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| Filter results15 paper(s) found. |
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1. Use Of Vegetation Indices In Variable Rate Application Of Potato Haulm Killing HerbicidesVariable rate application (VRA) of pesticides based on measured spatial variation in crop biomass is possible with currently available crop reflection sensors (remote and proximity), GNSS technology and modern field sprayers. VRA has the potential to contribute to a more sustainable use of pesticide. Dose rates are optimized based on local requirements at a scale of about 5-50 m2, leading to less adverse side effects, less costs and higher yields. In the longer term, we... C. Kempenaar, T. Been, F.V. Evert |
2. 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 |
3. Toward Geopolitical-Context-Enabled Interoperability in Precision Agriculture: AgGateway's SPADE, PAIL, WAVE, CART and ADAPTAgGateway is a nonprofit consortium of 240+ businesses working to promote, enable and expand eAgriculture. It provides a non-competitive collaborative environment, transparent funding and governance models, and anti-trust and intellectual property policies that guide and protect members’ contributions and implementations. AgGateway primarily focuses on implementing existing standards and collaborating with other organizations to extend them when necessary. In 2010 AgGateway identified... R. Ferreyra, D.B. Applegate, A.W. Berger, D.T. Berne, B.E. Craker, D.G. Daggett, A. Gowler, R.J. Bullock, S.C. Haringx, C. Hillyer, T. Howatt, B.K. Nef, S.T. Rhea, J.M. Russo, S.T. Nieman, P. Sanders, J.A. Wilson, J.W. Wilson, J.W. Tevis, M.W. Stelford, T.W. Shearouse, E.D. Schultz, L. Reddy |
4. Akkerweb: A Platform for Precision Farming Data, Science, and PracticeThe concept of precision farming (PF) was formulated about 40 years ago and the scientific knowledge for some applications of PF in The Netherlands has been available for almost 20 years. Also, in many cases equipment is available to implement PF in practice. In spite of all this PF uptake is still limited. An important reason for the limited uptake of PF is in the challenges that must be overcome to let data flow from sensors to data storage, to combine data sources and process them into recommendations,... F.K. Van Evert, T. Been, J.A. Booij, C. Kempenaar, G.J. Kessel, L.P. Molendijk |
5. Fostering Student Engagement and Leadership Development in Integrative Precision Agriculture Across BordersEfforts to advance integrative precision agriculture technologies are growing exponentially across the globe with the common interest of upholding food security and developing more sustainable food and fiber production systems. Countries such as the United States and Brazil are among the biggest crop producers in the world and will play an even bigger role in food security in the next decades. It is of utmost importance that countries can advance together to overcome future food production challenges... L. Lacerda, A. Felipe Dos Santos, E. Bedwell, A. Jakhar, T.O. Costa Barboza, M. Ardigueri |
6. Proximal, Drone, and Satellite Sensors for In-season Variable Nitrogen Rate Application in Corn: a Comparative Study of Fixed-rate and Sensor-based ApproachesEffective nitrogen (N) management is essential for optimizing corn yield and enhancing agricultural sustainability. Traditional N application methods, typically uniform split pre-plant and in-season applications, often neglect the spatial and temporal variability of N requirements across different fields and years, potentially leading to N overuse. With the rise of precision agriculture technologies, it is crucial to reassess these conventional practices. This study had two main objectives: first,... A. Jakhar, A. Bhattarai, L. Bastos, G. Scarpin |
7. Comparing Proximal and Remote Sensors for Variable Rate Nitrogen Management in CottonSensing and variable rate technology are becoming increasingly important in precision agriculture. These technologies utilize sensors to monitor crop growth and health, enabling informed decisions such as diagnosing nitrogen (N) stress and applying variable rates of N. Sensor-based solutions allow for customized N applications based on plant needs and environmental factors. This approach has led to notable reductions in N application rates, minimized N losses by improving N use efficiency (NUE),... A. Bhattarai, A. Jakhar, L. Bastos, G.J. Scarpin |
8. Comparison of Machine Learning Models for Within-field Cotton Yield Prediction: Assessing the Value of Temporal Data and GeneralizabilityCotton (Gossypium hirsitum L.) yield prediction is challenging due to the complex interactions between static environmental factors, such as soil properties and topography, and dynamic variables, including weather and crop management. These factors generate significant spatial and temporal variability within and across fields. While remote sensing technologies, particularly satellite-derived vegetation indices, provide spatially explicit data to evaluate crop health, translating... L. Bastos, L. Fuhrer, W. Porter, G.J. Scarpin, A. Kaur Dhaliwal, A. Bhattarai, A. Jakhar |
9. Sensor-based Variable Rate Nitrogen Recommendations: Comparing Proximal, Drone, and Satellite Sensors in CornNitrogen (N) represents 20–25% of corn (Zea mays L.) production costs, yet 15–65% is lost through volatilization and leaching. Conventional uniform-rate application ignores spatial variability and seasonal demand. Sensor-based variable rate nitrogen (VRN) addresses this by using real-time reflectance data, but the influence of sensing platform proximal, drone, or satellite on economic outcomes under varying N stress remains under-researched. The objective of this study at Iron Horse,... A. Jakhar, L. Bastos, A. Bhattarai, K. Poudel, A. Dhaliwal |
10. Evaluation of Kriging Models and Variogram Structures for Daily Weather Interpolation Across Georgia, United StatesSpatial interpolation fills gaps between scattered weather stations to create continuous maps of variables like temperature. In Georgia, USA—a state with rolling hills in the north, coastal plains in the south, and the Appalachian foothills—this process is vital for accurate climate monitoring, irrigation scheduling, and crop-yield forecasting. Without reliable grids, downstream models suffer from bias or uncertainty. This study aimed to assess... |
11. A Canopy-based Decision Framework for Selecting Sensor Platform and Vegetation Index in Variable-rate Nitrogen Management of Irrigated CornSensor-based variable-rate nitrogen (VRN) management promises field-specific N optimization, yet the choice of sensing platform fundamentally alters N recommendations. At early growth stages, a "double penalty" emerges: nitrogen-deficient plants produce smaller canopies, exposing more bare soil, which deflates vegetation index (VI) values and inflates N recommendations where accuracy matters most. This study developed a canopy coverage-based decision framework for selecting optimal sensor... A. Jakhar, L. Bastos, R. Roth, S. Virk, A. Bhattarai, K. Poudel, A. Dhaliwal |
12. AgGeoSampler: A Geospatial Open-Source Data Acquisition and Sampling Design Dashboard for Agricultural ApplicationsModern agricultural and environmental research increasingly depends on high-resolution geospatial data to support precise, site-specific decision-making. Advances in satellite remote sensing, unmanned aerial systems, and digital soil mapping have generated vast spatial datasets that capture fine-scale variability in vegetation health, soil properties, and terrain attributes. However, translating this wealth of information into effective field-sampling... A. Bhattarai, A. Jakhar, K. Poudel, A. Dhaliwal, L.M. Bastos |
13. Predicting Yield Stability Classes Using Satellite Imagery in the Absence of Yield Monitor DataSite-specific management is essential for improving agricultural productivity while reducing input costs and minimizing environmental impacts. Although yield monitor data are commonly used to characterize within-field yield variability, their availability is often limited by technological and economic constraints. The primary objective of this study was to compare spatial–temporal stability classes derived from yield monitor data and satellite imagery in cotton production... K. Poudel, A. Bhattarai, A. Jakhar, L. Bastos, A. Dhaliwal |
14. Main Environmental and Variety Drivers of Cotton Seed Quality: Historical Insights from the United States Cotton BeltCotton seed quality traits including oil content, nitrogen (protein), and gossypol significantly influence seed value and end-use applications, yet their predictability based on environmental conditions across varied U.S. growing regions remains poorly understood. This study aimed to: (i) identify critical environmental predictors of seed composition; (ii) build machine learning models to predict seed quality as a function of seasonal weather patterns; and (iii) assess differences... A. Dhaliwal, L. Bastos, K. Sv, A. Bhattarai, A. Jakhar, K. Poudel, D.M. Mccallister, S.Y. Jaconis |
15. Predicting Pre-harvest Cotton Fiber Quality: An Open-data and Machine Learning FrameworkIntra-field variability in soil properties and topography, and inter-field variability in weather patterns often leads to inconsistent cotton fiber quality and yield outcomes, posing challenges for growers. This study aims to: (i) predict within-field cotton fiber quality traits based on environmental and soil variables using machine learning models; (ii) identify the most influential environmental drivers (weather, vegetation indices, soil properties, terrain characteristics) affecting cotton... |