•The productive tillers/meter, thousand kernel weight, biological yield and grain yield were significantly affected by waterlogging.•Significant correlation was observed between biological yield and ...yield stability index.•The GMP, HM, MP, MRP, REI and STI indices may be used to select better performing genotypes under waterlogging.•The yield stability index (YSI) can be used as an alternative for reduction percentage to identify promising stable and tolerant genotypes.•The genotypes viz. CUNDERIN, DUCULA 4, HD 3086, KRL 105 and RW 3684 were identified to be waterlogging tolerant and can be utilized for enhancing waterlogging tolerance.
An experiment was conducted for evaluating the performance of 149 elite Indian and Australian germplasm lines under normal and waterlogged soil conditions over two years (2014–15 and 2015–16), revealed significant differences for all the studied traits under waterlogged as well as under normal soil conditions. Waterlogging adversely affected thousand kernel weight, tillers per meter, biological yield and grain yield. The three morphological traits namely; biological yield, tillers per meter and plant height significantly contributed towards waterlogging tolerance. The geometric mean productivity (GMP), harmonic mean (HM), stress tolerance index (STI), mean productivity (MP) and mean relative performance (MRP), were significantly and positively correlated with grain yield under waterlogged (Ys) as well as under normal soil conditions (Yp). Whereas, yield stability index (YSI) was found positively correlated with grain yield only under waterlogged soil conditions (Ys) in both years as well as over the years. Besides, GMP, HM, MP, MRP and STI were also significantly correlated with each other in individual year as well as across years, indicating thereby that any one of these parameters might be used as an alternative option to each other for selecting high yielding genotypes under both the conditions (waterlogging and normal). The promising lines identified based on GMP, STI, MP, HM and MRP could also be rated as tolerant being high yielding particularly for waterlogging condition. The two parameters (YSI and reduction percentage) identified as effective selection indices clearly discriminated the per se performance of genotypes based on stable performance under waterlogged conditions, and thus these could be used an indicative approach of germplasm lines possessing waterlogging tolerance genes. The correlation values also supported results and indicated strong association of biological yield with yield stability index. The germplasm lines namely; UP 2584, TINCURRIN, SSD-C2-172, SSD-C2-151, SSD-C2 204, SSD-C2 140, RW 3684, NW 4098, KRL 105, K 307, HD 3086, HD 2329, DUCULA 4 and CUNDERIN were identified to be capable of producing highest biomass and also higher values for YSI among all lines taken for this study. Based on both YSI and biological yield, lines CUNDERIN, DUCULA 4, HD 3086, KRL 105 and RW 3684 were identified to as tolerant and could be utilized as potential donor for increasing waterlogging tolerance of future wheat genotypes. Our research findings also imply that tolerant lines eventually would lead to higher productivity under stress situations, where heavy rainfall and stagnation of water for a prolonged period adversely affect the wheat crop. The results of the present study based on field screening technique and the selection criteria based upon indices will be rewarding for increasing grain yield of wheat under such harsh environments.
The introduction of
Lupinus mutabilis
(Andean lupin) in Europe will provide a new source of protein and oil for plant-based diets and biomass for bio-based products, while contributing to the ...improvement of marginal soils. This study evaluates for the first time the phenotypic variability of a large panel of
L. mutabilis
accessions both in their native environment and over two cropping conditions in Europe (winter crop in the Mediterranean region and summer crop in North-Central Europe), paving the way for the selection of accessions adapted to specific environments. The panel of 225 accessions included both germplasm pools from the Andean region and breeding lines from Europe. Notably, we reported higher grain yield in Mediterranean winter-cropping conditions (18 g/plant) than in the native region (9 g/plant). Instead, North European summer-cropping conditions appear more suitable for biomass production (up to 2 kg/plant). The phenotypic evaluation of 16 agronomical traits revealed significant variation in the panel. Principal component analyses pointed out flowering time, yield, and architecture-related traits as the main factors explaining variation between accessions. The Peruvian material stands out among the top-yielding accessions in Europe, characterized by early lines with high grain yield (e.g., LIB065, LIB072, and LIB155). Bolivian and Ecuadorian materials appear more valuable for the selection of genotypes for Andean conditions and for biomass production in Europe. We also observed that flowering time in the different environments is influenced by temperature accumulation. Within the panel, it is possible to identify both early and late genotypes, characterized by different thermal thresholds (600°C–700°C and 1,000–1,200°C GDD, respectively). Indications on top-yielding and early/late accessions, heritability of morpho-physiological traits, and their associations with grain yield are reported and remain largely environmental specific, underlining the importance of selecting useful genetic resources for specific environments. Altogether, these results suggest that the studied panel holds the genetic potential for the adaptation of
L. mutabilis
to Europe and provide the basis for initiating a breeding program based on exploiting the variation described herein.
Stylosanthes scabra Vogel is a tropical legume grown in dry tropical and subtropical environments. The objective of this research was to evaluate the genetic diversity of forage quality traits for 80 ...accessions of S. scabra. Seven plants from each accession were planted in a single-line plot with no replicates at Embrapa Cerrados, Brazil. All plants were harvested 90 days after planting. Crude protein (CP), in vitro dry matter digestibility (IVDMD), neutral detergent fibre (NDF), acid detergent fibre (ADF), lignin (LIG), hemicellulose (HEMIC) and cellulose (CELLU) were estimated. Data were submitted to principal component analysis (PCA) and a cluster analysis was performed to identify groups of similarity. Simpson and Shannon–Weaver diversity indices estimated the genetic diversity. The average values of CP, IVDMD, NDF, ADF, LIG, HEMIC and CELLU were 220g/kg, 560g/kg, 516.8g/kg, 368g/kg, 69.4g/kg, 148.8g/kg and 298.6g/kg, respectively. There was a significant difference among collection sites for IVDMD, ADF and CELLU. The first two principal components accounted for 73% of the total variation. The 80 accessions resulted in four clusters, among which significant differences were observed for CP, IVDMD and ADF. Group IV, with 24 accessions, had the highest CP and IVDMD concentrations and the lowest ADF concentration, being the highest-quality forage group. Diversity indices were 0.78 and 0.81 for Simpson’s and Shannon–Weaver’s, respectively. In conclusion, there is genetic diversity for forage quality traits among S. scabra.
The objective of the present study was to assess the accuracy of analyzing quantitative and qualitative descriptors separately or combined to differentiate 'Capsicum' accessions. We assessed 47 ...'Capsicum' accessions from the UFV Vegetable Germplasm Bank (BGH-UFV) with botanical classification previously known. The experiment was arranged in a randomized block design with four replicates. Variables consisted of 16 morphological descriptors proposed by the IPGRI. Analysis of quantitative descriptors alone was performed by calculating the Mahalanobis distance matrix (D1), while qualitative descriptors alone were analyzed using the simple coincidence index (D2). The joint analysis consisted of the sum of the distance matrices D1 and D2, and the joint calculation of both descriptors using Gower's algorithm. Association between the distance matrices was assessed by Mantel's correlation test, and accession clustering was performed using the UPGMA method. There was genetic variability amongst 'Capsicum' accessions for both quantitative and qualitative traits. Accession clustering was more accurate when performed using the distances obtained by the simple coincidence index (qualitative descriptors alone) and the Gower's algorithm (qualitative and quantitative descriptors combined).
Crop genetic improvements catalysed population growth, which in turn has increased the pressure for food security. We need to produce 70% more food to meet the demands of 9.5 billion people by 2050. ...Climate changes have posed challenges for global food supply, while the narrow genetic base of elite crop cultivars has further limited our capacity to increase genetic gain through conventional breeding. The effective utilization of genetic resources in germplasm collections for crop improvement is crucial to increasing genetic gain to address challenges in the global food supply. Genomic selection (GS) uses genome-wide markers and phenotype information from observed populations to establish associations, followed by genome-wide markers to predict phenotypic values in test populations. Characterizing an extensive germplasm collection can serve a dual purpose in GS, as a reference population for predicting model, and mining desirable genetic variants for incorporation into elite cultivars. New technologies, such as high-throughput genotyping and phenotyping, machine learning, and gene editing, have great potential to contribute to genome-assisted breeding. Breeding programmes integrating germplasm characterization, GS and emerging technologies offer promise for accelerating the development of cultivars with improved yield and enhanced resistance and tolerance to biotic and abiotic stresses. Finally, scientifically informed regulations on new breeding technologies, and increased sharing of genetic resources, genomic data, and bioinformatics expertise between developed and developing economies will be the key to meeting the challenges of the rapidly changing climate and increased demand for food.
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