The document discusses the increasing water requirements for agriculture, especially in dry climates where water sources are limited. It emphasizes research into efficient irrigation practices and crop water needs, utilizing technologies like satellite imagery and software for accurate calculations of evapotranspiration and irrigation schedules. The study area, methods of data acquisition, and findings regarding crop types and irrigation requirements are also detailed.

1. 1

2. 2
The water requirement has been increasing more and more
especially in agriculture. The agricultural sector makes use of 75% of
the water withdrawn from river, lakes and aquifers. In recent years
irrigated land has developed rapidly. Water increasingly often
becomes a limiting factor for food production especially in dry
climates. In dry climates water sources are very limited since the
amount of rain-fall is very low. As the total size of the hot dry areas in
the world is about 45-50 million square kilometres which means one
third of the total land area of the world.
In dry climate the availability of water for irrigation of crops is
limited, which restricts the possibility for cultivation of crops. For that
reason a lot of research has been done to develop methods to protect
water and using less amount of fresh water as far as possible without
effects on crops yield, and to increase water use efficiency in
irrigation without any negative effects on crop yields. Thus irrigation
scheduling is one of the best methods which can help us to realize
these aims.

3. 3
 M. Anadranistakis et al.(1999) :Evapotranspiration was calculated using
temperature,humidity,wind speed,root depth,crop height etc using penman
monteith method.the results showed a deviation of 8% form the
experimental results
 A.W. Abdelhadi et al.(1999) :Evapotranspiration was calculated using
penman monteith,farbrother method the results for penman monteith
method were more accurate than the farbrother method.the farbrother
method gave higher values
 J. G. Annandale et al. (2001) :Evapotranspiration was calculated using a
short programme named CROPWAT written in delphi and based on penman
monteith method.this was widely used by the food and agricultural
organization.5 day averages were taken instead of daily averages resulting in
more accurate predictions
 K H V Durga rao et al. (2001) :using different image processing techniques
crop type and their area coverage was calculated for a area in deheradun
evapotranspiration was calculated using CROPWAT the current irrigation
schedule was proved to be more than necessary thus a revision of the
irrigation schedule was proposed

4. 1. Identify the types of crops and the area occupied by
those crops using satellite or aerial images.
2. Estimate the water requirement for the different type
of crops during their base period.(sowing to
harvesting)
3. The product of the area occupied by the crops and
the base period water requirement will give the total
water requirement for that particular irrigation area.
4

5.  Efficient use of available water resource & Sustainable
watershed management
 we can decide the water requirements for suggested
cropping pattern by irrigation department)
 It helps in the design of irrigation project.
5

6.  Crops have low reflectance in the visible region and
have high reflectance in infrared region.
 The crops in the image cannot be distinguished
accurately with visible spectrum alone.
 Hence multispectral images are obtained by using
multispectral sensors mounted on satellites
 By obtaining the LISS-IV IMAGE , preparing the NDVI
Map for a particular region, we can identify the types
of crop grown in the area.
6

7. For example
True colour image:
The different crops cannot be
seen distinctively
False colour or infrared
image:
We can see the contrast between
different crops
7

8.  But before that we need to know how those crops appear at
different times of the year and their growing season
 lastly all the information is processed using a software such as
ERDAS and ARC-GIS to obtain the area under each crop
8

9.  Water is utilized in crops mainly for evapotranspiration
 Evapotranspiration (ET) is the sum of evaporation and plant transpiration from the
Earth's land and ocean surface to the atmosphere.
 Factors that affect evapotranspiration include the plant's growth stage or level of
maturity, percentage of soil cover, solar radiation, humidity, temperature, and wind.
9

10. PENMAN MONTEITH METHOD FOR ESTIMATION OF
REFERENCE CROP EVAPOTRANSPIRATION:
 this is one of the most accurate methods for the estimation of
evapotranspiration
 Reference crop evapotranspiration is defined as the
evapotranspiration from a hypothetical crop with an assumed height of
0.12m having a surface resistance of 70 s/m and an albedo of 0.23,
closely resembling the evaporation of an extension surface of green
grass of uniform height, actively growing and adequately watered.
 The software CROPWAT 8.0 was used to simplify the calculations
involved in the estimation of evapotranspiration
10

11.  The equation for reference evapotranspiration is: given by:
where:
ETo= Reference evapotranspiration [mm /day]
Rn = Net radiation at the crop surface [MJ /m2/day]
G = Soil heat flux density [MJ/ m2 /day]
T = Mean daily air temperature at 2 m height [°C]
u2 = Wind speed at 2 m height [m/s]
es = Saturation vapour pressure [kPa]
ea = Actual vapour pressure [kPa]
∆ = Slope vapour pressure curve [kPa /°C]
γ = Psychrometric constant [kPa/ °C]
11

12. The various parameters required for the equation
are:
 Latitude in degree and minutes
 Altitude in meters
 Maximum temperature
 Minimum temperature
 Wind velocity
 Sunshine hours
 Mean temperature
12

13. DATA USED:
The following data products are used for the present study:
 Survey of India (SOI) Topomap on 1:50,000 scale (to prepare the
base map to get information the command area)
 Hydro meteorological Data (Estimation of reference crop
evapotranspiration by penman monteith method )
 Satellite images ( LISS 3 and LISS 4) (to identify the cropping
pattern in the study area)
 Cadastral map (to know the area details like Survey Number &
Area under irrigation, from Revenue Department & Irrigation
Department)
 Crop data (as per the suggested cropping pattern from
irrigation department)
13

14. 14
Google image

15. 15
Study area location

16.  The study area is located south west of Davangere city in Davangere
district
 The study area is supplied by the right bank canal network from the
Bhadra reserviour
 The study area consists of the command area of the 10th distributary of
the Harihara branch canal.
 10th distributary having an area of 38.88sq.km(3888 hectares)
 The study area lies between from 75.796 to 75.886 decimal degrees
longitude and from 14.377 to 14.411 decimal degrees latitude
LAND COVER
 Major portion of the land is used for agriculture, horticulture
,plantations of area groundnut, coconut ,water bodies, barren scrubs
 The soil mainly consists of red soil followed by black soil
THE HARIHAR BRANCH CANAL
 The 10th distributary of the harihar branch canal has a design discharge
of 1.765cumecs
 The 10th distributary takes off from the Harihar branch canal at 15.3 km
16

17. METHOD OF DATA ACQUISITION
 Field Survey was carried out using a GPS device
 Borewells and wells were taken as reference points, a topomap
was developed using arc gis with the help of the GPS coordinates
17

18. NDVI map was generated using the LISS-III & LISS
IV map
Supervised classification was carried out on the
NDVI map. The steps taken for supervised
classification are as follows:
1. Defining training samples
2. Generate signature file
3. Perform most likelihood classification
Filters and corrections were applied to obtain the final
classified image
18

19.  The following weather data was entered into the
CROPWAT 8.0 software
19
Month Min TempMax Temp Humidity Wind Sun Rain
°C °C % km/day hours mm
January 16.3 30.1 66 69 8.5 0
February 18.2 32.8 65 77 8.4 0
March 20.8 35.5 57 34 8.8 21.2
April 22.9 36.5 57 41 9 25.2
May 23 35.2 61 65 7.6 115
June 22.1 30.5 27 49 4 89.2
July 21.5 28.1 75 75 1.9 153.6
August 21.5 28.3 73 79 3.8 99.2
September 20.8 29.3 72 35 4.6 330.2
October 20.6 30 69 48 5.2 105.2
November 18.3 29.3 67 172 9.8 26.2
December 16.1 29 66 79 3.8 0

20.  The following are the crop data for some crops
20
Banana:
Maize:

21. 21

22.  NDVI map generated using the LISS 3 map
22
RESULTS

23.  Classified map obtained by supervised classification
23

24.  After applying filtering and corrections the final
classified map was obtained
24

25.  The areal information of the cropping pattern
obtained was as follows:
25
land use area(sqm) area(hectares)
sugarcane 1225811.546 122.5811546
single rice 4889876.297 488.9876297
maize 737911.5978 73.79115978
double rice 27445981.8 2744.59818
coconut 841015.4332 84.10154332
built up area 1418468.603 141.8468603
barren land 731838.9495 73.18389495
banana 2004747.776 200.4747776
arecanut 1454758.992 145.4758992

26.  Reference evapotranspiration ET0 and effective rainfall
obtained from CROPWAT for the year 2015
26
Month ETo Eff rain
mm/day mm
January 3.5 0
February 4.11 0
March 4.49 20.5
April 5.03 24.2
May 4.91 93.8
June 3.75 76.5
July 2.95 115.9
August 3.41 83.5
September 3.32 158
October 3.33 87.5
November 4.43 25.1
December 2.73 0
Average 3.83 684.9

27.  The decadewise irrigation (mm/decade)for the various crops was
obtained as follows:
Irrigation requirement=ET0×Kc - effective rainfall
27
sugarcane banana barley maize rice(rabi) rice(khariff) arecanut coconut total irrigation(mm/dec)
Jan-01 39.2 30.4 9.9 9.9 36.4 0 33.84 37.92 197.56
Jan-02 42.5 34.1 12.5 10.8 39.5 0 38.07 42.66 220.13
Jan-03 49 40.7 27 18.3 45.9 0 43.3575 48.585 272.8425
Feb-01 46.5 40 38.7 27.1 44.2 0 41.2425 46.215 283.9575
Feb-02 48.5 42.3 45.1 37.9 46.8 0 43.3575 48.585 312.5425
Feb-03 39.6 34.6 36.9 37.2 38.5 0 34.7975 39.005 260.6025
Mar-01 44 39.5 42.4 43.5 44.5 0 40.3725 45.855 300.1275
Mar-02 40.2 38.1 41.1 42.3 43.3 0 38.93 44.54 288.47
Mar-03 44.9 45.2 49 50.3 51.5 0 46.2325 52.735 339.8675
Apr-01 42.7 43.4 41.8 48.9 50.1 0 47.275 53.65 327.825
Apr-02 43.2 43.7 30.1 40.4 50.3 0 49.99 56.62 314.31
Apr-03 30.2 20.1 5.8 17 36.4 0 40.2325 46.735 196.4675
May-01 13.4 0 0 0 0 0 25.5175 31.765 70.6825
May-02 0.7 0 0 0 0 103.9 15.36 21.48 141.44
May-03 1.5 0 0 0 0 161.3 19.7175 25.965 208.4825
Rabi season:

28. 28
Khariff season
sugarcane banana barley maize rice(rabi) rice(khariff) arecanut coconut total irrigation(mm/dec)
Jun-01 0 0 0 0 0 19.4 16.6 21.7 57.7
Jun-02 0 0 0 0 0 17.8 15.27 19.86 52.93
Jun-03 0 0 0 0 0 9.7 7.855 12.19 29.745
Jul-01 0 0 0 0 0 0 0 0 0
Jul-02 0 0 0 0 0 0 0 0 0
Jul-03 0 0 0 0 0 0 0 0.62 0.62
Aug-01 0 0 0 0 0 7.9 4.6825 8.635 21.2175
Aug-02 11.1 0 0 0 0 14.9 12.0975 16.305 54.4025
Aug-03 9.1 0 0 0 0 8.3 5.37 9.96 32.73
Sep-01 0 0 0 0 0 0 0 0 0
Sep-02 0 0 0 0 0 0 0 0 0
Sep-03 0 0 0 0 0 0 0 0 0
Oct-01 1.4 0 0 0 0 0 0 1.12 2.52
Oct-02 9 0 0 0 0 0 3.9825 7.935 20.9175
Oct-03 25.3 5.5 0 0 0 0 20.3 25.4 76.5
Nov-01 36.9 16.9 0 0 0 0 31.3725 36.855 122.0275
Nov-02 50.4 29.7 0 0 0 0 43.1025 49.095 172.2975
Nov-03 44.5 28 0 0 0 0 38.9575 44.185 155.6425
Dec-01 37.1 25.5 0 0 3.3 0 32.6825 36.635 135.2175
Dec-02 29 20.9 0 0 114.8 0 25.38 28.44 218.52
Dec-03 36.9 27.5 0 0 181.6 0 31.725 35.55 313.275

29.  The decadewise irrigation volume requirement obtained
was as follows:
Irrigation volume = Irrigation requirement × area under crop
29
crop arecanut rice khariff rice rabi maize coconut sugarcane banana total discharge(cum/sec)
Jan-01 49229.04 0 177991.4972 7305.32474 31891.31 48051.8126 60944.3324 375413.3167 0.434506154
Jan-02 55382.68 0 193150.1137 7969.44517 35877.72 52096.9907 68361.8992 412838.8423 0.477822734
Jan-03 63074.71 0 224445.322 13503.7821 40860.73 60064.76575 81593.2345 483542.5525 0.559655732
Feb-01 59997.9 0 216132.5323 19997.4041 38867.53 57000.23689 80189.9111 472185.5107 0.546511008
Feb-02 63074.71 0 228846.2107 27966.8493 40860.73 59451.85998 84800.8309 505001.199 0.584492129
Feb-03 50621.98 0 188260.2374 27450.3111 32803.81 48542.13722 69364.2731 417042.7421 0.482688359
Mar-01 58732.26 0 217599.4952 32099.1542 38564.76 53935.70802 79187.5372 480118.915 0.555693189
Mar-02 56633.77 0 211731.6437 31213.6603 37458.83 49277.62415 76380.8903 462696.4136 0.535528256
Mar-03 67257.15 0 251828.6293 37116.953 44350.95 55038.93841 90614.5995 546207.2145 0.632184276
Apr-01 68773.73 0 244982.8025 36083.8768 45120.48 52342.15301 87006.0535 534309.0955 0.618413305
Apr-02 72723.4 0 245960.7777 29811.6282 47618.29 52955.05878 87607.4778 536676.6388 0.621153517
Apr-03 58528.59 0 177991.4972 12544.497 39304.86 37019.50869 40295.4303 365684.381 0.423245811
May-01 37121.81 0 0 0 26714.86 16425.87472 0 80262.54273 0.092896461
May-02 22345.1 3359695.66 0 0 18065.01 858.0680821 0 3400963.833 3.936300733
May-03 28684.21 5215773.91 0 0 21836.97 1838.717319 0 5268133.804 6.097377087
irrigation discharge required(cum/decade)
Rabi season

30. 30
Khariff season
crop arecanut rice khariff rice rabi maize coconut sugarcane banana total discharge(cum/sec)
Jun-01 24148.9994 627315.647 0 0 18250.03 0 0 669714.6812 0.775132733
Jun-02 22214.16993 575578.274 0 0 16702.57 0 0 614495.0104 0.711221077
Jun-03 11427.13195 313657.823 0 0 10251.98 0 0 335336.9335 0.388121451
Jul-01 0 0 0 0 0 0 0 0 0
Jul-02 0 0 0 0 0 0 0 0 0
Jul-03 0 0 0 0 521.4296 0 0 521.4295686 0.000603506
Aug-01 6811.909018 255453.279 0 0 7262.168 0 0 269527.3562 0.311952959
Aug-02 17598.947 481804.286 0 0 13712.76 13606.5082 0 526722.4973 0.60963252
Aug-03 7812.05583 268387.622 0 0 8376.514 11154.8851 0 295731.0768 0.342281339
Sep-01 0 0 0 0 0 0 0 0 0
Sep-02 0 0 0 0 0 0 0 0 0
Sep-03 0 0 0 0 0 0 0 0 0
Oct-01 0 0 0 0 941.9373 1716.13616 0 2658.07345 0.003076474
Oct-02 5793.577718 0 0 0 6673.457 11032.3039 0 23499.33909 0.027198309
Oct-03 29531.6077 0 0 0 21361.79 31013.0321 11026.11 92932.54459 0.107560815
Nov-01 45639.42673 0 0 0 30995.62 45232.446 33880.24 155747.734 0.180263581
Nov-02 62703.7498 0 0 0 41289.65 61780.9019 59541.01 225315.3134 0.260781613
Nov-03 56673.77374 0 0 0 37160.27 54548.6138 56132.94 204515.5922 0.236707861
Dec-01 47545.16102 0 16136.59178 0 30810.6 45477.6084 51121.07 191091.0298 0.221170173
Dec-02 36921.78342 0 561357.7989 0 23918.48 35548.5348 41899.23 699645.8246 0.80977526
Dec-03 46152.22928 0 888001.5355 0 29898.1 45232.446 55130.56 1064414.873 1.231961659
irrigation discharge required(cum/decade)

31.  The violation in cropping pattern is observed as follows:
31
Crop Notified area(hectares) Actual area(hectares) %violation
Rice 62.04 3233.5858 5112.098324
sugarcane 120.21 122.5811 1.972464853
plantations 1262.74 430.0521 65.94294154

32. 1. Irrigation scheduling is the key element to proper management of irrigation system by
applying the correct amount of water at the right time to meet the requirement of water to
the plants.
2. From classification we can find huge violation of cropping area and because of that
shortage of supplied water in the tailrace. It’s clearly shows that there is proper water
management is required in the study area.
3. Scheduling efficiency was much lower for all treatments during the rainy summer season
compared to the other drier seasons indicating inaccuracy in determining site specific
rainfall.
4. Most crops will recover overnight from temporary wilting if less than 50 percent of the
plant available water has been depleted. Therefore, the allowable depletion volume generally
recommended is maximum 50 percent. However, the recommended volume may range from
40 percent or less in sandy soils to more than 60 percent in clayey soils.
5. The allowable depletion is also dependent on the type of crop, its stage of development,
and its sensitivity to drought stress
6. When the irrigation scheduling is designed according to historical climate data or
estimated by computer program, it is important to look at the crop in the field for color
change or measuring soil water status to make sure that the estimation is right, because this
kind of scheduling does not take into account weather extremes which are different
32

33.  The same procedure could be carried out for other
locations facing irrigation problems
 Suitable irrigation scheduling can be developed to
meet the deficit irrigation requirements
33

34. 34