Agriculture
Fariba Niroomandfad; Abbas Khashei Siuki; Seyed Reza Hashemi; Khalil Ghorbani
Abstract
Investigating and identifying the actual amount of water used for different agricultural products is of particular importance, and considering such evaluations, appropriate solutions can be provided to reduce agricultural water consumption, which is of great importance. The water footprint index as a ...
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Investigating and identifying the actual amount of water used for different agricultural products is of particular importance, and considering such evaluations, appropriate solutions can be provided to reduce agricultural water consumption, which is of great importance. The water footprint index as a global index shows the actual amount of water consumed by products based on the conditions and climate of each region. On the other hand, the phenomenon of climate change is one of the most important environmental challenges that has a significant impact on water resources. Therefore, it is important and necessary to evaluate this phenomenon in order to predict its impact on water consumption in the agricultural sector. In this research, the simulation of climate parameters using the MIROCES2L model of the 6th Report of the General Oceanic Atmospheric Circulation under three scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 in Birjand plain has been done and then using those results to calculate the footprint prediction The blue water and green water traces of the strategic product of saffron were carried out in the Birjand Plain region. The results of the first part showed that the minimum temperature and maximum temperature in all three scenarios in the future (2050-2022) generally increased and the precipitation parameter increased in autumn and winter and decreased in spring and summer. In the second part, the prediction of saffron crop performance by NIO model showed that under three scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the future (2022-2038) on average 0.13, 0.21 respectively and 0.05 kg/ha has decreased compared to the observation period (2005-2021) and the results showed that with the increase in water demand in the future period, the water footprint, the green water footprint and the total water footprint of the saffron crop under the influence of climate change In the future period, it has increased by almost 2 times compared to the observation period. Also, the ratio of blue water consumption to green water in this product has increased in the future under all three scenarios compared to the observation period from 1.91 to 2.04. Therefore, despite the phenomenon of climate change, increase in temperature, increase in water demand, and finally increase in the footprint of water consumption in surface and underground water sources in the Birjand plain in the coming years, it is necessary to implement a suitable model of water consumption in the plain and To use appropriate and effective solutions to reduce the water footprint in the study area, the methods of reducing the area under cultivation, less irrigation, changing the cultivation pattern and changing the agricultural calendar should also be proposed and implemented.
Agriculture
Ahmad Jafarzadeh; Abbas Khashei Siuki; Ali Shahidi
Abstract
Ensemble modelling is expanding in several areas of engineering, especially different aspects of water engineering. Accurate estimation of saffron water requirement (SWR), an essential strategic production of the agriculture sector, is a crucial and influencing act in local water planning of this region. ...
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Ensemble modelling is expanding in several areas of engineering, especially different aspects of water engineering. Accurate estimation of saffron water requirement (SWR), an essential strategic production of the agriculture sector, is a crucial and influencing act in local water planning of this region. Hence, this study aimed to check the applicability of ensemble modelling in enhancing SWR at Birjand, Southern Khorasan, Iran. The actual water requirement of saffron was recorded in the field lysimetric laboratory at the University of Birjand. The simulation of water requirement was conducted utilizing Decision Tree Regression (DTR) with input climate features. Additionally, Boosting and Bagging methods were employed to establish and enhance the ensemble process of soil water requirement (SWR) simulations. To track the effectiveness of any method, some comparative tests were designed, such as statistical criteria (RMSE and MAE) detection, Violin plot analysis, over/underestimation, times series comparison, and error improvement test. Results indicated that although the acceptable performance of DTR in simulating SWR, the probable improvement was potentially felt. Derived results confirmed that supervised ensemble modelling (Boosting) could enhance the accuracy of DTR by more than 30 percent (reducing absolute error from 36 mm to 23.65 mm), resulting in declining RMSE from 0.44 mm to 0.07 mm. Further, different experiment outcomes revealed that the Boosting algorithm quality is more appealing than DTR and Bagging outputs.
Other subject about saffron
Fahime KHadempour; Abbas Khashei Siuki; Mohammad Ali Behdani
Abstract
Saffron as the most precise agricultural and pharmaceutical product of the world has a specific place in industrial and export products of Iran. Nowadays, Iran is the largest producer and exporter of saffron in world and up to 93.7% of production of this valuable commodity belongs to Iran. Despite the ...
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Saffron as the most precise agricultural and pharmaceutical product of the world has a specific place in industrial and export products of Iran. Nowadays, Iran is the largest producer and exporter of saffron in world and up to 93.7% of production of this valuable commodity belongs to Iran. Despite the antiquity of saffron cultivation and added value of this product compared to other current crops of Iran, fewer shares of new technologies are dedicated to saffron and its production is mainly based on indigenous knowledge.In thispaper, multiple models are employed to evaluate and develop the performance of KStar and LWL in order to get an estimate on production yield of saffron based on climate parameters. Thecalibration and evaluation of models are obtained from the statistics of crop yield and climate factors betweenyears 1988–2017. In order to evaluate the employed models, the following statistical criteria were used: Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Nash- Sutcliffe (NSE). From among the proposed models, the KStar model is in the e-scenario with an R2 of 1.00, MAE and RMSE of 0.00 and NSE of 1.00, which has good accuracy in estimating production yield of the saffron plant. This precision of the KStar model has made it easy to estimate performance of saffron in different areas of the country based on the data available at different stations.
Other subject about saffron
Seeboyeh Aghamohamadi; abbas khashei; Ali Shahidi; Sayyed Reza Hashemi
Abstract
Climate changes and phenomena such as drought are effective in the yield of agricultural products. Replacing crisis management with risk management is one of the solutions for these phenomena. With risk assessment before crisis, the amount of damages will be reduced to the minimum amount. In this research, ...
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Climate changes and phenomena such as drought are effective in the yield of agricultural products. Replacing crisis management with risk management is one of the solutions for these phenomena. With risk assessment before crisis, the amount of damages will be reduced to the minimum amount. In this research, the risk assessment of drought by Monte Carlo method will be used in order to reduce the damages caused by drought as a natural and uncontrollable phenomenon on saffron product. The monthly Standardized Precipitation Index (SPI) of drought and the monthly average temperature are calculated as independent variables in the yield distribution function. The relationship between independent variables (temperature and SPI) and dependent variable (saffron yield) is established using Artificial Neural Network (ANN). After that, 2000 random data from independent variables are generated using MATLAB and 2000 simulated yields generated by a trained artificial neural network. Then, the cumulative distribution of the simulated yields are determined and these yields are standardized in order to unification of the yield data of each city. The risk factor is calculated by choosing a reference station and using the cumulative distribution. The relative risks of the stations are considered after drawing the diagram of Yield-Risk standard factor. The results of the research show that most of the studied years are in normal range and the drought frequency in the four stations of Khorasan Jonoobi province is twice the stations in Khorasan Razavi. Furthermore, the artificial neural network with a correlation coefficient of 0.85 could predict the yield of the product very well. The similarity of the cumulative distribution diagram of the real yield with the cumulative distribution of the yields simulated by Monte Carlo indicates that the results are correct. At the end The results of this research show that Ghayen has the highest relative risk compared to the reference station (Torbat-e- Heydariyeh) and Nehbandan has the lowest one.
Agriculture
Abbas Khashei Siuki; Mohsen Ahmadee; Sayyed Reza Hashemi; Amin Chaalaak
Abstract
Saffron (Crocus sativus L.) is a subtropical and valuable crop which is reproduced by corms. Due to the importance of corm weight in saffron yield, it is important to study the different factors that affect yield such as drought stress. For this purpose, this research was conducted as a factorial design ...
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Saffron (Crocus sativus L.) is a subtropical and valuable crop which is reproduced by corms. Due to the importance of corm weight in saffron yield, it is important to study the different factors that affect yield such as drought stress. For this purpose, this research was conducted as a factorial design based on completely randomized design (CRD) in the Agricultural Research Station of the University of Birjand during the period 2013-2015. The treatments consisted of Zeolite at four rates (0: Z0, 0.5: Z1, 1: Z2 and 2: Z3 as weight percentage) and irrigation management at three levels (traditional: I1, deficit irrigation as 70% moisture depletion: I2 and full irrigation: I3) with three replications. The results showed that zeolite rates has a significant effect on corm weight, number of corms less than 2gr, number of 6-8gr corms and number of replacement corms (P≤0.01). Irrigation management also has a significant effect on corm weight (P≤0.01), number of corms 6-8gr and number of replacement corms (P≤0.05). The treatments with no zeolite amended (Z0I1, Z0I2 and Z0I3) showed a reduction in corm weight compared to Z3I3 (P≤0.05). Z3I3, Z3I2 and Z3I3 showed an increase in the number of replacement corms while Z0I1 and Z0I2 had the least number of replacement corms. In conclusion, Z2I1 is recommended as the best treatment by considering the reduction in zeolite and water used, which increased corm weight by 26.64%, 23.88% and 17.81% compared to Z0I1, Z0I2 and Z0I3, respectively.
