One hundred fifty-two Diaporthe isolates were recovered from symptomatic soybean (Glycine max) stems sampled from the U.S. states of Iowa, Indiana, Kentucky, Michigan, and South Dakota. Using morphology and DNA sequencing, isolates were identified as D. aspalathi (8.6%), D. caulivora (24.3%), and D. longicolla (67.1%). Aggressiveness of five isolates each of the three pathogens was studied on cultivars Hawkeye (D. caulivora and D. longicolla) and Bragg (D. aspalathi) using toothpick, stem-wound, mycelium contact, and spore injection inoculation methods in the greenhouse. For D. aspalathi, methods significantly affected disease severity (P < 0.001) and pathogen recovery (P < 0.001). The relative treatment effects (RTE) of stem-wound and toothpick methods were significantly greater than for the other methods. For D. caulivora and D. longicolla, a significant isolate × method interaction affected disease severity (P < 0.05) and pathogen recovery (P < 0.001). Significant differences in RTEs were observed among D. caulivora and D. longicolla isolates only when the stem-wound and toothpick methods were used. Our study has determined that the stem-wound and toothpick methods are reliable to evaluate the three pathogens; however, the significant isolate × method interactions for D. caulivora and D. longicolla indicate that multiple isolates should also be considered for future pathogenicity studies.

Soybean time of flowering and maturity are genetically controlled by E genes. Different allelic combinations of these genes determine soybean adaptation to a specific latitude. The paper describes the first attempt to assess adaptation of soybean genotypes developed and realized at Institute of Field and Vegetable Crops, Novi Sad, Serbia [Novi Sad (NS) varieties and breeding lines] based on E gene variation, as well as to comparatively assess E gene variation in North-American (NA), Chinese, and European genotypes, as most of the studies published so far deal with North-American and Chinese cultivars and breeding material. Allelic variation and distribution of the major maturity genes (E1, E2, E3, and E4) has been determined in 445 genotypes from soybean collections of NA ancestral lines, Chinese germplasm, and European varieties, as well as NS varieties and breeding lines. The study showed that allelic combinations of E1–E4 genes significantly determined the adaptation of varieties to different geographical regions, although they have different impacts on maturity. In general, each collection had one major E genotype haplogroup, comprising over 50% of the lines. The exceptions were European varieties that had two predominant haplogroups and NA ancestral lines distributed almost evenly among several haplogroups. As e1-as/e2/E3/E4 was the most common genotype in NS population, present in the best-performing genotypes in terms of yield, this specific allele combination was proposed as the optimal combination for the environments of Central-Eastern Europe.

Soybean plant density is an important factor of successful agricultural production. Due to the high number of plants per unit area, early plant overlapping and eventual plant loss, the estimation of soybean plant density in the later stages of development should enable the determination of the final plant number and reflect the state of the harvest. In order to assess soybean plant density in a digital, nondestructive, and less intense way, analysis was performed on RGB images (containing three channels: RED, GREEN, and BLUE) taken with a UAV (Unmanned Aerial Vehicle) on 66 experimental plots in 2018, and 200 experimental plots in 2019. Mean values of the R, G, and B channels were extracted for each plot, then vegetation indices (VIs) were calculated and used as predictors for the machine learning model (MLM). The model was calibrated in 2018 and validated in 2019. For validation purposes, the predicted values for the 200 experimental plots were compared with the real number of plants per unit area (m2). Model validation resulted in the correlation coefficient—R = 0.87, mean absolute error (MAE) = 6.24, and root mean square error (RMSE) = 7.47. The results of the research indicate the possibility of using the MLM, based on simple values of VIs, for the prediction of plant density in agriculture without using human labor.

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