AMA, Agricultural Mechanization in Asia, Africa and Latin America
AMA, Agricultural Mechanization in Asia, Africa and Latin America (AMA) (issn: 00845841) is a peer reviewed journal first published online after indexing scopus in 1982. AMA is published by Farm Machinery Industrial Research Corp and Shin-Norinsha Co. AMA publishes every subjects of general engineering and agricultural engineering.
WOS Indexed (2026)
07 May 2026 (Vol - 57 , Issue- 05 )
31 May 2026 (Vol - 57 , Issue 05 )
Aim and Scope :
AMA, Agricultural Mechanization in Asia, Africa and Latin America
AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) is a peer-reviewed journal. The journal covers Agricultural and Biological Sciences and all sort of engineering topic. the journal's scopes are in the following fields but not limited to:
Agricultural and Biological Sciences
Electrical Engineering and Telecommunication
Electronic Engineering
Computer Science & Engineering
Civil and architectural engineering
Mechanical and Materials Engineering
Transportation Engineering
Industrial Engineering
Industrial and Commercial Design
Information Engineering
Chemical Engineering
Food Engineering
Correlation of major soil enzymes with population dynamics of Ralstonia solanacearum under different ginger based crop rotation system.
Ginger (Zingiber officinale Rose) is considered as one of the most important commercially grown spice crops. Green wilt disease caused by pathogen Ralstonia solanacearum is one of the devastating disease causing huge economic losses among the ginger growing communities. In hill regions of West Bengal, ginger is mostly grown organically and management of the pathogen and disease is challenging. Therefore, crop rotation with non-host crops plays a vital role in management of such soil borne pathogen by reducing the population load. Additionally left over compost and incorporation of FYM as an organic source of nutrients in organic cultivation of crop not only helps in increasing organic matter content and enhancing soil quality but also helps in enhancing various enzymatic activities. This enzymatic activity has been used as an indicator in enhancing beneficial soil microflora which helps in reducing the soil borne pathogens. The finding of the present experiments indicated cabbage-based crop rotation system with ginger as a beneficial one as it helps in reducing the pathogen population during the active tillering stage which is the crucial stage of pathogen infection. Also a significant negative correlation of R. solanacearum population with Dehydrogenase and β- glucosidase enzymes was recorded however no significant correlation was observed with the Acid phosphatase enzymes.
Compatibility study of entomopathogenic fungus (Beauveria bassiana) with promising plant oils
In recent years, plant oils and entomopathogens play a potential role in eco-smart management of insect pests due to their eco-friendly, effective and specific nature and hence needed to be prioritized as an alternative to chemical control in the pursuit of a healthy nation. The present investigation was made to study the compatibility of Beauveria bassiana with four plant oils at three different concentrations (neem oil @ 2.00, 1.00, 0.50 per cent; karanja oil @ 2.50, 1.00, 0.25 per cent, jatropha oil @ 4.00, 2.00, 0.50 per cent and citronella oil @ 0.30, 0.50 and 1.00 per cent). It was studied considering four aspects viz., radial growth, conidial production, sporulation and per cent inhibition for compatibility test. It revealed that citronella oil @ 0.30 + B.bassiana 1x107 conidia/ml combination was highly compatible with T value 79 followed by 74 and 72 in neem oil (0.50%) + B. bassiana (1x107 conidia/ml) and citronella oil (0.50%)+ B. bassiana (1x107) conidia/ml) respectively. Otherwise, jatropha oil (4.00%) + B. bassiana(1x107 conidia/ml) recorded the lowest T-value (45.70) indicating less compatibility showing moderate toxicity. The highly compatible combination of citronella oil @ 0.30 + B.bassiana (1x107 conidia/ml)would be a potential bioagent in pest management andcould enhance its virulence against insects at low cost too.
Phenotypic and Genetic Characterization of Diara Buffalo of Bihar
Diara buffaloes are dual purpose lesser known population distributed around south and north Gangetic plains of Bihar. These buffaloes are adapted to environmental conditions of Bihar, India and its genetic constitution getting diluted due to use of semen of other breeds. The phenotypic and genetic characterization studies assist to policy makers to take decisions for conservation and rational implementation of breeding programme. A total of 200 animals from all surveyed villages were included to record information on various management practices, physical characteristics and production and reproduction performances using structured questionnaire. The molecular characterization using FAO recommended 10 microsatellite markers were carried out on 50 unrelated individuals of buffaloes. Different measures of genetic diversity were estimated using various softwares. The average number observed of alleles (Na), Effective number of alleles (Ne), Shannon Information Index (I), Observed heterozygosity (Ho), Expected heterozygosity (HE), Average heterozygosity (HA), Polymorphism Information Content (PIC) at different microsatellites loci in Diara buffalo population were obtained 7.4, 5.7, 1.8, 0.68, 0.82, 0.80 and 0.79, respectively. The allele distribution followed the normal L-shaped form suggesting that the breed had not encountered a genetic bottleneck in the recent past. The result shows sufficiently high genetic diversity in the population. The information generated in this study may aid in formulation of effective breeding and conservation programme. However, genetic diversity study of Diara buffalo needs to be extended to include more microsatellites in a large sample size to further validate the research.
Production Performance and Carcass traits of Dual Purpose Crosses of Two Indigenous with Improved Chicken variety in Sub-tropical Environment of Bihar
This study aims to evaluate the body weight and carcass traits of Vanaraja poultry and its crosses with local Desi birds in the different agro-climatic conditions of Bihar, India. The research problem is the lack of information on the performance of Vanaraja and its crosses in this specific region. The study was conducted at the Instructional Livestock Farm Complex in Bihar Veterinary College, Patna. Vanaraja and its crosses with Desi fowl from different regions of Bihar were used as genetic groups. Body weight at different ages and various carcass traits were measured. The data were analyzed using a mixed model least-squares and maximum likelihood computer-based program. The results showed that Vanaraja and its crosses had significantly higher body weight and better carcass traits compared to the local Desi birds. The study concludes that the crosses exhibited heterotic effects and were superior to the Desi birds for these traits. However, the Vanaraja breed itself remained superior to the crosses. The findings have implications for promoting poultry production and improving income and nutrition in Bihar.
Multi-Class Semantic Segmentation of Caprine Parasites using Deep Lab V3+ Architecture
Parasitic infections are one of the main causes of infectious diseases in animals. Depending on the shape of the parasite, an exact parasite causing infection can be identified and suitable deworming agent can be suggested. Microscopic images are typically used to make a diagnosis, with error rates ranging from modest to substantial. Computational image analysis has been used to solve the problem. A total of 650 images of the seven most common species of parasitic ova are taken, namely Amphistome, Ascaris Egg, B. Coli, Moniezia Ova, Schistosoma Spindale, Strongyle, and Trichuris Egg. In this paper, input microscopic images are segmented to detect parasites in the image, it is accomplished by semantic segmentation using DeepLabv3+ architecture and Inception V3 is used as a backbone for prediction purpose. The model has been trained for 70 epochs considering the batch size as 4 and the number of classes as 8 (i.e. including the background as a class). Then the model is evaluated, the images are given for testing. Out of 650 images, 500 images are utilised for training and 150 for testing and validation. The obtained accuracy is 99.6%, precision 99.7%, Recall 99.9%, F1 score 99.8% and Jaccard 99.6%. The proposed technique is used for faster and accurate diagnosis of parasitic infection and expected to replace the problem of lack of skilled experts to some extent.