John W. Hummel

Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor

Sensors are needed to document the spatial variability of soil parameters for successful implementation of Site-Specific Management (SSM). This paper reports research conducted to document the ability of a previously developed near infrared (NIR) reflectance sensor to predict soil organic matter and soil moisture contents of surface and subsurface soils. Three soil cores (5.56 cm dia. ×1.5 m long) were collected at each of 16 sites across a 144 000 km 2 area of the US Cornbelt. Cores were subsampled at eight depth increments, and wetted to six soil moisture levels ranging from air-dry to saturated. Spectral reflectance data (1603–2598 nm) were obtained in the laboratory on undisturbed soil samples. Data were collected on a 6.6 nm spacing with each reflectance value having a 45 nm bandpass. The data were normalized, transformed to optical density [OD, defined as log10 (1/normalized reflectance)], and analyzed using stepwise multiple linear regression. Standard errors of prediction for organic matter and soil moisture were 0.62 and 5.31%, respectively. NIR soil moisture prediction can be more easily commercialized than can soil organic matter prediction, since a reduced number of wavelength bands are required (four versus nine, respectively).

Remote- and Ground-Based Sensor Techniques to Map Soil Properties

Farm managers are becoming increasingly aware of the spatial variability in crop production with the growing availability of yield monitors. Often this variability can be related to differences in soil properties (e.g., texture, organic matter, salinity levels, and nutrient status) within the field. To develop management approaches to address this variability, high spatial resolution soil property maps are often needed. Some soil properties have been related directly to a soil spectral response, or inferred based on remotely sensed measurements of crop canopies, including soil texture, nitrogen level, organic matter content, and salinity status. While many studies have obtained promising results, several interfering factors can limit approaches solely based on spectral response, including tillage conditions and crop residue. A number of different ground-based sensors have been used to rapidly assess soil properties “on the go” (e.g., sensor mounted on a tractor and data mapped with coincident position information) and the data from these sensors compliment image-based data. On-the-go sensors have been developed to rapidly map soil organic matter content, electrical conductivity, nitrate content, and compaction. Model and statistical methods show promise to integrate these groundand image-based data sources to maximize the information from each source for soil property mapping.

Soil macronutrient sensing for precision agriculture.

Accurate measurements of soil macronutrients (i.e., nitrogen, phosphorus, and potassium) are needed for efficient agricultural production, including site-specific crop management (SSCM), where fertilizer nutrient application rates are adjusted spatially based on local requirements. Rapid, non-destructive quantification of soil properties, including nutrient levels, has been possible with optical diffuse reflectance sensing. Another approach, electrochemical sensing based on ion-selective electrodes or ion-selective field effect transistors, has been recognized as useful in real-time analysis because of its simplicity, portability, rapid response, and ability to directly measure the analyte with a wide range of sensitivity. Current sensor developments and related technologies that are applicable to the measurement of soil macronutrients for SSCM are comprehensively reviewed. Examples of optical and electrochemical sensors applied in soil analyses are given, while advantages and obstacles to their adoption are discussed. It is proposed that on-the-go vehicle-based sensing systems have potential for efficiently and rapidly characterizing variability of soil macronutrients within a field.

Real-time multi ISFET/FIA soil analysis system with automatic sample extraction

Successful implementation of site-specific crop management relies on accurate quantification of spatial variation of important factors. Therefore, there is a tremendous need for the development of sensing technologies that will allow automated collection of soil, crop and pest data, to more accurately characterize within-field variability. The objective of this work was to develop an integrated multi-sensor soil analysis system. Ion-selective field effect transistor (ISFET) technology was coupled with flow injection analysis (FIA) to produce a real-time soil analysis system. Testing of the ISFET/ FIA system for soil analysis was carried out in two stages: (1) using manually extracted samples, and (2) the soil to be analysed was placed in the automated soil extraction system, and the extracted solution fed directly into the FIA system. The sensor was successful in measuring soil nitrates in manually extracted soil solutions (r2>0.9). The rapid response of the system allowed a sample to be analysed in 1.25 s, which is satisfactory for real-time soil sensing. Precision and accuracy of the system were highly dependent on maintaining precise, repetitive injection times and maintaining constant flow parameters during the calibration and testing cycle. The progress toward an automated soil extraction system was notable, but considerable effort will be necessary before commercialization can be realized. However, the concept of using ISFETs for the real-time analysis of soil nitrates is sound. The rapid response and low sample volumes required by the multi-sensor ISFET/FIA system make it a viable candidate for use in real-time soil nutrient sensing.

Soil property sensing for site-specific crop management

Abstract Site-specific crop management (SSCM) aims to improve production efficiency by adjusting crop inputs, especially fertilizers and agro-chemicals, to varying local conditions within a field. Sensors are needed to obtain site-specific data on factors affecting crop growth and yields, such as nutrient status, weed pressure, soil moisture status, landscape position, soil organic matter (SOM) content, soil acidity, and depth to a restrictive layer. Two SOM sensors have been licensed for commercial development: (1) a singlewavelength sensor that must be recalibrated for the soils and moisture conditions that prevail at the time of use, and (2) a multiple-wavelength sensor which can utilize a single calibration to predict SOM over a range of soil moistures and a range of soil types that occur within a geographical range of several hundreds of kilometers. The single-wavelength sensor requires operator acceptance of the need for frequent recalibration, but is relatively inexpensive and rugged. The multiple-wavelength sensor uses a single calibration applicable over a broader range of soil types and soil moistures, and can also be used to sense soil moisture and cation exchange capacity (CEC), but uses complex technology. A simple inexpensive sensor that can classify soils according to soil moisture has also been developed. Sensors for other soil parameters are being sought, and progress has been reported on nutrient and depth-to-claypan sensing.

SIMULTANEOUS SOIL MOISTURE AND CONE INDEX MEASUREMENT

Soil compaction can restrict root growth and water infiltration, resulting in yield reduction. Maps of yield monitor data aid in visualization of variations in yield, without identifying underlying factors for these variations. Soil penetration resistance can help identify areas where soil physical characteristics are negatively impacting yield. However, penetration resistance is a function of soil moisture content and soil type as well as compaction. A standard penetrometer cone was modified to collect near-infrared reflectance and estimate moisture content. The instrument was tested in the laboratory on a selection of soil types with varying moisture tension levels using stepwise and continuous probe insertions. Soil moisture, dry bulk density, and clay content were significant variables in predicting soil cone index at the lower moisture tension level.

DESIGN AND VALIDATION OF AN ON-THE-GO SOIL STRENGTH PROFILE SENSOR

Soil strength has traditionally been determined using the cone penetrometer, an instrument that provides highly variable discrete point measurements, making it difficult to detect statistically significant differences in the soil strength profile among treatments or locations. Generally, this problem has been addressed by obtaining a large number of measurements, a process that is time-consuming and labor-intensive. Our objective was to develop a soil strength profile sensor (SSPS) that could take measurements continuously and more efficiently while traveling across the field. The on-the-go SSPS was designed and fabricated using an array of load cells, each of which was interfaced with a soil-cutting tip. These multiple prismatic tips were extended forward from the leading edge of a vertical blade and spaced apart to minimize interference from the main blade and adjacent sensing tips. Prismatic soil strength index (PSSI, MPa) was defined as the force divided by the base area of the sensing tip. The sensing tip had a 60° cutting or apex angle and a base area of 361 mm2. The design maximum operating depth was 0.5 m, and the upper limit and resolution of soil strength were 19.4 and 0.14 MPa, respectively. Field tests determined that the optimum extension and spacing of the cutting tips were 5.1 and 10 cm, respectively. A significant (. = 0.01) linear relationship between PSSI and penetrometer cone index (CI), with a slope of approximately 0.6, was found for field data collected at a 30 cm depth. The ability to develop such relationships comparing penetrometer and SSPS data will allow SSPS data to be interpreted with respect to the available body of penetrometer literature.

EVALUATION OF NITRATE AND POTASSIUM ION-SELECTIVE MEMBRANES FOR SOIL MACRONUTRIENT SENSING

On-the-go, real-time soil nutrient analysis would be useful in site-specific management of soil fertility. The rapid response and low sample volume associated with ion-selective field-effect transistors (ISFETs) make them good soil fertility sensor candidates. Ion-selective microelectrode technology requires an ion-selective membrane that responds selectively to one analyte in the presence of other ions in a solution. This article describes: (1) the evaluation of nitrate and potassium ion-selective membranes, and (2) the investigation of the interaction between the ion-selective membranes and soil extractants to identify membranes and extracting solutions that are compatible for use with a real-time ISFET sensor to measure nitrate and potassium ions in soil. The responses of the nitrate membranes with tetradodecylammonium nitrate (TDDA) or methlytridodecylammonium chloride (MTDA) and potassium membranes with valinomycin were affected by both membrane type and soil extractant. A TDDA-based nitrate membrane would be capable of detecting low concentrations in soils to about 10 −5 mole/L NO3 − . The valinomycin-based potassium membranes showed satisfactory selectivity performance in measuring potassium in the presence of interfering cations such as Na + , Mg 2+ , Ca 2+ , Al 3+ , and Li + as well as provided a consistent sensitivity when DI water, Kelowna, or Bray P1 solutions were used as base solutions. The TDDA-based nitrate membrane and the valinomycin-based potassium membrane, used in conjunction with Kelowna extractant, would allow determination of nitrate and potassium levels, respectively, for site-specific control of fertilizer application.

Close-Range Sensing of Soil Organic Matter

ABSTRACT IN the past few years the use of herbicides in crop pro-duction has increased tremendously. Using herbicides at the recommended rates is very important. Applying too much herbicide may damage crops or cause excessive residues, and it is very costly. Using too little herbicide can result in poor weed control. Researchers have found that the amount of certain soil-applied herbicides needed depends upon the organic matter content of the soil. Table 1 shows that as the percentage of organic matter increases the recommended herbicide rate also increases. Alexander (1969) reported that soil color can be used to estimate organic matter content well enough to determine suitable herbicide ap-plication rates. In fields with varying organic matter content (such fields are quite common), that content must be deter-mined at several locations in the field and the herbicide application rate must be adjusted accordingly as the sprayer traverses the field. Hence an application system that would automatically react to changes in the soil organic matter content would be useful. Kunz (1970), Philippi (1976), and Bocksnick (1977) investigated techniques for sensing soil color by the use of light reflec-tance. Their results showed promise for application to a practical sensor of soil organic-matter content that would be automated and which would be a key component of an automatic herbicide application system. In this study a method based on direct spectral absorp-tion measurements was used. The method is unique in that it provides an electronic measurement based on a fundamental soil property that correlates with organic matter content. Using this method we analyzed the requirements for the determination of organic-matter content and developed instrumentation to make the necessary measurements.

RAPID NITRATE ANALYSIS OF SOIL CORES USING ISFETS

An intact core extraction procedure was tested that might be used in the field for real–time prediction of soil nitrates. An extraction solution was pushed through a soil core held between two filters, and an ion–selective field–effect transistor/flow injection analysis (ISFET/FIA) system was used to sense soil nitrates in real time. Laboratory tests were conducted using four soil types and two levels of nitrate concentration, soil moisture, core density, core length, core diameter, and extraction solution flow rate. The extraction solution flow was sampled at the exit face of the core and routed to the ISFET/FIA system. The ISFET output voltage was sampled at 100 Hz. Results of the test indicate that nitrate extraction of the soil cores was successful, and that data descriptors based on response curve peak and slope of the ISFET nitrate response curve might be used in tandem in a real–time prediction system.