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2019
2023

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Kumar, Adarsh

Development of Web-Based Combine Harvester Custom-Hiring Model for Rice–Wheat Cropping System

Abstract Views :181 | PDF Views:84

Authors

Roaf Ahmad Parray ¹, Indra Mani ², Adarsh Kumar ¹, Tapan Kumar Khura ¹

Affiliations
1 ICAR-Indian Agricultural Research Institute, New Delhi - 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi - 110 012

Source

Current Science, Vol 116, No 1 (2019), Pagination: 108-111

Abstract

A web-based custom hiring model was developed to help farmers and custom-hiring service providers take decisions regarding owning/custom hiring of combine harvester for rice-wheat cropping system. It also gives the break-even acreage for owning a combine harvester along with various cost economics. The model was evaluated for two situations: situation I with own area of 100 acres and custom-hiring catchment area of 160 acres combined under rice and wheat, and situation II with own area and custom-hiring catchment area being 60 and 276 acres respectively. For situation I the model guided the user to opt for custom-hiring, while for situation II it gave a decision to own a combine harvester.

Keywords

Custom Hiring, Combine Harvester, Rice-Wheat Cropping System, Web-Based Model.

Full Text

References

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Doubling Farmers' income

Machine Learning Algorithms for Categorization Of Agricultural Dust Emissions Using Image Processing of Wheat Combine Harvester

Abstract Views :91 | PDF Views:58

Authors

Utpal Ekka ¹, Himadri Shekhar Roy ², Adarsh Kumar ¹, S. P. Singh ¹, Apratim Kumar Pandey ¹, Kamalika Nath ²

Affiliations
1 ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, India., IN
2 ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012, India., IN

Source

Current Science, Vol 124, No 9 (2023), Pagination: 1074-1081

Abstract

India is the second largest wheat producer in the world after Russia. Wheat harvesting in the country was traditionally done using a sickle, a hand tool. However, in the last two decades, combined harvesters have been extensively used. The rapid development of mechanization has resulted in the production of dust and straw particles during the harvesting operation of wheat. These particles have severe health hazards for the machine operator. Exposure to various types of particulate matter has a variety of effects on human health. Such an effect can be minimized if the concentration of the generated particle is maintained within a permissible limit. Hence, the present study has been conducted to evaluate and categorize dust and straw particles in the workspace of a combine harvester operator during wheat harvesting. An image-processing technique was used to study a field data sample collected on sticky paper. It describes a novel method of collecting dust and straw particles while harvesting wheat. Few studies have been conducted in developing countries to analyse the characteristics of dust and wheat straw exposure of combined harvester operators. The number of dust and straw particles deposited per square millimetre was 9–12, with sizes ranging from 10 to 1400 mm. The extracted data were divided into three groups, viz. thoracic, inhalable and straw and modelled using machine learning algorithms, including support vector machine (SVM) and k-nearest neighbor. With an accuracy of 96%, SVM outperformed the other methods for categorising dust and straw particles, whereas linear discriminant analysis performed poorly with an accuracy of 88%.

Keywords

Agriculture, Combine Harvester, Dust and Straw Particles, Image Processing, Machine Learning.

Full Text

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Extreme Temperature and Rainfall Event Trends in the Middle Gangetic Plains From 1980 to 2018

Abstract Views :84 | PDF Views:62

Authors

S. Vijayakumar ¹, Sudhir Kumar Rajpoot ², N. Manikandan ³, R. Jayakumara Varadan ⁴, J. P. Singh ², Dibyendu Chatterjee ⁵, ‪Sumanta Chatterjee ⁶, Santosha Rathod ⁷, Anil Kumar Choudhary ⁸, Adarsh Kumar ⁹

Affiliations
1 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India; ICAR-National Rice Research Institute, Cuttack 753 006, India., IN
2 Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221 005, India., IN
3 ICAR-Central Research Institute for Dryland Agriculture, Hyderabad 500 059, India., IN
4 ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman & Nicobar Islands 744 101, India., IN
5 ICAR-National Rice Research Institute, Cuttack 753 006, India., IN
6 University of Wisconsin-Madison, Madison, WI 53706, USA., US
7 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India., IN
8 ICAR-Central Potato Research Institute, Shimla 171 001, India; ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
9 ICAR-National Bureau of Agriculturally Important Microorganisms, Mau Nath Bhanjan 275 103, India., IN

Source

Current Science, Vol 124, No 11 (2023), Pagination: 1300-1307

Abstract

Regional-level studies aimed at identifying and assessing various types of extreme weather events and comprehending their effects on various sectors are crucial. In the present study, we have utilized the RClimDex software to compute the trend in temperature and precipitation extreme events in the Varanasi district of Uttar Pradesh, India, from 1980 to 2018. We employed both Mann–Kendall test and linear regression to test the statistical significance of the computed trend. Out of 13 temperature indices, 8 showed a significant trend while the remaining showed a non-significant trend. The annual mean maximum temperature, warm days, diurnal temperature range and a monthly minimum of maximum temperature had decreased significantly by 0.029ºC, 0.159 days, 0.032ºC and 0.122ºC/yr respectively, whereas cool days and cold spell duration had increased significantly by 0.264ºC and 0.372 days/yr respectively, indicating an increased cooling effect over the study area. Similarly, out of the 11 rainfall indices, only two showed a significant trend, while the remaining showed a non-significant trend. The increasing drought over the study area is evident as the number of rainy days and consecutive wet days have decreased significantly by 0.262 days and 0.058 days/yr respectively, with a non-significant increase in consecutive dry days during the same period. The weak negative non-significant trend of a maximum of five consecutive days of rainfall, very heavy rainfall days and total annual precipitation indicate the decreasing trend of floods. This study stresses the development of adaptation plans to overcome the adverse consequences of extreme weather events in Varanasi district.

Keywords

Adaptation Plans, Climate Change, Extreme Weather Events, Temperature and Rainfall, Statistical Significance, Trends.

Full Text

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