Claypan Soil Field Research Articles

Inversion of soil electrical conductivity data to estimate layered soil properties

Bulk apparent soil electrical conductivity (ECa) sensors respond to multiple soil properties, including clay content, water content, and salt content (i.e. salinity). They provide a single sensor value for an entire soil profile down to a sensor-dependent measurement depth, weighted by a nonlinear response function. Because of this, it is generally difficult to elucidate strong relationships between ECa and the measured properties of individual soil layers. This research investigated inversion of the equations that govern the ECa-depth response relationship to reconstruct the soil conductivity in profile layers using data collected in multiple fields in the Midwest US. Layer conductivities obtained by inversion were first validated by comparison with true conductivities measured as a function of depth with an ECa-sensing penetrometer. Then, the validated layer conductivities were related to laboratory- measured soil properties. Inversion worked well but sometimes required iterative adjustment of initial conditions and other input parameters to obtain best results. Strong linear relationships (r2≥0.76) were obtained between inversion-estimated and measured layer conductivity data in all cases, sometimes with a truncated depth range. Layer conductivity data was successfully used to estimate soil texture fractions in the two alluvial fields examined. This was not the case for a claypan soil field, where there appeared to be parameters other than texture strongly affecting the EC response. Further examination of this approach is warranted to potentially provide improved ways to estimate depth-variable soil properties using ECa.

Mapping Depth to Argillic Soil Horizons Using Apparent Electrical Conductivity

Maps of apparent electrical conductivity [Formula: see text] of the soil profile are widely used in precision agriculture. A number of [Formula: see text] sensors are commercially available, each with a unique response function (i.e., the relative contribution of soil at each depth to the integrated [Formula: see text] reading). Our past research estimated depth to an argillic horizon (i.e., topsoil depth, TD) on claypan soils by fitting empirical equations to [Formula: see text] sensor data. The objective of this research was to determine if TD estimates could be improved by combining data from multiple [Formula: see text] sensors and by solving for TD by inverting a two-layer soil model incorporating instrument response functions. Data were obtained with three sensors having five different [Formula: see text] depth-response functions (Veris 3150 (mention of trade names or commercial products is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture or the University of Florida), Geonics EM38 vertical dipole mode, and DUALEM-2S) on two Missouri claypan-soil fields. Soil cores obtained in each field provided measured TD data for calibration and validation. Using a numerical optimization approach, response-function models were developed for [Formula: see text] variables individually and in combination. Similarly, linear regression was applied to single and multiple variables. Root mean square error of validation [Formula: see text] of single-variable TD estimates was [Formula: see text], with better results for those variables with moderately deep [Formula: see text] response functions. Results from the model-based approach were very similar to those obtained by regressing TD on [Formula: see text]. The best calibrations using multiple variables in model inversion or regression were somewhat better than those using single variables, with [Formula: see text] of [Formula: see text] and [Formula: see text], respectively. For all approaches, highest TD errors were localized to one area of one field, possibly because soils in this area violated the model assumption of spatially homogeneous soil layer conductivity. Although these calibrations are sufficiently accurate to be useful in TD mapping, a model solution allowing layer conductivities to vary spatially should be investigated for possible improvements.

Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model

Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season weather conditions.

Soil Bin and Field Tests of an On-The-Go Soil Strength Profile Sensor

An on-the-go soil strength profile sensor (SSPS) was previously developed to measure the within-field spatial variability in soil strength at five evenly spaced depths up to 50 cm. In this article, performance of the SSPS was evaluated using soil bin and field data. First, the SSPS was tested in a soil bin at different depths (10, 20, and 30 cm), forward speeds (from 0.5 to 3.0 m s-1), and compaction levels (high and low). Second, data were collected in two fields having variable soil texture, bulk density, and water content. Prismatic soil strength index (PSSI, defined as force divided by the base area of the horizontally operating prismatic tip) and penetrometer cone index were measured at five depths (10, 20, 30, 40, and 50 cm) across entire fields and also more intensively in four 10 10 m areas selected for soil texture differences. Auxiliary data collected were soil bulk density, soil water content, and apparent soil electrical conductivity (ECa). When the SSPS was tested in the soil bin, increases in PSSI with speed were less than 15% from 0.5 to 3.0 m s-1 operating speed. Based on soil bin results, we selected 1.5 m s-1 as a maximum field data collection speed, below which speed effects on PSSI were deemed negligible. Mean PSSI values collected in adjacent, parallel transects were not statistically different, confirming the repeatability and stability of soil strength sensing with the SSPS. Field data showed that, in general, PSSI was higher at locations with lower ECa, lower water content, and greater bulk density. Results of stepwise multiple linear regression showed that variability in PSSI was better explained when interactions among the soil variables were included as independent variables and when data were grouped into subsets by depth and/or ECa level. Over entire fields, R2 values for estimating PSSI were 0.66 and 0.61 for a claypan soil field and a flood plain field, respectively. These results will be useful for interpreting PSSI and for future applications of the SSPS in crop management, e.g., delineation of highly compacted within-field areas and control of variable tillage operations.

Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity

Efficient and cost-effective methods are needed for delineating sub-field productivity zones to improve soil and crop site-specific management. This investigation was conducted to answer the question of whether apparent soil electrical conductivity (EC a) and elevation could be used to delineate productivity zones (SPZ) for claypan soil fields that would agree with productivity zones delineated from yield map data (YPZ). Ten and seven years of combine-monitored yield maps were available for two Missouri claypan soil fields, designated Field 1 and Field 2, respectively. The fields were generally cropped in corn and soybean. Soil EC a data were collected with a non-contact, electromagnetic induction-based EC a sensor (Geonics EM38) and a coulter-based sensor (Veris model 3100). Elevation data were collected using a real-time kinematic GPS. Unsupervised fuzzy c-means clustering was independently used both on yield data to delineate three YPZ and on combinations of EC a and/or elevation data to delineate three SPZ. Outcomes of YPZ and SPZ were matched and agreement calculated with an overall accuracy statistic and a statistical index called the Kappa coefficient. Best performing combinations of EC a and elevation variables gave 60–70% agreement between YPZ and SPZ. We consider this level of agreement promising, especially considering that there were many other yield-limiting factors unrelated to EC a and elevation. Generally, multiple variables of EC a and elevation were better than a single variable for generating SPZ. The specific combinations of EC a and/or elevation variables that gave highest agreement between YPZ and SPZ were field specific. Based on these findings, we conclude EC a and elevation measurements can be reliably used for creating productivity zones on claypan soil fields.

Management Zone Analyst (MZA)

Producers using site‐specific crop management (SSCM) have a need for strategies to delineate areas within fields to which management can be tailored. These areas are often referred to as management zones Quick and automated procedures are desirable for creating management zones and for testing the question of the number of zones to create. A software program called Management Zone Analyst (MZA) was developed using a fuzzy c‐means unsupervised clustering algorithm that assigns field information into like classes, or potential management zones. An advantage of MZA over many other software programs is that it provides concurrent output for a range of cluster numbers so that the user can evaluate how many management zones should be used. Management Zone Analyst was developed using Microsoft Visual Basic 6.0 and operates on any computer with Microsoft Windows (95 or newer). Concepts and theory behind MZA are presented as are the sequential steps of the program. Management Zone Analyst calculates descriptive statistics, performs the unsupervised fuzzy classification procedure for a range of cluster numbers, and provides the user with two performance indices [fuzziness performance index (FPI) and normalized classification entropy (NCE)] to aid in deciding how many clusters are most appropriate for creating management zones. Example MZA output is provided for two Missouri claypan soil fields using soil electrical conductivity, slope, and elevation as clustering variables. Management Zone Analyst performance indices indicated that one field should be divided into either two (using NCE) or four (using FPI) management zones and the other field should be divided into four (using NCE or FPI) management zones.

Management Zone Analyst (MZA)

Producers using site-specific crop management (SSCM) have a need for strategies to delineate areas within fields to which management can be tailored. These areas are often referred to as management zones Quick and automated procedures are desirable for creating management zones and for testing the question of the number of zones to create. A software program called Management Zone Analyst (MZA) was developed using a fuzzy c-means unsupervised clustering algorithm that assigns field information into like classes, or potential management zones. An advantage of MZA over many other software programs is that it provides concurrent output for a range of cluster numbers so that the user can evaluate how many management zones should be used. Management Zone Analyst was developed using Microsoft Visual Basic 6.0 and operates on any computer with Microsoft Windows (95 or newer). Concepts and theory behind MZA are presented as are the sequential steps of the program. Management Zone Analyst calculates descriptive statistics, performs the unsupervised fuzzy classification procedure for a range of cluster numbers, and provides the user with two performance indices [fuzziness performance index (FPI) and normalized classification entropy (NCE)] to aid in deciding how many clusters are most appropriate for creating management zones. Example MZA output is provided for two Missouri claypan soil fields using soil electrical conductivity, slope, and elevation as clustering variables. Management Zone Analyst performance indices indicated that one field should be divided into either two (using NCE) or four (using FPI) management zones and the other field should be divided into four (using NCE or FPI) management zones.

Economic and Environmental Evaluation of Variable Rate Nitrogen and Lime Application for Claypan Soil Fields

Variable Rate Technology (VRT) has the potential to increase crop yields and improve water quality relative to Uniform Rate Technology (URT). The effects on profitability and water quality of adopting VRT for nitrogen (N) and lime were evaluated for corn production on four claypan soil fields in north central Missouri under average to better than average weather conditions. Variable N and lime rates were based on measured topsoil depth and soil pH, respectively. VRT rates were compared to two different uniform N applications (URT-Nl based on the topsoil depth within these claypan soil fields, and URT-N2 based on a typical N rate for corn production in this area). Expected corn yield was predicted based on topsoil depth, soil pH, N rate, and lime rate. Water quality benefits of VRT relative to URT were evaluated based on potential leachable N. Sensitivity analyses were performed using simulated topsoil data for topsoil depth and soil pH. Results showed that VRT was more profitable than URT in the four sample fields under URT-N1, and in two of the four fields under URT-N2. Greater variation in topsoil depth and soil pH resulted in higher profitability and greater water quality benefits with VRT. Results support adoption of VRT for N and lime application for other claypan soil fields with characteristics similar to those in the fields used in this study.