M. M. Ellsbury

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.

Precision Farming Protocols: Part 1. Grid Distance and Soil Nutrient Impact on the Reproducibility of Spatial Variability Measurements

To determine temporal changes in soil nutrient status, reproducible results must be obtained at each time step. The objective of this paper was to determine the impact of grid distance on the reproducibility of spatial variability measurements. Soil samples from the 0 to 15 cm depth were collected from a 30 by 30 m grid in May 1995 in a 65 ha notill corn (Zea mays L.) field. Each bulk sample contained 15 individual cores, collected at sample points located every 11.4 cm along a transect that transversed 3 corn rows (57 cm). At each sampling point latitude, longitude, elevation, landscape position, and soil series were determined. The 30 m grid was used to develop 4 and 9 independent data sets having a 60 and 90 m, grids, respectively. Semivariograms, nugget to sill ratios, and mean squared errors were calculated for each data set. At 60 m: (i) the total N, total C, and pH semivariograms, of different start points, were similar, while semivariograms for Olsen P, K, and Zn were different; (ii) the spatial dependence ratings, based on the nugget to sill ratio, for total N, total C, and pH semivariograms were consistent and suggested moderate spatial dependence; (iii) the spatial dependence rating for Olsen P, K, and Zn for the 4 semivariograms were not consistent and ranged from weak to moderate spatial dependence. At 90 m all soil nutrients had different semivariograms for each start point, while the spatial dependence rating for each total N, total C, and pH start point were consistent and showed moderate spatial dependence. The total C, P, K, Zn, and pH MSE values at 60 m, were 30, 30, 41, 28, and 72% lower than the variance, respectively. This study showed that semivariogram, semivariance, MSE, and nugget to sill ratios reproducibility were dependent on soil nutrient and grid distance.

Field scale variability of nitrogen and δ15N in soil and plants

Abstract Understanding the factors that influence soil and plant nitrogen (N) spatial variability may improve our ability to develop management systems that maximize productivity and minimize environmental hazards. The objective of this study was to determine the field (65 ha) scale spatial variability of N and δ15N in soil and corn (Zea mays). Soil, grain, and stover samples were collected from grids that ranged in size from 30 by 30 m to 60 by 60 m. Plant samples, collected following physiological maturity in 1995, were analyzed for total N and δ15N. Soil samples, collected prior to planting in the spring of 1995 and 1996, were analyzed for inorganic‐N, total N, and δ15N. All parameters showed strong spatial relationships. In an undrained portion of the field containing somewhat poorly and poorly drained soils there was a net loss of 95 kg N ha‐1, while in an adjacent area that was tile drained there was a net gain of 98 kg N ha‐1. Denitrification and N mineralization most likely were responsible for lo...

Precision farming protocols. part 2. comparison of sampling approaches for precision phosphorus management

Abstract Research is needed to compare the different techniques for developing site‐specific phosphorus (P) recommendations on a field‐wide basis. The objective of this study was to determine the impact different techniques for developing site‐specific P recommendation maps on yield and profitability. Enterprise analysis combined with a crop simulation model and detailed field characterization was used to estimate the value of spatial P information in a system where N was not limiting. The systems evaluated were continuous corn (Zea mays) and corn and soybean (Gfycine max) rotations where sampling and fertilizer applications were applied annually and semi‐annually, respectively. The sampling techniques tested were: (i) an unfertilized P control; (ii) whole field; (iii) whole field plus historic information (feedlot); (iv) landscape positions; (v) soil type; (vi) soil type plus historic information (feedlot); and (vii) 90‐m grid sampling. The finding of this study were based on soil samples collected from a 30 by 30‐m grid. The value of the spatial information was dependent on the crops response to P, the accuracy of the different sampling techniques, crop rotation, and the length of time between sampling dates. All of the sampling techniques produced different application maps. The recommendation map based on a single composite sample under fertilized 56.5% of the field. Increasing the sampling density reduced the percentage of under‐fertilized land. If corn had a low P response, then simulation/enterprise analysis indicated that applying P did not increased profits. For all scenarios tested: (i) the soil type + historic sampling approach had higher potential profits than the 90 m grid sampling approach; and (ii) there was no economic benefit associated with the 90‐m grid sampling. However, if research shows that amortization of sampling and analysis costs over 3 or 4 years is appropriate, then it may be possible to derive economic benefit from a 90‐m grid sampling. For a corn/soybean rotation, where fertilizer was applied when corn was planted and N and P was not applied to soybeans, enterprise/ simulation analysis (2.8 Mg ha‐1 soybean yield goal and a moderate P model) showed that soil + historic sampling approach increased profitability

Geostatistical Characterization of the Spatial Distribution of Adult Corn Rootworm (Coleoptera: Chrysomelidae) Emergence

3.74 ha‐1 when compared to the uniform P treatment.