Precision agriculture is now 'main stream' in agriculture and is playing a key role as the industry comes to terms with the environment, market forces, quality requirements, traceability, vehicle guidance and crop management. Research continues to be necessary and needs to be reported and disseminated to a wide audience.This book contains peer reviewed papers presented at the 9th European Conference on Precision Agriculture, held in Lleida, Spain. The papers reflect the wide range of disciplines that impinge on precision agriculture: technology, crop science, soil science, agronomy, information technology, decision support, remote sensing and others.The broad range of research topics reported will be a valuable resource for researchers, advisors, teachers and professionals in agriculture long after the conference has finished.

Organizing committee;Alexandre Escola, Joan R. Rosell, JaumeArno.- Scientific committee; JaumAlexandre Escola, Joan R. Rosell,Jaume Arno.- Editorial;John V. Stafford.-Section 1 Soil and crop proximal sensors.- Comparing the DUALEM and VERIS sensors for mapping soil properties;J. Serrano et al.- Three-layered soil maps based on sensor measurements;K. Piikki etal.- Real time soil sensing for determination of tropical soils pH;F.C.S. Silva, J.P. Molin.- Soil compaction sensor for site-specific tillage: design and assessment;J. Aguera et al.- Microphone sensor for grain yield monitoring;K. Shoji et al.- Improving the determination of plant characteristics by fusion of four different sensors;M. Weis et al.- Three-dimensional sensor for dynamic characterization of soil microrelief;F. Marinello et al.- Crop sensor readings in winter wheat as affected by nitrogen and water supply;R. Gebbers et al.- Rapid estimation of rice canopy LAI using multi-source proximal sensors;L.Q. Zhou et al.- Estimating rice nitrogen status with the Crop Circle multispectral active canopy sensor;Q. Cao et al.- Comparison of crop canopy sensors in sugarcane;L.R. Amaral et al.- Field comparison of ultrasonic and canopy reflectance sensors used to estimate biomass and N-uptake in sugarcane;G. Portz et al.- From theory to practice: using canopy reflectance to determine sidedress N rate in potatoes;F.K. van Evert et al.- The use of a laser scanner for measuring crop properties in three different crops in Central Greece;A. Chatzinikos et al.- The problem is not N deficiency: Active canopy sensors and chlorophyll meters detect P stress in corn and soybean;J.H. Grove, M.M. Navarro.- Development of sensor based detection of crop nitrogen status for utilization in variable rate nitrogen fertilization;J.J. Varco etal.- Portability of leaf chlorophyll empirical estimators obtained at Sentinel-2 spectral resolution;M. Vincini, E. Frazzi.-Section 2 Remote sensing.- Enhancement of micro Unmanned Aerial Vehicles for agricultural aerial sensor systems;J. Geipel et al.- Fieldcopter: unmanned aerial systems for crop monitoring services;T. van der Wal et al.- Aerial thermography for crop stress evaluation a look into the state of the technology;M.Meron et al.- Comparison of methods for field scale mapping of plant water status using aerial thermal imagery;O. Rosenberget al.- Imagery from unmanned aerial vehicles for early site specific weed management;J. Torres-Sanchez et al.- Mapping of vine vigor by UAV and anthocyanin content by a non-destructive fluorescence technique;A. Matese et al.- Predicting optimal soybean harvesting dates with satellite data;J.H. Meng et al.- Monitoring time-series crop leaf area index from higher resolution remotely sensed data;S. Jiao, Y. Qu.- Water status detection in California table grapes: from leaf to airborne;M.M.Alsina et al.-Section 3 Spatial variability and mapping.- Long-term effect of super phosphate fertilizer on accumulation of soil phosphorus on a pasture;J. Serrano et al.- Effect of sampling patterns and interpolation methods on prediction quality of soil variability mapping;H.H. Huang et al.- Spatial variability of drip irrigation in small vine fields of south of France;B. Tisseyre, A. Ducanchez.- A simple method for filtering spatial data;M. Spekken et al.- Spatial variability detection of crop height in a single field by terrestrial laser scanning;D.Hoffmeister et al.- Strip-crop rotations: yield spatial structure for spatially coincident and temporally subsequent corn and soybean production;E.M. Pena-Yewtukhiw, J.H. Grove.- Spatial variability of seed depth placement of maize under no tillage in Alentejo, Portugal;L. Conceicao et al.- Stochastic simulation of maize productivity: spatial and temporal uncertainty;A.R.L.Grifo, J. Marques da Silva.- Spatial and temporal variability of soybean and maize yield after 27 years of no-tillage in Sao Paulo, Brazil;S. Vieira et al.- Investigating geostatistical methods to model within-field yield variability of cranberries;R.Kerry et al.- Within-field zoning using a region growing algorithm guided by geostatistical analysis;L. Zane et al.- Understanding the effects of site-specific fertilization on yield and protein content in durum wheat;F. Morari et al.- Within-field variation in deoxynivalenol (DON) contents in oats;M.Soderstrom, T. Borjesson.-Section 4 Machinery, robotics and precision agriculture technologies.- On-line measurement of animal and bio slurry quality variations with near infrared spectroscopy;B. Stenberg, K. Gustafsson.- Automatic selection of vertical spray pattern in orchard sprayer;M. Tamagnone etal.- Management information system for spatial analysis of tractorimplement draft forces;Z. Tsiropoulos et al.- Using RTK-based GPS guidance for planting and inverting peanuts;G.Vellidis et al.- Hydraulic robot arm controlled by visual servoing;G. Raush et al.- Path planning to minimise distances and recharging instances for a small fleet of vehicles in an arable field;J. Conesa-Munoz et al.-Section 5 Management, data analyses and decision support systems.- Can fluorescence based sensing detect nitrogen variability at early growth stages of maize?;L. Longchamps et al.- Sub-paddock scale spatial variability between the pasture and cropping phases of mixed farming systems in Australia;P. McEntee et al.- The effect of long-term phosphorus and potassium precision fertilization;G.Kulczycki, P. Grocholski.- Theoretical basis for sensor-based in-season nitrogen management;V.I. Adamchuk.- A segmentation approach to delineate zones for differential nitrogen interventions;R.P. de Oliveira et al.- Practicable site-specific estimation of nitrate leaching risk from agricultural cropland;A.Kielhorn et al.- Yield variability linked to climate uncertainty and nitrogen fertilization;B. Dumont et al.- Variable rate application of side-dress nitrogen on cotton in Georgia, USA;V. Liakos etal.- Improving yield advisory models for precision agriculture with special regards to soil compaction in maize production;A.Nyeki et al.- A model-driven decision support system for vineyard water status management: a time dependent sensitivity analysis;A. Guaus et al.- Prediction of spatial variability of water status in a rain fed vineyard in Spain;I.Urretavizcaya et al.- A field information collecting system based on a wireless sensor network;X. Deng et al.- Site-specific land management of cereal crops based on management zone delineation by proximal soil sensing;G. Halcro et al.- A comparison of bivariate classification and segmentation approaches to delineating and interpreting grain yield-protein management units;J.A. Taylor et al.- Using profile soil electrical conductivity survey data to predict wheat establishment rates in the United Kingdom;S. Griffin, J. Hollis.- Geostatistical methods as auxiliary tools in field plot experimentation;J. Goaszewski etal.- Prediction of non-linear time-variant dynamic crop model using bayesian methods;M. Mansouri et al.-Section 6 Precision crop protection.- Gall mite inspection on dormant black currant buds using machine vision;M.R. Nielsen et al.- Assembly of a model for grapevine powdery mildew in a decision support system and search for evaluation criteria;G.Garin et al.- Advances in pesticide dose adjustment in tree crops;S. Planas et al.- Weed-crop discrimination using LiDAR measurements;D. Andujar et al.- Simulation of the effects of weed decision threshold, detection and treatment resolution on the errors in spraying decisions and on herbicide savings;C. SanMartin et al.- Crop and weed species recognition based on hyperspectral sensing and active learning;D. Moshou et al.- Effect of historical agronomic practices and proximity of infected plots on spatial patterns of broomrape in tomato crops;I. Roei et al.- Spray nozzle characterization using high speed imaging techniques;S. Vulgarakis Minov et al.- Site-specific disease management: a preliminary case with Orange Spotting in oil palm;S. Selvaraja et al.- Mapping redheaded cockchafer infestations in pastures are PA tools up to the job?;A. Cosbyet al.- Risk assessment of grapevine leafroll disease for developing future site-specific disease spread control tactics and strategies;T. Sokolsky et al.-Section 7 Advances in precision fructiculture/ viticulture/ oliviculture and horticulture in general.- Electronic characterization of the phenological stages of grapevine using a LIDAR sensor;M.Rinaldi et al.- Grape quality assessment by airborne remote sensing over three years;I. Bonilla et al.- Multispectral imagery acquired from a UAV to assess the spatial variability of a Tempranillo vineyard;C. Rey et al.- A simplified index to assess the opportunity for selective wine grape harvesting from vigour maps;A. Monso et al.- Using laser scanner to map pruning wood in vineyards;A. Tagarakis et al.- Agronomic significance of the zones defined within vineyards early in the season using NDVI and fruit load information;L.G. Santesteban et al.- Grape physiology, composition and sensory characteristics in a selective harvest winegrape vineyard;D.R. Smart et al.- Temporal evolution of within-season vineyard canopy response from a proximal sensing system;J.A. Taylor et al.- Automated determination of plum tree canopy cover with two different measurement techniques;J. Selbeck, F. Pforte.- Application of variable rate fertilizer in a commercial apple orchard;V. Liakoset al.- Obtaining yield maps in orchards by tracking machine behavior;A.F. Colaco et al.- Determination of field capacity and yield mapping in olive harvesting using remote data acquisition;J. Aguera-Vega et al.-Section 8 Advances in precision irrigation.- Scheduling vineyard irrigation based on mapping leaf water potential from airborne thermal imagery;J. Bellvert etal.- Assessment of drip irrigation sub-units using airborne thermal imagery acquired with an Unmanned Aerial Vehicle (UAV);M.A. Jimenez-Bello et al.- A soil moisture sensor-based variable rate irrigation scheduling system;G. Vellidis et al.- The potential of CWSI based on thermal imagery for in-season irrigation management in potato fields;R. Rud et al.- Variable rate irrigation and nitrogen fertilization of maize across landscape positions;R. Ferguson et al.- Response of alfalfa to precision fertigation in Saudi Arabia;K.A. Al-Gaadi et al.- Fusion of data from multiple soil sensors for the delineation of water holding capacity zones;A.M. Mouazen et al.-Section 9 Economics, practical adoption and emerging issues.- Precision agriculture and agro-environmental policy;J. Schieffer,C. Dillon.- Heuristic optimization for variable rate nitrogen and seeding decisions;C.R. Dillon.- Dispelling misperceptions regarding variable rate application;C.R. Dillon, Y. Kusunose.- Precision analysis of the effect of ephemeral gully erosion on vine vigour using NDVI images;J.A. Martinez-Casasnovas et al.- A survey of future farm automation a descriptive analysis of survey responses;C. Kester et al.- Service engineering in the domain of precision farming;S. Klingner et al.- A survey of wireless sensor technologies applied to precision agriculture;J.M. Barcelo-Ordinas et al.- Standardisation in precision agriculture through INSPIRE;P. Korduan, R. Bill.- Keyword index.- Author index.