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Validation of Dras Model for Rice Irrigation-2005

Dr M A Sattar
CSO and Head
IWMD, BRRI

Abstract/ Executive Summary:

The DRAS model predicted relatively optimum NIR for rice cultivation in boro/dry season in various water regimes imposed in the field for rice cultivation. Although the model predicted slightly higher values than BRRI recommended water management practice for rice, it seems to provide irrigation water requirements for rice with sufficient accuracy for the farming community. As the study conducted in two years, further study is needed to confirm the findings and to extend the model for on-farm water application for rice cultivation.

Keywords: DRAS model, NIR, CEGIS

Location: BRRI farm, Gazipur

Start Date: 01-07-2004

End Date: 30-06-2005

Program Area: Crop-Soil-Water Management

Commodity: Water management

Objectives:

To compare the model predicted NIR with the BRRI recommended water application for rice cultivation.
To determine rice yield by adopting model predicted NIR and BRRI recommended irrigation practices in boro season.

Methodology:

Experiment was set-up in randomized complete block design (RCBD) at BRRI farm, Gazipur in Boro season 2004-05 with seven treatments and was replicated thrice. The treatments were:T₁= Application of 50-70 mm irrigation water 3 days after ponded water disappeared from rice field (BRRI recommendation), T₂= NIR application considering ET, S&P and RF at 0.50p, T₃= NIR application considering instantaneous ET, S&P and RF at 0.50p, T₄= NIR application same as T₂ except seepage i.e., using reported percolation only and levee protected by poly sheet, T₅= NIR application same as T₃ except seepage i.e., using reported percolation only and levee protected by poly sheet, T₆= NIR application same as T₂ but considering 0.20 allowable depletion, T₇= NIR application same as T₄ but considering 0.20 allowable depletion. One of the highest yielding inbred rice variety, BRRI dhan29 was used. Fertilizer was applied at the rate of 125-26-60-13-7 Kg N-P-K-Zn-S/ha. The whole amounts of P, K, Zn and S were applied as basal at the time of final land preparation. Urea was top-dressed in three equal splits at 17, 43 and 62 days after transplanting. Weeding and spraying were done twice to control weeds and attack of insect-pest. Rice yield was assessed on the basis of whole plot yield. Harvested paddy was threshed, cleaned and weighed to determine yield. Finally, the yield was adjusted to 14% moisture content to determine yield per hectare. Irrigation water was applied as per model predicted NIR based on the water treatments and was measured by volumetric method. Seepage and percolation was measured by using a vertical gauge installed in the middle of experimental plot. A USWB Class A evaporation pan and a rain gauge was installed near the vicinity of the experimental field for determining rainfall and evaporation values during the growing season of rice. All the data were recorded at 7:30 AM daily to determine S&P, rainfall and evaporation from the experimental field.

Source of Information: Proceedings of BRRI Annual Internal Review for 2005

Article Link (Online Journal):

Funding Source:

1. Government

Budget:

Total Budget (Tk.) :

Achievement/Findings:

In BRRI recommended practice, irrigation water applied in almost all the decades except decade 9 and 10, due to 50 and 150 mm rainfall occurred in these decades . But application of irrigation water was needed in all decades as per model predicted value. But actually the rainfall was more than NIR demand in decade 9 and irrigation was not applied in decade 10 because of higher carry over water. It was observed that NIR prediction on the basis of current year rainfall data was almost similar to BRRI recommended water management practice.It is interesting to note that irrigation water application was 16-24% less than water requirements for the treatments T₁, T₃ and T₅ having options for water application. Other treatments without it required 5-10% more water than actually required for optimum yield of rice. Therefore, the model predicted reasonably accurate NIR for rice in boro/dry season.

Knowledge Management: Report/Proceedings