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 (2025)
07 Dec 2025 (Vol - 56 , Issue- 12 )
31 Dec 2025 (Vol - 56 , Issue 12 )
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
Impact of long-term fertilizer and manure application on rice biological yield, nutrient uptake, nutrient use efficiency, yield attributing characters in tropical rice ecosystem
India is the second largest producer of rice in world after China. Rice-rice cropping system is followed in eastern parts of India due to heavy rainfall and low land conditions. Efficient nutrient management is much important in rice cultivation. But, after long-term intensive cultivation of rice with traditional methods and imbalanced fertilizer application imparted a less sustainable system for future generation. So, we examined the various nutrient management strategies under a long-term fertilizer experiment with 4 replications and 11 treatments. The results demonstrated integrated nutrient management of organic manure and chemical fertilizer is a key element for maintaining rice yield and to keep the soil processes alive. 225.51 % biological yield advantage under farmyard manure (FYM) treated plots, respectively). Among the treatments, sulphur and boron uptake were observed highest in respective application plots and farmyard manure application plots (50% higher than 100%NPK). Similarly, internal utilization efficiency, agronomic efficiency, physiological efficiency, and recovery efficiency were observed highest under FYM treated plots. Sulphur uptake was 75.42% higher under NPK+FYM+Lime compared to control and NPK plots. Boron uptake was higher in 100%NPK+B+Zn by 50% compared to 100% NPK plots. Organic manure application along with NPK improved yield attributing characters viz., the plants per hill, tillers per plant, grains per panicle, and plant vigour compared to sole application of NPK. NPK+FYM and NPK+FYM+Lime plots showed the most effective management strategy among the treatments.
IoT based patient care model for highly communicable diseases in times of Covid19
As Covid-19 has become a matter of concern and provocation in the medical sector, the Internet Thing has enhanced humanity through advanced patient care in the healthcare zone. This is accomplished by the employment of a remote patient monitoring system. Consequently, the IoT-based monitoring systems are remarkably favorable for Covid-19 patients. The enactment of patient the health monitoring system is worthwhile as it remotely estimates the health condition of the patient. This has ameliorated the healthcare services by evading the physical contact, thereby facilitating the timely detection of coronavirus cases. The fundamental idea of this paper is to propose a patient monitoring system in times of Covid-19. The dataset of coronavirus patients is taken from API. This dataset comprises pulse rate and oxygen saturation, body temperature, and blood pressure monitor. The final evaluation and analysis have been done to depict the medical condition. The system will monitor mainly the pulse rate and the oxygen level, body temperature, and blood pressure monitor by the adoption of various sensors and the microprocessor Arduino Uno. In the presence of the ESP8266 Node MCU wi-fi module, Arduino will assemble the data from the numerous sensors and send this data to the server. The proposed design of this system is not only efficient in diminishing the fatality rates but also builds up the lives of humans the times of compulsion through its alert technique. Furthermore, this system will originate cautions to emergency services and the hospitals.
Preferences of Indigenous Fish Species according to Profitability by the Fish Farmers in Manipur, Northeast India
The current study was undertaken to determine the farmers' preferences for native fish species based on profitability in Manipur. The study involved 80 farmers from three villages in the Nambol block of Bishnupur district: Ishok, Oinam, and Naorem. A sample survey of farmers was conducted utilizing a personal interview method with a pretested and well-structured interview schedule. The respondents were chosen using a random sampling method. Ngakra, Pengba, Ngaton, Ngakichow, Nganap, and Ngasang are the six indigenous fish species. They're offered in pairs to the responders (fish farmers) in 15 different combinations. The respondents were asked to choose one Indigenous fish species over the other from each pair that they believe is more profitable. The Method of Paired Comparisons was used to analyze the data. According to the fish farmers, Ngakra is first in terms of profitability, followed by Pengba, Ngaton, Ngakichow, Ngasang, and Nganap.
Varietal screening of chickpea (Cicer arietinum L.) genotypes against gram pod borer under field environment condition
Chickpea is a valued food in terms of nutrition for an expanding world population. Wide range of insect pests and diseases leads to heavy losses in Chickpea Crop. It is infested by eleven insect pest species. Among these pests, the pod borer, Helicoverpa armigera (Hubner) (Lepidoptera) is the most deteriorating insect pest causing great losses in most of the chickpea growing areas of the world. It potentially causes serious damage to all plant parts at different growth stages which lead to devastating losses in yield up to 50 per cent or even more due to its incidence. Numerous control actions are taken in order to control this pest which predominantly includes the use chemical insecticides irrationally. The Indiscriminative use of Chemicals as a means of plant protection for such a long spell of time leads to numerous hostile effects that includes insecticidal resistance, environmental pollution, sudden outbreak and resurgence of pest, and also detrimental hazards to human life. To avoid such serious predicament, there is a very sincere need of promoting and enhancing the utilization of non-chemical measures as a core part of the pest control system, in view of which, continuous research efforts are needed to be focused on Development, promotion and enhancing the use of resistant plant material so as to overcome some serious issues emerging as a result of using chemical insecticides and thereby.
ARTIFICIAL INTELLIGENCE IN AGRICULTURE
Global population is expected to reach more than 9 billion by 2050 which will require an increase in agricultural production by 70% to fulfil the demand. Only about 105 of this increased production may come from the availability of unused lands and the rest of 90% should be fulfilled by intensification of current production. In this context use of the latest technological solutions to make farming more efficient, remains one of the greatest necessities. In the future, a farmer’s skills will increasingly be a mix of technology and biological skills rather than pure agricultural. The importance of Artificial Intelligence (AI) in agriculture can be applied cross-disciplinarity and it can also bring a revolution in the farming trend than what we are seeing today. AI-powered solutions will not only enable farmers to do more with less, but they will also improve quality and ensure a faster go-to-market for crops.