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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. Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) Zhonghua yi shi za zhi (Beijing, China : 1980)

Submission Deadline
24 Mar 2023 (Vol - 54 , Issue- 03 )
Upcoming Publication
31 Mar 2023 (Vol - 54 , Issue 03 )

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


Paper ID- AMA-27-01-2023-11974

This article used the primary data collected to examine the impact of transfer of integrated crop management technologies on the productivity and welfare of beneficiary cotton farmers. Results shown that the program is effective in improving the productivity and welfare outcomes of beneficiary farmers. The difference in labour needs of beneficiary and non-beneficiary farmers indicates that the program is marginally more cost-effective. Overall, the results revealed that the knowledge level and average productivity of beneficiary farmers are higher than those of non-beneficiary farmers, which is why the effect of transferring integrated crop management techniques to farmers is desirable.

Application of Drone Technology, ICT, GPS, IoT and AI in smart farming – A Review

Paper ID- AMA-26-01-2023-11972

Drone technology an advanced image data analytics with the capabilities it provides have the potential to become important parts of the technology mix that could fill the gap between current agricultural production and the needs of the future. The technology was first implemented in Japan in the 1980s when unmanned helicopters equipped with spraying equipment and pesticides tanks were used to spray crop fields. Typical modern day spraying drones have tank capacity of over ten litres of liquid pesticide with discharge rate of over a litre a minute, allowing them to cover a hectare in ten minutes. Smart Farming is an emerging concept that refers to managing farms using modern Information and Communication Technologies to increase the quantity and quality of products while optimizing the human labor required is a modern farming concept that looks into the use of technology to improve agricultural production while at the same time lowering the inputs significantly. Smart farming runs on the principles of precision farming such as the use of GPS guidance in the application of measures that are site-specific. Minimized or site-specific application of inputs, such as fertilizers and pesticides, in precision agriculture systems will mitigate leaching problems as well as the emission of greenhouse gases. Agriculture is undergoing a fourth revolution triggered by the exponentially increasing use of Information and communication Technology (ICT) in agriculture. In IoT-based smart farming, a system is built for monitoring the crop field with the help of sensors (light, humidity, temperature, soil moisture, etc.) and automating the irrigation system. The industry is turning to Artificial Intelligence technologies to help yield healthier crops, control pests, monitor soil, and growing conditions, organize data for farmers, help with the workload, and improve a wide range of agriculture-related tasks in the entire food supply chain. IoT based remote sensing utilizes sensors placed along the farms like weather stations for gathering data which is transmitted to analytical tool for analysis.

Unveiling the Yield Impact of Minor Millets in Tamil Nadu

Paper ID- AMA-26-01-2023-11971

The present study was undertaken to assess area and yield interaction of minor millets in Tamil Nadu. Data was consolidated for 20 years from 2002 – 2021. The linear, quadratic and exponential functions were fitted in order to analyze the angle of inclination in area, production and productivity of millets in Tamil Nadu. The results revealed that the ‘c’ value in quadratic functional forms for production and productivity are positive and significant for the total millets in Tamil Nadu but negative and non-significant for area coverage. Growing of millet crops is not risky as the CV of area, production and productivity of millet crops was found to be less than 0.438. It was found from the study that there is an increased production due to adoption of improved varieties of millets and recommended package of practices developed by Tamil Nadu Agricultural University.

Demand Driven and Doorstep Transfer of Technology through Convergence of Common Service Centres (CSC) – Case of Vellore District in Tamil Nadu

Paper ID- AMA-26-01-2023-11970

Demand Driven and Doorstep Transfer of Technology through Convergence of Common Service Centres (CSC) – Hub and Spokes Model is an innovative approach to bridge the gap between technology and farmers. The research gap of the study was found to be perception and expectation of the farmers towards CSC. Hence assessing the impact of the CSC and analysing the service quality of the centre was the main focus of the research. Results revealed that 97.00 % of the farmers reported that time has been efficiently managed at the CSC in disseminating agricultural technology. 78.40 per cent of the farmers reported that the charges obtained by the Village Level Entrepreneur for delivering service through CSC was not so high. It was also found that 92.40 % of the farmers had easy access to the centre. The implication of the study led to the development of empirical model showcasing the user satisfaction and benefits given by the CSC to the farmers and the actual expectation from the farmers. The study identified that there was a significant difference between farmer’s service expectation and farmer’s service perception. The technology had high impact since, the time of intervention was correlated with Nivar & Burevi flood and majority of the farmers welcomed this technology as a means of applying crop insurance.

Morphological and molecular characterization of rice root knot nematode, Meloidogyne graminicola infesting major rice cultivating regions of Tamil Nadu

Paper ID- AMA-25-01-2023-11969

Rice root knot nematode, Meloidogyne graminicola is a concern to global rice production, yet it is underexplored in many regions where it is cultivated. To gain a better understanding of M. graminicola prevalence and incidence in Tamil Nadu (India), M. graminicola isolates were obtained from soil and root samples and identified using perineal patterns and rDNA ITS-based sequencing. Galling index, root-knot nematode juveniles per root system and juveniles per 200 cc of soil were used to assess the severity of nematode infestation on rice roots and infested fields in various regions. Our findings show that rice is severely infested by a genetically varied and aggressive M. graminicola, demanding the successful implementation control strategies in rice. Both the conventional posterior cuticular pattern method as well as the molecular characterization method were used to validate the identity of the root-knot nematode (M. graminicola). Molecular study revealed that M. graminicola produced a single band of 790 bp for Meloidogyne graminicola isolate TNAU004 cytochrome c oxidase subunit I (COX1) gene and 764 bp Meloidogyne graminicola isolate TNAU003 large subunit ribosomal RNA gene. Each genus is given a detailed description, including morphometrics, illustrations, and key traits.