Water and Soil

Abstract Introduction Nowadays, the application of variable-rate fertilization is primarily based on soil analysis maps. These maps play a crucial role in decision-making for fertilizer management and the optimization of plant nutrient use. However, the sampling distance is often determined without considering the spatial variability of soil properties, which can lead to reduced map accuracy and inappropriate fertilization recommendations. Previous studies have shown that the density and spacing of soil samples have a significant impact on the accuracy of geostatistical models and the quality of maps produced using methods such as kriging. Moreover, soil management practices, including varying fertilizer application patterns, crop rotation, and historical land use, can strongly influence the spatial patterns of soil nutrients. Therefore, investigating the effect of sampling distance on the spatial distribution of major soil nutrients, particularly available potassium, can help optimize soil sampling and provide more precise recommendations for variable-rate fertilization. The present study aimed to examine the effect of sampling distance on the spatial structure of available potassium in the agricultural soils of the Shahrekord Plain.   Material and Methods For this purpose, 120 soil samples were collected using a regular grid sampling design with a spacing of 1335 meters from the surface layer of agricultural soils in the Shahrekord plain, covering an area of 21400 ha, and the available potassium content was determined in the laboratory. Descriptive statistics of the data, including the minimum, maximum, mean, median, coefficient of variation, and skewness of the variable, were calculated, and the normality of the data distribution was assessed using histograms and the Kolmogorov–Smirnov test. The experimental variogram of available potassium was constructed, and various theoretical models were fitted to it. Subsequently, the variogram parameters and the spatial dependency class of the variable were determined. Model validation was performed by adjusting the initial parameters so that the final optimized model had a mean error (ME) close to zero and a minimum mean squared error (MSE). Ordinary kriging in ArcGIS 10.3 was then used to produce maps of available potassium.   Results and Discussion The available potassium in the soils ranged from 120 to 812 mg kg⁻¹, with a coefficient of variation of 38.2%. The data were not normally distributed; however, the log-transformed data followed a normal distribution. Geostatistical analysis indicated that the exponential model best fitted the data. The nugget effect was 0.108, and the range of this variable was estimated at 53300 meters, indicating that the selected sampling distance is much smaller than the spatial variability range; therefore, the sampling distance appears to be sufficient. However, the exponential variogram is nearly flat at short distances, showing little change in semivariance, which reflects the influence of strong soil management practices, such as varying fertilizer application patterns, crop rotation, and historical land use, on data dispersion. The spatial dependence of the studied variable was 50.2%, which falls into the moderate spatial dependence class. The mean error (ME) of the estimated available potassium was –0.0008, and the mean squared error (MSE) was 0.011, indicating the unbiasedness and high accuracy of the estimation. The maps produced using the kriging method exhibited acceptable accuracy. This was achieved despite the increased variability at short distances and the disruption of regular spatial patterns caused by intensive soil management practices.   Conclusion According to the findings, using an appropriate sampling distance is essential to accurately represent the spatial variability of major soil nutrients, particularly potassium. The findings of this study indicated that the current sampling distance is not sufficient to accurately represent the spatial variability of available potassium in the soils under investigation. Therefore, for future studies, it is recommended to investigate the effect of sampling distance at distances shorter than 1,335 meters to evaluate the model sensitivity and the accuracy of kriging maps at larger scales. This can help optimize soil sampling and provide more precise recommendations for variable-rate fertilizer application. Furthermore, it is recommended that future studies also consider the role of soil management practices in the spatial modeling of potassium, as these factors can strongly influence the spatial distribution of soil nutrients.   Acknowledgements The authors would like to thank the Soil Science Department of the University of Shahrekord for providing equipment and facilities.

Abstract Introduction Increasing pressure on limited water resources, pronounced climatic variability, and the growing demand for agricultural production have made accurate agroclimatic assessment an essential component of sustainable agriculture in Iran. Identifying homogeneous agroclimatic zones enables more efficient resource management, optimized cropping calendars, and reduced production risk under variable environmental conditions. Agro-ecological zoning (AEZ) provides a scientific framework to integrate climatic, topographic, and crop phenological factors in order to delineate areas with similar production potential. Barley (Hordeum vulgare L.), as one of Iran’s major cereal crops, plays a vital role in food security, livestock feed supply, and agricultural livelihoods. Barley yield and stability are highly sensitive to planting date and length of the growing period, both of which are directly influenced by climatic gradients, elevation, and rainfall patterns. Despite the importance of these parameters, national-scale agroclimatic zoning of barley based explicitly on planting date and growing period has received limited attention. Therefore, this study aimed to delineate agroclimatic zones for barley cultivation across Iran using advanced spatial interpolation and clustering techniques, providing a decision-support tool for sustainable crop planning.   Materials and Methods This study adopted a spatial–analytical approach at the national scale of Iran. Point-based data on barley planting date and length of the growing period were obtained from the national Water Requirement System and related agroclimatic databases, covering approximately 790 stations distributed across the country. These data represent long-term average conditions relevant to barley phenology. To transform discrete point data into continuous spatial surfaces, the Empirical Bayesian Kriging Regression Prediction (EBK-RP) method was employed. This geostatistical technique combines regression modeling with Empirical Bayesian Kriging, allowing automatic estimation of semivariogram parameters and improved uncertainty handling, particularly in regions with sparse observations. Elevation derived from a digital elevation model (DEM) was incorporated as a covariate to enhance spatial prediction accuracy. The resulting raster layers for planting date and growing period length were then classified using the K-means clustering algorithm, a widely used unsupervised machine learning technique. The optimal number of clusters for each variable was determined using the elbow method based on within-cluster sum of squares (WCSS). Three clusters were identified for planting date and four clusters for growing period length. Finally, the classified layers were overlaid and combined to generate homogeneous agroclimatic zones for barley cultivation. To improve spatial coherence and reduce classification noise, focal statistics with a majority filter were applied.   Results and Discussion The spatial distribution of barley planting dates and growing period lengths exhibited strong correspondence with Iran’s climatic and altitudinal gradients. Early planting dates were predominantly observed in the humid Caspian coastal region and parts of western and northwestern Iran, where higher precipitation, moderate temperatures, and lower frost risk prevail. In contrast, delayed planting was common in the arid and semi-arid central, eastern, and southeastern regions, reflecting dependence on late and irregular rainfall events. The length of the growing period showed an inverse relationship with temperature and aridity. Longer growing seasons (approximately 230–300 days) were identified in high-altitude and humid regions such as the Caspian lowlands, Alborz and Zagros mountain ranges, and western provinces. Shorter growing periods (around 120–170 days) characterized the hot, dry southern and eastern regions, where high temperatures and moisture stress accelerate crop development and shorten phenological stages. The integration of planting date and growing period classes resulted in twelve agroclimatic zones for barley cultivation. Among these, zones characterized by early to intermediate planting and moderate to long growing periods demonstrated the highest suitability for barley production. These zones are mainly located in northern, western, and parts of northwestern Iran, where climatic conditions provide more stable growth environments. Conversely, zones with late planting and short growing periods, largely distributed across central, eastern, and southeastern Iran, were identified as high-risk areas with significant climatic limitations for barley cultivation. These findings highlight the dominant role of climate, elevation, and moisture availability in shaping barley phenology and production potential. The spatial patterns observed are consistent with previous agro-ecological zoning studies in Iran and other semi-arid regions, reinforcing the reliability of the applied methodology.   Conclusion This study demonstrated that integrating advanced geostatistical modeling (EBK Regression Prediction) with unsupervised clustering (K-means and elbow method) provides a robust framework for national-scale agroclimatic zoning of barley. The results clearly indicate that barley planting date and length of the growing period in Iran are strongly controlled by climatic–altitudinal gradients. Only a limited portion of the country—primarily humid and semi-humid northern and western regions—offers high and stable potential for sustainable barley production, while large arid and semi-arid areas face substantial climatic constraints. The derived agroclimatic zones can serve as a strategic decision-support tool for policymakers, planners, and extension services. They facilitate optimized cropping calendars, targeted selection of barley cultivars, improved water resource management, and risk reduction under climate variability. Ultimately, applying such zoning-based approaches can enhance productivity, promote sustainable land use, and support long-term food security in Iran.  

Abstract Introduction Precipitation is a fundamental component of the hydrological cycle and plays a vital role in water resource management, agriculture, ecosystem sustainability, hydropower generation, disaster mitigation, and urban planning. Accurate rainfall prediction is essential for effective water allocation, flood and drought risk management, and the development of early warning systems, thereby reducing potential damages to human communities and infrastructure. In agriculture, reliable precipitation forecasts support optimal planting schedules and resource management, leading to increased productivity and reduced weather-related losses. Furthermore, rainfall prediction is crucial for reservoir operation and sustainable long-term water resource planning. Due to the limited spatial coverage of ground-based meteorological stations, satellite-based precipitation products have gained increasing attention as reliable alternatives for regional-scale rainfall analysis. Among these, the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset is widely recognized for its extensive spatial coverage, relatively high accuracy in arid and mountainous regions, and free accessibility. CHIRPS integrates satellite observations with ground station data and provides precipitation estimates at approximately 5 km spatial resolution across multiple temporal scales since 1981. In this study, monthly mean CHIRPS precipitation data for the period 2000–2024 were extracted and analyzed for rainfall prediction purposes. The inherent nonlinearity and complex behavior of precipitation processes make traditional statistical approaches insufficient for accurate forecasting. Consequently, machine learning techniques have emerged as powerful tools for modeling complex climatic phenomena. Although numerous previous studies have applied machine learning algorithms, such as Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), to rainfall prediction, most have focused on single-model evaluations or simple model comparisons. However, combining multiple models has been shown to reduce prediction uncertainty and improve forecast robustness, an approach that has received comparatively limited attention. The primary objective of this research is to evaluate the performance of satellite-based CHIRPS precipitation data and to enhance monthly rainfall prediction using machine learning models implemented in the WEKA environment. To achieve this goal, fifteen individual machine learning models were first assessed independently. Subsequently, pairwise combinations of these models (105 combinations in total) were developed using simple averaging and brute-force weighting strategies to improve predictive accuracy. The novelty of this study lies not merely in the application of multiple machine learning algorithms but in proposing a systematic, transparent, and reproducible framework for satellite-based rainfall prediction in data-scarce basins. This framework is region-independent and can be readily applied to other climatic and hydrological settings.   Materials and Methods This study investigates monthly precipitation prediction in the Salt Lake Basin using satellite-based CHIRPS data for the period January 2000 to December 2025, extracted via the Google Earth Engine platform. CHIRPS was selected due to its adequate spatial resolution (0.05°), integration of satellite and ground-based observations, long-term temporal coverage since 1981, and suitability for arid and semi-arid regions with sparse rain-gauge networks. Data quality was assessed using the Interquartile Range (IQR) method, and no outliers were detected. As the dataset exhibited consistent scaling across all time steps, no normalization was applied. The data were divided into training (January 1999–December 2021) and testing (January 2022–December 2024) subsets. All modeling procedures were implemented in the WEKA environment using time-series modules. To validate the CHIRPS dataset, satellite-derived precipitation estimates were compared with observations from four ground stations over ten years (2007–2017), and basin-scale performance was evaluated using averaged statistical metrics. Initially, sixteen machine learning models were examined, and one model (Random Tree) was excluded due to poor performance. The final set of fifteen models included Gaussian Processes, Random Forest, MLP Regressor, RBF Regressor, Linear Regression, SMOreg, IBk, LWL, Additive Regression, Bagging, Random Committee, Random SubSpace, Decision Table, M5Rules, and M5P. Model hyperparameters were optimized using the Grid Search tool in WEKA. To improve prediction accuracy and robustness, pairwise ensemble models were generated using simple averaging, resulting in 105 combined configurations. Additionally, a brute-force weighting approach was applied by assigning weights between 0 and 1 with a step of 0.1 to each model, subject to a unity-sum constraint, in order to identify the optimal ensemble structure. Model performance was evaluated using the correlation coefficient, RMSE, MAE, MSE, bias, and Nash–Sutcliffe efficiency (NSE), and the most accurate configuration was selected based on testing results.   Results and Discussion The accuracy of the CHIRPS satellite precipitation product was first evaluated using observed rainfall data from four rain-gauge stations distributed across the Salt Lake Basin. The validation results indicated a satisfactory agreement between satellite-derived and observed precipitation, with an overall coefficient of determination (R²) of approximately 0.69 and an NSE of 0.70, confirming the suitability of CHIRPS data for monthly rainfall analysis in the study area. Subsequently, the performance of fifteen individual machine learning models was assessed for monthly precipitation prediction using the testing period (January 2022–December 2024). Model evaluation based on correlation coefficient, RMSE, MAE, MSE, bias, and Nash–Sutcliffe efficiency (NSE) revealed notable differences among algorithms. Rule-based and tree-based models, particularly M5Rules and Additive Regression, exhibited superior performance, characterized by higher NSE values, lower error magnitudes, and relatively small bias. In contrast, instance-based models such as IBk and function-based models like RBF Regressor showed weaker performance, with larger error dispersion and lower correlation with observed data. The combined analysis of bias, residual standard deviation, and median absolute error provided a more comprehensive understanding of model behavior than single error metrics alone. Visual assessments using Taylor diagrams and scatter plots further confirmed the robustness of M5Rules, followed by Additive Regression and Random Forest, in capturing both the variability and temporal patterns of observed precipitation. To enhance prediction accuracy and stability, pairwise combinations of the individual models were developed using simple averaging, resulting in 105 ensemble configurations. The results demonstrated that most ensemble models outperformed their corresponding single-model counterparts, as evidenced by higher NSE values and reduced error metrics. The combination of Additive Regression–M5Rules achieved the best overall performance, yielding the lowest MAE and MSE and the highest NSE among all tested configurations. Histogram analysis of NSE values showed that a large proportion of the ensemble models achieved NSE values above 0.75, indicating the general effectiveness of the ensemble approach rather than improvement limited to specific combinations. In addition, a brute-force weighting strategy was applied to optimize model contributions within the ensembles. Compared to simple averaging, the Brute Force approach consistently improved NSE values by reducing the influence of weaker models and assigning higher weights to more accurate ones. This improvement was particularly evident in combinations involving low-performing models such as IBk and RBF Regressor. Nevertheless, simple averaging also produced competitive and stable results without increasing computational complexity. Overall, the results indicate that monthly rainfall prediction accuracy depends not only on the choice of individual algorithms but also on the interaction and complementarity of model errors. The proposed ensemble framework effectively reduced bias and variance while avoiding overfitting through strict temporal data separation and independent testing. These findings demonstrate that systematic pairwise model combination, even with simple averaging, can substantially enhance the accuracy and robustness of precipitation forecasts in data-scarce and climatically complex regions. Conclusion The Salt Lake Basin is characterized by sparse rain-gauge coverage and high climatic variability, making accurate monthly precipitation estimation essential for water resources management. This study evaluated the CHIRPS satellite precipitation dataset and assessed the performance of individual and combined machine learning models for monthly rainfall prediction. Validation against four ground stations confirmed that CHIRPS reliably represents the temporal pattern of monthly precipitation in the basin, with acceptable correlation and Nash–Sutcliffe efficiency values, indicating its suitability for hydrological applications in arid and semi-arid regions. Among the fifteen evaluated machine learning models, M5Rules, Additive Regression, and Random Forest exhibited superior performance as single models. Ensemble modeling further improved prediction accuracy by reducing systematic errors and enhancing robustness. The Additive Regression–M5Rules combination achieved the best performance (NSE ≈ 0.88), outperforming all individual models. These results highlight the effectiveness of integrating CHIRPS data with ensemble machine learning approaches for improving monthly precipitation prediction in data-scarce basins. The proposed framework provides a reliable and transferable tool for supporting sustainable water resources management in arid and semi-arid regions.  

Abstract Introduction Soil is a fundamental natural resource that underpins ecosystem functioning, agricultural productivity, water regulation, and biogeochemical cycling. Vertical heterogeneity of soil properties along the profile reflects the combined influence of biological inputs, pedogenic processes, and land management. Soil depth strongly controls the distribution of organic matter, nutrients, soluble salts, and textural components, thereby affecting root development, nutrient uptake, and crop resilience under environmental stresses such as drought. Most previous studies relied primarily on classical statistical tests and comparisons of mean values between layers. While useful for detecting statistically significant differences, these approaches provide limited information on the magnitude, direction, and functional relevance of depth-related changes. In highly variable and non-normally distributed soil systems, reliance on p-values alone can lead to overinterpretation of minor differences or underestimation of ecologically important gradients. Recent advances emphasize effect size metrics and uncertainty estimation to quantify soil variability more robustly. Non-parametric indices, such as the rank-biserial effect size, allow depth-driven changes to be quantified independent of sample size and distributional assumptions. This study focuses on providing a comprehensive depth-oriented assessment of soil physicochemical properties using effect size–based approaches and classification into depth stability and management-relevant categories.   Materials and Methods Soil samples were collected from agricultural fields of Razavi Khorasan Province of Iran at two depths: 0–30 cm (surface) and 30–60 cm (subsurface), using a paired design to minimize spatial heterogeneity. A wide range of physicochemical properties was analyzed, including organic carbon, available P and K, exchangeable cations, EC, pH, carbonate parameters, and selected micronutrients. Paired non-parametric tests were applied for each variable, and the rank-biserial effect size (r_rb) was calculated to quantify the magnitude and direction of depth-related changes. Bootstrap resampling was used to estimate 95% confidence intervals. Based on effect size magnitude and confidence intervals, variables were classified into stable, quasi-stable, and depth-sensitive categories, which were further translated into practical management-oriented classes.   Results and Discussion Depth-sensitive properties included organic carbon, available phosphorus, potassium, and manganese, all exhibiting large effect sizes and pronounced accumulation in the surface layer. The enrichment of organic carbon reflects concentrated biological inputs, crop residue return, and intensified microbial activity in the topsoil, whereas its sharp decline with depth indicates restricted vertical transfer and limited subsoil biological functioning. Phosphorus accumulation in the surface layer is consistent with its low mobility and strong fixation in calcareous soils, underscoring the importance of depth-aware phosphorus management strategies to enhance root access in deeper horizons. The marked differences in potassium likely result from the combined effects of crop uptake, mineral weathering, and partial leaching, highlighting the necessity for root-zone–oriented monitoring and fertilization practices. Among micronutrients, manganese showed the greatest depth sensitivity, reflecting its responsiveness to redox dynamics and pH variations, and indicating potential risks of localized deficiency or toxicity within the soil profile. Copper and bicarbonate demonstrated moderate effect sizes, suggesting measurable but less pronounced depth-related variation. In contrast, clay, silt, pH, TNV, Zn, and Ca exhibited small effect sizes, indicating relative structural stability across depths. Sodium and chloride remained largely unchanged, implying that salinity distribution is relatively uniform within the studied profile and not strongly stratified under current conditions. The classification of variables into depth-stability and management-priority categories provides a practical framework for translating statistical outputs into agronomic decision-making. Overall, the findings reveal that reliance on surface soil analysis alone can lead to incomplete or potentially misleading interpretations of nutrient status. Depth-aware management, including precision placement technologies and fertilizer banding systems, can improve nutrient use efficiency, enhance subsoil fertility, and reduce environmental losses, thereby contributing to more resilient soil–plant systems under semi-arid conditions.   Conclusion By integrating paired depth-wise comparisons, PERMANOVA, and nonparametric effect size metrics, this study provides a robust framework for assessing vertical soil heterogeneity beyond conventional p-value–based approaches. The results showed pronounced and practically meaningful depth-related differences for key chemical properties, particularly soil organic carbon, available phosphorus, potassium, and manganese, while physical properties and basic indicators such as pH, clay, silt, and calcium carbonate exhibited limited variation. These patterns reflect the concentration of biological activity and management inputs in surface layers and the relative chemical depletion of deeper horizons, which constrains root development, nutrient uptake, and drought resilience in agricultural systems of Razavi Khorasan Province. Overall, the findings highlight that soil fertility assessments based solely on surface sampling can be misleading and underscore the need for targeted, depth-oriented soil management to improve nutrient use efficiency and enhance the sustainability of soil–plant systems, particularly under water-limited conditions.   Acknowledgements The authors would like to express their sincere appreciation to the Soil and Water Research Department of the Khorasan Razavi Agricultural and Natural Resources Research and Education Center for their support in conducting this study. The authors also gratefully acknowledge the valuable cooperation and technical assistance provided by the laboratory staff.

Abstract  Introduction Drought is one of the most complex and costly natural disasters, and its accurate monitoring is essential for water resources management and vulnerability reduction. The Standardized Precipitation-Evapotranspiration Index (SPEI), as a bivariate index, utilizes precipitation and temperature data (to estimate evapotranspiration) and has gained widespread application due to its multiscalar nature and sensitivity to climate change. However, limited access to long-term and uniform station-based data in a country with high climatic diversity and complex topography like Iran poses a significant challenge for accurate drought monitoring. The global gridded database SPEIbase, with a spatial resolution of 0.5 arc degrees, has been developed by integrating satellite data, climate models, and in-situ measurements. This study aims to evaluate the reliability of SPEIbase compared to station-based data across Iran's catchments to assess the feasibility of substituting this database due to its extensive spatial coverage and easier accessibility.   Materials and Methods Two datasets were used in this research: 1) Station data including monthly precipitation time series and monthly mean temperature from 43 meteorological stations in Iran for the statistical period 1986-2023, obtained from the Iran Meteorological Organization (IRIMO). 2) Gridded SPEIbase data, version 2.11, based on the CRU TS 4.09 dataset, extracted for the same period from the Climatic Research Unit (CRU) at the University of East Anglia. The station-based SPEI was calculated using potential evapotranspiration estimated via the Thornthwaite method at time scales of 1, 3, 6, 9, and 12 months. The SPEIbase index, based on potential evapotranspiration estimated via the FAO-56 Penman-Monteith method, was extracted for the corresponding period. The concordance and agreement between the two indices were evaluated using three statistical methods: 1) Pearson's correlation coefficient to assess linear relationship, 2) Weighted Cohen's Kappa statistic to measure agreement in drought classes (8 classes ranging from extreme drought to extremely wet), and 3) Bland-Altman plots to examine limits of agreement and systematic bias.   Results and Discussion Findings indicated that the Pearson correlation coefficient between SPEI and SPEIbase fell within the strong correlation range (0.70 to 0.90) for most stations and catchments in Iran. The highest correlations were observed at stations in mountainous areas, such as Khorramabad (0.92), Hamedan (0.87), and Urmia (0.86) at the 12-month scale. Conversely, coastal and lowland stations like Bandar Abbas, Ramsar, and Babolsar showed relatively weaker correlations (0.63 to 0.67) at shorter time scales. Results from the Weighted Cohen's Kappa statistic also demonstrated substantial agreement (0.60 to 0.80) in drought classification between the two indices in mountainous regions and moderate agreement (0.40 to 0.60) in lowland and coastal areas. Bland-Altman plots revealed narrow limits of agreement and random scatter of points around the mean line, indicating no significant systematic bias. A key finding was the increase in correlation and agreement with increasing time scale; the highest agreement was observed at 9 and 12-month scales (hydrological drought) and the lowest at the 1-month scale (meteorological drought). Bam station, with a mean annual precipitation of approximately 53.7 mm, exhibited the lowest correlation (0.64) and agreement (0.39) at the 12-month scale, possibly due to hyper-arid conditions and limitations of evapotranspiration estimation methods in such climates. Constructed violin plots confirmed the concentration of correlation and kappa coefficients at higher ranges with increasing time scale.   Conclusion The SPEIbase database demonstrates suitable accuracy for drought monitoring across Iran's catchments and shows high concordance and agreement with station-based data in many regions, particularly mountainous areas. As the time scale increases from 1 to 12 months, correlation and agreement improve, indicating higher reliability of this database for monitoring agricultural and hydrological droughts. In coastal and lowland regions, especially at shorter time scales, caution is recommended when using this database. Given its extensive spatial coverage, appropriate temporal resolution, and free accessibility, SPEIbase can serve as a reliable alternative to station-based data in data-sparse regions or areas with incomplete records. It is suggested that future research calculate station-based SPEI using the FAO-56 Penman-Monteith method to evaluate the impact of the evapotranspiration estimation method on the level of agreement with SPEIbase   Acknowledgement  The authors would like to thank the Iran Meteorological Organization (IRIMO) for providing the meteorological data used in this research free of charge.

Abstract Introduction  One of the main challenges of agricultural soils in arid and semi-arid regions is the low amount of organic matter, which has a negative effect on the physical and chemical properties of the soil. Soil organic matter plays a role in various soil properties and processes, including soil water retention capacity, soil water distribution, soil microbial activity, water and nutrient availability to plants, and subsequently plant nutrition and yield. The application of biochar as an organic amendment is one of the strategies that has received attention in recent years for increasing soil organic matter and improving the physical and chemical attributes of the soil. Another way to improve soil organic matter is compost application to the soil as an organic amendment. The low organic matter in the soils of sugarcane fields in Khuzestan Province and also the optimal amount of biochar and compost application to improve the physical and chemical properties of these soils, have not been studied. Therefore, this study was carried out to investigate the effect of applying different levels of biochar and compost derived from sugarcane green harvest residues on the physical and chemical properties of soil and water availability for sugarcane plants under field condition.   Materials and Methods  This study was conducted under field conditions at Research Station No. 1 of the Khuzestan Sugarcane Research and Training Institute. This experiment was conducted in a randomized complete block design with seven treatments, including control, biochar application at three levels of 5 (B5), 10 (B10), and 15 (B15) t ha^-1, and compost application at three levels of 15 (CO15), 30 (CO30), and 45 (CO45) t ha^-1, with three replications. For the application of biochar and compost treatments prior to sugarcane planting, the required amounts of each treatment were weighed, manually distributed and incorporated into the soil to an approximately 30 cm depth. Subsequently, sugarcane setts were planted. Soil sampling was conducted at the end of the growth period in different treatments and the physical and chemical properties of the soil were determined. In this study, the effects of different treatments on the soil chemical characteristics (soil pH, EC and organic carbon), soil phosphorus and potassium availability, physical properties (soil bulk density and mean weight diameter of soil aggregates), field capacity, and permanent wilting point (PWP) were measured and water availability for the sugarcane plant was determined.   Results and Discussion The results indicated that the application of biochar treatments (B10 and B15) and compost treatments (CO30 and CO45) decreased soil pH and bulk density, while significantly increasing mean weight diameter, soil organic carbon, available phosphorus, available potassium, and plant-available water. The mean weight diameter of soil aggregates in the CO30, CO45, B5, B10, and B15 treatments was 15.8%, 23.7%, 20.2%, 32.5%, and 36.0% higher than that of the control, respectively. The greatest increases in mean weight diameter of soil aggregates and field capacity plant water availability were observed in the B10 and B15 treatments. The amount of plant-available water in B10 and B15 was 38.9% and 37.5% higher than the control, respectively. In addition, the results also indicated a greater effect of biochar treatments on increasing plant-available water compared with compost treatments. Plant available water in B10 treatment was 21.7% higher than CO45 and 26.3% higher than CO30 treatment. The results indicated that there were no significant differences among B10, B15, and CO45 with respect to soil pH, organic carbon, and phosphorus availability. However, the biochar treatments (B10 and B15) were significantly more effective than CO45 in enhancing soil potassium availability, improving soil physical properties, and increasing plant-available water.   Conclusion Overall, it can be concluded that biochar application at 10 t ha^-1 and compost application at 45 t ha^-1 represent the optimal rates for improving soil chemical properties in calcareous soils of sugarcane fields. Furthermore, biochar at 10 t ha⁻¹ was the most effective treatment for improving soil physical properties and enhancing water availability for sugarcane.