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.

The Honeycomb Selection Design (HSD) is an innovative field experimentation pattern devised for plant breeding. With individual plants widely apart (nil-competition), the HSD can evaluate numerous progeny lines (entries) and apply single-plant selection from the very early segregating generations. Due to a systematic entry arrangement, complex statistical data analysis is necessary. This article presents 'rhoneycomb', a free and open-source R package concerning the construction, visualization and analysis of HSDs.

The coefficient of variation (CV) of yield may functionally be related to the mean. The expected exponential CV decline with increasing mean, i.e., the Taylor’s power law (TPL), is not always valid. Removal of this scale dependency allows for a scale-independent assessment of stability. The objective of this study was to interpret the validity of the homeostasis index (HI), i.e., the inverse CV value, suggested in breeding under nil competition as a selection criterion for progeny lines that oppose the acquired interplant variation. Data concerning the single-plant yield of various crops under a nil-competition regime were studies against the theoretical background of the above hypothesis. Simple correlations between logarithms of variances and respective means were used to assess the reliability of CV as a stability statistic in breeding trials under nil competition. A total of 8 of the 24 case analyses revealed a systematic variance dependence on the mean. The impact was more prevalent in experiments with extensive spatial heterogeneity and high CV scores. Conversion of variance to remove systematic dependence did not validate the CV~mean negative relationship. Because of variance dependence, caution is needed when interpreting the HI as a stability index. Misuse of the HI may entail the risk of bias, upgrading or downgrading a progeny line in its ability to withstand acquired dissimilarity between plants. Testing the validity of the variance seems necessary, and the calculation of HI on a converted variance may enhance the accuracy of identifying the most promising progeny lines.

The Honeycomb Selection Design (HSD) is an innovative experimental method whose main feature is the even and systematic entry arrangement. Its systematicity, if combined with the absence of inter-plant competition that maximizes the phenotypic expression and differentiation of individual plants, enables the implementation of single-plant selection as early as the initial generations of genetic segregation, facilitating plant breeders to identify superior genotypes. Due to the specificity of entry allocation and the complexity of statistical data analysis, a specialized software becomes necessary. This article provides a detailed presentation of the ‘rhoneycomb’, a free and open-source R package concerning the construction, visualization, and analysis of HSDs.

COPYRIGHT © 2022 Tokatlidis, Chauhan and Assefa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Crop response to density: Optimization of resource use to promote sustainability

Following their entrance into natural water reservoirs, the use of pesticides may adversely affect human health and the environment. Biobeds facilitate decontamination of agricultural wastewaters. Bioaugmentation could enhance biodegradation of pesticides and addition of an adsorbent, such as natural zeolite, may increase biobed efficacy. In this study, the bioaugmentation utility of Pseudomonas putida (biomixture A) and the impact of zeolites supplement (biomixture C) in biomixtures contaminated with the fungicides fluopyram (20 mg/kg), myclobutanil (10 mg/kg), and triticonazole (30 mg/kg) were examined. A biomixture devoid of zeolites and inoculant was used as control (biomixture B). Bioaugmentation enhanced the dissipation rate of all fungicides (DT50 values of 9.5, 7.7, and 10.5 days for myclobutanil, fluopyram, and triticonazole, respectively), contrary to zeolite-treated samples in which the dissipation was prolonged. According to alpha-diveristy results, bioaugmentation treatment displayed higher microbial diversity than control samples, while the highest diversity was recorded in the case of natural zeolites. The results of beta-diversity showed that bioaugmentation and natural zeolites affected microbial diversity during the first days of the experiment via the support of gammaproteobacteria (Proteobacteria) growth.

Plant-to-plant variability is a crop stability component. The objective of this study in maize (Zea mays L.) was to test the validity of the theoretical background of the hypothesis that the coefficient of variation (CV) for grain yield per plant and mean yield are connected exponentially, following the Taylor's Power Law (TPL) Model. Field experimentation was conducted across two sites, two seasons, and two planting densities. Densities were the main plots, corresponding to the typical practice of 8.89 plants m–2 (TCD) and the low-input optimal of 5.33 plants m–2 (LCD), while hybrids were the subplots. Data from 26 subplots in the first site averaged CV values of 22.6% at the TCD and 21.9% at the LCD, and mean yields of 19.1 and 13.9 t ha–1, respectively, following the TPL Model. The same was true for the second site, with average CVs and means of 41.5% and 14.3 t ha–1 at the TCD and 36.8% and 11.5 t ha–1 at the LCD. A test was performed on the simple correlation between the logarithms of variances and their respective means to investigate whether there is a systematic variance dependence on mean, thus questioning the reliability of TPL. The validity of TPL was verified in the first site. Nevertheless, there was a systematic dependence of yield variance on mean yield in the second site, implying that the CV-yield correlation might be not biologically meaningful. Conversion of the variance to remove its dependence on the mean did not validate the CV-yield negative relationship, meaning that caution is needed when interpreting the CV as a stability index for intra-crop variation. Whether the determinant factor of invalidity of TPL was the extensive intra-crop variation in the lower yielding second site can be assessed in future research.

Lentil is an important legume crop for human and animal dietary needs due to its high nutritional value. The effect of genotype and growing environment was studied on seed yield (SY), crude protein (CP) and mineral nutrients (macro and micronutrients) of five lentil genotypes grown at four diverse locations for two consecutive years under organic and conventional farming. The location within each year was considered as a separate environment (E). Data were subjected to over environment two-way analysis of variance, while a genotype (G) plus genotype × environment (GGE) biplot analysis was performed. Our results indicated the E as the main source of variation (62.3–99.8%) for SY, CP and macronutrients for both farming systems, while for micronutrients it was either the E or the G × E interaction. Different environments were identified as ideal for the parameters studied: E6 (Larissa/Central Greece/2020) produced the higher CP values (organic: 32.0%, conventional: 27.5%) and showed the highest discriminating ability that was attributed to the lowest precipitation during the crucial period of pod filling. E7 (Thessaloniki/Central Macedonia/2020) and E8 (Orestiada/Thrace/2020) had fertile soils and ample soil moisture and were the most discriminating for high micronutrient content under both farming systems. Location Orestiada showed the highest SY for both organic (1.87–2.28 t ha−1) and conventional farming (1.56–2.89 t ha−1) regardless the year of cultivation and is proposed as an ideal location for lentil cultivation or for breeding for high SY. Genotypes explained a low percentage of the total variability; however, two promising genotypes were identified. Cultivar “Samos” demonstrated a wide adaptation capacity exhibiting stable and high SY under both organic and conventional farming, while the red lentil population “03-24L” showed very high level of seed CP, Fe and Mn contents regardless E or farming system. This genetic material could be further exploited as parental material aiming to develop lentil varieties that could be utilized as “functional” food or consist of a significant feed ingredient.

Natural selection favors the competitive ideotype, enabling native plants to survive in the face of intense competition. The productive ideotype is the goal of artificial selection to achieve high crop yields via the efficient use of resources in a self-competition regime. When breeding is established under inter-genotypic competition, the competitive ideotype dominates and may fictitiously become selectable. The productive ideotype becomes selectable at the nil-competition regime, where widely spaced individuals prevent plant-to-plant interference for any input. Principal reasons bring to the fore the productive ideotype that combines low competitiveness and improved plant yield efficiency. Crop spacing via the productive ideotype is mandated to alleviate the varying optimum density and ensure efficient use of resources inter-seasonally, cope with intra-field variation and optimize resource use, compensate for missing plants and promote stability, counteract unpredictable stresses and offer a buffer against environmental diversity, and adopt low-input agriculture to conserve natural resources and the environment. For breeding toward the productive ideotype, nil-competition is the due condition to overcome the confounding effects of competition, maximize phenotypic differentiation and facilitate selection from an early segregating generation, optimize heritability due to moderated environmental variance and experimental designs that sample spatial heterogeneity, apply high selection pressure focusing exclusively on the targeted genotype, and avoid the risk of bias selection or loss of desired genotypes due to proximity to empty hills. The view of a modern crop variety composed of genotype(s) belonging to the productive ideotype is a viable option to reach crop resilience serving sustainability in enormously fluctuating agroecosystems.

Genetic differentiation between 40 lentil genotypes was tested using molecular markers. The genotypes were produced from a Greek landrace of commercial interest via the honeycomb breeding methodology, i.e., single-plant selection in the absence of competition, across three successive pedigree generations. The selected genotypes from each generation were examined for genetic relationships using 15 SSR molecular markers with HRM analysis. As expected, low variation among consecutive generations at the level of 2.5–7.7% was detected. Analysis of molecular variance (AMOVA) revealed that partitioning of this variation was at higher percentage within each generation’s population than between them. Population structure analysis indicated that ongoing selection could effectively shift the allelic composition in each generation. The applied honeycomb breeding methodology that effectively improved progeny yield and seed quality increased the percentage of favorable alleles altering allelic composition but not eliminating genetic variation of the breeding population.

The performance of Vicia sativa L. cultivars and cultivar mixtures used as green manure under two seed rates in a Mediterranean environment was investigated in a split plot design, over the period 2014–2016. Six common vetch cultivars grown in pure stands or in mixtures were established at two seed rates (S1 = 100 Kg ha−1 and S2 = 180 kg ha−1). Growth traits (e.g., plant height, biomass yield, LAI, CGR) were monitored until incorporation into topsoil. The nitrogen (N) concentration of the incorporated biomass along with the decomposition rate were determined, and the effects on the N supply to the soil were evaluated two, four and six months after biomass incorporation. The increment in residual soil Ν four months after green manuring fluctuated from 17 to 45 kg ha−1 among cultivars, and after six months, from 22 to 50 kg ha−1. The average decomposition rate of organic substances was slightly higher for S2 for all time intervals; however the highest value (62.6%) was recorded for S1 six months after biomass incorporation. Seed rate-independent genotypes were identified with regard to the decomposition of organic matter and are suggested as being suitable for green-manuring farming systems with a low seed rate, a fact that offers options with economic and environmental benefits.

Lentil is a versatile and profitable pulse crop with high nutritional food and feed values. The objectives of the study were to determine suitable locations for high yield and quality in terms of production and/or breeding, and to identify promising genotypes. For this reason, five lentil genotypes were evaluated in a multi-location network consisting of ten diverse sites for two consecutive growing seasons, for seed yield (SY), other agronomic traits, crude protein (CP), cooking time (CT) and crude protein yield (CPY). A significant diversification and specialization of the locations was identified with regards to SY, CP, CT and CPY. Different locations showed optimal values for each trait. Locations E4 and E3, followed by E10, were “ideal” for SY; locations E1, E3 and E7 were ideal for high CP; and the “ideal” locations for CT were E3 and E5, followed by E2. Therefore, the scope of the cultivation determined the optimum locations for lentil cultivation. The GGE-biplot analysis revealed different discriminating abilities and representativeness among the locations for the identification of the most productive and stable genotypes. Location E3 (Orestiada, Region of Thrace) was recognized as being optimal for lentil breeding, as it was the “ideal” or close to “ideal” for the selection of superior genotypes for SY, CP, CT and CPY. Adaptable genotypes (cv. Dimitra, Samos) showed a high SY along with excellent values for CP, CT and CPY, and are suggested either for cultivation in many regions or to be exploited in breeding programs.

Due to environmental diversity, genotype performance for yield and stability is essential for crop improvement. The GGE biplot, and 11 parametric and non-parametric stability models were employed to evaluate 23 wheat ( Triticum aestivum L.) genotypes, tested in randomized complete block trials across two contrasting fields (sandy and loamy) and four seasons. The sandy field yielded half compared to the loamy field, reflecting relatively low- and high-input environments, respectively. Analysis of variance showed significant differences between genotypes for grain yield and crossover genotype ranking across environments; the loamy field was more representative of an overall genotype performance. The stability models resulted in diverse genotype classification and were distinguished into two separate groups. The first group comprised measures that consider both G and GE focusing on the agronomic aspect of stability and high-yielding ability. The second group included tools that consider only GE focusing on the static aspect of stability and characterized most of the high-performing genotypes as undesirable. The GGE biplot highlighted genotypes that were characterized as either desirable or undesirable following most models in both groups. Therefore, the GGE biplot presented an effective statistical tool for assessing wheat genotypes in terms of general and specific adaptation without overlooking yielding ability. It is suggested the preference of favorable experimental conditions and application of the GGE model to identify genotypes that are more promising for stable performance across wide agroecosystems.

Previous research has found that, based on Taylor?s Power Law (TPL), the coefficient of variation (CV) of yield data may functionally be related to the mean, with an exponential mean yield decline with increasing CV. Recent proposals have been made how this scale-dependency can be removed in order to allow a scale-independent assessment of stability. The theoretical background of the hypothesis was studied in wheat (Triticum aestivum L.) throughout data obtained from two fields of the farm of the Mendel University of Brno, Czech Republic. Soils of the two fields were of the contrasting loamy and sandy textures. Yield variation was intense in the sandy field representing a low-input agro-ecosystem. TPL became more obvious when the two fields were considered together, so as to enlarge the range of means and CVs. On the simple correlation between logarithms of variances and respective means, there was a systematic dependence of yield variance on mean yield when the within-block CV was considered. Conversion of variance to remove dependence on mean did not validate the CV -yield negative relationship meaning that caution is needed when interpreting the CV of yield as a stability index. On the other hand, when the genotype CV for yield was considered variance was independent of the mean indicating agronomic essence in the CV- yield relationship, and coupled with the POLAR statistic, based on the negative residuals from the linear TPL regression that reflect low variability, revealed genotype cases exhibiting both high yield and stability. The findings corroborated that TPL is not always valid depending on scales and factors structuring the data. Interpreting crop variation via CV may entail a risk of bias due to variance dependence on mean, whereas the POLAR index offers an alternative stability measure that allows straight-forward interpretation providing the basis for developing more stable cropping systems. A trade-off between yield and stability does not exclude genotype cases of simultaneous occurrence of both, pinpointing realism in the pursuit of stable varieties without compromising yield.

Plant yield efficiency (PYE) reflects the ability of the single-plant to respond to additional inputs and is fully expressed at the nil-competition regime (an ultra-low density to preclude inter-plant interference for inputs). The purpose of this study was to determine if PYE could prevent the erratic optimum plant density-yield interaction effect in maize (Zea mays L.). Seven hybrids were evaluated across five environments at four densities, under both the normal-input regime (NIR) and low-input regime (LIR). Plant yield efficiency was measured at the lowest density approaching the nil-competition regime (0.74 plants m(-2)), while crop (per area) yield potential was estimated at the highest density corresponding to the typical farming density in the NIR (8.89 plants m(-2)). In terms of optimum density, the hybrids varied extensively in the NIR (6.64-8.81 plants m(-2)) but performed similarly in the LIR (5.11-5.61 plants m(-2)). The hybrid displaying the highest PYE also had high harvest index (HI) and low anthesis to silking interval (ASI) and was proved the most stable according to various stability statistics including the genotype and genotype by environment (GGE) biplot model. In conclusion, crop yield by density interaction is a matter of hybrid. Hybrids with low PYE have inconsistent optimum density, which is a causal factor of yield loss in rainfed maize. High PYE improves hybrid flexibility and performance at low densities ultimately enhancing crop resilience to extremely fluctuating environments.

The study pertains to field experimentation testing seven maize (Zea mays L.) hybrids at four densities, across five locations under normal (NIR) and low-input (LIR) regimes. The main objective was to assess the prognostic value of plant yield efficiency by homeostasis (PYEH) for breeding purposes at ultra-low plant density to predict hybrid yield potential and stability. PYEH comprises plant yield efficiency (PYE) that reflects the ability of individual plants to exploit resources, and plant yield homeostasis (PYH) that indicates the crop’s ability to evade acquired plant-to-plant variability. The same hybrids were also evaluated for stability by commonly used parametric and non-parametric statistics based on data at low (LCD) and high crop densities (HCD). Hybrid stability focused on potential yield loss due to erratic optimum density (OD). Most methods produced conflicting results regarding hybrid ranking for yield and stability especially at LCD. In contrast, PYEH consistently highlighted high-yielding and stable hybrids, potentially able to reach the attainable crop yield (ACY) inter-seasonally irrespective of crop spacing. Low density is common practice under resource-deficit conditions, so crop adaptation to crop spacing is a viable option to overcome erratic OD that constitutes a root source of crop instability in rainfed maize. The results were further supportive of breeding at ultra-low density to facilitate the identification and selection of superior genotypes, since such conditions promote phenotypic expression and differentiation, and ensure repeatability across diverse environments.

Twenty-three winter wheat (Triticum aestivum L.) varieties were tested in randomized complete blocks for four seasons in two fields (sandy and loamy soil), of the Mendel University in Brno, Czech Republic. Objectives were to find genotypes combining high grain with protein yield and cover the protein content standards. Stability was assessed on the coefficient of variation (CV) of grain yield and plant height (plot-to-plot variability), the level a variety lagged behind the maximum yield attained within a block (yield-gap index), and the GGE biplot analysis. Sandy and loamy fields simulated low- and high-input agroecosystems, respectively. The sandy field averaged 5.47 t/ha grain yield and 15.25% grain protein content, while the respective values for the loamy field were 10.4 t/ha and 13.17%. In the loamy field, nine varieties failed to meet the protein content benchmark of 13% for the breadmaking industry. The CV of plant height rather than of grain yield was a good criterion to show the effects of crop variation on yield and stability. The yield-gap index constituted a good measure of general adaptability to fluctuating conditions. Some varieties showed specific adaptation to sandy or loamy soil. On basis of CV of plant height, yield-gap index and GGE biplot, there were also varieties with general adaptability. Genotypes were found to have high protein content irrespective of grain yield, so selection of varieties with good yielding performance and protein concentration appears to be a sound pursuit.

Stability of maize (Zea mays L.) hybrids has been major research topics. So far, more emphasis has been placed on the per area grain yield variation, rather than on the plant-to-plant variability within the crop stand, i.e. intra-crop variation. The acquired intra-crop variation of different hybrids was recorded across five environments via the coefficient of variation (CV) of plant-to-plant variability for seven agronomic traits. Hybrids were less variable for plant height (PH) with CVs ranging up to 23%, and most variable for grain yield (GY) with a CV ceiling of 96%. A general negative association of plant-to-plant variability with GY pinpointed hybrids prone to intra-crop variation to be less efficient to capture the available inputs. An adverse affection of intra-crop variation on aboveground biomass (AB) was relatively moderate, while on harvest index (HI) occasional (albeit consistent in one environment). A test established to search whether variance and CV were systematically dependent on mean reflecting a meaningless CV ~ mean negative relationship, was occasionally positive only for AB but not for GY and HI, strengthening thus the value of CV for plant-to-plant variability as a measure of stability. There was also a tendency relationship of AB, GY or HI with interplant variation to follow the exponential than the linear declining pattern, i.e. the Taylor power law (TPL) model. The adverse effect of the interplant variation was more pronounced on GY compared to AB and HI. Consequently, crop stand uniformity seems imperative for optimal use of resources. The findings brought into the forefront the single-plant performance as a crucial breeding goal to moderate the intra-specific competition and acquired interplant variation.

Landraces are a valuable source of genetic variability for breeders to develop high-yielding lentil varieties. Apart from productivity, simultaneous breeding for lentil seed nutritional quality is of paramount importance for wider lentil consumption. This work examined the indirect effect of single plant selection for high yield on important seed quality traits within three Greek lentil landraces (“Elassona” (EL), “Lefkada” (L), and “Evros” (EV)). The breeding methodology applied was proved to help either maintain or improve such characteristics in the high-yielding second-cycle lines (SLs) selected. Compared to the parental landrace “Elassona”, the high-yielding lines showed increased crude fiber by 30–110%; the line 2-SL-EL-6 had higher starch content by 3.9% and reduced cooking time by 6.67 min, while the 2-SL-EL-10 line had higher crude fiber by 73%. In the case of “Lefkada”, the high-yielding lines selected maintained the protein content present in the parental landrace, apart from the 2-SL-L-1 where a decrease by 5% was recorded; however, most of them showed increased crude fiber (5.59–7.52%) in comparison with the parental landrace (4.65%). Finally, in relation to the “Evros” parental landrace, the 2-SL-EV-3 and 2-SL-EV-4 showed higher crude fiber and reduced cooking time. This study provides evidence that proper management of genetic variability could improve productivity without compromising or sometimes improving some seed quality traits.

Nil-competition (ultra-low plant density) has been asserted to highlight individual genotypes of high yielding potential. This was tested on three lentil (Lens culinaris Medikus) landraces originated from different regions of Greece, germplasm presumably comprising mixtures of homozygous genotypes due to the self-pollinating nature of the crop. Single-plant selection under ultra-low density (interplant distance of 50 or 80 cm) resulted in first- and second-generation sister lines. Progeny testing was conducted in three locations, while the final evaluation at farming density included an additional marginal environment. Wide interplant distance accelerated phenotypic expression of susceptibility to viruses, reflected by high coefficient of variation of single-plant yields. Compared to the mother populations, higher yields combined with reduced virus incidence was observed in the first-generation sister lines, and even higher yields in the second-generation lines partly attributable to further improvement of their sanitary status. Remarkably, at the farming density across five environments, second generation sister lines had mean grain yields by 8, 10 and 20% higher compared to their respective ancestors. Individual sister lines exhibited up to 32% higher yields and stability in ‘agronomic’ terms, i.e. on both the GGE biplot model and regression approach of G×E interaction. In conclusion, the procedure appears an efficient tool that allows the breeder to exploit the natural genetic variability within landraces and develop in short-time pure-line varieties adaptable to a wide range of conditions.