Search

Key message The morpho-agronomic and genetic studies recorded variations in vegetative and reproductive traits, and in molecular information through population structure and clustering approaches among South African Phaseolus vulgaris landraces. Abstract Phaseolus vulgaris L., commonly known as common beans, is widely used for its edible leaves, immature pods, and dry seeds. Studies on variation in morphology and genetics among P. vulgaris landraces are limited in South Africa. Therefore, the current study aimed to determine the morpho-agronomic and genetic variations among P. vulgaris landraces. Thirty-eight landraces from different agro-ecological origins, planted in a randomized complete block design, had their variation in vegetative and reproductive traits determined. These landraces were studied for their genetic diversity using simple sequence repeat (SSR) markers. The landraces were clustered in a biplot and dendrogram based on their seed coats, shape, similar morpho-agronomic traits, and their areas of origin. A total of 57 alleles were produced with a mean of 3.64 per SSR locus. The polymorphism information content ranged from 0.00 to 0.58. The population structure had the highest delta value K = 2, thus the 38 landraces were divided into two subpopulations based on the Bayesian approach. The population structure showed an overlap among the landraces as several from the Mesoamerican carried some seed traits or genes from the Andean gene pool, and showed a high level of admixtures. The principal coordinate analysis and the dendrogram had a similar clustering pattern as the population structure. This study revealed the potential markers with high diversity that can be used to determine genetically homogenous/heterogeneous landraces. Therefore, the use of PV-ctt001, PV-ag001, and PV-at003 could be beneficial in future breeding, conservation, and marker-assisted selection studies.

In multi-environment trials (METs), the study of genotype × environment (GE) interaction is important for identifying high-yielding and stable or specifically adapted genotypes. The main objective ...

Resumen Introducción. La arveja (Pisum sativum L.) es una leguminosa de gran importancia para la economía campesina de la zona altoandina del departamento de Nariño, Colombia. La mayor parte de la producción de arveja en este departamento, se comercializa en vaina en fresco; sin embargo, es necesario fortalecer el mercado local de grano seco, para eso, se requiere de variedades adaptadas a las condiciones regionales. Objetivo. Evaluar quince genotipos de arveja por la estabilidad fenotípica del rendimiento en grano seco, en la zona productora de Nariño. Materiales y métodos. Entre el 2013 y 2014, se evaluaron quince genotipos de arveja en tres ambientes de Nariño. Los experimentos se establecieron con un diseño de bloques completos al azar y cuatro repeticiones. Para el análisis de estabilidad de rendimiento en grano seco se utilizó el modelo GGE biplot. Resultados. En el análisis de varianza, a través de los tres ambientes, se presentaron efectos significativos (p

With regards to the importance of drought stress and genotype screening under stress conditions, the current study was conducted to evaluate barley varieties in response to drought stress and find ...

Under the climate change scenario, high-temperature stress declines crop yields. The present study was designed with the objectives to identify the stable heat-tolerant genotypes for grain yield under normal and heat stress conditions at three locations. The 80 mung bean genotypes and 3 check varieties were sown in three agro-ecological zones of Pakistan, namely, Chakwal, Piplan, and Bhakkar. The experiments were planted in two sowing dates, i.e., the normal sowing and late sowing by using an augmented randomized block design with 6 blocks at each location. The plot size for each genotype was 2.4 m2 at all the locations. All cultural and agronomic practices were carried out as per recommendations. Ten randomly selected plants were used to record the yield-related traits. An infrared gas analyzer (IRGA) was used to measure physiological traits like photosynthesis rate and stomatal conductance. To study the nature of relationship among traits, Pearson correlation analysis was done using SPSS software (v. 16.0). The GGE biplots were constructed to graphically represent the genotype and GE interaction of multi-environment data using the software plant breeding tools. Three views of the GGE biplot were generated to display the comparative performance, rank, and stability of each genotype in the tested environments. Correlation and principal component analysis (PCA) revealed a positive interaction of grain yield with pods plant−1, seed pod−1, 100 grain weight, and harvest index, while days to flowering and maturity had a negative association under normal and heat-stressed conditions. The GGE biplot analysis efficiently explained 85.1% of the total variation and demonstrated that among 8 vertex genotypes of polygon biplot, the G14 and G38 were heat tolerant as these genotypes outclassed others in all the heat-stressed environments. In contrast, the genotypes G51 and G74 produced maximum yield under normal conditions (E3 and E5). Among all the genotypes, genetic diversity was observed for grain yield and other morpho-physiological traits. Significant G × E interaction was observed. The genotypes G14, G38, and G51 were identified as stable and heat tolerant in most of the environments. These findings will be helpful for development of heat-tolerant mung bean breeding material in the future.

The subjectivity of index selection and unpredictable environmental variabilities cause ambiguities in multiple-trait selection across environments. In the present study, we tested and validated a recently proposed multivariate approach for genotypic selection based on multiple trait evaluations. A genotype by yield*trait (GY*T) biplot approach originally proposed was validated in this study to assess the comparative efficiency of GY*T approach over genotype by trait (GT) approach in genotypic selection on multiple traits in common bean. The analysis is based on six trials spread over four years from 2015–2018 at two locations. Based on the GY*T analysis, the biplot could improve the variation accounted for by PCI and PC2 from 71.37 to 92.81%. The genotypes WB-341, WB-1634 and WB-451 were identified as high yielding and possessed higher seed yield on account of higher values of pods per plant and seeds per pod indicating that these possess the ideal trait combination of yield, pod number and seeds per pod. The genotype WB-222 had the ideal trait combination of yield, pods per plant and plant height. Similarly the genotype WB-185 had the ideal trait combination of yield and 100-seed weight but was poor for pods per plant. The genotype WB-83 had an opposite trait profile to WB-185 especially for 100-seed weight, but was good for pods per plant, even though both had comparable yield levels. Based on the results of present study and few earlier studies, we conclude that GY*T approach based on multi-trait data is comparatively more efficient than GT biplot.

SummaryThe experiment was carried out in three crop cycles as plant cane, first ratoon, and second ratoon at five locations on Florida muck soils (histosols) to evaluate the genotypes, test locations, and identify the superior and stable sugarcane genotypes. There were 13 sugarcane genotypes along with three commercial cultivars as checks included in this study. Five locations were considered as environments to analyze genotype-by-environment interaction (GEI) in 13 genotypes in three crop cycles. The sugarcane genotypes were planted in a randomized complete block design with six replications at each location. Performance was measured by the traits of sucrose yield tons per hectare (SY) and commercial recoverable sugar (CRS) in kilograms of sugar per ton of cane. The data were subjected to genotype main effects and genotype × environment interaction (GGE) analyses. The results showed significant effects for genotype (G), locations (E), and G × E (genotype × environment interaction) with respect to both traits. The GGE biplot analysis showed that the sugarcane genotype CP 12-1417 was high yielding and stable in terms of sucrose yield. The most discriminating and non-representative locations were Knight Farm (KN) for both SY and CRS. For sucrose yield only, the most discriminating and non-representative locations were Knight Farm (KN), Duda and Sons, Inc. USSC, Area 5 (A5), and Okeelanta (OK).

Article Details: Received: 2020-11-30 | Accepted: 2020-12-09 | Available online: 2021-06-30 https://doi.org/10.15414/afz.2021.24.02.117-123 Multi-environment trials were conducted in two locations (Algiers and Setif) during two crop seasons in order to assess the responses of 17 genotype of barley (Hordeum vulgare L.) by evaluation of genotype-by-environment interactions (GEI) on grain yield and determine the stable genotypes. Results showed significant (p

Assessing the effectiveness of different molecular markers is essential for identification of appropriate ones for crop improvement and conservation, hence, inter-simple sequence repeat (ISSR) and start codon targeted (SCoT) markers were used for this study. Sixty-six accessions with different genomes obtained from International Transit Center, Belgium, were used for DNA extraction, amplification with ISSR and SCoT markers and agarose gel electrophoresis. The reproducible bands were scored for analyses. We identified high allelic richness of 299 (ISSR) and 326 (SCoT). Polymorphic information contents (ISSR: 0.9225; SCoT: 0.9421) were high but SCoT exhibited higher level of informativeness. The two markers demonstrated high percentage polymorphic loci (ISSR: 91.21–100%; SCoT: 96.97–100%). Other genetic indicators including effective number of alleles, Nei’s genetic diversity, and Shannon information index were higher in SCoT and further elucidated the usefulness of the markers. Intraspecific genetic diversity, interspecific genetic diversity, coefficient of gene differentiation and level of gene flow revealed extensive gene flow and larger variability within the accessions. Both ISSR and SCoT grouped the accessions via dendrogram, biplot and structure analyses. Though the two marker systems varied in their informativeness, they demonstrated high effectiveness in resolving genetic diversity (GD) of the different accessions, with higher efficiency in SCoT markers. Due to higher GD indices exhibited by SCoT, AS is the most genetically endowed one. Our study showed that SCoT markers are more informative than ISSR for GD exploration, assessment and cluster resolution of Musa species, thereby revealing the potential of SCoT markers for improved breeding and conservation.

Coffee is the stimulant crop plant that belongs to the genus Coffea, in the family Rubiaceae. The yield variation and pattern of the crop varied within short geographic distance and thus attributed to low productivity and unpredictable production. The objective of this study was to determine the extent of genotype × environment interaction on yields of promising Teppi coffee genotype. In this study, seventeen Arabica coffee genotypes, representing coffee growing areas of Teppi, southwestern Ethiopia were evaluated at six environments. This study was conducted by using completely randomized block design with two replications. The extent of genotype × environment interaction was analyzed by using additive main effect and multiplicative interaction model and genotype main effect and genotype × environment interaction biplot analysis model. The analysis of variance for yield revealed the presence of highly significant differences ( P < 0.01 ) within genotypes, environments and genotype by environment interaction. From the total sum of squares of genotype × environment interaction of yield, environment accounted for 51% while genotypic and GEI effects were 20.5% and 25.32%, respectively. Generally, genotypes with superior performance and suitable environments for coffee were identified. However, further researches have to target hybridization of those superior genotypes with elite lines in feature Arabica coffee breeding program.

This study assessed the genetic diversity of African yam bean (AYB) accessions using morphological and molecular markers. The accessions were grown, and morphological data collected were subjected to analysis of variance and multivariate analyses. Genomic DNA extracted from the accessions were amplified with inter simple sequence repeat (ISSR) markers. The diversity analysis was conducted using MEGA4 software. The accessions varied significantly (P < 0.05) in growth, flowering and seed-related parameters. Flowering commenced early in most accessions. Weight of 100-seed range from 15.01 to 21.15 g with the mean value of 18.30 g. Significant correlations existed between stem height, the number of leaves and leaf dimensions. Also, days to flowering correlated with pod formation; likewise, seed dimension had a positive association with seed weight. The principal biplot revealed that two components accounted for 41.77% of the observed variation. Analysis of the electropherogram showed 95 loci comprising 1351 alleles were detected by the ISSR markers with 65.26% polymorphism and combined polymorphic information content of 0.85. The principal coordinate analysis placed accessions together on a plane based on their spatial relationship. The dendrogram showed accession pairs (TSs-77, TSs-95) and (TSs-111, TSs-84) are closely related. The phylogram identified three kinships with a total length of 454. Accession TSs-115 is likely the progenitor while TSs-82 and TSs-86 are the most recent. The study concluded that a combination of morphological and ISSR markers is effective for the diversity study of AYB and the existing, genetic diversity in the accessions could be harnessed for its improvement, conservation and utilization.

Cowpea bean is a common crop quite appreciated in Brazil. However, it is known that its low yield depending on soil and climatic conditions. The aim of this research is the selection of high-performance black-eyed cowpea through GYT biplot analysis. Twelve (12) cowpea bean strains were assessed between 2016 and. The study followed a randomized complete block design, with four repetitions; two commercial cultivars were used ascheck. The following variables were assessed: number of days required for flowering, final stand, value for cultivation, pod yield, pod length, mean number of grains per pod and grain weight. The analysis of variance showed genetic variability between strains, differences between the assessed years and different strain-performance in each growing year. Based on the GYT biplot graph, cultivar BRS Itaim, and strains L9 and L7 recorded the best performance for the set of yield characteristics. Variable ‘pod length’ had positive correlation to bedding, number of days required for flowering and number of grains per pod. Variable ‘100-grain weight’ had positive correlation to final stand, pod yield and mean grain weight per pod. Strains L9, L7 and cultivar BRS Itaim recorded the best yield regarding the assessed variables

Despite much effort, little exotic germplasm has been introduced in temperate maize breeding programs. Tropical and subtropical maize germplasm from CIMMYT is the most genetically diverse in the world and should be useful in widening the genetic base of temperate maize germplasm. Extracting the most suitable material from exotic germplasm is a tough challenge for plant breeding programs. This study aimed to determine the optimal number of testers to screen a maize line derived from tropical and subtropical CIMMYT germplasm. For this purpose, four temperate maize testers (MO17, B73, K47/3, and K18) and nine lines from CIMMYT were crossed. The 36 crosses along with two hybrid checks were assessed in a randomized complete block design with three replications at four maize growing regions in Iran. The GGE biplot procedure and methodology proposed by Fan et al. (2010) were used to analyze the results indicating a possibility of direct use of 2, 3, 4, and 9 CIMMYT lines to improve grain yield in temperate environments. Temperate maize testers B73 and K47/3, which belong to the Reid Yellow Dent heterotic group, were very efficient in selecting superior tropical and subtropical CIMMYT germplasm, whereas testers MO17 and K18 from the Lancaster Sure Crop heterotic group were fairly good and weak, respectively. Actually just one temperate tester could select most, if not all, of the top exotic tropical and subtropical lines, and a second tester would provide backup.

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.

In the present study, fifty genotypes were evaluated over 5 years. The analysis of variance revealed significant interaction (P

African yam bean (AYB) is an affordable protein source capable of diversifying the food base in sub-Saharan Africa. However, research efforts made towards the crop's improvement and in expanding production are limited. This study characterized 169 AYB accessions at Jimma, Ethiopia, using 31 phenotypic characters. The analysis of variance revealed highly significant (P< 0.01) differences for days to 50% flowering, days to first flowering, leaf area, number of seeds per pod, pod length, seed thickness, total seed weight, petiole length and significant (P< 0.05) difference for terminal leaf length. Accession TSs62B produced the highest number of seeds per pod (17.65) and recorded the highest 100 seed weight (25.30 g), while 3A was the earliest to flower at an average of 84.50 d. Principal component analysis (PCA) of qualitative traits attributed 77.6% of observed variations to the first five principal components, of which the first two PC axes accounted for 53.6% of total variations. Cluster analysis and PCA biplot distinctly grouped the accessions into two major groups, cluster I had the highest number of accessions (108). The analytical approaches used confirmed considerable diversity across the germplasm with a distance matrix ranging from 0.37 to 0.85. The extent of diversity reflected in the current study provides breeders the baseline information to design breeding strategies, which might help identify materials for release as variety or parental lines for hybridization programmes.

To study the impact of terminal moisture stress, thirty-one genotypes of mungbean were screened under irrigated and moisture stress at pod filling stage during summer 2013–2014 in randomized block design. Mean performance revealed RMG 492 and GVIT-203 as best genotypes under irrigated and moisture stress, respectively. Individual and combined ANOVA revealed significant difference among the genotypes. The genotype-trait (GT) biplot analysis was done using multiple traits for irrigated and moisture stress separately. The polygon view of GT biplot revealed that AKM 9904, IPM-02-19, LGG 528, PUSA VISHAL and RMG 492 in irrigated condition; RMG 492 and GVIT-203 in moisture stress as best genotypes. The genotypes VG-6197A, IPM-02-19, COGG 974, ML 145 and PUSA 9531 under irrigated condition; EC-396117, RMG 492 and VG-6197A under moisture-stress condition are close to ideal genotypes. The harvest index (HI) was identified as the best and stable trait for all genotypes under both irrigated and moisture-stress conditions. The relationship among characters showed that seed yield per plant is positively associated with HI and relative water content under both irrigation regimes. The seed yield per plant and specific leaf area are more discriminative under both conditions, whereas SCMR and chlorophyll content are more representatives under irrigated and moisture stress, respectively. The genotypes RMG 492 and VG-6197A identified in this study could be used in different breeding programs and traits SCMR and harvest index portrayed in this study can be used for screening of moisture-stress tolerance in different selection processes.

While oat is a major cold-climate cereal in Marmara region of Turkey, little is known about the nitrogen use efficiencies (NUE) of Turkish oat cultivars. This research aims to evaluate NUE, grain yield, and several agronomic traits of 5 oat cultivars in 5 nitrogen applications doses. Field trials were conducted in the Canakkale, Turkey for two growing seasons where NUE, grain yield, plant height, panicle length, grain number per panicle, and grain weight per panicle were determined. Results of variance analysis indicated statistical significance for year, genotype, N dose, and Genotype x N dose effects for all traits. New oat cultivars (Kahraman, Yeniceri, and Sebat) were found to have higher grain yield and NUE compared to the older cultivars (Chekota and Seydisehir). Grain yield response of each cultivar to the increasing N levels were further investigated with regression analysis and Princial Component Analysis (PCA) Biplot. Results suggested that Kahraman has the highest grain yield potential when Sebat and Yeniceri can be recommended for the higher N doses. We conclude that oat breeding efforts of the last decade may have contributed to higher NUE along with improving grain yield of oat cultivars in Turkey.

In Bangladesh, maize stands second place after rice; since it faces diverse natural calamities during its highest growing season (rabi/winter), particularly strong storm during the reproductive stage. Sometimes in some regions, this crop is completely damaged by natural disasters. Considering the burning issue, thirteen hybrids, including 10 previously selected short stature hybrids were evaluated against three local and standard checks: ‘BHM-9’, ‘981’ and ‘Sunshine’ in two consecutive years in seven locations of Bangladesh. Combined analysis over locations and seasons instigated that genotypes ‘Sunshine’, ‘981’ and ‘G10’ were the top-high yielders, while genotypes ‘G1’, ‘G2’, ‘BHM-9’ and ‘Sunshine’ were found the most stable. On the other hand, five genotypes such as ‘G3’, ‘G4’, ‘G6’, ‘G8’ and ‘G9’ had the below-average mean yield and the genotypes ‘G6’ and ‘G9’ were the most unstable. Among the seven environments, Jamalpur, Joydebpur and Dinajpur were most discriminating and Ishwardi was the least discriminating; whereas Joydebpur was more representative and Borishal was the least representative of other test environments. In the case of plant and ear height, most of the genotypes showed a lower value than all the checks, which was desirable. But among the top three high yielders, local cross-genotype, the ‘G10’ had the lowest and more stable value for both plant height and ear height. Therefore, considering the plant and ear height, grain yield, and yield stability, the genotype ‘G10’ has been recommended for release as commercial variety and has been released as new maize variety in Bangladesh with the local name of ‘BARI Hybrid Maize-16’ (BHM-16).

Profitable canola production depends upon an adequate supply of nitrogen (N) and sulfur (S) fertilizer. However, agricultural soils in eastern Canada seldom receive S fertilizer and rely on inherent S supply from biogeochemical and anthropogenic sources, which could be a yield-limiting factor for canola. The objective of this study was to document N × S fertilizer interactions to develop site-specific best management practices for canola production in eastern Canada. Factorial field experiments were conducted for four consecutive cropping seasons at five locations across eastern Canada. The N × S and N × S × environment interactions on seed yields and yield-related traits were visualized with the graphical analytical genotype and genotype-by-environment biplot tool. The highest seed yield was achieved with 150 kg N ha−1 together with 20–40 kg S ha−1 in 15 of the 19 test site-years. Seed yield was closely related to harvest index and number of seeds pod−1. Fertilizing canola with 20 kg S ha−1 enhanced the nitrogen use efficiency (NUE) of canola receiving 75 kg N ha−1, but the benefit of S fertilizer on NUE was negligible when N fertilizer rates increased to 150 kg N ha−1. Since canola yield response to fertilizer inputs varied from one site to another, due to the variation in environmental and soil conditions, we conclude that N × S fertilizer recommendations for canola production need to be developed on a site-specific basis in eastern Canada.

Garden pea is an important off-season vegetable in the hills of North Western Himalayas that provides high remuneration to growers. Lack of high yielding wider adaptive varieties restricts its productivity. To identify high yielding stable genotypes across environments, 48 lines were evaluated in Alpha Lattice design over seven diverse environments spreading in five locations from sub-tropical to dry temperate conditions of Himachal Pradesh during winter 2016–2017 (5 environments) and one location each during summer 2017 and winter 2017–2018. The highest mean pod yield over environments was produced by SP-3 (83.35 g) which was 20% higher over check Pb-89 while SP-6 (79.45 g) and SP-22 (76.90 g) got second and third rank with significant increase of 15 and 11% for pod yield over the check, respectively. The joint regression analysis depicted significant G × E (linear) interaction for pods/plant and pod yield/plant. Of the seven genotypes depicting stability for pod yield, SP-6 showed stability (bi = 1 and S2di = 0) for all the traits whereas SP-22 showed the same for seeds/pod and shelling percentage. G + GE biplot described Dhaulakuan as the most representative and discriminating environment for pod yield. In the mean vs. stability GGE biplot, SP-6, SP-22 and SP-17 were highly stable while SP-3 possessed medium stability. ‘Which won where’ polygon view represented SP-3, SP-6 and SP-22 as most stable for yield and pod attributes across environments, constellated into majority of the environments and can be released for commercial cultivation.

Cotton is one of the most important crops among natural fibres. Fibre quality determines the spinning ability, which is negatively correlated with yield and yield-contributing traits. Limited efforts have been made to improve fibre quality and yield in diploid cotton. Therefore, screening a large panel of germplasm lines can help identify genotypes with better fibre quality and yield. We evaluated 712 desi cotton genotypes for fibre quality traits. The genotypes showed a significant difference for all the traits, suggesting considerable variability for fibre quality improvement. Fibre length and strength showed high phenotypic and genotypic coefficients of variation. Heritability was high for fibre strength, length, and elongation. Fibre length and strength were positively correlated; however, micronaire was negatively correlated with these two traits. Superior accessions were identified for fibre length (11), strength (20), uniformity (7), and elongation (25) among genotypes. Most of the desi lines (71%) had medium micronaire values. Twenty accessions identified were ideal for spinning, showing the fibre strength-to-length ratio of one. Cluster analysis based on Euclidean distance grouped all 712 accessions into four major clusters. Principal component analysis biplot revealed that accessions AC3418, 360-SP1, AC3522B, Kanpur A, Gao16CB-9, and AC3370 were genetically diverse. The superior accessions for fibre quality identified in this study are potential lines for the diploid cotton improvement programme.

META-R (multi-environment trial analysis in R) is a suite of R scripts linked by a graphical user interface (GUI) designed in Java language. The objective of META-R is to accurately analyze multi-environment plant breeding trials (METs) by fitting mixed and fixed linear models from experimental designs such as the randomized complete block design (RCBD) and the alpha-lattice/lattice designs. META-R simultaneously estimates the best linear and unbiased estimators (BLUEs) and the best linear and unbiased predictors (BLUPs). Additionally, it computes the variance-covariance parameters, as well as some statistical and genetic parameters such as the least significant difference (LSD) at 5% significance, the coefficient of variation in percentage (CV), the genetic variance, and the broad-sense heritability. These parameters are very important in the selection of top performing genotypes in plant breeding. META-R also computes the phenotypic and genetic correlations among environments and between traits, as well as their statistical significance. The genetic correlations between environments or traits can be visualized in a biplot graph or a tree diagram (dendrogram). Genetic correlations are very important for identifying environments with similar behavior or making indirect selection and identifying the most highly associated traits. META-R performs multi-environment analyses by using the residual maximum likelihood (REML) method; these analyses can be done by environment, across environments by grouping factors (stress conditions, nitrogen content, etc.) and across environments; the analyses across environments can be done with a pre-defined degree of heritability.

The evaluation of genetic diversity in a gene pool contributes to the effective selection of genotypes and truncates the breeding time. In the present study, 96 durum wheat genotypes were evaluated for (1) genotypic diversity using two DNA marker systems: CAAT box-derived polymorphism (CBDP) and inter-simple sequence repeats (ISSR) markers and (2) phenotypic diversity using several drought-adaptive traits under rainfed conditions for two cropping seasons. Our results revealed that the ISSR primers produced 99 bands across the genotypes, the average of which was 6 fragments per primer, whereas CBDP primers generated 70 polymorphic bands, with an average of 5.3 fragments per primer. The values of marker index (MI) and polymorphism information content (PIC) indicated that CBDP markers were more efficient than ISSR markers for detection of genetic diversity in durum wheat. The average of PIC values for the CBDP and ISSR markers were 0.40 and 0.23, respectively. Based on the values of Shannon index (I) ICARDA population had a higher than CIMMYT population and the heterozygosis values confirmed these results. Dendrogram was created with a neighbor-joining clustering method. The studied genotypes were divided into five and seven groups based on data from the CBDP and ISSR markers, respectively, which was supported by principal coordinate analyses. The results of genotype by trait biplot analysis showed different patterns of interaction accessions and traits across years. High level of diversity among the studied accessions based on each studies trait was observed.

It is difficult to identify superior muskmelon (Cucumis melo L.) genotypes due to their interaction in different irrigation regimes, but assessing their interaction with drought is important in ide...

To identify appropriate genotypes for breeding varieties suitable for semi-arid areas, yield components, as well as morphophonological traits of 127 safflower genotypes, were assessed at the Seed and Plant Improvement Institute, Iran. Cluster analysis revealed four distinct groups. The largest group (third cluster) includes 48 genotypes with characteristics such as mostly yellow-coloured flowers, mostly thorny with few thistle genotypes, relatively early-flowering, relatively high number of heads and grains per plant, highest 1000-grain weight with the highest grain yields. In contrast, the smallest group (second cluster) contains 13 genotypes with characteristics such as yellow florets, semi-dwarf, thorny, moderately late-flowering, low number of heads per plant and number of grains per head, low 1000-grain weight, and low grain yield. Principal component analysis results showed that the three components accounted for 29.5, 15.9 and 11.1% of the total variation, respectively. Graphic representation of the biplots based on the first and second principal components showed that the experimental genotypes were classified into four groups. The results also indicated that stem diameter, the number of branches, heads per plant and grains per head had the highest correlation with the first component, and grain yield, flower colour, flowering initiation and plant height had the highest correlation with the second component. These traits can be considered as an appropriate index for the selection of elite safflower genotypes for breeding varieties suited to cultivation in semi-arid conditions.

Cassava root yield under diverse environments is influenced by morpho-physiological traits that are in turn influenced by genotype, environment, and genotype × environment interaction (GEI). Most GEI analyses in cassava have been limited to root yield with less emphasis on stability of other yield-related traits. This study was carried out to assess the effect of GEI on some morpho-physiological traits in cassava and key traits that are useful for selection in different environments. The study utilized 20 cassava genotypes evaluated in six environments, namely, Fumesua 2013 and 2014, Nyankpala 2013 and 2014 (irrigation), and Nyankpala 2013 and 2014 (no irrigation). The genotypes were arranged in a randomized complete block design (RCBD) with three replications and assessed for a number of morpho-physiological traits, root yield and yield components. The data were subjected to analysis of variance using SAS statistical package. Subsequently, the GGE biplot was used to carry out genotype × environment, genotype × trait, and environment × trait interaction analyses to determine the specificity of genotypic performance and traits linked to particular environments. The results indicated significant (P<0.05) GEI effect on all traits except leaf temperature. The genotype × trait biplot analysis revealed closely related traits and those associated with root yield and genotypes that show weakness in these traits. Both environment × trait biplot and principal component analyses identified traits with higher discriminatory power among genotypes in various environments particularly in the dry environments. These can be used as secondary traits to select drought-tolerant genotypes. From this study, the GGE biplot was useful in identifying traits associated with specific genotypes in different environments to aid in selection. Moderate broad-sense heritability estimates werefound for storage root yield, harvest index, plant height, ratio of storage rootlength to girth, and cassava mosaic disease score indicating that progresscan be made through selection for these traits.

Inadequate information is available on trait relations and profiles of sorghum genotypes, yet this information is vital for precise decisions to be undertaken in breeding programs. Here, 17 sorghum experimental lines were evaluated together with three checks at five locations, representing the major sorghum production environments in Zimbabwe. Across site analysis of variance (ANOVA) showed significant genotypic effects on grain yield (GYD) as well as the other traits, including panicle length (PL) and stem diameter (SD). Distance-based clustering indicated the possibility of indirectly selecting for GYD using; SD, exertion (EXSTN), panicle length (PL), panicle width (PW) and number of leaves (NL). In addition, the vector view of the genotype-by-trait (GT) biplot also revealed strong correlations between GYD and, PW, SD and FYD, as well as the other physiological traits including, days to male flowering (DMA) and days to physiological maturity (DPM). Genotypes superior for a combination of traits were, G6, G7, G11, G18 and G20, which were strong for GYD, PL and PW, as well as, G5 which was strong for sugar content (SC), number of leaves (NL) and SD. Genotypes 15, 16 and 17, were specifically strong for GYD whereas, genotypes 4 and 9 were strong for FYD. Overall, results revealed the key traits which can be considered singularly or in combination, when selecting suitable sorghum genotypes, either for feed or food purposes, under arid and semi-arid conditions. This information is vital for decision making in sorghum breeding programs.

Climate variability and associated droughts are major environmental factors limiting crop productivity worldwide. Identification and better understanding of factors influencing genotype × environment (GE) interaction in drought-prone environments will allow breeders and producers to improve crop productivity. The objectives of this study were to identify genotypic and climatic factors responsible for GE interactions in durum wheat yield trials under highland cold and moderate cold rainfed areas of Iran. Field experiments were carried out using 20 durum wheat genotypes, differing in growth habit, in three highland rainfed research stations for three cropping seasons (2015–2018). Environments were characterized by climatic co-variables measured during the three seasons. Genotypes were described by co-variables related to phenology, agronomy and disease resistance. Possible factors influencing GE interaction were examined by factorial regression (FR), partial least squares (PLS) regression and the additive main effects and multiplicative interaction (AMMI) models. The results indicated that both AMMI IPCA1 and IPCA2 scores had positive and negative values, resulting in crossover GE interaction and leading to inconsistent yield performance of genotypes across environments. The AMMI, PLS and FR identified similar genotypic and climatic variables that explained a large proportion of the GE interaction for grain yield. Based on the results, the climatic variables of average temperature, rainfall, relative humidity, minimum and maximum temperature, and genotypic variables of heading date, plant height and 1000-kernel weight were among the most important factors responsible for differential genotype adaptation. The PLS biplot showed that the genotypic responses to environmental covariates depended strongly on the growth habit type. This information can contribute toward the identification and biological understanding of adaptive characteristics relevant for durum wheat breeding in highland cold rainfed conditions of Iran and facilitate cultivar recommendation at eco-regional level. In conclusion, the AMMI, PLS and FR complement each other and the PLS biplot offered further interpretations of the GE interaction including differential genotype adaptation.

The variable cowpea productivity across different environments demands evaluating the performance of genotypes in a breeding program prior to their release. The aim of this study was to assess yield stability of eight cowpea advanced breeding lines selected from participatory varietal selection in multilocational trials, and to identify mega-environments for cowpea production in Ghana. The genotypes were evaluated across five environments in 2016 and 2017 in randomized complete block design with three replications. The GEA-R version 4.0 software was used for genotype main effect plus genotype by environment interaction (GGE) biplot analyses. Analysis of variance (PROC GLM of SAS using a RANDOM statement with the TEST option) detected significant variations for location, year, genotype, environment, and their interactions. The results showed that the yield performances of the cowpea genotypes were highly influenced by genotype × environment interaction effects. The principal component 1 (PC1) and PC2 were significant components which accounted for 46.75% and 22.84% of GGE sum of squares, respectively. We showed for the first time, two mega-environments for cowpea production and testing in the major cowpea production agro-ecologies in Ghana. The genotypes SARI-6-2-6 and IT07K-303-1 were adapted to Damongo, Nyankpala, and Tumu, whereas SARI-2-50-80 was adapted to Yendi and Manga. The best ranking location was Damongo followed by Tumu, and Nyankpala. The high-yielding genotypes, IT86D-610, IT10K-837-1, IT07K-303-1, and SARI-2-50-80 had significant higher grain yields than the check (Bawutawuta) and were recommended for release as cultivars (or as breeding lines) to boost cowpea production in Ghana.

Comprehensive screening of rice (Oryza sativa L. subsp. japonica Kato) germplasm resources with different nitrogen (N) efficiency levels is effective for improving N use efficiency (NUE) while reducing pollution and providing high quality, yield, and efficiency agriculture. We investigated 14 indices of 38 varieties under three N application levels to assess differences among genotypes. Rice varieties were classified for screening and identifying N efficient. Descriptive statistical analysis results indicated significant differences in relative yield, and also in NUE indices (agronomic utilization rate and partial productivity of N fertilizer). The genotype main effects and genotype–environment interaction effects (GGE) biplot analysis was used to evaluate suitable varieties, compare the stable and high yield capabilities of different varieties, find the ideal variety, and describe the correlation, discrimination and representativeness of the indices under different N application levels. Descriptive statistical, discrimitiveness and representativeness and factor analysis were used to select indices, in which the panicle number per plant and soil and plant analyzer development (SPAD) value were the key indices for evaluation and identification. Heatmap and hierarchical cluster analysis based on the average value of evaluation indices, and scatter plot based on the comprehensive value of N efficiency (P) according to formula showed that all varieties could be divided into five types under different N treatments. Our findings work toward developing N efficient rice varieties to improve NUE, reduce N fertilizer application and thus N waste, consequently mitigating the effects of rice production on the environment to ensure food security and sustainable agricultural development.

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.

Drought is a serious global problem of agriculture. For this, an investigation was carried out with 25 wheat genotypes composed of cultivars and advanced lines to screen drought-tolerant bread wheat genotypes using multivariate analysis. The experiment was conducted in a split plot design with three replications and two treatments control and drought. The analysis of variance revealed significant differences among the genotypes and treatments for all the characters studied, viz. plant height, flag leaf collar height, flag leaf length, flag leaf breadth, chlorophyll a, chlorophyll b, proline content, number of tillers per plant, root fresh weight, shoot fresh weight and root–shoot ratio. High heritability was observed for all the traits except flag leaf length, flag leaf breadth and number of tillers per plant. Based on the genetic distances, all the 25 genotypes were grouped into three different clusters. In control conditions, cluster number I contained most tolerant genotypes and under drought conditions, cluster number III contained most drought-tolerant genotypes. In biplot, the genotypes SATYN-24, BARI Gom-24 and SATYN-2 contributed positively and correlated with number of tillers per plant, shoot fresh weight, flag leaf breadth, proline content and plant height under drought conditions. The drought susceptibility index (DSI) indicated that the genotypes BARI Gom-24 and SATYN-9 exhibited a highly drought-tolerant genotype in the present investigation. These genotypes might carry genes for drought tolerance and may be useful for further drought tolerance breeding.

A study was performed to assess nutrient release from biochar inoculated with solid vermicast (SVB), vermicast tea (VTB), deionized water (DWB), uninoculated biochar (Bioc), and Promix-BX (Pro-BX). The growth response of Swiss chard (Beta vulgaris subsp. vulgaris) cv. Rhubarb chard was also assessed. Comparatively, nutrients were released slowly from treatments SVB and VTB compared to the other treatments. The rate of nutrient release determined by total dissolved solids and electric conductivity from the Pro-BX was the highest. The trend for the plant growth components, total leaf surface area and leaf fresh weight at first harvest, was Pro-BX > Bioc > DWB = SVB > VTB. The only treatment that increased total leaf area and leaf fresh weight at the second harvest by approximately 1.02- and 1.88-fold was VTB. Leaf fresh weight was significantly reduced by approximately 0.33-fold for DWB, 0.28-fold for Bioc, and 0.70-fold for Pro-BX but was not altered by SVB at the second harvest as compared to the first harvest. A 2-dimensional principal component analysis (PCA) biplot confirmed that treatment Pro-BX increased plant growth components at the first harvest only. The locations of SVB and VTB on the PCA biplot confirmed their efficacies, which led to increases in the plant growth components at the second harvest. Overall, the VTB adsorbed more nutrients onto its surface that were slowly released to enhance the Swiss chard cv. Rhubarb chard plant growth at the second harvest. Further studies should consider microbial activities.

Seventeen sesame genotypes were tested at ten environments in Tigray, Northern Ethiopia during 2014-2015 cropping seasons. Randomized Complete Block Designs (RCBD) with three replications was used in the study. According to the GGE bi-plot different sesame growing environments grouped into two mega-environments: The first mega-environment contained the favorable environments Dansha area with a vertex G4 and Sheraro area with winner G3 and the second environment included medium to low environments E2 (Humera-2), E4 (Dansha-2), E5 (Sheraro-1), E7 (Wargiba-1), E8 (Wargiba-2) and E9 (Maykadra) for seed yield. Three mega-environments identified for oil content: The 1st environment contained G12, G7 and G2 in the mega-environment group of Humera, Dansha and Gendawuha, The 2nd environment, Sheraro location contained G9 and the 3rd environment Wargiba, was containing G17. G1 (HuRC-4) identified as an “ideal” genotype and E1 (Humera-1) also identified as an ideal environment the most representative of the overall environments and the most powerful to discriminate genotypes. The multivariate approaches AMMI and GGEbi-plot were better for partitioning the GEI into the causes of variation. According to different stability models, G1, G7, and G3 were high yielder and the most stable both in terms of seed yield and oil content. Moreover, showed yield advantages over the released and local varieties. The stable genotypes recommended for wider areas while G14 and G4 were for specific favorable environments Sheraro and Dansha, respectively.

An important objective of wheat improvement programmes is to breed varieties for high yield in optimum conditions and for minimum yield reduction under stress-prone conditions such as heat and drought. Analyses of yield and its components in multiple years allow a comprehensive and comparative understanding of genetic yield potential and stress-tolerance mechanisms in the study germplasm. The present study was carried out to evaluate performance of elite varieties and landraces of spring durum wheat under different water regimes, determine the repeatability of the examined traits, and identify superior genotypes for their potential use in breeding for drought tolerance. A total of 97 accessions of spring durum wheat (T. durum Desf.) were evaluated under rain-fed and well-watered conditions in the nursery of the Centre for Agricultural Research at Martonvásár, Hungary (2011–2013). The experiments were laid out in an unbalanced, incomplete alpha lattice block design. The trait with the lowest broad-sense repeatability was seed length (0.075), while high h2 values were observed for heading date (0.89), thousand-grain weight (0.85) and the protein content (0.85). Grain yield showed moderate level of repeatability (0.53) across the three years. The principal component analysis revealed that grain yield (t/ha) is positively associated with the fertile tiller number, chlorophyll content values at early waxy ripeness stages and plant height. Based on biplot analysis, ‘DP-133′, ‘DP-017′ and ‘DP-061′ proved to be the best durum cultivars in terms of yield whereas genotypes ‘DP-011′, ‘DP-185′, 'DP-126′ and 'DP-136′ preceded them with their good yield stability.

The present study was conducted to assess the physiological and biochemical responses in the leaves of ten chickpea accessions trifurcated into moderately tolerant, intermittent and moderately sensitive on the basis of their yield losses via drought susceptibility index. Moderately sensitive accessions recorded higher decline in number of leaves and dehydrogenase activity and increased membrane permeability index (MPI) under receding moisture conditions. However, moderately tolerant genotypes, viz. GL 29078 and ICC 4958, maintained number of leaves and optimal dehydrogenase activity and lower MPI in leaves at reproductive stages by enhanced enzymatic activity of superoxide dismutase, catalase and ascorbate peroxidase that upregulated their antioxidative defense system at 75% moisture reduction levels. Furthermore, the tolerance capacity was maintained in moderately tolerant genotypes (IPCK-2009-165 and ICC 4958) due to increased proline level that acted as a positive loading variable component in principal component biplot analysis and lowered malondialdehyde content levels in leaves at receding moisture levels. Assessment of leaf tissue-specific response under lysimetric screening and its final correlation with yield adds to paramount importance and uniqueness to the current study.

Identification and deployment of high-yielding and stress-tolerant maize hybrids adapted to stress-prone agro-ecologies is important for improving the food security and livelihoods of smallholder farmers in eastern Africa. The objectives of this study were to (i) assess the performance of maize hybrids under well-watered and drought stress conditions; (ii) evaluate grain yield stability of 65 intermediate-maturing and 55 early-maturing hybrids in 24 well-watered locations and seven drought stress locations; and (iii) identify representative and/or discriminative testing locations for increasing genetic gains for the target traits. There were significant differences for grain yield among early- and intermediate-maturing hybrids tested under well-watered and drought stress environments. Among the early-maturing hybrids, the top 10 hybrids produced 46.8%–73.9% and 31.2%–42.1% higher mean grain yields than the best commercial check under drought and well-watered conditions, respectively. Among the intermediate-maturing hybrids, the top 10 hybrids produced 25.2%–47.7% and 8.5%–13.5% higher grain yield than commercial checks under drought stress and well-watered conditions, respectively, suggesting improvement in the levels of drought tolerance in both early- and intermediate-maturing hybrids. GGE biplot analysis and a bi-segmented regression linear method identified specific early-maturing and intermediate-maturing hybrids that performed well under both well-watered and drought stress conditions. These hybrids could be recommended for commercial production in eastern Africa. Kakamega in Kenya was found to be the most representative and highly discriminating site among well-watered testing locations, while Kabuku in Tanzania was the least representative of test locations. For testing under drought stress conditions, Kiboko in Kenya was identified as the most representative location. This information could be useful for allocating resources and streamlining CIMMYT maize hybrid testing in eastern Africa. Keywords: Drought stress, Eastern Africa, Genotype by environment interaction, Yield stability, Zea mays L.

To investigate the effects of integrated nutrient management on oil, protein, grain yield and some traits of soybean, we conducted a factorial experiment involving 4 bio-fertilizer (no inoculation, inoculation with Barvar-2, inoculation with Biosoy and dual inoculation with Biosoy and Barvar-2) and 3 chemical fertilizer levels (no chemical fertilizer, 66 kg ha-1 diammonium phosphates + 50 kg ha-1 urea, 132 kg ha-1 diammonium phosphates + 100 kg ha-1 urea) with 100% chemical fertilizer (200 kg ha-1 diammonium phosphates + 150 kg ha-1 urea) as control base in a randomized complete block with four replications at the research farm of the University of Mohaghegh Ardabili, Iran. Analysis of variance showed that treatment combinations affected most of the traits (P>0.01). We used the biplot analysis as the treatment ? trait (TT) biplot to determine the best treatment combinations and traits. The first two principal components (PC1 and PC2) explained 94 and 96% of the total variant of the standardized data in 2017 and 2018, respectively. Accordingly, application of Biosoy and 150 kg ha-1 urea + 200 kg ha-1 diammonium phosphate significantly increased stem height at harvest, number of grains per plant, biomass, hundred-grain weight, oil and protein yield, protein percent and grain yield compared to the other treatment combinations. The results showed that there were positive correlations between these traits. Also, non-inoculated plants and no chemical fertilizer treatments significantly increased oil percent. The results indicate that higher-yielding treatment combinations had lower oil percent. The biplot was used for ranking of treatment combinations based on a single trait. These study results suggest that bio-fertilizers had a positive influence on soybean and that they could diminish the use of chemical fertilizers. The study reveals that the TT biplot was able to graphically show the interrelationships between traits and support visual comparison of treatments.

Drought is one of the major abiotic stresses often causing negative impacts on bean crops in the Andean region in Colombia. An experiment under the greenhouse conditions was carried out to assess the effect of a prolonged drought period (15 days) at two different phenological stages (vegetative or reproductive) on grain yield and yield components of five bush bean cultivars (ICA-Cerinza, Bachue, NUA35, Bianca, and Bacata). Nine tolerance indices including stress susceptibility index (SSI), tolerance (TOL), mean productivity (MP), geometric mean productivity (GMP), stress tolerance index (STI), yield stability index (YSI), yield index (YI), Harmonic mean (HM), and drought sensitivity index (DSI) were calculated based on grain yield under non-stressed (YP) and drought (YS) conditions. Based on the different drought indices, genotypes ICA-Cerinza and NUA35 had the best performance under drought conditions in both studied phases, which reflected in a reduction of grain yield ~≤40%. The biplot analysis also showed a clear superiority of these two genotypes at both phenological stages. Results also showed that SSI was more effective to identify genotypes less affected by drought. The above results allowed us to conclude that ICA-Cerinza and NUA35 may be considered for agricultural areas where long periods of water deficit are expected and can be used in breeding programs for drought tolerance.

Multi-location trials were conducted in 2016 and 2017 at Calabar, Ikom and Ogoja in Cross River State, Nigeria, to determine the yield stability of 17 ginger genotypes (G1-G17) using genotype and genotype by environment (GGE) biplot model. The location and year combination gave six environments (E1-E6). The experiment was laid out in split plots using a randomized complete block design with three replications. Yield related traits like rhizome fingers, rhizome length, and rhizome yield were determined. E3 (i.e. Ikom in 2016) was ranked as the ideal environment for ginger production in Cross River State. While G5 (UGI-5-04) was classified as the ideal genotype for rhizome yield in Calabar, Ikom and Ogoja. Ikom in 2016 (E3) and 2017 (E4) were identified as mega-environments for UG1-13-02, UG1-5-04, UG1-5-18, UG1-5-35, UG1-5-38, UG2-11-03 and UG2-9-01 while Ogoja in 2016 (E5) and 2017 (E6) were identified as mega-environments for UG1-2-35, UG1-5-48, UG1-5-52 and UG1-7-24 ginger mutants. Ikom is recommended as a suitable environment for the cultivation of generally adapted ginger genotypes namely, UG1-5-04, UG1-5-38, UG1-13-02 and UG2-9-01). Ogoja was suitable for specific adaptation of UG1-7-24 and UG1-5-48 ginger mutants. These mutants are recommended for consideration in subsequent ginger breeding and improvement programmes.

Understanding the genetic diversity and overcoming genotype-by-environment interaction issues is an essential step in breeding programs that aims to improve the performance of desirable traits. This study estimated genetic diversity and applied genotype + genotype-by-environment (GGE) biplot analyses in cotton genotypes. Twelve genotypes were evaluated for fiber yield, fiber length, fiber strength, and micronaire. Estimation of variance components and genetic parameters was made through restricted maximum likelihood and the prediction of genotypic values was made through best linear unbiased prediction. The modified Tocher and principal component analysis (PCA) methods, were used to quantify genetic diversity among genotypes. GGE biplot was performed to find the best genotypes regarding adaptability and stability. The Tocher technique and PCA allowed for the formation of clusters of similar genotypes based on a multivariate framework. The GGE biplot indicated that the genotypes IMACV 690 and IMA08 WS were highly adaptable and stable for the main traits in cotton. The cross between the genotype IMACV 690 and IMA08 WS is the most recommended to increase the performance of the main traits in cotton crops. RESUMO: Compreender a diversidade genética e contornar os problemas causados pela interação genótipos por ambientes é uma etapa importante em programas de melhoramento. Este estudo teve como objetivo estimar a diversidade genética e aplicar a metodologia de biplot genótipo + genótipo por ambiente (GGE biplot) em doze genótipos de algodão avaliados quanto ao rendimento da fibra, comprimento da fibra, resistência da fibra e micronaire. A estimativa dos componentes de variância e dos parâmetros genéticos foi feita através do método da máxima verossimilhança restrita e a predição dos valores genotípicos por meio da melhor predição linear não enviesada. Os métodos de Tocher modificado e análise de componentes principais (PCA) foram utilizados para quantificar a diversidade genética entre os genótipos. O método GGE biplot foi conduzido para encontrar os melhores genótipos em relação à adaptabilidade e estabilidade. As técnicas de Tocher e PCA permitiram a formação de clusters de genótipos semelhantes com base em uma estrutura multivariada. O GGE biplot indicou que os genótipos IMACV 690 e IMA08 WS foram altamente adaptáveis e estáveis para as principais características do algodão. O cruzamento dentre os genótipos IMACV 690 e IMA08 WS é o mais recomendado para aumentar o desempenho das principais características na cultura do algodão.

Genotype x enviroment (GE) interaction can difficult soybean breeding programs to atieve the aim of obtain more productive cultivars. Enviroment stratification is a way to circunvent this problem. This work aimed to gather GGE Biplot graphs of a network of trials unbalance multiyear soybean via matrices of coincidence and networks of enviroment to optimize environmental stratification. Data from an experimental network of 43 trials was used, these experiments were implanted during the crop seasons of 2011/12, 2012/13, 2013/14 and 2015/16 in Brazil. The GE interaction were statistically significant for all 43 trials. The step by step of our analses was: GGE Biplots graphs were obtained; the enviroment coincidence matrices were calculated; the values of matrices were used for to obtain the networks of environmental similarity. The study demonstrated that by the method was possible to identify, using unbalanced multiyear data, the formation of four mega-environments. Therefore, integrating GGE Biplot graphs and networks of environmental similarity is an efficient method to optimize a soybean program by environment stratification.

The root system is the important organ of a plant, helping to anchor the plant and take up nutrients from the soil. The purpose of this investigation was to determine the magnitude of the root network system (RNS) through phenotypic variability in a broad range of maize inbred lines. The GiA Root software was used to identify root attributes from images. After germination, the inbred lines were grown hydroponically for 15 days in a high-lux plant growth room with low phosphorus (LP) and normal phosphorus (NP) treatments. Variance analysis revealed a large range of variability present among the inbred lines, with intermediate to high heritabilities ranging from 0.59 to 0.95 for all RNS traits, demonstrating uniformity through the experiments. The proportions of genetic variance ranged from 0.01–0.60 in different maize RNS traits. A strong positive linear relationship between best linear unbiased predictors (BLUPs) with estimated means was found for all the RNS traits. The Euclidean genetic distances between the studied inbred lines ranged from 0.61 to 29.33, showing a higher amount of diversity. More than 79% of the overall genetic variation was explained by the first three principal components, with high loadings from the measurements of network length (NWL), network surface area (NWSA), network perimeter (NWP), network area (NWA), the maximum number of roots (MANR), median number of roots (MENR), network volume (NWV), network convex area (NWCA), specific root length (SRL), network depth (NWD), number of connected components (NCC), and network width (NWW). The biplot of genotype by trait interaction exposed superior genotypes with a relatively high expression of favorable trait combinations. Some outstanding genotypes with higher values of most RNS traits were identified through MGIDI analysis. These lines may be convenient for enhancing LP tolerance in maize.

Identification of cowpea genotypes with high protein content for specific environments, based on the genotype-environment interaction, has a positive impact in places where access to protein for human consumption is deficient. The objective of the study was to analyze the protein content of 10 cowpea bean genotypes in five environments in the Caribbean Region of Colombia. The randomized complete block design with four replications at each site was used. The analysis of the genotype-environment interaction (GEI) was performed using the AMMI (additive main effects and multiplicative interaction) and SREG (regression in sites) models, in which the main effects of genotypes (G) + GEI are part of the bilinear term of the model. The AMMI and SREG models and their biplots were useful in the analysis and interpretation of the protein content of cowpea beans from experiments carried out in multiple environments. The AMMI model identified genotypes 1, 4 and 8 as those with the greatest adaptability and stability, and the Montería (MO7B), Mahates (MA7B) and Cereté (CE7B) environments as the most favorable. The SREG model identified a potential mega-environment constituted by the PN7B, MA7B and CE7B environments, in which genotypes 1, 2 and 3 presented greater adaptability and stability, while genotype 8 showed specific adaptability in MO7B. In both models, genotypes 6, 7 and 10 showed absence of adaptability and stability in the studied environments. RESUMO A identificação de genótipos Vigna unguiculata com elevado conteúdo proteico, baseada na interação genótipo-ambiente, tem um impacto positivo em locais onde o acesso à proteína animal para consumo humano é deficiente e custoso. O objetivo do estudo foi analisar o conteúdo de proteínas no grão de 10 genótipos de V. unguiculata em cinco ambientes na Região Caribe da Colômbia. Foi utilizado um desenho de bloco completo aleatório com quatro repetições em cada sítio. A análise da interação genótipo-ambiente (GEI) foi realizada utilizando os modelos AMMI (efeitos principais aditivos e interação multiplicativa) e SREG (regressão em sítios) nos quais os principais efeitos dos genótipos (G) + GEI fazem parte do termo bi linear do modelo. Os modelos AMMI e SREG e os seus biplots foram úteis na análise e interpretação do conteúdo proteico de experimentos conduzidos em múltiplos ambientes. O modelo AMMI identificou os genótipos 1, 4 e 8 como os mais adaptáveis e estáveis, e os ambientes Montería (MO7B), Mahates (MA7B) e Cereté (CE7B) como os mais favoráveis. O modelo SREG identificou um potencial mega ambiente constituído pelos ambientes PN7B, MA7B e CE7B, em que os genótipos 1, 2 e 3 mostraram maior adaptabilidade e estabilidade, enquanto o genótipo 8 mostrou adaptabilidade específica no MO7B. Em ambos os modelos, os genótipos 6, 7 e 10 não mostraram adaptabilidade e estabilidade nos ambientes estudados.