Use of soil parameters to assess crop yield potential in Gallatin Valley, Montana : a study of soil quality
Year: 1999
Type: dissertation
Abstract: Soil survey and inventory activities involve collecting a variety of soil and environmental data and information which are subsequently available for numerous uses and applications.Some soil parameters have been identified to strongly influence quality of the soil, a term which has gained numerous definitions.Knowledge of using soil performance data in relation to crop yield may be a good learning process in approaching the study of soil quality.Understanding soil quality and its relation to dependent factors is, perhaps, helpful in making soil information more useful, especially in developing countries.The intent of the present investigation was not to develop yield estimate models based on soil parameters but rather to undertake a desktop exercise that would provide experience with the process of investigating soil quality and soil data.The main purpose of this study was to investigate important soil parameters which influence crop yield in Gallatin Valley.Variability in environmental conditions affecting crop yield was limited by taking a relatively small geographic area, Gallatin Valley, Montana for this study.Data and information about soils were primarily obtained from Natural Resources Conservation Service's URL Websites.Twenty nine soil pedons (with 162 horizons) representing eleven soil series were involved in this study.Soil data and information were obtained only for A and B horizons ; these data were used to calculate weighted-average values, which were used in subsequent analysis.Lack of available crop yield data was addressed by applying five different approaches to obtain crop yield estimates for winter and spring wheats (Triticum aestivum L.), and alfalfa (Medicago sativa L.).Eight soil variables from the entire soil survey database were used in conjunction with estimates of crop yield to assess the role of soil properties in soil quality characterization.Statistical Analysis System (SAS) programming was used for variable selection and regression analysis.Six 'best' regression equations were developed from this study.Two regressions predicting yield on the basis of soil properties were fit for each crop.Soil water holding capacity (WHC) proved to be the 'best' predictor of crop yield.Models involving WHC were defined as the first 'best' regressions for each crop.This independent variable was able to explain 91 percent of the variability in winter and spring wheat yields (models 1.1 and 6.1), and 87 percent of the yield variability in alfalfa (model 11).A combination of WHC and soil clay content (clay) comprised the second 'best' regression equations for each crop.This combination accounted for 64 percent variation in winter wheat yield (model 5.3), 56 percent yield variation in spring wheat (model 10.2), and 53 percent ofyield variation in alfalfa (model 15.2)._ Soil survey inventory results have been shown to be very useful for assessing yield potential of winter and spring wheats, and alfalfa on a limited basis.Of the five sources of dependent variables, yield estimates from NRCS-USDA were the most significantly correlated with soil parameters.Best guess yield estimates by experienced producers were very closely correlated with the estimated yields by the NRCS-USDA.By applying further manipulations, MAPS Atlas database I wish to express my sincere thanks to Dr. James W. Bander and Dr. CliffMontagne for their support, guidance, and progressive discussion during my work with this project.I thank Dr. Jerry Nielsen and Dr. Jon Wraith for serving as
Author: Arsil Saleh
Language: English
Subfield: Soil Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W82852283