Attack of the clones: Population genetics reveals clonality of Colletotrichum lupini, the causal agent of lupin anthracnose.

Colletotrichum lupini, the causative agent of lupin anthracnose, affects lupin cultivation worldwide. Understanding its population structure and evolutionary potential is crucial to design successful disease management strategies. The objective of this study was to employ population genetics to investigate the diversity, evolutionary dynamics, and molecular basis of the interaction of this notorious lupin pathogen with its host. A collection of globally representative C. lupini isolates was genotyped through triple digest restriction site-associated DNA sequencing, resulting in a data set of unparalleled resolution. Phylogenetic and structural analysis could distinguish four independent lineages (I-IV). The strong population structure and high overall standardized index of association (r̅d ) indicates that C. lupini reproduces clonally. Different morphologies and virulence patterns on white lupin (Lupinus albus) and Andean lupin (Lupinus mutabilis) were observed between and within clonal lineages. Isolates belonging to lineage II were shown to have a minichromosome that was also partly present in lineage III and IV, but not in lineage I isolates. Variation in the presence of this minichromosome could imply a role in host-pathogen interaction. All four lineages were present in the South American Andes region, which is suggested to be the centre of origin of this species. Only members of lineage II have been found outside South America since the 1990s, indicating it as the current pandemic population. As a seedborne pathogen, C. lupini has mainly spread through infected but symptomless seeds, stressing the importance of phytosanitary measures to prevent future outbreaks of strains that are yet confined to South America.

Genome-wide association study reveals white lupin candidate gene involved in anthracnose resistance.

KEY MESSAGE: GWAS identifies candidate gene controlling resistance to anthracnose disease in white lupin. White lupin (Lupinus albus L.) is a promising grain legume to meet the growing demand for plant-based protein. Its cultivation, however, is severely threatened by anthracnose disease caused by the fungal pathogen Colletotrichum lupini. To dissect the genetic architecture for anthracnose resistance, genotyping by sequencing was performed on white lupin accessions collected from the center of domestication and traditional cultivation regions. GBS resulted in 4611 high-quality single-nucleotide polymorphisms (SNPs) for 181 accessions, which were combined with resistance data observed under controlled conditions to perform a genome-wide association study (GWAS). Obtained disease phenotypes were shown to highly correlate with overall three-year disease assessments under Swiss field conditions (r > 0.8). GWAS results identified two significant SNPs associated with anthracnose resistance on gene Lalb_Chr05_g0216161 encoding a RING zinc-finger E3 ubiquitin ligase which is potentially involved in plant immunity. Population analysis showed a remarkably fast linkage disequilibrium decay, weak population structure and grouping of commercial varieties with landraces, corresponding to the slow domestication history and scarcity of modern breeding efforts in white lupin. Together with 15 highly resistant accessions identified in the resistance assay, our findings show promise for further crop improvement. This study provides the basis for marker-assisted selection, genomic prediction and studies aimed at understanding anthracnose resistance mechanisms in white lupin and contributes to improving breeding programs worldwide.

Untangling the Pea Root Rot Complex Reveals Microbial Markers for Plant Health.

Plant health is recognised as a key element to ensure global food security. While plant breeding has substantially improved crop resistance against individual pathogens, it showed limited success for diseases caused by the interaction of multiple pathogens such as root rot in pea (Pisum sativum L.). To untangle the causal agents of the pea root rot complex and determine the role of the plant genotype in shaping its own detrimental or beneficial microbiome, fungal and oomycete root rot pathogens, as well as previously identified beneficials, i.e., arbuscular mycorrhizal fungi (AMF) and Clonostachys rosea, were qPCR quantified in diseased roots of eight differently resistant pea genotypes grown in four agricultural soils under controlled conditions. We found that soil and pea genotype significantly determined the microbial compositions in diseased pea roots. Despite significant genotype x soil interactions and distinct soil-dependent pathogen complexes, our data revealed key microbial taxa that were associated with plant fitness. Our study indicates the potential of fungal and oomycete markers for plant health and serves as a precedent for other complex plant pathosystems. Such microbial markers can be used to complement plant phenotype- and genotype-based selection strategies to improve disease resistance in one of the world's most important pulse crops of the world.

A qPCR Assay for the Fast Detection and Quantification of Colletotrichum lupini.

White lupin (Lupinus albus) represents an important legume crop in Europe and other parts of the world due to its high protein content and potential for low-input agriculture. However, most cultivars are susceptible to anthracnose caused by Colletotrichum lupini, a seed- and air-borne fungal pathogen that causes severe yield losses. The aim of this work was to develop a C. lupini-specific quantitative real-time TaqMan PCR assay that allows for quick and reliable detection and quantification of the pathogen in infected seed and plant material. Quantification of C. lupini DNA in dry seeds allowed us to distinguish infected and certified (non-infected) seed batches with DNA loads corresponding to the disease score index and yield of the mother plants. Additionally, C. lupini DNA could be detected in infected lupin shoots and close to the infection site, thereby allowing us to study the disease cycle of this hemibiotrophic pathogen. This qPCR assay provides a useful diagnostic tool to determine anthracnose infection levels of white lupin seeds and will facilitate the use of seed health assessments as a strategy to reduce the primary infection source and spread of this disease.

Effect of Low-Input Organic and Conventional Farming Systems on Maize Rhizosphere in Two Portuguese Open-Pollinated Varieties (OPV), "Pigarro" (Improved Landrace) and "SinPre" (a Composite Cross Population).

Maize is one of the most important crops worldwide and is the number one arable crop in Portugal. A transition from the conventional farming system to organic agriculture requires optimization of cultivars and management, the interaction of plant-soil rhizosphere microbiota being pivotal. The objectives of this study were to unravel the effect of population genotype and farming system on microbial communities in the rhizosphere of maize. Rhizosphere soil samples of two open-pollinated maize populations ("SinPre" and "Pigarro") cultivated under conventional and organic farming systems were taken during flowering and analyzed by next-generation sequencing (NGS). Phenological data were collected from the replicated field trial. A total of 266 fungi and 317 bacteria genera were identified in "SinPre" and "Pigarro" populations, of which 186 (69.9%) and 277 (87.4%) were shared among them. The microbiota of "Pigarro" showed a significant higher (P < 0.05) average abundance than the microbiota of "SinPre." The farming system had a statistically significant impact (P < 0.05) on the soil rhizosphere microbiota, and several fungal and bacterial taxa were found to be farming system-specific. The rhizosphere microbiota diversity in the organic farming system was higher than that in the conventional system for both varieties. The presence of arbuscular mycorrhizae (Glomeromycota) was mainly detected in the microbiota of the "SinPre" population under the organic farming systems and very rare under conventional systems. A detailed metagenome function prediction was performed. At the fungal level, pathotroph-saprotroph and pathotroph-symbiotroph lifestyles were modified by the farming system. For bacterial microbiota, the main functions altered by the farming system were membrane transport, transcription, translation, cell motility, and signal transduction. This study allowed identifying groups of microorganisms known for their role as plant growth-promoting rhizobacteria (PGPR) and with the capacity to improve crop tolerance for stress conditions, allowing to minimize the use of synthetic fertilizers and pesticides. Arbuscular mycorrhizae (phyla Glomeromycota) were among the most important functional groups in the fungal microbiota and Achromobacter, Burkholderia, Erwinia, Lysinibacillus, Paenibacillus, Pseudomonas, and Stenotrophomonas in the bacterial microbiota. In this perspective, the potential role of these microorganisms will be explored in future research.

A High-Throughput Phenotyping Tool to Identify Field-Relevant Anthracnose Resistance in White Lupin.

The seed- and air-borne pathogen Colletotrichum lupini, the causal agent of lupin anthracnose, is the most important disease in white lupin (Lupinus albus) worldwide and can cause total yield loss. The aims of this study were to establish a reliable high-throughput phenotyping tool to identify anthracnose resistance in white lupin germplasm and to evaluate a genomic prediction model, accounting for previously reported resistance quantitative trait loci, on a set of independent lupin genotypes. Phenotyping under controlled conditions, performing stem inoculation on seedlings, showed to be applicable for high throughput, and its disease score strongly correlated with field plot disease assessments (r = 0.95, P < 0.0001) and yield (r = -0.64, P = 0.035). Traditional one-row field disease phenotyping showed no significant correlation with field plot disease assessments (r = 0.31, P = 0.34) and yield (r = -0.45, P = 0.17). Genomically predicted resistance values showed no correlation with values observed under controlled or field conditions, and the parental lines of the recombinant inbred line population used for constructing the prediction model exhibited a resistance pattern opposite to that displayed in the original (Australian) environment used for model construction. Differing environmental conditions, inoculation procedures, or population structure may account for this result. Phenotyping a diverse set of 40 white lupin accessions under controlled conditions revealed eight accessions with improved resistance to anthracnose. The standardized area under the disease progress curves (sAUDPC) ranged from 2.1 to 2.8, compared with the susceptible reference accession with a sAUDPC of 3.85. These accessions can be incorporated into white lupin breeding programs. In conclusion, our data support stem inoculation-based disease phenotyping under controlled conditions as a time-effective approach to identify field-relevant resistance, which can now be applied to further identify sources of resistance and their underlying genetics.

Heritable Variation in Pea for Resistance Against a Root Rot Complex and Its Characterization by Amplicon Sequencing.

Soil-borne pathogens cause severe root rot of pea (Pisum sativum L.) and are a major constraint to pea cultivation worldwide. Resistance against individual pathogen species is often ineffective in the field where multiple pathogens form a pea root rot complex (PRRC) and conjointly infect pea plants. On the other hand, various beneficial plant-microbe interactions are known that offer opportunities to strengthen plant health. To account for the whole rhizosphere microbiome in the assessment of root rot resistance in pea, an infested soil-based resistance screening assay was established. The infested soil originated from a field that showed severe pea root rot in the past. Initially, amplicon sequencing was employed to characterize the fungal microbiome of diseased pea roots grown in the infested soil. The amplicon sequencing evidenced a diverse fungal community in the roots including pea pathogens Fusarium oxysporum, F. solani, Didymella sp., and Rhizoctonia solani and antagonists such as Clonostachys rosea and several mycorrhizal species. The screening system allowed for a reproducible assessment of disease parameters among 261 pea cultivars, breeding lines, and landraces grown for 21 days under controlled conditions. A sterile soil control treatment was used to calculate relative shoot and root biomass in order to compare growth performance of pea lines with highly different growth morphologies. Broad sense heritability was calculated from linear mixed model estimated variance components for all traits. Emergence on the infested soil showed high (H 2 = 0.89), root rot index (H 2 = 0.43), and relative shoot dry weight (H 2 = 0.51) medium heritability. The resistance screening allowed for a reproducible distinction between PRRC susceptible and resistant pea lines. The combined assessment of root rot index and relative shoot dry weight allowed to identify resistant (low root rot index) and tolerant pea lines (low relative shoot dry weight at moderate to high root rot index). We conclude that relative shoot dry weight is a valuable trait to select disease tolerant pea lines. Subsequently, the resistance ranking was verified in an on-farm experiment with a subset of pea lines. We found a significant correlation (r s = 0.73, p = 0.03) between the controlled conditions and the resistance ranking in a field with high PRRC infestation. The screening system allows to predict PRRC resistance for a given field site and offers a tool for selection at the seedling stage in breeding nurseries. Using the complexity of the infested field soil, the screening system provides opportunities to study plant resistance in the light of diverse plant-microbe interactions occurring in the rhizosphere.

Advances in Breeding for Mixed Cropping - Incomplete Factorials and the Producer/Associate Concept.

Mixed cropping has been suggested as a resource-efficient approach to meet high produce demands while maintaining biodiversity and minimizing environmental impact. Current breeding programs do not select for enhanced general mixing ability (GMA) and neglect biological interactions within species mixtures. Clear concepts and efficient experimental designs, adapted to breeding for mixed cropping and encoded into appropriate statistical models, are lacking. Thus, a model framework for GMA and SMA (specific mixing ability) was established. Results of a simulation study showed that an incomplete factorial design combines advantages of two commonly used full factorials, and enables to estimate GMA, SMA, and their variances in a resource-efficient way. This model was extended to the Producer (Pr) and Associate (As) concept to exploit additional information based on fraction yields. It was shown that the Pr/As concept allows to characterize genotypes for their contribution to total mixture yield, and, when relating to plant traits, allows to describe biological interaction functions (BIF) in a mixed crop. Incomplete factorial designs show the potential to drastically improve genetic gain by testing an increased number of genotypes using the same amount of resources. The Pr/As concept can further be employed to maximize GMA in an informed and efficient way. The BIF of a trait can be used to optimize species ratios at harvest as well as to extend our understanding of competitive and facilitative interactions in a mixed plant community. This study provides an integrative methodological framework to promote breeding for mixed cropping.

Insights to plant-microbe interactions provide opportunities to improve resistance breeding against root diseases in grain legumes.

Root and foot diseases severely impede grain legume cultivation worldwide. Breeding lines with resistance against individual pathogens exist, but these resistances are often overcome by the interaction of multiple pathogens in field situations. Novel tools allow to decipher plant-microbiome interactions in unprecedented detail and provide insights into resistance mechanisms that consider both simultaneous attacks of various pathogens and the interplay with beneficial microbes. Although it has become clear that plant-associated microbes play a key role in plant health, a systematic picture of how and to what extent plants can shape their own detrimental or beneficial microbiome remains to be drawn. There is increasing evidence for the existence of genetic variation in the regulation of plant-microbe interactions that can be exploited by plant breeders. We propose to consider the entire plant holobiont in resistance breeding strategies in order to unravel hidden parts of complex defence mechanisms. This review summarizes (a) the current knowledge of resistance against soil-borne pathogens in grain legumes, (b) evidence for genetic variation for rhizosphere-related traits, (c) the role of root exudation in microbe-mediated disease resistance and elaborates (d) how these traits can be incorporated in resistance breeding programmes.

Cowpea (Vigna unguiculata L. Walp) hosts several widespread bradyrhizobial root nodule symbionts across contrasting agro-ecological production areas in Kenya.

Cowpea (Vigna unguiculata L. Walp.) is an important African food legume suitable for dry regions. It is the main legume in two contrasting agro-ecological regions of Kenya as an important component of crop rotations because of its relative tolerance to unpredictable drought events. This study was carried out in an effort to establish a collection of bacterial root nodule symbionts and determine their relationship to physicochemical soil parameters as well as any geographical distributional patterns. Bradyrhizobium spp. were found to be widespread in this study and several different types could be identified at each site. Unique but rare symbionts were recovered from the nodules of plants sampled in a drier in-land region, where there were also overall more different bradyrhizobia found. Plants raised in soil from uncultivated sites with a natural vegetation cover tended to also associate with more different bradyrizobia. The occurrence and abundance of different bradyrhizobia correlated with differences in soil texture and pH, but did neither with the agro-ecological origin, nor the origin from cultivated (n = 15) or uncultivated (n = 5) sites. The analytical method, protein profiling of isolated strains by Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS), provided higher resolution than 16S rRNA gene sequencing and was applied in this study for the first time to isolates recovered directly from field-collected cowpea root nodules. The method thus seems suitable for screening isolate collections on the presence of different groups, which, provided an appropriate reference database, can also be assigned to known species.

Evaluation of MALDI-TOF mass spectrometry for the competitiveness analysis of selected indigenous cowpea (Vigna unguiculata L. Walp.) Bradyrhizobium strains from Kenya.

Cowpea N2 fixation and yield can be enhanced by selecting competitive and efficient indigenous rhizobia. Strains from contrasting agro-ecologies of Kilifi and Mbeere (Kenya) were screened. Two pot experiments were established consisting of 13 Bradyrhizobium strains; experiment 1 (11 Mbeere + CBA + BK1 from Burkina Faso), experiment 2 (12 Kilifi + CBA). Symbiotic effectiveness was assessed (shoot biomass, SPAD index and N uptake). Nodule occupancy of 13 simultaneously co-inoculated strains in each experiment was analyzed by matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry (MS) to assess competitiveness. Strains varied in effectiveness and competitiveness. The four most efficient strains were further evaluated in a field trial in Mbeere during the 2014 short rains. Strains from bacteroids of cowpea nodules from pot and field experiments were accurately identified as Bradyrhizobium by MALDI-TOF based on the SARAMIS™ database. In the field, abundant indigenous populations 7.10 × 103 rhizobia g-1 soil, outcompeted introduced strains. As revealed by MALDI-TOF, indigenous strains clustered into six distinct groups (I, II, III, IV, V and VI), group III were most abundant occupying 80% of nodules analyzed. MALDI-TOF was rapid, affordable and reliable to identify Bradyrhizobium strains directly from nodule suspensions in competition pot assays and in the field with abundant indigenous strains thus, its suitability for future competition assays. Evaluating strain competitiveness and then symbiotic efficacy is proposed in bioprospecting for potential cowpea inoculant strains.

A Diagnosis of Biophysical and Socio-Economic Factors Influencing Farmers' Choice to Adopt Organic or Conventional Farming Systems for Cotton Production.

Organic agriculture is one of the most widely known alternative production systems advocated for its benefits to soil, environment, health and economic well-being of farming communities. Rapid increase in the market demand for organic products presents a remarkable opportunity for expansion of organic agriculture. A thorough understanding of the context specific motivations of farmers for adoption of organic farming systems is important so that appropriate policy measures are put in place. With an aim of understanding the social and biophysical motivations of organic and conventional cotton farmers for following their respective farming practices, a detailed farm survey was conducted in Nimar valley of Madhya Pradesh state in central India. The study area was chosen for being an important region for cotton production, where established organic and conventional farms operate under comparable circumstances. We found considerable variation among organic and conventional farmers for their social and biophysical motivations. Organic farmers were motivated by the sustainability of cotton production and growing safer food without pesticides, whereas conventional farmers were sensitive about their reputation in community. Organic farmers with larger holdings were more concerned about closed nutrient cycles and reducing their dependence on external inputs, whereas medium and small holding organic farmers were clearly motivated by the premium price of organic cotton. Higher productivity was the only important motivation for conventional farmers with larger land holdings. We also found considerable yield gaps among different farms, both under conventional and organic management, that need to be addressed through extension and training. Our findings suggest that research and policy measures need to be directed toward strengthening of extension services, local capacity building, enhancing availability of suitable inputs and market access for organic farmers.

Learning to Classify Organic and Conventional Wheat - A Machine Learning Driven Approach Using the MeltDB 2.0 Metabolomics Analysis Platform.

We present results of our machine learning approach to the problem of classifying GC-MS data originating from wheat grains of different farming systems. The aim is to investigate the potential of learning algorithms to classify GC-MS data to be either from conventionally grown or from organically grown samples and considering different cultivars. The motivation of our work is rather obvious nowadays: increased demand for organic food in post-industrialized societies and the necessity to prove organic food authenticity. The background of our data set is given by up to 11 wheat cultivars that have been cultivated in both farming systems, organic and conventional, throughout 3 years. More than 300 GC-MS measurements were recorded and subsequently processed and analyzed in the MeltDB 2.0 metabolomics analysis platform, being briefly outlined in this paper. We further describe how unsupervised (t-SNE, PCA) and supervised (SVM) methods can be applied for sample visualization and classification. Our results clearly show that years have most and wheat cultivars have second-most influence on the metabolic composition of a sample. We can also show that for a given year and cultivar, organic and conventional cultivation can be distinguished by machine-learning algorithms.

High-resolution analysis of a QTL for resistance to Stagonospora nodorum glume blotch in wheat reveals presence of two distinct resistance loci in the target interval.

Stagonospora nodorum glume blotch (SNG), caused by the necrotrophic fungus Stagonospora nodorum, is one of the economically important diseases of bread wheat (Triticum aestivum L.). Resistance to SNG is known to be quantitative and previous studies of a recombinant inbred line (RIL) population identified a major quantitative trait locus (QTL) for resistance to SNG on the short arm of chromosome 3B. To localize this QTL (QSng.sfr-3BS) with high resolution, we constructed a genetic map for the QTL target region using information from sequenced flow-sorted chromosomes 3B of the two parental cultivars 'Arina' and 'Forno', the physical map of chromosome 3B of cultivar 'Chinese Spring' and BAC-clone sequences. The mapping population of near-isogenic lines (NIL) was evaluated for SNG resistance in field infection tests. NILs segregated for disease resistance as well as for plant height; additionally, we observed a high environmental influence on the trait. Our analysis detected a strong negative correlation of SNG resistance and plant height. Further analysis of the target region identified two linked loci associated with SNG resistance. One of them was also associated with plant height, revealing an effect of QSng.sfr-3BS on plant height that was hidden in the RIL population. This result demonstrates an unexpectedly high genetic complexity of resistance controlled by QSng.sfr-3BS and shows the importance of the study of QTL in mendelized form in NILs.

Yield and economic performance of organic and conventional cotton-based farming systems--results from a field trial in India.

The debate on the relative benefits of conventional and organic farming systems has in recent time gained significant interest. So far, global agricultural development has focused on increased productivity rather than on a holistic natural resource management for food security. Thus, developing more sustainable farming practices on a large scale is of utmost importance. However, information concerning the performance of farming systems under organic and conventional management in tropical and subtropical regions is scarce. This study presents agronomic and economic data from the conversion phase (2007-2010) of a farming systems comparison trial on a Vertisol soil in Madhya Pradesh, central India. A cotton-soybean-wheat crop rotation under biodynamic, organic and conventional (with and without Bt cotton) management was investigated. We observed a significant yield gap between organic and conventional farming systems in the 1(st) crop cycle (cycle 1: 2007-2008) for cotton (-29%) and wheat (-27%), whereas in the 2(nd) crop cycle (cycle 2: 2009-2010) cotton and wheat yields were similar in all farming systems due to lower yields in the conventional systems. In contrast, organic soybean (a nitrogen fixing leguminous plant) yields were marginally lower than conventional yields (-1% in cycle 1, -11% in cycle 2). Averaged across all crops, conventional farming systems achieved significantly higher gross margins in cycle 1 (+29%), whereas in cycle 2 gross margins in organic farming systems were significantly higher (+25%) due to lower variable production costs but similar yields. Soybean gross margin was significantly higher in the organic system (+11%) across the four harvest years compared to the conventional systems. Our results suggest that organic soybean production is a viable option for smallholder farmers under the prevailing semi-arid conditions in India. Future research needs to elucidate the long-term productivity and profitability, particularly of cotton and wheat, and the ecological impact of the different farming systems.

Nutrient use efficiency and arbuscular mycorrhizal root colonisation of winter wheat cultivars in different farming systems of the DOK long-term trial.

BACKGROUND: For organic farming, cultivars are required with high nutrient use efficiency under nutrient limited conditions. Arbuscular mycorrhizal fungi (AMF) are known to contribute to nutrient uptake under low input conditions. We compared nutrient use efficiency (NUE) of old and modern organically and conventionally bred cultivars in organic and conventional systems and assessed AMF-root colonisation (AMF-RC) in relation to nutrient concentrations. RESULTS: Cultivars and systems had a statistically significant effect on nitrogen (N) and phosphorus (P) concentrations and NUE parameters, whereas no genotype x environment interactions appeared. In contrast to N and P uptake, the NUE parameters were higher under organic than under conventional conditions. NUE for N increased with the year of release of cultivars. In the organic systems, the organically bred cultivars could not outperform the conventionally bred cultivars in grain yield and NUE parameters. AMF-RC was higher in the organic than in the conventional system, but did not differ among cultivars. CONCLUSION: Cultivars achieving high NUE in the organic systems were found among modern cultivars, irrespective of the breeding programme. Nutrient conditions during the breeding programme did not affect AMF-RC. No clear evidence was found that AMF symbiosis contributed more to nutrient concentrations under low input than under high input conditions.