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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.

Submission Deadline
28 Sep 2022 (Vol - 53 , Issue- 10 )
Upcoming Publication
30 Sep 2022 (Vol - 53 , Issue 09 )

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

Milk Production Management of Ruminant Animals in Bhilwara District of Rajasthan

Paper ID- AMA-04-05-2022-11337

The present study was undertaken to find out the cost and returns of milk production in Bhilwara district of Rajasthan with a sample of 60 households. Milk production in India is mainly concentrated on small farms in rural area as a subsidiary occupation to agriculture. Dairy plays an important role to improve the economy of small milk producer’s households in different regions of India. India is leading country in milk in world with production of 176.35 million tonnes in 2017-18 and Rajasthan is the second largest milk producing state with 834 gm/day in the country. The results of study revealed overall average net cost per day was ₹ 154.09 for crossbreed cow, ₹ 106.20 for local cow and ₹ 136.36 for buffalo, respectively. The cost of milk production per litre for crossbreed cow, local cow and buffalo were found as ₹ 22.05, ₹ 33.29 and ₹ 29.26 respectively. The gross return was recorded as ₹ 202.48 for crossbreed cow, ₹ 114.84 for local cow and ₹ 194.05 for buffalo, respectively.

Studies on Genetic Variability and Character Association in Mustard for Seed Yield and its Contributing Characters

Paper ID- AMA-04-05-2022-11335

The current study used thirty mustard genotypes to evaluate the genetic variability, heritability, and genetic advance as a percentage of the mean. During Rabi 2020-21, all thirty genotypes were evaluated in a randomised block design with three replications. For all the features, analysis of variance revealed a significant level of variability across the genotypes, indicating a broad range of variability across the genotypes. Number of secondary branches per plant recorded the highest PCV and GCV followed by number of siliquae per plant, seed yield per plant, number of seeds per siliquae, number of primary branches per plant. This suggested that the environment had the least impact on the manifestation of these features. For all of the qualities, the difference between GCV and PCV values was obtained at low. This suggested that the prevalence of additive gene effects for these characters, and hence selection based on these qualities, might be profitable. High heritability coupled with high genetic advance as per cent of mean were observed for days to 50% flowering, plant height, number of primary branches per plant, number of secondary branches per plant, length of main raceme, number of siliquae on main raceme, number of siliquae per plant, length of siliqua, number of seeds per siliqua, 1000 seed weight, biological yield per plant, seed yield per plant and harvest index. Days to 50% flowering, days to maturity, number of primary branches per plant, number of secondary branches per plant, number of siliquae per plant, 1000 seed weight, biological yield per plant and harvest index has shown positive and high significant association with seed yield per plant, while negative and highly significant in length of siliquae and number of seeds per siliqua with seed yield per plant. Path analysis revealed that positivedirect effect on seed yield per plant per plant was observed by days to 50% flowering, plant height, number of siliquae per plant, number of seeds per siliqua, 1000 seed weight, biological yield per plant, harvest index. Whereas days to maturity, number of primary branches per plant, number of secondary branches per plant, length of main raceme, number of siliquae on main raceme, length of siliqua, oil content has shown the negative direct effect on the seed yield per plant. As a result, these traits should be prioritised in the selection of high-yielding mustard genotypes.

Effect of herbicidal treatments on the seed quality parameters and yield of Pearl millet

Paper ID- AMA-03-05-2022-11334

A laboratory experiment was conducted at Seed research farm of CCS Haryana Agricultural University, Hisar, Haryana during Kharif 2017. Observations on test weight, seedling length, seedling dry weight, seedling vigour index I, seedling vigour index II, grain yield, stover yield and biological yield of pearl millet were recorded. Different herbicides were applied to pearl millet plants in field plots. Treatments varied from hand weeded to pre emergence herbicides to post emergence herbicides. Pearl millet seeds were harvested and evaluated for different seed quality parameters. Herbicides had little effect on seed quality parameters such as test weight, percent germination, seedling length, seedling dry weight, seedling vigour index I and seedling vigour index II. Tembotrione 80g/ha PoE at 2-4 leaf/10-15 DAS + 1 HW at 30 DAS was found most effective which was at par with tembotrione 100g/ha PoE at 2-4 leaf/10-15 DAS + 1 HW at 30 DAS, two HW/hoeing at 15 and 30 DAS, atrazine + pendimethalin (0.4+0.75 kg/ha) PRE + 1HW at 21 DAS, pendimethalin 0.75 kg/ha PRE + 1HW at 21 DAS, atrazine 0.4 kg/ha PoE (10-14 DAS) + 1 HW at 30 DAS and atrazine 0.4 kg/ha PRE + 1HW at 21 DAS. Herbicides treatments with one hand weeding gave better results than sole herbicidal treatments.

Mapping of Rice Crop using Synthetic Aperture Radar (SAR) Data

Paper ID- AMA-28-04-2022-11331

Rice crop support the food need of billions of peoples and thus up-to-date and early statistics of acreage is essentially needed. Current study is being taken up to map the rice acreage for whole Haryana state covering a total of 4421200 ha area. Sentinel-1 SAR data were used for the mapping of Rice crop. A total of 28% of the state geographical area was found under the Rice coverage with a mapping accuracy of 90% using Maximum Likelihood (MXL) classification algorithm. Though the acreage was found to be less as compared to the statistical report of the year 2019 an early forecasting and yield assessment is possible using the current approach especially in monsoon season.

Research on lightweight sheep face recognition model based on double attention mechanism

Paper ID- AMA-27-04-2022-11330

To solve the problem that it is difficult to obtain the discriminant features of sheep face and the model is too large to be applied effectively in sheep face recognition. A lightweight sheep face recognition model based on double attention mechanism is proposed. The model is based on ShuffleNetV2 and add SKNet convolution attention module to adaptively adjust the convolution kernel size in order to capture sheep face features at different scales. Add an improved channel attention module in the channel dimension to suppress the interference of redundant information and enhance the expression of salient features of sheep face, so that the model could extract highly discriminant sheep face features. The model use Mish activation function to replace ReLU activation function to reduce the loss of feature information. Select adaptive scaling cosine metric function (Adacos) to train the model to speed up the convergence and further improve the recognition rate. On the constructed sheep face recognition datasets, the experimental results show that the recognition rate of the proposed sheep face recognition model is 91.52%, the model size is 4.45MB, and the computational quantity is 147MFLOPs. Experimental results show that the improved sheep face recognition model has better feature extraction ability and balance the recognition accuracy and complexity of the model, which can provide a reference for the practical application of sheep face recognition.