Spatial variation of soil phosphorus within a drainage ditch network

Agricultural drainage ditches serve as P transport pathways from fields to surface waters. Little is known about the spatial variation of P at the soil-water interface within ditch networks. We quantified the spatial variation of surficial (0-5 cm) soil P within vegetated agricultural ditches on a farm in Princess Anne, MD with an approximately 30-yr history of poultry litter application. Ditch soils from 10 ditches were sampled at 10-m intervals and analyzed for acid ammonium oxalate-extractable P, Fe, Al (P(ox), Fe(ox), Al(ox)), and pH. These variables were spatially autocorrelated. Oxalate-P (min = 135 mg kg(-1), max = 6919 mg kg(-1), mean = 700 mg kg(-1)) exhibited a high standard deviation across the study area (overall 580 mg kg(-1)) and within individual ditches (maximum 1383 mg kg(-1)). Several ditches contained distinct areas of high P(ox), which were associated with either point- or nonpoint-P sources. Phosphorus was correlated with Al(ox) or Fe(ox) within specific ditches. Across all ditches, Al(ox) (r = 0.80; p < 0.001) was better correlated with P(ox) than was Fe(ox) (r = 0.44; p < 0.001). The high level of spatial variation of soil P observed in this ditch network suggests that spatially distributed sampling may be necessary to target best management practices and to model P transport and fate in ditch networks.     

A graphical screening method for assessing stream water quality using specific conductivity and alkalinity data

In areas of varying geology, it is difficult to infer water quality from specific conductance or electrical conductivity (EC) data without an understanding of the expected range of EC values based on local bedrock composition. This paper describes a user-friendly graphical screening method that addresses this issue by plotting the EC against concurrent alkalinity data, which correlates well with the presence of carbonate bedrock under natural conditions, and thus serves as an index of bedrock type. The upper limit of EC vs. alkalinity expected in a stream is determined using regional groundwater quality data, based on the assumption that stream chemistry reflects groundwater under baseflow conditions. Stream samples with EC/alkalinity values that consistently plot above this limit are considered impacted by anthropogenic sources. The effect of dilution and runoff on the EC vs. alkalinity plot of stream samples is considered using a simple baseflow/storm runoff-mixing model. The graphical method's utility as a screening tool is demonstrated by application to stream chemistry data from watersheds of southeastern Pennsylvania and northwestern New Jersey in several distinct geologic settings; however the method is general and widely applicable to watersheds in humid temperate regions. Its use is intended for watershed stewards of both professional and nonprofessional qualification.     

Using GIS to Generate Spatially Balanced Random Survey Designs for Natural Resource Applications

Sampling of a population is frequently required to understand trends and patterns in natural resource management because financial and time constraints preclude a complete census. A rigorous probability-based survey design specifies where to sample so that inferences from the sample apply to the entire population. Probability survey designs should be used in natural resource and environmental management situations because they provide the mathematical foundation for statistical inference. Development of long-term monitoring designs demand survey designs that achieve statistical rigor and are efficient but remain flexible to inevitable logistical or practical constraints during field data collection. Here we describe an approach to probability-based survey design, called the Reversed Randomized Quadrant-Recursive Raster, based on the concept of spatially balanced sampling and implemented in a geographic information system. This provides environmental managers a practical tool to generate flexible and efficient survey designs for natural resource applications. Factors commonly used to modify sampling intensity, such as categories, gradients, or accessibility, can be readily incorporated into the spatially balanced sample design.