Modeling crop water requirement using weather model and spatial data of wheat under limited irrigations in North Western Himalayas
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Date
2021-10-29
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Palampur
Abstract
Water is considered as one of the most crucial inputs for agricultural production. The decreasing water resources for agriculture
production system in the face of climate change necessitates the use of real time weather data for reducing the water footprints
of the crop. A field experiment entitled “Modeling crop water requirement using weather model and spatial data of wheat under
limited irrigations in North Western Himalayas” was conducted during Rabi season 2018-19 and 2019-20 at the experimental
research farm of department of Agronomy, CSKHPKV, Palampur, Himachal Pradesh. The experiment comprising of five
irrigation treatments viz; Rainfed conditions (I1), two irrigations (I2), three irrigations (I3), Irrigation scheduling based on
Penman Monteith modified (I4), Irrigation scheduling based on spatial reference ET of grid (I5) with three dates of sowing (25th
October, 20th November & 10th December) was laid out in Split Plot Design with three replications. The soil of experimental site
was silty clay loam in texture, acidic in reaction, medium in available nitrogen (374.3 kg ha-1
), medium in available phosphorus
(21.7 kg ha-1
) and potassium (221.2 kg ha-1
). The experimental site is located 32°6ʹN latitude and 76°3ʹE longitude. The
experimental site received 562.2 mm and 454.2 mm rainfall during cropping season 2018-19 and 2019-20 respectively. The
study findings revealed that the growth parameters viz., plant height, LAI and dry matter recorded significantly higher when
crop sown on 25th October with three irrigations (I3) during both the years. The sowing window of 25th October observed to be
the best among the dates of sowing for yield parameters during both the years. However, irrigation schedule based on Penman
Monteith modified proved to be the best irrigation treatment being statistically at par with I2 and I3 irrigations schedules during
2018-19, whereas, I3 gave the highest yield in three irrigation schedules during 2019-20. Similarly, the yield parameters
reflected in significantly higher yield in I4 irrigation (4432 kg ha-1
) being at par with I3 irrigations (4140 kg ha-1
) and I2 (3922 kg
ha1) compared to I5 (3812 kg ha-1
) and I1 (3111 kg ha-1
) irrigations in 2018-19. The yield parameters reflected in significantly
higher yield in I3 irrigation (4109 kg ha-1
) compared to I2 irrigations (3916 kg ha-1
), I4 (3905 kg ha1), I5 (3798 kg ha-1
) and I1
(3441 kg ha-1
) irrigations in 2019-20. The highest benefit cost ratio (B:C) was observed for 25th October sowing (2.33), (2.35)
with I4 irrigation treatment (2.04), (2.12) during both years, respectively. The GDD was 1247, 1183, 1128 and 1194, 1106, 1028
for 1st, 2nd and 3rd dates of sowing during 2018-19 and 2019-20, respectively. The HTU, PTU, PTI and HUE did not show
variations.
The FAO-CROPWAT model was validated and the RMSE for estimated ET and AET worked out from the field water balance
method varied between 11.03-14.19% and 10.64-13.46% during 2018-19 and 2019-20, respectively. The RMSE of actual and
predicted dry matter accumulation was 0.017, 0.028, 0.017, 0.066 and 0.21 for 30, 60, 90, 120 DAS and at harvest during 2018-
19 and 2019-20. The validated model was used to simulate elevated temperature regimes of 1°,2°,3°C rise in both maximum and
minimum temperature, with 10%, 20% reduction and 10%, 20% increase in rainfall. With the 1°, 2°, and 3°C rise in
temperature. Increase in crop water requirement observed between 3.07-8.46 and 2.68-8.60% during 2018-19 and 2019-20,
respectively. The net irrigation water requirement increased ranging between 3.94-22.41 % and 3.87-25.64% for all elevated
temperature regimes with 20% decreases in rainfall, whereas it ranged between -5.81 to 4.62% and-7.49 to 6.19% with 20%
increase in rainfall compared to normal weather conditions during 2018-19 and 2019-20, respectively. During the year 2018-19
the irrigations were same under I3 and I4 treatment but the water productivity was higher in I4, while in 2019-20 the less
irrigation was applied under I4 treatment, still the water productivity was higher compared to I3 due to the use of real time
weather data.