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تحلیل حساسیت عملکرد گندم دیم نسبت به شاخصهای اقلیمی با استفاده از مدل یادگیری ماشین تفسیرپذیر XGBoost SHAP تحت شرایط تغییر اقلیم: مطالعه موردی استان زنجان | ||
| تحقیقات آب و خاک ایران | ||
| دوره 57، شماره 1، فروردین 1405، صفحه 1-20 اصل مقاله (2.1 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2026.401180.670000 | ||
| نویسندگان | ||
| سجاد عسگری1؛ زهرا آقاشریعتمداری* 2 | ||
| 1گروه مهندسی آبیاری آبادانی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران | ||
| 2استادیار/گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
| چکیده | ||
| این پژوهش با هدف تحلیل حساسیت عملکرد گندم دیم به شاخصهای اقلیمی، طی سالهای ۱۳۶۹ تا ۱۴۰۲ در سه ایستگاه زنجان، خدابنده و خرمدره انجام شد. برای این منظور، از مدل یادگیری ماشین (XGBoost) Extreme Gradient Boosting به همراه الگوریتم تفسیرپذیری SHAP استفاده گردید تا مهمترین شاخصهای اقلیمی تأثیرگذار شناسایی و بازههای بهینه و محدودکننده آنها بر عملکرد مشخص شوند. روششناسی پژوهش در چهار بخش تدوین شد : برآورد طول دوره رشد بر اساس دستورالعمل بهروزشده FAO، انطباق دادهها با دوره واقعی رشد گیاه، تحلیل تغییرات توزیع زمانی شاخصهای اقلیمی و انجام تحلیل تعاملی و تفسیرپذیر شاخصهای اقلیمی. نتایج نشان داد در هر سه منطقه، عملکرد گندم و طول دوره رشد روند افزایشی داشتند، اما روند طول دوره رشد در زنجان از نظر آماری معنی دار نبود (P > 0.1). همچنین، در هر سه ایستگاه، متغیرهای مرتبط با رطوبت مهمترین شاخصهای تعیینکننده عملکرد بودند، به طوری که در خدابنده بارش موثر (ERGP) ، در زنجان تعداد روزهای بارش (N_pr^GP) و در خرم دره شاخص ناهمواری بارش (URGP) است. افزون بر این، بارش موثر (1.8 ≤ ERGP ≤ 2.9 mm/Day) در خدابنده ، توزیع بهینه بارش (10.3 ≤ Ur/Er (GP) ≤ 12.4) در خرم دره و دمای میانگین بهینه (9 ≤ T ̅_mean^GP ≤ 9.2 ℃) موثرترین شاخصهای افزایش عملکرد در مقابل کوتاهی طول دوره رشد (50 ≤ LGP ≤ 68 Day) در خدابنده ، توزیع نامناسب بارش (1.1 ≤URGP ≤ 8.7 mm) در خرم دره و تعداد روزهای بارش کم (4 ≤ N_pr^GP ≤ 31 Day) در زنجان بهعنوان مهمترین محدودیتها شناخته شدند. | ||
| کلیدواژهها | ||
| طول دوره رشد (FAO)؛ بارش موثر؛ روند؛ توزیع بارش؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| Sensitivity Analysis of Rainfed Wheat Yield to Climatic Indices Using The Interpretable Xgboost–SHAP Model Under Climate Change Conditions: Zanjan Province Case Study | ||
| نویسندگان [English] | ||
| sajjad Asgary1؛ Zahra Aghashariatmadari2 | ||
| 1irrigation and reclamation engineering, agricultural and forest meteorology, University of tehran | ||
| 2Associate Prof., Irrigation & Reclamation Engrg. Dept. University of Tehran Karaj, Iran. | ||
| چکیده [English] | ||
| This study aimed to assess the sensitivity of rainfed wheat yield to climatic indices over the period 1990–2023 across three representative stations in Zanjan Province, Iran —Zanjan, Khodabandeh, and Khorramdarreh. The Extreme Gradient Boosting (XGBoost) algorithm, interpreted via SHapley Additive exPlanations (SHAP) values, was employed to identify the most influential climatic variables and to determine their optimal and yield-limiting thresholds. The methodological framework integrated four key approaches that have been seldom considered simultaneously in previous research: (i) estimating the length of the growing period (LGP) based on updated FAO guidelines, (ii) aligning climatic data with the actual crop growing period, (iii) focusing on the temporal distribution patterns of climatic indices rather than solely on their cumulative or seasonal means, and (iv) performing interactive and interpretable climatic analysis. The results indicated increasing trends in both yield and LGP across all three sites, although the LGP trend in Zanjan was statistically non-significant (P > 0.1). SHAP analysis revealed that moisture-related variables were the primary determinants of yield in all sites. Specifically, effective rainfall (ERGP: 1.8–2.9 mm/day) in Khodabandeh, the number of precipitation days (N_pr^GP: 4–31 days) in Zanjan, and uniform rainfall distribution (URGP: 11.2–31 mm) in Khorramdarreh emerged as the most influential positive drivers. Conversely, yield limitations were associated with shortened growing periods (LGP: 50–68 days) in Khodabandeh, poorly distributed rainfall (UR/ER: 5.4–10 mm) in Khorramdarreh, and a low number of precipitation days in Zanjan. | ||
| کلیدواژهها [English] | ||
| Length of Growing Period, Effective Rainfall, Rainfall Distribution Pattern, Trend Analysis, Machine Learning | ||
| مراجع | ||
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