A crop water stress index for tall fescue (Festuca arundinacea Schreb.) irrigation decision-making — a traditional method

Abstract A high irradiance plant growth chamber was used to study crop water stress indices (CWSI) and baselines with increasing soil water deficit for Tall Fescue (Festuca arundinacea Schreb.). Canopy temperatures for turf plugs were continuously measured with infrared thermometers, along with plant water use, measured with electronic mini-lysimeters. Net radiation, canopy and air temperatures, and vapor pressure deficit (VPD) levels were recorded and analyzed statistically. The canopy–air temperature differential (Tc−Ta) increased with a decrease in soil moisture content. Tc−Ta increased as net radiation became greater, independent of soil water deficit. Canopy temperature of well-watered plants decreased at rate of 2.4°C for each 1 kPa reduction in air vapor pressure deficit for all net radiation levels. For each 100 Wm−2 increase in net radiation, canopy temperature of well-watered plants increased at a rate of 0.6°C and was well correlated (well-watered baseline) with VPD. Increases in canopy temperature coupled with a decrease in transpiration rate were hallmark signs of water stress progression. However, (Tc−Ta) and VPD baseline relationships correlated poorly for moderate-stress and severe stress conditions regardless of net radiation levels. Thus, even with the increased precision and replications of a controlled environment study, lower limit crop water stress baselines were quite variable.

[1]  Estimation of plant water status from canopy temperature: an analysis of the inverse problem. , 1990 .

[2]  Madan M. Gupta,et al.  Introduction to Fuzzy Arithmetic , 1991 .

[3]  Daniel L. Schmoldt,et al.  Simulation of Plant Physiological Process Using Fuzzy Variables , 1991 .

[4]  Hamid R. Berenji,et al.  Fuzzy and neural control , 1993 .

[5]  Robert J. Reginato,et al.  field quantification of crop water stress , 1983 .

[6]  Terry A. Howell,et al.  Evaluation of Cotton Canopy Temperature to Detect Crop Water Stress , 1984 .

[7]  Donald C Slack,et al.  Irrigation Scheduling Using Crop Canopy-Air Temperature Difference , 1980 .

[8]  B. P. Verma A FUZZY PHOTOSYNTHESIS MODEL FOR TOMATO , 1997 .

[9]  David C. Nielsen,et al.  Infrared thermometry and the crop water stress index. II : sampling procedures and interpretation , 1992 .

[10]  Theodore W. Sammis,et al.  Relationships Between Crop Water Stress Index and Alfalfa Yield and Evapotranspiration , 1985 .

[11]  S. Idso,et al.  Normalizing the stress-degree-day parameter for environmental variability☆ , 1981 .

[12]  Gaylord V. Skogerboe Irrigation scheduling for water and energy conservation in the 80's : Proceedings, irrigation scheduling conference, Chicago, Illinois, Dec. 14-15, 1981. ASAE publication 23-31, American Society of Agricultural Engineers, 1981. 231 pp., US$19.50. ISBN , 1983 .

[13]  T Yamakawa A fuzzy logic controller. , 1992, Journal of biotechnology.

[14]  Thomas S. Colvin,et al.  A Fuzzy Logic Yield Simulator For Prescription Farming , 1994 .

[15]  R. Jackson Canopy Temperature and Crop Water Stress , 1982 .

[16]  A. Kaufmann,et al.  Introduction to fuzzy arithmetic : theory and applications , 1986 .

[17]  W. M. Edwards,et al.  Pasture canopy temperature under cloudy humid conditions , 1992 .

[18]  George E. Meyer,et al.  Calibration of Large Field-of-View Thermal and Optical Sensors for Plant and Soil Measurements , 1994 .

[19]  Panos J. Antsaklis,et al.  An introduction to intelligent and autonomous control , 1993 .

[20]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[21]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[22]  Yael Edan,et al.  A fuzzy logic expert system for dairy cow transfer between feeding groups , 1994 .

[23]  N. Eguchi,et al.  MACHINE LEARNING OF FUZZY RULES FOR CROP MANAGEMENT IN PROTECTED CULTIVATION , 1990 .

[24]  S. Idso,et al.  Remote-Sensing of Crop Yields , 1977, Science.

[25]  Y. W. Lee,et al.  FUZZY DECISION MAKING IN GROUND WATER NITRATE RISK MANAGEMENT , 1994 .

[26]  Donald C Slack,et al.  Crop water stress index models for bermudagrass turf: a comparison , 1993 .

[27]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[28]  J. Hatfield,et al.  Effect of Wind on the Crop Water Stress Index Derived by Infrared Thermometry1 , 1983 .

[29]  W. Ehrler Cotton Leaf Temperatures as Related to Soil Water Depletion and Meteorological Factors1 , 1973 .

[30]  George E. Meyer,et al.  DYNAMIC ANALYSIS OF MOISTURE STRESS IN TALL FESCUE (FESTUCA ARUNDINACEA) USING CANOPY TEMPERATURE, IRRADIATION, AND VAPOR DEFICIT , 2000 .

[31]  D. D. Fangmeier,et al.  Quantifying wheat water stress with the crop water stress index to schedule irrigations , 1994 .

[32]  S. Idso,et al.  Canopy temperature as a crop water stress indicator , 1981 .

[33]  Hongxing Li,et al.  Fuzzy Sets and Fuzzy Decision-Making , 1995 .

[34]  T. A. Howell,et al.  Canopy Temperature of Irrigated Winter Wheat , 1986 .

[35]  Richard S Gates,et al.  Knowledge-based control systems for commercial single stem rose production , 1997 .

[36]  J. C. Ótoole,et al.  Seasonal and Species Variation in Baseline Functions for Determining Crop Water Stress Indices in Turfgrass , 1989 .

[37]  George E. Meyer,et al.  Simulated Water Use and Canopy Resistance of New Guinea Impatiens (Impatiens X hb.) in Single Pots Using Infrared Heating , 1994 .

[38]  David J. Wehner,et al.  Models for Predicting the Lower Limit of the Canopy-Air Temperature Difference of Two Cool Season Grasses , 1994 .

[39]  N. J. Rosenberg,et al.  Microclimate: The Biological Environment. , 1976 .

[40]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .