Brucellosis in migratory sheep flock from Maharashtra,India

Brucellosis is a zoonotic disease worldwide distributed and having the economic as well as public health importance. The prevalence of brucellosis among sheep flock having history of abortions was studied. A total of 229 samples comprising of 157 blood and 72 clinical samples (vaginal swabs) were collected from 157 animals. Clinical samples were processed for the isolation of Brucella melitensis. Serum samples (n = 157) were tested by Rose Bengal plate test (RBPT) and i-ELISA. A total of 68 (43.31%) and 104 (66.24%) samples were positive by RBPT and ELISA, respectively. Brucella isolates (n = 2) were recovered from clinical samples. Both isolates demonstrated amplification for bcsp 31 and IS711 genes. On AMOS PCR, both the isolates amplified at 731 bp, i.e., belongs to B. melitensis species. The incidence of B. melitensis in a migratory flock warns the thorough testing and culling of Brucella-infected sheep from the flock on a continuous basis; otherwise, such incidence will be routine and poor farmers will be at a loss.     

Degradation of AM Nitrification Inhibitor in Soil of Subtropical Region

The aim of the study was to evaluate the degradation and persistence of 2-amino 4-chloro 6-methyl pyrimidine (AM), nitrification inhibitor at 1 and 2 µg g^?1 application rates in soil. The extraction of AM was done by QuEChER’s (Quick, Easy. Cheap. Rugged and Safe) method and the quantitative analysis by high-performance liquid chromatography (HPLC). AM decreased with time at both the levels of application with the decline being faster in the beginning up to 7 d. Dissipation of AM occurred in a single phase with the persistence data fitting well to the first-order kinetics. Half-lives of AM were determined to be 14.33 and 16.7 d at 1 and 2 µg g^–1 levels application rates. Since AM remains effective for an adequate period of time, it can be used for increasing efficiency of nitrogenous fertilizers in rice–wheat cropping systems as well as a safeguard for controlling environmental pollution in subtropical soils.     

Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging

Healthy wheat kernels and wheat kernels damaged by the feeding of the insects: rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum) were scanned using a near-infrared (NIR) hyperspecrtal imaging system (700-1100 nm wavelength range) and a colour imaging system. Dimensionality of hyperspectral data was reduced and statistical and histogram features were extracted from NIR images of significant wavelengths and given as input to three statistical discriminant classifiers (linear, quadratic, and Mahalanobis) and a back propagation neural network (BPNN) classifier. A total of 230 features (colour, textural, and morphological) were extracted from the colour images and the most contributing features were selected and used as input to the statistical and BPNN classifiers. The quadratic discriminant analysis (QDA) classifier gave the highest accuracy and correctly identified 96.4% healthy and 91.0-100.0% insect-damaged wheat kernels using the top 10 features from 230 colour image features combined with hyperspectral image features.     

Silicon and plant nutrition—dynamics,mechanisms of transport and role of silicon solubilizer microbiomes in sustainable agriculture: A review

Silicon (Si) is the second most abundant element in the Earth’s crust and has numerous roles in both soils and plants, although it is inaccessible to plants in its native state (insoluble silicate minerals). This inaccessibility can lead to insufficiency, which induces anomalies in plant growth and development.Specifically, Si alleviates various biotic and abiotic stresses in plants by enhancing tolerance mechanisms at different stages of uptake/deposition as a monosilicic acid. Exclusive utiliz...     

Development of somaclonal variants of wheat (Triticum aestivum L.) for yield traits and disease resistance suitable for heat stressed and zero-till conditions

In a substantial rice–wheat cropping system area of South Asia, wheat sowing often gets too delayed and exposed to terminal heat stress. Therefore, farmers prefer varieties that are able to perform well under a short growing period. Tissue culturally regenerated plants of wheat variety cv. HUW 234, the most widely cultivated variety of North Eastern Plain Zone (NEPZ) of India were screened using immature embryo as explant. Days to heading and maturity, yield and other yield components and resistance to leaf rust and spot blotch were evaluated. A few somaclones in R₃ and R₄ generations displayed significant earliness for days to heading and maturity, improved yield traits and resistance to leaf rust and spot blotch diseases. The superior performance of two of the variants was confirmed in the R₅ generation in 3 years of testing under two dates of conventional and zero-till sowing. Stability analysis also suggested superiority of the two somaclones across 12 environments. This appeared to confirm the possibility of obtaining useful somaclonal variants of wheat for very late sown as well as zero-till managed agriculture. The superior performing somaclones can be used as parents in the ongoing breeding programmes targeting late sown wheat in South Asia exposed to terminal heat stress.     

Detection of Sprouted and Midge‐Damaged Wheat Kernels Using Near‐Infrared Hyperspectral Imaging

Sprout damage which results in poor breadmaking quality due to enzymatic activity of α‐amylase is one of the important grading factors of wheat in Canada. Potential of near‐infrared (NIR) hyperspectral imaging was investigated to detect sprouting of wheat kernels. Artificially sprouted, midge‐damaged, and healthy wheat kernels were scanned using NIR hyperspectral imaging system in the range of 1000–1600 nm at 60 evenly distributed wavelengths. Multivariate image analysis (MVI) technique based on principal components analysis (PCA) was applied to reduce the dimensionality of the hyperspectral data. Three wavelengths 1101.7, 1132.2, and 1305.1 nm were identified as significant and used in analysis. Statistical discriminant classifiers (linear, quadratic, and Mahalanobis) were used to classify sprouted, midge‐damaged, and healthy wheat kernels. The discriminant classifiers gave maximum accuracy of 98.3 and 100% for classifying healthy and damaged kernels, respectively.     

Comparison of a Neural Network and a Non-parametric Classifier for Grain Kernel Identification

The performances of a four-layer backpropagation neural network and a non-parametric statistical classifier were compared for classification of barley, Canada Western Amber Durum wheat, Canada Western Red Spring wheat, oats, and rye. A total of 230 features (51 morphological, 123 colour, and 56 textural) from the high-resolution images of kernels of the five grain types were extracted and used as input features for classification. Different feature models, viz. morphological, colour, texture, and a combination of the three, were tested for their ability to classify these cereal grains. To make the classification process fast, the number of input features were reduced to 60 and 30. A set of features consisting of an equal number of morphological, colour, and textural features gave the best classification accuracies. The neural network classifier outperformed the non-parametric classifier in almost all the instances of classification.