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)
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:
Integrated nutrient management is the adjustment of plant nutrient supply to an optimum level for sustaining the desired crop productivity. In this study, a field experiment was conducted during two consecutive years 2019-20 and 2020-21 at High- tech unit, Department of Horticulture, Udaipur. The experiment consisting fourteen treatments under randomized block design with three replications. Application of different doses of farm yard manure, chemical fertilizers and silicon increased the yield, quality parameters and economics of cauliflower over the control. The results showed that maximum yield (277.53 q/ha), colour value (L* 87.05, a* 1.849 and b* 29.78), compactness of curd (9.21), TSS content (7.34 %), ascorbic acid content (75.26 mg/100g) and gross return (416292.00 Rs/ha) were recorded in treatment T14 (50 % RDN by FYM + 50 % RDF + 100 kg silicon/ha), while the maximum net return (325066.14 Rs/ha) and B:C ratio (4.10) were found in treatment T10 (75 % RDF + 100 kg Si/ha) as compared to control. It is concluded that the integrated use of silicon along with manures and chemical fertilizers enhanced growth, yield and quality parameters of cauliflower.
Disease forecasting and early warning are essential for better management of plant diseases. Being an important component of the disease triangle, weather parameters and soil characteristics are to be critically considered for developing prediction models for soil-borne diseases. An experiment was conducted during summer, kharif and rabi of 2020 to study the seasonal variation of the natural occurrence of sesame root rot disease, their correlation with weather factors and the derivation of a prediction model. Variations in root rot pathogen inoculum concerning soil depth, season, nutrient content and interaction with other rhizosphere microbes were studied. The experiment revealed that the disease initiated after 3 weeks of sowing and appeared throughout the seasons ranging from 5.00% to 34.6%, 0- 19.8% and 0-26% in summer, kharif and rabi season respectively with an increasing trend towards maturity. High disease incidence was recorded in summer when temperature ranged from 32.1℃ to 42℃ coupled with less to no rainfall followed by rabi and Kharif. Temperature showed a positive correlation with the disease incidence in all the seasons whereas rainfall is negatively correlated. The pathogen inoculum was observed more at 5-10 cm soil depth in the summer season. Prediction models were developed to forecast the disease by employing weather data and current disease incidence using statistical tools. Disease forecast using proposed models can be considered as a prototype for early warning of root rot disease.
Studies were conducted on Subhash Palekar Natural Farming (SPNF) [formerly known as Zero Budget Natural Farming (ZBNF)] practised at 72 ha farm of Gurukul Kurukshetra, Haryana, India. Soil samples at different times were collected and got analyzed from different premier institutes of India. The results revealed that there was significant enrichment of soils in terms of Organic Carbon (OC), available phosphorus, available potash, micronutrients and biological health with the adoption of ZBNF practices. The average OC in the soil samples collected from Gurukul farm in May 2017 and analysed at CCS Haryana Agricultural University (HAU), Hisar was 0.61% in which 19% soil samples were found rich in OC (>0.75%). After one year of cropping, it was observed that 95% soil samples were rich in OC with average OC value of 0.91% in the range of 0.82-1.12%. The results obtained in the analysis of the samples collected in October 2018 and May 2019 confirmed the earlier findings. Similar trend was observed in the analytical results of soil samples from other institutes. The impact on soil OC was more pronounced in rice-wheat system than in other cropping systems. There was 89, 32 and 179 % increase in mean values of available P in the soil samples collected in May 2018, October 2018 and May 2019, respectively, over that recorded in the soil samples collected in May 2017 and analysed at CCSHAU Hisar. Likewise, mean available K increased by 7, 17 and 66% in the samples collected at the respective time of sampling. The extent of increase in micronutrients was 32, 27, 31 and 114% in Zinc, Iron, Copper and Manganese, respectively, after one year of cropping from May 2017 to May 2018. Similar results on micronutrients were obtained in the soil samples analysed at Punjab Agricultural University (PAU) Ludhiana. The microbiological studies indicated that there was 528 times more colony forming units of bacteria per gram of soil in the soil samples of Gurukul farm as compared to that recorded in the soils of farmers’ fields practising chemical farming. Maximum microbial count in Jeevamrit (a liquid ZBNF formulation) was recorded on 12th day after its preparation. A multifold increase in microbial count with the addition of jaggery and pulse flour and their combined synergistic effect in Jeevamrit was also observed. Amendment of Jeevamrit with bio-inoculant, Azotobactor increased total microbial count by 2.66 times over Jeevamrit alone, whereas, addition of Azospirillum and Rhizobium in Jeevamrit failed to enhance microbial enrichment over Jeevamrit and Jeevamrit plus Azotobactor combination. Addition of soil (as innoculam in Jeevamrit) increased microbial count by about 105 times. Use of pre-made Jeevamrit as inoculum for further preparation of Jeevamrit could not emulate in terms of microbial count as compared to the Jeevamrit containing all the primary constituents. Total microbial count in the dung of native cow breeds was 363 and 25 times more than in the dung of buffalo and native breed of bull. The crop yields obtained at Gurukul Farm were highly comparable to the level achieved by the farmers. Average yields of non-scented high yielding cultivars of rice (including hybrids) were 74.45 q/ha in the range of 70-83 q/ha. Average wheat yields of Bansi, a desi (indigenous) variety, was 32.30 q/ha. Production of sugarcane achieved a level of 1300 q/ha with average cane yields of 850-1100 q/ha during the previous years. In vegetables, average tuber yield of potato ranged between 250-300 q/ha. The net returns from rice and wheat crops were 1.45 to 2.71 times higher than that of farmers produce.
With the prime motto of doubling farmer’s income by 2022 via production of high value crops and diversification of crops, fruit production is being encouraged to a large extent in India. Fruits being rich in nutrients and high value returns from fruits proved as a viable solution in stabilizing and raising the farm income and increasing the employment opportunities. The present study was an attempt to analyze the recent scenario of fruit production and export performance from India along with the export competitiveness of major important fruits exported from India. Revealed comparative advantage ratio was used to analyze the export competitiveness of major fruits exported from India. The area and production of fresh fruits changes over the years. During 2017-18, area under fruits was 65.06 lakh hectares and production was 973.58 lakh metric tonnes. The study revealed that exports of total fruits from India increased with the increase in production of fruits since 2010. Andhra Pradesh (13.88 percent) and Maharashtra (11.45 percent) served as the leading states in terms of production of total fruits during 2015-18. Among all the fruits, fresh grapes (38.67 per cent) and fresh mangoes (7.78 per cent) followed by banana (7.10 per cent) contributed highest share in total fruit exports by value from India during 2017-18. Revealed comparative advantage ratio for all the considered fruits was less than one except fresh grapes and the group of mangoes, mangostene and guava during the study period which signified that country must make efforts to integrate production, storage, marketing and processing of different fruits to get maximum export earnings.
In order to accurately diagnose the potassium content status of apple leaves in each growth period, a diagnostic model for potassium deficiency in apple leaves based on shape and color combination feature is constructed. Firstly, a series of image preprocessing work such as image denoising, leaf segmentation, etc. are carried out on leaf image samples in each growth period. Secondly, 9 color characteristics and 10 shape characteristics of a leaf are extracted by digital image processing technology, and the data dimension reduction and optimization are carried out through linear discriminant analysis method to obtain the key shape and color combination feature factors of apple tree leaves in each growth period; then, the established LDA-SVM, LDA-RF and LAD-KNN models are compared with the accuracy of potassium deficiency diagnosis of apple leaves at different periods to obtain the best diagnostic model for each growth period. Finally, the best diagnostic model is used for field experiments, and the generalization ability and robustness of the model are verified by the results. The test results show that the diagnostic accuracy of the model reaches an average of more than 93.6% in the whole growth cycle, which can accurately diagnose the potassium content of apple leaves in each growth period, and provide methods and ideas for the intelligent management of orchards and supplementary fertilizer application information.