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Active-optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant's N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (Zea mays L.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALGMU) to improve in-season (~V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALGMU were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALGMU and EONR (MUDIFF) was 81 and 74 kg N ha-1 for treatments receiving 0 and 45 kg N ha-1 applied at planting, respectively. When ALGMU was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha-1. Without adjustment, 20 and 29% of sites were within 34 kg N ha-1 of EONR with 0 and 45 kg N ha-1 at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha-1 of EONR. These results show that weather and soil information could be used to improve ALGMU N recommendation performance. [ABSTRACT FROM AUTHOR]

Year-to-year dynamics in weather affect both the timing of application and the potential hydrologic transport of pesticides. Further, the most commonly used pesticides dissipate in the environment during the growing season. Interactions among these factors - hydrology, timing of application and dissipation kinetics - hinder the detection of temporal trends in transport. It is increasingly important to be able to discern such trends, to judge effectiveness of management practices or to determine whether observed changes were caused by management or weather. In previous work, a cumulative vulnerability index was developed to account for these three factors. It explained 63% of annual variation in atrazine load in the Goodwater Creek Experimental Watershed (GCEW). The objectives of the current work were (i) to generalize the cumulative vulnerability index to explicitly account for variation in watershed size, area treated with atrazine and average application rate; (ii) to test the overall performance on watersheds showing such variation; and (3) to test whether the generalized index properly accounted for the additional input parameters. The generalized index was tested using data from GCEW (73.7 km2) and seven additional watersheds in the northeast Missouri claypan region that varied in size from 212 to 1180 km2 and from 4% to 23% of watershed area planted to corn or sorghum. Across 32 site-years, the generalized index explained 84% of variation in annual atrazine load. Further, tests of residuals showed no dependence on either watershed area or fraction of area planted to corn and sorghum, indicating that these parameters were properly integrated into the index. The performance of the index supports the conclusion that data obtained from GCEW is representative of the Mark Twain Lake Basin and likely the entire Central Claypan Major Land Resource Area. Published 2012. This article is a U.S. Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

Lerch, R.N., E.J. Sadler, C. Baffaut, N.R. Kitchen, and K.A. Sudduth, 2010. Herbicide Transport in Goodwater Creek Experimental Watershed: II. Long-Term Research on Acetochlor, Alachlor, Metolachlor, and Metribuzin. Journal of the American Water Resources Association (JAWRA) 1-15. DOI: 10.1111/j.1752-1688.2010.00504.x Farmers in the Midwestern United States continue to be reliant on soil-applied herbicides for weed control in crop production, and herbicide contamination of streams remains an environmental problem. The main objective of this study was to analyze trends in concentration and load of acetochlor, alachlor, metolachlor, and metribuzin in Goodwater Creek Experimental Watershed (GCEW) from 1992 to 2006. A secondary objective was to document the effects of best management practices (BMPs) implemented within GCEW on herbicide transport trends. Median relative herbicide loads, as a percent of applied, were 3.7% for metolachlor, 1.3% for metribuzin, 0.36% for acetochlor, and 0.18% for alachlor. The major decrease in alachlor use and increase in acetochlor use caused shifts in flow-weighted concentrations that were observed over the entire concentration range. The smaller decrease in metolachlor use led to a consistent decreasing time trend only for the upper end of the concentration distribution. Metribuzin also showed moderate decreases in concentration with time since 1998. Annual loads were generally correlated to second quarter discharge. Despite extensive education efforts in the watershed, conservation BMPs within GCEW were mainly implemented to control erosion, and therefore had no discernable impact on reducing herbicide transport. Overall, changes in herbicide use and second quarter discharge had the greatest effect on trends in flow-weighted concentration and annual load. [ABSTRACT FROM AUTHOR]

Optical diffuse reflectance sensing is a potential approach for rapid and reliable on-site estimation of soil properties. One issue with this sensing approach is whether additional calibration is necessary when the sensor is applied under conditions (e.g., soil types or soil moisture conditions) different from those used to generate an initial calibration, and if so, how many sample points are required in this additional calibration. In this study, these issues were addressed using data from ten fields in five states in the U.S. Corn Belt. Partial least squares (PLS) regression was used to develop calibrations between soil properties and reflectance spectra. Model evaluation was based on the ratio of standard deviation to RMS error (RPD), a statistic commonly used in spectral analysis. When sample data from the field where calibrations were to be applied (i.e., test field) were included in the calibration stage (full information calibration), RPD values of prediction models were increased by an average of 0.55 (from 1.08 to 1.63) compared with results from models not including data from the test field (calibration without field-specific information). Including some samples from the test field (hybrid calibration) generally increased RPD to 90% of that from full information calibration (average increase = 0.49) by using data from 8 to 20 soil cores, with little further improvement given additional data. Using test field points as a bias adjustment (two-stage calibration) increased RPD by an average of 0.29 with two to six sample points, a finding that was confirmed by Monte Carlo simulation. These results show the importance of including in a calibration set samples similar (i.e., obtained from the same or similar fields) to those in the test set. These similar samples could be included directly in the calibration or could be used to implement a post-calibration bias adjustment. Although results were more accurate with the recalibration approach, the bias adjustment approach was more efficient computationally and required less data. Thus, either might be preferred depending on specific circumstances. [ABSTRACT FROM AUTHOR]

Plant water and nutrient use for claypan soils are restricted by an argillic horizon (clay content > 500 g kg-1) that typically occurs 20 to 40 cm (8 to 16 in) below the soil surface. Identifying water infiltration characteristics for claypan soils under different management provides crucial information needed to optimize crop management and estimate watershed hydrology. The objectives of the study were: 1) to evaluate the influence of long-term annual cropping system (ACS) and perennial cropping system management (PCS) [such as Conservation Reservation Program (CRP) and hay crop] on water infiltration, and 2) to examine relationships between apparent soil electrical conductivity (ECa) and other claypan soil properties with water infiltration parameters. The effects of the ACS and PCS management on water infiltration parameters were evaluated using ponded water infiltration measurements in the field. Water infiltration parameters were estimated using the Green-Ampt infiltration equation. Apparent profile soil electrical conductivity was obtained using an EM38 sensor. Analysis of variance and orthogonal contrasts were used to determine effects of management treatments on water infiltration parameters and associated soil properties. Soil organic carbon and aggregate stability were significantly (P < 0.05) improved after 12 years of CRP management compared to 12 years of ACS management. Antecedent soil water content and ECa were lower and water infiltration was greater for PCS than for the ACS. For a hay crop treatment (PCS), water infiltration was greatly improved when compared to any ACS or other PCS management treatments. Antecedent soil water content and soil organic carbon were significantly correlated with the water infiltration parameters. Soil ECa was significantly correlated with infiltration-estimated saturated hydraulic conductivity (Ks). This relationship may be useful for mapping spatially-variable water infiltration within fields. In summary, PCS contributed to improved water infiltration as well as to increased soil organic carbon and soil aggregate stability. Soil ECa may be useful for characterizing management influence on water infiltration without labor intensive sampling. [ABSTRACT FROM AUTHOR]