Irrigation
B. Sadeghi; B. Farhadi Bansouleh; A. Bafkar; M. Ghobadi
Abstract
IntroductionThe rapid growth of the world's population, followed by an increase in the need for water, has put great pressure on water resources, so it is necessary to plan for the optimal use and increase of efficiency of this vital resource. Sunflower is one of the most important oilseed crops that ...
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IntroductionThe rapid growth of the world's population, followed by an increase in the need for water, has put great pressure on water resources, so it is necessary to plan for the optimal use and increase of efficiency of this vital resource. Sunflower is one of the most important oilseed crops that is mainly cultivated in Kermanshah province. Therefore, determining the appropriate sowing time of this crop for maximum production and water use efficiency is of particular importance. Because field experiments are costly and time-consuming, researchers use crop growth simulation models to determine the optimal planting time for each crop in a specific environment and climate. The use of simulation models minimizes the limitations of field experiments and allows the analysis of plant responses to environmental stresses and management scenarios. The objective of this study was to determine the optimal planting date of the Farrokh sunflower cultivar in four regions of Kermanshah province (Kermanshah, Islam Abad, Sarpol Zahab, and Kangavar) in order to maximize yield and water use efficiency using the AquaCrop model.Materials and MethodsA field experiment was conducted at the Research Farm of Razi University, Kermanshah, Iran in order to calibrate and validate the crop parameters in the AquaCrop model. The experiment was performed in a randomized complete block design with eight irrigation treatments in three replications. The irrigation treatments were the application of 60, 80, 100, and 120% of irrigation requirement (T1, T2, T3, and T4), 20 and 40% deficit irrigation in vegetative phase (T5 and T6), and 20 and 40% deficit irrigation in reproductive phase (T7 and T8). The crop water requirement was calculated based on the daily weather data collected from an automated meteorological station at the Research Farm using the FAO Penman-Monteith equation. During the growing season, canopy cover, biomass, and soil moisture were measured weekly. The crop parameters were calibrated based on the measured data in treatments T1, T3, T6, and T7 and validated with four treatments T2, T4, T6, and T8. In the calibration and validation stages, the statistical indices including compatibility index (d) and root mean square error (RMSE) were used to evaluate the model outputs. The calibrated model was used to simulate crop growth based on daily weather data for 30 years (1988-2017) in four synoptic stations in Kermanshah province (Kermanshah, Islam Abad, Sarpol Zahab, and Kangavar) and for several different planting dates. The crop water productivity was calculated based on simulated grain yield and seasonal crop evapotranspiration. Finally, the model outputs under different planting dates were analyzed to determine the most appropriate planting time from the perspective of maximum production and maximum water use efficiency.Results and Discussion Statistical indicators show that the model has simulated the parameters of biomass, crop canopy, and soil moisture in the calibration stage with good accuracy. T1 and T6 treatments in biomass simulation, T7, T6, and T3 treatments in crop canopy simulation, and T3 and T7 treatments in soil moisture simulation had the highest accuracy. The accuracy of the model outputs in the validation stage for biomass and canopy cover was as accurate as in the calibration stage, while the accuracy of the simulated soil moisture in the validation stage was not high except in T4 treatment. Based on the model results, grain yield, seasonal evapotranspiration and water productivity were determined. According to the results, it can be said that in the study period (1988 -2017), grain yield has generally increased with a slight slope. The results showed that the planting date, which maximizes grain yield and water productivity, varies in the studied regions. According to the model results, planting in the second decade of May and the second decade of June will lead to the highest grain yield and water productivity in Kermanshah, respectively. Planting in the third decade of May showed the highest grain yield and crop water productivity in Islam Abad. In Sarpol Zahab, which has the highest temperature among the studied stations, planting in the last decade of March and the first decade of April has the highest grain yield and water productivity, respectively. In Kangavar, which is located in the east of Kermanshah province and has the coldest climate, by cultivating sunflower in the last decade of May and the first decade of June, respectively, the highest grain yield and water productivity can be achieved.ConclusionDue to the fact that some crop parameters of crop growth simulation models are variety specific, in this study, the crop parameters of the AquaCrop model for Farrokh sunflower cultivar were calibrated and validated. The accuracy of the calibrated model for estimating biomass and canopy cover was higher than soil moisture. The simulation results showed that the values of the studied parameters (grain yield and seasonal evapotranspiration) have changes according to the planting time in each region. The highest crop yield can be obtained in Sarpol Zahab, Islam Abad, Kermanshah, and Kangavar regions (west to east of the province) by cultivation in the last decade of March, last decade of April, the second decade of May, and last decade of May, respectively. In all study areas except Islamabad, planting date that resulted in maximum water productivity was different from the planting date that had maximum grain yield station and delayed planting had the highest water productivity.
N. Afshar Bakeshloo; K. Zarafshani; B. Farhadi Bansouleh
Abstract
Introduction: Kermanshah Province with one million hectares of arable land play an important role in food security and economy of Kermanshah province. For example, Kermanshah province holds third in wheat yield per hectare; second in chickpea production; third in maize production; third in sugar beet ...
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Introduction: Kermanshah Province with one million hectares of arable land play an important role in food security and economy of Kermanshah province. For example, Kermanshah province holds third in wheat yield per hectare; second in chickpea production; third in maize production; third in sugar beet yield per hectare; and seventh in tomato production. However, unsustainable behavior of farmers in one hand and overuse of water consumption have depleted water reserves which in turn has developed “prohibited plains” in the region. For example, several regions such as Mahidasht, Islamabad, and Kangavar are consider as forbidden areas and still extending in size. In addition, with the continued overuse of water resources we will soon experience huge sinkholes across the province. Therefore, there is a need to study the content and value of virtual water in order to zone cultivated areas based on virtual water. This could be an effective way to maintain water resources and prevent environmental crises.
Materials and Methods: This study used quantitative documentary research method. Using secondary data source, we collected data from various sources such as FAO data bank, Agricultural Jihad Organization data source, Meteorological organization, Agricultural Research Center, and Department of Soil and Water Management. This documentary research sought to investigate the content and value of virtual water used in irrigated and rainfed farming across wheat, barley, chickpea, maize, sugar beet, and tomatoes during 2014-2015 using CROPWAT, AGWAT, and EXCEL software. In addition, the share of green and blue virtual water was estimated in the study. Finally, 12 provinces were zoned from classes A to Z based on virtual water content and value of the products.
Results and Discussion: Results revealed that wheat with 1.96 to 3.68 m3/kg is the most consumable product that about 60 percent of the cultivated areas of wheat are located in areas of the province that are inappropriate in terms of content and value of virtual water. Also, tomato with the value of 0.09 to 0.38 m3/kg had the lowest virtual water content and average value of virtual water. According to the results, the sugar beet product is in desirable condition in terms of virtual water content and had moderate conditions in virtual water value. Finally, about 80% of maize produced in areas that are not in a desirable position in terms of virtual water content and value.
Results of green and blue virtual water showed that spring products such as sugar beet, tomatoes and maize received their water requirement from surface and groundwater resources. In addition, the largest blue component of wheat was related to Harsin city and the lowest was related to Javanrod city. For irrigated barley, the smallest and the largest share of blue virtual water were related to cities of Qasr Shirin and Sahne respectively.
Conclusion: Overall, the results of this study revealed that irrigated wheat and barley have a poor condition in terms of the content and the value of virtual water. However, since wheat and barley are considered as a strategic products policymaker should take appropriate measures in order to provide sustainable cultivation of wheat and barley. For example, improved farming, plant breeding, changing the growing season according to climatic conditions, developing cultivation in suitable areas, as well as applying appropriate pricing and support policies, including training of beneficiaries, and improving insurance policies could provide appropriate measures if Iran is to be self-sufficient in wheat and barley production.
Results of this study has practical significance for agricultural policymakers in Iran in general and Kermanshah province in particular. For example, zoning of crop cultivation based on the content and value of virtual water provided in this study can be an effective tool in modelling cropping pattern and sound water management policies. In addition, effective cropping pattern as well as sound water management resources would encourage farmers to engage in climate smart agriculture. Moreover, cultivation zoning based on content and value of virtual water is considered as a climate smart agriculture technique. This in turn would create resilient farming system in the study area. Through resilient farming system, farmers better adapt to climatic condition more effectively.
Bahman Farhadi Bansouleh; Alireza Karimi; Hoamyoun Hesadi
Abstract
Introduction: Evapotranspiration (ET) is one of the key parameters in water resource planning and design of irrigation systems. ET could have spatial variations in a plain due to the diversity of plant species and spatial variability of meteorological parameters. Common methods of ET measurement are ...
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Introduction: Evapotranspiration (ET) is one of the key parameters in water resource planning and design of irrigation systems. ET could have spatial variations in a plain due to the diversity of plant species and spatial variability of meteorological parameters. Common methods of ET measurement are mostly point based and generalization of their results to the regional level are costly, time consuming and difficult. During the last three decades, several algorithms have been developed to estimate regional ET based on remote sensing techniques. Verstraeten et al. (2008) classified remote sensing-based methods for ET estimation into four categories i) methods based on the surface energy balance, ii) Penman-Monteith equation, iii) water balance and iv) the relationship between surface temperature and vegetation indices. SEBS (Surface Energy Balance System), SEBAL, METRIC and SEBI are examples of the algorithms which is developed based on the surface energy balance approach. SEBS is developed by Su (2002) and has been evaluated by several researchers. However this algorithm has been examined in the several studies in the world,it has been used rarely in Iran. The aim of the current study was to assess the results of SEBS algorithm in Mahidasht, Kermanshah, Iran. The study area is located at the latitude of 34º 5' – 34º 32' N and longitude of 46º 31' - 47º 06' E.
Materials and Methods: A brief description of the SEBS algorithm (in Persian) as well as its procedure to calculate ET based on Landsat images were presented in this paper. All equations of the algorithm were coded in the ERDAS Imagine package software using model maker tools. Actual ET over the study area was estimated using SEBS algorithm during the growth period of grain maize in the year 2010. For this purpose, available LANDSAT TM satellite images during the growing season of maize in 2010 (25 June, 11 July, 27 July and 12 August) were downloaded free of charge from the http://glovis.usgs.gov website (last visited: 26 November 2015).
A Lysimetric study was carried out to obtain reliable amounts of ET to assess the accuracy of calculating actual ET by SEBS algorithm. Because of the absence of the weighing Lysimeters in the study area, Drainable Lysimeter was used. Since the maize was the major crop in the study area, 10 ha maize was planted on 15 May 2010 at the research farm of the Mahidasht agricultural research station. At the same time, maize was cultivated in the Drainable Lysimeter (1m*1.5m*1.5m) which was located almost in the middle of the research farm. Actual ET of maize was calculated with the Lysimeter for each irrigation interval (10 days) based on water balance equation.
The Results of the SEBS algorithm were evaluated on two levels (farm and regional). At the farm level, average of calculating ET at the pixels of research farm was compared with the average of measured ET at the Lysimeter. The absolute and relative differences between the calculated and measured values of ET was used to describe the accuracy of the algorithm. Due to the absence of regional ET measurement, maximum ET estimated by the SEBS algorithm in the plain was compared with the calculated potential crop reference evapotranspiration (ETO). ETO was calculated using the Penman - Monteith formula based on daily weather data obtained from Mahidasht weather station.
Results and Discussion: Results indicated that an average of ET in the study area increased from June to August which coincides with increasing air temperature and vegetation density in the irrigated fields of the study area. The highest and lowest values of actual ET over the study area were determined in the irrigated farms and mountainous area, respectively. The results of Lysimetric study indicated that daily actual ET of maize on 25 June, 11 July, 27 July and 12 August was 4.13, 7.74, 7.45 and 8.05 mm.day-1, respectively. The value of ET estimated by SEBS algorithm was less than actual measured ET by Lysimeter for the all mentioned dates. The maximum absolute difference between estimated ET by SEBS and measured ET with the Lysimeter was occurred on 27 July with the amount 0.34 mm.day-1. Considering the maximum relative difference of 4.56 % between calculated and measured ET, it could be concluded that estimated ET by SEBS algorithm can be acceptable.
Due to the absence of ground-based measurements of evapotranspiration at the regional level, the maximum amount of ET estimated by SEBS algorithm was compared with ETO. The highest and lowest ratio of maximum ET over ETO were calculated as 1.02 and 1.22 which are acceptable values for the crop coefficient (Kc) in the studied period. The maximum difference between estimated ET by SEBS algorithm with ETO was 1.53 mm.day-1 which is equal to 21.86% of ETO in the same date (12 August).
Conclusions: The results of the current study showed that the SEBS algorithm can estimate the actual ET of maize with the acceptable accuracy in the Mahidasht. In the absence of measured ET data at the regional level, it was difficult to have a reasonable judgment on the accuracy of the estimated values of ET by SEBS algorithm at this scale. It is recommended to do the same study on other remote sensing-based approaches of ET estimation.
M. Esmaeili; Bahman Farhadi Bansouleh; M. Ghobadi
Abstract
Introduction: Expansion of the area of oilseed crops such as soybean is one of the policies of Iranian agricultural policy makers as Iran is one of the major oilseed importers in the world. However, the area of this crop in Kermanshah province is negligible, but it could be cultivated in most parts ...
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Introduction: Expansion of the area of oilseed crops such as soybean is one of the policies of Iranian agricultural policy makers as Iran is one of the major oilseed importers in the world. However, the area of this crop in Kermanshah province is negligible, but it could be cultivated in most parts of this province. The quantity and quality of the produced grain could be affected by environmental factors such as weather parameters and water availability. The aim of the current study was to investigate the effects of levels of deficit irrigation on the quantity and quality of soybean crop yield in Kermanshah, Iran.
Materials and Methods: For this purpose, a field study was conducted as randomized complete block design with four replications and four irrigation treatments at the research farm of Razi University, Kermanshah in 2012. The size of each plot was 4 * 4 m. Irrigation treatments consisted of four irrigation levels: 20% over irrigation (T4), full irrigation (T3 as control), 20% less irrigation (T2) and 40% less irrigation (T1). The reason to choose T4 treatment was the lack of confidence in estimated crop evapotranspiration as there was no local calibration of crop coefficient (Kc) for this crop. The required water for T3 treatment was calculated based on daily weather data using FAO-Penman-Montith equation. Daily weather data was recorded in a weather station which was located in the research farm and is available in the www.fieldclimate.com. As there was no rainfall during the crop season, all of the required water was supplied through irrigation. The required water for treatments of T1, T2 and T4 was considered as 60%, 80% and 120% of T3 treatment. The required water was applied using a hose connected to a volumetric flow meter with a liter precision. Total amount of applied water during the crop season was 4399, 5865, 7331 and 8797 m3.ha-1 in the treatments. Fertilizers were applied based on the recommendations of soil fertility experts. Weeds were controlled manually. Finally, the area of two square meters in the middle of each plot was harvested in order to determine crop yield in terms of grain, biomass, stem, pod, seed protein content and fat percentage and also water productivity index. Dry weights of the samples were measured after drying samples in the oven for48hours at 70° C. The percentage of fat and protein in the grains are also measured in the laboratory. Water productivity index was calculated for each treatment by dividing crop yield (in terms of grain, biomass, protein and fat) over seasonal water use. Statistical analysis of the results is also done using MSTATC software.
Results and Discussion: The highest and lowest crop yields were measured respectively in the treatments T4 and T1.The mean value of grain yield was 1084, 1367, 1716 and 1940 kg.ha-1,respectively in the treatments T1, T2, T3 and T4. These results showed a 36% decrease in the grain yield by decreasing 40% in the amount of supplied water. However, biological yield was decreasedby the level of irrigation, but the rate of reduction was lower than that of grain yield. By reducing irrigation application, thepercentage of grain protein content increased while the percentage of fat in the grain decreased. Considering simultaneous reduction in grain yield and fat content in the grain, severe reductions in fat yield (oil content) were observed under water stress conditions. Crop yield in terms of fat was reduced by 26.2 and 50.1 %, respectively in treatments T2 and T1 in comparison with T3 (control treatment). The maximum and minimum percentages of protein in the treatments were 31% and 27%, respectively in the treatments T1 and T4. Maximum water productivity in terms of grain, biomass and protein was achieved in T1 treatment respectively with the amounts of 0.24, 0.81 and 0.077 kg.m-3. Maximum and minimum fat percentage was 0.052 and 0.040 kg.m-3, respectively in the T4 and T1 treatments. In addition,the results indicated that water productivity index in terms of grain, biomass and protein increased while they decreased in terms of fat yield.The results of statistical analysis indicated that water productivity index in all terms except protein had significant differences (at 5%) with T3 treatment.
Conclusion: Crop yield and water productivity (except in terms of fat) was increased by increasing applied water. Considering all indices of treatment T2 (20% deficit irrigation), itwas suggested as the best treatment.
