1. Automated Weather Stations (AWS)
Automated Weather Stations (AWS). Key technologies in Precision Agriculture.
Dr. A. Gertsis
Fall 2014

2. Contribution of weather data to system evaluation
Plants grow and develop within the Soil-Plant-atmosphere continuum (SPAC).
Many processes which affect plant growth are driven by “climatic “ factors.
Air Temperature-Relative Humidity (RH%)-wind speed & direction –Rainfall (precipitation in general) and solar radiation are the dominant climatic (weather) variables

3. Q=how much H2O is included in 126 m3 of air , if it is 1% v/v?
1m3=1000 lt (volume)
I lt=1000 cm3
1 mt=1000 kg (mass=weight)
Density of water= m/V= 1,3245 g/cm3=1mt/m3
Answer= 1260 lt = (7 m x 6 m x DEPTH)
Depth=1260 lt/ (42 m2)=30 lt/m2

4. RH% relative humidity Examples Word Origin
noun1.the amount of water vapor in the air, expressed as a percentage of the maximum amount that the air could hold at the given temperature; the ratio of the actual water vapor pressure to the saturation vapor pressure.
Abbreviation: RH%, rh.

5. Relative humidity (abbreviated RH) is the ratio of the partial pressure of water vapor to the equilibrium vapor pressure of water at the same temperature. Relative humidity depends on temperature and the pressure of the system of interest.
Source:

6. Humans are sensitive to humidity because the human body uses evaporative cooling, enabled by perspiration, as the primary mechanism to rid itself of waste heat. Perspiration evaporates from the skin more slowly under humid conditions than under arid conditions. Because humans perceive a low rate of heat transfer from the body to be equivalent to a higher air temperature,[2] the body experiences greater distress of waste heat burden at high humidity than at lower humidity, given equal temperatures.
For example, if the air temperature is 24 °C (75 °F) and the relative humidity is zero percent, then the air temperature feels like 21 °C (69 °F).[3] If the relative humidity is 100 percent at the same air temperature, then it feels like 27 °C (80 °F).[3] In other words, if the air is 24 °C (75 °F) and contains saturated water vapor, then the human body cools itself at the same rate as it would if it were 27 °C (80 °F) and dry.[3] The heat index and the humidex are indices that reflect the combined effect of temperature and humidity on the cooling effect of the atmosphere on the human body.
Humans can be comfortable within a wide range of humidities depending on the temperature — from thirty to seventy percent - [4] but ideally between 50%[5] and 60%.[6]

7. AWS in the Perrotis College’s research and educational facilities
There are currently 3 AWS located in various points in AFS.
One sensor is located outside of the Greenhouse, with the console (data logger) located inside and recording both in and out air temperature, RH% and outside wind speed and direction and rainfall (additionally the barometric pressure is recorded)
All data are recorded at 30 min time intervals (the time of record can be adjusted through the software).
Data are downloaded to a PC using a software and a USB cable.

8. Commonn charcteristics of all stations
1. programmable (time intervals for recording data, i.e: 5 min, 30 min 5 days)
2. wireless communication between sensors and the console)
3. minimum range 100 m (maximum 1000 m)
4. data are downloadable (using USB cable or INTERNET)

9. AWS in the Perrotis College’s research and educational facilities
There are currently 3 AWS located in various points in AFS.
The 2nd sensor is located in the first line of the AFS vineyard and the console is outside the window of Dr. Gertsis office (about 60 m in straight line)
All 3 AWS are wireless transmitting data wireless and their data transmission length ranges from 100 m (for the first 2 stations) to 1000 m for the new Davis Vantage Vue (see next slide)

10. AWS in the Perrotis College’s research and educational facilities
The 3rd AWS and the latest acquisition:
Davis Vantage View model- established in the Olive Grove in March 29th, 2014.
On September 20, 2014 it was connected to the Internet, through the WeatherLinkIP cable.
It is accessible from (with a username and a password)

23. Download information
This weather station was set to record every 30 m all weather parameters and save in a file, which can be downloaded using a micro USB cable.
Then the data are exported in an Excel file and further processing and analyses (statistics) can take place.

24. Sample uses of raw and processed weather data.
Example #1: rainfall data.
When a rainfall event takes place it is important to know both, the TOTAL rainfall (mm) in a given period but more important is to know the DISTRIBUTION of the total amount in the given time period.
From this information we can evaluate potential soil water erosion impact on soils.

25. Sample uses of raw and processed weather data.
Example #2:Wind speed and Direction.
Following the wind speed we can also estimate potential wind erosion and considering the prevailing wind Direction, we can establish protective measures for our crops.

26. Sample uses of raw and processed weather data.
Example #3: Air temperature.
Temperature drives plant growth!
For many cultivated species, there are reliable models based exclusively in Air Temperature, which can be used to accurately predict critical crop growth stages.
Temperature also drives soil evaporation and plant transpiration!
These two terms are coined as : ET (Evapotranspiration).

27. Sample uses of raw and processed weather data.
Example #4: Irrigation scheduling and amount
Irrigation scheduling and estimation of water needs are also strongly based on climatic data.
Many models exist to predict irrigation water needs for crops.

28. Precision Agriculture & Weather data
Precision Irrigation (one application of PA) depends on soil water sensors and weather data, to determine more accurately the crop specific needs.
Soil sensors provide information on “how much water is in the soil”, while weather data tell us “how much water is used/needed by the crops for each critical growth stage”.
A combination of both information must be sued to provide the most accurate –precise irrigation input.

29. Decision Support Systems (DSS)- Simulation models & Weather data
The advance of PCs brought up a revolution in development of DSS and Crop & Soil Simulation models, quite before the technologies of Precision agriculture became so popular and affordable.
All DSS require as necessary inputs accurate weather data, to provide their specific outputs.

30. Step 3. Apply VRT! (Variable Rate Technologies) or VRA…applications.
Question: you are given all possible technologies for PA. (GPS-GIS-Sensors….and money!) How will you apply it in a field , i.e. a 100 ha cotton field Note: make it in steps.
Step 1. Measure NDVI, EC, pH, weather data, yield, soil texture, soil OC, etc.
Step 2. use software (GIS & MZA) to make maps of MZ’s the above parameters measured. VERY important to determine criteria (ranges) of the variables used when making MZs.
Step 3. Apply VRT! (Variable Rate Technologies) or VRA…applications.

31. Step 3. Apply VRT! (Variable Rate Technologies) or VRA…applications.
Question: you are given only sensor technologies for PA. (Sensors….and money!) How will you apply it in a field , i.e. a 100 ha cotton field Note: make it in steps.
Step 1. Measure NDVI, EC, pH, weather data, yield, soil texture, soil OC, etc.
Step 2. use software (GIS & MZA) to make maps of MZ’s the above parameters measured. VERY important to determine criteria (ranges) of the variables used when making MZs.
Step 3. Apply VRT! (Variable Rate Technologies) or VRA…applications.