Assessment of the Persistence of Avena sterilis L. Patches in Wheat Fields for Site-Specific Sustainable Management

This paper aims to evaluate the spatial persistence of wild oat patches in four wheat fields over time to determine the economic feasibility of using late-season wild oat maps for early site-specific weed management (SSWM) next season. The spatial persistence of wild oat patches was analyzed by three tests: land use change detection between years, spatial autocorrelation, and análisis of spreading distance. The temporal trend of wild oat patch distribution showed a clear persistence and a generalized increase in the infested area, with a noticeable level of weed aggregation and a tendency in the new weed patches to emerge close to older ones. To economically evaluate the SSWM, five simulations in four agronomic scenarios, varying wheat yields and losses due to wild oat, were conducted. When yield losses due to wild oat were minimal and for any of the expected wheat yields, some SSWM simulations were more economically profitable than the overall application in most of the fields. Nevertheless, when the yield losses due to wild oat were maximal, all SSWM simulations were less profitable than overall treatment in all the analyzed fields. Although the economic profit variations achieved with SSWM treatments were modest, any of the site-specific treatments tested are preferred to herbicide broadcast over the entire field, in order to reduce herbicide and environmental pollution

Clasificación de cultivos y de sus medidas agroambientales mediante segmentación de imágenes QuickBird

En la últimas décadas han ido creciendo considerablemente los conocimientos y la sensibilización sobre la protección al medioambiente en muy diversas áreas, entre las que se encuentra la Agricultura. El uso intensivo del laboreo ocasiona graves daños medioambientales como la erosión del suelo, la contaminación de las aguas superficiales (escorrentía y colmatación de embalses), el descenso del contenido de la materia orgánica y de la biodiversidad de los suelos labrados, y el aumento de la emisión de CO2 del suelo a la atmósfera. Actualmente, la Unión Europea sólo subvenciona a los agricultores que cumplen lo que se conoce como “Medidas Agroambientales o de Condicionalidad” cuyo diseño ha estado dentro de las competencias de las Políticas Agrarias Autonómicas, Nacionales y Europeas. Estas medidas consisten en alterar el perfil y la estructura del suelo lo menos posible, dejando éste sin labrar y permanentemente protegido por cubiertas vegetales (rastrojo) en el caso de cultivos herbáceos (ej. trigo, maíz, girasol), o por cubiertas vegetales vivas o inertes (restos de poda) en el caso de cultivos leñosos (principalmente cítricos y olivar). El seguimiento del cumplimiento de estas medidas se realiza a través de visitas presenciales a un 1% de los campos susceptibles de recibir ayudas. Este método es ineficiente y provoca muchos errores con la consiguiente presentación de un ingente número de reclamaciones. Para subsanar esta problemática, en este artículo presentamos los resultados obtenidos en la clasificación de los cultivos y las medidas agroambientales asociadas a éstos en una imagen multiespectral QuickBird tomada a principios de Julio de una zona típica de cultivos en régimen de secano de Andalucía. Se aplicaron 5 métodos de clasificación (Paralelepípedos, P; Mínima Distancia, MD; Distancia de Mahalanobis, MC; Mapeo del Ángulo Espectral, SAM; y Máxima Probabilidad, ML) para la discriminación de rastrojo de trigo quemado y sin quemar, arbolado, carreteras, olivar, cultivos herbáceos de siembra primaveral y suelo desnudo. Además, la imagen es segmentada en objetos para comparar la fiabilidad obtenida aplicando los métodos anteriores partiendo tanto de píxeles como de objetos como Unidades Mínimas de Información (MIU). El análisis de los resultados permite concluir que las clasificaciones de todos los usos de suelo basadas en objetos claramente mejoraron las basadas en píxeles, obteniéndose precisiones (overall accuracy) mayores al 85%. La elección de un método de clasificación u otro influye en gran medida en la precisión de los mapas obtenidos. Debido a que la precisión del mapa temático que necesitamos obtener ha de ser muy elevada para tomar decisiones sobre Conceder / No conceder las ayudas, sería interesante estudiar si el incremento de la resolución espacial que se obtenga gracias a la fusión de imágenes multiespectral y pancromática de QuickBird para obtener una imagen fusionada con resolución espacial de la pancromática (0.7 m) y espectral de la multiespectral (4 bandas) mejora la precisión de cualquiera de los métodos de clasificación estudiadosSoil management in crops is mainly based on intensive tillage operations, which have a great relevancy in terms of increase of atmospheric CO2, desertification, erosion and land degradation. Due to these negative environmental impacts, the European Union only subsidizes cropping systems which require the implementation of certain no-tillage systems and agro-environmental measures, such as keeping the winter cereal residues and non-burning of stubble to reduce erosion, and to increase the organic matter, the fertility of soils and the crop production. Nowadays, the follow-up of these agrarian policy actions is achieved by ground visits to sample targeted farms; however, this procedure is time-consuming and very expensive. To improve this control procedure, a study of the accuracy performance of several classification methods has been examined to verify if remote sensing can offer the ability to efficiently identify crops and their agro-environmental measures in a typical agricultural Mediterranean area of dry conditions. Five supervised classification methods based on different decision rule routines, Parallelepiped (P), Minimum Distance (MD), Mahalanobis Classifier Distance (MC), Spectral Angle Mapper (SAM), and Maximum Likelihood (ML), were examined to determine the most suitable classification algorithm for the identification of agro-environmental measures such as winter cereal stubble and burnt stubble areas and other land uses such as river side trees, vineyard, olive orchards, spring sown crops, roads and bare soil. An object segmentation of the satellite information was also added to compare the accuracy of the classification results of pixel and object as Minimum Information Unit (MIU). A multispectral QuickBird image taken in early summer was used to test these MIU and classification methods. The resulting classified images indicated that object-based analyses clearly outperformed pixel ones, yielding overall accuracies higher than 85% in most of the classifications. The choice of a classification method can markedly influence the accuracy of classification maps

Mapeo y cuantificación de las infestaciones de Orobanche crenata en guisantes mediante teledetección

Póster presentado en el XIII Congreso Nacional de Malherbología celebrado en La Laguna (Tenerife) en noviembre de 2011.Los jopos (Orobanche crenata Forsk.) son especies parásitas de cultivos leguminosos, muy extendidas en el área mediterránea (García-Torres et al., 1994). La agricultura de precisión trata de determinar y manejar la distribución espacial de factores bióticos, tales como malas hierbas y patógenos, y de factores abióticos y así fundamentar la aplicación de inputs a dosis variables, ajustados a las necesidades de pequeñas aéreas o sub-parcelas. El objetivo de este trabajo es describir brevemente la discriminación de rodales de jopos en el cultivo de guisante (Pisum sativum L.) mediante imágenes remotas multiespectrales y su manejo de precisión mediante el software SARI® (Sectioning and Assessment of Remote Images) un módulo complementario de ENVI® que divide y cuantifica la imagen de una parcela en sub-parcelas.Esta investigación se ha financiado en parte a través de los proyectos AGL2007-60926 (FEDER) y AGL2010-15506 (FEDER).Peer reviewe

Geo-referencing remote images for precision agriculture using artificial terrestrial targets

The aim of this paper is to assess co-registration errors in remote imagery through the AUGEO system, which consists of geo-referenced coloured tarps acting as terrestrial targets (TT), captured in the imagery and semi-automatically recognised by AUGEO2. 0® software. This works as an add-on of ENVI® for image co-registration. To validate AUGEO, TT were placed in the ground, and remote images from satellite Quick Bird (QB), airplanes and unmanned aerial vehicles (UAV) were taken at several locations in Andalusia (southern Spain) in 2008 and 2009. Any geo-referencing system tested showed some error in comparison with the Differential Global Positioning System (DGPS)-geo-referenced verification targets. Generally, the AUGEO system provided higher geo-referencing accuracy than the other systems tried. The root mean square errors (RMSE) from the panchromatic and multi-spectral QB images were around 8 and 9 m, respectively and, once co-registered by AUGEO, they were about 1.5 and 2.5 m, for the same images. Overlapping the QB-AUGEO-geo-referenced image and the National Geographic Information System (NGIS) produced a RMSE of 6.5 m, which is hardly acceptable for precision agriculture. The AUGEO system efficiently geo-referenced farm airborne images with a mean accuracy of about 0.5-1.5 m, and the UAV images showed a mean accuracy of 1.0-4.0 m. The geo-referencing accuracy of an image refers to its consistency despite changes in its spatial resolution. A higher number of TT used in the geo-referencing process leads to a lower obtained RMSE. For example, for an image of 80 ha, about 10 and 17 TT were needed to get a RMSE less than about 2 and 1 m. Similarly, with the same number of TT, accuracy was higher for smaller plots as compared to larger plots. Precision agriculture requires high spatial resolution images (i.e., <1.5 m pixel-1), accurately geo-referenced (errors <1-2 m). With the current DGPS technology, satellite and airplane images hardly meet this geo-referencing requirement; consequently, additional co-registration effort is needed. This can be achieved using geo-referenced TT and AUGEO, mainly in areas where no notable hard points are available. © 2011 Springer Science+Business Media, LLC.This research was partially financed by the Spanish Ministry of Science and Innovation through the projects AGL2007-60926 and AGL2010-15506.Peer Reviewe

A digital elevation model to aid geostatistical mapping of weeds in sunflower crops

A major concern in landscape management and precision agriculture is the variable-rate application of herbicides in order to reduce herbicide treatment load. These applications require a correct assessment and knowledge of the density and potential spatial variability of weed species within fields. This article addresses the issue of incorporating a digital elevation model as secondary spatial information into the mapping of main weed species present in two sunflower crops in Andalusia, Spain. Two prediction methods were used and compared for mapping weed density for precision agriculture. The primary information was obtained from an intensive grid weed density sampling and the secondary spatial information, e.g., elevation from a digital elevation model. The prediction methods were two geostatistical algorithms: ordinary kriging and kriging with an external drift, which takes into account the influence of landscape. Mean squared error was used to evaluate the performance of the map prediction quality. The best prediction method for mapping most of the weed species was kriging with an external drift, with the smallest mean squared error, indicating the highest accuracy. The results showed that kriging with an external drift with elevation reduced the prediction variance compared with ordinary kriging. Maps obtained from these kriged estimates showed that the incorporation of a digital elevation model as secondary exhaustive information can improve the accuracy of predicted weed densities within fields. These results suggest that kriging with an external drift of weed density data with elevation as a secondary exhaustive variable could be used in such situations, and in this way, the accuracy of maps for precision agriculture, which is the preliminary step in a precision agricultural management program, could be improved with little or no additional cost, since a digital elevation model could be obtained as part of other analyses

A multi-objective neural network based method for cover crop identification from remote sensed data

One of the objectives of conservation agriculture to reduce soil erosion in olive orchards is to protect the soil with cover crops between rows. Andalusian and European administrations have developed regulations to subsidise the establishment of cover crops between rows in olive orchards. Current methods to follow-up the cover crops systems by administrations consist of sampling and on ground visits of around 1% of the total olive orchards surface at any time from March to late June. This paper outlines a multi-objective neural network based method for the classification of olive trees (OT), bare soil (BS) and different cover crops (CC), using remote sensing data taken in spring and summer. The main findings of this paper are: (1) the proposed models performed well in all seasons (particularly during the summer, where only 48 pixels of CC are confused with BS and 10 of BS with CC with the best model obtained. This model obtained a 97.80% of global classification, 95.20% in the class with the worst classification rate and 0.9710 in the KAPPA statistics), and (2) the best-performing models could potentially decrease the number of complaints made to the Andalusian and European administrations. The complaints in question concern the poor performance of current on-ground methods to address the presence or absence of cover crops in olive orchards. © 2012 Elsevier Ltd. All rights reserved.This work was supported in part by the Spanish Inter-Ministerial Commission of Science and Technology under Project TIN2011–22794, the Spanish Minister of Science and Innovation by project AGL2011–30442-CO2–01 (FEDER), the European Regional Development fund and the “Junta de Andalucía” (Spain), under Project P2011-TIC-7508. M. Cruz-Ramírez’s research has been subsidized by the FPU Predoctoral Program (Spanish Ministry of Education and Science), grant reference AP2009–0487.Peer Reviewe