پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,230 |
| تعداد مقالات | 67,764 |
| تعداد مشاهده مقاله | 115,183,461 |
| تعداد دریافت فایل اصل مقاله | 89,931,272 |
عارضه‌یابی و اولویت‌بندی عوامل اجرایی و مدیریتی مؤثر بر بهره‌وری آب در تولید نیشکر و ارائه راهکارهای عملی برای افزایش آن | ||
| مهندسی بیوسیستم ایران | ||
| مقاله 9، دوره 51، شماره 1، فروردین 1399، صفحه 99-112 اصل مقاله (959.96 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2019.289835.665228 | ||
| نویسنده | ||
| نسیم منجزی* | ||
| استادیار گروه مهندسی بیوسیستم دانشکده کشاورزی دانشگاه شهید چمران اهواز | ||
| چکیده | ||
| هدف از این پژوهش، عارضه‌یابی، شناسایی و اولویت‌بندی عوامل مؤثر بر بهره‌وری آب در تولید نیشکر در استان خوزستان می‌باشد. در این تحقیق، برای محاسبه بهره‌وری آب از شاخص‌هایی چونCPD،BPD وNBPDاستفاده شده است. با توجه به این که هدف تحقیق، اولویت‌بندی عوامل مؤثر بر بهره‌وری نهاده آب طی فرآیند تولید نیشکر است، عوامل شناسایی شده با استفاده از تکنیک تحلیل سلسله مراتبی (AHP) ارزیابی شدند. سپس در مدل‌سازی بهره‌وری نهاده آب و بررسی عوامل مؤثر بر آن از الگوریتم‌های تصمیم CART و CHAID استفاده شد. بر اساس نتایج تحقیق، متوسط شاخص‌های CPD، BPD و NBPD برای محصول نیشکر به ترتیب برابر 37/2 کیلوگرم بر متر مکعب، 71/1082 تومان بر مترمکعب، 03/528 تومان بر مترمکعب به دست آمد. متغیرهای بهره‌وری اقتصادی آب، ارزش فروش محصول (درآمد)، مقدار شکر تولیدی، هزینه‌های تولید، مصرف آب در هر دور آبیاری، فواصل آبیاری، هدایت الکتریکی خاک بعد از برداشت، هدایت الکتریکی آب رودخانه، متوسط زمان هر دور آبیاری، مساحت مزرعه، رقم گیاه، سن گیاه، تعداد دورهای آبیاری، pH آب خروجی زهکش و عرض خط کشت قبل از دیسکاور مهم‌ترین و تأثیرگذارترین متغیرها در الگوریتم‌های تصمیم CART و CHAID می‌باشند. دقت مدل CART در قسمت آموزش و تست به ترتیب برابر 96 و 92 درصد بوده است و دقت مدل CHAID در بخش آموزش و تست به ترتیب برابر 97 و 90 درصد می‌باشد. همچنین بر اساس اولویت‌بندی عوامل مؤثر بر بهره‌وری با استفاده از روش تحلیل سلسله مراتبی به ترتیب کمیت و کیفیت آب آبیاری، شرایط آب و هوایی، شرایط گیاه، عوامل مدیریتی و انسانی و شرایط خاک با ضریب 459/0، 231/0، 150/0، 091/0 و 069/0 رتبه بندی شدند. | ||
| کلیدواژه‌ها | ||
| آب؛ بهره‌وری؛ تحلیل سلسله مراتبی؛ درخت تصمیم؛ نیشکر | ||
| عنوان مقاله [English] | ||
| Diagnose and Prioritizing of Effective Managerial and Executive Factors on Water Productivity in Sugarcane Production and Providing Practical Solutions to Increase It | ||
| نویسندگان [English] | ||
| nasim monjezi | ||
| Assistant professor, Biosystems engineering Dept., Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| چکیده [English] | ||
| The purpose of this study was to identify, prioritize and control the factors affecting water productivity in sugar cane production. In this study, to calculate water productivity, the indices such as BPD and CPD, NBPD is used. Given that the research goal was to prioritize the factors affecting the productivity of water input during the sugar cane production process, the identified factors were evaluated using Analytical Hierarchy Process Analysis (AHP). Then, CART and CHAID decision trees were used in modeling the water input efficiency and the factors influencing it. According to the results, the average CPD, BPD and NBPD indices for sugar cane were 2.37 kg m-3, 1082.71 toman m-3, and 528.03 toman m-3, respectively. The variables of economic productivity of water, the value of sales of the product (income), the amount of sugar produced, production costs, water consumption per irrigation interval, irrigation intervals, electrical conductivity of the soil after harvest, river electrical conductivity, mean time of each irrigation interval, Plant height, plant age, number of irrigation cycles, drainage water drainage PH and width of the cultivating line before discovery are the most important and influential variables in the decision tree models of CHAR and CHAID. The accuracy of the CART model in training and testing was 96% and 92%, respectively, and the accuracy of the CHAID model in education and testing is 97% and 90%, respectively. Also, based on the prioritization of the factors affecting productivity, using the hierarchical analysis method, the quantity and quality of irrigation water, climate conditions, plant conditions, managerial and human factors, and soil conditions were ranked with a coefficient of 0.459, 0.231, 0, 0.091 and 0.069, respectively. | ||
| کلیدواژه‌ها [English] | ||
| Analytical Hierarchy process, Decision Tree, productivity, Water, Sugarcane | ||
| مراجع | ||
|
Abbasi, F. & Sheini Dashtegol, A. (2017). Evaluating and Improving the Sugarcane Furrow Irrigation Management in Khuzestan. Water and Soil Science- University of Tabriz, 26 (4.2), 109-121. (In Farsi) Aghabeigi, A., Gholami Sefid Koohi, M., Raeeini Sarjaz, M. & Yazdani, M. R. (2019). Evaluation and Comparison of Water Productivity in Traditional and Consolidated Paddy Fields in Astaneh Ashrafieh County. Water Research in Agriculture, 32.4 (4), 485-497. (In Farsi) Akbari, M., Shani Dashtgol, A. & hajisharafi, H. (2016). Evaluation of water consumption in sugar cane fields and water consumption reduction strategies. Case study: Amirkabir sugarcane cultivation and industry. Iranian National Irrigation and Drainage Congress. Isfahan University of Technology, 23-25 August. Esfahan. (In Farsi) Ali, M. H. & Talukder, M. S. U. (2008). Increasing water productivity in crop production –A synthesis. Agricultural Water Management, 95, 1201–1213. Amini, A., Porhemat, J. & Kazemi, S. (2017). Economic and physical productivity of water of cucumber and rapeseed products in eastern plains of Kurdistan province. First International Conference on Economic Planning, Regional Sustainable Development and Balanced Development. May 13-14. University of Kordestan (In Farsi) Arumugam, A. (2017). A predictive modeling approach for improving paddy crop productivity usingdata mining techniques. Turkish Journal of Electrical Engineering & Computer Sciences, 25, 4777-4787. Everingham, Y., Sexton, J., Skocaj, D. & Inman-Bamber, G. (2016). Accurate prediction of sugarcane yield using a randomforest algorithm. Agronomy for Sustainable Development, 36 (2), 27-36. Farhate, C. V. V., de Souza, Z. M., Oliveira , S. R., Tavares, R. L. M. & Carvalho, J. L. N. (2018). Use of data mining techniques to classify soil CO2 emission induced by crop management in sugarcane field. Plos One, 13 (3), 1-18. Heidari, N. (2011). Determination and evaluation of water use efficiency of some major crops under farmer’s management in Iran. Journal of Water and Irrigation Management, 1 (2), 43-57. (In Farsi) Heydari, N. (2014). Water productivity in agriculture: Challenges in concepts, terms and values. Irrigation and Drainage, 63(1), 22–28. Jafari, S., Noori, M., Naseri, A. A., Safari Rad, S. & Al Ali, M. H. (2108). Evaluation of water use efficiency in sugar and sugarcane production in Karun cultivation and industry. 5th National Conference on Irrigation and Drainage Management and Third Iranian National Irrigation and Drainage Congress, Shahid Chamran University of Ahvaz, March 21-23, Ahvaz. (In Farsi) Karimi, M. & Jolaini, M. (2017). Evaluation of Agricultural Water Productivity Indices in Major Field Crops in Mashhad Plain (Technical Note). Journal of Water and Sustainable Development, 4 (1), 133-138. (In Farsi) Leal, D., Coelho, R. D., Barbosa, F., Fraga Junior, E. F., Mauri, R. & Santos, L. (2017). Water productivity for sugar and biomass of sugarcane varieties. Revista Brasileira de Engenharia Agrícola e Ambiental, 21 (9), 618-622. Mittrapiyanuruk, P. & Charoen-Ung, P. (2018). Sugarcane Yield Grade Prediction using Random Forest and Gradient Boosting Tree Techniques. The 15th International Joint Conference on Computer Science and Software Engineering (JCSSE2018). DOI: 10.1109/JCSSE.2018.8457391. Naseri, A. A, Kianizadeh, A., Houshmand, R., Hamedan Nezhad, M., Josie, M. & Judy, F. (2011). Irrigation water productivity and water use efficiency of cane sugar plant in drought conditions (case study in Farabi province). Third National Conference on Water Supply and Drainage Management. 10-12 March of the month. Chamran martyr of Ahwaz University (In Farsi) Peloia, P. R., Bocca, F. F. & Rodrigues, L. H. (2019). Identification of patterns for increasing production with decision trees in sugarcane mill data. Scientia Agricola, 76 (4),281-289. Rousta, A. (2010). Agricultural productivity in drought-affected areas (case study of Marvdasht city). Fifth National Conference on New Ideas in Agriculture. Islamic Azad University of Khorasgan Branch. February 27-28, Isfahan (In Farsi) Safi, R., Mir Latifi, M. & Akbari, M. (2016). Assessment of Water Productivity of the Amirkabir Sugarcane Agro-Industry Farms Using Landsat 8 Satellite Data. Iranian Journal of Irrigation and Drainage, 10 (1), 36-47. (In Farsi) Seydan, M., Bahramloo, R. & Naseri, A. (2018). Determination of water productivity (WP) in wheat cultivation with sprinkler irrigation and traditional system in Hamadan province. Iranian Journal of Irrigation and Drainage, 12 (3), 732-743. (In Farsi) Shoja, P., Hemmat, A., Amirfattahi, R. & Gheysari, M. (2016). Water stress monitoring in olive trees using thermal imaging. Iranian Journal of Biosystem Engineering, 46 (4), 339-443. (In Farsi) Singh, S., Kaur Sidana, B. & Kumarm S. (2018). Water Productivity of Sugarbeet Vs Sugarcane Cultivation in Punjab. International Journal for Innovative Research in Science and Technology, 4 (9), 61-69. Sujata Terdal, R. (2019). Evaluation ofMachine Learning Algorithms forCrop Yield Prediction. International Journal of Engineering and Advanced Technology, 8 (6), 4082-4086. Tavakkoli, A. R. & Oweis, T. (2004). The role of supplemental irrigation and nitrogen in producing bread wheat in the highlands of Iran. Agricultural water management, 65, 225-236. Tavakoli, A., Liaghat, A., Oweis, T. & Alizadeh, A. (2012). The role of limited irrigation and advanced management on improving water productivity of rainfed wheat at semicold region of upper Karkheh River Basin, Iran. International Journal of Agriculture and Crop Sciences. Available online at www.ijagcs.com .IJACS/2012/4-14/939-948. Zakidizaji, H., Bahrami, H., Monjezi, N. & Shiekhdavoodi, M. J. (2019). Modeling of the variables that influence sugarcane yield using C5.0 and QUEST decision tree algorithms. Journal of Agricultural Machinery, 9 (2), 231-246. Zamani, A., Mortazavi, A. & Balali, M. (2015). Economical Water Productivity of Agricultural Products in Bahar Plain, Hamadan. Water Research in Agriculture, 28.1 (1), 51-62. (In Farsi) Zhang, H. (2003). Improving water productivity through deficit irrigation: Examples from Syria, the North China Plain and Oregon, USA. In: Water productivity in agriculture: Limits and opportunities for improvement. Kijne J.W., Barker R., Molden D. (Eds.). Comprehensive assessment of water management in agriculture series 1. CABI/IWMI, Wallingford/Colombo, 301-309. Zhang, H. & Oweis, T. (1999). Water -yield relations and optimal irrigation scheduling of wheat in the Mediterranean region. Agricultural water management, 38, 195-211. Zwart, S. J. & Bastiaanssen, G. M. (2004). Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agricultural water management, 69, 115-133. | ||
|
آمار تعداد مشاهده مقاله: 328 تعداد دریافت فایل اصل مقاله: 237 |
||