Relationship of Turfgrass Growth and Quality to Soil Nitrate Desorbed from Anion Exchange Membranes

Anion exchange membranes (AEMs) are a potential method for determining the plant available N status of soils; however, their capacity for use with turfgrass has not been researched extensively. The main objective of this experiment was to determine the relationship between soil nitrate desorbed from AEMs and growth response and quality of turfgrass managed as a residential lawn. Two field experiments were conducted with a bluegrass-ryegrass-fescue mixture receiving four rates of N fertilizer (0, 98, 196, and 392 kg N ha(-1) yr(-1)) with clippings returned or removed. The soils at the two sites were a Paxton fine sandy loam (coarse-loamy, mixed, active, mesic Oxyaquic Dystrudepts) and a variant of a Hinckley gravelly sandy loam (sandy-skeletal, mixed, mesic Typic Udorthents). Anion exchange membranes were inserted into plots and exchanged weekly during the growing seasons of 1998 and 1999. Nitrate-N was desorbed from AEMs and quantified. As N fertilization rates increased, desorbed NO3-N increased. The relationship of desorbed NO3-N from AEMs to clipping yield and turfgrass quality was characterized using quadratic response plateau (QRP) and Cate-Nelson models (C-Ns). Critical levels of desorbed NO3-N ranged from 0.86 to 8.0 microgram cm(-2) d(-1) for relative dry matter yield (DMY) and from 2.3 to 12 microgram cm(-2) d(-1) for turfgrass quality depending upon experimental treatment. Anion exchange membranes show promise of indicating the critical levels of soil NO3-N desorbed from AEMs necessary to achieve maximum turfgrass quality and yield without overapplication of N.

Extractable Soil Phosphorus Concentrations and Creeping Bentgrass Response on Sand Greens

Few studies have directly related turfgrass growth and quality responses to extractable soil P concentrations in sand greens. A 3-yr field experiment was conducted on a sand-based putting green to determine creeping bentgrass (Agrostis stolonifera L.) growth and quality responses to extractable soil P. Extractable soil P concentrations were obtained by using the modified-Morgan, Mehlich-1, and Bray-1 extractants. Critical extractable P concentrations (above which there is a low probability of response to increasing soil P concentrations) for shoot counts, thatch thickness, relative clipping yields, quality ratings, P deficiency ratings, tissue P concentrations, and root weights were determined using Cate-Nelson (CN) and quadratic response and plateau (QRP) models. Both models fit the data relatively well in most cases (R2 values from 0.12 to 0.89), and critical concentrations for the QRP models were always greater than the CN models. Critical extractable P concentrations were lowest for the modified-Morgan extractant (1.4 to 12.0 mg kg(-1)) and greatest for the Mehlich-1 extractant (14.1 to 63.6 mg kg(-1)). Application of estimated critical extractable P concentrations in this study could be used to substantiate observed responses or explain lack of responses in other previously reported creeping bentgrass P studies. We found better model fits with modified-Morgan extractable P for bentgrass quality ratings, deficiency ratings, and tissue P concentrations than with P extracted by the Mehlich or Bray methods. This suggests that the modified-Morgan extractant may have advantages over stronger-acid extractants when used on sand-based media. The results can be used to revise or update existing P fertilization recommendations for bent-grass grown on sand-based media.

Extracting more out of relocation data: building movement models as mixtures of random walks

We present a framework for fitting multiple random walks to animal movement paths consisting of ordered sets of step lengths and turning angles. Each step and turn is assigned to one of a number of random walks, each characteristic of a different behavioral state. Behavioral state assignments may be inferred purely from movement data or may include the habitat type in which the animals are located. Switching between different behavioral states may be modeled explicitly using a state transition matrix estimated directly from data, or switching probabilities may take into account the proximity of animals to landscape features. Model fitting is undertaken within a Bayesian framework using the WinBUGS software. These methods allow for identification of different movement states using several properties of observed paths and lead naturally to the formulation of movement models. Analysis of relocation data from elk released in east-central Ontario, Canada, suggests a biphasic movement behavior: elk are either in an "encamped" state in which step lengths are small and turning angles are high, or in an "exploratory" state, in which daily step lengths are several kilometers and turning angles are small. Animals encamp in open habitat (agricultural fields and opened forest), but the exploratory state is not associated with any particular habitat type.

Anion exchange membrane soil nitrate predicts turfgrass color and yield.

Desirable nitrogen (N) management practices for turfgrass supply sufficient N for high quality turf while limiting excess soil N. Previous studies suggested the potential of anion exchange membranes (AEMs) for predicting turfgrass color, quality, or yield. However, these studies suggested a wide range of critical soil nitrate-nitrogen (NO3-N) values across sample dates. A field experiment, in randomized complete block design with treatments consisting of nine N application rates, was conducted on a mixed species cool-season turfgrass lawn across two growing seasons. Every 2 wk from May to October, turfgrass color was assessed with three different reflectance meters, and soil NO3-N was measured with in situ AEMs. Cate-Nelson models were developed comparing relative reflectance value and yield to AEM desorbed soil NO3-N pooled across all sample dates. These models predicted critical AEM soil NO3-N values from 0. 45 to 1.4 micro g cm-2 d-1. Turf had a low probability of further positive response to AEM soil NO3-N greater than these critical values. These results suggest that soil NO3-N critical values from AEMs may be applicable across sample dates and years and may serve to guide N fertilization to limit excess soil NO3-N.