m m; h b; f k; n e
Abstract
The agricultural sector is known as the largest consumer of water. Due to limited water resources, water productivity needs to be enhanced in this sector. The concept of water productivity has attracted the attention of policy makers in food and water sector at large scale. Remote sensing is used in ...
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The agricultural sector is known as the largest consumer of water. Due to limited water resources, water productivity needs to be enhanced in this sector. The concept of water productivity has attracted the attention of policy makers in food and water sector at large scale. Remote sensing is used in the assessment and management of soil and water resources in recent decades. In the present research, this method was used to estimate water productivity. Evapotranspiration and actual production levels of dry matter were calculated using SEBAL algorithms and five images from the Landsat 5TM satellite in Qazvin Plain. The results of SEBAL algorithm in five images and lysimeter data were compared and evaluated in the region. The coefficient of determination ( 15R2"> ) and their mean absolute difference were 0.9948 and 0.446 mm/day, respectively, which demonstrated the accuracy of remote sensing methods in estimating agricultural water productivity at the basin level. The results showed that water productivity varied from 0.18 to 1.35 in the field. The wheat water productivity values from Landsat 5TM images and lysimeter data were 0.73 and 0.85 kg/m3, respectively, which are relatively close to each other.
Arash Tafteh; Niazali Ebrahimipak; Hossin Babazadeh; Fereydoon Kaveh
Abstract
Management of water distribution in the Qazvin Plain is planned on monthly intervals. Therefore, production functions which can accurately predict yield reduction under deficit irrigation on monthly basis are needed. This study was conducted with the following purpose: assessment of the production functions ...
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Management of water distribution in the Qazvin Plain is planned on monthly intervals. Therefore, production functions which can accurately predict yield reduction under deficit irrigation on monthly basis are needed. This study was conducted with the following purpose: assessment of the production functions using different methods including, minimum, average, multiplicative, Raes method, and product with yield response factor (Ky) power as applied by FAO and Najarchi yield response factors. To estimate tomato yield under different deficit irrigations and evaluation of empirical methods, a study was conducted by using randomized complete block design with irrigation interval treatments including T1, T2, T3, and T4 representing, respectively, 60, 90,120, and 150 mm evaporation from class A pan between consecutive irrigations. The study had three replications and was carried out at the Faizabad Agricultural Research Station, in Qazvin. The results showed that maximum water requirement of tomato plant was 1073 mm, T1 treatment had the maximum yield with 88500 kg/ha and T4 treatment had the minimum yield with 57000 kg/ha. Also, according to statistical comparisons, the proposed method that estimated the plant response factor based on monthly power had the minimum root mean square error (RMSE) and normal root mean square error (NRMSE), while it had the highest agreement index and coefficient of determination (R2).The plant yield response factors were determined in June, July, August, September, and October as, respectively, 0.7, 1.1, 1.1, 1.14 and 0.4. The value of this factor for initial growth, plant development satge, mid-season, and late-season were, respectively, 0.7, 1.1, 1.14, and 0.4, while the average for the whole growing period was determined as 0.89 by using the proposed method. As a result, the proposed method is suggested as a convenient method.