Cool-season turfgrass color and growth calibrated to leaf nitrogen

Tissue N analysis a tool available for N management of turfgrass. However, peer-reviewed calibration studies to determine optimum tissue N values are lacking. A field experiment with a mixed cool-season species lawn and a greenhouse experiment with Kentucky bluegrass (Poa pratensis L.) were conducted across 2 yr, each with randomized complete block design. Treatments were N application rates between 0 and 587 kg N ha-1 yr-1. In the field experiment, clipping samples were taken monthly from May to September, dried, ground, and analyzed for total N. Clippings samples were collected one to two mowings after plots were fertilized. Linear plateau models comparing relative clipping yield, Commission Internationale de l' Eclairage hue, and CM1000 index to leaf N concentrations were developed. In the greenhouse experiment, clipping samples were taken every 2 wk from May to October and composited across sample dates for leaf N analysis. Color and clipping yields were related to leaf N concentrations using linear plateau models. These models indicated small marginal improvements in growth or color when leaf N exceeded 30 g kg-1, suggesting that a leaf N test can separate turf with optimum leaf N concentrations from turf with below optimum leaf N concentrations. Plateaus in leaf N concentrations with increasing N fertilizer rates suggest, however, that this test may be unable to identify sites with excess available soil N when turf has been mowed before tissue sampling.

Fall Fertilization Timing Effects on Nitrate Leaching and Turfgrass Color and Growth

Fall season fertilization is a widely recommended practice for turfgrass. Fertilizer applied in the fall, however, may be subject to substantial leaching losses. A field study was conducted in Connecticut to determine the timing effects of fall fertilization on nitrate N (NO3-N) leaching, turf color, shoot density, and root mass of a 90% Kentucky bluegrass (Poa pratensis L.), 10% creeping red fescue (Festuca rubra L.) lawn. Treatments consisted of the date of fall fertilization: 15 September, 15 October, 15 November, 15 December, or control which received no fall fertilizer. Percolate water was collected weekly with soil monolith lysimeters. Mean log10 NO3-N concentrations in percolate were higher for fall fertilized treatments than for the control. Mean NO3-N mass collected in percolate water was linearly related to the date of fertilizer application, with higher NO3-N loss for later application dates. Applying fall fertilizer improved turf color and density but there were no differences in color or density among applications made between 15 October and 15 December. These findings suggest that the current recommendation of applying N in mid- to late November in southern New England may not be compatible with water quality goals.

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.

Institutional Infrastructure to Support 'Super Growth' in Kenya: Governance Thresholds, Reversion Rates and Economic Development

Kenya Growth Vision 2030 proposes policy and institutional reforms that make it possible for the country to achieve development status of a middle income country by 2030. This paper outlines the institutional framework necessary to achieve ÈSuper Growth,É which describes the character of growth required to meet targets stipulated in the Vision. The paper provides evidence confirming the importance of improving the quality of governance to the achievement of the Vision. The paper also demonstrates that the country is characterized by a high probability of reverting to poor governance. It is argued that, to achieve super growth, the country must attain an institutional tipping point which associates with low reversion rates to weaker institutions. The paper provides suggestions for institutional reforms that result in the achievement of an institutional tipping point and super growth.