Nearest Neighbor Soil Water Retention Estimator

The k-nearest neighbor (k-NN) technique is a non-parametric technique that can be used to make predictions of discrete (class-type) as well as continuous variables. The k-NN technique and many of its derivatives belong to the group of .lazy learning algorithms.. It is lazy, as it passively stores the development data set until the time of application; all calculations are performed only when estimations need to be generated.

Agroecosystems & Environment

RETC

Analyzes the hydraulic properties of unsaturated soils.

Agroecosystems & Environment

Data from: Eleven years of mountain weather, snow, soil moisture and stream flow data from the rain-snow transition zone - the Johnston Draw catchment, Reynolds Creek Experimental Watershed and Critical Zone Observatory, USA. v1.1

Detailed hydrometeorological data from the mountain rain-to-snow transition zone are present for water years 2004 through 2014. The Johnston Draw watershed (1.8 km2), ranging from 1497 – 1869 m in elevation, is a sub-watershed of the Reynolds Creek Experimental Watershed (RCEW) in southwestern Idaho. The dataset includes continuous hourly hydrometeorological variables across a 372 m elevation gradient, on north- and south-facing slopes, including air temperature, relative humidity and snow depth from 11 sites in the watershed. Hourly measurements of solar radiation, precipitation, wind speed and direction, and soil moisture and temperature are available at selected stations. The dataset includes hourly stream discharge measured at the watershed outlet. These data provide the scientific community with a unique dataset useful for forcing and validating models in interdisciplinary studies and will allow for better representation and understanding of the complex processes that occur in the rain-to-snow transition zone.

Agroecosystems & Environment

Data from: Soil Water Holding Capacity Mitigates Downside Risk and Volatility in US Rainfed Maize: Time to Invest in Soil Organic Matter?

This dataset includes county-level annual data on maize (Zea mays L.) yield, soil physical and chemical characteristics, and mean weather data for 2000 through 2014 for IL, MI, MN and PA. The data were aggregated from public databases, including NASS Quick Stats, NOAA Climate Data Online, and the USDA-NRCS Web Soil Survey. U.S. counties were the experimental unit for this study, and all data are county-level averages. Covariances among county-level maize yield stability and environmental variability were analyzed using structural equation models (SEM) and linear mixed effects (LME) models.

Agroecosystems & Environment

Data from: Hydrological and ecological observations from the rain-to-snow transition zone: a dataset for the Johnston Draw catchment, Reynolds Creek Experimental Watershed, Idaho, USA

Detailed hydrometeorological data from the mountain rain-to-snow transition zone are present for water years 2004 through 2014. The Johnston Draw watershed (1.8 km2), ranging from 1497 – 1869 m in elevation, is a sub-watershed of the Reynolds Creek Experimental Watershed (RCEW) in southwestern Idaho. The dataset includes continuous hourly hydrometeorological variables across a 372 m elevation gradient, on north- and south-facing slopes, including air temperature, relative humidity and snow depth from 11 sites in the watershed. Hourly measurements of solar radiation, precipitation, wind speed and direction, and soil moisture and temperature are available at selected stations. The dataset includes hourly stream discharge measured at the watershed outlet. These data provide the scientific community with a unique dataset useful for forcing and validating models in interdisciplinary studies and will allow for better representation and understanding of the complex processes that occur in the rain-to-snow transition zone.

Agroecosystems & Environment

APLE : Annual Phosphorus Loss Estimator Tool

APLE is a Microsoft Excel spreadsheet model that runs on an annual time-step and estimates field-scale, sediment bound and dissolved P loss (kg ha−1) in surface runoff for agricultural field. APLE is intended to quantify P loss through process-based equations. It has been tested for its ability to reliably predict P loss in runoff for systems with machine-applied manure and for soil P cycling using data from a wide variety of agricultural fields and regions.

Agroecosystems & Environment