Charcoal rot disease caused byMacrophomina phaseolina(Tassi) Goid is one of the most devastating diseases in soybean in India. During 2018, 226 diverse soybean genotypes were evaluated for genetic resistance under hot-spot conditions. Out of them, a subset of 151 genotypes were selected based on Percent Disease Incidence (PDI) and better agronomic performance. Out of these 151 genotypes evaluated during 2019, 43 genotypes were selected based on PDI and superior agronomic performance for further field evaluation and molecular characterization. During 2020 and 2021, these forty-three genotypes, were evaluated for PDI, Area Under Disease Progress Curve (AUDPC), and grain yield. In 2020, genotype JS 20-20 showed least PDI (0.42) and AUDPC (9.37).Highest grain yield was recorded by the genotype JS 21-05 (515.00 g). In 2021, genotype JS 20-20 exhibited least PDI (0.00) and AUDPC (0.00).Highest grain yield was recorded in JS 20-98 (631.66 g). Across both years, JS 20-20 had the least PDI (0.21) and AUDPC (4.68), while grain yield was highest in JS 20-98 (571.67 g). Through MGIDI (multi-trait genotype-ideotype distance) analysis, JS 21-05 (G19), JS 22-01 (G43), JS 20-98 (G28) and JS 20-20 (G21) were identified as the ideotypes with respect to the traits that were evaluated. Two unique alleles, Satt588 (100 bp) on linkage group K (Chromosome no 9) and Sat_218 (200 bp) on linkage group H (Chromosome no 12), were specific for thetwo resistant genotypes JS 21-71and DS 1318, respectively. Through cluster analysis, it was observed that the genotypes bred at Jabalpur were more genetically related.

Anthracnose caused byColletotrichum truncatumis a major soybean disease in India. Genetic resistance is the viable option to combat yield losses due to this disease. In the current study, 19 soybean genotypes were evaluated for anthracnose disease resistance at five locations (Medziphema, Palampur, Dharwad, Jabalpur and Indore) for three consecutive years (2017–2019) to identify stable and superior genotypes as resistant sources and to elucidate genotype (G) × environment (E) interactions. Genotype effect, environment effect and G × E interactions were found significant (P< 0.001) where G × E interactions contributed highest (42.44) to the total variation followed by environment (29.71) and genotype (18.84). Through Weighted Average of Absolute Scores (WAASB) stability analysis, PS 1611 (WAASB score = 0.33) was found to be most stable and through WAASBY superiority analysis NRC 128 (WAASBY score = 94.31) and PS 1611 (WAASBY score = 89.43) were found to be superior for mean performance and stability. These two genotypes could be candidate parents for breeding for durable and stable anthracnose resistance. Through principal component analysis, disease score was found to be positively associated with relative humidity, wind speed at 2 m above ground level, effect of temperature on radiation use efficiency and global solar radiation based on latitude and Julian day. Among the five locations, Indore was found to be highly discriminative with the highest mean disease incidence and could differentiate anthracnose-resistant and susceptible genotypes effectively, therefore can be considered an ideal location for breeding for field resistance against anthracnose disease.

Background: In this study, whole genome re-sequencing of rust resistant soybean genotype EC241780 was performed to understand the genomic landscape involved in the resistance mechanism. Methods: A total of 374 million raw reads were obtained with paired-end sequencing performed with Illumina HiSeq 2500 instrument, out of which 287.3 million high quality reads were mapped to Williams 82 reference genome. Comparative sequence analysis of EC241780 with rust susceptible cultivars Williams 82 and JS 335 was performed to identify sequence variation and to prioritise the candidate genes. Results: Comparative analysis indicates that genotype EC241780 has high sequence similarity with rust resistant genotype PI 200492 and the resistance in EC241780 is conferred by the Rpp1 locus. Based on the sequence variations and functional annotations, three genes Glyma18G51715, Glyma18G51741 and Glyma18G51765 encoding for NBS-LRR family protein were identified as the most prominent candidate for Rpp1 locus. Conclusion: The study provides insights of genome-wide sequence variation more particularly at Rpp1 loci which will help to develop rust resistant soybean cultivars through efficient exploration of the genomic resource.

Charcoal rot is one of the most destructive fungal diseases of soybean, caused by the pathogen called Macrophomina phaseolina. This disease thrives in warm and dry conditions, affecting the yield of soybean and other important agronomic crops. Existing methods used to screen the disease suffer from several drawbacks including, manual rating, low accuracy, high operating time, and high system complexity. To circumvent these drawbacks, we developed a laser biospeckle based sensor to characterize the charcoal rot in soybean crop. Applicability of the proposed sensor was tested to analyze three major aspects of plant disease management, viz. characterization of disease progression, early identification of disease symptoms, and analysis of genetic resistance of the given cultivar towards the disease. The experiments were conducted during Kharif season for two consecutive years (2019 and 2020) on two cultivars of soybean, namely, JS 90-41 and AMS-MB-5-18. The proposed sensor as well as standard rating protocol (i.e. measuring the length of necrosis) were used to analyze the extent of disease. To characterize the disease progression and the genetic resistance of different cultivars against M. phaseolina, two new metrics, charcoal rot severity index and disease susceptibility index were introduced. Biospeckle activity was found to be strongly correlated with the lesion length of infected plant stems (r = +0.96, p <; .01, two-tailed (for JS 90-41) and (r = +0.95, p <; .01, two-tailed (for AMS-MB-5-18) for the year 2019; and r = +0.97, p <; .01, two-tailed (for JS 90-41) and r = +0.93, p <; .01, two-tailed (for AMS-MB-5-18) for the year 2020). Experimental results clearly indicate that the proposed sensor can be used as an efficient tool to detect the disease in its early stages of pathogen development. This study provides insights into development and implementation of disease control measures for increasing soybean crop production.