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 GallatinValley.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
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Claims 11
Claim · model-written summary
Six regression equations were developed.
Verbatim
e0“Six 'best' regression equations were developed from this study.”
Claim · model-written summary
Soil water holding capacity was the best predictor of crop yield.
Not reportedSettingDirection
Verbatim
e0“Soil water holding capacity (WHC) proved to be the 'best' predictor of crop yield.”
t0“crop yield”
Claim · model-written summary
WHC explained 91 percent of winter wheat yield variability.
Not reportedDirection
Verbatim
e0“This independent variable was able to explain 91 percent of the variability in winter and spring wheat yields (models 1.1 and 6.1)”
e1“winter and spring wheats (Triticum aestivum L.)”
Claim · model-written summary
WHC explained 91 percent of spring wheat yield variability.
TriticumNot reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“This independent variable was able to explain 91 percent of the variability in winter and spring wheat yields (models 1.1 and 6.1)”
e1“winter and spring wheats (Triticum aestivum L.)”
Claim · model-written summary
WHC explained 87 percent of alfalfa yield variability.
- Taxon
- alfalfa
Medicago sativae1 - Outcome
- yield variability in alfalfae0
- Value
- 87 percente0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“87 percent of the yield variability in alfalfa (model 11)”
e1“alfalfa (Medicago sativa L.)”
Claim · model-written summary
WHC plus clay accounted for 64 percent of winter wheat yield variation.
Not reportedDirection
Verbatim
e0“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)”
e1“winter and spring wheats (Triticum aestivum L.)”
Claim · model-written summary
WHC plus clay accounted for 56 percent of spring wheat yield variation.
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“56 percent yield variation in spring wheat (model 10.2)”
e1“spring wheats (Triticum aestivum L.)”
Claim · model-written summary
WHC plus clay accounted for 53 percent of alfalfa yield variation.
- Taxon
- alfalfaMedicago sativae1
- Outcome
- ofyield variation in alfalfae0
- Value
- 53 percente0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“53 percent ofyield variation in alfalfa (model 15.2)”
e1“alfalfa (Medicago sativa L.)”
Claim · model-written summary
Soil survey inventories were useful for limited yield potential assessment.
Not reportedComparatorValue
Verbatim
e0“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.”
Claim · model-written summary
NRCS-USDA yield estimates correlated most significantly with soil parameters among five sources.
- Taxon
- winter and spring wheatse1
- Outcome
- yield estimates from NRCS-USDA were the most significantly correlated with soil parameterse0
- Comparator
- five sources of dependent variablese0
Not reportedDirection
Verbatim
e0“Of the five sources of dependent variables, yield estimates from NRCS-USDA were the most significantly correlated with soil parameters.”
e1“winter and spring wheats (Triticum aestivum L.), and alfalfa (Medicago sativa L.)”
Claim · model-written summary
Producer best-guess yield estimates were closely correlated with NRCS-USDA estimates.
- Taxon
- winter and spring wheatse1
- Outcome
- Best guess yield estimates by experienced producerse0
- Comparator
- estimated yields by the NRCS-USDAe0
Not reportedDirection
Verbatim
e0“Best guess yield estimates by experienced producers were very closely correlated with the estimated yields by the NRCS-USDA.”
e1“winter and spring wheats (Triticum aestivum L.), and alfalfa (Medicago sativa L.)”