240-9 Maize Yield Estimation As a Function of Soil Texture and Chemical and Physical Soil Characteristics.

Poster Number 242

See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: General Agroclimatology and Agronomic Modeling: II
Tuesday, November 4, 2014
Long Beach Convention Center, Exhibit Hall ABC
Share |

Bernardo Chaves-Cordoba1, Gerrit Hoogenboom1 and Philip Thornton2, (1)Washington State University, Prosser, WA
(2)International Livestock Research Institute (ILRI), CGIAR/ESSP Program on Climate Change, Agriculture and Food Security (CCAFS), Nairobi, Kenya
The global soil database of the World Inventory of Soil Emission Potentials (WISE) provides a homogenized set of primary profile soil data for a wide range of environmental conditions. WISE Version 3.1 provided by the International Soil Reference and Information Centre (ISRIC) (www.isric.org) includes soil attribute data for 10,250 soil profiles. Soil texture is one of most frequently soil characteristics measured often used to estimate some other physic soil characteristics. The objective of this study was to establish relationships between the maize yield and the chemical and physical characteristics of the soil textural classes and to determine the effects of water and nitrogen. 9613 WISE soil profiles were converted into crop model format of the Decision Support System for Agrotechnology Transfer (DSSAT). Local crop management for maize grown at the University of Florida in Gainesville was used. 30 years of daily weather data were generated using the WGEN. The treatments included rainfed & low nitrogen, rainfed & high nitrogen, automatic irrigation & low nitrogen, and automatic irrigation & high nitrogen. The Cropping System Model (CSM)-CERES-Maize of DSSAT was used to simulate the yield for each soil profile and treatment combinations. Principal Component Analysis using the SAS procedure PRINQUAL was applied to each treatment and soil texture class to determine the relationship of the chemical and soil characteristics and yield. Two components that explained more than 90% of the total variability were used to determine the relationship among soil profile variables and the yield percentiles of each treatment. The structure of the correlations changed according to the treatments and texture, and the yield percentiles were distributed differently. In general, the highest yield was obtained when nitrogen, organic carbon contents and saturated upper limit were high and bulk density low. Low yield was related to higher root growth factor and shallow soils.
See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: General Agroclimatology and Agronomic Modeling: II