D. Brenton Myers

Interaction effects of climate and land use/land cover change on soil organic carbon sequestration

Historically, Florida soils stored the largest amount of soil organic carbon (SOC) among the conterminous U.S. states (2.26 Pg). This region experienced rapid land use/land cover (LULC) shifts and climate change in the past decades. The effects of these changes on SOC sequestration are unknown. The objectives of this study were to 1) investigate the change in SOC stocks in Florida to determine if soils have acted as a net sink or net source for carbon (C) over the past four decades and 2) identify the concomitant effects of LULC, LULC change, and climate on the SOC change. A total of 1080 sites were sampled in the topsoil (0-20 cm) between 2008 and 2009 representing the current SOC stocks, 194 of which were selected to collocate with historical sites (n = 1251) from the Florida Soil Characterization Database (1965-1996) for direct comparison. Results show that SOC stocks significantly differed among LULC classes--sugarcane and wetland contained the highest SOC, followed by improved pasture, urban, mesic upland forest, rangeland, and pineland while crop, citrus and xeric upland forest remained the lowest. The surface 20 cm soils acted as a net sink for C with the median SOC significantly increasing from 2.69 to 3.40 kg m(-2) over the past decades. The SOC sequestration rate was LULC dependent and controlled by climate factors interacting with LULC. Higher temperature tended to accelerate SOC accumulation, while higher precipitation reduced the SOC sequestration rate. Land use/land cover change observed over the past four decades also favored the C sequestration in soils due to the increase in the C-rich wetland area by ~140% and decrease in the C-poor agricultural area by ~20%. Soils are likely to provide a substantial soil C sink considering the climate and LULC projections for this region.

Mapping Depth to Argillic Soil Horizons Using Apparent Electrical Conductivity

Maps of apparent electrical conductivity (ECa) of the soil profile are widely used in precision agriculture. A number of ECa sensors are commercially available, each with a unique response function (i.e., the relative contribution of soil at each depth to the integrated ECa reading). Our past research estimated depth to an argillic horizon (i.e., topsoil depth, TD) on claypan soils by fitting empirical equations to ECa sensor data. The objective of this research was to determine if TD estimates could be improved by combining data from multiple ECa 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 ECa depthresponse functions (Veris 3150 * , 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 ECa variables individually and in combination. Similarly, linear regression was applied to single and multiple variables. Root mean square error of validation (RMSEv) of single-variable TD estimates was 22 to 25 cm, with better results for those variables with moderately deep ECa response functions. Results from the model-based approach were very similar to those obtained by regressing TD on ECa 21 . The best calibrations using multiple variables in model inversion or regression were somewhat better than those using single variables, with RMSEv of 22 cm and 20 cm, 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.

Influence of the spatial extent and resolution of input data on soil carbon models in Florida, USA

(R 2 from 0.10 in the cattle station to 0.61 in Florida) and with a decrease in the resolution of input data (R 2 from 0.33 at 1920-m resolution to 0.61 at 30-m resolution in Florida). Soil and hydrologic variables were the most important across the seven resolutions both in Florida and in the watershed. The spatial extent and resolution of environmental covariates modulate soil C variation and soil-landscape correlations influencing soil C predictive models. Our results provide scale boundaries to observe environmental data and assess soil C spatial patterns, supporting C sequestration, budgeting and monitoring programs.

Yield Editor 2.0: Software for Automated Removal of Yield Map Errors

Yield maps provide important information for developing and evaluating precision management strategies. The high-quality yield maps needed for decision-making require screening raw yield monitor datasets for errors and removing them before maps are made. To facilitate this process, we developed the Yield Editor interactive software which has been widely used by producers, consultants and researchers. Some of the most difficult and time consuming issues involved in cleaning yield maps include determination of combine delay times, and the removal of “overlapped” data, especially near end rows. Our new Yield Editor 2.0 automates these and other tasks, significantly increasing the reliability and reducing the difficulty of creating accurate yield maps. This paper describes this new software, with emphasis on the Automated Yield Cleaning Expert (AYCE) module. Application of Yield Editor 2.0 is illustrated through comparison of automated AYCE cleaning to the interactive approach available in Yield Editor 1.x. On a test set of fifty grain yield maps, AYCE cleaning was not significantly different than interactive cleaning by an expert user when examining field mean yield, yield standard deviation, and number of yield observations remaining after cleaning. Yield Editor 2.0 provides greatly improved efficiency and equivalent accuracy compared to the interactive methods available in Yield Editor 1.x.

Factors Affecting Soil Phosphorus and Potassium Estimation by Reflectance Spectroscopy

Abstract. Visible and near infrared (VNIR) diffuse reflectance spectroscopy has potential in site-specific measurement of soil properties. However, previous studies have reported VNIR estimates of plant available soil phosphorus (P) and potassium (K) to be of variable accuracy. In this study, we used a database of over 1500 soil samples to investigate what factors influenced P and K estimation accuracy. Specifically, the effect of classifying soil samples by major land resource areas (MLRAs), cation exchange capacity (CEC) or organic matter (OM) was investigated. Additionally, calibrations using only those samples within the approximate range of interest for fertilizer application to field crops – P from 0 to 27 mg kg -1 and K from 0 to 192 mg kg -1 – were compared to calibrations using the full range of soil samples. Pretreatments of log 10 (1/reflectance) plus mean normalization plus median filter smoothing with or without direct orthogonal signal correction (DOSC) were investigated. Results from partial least squares regression (PLSR), principal component regression (PCR) and support vector regression (SVR) were compared. Reasonable estimates of P and K were obtained for soil samples from two Missouri MLRAs (109 and 115B) out of the eight analyzed. Model estimates were poor when soil samples were grouped by CEC or OM; however, there was some indication that VNIR estimation of P and K might be possible for soils low in OM. Accuracy was maintained when analyzing a reduced wavelength range from 1100 to 2450 nm, suggesting this narrower sensing range might be used for on-the-go sensors. PLSR provided better accuracy than PCR or SVR for both P and K. The DOSC pretreatment significantly improved P and K estimation accuracy. The results of this research provided some insight into the factors affecting the accuracy of P and K estimation by VNIR models, but additional research is needed to determine if these findings can lead to P and K estimations sufficiently accurate to guide variable-rate fertilization.

New Methods for Automatic Delay Time Compensation in Grain Yield Maps

Crop yield data is a key component of precision agriculture, critical for both development and evaluation of precision management strategies. Ideally, software that generates grain yield maps from raw yield monitor data should automatically correct common errors associated with machine and operating characteristics. Perhaps the most basic correction required is to properly compensate for the time lag (or position lag) between the cutting of the crop from the field and the grain flow measurement by the flow sensor in the combine. Past research has suggested several approaches to automatically determine delay time but for various reasons these have not been implemented in mapping software. In this paper we present a new, computationally efficient method that can accurately determine delay time for individual fields using the image processing method of phase correlation. This method was evaluated using a number of yield maps with varying degrees of harvest complexity and results were compared to a geostatistical method. The new method performed satisfactorily on larger datasets with a significant amount of spatial variability. Additionally, this method was more computationally efficient than previous methods. Results of this study will increase the feasibility of including automatic delay time compensation in yield mapping software.