A new scheme to optimize irrigation depth using a numerical model of crop response to irrigation and quantitative weather forecasts

Irrigation management can be improved by utilizing advances in numerical models of water flow in soils that can consider future rainfall by utilizing data from weather forecasts. Toward this end, we developed a numerical scheme to determine optimal irrigation depth on scheduled irrigation days based on a concept of virtual net income as a function of cumulative transpiration over each irrigation interval; this scheme combines a numerical model of crop response to irrigation and quantitative weather forecasts. To evaluate benefits, we compared crop growth and net income of this proposed scheme to those of an automated irrigation method using soil water sensors. Sweet potato (Ipomoea batatas (L.), cv. Kintoki) was grown in 2016 in a sandy field of the Arid Land Research Center, Tottori University, Japan under either a non-optimized automated irrigation or the proposed scheme. Under the proposed scheme, 18% less water was applied, yield increased by 19%, and net income was increased by 25% compared with the results of the automated irrigation system. In addition, soil water content simulated by the proposed scheme was in fair agreement with observed values. Thus, it was shown that the proposed scheme may enhance net income and be a viable alternative for determining irrigation depths

Double-Stranded RNA of Intestinal Commensal but Not Pathogenic Bacteria Triggers Production of Protective Interferon-β

SummaryThe small intestine harbors a substantial number of commensal bacteria and is sporadically invaded by pathogens, but the response to these microorganisms is fundamentally different. We identified a discriminatory sensor by using Toll-like receptor 3 (TLR3). Double-stranded RNA (dsRNA) of one major commensal species, lactic acid bacteria (LAB), triggered interferon-β (IFN-β) production, which protected mice from experimental colitis. The LAB-induced IFN-β response was diminished by dsRNA digestion and treatment with endosomal inhibitors. Pathogenic bacteria contained less dsRNA and induced much less IFN-β than LAB, and dsRNA was not involved in pathogen-induced IFN-β induction. These results identify TLR3 as a sensor to small intestinal commensal bacteria and suggest that dsRNA in commensal bacteria contributes to anti-inflammatory and protective immune responses

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

Low-Error Soil Moisture Sensor Employing Spatial Frequency Domain Transmissometry

A new type of soil moisture sensor using spatial frequency domain transmissometry (SFDT) was evaluated. This sensor transmits and receives ultrawideband (1 to 6 GHz) radio waves between two separated antennas and measures the propagation delay time in the soil related to the dielectric constant. This method is expected to be less affected by air gaps between the probes and the soil, as well as being less affected by soil electrical conductivity (EC), than typical commercial sensors. The relationship between output and volumetric water content (θ), and the effects of air gaps and EC were evaluated through experiments using sand samples and the prototype SFDT sensor. The output of the SFDT sensor increased linearly with θ and was not affected by even a high salt concentration for irrigation water, such that the EC of the pore water was 9.2 dS·m−1. The SFDT sensor was almost unaffected by polyethylene tapes wrapped around the sensor to simulate air gaps, whereas a commercially available capacitance sensor significantly underestimated θ. Theoretical models of the SFDT sensor were also developed for the calibration equation and the air gaps. The calculation results agreed well with the experimental results, indicating that analytical approaches are possible for the evaluation of the SFDT sensor

Predicting Soil Infiltration and Horizon Thickness for a Large-Scale Water Balance Model in an Arid Environment

Prediction of soil characteristics over large areas is desirable for environmental modeling. In arid environments, soil characteristics often show strong ecological connectivity with natural vegetation, specifically biomass and/or canopy cover, suggesting that the soil characteristics may be predicted from vegetation data. The objective of this study was to predict soil infiltration characteristics and horizon (soil layer) thickness using vegetation data for a large-scale water balance model in an arid region. Double-ring infiltrometer tests (at 23 sites), horizon thickness measurements (58 sites) and vegetation surveys (35 sites) were conducted in a 30 km × 50 km area in Western Australia during 1999 to 2003. The relationships between soil parameters and vegetation data were evaluated quantitatively by simple linear regression. The parameters for initial-term infiltration had strong and positive correlations with biomass and canopy coverage (R2 = 0.64 − 0.81). The horizon thickness also had strong positive correlations with vegetation properties (R2 = 0.53 − 0.67). These results suggest that the soil infiltration parameters and horizon thickness can be spatially predicted by properties of vegetation using their linear regression based equations and vegetation maps. The background and reasons of the strong ecological connectivity between soil and vegetation in this region were also considered