WOS Indexed (2022)
clarivate analytics

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.

Submission Deadline
03 Jul 2022 (Vol - 53 , Issue- 07 )
Upcoming Publication
31 Jul 2022 (Vol - 53 , Issue 07 )

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:

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
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

Modelling and forecasting of rice production in south Asian countries

Paper ID- AMA-11-08-2021-10606

This study aimed mainly at forecasting rice production in SAARC and Iran and is linked to the univariate time series prevision. The rice production forecast was done with the BATS and TBATS models, Holt's Linear Trend, (NNAR) model and ARIMA model. All the data series have been divided into training sets for model construction purposes between 1961 and 201 4, and testing sets for validation purposes between 2015 and 2019, and after the best model was selected, prediction was used between 2020 and 2025. From the forecast values, we conclude that rice production in SAARC countries including Iran will continue to increase in the next couple of years. In Pakistan, the highest production growth rate was estimated at 2.59%, while in Afghanistan the lowest growth rate was estimated at 0.13%. The highest SAARC country rice producer, India, is estimated to increase by 2.24 per cent. These findings may be important in building a successful policy on production by providing an idea of the expected production values or by assessing the performance of these policies and by predicting the food gap in rice crops.

State of art of ARIMA modeling in top five egg production countries in World

Paper ID- AMA-11-08-2021-10605

Eggs constitute a good source for animal proteins and are considered as a food of great nutritional value. In this way, Eggs production has shown a marked increasing in the last years through the world. The objective of this work is to predict eggs production in the next years in the five highest producing countries in the world including China, the Unites States of America, India, Mexico and Indonesia using time series data from 1961 to 2019. Five ARIMA models (1,2,0), (5,2,0), (1,2,1), (4,2,1) and (2,2,0) were used. Results showed that the best model for China, India, Indonesia, Mexico and United States of America are ARIMA (1,2,0), (5,2,0), (1,2,1), (4,2,1) and (2,2,0) respectively. Eggs production prevision for the cited countries showed that except for the United States of America where the production would decrease for about (- 2.6%), an increasing of production of (16.7%), (15%), (13,7%) and (11.5%) would be observed in 2026 in China, India, Indonesia and Mexico respectively. The estimated production willbe 930104, 100920,83806, 71402, 42855 million numbers in 2026in China, India, United States, Mexico and Indonesia respectively. This study showed the importance of ARIMA models in the prediction of eggs production which could be helpful for decision-makers in planning for future food policy.

Intel Mot: An Internet of Things Based Smart Watering System Using Decision Trees

Paper ID- AMA-11-08-2021-10604

Watering to the plants in the agriculture field is done promptly in conventional farming, without considering important parameters such as the water requirement of a crop, the possibility of rain on the next day, etc. In some instances, soil moisture, humidity, and temperature are taken into consideration for crop watering which may not be sufficient to conserve the water. The atmospheric conditions play a main role in the water requirements of the crop. The internet of things and machine learning are extensively becoming popular during the last decade and find applications in all domains such as agriculture, banking, smart home, etc. Internet of things is used in this agriculture application, where the data from soil moisture sensor, humidity, and temperature sensor is combined along with weather data to water the crops. In this research paper, an IoT-based smart watering system for assessing the watering requirements of the crop is proposed. The soil moisture parameters in multi-crop environments at various locations are measured using a ground moving robot with a moisture sensor embedded in it. Information gain and entropy statistics of the decision tree are applied to find the status of the output actuator (sprinkler motor). Entropy methods make our system more efficient by using decision tree split criteria, and it is implemented using MATLAB where the information gain and entropy are used for the selection of the best feature to split the tree. Accuracy rate and decision tree performance are improved by the efficient feature split of decision trees. An accuracy of 96.5 % is achieved for the proposed system. The proposed model is a low-cost system where a single ground moving robot is used to collect the crop parameters. The system will reduce the time a farmer spent in the agriculture field as he need not visit the farm regularly.

Profitability of Cauliflower Cultivation in Sonepat District of Haryana

Paper ID- AMA-11-08-2021-10603

Cauliflower (Brassica oleracea) is one of the important cruciferous vegetable crops of India. It is widely cultivated throughout the sub-tropical parts of north India. The present investigation has analyzed the costs, returns and marketing in Sonepat district of Haryana during the year 2017-18 on the basis of highest production. A total of thirty farmers were randomly selected from the various villages of Sonepat District of Haryana. Results indicated that cost of cultivation of cauliflower per hectare was Rs.211685. The average yield of cauliflower per hectare was observed 246 quintals on sample farms. The net profit per hectare was Rs.96799. The major findings of this study revealed that production of the cauliflower was profitable. Benefit cost ratios of cauliflower was 1.46. The study was found that direct marketing of cauliflower more profitable in Channel-IV Producer-consumer among all other marketing channels due to the non-existence of intermediaries between the producer and ultimate consumer.

Marketing and Constraints faced by Kinnow Growers in Sirsa District of Haryana

Paper ID- AMA-11-08-2021-10602

Kinnow is being widely cultivated in North-Western part of India comprising the states of Punjab, Haryana and Rajasthan. The present study was conducted in Sirsa district of Haryana. A sample of thirty kinnow respondents was taken purposively from various villages in Mandi Dabwali block of Sirsa district of Haryana. A comparison of price spread through different marketing channels has revealed that producers’ share in consumers’ rupee was the highest (about 93.05%) in channel-V, due to self-sale in the local market. The marketing efficiency has been found to be highest in channel-V. The producer got maximum benefits in channel-V, therefore this channel should be followed to make producer highest beneficiary; although this channel has its own limitations. The major problems faced by the farmers in the production of kinnow are indicates that 90.00 per cent of the respondents were claiming high cost of pesticides, high cost of seed (83.33%), high cost of fertilizer (80.00%), lack of knowledge of recommended fertilizer doses (80.00%), lack of knowledge about the control measures for various pests and diseases (76.67%). These were followed by difficulty in identifying the pests and diseases (66.67%), shortage of electricity power for processing (73.33%), lack of technical manpower (66.67%), problems in the arrangement of finance (63.33%), lack of processing unit (60.00%), lack of good quality packaging material (53.33%),fluctuation in raw material and procurement (50.00%), constraints regarding location of site (46.67%).