Development and analysis of the Soil Water Infiltration Global database

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type ( ∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it

Optimización de un sistema de inferencia neuro-fuzzy adaptable para el mapeo del potencial de aguas subterráneas

The main goal of this study was to optimize an adaptive neuro-fuzzy inference system (ANFIS) using three meta-heuristic optimization algorithms—genetic algorithm (GA), biogeography-based optimization (BBO) and simulated annealing (SA)—to prepare groundwater potential maps. The methodology was applied to the Booshehr plain, Iran. The results of optimized models were compared with ANFIS individually and three bivariate models: frequency ratio (FR), evidential belief function (EBF), and the entropy model. First, 339 wells with groundwater yield higher than 11 m3/h were selected and randomly divided into two groups. In all, 238 wells (70%) were used for training the models and 101 wells (30%) were used for testing and validating the models. Fifteen conditioning factors were selected as input parameters for the modeling. The accuracy of the groundwater potential maps for the study area was determined using root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and standard deviation of error (SD), as well as the area under the receiver operating characteristic (ROC) curve (AUC). Overall, the results demonstrated that ANFIS-GA had the highest prediction capability (AUC = 0.915) for groundwater potential mapping followed by ANFIS-BBO (0.903), entropy (0.862), FR (0.86), ANFIS-SA (0.83), ANFIS (0.82) and EBF (0.80). According to the entropy model, land-use, soil order and rainfall factors had the highest impact on groundwater potential in the study area. The results of this research show that the ANFIS models combined with meta-heuristic optimization algorithms can be a useful decision-making tool for assessment and management of groundwater resources.</p

Effects of applying liquid swine manure on soil quality and yield production in tropical soybean crops (Paraná, Brazil)

Brazil is one of the main producers of pork meat in the world. It is well-known that the agricultural sector is a key component of the economic development of this country, where super-intensive fields are only competitive in the globalized market. For the farmers, the application of swine manure to fertilize the soil can increase the yearly income, but it also may cause serious environmental problems related to soil health and soil quality. In this research, we assessed the effects of applying liquid swine manure in a tropical soybean (Glycine max) plantation to better understand when this technique stops being effective and starts causing a threat to soil health and quality. Therefore, we compared values of several soil properties and the soybean yield on treated fields at 10 random points belonging to 7 different plots that were treated with the liquid swine manure over a period ranging from 0 to 15 years. The results showed a positive linear trend in soybean production from 2.45 to 3.08 Mg ha-1 yr-1. This positive trend was also recorded for some key soil parameters such as porosity and exchangeable cations content (Ca, Mg, K, and Al). Additionally, positive effects were also found for organic matter content after 10 years of application. Our findings suggest that the use of liquid swine manure has a positive effect on soybean yield and improves soil quality, particularly on mixed farms where pigs are intensively raised nearby cultivated fields.</p

Long-term impact of rainfed agricultural land abandonment on soil erosion in the Western Mediterranean basin

[EN] Land abandonment is widespread in the Mediterranean mountains. The impact of agricultural abandonment results in a shift in ecosystem evolution due to changes in soil erosion, but little is known about long-term soil and water losses. This paper uses 11 years of measurements in two paired plots (abandoned vs control) with four subplots to determine how soil and water losses evolved after abandonment within an agricultural parcel. For two years (2004¿2005) both plots were under tillage, and after 2006 one plot was abandoned. The monitored plots measured runoff and sediment concentration after each rainfall event.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 603498 (RECARE project), POST-FIRE Project (CGL2013-47862-C2-1 and 2-R) and POSTFIRE_CARE Project (CGL2016-75178-C2-2-R) sponsored by the Spanish Ministry of Economy and Competitiveness.Cerda, A.; Rodrigo Comino, J.; Novara, A.; Brevik, E.; Reza, A.; Pulido, M.; Giménez Morera, A.... (2018). Long-term impact of rainfed agricultural land abandonment on soil erosion in the Western Mediterranean basin. Progress in Physical Geography. 42(2):202-219. https://doi.org/10.1177/0309133318758521S20221942

Development and analysis of the Soil Water Infiltration Global database

© Author(s) 2018. In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (∼76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76ĝ€% of the experimental sites with agricultural land use as the dominant type (∼40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in ∗.xlsx and ∗.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.status: publishe