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Salina, KS, United States

Kweon G.,Veris Technologies Inc
Agronomy Journal | Year: 2012

Many studies have found the range of spatial dependence is shorter than the distances used in most grid sampling, and government soil surveys have limited utility in many precision applications. A new multi-sensor platform was developed that records apparent soil electrical conductivity (ECa), optical reflectance readings with red and near-infrared light-emitting diodes and pH along with topography data. The objective of this study was to evaluate its performance for estimation of soil cation exchange capacity (CEC), organic matter (OM), and pH on eight fields in four states, comparing results with lab-analyzed samples, USDA soil surveys, and 1-ha grid maps. Proximal soil sensor measurements correlated well with lab-analyzed soil samples. The OM calibrations using ECa, optical, and/or topographic data showed good performance with R2 of 0.8 or higher and ratio of prediction to deviation (RPD) of 2.5 or greater in all fields. In CEC calibrations, five of six fields had higher than 0.86 for R2 and greater than 2.8 for RPD. The pH calibration results showed RPD of 2.1 or greater and R2 of 0.76 or higher in seven fields. The sensor maps showed small-scale variability not detected at conventional grid sample scales or with USDA soil surveys. Using the proximal soil sensors, the average root mean square error of prediction for OM was 2.78 g kg-1, CEC 1.20 cmolc kg-1, and pH 0.26 for the project fields. These values are significantly lower than the soil property ranges found in the soil surveys. This is a promising development for improving farm practices and management. © 2012 by the American Society of Agronomy. Source


Kweon G.,Gyeongsang National University | Maxton C.,Veris Technologies Inc
Biosystems Engineering | Year: 2013

This research was conducted to develop an inexpensive on-the-go optical sensor for soil organic matter (OM) sensing. Diffuse reflectance for 86 soil samples from Kansas and Illinois was measured by a spectrometer in a laboratory. Stepwise multiple linear regression (SMLR) and B-matrix in partial least squares (PLS) were used to determine important wavelengths for soil OM measurement. The wavelengths of 660 and 940nm, identified by both SMLR and PLS, were used for an optical sensor. The developed optical sensor with dual wavelength was evaluated with dry and wet soils in the lab and the relationship between reflectance and OM showed a coefficient of determination (R2) as high as 0.91. Gaps between soil and the sensor window reduced the ability to estimate soil organic matter, thus the sensor window should press firmly against soil. In field tests, all fields gave good results, with RPD (ratio of prediction to deviation=standard deviation/root mean square error) of 2 or greater for OM estimation. In comparison with a NIR spectrophotometer shank unit, the optical sensor showed similar results for OM mapping pattern with coefficient of determination as 0.86. The level of agreement between the two maps was 0.56 for overall accuracy and 0.34 for kappa coefficient. Further field tests need to be implemented to evaluate the soil organic matter estimation with the sensor over different types of soils in a wider set of locations. © 2013 IAgrE. Source


A delineation procedure for site-specific productivity zones was developed with a fuzzy logic system using soil properties obtained from on-the-go electrical conductivity (EC) and organic matter (OM) sensors and topographic attributes. EC, OM, slope and curvature were used as input variables, and productivity was set as an output variable. The fuzzy rules were developed with grower's knowledge for typical central Kansas upland fields; areas within the field having high OM, low EC and low slope have the highest productivity potential, and areas within the field with low OM, high EC and high slope have the lowest productivity potential. The fuzzy logic system performed properly and generated productivity as designed by the fuzzy logic and inference scheme. To validate the system, an adjacent field with 5 years of wheat yield data was selected. The spatial agreement between productivity and yield showed as high as 0.57 and 0.35 for overall accuracy and kappa coefficient. The level of agreement is promising, considering there were many other yield-limiting factors such as precipitation, temperature and management effects. From comparison of the productivity map with the map generated by a fuzzy c-means clustering algorithm (FCM map), agreement between the productivity and yield exhibited generally higher in overall accuracy and Kappa coefficient than the agreement between FCM map and yield. Results of this study can benefit producers and consultants who utilise site-specific management by delineating productivity zones using EC, OM, slope and curvature from the on-the-go sensors. © 2012 IAgrE. Source


Grant
Agency: Department of Agriculture | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 79.68K | Year: 2008

Fertilizer application rates that do not match crop usage pose an economic loss for farmers, and excess applications can result in environmental degradation of water and atmosphere. A significant portion of the nitrogen applied to U.S. fields is not needed, due to the availability of nitrogen from the soil. Soil nutrients, especially nitrogen, vary spatially and temporally, within the field and soil profile. In order to deal with the significant variability challenges, a large number of soil measurements must be taken on each field. Using conventional lab analysis, this is not cost-effective. In-field measurements represent an appealing alternative, yet these must be accurate and affordable. This project will develop the field-deployable test equipment required to evaluate several sensor technologies in a side-by-side comparison. Soil sensing technology used for this application will be electrical, optical, and electro-chemical. The sensors to be used in this project represent the most viable candidates from those categories, and have shown initial feasibility to meet the criteria. However, most have not been widely tested under in-field conditions, nor has a thorough side-by-side comparison been conducted. In order to perform this feasibility comparison test, equipment will be devised to collect and process the soil cores, and bring them into contact with the sensors. Results from in-field sensors will be compared with laboratory-analyzed soil tests.


Grant
Agency: Department of Agriculture | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 89.69K | Year: 2010

Crop growers in the United States, and in developed countries worldwide, apply large amounts of phosphate fertilizers to their fields. In the US for example, growers annually apply over 4,000,000 tons of phosphate fertilizer. Fertilizer rates are typically applied in excess of the crop need, since the cost of fertilizer is low relative to the loss in crop yield if a given nutrient is yield-limiting. Over-application of fertilizers can be harmful in some situations. Phosphorus increases may cause algae blooms in fresh-water ecosystems. An excess of P in the soil also can increase the risk of P runoff and leaching. Soils that are P-deficient, due to poor management or inaccurate sampling and application methods, can experience yield reductions. The economic and environmental concerns of both extremes demonstrate the importance of keeping phosphorus levels within a proper range. Phosphorus levels within a field often range from soils that are P-deficient to those are significantly above a sufficiency threshold. In order to deal with spatial variability, a large number of soil measurements must be taken on each field. Using conventional lab analyses, sampling at the scale needed for accurate mapping of phosphorus levels is not feasible. In-field measurements represent an appealing alternative. This project will develop the test equipment required to evaluate sensor technologies in a side-by-side comparison. The sensors to be used in this project have shown initial feasibility to meet the criteria. However, they have not been widely tested using unprocessed soil cores, nor has a thorough side-by-side comparison been conducted. In order to perform this feasibility comparison test, equipment will be devised to collect and process the soil cores, and bring them into contact with the sensors. Results from in-field sensors will be compared with laboratory-analyzed soil tests. The results of this Phase I project will establish a clear direction for the development of an in-field sensing system during a Phase II.

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