Effect of mycorrhizal provision and watering frequency on productivity and fresh material of forage sorghum (Sorghum bicolor (L.) Moench)

Sorghum is a cereal crop that has the potential to be developed as animal feed. Sorghum plant experiencing drought stress will decrease their productivity. Efforts to overcome this problem are by adding mycorrhizae. Mycorrhizae increase the ability of plants to adapt to the environment in the form of absorption of water and nutrients. This study aimed to determine the productivity of sorghum (plant height increase, plant length increase, number of leaves and stem diameter) and fresh weight of sorghum plant. The research treatments were mycorrhizal levels and watering frequency using a completely randomized design with a 3x3 factorial pattern. The mycorrhizal fungi provision consisted of three levels, namely without mycorrhizal (M0), mycorrhizal 10 g/polybag (M10), and mycorrhizal 20 g/polybag (M20). The frequency of watering consisted of watering everyday (A1), watering every four days (A4), and watering every eight days (A8). The provision of mycorrhizae significantly affected the productivity of sorghum and fresh weight of sorghum (p<0.05). The difference in watering frequency affected the increase in sorghum plant height (p<0.05). However, it did not significantly affect the increase in length, stem diameter, number of leaves, and fresh weight of sorghum. In conclusion, mycorrhizae can significantly improved the productivity of sorghum plants developed in areas with high drought levels.

The growth and yield of several sorghum varieties in the first ratoon

At harvest, the stems of the sorghum plant are generally cut down and then it is left abandoned. Meanwhile, the remaining sorghum stems from the harvest have the potential to be regrown for the cultivation of ratoon. The advantages of ratoon cultivation are it has a relatively shorter harvest time than the main crop, requires less water, and lower production costs. The aim of this study was to test the growth and yield of five varieties of sorghum, namely Numbu, Super 1, Suri 3, Keller, and Kawali in ratoon cultivation and to test the growth and yield of sorghum with several selections of the number of shoots in ratoon cultivation. The study used a factorial - Randomized Completed Block Design with 2 treatment factors, repeated 3 times. The first treatment was sorghum varieties consisting of Numbu, Super 1, Suri 3, Keller, and Kawali. The second treatment was the number of shoots consisting of 1 shoot, 2 shoots, 3 shoots, and 4 shoots. Observational data were analyzed using analysis of variance with the F test at 5% level followed by Duncan’s multiple distance test. Observation variables included the height of plant, stem diameter, grain weight per clump, number of grains per clump, the weight of 1000 grains, and yield per plant. The results indicated that the highest plant in the Numbu variety, while the largest stem diameter was in the Kawali variety. The number of shoots 4 increased the grain yield per clump but decreased the plant height compared to the number of shoots that were less. The yield of grains per plot was higher on Super1 varieties as much as 604.33 g or 3.3 ton/ha, however the highest number of grains per clump was on Kawali varieties.

Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery

In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from “stitched” mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.

Complete Genome Report of Sphingobium Yanoikuyae S72: A Bacterium Capable of Degrading Hydrocarbons

Sphingobium yanoikuyae S72 was isolated from the rhizosphere of sorghum plant, Mexico. Its role on the degradation of polycyclic aromatic hydrocarbon (PAH) was evaluated in a minimal medium containing one of biphenyl, naphthalene, phenanthrene, xylene and Toluene as the only carbon source. It grew effectively in each minimal medium (Bushnell Haas, BH). Gas column chromatography–mass spectrometry (GC-MS) analysis on the metabolite recovered after S72’s growth in BH showed that it successfully degraded the PAHs tested resulting in the degradation of biphenyl (85%), phenanthrene (93%), naphthalene (81%), xylene (19%) and toluene (30%). The complete genome of the S72 was sequenced to evaluate the genetic bases of S72`s degradation of PAH. The genome was assembled de novo and reduced to 1 contig and consists of 5,532,623 bp, 5005 protein-coding genes. The analysis of the COG categories in S72 in comparison with other Sphingobium yanoikuyae and other PAH degrading gene, showed that it has in higher abundance in some categories that have been associated with the degradation of PAH (energy production and conversion (COG C, 6.35), carbohydrate transport and metabolism (COG G, 6.20%), lipid transport and metabolism (COG I, 6.99), secondary metabolites biosynthesis (COG Q, 4.52%), general function prediction only in transport and catabolism (COG R, 9.93),) Pan-core genome analysis revealed that about 126 unique genes in S72 are associated with the degradation of xenobiotics and PAHs. Most gene found in S72 are associated with the peripheral degradation pathway for PAH. Based on our observation we proposed a possible benzoate degradation pathway.

KARAKTERISTIK MORFOLOGI PLASMA NUTFAH SORGUM DARI BEBERAPA DAERAH POTENSIAL SUMATERA BARAT

Sorghum (Sorghum bicolor (L.) Moench) is a cereal plant that has the potential to be cultivated and developed as a source of carbohydrates. The research objective was to identify the morphology and varieties of sorghum plants from several potential areas of West Sumatra, such as Limapuluh Kota Regency, Payakumbuh City, and Sijunjung Regency be used as a source of germplasm. The research was carried out in Limapuluh Kota District, Payakumbuh City, and Sijunjung Regency for six months starting from June-November 2020. The research was carried out with stages beginning with a survey of the location of sorghum planting, identification of plant morphological characteristics, identification of varieties/cultivars/lines, and collection of germplasm sources. Observations were made on the morphological characteristics of plants in the vegetative and generative phases based on the sorghum plant description book. The results showed differences between morphological features in the field and existing descriptions in terms of plant properties, plant height, leaf color, leaf bone, panicle density, panicle shape, panicle length starting from the neck of the panicle, shape, and color of the neck of the panicle.

Effect of organic fertilizers on yield and quality indicators of cotton and sorghum plant against mineral fertilizer background

In order to study the influence of organic fertilizers against the background of minerals on the yield and quality of cotton and sorghum, experiments were laid under the conditions of gray-meadow soil of the Shirvan zone. As a result of the studies, it was found that the highest crop was obtained in versions where biohumus and mineral fertilizers were co-introduced. So in these versions, the cotton crop is 12.9 c/ha or 66.1%, and the sorghum crop is 8.5 c/ha or 42.5%, respectively, compared to a control without fertilizers. Key words: organic fertilizers, mineral fertilizers, cotton, sorghum, productivity

Response of biomass, grain production, and sugar content of four sorghum plant varieties (Sorghum bicolor (L.) Moench) to different plant densities

Sorghum (Sorghum bicolor (L.) Moench) is a potential plant for food, livestock feed, biofuel, sugar, alcohol, and other bioindustry products. Sorghum could be adaptable to grow and expand in marginal areas of the world. Varieties of sorghum have their specific morpho-agronomic characters. It would be significant to compare the performance of multiple sorghum varieties to identify a suitable one for the intended use. The increase in biomass plant production could be caused by cultivation factors, such as an increased planting density. This study aims to determine the response of four different sorghum varieties to the treatment of the plant density on the biomass, grain production, and sugar content of stem juice. This research was conducted using two factors: sorghum variety (“Super 1,” “Keler,” “Lepeng,” and “Rio”) and the plant density (two, four, and six plants per hole or 106,667; 213,333; and 320,000 plants ha−1, respectively). The results of the study showed that all four sorghum varieties tested could be used as biomass resources. The highest plant dry biomass was gained from six plants per hole with 44.0 t ha–1, whereas the lowest one was two plants per hole with 30.4 t ha–1. “Super 1” was a superior variety due to the significant highest sugar content of the stem juice (13.9°Brix) and grain production. “Lepeng” variety was the lowest in both sugar content (8°Brix) and grain production, whereas “Keler” and “Rio” varieties contained sugar in between 8.5 and 10.8°Brix of the stem juice.

Measuring Stem Diameter of Sorghum Plants in the Field Using a High-Throughput Stereo Vision System

HighlightsA custom-built camera module named PhenoStereo was developed for high-throughput field-based plant phenotyping.Novel integration of strobe lights facilitated application of PhenoStereo in various environmental conditions.Image-derived stem diameters were found to have high correlations with ground truth, which outperformed any previously reported sensing approach.PhenoStereo showed promising potential to characterize a broad spectrum of plant phenotypes.Abstract. The stem diameter of sorghum plants is an important trait for evaluation of stalk strength and biomass potential, but it is a challenging sensing task to automate in the field due to the complexity of the imaging object and the environment. In recent years, stereo vision has offered a viable three-dimensional (3D) solution due to its high spatial resolution and wide selection of camera modules. However, the performance of in-field stereo imaging for plant phenotyping is adversely affected by textureless regions, occlusion of plants, variable outdoor lighting, and wind conditions. In this study, a portable stereo imaging module named PhenoStereo was developed for high-throughput field-based plant phenotyping. PhenoStereo features a self-contained embedded design, which makes it capable of capturing images at 14 stereoscopic frames per second. In addition, a set of customized strobe lights is integrated to overcome lighting variations and enable the use of high shutter speed to overcome motion blur. PhenoStereo was used to acquire a set of sorghum plant images, and an automated point cloud data processing pipeline was developed to automatically extract the stems and then quantify their diameters via an optimized 3D modeling process. The pipeline employed a mask region convolutional neural network (Mask R-CNN) for detecting stalk contours and a semi-global block matching (SGBM) stereo matching algorithm for generating disparity maps. The correlation coefficient (r) between the image-derived stem diameters and the ground truth was 0.97 with a mean absolute error (MAE) of 1.44 mm, which outperformed any previously reported sensing approach. These results demonstrate that, with proper customization, stereo vision can be an effective sensing method for field-based plant phenotyping using high-fidelity 3D models reconstructed from stereoscopic images. Based on the results from sorghum plant stem diameter sensing, this proposed stereo sensing approach can likely be extended to characterize a broad range of plant phenotypes, such as the leaf angle and tassel shape of maize plants and the seed pods and stem nodes of soybean plants. Keywords: Field-based high-throughput phenotyping, Point cloud, Stem diameter, Stereo vision.