Xuxiao Zong

A comprehensive study of the genome and genetics of superior germplasms is fundamental for crop improvement. As a widely adapted protein crop with high yield potential, the improvement in breeding and development of the seeds industry of faba bean have been greatly hindered by its giant genome size and high outcrossing rate. To fully explore the genomic diversity and genetic basis of important agronomic traits, we first generate a de novo genome assembly and perform annotation of a special short-wing petal faba bean germplasm (VF8137) exhibiting a low outcrossing rate. Comparative genome and pan-genome analyses reveal the genome evolution characteristics and unique pan-genes among the three different faba bean genomes. In addition, the genome diversity of 558 accessions of faba bean germplasm reveals three distinct genetic groups and remarkable genetic differences between the southern and northern germplasms. Genome-wide association analysis identifies several candidate genes associated with adaptation- and yield-related traits. We also identify one candidate gene related to short-wing petals by combining quantitative trait locus mapping and bulked segregant analysis. We further elucidate its function through multiple lines of evidence from functional annotation, sequence variation, expression differences, and protein structure variation. Our study provides new insights into the genome evolution of Leguminosae and the genomic diversity of faba bean. It offers valuable genomic and genetic resources for breeding and improvement of faba bean.

Phaseolus vulgaris is a globally important legume cash crop, which can carry out symbiotic nitrogen fixation with rhizobia. The presence of suitable rhizobia in cultivating soils is crucial for legume cropping, especially in areas beyond the plant-host native range, where soils may lack efficient symbiotic partners. We analyzed the distribution patterns and traits of native rhizobia associated with P. vulgaris in soils of Yunnan, where the common bean experienced a recent expansion. A total of 608 rhizobial isolates were tracked from soils of fifteen sampling sites using two local varieties of P. vulgaris. The isolates were discriminated into 43 genotypes as defined by IGS PCR-RFLP. Multiple locus sequence analysis based on recA, atpD and rpoB of representative strains placed them into 11 rhizobial species of Rhizobium involving Rhizobium sophorae, Rhizobium acidisoli, Rhizobium ecuadorense, Rhizobium hidalgonense, Rhizobium vallis, Rhizobium sophoriradicis, Rhizobium croatiense, Rhizobium anhuiense, Rhizobium phaseoli, Rhizobium chutanense and Rhizobium etli, and five unknown Rhizobium species; Rhizobium genosp. I~V. R. phaseoli and R. anhuiense were the dominant species (28.0% and 28.8%) most widely distributed, followed by R. croatiense (14.8%). The other rhizobial species were less numerous or site-specific. Phylogenies of nodC and nifH markers, were divided into two specific symbiovars, sv. phaseoli regardless of the species affiliation and sv. viciae associated with R. vallis. Through symbiotic effect assessment, all the tested strains nodulated both P. vulgaris varieties, often resulting with a significant greenness index (91–98%). However, about half of them exhibited better plant biomass performance, at least on one common bean variety, and two isolates (CYAH-6 and BLYH-15) showed a better symbiotic efficiency score. Representative strains revealed diverse abiotic stress tolerance to NaCl, acidity, alkalinity, temperature, drought and glyphosate. One strain efficient on both varieties and exhibiting stress abiotic tolerance (BLYH-15) belonged to R. genosp. IV sv. phaseoli, a species first found as a legume symbiont.

Peas are one of the most important cultivated legumes worldwide, for which early yield estimations are helpful for agricultural planning. The unmanned aerial vehicles (UAVs) have become widely used for crop yield estimations, owing to their operational convenience. In this study, three types of sensor data (red green blue [RGB], multispectral [MS], and a fusion of RGB and MS) across five growth stages were applied to estimate pea yield using ensemble learning (EL) and four base learners (Cubist, elastic net [EN], K nearest neighbor [KNN], and random forest [RF]). The results showed the following: (1) the use of fusion data effectively improved the estimation accuracy in all five growth stages compared to the estimations obtained using a single sensor; (2) the mid filling growth stage provided the highest estimation accuracy, with coefficients of determination (R²) reaching up to 0.81, 0.8, 0.58, and 0.77 for the Cubist, EN, KNN, and RF algorithms, respectively; (3) the EL algorithm achieved the best performance in estimating pea yield than base learners; and (4) the different models were satisfactory and applicable for both investigated pea types. These results indicated that the combination of dual-sensor data (RGB + MS) from UAVs and appropriate algorithms can be used to obtain sufficiently accurate pea yield estimations, which could provide valuable insights for agricultural remote sensing research.

Early and high-throughput estimations of the crop harvest index (HI) are essential for crop breeding and field management in precision agriculture; however, traditional methods for measuring HI are time-consuming and labor-intensive. The development of unmanned aerial vehicles (UAVs) with onboard sensors offers an alternative strategy for crop HI research. In this study, we explored the potential of using low-cost, UAV-based multimodal data for HI estimation using red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors at 4 growth stages to estimate faba bean (Vicia faba L.) and pea (Pisum sativum L.) HI values within the framework of ensemble learning. The average estimates of RGB (faba bean: coefficient of determination [R²] = 0.49, normalized root-mean-square error [NRMSE] = 15.78%; pea: R² = 0.46, NRMSE = 20.08%) and MS (faba bean: R² = 0.50, NRMSE = 15.16%; pea: R² = 0.46, NRMSE = 19.43%) were superior to those of TIR (faba bean: R² = 0.37, NRMSE = 16.47%; pea: R² = 0.38, NRMSE = 19.71%), and the fusion of multisensor data exhibited a higher estimation accuracy than those obtained using each sensor individually. Ensemble Bayesian model averaging provided the most accurate estimations (faba bean: R² = 0.64, NRMSE = 13.76%; pea: R² = 0.74, NRMSE = 15.20%) for whole growth stage, and the estimation accuracy improved with advancing growth stage. These results indicate that the combination of low-cost, UAV-based multimodal data and machine learning algorithms can be used to estimate crop HI reliably, therefore highlighting a promising strategy and providing valuable insights for high spatial precision in agriculture, which can help breeders make early and efficient decisions.

Cultivated soils need to shelter suitable rhizobia for legume cropping, especially in areas outside of the plant-host native range, where soils may lack efficient symbiotic partners. We analyzed the distribution patterns and traits of native rhizobia associated with Pisum sativum L. in soils of Hebei Province, a region that has recently experienced an expansion of pea production in China. A total of 43 rhizobial isolates were obtained from root-nodules and characterized genetically and symbiotically. The isolates discriminated into 12 genotypes as defined by PCR-RFLP of IGS DNA. Multiple locus sequence analysis (MLSA) based on the 16S rRNA, recA, atpD and gyrB of representative strains placed them into five clusters of four defined species (R. sophorae, R. indicum, R. changzhiense, and R. anhuiense) and a novel Rhizobium genospecies. R. sophorae was the dominant group (58%) followed by R. indicum (23%). The other groups composed of R. changzhiense (14%), R. anhuiense (1 isolate) and the new genospecies (1 isolate), were minor and site-specific. Based on nodC phylogeny, all representatives were intermingled within the symbiovar viciae with R. sophorae and R. changzhiense being a new record. All the tested strains showed efficient symbiotic fixation on pea plants, with half of them exhibiting better plant biomass performance. This suggests that the pea-nodulating rhizobia in Hebei Province form a specific community of efficient symbiotic rhizobia on pea, distinct from those reported in other countries.

Faba bean is an important member of legumes, which has richer protein levels and great development potential. Yield is an important phenotype character of crops, and early yield estimation can provide a reference for field inputs. To facilitate rapid and accurate estimation of the faba bean yield, the dual-sensor (RGB and multi-spectral) data based on unmanned aerial vehicle (UAV) was collected and analyzed. For this, support vector machine (SVM), ridge regression (RR), partial least squares regression (PLS), and k-nearest neighbor (KNN) were used for yield estimation. Additionally, the fusing data from different growth periods based on UAV was first used for estimating faba bean yield to obtain better estimation accuracy. The results obtained are as follows: for a single-growth period, S2 (12 July 2019) had the best accuracy of the estimation model. For fusion data from the muti-growth period, S2 + S3 (12 August 2019) obtained the best estimation results. Furthermore, the coefficient of determination (R²) values for RF were higher than other machine learning algorithms, followed by PLS, and the estimation effects of fusion data from a dual-sensor were evidently better than from a single sensor. In a word, these results indicated that it was feasible to estimate the faba bean yield with high accuracy through data fusion based on dual-sensor data and different growth periods.

Novel germplasm resources are the key to crop breeding, with their genetic diversity and population structure analysis being highly significant for future faba bean breeding. We genotyped 410 global faba bean accessions using the 130K targeted next-generation sequencing (TNGS) genotyping platform, resulting in a total of 38,111 high-quality SNP loci by high-standard filtering. We found the polymorphism information content (PIC) and Nei’s gene diversity were 0.0905–0.3750 and 0.0950–0.5000, with averages of 0.2471 and 0.3035, respectively. After evaluating the genetic diversity of 410 accessions using Nei’s gene diversity and PIC, on the basis of their geographical origin (continent) and structure-analysis-inferred subpopulations, we found that the faba bean accessions from Asia (except China) and Europe had rich genetic diversity, while those from the winter sowing area of China were low. The 410 faba bean accessions were divided into four subpopulations according to population structure analysis and clustering analysis based on Nei’s (1972) genetic distance using the neighbor-joining (NJ) method. However, the same subpopulation contained materials from different geographical origins, thereby indicating that the gene flow or introgression occurred among the accessions. Results from NJ clustering based on shared allele genetic distance indicated that the 410 accessions were divided into three groups according to their dissemination routes. The genetic diversity analysis results demonstrated that the genetic relationships among the faba bean groups with similar ecological environments and geographic origins in neighboring regions or countries were closer and frequently found within the same group, while genetic variation among individuals was the main source of their total genetic variation.

Complete and accurate reference genomes and annotations provide fundamental resources for functional genomics and crop breeding. Here we report a de novo assembly and annotation of a pea cultivar ZW6 with contig N50 of 8.98 Mb, which features a 243-fold increase in contig length and evident improvements in the continuity and quality of sequence in complex repeat regions compared with the existing one. Genome diversity of 118 cultivated and wild pea demonstrated thatPisum abyssinicumis a separate species different fromP. fulvumandP. sativumwithinPisum. Quantitative trait locus analyses uncovered two known Mendel’s genes related to stem length (Le/le) and seed shape (R/r) as well as some candidate genes for pod form studied by Mendel. A pan-genome of 116 pea accessions was constructed, and pan-genes preferred inP. abyssinicumandP. fulvumshowed distinct functional enrichment, indicating the potential value of them as pea breeding resources in the future.

Pea (Pisum sativum L.) is an important legume crop. However, the yield of pea is adversely affected by heat stress in China. In this study, heat-tolerant germplasms were screened and evaluated in the field under multi-conditions. The results showed that heat stress could significantly affect pea yield. On the basis of grain weight per plant, 257 heat-tolerant and 175 heat-sensitive accessions were obtained from the first year’s screening, and 26 extremely heat-tolerant and 19 extremely heat-sensitive accessions were finally obtained in this study. Based on SNaPshot technology, two sets of SNP markers, including 46 neutral and 20 heat-tolerance-related markers, were used to evaluate the genetic diversity and population genetic structure of the 432 pea accessions obtained from the first year’s screening. Genetic diversity analysis showed that the average polymorphic information content was lower using heat-tolerance-related markers than neutral markers because of the selective pressure under heat stress. In addition, population genetic structure analysis showed that neutral markers divided the 432 pea accessions into two subpopulations associated with sowing date type and geographical origin, while the heat-tolerance-related markers divided these germplasms into two subpopulations associated with heat tolerance and sowing date type. Overall, we present a comprehensive resource of heat-tolerant and heat-sensitive pea accessions through heat-tolerance screenings in multi-conditions, which could help genetic improvements of pea in the future.