Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: Ward S. Upham x
  • All content x
Clear All Modify Search
Free access

Jack D. Fry and Ward S. Upham

In 1992 and 1993, 12 postemergence herbicide treatments were applied to field-grown buffalograss [Buchloe dactyloides (Nutt.) Engelm.] seedlings having 1 to 3 leaves and 2 to 4 tillers, respectively. The only herbicide treatments that did not cause plant injury at 1 or 2 weeks after treatment (WAT) or reduce turf coverage 4 or 6 WAT compared to nontreated plots (in 1992 or 1993) were (in kg·ha–1) 0.6 dithiopyr, 0.8 quinclorac, 2.2 MSMA, and 0.8 clorpyralid. Evaluated only in 1993, metsulfuron methyl (0.04 kg·ha–1) also caused no plant injury or reduction in coverage. Fenoxaprop-ethyl (0.2 kg·ha–1) caused severe plant injury and reduced coverage by >95% at 6 WAT. Dicamba reduced coverage by 11% at 6 WAT in 1992 but not 1993. The chemicals (in kg·ha–1) triclopyr (0.6), 2,4-D (0.8), triclopyr (1.1) + 2,4-D (2.8), 2,4-D (3.1) + triclopyr (0.3) + clorpyralid (0.2), and 2,4-D (2.0) + mecoprop (1.1) + dicamba (0.2) caused plant injury at 1 or 2 WAT in 1992 or 1993, but coverage was similar to that of nontreated turf by 6 WAT. Chemical names used: 3,6-dichloro-2-pyridinecarboxylic acid (clorpyralid); 3,6-dichloro-o-anisic acid (dicamba); (+/–)-2-[4-(2,4-dichlorophenoxy)phenoxy]propanoic acid (diclofop); 3,5-pyridinedicarbothioic acid, 2-(difluoromethyl)-4-(2-methylpropyl)-6-(trifluoromethyl)-S,S-dimethyl ester (dithiopyr); 2-[4-[(6-chloro-2-benzoxazolyl)oxy]phenoxy] propanoate (fenoxaprop-ethyl); 2-(2,4-dichlorophenoxy)propionic acid (mecoprop); methyl 2-[[[[(4-methoxy-6-methyl-1,3,5-triazin-2-yl)-amino]carbonyl]amino]sulfonyl]benzoate (metsulfuron methyl); monosodium salt of methylarsonic acid (MSMA); 3,7-dichloro-8-quinolinecarboxylic acid (quinclorac); [(3,5,6-trichloro-2-pyridinyl)oxy] acetic acid (triclopyr); (2,4-dichlorophenoxy) acetic acid (2,4-D).

Free access

Jack D. Fry, Steven C. Wiest, and Ward S. Upham

Evapotranspiration from tall fescue, perennial ryegrass and zoysiagrass turfs during the summers of 1992-3 was compared to evapotranspiration estimates from an evaporation pan, a black Bellani plate, and several empirical combination models, Actual measurement of turf water use was made with small weighing lysimeters. Soil was maintained at field capacity. Data were collected on 51 dates between June and September. Tall fescue was clipped weekly at 7.6 cm whereas ryegrass and zoysiagrass were clipped 3 times weekly at 2.5 cm, Although differences between the grass species existed, in general the rankings of estimate precision were Bellani plate > evaporation pan > empirical models when compared with measured evapotranspiration rates.

Free access

Steven C. Wiest, Jack D. Fry, and Ward S. Upham

A relatively accurate estimate of turfgrass evapotranspiration (ET) using environmental parameters readily obtainable from a local weather station would be of benefit to golf course superintendents, landscape managers, and homeowners. The Penman–Monteith model is clearly a poorer estimate than that obtained by Bellani plates or spheres. It has been suggested that, while the Penman–Monteith model is good in the drier climate of the southwestern United States, other models may be of greater practicable utility in climates such as are common in Kansas. Thus, other models have been evaluated for their suitability as turfgrass ET estimates in Kansas-like climates. Turfgrass ET was measured via lysimeters in 1992–94. Specifically, measurements were taken on three tall fescue varieties mowed at 6.35 or 7.62 cm, and zoysiagrass and perennial ryegrass mowed at 2.54 cm. Evaporation from black Bellani plates was measured simultaneously. These evaporation and ET rates were compared to those estimated by various empirical models whose data came from a weather station located within 31 m of the Bellani plates and lysimeters. Empirical models included temperature methods (e.g., FAO-24 Blaney–Criddle), radiation methods (e.g., Jensen–Haise, Hargreaves–Samani), combination equations (e.g., Priestly–Taylor, Penman), and variants. The best model(s) determined from these comparisons will likely become the method(s) of choice for estimating turfgrass ET in Kansas.