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| Filter results9 paper(s) found. |
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1. Response Of Rhodes Grass (Chloris Gayana Kunth) To Variable Rate Application Of Irrigation Water And Fertilizer NitrogenRhodes grass is cultivated extensively in Saudi Arabia under center pivot sprinkler irrigation system. The research work was carried out to optimize irrigation water and fertilizer nitrogen levels for the crop. The objectives of the study were: 1. To delineate the field in to management zones, 2. To study the effects of variable rate application (VRA) of irrigation water and fertilizer nitrogen on the yield of Rhodes grass. A field experiment was carried out from... V. Patil, R. Madugundu, E. Tola, S. Marey, D.J. Mulla, S.K. Upadhyaya, K.A. Al-gaadi |
2. Accuracy of Differential Rate Application Technology for Aerial Spreading of Granular Fertiliser Within New ZealandAerial topdressing of granular fertilizer is common practice on New Zealand hill country farms because of the challenging topography. Ravensdown Limited is a New Zealand fertilizer manufacturer, supplier and applicator, who are funding research and development of differential rate application from aircraft. The motivation for utilising this technology is to improve the accuracy of fertilizer application and fulfil the variable nutrient requirements of hill country farms. The capability of... I.J. Yule, S.E. Chok, M.C. Grafton, M. White |
3. Hyperspectral Imaging to Measure Pasture Nutrient Concentration and Other Quality ParametersManaging pasture nutrient requirements on large hill country sheep and beef properties based on information from soil sampling is expensive because of the time and labor involved. High levels of error are also expected as these properties are often greatly variable and it is therefore extremely difficult to sample intensively enough to capture this variation. Extensive sampling was also not considered viable as there was no effective means of spreading fertilizer with a variable rate capability... I.J. Yule, R.R. Pullanagari, G. Kereszturi, M.E. Irwin, P.J. Mcveagh, T. Cushnahan, M. White |
4. Field Potential Soil Variability Index to Identify Precision Agriculture OpportunityPrecision agriculture (PA) technologies used for identifying and managing within-field variability are not widely used despite decades of advancement. Technological innovations in agronomic tools, such as canopy reflectance or electrical conductivity sensors, have created opportunities to achieve a greater understanding of within-field variability. However, many are hesitant to adopt PA because uncertainty exists about field-specific performance or the potential return on investment. These concerns... C.W. Bobryk, M. Yost, N. Kitchen |
5. Evaluation of the Potential for Precision Agriculture and Soil Conservation at Farm and Watershed Scale: A Case StudyPrecision agriculture and soil conservation have the potential to increase crop yield and economic return while reducing environmental impacts. Landform, spatial variability of soil processes, and temporal trends may affect crop N response and should be considered for precision agriculture. The objective of this research was to evaluate the viability of precision agriculture in improving N use efficiency and profitability at the farm and watershed level in western Canada. Two studies are described... M. Khakbazan, A. Moulin, J. Huang, P. Michiels, R. Xie |
6. Determining the Marginal Value of Extra Precision in Precision Grazing Systems – an Ex Ante Analysis of Impacts on System Productivity, Sustainability and EconomicsThe development of precision livestock farming (PLF) technologies for application in grazing systems is rapidly evolving. PLF technologies that facilitate the spatial and temporal management of variability in landscapes, pastures and animals promise to improve the efficiency, profitability and sustainability of livestock farming. However, such technologies as a complete package do not yet exist in grazing systems and the question of impacts at the farm system level remains unresolved. Other potential... K. Behrendt, T. Takahashi, M.S. Rutter |
7. A Multi-objective Optimisation Analysis of Virtual Fencing in Precision GrazingVirtual fencing is a precision livestock farming tool consisting of invisible boundaries created via Global Navigation Satellite Systems (GNSS) and managed remotely and in real time by app-based technology. Grazing livestock are equipped with battery-powered collars capable of delivering audio or vibration cues and possibly electric shocks when approaching or crossing an invisible boundary. Virtual fencing makes precision grazing possible without the need for physical fences. This technology originated... E. Maritan, K. Behrendt, J. Lowenberg-deboer, S. Morgan, M.S. Rutter |
8. 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 |
9. The Relationship Between Vegetation Indices Derived from UAV Imagery and Maturity Class in Potato Breeding TrialsIn potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. Unmanned aerial vehicles (UAVs) equipped with sensors can acquire images with high spatial and temporal resolution. The objectives of this study were to 1) establish the relationship between vegetation indices (VIs) derived from UAV imagery at three... S.M. Samborski, U. Torres, R. Leszczyńska, A. Bech, M. Bagavathiannan |