Proceedings
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| Filter results7 paper(s) found. |
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1. Adoption Level Of Precision Agriculture For Brazilian Farmers - 2011/12 Crop YearAlthough Precision Agriculture (PA) concepts and technologies are widespread in Brazil, its application still little used in some important crop production regions. The purpose of this study was to survey the current adoption level of PA by printed and online questionnaire. We started making a specific questionnaire to farmers and PA service companies using some technology related to PA. The questionnaires were developed based on the methodology of Whipker and Akridge (2009),... E. Borghi, A. Luchiari Junior, L. Bortolon, E.S. Bortolon, R.Y. Inamasu, A.C. Bernardi, J.C. Avanzi |
2. Strategies For Scientific Communication Of Precision Agriculture In BrazilScientific knowledge popularization is the way to the society access technical scientific advances. The challenge is to increase the means, channels and processes of information and relationship with society and decode scientific issues into a format that makes knowledge accessible. The Embrapa Precision Agriculture Network has been used scientific communication strategies at the traditional and new media, as a way of approach with various stakeholders, contributing to the construction... C.V. Fragalle, J.C. Silva, E.P. Fragalle, R.Y. Inamasu, A.C. Bernardi |
3. Spatial Variability Of Soil Properties And Yield Of An Alfalfa Pasture Under Grazing In BrazilAlfalfa is extremely demanding in fertility, and an adequate supply of nutrients is important for forage production and is essential to maintain high forage quality and profitable yields. Tropical acid soils are naturally poor in plant nutrients, therefore, soil liming and balanced nutrient supply essential to ensure high yields and high alfalfa forage quality. The knowledge of soil properties spatial variability and forage yield is useful for the rational use of inputs, as in the variable... A. Bernardi |
4. Precision Agriculture Use In Selected Agricultural Regions In BrazilInvestment in technology brought Brazil to the position among the top agricultural producers in the world. Brazilian agricultural production has increased drastically as a result of productivity growth instead expansion in area. In this scenario the use of Precision Agriculture (PA) in the farm management, considering the spatial variability for maximizing economic return and minimizing the risk of damage to the environment can be decisive. However, the adoption of PA by Brazilian... R.Y. Inamasu, A.C. Bernardi |
5. Spatial Variability of Soil Nutrients and Site Specific Nutrient Management in MaizeA field study was conducted during kharif 2014 and rabi 2014-15 at Southern Transition Zone of Karnataka under the jurisdiction of University of Agricultural Sciences, GKVK, Bangalore, India to know the spatial variability for available nutrient content in cultivator’s field and effect of site specific nutrient management in maize. The farmer’s fields have been delineated with each grid size of 50 m x 50 m using geospatial technology. Soil samples from 0-15 cm were... S. T, M. Giriyappa, D. Hanumanthappa, N. Dr., S. K, S. Yogananda, A. Kiran |
6. Estimating Cotton Water Requirements Using Sentinel-2Crop coefficient (Kc)-based estimation of crop water consumption is one of the most commonly used methods for irrigation management. Spectral modeling of Kc is possible due to the high correlations between Kc and the crop phenologic development and spectral reflectance. In this study, cotton evapotranspiration was measured in the field using several methods, including eddy covariance, surface renewal, and heat pulse. Kc was estimated as the ratio between reference evapotranspiration... O. Rozenstein, N. Haymann, G. Kaplan , J. Tanny |
7. Data-driven Agriculture and Sustainable Farming: Friends or Foes?Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies... O. Rozenstein, Y. Cohen, V. Alchanatis , K. Behrendt, D.J. Bonfil, G. Eshel, A. Harari, W.E. Harris, I. Klapp, Y. Laor, R. Linker, T. Paz-kagan, S. Peets, M.S. Rutter, Y. Salzer, J. Lowenberg-deboer |