موسسه تحقیقات خاک و آب مدیریت اراضی 2345-6205 14 1 2026 08 23 Effects of different levels of Fe, Zn, and Mg on cucumber performance under greenhouse conditions ( A case study in Jiroft) بررسی اثر مقادیر مختلف آهن، روی و منگنز بر عملکرد خیار گلخانه‌ای (مطالعه موردی: در شرایط جیرفت) 1 11 135526 10.22092/lmj.2026.372083.400 FA جواد سرحدی استادیار پژوهشی.بخش تحقیقات خاک و آب.مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی جنوب کرمان صابر حیدری استادیار پژوهشی بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی جنوب استان کرمان، سازمان تحقیقات، آموزش و ترویج مهری شریف کارشناس بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی جنوب استان کرمان، سازمان تحقیقات، آموزش و ترویج کشاورزی، Journal Article 2026 01 25 Objectives This study was designed to address a critical agronomic challenge in one of Iran's foremost greenhouse production hubs. The primary objectives were: 1) to determine the precise optimal application rates of iron (Fe), zinc (Zn), and manganese (Mn) micronutrient fertilizers for greenhouse cucumber (Cucumis sativus L.) cultivation in the distinctive alkaline, sandy, and organic-poor soils in Jiroft region; 2) to evaluate the individual and combined effects of these micronutrients on key quantitative and qualitative yield parameters, including total yield, fruit diameter, and fruit nitrate concentration; 3) to investigate the presence and strength of synergistic interactions among Fe, Zn, and Mn in influencing cucumber performance; and 4) to develop a scientifically-grounded, efficient, and localized fertilization strategy that maximizes productivity and fruit quality while preventing the economic losses and environmental risks associated with both deficiency and toxicity from imbalanced micronutrient application. Material and Methods The research was conducted during the 2021-2022 growing season in a fully equipped polyethylene greenhouse at the Agricultural and Natural Resources Research Center of southern Kerman Province, Jiroft. The experiment employed a factorial arrangement based on a Randomized Complete Block Design (RCBD) with three replications. The three fertilizer factors were: Fe-EDDHA chelate at three levels (namely, 0, 11, and 13 kg/ha), zinc sulfate (ZnSO₄) at three levels (namely, 0, 50, and 80 kg/ha), and manganese sulfate (MnSO₄) at three levels (namely, 0, 50, and 120 kg/ha). This yielded 27 combined treatment combinations plus a control (i.e., no micronutrient application). Prior to planting, an exhaustive soil analysis was performed to find that the soil was sandy loam in texture, alkaline (pH 7.7), low in electrical conductivity (EC 2.50 dS/m), very low in organic carbon content (0.18%), and deficient in available Fe (3.60 mg/kg), Mn (2.30 mg/kg), and Zn (0.80 mg/kg) as extracted by the DTPA method. Uniform cucumber seedlings at the 3-4 leaf stage were transplanted. All the micronutrient treatments were applied in solution through a drip irrigation system in three splits: at planting, at the beginning of flowering, and at early fruit set. Macronutrient (N, P, K) application was uniform across the plots based on soil test recommendations. Standard pest, disease, and crop management practices were applied uniformly. At final harvest, the following traits were measured: total marketable yield (t/ha), fruit diameter (cm) as measured by a digital caliper, and spectrophotometrically determined fruit nitrate concentration (mg/kg). The data thus collected were subjected to Analysis of Variance (ANOVA) using SAS software (v. 9.4), and mean comparisons were performed using Duncan's Multiple Range Test at the 5% probability level. Results Soil analysis confirmed the initial micronutrient deficiencies. ANOVA revealed that the main effects of Fe, Zn, and Mn fertilization were highly significant (p≤0.01) on all the measured traits of total yield, fruit diameter, and nitrate concentration. Furthermore, all the two-way interactions (Fe×Zn, Fe×Mn, Zn×Mn) and the three-way interaction (Fe×Zn×Mn) were found to have significant effects on yield and fruit diameter, underscoring the complex interplay among these nutrients. The separate application of optimal levels of each single micronutrient was observed to outperform the control treatment in a significant manner. The optimal levels were identified as: 11 kg/ha for Fe-EDDHA (yield: 311.18 t/ha; fruit diameter: 14.56 cm), 50 kg/ha for ZnSO₄ (yield: 310.10 t/ha; fruit diameter: 14.60 cm; nitrate: 48.57 mg/kg), and 50 kg/ha for MnSO₄ (yield: 309.73 t/ha). Application of Zn at 50 kg/ha also resulted in a significant reduction (16.5%) in fruit nitrate concentration compared to the control (58.20 mg/kg). The most compelling results emerged from the interaction studies. More specifically, the two-way interactions consistently showed that the combination of the optimal level of one element with that of another (e.g., Fe11 with Zn50) produced significantly higher yields and larger fruits than any sub-optimal combination did. The pinnacle of this synergistic effect was observed in the three-way interaction such that the combined application of all the three optimal levels (Fe11Zn50Mn50) produced the absolute maximum values: a yield of 364.30 t/ha and a fruit diameter of 14.83 cm. This result was significantly superior to those obtained from all the other treatment combinations and the individual optimal applications, demonstrating a powerful synergistic interaction. Importantly, applying levels higher than these identified optima (e.g., 13 kg/ha Fe, 80 kg/ha Zn, or 120 kg/ha Mn) provided no additional benefit and, in some cases, even led to a relative decrease in performance, indicating the risk of imbalance or incipient toxicity. Conclusion This study conclusively establishes that balanced micronutrient fertilization is a decisive factor contributing to successful greenhouse cucumber production in the alkaline, impoverished soils in Jiroft region. The research identifies a highly efficient and specific fertilization formula of 11 kg/ha of Fe-EDDHA chelate, 50 kg/ha of zinc sulfate, and 50 kg/ha of manganese sulfate. While the individual application of each of these optimal levels significantly enhances yield and fruit size, the key finding is the pronounced synergistic interaction among Fe, Zn, and Mn so that their simultaneous application at optimal rates yields a supra-additive effect, leading to the highest possible productivity and fruit quality. Hence, the critical superiority of an integrated and balanced nutritional program over the single-element fertilization cannot be overemphasized. A significant quality-related outcome was the role optimal zinc nutrition played in significantly reducing nitrate accumulation in fruits to enhance the product's safety for consumption. The results also delivers a crucial cautionary note: application rates exceeding the identified optima are economically unjustified and potentially counterproductive, as they do not improve yield but may induce nutrient imbalances or environmental loading. In summary, this research provides a robust, evidence-based protocol for micronutrient management in greenhouse cucumber cultivation under conditions similar to those in Jiroft. This strategy promises maximized yield and quality, improved economic returns for growers, and enhaced environmental sustainability by preventing both deficient and excessive fertilizer application. هدف این مطالعه، تعیین سطوح مصرف کودهای ریزمغذی آهن، روی و منگنز به‌منظور بهبود عملکرد و کیفیت خیار گلخانه‌ای در شرایط خاک‌های قلیایی و فقیر منطقه جیرفت بود. آزمایش به‌صورت فاکتوریل در قالب طرح بلوک‌های کامل تصادفی با سه تکرار و با استفاده از سه فاکتور کودی (کلات EDDHA آهن در مقادیر صفر، ۱۱ و ۱۳ کیلوگرم در هکتار، سولفات روی در مقادیر صفر، ۵۰ و ۸۰ کیلوگرم در هکتار و سولفات منگنز در مقادیر صفر، ۵۰ و ۱۲۰ کیلوگرم در هکتار) اجرا شد. صفات اندازه‌گیری شده شامل عملکرد کل، طول میوه، طول ساقه و غلظت نیترات در میوه بودند. نتایج نشان داد که کاربرد جداگانه سطوح ۱۱ کیلوگرم در هکتار آهن، ۵۰ کیلوگرم در هکتار روی و ۵۰ کیلوگرم در هکتار منگنز، در مقایسه با تیمار شاهد، به‌طور معنی‌داری عملکرد و طول میوه را افزایش داد. با این حال، قوی‌ترین اثر مربوط به کاربرد همزمان این سه سطح بهینه (Fe11Zn50Mn50) بود که بالاترین عملکرد (364/30 تن در هکتار) و بزرگ‌ترین طول میوه (14/83 سانتی‌متر) را تولید کرد که برهمکنش هم‌افزایی قوی بین این عناصر را نشان داد. مصرف روی (۵۰ کیلوگرم در هکتار) به کاهش معنی‌دار غلظت نیترات میوه منجر شد. در مقابل، مصرف سطوح بالاتر از مقادیر بهینه، بهبود اضافی در پی نداشت. به‌طورکلی، این پژوهش سطوح ۱۱، ۵۰ و ۵۰ کیلوگرم در هکتار را به ترتیب برای کودهای کلات آهن، سولفات روی و سولفات منگنز، به‌عنوان الگوی تغذیه‌ای کارآمد برای دستیابی به حداکثر عملکرد و کیفیت خیار گلخانه‌ای در شرایط مشابه با خاک مورد آزمایش معرفی می‌کند.

https://lmj.areeo.ac.ir/article_135526_e1980db712a019a8c53c1e5a32302891.pdf

موسسه تحقیقات خاک و آب مدیریت اراضی 2345-6205 14 1 2026 08 23 Temporal analysis of the origin of trans-regional dust in Golestan Province using the NOAA HYSPLIT model (A case study of the dust event of September 12-16, 2025) تحلیل زمانی منشأ گردوغبارهای فرا منطقه‌ای استان گلستان با استفاده از مدل NOAA HYSPLIT (در بازه 21 تا 25 شهریور 1404) 13 27 135644 10.22092/lmj.2026.372066.399 FA بهروز محسنی استادیار پژوهشی بخش تحقیقات منابع طبیعی مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان، سازمان تحقیقات، آموزش و ترویج قربانعلی روشنی دانشیار پژوهشی، بخش تحقیقات خاک و آب مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، گرگان، ایران. محمد ججوزاده محقق بخش تحقیقات منابع طبیعی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، Journal Article 2026 01 22 Objectives It was the objective of this study to identify the temporal origin and transport pathways of extra-regional dust storms affecting Golestan Province in northeastern Iran, during the period 12–16 September 2025. For this purpose, the NOAA HYSPLIT backward trajectory model and the Sentinel-5P satellite observations were integrated to determine dust source regions, transport dynamics, and atmospheric conditions at different temporal scales (12, 24, and 72 hours). In addition, the study endeavored to evaluate the aerosol layer height and the Absorbing Aerosol Index (AAI) to verify dust occurrence and assess its spatial distribution. Satellite-derived information was further validated using ground-based PM10, PM2.5, and horizontal visibility measurements. The findings help yield a deeper understanding of the transboundary dust transport from Central Asia to northern Iran and provide scientific evidence to support air quality management, environmental monitoring, and developing early warning systems for dust-related hazards in Golestan Province. Material and Methods An integrated remote sensing and atmospheric trajectory analysis approach was employed to investigate the origin and transport pathways of extra-regional dust events affecting Golestan Province, in northeastern Iran, during the above-mentioned study period. Backward air-mass trajectories were simulated using the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model together with GDAS meteorological data. Trajectories were calculated for 12, 24, and 72-hour intervals at an arrival height of 100 m above ground level to identify potential dust source regions and atmospheric transport routes. Satellite observations from the Sentinel-5P platform were used to characterize aerosol conditions during the study period. Aerosol Layer Height (ALH) products were analyzed to determine the vertical distribution of aerosol plumes while the Absorbing Aerosol Index (AAI) was employed both to detect UV-absorbing aerosols associated with mineral dust and to evaluate their spatial extent and intensity. The combined interpretation of HYSPLIT trajectories and satellite-derived aerosol products allowed not only the probable dust source regions but also the long-range transport mechanisms to be identified. To validate the remote sensing and trajectory modeling results, use was made of the ground-based meteorological and air quality observations collected from the synoptic stations in Golestan Province. Daily PM10 and PM2.5 concentrations together with horizontal visibility records were analyzed to confirm the occurrence and severity of dust events. Finally, spatial and temporal comparisons were performed among the trajectory outputs, satellite observations, and surface measurements to evaluate the consistency of the results and to improve the reliability of dust source identification and transport pathway analysis. Results The integrated analysis of HYSPLIT backward trajectories, Sentinel-5P satellite observations, and ground-based measurements revealed the occurrence of significant extra-regional dust events over Golestan Province during the study period. The 72-hour backward trajectory analysis indicated that the principal dust air masses originated from Central Asia, particularly Kazakhstan, Uzbekistan, and Turkmenistan, and were transported toward northeastern Iran by the prevailing northeasterly atmospheric circulation. Trajectories at higher altitudes (2–4 km) suggested that part of the dust transport occurred within the middle troposphere before descending over the study area. The 24-hour trajectory analysis demonstrated that shorter-range transports were dominated by air masses moving from the Caspian Sea region and southern Caucasus, whereas the 12-hour trajectories indicated that local and regional winds from Turkmen Sahara and northern Khorasan contributed to near-surface dust accumulation. These findings suggest that the observed events resulted from the combined influence of long-range transboundary transport and local atmospheric circulation. Sentinel-5P Aerosol Layer Height (ALH) products showed elevated aerosol layers over eastern Iran and Turkmenistan prior to the event, supporting the hypothesis of long-distance aerosol transport. Moreover, the Absorbing Aerosol Index (AAI) identified high aerosol concentrations over eastern Caspian coastal areas and Golestan Province, with values indicating intense mineral dust loading. Spatial correspondence between satellite observations and model trajectories confirmed Central Asia as the dominant source region od dust air masses. Ground observations further validated these findings as evidenced by the PM₁₀ concentrations reaching approximately 110 μg m⁻³ on 15 September 2025 accompanied by a substantial reduction in horizontal visibility, all of which confirm severe dust conditions. The consistency observed among trajectory simulations, satellite-derived aerosol products, and surface observations demonstrates the reliability of the integrated methodology proposed herein for identifying dust sources and transport pathways affecting Golestan Province. Conclusion The results of the present study demonstrated the effectiveness of integrating the NOAA HYSPLIT trajectory model, Sentinel-5P satellite observations, and ground-based air quality measurements in identifying the origin and transport pathways of extra-regional dust events affecting Golestan Province. Furthermore, the combined analyses revealed that the dust episode investigated was primarily associated with long-range atmospheric transport from Central Asian source regions, particularly Turkmenistan, Uzbekistan, and Kazakhstan. Backward trajectory simulations at different temporal scales showed that regional and local atmospheric circulation also contributed to the final distribution and accumulation of dust over the study area. Meanwhile, satellite-derived Aerosol Layer Height and Absorbing Aerosol Index products confirmed the presence of elevated aerosol concentrations over northeastern Iran and the eastern Caspian region before and during the event. These observations were consistent with HYSPLIT simulations and were further supported by increased PM₁₀ concentrations and reduced horizontal visibility recorded at synoptic stations, confirming the occurrence of a severe transboundary dust episode. Overall, the results highlight the Central Asian deserts as dominant external dust sources influencing northern Iran and reiterate the importance of atmospheric circulation patterns in controlling dust transport. Hence, the integrated methodology adopted in this study provides a reliable framework for identifying dust source regions and validating transport mechanisms through multiple independent datasets. Clearly, the findings contribute to a better understanding of transboundary dust dynamics in northeastern Iran that can be exploited toward developing early warning systems, air quality management strategies, and regional environmental policies. Future studies are recommended to investigate long-term dust variability using multi-year datasets, higher-resolution atmospheric models, and additional satellite products to improve the prediction and mitigation of dust-related environmental hazards. رویداد گردوغبار یک فرآیند پیچیده بوده که به ترکیبی از عوامل زمینی و جوی، از جمله زمان، مکان، مدت و بزرگی شیوع گردوغبار بستگی دارد. در شمال ایران و مخصوصاً استان گلستان این پدیده رایج است. هدف از این مطالعه، شناسایی منشأ توده‌های گردوغبار فرا منطقه‌ای به استان گلستان در بازه زمانی 21 تا 25 شهریور 1404 با استفاده از مدل NOAA HYSPLIT و داده‌های ماهواره‌ای بوده است. برای تعیین میزان آلودگی هوا و تشخیص توده‌های گردوغبار از شاخص آئروسل‌های جاذب نور (AAI) بهره گرفته شد. مسیرهای ردیابی‌شده بازه زمانی 72 ساعته، نشان داد که منشأ گردوغبار در روز 21 شهریور از کشورهای ازبکستان و ترکمنستان بوده است. در مقابل، بازه زمانی 24 ساعته نشان داد که منشأ گردوغبار در روز 24 شهریور قزاقستان و ازبکستان است. همچنین، تحلیل بازه زمانی 12 ساعته نشان داد که توده گردوغبار در این بازه از سمت شمال شرق (ترکمنستان و شمال خراسان) وارد منطقه شده است. تصاویر ماهواره‌ای نیز نشان داد که در تاریخ 25 شهریور 1404، شمال شرق ایران و به‌ویژه منطقه اطراف دریای خزر تحت تأثیر یک توده گردوغبار با غلظت بالا قرار داشته است. شاخص AAI در شرق دریای خزر تا استان گلستان نشان‌دهنده آلودگی هوای قابل‌توجه ناشی از گردوغبار بوده است. درمجموع، تحلیل مدل HYSPLIT و تصاویر ماهواره‌ای نشان می‌دهد که منشأ و جریان گردوغبار فرا منطقه‌ای وارد شده به استان گلستان در بازه‌های زمانی ذکرشده، عمدتاً از سمت شمال شرق و سه کشور ترکمنستان، ازبکستان و قزاقستان بوده و نشان می‌دهد که شرایط جوی و جریان‌های باد در این بازه نقش مهمی در انتقال این توده‌ها داشته‌اند. همچنین، تأیید این یافته‌ها در تطابق با داده‌های ماهواره‌ای و زمینی (مقادیر PM و دید افقی) نشان می‌دهد که این پدیده یک رویداد واقعی و قابل ردیابی است.

https://lmj.areeo.ac.ir/article_135644_aa252f9414dc27d0d063c6ac5ea9c9f4.pdf

موسسه تحقیقات خاک و آب مدیریت اراضی 2345-6205 14 1 2026 08 23 Impacts of input consumption management on sustainable production of Quinoa: energy use, water use, and environmental implications ارزیابی تأثیر مدیریت مصرف نهاده‌ها بر پایداری تولید کینوا: مصرف انرژی، مصرف آب و پیامدهای محیط‌زیستی 29 42 135076 10.22092/lmj.2026.371102.397 FA رضا محمدی کیا محقق بخش مدیریت آب در مزرعه، مؤسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران Journal Article 2025 11 10 Objectives Quinoa (Chenopodium quinoa Willd.) has emerged as a promising climate-resilient crop due to its exceptional adaptability to harsh environmental conditions, including drought, salinity, and poor soil fertility. Its high nutritional value, coupled with its ability to maintain acceptable productivity under limited water availability, has made quinoa an attractive option for diversifying cropping systems and improving food security in arid and semi-arid regions. However, despite its recognized tolerance to environmental stresses, achieving sustainable quinoa production still depends on efficient management of agricultural inputs, particularly water, energy, and agrochemicals, to ensure both economic viability and environmental sustainability. With increasing limitations on water and energy resources and the pressing need for sustainable agriculture in arid and semi-arid regions, optimizing the management of water and agricultural inputs has become crucial for quinoa production. This study is intended to evaluate the effects of optimal input management on quinoa cultivation sustainability, focusing on Energy Use Efficiency (EUE), Water Use Efficiency (WUE), and Environmental Impact Index (EII). Understanding how input management affects energy consumption, water productivity, and environmental impacts is essential for enhancing resource-use efficiency and promoting sustainable cropping systems in water-limited areas. Material and Methods A field-based study using lysimeters under controlled management conditions was conducted to quantify system inputs and outputs. System inputs included energy from fuel, water, and electricity as well as fertilizers, pesticides, seeds, and human labor while output energy was calculated based on quinoa grain yield. EUE, WUE, and EII were computed using standard formulas. Additionally, a Monte Carlo simulation was employed for sensitivity analysis to determine the relative effects of different efficiency indicators on system sustainability. To ensure exhaustive sustainability assessment, all the energy equivalents and environmental coefficients associated with agricultural inputs were derived from established conversion factors reported in the literature. Water Use Efficiency was determined bsed on grain yield relative to the total volume of irrigation water applied during the growing season. The environmental impact index was estimated based on greenhouse gas emissions associated with the use of major agricultural inputs, enabling a comparative evaluation of management performance from both resource-use and environmental perspectives. The integration of deterministic efficiency calculations with probabilistic sensitivity analysis provided a robust framework for identifying the most influential factors affecting the sustainability of quinoa production under arid and semi-arid conditions. Results Optimal input management significantly reduced total energy inputs and the environmental impact index but enhanced energy and water use efficiencies. Specifically, total system input energy was estimated at 20,208 MJ ha⁻¹ whereas output energy from quinoa grain reached 49,406 MJ ha⁻¹, resulting in an EUE of 2.44. Water use efficiency was calculated at 0.81 kg m⁻³, indicating effective water utilization throughout the growing season. The environmental impact index (EII) was calculated at 2,821 kg CO₂-eq ha⁻¹, demonstrating a substantial reduction in environmental burden. Sensitivity analysis revealed that system sustainability was most responsive to variations in WUE and EUE, highlighting the critical role of precise management of water and energy inputs in arid and semi-arid cropping systems. Conclusion The findings highlight the pivotal role of optimal management of inputs for enhancing energy and water efficiencies and mitigating environmental impacts in quinoa cultivation. A holistic approach integrating precise irrigation scheduling and judicious input application can substantially improve the sustainability of quinoa production in water-limited regions. These results provide valuable insights for policymakers and land managers seeking sustainable agricultural practices and resource-efficient cropping strategies. Furthermore, the integration of energy, water, and environmental indicators in evaluating quinoa production systems provides a comprehensive framework for assessing agricultural sustainability under resource-constrained conditions. The results of this study suggest that improving input-use strategies not only enhances production efficiency but also contributes to reduced ecological footprint of crop production. It is, therefore, essential to adopt such integrated management approaches in order to develop resilient and sustainable farming systems, particularly in arid and semi-arid regions that face increasing pressures from climate change and water scarcity. با افزایش محدودیت منابع آب و انرژی و نیاز به توسعه کشاورزی پایدار در مناطق خشک و نیمه‌خشک، مدیریت مصرف بهینه آب و نهاده‌های کشاورزی در تولید کینوا اهمیت ویژه‌ای یافته است. هدف این پژوهش، ارزیابی اثر مدیریت بهینه مصرف نهاده‌ها بر پایداری کشت کینوا با تمرکز بر بهره‌وری انرژی (EUE)، کارایی مصرف آب(WUE) و شاخص محیطزیستی (EII) بود. این مطالعه بهصورت لایسیمتری و تحت مدیریت کنترل‌شده انجام شد. انرژی ورودی سیستم شامل انرژی ناشی از مصرف سوخت، آب، برق، کود و سم، بذر و نیروی انسانی و انرژی خروجی شامل انرژی حاصل از عملکرد دانه کینوا در نظر گرفته شد. شاخص‌های بهره‌وری انرژی، کارایی مصرف آب و شاخص محیطزیستی بر اساس روابط استاندارد محاسبه گردیدند. نتایج نشان داد که مدیریت بهینه مصرف نهاده‌ها در کشت کینوا منجر به کاهش چشمگیر انرژی ورودی و شاخص محیطزیستی و افزایش بهره‌وری انرژی و کارایی مصرف آب شد. به‌طور مشخص، انرژی ورودی سیستم برابر 20209/8 مگاژول بر هکتار بود، در حالی که انرژی خروجی حاصل از محصول برابر 49406/7 مگاژول بر هکتار برآورد شد که منجر به بهره‌وری انرژی (EUE) برابر با 2/44 شد. کارایی مصرف آب (WUE) نیز برابر 0/81کیلوگرم بر مترمکعب محاسبه شد که نشان‌دهنده استفاده مؤثر از آب در طول فصل رشد است. شاخص محیطزیستی (EII) نیز با مقدار 2821/6 کیلوگرم دیاکسیدکربن معادل در هکتار کاهش قابل توجه اثرات محیطزیستی را نشان می‌دهد. مقادیر به‌دست‌آمده برای شاخص‌ها بیانگر نقش تعیین‌کننده مدیریت آبیاری و مصرف نهاده‌ها در افزایش پایداری تولید کینوا هستند. تحلیل حساسیت با استفاده از شبیه‌سازی مونت‌کارلو نشان داد که پایداری سیستم بیشترین حساسیت را نسبت به تغییرات شاخص‌های کارایی مصرف آب و بهره‌وری انرژی دارد، که بر اهمیت مدیریت دقیق این دو مؤلفه در سیستم‌های زراعی مناطق خشک و نیمه‌خشک تأکید می‌کند.

https://lmj.areeo.ac.ir/article_135076_2293f73679abbb550fab6b73e07e0e44.pdf

موسسه تحقیقات خاک و آب مدیریت اراضی 2345-6205 14 1 2026 08 23 Production and characterization of morphological and moisture properties of nanosilica from rice husk: A step toward clean technologies in land management تولید و شناسایی ویژگی‌های مورفولوژی و رطوبتی نانوسیلیس حاصل از پوسته برنج: گامی به‌سوی فناوری‌های پاک در مدیریت اراضی 43 54 135736 10.22092/lmj.2026.371228.398 FA میثاق پرهیزکار استادیار سازمان تحقیقات، آموزش و ترویج کشاورزی، موسسه تحقیقات برنج کشور، رشت، ایران معصومه ا یزدپناه نشرودکلی دانشجوی دکتری مدیریت منابع خاک – فیزیک خاک و حفاظت خاک، دانشکده کشاورزی، دانشگاه گیلان، ایران Journal Article 2025 11 01 Objectives This study was conducted to address the growing need for sustainable utilization of agricultural waste and the development of environmentally friendly nanomaterials for land management applications. The primary objectives included: 1) extraction and synthesis of silica nanoparticles from the abundantly available rice husk, as an agricultural by-product, using a controlled acid-leaching and calcination process, 2) characterization of the morphological, structural, and moisture properties of the nanosilica thus produced using advanced analytical techniques including Fourier-transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (FESEM), 3) evaluation of the quality, structural stability, and physicochemical characteristics of rice husk-derived nanosilica and assessemt of its potential for application as a sustainable material in soil improvement, land management, and environmental technologies. Material and Methods The study was carried out using rice husk as the primary raw material collected from the Rice Research Institute of Iran. Prior to processing, the rice husk samples were washed thoroughly with distilled water and dried at ambient temperature. The dried samples were then subjected to acid leaching using a 10% HCl solution under continuous heating for two hours to remove metallic impurities. After repeated washing and drying at 100°C for 24 hours, the treated samples were calcined at 700°C for two hours to obtain silica-rich ash. Subsequently, the silica powder thus obtained was treated with 0.5 N potassium nitrate solution under continuous stirring for one hour to induce partial crystallization. The resulting material was filtered, dried at 105°C for four hours, and finally calcined at 800°C for eight hours to produce porous semi-crystalline silica nanoparticles. The structural properties of the synthesized nanosilica were characterized using Fourier-transform infrared spectroscopy (FTIR) to identify functional groups and confirm the silica bonding structures. Morphological characteristics and particle size distribution were determined using field emission scanning electron microscopy (FESEM) at a magnification of 200 kx. Particle dimensions and shape factors were determined using the ImageJ software. Specific surface area was estimated based on geometric relationships between particle diameter and density. Moisture content was determined using the oven-drying method. Production yield was calculated as the ratio of final nanosilica mass to that of initial rice husk ash. Statistical measures, including mean, standard deviation, coefficient of variation, skewness, and frequency distribution, were calculated using XLSTAT software to evaluate data consistency and reproducibility. Results The FTIR analysis confirmed the successful synthesis of silica nanoparticles through identification of characteristic absorption bands corresponding to Si–O–Si and Si–OH functional groups. A strong absorption peak observed at 1017 cm⁻¹ represented the asymmetric stretching vibration of Si–O–Si bonds while that near 808 cm⁻¹ corresponded to the symmetric stretching vibration of the siloxane framework. Moreover, a broad absorption band around 3400 cm⁻¹ indicated the presence of surface hydroxyl groups and the water molecules adsorbed, confirming the hydrophilic nature and high surface activity of the synthesized nanosilica. FESEM images revealed that the silica nanoparticles exhibited a nearly spherical morphology with a highly uniform particle distribution and minimal aggregation. Particle sizes ranged from 8 to 24 nm, with an average particle diameter of 15.56 nm. The observed morphology confirmed the successful conversion of rice husk into highly pure nanosilica with a homogeneous porous structure. Statistical analysis demonstrated a low standard deviation (2.96 nm) and a coefficient of variation of 0.194, indicating high reproducibility and uniformity of the synthesis process. The estimated specific surface area of the synthesized nanosilica was approximately 178.5 m² g⁻¹, reflecting the presence of a highly porous nanostructure with abundant active surface sites. The calculated particle shape factor was 0.95, indicating a highly spherical morphology and excellent geometric uniformity. Moisture analysis showed an average moisture content of 1.84%, with a standard deviation of 0.09 and a coefficient of variation of 4.9%, confirming the stability and consistency of the nanoparticles produced. Furthermore, the production process yielded approximately 70 g of nanosilica from 200 g of dried rice husk, corresponding to a production efficiency of approximately 35%. Conclusion This study demonstrated the successful production of high-quality silica nanoparticles from rice husk through a controlled acid-leaching and thermal treatment process. The synthesized nanosilica exhibited desirable physicochemical characteristics, including an amorphous to semi-crystalline structure, spherical morphology, narrow particle size distribution, high specific surface area, and low moisture content. The presence of active Si–OH functional groups and the high surface area indicate significant potential for adsorption, ion exchange, and environmental applications. The findings revealed that the combined acid treatment and controlled calcination at 800°C represent an efficient and sustainable approach for producing high-purity nanosilica from agricultural waste materials. The nanoparticles thus obtained exhibited excellent structural stability, high surface activity, and favorable morphological characteristics that make them a suitable candidate for soil amendment, environmental remediation, nutrient management, and sustainable land management applications. In addition to providing an environmentally friendly strategy for agricultural waste recycling, this study contributes to the advancement of green nanotechnology and circular economy principles. Nevertheless, further investigations are recommended to evaluate the long-term behavior, environmental interactions, and biological impacts of rice husk-derived nanosilica in soil-plant systems under field conditions. Overall, the present research demonstrates that rice husk-derived nanosilica enjpys a substantial potential as a multifunctional nanomaterial for sustainable agriculture, environmental protection, and clean technology development. پوسته برنج به‌عنوان یکی از پسماندهای فراوان کشاورزی در کشورهای تولیدکننده برنج، حاوی مقادیر قابل‌توجهی سیلیس است که در صورت فرآوری مناسب می‌تواند به منبعی ارزشمند برای تولید نانوذرات سیلیس تبدیل شود. ضرورت این پژوهش از آنجا ناشی می‌شود که استفاده از منابع زیستی ارزان و در دسترس، علاوه بر کاهش آلودگی ‌محیطزیستی ناشی از دفع پسماندهای کشاورزی، می‌تواند در توسعه فناوری‌های سبز و مدیریت پایدار اراضی نقش مهمی ایفا کند. هدف مطالعه حاضر، استخراج و شناسایی نانوذرات سیلیس از پوسته برنج و بررسی ویژگی‌های آن‌ها و محتوای رطوبت این ذرات بود. تحلیل‌ آنالیز طیف‌سنجی تبدیل فوریه فروسرخ (FTIR) وجود باندهای مشخصه Si–O–Si و Si–OH را نشان داد که بیانگر ساختار ساختار کم‌بلوری و سطح فعال بالای نانوذرات بود. تصاویر میکروسکوپ الکترونی روبشی اثر میدانی (FESEM) ریخت‌شناسی یکنواخت و کروی ذرات در محدوده ۸ تا ۲۴ نانومتر با میانگین اندازه ذرات 15/56 نانومتر را تأیید کردند. سطح ویژه نانوذرات برابر با 178/5 مترمربع بر گرم، رطوبت 1/84 درصد و بازده تولید حدود ۳۵ درصد محاسبه شد. نتایج نشان داد روش مورد استفاده با کنترل شرایط اسیدشویی و کلسیناسیون قادر است نانوذراتی با خلوص بالا، سطحویژه زیاد و پایداری ساختاری مطلوب تولید کند.

https://lmj.areeo.ac.ir/article_135736_0fc02d7758cad4ddcd4aef20eb8794b1.pdf

موسسه تحقیقات خاک و آب مدیریت اراضی 2345-6205 14 1 2026 08 23 Digital mapping of soil pH using Landsat 8 satellite images and auxiliary data by machine learning models in Badr Watershed, Kurdistan Province نقشه‌برداری رقومی اسیدیته خاک با استفاده از تصاویر ماهواره‌ای لندست 8 و داده‌های کمکی توسط مدل‌های یادگیری ماشین در حوزه آبخیز بدر، استان کردستان 55 73 135535 10.22092/lmj.2026.368002.377 FA مسلم زرینی بهادر کارشناس تحقیقات خاک و آب مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان تهران، سازمان تحقیقات، آموزش و ترویج کشاورزی(تات)، تهران، ایران. Journal Article 2024 12 23 Objectives Soil pH is a key chemical property that governs nutrient availability, microbial activity, and overall soil fertility, thereby exerting a significant influence on plant growth and productivity. Accurate spatial prediction of soil pH is essential for site-specific soil management and precision agriculture. The present study aims: 1) to prepare a digital map of the spatial distribution of soil pH in the 0–25 cm soil layer at Badr Watershed, southern Qorveh County, using environmental covariates derived from terrain attributes, remote sensing data, and geopedological information; 2) to evaluate the predictive performance of machine learning and statistical models, including Random Forest (RF), Artificial Neural Network (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), and Multiple Linear Regression (MLR) used for soil pH estimation; 3) to identify the most influential environmental variables controlling the spatial variability of soil pH; and 4) to determine the modeling approach capable of producing the most accurate and reliable digital soil pH maps to support precision agriculture and sustainable soil management. Material and Methods This study was conducted in Badr Watershed located in the southern part of Qorveh County, Kurdistan Province, Iran, covering an area of approximately 6,700 ha. The objective was to develop a digital soil pH map of the 0–25 cm soil layer by integrating environmental covariates with machine learning and statistical modeling approaches. A geopedological map was first generated in a Geographic Information System (GIS) environment based on the Zink geopedological approach using geological and topographic information. Soil sampling locations were subsequently selected using the Latin Hypercube Sampling (LHS) technique to ensure representative coverage of the environmental variability across the study area. A total of 125 surface soil samples (0–25 cm) were collected and soil pH was determined in saturated soil paste using a calibrated pH meter following standard laboratory procedures. A comprehensive set of environmental covariates was prepared to represent the soil-forming factors, including terrain attributes derived from a Digital Elevation Model (DEM), remote sensing indices extracted from Landsat 8 Imagery, and geopedological variables. These covariates were used as predictor variables for digital soil mapping. Soil pH was modeled using five predictive approaches, including Random Forest (RF), Artificial Neural Network (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), and Multiple Linear Regression (MLR), implemented in the R statistical software environment. Model performance was evaluated using both 10-fold cross-validation and 5-fold random validation procedures. Predictive accuracy was assessed based on the coefficient of determination (R²), root mean square error (RMSE), and other relevant statistical performance indices. The resulting prediction maps were subsequently generated to characterize the spatial distribution of soil pH across the study area. Results The environmental covariates exhibited varying degrees of influence on the spatial prediction of soil pH. Variable importance analysis identified geomorphology as the most influential predictor, followed by watershed network base level, carbonate index, slope aspect, relative slope position, slope length factor (LS), and curvature index. The the above-mentioned five statistical and machine learning models (namely, MLR, RF, ANN, DT, and KNN) were evaluated in terms of their predictive performance using both 10-fold cross-validation and 5-fold random validation procedures. The results revealed differences in prediction accuracy among the models evaluated. Based on the 10-fold cross-validation results, the Multiple Linear Regression (MLR) model achieved the highest predictive performance, with a coefficient of determination (R²) of 0.698 and a root mean square error (RMSE) of 0.190, indicating its strong capability of soil pH estimation across the study area. In contrast, the 5-fold random validation results identified the K-Nearest Neighbor (KNN) model as the most accurate and precise approach for soil pH prediction. Comparison of the validation approaches further indicated that ensemble or combined prediction strategies would generally outperform individual models. In this regard, integrating the prediction outputs from multiple models improved the overall reliability and spatial accuracy of the digital soil pH maps generated, resulting in a more robust characterization of soil pH variability across the study watershed. Conclusion This study demonstrated the effectiveness of integrating environmental covariates with statistical and machine learning approaches for digital mapping of soil pH in Badr Watershed. The results confirmed that terrain- and geopedology-related variables played fundamental roles in explaining the spatial variability of soil pH, highlighting the importance of incorporating multiple environmental factors into digital soil mapping frameworks. Model evaluation revealed that predictive performance varied depending on the validation strategy adopted. This is evidenced by the fact that Multiple Linear Regression (MLR) provided the highest predictive accuracy under the 10-fold cross-validation scheme, whereas the K-Nearest Neighbor (KNN) model performed best under the 5-fold random validation approach. These findings indicate that no single model is universally superior and that model performance depends on both the characteristics of the validation procedure and the spatial structure of the dataset. Furthermore, the superior performance of combined prediction approaches suggests that, compared to individual models, ensemble modeling can improve the reliability and spatial accuracy of digital soil pH maps. The soil pH maps thus generated provide valuable spatial information that can support site-specific soil management, precision agriculture, and sustainable land-use planning in the study area. Overall, the integration of geopedological information, terrain derivatives, remote sensing data, and advanced predictive models yields a robust framework for digital soil pH mapping. Future studies may be recommended to investigate hybrid and ensemble machine learning techniques, incorporate additional environmental covariates and higher-resolution remote sensing data, and evaluate the transferability of the modeling framework proposed herein to other regions with different soil and environmental conditions. pH خاک یکی از ویژگی‌های مهم خاک است که معیاری از اسیدی یا قلیایی بودن خاک می‌باشد و عمیقاً بر دسترسی به مواد مغذی و فعالیت میکروبی تأثیر می‌گذارد و مستقیماً بر رشد و سلامت گیاه تأثیر دارد. محصولات مختلف در محدوده‌های pH خاص رشد می‌کنند و حفظ سطح pH بهینه تضمین می‌کند که مواد مغذی ضروری به راحتی در دسترس گیاهان باشند. مطالعه حاضر با هدف نقشه‌برداری رقومی pH خاک با استفاده از متغیرهای کمکی محیطی و تصاویر ماهواره لندست 8 و مدل‌های پیش‌بینی کننده و معرفی بهترین مدل‌ها، در حوضه آبخیز بدر در جنوب شهرستان قروه انجام گرفت. برای انجام این پژوهش در مرحله اول، نقشه‌ی ژئومورفولوژی با استفاده از نقشه زمین‌شناسی و بر اساس روش ژئوپدولوژی زینک در محیط سامانه اطلاعات جغرافیائی ترسیم گردید. در مرحله‌ی دوم، محل125 خاکرخ مطالعاتی بر اساس تکنیک ابر مکعب لاتین تعیین گردید، و pH در گل اشباع توسط دستگاه پ‌هاش‌متر اندازه‌گیری شد. متغیرهای کمکی شامل مشتقات مدل رقومی ارتفاع، شاخص‌های سنجش از دور دریافتی از ماهواره لندست 8 و نقشه ژئوپدولوژی بودند که انتخاب متغیرهای کمکی مناسب با استفاده از روش تجزیه مؤلفه‌های اصلی (PCA) انجام گرفت. در مرحله سوم، مدلسازی انجام، نقشه‌های رقومی کلاس‌ها و ویژگی‌های خاک تهیه گردید و ارزیابی مدل‌ها صورت گرفت. متغیرهای کمکی مهم در پیش‌بینی مقدار pH خاک به ترتیب اهمیت عبارت‌اند از: ژئومورفولوژی، سطح‌مبنای شبکه آبراهه‌ای، شاخص کربنات، جهت شیب، موقعیت نسبی شیب، عامل طول شیب و شاخص انحنا. پیش‌بینی pH توسط مدل‌های نزدیک‌ترین همسایه K، تحلیل درخت تصمیم، شبکه عصبی مصنوعی، جنگل تصادفی و رگرسیون خطی چندگانه صورت گرفت. در میان مدل‌های استفاده‌شده برای پیش‌بینی pH، با استفاده از روش اعتبارسنجی کافلد 10 مکانی، مدل رگرسیون خطی چندگانه با ضریب تبیین 0/698 و ریشه دوم متوسط مربعات خطا 0/190 از بیشترین دقت برای پیش‌بینی برخوردار بود. بر اساس روش اعتبارسنجی کافلد 5 تصادفی، دقت و صحت مدل نزدیک‌ترین همسایه بهترین عملکرد را در پیش‌بینی pH خاک دارا بوده است. به طور کلی، بر اساس روش‌های ارزیابی کافلد 10 مکانی و کافلد 5 تصادفی، می‌توان بیان داشت که روش‌های ترکیبی دارای قابلیت بیشتری برای پیش‌بینی pH می‌باشند. این بدین معنی است که ترکیب نتایج پیش‌بینی سایر مدل‌ها می‌تواند نقشه‌هایی با دقت بالاتر را تولید کند.

https://lmj.areeo.ac.ir/article_135535_6d1bb6c752a042c9407b24240d8b1352.pdf

موسسه تحقیقات خاک و آب مدیریت اراضی 2345-6205 14 1 2026 08 23 A review of artificial intelligence and its applications in soil science مروری بر هوش مصنوعی و کاربردهای آن در علوم خاک 75 111 135159 10.22092/lmj.2026.370937.396 FA رضا حسن پور محقق بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی آذربایجان شرقی، سازمان تحقیقات، آموزش و ترویج کشاورزی، تبریز، ایران. 0000-0001-9262-6251 محمدرضا خانزاده ریک‌آبادی دانشجوی کارشناسی گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران. علیرضا توسلی استادیار بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی آذربایجان شرقی، سازمان تحقیقات، آموزش و ترویج کشاورزی، تبریز، ایران. حسین رضایی استادیار گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران. Journal Article 2025 11 10 Objectives Artificial Intelligence (AI) has emerged as a transformative tool in the field of soil science, providing capabilities for data analysis, pattern recognition, and decision-making that go beyond those of the traditional methods. Extensive research has focused on developing and applying AI in soil science, driven by the growth of digital data, computational power, and algorithmic advancements. While machine learning (ML) currently dominates AI applications, the field encompasses broader areas including digital image analysis, natural language processing (NLP), expert systems, and knowledge representation. In soil science, AI supports diverse tasks, from predicting soil carbon stocks with ML algorithms to evaluating soil functions via decision support systems, extracting metadata from scientific texts, and analyzing digital soil images. Despite this progress, a precise consensus on AI's definition remains elusive, often conflated with ML, even though its scope extends to computer vision, control theory, and robotics. Early predictions anticipated revolutions in soil science through expert systems and natural language interfaces, some realized as fuzzy logic and decision support applications. Recently revitalized interest in AI owes much to advances in ML and digital convergence, that is, the integration of digital data, databases, the internet, and advanced tools for big data management. Nevertheless, interdisciplinary dialogue among the subfields remains limited, constraining the full exploitation of AI potentials. This review study is dedicated to providing a comprehensive overview of AI’s role and applications in soil science, review of its history and definitions, its classification into key domains, and documentation of its diversity across subdisciplines as well as identification of trends, research gaps, challenges, and future opportunities, with emphasis laid on its overreliance on predictive ML and the need for explainability and knowledge integration. Methodology This study is a narrative review derived from an extensive body of scientific literature. For this purpose, systematic searches were conducted in major databases including Scopus, Web of Science, Google Scholar, and the Scientific Information Database (in Persian). The search strategy combined keywords such as “artificial intelligence in soil science”, “machine learning”, “expert systems”, “digital soil mapping”, “natural language processing”, “pedotransfer functions”, “soil image analysis”, and “explainable AI” (all in English) along with Persian equivalents for broader coverage. Inclusion criteria prioritized seminal works on AI history and definitions, foundational studies on soil applications, recent reviews, and empirical papers demonstrating AI techniques under various soil contexts. The information thus collected was structured around a taxonomy classifying AI into three main domains, with critical analysis of applications, algorithms, and limitations. Quantitative trends, such as publication growth, were also considered based on annual AI reports. Results AI traces its origins to early neural models and foundational conferences, evolving through periods of limited progress to modern breakthroughs in protein structure prediction and image generation. Definitions of AI range from emulating human intelligence to rational decision-making for goal achievement. In soil science, AI applications cover sensing and interacting, reasoning and decision-making, as well as learning and prediction. In sensing and interacting, NLP is used to extract data from legacy soil profiles, computer vision to support soil image classification, proximal and remote sensing with ML to predict soil properties, robotics to enable automated sampling, and explainable AI (XAI) to interpret model outputs using techniques such as Shapley values. Reasoning and decision-making applications include expert systems for soil classification, fuzzy logic for handling uncertainty, decision support systems for conservation planning, and optimization algorithms for land evaluation. Learning and prediction (i.e., the dominant domain) employs ML techniques such as random forests and neural networks for digital soil mapping (DSM), pedotransfer functions (PTFs) for estimating hydraulic properties, and predictive modeling for estimating soil salinity or nutrient content. Across soil subdisciplines, AI has proven effective in DSM, PTF development, image classification, and expert systems. However, the majority of applications remain ML-focused and predictive, such as mapping soil organic carbon, with relatively few studies emphasizing explainability or integration of pedological knowledge. Exceptions include NLP for meta-analysis and cognitive soil models, which demonstrate its potentials for more holistic AI uses. Persistent challenges involve overemphasis on prediction, reduced model interpretability, and limited synergy across soil science subfields. Conclusion AI has profoundly impacted soil science, offering tools for predictiing, interacting, and decision-making, yet its applications remain heavily skewed toward ML-based forecasting. While AI effectively handles big data and complex patterns, as evidenced by DSM and PTF advancements, it often lacks interpretability and domain-specific knowledge integration, resulting in black-box models that might impede scientific understanding. Notable exceptions such as XAI and NLP for legacy data extraction highlight pathways for broader, more holistic applications. Future directions include mining historical soil profile texts to extract new insights, constructing meta-analyses from soil literature using NLP, developing interpretable ML models, and integrating cognitive models for simulating soil processes. Addressing these challenges will require interdisciplinary collaboration, fostering synergies across subfields and embedding pedological expertise into algorithms. Recommendations include establishing standardized benchmarks for AI applications, promoting open-source datasets and tools for reproducibility, investing in XAI to enhance trust and usability, and exploring hybrid approaches that combine AI with traditional methods for sustainable soil management. Responsible AI development, aligned with ethical principles such as bias mitigation, will ensure its contribution to addressing global challenges including climate resilience and food security. در دهه‌های اخیر، تحقیقات گسترده‌ای به توسعه و کاربرد هوش مصنوعی در علوم خاک اختصاص یافته است. اگرچه بخش عمده‌ای از کاربردهای کنونی هوش مصنوعی در علوم خاک به یادگیری ماشین مربوط می‌شود، اما این فناوری حوزه‌های دیگری مانند تجزیه‌وتحلیل تصویر دیجیتال، پردازش زبان طبیعی، سیستم‌های خبره و نمایش دانش را نیز در برمی‌گیرد. هدف این مقاله مروری، ارائه یک بررسی جامع از نقش و کاربردهای هوش مصنوعی در علوم خاک است. در ابتدا، تاریخچه و تعاریف هوش مصنوعی مرور شده و سپس یک طبقه‌بندی معمول از هوش مصنوعی در سه حوزه سنجش و تعامل، استدلال و تصمیم‌گیری و یادگیری و پیش‌بینی ارائه شده است. در ادامه، کاربردها و الگوریتم‌های مرتبط با هر حوزه‌ در علوم خاک بررسی شده‌اند. یافته‌های اصلی نشان می‌دهد: الف) کاربردهای هوش مصنوعی در علوم خاک متنوع بوده و شامل سیستم‌های پشتیبانی تصمیم‌گیری، طبقه‌بندی تصویر، پیش‌بینی با یادگیری ماشین و سیستم‌های خبره می‌شوند. ب) در حال حاضر، کاربرد هوش مصنوعی در علوم خاک تقریباً به‌طور کامل با یادگیری ماشین گره‌خورده است؛ ج) کاربردهای یادگیری ماشین به‌طور عمده در نقشه‌برداری رقومی خاک و توسعه توابع انتقالی خاک دیده می‌شوند و د) بخش عمده‌ای از کاربردهای هوش مصنوعی بر اهداف پیش‌بینی متمرکز هستند. بااین‌حال، چند استثنای قابل‌توجه فراتر از این کاربردها، به‌ویژه در پردازش زبان طبیعی، توسعه مدل‌های شناختی خاک و یادگیری ماشین تفسیرپذیر مشاهده می‌شود. بر اساس این یافته‌ها، تمرکز بیش از حد بر پیش‌بینی با هوش مصنوعی، همراه با کاهش قابلیت توضیح و نبود ادغام مؤثر دانش خاک در الگوریتم‌ها، از چالش‌های مهم این حوزه می‌باشد. افق‌های آینده در این زمینه شامل بهره‌گیری از هوش مصنوعی برای استخراج داده‌ها از متون نیم‌رخ‌‌های خاک قدیمی به‌منظور بازیابی منابع جدید اطلاعات خاک و نیز به‌کارگیری پردازش زبان طبیعی برای ساخت فراتحلیل‌هایی از متون علمی خاک‌شناسی است. این کاربردهای نوظهور می‌توانند سهم بسزایی در تحقیقات علوم خاک ایفا کنند.

https://lmj.areeo.ac.ir/article_135159_6b15d27badf20c90e1e233a3233be44f.pdf

موسسه تحقیقات خاک و آب مدیریت اراضی 2345-6205 14 1 2026 08 23 A comprehensive review of the effects of biological soil crusts on soil fertility, moisture conservation, erosion control, and ecosystem resilience ارزیابی جامع نقش پوسته‌های زیستی خاک در ارتقای حاصلخیزی، حفظ رطوبت، کاهش فرسایش و تاب‌آوری بوم‌سازگان 113 146 135764 10.22092/lmj.2026.368649.388 FA راشین محمدی دانشجوی دکتری مدیریت مناطق بیابانی، گروه مدیریت مناطق بیابانی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران محسن حسینعلی زاده دانشیار گروه مدیریت مناطق بیابانی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران. Journal Article 2025 05 13 Objectives This article presents a comprehensive review of the roles played by biological soil crusts (BSCs) in improving soil fertility, regulating hydrological processes, and reducing soil erosion in arid and semi-arid ecosystems, with special attention paid to application of artificial inoculation approaches. The study addresses a critical environmental challenge in Iran, where arid and semi-arid areas account for approximately 75% of the ecosystems facing such dire consequences as desertification, sand and dust storms, as well as accelerated wind erosion. Due to the moisture deficiency along with other adverse climatic conditions, soils in these ecosystems are highly vulnerable to degradation as a result of their generally sparse vascular plant cover. However, many of these areas are home to intersperse spaces that are covered by communities of plants and microbial microorganisms known as BSCs. These crusts, formed through the interactions of mineral particles with cyanobacteria, algae, fungi, lichens, and bryophytes are recognized as common covers in arid and semi-arid regions. By significantly influencing early ecosystem processes, these crusts are appropriately described as "ecosystem engineers" in arid lands. Numerous field and farm studies have been recently conducted in Iran to investigate the roles of BSCs in improving soil physicochemical properties, regulating hydrological responses, and reducing erosion. Methodology A systematic review was conducted using study reports published between 1980 and 2025. The methodology included searching both international databases (using such relevant terms in English as cyanobacteria, algae, fungi, lichens, bryophytes, biological soil crust, soil erosion, runoff, and land restoration) and domestic databases (using equivalent Persian terms). Inclusion criteria involved studies addressing at least one of the following three main themes: 1) erosion and sedimentation, 2) hydrological processes, and 3) fertility and soil quality characteristics, with provision of quantitative or qualitative data. General reports and case studies that merely addressed taxonomic aspects without direct connection to ecosystem functions of crusts were excluded from the present review. Ultimately, approximately 160 sources forming the basis for the current analysis and synthesis were selected that included field and experimental studies, modeling studies, previous reviews, and domestic research. Results The comprehensive review of the 160 studies from 1980 to 2025 demonstrates that BSCs significantly reduce soil erosion depending on crust type, successional stage, coverage percentage, and spatial scale. Cyanobacterial crusts seem to have achieved reductions of approximately 77-99% in erosion as reported in many field and laboratory experiments while moss crusts with adequate coverage have reportedly resulted in nearly 100% reduction in surface erosion and, in some cases, dust emission. During early successional stages, cyanobacteria and bacteria are reported to have provided the initial core of surface restoration through extracellular polysaccharides (EPS) generation, nitrogen fixation, soil aggregate formation, and increased structural cohesion. The experimental studies reviewed show that single-application artificial inoculation of these microorganisms in late autumn on loose, erosion-prone soils substantially reduces runoff and sedimentation while it also enables faster formation of stable crusts which reduces ecosystem recovery time from the natural multi-decadal periods to approximately 5-10 years. The implementation cost of this approach is reported to be typically around $350 per hectare, making it a cost-effective strategy for large-scale soil restoration. Upon achieving any coverage greater than 36%, mosses in advanced successional stages become the most effective factor involved in reducing erosion and improving surface stability. By altering the moisture and thermal regimes of the surface layer, they simultaneously affect evaporation, infiltration, and water redistribution on both patch and bulk scales. Lichens also play a role in long-term stabilization under specific conditions (such as rocky or sloped surfaces). These results indicate that the targeted use of artificial BSC inoculation, when combined with moss cover management, represents an efficient and accelerating approach to erosion control, enhanced fertility, and optimized soil hydrological behavior in land restoration programs. Given the moisture-thermal regimes of Iran, the inoculation of such native cyanobacteria as Phormidium spp. and Scytonema spp. at optimal concentrations of 100 million cells per liter (10⁸ cells L⁻¹) during late autumn may be recommended as a practical operational strategy. This approach expectedly strikes a balance among cost, microbial survival, restoration efficiency in arid climates, and ecosystem equilibrium. Conclusion The present systematic review of 160 sources demonstrates that biological soil crusts are effective tools for sustainable soil management in arid and semi-arid ecosystems, with their efficacy significantly influenced by the type of constituent microorganisms, successional stage, and environmental conditions. Cyanobacteria, particularly the species Phormidium ambiguum and Scytonema javanicum (used at optimal concentrations of 100 million cells per liter in late autumn), are identified as the most suitable options in arid climates for initial artificial inoculation stages on erosion-prone soils on the grounds that they rapidly stabilize soil particles and enhance fertility through nitrogen fixation and EPS secretion to reduce ecosystem recovery time from several decades to 5-10 years. In contrast, mosses in advanced successional stages, especially with a coverage exceeding 36%, are most effective in reducing erosion (up to nearly 100%) and improving surface stability while they simultaneously moderate soil moisture and temperature regimes to affect runoff, infiltration, and evaporation. Lichens also play a role in long-term stabilization under specific conditions (such as rocky or sloped surfaces). BSCs possess significant operational and economic potentials as demonstrated by scientific evidence for large-scale soil management, particularly through the use of native cyanobacteria inoculation as a low-cost and effective method. The key challenge to be addressed in future is the upscaling of these laboratory findings to the watershed level in which case not only might they be integrated with operational guidelines but also help local communities participate in the continuous monitoring of these living soil health indicators and their adaptation to climate change. Smart integration of microbial inoculation using native plant species along with socio-economic evaluation of technology appraisal by local communities in land restoration programs may be recommended as future research to achieve enhanced ecosystem resilience. این مقاله مروری با تحلیل پژوهش‌های طی سال‌های ۱۹۸۰ تا ۲۰۲۵، نقش پوسته‌های زیستی خاک (BSCs) را در بهبود حاصلخیزی، تنظیم فرآیندهای هیدرولوژیکی و کاهش فرسایش خاک در بوم‌سازگان‌های خشک و نیمه‌خشک، با تأکید ویژه بر کارآیی رویکردهای تلقیح مصنوعی، بررسی می‌کند. شواهد کمی نشان می‌دهد که BSCs بسته به نوع، مرحله توالی، درصد پوشش و مقیاس مکانی، فرسایش را به‌طور معنی‌داری کاهش می‌دهند؛ به‌گونه‌ای که پوسته‌های سیانوباکتریایی در بسیاری از آزمایش‌های میدانی و آزمایشگاهی، کاهش حدود ۷۷ تا ۹۹ درصدی و پوسته‌های خزه‌ای در پوشش‌های کافی، کاهش نزدیک به ۱۰۰ درصدی در فرسایش سطحی و گاه انتشار غبار را رقم زده‌اند. در مقیاس‌های مختلف، این کارکرد به ریزعارضه‌نگاری، ناهمگنی درون‌جامعه‌ای و الگوی استقرار آن‌ها در مراحل اولیه توالی، سیانوباکتری‌ها و باکتری‌ها از طریق تولید پلی‌ساکاریدهای خارج‌سلولی، تثبیت نیتروژن، تشکیل خاکدانه و افزایش انسجام ساختاری خاک، هسته آغازین فرآیند احیای سطح را فراهم می‌کنند. مرور گستره‌ای از مطالعات تجربی نشان می‌دهد که تلقیح مصنوعی یک‌باره در اواخر پاییز این ریزموجودات بر روی خاک‌های سست و مستعد فرسایش، ضمن کاهش محسوس رواناب و رسوب، تشکیل سریع‌تر پوسته‌های پایدار را ممکن ساخته و زمان بازیابی را از بازه‌های طبیعی چندده‌ساله به حدود ۵ تا ۱۰ سال کاهش می‌دهد؛ این در حالی است که هزینه اجرای این رویکرد معمولاً در حدود ۳۵۰ دلار در هکتار گزارش شده است. در مراحل پیشرفته‌تر توالی، خزه‌ها با دستیابی به پوشش بالاتر از حدود ۳۶ درصد، به مؤثرترین عامل کاهش فرسایش و بهبود پایداری سطحی تبدیل می‌شوند و در عین حال، با تغییر رژیم رطوبتی و دمایی لایه سطحی، بر تبخیر، نفوذپذیری و توزیع مجدد آب در مقیاس لکه‌ای و توده‌ای اثر می‌گذارند. این نتایج نشان می‌دهد که استفاده هدفمند از تلقیح مصنوعی BSCs، در ترکیب با مدیریت پوشش خزه‌ای، رویکردی کارآمد و تسریع‌کننده برای کنترل فرسایش، افزایش حاصلخیزی و بهینه‌سازی رفتار هیدرولوژیکی خاک در برنامه‌های احیای سرزمین محسوب می‌شود. با عنایت به رژیم‌های رطوبتی-حرارتی ایران، تلقیح یک‌باره سیانوباکتری‌های بومی نظیرPhormidium spp. وScytonema spp. با غلظت بهینه ۱۰۰ میلیون سلول در لیتر در اواخر پاییز، به‌عنوان راهکار عملیاتی پیشنهاد می‌گردد. این رویکرد، تعادل مناسبی میان هزینه، بقای میکروبی و کارایی احیا در اقلیم‌های خشک و تعادل بوم‌سازگان را برقرار می‌سازد.

https://lmj.areeo.ac.ir/article_135764_0f66c2e6642f32c1a5048da0df7123fb.pdf