Proceedings

Find matching any: Reset
Paz-Kagan, T
Perez-Parmo, R
Pujari, B
Add filter to result:
Authors
Upadhyaya, S
Balakrishnan, P
Pujari, B
Patil, M
Kanannavar, P
Upadhyaya, S
Balakrishnan, P
Pujari, B
Patil, M
Kanannavar, P
Unamunzaga, O
Castell, A
Besga, G
Perez-Parmo, R
Aizpurua, A
Rozenstein, O
Cohen, Y
Alchanatis , V
Behrendt, K
Bonfil, D.J
Eshel, G
Harari, A
Harris, W.E
Klapp, I
Laor, Y
Linker, R
Paz-Kagan, T
Peets, S
Rutter, M.S
Salzer, Y
Lowenberg-DeBoer, J
Topics
Spatial Variability in Crop, Soil and Natural Resources
Precision Nutrient Management
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Type
Poster
Oral
Year
2012
2010
2024
Home » Authors » Results

Authors

Filter results4 paper(s) found.

1. Impact Of Precision Leveling On Spatial Variability Of Moisture Conservation In Arid Zones Of Karnataka

... S. Upadhyaya, P. Balakrishnan, B. Pujari, M. Patil, P. Kanannavar

2. Laser Leveling Holds a Lot Of Promise in Water Conservation and Saving in Dry Zones (Drought Prone Areas) of Karnataka

... S. Upadhyaya, P. Balakrishnan, B. Pujari, M. Patil, P. Kanannavar

3. Spatial And Vertical Distribution Of Soil P, K, And Mg Content In A Vineyard Of The Do Ca Rioja Using Grid And Target Sampling Methods

  Knowledge of spatial variability of soil nutrient contents is very important to design a fertilization strategy based on the needs of the vine. Matching fertilization and nutritional plant needs is very important due to the influence of nutritional status of vineyards on productive and qualitative factors. The aim of this work was to study the spatial and vertical variability of P, K and Mg in a vineyard soil by two methods: (i) the grid sampling at three depth ranges (0-30,... O. Unamunzaga, A. Castell, G. Besga, R. Perez-parmo, A. Aizpurua

4. 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