| تعداد نشریات | 285 |
| تعداد نشریات سازمان | 83 |
| تعداد شمارهها | 3,139 |
| تعداد مقالات | 34,918 |
| تعداد مشاهده مقاله | 50,384,145 |
| تعداد دریافت فایل اصل مقاله | 90,277,407 |
شبیهسازی اثر تغییر اقلیم بر عملکرد، بهرهوری آب و طول دوره رشد چغندرقند در قزوین با استفاده از مدل AquaCrop | ||
| چغندرقند | ||
| مقاله 3، دوره 40، شماره 2، مهر 1403، صفحه 165-184 اصل مقاله (796.56 K) | ||
| نوع مقاله: کامل علمی - پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/jsb.2025.365259.1351 | ||
| نویسندگان | ||
| قاسم مرعی1؛ اصلان اگدرنژاد* 2؛ نیازعلی ابراهیمی پاک3 | ||
| 1دانشجوی کارشناسی ارشد منابع آب، گروه علوم و مهندسی آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران. | ||
| 2استادیار، گروه علوم و مهندسی آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران. | ||
| 3بخش آبیاری و فیزیک خاک، موسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران | ||
| چکیده | ||
| تغییر اقلیم از عواملی است که میتواند در دهههای آینده اثرات بسیاری بر رشد و عملکرد گیاهان زراعی داشته باشد. چغندرقند یکی از محصولهای راهبردی است که رشد و عملکرد آن تحت تأثیر دو تنش آبی و کودی تغییر میکند، بنابراین اطلاع از تأثیر این تنشها بر گیاه در مزرعه و تغییر اقلیم برای مدیریت تولید چغندرقند ضروری است. برای دستیابی به این هدف، پژوهش حاضر در ایستگاه تحقیقاتی فیضآباد قزوین در طی سه سال زراعی انجام شد. عوامل مورد بررسی شامل مدیریت آبیاری در چهار دور آبیاری (I1، I2، I3 و I4 به ترتیب 6، 9، 12 و 15 روزه) و عامل کودی (از منبع اسید بوریک) در سه سطح (F0: 21، F1: 30 و F2: 39 کیلوگرم در هکتار به ترتیب نشاندهندهی سطوح کم، متوسط و زیاد بود. برای شبیهسازی اثر تغییر اقلیم، دادههای هواشناسی بر اساس سناریوهای SSP2-4.5 (حد واسط) و SSP5-8.5 (خیلی بدبینانه) و تحت دو مدل GFDL-ESM4 (مدل G) و MRI_ESM2-0 (مدل M) در طی دورهی 64 ساله، 1404 الی 1468 شبیهسازی شدند. برای شبیهسازی اثر تغییر اقلیم بر عملکرد و رشد چغندرقند از مدل AquaCrop استفاده شد. نتایج شبیهسازی مدل AquaCrop نشان داد که در دوره زمانی آینده نزدیک میانگین عملکرد ریشه در سناریوهای خوشبینانه و بدبینانه در مدل G به ترتیب 40746 و 38046 کیلوگرم در هکتار و در مدل M به ترتیب 41546 و 38746 کیلوگرم در هکتار خواهد بود. در دوره زمانی آینده دور، متوسط عملکرد ریشه برای دو سناریوی خوشبینانه و بدبینانه در مدل G به ترتیب 52077 و 49377 کیلوگرم در هکتار و در مدل M به ترتیب 52877 و 50077 کیلوگرم در هکتار محاسبه شد، بنابراین، بهصورت متوسط، در آینده بیش از 40 درصد عملکرد نسبت به شرایط فعلی کاهش مییابد. برای جبران این کاهش عملکرد، با در نظر گرفتن شرایط فعلی مدیریت آب و کود در مزرعه، افزایش تراکم بوته توصیه میشود. | ||
| کلیدواژهها | ||
| تنش آبی؛ تنش کودی؛ تغییر اقلیم؛ چغندرقند | ||
| عنوان مقاله [English] | ||
| Simulating the effect of climate change on sugar beet yield, water productivity and growing period in Qazvin using the AquaCrop model | ||
| نویسندگان [English] | ||
| Ghasem Marei1؛ Aslan Egdernezhad2؛ niaz ebrahimipak3 | ||
| 1M.Sc. Student of Water Resources, Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran. | ||
| 2Assistant professor, Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran. | ||
| 3Department of irrigation and soil physics, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. | ||
| چکیده [English] | ||
| Extended Abstract Introduction Global warming has accelerated since the industrial revolution, but due to human activities and environmental degradation, this phenomenon has received increased attention in recent years. Future trends in global warming are expected to be influenced by scenarios projecting continued increases in greenhouse gas emission, particularly carbon dioxide. These scenarios predict varying levels of carbon dioxide production worldwide, which in turn affect climate factors, crop growth, and yield. Changes in climate can significantly impact agricultural production environments. To assess and predict the extent of these impacts on crop growth and yield, crop simulation models are essential tools. Among these, the AquaCrop model is widely used due to its user-friendliness, high accuracy and reliability. This model is capable of simulating variouse field conditions and is applicable to a wide range of agricultural crops, including corn, canola, wheat, saffron, and quinoa.. Sugar beet is considered a strategic crop in Iran, with its yield highly dependent on irrigation water and fertilizer application. Materials and Methods All data were collected from a research field located at the Faizabad research station in Qazvin, Iran, over three agricultural years. The experimental factors included irrigation management at four intervals (I1: 6, I2: 9, I3: 12, and I4: 15 days) and fertilizer application at three levels (F0: 21, F1: 30, and F2: 39 kh ha-1). The LARS-WG model was used to simulate meteorological data for Qazvin under two climate change scenarios: an optimistic scenario (SSP2-4.5) and a pessimistic scenario (SSP5-8.5). These simulations were performed using two atmospheric general circulation models, GFDL-ESM4 (G model) and MRI_ESM2-0 (M model), across two future periods: 2026-2045 (near-future) and 2046-2099 (-uture). The AquaCrop model was then used to simulate sugar beet yield and water productivity based on these climate projections. Some statistical criteria were applied to evaluate the performance and accuracy of both the LARS-WG and AquaCrop models.. Results and Discussion During the calibration stage, the RMSE statistics for the AquaCrop model showed a yield prediction error of 3511 kg ha-1, placing its accuracy in the excellent category (NRMSE<0.1). For water productivity, the model’s error was 1.2 kg m-3, with accuracy evaluated as good (0.1<NRMSE<0.2). In the validation stage, the AquaCrop model showed some errors and underestimations in simulating yield and water productivity, as indicated by the mean bias error (MBE) exceeding acceptable thresholds. The RMSE for yield during validation was 2797 kg ha-1. The LARS-WG model was evaluated using observed and simulated climate data for the baseline period (1950-2014). While slight underestimation of temperature and overestimation of some parameters were observed, the RMSE values were small enough to be considered negligible. The NRMSE values for all three variables studied were below 0.1, indicating high model accuracy. Furthermore, the efficiency coefficient (EF) and the index of agreement (d) were both greater than 0.8, demonstrating strong model performance. The coefficient of determination (R²) exceeded 0.9, indicating that the LARS-WG model explained over 90% of the variance in the observed data. Comparing rainfall between models, the baseline rainfall predicted by model G was lower than that of model M. Future projections revealed that under the optimistic scenario, rainfall is expected to increase compared with the current conditions, while the pessimistic scenario predicts a slight decrease. Minimum temperature changes were less consistent; the pessimistic scenario showed minimal increases, whereas the optimistic scenario exhibited more variability. Model M predicted the lowest minimum temperature, while model G predicted an increase. These climatic changes imply an increase in evapotranspiration, which, along with effects on crop growth duration, can alter the length of growing period. Since increases in evapotranspiration surpass increases in minimum temperature, the overall length of the growing period is expected to decrease. Rainfall is projected to increase by approximately 6 mm in the near-future and 8 mm in the far-future. Correspondingly, the sugar beet growing period (time from planting to harvest) is predicted to shorten comapred with the baseline period. The average growth period was 160 days during the baseline period, decreasing to 154 days in the near-future and 153 days in the far-future. Simulations using models G and M for the near future under optimistic and pessimistic scenarios yielded growth periods of 156 and 152 days, respectively. For the far future, these values were 155 and 150 days, respectively. These findings indicate that, under climate change, the growth and development phases of many crops will shorten, with the magnitude depending on crop type and climatic severity. Yield changes under water and fertilizer stress mirrored observed treatments, suggesting similar trends will continue in coming decades. In model G, the average sugar beet yield for the near future under optimistic and pessimistic scenarios were 40746 and 38046 kg.m-3, respectively representing decreases of 40 and 44% compared with current conditions. Furthermore, yield reductions are projected to be more severe in the far-future than in the near-future. Conclusion Based on climate change scenarios and growth simulations using the Aquacrop model, the results showed a yield gap of approximately 40 and 70% between potential and actual sugar beet yield. To address this issue, it is recommended to increase the planting density compared with current practices. By reducing the difference between potential and actual evapotranspiration, this strategy may help narrow the yield gap and improve overall productivity under changing climatic conditions. | ||
| کلیدواژهها [English] | ||
| Climate Change, Fertilizer Stress, Sugarbeet, Water Stress | ||
| مراجع | ||
|
Akbari F, Shourian M, Moridi A. Assessment of the climate change impacts on the watershed-scale optimal crop pattern using a surface-groundwater interaction hydro-agronomic model. Agricultural Water Management. 2022; 265: 107508. Doi:https://doi.org/10.1016/j.agwat.2022.107508 Akumaga U, Tarhule A, Yusuf AA, Validation and testing of the FAO AquaCrop model under different levels of nitrogen fertilizer on rainfed maize in Nigeria, West Africa. Agricultural and Forest Meteorology. 2017; 232, 225–234. Doi: https://doi.org/10.1016/j.agrformet.2016.08.011 Alishiri R, Paknejad F, Aghayari F. Simulation of sugar beet growth under different water regimes and nitrogen levels by AquaCrop. Bioscience. 2014; 4(4): 1-9. Doi:https://doi.org/10.12692/ijb/4.4.1-9 Andarzian B, Bannayan M, Steduto P, Mazraeh H, Barati, ME, Barati MA, Rahnama A. Validation, and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agricultural Water Management. 2011; 100:1-8. Doi:https://doi.org/10.1016/j.agwat.2011.08.023 Behmanesh A, Egdernezhad A, Sepehri S. Evaluation of AquaCrop model in simulating safflower yield, biomass and water productivity under different Irrigation amounts. Iranian Journal of Soil and Water Research. 2021; 52(9): 2399-2413. Doi:https://doi.org/10.22059/ijswr.2021.327254.669026. (in Persian) Daneshfaraz R, razzaghpour H. Evaluation of climate change impacts on potential evapotranspiration in the West Azerbaijan province. Geographic Space. 2014; 46(4), 199-211. (in Persian) Ebrahimipak NA, Mostashari M. Evaluation of irrigation water management and boron fertilizer to increase water use efficiency of sugar beet. Water and Irrigation Management. 2013; 2(2), 53-67. Doi:https://doi.org/10.22059/jwim.2013.30340. (in Persian) Ebrahimipak NA, Tafteh A. Determination of yield –water use function for sugar beets in Qazvin. Journal of Sugar Beet. 2017; 33(1): 47-63. (In Persion with English abstract). Doi:https://doi.org/10.22092/jsb.2017.1319. Ebrahimipak NA, Ahmadee M, Egdernezhad A, KhasheiSiuki A. Evaluation of AquaCrop to simulate saffron (Crocus sativus L.) yield under different water management scenarios and zeolite amount. Journal of Water and Soil Resources Conservation. 2018; 8(1), 117-132. (in Persian). Farahani HJ, Izzi G, Steduto P, Oweis TY. Parameterization and evaluation of AquaCrop for full and deficit irrigated cotton. Agronomy. 2009; 101: 469-476. Doi:https://doi.org/10.2134/agronj2008.0182s Garcia-Vila M, Fereres E, Mateos L, Orgaz F, Steduto P, Deficit irrigation optimization of cotton with AquaCrop. Agronomy. 2009; 101: 477-487. Doi:https://doi.org/10.2134/agronj2008.0179s Geerts S, Raes D, Garcia M, Miranda R, Cusicanqui JA. Simulating yield response to water of quinoa (Chenopodium quinoaWilld) with FAO-AquaCrop. Agronomy. 2009; 101: 499-508. Doi:https://doi.org/10.2134/agronj2008.0137s Gholami A, Egdernezhad A, Ebrahimipak NA. Simulation of the effect of irrigation management on yield, biomass and water use efficiency of canola (Brassica napus L.) using AquaCrop model. Journal of Crop Ecophysiology. 2023; 17(2): 205-222. (in Persian). Doi:https://doi.org/10.30495/JCEP.2023.1930748.1804 Heng Lk, Hsiao T. C. Evett S, Howell T, Steduto P. Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agronomy. 2009; 101(3): 488-498. Doi:https://doi.org/10.2134/agronj2008.0029xs Huo Z, Dai X, Feng Sh, Kang Sh, Huang G. Effect of climate change on reference evapotranspiration and aridity index in arid region of China. Journal of Hydrology. 2013; 492, 24-34. Doi:https://doi.org/10.1016/j.jhydrol.2013.04.011 Hsiao TC, Heng LK, Steduto P, Raes D, Fereres E. AquaCrop-model parameterization and testing for maize. Agronomy. 2009; 101: 448-459. Doi: https://doi.org/10.2134/agronj2008.0218s Koocheki A, Nasiri Mahalati M. Climate change Effects on agricultural production of Iran: II. Predicting productivity of field crops and adaptation strategies. Iranian Journal of Field Crops Research. 2016; 14(1), 1-20. Doi:https://doi.org/10.22067/gsc.v14i1.51157. (in Persian) Kulan EG, Kaya MD. Effects of weed-control treatments and plant density on root yield and sugar content of sugar beet. Sugar Tech. 2023; 25: 805–819. Doi:https://doi.org/10.1007/s12355-023-01249-0 Kunz R, Schulze R, Mabhaudhi T, Mokonoto O. Modeling the potential impacts of climate change on yield and water use of sugarcane and sugar beet: preliminary results based on the AquaCrop model. South African SugarAssociation. 2014; 87: 285-289. Lhomme JP, Mougou R, Mansour M. Potential impact of climate change on durum wheat cropping in Tunisia. Journal of Climatic Change. 2009; 96(4), 549-564. Doi:https://doi.org/10.1007/s10584-009-9571-9 Malik A, Shakir AS, Ajmal M, Jamal Khan M, Ali Kan T. Canopy cover, biomass and root yield under different irrigation and field management practices in semi-arid regions of Pakistan. Water Resources Management. 2017; 31: 4275-4292. Doi:https://doi.org/10.1007/s11269-017-1745-z Mohammadzadeh Z, Soltani A, ajamnorozei H, Bazrgar AB. Modeling of sugar beet yield gap and potential in Iran. Journal of Sugar Beet. 2020; 36(1): 27-46. (In Persion with English abstract). Doi:https://doi.org/10.22092/jsb.2021.352324.1255 Moradi R, Koocheki A, Nassiri Mahallati M. Effect of climate change on maize production and shifting of planting date as adaptation strategy in mashhad. Journal of Agricultural Science and Sustainable Production. 2014; 23(4): 111-130. (in Persian) Raes D, Steduto P, Hsiao TC, Fereres E. AquaCrop-the FAO Crop model to simulate yield response to water II. Main algorithms and software description. Agronomy Journal. 2009; 101, 438-447. Doi:https://doi.org/10.2134/agronj2008.0140s Ranjbar A, Rahimikhoob A, Ebrahimian H. Evaluating Semi-Quantitative Approach of the AquaCrop model for simulating maize response to nitrogen fertilizer. Iranian Journal of Irrigation and Drainage. 2017; 11(2): 286-298. Saadati Z, Delbari M, Panahi M, Amiri E, Rahimian M, Ghodsi M. Evaluation of the effects of climate change on wheat growing period and evapotranspiration using the CERES-wheat model (Case Study: Mashhad). Water and Soil Science. 2016; 26(3), 67-79. (in Persian) Saadati Z, Delbari M, Amiri E. Simulation of sugar beet growth under water stress using AquaCrop model. Journal of Water and Soil Resources Conservation. 2018; 7(3): 1-19. (in Persian) Shirdeli A, Khani Temeliyeh Z, Fakhimi P, khani temeliyeh S, Mirabbasi-Najafabadi R. Evaluation of climate change and its effects on tomato yield in Abhar Plain. Water and Soil Management and Modelling. 2022; 2(1), 63-75. Doi:https://doi.org/10.22098/mmws.2022.9429.1041. (in Persian) Soltani A, Gholipoor M. Simulating the impact of climate change on growth, yield and water use of chickpea. Journal of agricultural sciences and natural resources. 2006; 13(2): 69-79. (in Persian) Steduto P, Hsiao TC, Raes D, Fereres E. AquaCrop-the FAO Crop model to simulate yield response to water I. Concepts and underlying principles. Agronomy Journal. 2009; 101, 426-437. Doi:https://doi.org/10.2134/agronj2008.0139s Stricevic R, Cosic M, Djurovic N, Pejic B, Maksimovic L. Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower. Agricultural Water Management. 2011; 98: 1615-1621. Doi: https://doi.org/10.1016/j.agwat.2011.05.011 Sun SK, Li C, Wu PT, Zhao XN, Wang YB. Evaluation of agricultural water demand under future climate change scenarios in the Loess Plateau of Northern Shaanxi, China. Ecological Indicators. 2018; 84: 811-819. Doi:https://doi.org/10.1016/j.ecolind.2017.09.048 Todorovic M, Albrizio R, Zivotic L, Abisaab M, Stwckle C. Assessment of AquaCrop, CropSyst and WOFOST models in the simulation of sunflower growth under different water regimes. Agronomy. 2009; 101: 509-521. Doi:https://doi.org/10.2134/agronj2008.0166s Van Gaelen H, Tsegay A, Delbecque N, Shrestha N, Garcia M, Fajardo H, Miranda R, Vanuytrecht E, Abrha B, Diels J, Raes D. Asemi-quantitative approach for modelling crop response to soil fertility: evaluation of the Aqua crop procedure. Journal of Agricultural Science. 2014; 1–16. Doi:https://doi.org/10.1017/S0021859614000872. | ||
|
آمار تعداد مشاهده مقاله: 286 تعداد دریافت فایل اصل مقاله: 217 |
||