WinFlume is a Windows-based computer program used to design and calibrate long-throated flume and broad-crested weir flow measurement structures. The software was developed through the cooperative efforts of the Bureau of Reclamation, the Agricultural Research Service, and the International Institute for Land Reclamation & Improvement . Primary funding for WinFlume's development has come from the Bureau of Reclamation's Water Conservation-Field Services Program.
DRIFTSIM can be used to determine the effects of major drift-causing factors on the mean drift distances up to 656 feet from the release point for individual water droplets or classes of droplets.
Stored Grain Advisor (SGA) is a decision support system for managing insect pests of farm-stored wheat. The program predicts the likelihood of insect infestation, and recommends appropriate preventative actions . It also provides advice on how to sample and identify insect pests of stored wheat. SGA Pro was designed for use in commercial elevators as part of the Areawide IPM Project for stored grain. Grain samples are taken with a vacuum probe and processed over an inclined sieve. SGA Pro analyzes the insect data, grain temperatures and moistures, and determines which bins need to be fumigated.
Data from: Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design
The type 2 modified augmented design (MAD2) is an efficient unreplicated experimental design used for evaluating large numbers of lines in plant breeding and for assessing genetic variation in a population. Statistical methods and data adjustment for soil heterogeneity have been previously described for this design. In the absence of replicated test genotypes in MAD2, their total variance cannot be partitioned into genetic and error components as required to estimate heritability and genetic correlation of quantitative traits, the two conventional genetic parameters used for breeding selection. We propose a method of estimating the error variance of unreplicated genotypes that uses replicated controls, and then of estimating the genetic parameters. Using the Delta method, we also derived formulas for estimating the sampling variances of the genetic parameters. Computer simulations indicated that the proposed method for estimating genetic parameters and their sampling variances was feasible and the reliability of the estimates was positively associated with the level of heritability of the trait. A case study of estimating the genetic parameters of three quantitative traits, iodine value, oil content, and linolenic acid content, in a biparental recombinant inbred line population of flax with 243 individuals, was conducted using our statistical models. A joint analysis of data over multiple years and sites was suggested for genetic parameter estimation. A pipeline module using SAS and Perl was developed to facilitate data analysis and appended to the previously developed MAD data analysis pipeline (http://probes.pw.usda.gov/bioinformatics_tools/MADPipeline/index.html).
The measured and simulated data for corn and soybean yields, tileflow, N concentration, and N loading by plot, year, treatment, rotation, tillage and N application from 36 1-acre plots located on the Northeast Research and Demonstration Farm near Nashua, Iowa are presented.
The LDMI experiment (Low-Disturbance Manure Incorporation) was designed to evaluate nutrient losses with conventional and improved liquid dairy manure management practices in a corn silage (Zea mays) / rye cover-crop (Secale cereale) system. The improved manure management treatments were designed to incorporate manure while maintaining crop residue for erosion control. Field observations included greenhouse gas (GHG) fluxes from soil, soil nutrient concentrations, crop growth and harvest biomass and nutrient content, as well as monitoring of soil physical and chemical properties. Observations from LDMI have been used for parameterization and validation of computer simulation models of GHG emissions from dairy farms (Gaillard et al., submitted). The LDMI experiment was performed as part of the Dairy CAP.
The MAMA experiment (Manure Application Methods for Alfalfa-Grass), from the USDA-ARS research station in Marshfield, WI was designed to evaluate nutrient and pathogen losses with conventional and improved liquid dairy manure management practices for alfalfa-grass production. Observations from MAMA have also been used for parameterization and validation of computer simulation models of greenhouse gas (GHG) emissions from dairy farms.