Agricultural Engineering

Abstract Introduction: Darab region is one of the most important citrus production hubs, especially oranges, in Fars province. Given the frequent reports of iron deficiency chlorosis in citrus grown in calcareous soils of Iran, which reduces fruit production and quality, a better understanding of the mechanisms and kinetics of iron release from soil can provide useful information about its bioavailability and the factors affecting it and help us take the necessary measures to optimize soil conditions to prevent iron deficiency in plants and increase its availability in the soil. The aim of the present study is to investigate the kinetics of iron release in some calcareous soils of orange orchards (Citrus sinensis L.) in the Darab region, southern Iran, using six mathematical kinetic models and to find the effective soil characteristics in the release of iron from the solid phase to the soil solution.

Materials and Methods: 21 Washington Novel orange orchards (Citrus sinensis L. Osbeck) grafted onto orange (Citrus aurantium) rootstock were selected from different locations in Darab region in southeastern Fars Province through field observations and aerial photo analysis. Soil sampling was carried out from the selected tree bases and their shaded areas in four different directions (from 0 to 30 cm depth) in the vicinity of the drippers. Thus, a total of 10 soil samples were selected with different physical and chemical properties. The kinetics of iron release study was carried out using DTPA solution at pH 7.3 as the extractant, which was as follows: 10 g of each of the three soil sample replicates were placed in a 50 ml centrifuge tube and extracted with 20 ml of DTPA solution on a shaker (120 rpm) for 0.083, 0.25, 0.5, 1, 2, 6, 12, 24 and 48 hours at 25 ± 2°C. After shaking, the samples were immediately centrifuged for 10 minutes at 4000 rpm and the supernatant was passed through Whatman filter paper No. 42. The iron concentration in the resulting solution was measured by atomic absorption spectrometry (PG 990, PG Instruments Ltd. UK). The cumulative concentration of released iron (q) was evaluated as a function of time (t) using six different kinetic models. Relatively high values of the coefficient of determination (R2) and low values of the standard error of estimate (SEE) were used as criteria for selecting the best models. Data analysis was performed using SPSS 17.0 software.

Results and Discussion: The pattern of iron release in all soil samples was similar and consisted of a short-term rapid phase (fast phase) and a long-term slow phase (slow phase), so that about 44% (on average) of the total iron released during 48 hours was desorbed in the first two hours. Therefore, this result can confirm the two-stage iron release process from the soil samples. The highest iron release content within 48 hours was observed in soil "A" (17.3 mg kg-1), which could be due to the fact that among the studied soils, the highest organic matter content (7%), cation exchange capacity (2.15 meq 100 g-1 soil) and total iron (7560 mg kg-1) were found in soil A. The power function, parabolic diffusion, and simple Elovich kinetic models had the highest R2 values and the lowest SEE values among the kinetic models, respectively. In the power function kinetic model, the value of the constant “a” ranged from 0.829 to 1.260 with a mean of 1.02, while these values for the constant “b” ranged from 0.23 to 0.30 with a mean of 0.25. The KP constant in the parabolic diffusion kinetic model represents the rate coefficient of iron diffusion (mg Fe kg-1h-0.5) from the solid phase to the soil solution. Among the studied soils, the highest and lowest KP values were for soil A (0.399) and soil B (0.198), respectively.

Conclusion: The soil properties such as organic matter, cation exchange capacity, pH, salinity, percentage of sand, silt and clay (soil texture) were effective on the rate of iron release from calcareous soils, while the total soil iron and equivalent calcium carbonate had no effect on iron release. Soil organic matter and percentage of sand had a positive and significant correlation with the rate of iron release in the soil from the solid phase to the soil solution, while there was a significant negative correlation between the rate of iron release and soil salinity, pH, percentage of silt and clay. Finally, it is suggested that management methods, including creating lighter soil texture by adding sand and reducing salinity (leaching) of calcareous soils under orange cultivation in the Darab region, can significantly contribute to the release of native soil iron from the solid phase to the soil solution, and consequently improve iron nutrition.

Abstract Introduction: The generation of waste and the emission of pollutants are significant challenges in production processes, leading to increased economic costs and environmental degradation. Addressing these challenges has become a priority, as unsustainable production practices not only harm the environment but also hinder long-term economic development. Sustainable production and environmental efficiency are critical goals for manufacturing units aiming to achieve sustainable development. Achieving these goals requires innovative tools and methodologies that can assess and optimize resource use across various production systems. Cleaner production approaches, which focus on optimizing production processes and reducing negative environmental impacts, have emerged as key strategies in this context. One such tool is Material Flow Cost Accounting (MFCA), developed under ISO 14051:2011. MFCA has proven to be a powerful tool for simultaneously improving environmental and financial performance by quantifying material flows and associated costs within production systems. This study applies MFCA to analyze the energy and economic performance of olive production in Tarom County, Iran, a region known for its significant olive cultivation. The research seeks to identify inefficiencies, reduce resource waste, and promote sustainable agricultural practices through an integrated assessment of energy and economic parameters.
Materials and Methods: This study was conducted in olive orchards in Tarom County during the 2022 agricultural year, using an approach grounded in both scientific rigor and practical applicability. Data were collected through comprehensive questionnaires covering all major agricultural activities such as plowing, pruning, irrigation, fertilization, pest control, labor, and fuel and electricity consumption. A statistically reliable sample of 50 orchards was selected using Cochran's formula to ensure adequate representation of the region’s olive production practices. MFCA was employed to quantify material and energy flows, focusing on input resources (e.g., water, fertilizers, electricity) and output products (e.g., olives, waste, and emissions). The methodology strictly adhered to ISO 14051 guidelines, which define four cost categories for each quantity center (QC): system costs, material costs, energy costs, and waste management costs. Energy equivalents for inputs and outputs were calculated using standard conversion factors. Additionally, energy indices such as energy efficiency, energy productivity, and net energy were evaluated to provide a comprehensive understanding of the system’s energy dynamics. The economic analysis encompassed parameters such as gross production value, net income, benefit-to-cost ratio, and economic productivity, offering insights into the financial implications of production practices.
Results and Discussion: The total energy input for olive production was calculated at 77,417.56 MJ/ha, with electricity (59%), urea fertilizer (15%), and fossil fuels (14%) identified as the primary contributors. Positive energy outputs totaled 40,221.48 MJ/ha, while negative energy outputs, which included product loss and fertilizer waste, amounted to 6,853.04 MJ/ha. Among negative outputs, product loss (71%) and nitrate leaching (21%) represented the largest shares. The energy efficiency ratio was 0.52 using conventional methods but dropped to 0.43 when analyzed with MFCA, underscoring the hidden costs of waste and inefficiencies that traditional accounting methods fail to capture. These findings highlight the need for improved management practices to enhance both energy efficiency and economic viability. Economic analysis revealed that labor costs constituted 67% of variable production costs, whereas electricity, despite its high contribution to energy inputs, accounted for a mere 0.28% of financial costs. This discrepancy points to the critical need for better energy management strategies, including the adoption of renewable energy sources to reduce dependency on fossil fuels. Several inefficiencies were identified, particularly excessive water use during irrigation and significant product loss during harvesting, which adversely affect both energy and economic performance. To address these issues, implementing precision irrigation systems, mechanizing harvesting processes, and integrating renewable energy sources for pumping were proposed as effective strategies to reduce resource waste and improve sustainability. These findings align with those of previous studies on other crops, underscoring the universal applicability of MFCA in identifying inefficiencies and fostering sustainable agricultural practices. Furthermore, the study emphasizes that adopting cleaner production methods not only enhances resource efficiency but also supports broader sustainable development goals. This dual benefit makes MFCA a valuable tool for promoting both environmental stewardship and economic competitiveness in agriculture.
Conclusion: This study demonstrates the effectiveness of MFCA in providing a comprehensive analysis of energy and economic performance in olive production systems. By quantifying material and energy flows, MFCA revealed significant inefficiencies in critical areas such as water use and product loss, which are often overlooked in conventional accounting methods. The results emphasize the importance of adopting cleaner production practices, including precision irrigation, mechanized harvesting, and renewable energy integration, to improve both sustainability and economic viability. The application of MFCA not only enhances resource efficiency but also contributes to achieving sustainable development goals by reducing environmental impacts and lowering production costs. Future research should focus on the practical implementation of the proposed strategies and assess their long-term effects on carbon footprint reduction, resource optimization, and market competitiveness. This study underscores the transformative potential of MFCA as a tool for advancing sustainable agriculture, offering valuable insights for policymakers, farmers, and researchers aiming to optimize resource use and promote environmental stewardship in olive production and beyond.

Abstract Introduction: Soil physical and chemical properties play critical roles in soil health, agricultural productivity, and natural resource management. These properties are key indicators for assessing soil sustainability and carbon storage. Understanding their spatial distribution is essential for effective land management and resource modeling, particularly within the context of sustainable agriculture. The mean weight diameter of soil aggregates (MWD) is a crucial functional parameter that significantly influences the carbon cycle, soil structural stability, and agricultural productivity. Similarly, soil organic carbon (SOC) plays a vital role in environmental sustainability. Digital soil mapping (DSM), which integrates data from environmental covariates and machine learning (ML) algorithms, is a powerful tool for predicting soil properties and enhancing sustainable land management practices. The DSM techniques have proven highly effective in estimating soil parameters across diverse landscapes, minimizing the need for extensive field surveys and enabling data-driven decision-making in precision agriculture. The primary objective of this study was to predict the spatial distribution of MWD and SOC using DSM. Additionally, this study aimed to rank the most influential environmental variables affecting these properties through ML-based modeling and compare the performance of various ML methods.

Materials and Methods: This study was conducted in the Fereydan region (50º 6' E, 32º 56' N), Isfahan Province, Iran. A total of 100 surface soil samples (0 to 20 cm depth) were collected from various land uses, including irrigated croplands, dryland farms, and pastures, using a systematic sampling network. The collected samples were air-dried, sieved through a 2 mm mesh, and then subjected to laboratory analyses. Six ML algorithms—artificial neural network (ANN), random forest (RF), cubist model, support vector machines (SVM), k-nearest neighbors (KNN), and boosted regression trees (BRT)—were employed to relate environmental variables to the soil properties under investigation. For the modeling process, four datasets were used as predictor environmental covariates: remote sensing data, topography derived from a digital elevation model (DEM), soil properties, and classified land data. The models' accuracy was evaluated across four different scenarios, each using one or more of these datasets. The most suitable environmental variables were selected through a multicollinearity test using the variance inflation factor (VIF). Finally, to model the two soil properties, the dataset was divided into calibration (70%) and validation (30%) subsets. The models' performance was evaluated using various statistical metrics, including the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE).

Results and Discussion: The results of model validation revealed that the highest prediction accuracy for MWD was achieved using the KNN model in the fourth scenario (R²=0.59, RMSE=0.19, MAE=0.16). These values indicate a moderate yet significant ability of the KNN model to predict MWD. In contrast, for SOC, the cubist model in the fourth scenario yielded the highest accuracy (R²=0.78, RMSE=4.12, MAE=3.53). These results suggest that the cubist model was particularly effective in capturing the complex relationships between environmental variables and SOC, providing a robust framework for predicting soil carbon content. These findings highlight the strong potential of ML models to capture the intricate interactions between soil properties and environmental factors, allowing for precise spatial predictions of soil characteristics. The ability of these models to process large volumes of data and uncover hidden patterns is particularly useful in soil science, where the relationship between soil properties and environmental variables can be complex and nonlinear. The results also revealed that different environmental variables played varying roles in explaining the spatial variability of MWD and SOC. For MWD prediction, silt content was found to be the most influential variable, followed by VV and CHND, both of which were also significant factors affecting the spatial distribution of MWD. In contrast, for SOC prediction, the most important variables were the multi-resolution valley bottom flatness index (MRVBF) and clay content. These factors are critical for understanding the storage and cycling of organic carbon in soil, as both moisture retention and clay particles are known to influence the stabilization of soil organic matter. The relationship between these variables and SOC highlights the importance of considering soil texture and moisture dynamics when predicting soil carbon stocks.

Conclusion: The results of this study demonstrated that ML models, such as cubist and KNN can effectively predict soil quality properties across different regions, particularly when combined with topographic and climatic data. These approaches offer an efficient and rapid method for generating digital soil maps and supporting sustainable land management. By leveraging multiple environmental datasets, ML-based DSM enhances soil modeling accuracy while reducing reliance on extensive soil sampling. These models are especially useful in areas with similar climates where full-scale soil sampling may not be feasible, providing a valuable tool for estimating soil quality attributes and enhancing agricultural productivity. Ultimately, the findings of this research can help policymakers and farmers identify critical areas and adopt optimal strategies to improve soil quality and mitigate adverse environmental impacts. To further improve the accuracy and stability of predictions, future studies should incorporate high-resolution spatiotemporal data, advanced algorithms such as cubist and KNN, and dynamic models for analyzing temporal variations. Additionally, investigating soil–water–plant interactions and assessing the impacts of climate change could play a crucial role in enhancing agricultural productivity and promoting the sustainable management of soil resources.

Abstract Introduction: In recent years, ensuring the continuous and sustainable production of healthy food products along with environmental protection and paying attention to agricultural economic and environmental problems is very important. Potassium is one of the major nutrients that its deficiency decreased the growth and yield of rapeseed. After soybean, rapeseed is the world's second produced oilseed. soil inorganic amendments such as zeolite are good strategies to improve soil quality and increase soil potassium content. The addition of zeolite plays an important role in reducing the nutrients loss and increasing fertilizer use efficiency. Considering unique characteristics of zeolites, such as the low-cost and abundance of its mines in Iran, having a special physical and chemical structure and it plays an important role in enhancing the water and nutrients uptake by plant. This research was conducted with the aim of determining the effects of natural and enriched zeolite with potassium on rapeseed yield, component yields and concentration of potassium in soil and rapeseed in two soils with different texture (sandy loam and silty clay loam).

Materials and Methods: This experiment was conducted as a split plot in pots conditions for three replicates in the research greenhouse of Sari Agricultural Sciences and Natural Resources University. In this experiment, the main factors include two types of soil (silty clay loam and sandy loam) and the secondary factors in twelve levels include the control (without zeolite and potassium sulphate) (T0), 32 mg. kg-1 potassium sulphate fertilizer (T1), 46 mg. kg-1 potassium sulphate fertilizer (T2), 2 gr zeolite with 32 mg. kg-1 potassium sulphate(T3), 4 gr zeolite with 32 mg. kg-1 potassium sulphate(T4), 6 gr zeolite with 32 mg. kg-1 potassium sulphate (T5), 2 gr zeolite with 46 mg. kg-1 potassium sulphate (T6), 4 gr zeolite with 46 mg. kg-1 potassium sulphate (T7), 6 gr zeolite with 46 mg. kg-1 potassium sulphate (T8), 2 gr enriched zeolite (T9), 4 gr enriched zeolite (T10) and 6 gr enriched zeolite (T11) were considered. Zeolite was enriched by ion saturation method. Rapeseed cultivation was done in 2021. At the end of cultivation season, harvest was done from each plot, grain yield, some yield components (pod number, thousand grain weight, grain oil), leaf potassium and grain potassium were measured. In addition, soil samples were collected from plots after harvest, and soil potassium was measured. Data analysis was done in the Statistic software and the mean comparisons were made by using LSD test.

Results and Discussion: The results showed that the effects of the soil type treatments were significant on all studied traits except seed potassium. The effects of amendment treatment were significant on all studied and the interaction effect of soil type and amendment treatment was only significant on grain yield. The highest amount of grain yield (6.46 gr) was observed in silty clay loam soil and 4 gr zeolite with 46 mg. kg-1 potassium sulphate (T7) with increasing about 49% compared the lowest amount (4.33 gr per pod) was obtained in sandy clay loam soil and control (T0). The use of 4 gr zeolite with 46 mg. kg potassium sulphate (T7) compared with the control (T0), was increased by 22% of thousand grain weight. Also the highest amount of grain oil (36.36 %) and grain potassium (2.75 %) was obtained in the treatment of 4 gr zeolite with 46 mg. kg potassium sulphate (T7). The results also revealed that the number of pod (85.16 %), leaf potassium (2.28%) and soil potassium (155.83 mg. kg-1) with increasing about 40%, 19% and 21 % respectively, compared with the control were significantly affected by 6 gr zeolite with 46 mg. kg potassium sulphate (T8). Between the enriched zeolite with potassium treatments, 4 gr enriched zeolite (T11) had the greatest effect on the studied traits.

Conclusion: overall, it can be concluded that, all studied traits except grain potassium in soil with silty clay loam texture are more than soil with sandy loam texture. The best results were obtained in 4 gr zeolite with 46 mg. kg-1 potassium sulphate (T7). Between the enriched zeolite with potassium treatments, application of 4 gr enriched zeolite with potassium was the most effective treatment on the studied traits. It can be concluded that the combined use of zeolite and potassium sulphate can be a suitable solution for improving rapeseed yield

Abstract Introduction: Previous studies indicate that many soils worldwide lack zinc or contain it in a form that is unavailable to plants. In Iran, over 56% of agricultural soils have DTPA-extractable zinc levels below 0.75 mg/kg, while only 31% exceed 1 mg/kg. Therefore, it is crucial to understand the factors influencing zinc concentration and prioritize their significance in the distribution of this essential element in soil. Employing an effective model to comprehend the logical connections within the results is essential. Artificial neural networks, known for their ability to model complex and non-linear relationships between soil variables, have been widely used in numerous studies over the past few decades. Khuzestan province, located in the southwest of Iran, possesses high potential for producing a variety of agricultural and horticultural products due to its water resources and fertile soils. With an output of 17.5 million tons of agricultural and horticultural products, Khuzestan plays a crucial role in ensuring the country's food security. Considering the diverse topography and land uses, this study aims to estimate the available zinc content in the surface soils of a selected area in northern Khuzestan province using artificial neural networks and to identify the key factors controlling its distribution across the study area.
Materials and Methods: Most of the study area is within the catchment area of the Karun River, which forms the backbone of the hydrographic network in Khuzestan province. The eastern sectors are located in the catchment area of the Zohre-Jarhari Rivers. The region has a hot and dry climate, with average rainfall ranging from 240 mm in the center to 590 mm in the eastern parts. Various geological formations, such as Asmari, Gachsaran, and Mishan, are present in this area. The soil moisture regimes include aridic, ustic, and xeric (in the eastern parts), while the temperature regimes are classified as thermic and hyperthermic. The soils in the study area are primarily classified as Aridisol, Inceptisol, and Entisol. Two hundred and three (203) surface (0-10 cm) composite samples were randomly collected from an area of 27,452 square kilometers in northern Khuzestan province. Sampling took place under sunny conditions during winter. The soil samples were air-dried and then passed through a 2 mm sieve. The pH and electrical conductivity of the soil were measured in pastes and saturated extracts, respectively. Soil texture was determined using the pipette method; organic matter was assessed by the wet oxidation method with potassium dichromate; and calcium carbonate equivalent was measured through back titration. To measure the available zinc content in the soils, 10 grams of each soil sample were weighed, and 20 ml of a 0.005 M DTPA solution was added. The suspension was shaken for 2 hours at 120 rpm. Subsequently, the solid and solution phases were separated using Whatman 42 filter paper. Finally, the zinc content of the extracts was measured using a GBS Variant AA atomic absorption device. In the dataset, zinc concentrations of less than 3 mg/kg, between 3 and 6 mg/kg, and greater than 6 mg/kg were categorized as low, medium, and high classes of available zinc content, respectively. A Multilayer Perceptron (MLP) neural network was employed to model available zinc content alongside other soil variables in the SPSS v26 environment, with the optimal structure of the neural network determined through a trial-and-error approach. The normality of the data was assessed using the Kolmogorov-Smirnov test within SPSS v26 software.
Results and Discussion: The results indicated that the average content of available soil zinc in the study area is 2.76 mg/kg. The average calcium carbonate equivalent of the soils is 42.25%, suggesting that the soils can be classified as calcareous. Additionally, with an average pH of 7.65, the soils in the region exhibit alkaline reactions. The average soil organic carbon content is 12.6 g/kg. The findings also suggest that the studied soils primarily belong to the loam family. Among the examined soil properties, the highest coefficient of variation is associated with soil salinity (131.9%), while the lowest is related to soil pH (4.2%). The results of the Kolmogorov-Smirnov test revealed that only the silt content of the soils exhibited a normal distribution pattern; the other variables did not pass the test of normality. When various configurations of multilayer perceptron artificial neural networks were evaluated, it was found that the MLPSO7 model, with a stable arrangement of 3 output classes, 5 hidden layer neurons, and 7 input layers, using the sigmoid function for both input and output activation, performed best in predicting available zinc values in soil samples. The results also indicate that organic carbon, calcium carbonate equivalent, and electrical conductivity were the most influential factors in developing the model. The analysis of the estimation matrix for available zinc classes shows that the MLPSO7 model accurately estimated the class with low concentrations of available Zn more effectively than the other two classes. Furthermore, the soil variables used to model the available zinc contents in the study area were able to accurately estimate these contents (97.4% for the training dataset, 97.1% for the test dataset, and an overall accuracy of 90.8%). In contrast, the class with high concentrations of Zn (>6 mg/kg) produced weaker estimates, which can be attributed to the small number of samples examined in this category.
Conclusion: It can be concluded that the estimates derived from the optimal neural network model demonstrate good flexibility and efficiency in justifying the available Zn contents in the study area. For future large-scale studies, greater attention must be given to the significant roles of organic matter, the abundance of calcareous compounds, and the salt content of the soils in determining available Zn levels.

Abstract Introduction: Abiotic stresses are the most important limiting factors affecting agricultural productivity. Soil salinity is considered one of the most severe abiotic stresses in plants. Crop plants must use biological mechanisms to cope with unfavorable environmental conditions. Their growth, development, and yield will be reduced if they fail in coping with such conditions. The plant microbiome provides plants with the main basic support they need to obtain nutrients, resist diseases, and tolerate abiotic stresses. The metabolic and genetic potential of the plant microbiome transforms plants into appropriate organisms for fighting unfavorable environmental conditions. Hence, the use of salt-tolerant halophyte rhizosphere bacteria is an effective strategy for improving plant growth in saline soils.

Materials and methods: Consequently, this research intended to isolate indigenous resistant bacteria adapted to salinity stress conditions from the rhizosphere of desert plants in Kerman Province and determine their effects on some physiological and biochemical characteristics of maize plants in soil under salt stress. First, the rhizosphere bacteria of desert trees were isolated and purified. Next, their growth was studied at various sodium chloride concentrations (0, 100, 200, 300, 400, 500, 600, and 650 mM) in solid microbial culture media and then in quantitative testing at various sodium chloride concentrations (0, 320, 650, and 850 mM) in liquid microbial culture media. Four bacterial strains (BZ79, BR90, BG59 and BG71) were selected for the greenhouse stage. The greenhouse experiment was performed on maize plants with three replications using a completely randomized design arranged as a factorial consisting of four salt-tolerant strains and a control at two soil salinity levels: non-saline soil (1.32 dS/m) and saline soil (12 dS/m). The experiment was completed in 90 days. Finally, dry weight of the aerial organs, fresh weight of the aerial organs and of the roots, length of the aerial organs and of the roots, concentrations of chlorophyll a, b, total chlorophyll, and carotenoid content of maize plants in saline and non-saline soils were measured.

Results and Discussion: The results of the statistical analysis showed that the bacterial strains differed significantly in salt tolerance, and some of them were even able to tolerate high sodium chloride concentrations (up to 850 mM) and grow well. Salt stress causes osmotic stress, ionic imbalance, and oxidative damage in maize plants thereby decreasing growth and photosynthetic efficiency. The results indicated that increases in salinity level reduced dry matter of the aerial organs (51.4%), fresh weight of the aerial organs (24.4%), fresh weight of the roots (15.9%), length of the aerial organs (7.8%), chlorophyll a content (64.2%), chlorophyll b content (115%), and carotenoid content (3%). However, inoculation of the maize plants with salt-tolerant bacteria significantly increased dry weight of the maize plants, fresh weight of the aerial organs and of the roots, length of the aerial organs and of the roots, concentrations of chlorophyll a, chlorophyll b, and total chlorophyll, and carotenoid content in non-saline and saline soils. Of course, the increases in these plant parameters caused by inoculation with salt-tolerant bacteria were more pronounced in saline soil compared to non-saline soil. The results demonstrated that, compared to the uninoculated control (B0), inoculation of the plants with the bacterial strains BZ79, BR90, BG59, and BG71 increased dry weight of the aerial organs in the non-saline soil by 20%, 12.3%, 13%, and 3%, respectively, and by 62.2%, 39.5%, 59%, and 41.4%, respectively, in the saline soil. The largest dry weight of the aerial organs (25.95 g) was recorded in the maize plants inoculated with BZ79 and the smallest (1 g) in those uninoculated with the control (B0). Compared to the uninoculated control (B0), inoculation of the maize plants with BZ79, BG59, and BG71 in the saline soil improved chlorophyll a concentration by 1.7%, 1.9%, and 2.4%, respectively, and increased chlorophyll b concentration by 2.6%, 1.7%, and 1.8%, respectively. However, inoculation with BR90 did not increase chlorophyll a content. In the saline soil, inoculation with both BR90 and BG59 led to a three-fold increase in carotenoid content compared to the uninoculated control.

conclusion: Among the indigenous rhizosphere bacteria, BZ79 was the most effective in improving maize plant tolerance to salt stress. These findings indicated the potential of these indigenous salt-tolerant bacteria as an effective factor for reducing the damage caused by salt stress by increasing the physiological performance of the maize plants and the photosynthetic pigments in them. Given the beneficial effect of the indigenous salt-tolerant bacteria under saline soil conditions, these microorganisms have the potential to be candidate as biofertilizer in the future.

Abstract Introduction: Beetroot (Beta vulgaris) is a great source of diverse essential nutrients such as dietary fibers, betalains, vitamins and carbohydrates. Drying of fruits and vegetables is considered as an engineering strategy in food industry, pointed at applying a set of operations to enhance shelf life, ensure perennial market supply, reduce packing and transportation costs, and enhance the quality of the final product. Simultaneously this is a massive energy consumption among food industries. Numerous efforts have been made to reduce the cost of drying agricultural products through various methods, including the manufacturing of new equipment and the efficiency maximization of existing systems. In the present work, it was appraised whether by means of new drying technique of step-down mode of stepwise temperature change conditions in a vacuum dryer apparatus, could be a probable proper alternative for manufacturing new devices which tremendously, requires expenditure of time and disbursement.
Materials and Methods: In this research the impact of a laboratory infrared-vacuum dryer and a new drying technique of stepwise temperature with the same device were investigated. The new strategy was assisted by a microwave treatment using a home scale Toshiba microwave. The present study was purposed to comprehend the effects of above-mentioned drying methods on the drying behavior of 4 mm red beetroot slices in addition to physical properties including color indices of (ΔL*, Δa*, Δb*) and shrinkage; Chemical properties of vitamin C and antioxidant component; Thermal properties of effective moisture diffusivity and drying kinetics and specific energy consumption as well as environmental aspects of greenhouse gasses (GHG) emissions. Beetroot slices were exposed to three levels of temperature (45, 60 and 75 °C) and absolute pressure (20, 50 and 80 kPa) as normal drying. Also, three levels of microwave power and times (at 150, 300, and 450 W) and (0, 10, and 20s) for stepwise conditions were applied.
Results and Discussion: Results showed that R² value of normal and stepwise drying methods were between (92.73 – 99.92%) & (94.34 – 99.92%) respectively. The high values of R² indicates that there is a strong relationship between experimental foundlings and predicted proposed model quantities. Furthermore, the CV percentage for them was less than 6.93 and 6.16 for normal and stepwise drying methods correspondingly; that illustrates a good repeatability of related models. All studied variables demonstrate non-significant lack of fit values that exhibit a good prediction ability for dependent variables. Therefore, models offer a good reliability in all dependent variables. Temperature factor had significant effect on all studied parameters in normal vacuum drying at 0.1% level. Absolute vacuum pressure affected significantly at different levels on all variables excluding SECtotal, CO₂ and NO_x for both methods; Unless otherwise the effect of temperature on dependent variables was more considerable comparing to absolute vacuum pressure. Power level and duration of microwave application in stepwise strategy had significant effect at 0.1% level on all dependent variables excluding AA. Interaction effect of temperature and vacuum pressure was more significant in normal vacuum drying. Stepwise method showed more considerable effect of microwave power application time interacting with vacuum pressure and microwave power level. The desirability values were found 0.606 and 0.610 for normal vacuum and stepwise method respectively. The optimization by using the Response Surface Methodology (RSM), was detected at 65 ℃ and 20 kPa for normal vacuum drying. Besides that, pressure vacuum of 45 kPa, 360 W of microwave power and 10 s of microwave power application was found as optimum point for stepwise drying method. Compared to normal vacuum drying at optimal conditions, the stepwise method reduced NO_x and CO₂ emissions by 27.91% and 27.80%, respectively, and decreased specific energy consumption by 28.18%. Additionally, improvements in color parameters were observed, with Δa* and ΔL* increasing by 18.80% and 9.87%, respectively. However, Δb* showed a 14% color degradation, and shrinkage increased by 38.62%. Furthermore, reductions in vitamin C loss index and antioxidant activity were recorded at 11.31% and 11.23%, respectively.
Conclusion: Using stepwise drying strategy had a close but lower impact on quality and physical parameters; whereas, showed a great positive difference on greenhouse gasses emission and specific energy consumption. This method also needs less energy to create absolute vacuum pressure (45 kPa compared to 20 kPa for normal method) to gain the mentioned results. Therefore, stepwise strategy is recommended for drying of red beetroot, as it offers a more energy-efficient approach while maintaining product quality and reducing environmental impact. Furthermore; this technique is proposed for new research studies that can be experimented with different agricultural products. 
 

Abstract Introduction

Sugarcane is cultivated on 135,000 hectares of agricultural land in Khuzestan Province. In some of the farms of Sugarcane and By Products Development Company, an average of 125 tons per hectare is harvested annually, which indicates the favorable potential of Khuzestan province for sugarcane cultivation. In recent years, sugarcane production yield per unit area has decreased. Therefore, extensive studies are needed in this field to investigate the causes of the decline in sugarcane yields and provide solutions to improve crop yield. Sugarcane yield is affected by various parameters; given that simultaneous examination of the effects of management, water, soil, and climate on sugarcane plants is not possible in the field method; therefore, modeling is a solution to this problem. Data mining and machine learning tools can analyze data and identify factors affecting product performance. The purpose of this research is to identify problems and estimate the performance of sugarcane farms in Dehkhoda Agro-Industry using the support vector machine and random forest algorithms.

Materials and Methods

To collect data and information, statistics from Sugarcane and By Products Development Company and the Dehkhoda Sugarcane Agro-Industry Company in the 2019-2023 crop years were used, as well as field data collection and measurement. The data used includes 2032 records, which consist of four categories of datasets: meteorological data, crop data, soil data, and farm management data. The variables studied in the two algorithms of support vector machine and random forest include: crop variety, harvest month, nitrogen chemical fertilizer, phosphorus chemical fertilizer, plant age (first crop or ratoon), number of times the field is irrigated during the growing season, ratio of the field's sprayed area (ratio of the sprayed area to the total field area), soil texture, soil electrical conductivity, amount of water consumption per hectare, drainage rate, field management (production management), length of the growing season, field area, sugarcane yield, and climatic variables. The indicators used to evaluate the accuracy of the model included: root mean square error, average absolute value of error, and correlation coefficient. Python software version 3.12.4 was used for modeling. In this study, 70% of the data, which includes 1626 samples, were selected for the training set and 30% of the data, which includes 406 samples, were selected for the testing set.

Results and Discussion

The scores of the support vector machine and random forest models, considering sugarcane yield as the target variable in two training and testing datasets, were 84.5 and 85.6 percent on the testing data, respectively, and 86.9 and 87.1 percent on the training data, respectively. In the experimental phase, the performance of the random forest model was significant with a correlation coefficient (R2) of 0.92, a mean absolute error (MAE) of 0.34, and a root mean square error (RMSE) of 62.81. In this study, various metrics and methods were used to evaluate the accuracy of the models and their stability and reliability in dealing with new data. The constructed models were evaluated using data cross-validation, confusion matrix, performance characteristic curve, and sensitivity accuracy curve. For sugarcane yield prediction, the accuracy of the hybrid classifier with soft voting on training and test data was 93% and 89%, respectively. The accuracy-frequency for the sugarcane yield prediction model using random forest model was 96%, which is a significant achievement in this research. This high accuracy indicates that the model was able to well identify and model the complex and nonlinear patterns present in sugarcane data. Also, this high accuracy indicates that the input variables selected for the model had high predictive power. The obtained random forest output curve shows that the random forest algorithm has also been able to perform acceptable in predicting sugarcane yield. The upward trend and then stabilization of the curve indicates that the model has converged well and reached the optimal point.

Conclusion

The results showed significant success in using machine learning techniques to predict the performance of sugarcane fields. The 97% accuracy of the random forest model, confirmed by 10-layer cross-validation, indicates that this model can be used as a reliable tool in sugarcane plantation management. Complementary results from the vector machine algorithm also confirm these findings. The balanced distribution of data in the box plot, good performance in the confusion matrix, and favorable ROC curve all indicate that these models can play an important role in sugarcane plantation management decisions. These results also show that using the random forest technique in agriculture can help improve productivity and better resource management.