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1. Estimation Of Sugar Beet Yield Brfore Harvesting Using Meteorological Data And Spot Satellite DataIn Japan, sugar beet is only cultivated in Hokkaido, the northernmost island. The area of sugar beet cultivation in Tokachi District is 30,000ha, which is equal to about 45% of the total national production area. Because sugar beet is suited to cool weather conditions, it is an important rotation crop in Hokkaido. The production of beet sugar in Hokkaido is about 640,000 tons, which is 75... C. Hongo, K. Niwa |
2. On The Go Soil Sensor For Soil Ec MappingThis paper describes spatial variation maps of soil electrical conductivity (EC) obtained by both spectroscopic and capacitance methods using on the go soil sensor ( a real-time soil sensor -RTSS) SAS 1000, commercialized by Shibuya Kogyo Co. The experiments were conducted over a 2 year period on an experimental Hokkaido farm with an alluvial soil type. The comparison in soil EC records between the spectroscopy and the capacitance were also discussed. The spectroscopic approach used the soil... N. Sulastri, S. Shibusawa, M. Kodaira |
3. Monitoring Dairy Cow Activity With GPS-tracking And Supporting TechnologiesNutrient loss from dairy farms is an issue of serious concern to most dairy farmers around the world. On grazed systems such as those practiced in New Zealand animal excreta has been identified as a major source of nutrient loss, which for nitrogen (N) relates to cattle urine in particular. A study was commissioned to examine nutrient transfer around dairy farms associated with the cows with a view to developing improved precision nutrient application... I. Draganova, I.J. Yule, K. Betteridge, M.J. Hedley, K.J. Stafford |
4. Development Of Batch Type Yield Monitor For Small FieldsAbstract The yield monitor is intended to give the user an accurate assessment of yield variations y within a field. A yield monitor can assist grain producers in many aspects of crop management. A yield monitor by itself can provide useful information and enhance on-farm research. Yield data can... M. Singh, A. Sharma, G. Singh, P. Fixen |
5. Sensor And System Technology For Individual Plant Crop ScoutingSensor and system technologies are key components for automatic treatment of individual plants as well as for plant phenotyping in field trials. Based on experiences in research and application of sensors in agriculture the authors have developed phenotyping platforms for field applications including sensors, system and software development and application-specific mountings. Sensor and data fusion have a high potential by compensating varying selectivities... A. Ruckelshausen, K.V. Alheit, L. Busemeyer, R. Klose, A. Linz, K. Moeller, F. Rahe, M. Thiel, D. Trautz, U. Weiss |
6. Exploiting the Dmc Satellite Constellation for Applications in Precision AgricultureThis paper presents the unique capabilities of the DMC constellation of optical sensors, and examples of how a number of organisations around the world are exploiting this powerful data source for applications in precision farming. The DMC consists of five satellites built in the UK by Surrey Satellite Technology Ltd, each carrying a wide swath (650km) optical sensor. It is an international programme of satellite ownership and groundstations, with joint campaigns being coordinated centrally... P. Stephens, S. Mackin, G. Holmes |
7. Influence Of Phosphorus Application With Or Without Nitrogen On Oat (Avena Sativa) Grass Nutritive Value And In Situ Digestion Kinetics In Buffalo BullsFodder is the mainstay of ruminant production in majority of developing countries. However, its low yield and poor quality are considered considerable constrains which impede ruminant productivity. Fodder production and its nutritive value can be enhanced by ensuring adequate supply and utilization of nutrients... M.U. Nisa, I. Babar, M. Sarwar, N.A. Tauqir, M.A. Shahzad |
8. Applications for Precision Agriculture: the Italian Experience of SIRIUS ProjectThis paper reports the results of the project SIRIUS (Sustainable Irrigation water management and River-basin... P. Nino, S. Vanino, F. Lupia, F. Altobelli, F. Vuolo, I. Namdarian, C. De michele |
9. Using Multiplex® to Manage Nitrogen Variability in Champagne Vineyard... L. Marine, M. Manon, G. Claire, P. Laurent, F. Mostafa, C. Zoran, B. Naima, D. Sébastien, G. Olivier |
10. The Effect of Leaf Orientation on Spray Retention on BlackgrassSpray application efficiency depends on the pesticide application method as well as target properties. A wide range of drop impact angles exists during the spray application process because of drop trajectory and the variability of the leaf orientation. As the effect of impact angle on retention is still poorly documented, laboratory studies were conducted... F. Lebeau, M. Massinon, P. Maréchal, H. Boukhalfa |
11. Comparison of Algorithms for Delineating Management Zones... A.M. Saraiva, R.T. Santos, J.P. Molin |
12. BrainWeed - Teach-In System for Adaptive High Speed Crop / Weed Classification and TargetingConducting inter row mechanical weeding requires the precise location of each individual crop plant is known. One technique is to record the global position of each seed when sown using RTK-GPS systems. Another... R.N. J�??�?�¸rgensen, H.S. Midtiby, T.M. Giselsson |
13. Physiological Repsonses Of Corn To Variable Seeding Rates In Landscape-Scale Strip TrialsMany producers now have the capability to vary seeding rates on-the-go. Methods are needed to develop variable rate seeding approaches in corn but require an understanding of the physiological response of corn to soil-landscape and weather conditions. Interplant competition fundamentally differs at varied seeding rate and may affect corn leaf area, transpiration, plant morphology, and assimilate partitioning. Optimizing these physiological effects with optimal seeding rates in a site-specific... D.B. Myers, N.R. Kitchen, K.A. Sudduth, B.J. Leonard |
14. Creation Of Prescription For Optimal Nitrogen Fertilization Through Evaluation Of Soil Carbon Amount Using Remotely Sensed DataIn these years, drastic increase of agricultural production costs has been induced, which was triggered by the sharp rise of costs relating to agricultural production materials such as fertilizers and oil. In Japan, the substantial negative influence is anticipated to spread over to management of the farmers particularly in Hokkaido, the northern part of Japan. As one of the measures against this influence, a plan of effective fertilizer application and also... E. Tamura, K. Aijima, K. Niwa, O. Nagata, K. Wakabayashi, C. Hongo |
15. Beyond The 4-Rs Of Nutrient Management In Conjunction With A Major Reduction In TillageAgribusiness and government agencies have embraced the 4-R concept (right form, rate, time, and place) to improve nutrient management and environmental quality. No-tillage... J.S. Schepers, B. Mclure, G. Swanson |
16. Mapping Spatial Production Stability in Integrated Crop and Pasture Systems: Towards Zonal Management That Accounts for Both Yield and Livestock-landscape Interactions.Precision farming technologies are now widely applied within Australian cropping systems. However, the use of spatial monitoring technologies to investigate livestock and pasture interactions in mixed farming systems remains largely unexplored. Spatio-temporal patterns of grain yield and pasture biomass production were monitored over a four-year period on two Australian mixed farms, one in the south-west of Western Australia and the other in south-east Australia. A production stability index was... P. Mcentee, S. Bennett, M. Trotter, R. Belford, J. Harper |
17. 'Spatial Discontinuity Analysis' a Novel Geostatistical Algorithm for On-farm ExperimentationTraditional agronomic experimentation is restricted to small plots. Under appropriate experimental designs the effects of uncontrolled environmental variables are minimized and the measured responses (e.g. in yields) are compared to controllable inputs (seed, tillage, fertilizer, pesticides) using well-trusted design-based statistical methods. However, the implementation of such experiments can be complex and the application, management, and harvesting of treated areas might have to... S. Rudolph, B.P. Marchant, V. Gillingham, D. Kindred, R. Sylvester-bradley |
18. Measuring Pasture Mass and Quality Indices Over Time Using Proximal and Remote SensorsTraditionally pasture has been measured or evaluated in terms of a dry matter yield estimate, which has no reference to other important quality factors. The work in this paper measures pasture growth rates on different slopes and aspects and pasture quality through nitrogen N% and metabolizable energy and ME concentration. It is known that permanent pasture species vary greatly in terms of quality and nutritional value through different stages of maturity. Pasture quality decreases as grass tillers... I.J. Yule, M.C. Grafton, L.A. Willis, P.J. Mcveagh |
19. Towards Data-intensive, More Sustainable Farming: Advances in Predicting Crop Growth and Use of Variable Rate Technology in Arable Crops in the NetherlandsPrecision 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 |
20. 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 |
21. Precision Agriculture Techniques for Crop Management in Trinidad and Tobago: Methodology & Field LayoutAgriculture in Trinidad and Tobago has not advanced at the same rate at which new agricultural technology has been released. This has led to large-scale abandonment of crop lands as challenges posed by labor availability and their agronomic capability could not meet the technological demands for agricultural production, competitiveness and sustainability. There is an urgent need to develop technology-based agriculture models to meet the demands of a modern agricultural sector and to maintain its... G. Seepersad, T. Sampson, S. Seepersad, D. Goorahoo |
22. A Tree Planting Site-Specific Fumigant Applicator for Orchard CropsThe goal of this research was to use recent advances in the global positioning system and computer technology to apply just the right amount of fumigant where it is most needed (i.e., in the neighborhood of each tree planting site or tree- planting-site-specific application) to decrease the incidence of replant disease, and achieve the environmental and economical benefits of reducing the application of these toxic chemicals. In the first year of this study we retrofitted a chemical applicator... S.K. Upadhayaya, V. Udompetaikul, M.S. Shafii, G.T. Browne |
23. Agronōmics: Eliciting Food Security from Big Data, Big Ideas and Small FarmsMost farmers globally could make their farms more productive; few are limited by ambient availabilities of light energy and water. Similarly the sustainability of farming practices offers large scope for innovation and improvement. However, conventional ‘top-down’ Agricultural Knowledge and Innovation Systems (AKISs) are commonly failing to maintain significant progress in either productivity or sustainability because multifarious and complex agronomic interactions thwart accurate... R. Sylvester-bradley, D. Kindred, P. Berry |
24. Prototype Unmanned Aerial Sprayer for Plant Protection in Agricultural and Horticultural CropsAerial application of pesticides has the potential to reduce the amount of pesticides required as chemicals are applied where needed. A prototype Unmanned Aerial Sprayer with a payload of 20 kg; a spraying rate of 6 liters per minute; a spraying swathe of 3 meters, coverage rate of 2 to 4 meters per second and 10 minutes of flight time was built using state of the art technologies. The project is a joint development by University of Agricultural Sciences, Dharwad, KLE Technological University,... S. Reddy, D.P. Biradar, V.C. Patil, B.L. Desai, V.B. Nargund, P. Patil, V. Desai, V. Tulasigeri, S.M. Channangi, W. John |
25. Practical and Affordable Technologies for Precision Agriculture in Small Fields: Present Status and Scope in IndiaThe objective of this review paper is to find out practical and affordable precision agriculture(PA) technologies present status and scope in India that are suitable for small fields. The judicious use of inputs like water, fertilizers, herbicides, pesticides and better management of farm equipments will increase the net profit for farmers. The important components of PA in India which are being used for small lands are Geographic Information System(GIS), laser land leveler, leaf color chart,... S. Kumar, M. Singh, H. Mirzakhaninafchi, R.U. Modi, M. Ali, M. Bhardwaj, R. Soni |
26. Corn Nitrogen Fertilizer Recommendation Models Based on Soil Hydrologic Groups Aid in Predicting Economically Optimal Nitrogen RatesNitrogen (N) fertilizer recommendations that match corn (Zea mays L.) N needs maximize grower profits and minimize water quality consequences. However, spatial and temporal variability makes determining future N requirements difficult. Studies have shown no single soil or weather measurement is consistently increases accuracy, especially when applied over a regional scale, in predicting economically optimal N rate (EONR). Basing site N response on soil hydrological group could help account for... G.M. Bean, N.R. Kitchen, J.J. Camberato, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.E. Sawyer, P.C. Scharf |
27. Supporting and Analysing On-Farm Nitrogen Tramline Trials So Farmers, Industry, Agronomists and Scientists Can LearN TogetherNitrogen fertilizer decisions are considered important for the agronomic, economic and environmental performance of cereal crop production. Despite good recommendation systems large unpredicted variation exists in measured N requirements. There may be fields and farms that are consistently receiving too much or too little N fertilizer, therefore losing substantial profit from wasted fertilizer or lost yield. Precision farming technologies can enable farmers (& researchers) to test appropriate... D. Kindred, R. Sylvester-bradley, S. Clarke, S. Roques, D. Hatley, B. Marchant |
28. Can Optimization Associated with On-Farm Experimentation Using Site-Specific Technologies Improve Producer Management Decisions?Crop production input decisions have become increasingly difficult due to uncertainty in global markets, input costs, commodity prices, and price premiums. We hypothesize that if producers had better knowledge of market prices, spatial variability in crop response, and weather conditions that drive crop response to inputs, they could more cost-effectively make profit-maximizing input decisions. Understanding the drivers of variability in crop response and designing accompanying management strategies... B.D. Maxwell, A. Bekkerman, N. Silverman, R. Payn, J. Sheppard, C. Izurieta, P. Davis, P.B. Hegedus |
29. Using Deep Learning in Yield and Protein Prediction of Winter Wheat Based on Fertilization Prescriptions in Precision AgriculturePrecision Agriculture has been gaining interest due to the significant growth in the fields of engineering and computer science, hence leading to more sophisticated methods and tools to improve agricultural techniques. One approach to Precision Agriculture involves the application of mathematical models and machine learning to fertilization optimization and yield prediction, which is what this research focuses on. Specifically, in this work we report the results of predicting yield and protein... J. Sheppard, A. Peerlinck, B. Maxwell |
30. Prototype Unmanned Aerial Sprayer for Plant Protection in Agricultural and Horticultural CropsAerial application of pesticides has the potential to reduce the amount of pesticides required as chemicals are applied where needed. A prototype Unmanned Aerial Sprayer with a payload of 20 kg; a spraying rate of 6 liters per minute; a spraying swathe of 3 meters, coverage rate of 2 to 4 meters per second and 10 minutes of flight time was built using state of the art technologies. The project is a joint development by University of Agricultural Sciences, Dharwad, KLE Technological University,... S. G, D.P. Biradar, B.L. Desai, V.C. Patil, P. Patil, V.B. Nargund, V. Desai, W. John, S.M. Channangi, V. Tulasigeri |
31. Optimizing Nitrogen Application to Maximize Yield and Reduce Environmental Impact in Winter Wheat ProductionField-specific fertilizer rate optimization is known to be beneficial for improving farming profit, and profits can be further improved by dividing the field into smaller plots and applying site-specific rates across the field. Finding optimal rates for these plots is often based on data gathered from said plots, which is used to determine a yield response curve, telling us how much fertilizer needs to be applied to maximize yield. In related work, we use a Convolutional Neural Network, known... A. Peerlinck, J. Sheppard, G.L. Morales luna, P. Hegedus, B. Maxwell |
32. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US MidwestEffective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly across... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan |
33. Using Informative Bayesian Priors and On-farm Experimentation to Predict Optimal Site-specific Nitrogen RatesMost U.S. Corn Belt states now recommend the Maximum Return to Nitrogen (MRTN) method for determining optimal nitrogen rates, which is based on 15 years of on-farm yield response to nitrogen trials. The MRTN method recommends a uniform rate for a region of a state. This study combines Illinois MRTN data, Bayesian methods, and on-farm experimentation from the Data Intensive Farm Management (DIFM) project to provide site-specific nitrogen recommendations. On-farm trials are now being used to provide... W. Brorsen, D. Poursina, C. Patterson, T. Mieno, B. Edge, E.D. Nafziger |
34. 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 |