24 research outputs found
Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate
Geostatistical analysis for soil moisture content under the no tillage cropping system
Assesment of soil erosion by 137Cs technique in native forests in Londrina City, Parana, Brazil
Soil organic carbon and clay content as deciding factors for net nitrogen mineralization and cereal yields in boreal mineral soils
Delta yieldâbased optimal nitrogen rate estimates for corn are often economically sound
Scale-dependent covariance of soil physical properties above and below a soil horizon interface: Pedogenic versus anthropogenic influences on total porosity
Testing Corn (Zea mays L.) Preseason Regional Nitrogen Recommendation Models in South Dakota
The purpose of a N recommendation model is to maximize profitability and minimize the impacts of agriculture on the environment. To achieve this goal, reliable recommendations must be developed and systematically tested. The objective of this study was to evaluate and test regional N recommendation models from South Dakota, western Minnesota, Iowa, and Nebraska for their suitability to improve South Dakota N recommendations. Data used to test the models were collected between 2002 and 2004 at Aurora and between 2004 and 2006 at Beresford and Watertown in eastern South Dakota. In this experiment, corn was responsive to N fertilizer, soil organic matter was relatively high (\u3e30 g kgâ1), manure was not applied, and drought conditions were not observed. Root mean square errors and bias of the different regional models were determined. Results showed that: (i) all models were unique and produced different N recommendations; (ii) economically optimum N rates (EONR) were sensitive to changing fertilizer costs and corn selling prices; (ii) water had a large impact on yield and N use efficiency; (iv) yields at the EONR were highly correlated (r = 0.60â0.73, P \u3c 0.01) to the yield difference between fertilized and unfertilized plots; and (v) a modified South Dakota N recommendation model can be used to predict the impact of synergistic relationships between N and water