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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
10 Feb 2023 (Vol - 54 , Issue- 02 )
Upcoming Publication
03 Feb 2023 (Vol - 54 , Issue 01 )

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

Design and Experimental Research on Distribution Mechanism of Liquid Manure Spreader

Paper ID- AMA-25-01-2022-11053

To solve the problems of high error on both sides and high coefficient of variation during the liquid manure distribution, This work designed a distribution mechanism integrating conveying, stirring and distribution functions, combing with the physical properties of selected liquid manure. Taking rotor speed, inlet flow and moving cutter structure as test factors, the Design-Expert 8.0.6 software was used to design "three-factor three-level quadratic regression" orthogonal test and establish response surface regression model. Through observing relative error and coefficient of variation, we performed uniform distribution characteristics test and parameter optimization of liquid manure. The results showed that the primary and secondary order of influencing factors on the relative error is rotor speed> inlet flow > moving cutter structure; the primary and secondary order of influencing factors on the coefficient of variation is inlet flow > moving cutter structure> rotor speed. Further, the optimization test indicated that 170 r/min rotor speed, 80 m3/h inlet flow, combined with arc-shaped moving cutter structure could output 10.50% relative error and 9.30% variation rate, which was less than 5% relative to the model predicted value.

Application of Different Deep Learning Algorithms in Forestry Production Management and Processing

Paper ID- AMA-25-01-2022-11052

Forest resources are the most precious natural resources of human beings. According to the national strategic planning and guidance, China's forest area increases continuously, quality rises steadily, and efficiency increases continuously. In recent years, the deep integration of artificial intelligence technology represented by deep learning and forestry production management and processing industry is one of the important trends to realize the green and intelligent development of forestry. This paper summarizes the research progress of target detection, recognition and classification based on different deep learning algorithms and models in forestry production management and processing, comprehensively compares the advantages and disadvantages of different model algorithms, and puts forward some research suggestions, such as establishing multi-source database, optimizing algorithm model, improving hardware configuration, etc., which provides a reference for the intelligent development of forestry industry.

Effect of soil and foliar application of zinc and iron on Yield, Quality and Economics of Maize

Paper ID- AMA-23-01-2022-11049

A field experiment was conducted during two consecutive Kharif, seasons of 2018 and 2019 at Instructional Farm, Rajasthan College of Agriculture, Udaipur. The experiment was laid out under a split-plot design with three replications, including seven levels of soil application in main plots and four levels of foliar spray in the subplot. Significantly highest grain, stover and biological yield (4068.47, 6532.07 and 10600.54 kg ha-1) in the main plot under treatment 25 kg ZnSO4 ha-1 + 25 kg FeSO4 ha-1. In subplot highest grain, stover and biological yield (4068.47, 6532.07 and 10600.54 kg ha-1) were observed under treatment 0.5% ZnSO4 ha-1 + 0.5% FeSO4 ha-1. The interactive effect of soil and foliar application of zinc and iron significantly increases the grain and stover yield. The highest grain and stover yield (4609.45 and 7749.30 kg ha-1) was found with treatment combination S6F3. Harvest index was found non-significant in both soil and foliar application. The highest protein content (11.08 % and 11.07 %) was found in S6 and F3 treatments in the main and subplot, respectively. The highest net return and B:C ratio were found under soil application of 25 kg ZnSO4 ha-1 + 25 kg FeSO4 ha-1 (₹ 61986.1 and 2.53) and foliar application of 0.5% ZnSO4 ha-1 + 0.5% FeSO4 ha-1 (₹ 63479.1 and 2.70).

Deciphering Molecular Characterization in Wheat Genotypes using ISSR Marker

Paper ID- AMA-23-01-2022-11047

A total of 37 allele was amplified with each primer thus produced on an average 7.4 polymorphic bands and The number of alleles per locus ranged from 5 to 12. The polymorphism index content (PIC) value varied from 0.675 to 0.868. ISSR primers performed well in the detection of in wheat genetic diversity can be recommended for future wheat improvement program. Genomic DNA was extracted from thirty three wheat genotypes using CTAB procedure and amplified using five ISSR primers. Matrix similarity of genotypes was calculated by using NTSYSpc.2.1 with Sanh-clustering using the UPGMA (Unweighted Paired Group Method Using Arithmetic Averages) method. The prime aim of this study were to determination of the genetic polymorphism, genetic diversity, selection promising diverse progenitor, identification of putative and informative markers can be recommended for future crop improvement program.

Effect of different environmental Condition on Seed Yield and Its Attributes in Sesame (Sesamum Indicum L.)

Paper ID- AMA-23-01-2022-11046

The analysis of Sesame (Sesamum Indicum L.) yield and yield attributes in three environmental conditions produced by three different dates of sowing viz., normal, late and very late sowing for yield and its attributes by performing (half diallel fashion excluding reciprocals). To achieve the objectives of the present investigation, ten diverse parents along with their 45 F1 progenies were evaluated in a randomized block design with three replications during kharif 2020-21 at S.K.N. College of Agriculture, Jobner (Rajasthan) in Kharif 2019-20. This Analysis of variance revealed that the existence of significant genotypic differences among the genotypes and F1’s in individual environment. The mean of seed yield per plant was decreased by 19.40% and 32.38% in normal to late environmental condition among the parents and hybrids respectively. In normal to very late environmental condition 60.32% and 65.41% reduction was observed among the parents and hybrids respectively.