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-180809-603

Cited by 9 publications

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“…The field was tile drained, but there were no surface inlets and surface water frequently accumulates and remains in closed depressions for several days after high intensity and/or high volume precipitation events. Detailed information on the soils can be found in Steinwand and Fenton (1995) and Steinwand et al (1996) Field management is considered typical for Iowa and is described in detail in Karlen and Colvin (1992) and Colvin et al (1997) The field has been in a 2‐yr corn–soybean rotation since 1957 with corn planted in odd numbered years. From 1932 to 1981, the primary tillage was fall moldboard plowing followed by disking and harrowing.…”

Section: Methodsmentioning

confidence: 99%

Relationship of Corn and Soybean Yield to Soil and Terrain Properties

Kaspar¹,

Pulido

Fenton

et al. 2004

Agronomy Journal

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yield data collected over many years in the same field and a larger set of measured soil and terrain variables Farmers will be better able to implement site-specific management would have a better chance of accomplishing this goal. practices when they understand the causes of spatial and temporal variability of corn (Zea mays L.) and soybean [Glycine max (L.) Data collection and analysis, however, are costly and Merr.] yield in their fields. Our objectives were to determine if a data labor-intensive, and questions remain about the kind set containing 20 soil and terrain variables could explain spatial yield and amount of data that are needed to adequately unvariability better than a subset of seven more easily measured variables derstand spatial yield patterns. Additionally, it is unclear and to determine whether the relative importance of factors in exwhether different variables should be measured for soyplaining yield variability differed between corn and soybean or bebean and corn or for wet and dry years. tween wet and dry years. Yield data were collected for 11 yr in a 16-ha Growing season precipitation often interacts with terfield in central Iowa. Soil and terrain variables measured included: A rain attributes and soil properties to influence crop yield horizon depth, carbonate depth, pH, coarse sand, sand, silt, clay, (Timlin et al., 1998; Jaynes et al., 2002; Kaspar et al., organic C, N, Fe, K, P, and Zn; and seven easily measured variables: 2003). In years with below average rainfall, field areas electrical conductivity, soil color, elevation, slope, profile curvature,higher on a hillslope with greater slopes and convex plan curvature, and depression depth. Factor analysis of the variables followed by regression of yield on the resulting factors showed that curvatures usually have less available water and lower the 20-variable set explained more of the spatial variation in yield than yield than areas lower on the hillslope, with lesser slopes the subset of seven variables. Further, the analysis of the 20-variable and concave curvatures (Ciha, 1984; Halvorson and data set showed that soybean yield was affected more by pH, more Doll, 1991; Afyuni et al., 1993;Timlin et al., 1998; Jaynes by closed depressions in wet years, and less by curvature in dry years Kaspar et al., 2003). Similarly, eroded soils, than corn yield. Similarly, yield was negatively affected by closed which commonly have substantial slopes, convex curvadepressions and lower landscape positions in wet years, whereas these tures, and shallow topsoils (Pennock and de Jong, 1987; factors had either no effect or a positive effect in dry years. Alternately, Lindstrom et al., 1992), show a greater yield decline curvature had a negative effect in dry years and no effect in wet years.

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Section: Methodsmentioning

confidence: 99%

Relationship of Corn and Soybean Yield to Soil and Terrain Properties

Kaspar¹,

Pulido

Fenton

et al. 2004

Agronomy Journal

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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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outputs in time. Prescription maps of fertilizer rates are usually derived from one or more condition maps, Zone sampling for site-specific N application has been shown to modified perhaps by performance maps. The four condibe effective in North Dakota and other areas of the Great Plains. Printed and sometimes digitized soil surveys are presently available tion-map categories for nutrient management are (i) for most agricultural counties in the USA. Order 2 soil surveys gener-soil surveys, (ii) interpolation of a network of point ally have scales that range from 1:12 000 to 1:31 680. These surveys samples (i.e., grid sampling), (iii) yield monitor data or were developed for general planning purposes. There is interest in remotely sensed images, and (iv) modeling to estimate using Order 2 soil surveys as a basis for delineating N management spatial nutrient patterns. zone patterns, especially where the soil-mapping units have been Soil surveys are compendiums of soils in a region. digitized. This study was conducted to evaluate soil survey scales at Compendiums contain information on boundaries of the Order 1 (scale Ͼ1:15 840) and Order 2 level against grid-and soil series, types, associations, or complexes that are topography-based zone sampling to determine whether soil surveys usually traced on aerial photographs of the region. Most at these scales could be used to delineate N management zones for soil surveys in the USA are designated second order site-specific fertilizer application. Fields mapped at a finer scale (Order 1 survey) showed some similarity between mapping units and N man-

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“…This could be due to an error in the measured data because samples taken 12 m on either side of the suspect sample, as well as another sample of the same location from a later survey, all returned SOC contents at least twice as great as that measured; it could also indicate the presence of small inclusions not mapped in the survey of Steinwand et al ( 1996), which is likely (Fenton, personal communication, 2009). Conversely, Steinwand et al (1996) also mapped Okoboji and Delft clay loam soils in and around the localized depression just west of the center of the field. Inclusion of Okoboji's 46.4 to 69.6 g kg" SOC range in the models would have raised SOC estimates, negating the improvement from including Zenor s lower SOC range.…”

Section: Measured Vs Estimated Organic Carbonmentioning

confidence: 94%

“…These soils are in low-relief prairie pothole topography typical of the Des Moines lobe of the Cary substage of the Wisconsin glaciation. Field 1 was mapped at a higher resolution by Steinwand and Fenton (1995) and Steinwand et al (1996), revealing high-organic-matter depressional soils such as Okoboji and Canisteo and low-organic-matter summit soils such as Storden and Zenor (Table 1). …”

Section: Materials and Methods: Site Descriptionmentioning

confidence: 99%

“…To determine if a Level 1 soil stirvey could improve the results, the 1:3305 soil survey of Field 1 conducted by Steinwand et al (1996) was consulted, revealing a number of soils not mapped in the corresponding areas of the Level 2 soil maps. The survey mapped Storden and Zenor loam soils on the eroded hilltop areas in the center north of Field 1.…”

Section: Measured Vs Estimated Organic Carbonmentioning

confidence: 99%

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Estimating Soil Organic Carbon in Central Iowa Using Aerial Imagery and Soil Surveys

Gelder

Anex

Kaspar

et al. 2011

Soil Science Society of America Journal

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Widespread implementation of precision agriculture practices requires low-cost, high-quality data such as soil organic C (SOC) content, but SOC mapping currently requires expensive sample collection and analysis techniques. Soils higher in organic C appear darker than surrounding soils in aerial imagery after tillage, although this difference is only relative without knowledge ot the range of SOC. This range could be estimated from Soil Survey Geographic (SSURGO) database. To verify this, the SSURGO database was used to estimate the SOC range at diree sites in central Iowa. Soil organic C content across each Held was then linearly interpolated within the SSURGO-estimated range for each field using the brightness values at each pixel in the aerial photograph as a scaling factor. Measured SOC data trom the three sites ranged from 3.4 to 50 g kg"' and the R-and RMSE values between the measured and estimated SOC concentrations ranged from 0.60 to 0.82 and 3.5 to 7.6 g kg"', respectively. Limited thresholding of the brightest and darkest pixels improved the accuracy and precision oiSOC estimates over raw imagery. These results imply that aerial imagery supplemented by SSURGO-estimated SOC ranges can provide georeferenced SOC estimates suitable for site-specific recommendations and analysis without field sampling.

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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

Relationship of Corn and Soybean Yield to Soil and Terrain Properties

Relationship of Corn and Soybean Yield to Soil and Terrain Properties

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

Estimating Soil Organic Carbon in Central Iowa Using Aerial Imagery and Soil Surveys

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