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Ranieri, E
Young, J
Amouzou, K.A
Seger, J
Nysten, S
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Authors
Kempenaar, C
van Evert, F
Been, T
Kocks, C
Westerdijk, K
Nysten, S
Morris, T
Tremblay, N
Kyveryga, P.M
Clay, D.E
Murrell, S
Ciampitti, I
Thompson, L
Mueller, D
Seger, J
Straw, C
Bolton, C
Young, J
Hejl, R
Friell, J
Watkins, E
Sogbedji, J.M
Lare, M
Lotsi, A.K
Amouzou, K.A
Agneroh, T
Karampoiki, M
Todman, L
Mahmood, S
Murdoch, A
Paraforos, D
Hammond, J
Ranieri, E
Topics
Decision Support Systems in Precision Agriculture
Standards & Data Stewardship
Drainage Optimization and Variable Rate Irrigation
Precision Agriculture and Global Food Security
Big Data, Data Mining and Deep Learning
Type
Oral
Poster
Year
2016
2022
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1. Towards Data-intensive, More Sustainable Farming: Advances in Predicting Crop Growth and Use of Variable Rate Technology in Arable Crops in the Netherlands

Precision farming (PF) will contribute to more sustainable agriculture and the global challenge of producing ‘More with less’. It is based on the farm management concept of observing, measuring and responding to inter- and intra-field variability in crops. Computers enabled the use of Farm Management Information Systems (FMIS) and farm and field specific Decision Support Systems (DSS) since mid-1980s. GIS and GNSS allowed since ca. 2000 geo-referencing of data and controlled traffic... C. Kempenaar, F. Van evert, T. Been, C. Kocks, K. Westerdijk, S. Nysten

2. Rationale for and Benefits of a Community for On-Farm Data Sharing

Most data sets for evaluating crop production practices have too few locations and years to create reliable probabilities from predictive analytical analyses for the success of the practices. Yield monitors on combines have the potential to enable networks of farmers in collaboration with scientists and farm advisors to collect sufficient data for calculation of more reliable guidelines for crop production showing the probabilities that new or existing practices will improve the efficiency of... T. Morris, N. Tremblay, P.M. Kyveryga, D.E. Clay, S. Murrell, I. Ciampitti, L. Thompson, D. Mueller, J. Seger

3. Soil Moisture Variability on Golf Course Fairways Across the United States: an Opportunity for Water Conservation with Precision Irrigation

Fairways account for an average of 11.3 irrigated hectares on each of the 15,000+ golf courses in the US. Annual median water use per hectare on fairways is between ~2,800,000 and 14,000,000 liters, depending on the region. Conventional fairway irrigation relies on visual observation of the turfgrass, followed by secondary considerations of short-term weather forecasts, which oftentimes lead to “blanket” applications to the entire area. The concept of precision irrigation is a strategy... C. Straw, C. Bolton, J. Young, R. Hejl, J. Friell, E. Watkins

4. Variability in Yield Response of Maize to N, P and K Fertilization Towards Site-specific Nutrient Recommendations in Two Maize Belts in Togo

Savannah and central regions are the major maize production zones in Togo, but with maize grain yields at a threshold of only 1.5 Mg ha-1. We use a participatory approach to assess the importance of the major three macro elements (N, P and K) for maize cropping in the two regions in order to further allow for site-specific and scalable fertilizer recommendations. Thirty farmers’ fields served as pilot sites, allocated within the two regions to account for spatial variability in... J.M. Sogbedji, M. Lare, A.K. Lotsi, K.A. Amouzou, T. Agneroh

5. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical Data

Bayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri