Agricultural Engineering

Abstract Introduction: Land-use change, driven primarily by anthropogenic activities and, to a lesser extent, natural factors, poses a critical environmental challenge that profoundly affects global ecosystems and human communities. Accurate identification of land-use change patterns is vital for the sustainable management of natural resources. Consequently, the integration of remote sensing technologies, Geographic Information Systems (GIS), and machine learning methods has become increasingly prevalent in monitoring these complex dynamics. Among the predictive tools, Markov chain models and Cellular Automata (CA)-based models are widely employed to simulate and forecast future land-use scenarios, with numerous researchers successfully utilizing them alongside satellite data across various regions. This study focuses on Ahvaz County, a region in Iran facing pressing challenges related to land-use change and environmental degradation. Given the growing importance of sustainable land management in Ahvaz County, and the necessity of anticipating the potential consequences of future development, proactive policy intervention is essential. Therefore, this research aims to predict the future land-use trend in Ahvaz County until the year 2040. Utilizing Google Earth imagery and a combined Cellular Automata-Markov Chain model, this study seeks to provide valuable, spatially explicit information for urban planners, environmental managers, and policymakers.

Materials and Methods: The study area, located within the Khuzestan plains and including the counties of Ahvaz, Bavi, Karun, and Hamidieh, faces significant challenges, including water scarcity and environmental crises. In recent decades, urbanization coupled with industrial and agricultural development has led to significant land use changes in this region. This research focused on extracting land use maps for the period 2000–2020. The methodology employed a Convolutional Neural Network (CNN) implemented using Python software, specifically the TensorFlow library. Google Earth images were collected, labeled, and divided into three datasets (training, validation, and testing) for the CNN analysis. The U-Net model was employed for image segmentation and land use identification, utilizing ReLU and Softmax activation functions to enhance classification accuracy. The accuracy of the resulting land use maps was assessed using the Kappa coefficient and overall accuracy indices. To predict future land use changes, a Cellular Automata–Markov Chain model was utilized. This model overcomes the limitations of the standard Markov chain model by incorporating spatial information and the influence of neighboring cells. For the prediction, land use maps from 2000, 2007, 2014, and 2020 were input into the CA-Markov chain model to forecast land use status in 2040. The prediction process was conducted using IDRISI TerrSet 2020 software.

Results and Discussion: This study employed convolutional neural networks to generate highly accurate land use maps for the years 2000 and 2020. Specifically, the 2020 map achieved a Kappa coefficient of 97.89% and an overall accuracy of 96/06%. The results indicate that over this 20-year period, man-made lands experienced a substantial increase of 251%, rising from 16124 hectares to 56635 hectares. This expansion is primarily attributed to population growth, the necessity for urban infrastructure development, and securing the food supply. Agricultural lands also expanded significantly, growing from 64451 hectares to 117530 hectares. Conversely, barren lands decreased dramatically, with their share of the total study area falling from 49.69% in 2000 to 36.63% in 2020. Future land use changes were predicted using the Cellular Automata–Markov chain model. The projections suggest that by 2040, man-made lands will grow to 79727 hectares, constituting 9.49% of the total area. Although this rate of increase is slower compared to the 2000–2020 period, it still represents a significant component of the projected land use transformation. Agricultural lands are expected to rise by an additional 17578 hectares, reaching a total of 135108 hectares, which will cover approximately 16.1% of the total area. In contrast, natural land uses such as pastures and barren lands are projected to decline: grasslands will decrease by 25108 hectares, and barren lands will shrink by 19348 hectares, resulting in final areas of 323634 hectares and 287553 hectares, respectively. These significant land use shifts could carry considerable ecological consequences, including reduced biodiversity and negative impacts on overall ecosystem structure.

Conclusion: The application of the cellular automata-Markov model enables more accurate predictions; however, it is recommended to integrate this model with new technologies and scenario-based approaches to evaluate the interactive effects of various factors. In Ahvaz County, human pressures have resulted in an observable increase in man-made and agricultural lands, concurrent with a decrease in barren lands, underscoring the urgent need for sustainable land management strategies. Predictions for the year 2040 suggest that this trend- the expansion of man-made and agricultural areas alongside the diminishing extent of grasslands and barren lands- will continue, thereby necessitating comprehensive environmental policies. While the application of the Cellular Automata–Markov chain model facilitates more accurate predictions, it is recommended that this model be integrated with emerging technologies and scenario-based approaches to effectively evaluate the interactive effects of various influencing factors.

Abstract Introduction: Basil, Thymus and Rosemarinus are members of the mint family, which have antibacterial, anti-inflammatory, antifungal, and antioxidant properties. Agricultural products are dried to prevent physical and chemical changes. Drying agricultural products and medicinal plants changes their aroma and color. The aim of this study was to determine the drying type of basil, thyme, and rosemary plants in three drying methods: infrared controlled atmosphere, infrared conventional atmosphere, and shade drying using electronic nose and image processing systems.
Materials and Methods: After purchasing, all samples were stored in a refrigerator (4°C) until the experiments were performed. Three methods of controlled atmosphere infrared, normal atmosphere infrared and shade mathodes were used to dry the samples. Dehumidification time, moisture content, dehumidification rate, and effective diffusion coefficient were evaluated in three drying methods. An electronic nose system consisting of 15 sensors was used to detect odors of fresh and dried samples, and two PCA and SVM methods were used to analyze the data. A machine vision system equipped with a mobile phone camera and light source was used to capture images of fresh and dried plant leaves. The images were recorded in RGB color space format, which was converted to LAB color space using equations.
Results and Discussion: According to the results obtained, the longest moisture removal time for Basil, Thymus and Rosemarinus samples in the shade drying method was 2700, 900, and 5520 minutes, respectively, and the shortest time in the infrared atmosphere control method was 550, 385, and 1300 minutes, respectively.The reason for this is that in the shade drying method, due to high relative humidity and ambient temperature, the rate of moisture removal from the samples is slow, while in the infrared controlled atmosphere dryer, the dehumidification process was accelerated due to the presence of a vacuum in the chamber and the use of infrared lamps due to the depth of penetration into the sample. The results showed that the highest dehumidification rates were for Basil and Rosemarinus samples in the infrared controlled atmosphere dryer and for Thymus samples in the infrared normal atmosphere dryer. The results showed that the first (PC-1) and second (PC-2) principal components were able to cover 53% and 25% of the variance of the input data variable, respectively, and distinguish all three samples well. The MQ2, TGS822, and TGS2610 sensors had the greatest impact on this component. In the discussion of differentiating the type of dryers according to the second main component as well as the loading diagram, the differentiating the type of dryers was done well and the sensors MQ4, MQ2, TGS813, TGS822 and TGS2610 had the highest performance respectively. Based on the results, in the support vector machine method, the training accuracy and classification accuracy for distinguishing samples were 100 and 75 percent, respectively, and based on the type of dryers, they were 41.67 percent and zero, respectively; Therefore, this method was used separately to classify the different drying methods of all three samples, and the training accuracy and classification accuracy were 100% and zero, respectively. The amount of color index G in the dry shade method is closer to fresh Basil and Thymus plants, which could be due to the ambient temperature. The amount of color index B in the normal atmospheric drying method is closer to fresh Basil and rosemarinus, which can be due to the use of infrared lamps, the duration of dehumidification and the pressure inside the chamber. On the other hand, the amount of index B in the dry shade method is closer to the fresh Thymus plant, which is probably due to the ambient temperature. The amount of R index in the normal atmospheric drying method is closer to fresh Rosemarinus plant, which can be due to the use of infrared lamp, the pressure inside the chamber and the time of dehumidification. In the controlled atmosphere dryer, the color index L* is closer to fresh Basil and Thymus plants, which may be due to the use of nitrogen gas, the duration of dehumidification, and infrared lamps. The amount of color index b* in the normal atmospheric drying method is closer to the fresh Basil plant than the dry shade method, which can be due to the use of an infrared lamp, the pressure inside the chamber and the duration of dehumidification. The a* color index in the normal atmosphere drying method is closer to that of fresh Rosemarinus than in the shade drying method.
Conclusion: Compared to the other two methods, the use of infrared atmosphere control dryer has the shortest dehumidification time, the highest coefficient of explanation for Thymus and Rosemarinus samples, and the lowest for Basil samples, and also the highest effective diffusion coefficient. The accuracy of the PCA method for differentiating plants and dryers was 78%, while the training accuracy and classification accuracy of the SVM method for differentiating plants were 100% and 75%, respectively, and 100% and zero for the type of dryers of each plant, respectively. The indices of the two color spaces RGB and Lab depend on the drying conditions in dryers, such as temperature, dehumidification time, air pressure, and heat source. In the RGB color space, the color index R for Rosemarinus plants in the normal atmosphere drying method has less changes than the fresh plant. Also, the color index G for Basil and Thymus plants dried in the shade drying method has less changes than the fresh plant. On the other hand, the color index B for Basil and Rosemarinus plants dried in the normal atmosphere drying method has less changes than the fresh plant. In the Lab color space, the L* color index for Basil and Thymus plants in the controlled atmosphere drying method has less changes than the fresh plant, and the a* and b* color indices for Basil and Rosemarinus plants in the normal atmosphere drying method, respectively, have less changes than the fresh plant.

Abstract Introduction Phosphorus is an essential nutrient for plant growth, but its bioavailability is low in calcareous soils due to precipitation with calcium. Cow bone char (containing hydroxyapatite) can act as a slow-release phosphorus source. The particle size of bone char significantly influences phosphorus release kinetics; reducing the particle size to the nano-scale increases the specific surface area and accelerates phosphorus release. Despite studies on macro-sized bone char, limited information is available regarding the effect of nano-sized bone char on the chemical forms of phosphorus and its release kinetics in calcareous soil. Therefore, this study was conducted to compare the effects of macro- and nano-sized cow bone char on phosphorus availability in a calcareous soil.

Materials and Methods This experiment was conducted as a completely randomized design with three replicates to evaluate the effects of cow bone char and nano cow bone char at rates of 200 and 400 mg P kg⁻¹ soil compared to a control treatment. To prepare bone char, raw cow bones were cleaned several times and then dried at 110°C for 24 hours. The obtained bone char was ground to nano-scale using a mechanical ball mill. The treatments were incubated for two months at 25 ± 2°C, and soil moisture was maintained at 70% of field capacity (FC); then the samples were air-dried. Mineral phosphorus fractions were measured using the Jiang and Guo method. Phosphorus release kinetics were determined by adding 20 mL of 0.5 M NaHCO₃ to 1 g of each treatment, shaking for 1 to 72 hours (seven time points), and then centrifuging. Phosphorus concentration in the extracts was determined using a colorimetric method. Subsequently, the data obtained from the phosphorus release kinetics experiment were fitted to pseudo-first-order, pseudo-second-order, and Elovich equations. Analysis of variance (ANOVA) was performed using SPSS 26 software to evaluate the effects of different phosphorus fertilizer levels on various P forms and release kinetics. Mean comparisons were conducted using Duncan's test at a significance level of p < 0.05.

Results and Discussion Based on the results, reducing the size of bone char to the nano-scale caused a significant effect on the amount of available-P and inorganic forms of phosphorus (Ca₂-P, Ca₈-P, Ca₁₀-P, Fe-P, and Al-P) compared to the control. The application of nano bone char, especially at the rate of 400 mg kg-1, increased the amount of available phosphorus, dicalcium phosphate, and octacalcium phosphate compared to other treatments. The treatment of nano cow bone char at 400 mg kg⁻¹ increased the amounts of available phosphorus (Available-P), dicalcium phosphate (Ca₂-P), and octacalcium phosphate (Ca₈-P) by 8.4, 3.3, and 3.8 times, respectively, compared to the control treatment. The pseudo-second-order kinetic model provided the best fit to the kinetic release data due to its high coefficient of determination (R²) and low standard error (SE). The maximum release capacity (qₑ) of phosphorus increased with the use of nano bone char at 400 mg kg-1. Therefore, it can be concluded that nano bone char at 400 mg kg⁻¹ resulted in the highest phosphorus release in the soil.

Conclusion The results of this research showed the effect of bone char particle size on the release and distribution of phosphorus in different forms. In general, the study findings indicated that nano bone char demonstrated improvement compared to macro-sized bone char treatments. Nano bone char applied at a rate of 400 mg kg-1 was the best treatment for reducing insoluble forms of phosphorus to soluble forms, due to its higher specific surface area and reactivity. Thus, nano bone char can be considered a promising and environmentally friendly strategy for sustainable agriculture.

Abstract Introduction
Iron deficiency is a major nutritional problem in calcareous soils because high pH and carbonate content reduce Fe solubility and limit its uptake by plants. Although synthetic chelates such as Fe-EDDHA are effective in correcting Fe deficiency, their high cost and limited sustainability have encouraged the search for alternative Fe sources. Iron-loaded biochar has attracted attention as a slow-release amendment due to its porous structure, high surface area, and reactive functional groups. In addition, converting agricultural wastes into biochar provides an environmentally sound strategy for recycling residues and improving soil fertility. Therefore, this study was conducted to identify a suitable biomass for preparing Fe-loaded biochar and to compare its effect with Fe-EDDHA on soil Fe availability and Fe concentration in sorghum grown in calcareous soil.
Materials and Methods
Tea waste was collected from Lahijan, Guilan Province, and citrus residues including lemon, orange, and bitter orange were collected from Babol, Mazandaran Province, Iran. The biomasses were washed, oven-dried, ground, and sieved to less than 2 mm. Biochar was produced by slow pyrolysis at 450°C for 2 h with a heating rate of 10°C min−1. The biochars were then crushed and sieved to obtain particles of 0.5-1 mm. Iron loading was performed by wetness impregnation method using FeSO4.7H2O solution containing 1000 mg L−1 Fe(II) at a solid-to-solution ratio of 1:50 and pH 6. The suspension was stirred for 8 h, filtered, dried, and calcined at 600°C for 1 h. To select the best biochar, total Fe and DTPA-extractable Fe were measured, and tea-waste biochar was selected for further study. An incubation experiment was carried out as a factorial in a completely randomized design with three replications. Treatments included control, Fe-loaded biochar at 10 mg Fe kg−1 soil, and Fe-EDDHA at 10 mg Fe kg−1 soil, sampled after 3, 360, and 720 h. DTPA-extractable Fe was measured at each time. A greenhouse experiment was also conducted in a completely randomized factorial design with three replications. The first factor was foliar application at two levels: distilled water and Fe-EDTA at 3%. The second factor was soil Fe application including 0, 5, and 10 mg Fe kg−1 from Fe-loaded biochar or Fe-EDDHA. Sorghum was grown in 5 kg soil pots under greenhouse conditions. After four months, shoot Fe concentration and post-harvest soil DTPA-extractable Fe were determined. Data were analyzed using SAS software, and means were compared by LSD at the 5% level.
Results and Discussion
Among the tested feedstocks, tea-waste biochar had the highest total Fe content, about 373 mg kg−1, and was therefore selected as the superior biochar. The results also showed that pyrolysis reduced DTPA-extractable Fe compared with the original biomasses, indicating transformation of Fe into more stable and less soluble forms during biochar production. In the incubation experiment, soil Fe treatment, incubation time, and their interaction had significant effects on DTPA-extractable Fe at the 1% probability level. Both Fe-loaded biochar and Fe-EDDHA increased soil Fe availability compared with the control. In the Fe-loaded biochar treatment, DTPA-extractable Fe increased by about 3.1%, 13%, and 30% over the control after 3, 360, and 720 h, respectively. In the Fe-EDDHA treatment, the corresponding increases were about 12.5%, 58%, and 87%. These results indicate that Fe-EDDHA was more effective in maintaining Fe in a readily available form in the short term, whereas Fe-loaded biochar showed a slower but progressive release pattern. In the greenhouse experiment, foliar application, soil Fe source, and their interaction significantly affected shoot Fe concentration and soil DTPA-extractable Fe. The lowest shoot Fe concentration was observed in the untreated control 126 mg kg−1. Soil application of Fe-loaded biochar increased shoot Fe concentration to 169.39 and 280 mg kg−1 at 5 and 10 mg Fe kg−1, respectively. The corresponding values for Fe-EDDHA were 205 and 270 mg kg−1. Foliar Fe-EDTA further improved plant Fe status in all treatments. The highest shoot Fe concentration, 321 mg kg−1, was recorded in the treatment receiving 10 mg Fe kg−1 as Fe-loaded biochar plus foliar Fe-EDTA. Soil DTPA-extractable Fe after harvest ranged from 3.18 to 5.49 mg kg−1, with the highest value also observed in the same treatment. These findings suggest that Fe-loaded biochar can act as a slow-release Fe source and that its combined use with foliar Fe application enhances both soil Fe availability and plant Fe nutrition
Conclusion
The results showed that tea waste is a suitable feedstock for producing Fe-loaded biochar. Although Fe-EDDHA was more effective in rapidly increasing DTPA-extractable Fe in calcareous soil, Fe-loaded tea-waste biochar significantly improved both soil Fe availability and Fe concentration in sorghum, especially at the higher application rate. The combined use of Fe-loaded biochar and foliar Fe-EDTA produced the best overall response. Therefore, Fe-loaded tea-waste biochar can be considered a promising and sustainable amendment for improving Fe nutrition in calcareous soils and reducing dependence on synthetic Fe chelates.

Abstract Introduction Wheat (Triticum aestivum L.) serves as Iran's most strategic crop for food security, yet its production in arid regions faces severe constraints from limited water resources and erratic rainfall patterns. Dryland farming systems in khuzestan province experience significant yield gaps due to moisture stress during critical growth stages. While supplemental irrigation offers potential relief, its effectiveness depends fundamentally on concurrent nutrient management. This study investigated the integrated effects of supplemental irrigation and soil test-based optimal nutrition on yield, water use efficiency, and economic viability of dryland wheat in Baghmalek County, Khuzestan province.
Materials and Methods A field experiment was conducted during the 2023-2024 growing season in Baghmalek County, Khuzestan (Coordinates: 32°49' N, 49°50' E, 827 m altitude). The soil at the experimental site had a pH of 7.79, EC of 1.57 dS m⁻¹, 0.84% organic carbon, 7.54 mg kg⁻¹ available P, and 138 mg kg⁻¹ available K. The experiment followed a randomized complete block design with three treatments and three replications, using plots of 50 m² (10m × 5m). The wheat cultivar 'Dehdasht' was sown at a rate of 200 kg ha⁻¹ with 13 cm row spacing. Treatments included: 1) Farmer's conventional practice (rainfed, control); 2) Farmer's conventional practice + combined package of supplemental irrigation and improved nitrogen management; and 3) Soil test-based optimal nutrition + supplemental irrigation. In treatments with supplemental irrigation (T2 and T3), nitrogen fertilizer was split-applied to enhance use efficiency, as top-dressing without adequate moisture is ineffective under rainfed conditions. This approach aligns with the recommendations of the Dryland Agricultural Research Institute of Iran. The optimal nutrition treatment involved: basal application of 100 kg ha⁻¹ ammonium phosphate and 100 kg ha⁻¹ potassium sulfate based on soil test, seed priming with humic acid and zinc (1.5 L per 100 kg seed), 120 kg ha⁻¹ urea at first irrigation, foliar application of amino acids and seaweed extract at tillering, foliar application of potassium silicate at stem elongation, and 80 kg ha⁻¹ urea at second irrigation. Supplemental irrigation consisted of two events: 50 mm (2,500 L per plot) at planting and 30 mm (1,500 L per plot) at the booting stage. All other agronomic practices were uniform. At maturity, yield components (spikes m⁻², grains per spike, thousand kernel weight), biological yield, and grain yield were measured. Water use efficiency (WUE) was calculated using both total seasonal rainfall and effective rainfall (estimated using a reliable rainfall method). Economic analysis was performed using partial budgeting with wheat priced at 20,500 Tomans kg⁻¹. Data were analyzed using SPSS software with Duncan's test at p≤0.05.
Results and Discussion The results revealed that the integrated treatment (optimal nutrition + supplemental irrigation) significantly outperformed the others. It achieved the highest grain yield at 4368 kg ha⁻¹, representing a 47% increase over the control (2962 kg ha⁻¹). This treatment also recorded the highest thousand kernel weight (44.65 g), a 21% increase over the control. Notably, while the combined package of supplemental irrigation and improved nitrogen management (T2) significantly increased the number of spikes per unit area (231 spikes m⁻² compared to 165 in the control), it failed to significantly boost grain yield (3660 kg ha⁻¹) or thousand kernel weight. It should be noted that the observed yield increase in T2 is attributable not solely to supplemental irrigation, but also to the split application of nitrogen—a component inherently enabled by water availability in this treatment. This demonstrates that the conjunctive use of water and nitrogen, without balanced nutrition, cannot effectively translate vegetative growth into grain yield. The synergy between water and all essential nutrients was crucial. WUE calculations based on effective rainfall plus irrigation showed the integrated treatment reached 1.54 kg m⁻³, the highest among all treatments. Economic analysis using the partial budgeting method demonstrated a superior benefit-to-cost ratio of 1.36 for the integrated management, confirming it as the most profitable option for farmers. The study confirms that while the combined use of water and nitrogen primarily influences yield component establishment (spike number) a soil test-based comprehensive optimal nutrition program is essential for grain filling and final yield realization.
Conclusion This study conclusively demonstrates that a targeted, integrated approach combining supplemental irrigation with a balanced, soil test-based nutrition program is essential for unlocking the yield potential of dryland wheat in arid regions. The synergy between water and nutrients not only achieves substantial yield gains (47% increase) but also enhances the efficiency of water use and provides strong economic justification (benefit-to-cost ratio of 1.36). This integrated crop management strategy offers a viable pathway toward sustainable intensification, ensuring greater productivity and resilience in rainfed farming systems of Khuzestan and similar agro-ecologies.

Abstract Introduction: Phosphorus is one of the essential macronutrients required for plant growth, playing a critical role in processes such as photosynthesis, energy transfer, and cell division. However, inorganic phosphorus in soil is often present in insoluble and plant-unavailable forms, particularly in soils with high pH or elevated levels of phosphorus-fixing elements. Consequently, phosphorus use efficiency in agricultural systems tends to be low, leading to excessive application of chemical fertilizers, which in turn poses significant environmental concerns.
In recent years, the use of biological soil amendments such as biochar and carbon black has gained attention as a sustainable approach to improving the stability and availability of nutrients in soil. Biochar, produced through the pyrolysis of biomass under low oxygen conditions, is characterized by its porous structure, high cation exchange capacity, and considerable sorption ability, all of which contribute to nutrient retention and controlled release. Similarly, carbon black—a by-product of incomplete combustion of carbonaceous materials—can influence soil phosphorus dynamics due to its unique chemical properties.
Moreover, soil moisture acts as a crucial factor in nutrient dynamics. Variations in moisture levels can affect the physical, chemical, and biological processes in soil, thereby altering the distribution and chemical forms of inorganic phosphorus. Given the increasing occurrence of droughts and irregular irrigation due to climate change, it is essential to investigate the combined effects of moisture regimes and soil amendments on phosphorus behavior.
Therefore, this study aims to evaluate the simultaneous effects of carbon black and biochar on the chemical distribution of inorganic phosphorus in soil under different moisture conditions. The findings of this research are expected to provide valuable insights for optimizing phosphorus management and enhancing the sustainability of agricultural systems.In recent years, the use of biological soil amendments such as biochar and carbon black has gained attention as a sustainable approach to improving the stability and availability of nutrients in soil. Biochar, produced through the pyrolysis of biomass under low oxygen conditions, is characterized by its porous structure, high cation exchange capacity, and considerable sorption ability, all of which contribute to nutrient retention and controlled release. Similarly, carbon black—a by-product of incomplete combustion of carbonaceous materials—can influence soil phosphorus dynamics due to its unique chemical properties.
Moreover, soil moisture acts as a crucial factor in nutrient dynamics. Variations in moisture levels can affect the physical, chemical, and biological processes in soil, thereby altering the distribution and chemical forms of inorganic phosphorus. Given the increasing occurrence of droughts and irregular irrigation due to climate change, it is essential to investigate the combined effects of moisture regimes and soil amendments on phosphorus behavior.
Therefore, this study aims to evaluate the simultaneous effects of carbon black and biochar on the chemical distribution of inorganic phosphorus in soil under different moisture conditions. The findings of this research are expected to provide valuable insights for optimizing phosphorus management and enhancing the sustainability of agricultural systems.


Materials and Methods: In this study, a factorial experiment with four replications was conducted in 2023 using a completely randomized design to investigate the chemical distribution of inorganic phosphorus in soil affected by carbon black and biochar under different moisture conditions. The experimental factors included: 5% carbon black, 5% biochar, a combination of 2.5% carbon black and 2.5% biochar, and a control; two moisture levels (field capacity and saturation); and two incubation periods (30 and 60 days). The chemical forms of phosphorus were determined using the sequential fractionation method proposed by Jiang and Gu (1989).

Results and Discussion: The results showed that the application of biochar and carbon black under different moisture conditions led to significant changes in the availability of various chemical forms of phosphorus. Specifically, the use of carbon black as a soil amendment resulted in increases of 76.5% in octacalcium phosphate, 99% in occluded phosphate, and 46% in total phosphorus. Application of biochar led to an 88% increase in dicalcium phosphate, an 81% increase in available phosphorus, and a 10.9% increase in soil pH. The combined application of both amendments caused a 37% increase in aluminum phosphate and a 53% increase in apatite. However, this treatment also led to a 1% reduction in soil iron phosphate.

Conclusion: Overall, the use of biochar and carbon black presents a promising solution for enhancing phosphorus availability in soil and improving plant health.