Sequencing-based genotyping of heterozygous diploids requires sufficient depth to accurately call heterozygous genotypes. In interspecific hybrids, alignment of reads to both parental genomes simultaneously can generate haploid data, potentially eliminating the problem of heterozygosity. Two populations of interspecific hybrid rootstocks of walnut (Juglans) and pistachio (Pistacia) were genotyped using alignment to the maternal genome, paternal genome, and dual alignment to both genomes simultaneously. Downsampling was used to examine concordance of imputed genotype calls as a function of sequencing depth. Dual alignment resulted in datasets essentially free of heterozygous genotypes, simplifying the identification and removal of cross-contaminated samples. Concordance between full and downsampled genotype calls was always highest after dual alignment. Nearly all SNPs in dual alignment datasets were shared with the corresponding single-parent datasets, but 60-90% of single-parent SNPs were private to that dataset. Private SNPs in single-parent datasets had higher rates of heterozygosity, lower levels of concordance, and were enriched in fixed differences between parental genomes (“homeo-SNPs”) compared to shared SNPs in the same dataset. In multi-parental walnut hybrids, the paternal-aligned dataset was ineffective at resolving population structure in the maternal parent. Overall, the dual alignment strategy effectively produced phased, haploid data, increasing data quality and reducing cost.
Maize (Zea mays L.) is a crop of major economic and food security importance globally. The fall armyworm (FAW), Spodoptera frugiperda, can devastate entire maize crops, especially in countries or markets that do not allow the use of transgenic crops. Host-plant insect resistance is an economical and environmentally benign way to control FAW, and this study sought to identify maize lines, genes, and pathways that contribute to resistance to FAW. Of 289 maize lines phenotyped for FAW damage in artificially infested, replicated field trials over three years, 31 were identified with good levels of resistance that could donate FAW resistance into elite but susceptible hybrid parents. The 289 lines were genotyped by sequencing to provide SNP markers for a genome-wide association study (GWAS), followed by a metabolic pathway analysis using the Pathway Association Study Tool (PAST). GWAS identified 15 SNPs linked to 7 genes, and PAST identified multiple pathways, associated with FAW damage. Top pathways, and thus useful resistance mechanisms for further study, include hormone signaling pathways and the biosynthesis of carotenoids (particularly zeaxanthin), chlorophyll compounds, cuticular wax, known antibiosis agents, and 1,4-dihydroxy-2-naphthoate. Targeted metabolite analysis confirmed that maize genotypes with lower levels of FAW damage tend to have higher levels of chlorophyll a than genotypes with high FAW damage, which also tend to have lower levels of pheophytin, lutein, chlorophyll b and β-carotene. The list of resistant genotypes, and the results from the genetic, pathway, and metabolic study, can all contribute to efficient creation of FAW resistant cultivars.
Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral species. As such genomic prediction in sugarcane presents an interesting case for machine learning methods, which are purportedly able to deal with high levels of complexity in prediction. Here we investigate deep learning networks (DL), including Multilayer networks (MLP) and convolution neural networks (CNN), and Random Forest (RF) for genomic prediction in sugarcane. The data set was 2912 sugarcane clones, scored for 26,086 genome wide SNP markers, with final assessment trial (FAT) data for total cane harvested (TCH), Commercial cane sugar (CCS) and Fibre content. The clones in the latest trial (2017) were used as a validation set. We compared performances of these methods to GBLUP extended to include dominance and epistatic effects. The prediction accuracies from GBLUPs were 0.37 for TCH, 0.37 for CCS and 0.48 for Fibre, while the DL models had accuracies of 0.33 for TCH prediction, 0.38 for CCS prediction and 0.43 for Fibre. Optimised RF achieved a prediction accuracy of 0.35 for TCH, 0.38 for CCS and 0.48 for Fibre. Both DL and RF predictions were more accurate additive GBLUP but generally lower than extended GBLUP. Finally, we identified a partially shared distribution of SNP selections between RF and GBLUP models. We conclude RF may have some utility for genomic prediction for crops with highly complex genomes, particularly if non-additive interactions can be captured with clonal propagation.
Climate influences on below-ground plant traits seldom receive due attention. Climate change is varying the availability of resources, soil physical properties, rainfall events, soil mineral weathering and leaching intensity which collectively determines soil physical and chemical properties. Soil constraints – acidity (pH<6), salinity(pH≤8.5), sodicity and dispersion (pH>8.5) are major causes of wheat yield loss in arid and semi-arid cropping systems. To cope with changing environment, plants employ adaptive strategies such as phenotypic plasticity; a key multifaceted trait, to promote shifts in phenotypes. Adaptive strategies are complex, determined by key functional traits and Genotype × Environment interactions. The understanding of molecular basis of stress tolerance is particularly challenging for plasticity traits. Advances in sequencing and high-throughput genomics technologies has identified functional alleles in gene-rich regions, haplotypes, candidate genes, mechanisms and in silico gene expression profiles at various growth developmental stages. Our review focuses on favourable alleles for enhanced gene expression, QTLs and epigenetic regulation of plant responses to soil constraints including heavy metal stress and nutrient limitations. A strategy is then described for quantitative traits in wheat by investigating significant alleles, functional characterization of variants, followed by gene validation using advanced genomic tools and marker development for molecular breeding and genome editing. Also, the review highlights the progress of gene editing in wheat, multiplex gene editing and novel alleles for smart control of gene expression. Integration of these genomic technologies will be effective to enhance plasticity traits and stabilizing wheat yields on constrained soils in the face of climate change.
Large and publicly available soil spectral libraries, such as the USDA National Soil Survey Center – Kellogg Soil Survey Laboratory (NSSC-KSSL) mid infrared (MIR) spectral library, are enormously valuable resources enabling laboratories around the world to make rapid estimates of a number of soil properties. A limitation to widespread sharing of soil spectral data is the need to ensure that spectra collected on a local spectrometer are compatible with the spectra in the primary or reference library. Various spectral preprocessing and calibration transfer techniques have been proposed to overcome this limitation. We tested the transferability of models developed using the USDA NSSC-KSSL MIR library to a secondary instrument. For the soil properties, total C (TC), pH and clay content, we found that good performance (RPD = 4.9, 2.0 and 3.6, respectively) could be achieved on an independent test set with Savitzky-Golay (SG) smoothing and first derivative preprocessing of the secondary spectra using a memory-based learning chemometric approach. We tested three calibration transfer techniques (direct standardization (DS), piecewise direct standardization (PDS), and spectral space transformation (SST)) using different size transfer sets selected to be representative of the entire NSSC-KSSL library. Of the transfer methods, SST consistently outperformed DS and PDS with 50 transfer samples being an optimal number for transfer model development. For TC and pH, performance was improved using the SST transfer (RPD = 7.7 and 2.2, respectively) primarily through the elimination of bias. Calibration transfer could not improve predictions for clay. These findings suggest calibration transfer may not always be necessary but users should test to confirm this assumption using a small set of representative samples scanned at both laboratories.
Soil health is the capability of soil to provide ecosystem services. These can be quantified through multiple separate indicators (N-mineralization, water infiltration, aggregate stability, etc.) or by a single proxy that integrates many soil processes. Two commonly used integrative measurements are the soil 24h-respiration test (CO2burst) and the visual evaluation of soil structure (VESS). Both are fast, but capture only a part of whole phenomenon of soil health. Soil redox potential is a promising soil and plant health indicator. The redox potential is controlled by soil chemical oxidation-reduction reactions and therefore integrates several processes. However, this method has been tested only on a few soils. In this study, we evaluated redox by comparing it with other established soil health indicators on 35 fields in Finland. Based on the results, redox correlated well with soil biological activity, structure, and texture. Soils with good structure had an oxidized redox status. A low redox state was connected to high biological activity. The carbon farming practices resulted in lower oxidation. A combination of redox and pH could be used to classify soils. The analysis supports the use of redox as a soil health indicator, but further research is needed in identifying the processes and properties the redox is an indicator for.
Growing media constituents have heterogeneous particle size and shape, and their physical properties are partly related to them. Particle size distribution is usually analyzed through sieving process, segregating the particles by their width. However, sieving techniques are best describing more granular shapes and are not as reliable for materials exhibiting large varieties of shapes, like growing media constituents. A dynamic image analysis has been conducted for a multidimensional characterization of particle size distribution of several growing media constituents (white and black peats, pine bark, coir, wood fiber, and perlite), from particles that were segregated and dispersed in water. Diameters describing individual particle width and length were analyzed, then compared to particle size distribution obtained by sieving DM and HM methods. This work suggests the relevance of two parameters, Feret MAX and Chord MIN diameters for assessing particle length and width, respectively. They largely varied among the growing media constituents, confirming their non-spherical (i.e. elongated) shapes, demonstrating the advantages in using dynamic image analysis tools over traditional sieving methods. Furthermore, large differences in particle size distribution were also observed between dynamic image analysis and sieving procedures, with a finer distribution for dynamic image analysis. The discrepancies observed between methodologies were discussed (particle segregation, distribution weighing, etc.), while describing in details methodological limitations of dynamic image analysis.
Advances in technology have increased adoption of high-throughput phenotyping (HTP) methodologies, potentially replacing laborious and time-consuming measurements and data recording. One promising HTP tool for fine-featured and small-sized characteristics are 3-dimensional (3-D) scanning and imaging systems, but the utility of present 2-D technology has not been fully explored for this purpose. The objective of this work was to develop 2-D photogrammetric and 3-D topometric imaging methods for HTP of spike characteristics in perennial ryegrass (Lolium perenne L.) with special attention to traits that might be associated with seed retention. These HTP imaging systems were compared with direct data capture by hand in spikes of 21 diverse global accessions of perennial ryegrass. The Fiji (ImageJ) open source imaging software was used for photogrammetric analysis of spike structure including spike length, spikelet number, internode length and 2-D curvature of the spike. The optical sensor Artec Space Spider 3-D scanner was used to generate dense 3-D point clouds to measure spike length, spikelet number, internode length, spikelet length, spikelet angle, and 3-D curvature of the spike. Both methods were found to accurately characterize the subject, the 3-D method was slower than 2-D but was more (P ≤ 0.01) precise than 2-D image analysis with a linear measurement deviation of only 0.17%. Fiji was effectively used for post-processing image analysis and the Space Spider can be used directly in the field to support HTP data collection. This non-destructive field measurement system facilitates HTP in perennial ryegrass spikes and likely in other applications.
Groundwater recharge can be significantly influenced by the macropores, especially in fine structured soils. However, models considering macropores require a number of additional parameters which are difficult to determine by conventional methods. Thus, inverse modeling is often applied to estimate soil hydraulic and solute transport properties of the unsaturated zone. In this study, an efficient method for recharge prediction and parameter uncertainty quantification by coupling a dual-porosity model (DPM) to the null-space Monte Carlo (NSMC) algorithm was developed, and the impact of uncertainty in the key model parameters on groundwater recharge were analyzed. Recharge estimates were further compared to the one by tritium peak method. Results showed that the estimated recharge was much smaller than the one estimated from the tritium peak method, indicating the possible overestimation of recharge by conventional tritium peak method with piston flow model. Our study further demonstrated that the conventional practice of deriving single set of parameters through inverse modeling could result in biased recharge prediction, and that for the complex subsurface flow and transport models such as the DPM, NSMC method can provide a practical solution for predictive uncertainty analysis.
Drought alone and with associated abiotic stress such as heat and nutrient deficiency leads to significant agricultural crop loss. Thus, with changing climatic conditions, it is important to develop resilience measures in agricultural systems against drought stress. In this review, we discuss the modifications in plants while responding to drought giving special focus on roots as they are the primary sense organs in this context. Prospects of genomic crop improvement by pointing out the focus areas to engineer root system architecture and genomic regions involved in the related traits are also discussed. We have also listed instruments and software facilitating high throughput phenotyping of root system in field conditions as the phenotyping of root system architecture in the field is a challenge.
Maintaining a healthy soil microbiome is important for key soil functions and plant growth. However, little is known about temporal changes in soil microbial communities across different soils and nitrogen fertilization in production soils. The aim of this investigation was to determine soil bacterial and fungal baseline communities and seasonal changes in cornfields, under contrasting soil orders with and without nitrogen fertilization. Three Missouri soil orders (Entisol, Alfisol, and Mollisol) and two nitrogen fertilizer rates (0 and 225 kg nitrogen ha-1) were used for this research. Soil samples (0-5 and 5-15 cm) were taken six times during the season, starting prior to planting up to the R2 corn growth stage. Samples were used to determine bacterial and fungal abundancies and biomass. Soil characteristics (e.g., CEC, pH, organic matter) and nitrogen fertilization showed significant but minor influence on bacterial abundance and biomass, while soil order and corn growth stage had major influence. Each soil order had a distinct and significantly different bacterial and fungal community. Soil depth significantly influenced all Beta diversity metrics, and bacterial and fungal biomass were greater in the 0-5 cm depth. No microbial interactions influenced corn growth more than nitrogen. Though strong relationships between microbes and soil and plant health have been shown, linkages of microbiome information to agronomic decisions are rare. Before developing soil microbial information based decision aids for farmers, longer temporal sampling in more growing environments are needed to identify links between management practices and microbial information.
Date palm (Phoenix dactylifera) fruit are an economically and culturally significant crop in the Middle East and North Africa. There are hundreds of different commercial cultivars producing dates with distinctive shapes, colors, and sizes. Genetic studies of some Date palm traits have been performed, including for date palm sex-determination, sugar content and fresh fruit colour. In this study, we used genome sequences and image data of 199 dry date fruit (Tamar) samples collected from 14 countries to identify genetic loci associated with the color of this fruit stage. Here, we find loci across multiple linkage groups (LG) associated with dry fruit color phenotype. We recover the previously identified VIR genotype associated with fresh fruit yellow or red color and new associations with the lightness and darkness of dry fruit. This study will add resolution to our understanding of the date palm fruit color phenotype especially at the most commercially important tamar stage.
The development of strawberry (Fragaria × ananassa) cultivars resistant to Phytophthora crown rot (PhCR), a devastating disease caused by the soil-borne pathogen Phytophthora cactorum, has been challenging, partly because resistance phenotypes are quantitative and only moderately heritable. To develop deeper insights into the genetics of resistance and build the foundation for applying genomic selection, a genetically diverse training population was screened for resistance to California isolates of the pathogen. Here we show that genetic gains in breeding for resistance to PhCR have been negligible (3% of the cultivars tested were highly resistant and none surpassed early twentieth century cultivars). Narrow-sense heritability for PhCR resistance ranged from 0.35-0.57. Using multivariate GWAS, we identified a large-effect locus (predicted to be RPc2) that appears to be ubiquitous, slowed symptom development, explained 43.6-51.6% of the genetic variance, was necessary but not sufficient for resistance, and was strongly associated with calcium channel and other genes with known plant defense functions. The addition of underutilized gene bank resources to our training population doubled additive genetic variance, increased the accuracy of genomic selection, and enabled the discovery of individuals carrying favorable alleles that are either rare or not present in modern cultivars. The incorporation of an RPc2-associated SNP as a fixed effect increased genomic prediction accuracy from 0.40 to 0.55. Finally, we show that parent selection using genomic-estimated breeding values, genetic variances, and cross-usefulness holds promise for enhancing resistance to PhCR in strawberry.
A more vital soil science future 11Acceptance speech at the Presidential Award ceremony of the 2022 Soil Science Society of America (SSSA) meeting in BaltimoreAlfred E. HarteminkUniversity of Wisconsin-Madison, Department of Soil Science, FD Hole Soils Lab, 1525 Observatory Drive, Madison Wi 53711, USA. E-mail email@example.comBy some measures, soil science is doing fine. We have gained a battery of aspiring young scientists, and became more gender-balanced although still not as diverse as we should be (Carter et al., 2021). The number of soil scientific papers and books are growing at an almost exponential rate reflecting increased research and funding and, hopefully, an expanding knowledge base. An enormous amount of soil information is available, and increasingly peer-reviewed publications are freely accessible. Soon there will be no barrier for humanity between what is known and what information can be retrieved about soil.Soil awareness has grown among policy makers and the general public, following enduring campaigns by national and international soil organizations. It is not uncommon to hear podcasts, talks on the tv, or read articles about soil health, regenerative agriculture, or the relationships between soil management, greenhouse gases and the changing climate. Scientific disciplines ranging from medical geology to urban planning recognize the relevance of soils and have embarked on soil research.Countless technological advances enable us to observe, measure, model and monitor soil attributes at accelerating speed (Wadoux and McBratney, 2021). The private sector and industry have entered the soil carbon market, discovered the commercial value of the soil microbiome, and continues to develop technologies to optimize soil water use. But as soil science is rapidly evolving with a particular focus to solve the grand environmental challenges, it is vital that the science is done efficiently and impactful, and that it stays well-ahead of the technology.Some soil research approaches are holding us back. First, a lot of soil science is conducted at the fringe of traditional soil science centers and departments by a community that has not had the benefit of primary schooling in soil science. Our discipline has always been enriched by approaches and theory from other scientific disciplines but, at the same time, a lot of fundamental knowledge about our soils, knowledge that was learned the hard way, is ignored (Hartemink, 2015; Schimel and Chadwick, 2013). This particularly refers to the lack of viewing soils as a four-dimensional system that cannot be reduced to measuring a limited set of attributes in one or a few timesteps and from shallow soil depth.Quite a lot of research purports to investigate systems and aims to derive knowledge from differences among the systems. For example, soils under different vegetation or cropping systems are sampled for certain fractions, say, soil organic carbon, and the differences are attributed to land use and management. Too often, samples are collected only from the uppermost 20 cm (Yost and Hartemink, 2020) and, in many studies, diverse vegetation or a ‘range of agricultural soils’ are sampled across the continent so as to maximize variation. No wonder that differences are unearthed, but such studies rarely progress beyond the description of loosely connected phenomena that are hard to extrapolate, or interpolate.With research framed within one or other grand environmental challenge (Wortman and Lovell, 2013), it remains unclear how the results translate or contribute to solving those challenges. Studying and understanding soils is noy easy. The best studies are those that investigate a multitude of soils and their attributes across a range of spatial and timescales using a wide range of tools and, above all are based on a solid framework and sound theory. We need to speculate more and think deeper and longer, and require more explorations that combine measurements with modelling and predictions across time and space.Next, there is soil anxiety – that might be described as the hidden apprehension of doing something new and novel. This seems to occur, for example, at the research proposal stage whereby new ideas are met with: ‘Why?’ instead of a firm: ‘Why not!’. This goes along with the lack of study whether the research has already been done: it is easier to repeat than to formulate and invent. Part of the soil anxiety is the strictly regional approach that can be summed up as: ‘We have done in North Dakota. Now we do it in South Dakota.’ Science needs verification but there is quite a limit to progress in this direction. Progress comes from tirelessly chasing of new ideas, quite often with only partial success, but the lack of ideas - equivalent to maintaining a status quo - is regressive. It is not: ‘Go big or go home’ but: ‘Do something new or go home. Dare to fail - and dare to publish it.’And, finally, a lot more effort is needed to develop sound theory. Sound theory is the bedrock of all the soil science subdisciplines. This could be the last chapter of every PhD thesis. It is not the same thing as hypothesis formulation and testing. Research considered ‘high impact-high risk’ is no guarantee of novelty or an overture for theory development if that is not the overarching aim. Funding bodies might hesitate to support foundational soil science research but it is good to see that methodology development is advancing in most soil science subdisciplines. It seems to go hand in hand with increased technological availability.So, soil science is thriving, but it can do better. As much of our research findings are available, we have all the more responsibility to deliver the best. The future directions of our science are by no means solely determined by funding or the environmental challenges but, in particular, by the community, its education and willingness to strive for excellence. There is surely a role for the soil science journals to weed out problematic research approaches, but it is the task of the community, research centers and universities to guarantee that the research approaches are solid, reproducible, and innovative. Innovation should be the foundation of our research and not a special program defined as ‘high risk’ or ‘quick impact’. Concerted efforts should be made to force breakthroughs, for the growth of soil science theory and frameworks, and to think about the vitality of our discipline – now and in the future. And in all that, there is no need to think outside the box. There is no box.
The use of heavy machinery is increasing in agricultural industries in particular cotton farming systems, which induces an increased risk of soil compaction and yield reduction. Hence, there is a need for a technical solution to use available tools to measure projected soil compaction due to farm machinery traffics. The aim of this work was to compare the effects of static and dynamic loads on soil compaction. In this study, three vertosols (common soil for cotton production) were selected to examine soil compaction under a range of static and dynamic loads using uniaxial compression equipment and a modified proctor test, respectively. In general, soils behaved similarly under static and dynamic loads with no significant difference between bulk density values for all moisture contents with a high index of agreement (d=0.96, RMSE= 0.056). The results further indicate better agreement between soil compaction for static and dynamic loads Uniaxial compression test (static loads) produced higher compaction compared to the modified proctor test (dynamic loads) in particular at moisture contents lower than the plastic limit condition. The variation in soil compaction for static and loads was often evident for loads ≥600 kPa, with the highest soil compaction induced under loads ≥1200 kPa. The findings of this study confirm the suitability of a modified proctor method to assess soil compaction as an alternative tool under a range of moisture contents and machinery loads for vertosols.
Tree training systems for temperate fruit have been developed throughout history by pomologists to improve light interception, fruit yield, and fruit quality. These training systems direct crown and branch growth to specific configurations. Quantifying crown architecture could aid the selection of trees that require less pruning or that naturally excel in specific growing/training system conditions. Regarding peaches [Prunus persica (L.) Batsch], access tools such as branching indices (BIs) have been developed to characterize tree crown architecture. However, the required branching data to develop these indices are difficult to collect. Traditionally, branching data have been collected manually, but this process is tedious, time-consuming, and prone to human error. These barriers can be circumnavigated by utilizing terrestrial LiDAR (TLS) to obtain a digital twin of the real tree. TLS generates three-dimensional (3D) point clouds of the tree crown, wherein every point contains 3D coordinates (x, y, z). To facilitate the use of these tools for peach, we selected four young peach trees scanned in 2021 and 2022. These four young trees were then modeled and quantified using the open-source software TreeQSM. As a result, “in silico” branching and biometric data for the young peach trees were calculated to demonstrate the capabilities of TLS phenotyping of peach tree-crown architecture. The comparison and analysis of field measurements (in situ) and in silico branching data (BD), biometric data, and residual ground truth data were utilized to determine the reconstructive model’s reliability as a source substitute for field measurements. Mean average deviation (MAD) when comparing young tree height was approx. 8.2%, with crown volume (crV) was approx. 7.6% across both 2021 and 2022. All point clouds of the young trees in 2022 showed residuals < 10mm to cylinders fitted to all branches, and mean surface coverage >50% across all branching orders.
In view of the fact that the practical salinity of the Bohai Sea in January 2007 was significantly higher than the multi-year mean with its horizontal distribution opposite to the latter, the factors that affect the interannual variation of practical salinity in the Bohai Sea are quantitatively analyzed based on the in-situ hydrological measurements, the annual runoff data of the Yellow River into the sea, as well as the precipitation and evaporation reanalysis data of EAR5. The results show that the local freshwater supply not only dominates the magnitude of salinity change in the Bohai Sea, but also causes the salinity in the central Bohai Sea is higher than that in the Bohai Strait in winter in some years which is in inverse to the climatological salinity field. The seesaw distribution characteristics of the freshwater supply of the Bohai, Yellow and East China Seas also strengthen the characteristics that the salinity horizontal distribution opposite to the climatological in the Bohai Sea in some years. Available observations also show that the nutrient and inorganic carbon of the Bohai Sea are much higher than that of the open ocean, which gives a rise of 0.02 ~0.2 g·kg-1 in Absolute Salinity. Therefore, it is necessary to replace the Practical Salinity with the Absolute Salinity for the accurately salinity changes study in the Bohai Sea.
A three-dimensional (3D) P-wave seismic velocity (Vp) model of the crust at the northern South China Sea margin drilled by IODP Expeditions 367/368/368X has been obtained with first-arrival travel-time tomography using wide-angle seismic data from a network of 49 OBSs and 11 air-gun shot lines. The 3D Vp distribution constrains the extent, structure and nature of the continental, continent to ocean transition (COT), and oceanic domains. Continental crust laterally ranges in thickness from ~8 to 20 km, a ~20 km-width COT contains no evidence of exhumed mantle, and crust with clear oceanic seismic structure ranges in thickness from ~4.5 to 9 km. A high-velocity (7.0-7.5 km/s) lower crust (HVLC) ranges in thickness from ~1 to 9 km across the continental and COT domains, which is interpreted as a proxy of syn-rift and syn-breakup magma associated to underplating and/or intrusions. Continental crust thinning style is abrupter in the NE segment and gradual in the SW segment. Abrupter continental thinning exhibits thicker HVLC at stretching factor (β) <~3, whereas gentler thinning associates to thinner HVLC at β>~4. Opening of the NE segment thus occurred by comparatively increased magmatism, whereas tectonic extension was more important in the SW segment. The Vp distribution shows the changes in deformation and magmatism are abrupt along the strike of the margin, with the segments possibly bounded by a transfer fault system. No conventional model explains the structure and segmentation of tectonic and magmatic processes. Local inherited lithospheric heterogeneities during rifting may have modulated the contrasting opening styles.