Etissa E, Dechassa N and Alemayehu Y
Field experiment was conducted at Melkassa Agricultural Research Center with the objectives to determine the optimal irrigation levels for maximum tomato production and to assess the effect of limited water supply on field grown tomato yield and to estimate ‘yield response of tomato to soil water (Ky)’ and to validate CropWat irrigation model using the data for tomato cultivation during hot-dry season conditions. Three irrigation scheduling levels such as 1) 100% of crop water requirement (ETc) (Full irrigation) 2) 80% ETc (Full) (= 0.80 ETc) and finally 3) 60% ETc (= 0.60 ETc) were used using drip irrigation replicated three times; the tomato was subjected to various levels of water stresses over whole growth period. Yield data such as marketable, unmarketable and total fruit yield were collected at each harvesting and summed at the end of harvesting. The results of data analysis showed that use of various irrigation depth brought a significant effect (P<0.01) effect on the marketable yield of tomato whereas application of various irrigation depths did not bring significant difference (P<0.05) on unmarketable fruit yield of tomato. Use of various irrigation depths had a significant effect (P<0.05) on the total fruit yield of tomato. The mean separation indicated that the highest fresh fruit yield was obtained from full irrigation and the lowest was obtained from 60% irrigation. Thus, the total fresh fruit yield obtained from fully irrigated tomato plot exceeded the fresh fruit yield obtained from tomato plot irrigated with only 60% of full irrigation water by 62.8%. The results showed that with decrease in the depth of irrigation, there was a decrease in total fruit yield in tomato due to reduced uptake of water. The yield response (Ky) of tomato throughout the crop cycle was calculated and found to be 0.999, indicating that the yield reduction is directly proportional to reduced water use. Then the CropWat irrigation model was validated using field data for tomato cultivation. Accordingly, the efficiency of the model was found to be 94%, indicating that the model is a useful decision support system to help tomato growers
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