S1018 Irrigation Management for Humid and SubHumid Areas

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S1018 Irrigation Management for Humid and
Sub-Humid Areas
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Objective 2

• Improve irrigation scheduling methods and the
  knowledge application database associated with
• crop coefficients,
• reference ET predictions,
• precipitation forecasting, and
• field-based sensor systems
• as they relate to plant water use.

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Approach a)

• Development of reference ET crop coefficients
• Weighing lysimeters,
• Remote sensing for spectral response,
• Doppler rainfall information,
• Modeling to estimate effective rainfall,
• ET determination water balance calculations
• Cotton, soybean, peanut

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Approach a) Development of reference ET crop
coefficients

• Weighing lysimeters
• MS, LA, FL, SC using weighing lysimeters for
  reference ET and crop coefficients
• Improve ET determination under humid conditions
• Improve models from checkbook to more complex
  physiological models

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Approach a) Development of reference ET crop
coefficients

• Remote sensing for spectral response
• ARS in MS, GA, SC, and GA exploring remote
  sensing crop condition with various imaging
  systems mounted on airplanes, balloons, unmanned
  airplanes, as well as closer to ground units on
  tractor mounts, extension poles and irrigation
  equipment.
• Frequency and timeliness of imaging in a region
  of unpredictable rainfall make add challenges
• Canopy temperature normalized difference
  vegetative index (NDVI) most commonly evaluated
• Examination of effects of background bare soil,
  crop residues
• Cotton and Peanut

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Approach b)

• Field-based approaches to irrigation scheduling
• Water content sensors - Sentec, TDR
• Water pressure sensors - Watermark
• Evaporation scheduling Easy Pan, checkbook
• Temperature-based Arkansas scheduler,
  IrrigatorPro
• Yield responses, economic returns
• Deficit irrigation limited water supplies
• Cotton, corn, soybean, peanut, rice

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Approach b) Field-based approaches to irrigation
scheduling

• Scheduling models using ET estimates, sometimes
  with sensors
• AR, MO Arkansas Irrigation Scheduler applied to
  rice improved with weather data for better ET
• LA evaluating Arkansas Irrigation Scheduler for
  soybean cotton
• Scheduling of drainage of rice fields based on
  whc of soil
• GA IrrigatorPro models improved for corn, cotton,
  peanut using sensor-based inputs for corn
  cotton continued use of soil temperature for
  protecting peanut quality yield
• FL developed scheduling guidelines for citrus
  that use reduced applications and earlier cutoff
  to meet Fl demands for reduced irrigation.

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Approach b) Field-based approaches to irrigation
scheduling

• Sensor-based scheduling
• Nearly all states exploring and testing soil
  water sensors for scheduling.
• Soil water content sensors
• Capacitance probes Sentec TDR, TDT
• Dual-frequency capacitance probes - Triscan
• Soil water pressure sensors
• Electrical resistance Irrometer Watermark,
  tensiometers
• Many involve continuous logging devices in field
• Some of those use radio, cell, satellite
  communications with irrigation controllers (like
  turf applications), base stations, internet
  hosted data.
• GA, ARS in MS developing low-cost sensing
  logging systems

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Approach b) Field-based approaches to irrigation
scheduling

• Sensor-based scheduling
• Nearly all states exploring and testing soil
  water sensors for scheduling.
• Some of those use radio, cell, satellite
  communications with irrigation controllers (like
  turf applications), base stations, internet
  hosted data.
• GA, ARS in MS developing low-cost sensing
  logging systems
• As VRI options improve we need systems for
  site-specific scheduling tools
• GA, SC sensor placement in varying
  soil/application

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Approach b) Field-based approaches to irrigation
scheduling

• Sensor-based scheduling
• Nearly all states exploring and testing soil
  water sensors for scheduling.
• So you know the water content or pressure
• What trigger water content/pressure?
• Should that change through the season?
• How do you deal with changing zones of water
  extraction as roots develop?
• Sensors have advantage of confirming that an
  irrigation applied water deeply enough or whether
  an excess was applied
• How many sensor locations needed to manage an
  irrigation system in various sized fields?
• How do you use sensors if you have to schedule
  water deliveries or withdrawals?

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Approach b) Field-based approaches to irrigation
scheduling

• Easy Pan-based scheduling
• Easy-Pan scheduling
• GA, LA, ARS-MS peanut, cotton, (corn), pecans
• Atmometer based ET
• NC calibrating atmometers with ref-ET and ET
  equations

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Approach c)

• Crop physiological response to water
• Stress levels
• Irrigation methods
• Tillage regimes
• Observations of physiological growth stages,
  maturity, yield components
• Accurate crop water use functions with
  characterization based on irrigation and soil
  type

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Approach c) Crop physiological response to water

• Crop response and irrigation affected by tillage
• ARS in MS GA, GA comparing water availability
  in conservation vs conventional tillage
• Corn, cotton, peanut

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Approach c) Crop physiological response to water

• Crop response and irrigation affected by
  irrigation method
• AL, DE, GA, FL examined scheduling in drip
  irrigation, including SDI
• Type and placement of sensors, dual measurements
  salts fertilizers
• Vegetables, citrus, corn, cotton, peanut