Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe.

Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343⁻2222 nm), apparent electrical conductivity (ECa), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, ECa, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties.

Long-term agroecosystem research in the central Mississippi river basin: introduction, establishment, and overview.

Many challenges currently facing agriculture require long-term data on landscape-scale hydrologic responses to weather, such as from the Goodwater Creek Experimental Watershed (GCEW), located in northeastern Missouri, USA. This watershed is prone to surface runoff despite shallow slopes, as a result of a significant smectitic clay layer 30 to 50 cm deep that restricts downward flow of water and gives rise to a periodic perched water table. This paper is the first in a series that documents the database developed from GCEW. The objectives of this paper are to (i) establish the context of long-term data and the federal infrastructure that provides it, (ii) describe the GCEW/ Central Mississippi River Basin (CMRB) establishment and the geophysical and anthropogenic context, (iii) summarize in brief the collected research results published using data from within GCEW, (iv) describe the series of papers this work introduces, and (v) identify knowledge gaps and research needs. The rationale for the collection derives from converging trends in data from long-term research, integration of multiple disciplines, and increasing public awareness of increasingly larger problems. The outcome of those trends includes being selected as the CMRB site in the USDA-ARS Long-Term Agro-Ecosystem Research (LTAR) network. Research needs include quantifying watershed scale fluxes of N, P, K, sediment, and energy, accounting for fluxes involving forest, livestock, and anthropogenic sources, scaling from near-term point-scale results to increasingly long and broad scales, and considering whole-system interactions. This special section informs the scientific community about this database and provides support for its future use in research to solve natural resource problems important to US agricultural, environmental, and science policy.

Long-term agroecosystem research in the central Mississippi river basin: goodwater creek experimental watershed weather data.

Knowledge of weather, particularly precipitation, is fundamental to interpreting watershed and hydrologic processes. The long-term weather record in the Goodwater Creek Experimental Watershed (GCEW) complements hydrologic and water quality data in the region. The GCEW also is the core of the Central Mississippi River Basin (CMRB) node of the Long-Term Agroecosystem Research network. Our objectives are to (i) describe the climatological context of the GCEW and CMRB settings, (ii) document instrumentation and the data collection, quality assurance, and reduction processes; (iii) provide examples of the data obtained and descriptive statistics; and (iv) document the availability of and access methods to obtain the data from the web-based data access portal at . These objectives support an overall goal to make these long-term data available to the public for use in further analyses and modeling in support of research and public policy on watershed management.

Long-term agroecosystem research in the central Mississippi river basin: hyperspectral remote sensing of reservoir water quality.

In situ methods for estimating water quality parameters would facilitate efforts in spatial and temporal monitoring, and optical reflectance sensing has shown potential in this regard, particularly for chlorophyll, suspended sediment, and turbidity. The objective of this research was to develop and evaluate relationships between hyperspectral remote sensing and lake water quality parameters-chlorophyll, turbidity, and N and P species. Proximal hyperspectral water reflectance data were obtained on seven sampling dates for multiple arms of Mark Twain Lake, a large man-made reservoir in northeastern Missouri. Aerial hyperspectral data were also obtained on two dates. Water samples were collected and analyzed in the laboratory for chlorophyll, nutrients, and turbidity. Previously reported reflectance indices and full-spectrum (i.e., partial least squares regression) methods were used to develop relationships between spectral and water quality data. With the exception of dissolved NH, all measured water quality parameters were strongly related ( ≥ 0.7) to proximal reflectance across all measurement dates. Aerial hyperspectral sensing was somewhat less accurate than proximal sensing for the two measurement dates where both were obtained. Although full-spectrum calibrations were more accurate for chlorophyll and turbidity than results from previously reported models, those previous models performed better for an independent test set. Because extrapolation of estimation models to dates other than those used to calibrate the model greatly increased estimation error for some parameters, collection of calibration samples at each sensing date would be required for the most accurate remote sensing estimates of water quality.

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.

Economically optimal nitrogen rate reduces soil residual nitrate.

Post-harvest residual soil NO(3)-N (RSN) is susceptible to transfer to water resources. Practices that minimize RSN levels can reduce N loss to the environment. Our objectives were (i) to determine if the RSN after corn (Zea mays L.) harvest can be reduced if N fertilizer is applied at the economically optimal N rate (EONR) as compared to current producer practices in the midwestern USA and (ii) to compare RSN levels for N fertilizer rates below, at, and above the EONR. Six experiments were conducted in producer fields in three major soil areas (Mississippi Delta alluvial, deep loess, claypan) in Missouri over 2 yr. Predominant soil great groups were Albaqualfs, Argiudolls, Haplaquolls, and Fluvaquents. At four transects in each field, six treatment N rates from 0 to 280 kg N ha(-1) were applied, the EONR was determined, and the RSN was measured to a 0.9-m depth from five treatment plots. The EONR at sampling sites varied from 49 to 228 kg N ha(-1) depending on site and year. Estimated average RSN at the EONR was 33 kg N ha(-1) in the 0.9-m profile. This was at least 12 kg N ha(-1) lower than RSN at the producers' N rates. The RSN increased with increasing Delta EONR (total N applied - EONR). This relationship was best modeled by a plateau-linear function, with a low RSN plateau at N rates well below the EONR. A linear increase in RSN began anywhere from 65 kg N ha(-1) below the EONR to 20 kg N ha(-1) above the EONR at the three sites with good data resolution near the EONR. Applying N rates in excess of the EONR produced elevated RSN values in all six experiments. Our results suggest that applying the EONR will produce environmental benefits in an economically sound manner, and that continued attempts to develop methods for accurately predicting EONR are justified.