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Water quality index for agricultural systems in Northwest Uruguay
Authors:Gabriela Eguren  Noelia Rivas-Rivera  Claudio García  Bernardo Böcking  Santiago Bandeira
Institution:1.Ecology and Environmental Sciences Institute,Sciences School (de la Republica University),Montevideo,Uruguay;2.National Agricultural Research Institute (INIA), Experimental Station Wilson Ferreira Aldunate,Canelones,Uruguay;3.Los Tordos S en C. Rambla B. Brum 2819,Montevideo,Uruguay
Abstract:Agricultural systems have experienced rapid expansion and intensification in the last several decades. In Uruguay, since the beginning of 2000, the most common cropping systems have included soybeans. Currently, this crop is expanding towards lowlands traditionally occupied by rice in rotation with pastures. However, the environmental effects of agricultural intensification and diversification are not well known. Thus, some indices have been proposed to quantify the changes in agricultural production systems and assess water quality. The main goal of this study was to develop a water quality index (WQI) to assess the impacts of the diversification of rice production systems in northwest Uruguay. The study was carried out in an agricultural basin where other summer crops have been incorporated in the rice-pasture sequence. Agriculture intensification and crop diversification indices were calculated using information provided by farmers. Water samples were collected downstream of the production area before crop sowing and after crop harvest (2008–2009 to 2010–2011 and 2016–2017 to 2017–2018). Biochemical oxygen demand, nitrates, total phosphorus, fecal coliforms, and total suspended solids were the variables that mainly explained the effects of the agricultural activities on water quality. The proposed water quality index included these unweighted variables, which allowed for the pre-sowing and post-harvest to be differentiated, as well as the degree of diversification. Therefore, the proposed WQI constitutes a tool that can be used to evaluate the water quality in an agricultural basin. Likewise, it can be used to select agricultural sequences that generate the least possible impacts on the associated water resources.
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