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| Filter results6 paper(s) found. |
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1. Factors Influencing the Timing of Precision Agriculture Technology Adoption in Southern U.S. Cotton ProductionTechnology innovators in cotton production adopted precision agriculture (PA) technologies soon after they became commercially available, while others adopted these technologies in later years after evaluating the success of the innovators. The timing of... D.M. Lambert, J.A. Larson, B.C. English, R.M. Rejesus, M.C. Marra, A.K. Mishra, C. Wang, P. Watcharaanantapong, R.K. Roberts, M. Velandia |
2. Maximizing Agriculture Equipment Capacity Using Precision Agriculture TechnologiesGuidance systems are one of the primary Precision Agriculture technologies adopted by US farmers. While most practitioners establish their initial AB lines for fields based on previous management patterns, a potential exists in conducting analyses to establish AB lines or traffic patterns which maximize field capacity. The objective of this study was to... A.M. Poncet, T.P. Mcdonald, G. Pate, B. Tisseyre, J.P. Fulton |
3. Effect Of A Variable Rate Irrigation Strategy On The Variability Of Crop Production In Wine Grapes In CaliforniaPruning and irrigation are the cultural practices with the highest potential impact on yield and quality in wine grapes. In particular, irrigation start date, rates and frequency can be synchronized with crop development stages to control canopy growth and, in turn, positively influence light microclimate, berry size and fruit quality. In addition, canopy management practices can be implemented in vineyards with large canopies to ensure fruit zone microclimate... L.A. Sanchez, L.J. Klein, A. Claassen, D. Lew, M. Mendez-costabel, B. Sams, A. Morgan, N. Hinds, H.F. Hamann, N. Dokoozlian |
4. Site-specific Scale Efficiency Determined by Data Envelopment Analysis of Precision Agriculture Field DataSince its inception and acceptance as a benchmarking tool within the economics literature, data envelopment analysis (DEA) has been used primarily as a means of calculating and ranking whole-farm entities marked as decision making units (DMU) against one another. Within this study, instead of ranking the entire farm operation against similar peers that encompass the study, individual data points from within the field are evaluated to analyze the site-specific technical efficiencies estimated... J.L. Maurer, T.W. Griffin, A. Sharda |
5. Field Phenotyping Infrastructure in a Future World - Quantifying Information on Plant Structure and Function for Precision Agriculture and Climate ChangePhenotyping in the field is an essential step in the phenotyping chain. Phenotyping begins in the well-defined, controlled conditions in laboratories and greenhouses and extends to heterogeneous, fluctuating environments in the field. Field measurements represent a significant reference point for the relevance of the laboratory and greenhouse approaches and an important source of information on potential mechanisms and constraints for plant performance tested at controlled conditions. In this... O. Muller, M.P. Cendrero mateo, H. Albrecht, F. Pinto, M. Mueller-linow, R. Pieruschka, U. Schurr, U. Rascher, A. Schickling, B. Keller |
6. Field Phenotyping and an Example of Proximal Sensing of PhotosynthesisField phenotyping conceptually can be divided in five pillars 1) traits of interest 2) sensors to measure these traits 3) positioning systems to allow high throughput measurements by the sensors 4) experimental sites and 5) environmental monitoring. In this paper we will focus on photosynthesis as trait of interest, measured by remote active fluorescence. The sensor presented is the Light Induced Fluorescence Transient (LIFT) instrument. The LIFT instrument is integrated in three positioning systems.... O. Muller, B. Keller, L. Zimmermanm, C. Jedmowski, V. Pingle, K. Acebron, N. Zendonadi, A. Steier, R. Pieruschka, U. Schurr, U. Rascher, T. Kraska |