]
  44. SAS Institute. JMP 11.0.0; SAS Institute Inc.: Cary, NC, USA, 2013. [Google Scholar]
  45. Yan, W.; Kang, M.S. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists, 1st ed.; CRC Press: Boca Raton, FL, USA, 2002. [Google Scholar]
  46. Frutos, E.; Galindo, M.P.; Leiva, V. An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stoch. Environ. Res. Risk. Assess. 2014, 28, 1629–1641. [Google Scholar] [CrossRef]
  47. Kang, M.S. Using genotype-by-environment interaction for crop cultivar development. Adv. Agron. 1997, 62, 199–252. [Google Scholar]
  48. Annicchiarico, P. Advances in the definition of adaptation strategies and yield-stability targets in breeding programs. In Quantitative Genetics, Genomics and Plant Breeding, 2nd ed.; Kang, M.S., Ed.; CAB International: Wallingford, UK, 2020; pp. 194–209. [Google Scholar]
  49. Duc, G.; Agrama, H.; Bao, S.; Berger, J.; Bourion, V.; De Ron, A.M.; Gowda, C.L.L.; Mikic, A.; Millot, D.; Singh, K.B.; et al. Breeding Annual Grain Legumes for Sustainable Agriculture: New Methods to Approach Complex Traits and Target New Cultivar Ideotypes. Crit. Rev. Plant Sci. 2015, 34, 381–411. [Google Scholar] [CrossRef]
  50. Dehghani, H.; Sabaghpour, S.H.; Sabaghnia, N. Genotype × environment interaction for grain yield of some lentil genotypes and relationship among univariate stability statistics. Span. J. Agric. Res. 2008, 6, 385–394. [Google Scholar] [CrossRef]
  51. Silim, S.N.; Saxena, M.C.; Erskine, W. Adaptation of Lentil to the Mediterranean Environment. II. Response to Moisture Supply. Exper. Agric. 1993, 29, 21–28. [Google Scholar] [CrossRef]
  52. Siddique, K.H.M.; Loss, S.P.; Pritchard, D.L.; Regan, K.L.; Tennant, D.; Jettner, R.L.; Wilkinson, D. Adaptation of lentil (Lens culinaris Medik.) to Mediterranean-type environments: Effect of time of sowing on growth, yield, and water use. Austr. J. Agric. Res. 1998, 49, 613–626. [Google Scholar] [CrossRef]
  53. Zhang, H.; Pala, M.; Oweis, T.; Harris, H. Water use and water-use efficiency of chickpea and lentil in a Mediterranean environment. Austr. J. Agric. Res. 2000, 51, 295–304. [Google Scholar] [CrossRef]
  54. Liu, K.; Blackshaw, R.E.; Johnson, E.N.; Hossain, Z.; Hamel, C.; St-Arnaud, M.; Gana, Y. Lentil enhances the productivity and stability of oilseed-cereal cropping systems across different environments. Eur. J. Agron. 2019, 105, 24–31. [Google Scholar] [CrossRef]
  55. Ramegowda, V.; Senthil-Kumar, M. The interactive effects of simultaneous biotic and abiotic stresses on plants: Mechanistic understanding from drought and pathogen combination. J. Plant Physiol. 2015, 176, 47–54. [Google Scholar] [CrossRef]
  56. Prysiazhniuk, O.; Mostoviak, I.; Yevchuk, Y.; Tkachenko, H.; Prokopenko, E.; Kononenko, L.; Prykhodko, V.; Vyshnevska, L.; Poltoretska, N. Lentil Yield Performance and Quality as Affected by Moisture Supply. Ecol. Eng. Environ. Technol. 2022, 23, 25–33. [Google Scholar] [CrossRef]
  57. El Fakhouri, K.; Sabraoui, A.; Kehel, Z.; El Bouhssini, M. Population Dynamics and Yield Loss Assessment for Pea Aphid, Acyrthosiphon pisum (Harris) (Homoptera: Aphididae), on Lentil in Morocco. Insects 2021, 12, 1080. [Google Scholar] [CrossRef] [PubMed]
  58. Pradhan, A.; Nag, S.K.; Mukherjee, S.C. Thermal requirement of small millets in Chhattisgarh plateau under rainfed cropping situation. J. Agro Meteorol. 2018, 20, 244–245. [Google Scholar] [CrossRef]
  59. Maphosa, L.; Preston, A.; Richards, M.F. Effect of Sowing Date and Environment on Phenology, Growth and Yield of Lentil (Lens culinaris Medikus.) Genotypes. Plants 2023, 12, 474. [Google Scholar] [CrossRef]
  60. Ludlow, M.M. Strategies of response to water stress. In Structural and Functional Responses to Environmental Stresses; Kreeb, K.H., Ritcher, H., Hinckley, T.M., Eds.; SPB Academic Publishing: Gravenhage, The Netherlands, 1989; p. 269281. [Google Scholar]
  61. Alia Johnson, D.L.; Stushnoff, C. Screening lentil (Lens culinaris) for cold hardiness under controlled conditions. J. Agric. Sci. 1999, 133, 313–319. [Google Scholar]
  62. Yan, W.; Hunt, L.A.; Sheng, Q.; Szlavnics, Z. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 2000, 40, 597–605. [Google Scholar] [CrossRef]
  63. Tsialtas, I.T.; Irakli, M. Bruchid Infestation Was Associated With Agronomic Traits in Field-grown Faba Bean Genotypes. J. Crop Health 2024, 76, 461–470. [Google Scholar] [CrossRef]
  64. Chopra, N.; Pajni, H.R. Resistance of different lentil varieties to the attack of Bruchus lentis. Lens Newslett. 1987, 23, 45–48. [Google Scholar]
  65. Pajni, H.R.; Sidhu, N.S.; Girdhar, A.K. Infestation of Lens culinaris Medik. by Bruchus lentis Froel. as affected by cropping pattern and other practices in Chandigarh Area, India. Lens Newslett. 1996, 23, 45–48. [Google Scholar]
  66. Khazaei, H.; Subedi, M.; Nickerson, M.; Martínez-Villaluenga, C.; Frias, J.; Vandenberg, A. Seed Protein of Lentils: Current Status, Progress, and Food Applications. Foods 2019, 8, 391. [Google Scholar] [CrossRef]
  67. Bhatty, R.S.; Slinkard, A.E.; Sosulski, F.W. Chemical composition and protein characteristics of lentils. Can. J. Plant Sci. 1976, 56, 787–794. [Google Scholar] [CrossRef]
  68. Lizarazo, C.I.; Lampi, A.M.; Liu, J.; Sontag-Strohm, T.; Piironen, V.; Stoddard, F.L. Nutritive quality and protein production from grain legumes in a boreal climate. J. Sci. Food Agric. 2015, 95, 2053–2064. [Google Scholar] [CrossRef] [PubMed]
  69. Stoddard, F.L.; Marshall, D.R.; Ali, S.M. Variability in grain protein concentration of peas and lentils grown in Australia. Aust. J. Agric. Res. 1993, 44, 1415–1419. [Google Scholar] [CrossRef]
  70. Hamdi, A.; Erskine, W.; Gates, P. Adaptation of Lentil Seed Yield to Varying Moisture Supply. Crop Sci. 1992, 32, 987–990. [Google Scholar] [CrossRef]
  71. Nikolova, I. Pea weevil damage and chemical characteristics of pea cultivars determining their resistance to Bruchus pisorum L. Bull. Entomol. Res. 2016, 106, 268–277. [Google Scholar] [CrossRef] [PubMed]
  72. Zubareva, C. Structural and Biochemical Characteristics of Pisum sativum L., Which Determine Resistance to Bruchus pisorum L. Ph.D. Thesis, Orel State Agrarian University, Orel, Russia, 2006. [Google Scholar]
  73. Lawrence, P.K.; Koundal, K.R. Plant protease inhibitors in control of phytophagous insects. Electron. J. Biotechnol. 2002, 5, 93–109. [Google Scholar] [CrossRef]
  74. Erskine, W.; Williams, P.C.; Nakkoul, H. Genetic and environmental variation in the seed size, protein, yield, and cooking quality of lentils. Field Crop. Res. 1985, 12, 153–161. [Google Scholar] [CrossRef]
  75. Ross, K.A.; Alejo-Lucas, D.; Malcolmson, L.; Arntfield, S.D.; Cenkowski, S. Interpretation of the force–deformation curves of cooked red lentils (Lens culinaris). J. Texture Stud. 2009, 40, 109–126. [Google Scholar] [CrossRef]
  76. Wang, N. Effect of variety and crude protein content on dehulling quality and on the resulting chemical composition of red lentil (Lens culinaris). J. Sci. Food Agric. 2008, 88, 885–890. [Google Scholar] [CrossRef]
Agronomy 14 00790 g001
Figure 1. Locations (L1–L5) of field trials conducted in organic environments across Greece.
Figure 1. Locations (L1–L5) of field trials conducted in organic environments across Greece.
Agronomy 14 00790 g001
Agronomy 14 00790 g002
Figure 2. GGE biplot analysis for grouping environments based on the best genotype for seed yield among the five lentil genotypes (abbreviated as G1–G5) across five locations (abbreviated as L1–L5) during 2019 and 2020 (abbreviated 19 and 20). The vertex genotype for each sector, marked by red lines, represents the genotype that yielded the highest for the environments within that sector.
Figure 2. GGE biplot analysis for grouping environments based on the best genotype for seed yield among the five lentil genotypes (abbreviated as G1–G5) across five locations (abbreviated as L1–L5) during 2019 and 2020 (abbreviated 19 and 20). The vertex genotype for each sector, marked by red lines, represents the genotype that yielded the highest for the environments within that sector.
Agronomy 14 00790 g002
Agronomy 14 00790 g003
Figure 3. GGE biplot ranking five lentil genotypes (abbreviated G1−G5) for mean seed yield and stability during 2019 and 2020. Entries with vertical projections farther left along the one-arrow horizontal axis represent the highest yielding, while those with vertical projections furthest from the stability line (vertical to the horizontal) are considered less stable.
Figure 3. GGE biplot ranking five lentil genotypes (abbreviated G1−G5) for mean seed yield and stability during 2019 and 2020. Entries with vertical projections farther left along the one-arrow horizontal axis represent the highest yielding, while those with vertical projections furthest from the stability line (vertical to the horizontal) are considered less stable.
Agronomy 14 00790 g003
Table 1. Pedoclimatic conditions of the 5 locations (L1–L5) organically cultivated with lentil across Greece over 2019 and 2020.
Table 1. Pedoclimatic conditions of the 5 locations (L1–L5) organically cultivated with lentil across Greece over 2019 and 2020.
LocationOrestiada
(L1)		Thessaloniki
(L2)		Anatoliko
(L3)		Larissa
(L4)		Domokos
(L5)
Soil Order 1FluvisolsFluvisolsLuvisolsFluvisolsVertisols
Soil textureSiCLCLCLCC
pH (1:1)7.38.07.27.87.0
EC 20.720.700.570.520.39
SOM 3 %2.01.32.01.81.4
CaCO3 %2.04.51.31.21.5
POlsen mg kg−17.36.713.07.318.5
CEC 428.638.344.913.327.7
 Cu 5 2.52.12.02.25.2
 Fe 5 1912211040
 Mn 53116321959
 Zn 5 3.00.860.600.681.10
B mg kg−11.401.230.541.400.74
Altitude (m)26562477570
Year2019202020192020201920202019202020192020
PrS 6 (mm)367.2432.6399.6524.8558.3402.5479.2453.2566.1684.7
PrA-M 7 (mm)118.3182.8107.8168.4144.7158.472.099.081.8186.4
Ts (°C) 812.612.514.014.410.111.113.914.411.311.5
TA-M (°C) 915.014.516.415.613.213.516.416.213.613.7
Tmin Jan (°C) 100.7−3.10.50.4−5.9−3.7−0.2−1.0−2.2−3.5
TDmin Jan (°C) 11−6.2−9.5−8.7−6.1−17.9−9.4−10.7−6.1−12.8−10.5
1 Soil class [29], 2 EC = Elec. Cond. (mS cm−1) (25 °C), 3 SOM = Soil organic matter, 4 CEC = Cation Exch. Cap., (cmolc kg−1), 5 DTPA-extractable trace elements mg kg−1, 6 PrS = Prec. during the growing season (November to July), 7 PrA-M = Precip. April–May, 8 Season Avg. Ts (°C) = Average temp. in the growing season (November to July), 9 TA-M = Average temp. April–May, 10 Tmin Jan = Average min. temp. in January, 11 TDmin Jan = Daily record min. temp. in January.
Table 2. Seed characteristics of the five lentil genotypes.
Table 2. Seed characteristics of the five lentil genotypes.
Name/CodeCotyledon ColorSeed SizeFlowering 1 Maturity
Samos (G1)YellowMedium22Medium
Dimitra (G2)YellowSmall20Medium
Thessalia (G3)YellowMedium21Medium
Elpida (G4)YellowLarge4Very early
03-04L (G5)RedMedium12Early
1 Days after 1 April in L4 (Larissa).
Table 3. Analysis of variance, sum of squares (SS), and percentage (%) contribution to treatment SS (SSTRMT) of the five lentil genotypes tested across five locations for two growing seasons (2019 and 2020) under organic cropping systems.
Table 3. Analysis of variance, sum of squares (SS), and percentage (%) contribution to treatment SS (SSTRMT) of the five lentil genotypes tested across five locations for two growing seasons (2019 and 2020) under organic cropping systems.
SourcedfSY PP1000 SWPHSLYLCPCT
Year (Y) 12.07 **933.12 **189.54 **22.78 ns999.04 **0.07 **8.60 **3.54 **
Location (L) 462.37 **11,680.93 **1280.83 **1390.08 **35,074.40 **6.18 **848.13 **680.23 **
Genotype (G) 41.3 *1632.83 **13,652.65 **1111.58 **350.20 **0.10 **67.51 **1418.79 **
Y × L48.19 **802.03 *1711.044 **104.4 *816.44 **0.06 **187.84 **223.07 **
G × Y41.15 *220.93 ns147.787 **7.95 ns90.11 ns0.01 ns21.55 **120.00 **
G × L163.74 *1498.07 *944.12 **543.67 **1329.49 **0.15 **77.83 **75.61 **
Y × L × G168.29 **482.67 ns570.49 **76.4 ns731.83 **0.09 **44.65 **31.45 **
Blocks (LY) 163.76 ns1044.8 ns37.124 ns273.83 ns506.02 ns0.05 ns3.48 *11.74 ns
Error 120100.35388494.09730.115160.188.2435.58
% of the SSTRMT
SSY 2.385.411.020.702.541.050.690.14
SSL 71.6067.726.9242.6989.0492.7967.5326.65
SSG 1.499.4773.8134.140.891.505.3855.58
SSY×L 9.404.659.253.212.070.9014.968.74
SSG×Y 1.321.280.800.240.230.151.724.70
SSG×L 4.298.685.1016.703.382.256.202.96
SSY×L×G 9.522.803.082.351.861.353.561.23
df, degree of freedom; SY, seed yield (t ha−1); PP, number of pods per plant; 1000 SW, thousand seed weight; PH, plant height (cm); SL, seed loss percentage; YL, yield loss (t ha−1); CP, crude protein concentration (%); CT, cooking time (min); * and **: significance at p < 0.05 and 0.01, respectively; ns, not significant.
Table 4. Seed yield t ha−1 (SY), number of pods per plant (PP), thousand-seed weight in g (1000 SW), and plant height in cm (PH) of the lentil genotypes tested across five locations for two growing seasons (2019 and 2020) under organic cropping systems.
Table 4. Seed yield t ha−1 (SY), number of pods per plant (PP), thousand-seed weight in g (1000 SW), and plant height in cm (PH) of the lentil genotypes tested across five locations for two growing seasons (2019 and 2020) under organic cropping systems.
SYPP1000 SWPH
GenotypesMean20192020Mean20192020Mean20192020Mean20192020
G11.29a1.25ab1.34a35.10a36.95b33.25a39.41d37.67d41.16c37.20a37.65a36.75a
G21.16bc1.26ab1.06b30.92b33.05c28.80b32.98e31.75e34.22d34.43c34.45b34.40b
G31.20ab1.37a1.02b35.10a35.80a34.40a45.69b43.93b47.46b37.24a37.50a36.98a
G41.11bc1.25ab0.97b30.42b34.40c26.45b57.85a57.17a58.53a36.08b36.70a35.45ab
G51.06c1.19b0.92b27.75c29.90d25.60b41.72c42.27c41.17c30.93d31.25c30.60c
Locations
L12.00a1.78b2.21a38.68a38.30a39.05a47.05a40.59b53.52a33.54c34.15c32.93bc
L21.63b1.97a1.30b28.85c32.25bc25.45c44.67b46.30a43.04b39.73a41.30a38.15a
L30.53e0.58d0.48d40.90a43.20ab38.60a44.65b45.81a43.50b32.18d32.10d32.25c
L40.75d1.06c0.44d31.45b31.80ab31.10b40.21d40.35b40.08d36.30b35.75b36.85a
L50.90c0.94c0.87c19.42d24.55c14.30d41.07c39.75c42.40c34.13c34.25bc34.00b
Mean1.161.261.0631.8634.0229.7043.5342.5644.5135.1835.5134.84
LSD0.050.1240.1470.2072.5183.4083.7740.3920.5470.6411.0901.571.67
CV (%)24.1318.2531.0317.8211.6420.162.022.012.286.997.017.63
Different letters within a column indicate significant differences according to LSD test (p < 0.05).
Table 5. Comparison for seed yield (SY t ha−1) in each location (L1–L5) of the lentil genotypes tested across five locations for two growing seasons (2019 and 2020) under organic cropping systems.
Table 5. Comparison for seed yield (SY t ha−1) in each location (L1–L5) of the lentil genotypes tested across five locations for two growing seasons (2019 and 2020) under organic cropping systems.
Orestiada
(L1)		Thessaloniki
(L2)		Anatoliko
(L3)		Larissa
(L4)		Domokos
(L5)
Cultivar Mean 20192020Mean 20192020Mean 20192020Mean 20192020Mean20192020
G12.12ab1.72b2.521.97a1.70c2.24a0.73a0.810.65a0.79c1.08b0.45b0.870.910.83
G22.30a2.29a2.321.35b1.69c1.00bc0.49b0.470.50a0.92a1.13a0.54a0.970.881.07
G32.19a2.41a1.971.67ab1.83bc1.50b0.57b0.640.50a0.73c1.05b0.38c0.840.910.77
G41.70b1.37bc2.041.61ab2.20ab1.03bc0.52b0.550.50a0.73c0.95c0.42b0.971.170.77
G51.68b1.14c2.231.58b2.43a0.72c0.35c0.420.27b0.81b1.09b0.40b0.870.830.90
Mean2.001.782.211.631.971.300.530.580.480.751.060.440.900.940.87
LSD0.050.4670.461ns 1.2690.3780.6440.25ns0.1800.1010.1020.025nsnsns
CV%22.916.824.422.612.232.331.531.524.220.315.319.922.818.725.7
Different letters within a column indicate significant differences according to LSD test (p < 0.05). ns, not significant.
Table 6. Seed loss percentage (SL), yield loss in t ha−1 (YL), crude protein percentage (CP), and cooking time in min. (CT) of the lentil genotypes tested across five locations for two growing seasons (2019 and 2020) under organic cropping systems.
Table 6. Seed loss percentage (SL), yield loss in t ha−1 (YL), crude protein percentage (CP), and cooking time in min. (CT) of the lentil genotypes tested across five locations for two growing seasons (2019 and 2020) under organic cropping systems.
SLYLCPCT
Genotypes Mean 20192020Mean 20192020Mean 20192020Mean 20192020
G111.66b9.70a13.6b0.16bc0.14ab0.18b25.14d25.65d24.63d25.12b25.12b25.12b
G210.50bc8.47ab12.6b0.12d0.10c0.14c25.97b26.20c25.73b24.24a23.71a24.77a
G313.60a10.00a17.2a0.19a0.16a0.22a25.78c26.36b25.19c26.87c26.87c26.87c
G410.51bc8.85ab12.20b0.17ab0.16a0.18b24.83e24.52e25.15c27.51d27.02d28.01d
G59.82c7.90b11.80b0.15c0.13b0.17b26.45a26.48a26.43a27.49d28.27e26.72c
Locations
L10.88d1.40d0.38d0.03d0.04d0.02d23.73c25.01b22.45d29.61d29.61d30.57d
L210.37b6.50b14.30b0.12b0.08b0.16b27.99a28.61a27.38a24.91a24.91a25.01b
L32.33d1.77cd2.90d0.06c0.05cd0.08c23.17d23.35d22.98c26.14c26.14c26.17c
L436.90a32.00a41.80a0.51a0.47a0.55a28.04a28.59a27.49a25.54b25.54b25.22b
L55.65c3.35c7.95c0.07c0.06bc0.08c25.24b23.65c26.84b24.99a24.99a24.52a
Mean11.239.0013.460.150.140.1725.6325.8425.4226.2426.2426.29
LSD0.051.571.5922.8230.0170.0210.2860.1140.0950.2260.1140.1580.189
CV (%)31.1628.0133.224.6823.7425.281.030.581.371.3624.221.14
Different letters within a column indicate significant differences according to LSD test (p < 0.05).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Baxevanos, D.; Kargiotidou, A.; Noulas, C.; Kouderi, A.-M.; Aggelakoudi, M.; Petsoulas, C.; Tigka, E.; Mavromatis, A.; Tokatlidis, I.; Beslemes, D.; et al. Lentil Cultivar Evaluation in Diverse Organic Mediterranean Environments. Agronomy 2024, 14, 790. https://doi.org/10.3390/agronomy14040790

AMA Style

Baxevanos D, Kargiotidou A, Noulas C, Kouderi A-M, Aggelakoudi M, Petsoulas C, Tigka E, Mavromatis A, Tokatlidis I, Beslemes D, et al. Lentil Cultivar Evaluation in Diverse Organic Mediterranean Environments. Agronomy. 2024; 14(4):790. https://doi.org/10.3390/agronomy14040790

Chicago/Turabian Style

Baxevanos, Dimitrios, Anastasia Kargiotidou, Christos Noulas, Antigoni-Maria Kouderi, Maria Aggelakoudi, Christos Petsoulas, Evangelia Tigka, Athanasios Mavromatis, Ioannis Tokatlidis, Dimitrios Beslemes, and et al. 2024. "Lentil Cultivar Evaluation in Diverse Organic Mediterranean Environments" Agronomy 14, no. 4: 790. https://doi.org/10.3390/agronomy14040790

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop