Agriculture provides food, raw materials, and employment opportunities for a significant percentage of the world's population. Climate, economic, political, social, and other conditions affect decision making in agricultural processes. In many cases, these conditions imply the loss of suitability of many areas for some traditional crops. In contrast, these areas can produce new crops by taking advantage of changing conditions. In this sense, having reliable tools and information for decision making is essential in adapting to new agricultural productivity scenarios. The above implies having sufficient and relevant data sources to reduce the uncertainty in the decision‐making processes. However, data by nature tend to be diverse in structure, storage formats, and access protocols. Data fusion tasks have been immersed in a multitude of applications and have been approached from different points of view when implementing a suitable solution. We propose a multi‐domain data fusion strategy to support data analysis tasks in agricultural contexts. We also describe all the data sources collected, which are the main input to the proposed strategy. The combined data sources were also evaluated through a preliminary exploratory analysis in a multi‐label learning approach. Finally, the data fusion strategy is explained through an example in agricultural crop production.
Agriculture is the backbone of a country's economic system, considering that it not only provides food and raw materials but also employment opportunities for a large percentage of the population. In this way, determining the degree of agricultural vulnerability represents a guide for sustainability and adaptability focused on changing future conditions. In many cases, vulnerability analysis data is restricted to use by authorized personnel only, leaving open data policies aside. Furthermore, data in its native format (raw data) by nature tend to be diverse in structure, storage formats, and access protocols. In addition, having a large amount of open data is important (though not sufficient) to obtain accurate results in data-driven analysis. These data require a strict preparation process and having guides that facilitate this process is becoming increasingly necessary. In this study, we present the step by step processing of several open data sources in order to obtain quality information for feedback on different agricultural vulnerability analysis. The data preparation process is applied to a case study corresponding to the upper Cauca river basin in Colombia. All data sources in this study are public, official and are available from different web platforms where they were collected. In the same way, a ranking with the importance of variables for each dataset was obtained through automatic methods and validated through expert knowledge. Experimental validation showed an acceptable agreement between the ranking of automatic methods and the ranking of raters. The result of this study corresponds to 16 processed data sources ready to feed data-driven systems, as well as agricultural vulnerability methodologies.INDEX TERMS Agricultural vulnerability analysis, climate variability, data cleaning, data preparation.
Due to the renewable nature of water, this resource has been treated and managed as if it were unlimited; however, increase the indiscriminate use has brought with it a rapid deterioration in quality; so as predicting water quality has a very important role for many socioeconomic sectors that depend on the use of the precious liquid. In this study, a systematic literature mapping was performed about water quality prediction using computational intelligence techniques, including those used to calibrate predictive models in order to improve accuracy. Based on research questions formulated in the systematic mapping, a gap is identified oriented to creation of an adaptive mechanism for predicting water quality that can be applied in different water uses without raised the accuracy of the predictions is affected.
<p>En el presente estudio se colectaron cuatro parasitoides de <em>Dasiops inedulis </em>Steyskal (Diptera: Lonchaeidae), i.e., uno de tipo larva-pupa identificado como <em>Utetes anastrephae </em>(Viereck) (Hymenoptera: Braconidae), y tres parasitoides de pupas identificados como <em>Pachycrepoideus vindemmiae </em>Rondani, <em>Spalangia </em>sp. (Hymenoptera: Pteromalidae) y <em>Aganaspis </em>sp. (Hymenoptera: Figitidae). En el campo se observaron daños ocasionados por una larva de Chrysopidae (Neuroptera) sobre pupas centinelas de<em>D. inedulis</em>. Se realizaron experimentos con un cebo tóxico de origen natural a base de <em>Saccharopolyspora spinosa </em>en los municipios de Palmira y Toro, en el departamento del Valle del Cauca, destacándose como una nueva alternativa para el control de <em>D. inedulis </em>en maracuyá amarillo, ya que en las dos localidades mantuvo los niveles de daño por debajo del manejo convencional del agricultor y el testigo absoluto. Se provee una lista de enemigos naturales de <em>D. inedulis </em>tomada mediante muestreos de botones florales de maracuyá, uso de pupas centinelas, e información en la literatura científica. Para el control de la mosca del botón floral del maracuyá, <em>D. inedulis</em>, se propone una estrategia de manejo integrado de plagas que le permitirán al productor mantener las poblaciones de <em>D. inedulis </em>reguladas con diferentes herramientas, i.e., enemigos naturales que se pueden incorporar en diferentes etapas de la fenología de <em>D. inedulis</em>, uso de cebos tóxicos de baja toxicidad, recolección de botones con síntomas de daño, y monitoreo con el uso de trampas McPhail cebadas con proteína hidrolizada, interviniendo en diferentes estados de desarrollo, cortando ciclos y disminuyendo así futuras generaciones del insecto. </p><p> </p><p><strong>Integrated pest management as a strategy to control the passionfruit flower-bud fly, <em>Dasiops inedulis</em>Steyskal (Diptera: Lonchaeidae)</strong></p><span> Four parasitoids of the passion fruit flower bud fly, </span><em>Dasiops inedulis </em><span>Steyskal (Diptera: Lonchaeidae) were collected in the present study, i.e., a larva-pupa type parasitoid, </span><em>Utetes anastrephae </em><span>(Viereck) (Hymenoptera: Braconidae), and three pupal parasitoids, namely </span><em>Pachycrepoideus vindemmiae</em><span>Rondani, </span><em>Spalangia </em><span>sp. (Hymenoptera: Pteromalidae) and</span><em>Aganaspis </em><span>sp. (Hymenoptera: Figitidae). In the field we observed a species of Chrysopidae (Neuroptera) larva feeding on the sentinel pupae of </span><em>D. inedulis</em><span>. We conducted experiments in the municipalities of Palmira and Toro, in the State of Valle del Cauca, Colombia, on the efficiency of a toxic bait made from the bacteria </span><em>Saccharopolyspora spinosa</em><span>, standing out as a new alternative for controlling </span><em>D. inedulis</em><span>on yellow passion fruit. This toxic bait maintained the injury levels below the conventional management used by the farmers and the control plots in both study areas. A list of natural enemies of </span><em>D. inedulis </em><span>was compiled by inspecting passion fruit flower buds, using sentinel pupae, and information taken from the literature. For the control of </span><em>D. inedulis</em><span>, an integrated pest management strategy is proposed that will allow the farmer to maintain </span><em>D. inedulis </em><span>populations under control with different management tools, i.e., natural enemies which can be incorporated at different stages of development of </span><em>D. inedulis</em><span>, low toxicity baits, recollection of flower buds with symptoms of damage, monitoring with McPhail traps baited with protein hydrolysate, thus intervening at different stages the development of the lonchaeid fly pest, breaking its life cycle and thus decreasing their populations in future generations.</span>
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