The scientific community strongly recommends the adoption of indicators for the evaluation and monitoring of progress towards sustainable development. Furthermore, international organizations consider that indicators are powerful decision-making tools. Nevertheless, the quality and reliability of the indicators depends on the application of adequate and appropriate criteria to assess them. The general objective of this study was to evaluate how indicators related to water use and management perform against a set of sustainability criteria. Our research identified 170 indicators related to water use and management. These indicators were assessed by an international panel of experts that evaluated whether they fulfil the four sustainability criteria: social, economic, environmental, and institutional. We employed an evaluation matrix that classified all indicators according to the DPSIR (Driving Forces, Pressures, States, Impacts and Responses) framework. A pilot study served to test and approve the research methodology before carrying out the full implementation. The findings of the study show that 24 indicators comply with the majority of the sustainability criteria; 59 indicators are bi-dimensional (meaning that they comply with two sustainability criteria); 86 are one-dimensional indicators (fulfilling just one of the four sustainability criteria) and one indicator do not fulfil any of the sustainability criteria.

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The climate change has caused threats to agricultural production; the extremes of temperature and humidity, and other abiotic stresses are contributing factors to the etiology of disease and pest on crops. About the matter, recent research efforts have focused on predicting disease and pest crops using techniques such as supervised learning algorithms. Therefore in this paper, we present an overview of supervised learning algorithms commonly used in agriculture for the detection of pests and diseases in crops such as corn, rice, coffee, mango, peanut, and tomato, among others, with the aim of selecting the algorithms that give the best performance for the agricultural sector.

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RESUMEN La agricultura como principal actividad del desarrollo económico rural en

Productive activities such as agriculture and livestock have transformed the Andean basins in South America. In accordance with this statement, the objective of this study was to assess the water quality of "Las Piedras" river located in an Andean basin with 6626 ha in Colombian Highlands. This study analyzed nutrient inputs from agricultural production, spatial crops distribution, human activities and their influence on the ecological state of the river. The evaluation integrated physicochemical and biological parameters in the indexes of water quality, pollution and the aquatic macroinvertebrates community. Results showed that aggregated crop fields occupy 25.2 % in the basin and the biological water quality through the Biological Monitoring Working Party (BMWP) index denotes the presence of tolerant-pollution organisms, additionally the biodiversity was low (Shannon H´1.1). The water quality in the river satisfies the Colombian regulation criteria for human consumption, even higher concentrations of nutrients in the lower area with 2.20 mg/L nitrates and 1.49 mg/L of phosphates, besides the loss of nutrients by runoff, which was 2.57 mg/L nitrates in the middle area and 0.18 mg/L phosphates in the upper area. In this sense, the nutrient concentration input increases toward the lower area of the basin because of the crop distribution. The agricultural land use modified the hydrological ecosystem services by decreasing the basin water regulatory capacity and nutrients input to the main stream.Palabras clave: contaminación hídrica, agricultura, distribución espacial de los cultivos, biodiversidad, agua potable RESUMENLas actividades productivas como la agricultura y la ganadería han transformado las cuencas andinas de América del Sur. El objetivo de este estudio fue evaluar la calidad del agua en la cuenca del río Las Piedras (6626 ha), localizada en la alta montaña colombiana. Se analizaron los aportes de nutrientes a partir de la producción agrícola, distribución espacial de cultivos, actividades humanas y su influencia en el estado ecológico del río. La evaluación integró los parámetros físicos, químicos y biológicos en índices de calidad del agua, de contaminación y de macroinvertebrados acuáticos. Los resultados mostraron que los cultivos ocupan el 25.2 % de la cuenca, la calidad biológica del agua según el 362índice del Grupo de Trabajo de Monitoreo Biológico (BMWP, por sus siglas en inglés) evidenció la presencia de organismos tolerantes a la contaminación y baja biodiversidad (Shannon H' 1.1). La calidad del agua es apta para consumo humano según los estándares establecidos por la normatividad Colombiana. Sin embargo, se encontraron altas concentraciones de nutrientes en la zona baja del río con 2.20 mg/L de nitratos y 1.49 mg/L de fosfatos, adicionalmente los aportes de nutrientes en escorrentía superficial fueron de 2.57 mg/L de nitratos en la zona media y 0.18 mg/L de fosfatos en la zona alta. En este sentido, se observó que el ingreso de nutrientes aumenta hacia la zona baja de la cuenca deb...

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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.