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| Filter results8 paper(s) found. |
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1. Site-specific Evaluation of Sensor-based Winter Wheat Nitrogen Tools Via On-farm ResearchCrop producers face the challenge of optimizing high yields and nitrogen use efficiency (NUE) in their agricultural practices. Enhancing NUE has been demonstrated by adopting digital agricultural technologies for site-specific nitrogen (N) management, such as remote-sensing based N recommendations for winter wheat. However, winter wheat fields are often uniformly fertilized, disregarding the inherent variability within the fields. Thus, an on-farm evaluation of sensor-based N tools is needed to... J. Cesario Pinto, L. Thompson, N. Mueller, T. Mieno, L. Puntel, P. Paccioretti, G. Balboa |
2. Barriers and Adoption of Precision Ag Tehcnologies for Nitrogen Management NebraskaA statewide survey of Nebraska farmers shows that they determine the N rate based on soil lab recommendations (82%), intuition, traditional rate, and own experience (67%). The adoption of dynamic site-specific models (23%), and sensor-based algorithms (11%) remains low. The survey identified the main barriers to the adoption of these N management technologies. ... G. Balboa, L. Puntel, L. Thompson, P. Paccioretti |
3. Quantifying Prediction Uncertainty in Field-scale Soil Maps Generated by Machine Learning.Field-scale maps of soil properties are a key component of precision agriculture, as they are routinely used as inputs for variable-rate fertilization, zone delineation, and site-specific management. While machine learning models have substantially improved the accuracy of spatial predictions, uncertainty associated with these predictions is often ignored, limiting the reliability of soil maps as decision-support tools. Quantifying prediction uncertainty is essential not only to assess map quality,... F. García Seleme, P. Paccioretti, M. Balzarini, M. Córdoba |
4. Less Nitrogen, Same Yield: Evidence from 45 Site-Years of Sensor-Based Maize Nitrogen Management in the U.S. MidwestImproving nitrogen use efficiency (NUE) while maintaining maize productivity remains a central challenge for irrigated corn systems in the U.S. Midwest. Sensor-based, in-season nitrogen (N) management has been around for many years. Yet, US Midwest farmers reported that a lack of information about the value of this approach and fear of yield loss when reducing the N rate were the top barriers to adoption. In-season N management enables better synchronization of N supply with crop demand, yet multi-environment... G. Balboa, P. Paccioretti, J.D. Luck |
5. An Online Decision Support Tool for Homogeneous Zone Delineation in Precision AgricultureManagement zone delineation is a key component of site-specific management in precision agriculture, enabling the spatial optimization of inputs and an improved understanding of within-field variability. Traditionally, homogeneous zones have been derived from historical yield maps or soil-related variables obtained through proximal sensing. More recently, the increasing availability of multispectral satellite imagery and derived vegetation indices has expanded the range of data sources available... |
6. Temporal Stability of Management Zones Derived from Vegetation Indices and Yield Data in Contrasting Production SystemsThe delineation of management zones is a central component of site-specific crop management in precision agriculture. However, the temporal stability of zones derived from different data sources remains a key challenge, particularly when vegetation indices and yield data are combined across multiple seasons. This study evaluates the temporal stability of management zones delineated using vegetation indices and yield data derived from long-term commercial field datasets. The proposed methodology... |
7. Statistical Mean Comparisons in Unreplicated Yield Trials with Georeferenced DataPrecision agriculture technologies have enabled the collection of large volumes of georeferenced yield data within experimental fields. In practice, many on-farm experiments (OFE) are implemented as large unreplicated strips or field zones containing numerous observations within each zone. The lack of replication prevents the use of classical statistical models for comparing zone means. Although many yield observations are available per zone, spatial autocorrelation violates independence assumptions... M. Córdoba, P. Paccioretti, M. Balzarini |
8. Data Analytics in Precision Agriculture: Statistical Modelling and Machine Learning... M. Córdoba, P. Paccioretti |