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1. Use of Satellite Data to Improve Damage Assessment Process for Agricultural Insurance Scheme in IndonesiaGoal is to develop new method utilizing satellite data for assessment of damage in paddy field which can contribute toward substantial reduction of the damage assessment time and costs in framework of agricultural insurance in Indonesia. For the damage assessment, estimation of yield in each paddy plot is a key, so the research on the estimation of rice yield was carried out using satellite data which was acquired in harvesting season. Multiple linear regression analysis was conducted for the... C. Hongo, C. Ogasawara, E. Tamura, G. Sigit |
2. Open Data for Food Quality and Food Security Control: a Case Study of the Czech RepublicFood quality and food security is of a high public interest in the European Union. In the Czech Republic, food quality and food security is under control of three different public authorities: the Czech Trade Inspection Authority (CTIA) that is affiliated with the Ministry of Industry and Trade of the Czech Republic, the Czech Agriculture and Food Inspection Authority (CAFIA) that is affiliated with the Ministry of Agriculture of the Czech Republic and the regional network of hygienic station... M. Ulman, M. Stoces, J. Jarolimek, P. Simek |
3. Nitrogen Management in Lowland RiceRice is staple diet for more than fifty percent of the world population and nitrogen (N) deficiency is one of the major yields limiting constraints in most of the rice producing soils around the world. The lowland rice N recovery efficiency is <50% of applied fertilizers in most agro-ecological regions. The low N efficiency is associated with losses caused by leaching, volatilization, surface runoff, and denitrification. Hence, improving N use efficiency is crucial for higher yields, low c... N.K. Fageria, A.B. Santos |
4. Prediction of Nitrogen Needs with Nitrogen-rich Strips and Ramped Nitrogen StripsBoth nitrogen rich strips and ramped nitrogen strips have been used to estimate topdress nitrogen needs for winter wheat based on in-season optical reflectance data. The ramped strip system places a series of small plots in each field with increasing levels of nitrogen to determine the application rate at which predicted yield response to nitrogen reaches a plateau. The nitrogen-rich strip system uses a nitrogen fertilizer optimization algorithm based on optical reflectance measures from the ... D.C. Roberts, B.W. Brorsen, W.R. Raun, J.B. Solie |
5. Spatial Patterns of Nitrogen Response Within Corn Production FieldsCorn (Zea mays L.) yield response to nitrogen (N) application is critical to being able to develop management practices that reduce N application or improve N use efficiency. Nitrogen rate studies have been conducted within small plots; however, there have been few field scale evaluations. This study was designed to evaluate N response across 14 corn fields in central Iowa using different rates of N applied in strips across fields. These fields ranged in size from 15 to 130 ha with N... J.L. Hatfield |
6. Developing Nitrogen Algorithms for Corn Production Using Optical SensorsRemote sensing for nitrogen management in cereal crops has been an intensive research area due to environmental concerns and economic realities of today’s agronomic system. In the search for improved nitrogen rate decisions, what approach is most often taken and are those approaches justified through scientific investigation? The objective of this presentation is to educate decision makers on how these algorithms are developed and evaluate how well they work in the field on a small-plot... R.W. Mullen, S.B. Phillips, W.R. Raun, W.E. Thomason |
7. Variability in Observed and Sensor Based Estimated Optimum N Rates in CornRecent research showed that active sensors such as Crop Circle can be used to estimate in-season N requirements for corn. The objective of this research was to identify sources of variability in the observed and Crop Circle-estimated optimum N rates. Field experiments were conducted at two locations for a total of five sites during the 2007 growing season using a randomized complete block design with increasing N rates applied at V6-V8 (NV6) as the treatment factor. Field sites were selected ... R.P. Sripada, J.P. Schmidt |
8. Controller Performance Criteria for Sensor Based Variable Rate ApplicationSensor based variable rate application of crop inputs provides unique challenges for traditional rate controllers when compared to map based applications. The controller set point is typically changing every second whereas with a map based systems the set point changes much less frequently. As applied data files for a sensor based variable rate nitrogen applicator were obtained from a wheat field in north central Oklahoma. These data were analyzed to determine the magnitude and frequency of r... R.K. Taylor, P. Bennur, J.B. Solie, N. Wang, P. Weckler, W.R. Raun |
9. Variability in Yield Response of Maize to N, P and K Fertilization Towards Site-specific Nutrient Recommendations in Two Maize Belts in TogoSavannah 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 ... J.M. Sogbedji, M. Lare, A.K. Lotsi, K.A. Amouzou, T. Agneroh |
10. Suitability of ML Algorithms to Predict Wild Blueberry Harvesting LossesThe production of wild blueberries (Vaccinium angustifolium.) is contributing 112.2 million dollars to the Canada’s revenue which can be further increased through controlling harvest losses. A precise prediction of blueberry harvesting losses is necessary to mitigate such losses. In this study, the performance of three machine learning (ML) models was evaluated to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada we... H. Khan, T. Esau, A. Farooque, F. Abbas |
11. Comparative Analysis of Light-weight Deep Learning Architectures for Soybean Yield Estimation Based on Pod Count from Proximal Sensing Data for Mobile and Embedded Vision ApplicationsCrop yield prediction is an important aspect of farming and food-production. Therefore, estimating yield is important for crop breeders, seed-companies, and farmers to make informed real-time financial decisions. In-field soybean (Glycine max L.(Merr.)) yield estimation can be of great value to plant breeders as they screen thousands of plots to identify better yielding genotypes that ultimately will strengthen national food security. Existing soybean yield estimation too... J.J. Mathew, P.J. Flores, J. Stenger, C. Miranda, Z. Zhang, A.K. Das |
12. Agriculture Machine Guidance Systems: Performance Analysis of Professional GNSS ReceiversGNSS (Global Navigation Satellite Systems) plays nowadays a major role in different civilian activities and is a key technology enabling innovation in different market sectors. For instance, GNSS-enabled solutions are widespread within the Precision Agriculture and, among them, applications in the field of machinery guidance are commonly employed to optimize typical agriculture practices. The scope of this paper is to present the outcomes of the agriculture testing campaign performe... J. Capolicchio, D. Mennuti, I. Milani, M. Fortunato, R. Petix, J. Reyes gonzalez, M. Sunkevic |
13. Enhancing PA Adoption Through Value ConnectionsDespite an increase in breadth of precision agriculture over time, and the attendant elements of digital agriculture that either support PA or integrates the outputs of PA, the pace of adoption of digital agriculture in our farming systems remains slow. In assessing impediments to adoption of digital agriculture, much work to date has focused on the value proposition as considered by individual producers or value chain actors. At this level, adoption remains constrained by perceptions o... D.W. Lamb, M.T. Schaefer |
14. Farmer Charlie - Low Cost Smart Local Data Available to Remote FarmersFarmer Charlie brings connectivity and information to farmers, who receive tailored agronomic data to improve their agricultural practice. Farmer Charlie is based on on-site sensors through which soil data can be detected, gathered, and processed by a dedicated server. Broadband communication allows farmers to receive real-time, localised information on tablet or mobile phone. Farmer Charlie is a low-cost solution, it can be adapted to various crops and to detect soil humidity, pH, temperatur... B. Bonnardel |
15. Smart Food Oases: Development of a Distributed Point-to-point Urban Food Ecosystem in Food Desert AreasUrban agriculture has been getting much attention in the past decade as a solution to overcome food insecurity and accessibility of food for urban residents and to have better green environments in cities. Urban agriculture is expected to provide better nutrients to residents, reduce transportation and environmental costs, and help urban dwellers access food efficiently. The present study is to build a collaborative ecosystem among urban growers/producers and create bridges from these farmers... J. Lee, S. Song, S. Oh, K. Krishnaswamy, C. Sun, Y. Adu-gyamfi |
16. The ISO Strategic Advisory Group for Smart Farming: a Multi-pronged Opportunity for Greater Global InteroperabilityAgriculture is becoming increasingly complex and producers must secure their profitability, sustainability, and freedom to operate under a progressively more challenging set of constraints such as climate change, regulatory pressure, changes in consumer preferences, increasing cost of inputs, and commodity price volatility. We have not, however, yet reached the level of data interoperability required for a truly "smart" farming that can tackle the aforementioned probl... R. Ferreyra, J. Lehmann |