A soil database from Queretaro, Mexico for assessment of crop and irrigation water requirements

Abstract Several studies have assessed crop water requirements based on soil properties, but these have been on a small scale or on soils with similar textures. Here, a data base of soil measurements in the field and laboratory from sites across Irrigation District 023, San Juan del Rio, Queretaro, Mexico was sampled, collected, analyzed, and integrated. The data base, named, NaneSoil, contains information on 900 samples obtained from irrigated plots. NaneSoil cover 10 of the 12 textural classes with the following information: sand, silt, clay contents, bulk density, saturated volumetric water content, field capacity, permanent wilting point and saturated hydraulic conductivity. The aim of this work is to provide the scientific community with sufficient information to perform a large number of analyses, for example, development of pedotransfer functions, calculation of water requirements of plants in soils with similar characteristics, modeling of infiltration, optimal irrigation discharge calculation, among others. The dataset also promotes the scientific community to contribute their own measurements to further strengthen the knowledge of flow in the porous medium

Evaluation and Development of Pedotransfer Functions and Artificial Neural Networks to Saturation Moisture Content Estimation

Modeling of irrigation and agricultural drainage requires knowledge of the soil hydraulic properties. However, uncertainty in the direct measurement of the saturation moisture content (θs) has been generated in several methodologies for its estimation, such as Pedotransfer Functions (PTFs) and Artificial Neuronal Networks (ANNs). In this work, eight different PTFs were developed for the (θs) estimation, which relate to the proportion of sand and clay, bulk density (BD) as well as the saturated hydraulic conductivity (Ks). In addition, ANNs were developed with different combinations of input and hidden layers for the estimation of θs. The results showed R2 values from 0.9046≤R2≤0.9877 for the eight different PTFs, while with the ANNs, values of R2>0.9891 were obtained. Finally, the root-mean-square error (RMSE) was obtained for each ANN configuration, with results ranging from 0.0245≤RMSE≤0.0262. It was found that with particular soil characteristic parameters (% Clay, % Silt, % Sand, BD and Ks), accurate estimate of θs is obtained. With the development of these models (PTFs and ANNs), high R2 values were obtained for 10 of the 12 textural classes