Analysis of the Genetic Diversity of Crops and Associated Microbiota

Plant genetic resources are the basis for genetic improvements in cultivated plants and in future food and feed security. The interactions of plants with their agrosystem, especially the microbiota associated with crops under the current changing climate conditions, are relevant aspects of this. Some microorganisms that are symbiotic with plants promote biological nitrogen fixation, while others promote plant growth and help to prevent or combat crop diseases and other stresses.

Agricultural biodiversity is the diversity of crops, their wild relatives and associated microbes, and other species relevant to agricultural production. The genetic diversity of several crop species and their associated microbiota is presented in this publication. This biodiversity was assessed through an examination of morphological and agronomical traits and nutritional characteristics, including molecular diversity and tolerance/resistance to abiotic and biotic stresses. This analysis of biodiversity in crops and their associated microbiota is focused on different goals: the use of germplasm for breeding; the adaptation of germplasm to different environments; the evolution of adapted varieties; and the nutritional properties, attributes, and efficiency of the microbiota–plant symbiotic system.

Genetic diversity characteristics were studied in the different germplasm collections of two crops of the Poaceae family, namely sugarcane (Saccharum spp.) [1] and rice (Oryza sativa L.) [2]), two horticultural crops, namely carrot (Daucus carota L.) [3] and turnip (Brassica rapa L.) [4], and the legume Bambara groundnut (Vigna subterranea (L.) Verdc.) [5]. Symbiotic bacterial isolates were analyzed in another legume crop, the narrow-leafed or blue lupin (Lupinus angustifolius L.) [6].

Sugarcane (Saccharum spp. hybrids) is one of the most important commercial crops for the production of sugar, ethanol, and other byproducts; therefore, carrying out genetic research is highly significant. The assessment of the genetic population structure and diversity plays a vital role in managing genetic resources and gene mapping. In one study [1], the genetic diversity and population structure among 196 Saccharum accessions were assessed, including 34 S. officinarum, 69 S. spontaneum, 17 S. robustum, 25 S. barberi, 13 S. sinense, 2 S. edule, and 36 Saccharum spp. hybrids. A total of 624 polymorphic SSR alleles were amplified by PCR with 22 pairs of fluorescence-labeled, highly polymorphic SSR primers and identified on a capillary electrophoresis (CE) detection system, including 109 new alleles. Three approaches (model-based clustering, principal component analysis, and phylogenetic analysis) were adopted for the analysis of population structure and genetic diversity. The results showed that the 196 accessions could be grouped into either three (Q) or eight (q) sub-populations. Phylogenetic analysis indicated that most accessions from each species merged. The species S. barberi and S. sinense formed one group. The species S. robustum, S. barberi, S. spontaneum, S. edule, and sugarcane hybrids merged into the second group. The S. officinarum accessions formed the third group, located between the other two groups. Two-way chi-square tests derived a total of 24 species-specific or species-associated SSR alleles, including four alleles each for S. officinarum, S. spontaneum, S. barberi, and S. sinense, five alleles for S. robustum, and three alleles for Saccharum spp. hybrids. These species-specific or species-associated SSR alleles will have a wide application value in sugarcane breeding and species identification. The overall results provide useful information for the future genetic study of the Saccharum genus and efficient utilization of sugarcane germplasm resources in sugarcane breeding.

The objective of the rice diversity study was to determine the presence and relative concentration of lectins in different accessions of rice obtained from IABGR/NARC Islamabad, mainly originating from Pakistan [2]. Around 210 rice accessions, including 2 local varieties and 5 transgenic varieties, were screened for seed lectins using a hemagglutination (HA) assay with 5% Californian bred rabbits’ erythrocytes. A protein concentration of 3–8 mg/100 mg of seed flour was measured for all the rice accessions; the highest was 8.03 mg for accession 7600, while the lowest noted was 3.05 mg for accession 7753. Out of 210 accessions, 106 showed the highest HA activity. These 106 genotypes were further screened for titer analysis and specific activity. The highest titer and specific activity were observed for accession 7271 as 1024 and 236 hemagglutination unit (HAU), respectively. The selected accessions’ relative affinity and HA capability were evaluated using blood from four different sources: human, broiler chicken, local rabbit, and Californian-breed rabbit. The highest HA activity was observed with Californian-breed rabbit RBCs. The lectin assay was stable for about 1–2 h. After the required investigations, the accessions with higher lectin concentration and HA capability could be used as a readily available source of lectins for further characterization and utilization in crop improvement programs.

The multivariate analysis method was used in a study about carrots, which was helpful in resolving the different phenotypic and genotypic parameters/measurements of large collections of this horticultural crop into easily interpretable dimensions [3]. The research work was carried out with eighty-one genotypes to evaluate the genetic diversity of a germplasm collection through multivariate analysis. The divergence analysis grouped all 81 genotypes into 10 clusters and cluster VI was found to be the biggest, comprising 30 genotypes, followed by IV, which comprised 16 genotypes. Cluster X exhibited a high mean value for root weight and anthocyanin content; cluster III showed a high value for number of days until first root harvest and root girth, and cluster V for dry matter content, total sugar content, and carotene content, respectively. The maximum distance between clusters was recorded between the II and X clusters (43,678.5), followed by the I and X (43,199.7) clusters, and it was indicated that the genotypes from these far away clusters could be used efficiently in breeding programs to obtain superior hybrids. Total sugar content (36.14%) contributed the most to genetic divergence, followed by anthocyanin content (35.74%). Out of the four principal components, PC1 largely contributed to total variation, followed by PC2. The partial variances (%) from the first to fourth PC-axes were 36.77, 25.50, 12.67, and 10.17, respectively. Genotypes like PC-161, PC-173, PAU-J-15, PC-103, and PC-43 were considered superior with respect to marketable yield and its associated traits, such as root length and root weight, and hence can be released directly as a variety.

Turnip, one of the oldest groups of cultivated Brassica rapa species, is a traditional crop as well as a form of animal fodder, a vegetable, and an herbal medicine that is widely cultivated in farming and pastoral regions in Tibet. The different regions of the Qinghai–Tibet Plateau (QTP) are home to a rich diversity of turnip, owing to their high altitudes and variable climate types. However, information on the morphology and genetic diversity of Tibetan turnip remains limited. Therefore, in another study, the genetic diversity of 171 turnip varieties from China and elsewhere (Japan, Korea, and Europe) was analyzed using 58 morphological characteristics and 31 simple sequence repeat (SSR) markers [4]. The varieties showed that the genetic distance ranged from 0.12 to 1.00, and the genetic similarity coefficient ranged between 0.73 and 0.95. Cluster tree analysis showed two distinct clusters. Both morphotype and geography contributed to the group classification. A combination of morphological traits and molecular markers could refine the precision and accuracy of identification compared to the separate morphological and molecular data analyses. A sampling ratio of 15%, in order to most precisely represent the initial population, was compared to ratios of 10% and 20%, and the sampling ratio of 15% was recommended for future works when a primary core collection of turnip resources is constructed. These results could furnish a foundation for germplasm conservation and effective turnip breeding in future studies.

The goal of the study on Bambara groundnut [5] was to find the winning genotype(s) for the test settings in a part of the Southwest region of Nigeria, as well as to investigate the nature and extent of genotype–environment interaction (GEI) effects on Bambara groundnut (BGN) production. The experiment was carried out in four environments (two separate sites, Ibadan and Ikenne, for two consecutive years, 2018 and 2019) with ninety-five BGN accessions. According to the combined analysis of variance over the environments, genotypes and GEI both had a substantial (p < 0.001) impact on BGN yield. The results revealed that BGN accessions performed differently in different test conditions, indicating that the interaction was crossover in nature. In order to examine and show the pattern of the interaction components, biplots showing the genotype main effect and genotype–environment interaction (GEI) were used. The first two PCs explained 80% of the total variation of the GGE model (i.e., G + GE) (PC1 = 48.59%, PC2 = 31.41%). The accessions that performed best in each environment based on the “which-won-where” polygon were TVSu-2031, TVSu-1724, TVSu-1742, TVSu-2022, TVSu-1943, TVSu-1892, TVSu-1557, TVSu-2060, and TVSu-2017. Among these accessions, TVSu-2017, TVSu-1557, TVSu-2060, TVSu-1892, and TVSu-1943 were among the highest-yielding accessions on the field. The adaptable accessions were TVSu-1763, TVSu-1899, TVSu-2019, TVSu-1898, TVSu-1957, TVSu-2021, and TVSu-1850, and the stable accessions were TVSu-1589, TVSu-1905, and TVSu-2048. In terms of discerning the representativeness of the environments, Ibadan 2019 was deemed to be a superior environment. The selected accessions are recommended as parental lines in breeding programs for the improvement of grain yield in Ibadan or Ikenne or similar agro-ecological zones.

Thirty-two bacterial isolates were obtained from the root nodules of Lupinus angustifolius growing in Northern Tunisia [6]. Phylogenetic analyses, based on recA and gyrB partial gene sequences, grouped the strains into six clusters: four clusters belonged to the genus Bradyrhizobium (22 isolates), one to Microvirga (8 isolates), and one to Devosia (2 isolates), a genus that has not been previously reported to nodulate lupin [6]. Representative strains of each group were further characterized. Multi-Locus Sequence Analysis (MLSA), based on recA and glnII gene sequences, separated the strains within the genus Bradyrhizobium into four divergent clusters related to B. canariense, B. liaoningense, B. lupini, and B. algeriense, respectively. The latter might constitute a new Bradyrhizobium species. The strains in the Microvirga cluster showed high identity with M. tunisiensis. The Devosia isolates might also represent a new species within this genus. An additional phylogenetic analysis, based on the symbiotic gene nodC, affiliated the strains to symbiovars genistearum, mediterranense, and possibly to a new symbiovar. Altogether, these results contributed to the existing knowledge on the genetic diversity of lupin-nodulating microsymbionts and revealed a likely new, fast-growing, salt-tolerant rhizobial species within the genus Devosia as a potentially useful inoculant in agricultural practices or landscape restoration.

In conclusion, in sustainable agricultural production, agrobiodiversity supports long-term productivity, resilience, and multiple ecosystem services, boosting yields in quality and quantity, and increasing soil and water quality. It is relevant to consider reducing the need for synthetic fertilizers by means of an efficient use of the crops’ associated microbiota. This would also make farmers’ livelihoods more resilient, reducing yield losses due to climate change and damage due to environmental stresses. Agrobiodiversity, meaning both crops and their associated microbiota, when adequately maintained, could support options for unknown future needs; additionally, using biodiversity-based solutions on farms could also decrease emissions of greenhouse gases and help mitigate climate change.