Agricultural Research,Education and Extension Organization Iranian Journal of Soil Research 2228-7124 40 1 2026 05 22 Spatial Modeling and Prediction of the Soil Fertility Index in Two Arid and Semi-Arid Regions of Ilam Province, Iran Spatial Modeling and Prediction of the Soil Fertility Index in Two Arid and Semi-Arid Regions of Ilam Province, Iran 1 21 135356 10.22092/ijsr.2026.371527.803 FA Asghar Rahmani Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Mahmood Rostaminya Water and Soil Department, Agriculture Faculty, Ilam University, Ilam, Iran. Nasibeh Sayedi Department of Soil and Water, Faculty of Agriculture, University of Tehran, Tehran, Iran. Seyed Roohollah Mousavi Department of Soil Science, University of Tehran, Karaj, Iran. Journal Article 2025 11 29 Background and Objectives: Soil fertility is one of the most critical factors determining the sustainability of agricultural ecosystems and ensuring food security. Declining soil fertility directly affects crop productivity and, consequently, food security. Understanding the spatial variability of fertility distribution patterns is essential for efficient soil management. In recent years, the integration of advanced machine learning algorithms with geostatistical methods has provided powerful tools for modeling and predicting soil fertility indicators such as the Soil Fertility Index (SFI). In arid and semi-arid regions, water scarcity, soil salinity, and climatic variability are major challenges for sustainable agricultural production. Therefore, spatial modeling of soil fertility and identification of its driving factors can serve as a scientific basis for regional land-use planning and resource management. The present study aimed to model and predict the spatial distribution of SFI in two arid and semi-arid regions, Miameh–Dehloran and Valiasr–Badreh (Ilam Province, western Iran), using Random Forest (RF) and Cubist (CB) machine learning algorithms, and to compare their performance with the conventional Ordinary Kriging (OK) method. Ultimately, this research seeks to develop a region-based spatial model for SFI prediction in the agricultural lands of the Zagros region in western Iran. Materials and Methods: Soil sampling was conducted in the Miameh–Dehloran and Valiasr–Badreh areas using the conditioned Latin hypercube sampling (cLHS) method. A total of 133 and 71 surface soil samples were collected from the respective regions. The samples were analyzed for physical, chemical, and biological properties to calculate the Soil Fertility Index (SFI). Auxiliary environmental variables, including topographic parameters derived from the Digital Elevation Model (DEM) and remote sensing (RS) indices, were used as predictors. The most relevant variables were selected using the Variance Inflation Factor (VIF) and Boruta algorithms, resulting in 9 and 12 selected predictors for the Miameh–Dehloran and Valiasr–Badreh sites, respectively. Additionally, two climatic variables—mean annual precipitation (MAP) and mean annual temperature (MAT)—were included based on expert judgment. The RF, CB, and OK models were trained and validated, and their predictive performances were evaluated using the coefficient of determination (R²) and root mean square error (RMSE). Spatial prediction maps of SFI were generated in ArcGIS based on the best-performing model. Results: The results indicated that the Random Forest model outperformed both the Cubist and Ordinary Kriging models in predicting SFI values. The R² of RF was 0.79 for Miameh–Dehloran and 0.60 for Valiasr–Badreh, while the RMSE values were 0.64 and 0.69, respectively. These results demonstrate the superior ability of RF in capturing nonlinear relationships between soil fertility and environmental covariates. According to the spatial distribution maps, approximately 74.14% of the Miameh–Dehloran and 77.33% of the Valiasr–Badreh areas fell within the “very high” (F1) and “high” (F2) fertility classes, indicating considerable potential for agricultural productivity. The climatic variables MAT and MAP were identified as the most influential predictors of SFI. In Miameh–Dehloran, remote sensing indices—especially vegetation and spectral reflectance indicators—played a major role, whereas in Valiasr–Badreh, topographic parameters such as elevation, slope, and aspect were more dominant. These spatial differences reflect the contrasting climatic and geomorphological conditions of the two regions. Combining RF with RS and topographic data significantly improved prediction accuracy and enabled the generation of high-resolution fertility maps suitable for precision agriculture applications. Conclusion: The findings highlight the potential of the Random Forest algorithm as a robust and reliable approach for spatial modeling of soil fertility in arid and semi-arid environments. RF effectively captured complex interactions among climatic, topographic, and spectral variables, leading to accurate and detailed SFI prediction maps. The study confirmed that climatic variables, particularly temperature and precipitation, play a decisive role in determining the spatial variability of soil fertility. The resulting maps can serve as valuable tools for agricultural planning, selection of suitable crop types, and sustainable management of soil and water resources. Overall, the proposed modeling framework provides an efficient strategy for optimizing land potential, improving crop yields—especially for wheat—and contributing to food security and sustainable agricultural development in the drylands of western Iran. Background and Objectives: Soil fertility is one of the most critical factors determining the sustainability of agricultural ecosystems and ensuring food security. Declining soil fertility directly affects crop productivity and, consequently, food security. Understanding the spatial variability of fertility distribution patterns is essential for efficient soil management. In recent years, the integration of advanced machine learning algorithms with geostatistical methods has provided powerful tools for modeling and predicting soil fertility indicators such as the Soil Fertility Index (SFI). In arid and semi-arid regions, water scarcity, soil salinity, and climatic variability are major challenges for sustainable agricultural production. Therefore, spatial modeling of soil fertility and identification of its driving factors can serve as a scientific basis for regional land-use planning and resource management. The present study aimed to model and predict the spatial distribution of SFI in two arid and semi-arid regions, Miameh–Dehloran and Valiasr–Badreh (Ilam Province, western Iran), using Random Forest (RF) and Cubist (CB) machine learning algorithms, and to compare their performance with the conventional Ordinary Kriging (OK) method. Ultimately, this research seeks to develop a region-based spatial model for SFI prediction in the agricultural lands of the Zagros region in western Iran. Materials and Methods: Soil sampling was conducted in the Miameh–Dehloran and Valiasr–Badreh areas using the conditioned Latin hypercube sampling (cLHS) method. A total of 133 and 71 surface soil samples were collected from the respective regions. The samples were analyzed for physical, chemical, and biological properties to calculate the Soil Fertility Index (SFI). Auxiliary environmental variables, including topographic parameters derived from the Digital Elevation Model (DEM) and remote sensing (RS) indices, were used as predictors. The most relevant variables were selected using the Variance Inflation Factor (VIF) and Boruta algorithms, resulting in 9 and 12 selected predictors for the Miameh–Dehloran and Valiasr–Badreh sites, respectively. Additionally, two climatic variables—mean annual precipitation (MAP) and mean annual temperature (MAT)—were included based on expert judgment. The RF, CB, and OK models were trained and validated, and their predictive performances were evaluated using the coefficient of determination (R²) and root mean square error (RMSE). Spatial prediction maps of SFI were generated in ArcGIS based on the best-performing model. Results: The results indicated that the Random Forest model outperformed both the Cubist and Ordinary Kriging models in predicting SFI values. The R² of RF was 0.79 for Miameh–Dehloran and 0.60 for Valiasr–Badreh, while the RMSE values were 0.64 and 0.69, respectively. These results demonstrate the superior ability of RF in capturing nonlinear relationships between soil fertility and environmental covariates. According to the spatial distribution maps, approximately 74.14% of the Miameh–Dehloran and 77.33% of the Valiasr–Badreh areas fell within the “very high” (F1) and “high” (F2) fertility classes, indicating considerable potential for agricultural productivity. The climatic variables MAT and MAP were identified as the most influential predictors of SFI. In Miameh–Dehloran, remote sensing indices—especially vegetation and spectral reflectance indicators—played a major role, whereas in Valiasr–Badreh, topographic parameters such as elevation, slope, and aspect were more dominant. These spatial differences reflect the contrasting climatic and geomorphological conditions of the two regions. Combining RF with RS and topographic data significantly improved prediction accuracy and enabled the generation of high-resolution fertility maps suitable for precision agriculture applications. Conclusion: The findings highlight the potential of the Random Forest algorithm as a robust and reliable approach for spatial modeling of soil fertility in arid and semi-arid environments. RF effectively captured complex interactions among climatic, topographic, and spectral variables, leading to accurate and detailed SFI prediction maps. The study confirmed that climatic variables, particularly temperature and precipitation, play a decisive role in determining the spatial variability of soil fertility. The resulting maps can serve as valuable tools for agricultural planning, selection of suitable crop types, and sustainable management of soil and water resources. Overall, the proposed modeling framework provides an efficient strategy for optimizing land potential, improving crop yields—especially for wheat—and contributing to food security and sustainable agricultural development in the drylands of western Iran.

https://srjournal.areeo.ac.ir/article_135356_c0a44c0da8232f01050f588429b4d571.pdf

Agricultural Research,Education and Extension Organization Iranian Journal of Soil Research 2228-7124 40 1 2026 05 22 Trends of nitrogen changes and the effect of amino acids on flowering and fruit set of citrus trees Trends of nitrogen changes and the effect of amino acids on flowering and fruit set of citrus trees 23 43 135477 10.22092/ijsr.2026.372348.814 FA Ali Asadi Kangarshahi Mazandaran Agricultural and Natural Resources Research and Education Center,Agricultural Research, Education and Extension Organization (AREEO), Mazandaran, Iran. Negin Akhlaghi Amiri Mazandaran Agricultural and Natural Resources Research and Education Center,Agricultural Research, Education and Extension Organization (AREEO), Mazandaran, Iran. Journal Article 2026 02 22 Background and Objectives: Amino acids are a large group of biological compounds that contain an amino group and a carboxyl group. Amino acids used in foliar nutrition are usually mixtures of different amino acids and short-chain peptides. These amino acids are plant growth stimulants that can be used by foliar spraying and fertigation. Plants may consume amino acids as a source of nitrogen, and in some cases, the amino acid may also be a plant stimulant. On the other hand, the flowering and fruiting period in citrus trees is the most important and critical stage of fruit development in fruit trees. The maintenance of reproductive organs and fruits during this period is directly related to the final yield of the trees. There is a high demand for nitrogen during the flowering and fruiting period. For this purpose, the trend of leaf nitrogen changes during the flowering stage of citrus trees and the effect of amino acids on nitrogen concentration, flower drop, and fruit formation of citrus trees were investigated in two separate experiments. Materials and Methods: In the first experiment, trends of nitrogen changes in the leaves of Satsuma mandarin (C. unshiu cv. Miyagawa) trees on Carrizo citrange (Citrus sinensis Osb. × Poncirus trifoliata L. Raf.), rootstock, and on Thomson Navel oranges on Sour Orange (C. aurantium L.) rootstock were measured in the eastern Mazandaran region. In the second experiment, the effect of foliar spraying of amino acids (combined amino acids) was carried out in a randomized complete block design with three treatments and three replications for three years with Satsuma mandarin (C. unshiu cv. Miyagawa) trees on Carrizo citrange (Citrus sinensis Osb. × Poncirus trifoliata L. Raf.), rootstock. The treatments included: T1. Control; T2. amino acid 1 g L-1; T3. amino acid 3 g L-1. The amino acid mixture used contained 8% aspartic acid, 12% glutamic acid, 14% serine, 8% glycine, 2% histidine, 6% arginine, 6% alanine, 6% threonine, 12% proline, 6% valine, 7% leucine, 5% phenylalanine, and about 1% methionine, cysteine, lysine, isoleucine, and tyrosine, and 13% total nitrogen. Results: The results of the first experiment showed that the nitrogen concentration of leaves in Satsuma gradually began to decrease from the time of bud break and the beginning of spring shoot growth, and reached a minimum at the flower opening stage, from 2.65% at the beginning of sampling to 1.84% at full bloom. From full bloom, the amount of nitrogen in the leaves gradually increased again, from 1.84% to 2.66% at the physiological fruit drop (June drop) stage. In the case of Thomson Navel orange trees, the results showed that the nitrogen concentration decreased from 2.57% at the time of sampling to 1.85% at full bloom. After flowering, the nitrogen in the leaves gradually increased and reached from 1.85% to 2.56% at the physiological fruit drop (June drop) stage. The results of the second experiment showed that foliar spraying of amino acids at concentrations of one and three parts per thousand (W/V) showed that foliar spraying treatments had no significant effect on reducing flower and fruit drop, increasing fruit set, leaf nitrogen concentration, and increasing the yield of Satsuma mandarin trees. Conclusion: According to the results of this study, the flowering time in citrus trees coincides with the minimum nitrogen concentration in the leaves. This indicates the limitation and need for nitrogen in this sensitive stage of phenology, but foliar spraying of amino acids did not have a significant effect on increasing nitrogen concentration and fruit set. Therefore, foliar application of amino acids for citrus trees during the flowering and fruit set stages in the climatic conditions of the north of the country is not recommended for citrus orchards Background and Objectives: Amino acids are a large group of biological compounds that contain an amino group and a carboxyl group. Amino acids used in foliar nutrition are usually mixtures of different amino acids and short-chain peptides. These amino acids are plant growth stimulants that can be used by foliar spraying and fertigation. Plants may consume amino acids as a source of nitrogen, and in some cases, the amino acid may also be a plant stimulant. On the other hand, the flowering and fruiting period in citrus trees is the most important and critical stage of fruit development in fruit trees. The maintenance of reproductive organs and fruits during this period is directly related to the final yield of the trees. There is a high demand for nitrogen during the flowering and fruiting period. For this purpose, the trend of leaf nitrogen changes during the flowering stage of citrus trees and the effect of amino acids on nitrogen concentration, flower drop, and fruit formation of citrus trees were investigated in two separate experiments. Materials and Methods: In the first experiment, trends of nitrogen changes in the leaves of Satsuma mandarin (C. unshiu cv. Miyagawa) trees on Carrizo citrange (Citrus sinensis Osb. × Poncirus trifoliata L. Raf.), rootstock, and on Thomson Navel oranges on Sour Orange (C. aurantium L.) rootstock were measured in the eastern Mazandaran region. In the second experiment, the effect of foliar spraying of amino acids (combined amino acids) was carried out in a randomized complete block design with three treatments and three replications for three years with Satsuma mandarin (C. unshiu cv. Miyagawa) trees on Carrizo citrange (Citrus sinensis Osb. × Poncirus trifoliata L. Raf.), rootstock. The treatments included: T1. Control; T2. amino acid 1 g L-1; T3. amino acid 3 g L-1. The amino acid mixture used contained 8% aspartic acid, 12% glutamic acid, 14% serine, 8% glycine, 2% histidine, 6% arginine, 6% alanine, 6% threonine, 12% proline, 6% valine, 7% leucine, 5% phenylalanine, and about 1% methionine, cysteine, lysine, isoleucine, and tyrosine, and 13% total nitrogen. Results: The results of the first experiment showed that the nitrogen concentration of leaves in Satsuma gradually began to decrease from the time of bud break and the beginning of spring shoot growth, and reached a minimum at the flower opening stage, from 2.65% at the beginning of sampling to 1.84% at full bloom. From full bloom, the amount of nitrogen in the leaves gradually increased again, from 1.84% to 2.66% at the physiological fruit drop (June drop) stage. In the case of Thomson Navel orange trees, the results showed that the nitrogen concentration decreased from 2.57% at the time of sampling to 1.85% at full bloom. After flowering, the nitrogen in the leaves gradually increased and reached from 1.85% to 2.56% at the physiological fruit drop (June drop) stage. The results of the second experiment showed that foliar spraying of amino acids at concentrations of one and three parts per thousand (W/V) showed that foliar spraying treatments had no significant effect on reducing flower and fruit drop, increasing fruit set, leaf nitrogen concentration, and increasing the yield of Satsuma mandarin trees. Conclusion: According to the results of this study, the flowering time in citrus trees coincides with the minimum nitrogen concentration in the leaves. This indicates the limitation and need for nitrogen in this sensitive stage of phenology, but foliar spraying of amino acids did not have a significant effect on increasing nitrogen concentration and fruit set. Therefore, foliar application of amino acids for citrus trees during the flowering and fruit set stages in the climatic conditions of the north of the country is not recommended for citrus orchards

https://srjournal.areeo.ac.ir/article_135477_5e0631da031492307a15cc427a1ae382.pdf

Agricultural Research,Education and Extension Organization Iranian Journal of Soil Research 2228-7124 40 1 2026 05 22 The Effect of Seed Coating with Biostimulants on Yield and Yield Components of Canola (Brassica napus L.) The Effect of Seed Coating with Biostimulants on Yield and Yield Components of Canola (Brassica napus L.) 45 60 135496 10.22092/ijsr.2026.370813.790 FA Parsa Sekooti Department of Crop Physiology, University of Mohaghegh Ardabili, Ardabil, Iran. Salim Farzaneh Department of Agronomy and Plant Breeding, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. Abdolghayoum Gholipouri Department of Agronomy, Faculty of plant production, Gorgan University of Agricultural Science and Natural resources, Gorgan, Iran. Shahram Khodadadi Sugar Beet Seed Institute(SBSI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Bahman Khoshru Soil and Water Research Institute(SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Negin Taleschian Tabrizi Crop and Horticultural Science Research Department, Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Moghan, Iran. Journal Article 2025 09 28 Background and Objectives: Canola (Brassica napus L.) is a vital global oilseed crop, critical for food security due to its high-quality edible oil and protein-rich meal. Enhancing productivity is a primary goal in modern sustainable agriculture, especially in semi-arid regions where suboptimal environmental conditions often hamper germination and seedling establishment. These early-stage stressors lead to poor crop stands, reduced seedling vigor, and ultimately lower final yields. To address these limitations, seed coating has emerged as a precise and resource-efficient method for improving seed performance. By creating a favorable micro-environment (spermosphere) and delivering beneficial substances directly to the embryo, this technique enhances establishment. Biostimulants, such as seaweed extracts (rich in phytohormones like auxins and cytokinins), humic acids (known for chelating nutrients and stimulating root development), and amino acids (essential for protein synthesis and stress tolerance), are effective additives. While their benefits in foliar applications are well-known, their efficacy and optimal concentrations via seed coating require further investigation. This study aimed to comprehensively evaluate the effects of seed priming and coating with various concentrations of humic acid, seaweed extract, and amino acids, individually and in combination, on the yield and yield components of spring canola. Materials and Methods: A field experiment was conducted during the 2018-2019 growing season at the research farm of the University of Mohaghegh Ardabili in northwestern Iran, a region characterized by a semi-arid, cold climate. The soil was a calcareous loam (pH 7.8, EC 1.15 dS/m) with low organic matter. The experiment utilized a Randomized Complete Block Design (RCBD) with three replications. The study involved 16 distinct seed pre-treatments applied to the spring canola cultivar 'Hyola 50'. Treatments included three levels of humic acid (3, 6, and 9 g/kg seed), three levels of seaweed extract (3, 6, and 9 g/kg seed), and three levels of amino acids (2, 4, and 6 g/kg seed), alongside specific combined formulations and hydropriming controls. An aqueous slurry containing carboxymethyl cellulose (CMC) as a filler and polyvinyl acetate (PVA) as a binder was used for uniform coating via a laboratory rotary coater. Standard agronomic practices were followed throughout the season. At physiological maturity, plants were harvested to determine grain yield, biological yield, harvest index, and key yield components. Data were analyzed using SAS software (ANOVA), and means were compared using Duncan's Multiple Range Test (DMRT) at the 5% probability level. Results: Analysis of variance revealed significant effects (P < 0.01) of seed treatments on grain yield, biological yield, the number of pods per plant, 1000-grain weight, and plant height. However, no statistically significant differences were observed for harvest index, pod length, and the number of seeds per pod, suggesting these traits may be less responsive to seed treatments. Grain yield was significantly impacted; the highest yield of 2185.3 kg/ha was achieved by coating seeds with 9 g/kg seaweed extract, representing a substantial 38.76% increase over the control (1574.8 kg/ha). Similarly, coating with 9 g/kg humic acid resulted in the highest biological yield (6941.3 kg/ha) and the maximum number of pods per plant (96.22), showing increases of 21.98% and 62.50% compared to the control, respectively. The 1000-grain weight was also significantly improved by 9 g/kg seaweed extract. Among the combined treatments, the specific combination of 2 g amino acid + 6 g seaweed extract + 3 g humic acid per kg seed demonstrated superior performance in improving plant height and overall yield stability compared to other combinations, highlighting synergistic effects at specific ratios. Conclusion: This study confirms that seed coating with biostimulants is a potent strategy for enhancing spring canola productivity in semi-arid regions. The targeted application of these substances leads to significant improvements in seedling establishment, vegetative growth, and final grain yield. Application of 9 g/kg seaweed extract is specifically recommended for maximizing grain yield due to its hormonal influence, while high concentrations of humic acid (9 g/kg) are superior for boosting biomass and sink capacity, i.e., pods. The results also indicated that optimized combined treatments can further enhance plant performance. Therefore, seed coating serves as a valuable, precise tool for sustainable production. However, since this study was conducted over a single growing season, further multi-year and multi-location trials are recommended to confirm the stability and generalizability of these findings under varying climatic conditions. Background and Objectives: Canola (Brassica napus L.) is a vital global oilseed crop, critical for food security due to its high-quality edible oil and protein-rich meal. Enhancing productivity is a primary goal in modern sustainable agriculture, especially in semi-arid regions where suboptimal environmental conditions often hamper germination and seedling establishment. These early-stage stressors lead to poor crop stands, reduced seedling vigor, and ultimately lower final yields. To address these limitations, seed coating has emerged as a precise and resource-efficient method for improving seed performance. By creating a favorable micro-environment (spermosphere) and delivering beneficial substances directly to the embryo, this technique enhances establishment. Biostimulants, such as seaweed extracts (rich in phytohormones like auxins and cytokinins), humic acids (known for chelating nutrients and stimulating root development), and amino acids (essential for protein synthesis and stress tolerance), are effective additives. While their benefits in foliar applications are well-known, their efficacy and optimal concentrations via seed coating require further investigation. This study aimed to comprehensively evaluate the effects of seed priming and coating with various concentrations of humic acid, seaweed extract, and amino acids, individually and in combination, on the yield and yield components of spring canola. Materials and Methods: A field experiment was conducted during the 2018-2019 growing season at the research farm of the University of Mohaghegh Ardabili in northwestern Iran, a region characterized by a semi-arid, cold climate. The soil was a calcareous loam (pH 7.8, EC 1.15 dS/m) with low organic matter. The experiment utilized a Randomized Complete Block Design (RCBD) with three replications. The study involved 16 distinct seed pre-treatments applied to the spring canola cultivar 'Hyola 50'. Treatments included three levels of humic acid (3, 6, and 9 g/kg seed), three levels of seaweed extract (3, 6, and 9 g/kg seed), and three levels of amino acids (2, 4, and 6 g/kg seed), alongside specific combined formulations and hydropriming controls. An aqueous slurry containing carboxymethyl cellulose (CMC) as a filler and polyvinyl acetate (PVA) as a binder was used for uniform coating via a laboratory rotary coater. Standard agronomic practices were followed throughout the season. At physiological maturity, plants were harvested to determine grain yield, biological yield, harvest index, and key yield components. Data were analyzed using SAS software (ANOVA), and means were compared using Duncan's Multiple Range Test (DMRT) at the 5% probability level. Results: Analysis of variance revealed significant effects (P < 0.01) of seed treatments on grain yield, biological yield, the number of pods per plant, 1000-grain weight, and plant height. However, no statistically significant differences were observed for harvest index, pod length, and the number of seeds per pod, suggesting these traits may be less responsive to seed treatments. Grain yield was significantly impacted; the highest yield of 2185.3 kg/ha was achieved by coating seeds with 9 g/kg seaweed extract, representing a substantial 38.76% increase over the control (1574.8 kg/ha). Similarly, coating with 9 g/kg humic acid resulted in the highest biological yield (6941.3 kg/ha) and the maximum number of pods per plant (96.22), showing increases of 21.98% and 62.50% compared to the control, respectively. The 1000-grain weight was also significantly improved by 9 g/kg seaweed extract. Among the combined treatments, the specific combination of 2 g amino acid + 6 g seaweed extract + 3 g humic acid per kg seed demonstrated superior performance in improving plant height and overall yield stability compared to other combinations, highlighting synergistic effects at specific ratios. Conclusion: This study confirms that seed coating with biostimulants is a potent strategy for enhancing spring canola productivity in semi-arid regions. The targeted application of these substances leads to significant improvements in seedling establishment, vegetative growth, and final grain yield. Application of 9 g/kg seaweed extract is specifically recommended for maximizing grain yield due to its hormonal influence, while high concentrations of humic acid (9 g/kg) are superior for boosting biomass and sink capacity, i.e., pods. The results also indicated that optimized combined treatments can further enhance plant performance. Therefore, seed coating serves as a valuable, precise tool for sustainable production. However, since this study was conducted over a single growing season, further multi-year and multi-location trials are recommended to confirm the stability and generalizability of these findings under varying climatic conditions.

https://srjournal.areeo.ac.ir/article_135496_ba516116a8a0b4c18fa4f8a7a3a4bac7.pdf

Agricultural Research,Education and Extension Organization Iranian Journal of Soil Research 2228-7124 40 1 2026 05 22 Multicriteria Analysis of the Effects of Organic Growth Stimulants on Reducing Damage Caused by Drought Stress in Sugar Beet Multicriteria Analysis of the Effects of Organic Growth Stimulants on Reducing Damage Caused by Drought Stress in Sugar Beet 61 79 135501 10.22092/ijsr.2026.370715.807 FA Reza Mohammadikia Soil and Water Research Institute(SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Mohammad Passandideh Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Farahnaz Hamdi Holasoo Sugar Beet Seed Institute(SBSI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Vali-Allah Yousef Abadi Sugar Beet Seed Institute(SBSI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Journal Article 2025 12 24 Background and Objectives: Sugar beet (Beta vulgaris L.) is one of the most important sugar-producing crops in arid and semi-arid regions, where water scarcity is a major limiting factor for yield and quality. Drought stress often reduces root growth and sugar yield, necessitating the development of sustainable management strategies to enhance drought tolerance. In recent years, organic growth stimulants such as amino acids and humic substances have attracted increasing attention due to their environmentally friendly nature and potential to improve plant performance under abiotic stresses. These stimulants not only promote physiological and biochemical processes in plants but also enhance soil health and nutrient availability, contributing to overall crop resilience. The objective of this pilot study was to preliminarily assess the effectiveness of different organic growth stimulants in alleviating drought stress effects on sugar beet and to identify superior treatments using an integrated multi-criteria decision-making approach. The findings of this research are expected to provide valuable insights for developing practical, eco-friendly strategies to improve sugar beet productivity under water-limited conditions. Materials and Methods: This study was conducted at the Kamalshahr Research Station, Karaj, Iran, using a split-plot arrangement within a randomized complete block design. Two irrigation regimes, including normal irrigation and drought stress, were assigned to main plots, while four nutritional treatments (control, amino acid, humic acid, and combined application of growth stimulants) were allocated to subplots. Several quantitative and qualitative traits related to root yield and sugar quality were measured during the growing season. In addition to conventional analysis of variance, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank treatments by simultaneously considering multiple criteria with equal weights. To evaluate the robustness and stability of the rankings, Monte Carlo simulation was applied based on repeated random perturbations of the input data. Results: The results showed that drought stress markedly reduced root yield and root weight, while increasing root dry matter content (dry root pulp weight / fresh root pulp weight) and pure sugar percentage. Growth stimulant treatments generally improved root yield under both irrigation regimes, with humic acid and the combined application showing more pronounced positive effects. Multi-criteria analysis indicated that under normal irrigation conditions, the amino acid treatment achieved the highest closeness to the ideal solution. In contrast, under drought stress conditions, the combined application of growth stimulants was clearly identified as the superior treatment and ranked first in more than 95 % of Monte Carlo simulation runs, demonstrating a high level of ranking stability. Conclusion: Although the outcomes of this research are preliminary and derived from a pilot study, the results suggest that organic growth stimulants, particularly the combined application, have considerable potential for mitigating drought stress effects in sugar beet. Moreover, the integration of TOPSIS with Monte Carlo simulation proved to be a powerful and reliable framework for multi-criteria evaluation and early-stage screening of management options. These findings provide a scientific basis for designing more comprehensive field experiments and developing sustainable irrigation and nutritional strategies for sugar beet production under water-limited conditions. Background and Objectives: Sugar beet (Beta vulgaris L.) is one of the most important sugar-producing crops in arid and semi-arid regions, where water scarcity is a major limiting factor for yield and quality. Drought stress often reduces root growth and sugar yield, necessitating the development of sustainable management strategies to enhance drought tolerance. In recent years, organic growth stimulants such as amino acids and humic substances have attracted increasing attention due to their environmentally friendly nature and potential to improve plant performance under abiotic stresses. These stimulants not only promote physiological and biochemical processes in plants but also enhance soil health and nutrient availability, contributing to overall crop resilience. The objective of this pilot study was to preliminarily assess the effectiveness of different organic growth stimulants in alleviating drought stress effects on sugar beet and to identify superior treatments using an integrated multi-criteria decision-making approach. The findings of this research are expected to provide valuable insights for developing practical, eco-friendly strategies to improve sugar beet productivity under water-limited conditions. Materials and Methods: This study was conducted at the Kamalshahr Research Station, Karaj, Iran, using a split-plot arrangement within a randomized complete block design. Two irrigation regimes, including normal irrigation and drought stress, were assigned to main plots, while four nutritional treatments (control, amino acid, humic acid, and combined application of growth stimulants) were allocated to subplots. Several quantitative and qualitative traits related to root yield and sugar quality were measured during the growing season. In addition to conventional analysis of variance, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank treatments by simultaneously considering multiple criteria with equal weights. To evaluate the robustness and stability of the rankings, Monte Carlo simulation was applied based on repeated random perturbations of the input data. Results: The results showed that drought stress markedly reduced root yield and root weight, while increasing root dry matter content (dry root pulp weight / fresh root pulp weight) and pure sugar percentage. Growth stimulant treatments generally improved root yield under both irrigation regimes, with humic acid and the combined application showing more pronounced positive effects. Multi-criteria analysis indicated that under normal irrigation conditions, the amino acid treatment achieved the highest closeness to the ideal solution. In contrast, under drought stress conditions, the combined application of growth stimulants was clearly identified as the superior treatment and ranked first in more than 95 % of Monte Carlo simulation runs, demonstrating a high level of ranking stability. Conclusion: Although the outcomes of this research are preliminary and derived from a pilot study, the results suggest that organic growth stimulants, particularly the combined application, have considerable potential for mitigating drought stress effects in sugar beet. Moreover, the integration of TOPSIS with Monte Carlo simulation proved to be a powerful and reliable framework for multi-criteria evaluation and early-stage screening of management options. These findings provide a scientific basis for designing more comprehensive field experiments and developing sustainable irrigation and nutritional strategies for sugar beet production under water-limited conditions.

https://srjournal.areeo.ac.ir/article_135501_796be366020fe9d26a060edfdc937944.pdf

Agricultural Research,Education and Extension Organization Iranian Journal of Soil Research 2228-7124 40 1 2026 05 22 Various aspects of analysis, interpretation and diagnosis of nutrient status using PCA, CND-clr and CND-ilr methods (a case study on sugar beet) Various aspects of analysis, interpretation and diagnosis of nutrient status using PCA, CND-clr and CND-ilr methods (a case study on sugar beet) 81 104 135502 10.22092/ijsr.2026.371888.808 FA Abdolmohammad Daryashenas Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Karim Shahbazi Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran. Kambiz Bazargan Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran. Journal Article 2026 01 04 Background and Objectives: Optimizing fertilizer use through plant analysis requires robust nutrient standards based on growth stage and a thorough understanding of nutrient interactions (the plant ionome). Statistical methods based on Compositional Data Analysis (CDA)—such as Principal Component Analysis (PCA) and Compositional Nutrient Diagnosis (CND)—overcome major limitations of single-factor approaches, provided they minimize bias in result interpretation. In this study, nutrient concentrations and root yield data from 170 sugar beet fields were compared using three models: PCA, CND-clr, and CND-ilr. This research aims to: (1) introduce the theoretical foundations of PCA, CND-clr, and CND-ilr; (2) validate nutrient indices through two interpretive approaches (minimum limit-maximum limit, LMi-LMa, and lower limit-upper limit, LL-LU) within the CND-clr model; (3) derive critical concentrations and sufficiency ranges using CND-clr indices; (4) validate CND-ilr reference standards and compare them with other models; and (5) assess nutrient status using PCA and compare it with CND-clr and CND-ilr. Materials and Methods: Leaf concentrations of N, P, K, Fe, Mn, Zn, and Cu, along with root yield, were collected from 170 sugar beet farms in Khuzestan Province, southwestern Iran. Leaf samples were taken from plants aged 90–120 days, washed, oven-dried at 65 °C for 48 h, ground, and sieved. Nutrients were analyzed using standard laboratory methods: micro-Kjeldahl for N, spectrophotometry for P, flame photometry for K, and atomic absorption spectrophotometry for Fe, Mn, Zn, and Cu. At harvest, average root yield per hectare was recorded. The study area soils had a saturated extract pH of 7.5–7.8, salinity <1 dS m⁻¹, lime content of 30–50%, and silty loam to silty clay loam textures. Results: Principal Component Analysis using absolute nutrient concentrations showed that four components explained approximately 85 % of the total variance (eigenvalues > 1). In the first principal component (PC1), potassium, zinc, and copper exhibited the highest positive correlations, while nitrogen showed the highest negative correlation with root yield. However, interpreting nutrient status based on the nutrient index (IX) within the PCA framework led to bias. In contrast, the same nutrient index produced unbiased results when used with Pearson correlation. Consequently, PCA is capable of prioritizing nutrient–yield correlations at a macro scale (regional level) but lacks standard criteria for plot, farm, or orchard scale evaluation. Using the CND-clr method, critical concentrations and sufficiency ranges for N, P, K, Fe, Mn, Zn, and Cu were established. Validation of these standards on multiple farms using the two approaches revealed that the lower limit-upper limit (LL-LU) approach is more stringent than the minimum-maximum (LMi-LMa) approach. After determining CND-ilr reference standards, farm level validation effectively detected nutrient balances indicating synergistic and antagonistic effects, with the CND-ilr method providing the most diagnostically informative outputs. Comparative analysis demonstrated that both CND-clr and CND-ilr, supported by credible reference standards, are capable of assessing plant nutritional status at both micro scale (individual field) and macro scale (regional) levels. Conclusion: PCA is a valuable tool for macro scale prioritization of nutrient yield correlations, but its lack of micro scale evaluation standards limits its application at the farm level. By contrast, the CND-clr and CND-ilr methods, equipped with robust reference standards, effectively assess nutrient status across both spatial scales. Critical concentrations and sufficiency ranges for N, P, K, Fe, Mn, Zn, and Cu were determined as reference standards indicative of nutrient interactions. The LL-LU validation approach proved more stringent than LMi-LMa. Furthermore, the CND-ilr method enabled a more accurate diagnosis of synergistic and antagonistic nutrient interactions, making it particularly suitable for site-specific nutrient management. Background and Objectives: Optimizing fertilizer use through plant analysis requires robust nutrient standards based on growth stage and a thorough understanding of nutrient interactions (the plant ionome). Statistical methods based on Compositional Data Analysis (CDA)—such as Principal Component Analysis (PCA) and Compositional Nutrient Diagnosis (CND)—overcome major limitations of single-factor approaches, provided they minimize bias in result interpretation. In this study, nutrient concentrations and root yield data from 170 sugar beet fields were compared using three models: PCA, CND-clr, and CND-ilr. This research aims to: (1) introduce the theoretical foundations of PCA, CND-clr, and CND-ilr; (2) validate nutrient indices through two interpretive approaches (minimum limit-maximum limit, LMi-LMa, and lower limit-upper limit, LL-LU) within the CND-clr model; (3) derive critical concentrations and sufficiency ranges using CND-clr indices; (4) validate CND-ilr reference standards and compare them with other models; and (5) assess nutrient status using PCA and compare it with CND-clr and CND-ilr. Materials and Methods: Leaf concentrations of N, P, K, Fe, Mn, Zn, and Cu, along with root yield, were collected from 170 sugar beet farms in Khuzestan Province, southwestern Iran. Leaf samples were taken from plants aged 90–120 days, washed, oven-dried at 65 °C for 48 h, ground, and sieved. Nutrients were analyzed using standard laboratory methods: micro-Kjeldahl for N, spectrophotometry for P, flame photometry for K, and atomic absorption spectrophotometry for Fe, Mn, Zn, and Cu. At harvest, average root yield per hectare was recorded. The study area soils had a saturated extract pH of 7.5–7.8, salinity <1 dS m⁻¹, lime content of 30–50%, and silty loam to silty clay loam textures. Results: Principal Component Analysis using absolute nutrient concentrations showed that four components explained approximately 85 % of the total variance (eigenvalues > 1). In the first principal component (PC1), potassium, zinc, and copper exhibited the highest positive correlations, while nitrogen showed the highest negative correlation with root yield. However, interpreting nutrient status based on the nutrient index (IX) within the PCA framework led to bias. In contrast, the same nutrient index produced unbiased results when used with Pearson correlation. Consequently, PCA is capable of prioritizing nutrient–yield correlations at a macro scale (regional level) but lacks standard criteria for plot, farm, or orchard scale evaluation. Using the CND-clr method, critical concentrations and sufficiency ranges for N, P, K, Fe, Mn, Zn, and Cu were established. Validation of these standards on multiple farms using the two approaches revealed that the lower limit-upper limit (LL-LU) approach is more stringent than the minimum-maximum (LMi-LMa) approach. After determining CND-ilr reference standards, farm level validation effectively detected nutrient balances indicating synergistic and antagonistic effects, with the CND-ilr method providing the most diagnostically informative outputs. Comparative analysis demonstrated that both CND-clr and CND-ilr, supported by credible reference standards, are capable of assessing plant nutritional status at both micro scale (individual field) and macro scale (regional) levels. Conclusion: PCA is a valuable tool for macro scale prioritization of nutrient yield correlations, but its lack of micro scale evaluation standards limits its application at the farm level. By contrast, the CND-clr and CND-ilr methods, equipped with robust reference standards, effectively assess nutrient status across both spatial scales. Critical concentrations and sufficiency ranges for N, P, K, Fe, Mn, Zn, and Cu were determined as reference standards indicative of nutrient interactions. The LL-LU validation approach proved more stringent than LMi-LMa. Furthermore, the CND-ilr method enabled a more accurate diagnosis of synergistic and antagonistic nutrient interactions, making it particularly suitable for site-specific nutrient management.

https://srjournal.areeo.ac.ir/article_135502_ce0484ba20d2807b83f55c4c0fc6adf1.pdf

Agricultural Research,Education and Extension Organization Iranian Journal of Soil Research 2228-7124 40 1 2026 05 22 Impact of Spatial Resolution of Environmental Covariates on the Accuracy of Digital Soil Mapping: A Review Based on the SCORPAN Conceptual Framework Impact of Spatial Resolution of Environmental Covariates on the Accuracy of Digital Soil Mapping: A Review Based on the SCORPAN Conceptual Framework 105 126 135616 10.22092/ijsr.2026.372398.815 FA Rasoul Kharazmi Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Mohsen Bagheri Bodaghabadi Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Journal Article 2026 03 07 Background and Objectives: Digital Soil Mapping (DSM), as a modern data-driven approach, predicts the spatial distribution of soil physical and chemical properties based on the SCORPAN model. However, one of the keys and often overlooked factors influencing mapping accuracy is the spatial resolution of environmental covariates, which may either enhance or distort the true soil-forming patterns. This review article aims to examine and analyze the impact of the spatial resolution of environmental variables on the accuracy of DSM, particularly in arid and semi-arid regions of Iran. The specific objectives include: (i) identifying scalability challenges such as scale mismatch, noise amplification at very fine resolutions, and computational costs; (ii) providing optimized resolution recommendations based on landscape type; and (iii) proposing multi-scale approaches and advancements in machine learning to improve local accuracy and global generalizability. This review emphasizes the importance of adaptive spatial resolution selection for practical applications such as sustainable agriculture and evidence-based environmental policymaking under climate change. The focus on arid and semi-arid regions stems from the high sensitivity of these ecosystems to micro-scale variations, where inappropriate resolution may increase prediction errors by 30-50%. Ultimately, this study seeks to bridge theory and practice to enhance DSM as a more operational and effective tool. Materials and Methods: This targeted review was conducted in accordance with the PRISMA 2020 statement. A comprehensive search was performed across major international and Persian databases, covering the period from 2000 to 2025. Search terms consisted of combinations of key DSM-related terminology. A total of 438 articles were initially identified. After removing duplicates, 302 articles remained for preliminary screening. Inclusion criteria comprised studies that directly or indirectly examined the effect of spatial resolution on DSM accuracy and evaluated at least one SCORPAN factor. Following full-text assessment, 150 articles were reviewed in detail, and ultimately 56 studies were included in the final analysis. Data extraction involved categorizing variables according to the SCORPAN framework, evaluating methodological approaches, validation metrics, strengths and limitations, and identifying emerging trends. Results: The findings indicate that, in complex arid and semi-arid terrains, the spatial resolution of topographic variables should be as fine as 30 m to adequately capture local features such as rills and erosion patterns and to prevent excessive smoothing. Otherwise, the prediction accuracy of properties such as clay content or soil water storage may decline by 30-40%. For climatic variables, a spatial resolution finer than 250 m is essential in these regions to better model microclimates and their interactions with topography, thereby reducing unexplained variance. Biological and remote sensing covariates require a spatial resolution of 10-30 m to capture seasonal and patchy vegetation dynamics in dry ecosystems, potentially improving prediction accuracy by up to 25%. Parent material and geological variables are generally adequate at 90-100 m resolution; however, in highly heterogeneous settings, integration with topographic data is necessary to improve the prediction of soil chemical properties. Soil age and spatial position variables play complementary roles, and their integration at moderate resolutions may reduce uncertainty by 10-20%. Recent advancements in machine learning algorithms and multi-scale modeling approaches have improved prediction accuracy across multiple spatial scales while addressing challenges such as scale mismatch. The recommendation framework suggests that in humid and temperate lowland regions, moderate spatial resolution is generally sufficient, whereas in arid and rugged landscapes, high spatial resolution for topography and vegetation is essential. Conclusion: The analysis underscores that spatial resolution selection should be adaptive and dependent on landscape complexity, modeling objectives, and practical constraints to balance local accuracy, computational efficiency, and generalizability. In arid and semi-arid regions, high spatial resolution more effectively captures micro-scale patterns of erosion, salinization, and moisture distribution. However, it also introduces challenges such as increased noise, overfitting, and large data processing costs, which require careful methodological management. Scale mismatch among covariates increases unexplained variance and highlights the need for spatial harmonization. Advances in deep learning and three-dimensional modeling are transforming DSM from a static to a dynamic framework, improving predictive performance in environmentally sensitive ecosystems. Nevertheless, critical gaps remain, particularly the scarcity of historical soil age data in specific biomes. Ultimately, this study demonstrates that spatial resolution is not merely a technical parameter but a key determinant of uncertainty reduction, enabling digital soil mapping to evolve into a more effective tool for environmental policymaking and sustainable agriculture. Background and Objectives: Digital Soil Mapping (DSM), as a modern data-driven approach, predicts the spatial distribution of soil physical and chemical properties based on the SCORPAN model. However, one of the keys and often overlooked factors influencing mapping accuracy is the spatial resolution of environmental covariates, which may either enhance or distort the true soil-forming patterns. This review article aims to examine and analyze the impact of the spatial resolution of environmental variables on the accuracy of DSM, particularly in arid and semi-arid regions of Iran. The specific objectives include: (i) identifying scalability challenges such as scale mismatch, noise amplification at very fine resolutions, and computational costs; (ii) providing optimized resolution recommendations based on landscape type; and (iii) proposing multi-scale approaches and advancements in machine learning to improve local accuracy and global generalizability. This review emphasizes the importance of adaptive spatial resolution selection for practical applications such as sustainable agriculture and evidence-based environmental policymaking under climate change. The focus on arid and semi-arid regions stems from the high sensitivity of these ecosystems to micro-scale variations, where inappropriate resolution may increase prediction errors by 30-50%. Ultimately, this study seeks to bridge theory and practice to enhance DSM as a more operational and effective tool. Materials and Methods: This targeted review was conducted in accordance with the PRISMA 2020 statement. A comprehensive search was performed across major international and Persian databases, covering the period from 2000 to 2025. Search terms consisted of combinations of key DSM-related terminology. A total of 438 articles were initially identified. After removing duplicates, 302 articles remained for preliminary screening. Inclusion criteria comprised studies that directly or indirectly examined the effect of spatial resolution on DSM accuracy and evaluated at least one SCORPAN factor. Following full-text assessment, 150 articles were reviewed in detail, and ultimately 56 studies were included in the final analysis. Data extraction involved categorizing variables according to the SCORPAN framework, evaluating methodological approaches, validation metrics, strengths and limitations, and identifying emerging trends. Results: The findings indicate that, in complex arid and semi-arid terrains, the spatial resolution of topographic variables should be as fine as 30 m to adequately capture local features such as rills and erosion patterns and to prevent excessive smoothing. Otherwise, the prediction accuracy of properties such as clay content or soil water storage may decline by 30-40%. For climatic variables, a spatial resolution finer than 250 m is essential in these regions to better model microclimates and their interactions with topography, thereby reducing unexplained variance. Biological and remote sensing covariates require a spatial resolution of 10-30 m to capture seasonal and patchy vegetation dynamics in dry ecosystems, potentially improving prediction accuracy by up to 25%. Parent material and geological variables are generally adequate at 90-100 m resolution; however, in highly heterogeneous settings, integration with topographic data is necessary to improve the prediction of soil chemical properties. Soil age and spatial position variables play complementary roles, and their integration at moderate resolutions may reduce uncertainty by 10-20%. Recent advancements in machine learning algorithms and multi-scale modeling approaches have improved prediction accuracy across multiple spatial scales while addressing challenges such as scale mismatch. The recommendation framework suggests that in humid and temperate lowland regions, moderate spatial resolution is generally sufficient, whereas in arid and rugged landscapes, high spatial resolution for topography and vegetation is essential. Conclusion: The analysis underscores that spatial resolution selection should be adaptive and dependent on landscape complexity, modeling objectives, and practical constraints to balance local accuracy, computational efficiency, and generalizability. In arid and semi-arid regions, high spatial resolution more effectively captures micro-scale patterns of erosion, salinization, and moisture distribution. However, it also introduces challenges such as increased noise, overfitting, and large data processing costs, which require careful methodological management. Scale mismatch among covariates increases unexplained variance and highlights the need for spatial harmonization. Advances in deep learning and three-dimensional modeling are transforming DSM from a static to a dynamic framework, improving predictive performance in environmentally sensitive ecosystems. Nevertheless, critical gaps remain, particularly the scarcity of historical soil age data in specific biomes. Ultimately, this study demonstrates that spatial resolution is not merely a technical parameter but a key determinant of uncertainty reduction, enabling digital soil mapping to evolve into a more effective tool for environmental policymaking and sustainable agriculture.

https://srjournal.areeo.ac.ir/article_135616_ec65dcc7f3de131680709e5827db94d0.pdf