issues ( Cahn et al., 2014 ). A need exists for low-cost management strategies including use of irrigation scheduling technologies that incorporate information on meteorological conditions and crop growth stage. The CIMIS, operated by the California
Lee F. Johnson, Michael Cahn, Frank Martin, Forrest Melton, Sharon Benzen, Barry Farrara, and Kirk Post
Abby B. Griffin, Amy N. Wright, Kenneth M. Tilt, and D. Joseph Eakes
and Apr. 2009. Weed control was by hand-weeding around plants and by herbicide (glyphosate) between rows. Five irrigation scheduling treatments were assigned in a randomized complete block design within each of two plots with five blocks per taxa
Alberto Pardossi and Luca Incrocci
irrigation water while contributing to crop return by 39%. Irrigation scheduling is important in vegetable production. Under-irrigation generally results in yield loss and low produce quality. Conversely, overirrigation increases the crop's susceptibility to
Ioannis Tsirogiannis, Nikolaos Katsoulas, and Constantinos Kittas
Optimal irrigation scheduling could lead to higher water use efficiency, an objective of very high importance nowadays. Adequate supply of water and nutrients results in higher water and nutrient use efficiency, better production control, and
Jeffery C. Kallestad, John G. Mexal, Theodore W. Sammis, and Richard Heerema
Irrigation scheduling is a process by which the timing and amount of water applied are determined to meet the evapotranspiration demands of the crop. Both the water delivery system and the availability of water to the plant need to be considered in
Jeff B. Million, Thomas H. Yeager, and Joseph P. Albano
-leach irrigation strategies to reduce nutrient leaching may be limited in regions with significant precipitation. The objective of our research was to determine if an ET-based irrigation schedule could reduce irrigation runoff volume and nutrient loss compared with
Luke Miller, George Vellidis, and Timothy Coolong
irrigation scheduling applications use data generated by nearby weather stations in Georgia or Florida to schedule irrigation. Migliaccio et al. (2016) reported up to a 37% reduction in water use for growers using an irrigation scheduling application for
James E. Ells, E. Gordon Kruse, and Ann E. McSay
An irrigation scheduling program has been developed for zucchini squash that produced high yields and high water use efficiency with, a minimum number of irrigations. The irrigation program is based upon a soil water balance model developed by the USDA. This irrigation program is available in diskette form and may be used with any IBM compatible personal computer provided wind run, temperature, solar radiation, humidity and precipitation data are available.
Eric Simonne and Doyle A. Smittle
An irrigation scheduling model for turnip greens (Brassica rapa L.) was developed and validated.. The irrigation scheduling model is represented by the equation: 12.7 (i-3) * 0.5 ASW = 0i-1 + Ei(0.365+0.00154i+0.00011i2) - R - I where crop age is i; effective root depth is 12.7 * (i-3) with a maximum of 300 mm; usable water (cm/cm of soil) is 0.5 ASW; deficit on the previous day is Di-1 evapotranspiration; is pan evaporation (Ei) times 0.365+0.0154i+0.00011i2; rainfall (R) and irrigation (I) are in millimeters. Yield measured as leaf weight, and quality analyzed in terms of color (Gardner XL20 cronameter L, a, b), leaf blade and blade: stem weight ratio were determined. Leaf yield and quality responses were affected by both irrigation and fertilizer rates. Yield increased quadratically as irrigation rates increased from 0 to 190% of the model rate. Maximum leaf yields were produced by irrigations at 100% of the model rate. Leaf quality parameters also tended to change quadratically with irrigation rates. Leaf yield and quality changed quadratically as nitrogen fertilizer rates increased from 80 to 120% of the median recommended N rate for Georgia.
Doyle A. Smittle, W. Lamar Dickens, and James R. Stansell
An irrigation scheduling model for snap bean (Phaseolus vulgaris L.) was developed and validated. The irrigation scheduling model is represented by the equation: 12.7(i - 4) × 0.5ASW = Di-1 + [E(0.31 + 0.01i) - P - I]i, where crop age is i; effective root depth is 12.7(i - 4) with a maximum of 400 mm; usable water (cm3·cm-3 of soil) is 0.5 ASW, deficit on the previous day is Di-1; evapotranspiration is pan evaporation (E) times 0.31 + 0.01i; rainfall (mm) is P, and irrigation (mm) is I. The model was validated using a line source irrigation system with irrigation depths ranging from 3% to 145% of tbe model rate in 1985 and from 4% to 180% of the model rate in 1986. Nitrogen fertilization rates ranged from 50% to 150% of the recommended rate both years. Marketable pod yields increased as irrigation rate increased in 1985. Irrigation at 4%, 44%, 65%, 80%, 150%, and 180% of the model rate produced yields that were 4%, 39%, 71%, 85%, 92%, and 55% as great as yields with the model rate in 1986. Marketable pod yields increased as N rate increased when irrigation was applied at 80%, 100%, or 150% of the model rate in 1986, but pod yields varied less with N rate when irrigation was applied at 4%, 44%, 65%, or 180% of the model.