B. Ababaei; H. Ramezani Etedali
Abstract
Introduction: Water use and pollution have raised to a critical level in many compartments of the world. If humankind is to meet the challenges over the coming fifty years, the agricultural share of water use has to be substantially reduced. In this study, a modern yet simple approach has been proposed ...
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Introduction: Water use and pollution have raised to a critical level in many compartments of the world. If humankind is to meet the challenges over the coming fifty years, the agricultural share of water use has to be substantially reduced. In this study, a modern yet simple approach has been proposed through the introduction concept ‘Water Footprint’ (WF). This concept can be used to study the connection between each product and the water allocation to produce that product. This research estimates the green, blue and gray WF of wheat in Iran. Also a new WF compartment (white) is used that is related about irrigation water loss.
Materials and Methods: The national green (Effective precipitation), blue (Net irrigation requirement), gray (For diluting chemical fertilizers) and white (Irrigation water losses) water footprints (WF) of wheat production were estimated for fifteen major wheat producing provinces of Iran. Evapotranspiration, irrigation requirement, gross irrigation requirement and effective rainfall were got using the AGWAT model. Yields of irrigated and rain-fed lands of each province were got from Iran Agricultural-Jihad Ministry. Another compartment of the wheat production WF is related about the volume of water required to assimilate the fertilizers leached in runoff (gray WF). Moreover, a new concept of white water footprint was proposed here and represents irrigation water losses, which was neglected in the original calculation framework. Finally, the national WF compartments of wheat production were estimated by taking the average of each compartment over all the provinces weighted by the share of each province in total wheat production of the selected provinces.
Results and Discussion: In 2006-2012, more than 67% of the national wheat production was irrigated and 32.3% were rain-fed, on average, while 37.9% of the total wheat-cultivated lands were irrigated and 62.1% was rain-fed from more than 6,568 -ha. The total national WF of wheat production for this period was estimated as 42,143 MCM/year, on average. Different compartments of wheat WF were estimated for 236 plains in fifteen selected provinces. For irrigated areas, the green WFs ranged from 499 to 1,023 m3/ton, the blue WFs from 521 to 1,402 m3/ton, the gray WFs from 337 to 822 m3/ton, and the white WFs from 701 to 2,301 m3/ton. The average total WF for irrigated areas among all the selected provinces is about 3,188 m3/ton, with almost equal shares of blue and green water. For rain-fed areas, the green WFs ranged from 1,282 to 4,166 m3/ton and the gray WFs from 100 to 740 m3/ton. The average total WF for rainfed areas is about 3,071 m3/ton with the share of green WF being nine times the gray WF. In irrigated areas, the percentages of green, blue, gray and white WFs are 23, 25, 17 and 35% of total WF, respectively in each province. The average total WF for irrigated areas is about 3,188 m3/ton with comparable shares of blue and green water. In irrigated areas, Fars, Khorasan and Khuzestan provinces have the largest of the total WF with 5,575, 5,028 and 4,123 MCM/year, respectively. In addition to large cultivated areas and high rates of potential evapotranspiration, high values of gray and white water is another reason for the high volume of total WF in these provinces.
Conclusions: The results showed that the green WF related about wheat production in our country is about 2.3 times the blue WF. It confirmed the importance of green water in wheat production. Also the gray water footprint was assessed which is related about nitrogen application. Besides, the white water footprint was proposed here, which represents irrigation water losses. Results showed that the total water footprint in wheat production for the whole country is about 42,143 MCM/year on average over the period of 2006-2012. The ratios of green, blue, gray and white water were 41, 18, 16 and 25%, respectively. Different compartments of wheat WF were estimated for 236 plains over fifteen selected provinces. Total shares of gray and white water footprint were 41% of total wheat production water footprint. The average total WF for irrigated areas among all selected provinces is about 3,188 m3/ton, with almost equal shares of blue and green water. The authors admit that the accuracy of these results is subject to the quality of the input data.
vahid Rezaverdinejad; M. Hemmati; H. Ahmadi; A. Shahidi; B. Ababaei
Abstract
In this study, the FAO agro-hydrological model was investigated and evaluated to predict of yield production, soil water and solute balance by winter wheat field data under water and salt stresses. For this purpose, a field experimental was conducted with three salinity levels of irrigation water include: ...
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In this study, the FAO agro-hydrological model was investigated and evaluated to predict of yield production, soil water and solute balance by winter wheat field data under water and salt stresses. For this purpose, a field experimental was conducted with three salinity levels of irrigation water include: S1, S2 and S3 corresponding to 1.4, 4.5 and 9.6 dS/m, respectively, and four irrigation depth levels include: I1, I2, I3 and I4 corresponding to 50, 75, 100 and 125% of crop water requirement, respectively, for two varieties of winter wheat: Roshan and Ghods, with three replications in an experimental farm of Birjand University for 1384-85 period. Based on results, the mean relative error of the model in yield prediction for Roshan and Ghods were obtained 9.2 and 26.1%, respectively. The maximum error of yield prediction in both of the Roshan and Ghods varieties, were obtained for S1I1, S2I1 and S3I1 treatments. The relative error of Roshan yield prediction for S1I1, S2I1 and S3I1 were calculated 20.0, 28.1 and 26.6%, respectively and for Ghods variety were calculated 61, 94.5 and 99.9%, respectively, that indicated a significant over estimate error under higher water stress. The mean relative error of model for all treatments, in prediction of soil water depletion and electrical conductivity of soil saturation extract, were calculated 7.1 and 5.8%, respectively, that indicated proper accuracy of model in prediction of soil water content and soil salinity.
B. Ababaei; V. R. Verdinejad
Abstract
In this research, replacement of hydraulic models with statistical models and artificial neural networks were studied in order to estimate the criteria of pressurized irrigation systems hydraulic performance. The Coefficient of Uniformity of Christiansen (CU) was accepted as a hydraulic performance index. ...
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In this research, replacement of hydraulic models with statistical models and artificial neural networks were studied in order to estimate the criteria of pressurized irrigation systems hydraulic performance. The Coefficient of Uniformity of Christiansen (CU) was accepted as a hydraulic performance index. Using an automated algorithm, the values of this index were calculated for different combinations of inlet pressure, number and spacing of outlets, pipe roughness coefficient, inside diameter, slope, outlets nominal outflow and pressure and the exponent of the formula of outlet outflows (x) (4320 different combinations). Two different architecture of artificial neural networks were studied including a multi-layer perceptron (MLP) model and a generalize regression model (GRNN). Again, K-nearest neighbor (KNN) algorithm, as a nonparametric regression model was analyzed too. The results showed that MLP model could estimate the CU values of pressurized irrigation system laterals very closely (2-3% error) using its hydraulic and physical characteristics. The performance of GRNN model was also acceptable, especially related to the whole data set. But, the KNN algorithm was unable to predict standard deviation of CU values, although it was capable in estimating the mean value. The deviations of the KNN algorithm were the largest among all the models. The lowest values of error indices of the KNN algorithm was related to the K values of 10 and 15. The results of this study revealed the possibility of simplification of sophisticated hydraulic models by replacing the whole or some parts of these models with simpler statistical models and artificial neural networks. This is very interesting because of the complexity of hydraulic models, especially in optimization processes of irrigation systems.
B. Ababaei; T. Sohrabi
Abstract
چکیده
از جمله روشهای مرسوم طراحی سیستمهای آبیاری تحت فشار، میتوان به روش افت بار واحد (حداکثر گرادیان هیدرولیکی)، روش حداکثر سرعت جریان و روش درصد افت بار اشاره ...
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چکیده
از جمله روشهای مرسوم طراحی سیستمهای آبیاری تحت فشار، میتوان به روش افت بار واحد (حداکثر گرادیان هیدرولیکی)، روش حداکثر سرعت جریان و روش درصد افت بار اشاره کرد. در این مطالعه، یک الگوریتم دو مرحله ای برای طراحی سیستمهای آبیاری تحت فشار معرفی شده و در محیط برنامه نویسی LINGO توسعه داده شد. نتایج این مدل در یک سیستم آبیاری بارانی فرضی شامل سه لولة آبرسان (منیفلد) با روشهای فوق مقایسه شد تا عملکرد این الگوریتم مورد ارزیابی قرار گیرد. نتایج نشان داد که الگوریتم بهینه سازی، به کاهش هزینههای یک سیستم آبیاری کوچک تا بیش از 3 درصد و حصول یکنواختی توزیع مطلوب منجر می شود. همچنین مشاهده گردید که طراحی براساس معیار حداکثر گرادیان هیدرولیکی m.m-1 01/0، به بالاترین مقدار یکنواختی توزیع منجر شده و پس از آن به ترتیب مدل بهینه سازی اقتصادی، روش حداکثر گرادیان هیدرولیکی m.m-1 02/0 و روش حداکثر سرعت مجاز قرار گرفتند. مقدار انحراف استاندارد دبی واقعی خروجی از هر آبپاش نسبت به دبی اسمی آبپاشهای به کار رفته در سیستم، برای روشهای طراحی بهینه سازی، حداکثر گرادیان هیدرولیکی m.m-1 01/0، حداکثر گرادیان هیدرولیکی m.m-1 02/0 و روش حداکثر سرعت مجاز، به ترتیب 1/0، 03/0، 05/0 و 12/0 لیتر در ثانیه برآورد شدند.
واژههای کلیدی : الگوریتم بهینه سازی اقتصادی، آبیاری تحت فشار، LINGO، WaterGEMS
