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 Interventional Pulmonology (middletown, de.)
A Field experiment was conducted to evaluate the residual effect of nutrient management on growth, yield attributes and yield of mungbean. The experiment was conducted in a Randomized Block Design (RBD) with 10 treatments and 4 replications during kharif, 2019 at Anand Agricultural University, Anand, Gujarat. All the nutrient management practices were followed in the previously sown pearlmillet crop. The Present experiment evaluates the residual effect of these nutrient management practices on mungbean. The residual effect of 100 % RDF + 15 t FYM ha-1 + Bio NP Consortia was recorded in plant height, number of pods per plant, test weight and protein content. The total number of nodules and the dry weight of nodules were significantly higher under the residual effect of 15 t FYM ha-1 + 5.0 t Vermicompost ha-1 + Bio NP Consortia (T10). The partial nutrient balance of the whole cropping system showed that nitrogen fertilization showed a positive balance, whereas phosphorus and potassium showed a negative nutrient balance under different nutrient management practices.
The study was conducted at Sagolkhong Watershed located in the Imphal west district of Manipur, India in the year 2018-19. A case study was done so as to analyze the impact of this watershed project on the income-employment and equity of the beneficiary households. For this, a two-stage-stratified random sampling method was used for the selection of three villages and 100 sample households from the Sagolkhong watershed using proportionate allocation random sampling technique. A preliminary survey preceded the actual survey by using well-prepared and pre-tested personal interview schedule so as to gain a firsthand knowledge of the various existing resources and other socio-economic features too. The primary objective of the study was to assess the employment, income generation and equity level of the project beneficiaries. Both conventional and mathematical tools were used for the analysis of the sample households. Also, the Gini concentration ratio & Lorentz curve for income distribution and equity were used for the analysis of the data. Due to the intensification of cropping pattern, adoption of various conservative measures and introduction of livestock sector, the sample households were provided ample room for giving employment. So, the per family employment level has significantly increased to 24 per cent. Hence, per hectare and per worker employment level have reduced to 1.5 and 3.7 per cent respectively after the project. With these, the average annual income of the beneficiaries households from both the farm and off-farm has significantly increased from Rs. 30050.61 to Rs. 41149 i.e. an increased of 36 per cent. This increased income has not been dispersed to the upper section of the community i.e., not to the richer fellows but also somewhat distributes among the weaker and poorer section too and particularly to the medium income group However, the increased income has not been evenly distributed to the marginal and landless farmers and is indicated by the Gini-Concentration ratio and Lorentz curve. The Gini- Concentration ratio before and after the project were found to be 0.38 to 0.36 respectively.
The experiment was established during kharif season 2019 and 2020 with different cropping systems were evaluated in a randomized complete block design with four replications at Punjab Agricultural University Ludhiana. The result revealed that the more no. of tiller count, PARI (%), no. of grain panicle-1, weight of grains/panicle (g), panicle length (cm), harvest index and 1000-grain weight (g) were produced under CA based management scenario PTR-Happy seeder wheat-summermoong(ZT)(+R) -Sc2 and lowest under PTR – CT wheat–summermoong(R0)-Sc1 during both the years. Scenario Sc2 (74.5 and 77.8 q ha-1) produced 8.96 and 11.78 per cent during first year and second year higher grain yield compared to Sc1 (68.3 and 69.5 q ha-1) in first and second years, respectively. Significantly the maximum root traits i.e. total root length, stem and system width were observed under Sc2 as compared to Sc1. Irrigation water applied varied from 1125-1275 and 1425-1725 cm ha-1 during first and second years, respectively. CA scenario Sc2 recorded 23.50 per cent higher IWP during the first year and 35.23 per cent higher IWP during the second year compared to Sc1 scenario. Whereas, the highest benefit cost ratio (B: C ratio) was recorded under Sc2 (2.36 &2.65) as compared to Sc1 (1.64 &1.71) during both the year. The system productivity recorded highest in CA based cropping system as compared to conventional till.
Rice pest control effect is a key factor of rice harvest, and the prediction of pest development trend is a necessary step of pest control. To improve rice pest control effect and rice quality, a deep learning-based SD-Mask R-CNN convolutional neural network is proposed to accomplish accurate identification of segmented pest pictures. The training and testing samples of the network are collected in a multi-point manner to collect pest samples from different regions and various developmental ages to realize the diversity of rice pest samples. Based on the Mask R-CNN recognition network, and the Resnet swish DISN feature extraction network is proposed to solve the problems of gradient explosion and network degradation that easily occur in the recognition network. It is found that the recognition average accuracy AP of this recognition network reaches 98.08% and the recall rate Recall is 95.19%, which are improved by 9.8% and 17.56% respectively compared with Mask R-CNN recognition network.
Many flowers are morphologically adapted to take advantage of electrostatic forces during pollination. Hence the application of electrostatic force in non-contact type mechanical pollen collection has been gaining more importance since most of the mechanical pollinators are contact in type and cause mechanical injury to the flower which leads to decrease in fruit set efficiency. This study investigated different parameters for the development of an electrostatic pollinator for vegetables crops under protected cultivation. Tomato and bitter gourd were selected for the study. Electrostatic pollinator mainly consists of an electrode and a high voltage amplification circuit. Spherical shaped electrodes, E1 and E2 with diameter 10 mm and 7.5 mm and electrostatic induction charging system was selected for uniform distribution of charges. The pollen collection and deposition capacity of electrodes were evaluated at different voltage potentials of 3 kV, 4 kV, 5 kV, 6 kV and 7 kV. Fruit set efficiency of the flowers after electrostatic and conventional pollination were recorded. The maximum number of pollens was collected by electrode E1 with a charging potential of 6 kV at 5 mm distance from the anther tip of flower. At 7 kV electrode potential, repulsion of pollen grains was observed just before reaching the electrode after detaching from the anther and some of the repelled pollens are deposited on the stigma of same flower. The fruit set efficiency of was 70% in tomato and 100% in bitter gourd with electrostatic pollination whereas it was 30% after hand pollination.