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1. The Use Of A Ground Based Remote Sensor For Winter Wheat Grain Yield Prediction In Northern PolandThe aim of the research was to investigate if algorithms developed for winter wheat, cv. Trend, yield predictions, based on ground measured GNDVI, differ significantly between 2 sequent years. The research was conducted in Pomerania, northern Poland (54° 31' N 17° 18' E) on sandy loam soils. The strip-trial design was used to compare the effect of 6 N treatments: 0, 50, 100, 150, 200 and 250 kg ha-1, applied as one dose at the beginning... S.M. Samborski, D. Gozdowski, S.E. Dobers |
2. Adoption And Use Of Precision Agriculture Technologies By PractitionersA survey of farmers and farm service providers were initiated to ascertain the adoption and use of precision agriculture technologies as well as the barriers to and incentives for adoption. Farm-level data were collected via audience response system at the 2009 Alabama Precision Ag and Field Crops Conference and local winter production meetings across the six crop reporting districts in Alabama. Service provider data were collected using an online survey. Questions common to farmers and service... A.T. Winstead, S.H. Norwood, T. Griffin, A.M. Adrian, M. Runge, J.P. Fulton |
3. Estimating Soil Productivity And Energy Efficiency Using Websoil Survey, Soil Productivity Index Calculator, And Biofuel Energy Systems SimulatorSoils have varying production capacities for a specific plant or sequence of plants under defined management strategies. The production capacity or “productivity” can be quantified as a mathematical function of a soils ability to sufficiently sustain plant growth... K.D. Reitsma, T.E. Schumacher |
4. Indirect Measurement Of Creeping Bentgrass N, Chlorophyll, And Color For Precision Golf Green ManagementIndirect measurement of turfgrass tissue through optical sensing may provide golf course managers with non-destructive and relatively simple real-time measurements of golf green N requirements. The objective of this study was to determine the effect of N rate on ‘Crenshaw’ creeping bentgrass (Agrostis stolonifera L.) tissue N, chlorophyll concentration, and color using the GreenSeeker (NTech Industries, Ukiah, CA) handheld sensor. Plots... J.Q. Moss, G.E. Bell |
5. Cotton Precision Farming Adoption In The Southern United States: Findings From A 2009 SurveyThe objectives of this study were 1) to determine the status of precision farming technology adoption by cotton producers in 12 states and 2) to evaluate changes in cotton precision farming technology adoption between 2000 and 2008. A mail survey of cotton producers located in Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, Missouri, North Carolina, South Carolina, Tennessee, Texas and Virginia was conducted in February and March of 2009 to establish the use of precision farming technologies... M. Velandia, D.F. Mooney, R.K. Roberts, B.C. English, J.A. Larson, D.M. Lambert, S.L. Larkin, M.C. Marra, R. Rejesus, S.W. Martin, K.W. Paxton, A. Mishra, C. Wang, E. Segarra, J.M. Reeves |
6. Development Of A Precision Sensing Sprayer For The Application Of Nitrogen Fertilizer To TurfgrassNormalized difference vegetation index (NDVI) may be very useful for turfgrass managers to measure turf quality and obtain an indirect measurement of turf N status. The objective of this research was to develop a Nitrogen Fertilization Optimization Algorithm (NFOA) for use in a turfgrass variable rate N applicator on bermudagrass [Cynodon dactylon (L.) Pers] fairways and creeping bentgrass (Agrostis stolonifera L.) greens in Oklahoma. Plots (0.9 X 1.5 m)... J.Q. Moss, G.E. Bell, J.B. Solie, M.L. Stone, D.L. Martin, M.E. Payton |
7. Spatial Variability Of Spikelet Sterility In Temperate Rice In ChileSpikelet sterility (blanking) causes large economic losses to rice farmers in Chile. The most common varieties are susceptible to low air and water temperatures during pollen formation and flowering, which is the main responsible for the large year to year variation observed in terms of blanking and, therefore, of grain yield. The present work had for objective to study the spatial variability of spikelet sterility within two rice fields, during two consecutive seasons, and relate it to water... R.A. Ortega, D.E. Del solar, E. Acevedo |
8. Active Optical Sensor Algorithms For Corn Yield Prediction And In-Season N Application In North DakotaA recent series of seventy seven field N rate experiments with corn (Zea mays, L.) in North Dakota was conducted. Multiple regression analysis of the characteristics of the data set indicated that segregating the data into those with high clay soils and those with medium textures increased the relationship between N rate and corn yield. However, the nearly linear positive slope relationship in high clay soils and coarser texture soils with lower yield productivity indicated... L. Sharma, H. Bu, R. Ashley, G. Endres, J. Teboh, D.W. Franzen |
9. Development Of An Index-Based Insurance Product: Validation Of A Forage Production Index Derived From Medium Spatial Resolution fCover Time SeriesAn 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 |
10. Use Of Quality And Quantity Information Towards Evaluating The Importance Of Independent Variables In Yield PredictionYield predictions based on remotely sensed data are not always accurate. Adding meteorological and other data can help, but may also result in over-fitting. Working with American Crystal Sugar, we were able to demonstrate that the relevance of independent variables can be tested much more reliably when not only yield but also quality attributes are known, such as the sugar content and the sugar... E. Momsen, J. Xu, D.W. Franzen, J.F. Nowatzki, K. Farahmand, A.M. Denton |
11. Integrated Analysis of Multilayer Proximal Soil Sensing DataData revealing spatial soil heterogeneity can be obtained in an economically feasible manner using on-the-go proximal soil sensing (PSS) platforms. Gathered georeferenced measurements demonstrate changes related to physical and chemical soil attributes across an agricultural field. However, since many PSS measurements are affected by multiple soil properties to different degrees, it is important to assess soil heterogeneity using a multilayer approach. Thus, analysis of multiple layers of geospatial... V.I. Adamchuk, N. Dhawale, A. Biswas, S. Lauzon, P. Dutilleul |
12. Levels of Inclusion of Crambe Meal (Crambe Abyssinica Hochst) in Sheep Diet on the Balance of Nitrogen and Ureic Nitrogen in the Blood SerumCrambe meal, which is a co-product of biodiesel production, is a potential substitute for conventional protein sources in ruminant diets. The objective of this study was to evaluate the effect of the substitution of crude protein of the concentrate by crude protein of crambe meal with increasing levels (0, 25, 50, and 75%) on nitrogen balance and blood plasma urea nitrogen concentration in sheep. Four male sheep, rumen fistulated, were placed in metabolic crates and distributed in a 4 x 4 Latin... K.K. De azevedo, D.M. Figueiredo, M.G. De sousa, G.M. Dallago, R.R. Silveira, L.D. Da silva, L.N. Rennó, R.A. Santos |
13. Efficiency of Microbial Synthesis and the Flow of Nitrogen Compounds in Sheep Receiving Crambe Meal (Crambe Abyssinica Hochst) Replacing the Concentrade Crude ProteinThe objective of this study was to evaluate the effect of increasing levels (0, 25, 50, 75%) of crude protein substitution of the concentrate by crude protein of crambe meal on microbial protein synthesis and the flow of microbial nitrogen compounds in sheep. Four rumen fistulated sheep (18 months and initial average body weight of 50 kg) were distributed in a 4 x 4 Latin square design. Diets were balanced to meet the requirements for minimum gains, containing approximately 14% crude protein and... K.K. De azevedo, D.M. Figueiredo, G.M. Dallago, J.A. Vieira, R.R. Silveira, L.D. Da silva, R.A. Santos, L.N. Rennó, G.B. Pacheco |
14. Tracking Two Decades of Precision Agriculture Through the Croplife Purdue SurveyThe CropLife/Purdue University precision dealer survey is the longest-running continuous survey of precision farming adoption. The 2017 survey is the 18th, conducted every year from 1997 to 2009, and then every other year following. For individuals working in agriculture there is great value in knowing who is doing what and why, to get a better understanding of the utilities and applications, and to guide investments. A major revision in survey questions was made... B. Erickson, J. Lowenberg-deboer, J. Bradford |
15. Optimization of Batch Processing of High-density Anisotropic Distributed Proximal Soil Sensing Data for Precision Agriculture PurposesThe amount of spatial data collected in agricultural fields has been increasing over the last decade. Advances in computer processing capacity have resulted in data analytics and artificial intelligence becoming hot topics in agriculture. Nevertheless, the proper processing of spatial data is often neglected, and the evaluation of methods that efficiently process agricultural spatial data remains limited. Yield monitor data is a good example of a well-established methodology for data processing... F. Hoffmann silva karp, V. Adamchuk, A. Melnitchouck, P. Dutilleul |
16. Predicting, Mapping, and Understanding the Drivers of Grain Protein Content Variability – Utilising John Deere’s New Harvestlab 3000 Grain Sensing SystemGrain protein content (GPC) is a key determinant of the prices that grain growers receive, and the rising cost of production is shifting management focus towards optimising this to maximise return on investment. In 2023, John Deere released the HarvestLab 3000TM Grain Sensing system in Australia for real-time, on-the-go measurement of protein, starch, and oil values for wheat, barley, and canola. However, while the uptake of these sensors is increasing, GPC maps are not available for... M.J. Tilse, P. Filippi, T. Bishop |
17. Are Pulses Really More Variable Than Cereals? a Country-wide Analysis of Within-field VariabilityIn Australia, pulses are underutilised by growers relative to cereal crops. There is significant global interest in growing pulses to provide more plant protein, and they also provide a string of agronomic and environmental benefits, such as their ability to fix nitrogen, and provide a pest and disease break for cereal crops. Many studies attribute this underutilisation to pulses exhibiting greater within-field yield variability than cereals. However, this has never been comprehensively examined... P. Filippi, T. Bishop, D. Al-shammari, T. Mcpherson |
18. Deposition Characteristics of Different Style Spray Tips at Varying Speeds and Altitudes from an Unmanned Aerial SystemThe application of pesticides with a UAS has become a popular practice over the past few years within crop production. The ability to carry larger volumes of liquid i onboard, reduced costs, and simple operation has attributed to the increased popularity. Additionally, the increased number of fungicide applications in corn due to the tar spot disease has shown that the demand for aerial applications of all types has increased with UAS pesticide application technology providing the opportunity... A. Leininger, K. Verhoff, K. Lovejoy, A. Thomas, G. Davis, A. Emmons, J.P. Fulton |
19. On Data-driven Crop Yield Modelling, Predicting, and Forecasting and the Common Flaws in Published StudiesThere has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), and abundance of machine learning modelling approaches. This is a particular problem in the field of Precision Agriculture, where many studies will take a crop yield map (or a small number), create... P. Filippi, T. Bishop, S. Han, I. Rund |
20. Predicting Soil Chemical Properties Using Proximal Soil Sensing Technologies and Topography Data: a Case StudyUsing proximal soil sensors (PSS) is widely recognized as a strategy to improve the quality of agricultural soil maps. Nevertheless, the signals captured by PSS are complex and usually relate to a combination of processes in the soil. Consequently, there is a need to explore further the interactions at the source of the information provided by PSS. The objectives of this study were to examine the relationship between proximal sensing techniques and soil properties and evaluate the feasibility... F. Hoffmann silva karp, V. Adamchuk, P. Dutilleul, A. Melnitchouck, A. Biswas |