Search

The agricultural extension system of India has various kinds of service providers like state agriculture departments, universities, research institutes, Farm Science Centers and private players. This study was conducted in 2020 and attempted to explore the association between average annual net income earned from citrus cultivation and the source of availing citrus extension services through Correspondence Analysis (CA) method of 300 citrus farmers selected from three districts in Maharashtra, India. The farmers with high income (1808.31 to 2,411.09 USD ha-1) received advisory services of the public research institute ICAR-Central Citrus Research Institute (CCRI) through either personal contact or electronic platforms. Awareness about CCRI services, source of seeking citrus cultivation related information, source of purchasing citrus planting material, using CCRI mobile app and website for citrus advisories, and contacting CCRI scientists for solving citrus farming related issues was found to have significant (P< 0.05) positive relationship with net income from citrus farming through Pearsons' correlation coefficient, while cost of cultivation and orchard age had negative significant relationship. The multi-linear regression analysis, depicted cost of cultivation, awareness about CCRI services, source of seeking citrus advisories, and source of purchasing planting material had significant association with net income. The findings of correlation and regression thus emphasized the positive significant association of CCRI's extension services to income from citrus farming. Identifying homogenous target groups of citrus farmers through Classification and Regression Tree (CART) method can serve as policy implication for extension service providers to deliver customized need-based advisories to target clientele. [ABSTRACT FROM AUTHOR]

Inconsistency in fruit yield and quality production in 'Nagpur' mandarin coupled with limited rootstock base is the problem throughout the country in general and central zone in particular. Therefore 'Nagpur' mandarin as in scion was evaluated on 18 diverse rootstocks during 2002-05 on an alkaline Inceptisol under the sub-humid tropical climate of central India. Canopy volume was observed directly proportional to fruit yield with respect to Rangpur lime (29.4 m-3 and 156.3 kg tree-1 with the yield efficiency of 5.3 kg m-3) and trifoliate hybrid rootstocks (28.3 m-3 and 79.5 kg tree-1 with the yield efficiency of 2.81 kg m-3). While the reverse was true with Cleopatra mandarin (21.7 m-3 and 54.5 kg tree-1 with the yield efficiency of 2.51 kg m-3) and rough lemons (34.8 m-3 and 102.4 kg tree-1 with the yield efficiency of 2.94 kg m-3), suggesting that trees with highest canopies are not always heavy producers. Different strains within the rootstock type exhibited a large variation in TSS/acid ratio. On all the strains of Cleopatra mandarin rootstock, Nagpur mandarin had significantly (P > 0.05) higher N and Zn content (1.99% and 21.5 ppm) than on rough lemon strains (1.70% and 14.5 ppm) and Rangpur lime (1.88% and 17.7 ppm) rootstock. Hybrid rootstock (Troyer and Carrizo strains) showed a much better nutrient foraging ability with trifoliate orange rootstocks. Rangpur lime (Tirupati and 8784 strains) was adjudged to be the most promising rootstock for Nagpur mandarin. [ABSTRACT FROM AUTHOR]

Crowd analysis has become an extremely famous research point in the territory of computer vision. Computerized examination of group exercises utilizing reconnaissance recordings is a significant issue for public security since it permits the identification of hazardous groups and where they’re going. We all see how many problems are faced because of the crowd. In our country, many terrorists are there. They plant a bomb in a crowded area which causes a lot of injuries. Thieves are mostly found or always leave in crowded areas so they can easily get an advantage of the crowd. In that situation, crowd analysis is very important. This paper presents the design of the deep learning architecture that provides control over the crowd behavior that will help to avoid violence or any other act which occurs because of the crowd which causes harmful effects to the society. So we are proposing a system that detects abnormal behavior of crowds using deep learning techniques.