Cheryl Reese

Factors influencing spatial variability of soil apparent electrical conductivity

ABSTRACT Soil apparent electrical conductivity (ECa) can be used as a precision farming diagnostic tool more efficiently if the factors influencing ECa spatial variability are understood. The objective of this study was to ascertain the causes of ECa spatial variability in soils developed in an environment with between 50 and 65 cm of annual rainfall. Soils at the research sites were formed on calcareous glacial till parent materials deposited approximately 10,000 years ago. Soil samples (0–15 cm) collected from at least a 60 by 60 m grid in four fields were analyzed for Olsen phosphorus (P) and potassium (K). Elevation was measured by a carrier phase single frequency DGPS and ECa was measured with an EM 38 (Geonics Ltd., ON, Canada) multiple times between 1995 and 1999. Apparent electrical conductivity contained spatial structure in all fields. Generally, the well drained soils in the summit areas and the poorly drained soil in the toeslope areas had low and high ECa values, respectively. The landscape differences in ECa were attributed to: (i) water leaching salts out of summit areas and capillary flow combined with seepage transporting water and salts from subsurface to surface soils in toeslope areas; (ii) lower water contents in summit than toeslope soils; and (iii) water erosion which transported surface soil from summit/shoulder areas to lower backslope/footslope areas. A conceptual model based on these findings was developed. In this model, topography followed a sine curve and ECa followed a cosine curve. Field areas that did not fit the conceptual model were: (i) areas containing old animal confinement areas; (ii) areas where high manure rates had been applied; and (iii) areas where soils were outside the boundary conditions of the model, i.e., soils not developed under relatively low rainfall conditions in calcareous glacial till with temperatures ranging between mesic and frigid. This research showed that the soil forming processes as well as agricultural management influenced ECa and that by understanding how landscape position influences salt loss and accumulation, water redistributions following precipitation, and erosion areas that do not fit the conceptual model can be identified. This information can be used to improve soil sampling strategies.

Spatial variability of 13C isotopic discrimination in corn

Water stress influences photosynthesis induced isotopic 13C discrimination (Δ) in C3 and C4 plants. In C4 plants, Δ increases with increasing water stress, while in C3 plants the opposite is true. The amount of Δ that occurs is a function of plant type, interactions between climatic conditions, plant available water, water stress, photosynthesis capacity, plant water demand, and yield. The objective of this study was to determine if Δ measured in corn (Zea mays) tissue collected from summit, shoulder, backslope, and toeslope areas contains spatial structure. Corn grain or fodder samples collected from grid points located at five sites were analyzed for total nitrogen (N) and Δ on a Europa 20:20 ratio mass spectrometer. At sites where crop growth was not N limited, Δ spatial dependence was described using linear models. Spatial dependence most likely was generated by environmental and physical factors that interacted to influence plant available water. Spatial dependence indicates that the basic physiological response of corn to water stress, i.e., stomatal closure, can be measured at the plant and integrated to the landscape scale. A relationship between relative yields and Δ suggests that Δ provides an index for water stress under non nutrient limiting conditions. However, because Δ can be influenced by many factors, Δ as a direct measure of water stress must be used with caution.

Spatial distribution, temporal stability, and yield loss estimates for annual grasses and common ragweed (Ambrosia artimisiifolia) in a corn/soybean production field over nine years

Abstract Weeds generally occur in patches in production fields. Are these patches spatially and temporally stable? Do management recommendations change on the basis of these data? The population density and location of annual grass weeds and common ragweed were examined in a 65-ha corn/soybean production field from 1995 to 2004. Yearly treatment recommendations were developed from field means, medians, and kriging grid cell densities, using the hyperbolic yield loss (YL) equation and published incremental YL values (I), maximum YL values (A), and YL limits of 5, 10, or 15%. Mean plant densities ranged from 12 to 131 annual grasses m−2 and < 1 to 37 common ragweed m−2. Median weed densities ranged from 0 to 40 annual grasses m−2 and were 0 for common ragweed. The grass I values used to estimate corn YL were 0.1 and 2% and treatment was recommended in only 1 yr when the high I value and either the mean or median density was used. The grass I values used for soybean were 0.7 and 10% and estimated YL was over...

Predicting Soil Electrical Conductivity of the Saturation Extract from a 1:1 Soil to Water Ratio

ABSTRACT Since 1954, the electrical conductivity of the saturated paste extract (ECe) has been the preferred index for soil salinity. Based on this value, remediation strategies were developed and widely used but this approach is time consuming and not routinely offered by many soil testing facilities. However, many laboratories determine the EC1:1 value of a 1:1 soil to solution ratio extract. The objective of this study was to identify the relationship between ECe and EC1:1 and determine if EC1:1 can be used as a proxy in the northern Great Plains for ECe. Samples were collected across five studies and from AGVISE Laboratory. The samples were analyzed for EC1:1 and ECe. The relationship between the ECe and EC1:1 showed that soil parent materials need to be considered in the conversion of EC1:1 values to ECe values. A failure to consider parent materials in this conversion may have short and long-term sustainability ramifications.

Characterization of Soybean Yield Variability Using Crop Growth Models and 13C Discrimination

During the past several years, crop models have successfully been used to test the hypothesis that water stress is the primary factor that causes spatial yield variability in soybean [Glycine max (L.) Merr.] fields. However, there have been few attempts to validate this hypothesis through direct temporal and spatial measurements of water stress during the season. Recently, a technique has been developed to relate plant tissue 13C levels to the temporal water stress experienced by soybean plants. The purpose of this work was to compare the spatial yield loss simulated by a crop model with yield loss measured by 13C discrimination ( .) for a soybean field in South Dakota. The field was divided into 0.9-ha grids and the CROPGRO-Soybean model was calibrated to minimize error between simulated and observed yield in each grid over two seasons (1998, 2000). 13C discrimination was measured at 50 points representing 23 of the grids used in the crop modeling analysis in 2000. Simulated yield loss in grids that encompassed each 13C point in 2000 were compared to measurements of yield loss using the 13C discrimination technique. Initially, the root mean square error and r2 between simulated and measured yield loss was 259 kg ha-1 and 0.24, respectively. Upon closer inspection, it was observed that yield in 5 grids with the highest error likely were influenced by processes that are not represented in the crop model. Removing these values dramatically improved the agreement between simulated and observed yield loss, giving an RMSE of 216 kg ha-1 and r2 of 0.81. Both 13C discrimination and simulation results indicated that substantial yield loss occurred due to water stress in the summit/backslope areas of the field.