Contributions of soil organic carbon-induced root- and soil properties complexity to water flow in eastern China.

Water flow within the soils affects the efficiency of materials transfer mediated by water. Soil organic carbon (SOC) as an important role in active water flow events can drive the complexity of root-soil synthesis by improving root and soil properties. However, contributions of SOC-induced root- and soil properties complexity to water flow are not well understood. In this study, dye tracing experiments at the three forest stands (oak, pine, and bamboo forests) were conducted to explore water flow patterns, i.e., preferential flow paths (PFP), stream buffer zones (SBZ), and water flow zones (WFZ). X-ray microtomography (CT) scanning was performed to reconstruct the root architecture. The partial least squares path model was applied to quantitatively explore the effects of root- and soil properties on water flow. The results showed that the index of water flow connectivity (IWFC) in the PFP and WFZ patterns decreased with increasing soil depth, while IWFC in the SBZ pattern increased at first and then decreased. In the PFP pattern, soil physical properties had the larger total effects (TE = 0.624) on IWFC change compared with root properties (TE = 0.257). In the SBZ pattern, the total effects of root properties controlling IWFC change (TE = 0.510) were greater than soil physical properties (TE = -0.386). Both of them can equally affect the IWFC in the WFZ pattern. In conclusion, the influences of SOC by driving the changes of soil properties on gravity-driven convective flow process were dramatically stronger than root properties, while SOC could primarily drive the changes of root properties and thereby affect capillary-driven convective flow process. The present results can provide a scientific basis for sustainable forestry management and also a better understanding of the forestry hydrology.

Genome-Wide Association Study to Map Genomic Regions Related to the Initiation Time of Four Growth Stage Traits in Soybean.

The time to flowering (DF), pod beginning (DPB), seed formation (DSF), and maturity initiation (DMI) in soybean (Glycine max [L.] Merr) are important characteristics of growth stage traits (GSTs) in Chinese summer-sowing soybean, and are influenced by genetic as well as environmental factors. To better understand the molecular mechanism underlying the initiation times of GSTs, we investigated four GSTs of 309 diverse soybean accessions in six different environments and Best Linear Unbiased Prediction values. Furthermore, the genome-wide association study was conducted by a Fixed and random model Circulating Probability Unification method using over 60,000 single nucleotide polymorphism (SNP) markers to identify the significant quantitative trait nucleotide (QTN) regions with phenotypic data. As a result, 212 SNPs within 102 QTN regions were associated with four GSTs. Of which, eight stable regions were repeatedly detected in least three datasets for one GST. Interestingly, half of the QTN regions overlapped with previously reported quantitative trait loci or well-known soybean growth period genes. The hotspots associated with all GSTs were concentrated on chromosome 10. E2 (Glyma10g36600), a gene with a known function in regulating flowering and maturity in soybean, is also found on this chromosome. Thus, this genomic region may account for the strong correlation among the four GSTs. All the significant SNPs in the remaining 7 QTN regions could cause the significant phenotypic variation with both the major and minor alleles. Two hundred and seventy-five genes in soybean and their homologs in Arabidopsis were screened within ± 500 kb of 7 peak SNPs in the corresponding QTN regions. Most of the genes are involved in flowering, response to auxin stimulus, or regulation of seed germination, among others. The findings reported here provide an insight for genetic improvement which will aid in breeding of soybean cultivars that can be adapted to the various summer sowing areas in China and beyond.

The concept, approach, and future research of hydrological connectivity and its assessment at multiscales.

In this review, we explore the concept, approach, and future research of hydrological connectivity and its assessment at multiscales, because according to the literature, an integrated review upon hydrological connectivity is lack. Systematic studies illustrate the effects of (i) human activities (i.e., dam construction, groundwater extraction, water flow regulation and diversion, and land management) and (ii) natural factors (i.e., climate, soil characteristics, vegetation, and topography) on hydrological connectivity. Approaches (i.e., soil water content patterns, runoff patterns and processes, numerical models, and index of hydrological connectivity) applied to evaluate hydrological connectivity are examined in detail. Lastly, hydrological connectivity at multiscales is indicated. This review concludes with a discussion of potential research trends that can improve understanding of hydrological connectivity. Reported records showed that few studies were published on hydrological connectivity from 1980 to 2003, whereas the evolution of these studies is temporally promising since 2003. We cannot define a standard concept of hydrological connectivity that works in all environments. We desire to show different concepts of hydrological connectivity in different environments. The degree and nature of hydrological connectivity are not static due to the influences of human activities and changes of natural factors. The index of hydrological connectivity and numerical models are the most significant approaches to assess the changes in hydrological connectivity. This study showed that considering hydrological connectivity in social-economical-ecological-hydrological frameworks can prevent its negative effects on surface or subsurface water quantity and quality and is beneficial for sound water sources management.

Hydrological connectivity improves soil nutrients and root architecture at the soil profile scale in a wetland ecosystem.

Hydrological connectivity is an essential driver of the stability, structure, and function of wetland ecosystems. Small-scale hydrological connectivity restricts large-scale hydrological cycle processes. This study aimed to investigate the response of soil and root properties to hydrological connectivity at the soil profile scale. Tamarix chinensis, which is a typical plant of the Yellow River Delta wetland, was sampled for analysis. We investigated soil and root properties in the three study plots where T. chinensis distributed and compared them at different soil depths and under different hydrological connectivity conditions. We found that the soil organic carbon (SOC), soil organic matter (SOM), and soil total nitrogen (STN) were significantly higher in shallow soil (0-10 cm deep), and that root architecture parameters such as root length and width at soil depth of 0-10 cm were also significantly higher than at other soil depths. Both the soil nutrients and root architecture parameters were significantly influenced by hydrological connectivity. Specifically, SOC, SOM, and STN were significantly higher in regions of high hydrological connectivity than in regions of low hydrological connectivity. Additionally, root length, root surface area, root projected area, and root volume were markedly higher in regions of high hydrological connectivity. Furthermore, root length and width had significant positive correlations with both SOC and SOM in regions of high hydrological connectivity. Taken together, these results indicate that higher hydrological connectivity promotes soil nutrients and root architecture at the soil profile scale. In the process of wetland protection and restoration, we should not only pay attention to hydrological connectivity at a watershed scale, but also to improving hydrological connectivity at smaller scales so as to restore soil nutrients and promote plant growth.

Detecting the QTL-Allele System of Seed Oil Traits Using Multi-Locus Genome-Wide Association Analysis for Population Characterization and Optimal Cross Prediction in Soybean.

Soybean is one of the world's major vegetative oil sources, while oleic acid and linolenic acid content are the major quality traits of soybean oil. The restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS), characterized with error and false-positive control, has provided a potential approach for a relatively thorough detection of whole-genome QTL-alleles. The Chinese soybean landrace population (CSLRP) composed of 366 accessions was tested under four environments to identify the QTL-allele constitution of seed oil, oleic acid and linolenic acid content (SOC, OAC, and LAC). Using RTM-GWAS with 29,119 SNPLDBs (SNP linkage disequilibrium blocks) as genomic markers, 50, 98, and 50 QTLs with 136, 283, and 154 alleles (2-9 per locus) were detected, with their contribution 82.52, 90.31, and 83.86% to phenotypic variance, corresponding to their heritability 91.29, 90.97, and 90.24% for SOC, OAC, and LAC, respectively. The RTM-GWAS was shown to be more powerful and efficient than previous single-locus model GWAS procedures. For each trait, the detected QTL-alleles were organized into a QTL-allele matrix as the population genetic constitution. From which the genetic differentiation among 6 eco-populations was characterized as significant allele frequency differentiation on 28, 56, and 30 loci for the three traits, respectively. The QTL-allele matrices were also used for genomic selection for optimal crosses, which predicted transgressive potential up to 24.76, 40.30, and 2.37% for the respective traits, respectively. From the detected major QTLs, 38, 27, and 25 candidate genes were annotated for the respective traits, and two common QTL covering eight genes were identified for further study.

Establishment of a 100-seed weight quantitative trait locus-allele matrix of the germplasm population for optimal recombination design in soybean breeding programmes.

A representative sample comprising 366 accessions from the Chinese soybean landrace population (CSLRP) was tested under four growth environments for determination of the whole-genome quantitative trait loci (QTLs) system of the 100-seed weight trait (ranging from 4.59g to 40.35g) through genome-wide association study (GWAS). A total of 116 769 single nucleotide polymorphisms (SNPs) were identified and organized into 29 121 SNP linkage disequilibrium blocks (SNPLDBs) to fit the property of multiple alleles/haplotypes per locus in germplasm. An innovative two-stage GWAS was conducted using a single locus model for shrinking the marker number followed by a multiple loci model utilizing a stepwise regression for the whole-genome QTL identification. In total, 98.45% of the phenotypic variance (PV) was accounted for by four large-contribution major QTLs (36.33%), 51 small-contribution major QTLs (43.24%), and a number of unmapped minor QTLs (18.88%), with the QTL×environment variance representing only 1.01% of the PV. The allele numbers of each QTL ranged from two to 10. A total of 263 alleles along with the respective allele effects were estimated and organized into a 263×366 matrix, giving the compact genetic constitution of the CSLRP. Differentiations among the ecoregion matrices were found. No landrace had alleles which were all positive or all negative, indicating a hidden potential for recombination. The optimal crosses within and among ecoregions were predicted, and showed great transgressive potential. From the QTL system, 39 candidate genes were annotated, of which 26 were involved with the gene ontology categories of biological process, cellular component, and molecular function, indicating that diverse genes are involved in directing the 100-seed weight.

Genome-wide genetic dissection of germplasm resources and implications for breeding by design in soybean.

"Breeding by Design" as a concept described by Peleman and van der Voort aims to bring together superior alleles for all genes of agronomic importance from potential genetic resources. This might be achievable through high-resolution allele detection based on precise QTL (quantitative trait locus/loci) mapping of potential parental resources. The present paper reviews the works at the Chinese National Center for Soybean Improvement (NCSI) on exploration of QTL and their superior alleles of agronomic traits for genetic dissection of germplasm resources in soybeans towards practicing "Breeding by Design". Among the major germplasm resources, i.e. released commercial cultivar (RC), farmers' landrace (LR) and annual wild soybean accession (WS), the RC was recognized as the primary potential adapted parental sources, with a great number of new alleles (45.9%) having emerged and accumulated during the 90 years' scientific breeding processes. A mapping strategy, i.e. a full model procedure (including additive (A), epistasis (AA), A × environment (E) and AA × E effects), scanning with QTLNetwork2.0 and followed by verification with other procedures, was suggested and used for the experimental data when the underlying genetic model was usually unknown. In total, 110 data sets of 81 agronomically important traits were analyzed for their QTL, with 14.5% of the data sets showing major QTL (contribution rate more than 10.0% for each QTL), 55.5% showing a few major QTL but more small QTL, and 30.0% having only small QTL. In addition to the detected QTL, the collective unmapped minor QTL sometimes accounted for more than 50% of the genetic variation in a number of traits. Integrated with linkage mapping, association mappings were conducted on germplasm populations and validated to be able to provide complete information on multiple QTL and their multiple alleles. Accordingly, the QTL and their alleles of agronomic traits for large samples of RC, LR and WS were identified and then the QTL-allele matrices were established. Based on which the parental materials can be chosen for complementary recombination among loci and alleles to make the crossing plans genetically optimized. This approach has provided a way towards breeding by design, but the accuracy will depend on the precision of the loci and allele matrices.