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.
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
Okra shoot and fruit borer, Earias spp. is the major pest of okra. It is important to understand to what extent, this pest causes damage in okra field, and how do they respond to the prevailing environmental conditions. We studied extent of damage by Earias spp., and its correlation with abiotic factors. The study was done at Department of Entomology, CCS Haryana Agricultural University, Hisar, Haryana, during kharif season, 2019. The study was carried out from August, 2019 (32nd standard meteorological week) to October 2019 (42nd standard meteorological week). The initiation of larval population was found to be from 32nd SMW, while it reached at its peak (8.67 larvae/ 5 plants) during 39th SMW. Infestation of fruits initiated from 34th SMW (0.48%), and increased gradually till 40th SMW (39.41%). Temperature was recorded to be the major factor affecting the population of this pest. Larval population showed a significant and negative correlation with the maximum (r= -0.568*), minimum (r= -0.643*) and average (r=-0.664*) temperature, along with evening relative humidity (r= -0.590*) and rainfall (r= -0.590*). This study provides basic knowledge about the incidence and damage caused by this pest, and its behaviour towards various abiotic factors.
Many countries ordered closure of all educational institutes due to COVID-19. A sample of 300 students was selected from all the 6 constituent colleges of Maharana Pratap University of Agriculture and Technology, Udaipur, Rajasthan. Students from 2nd and 3rd year were selected from which 50 students was selected randomly from each college to assess the benefits perceived by the students regarding e-learning platforms during COVID-19. Data collection was done through “Google Forms” which was sent on Whatsapp and e- mail. Frequency, percentage and mean per cent score were used for analysing the data statistically. On the basis of the data obtained it can be concluded that a flexible schedule and convenience (MPS 81.88) was found to be the major benefits perceived by the respondents in the e-learning followed by favourable for people with restricted mobility (MPS 81.11) and it also helps in an easy and quick share of educational material. A number of the respondents also believed that e-learning is helpful in saving time and money and in increasing general awareness of the respondents.
The study was conducted in randomly selected two villages of (Palana and Barsinghsar) of Bikaner Panchayat samiti with a sample of 60 rural women (30 from each village). Pre and Post- test experimental research design was used for the present study. To find out the visual perception and comprehension unstructured open ended questions were framed. Pre-test was done by the help of developed knowledge test with the rural women, to know the existing knowledge on organic farming and after this developed video programme was exposed to the rural women. Post-test was carried out to find out the gain in knowledge. Regarding visual perception and comprehension findings indicate that majority of respondents have perceived and comprehended the messages of video programme very well. Significant improvement in the knowledge of respondents was found as a result of exposure of video programme with the increased in mean percent score is from 22 to 62.90 per cent with the gain in knowledge of about 40.90 per cent. On the basis of these findings, it is concluded that the developed video programme on "Management of organic farming" was good and is useful for field functionaries, extension workers, and all those agencies / organization working in rural areas for transfer of scientific information to rural women.
A specific variable selection method was proposed based on three-step hybrid strategy for near infrared spectral analysis. By analyzing functions of each step and characteristics of various variable selection methods, synergy interval partial least squares, iterative variable subset optimization and bootstrapping soft shrinkage were chosen for three steps. To test effect of the three-step hybrid method, it was applied into corn and soil spectral data and compared to other common methods. Results showed that three-step hybrid variable selection method selected 1% variables of full spectrum, calibration determination coefficient and prediction determination coefficient reached 0.9976 and 0.9932. It could effectively extract variables related to tested substance and provide a new variable selection method for near infrared spectral analysis.
Agricultural mechanization level in two governorates in northwestern Tunisia has been assessed and analyzed. Interviews, observations, and a structured questionnaire were used for the collection of the database. Five artificial neural networks (ANN) models with two hidden layers were used to estimate the mechanization level. Initially, 80 attributes were used as input variables to predict the level of mechanization. The Forward regression method (SPSS 22 software) followed by the significance analysis method was used to select the typical variables. Ten variables were selected as models’ inputs. The performance of the ANN model was evaluated with various statistical measures including coefficient of determination (R2), mean square error (MSE), and mean absolute error (MAE). The optimal ANN models had correlations of 0.94 with calculated mechanization levels. Sensitivity analysis of the models showed that farm area, labor, dominant crop area, and the number of tractors is the typical inputs affecting the level of mechanization. The results presented in this study justify the low level of mechanization according to farm area (Maximum value less than 30%), dominant crop area (average value less than 25%), number of tractors (Maximum value less than 30%). However, the availability of labor gives an acceptable level (on average 50%) for farms with some skilled labor between 3 and 4 and a low mechanization level for farms with some skilled labor is less than 2 (on average 10%).