U.S. flag

An official website of the United States government

RZWQM2

    Root Zone Water Quality Model 2 (RZWQM2) is a whole-system model for studying crop production and environmental quality under current and changing climate conditions. It emphasizes the effects of agricultural management practices on physical, chemical and biological processes. RZWQM2 is a one-dimensional model with a pseudo 2-dimensional drainage flow. Crop simulation options include the generic plant growth model, DSSAT-CSM 4.0 and HERMES SUCROS models. It also can simulate surface energy balance with components from the SHAW model and water erosion from the GLEAMS model. An automated parameter estimation algorithm (PEST) was added to RZWQM2 for objective model calibration and uncertainty analysis.

    Data from: Starch and dextrose at 2 levels of rumen-degradable protein in iso-nitrogenous diets: Effects on lactation performance, ruminal measurements, methane emission, digestibility, and nitrogen balance of dairy cows.

      This feeding trial was designed to investigate two separate questions. The first question is, “What are the effects of substituting two non-fiber carbohydrate (NFC) sources at two rumen-degradable protein (RDP) levels in the diet on apparent total-tract nutrient digestibility, manure production and nitrogen (N) excretion in dairy cows?”. This is relevant because most of the N ingested by dairy cows is excreted, resulting in negative effects on environmental quality. The second question is, “Is phenotypic residual feed intake (pRFI) correlated with feed efficiency, N use efficiency, and metabolic energy losses (via urinary N and enteric CH4) in dairy cows?”. The pRFI is the difference between what an animal is expected to eat, given its level of productivity, and what it actually eats. The goal was to determine whether production of CH4, urinary N or fecal N is a driver of pRFI.

      Data from: Agro-environmental consequences of shifting from nitrogen- to phosphorus-based manure management of corn.

        This experiment was designed to measure greenhouse gas (GHG) fluxes and related agronomic characteristics of a long-term corn-alfalfa rotational cropping system fertilized with manure (liquid versus semi-composted separated solids) from dairy animals. Different manure-application treatments were sized to fulfill two conditions: (1) an application rate to meet the agronomic soil nitrogen requirement of corn (“N-based” without manure incorporation, more manure), and (2) an application rate to match or to replace the phosphorus removal by silage corn from soils (“P-based” with incorporation, less manure). In addition, treatments tested the effects of liquid vs. composted-solid manure, and the effects of chemical nitrogen fertilizer. The controls consisted of non-manured inorganic N treatments (sidedress applications). These activities were performed during the 2014 and 2015 growing seasons as part of the Dairy Coordinated Agricultural Project, or Dairy CAP, as described below. The data from this experiment give insight into the factors controlling GHG emissions from similar cropping systems, and may be used for model calibration and validation after careful evaluation of the flagged data.

        Wind Erosion Prediction System (WEPS)

          This site provides access to the WEPS software version used for official purposes by NRCS field offices and Technical Service providers. NRCS developed and maintains the components of the WEPS Databases and information on this site. The USDA-Agricultural Research Service is the lead agency for developing the science in the WEPS model and the model interface. WEPS predicts many forms of soil erosion by wind such as saltation-creep and suspension including PM-10.

          Drainage Index and Productivity Index

            This website is designed to help you calculate the Drainage Index (DI) and Productivity Index (PI) of all soils that are classified within the US system of Soil Taxonomy. These data aid in the identification of areas at risk to various forest insects and diseases because of their ability to identify regions of potential tree stress.