GOSSYM is a dynamic, process-level simulation model of cotton growth and yield. GOSSYM essentially is a materials balance model which keeps track of carbon and nitrogen in the plant and water and nitrogen in the soil root zone. GOSSYM predicts the response of the field crop to variations in the environment and to cultural inputs. Specifically, the model responds to weather inputs of daily total solar radiation, maximum and minimum air temperatures, daily total wind run, and rainfall and/or irrigation amount. The model also responds to cultural inputs such as preplant and withinseason applications of nitrogen fertilizer, row spacing and within row plant density as they affect total plant population, and cultivation practices.
SHOOTGRO emphasizes the development and growth of the shoot apex of small-grain cereals such as winter and spring wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.). To better incorporate the variability typical in the field, up to six cohorts, or age classes, of plants are followed using a daily time step.
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.
The Nitrogen Index is a tool written in the programming language Java that is used to calculate nitrogen uptake and leaching in farming techniques.
Computer Software for Evaluating Solute Transport in Porous Media Using Analytical Solutions of the Convection-Dispersion Equation.
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.