An Evaluation of Soil Survey Crop Yield Interpretations for Two Central Iowa Farms

Steinwand, A. L.; Karlen, Douglas L.; Fenton, Thomas E.

doi:10.31274/icm-180809-573

Cited by 9 publications

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“…The field was characterized by low‐relief swell and swale topography representative of the Des Moines lobe of the Cary substage of glaciation. Detailed information on the soils can be found in Steinwand and Fenton (1995) and Steinwand et al (1996) Field management was typical for Iowa and has been described in detail in Karlen and Colvin (1992) and Colvin et al (1997) The field has been in a 2‐yr rotation of corn and soybean since 1957. For the past 16 yr, tillage has been by chisel plow and field cultivator, and a 76‐cm row spacing has been used.…”

Section: Methodsmentioning

confidence: 99%

Cluster Analysis of Spatiotemporal Corn Yield Patterns in an Iowa Field

Jaynes¹,

Kaspar²,

Colvin³

et al. 2003

Agronomy Journal

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relationships can be developed between spatial yield patterns for a given year and soil properties such as Crop yields are frequently heterogeneous across space and time. texture, apparent electrical conductivity, organic matter We performed this study to determine if cluster analysis could be used to decipher the temporal and spatial patterns of corn (Zea mays content, or terrain attributes (Yang et al., 1998). HowL .) yield within a field. Nonhierarchal cluster analysis was applied to ever, the relationships are not the same among years 6 yr of corn yield data collected for 224 yield plots on a regular grid (Jaynes et al., 1995b; Halvorson and Doll, 1991), unless on the southern half of a 32-ha field. We were able to group the yield the yield data is divided into subsets based on growing observations into five temporal yield patterns or clusters. The clusters season climatic conditions or field attributes (Kaspar et were not randomly distributed across the field but instead formed al., 2003; Timlin et al., 1998). For example, Kaspar et contiguous areas roughly equivalent to landscape positions. Cluster al. (2003) were able to develop a multiple-regression membership was determined primarily by yield differences in years equation based on terrain attributes that accounted for with growing season precipitation greater than the 40-yr average. A 78% of the yield variability for 6 yr of corn grown on multiple discriminant analysis was used to predict the spatial occura 16-ha field by restricting the analysis to the 4 yr having rence of the clusters from easily determined field attributes: soil electrical conductivity, elevation, slope, and plan and profile curvature. below-normal seasonal precipitation. When they in-The multiple discriminant functions were unable to distinguish be-cluded the 2 yr with above-normal growing season pretween the two clusters located on the lowest portions of the landscape. cipitation, only 26% of the yield variability could be Because of similar temporal yield patterns in these two clusters, they accounted for by multiple regression. were combined and the multiple discriminant analysis repeated for Rather than trying to predict specific yields within a four clusters. Using a holdout sample approach, we achieved 76 and field, we may be more successful in identifying areas 80% success rates in classifying the yield plots into the correct yield within a field that behave similarly among years. Most clusters. If response curves for inputs such as N prove to be unique studies have approached this problem by first identifor the different yield clusters, then clustering of multiple-year yield fying areas of similar soil properties or terrain attributes data may prove an effective method for determining management and then testing for reduction in yield variability or yield zones within fields. response to inputs within these areas (Fraisse et al., 2001; Fleming et al., 2000). These methods rely on using general knowledge of crop production to identify the

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Section: Methodsmentioning

confidence: 99%

Cluster Analysis of Spatiotemporal Corn Yield Patterns in an Iowa Field

Jaynes¹,

Kaspar²,

Colvin³

et al. 2003

Agronomy Journal

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“…The Larson Field soils are primarily Hapludolls and Endoaquolls and are highly uniform across the fi eld, with only four SMUs representing the four soil series identifi ed during Order 1 mapping (Brevik et al, 2003a) (Table 1). Two of the SMUs, Ott osen (288) and Kossuth (388), comprise more than 89% of fi eld's area, and their component series qualify as similar inclusions (Brevik et al, 2003a), meaning there is litt le expected diff erence in agricultural management between the two soils (Steinwand et al, 1996). Therefore, approximately 90% of this fi eld is essentially uniform in terms of soil properties and anticipated response to agricultural management.…”

Section: Methodsmentioning

confidence: 99%

The Use of Soil Electrical Conductivity to Investigate Soil Homogeneity in Story County, Iowa, USA

2012

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Precision agriculture, environmental applications, and land use planning needs have led to demands for more detailed soil maps. A remote sensing technique that can differentiate soils with a high degree of accuracy would be ideal for soil survey purposes. One technique that has shown promise in Iowa is electrical conductivity (ECa) measured by the Geonics EM‐38. Electrical conductivity readings obtained with this technique integrate the soils’ water content, clay content, soluble salt content, and temperature. One way ECa techniques have been evaluated as soil survey tools involves grid mapping a field, performing an ECa survey, and then comparing maps obtained using each method. Good results have been obtained in this way for several fields in central Iowa that contain soils that are quite variable over relatively short distances. However, these fields had heterogeneous soil properties. There is a need to investigate the application of ECa techniques in fields with more homogenous soil properties. Therefore, a field with lacustrine‐derived soils that exhibits only slight spatial variability in soil properties was chosen for this study. Consequently, highly uniform ECa readings were obtained, and it was not possible to differentiate soil map units (SMUs) using the ECa data. However, the results did confirm the uniform nature of the soils in the field, a critical criterion for precision agriculture applications.

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“…Carr et al (1991) suggested that soil series could be used to direct site‐specific management as long as soil series were identified and mapped accurately. Steinwand et al (1996) examined a 64‐ha (158 acre) field in Iowa in a Clarion–Webster–Nicollet association and found that the 1:15840 and 1:3305 scales were similar in terms of predicting crop yields within a 3‐yr study. They concluded that soil survey mapping could be used to direct site‐specific decisions.…”

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confidence: 99%

Evaluation of Soil Survey Scale for Zone Development of Site‐Specific Nitrogen Management

et al. 2002

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Zone sampling for site‐specific N application has been shown to be effective in North Dakota and other areas of the Great Plains. Printed and sometimes digitized soil surveys are presently available for most agricultural counties in the USA. Order 2 soil surveys generally have scales that range from 1:12000 to 1:31680. These surveys were developed for general planning purposes. There is interest in using Order 2 soil surveys as a basis for delineating N management zone patterns, especially where the soil‐mapping units have been digitized. This study was conducted to evaluate soil survey scales at the Order 1 (scale >1:15840) and Order 2 level against grid‐ and topography‐based zone sampling to determine whether soil surveys at these scales could be used to delineate N management zones for site‐specific fertilizer application. Fields mapped at a finer scale (Order 1 survey) showed some similarity between mapping units and N management zones defined by topography. Order 2 soil‐mapping units, which are the present mapping scale of most agricultural soil surveys, were often not similar to N management zones. Published Order 2 soil surveys should not be used to develop N management zones for site‐specific agriculture unless the soil patterns are verified with other zone development tools of site‐specific management. Alternatively, a major benefit of Order 1 soil surveys would be to reinforce or redefine apparent N management zones.

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An Evaluation of Soil Survey Crop Yield Interpretations for Two Central Iowa Farms

Abstract: Hillel. D. 1982. [ncrodu~cion co soil physics. Acade• mic Press. :-:ew York. Jury. W.A .. and E.E. ,\tiller. 1974 , 'vfeasuremenc of che cransporc coefficients for coupled How of h"c •nd moisture 1n a rr:edium soil. Soil Sci• ence Sociery of Amer~ca Proceedings 38:55l-55i.

Cited by 9 publications

References 0 publications

Cluster Analysis of Spatiotemporal Corn Yield Patterns in an Iowa Field

Cluster Analysis of Spatiotemporal Corn Yield Patterns in an Iowa Field

The Use of Soil Electrical Conductivity to Investigate Soil Homogeneity in Story County, Iowa, USA

Evaluation of Soil Survey Scale for Zone Development of Site‐Specific Nitrogen Management

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