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Hagolle, O
Holmes, A
Harsányi, E
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Authors
Jacquin, A
Sigel, G
Hagolle, O
Lepoivre, B
Roumiguié, A
Poilvé, H
Rátonyi, T
Ragán, P
Sulyok, D
Nagy, J
Harsányi, E
Vántus, A
Csatári, N
Ekanayake, D.C
Owens, J
Werner, A
Holmes, A
Ragán, P
Harsányi, E
Nagy, J
Ágnes, T
Rátonyi, T
Vántus, A
Csatári, N
Nándor, C
Rátonyi, T
Harsányi, E
Ragán, P
Hagymássy, Z
Nagy, J
Vántus, A
Werner, A
Holmes, A
Topics
Sensor Application in Managing In-season CropVariability
On Farm Experimentation with Site-Specific Technologies
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Precision Dairy and Livestock Management
Type
Oral
Poster
Year
2014
2018
2025
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Filter results6 paper(s) found.

1. Development Of An Index-Based Insurance Product: Validation Of A Forage Production Index Derived From Medium Spatial Resolution fCover Time Series

An index-based insurance solution is developed by Pacifica Crédit Agricole Assurances and Astrium GEO-Information to estimate and monitor the near real-time forage production in France. In this system, payouts are indexed on an indicator, called Forage Production Index (FPI), calculated using a biophysical characterization of the grassland from medium spatial resolution remote sensing time series. We used the Fraction of green Vegetation Cover (fCover) integral as... A. Jacquin, G. Sigel, O. Hagolle, B. Lepoivre, A. Roumiguié, H. Poilvé

2. Evaluation of Strip Tillage Systems in Maize Production in Hungary

Strip tillage is a form of conservation tillage system. It combines the benefits of conventional tillage systems with the soil-protecting advantages of no-tillage. The tillage zone is typically 0.25 to 0.3 m wide and 0.25 to 0.30 m deep. The soil surface between these strips is left undisturbed and the residue from the previous crop remain on the soil surface. The residue-covered area reaches 60-70%. Keeping residue on the surface helps prevent soil structure and reduce water loss from the soil.... T. Rátonyi, P. Ragán, D. Sulyok, J. Nagy, E. Harsányi, A. Vántus, N. Csatári

3. Delineation of 'Management Classes' Within Non-Irrigated Maize Fields Using Readily Available Reflectance Data and Their Correspondence to Spatial Yield Variation

Maize is grown predominantly for silage or gain in North Island, New Zealand. Precision agriculture allows management of spatially variable paddocks by variably applying crop inputs tailored to distinctive potential-yield limiting areas of the paddock, known as management zones. However, uptake of precision agriculture among in New Zealand maize growers is slow and limited, largely due to lack of data, technical expertise and evidence of financial benefits. Reflectance data of satellite and areal... D.C. Ekanayake, J. Owens, A. Werner, A. Holmes

4. Examining the Relationship Between SPAD, LAI and NDVI Values in a Maize Long-Term Experiment

In Hungary, the preconditions for the use of precision crop production have undergone enormous development over the last five years. RTK coverage is complete in crop production areas. Consultants are increasingly using the vegetation index maps from Landsat and Sentinel satellite data, but measurements with on-site proximal plant sensors are also needed to exclude the influence of the atmosphere. The aim of our studies was to compare the values measured by proximal plant sensors in the... P. Ragán, E. Harsányi, J. Nagy, T. Ágnes, T. Rátonyi, A. Vántus, N. Csatári

5. The Spread of Precision Livestock Farming Technology at Dairy Farms in East Hungary

During the survey, 25 dairy farms were examined in East Hungary in Hajdú-Bihar (H-B) County between 2017 and 2018 by methodical observation and oral interviews with the farm managers, about the spread of Precision Livestock Farming (PLF) technologies. Among Holstein Friesian dairy farms in the County 60% were questioned, and the representativity was above 47 percent ins each size category. Nine precision farming equipment were examined on the farms: milking robot or robotic carousel milking... C. Nándor, T. Rátonyi, E. Harsányi, P. Ragán, Z. Hagymássy, J. Nagy, A. Vántus

6. Measure, Model, Manage: the Unfinished Revolution in Agriculture

Over the last 40 years, the paradigm of Measure, Model, Manage has promised an agricultural revolution through data-informed precision management. This shift remains largely incomplete, lagging concurrent innovations in genetics and pesticides. Significant barriers persist in achieving breakthrough innovations for crop data collection and the development of data analysis/decision-making systems. These hurdles include a decades-old "Sensor Crisis" (a lack of appropriate tools),... A. Werner, A. Holmes