A Model for Agro-Economic Analysis of Soil pH Mapping

Core soil sampling followed by laboratory analysis is the traditional method used to map soil pH prior to variable rate application (VRA) of lime on cropland. A recently developed automated soil sampling system capable of measuring soil pH on-the-go has significantly increased sampling resolution. However, adoption of such systems must be justified economically. This paper presents a method for assessing the economic benefit from automated mapping of soil pH prior to variable rate lime application. In this work, geostatistical, agronomic, and economic methods were used to generate a comprehensive numerical model for quantitative assessment of the net return over cost of liming for different lime management strategies. The strategies included: automated pH mapping, manual grid soil sampling, and whole field sampling used in combination with either variable or fixed rate liming. The model was demonstrated using a simulated field with known average pH and semivariogram model. The analysis showed the largest benefit ($6.13ha^–1year^–1) from using VRA with automated soil pH mapping versus VRA based on 1ha (2.5acres) manual grid point sampling for the selected simulated field conditions. A sensitivity analysis demonstrated that for a wide range of field conditions and crop prices, VRA plus automated mapping promises higher relative benefits than VRA based on either manual grid point or grid cell sampling.     

Influence of failure mode induced by a horizontally operated single-tip penetrometer on measured soil resistance

Excessive soil compaction has negative effects for agriculture and the environment. Measurement of soil strength is a common indirect measure of soil compactness. In the context of precision farming, on-the-go soil mechanical resistance measurements using single- and multiple-tip horizontal sensors have been developed. It has been reported that there was a significant relationship between soil mechanical resistance values measured with both vertically operated cone penetrometer and horizontally operated sensors only for relatively deep layers. It was hypothesized that the differences in horizontally measured soil resistance in different soil layers could be explained by different failure modes. The objective of this research was to develop a horizontal soil mechanical resistance sensor and to observe the failure mode in front of it while penetrating soil at three different depths. A single-tip horizontal penetrometer was equipped with a 30° prismatic tip and had a base area of 324 mm². The prismatic tip was mounted horizontally to an S-shaped load cell housed inside a shank. A data-logging system was also developed to record measurements with 10 Hz sampling rate. The sensor was tested in a field with silty clay loam soil at three depths of 20, 25 and 30 cm. Cone index (CI) values were obtained with 1 cm depth increments and 1 m horizontal intervals along each transect for comparison using a standard cone penetrometer. The results showed that average horizontal soil mechanical resistance index (HRI) values for both depths of 20 and 25 cm were similar due to the brittle failure mode in both cases. However, when the tip was operated below the critical depth of the sensor, the value of HRI at 30 cm depth increased three times when compared with 20 or 25 cm depth values. This was due to change in failure mode from brittle to compressive mode below the critical depth. There was a significant relationship (R² = 0.75) between HRI and CI for the 30-cm depth, whereas for shallower depths the relation was not significant. It can be concluded that the correlation between measurements obtained with the vertically and horizontally operated penetrometers would be significant as long as both produced the same soil failure mode.     

Evaluation of an on-the-go technology for soil pH mapping

Since conventional sampling and laboratory soil analysis do not provide a cost effective capability for obtaining geo-referenced measurements with adequate frequency, different on-the-go sensing techniques have been attempted. One such recently commercialized sensing system combines mapping of soil electrical conductivity and pH. The concept of direct measurement of soil pH has allowed for a substantial increase in measurement density. In this publication, soil pH maps, developed using on-the-go technology and obtained for eight production fields in six US states, were compared with corresponding maps derived from grid sampling. It was shown that with certain field conditions, on-the-go mapping can significantly increase the accuracy of soil pH maps and therefore increase the potential profitability of variable rate liming. However, in many instances, these on-the-go measurements need to be calibrated to account for a field-specific bias. After calibration, the overall error estimate for soil pH maps produced using on-the-go measurements was less than 0.3 pH, while non-calibrated on-the-go and conventional field average and grid-sampling maps produced errors greater than 0.4 pH.     

Development of an instrumented blade system for mapping soil mechanical resistance represented as a second-order polynomial

An instrumented blade system for mapping soil mechanical resistance on-the-go was developed. The system consists of a cutting edge that transfers the load from cutting through soil media to a vertical shank equipped with an array of strain gage bridges. The resulting force is transferred between the cutting edge and the blade at three distinct locations and the soil mechanical resistance profile is modeled as a second-order polynomial. The geometry of the blade was determined using an optimization procedure minimizing the total error associated with potential strain gage noise. The instrument was built and calibrated using a set of known point loads. It was found that signal adjustment was needed in order to account for the mechanical transfer of force through the cutting edge. This adjustment was applied using a set of coefficients found through a multiple linear regression. Coefficients of determination, R², for regressions between adjusted and theoretical second-order profiles were greater than 0.99. Results of field evaluation and discussion on the potential applicability of the system developed will be presented in a follow-up publication.     

Agitated soil measurement method for integrated on-the-go mapping of soil pH, potassium and nitrate contents

Knowledge of spatial variability of soil attributes within an agricultural field is critical for successful site-specific crop management. Soil sensing techniques to assess this variability on-the-go are being developed as an alternative to tedious manual soil sampling and laboratory testing. The goal of this study was to evaluate an Agitated Soil Measurement (ASM) method for integrated on-the-go mapping of soil pH, soluble potassium and residual nitrate contents using ion-selective electrodes. To implement ASM, an Integrated Agitation Chamber Module (IACM) was developed and attached to a commercial soil pH mapping implement. Precision of the tested electrodes was assessed through the root mean squared error (RMSE) and ranged from 0.10 for pK to 0.22 for pNO₃ (units represent the negative base 10 logarithm of the molar concentration of specified ions). The accuracy of the electrodes was assessed by comparing test results against reference measurements conducted in a commercial soil laboratory using the linear regression method. Average accuracy error ranged from 0.11 for pK to 0.23 for pNO₃. In a field simulation test, neither precision nor accuracy errors obtained with ASM were lower than for a previously investigated Direct Soil Measurement (DSM) method, which produced precision errors ranging from 0.11 for pH to 0.22 for pNO₃ and accuracy errors ranging from 0.12 for pNO₃ to 0.20 for pH. The coefficients of determination (r²) of linear regressions between individual field simulation measurements and corresponding average reference measurements were 0.85–0.89 (pH), 0.50–0.54 (pK), and 0.14–0.32 (pNO₃). However, laboratory evaluation of the ASM method revealed substantially lower measurement errors and increased r² values when compared to the field simulation, indicating that the proposed ASM method retains the potential for improving on-the-go field mapping. Except for reduced electrode abuse and the ability to use less expensive half-cell ion-selective electrodes, physical implementation of ASM through the IACM did not bring substantial improvement over conventionally available DSM. This could be attributed to the design of the IACM and use of half-cell electrodes. Further research is necessary to improve the design of the solution-based measuring equipment and to develop an algorithm integrating on-the-go measurements with other sources of spatial data for an improved decision-making process.     

Estimation of surface soil organic matter using a ground-based active sensor and aerial imagery

Active canopy sensors are currently being studied as a tool to assess crop N status and direct in-season N applications. The objective of this study was to use a variety of strategies to evaluate the capability of an active sensor and a wide-band aerial image to estimate surface soil organic matter (OM). Grid soil samples, active sensor reflectance and bare soil aerial images were obtained from six fields in central Nebraska before the 2007 and 2008 growing seasons. Six different strategies to predict OM were developed and tested by dividing samples randomly into calibration and validation datasets. Strategies included uniform, interpolation, universal, field-specific, intercept-adjusted and multiple-layer prediction models. By adjusting regression intercept values for each field, OM was predicted using a single sensor or image data layer. Across all fields, the uniform and universal prediction models resulted in less accurate predictions of OM than any of the other methods tested. The most accurate predictions of OM were obtained using interpolation, field-specific and intercept-adjusted strategies. Increased accuracy in mapping soil OM using an active sensor or aerial image may be achieved by acquiring the data when there is minimal surface residue or where it has been excluded from the sensor’s field-of-view. Alternatively, accuracy could be increased by accounting for soil moisture content with supplementary sensors at the time of data collection, by focusing on the relationship between soil reflectance and soil OM content in the 0–1 cm soil depth or through the use of a subsurface active optical sensor.     

Analysis of soil water availability by integrating spatial and temporal sensor-based data

An efficient irrigation system should meet crop demands for water. A limited water supply may result in reductions in yield, while excess irrigation is a waste of resources. To investigate water availability throughout the growing season, on-the-go sensing technologies (field elevation and apparent electrical conductivity) were used to analyze the spatial variability of soil relevant to its water-holding capacity. High-density data layers were used to identify strategic sites to monitor changes in plant-available water over time. To illustrate this approach, nine locations in a 37-ha agricultural field were selected for monitoring the soil matric potential and temperature at four depths (18, 48, 79 and 109 cm) using wireless technology. Using a linear regression approach, a field-specific model was developed that quantified plant-available water at every field location and at specific points in time. Further analysis was used to quantify the percentage of the field that undergoes a potential shortage in water supply. These results could be used to optimize irrigation scheduling and to assess the potential for variable-rate irrigation.     

Delineating site-specific management zones for pH-induced iron chlorosis

Iron chlorosis can limit crop yield, especially on calcareous soil. Typical management for iron chlorosis includes the use of iron fertilizers or chlorosis tolerant cultivars. Calcareous and non-calcareous soil can be interspersed within fields. If chlorosis-prone areas within fields can be predicted accurately, site-specific use of iron fertilizers and chlorosis-tolerant cultivars might be more profitable than uniform management. In this study, the use of vegetation indices (VI) derived from aerial imagery, on-the-go measurement of soil pH and apparent soil electrical conductivity (EC_a) were evaluated for their potential to delineate chlorosis management zones. The study was conducted at six sites in 2004 and 2005. There was a significant statistical relationship between grain yield and selected properties at two sites (sites 1 (2005) and 3), moderate relationships at sites 2 and 4, and weak relationships at site 5. For sites 1 (2005) and 3, and generally across all sites, yield was predicted best with the combination of NDVI and deep EC_a. These two properties were used to delineate chlorosis management zones for all sites. Sites 1 and 3 showed a good relationship between delineated zones and the selected properties, and would be good candidates for site-specific chlorosis management. For site 5, differences in the properties between mapped zones were small, and the zones had weak relationships to yield. This site would be a poor candidate for site-specific chlorosis management. Based on this study, the delineation of chlorosis management zones from aerial imagery combined with soil EC_a appears to be a useful tool for the site-specific management of iron chlorosis.     

Proximal soil sensing of soil texture and organic matter with a prototype portable mid‐infrared spectrometer

Recent advances in semiconductor technologies have given rise to the development of mid‐infrared (mid‐IR) spectrometers that are compact, relatively inexpensive, robust and suitable for in situ proximal soil sensing. The objectives of this research were to evaluate a prototype portable mid‐IR spectrometer for direct measurements of soil reflectance and to model the spectra to predict sand, clay and soil organic matter (SOM) contents under a range of field soil water conditions. Soil samples were collected from 23 locations at different depths in four agricultural fields to represent a range of soil textures, from sands to clay loams. The particle size distribution and SOM content of 48 soil samples were measured in the laboratory by conventional analytical methods. In addition to air‐dry soil, each sample was wetted with two different amounts of water before the spectroscopic measurements were made. The prototype spectrometer was used to measure reflectance (R) in the range between 1811 and 898 cm^?1 (approximately 5522 to 11 136 nm). The spectroscopic measurements were recorded randomly and in triplicate, resulting in a total of 432 reflectance spectra (48 samples × three soil water contents × three replicates). The spectra were transformed to log₁₀ (1/R) and mean centred for the multivariate statistical analyses. The 48 samples were split randomly into a calibration set (70%) and a validation set (30%). A partial least squares regression (PLSR) was used to develop spectroscopic calibrations to predict sand, clay and SOM contents. Results show that the portable spectrometer can be used with PLSR to predict clay and sand contents of either wet or dry soil samples with a root mean square error (RMSE) of around 10%. Predictions of SOM content resulted in RMSE values that ranged between 0.76 and 2.24%.