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1. Application of Information Technologies in Precision ApicultureApiculture, widely known as beekeeping, is one of the agriculture’s sub directions, where Precision Agriculture (PA) methods can be successfully applied. Adaptation of PA methods and technics into Apiculture, as well as integrating information technologies into beekeeping process can change and improve the beekeepers understanding of bee... E. Stalidzans, A. Zacepins, J. Meitalovs |
2. Evaluation of Photovoltaic Modules at Different Installation Angles and Times of the DaySeveral electricity-consuming components for cooling and heating, illumination, ventilation, and irrigation are used to maintain proper environments of protected crop cultivation facilities. Photovoltaic system is considered as one of the most promising alternative power source for protected cultivation. Effects of environm... S. Chung, J. Kong, Y. Huh, K. Bae, S. Hur, D. Lee, Y. Chae |
3. Climatological Diagnostic Analysis: A Case Study for Parbhani District in Marathwada Region of India... S.N. Pawar, A.K. Gore, G.U. Shinde, M.S. Pendke |
4. Using Drone Based Sensors to Direct Variable-Rate, In-Season, Aerial Nitrogen Application on CornImproving nutrient management on farms is a critical issue nationwide. Applying a portion of N fertilizer during the growing season, alongside the growing corn crop is one way to improve nitrogen management. Sidedress N applications allow the availability of N fertilizer to more closely match the time when the crop is rapidly uptaking N. Additionally, waiting to apply a portion of the N during the growing season allows for management which is responsive to current growing season conditions.... L.J. Thompson |
5. Utilization of Spatially Precise Measurements to Autocalibrate the EPIC Agroecosystem ModelCorn nitrogen recommendations for individual fields must improve to minimize the negative influence that agriculture has on the environment and society. Two adaptive N management approaches for making in-season N fertilizer recommendations are remote sensing and crop systems modeling. Remote sensing has the advantage of characterizing the spatial variability at a high spatial resolution, and crop models are prognostic and can assess expected additions and losses that are not yet reflected by ... T. Nigon, D. Mulla, C. Yang |
6. 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 fo... 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 |
7. Active Canopy Sensors for the Detection of Non-Responsive Areas to Nitrogen Application in WheatActive canopy sensors offer accurate measurements of crop growth status that have been used in real time to estimate nitrogen (N) requirements. NDVI can be used to determine the absolute amount of fertilizer requirement, or simply to distribute within the field an average rate defined by decision models using other diagnostics. The objective of this work was to evaluate the capacity of active canopy sensors to determine yield and N application requirements within a site at jointing stage (Fee... A.G. Berger, E. Hoffman, N. Fassana, F. Alfonso |
8. Using a UAV-Based Active Canopy Sensor to Estimate Rice Nitrogen StatusActive canopy sensors have been widely used in the studies of crop nitrogen (N) estimation as its suitability for different environmental conditions. Unmanned aerial vehicle (UAV) is a low-cost remote sensing platform for its great flexibility compared to traditional ways of remote sensing. UAV-based active canopy sensor is expected to take the advantages of both sides. The objective of this study is to determine whether UAV-based active canopy sensor has potential for monitoring rice N statu... S. Li, Q. Cao, X. Liu, Y. Tian, Y. Zhu |
9. Deriving Fertiliser VRA Calibration Based on Ground Sensing Data from Specific Field ExperimentsNitrogen (N) fertilisation affects both rice yield and quality. In order to improve grain yield while limiting N losses, providing N fertilisers during the critical growth stages is essential. NDRE is considered a reliable crop N status indicator, suitable to drive topdressing N fertilisation in rice. A multi-year experiment on different rice varieties (Gladio, Centauro, and Carnaroli) was conducted between 2011 and 2017 in Castello d’Agogna (PV), northwest Italy, with the aim of i) est... E. Cordero, D. Sacco, B. Moretti, E.F. Miniotti, D. Tenni, G. Beltarre, M. Romani, C. Grignani |
10. Active and Passive Sensor Comparison for Variable Rate Nitrogen Determination and Accuracy in Irrigated CornThe objectives of this research were to (i) compare active and passive crop canopy sensors’ sidedress variable rate nitrogen (VRN) derived from different vegetation indices (VI) and (ii) assess VRN recommendation accuracy of active and passive sensors as compared to the agronomic optimum N rate (AONR) in irrigated corn. This study is comprised of six site-years (SY), conducted in 2015, 2016 and 2017 on different soil types (silt loam, loam and sandy loam) and with a range of preplant-ap... L. Bastos, R.B. Ferguson |
11. Use of Field Diagnostic Tools for Top Dressing Nitrogen Recommendation When Organic Manures Are Applied in Humid Mediterranean ConditionsNitrogen is often applied in excessive quantities, causing nitrogen losses. In recent years, the management of large quantities of manure and slurry compounds has become a challenge. The aim of this study was to assess the usefulness of the proxy tools Yara N-testerTMand RapidScan CS-45 for diagnosing the N nutritional status of wheat crops when farmyard manures were applied. Our second objective was to start designing a N fertilization strategy based on these measurements. To achieve these o... A. Castellón, A. Aizpurua, M. Aranguren |
12. Predicted Nitrate-N Loads for Fall, Spring, and VRN Fertilizer Application in Southern MinnesotaNitrate-N from agricultural fields is a source of pollution to fresh and marine waters via subsurface tile drainage. Sensor-based technologies that allow for in-season monitoring of crop nitrogen requirements may represent a way to reduce nitrate-N loadings to surface waters by allowing for fertilizer application on a more precise spatial and temporal resolution. However, little research has been done to determine its effectiveness in reducing nitrate-N losses. In this study... G.L. Wilson, D.J. Mulla, J. Galzki, A. Laacouri, J. Vetsch |
13. Improving Corn Nitrogen Rate Recommendations Through Tool FusionImproving corn (Zea maysL,) nitrogen (N) fertilizer rate recommendation tools can improve farmer’s profits and help mitigate N pollution. One way to improve N recommendation methods is to not rely on a single tool, but to employ two or more tools. Thiscould be thoughtof as “tool fusion”.The objective of this analysis was to improve N management by combining N recommendation tools used for guiding rates for an in-seasonN application. This evaluation ... C.J. Ransom, N.R. Kitchen, J.J. Camberato, P.R. Carter, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J. Shanahan, J.E. Sawyer |
14. Utilizing Weather, Soil, and Plant Condition for Predicting Corn Yield and Nitrogen Fertilizer ResponseImproving corn (Zea mays L.) nitrogen (N) fertilizer rate recommendation tools should increase farmer’s profits and help mitigate N pollution. Weather and soil properties have repeatedly been shown to influence crop N need. The objective of this research was to improve publicly-available N recommendation tools by adjusting them with additional soil and weather information. Four N recommendation tools were evaluated across 49 N response trials conducted in eight U.S. states over three gr... N.R. Kitchen, M.A. Yost, C.J. Ransom, G. Bean, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer |
15. 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 Lat... 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 |
16. Evaluating Remote Sensing Based Adaptive Nitrogen Management for Potato ProductionConventional nitrogen (N) management for potato production in the Upper Midwest, USA relies on using split-applications of N fertilizer or a controlled release N product. Using remote sensing to adaptively manage N applications has the potential to improve N use efficiency and reduce losses of nitrate to groundwater, which are important regional concerns. A two-year plot-scale experiment was established to evaluate adaptive N-management using remote sensing compared to conventional practices ... B. Bohman, D. Mulla, C. Rosen |
17. Improving Active Canopy Sensor-Based In-Season N Recommendation Using Plant Height Information for Rain-Fed Maize in Northeast ChinaThe inefficient utilization of nitrogen (N) fertilizer due to leaching, volatilization and denitrification has resulted in environmental pollution in rain-fed maize production in Northeast China. Active canopy sensor-based in-season N application has been proven effective to meet maize N requirement in space and time. The objective of this research was to evaluate the feasibility of using active canopy sensor for guiding in in-season N fertilizer recommendation for rain-fed maize in Northeast... X. Wang, Y. Miao, T. Xia, R. Dong, G. Mi, D.J. Mulla |
18. Precision Nitrogen and Water Management for Enhancing Efficiency and Productivity in Irrigated MaizeNitrogen and water continue to be the most limiting factors for profitable maize production in the western Great Plains. The objective of this research was to determine the most productive and efficient nitrogen and water management strategies for irrigated maize. This study was conducted in 2016 at Colorado State University’s Agricultural Research Development and Educational Center, in Fort Collins, Colorado. The experiment included a completely randomized block design with ... E. Phillippi, R. Khosla, L. Longchamps, P. Turk |
19. Practical Prescription of Variable Rate Fertilization Maps Using Remote Sensing Based Yield PotentialThis paper describes a practical approach for the prescription of variable rate fertilization maps using remote sensing data (RS) based on satellite platforms, Landsat 8 and Sentinel-2 constellation. The methodology has been developed and evaluated in Albacete, Spain, in the framework of the project FATIMA (http://fatima-h2020.eu/). The global approach considers the prescription of N management prior to the growing season, based on a spatially distributed N balance. Although the diagnosis of ... A. Osann, I. Campos, M. Calera, C. Plaza, V. Bodas, A. Calera, J. Villodre, J. Campoy, S. Sanchez, N. Jimenez, H. Lopez |
20. Estimating Litchi Canopy Nitrogen Content Using Simulated Multispectral Remote Sensing DataThis study aims at evaluating the performance of seven highly spatial resolution remote sensing data in litchi canopy nitrogen content estimation. The litchi canopy reflectance were collected by ASD field spectrometer. Then the canopy spectral data were resampled based on the spectral response functions of each satellite sensors (Geo-eye, GF-WFV1, Rapid-eye, WV-2, Landsat 8, WV-3, and Sentinel-2). The spectral indices in literature were derived based on the simulated data. Meanwhile, the succ... D. Li, H. Jiang, S. Chen, C. Wang |
21. Grassland System Impacts on Spatial Variability of Soil Phosphorus in Eastern CanadaPhosphorus (P) is an essential nutrient for plants, including grasslands. However, continuous applications of P fertilizer result in P accumulations in the soil, increasing the risk of P losses through runoff and erosion. Since 2008, more than 31 million tonnes of organic fertilizers, representing more than 95,000 tonnes of P2O5, were applied to agricultural fields in Eastern Canada. Thus, grassland systems were fertilized intensively using organic fertilizers with high ... J.D. Nze memiaghe, A. Cambouris |