World Academy of Science, Engineering and Technology

Authors: Nadia Khater, Kenza Garah

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The rich and varied flora of Algeria is distinguished by the abundance of indigenous species. This extraordinary biodiversity is correlated with the presence of extremely diverse ecological circumstances. A prime illustration of Algeria's diversity of biogeographical types at the nexus of many flora sources, including subtropical, Mediterranean, and Saharan, is the Argan grove in Tindouf. Tegumentary dormancy, especially in Argania spinosa, L. Skeels, influences the germination rate and longevity. Thus, one of the main reasons why the production of argan seedlings in the Tindouf region has failed is salinity. Increasing salt tolerance is a crucial step in the process of growing these species' seedlings. This study found that combining salicylic acid (AS) with NaCl increased argan seedling germination and growth. The use of 1.25 g/L and 2.5 g/L of AS in combination with 5 g/L and 12.5g of NaCl resulted in significant improvements in all parameters evaluated. The study demonstrated that the application of a low concentration of AS could mitigate the adverse effects of salt stress by stimulating germination and regulating the morphophysiological mechanisms of the Arganier plants.

Keywords: salinity, salycilic acid, argania spinosa, NaCl

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Authors: Narendra Nayak, Vinita Nayak, Reena Dhakad

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A study was carried out to develop functional Kadaknath meat cookies” with the prime objective of increasing fibre content and reducing the fat content by incorporating psyllium husk and poppy seed, respectively. The efficacy of psyllium husk at three different levels was assessed to increase the fibre content in Kadaknath meat cookies. The results indicated that the total dietary fibre, ash, and thickness value were significantly (P<0.05) increased. However, fat content was decreased significantly (P<0.05) with the addition of psyllium husk. The hardness value, gumminess value, and chewiness value increased significantly (P<0.05) with the increasing level of psyllium husk. The sensory attributes of Kadaknath meat cookies with 12% psyllium husk were either higher or comparable to control. Hence, cookies with 12% psyllium husk were selected for development of fibre enriched low fat Kadaknath meat cookies and further used to develop low fat fibre enriched meat cookies using poppy seed as fat replacer. Low fat Kadaknath meat cookies were prepared by incorporating three different levels of poppy seed by replacing added fat. A significant (P<0.05) difference in the pH, moisture content, fat content, total dietary fibre, ash content, moisture-protein ratio, moisture retention of Kadaknath meat cookies was recorded among the treatments. Poppy seed added low-fat Kadaknath meat cookies had significantly (P<0.05) lower fat content compared to the control. Fat retention was significantly (P<0.05) lower in control as compared to poppy seed-incorporated Kadaknath meat cookies. The hardness, gumminess, and chewiness values differ significantly (P<0.05) among the treatments. Sensory attributes indicate that 2% of poppy seed incorporation showed either comparable or higher scores compared to control. Hence, it is concluded that psyllium husk and Poppy seed may be used as a source of fiber and as fat replacers, respectively, to develop fibre-enriched low-fat functional Kadaknath meat cookies without affecting the quality and sensory attributes of the products.

Keywords: fibre enriched, functional, Kadaknath low fat

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Authors: Mohammad Anamul Haque, Rafiqul Islam, Rakibul Hasan, M. Zulfikar Rahman

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The primary aim of this study was to examine the socio-economic impacts of climate change management on rice farmers in the High Barind Tract, Bangladesh. The research was conducted in this drought-prone region, specifically within two upazilas—Tanore and Godagari—both located in the Rajshahi district. To gather data, a questionnaire survey was administered to a randomly selected sample of 300 rice farmers. Data collection took place through personal interviews using a pre-tested structured interview schedule between February and June 2024. To measure the relevant variables, appropriate scales were developed and employed. The study considered twelve farmer characteristics as independent variables, while the socio-economic impacts of climate change management on rice farmers served as the dependent variable. Additionally, climate data spanning 37 years (1987–2024)—including temperature and rainfall—were obtained from the Bangladesh Meteorological Department (BMD) for the Dhaka weather station. This data was analyzed to assess climate trends and to evaluate the level of awareness among rice farmers regarding climate change, identify the adaptation strategies adopted, and determine the factors influencing the adoption of these strategies. Furthermore, the study calculated the marginal cost of rice production for farmers. Climate data analysis, utilizing a modified Mann-Kendall trend test, revealed no significant statistical trend in rainfall amounts during the study period (p > 0.05), although a slight decrease was observed. Conversely, temperature trends indicated a significant increase (p < 0.05) over the same period. The study found that 36% of farmers were aware of climate change, and 32% perceived its effects, based on a five-level Likert scale. It was also observed that factors such as education level (p = 0.019), access to extension services (p = 0.001), market distance (p = 0.002), and rice income (p < 0.001) significantly influenced farmers’ perceptions of climate change, highlighting the importance of awareness and understanding in adaptation. Further analysis using the Hackman step-two model identified that extension access (p < 0.001), household size (p = 0.098), market distance (p = 0.047), rice income (p = 0.032), contact with fellow farmers (p < 0.001), and the perceived effects of climate change on rice (p = 0.038) significantly impacted farmers’ choices of adaptation strategies. The profit margin for farmers was estimated at 0.298, indicating the economic implications of climate adaptation measures. In conclusion, the study emphasizes that access to information facilities and rice income significantly influence farmers’ perceptions of climate change. Moreover, extension services, market proximity, peer interactions, and the perceived impact of climate change on rice yields strongly affect farmers’ selection of adaptation strategies. Based on these findings, the study recommends that government agencies and local administrators develop comprehensive strategies to improve farmers’ access to information and technology related to climate change adaptation. Such initiatives could enhance rice production yields and help farmers better cope with the adverse effects of climate variability in the High Barind Tract.

Keywords: socio-economic, impact, climate change, management, rice farmers, Bangladesh

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Authors: Ketounou Sewanou Tiburce, Toshiro Takahara, Elie Antoine Padonou

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Agriculture in Benin faces growing challenges due to climate variability, soil degradation, and limited access to data-driven decision-making tools. To address these issues, this research presents a smart, automated system that leverages the Internet of Things (IoT) and Machine Learning (ML) to assist smallholder farmers in making optimal crop selection decisions based on real-time soil and environmental data. The system is built on a combination of calibrated sensors, measuring soil nutrients (nitrogen, phosphorus, potassium), pH, temperature, humidity, and rainfall, interfaced with an Arduino Mega and ESP32 microcontroller. These sensors continuously gather field-level data and transmit it to a cloud database through Wi-Fi, using MQTT protocols for real-time communication.A key feature of the system is its crop recommendation engine, powered by a Random Forest machine learning algorithm. The model is trained using a robust dataset encompassing the agroecological requirements of major crops cultivated in Benin. It analyzes the live sensor data to predict the most suitable crops for a given location and soil condition. A user-friendly web interface was developed using Django, HTML, CSS, JavaScript, and PostgreSQL, offering farmers access to interactive dashboards, real-time monitoring, and AI-generated crop suggestions.The platform supports precision agriculture and aims to reduce the dependency on traditional guesswork in farming. It empowers farmers to optimize land use, minimize input waste, and improve yields, while contributing to sustainable land management. Furthermore, the system is scalable and designed to integrate future modules such as pest detection, weather forecasting, and yield prediction. Initial testing with farmers in southern Benin demonstrated high usability and relevance, with prediction accuracy reaching up to 97% in controlled tests. The solution promises to enhance food security, increase farmer income, and promote climate-resilient agriculture in West Africa.

Keywords: IoT, machine learning, crop recommendation, soil monitoring, precision farming

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Authors: Saeid Hazrati, Naser Siavoushi Oskuei

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In organic agriculture, the symbiotic relationship between endophytic fungi and plants, as well as the use of biochar, is of great importance due to its significant effects on plant growth and yield. In order to evaluate the simultaneous use of biochar and inoculation with arbuscular mycorrhizal fungi (Glomus) on the growth and yield of Nigella sativa in poor soils, a factorial experiment in the form of a completely randomized block design was carried out in the spring of 2021-2022 at the research farm of the Faculty of Agriculture, Azarbaijan Shahid Madani University, East Azarbaijan Province. In this experiment, the first factor includes biochar with three levels (no application (control), application of 5 and 10 t ha-1) and the second factor of inoculation treatment with arbuscular mycorrhizal fungi of genus Glomus (no inoculation (control), seed inoculation with fungi and seed and biochar inoculation with fungi) was carried out in three replications. The results showed that the characteristics of chlorophyll a, chlorophyll b, total chlorophyll, biomass, number of capsules, plant height, number of branches per plant, 1000 seed weight, number of seeds, seed yield, oil yield, oil content and harvest index were significantly affected by the interaction of biochar × fungal inoculation. The results showed that the highest amount of total chlorophyll was associated with the application of 10 t ha-1 biochar + seed inoculation and biochar with fungi (3.3 mg per g fresh weight). The highest number of branches per plant was obtained in all treatments studied, except the treatments of no biochar + no inoculation with fungi (9.67 per plant) and no biochar + seed inoculation with fungi (10.33 per plant), which had the lowest number of branches per plant. The highest amount of total biomass was obtained in the 5 and 10 t ha-1 biochar treatments with no inoculation and seed inoculation and fungal inoculation. This research demonstrates that the combined application of biochar and mycorrhizal fungi can substantially improve black cumin productivity and oil quality in poor soils. The optimal combination (5 t ha-1 biochar with seed and biochar fungal inoculation) offers a promising strategy for sustainable cultivation of this valuable medicinal plant in organic agriculture systems.

Keywords: fatty acid, fertility, linolenic, oil content, soil amendment

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Authors: Golnaz Majdizadeh

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One of the problems that significantly affects the nutritional status of everyone in society, but particularly of children, is food insecurity. Since food insecurity can be a sign of nutritional, developmental, and health issues, it is also crucial to identify the factors that contribute to it in any community. In light of this, the purpose of this study was to ascertain how food security, socioeconomic status, and anthropometric indices relate to children in Hamedan age ages 2 to 5, from Hamedan city and villages (360 boys and 323 girls), were chosen for this analytical descriptive cross-sectional study using systematic cluster sampling techniques from the Hamedan University of Medical Sciences. Mothers of children were interviewed to complete a general questionnaire and a 9-item HFLAS questionnaire to look into food security. Based on WHO 2007 guidelines, the World Health Organization's (WHO) Anthro software was also used to measure the anthropometric Z scores of children. SPSS software eventually analyzed the data. This study found that the frequency of snacks, food security, and the mother's and father's occupation and education were significantly correlated with the weight of the children for their age (p <0.05). Additionally, a significant correlation (p <0.05) was found between the frequency of snacks and the children's weight for height and the jobs of the mother and father. On the other hand, maternal education and BMI for age were significantly correlated.

Keywords: food security, anthropometric incidence, Hamedan city, children

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Authors: Omidali Akbarpour, Samaneh Akbari, Amin Taheri-Garavand

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Accurate classification of corn seed varieties is crucial for maintaining genetic purity and optimizing crop yield. Traditional methods often face limitations in terms of efficiency, accuracy, and reliance on expert knowledge. This study proposes a deep learning-based approach leveraging Convolutional Neural Networks (CNNs) for the rapid and non-destructive classification of five corn seed cultivars (KSC201, KSC260, KSC400, KSC703, and KSC705). A total of 9,374 high-resolution seed images were collected and preprocessed under controlled conditions to ensure data consistency. The CNN model was trained using TensorFlow and Keras, incorporating data augmentation techniques such as rotation, zooming, and flipping to enhance generalization. The architecture included convolutional, ReLU activation, max pooling, dropout, and dense layers, achieving robust feature extraction and classification. The model demonstrated exceptional performance, reaching a test accuracy of 97.11%, with training and validation accuracies of 95.44% and 94.45%, respectively. Evaluation metrics including precision (96.74%), sensitivity (96.67%), specificity (99.18%), and F1-score (96.67%) further confirmed the model's reliability. Precision-recall curves yielded average precision scores above 0.96 for all classes, while ROC analysis showed near-perfect separability, with four cultivars achieving an AUC of 1.0 and KSC201 achieving 0.99. Confusion matrix analysis revealed high true positive rates across all classes, particularly for KSC260 and KSC703, which achieved 98% and 97% class-specific accuracy, respectively. These results surpass previous studies utilizing traditional machine learning or less advanced CNN architectures. The proposed system offers a scalable and automated solution for seed classification, significantly reducing manual labor and improving sorting accuracy. This work highlights the transformative potential of CNNs in agricultural applications, demonstrating their capability to handle subtle morphological differences among seed types. Future research will explore hyperparameter optimization, transfer learning, and integration into real-time seed sorting systems.

Keywords: agricultural automation, agricultural technology, convolutional neural network, deep learning, maize seed classification

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Authors: Farid Ahmed

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Land degradation, encompassing soil erosion and the formation of badlands, poses a significant threat to food security. The Chambal Division, characterized by its unique and fragile badlands ecosystem, has historically faced socio-economic challenges, including the problem of dacoits in the past. Decades of soil erosion, exacerbated by the Chambal River, have sculpted extensive ravine systems. Current efforts to flatten these ravines for agricultural conversion pose a serious threat to the region's ecological integrity. This study pioneers a remote sensing-based analysis of land degradation in the Chambal Division, utilizing multi-temporal satellite data to assess critical indicators, including land use/land cover change, ravine dynamics, vegetation health and productivity, soil quality, soil erosion, and desertification. Our research indicates that land levelling, coupled with persistent soil erosion and ravine formation, adversely affects food security in Chambal, particularly for small and landless farmers, underscoring the critical necessity for sustainable land management strategies that reconcile agricultural demands with ecosystem conservation.

Keywords: badlands, food security, land degradation, soil erosion

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Authors: Mohammad Hasounizadeh

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Side weirs are flow-controlling hydraulic structures widely used in irrigation and drainage networks, urban sewage collection systems, and flood management. These structures are constructed on the wall of the main channel and discharge excess water when the water surface level exceeds the crest elevation. The flow over these weirs is spatially varied and typically exhibits a decreasing discharge along the weir length. In the present study, the effect of flexible rubber barriers with varying heights and thicknesses, placed in different Froude numbers within the main channel, on the discharge coefficient and water surface profile of a sharp-crested rectangular side weir was investigated. The experimental variables in this research include the Froude number (discharge) and the thickness and height of the rubber barrier. The results indicated that the presence of a flexible rubber barrier in the main channel led to an average increase of 17.92% in the flow rate over the side weir, with a maximum increase of 60% observed for a barrier with a thickness-to-height ratio of 0.038 (13 cm height and 5 mm thickness). Furthermore, the water surface profile became more uniform, and the 5 mm thick flexible barrier demonstrated the best overall performance on average.

Keywords: side weir, flow profile, water surface, discharge, flexible rubber

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Authors: Vanke Ibrahim, Felix A. Modi, Abdulkadir S. Ahmed

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This study applies the Autoregressive Integrated Moving Average (ARIMA) modelling technique to forecast key water quality indicators of the River Benue in Jimeta, Yola, Nigeria. Utilizing a ten-year dataset (2011–2021) obtained from the Adamawa State Ministry of Water Resources, the research focused on three essential parameters: pH, calcium (mg/L), and iron (mg/L). Following the Box-Jenkins methodology, the data underwent stationarity testing using the Augmented Dickey-Fuller test, model identification via ACF/PACF analysis, parameter estimation, diagnostic checking, and forecasting. Results indicate that the ARIMA (0,0,1) model best fits the pH and iron data, while the ARIMA (1,0,0) model suits calcium. Forecasting results showed a stable average pH of 7.23, fluctuating calcium levels averaging around 61.85 mg/L, and a consistent iron concentration of approximately 0.1523 mg/L over the projected ten-year period (2023–2032). Diagnostic checks confirmed that all selected models were stable, stationary, and invertible, with no unit roots. These findings demonstrate ARIMA’s effectiveness in capturing temporal dynamics in water quality and provide a reliable foundation for proactive environmental monitoring, planning, and decision-making.

Keywords: modeling, river benue, water, forecasting, time series, concentration, stationarity test, management

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Authors: Omar Mohamed Mumin

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One of the world's oldest and most important oil crops, sesame (Sesamum indicum L.), is grown in hot areas and has a rich historical background. Sesame has extended from tropical African regions to Western Asia, China, and Japan. Over its extensive 5000-year agricultural history in Asia. Sesame is a highly valuable agricultural crop, renowned for its oil-rich seeds that contain 25–60% oil and 25% protein. Sesame, also known as ‘Sisin,’ is a significant cash crop in Somalia, contributing significantly to the economy. Despite ranking eighth globally in sesame seed exports, the country faces challenges like low productivity due to inadequate agricultural practices, biotic and abiotic stresses, and a lack of a robust breeding program. Also, Sesame cultivation in Somalia is vital for economic growth and job creation, but challenges like traditional farming methods, inadequate seeds, and fertilizer misuse hinder optimal productivity, particularly in the Shabelle areas. Generally, Sesame production in the world reached 6.74 million tons in 2022, a significant increase from its low of 1.7 million tons in 1976. Sudan, India, and Myanmar are the largest producers, accounting for over 40% of the total production. Other notable producers include Tanzania, China, Nigeria, Burkina Faso, Chad, Ethiopia, South Sudan, Brazil, and Pakistan. Also, Uganda, Mozambique, Niger, Cameroon, Mexico, Egypt, and Somalia contribute to global sesame production, though at lower levels. The remaining countries contribute less than 60% of the total production. In 2020, 6.8 million tons of sesame were produced in 14.8 million hectares, while in 2022, 6.74 million tons of sesame were produced in 12.8 million hectares. The total cultivated area for sesame decreased from 14.8 million hectares to 12.8 million hectares. The cultivated area for sesame in Somalia decreased from 110,000 hectares to 78,235 hectares between 1989 and 2022. Despite an increase in yield from 455 kg to 4,366 kg per hectare, the decrease in cultivation area caused a significant decrease in production. Sesame production decreased from 50,040 tons to 34,157.31 tons, resulting in a 68.3% decrease in production. The review examines sesame production in Somalia from 1989 to 2022, revealing a decline in cultivation area despite being the world's largest producer. Recommendations include improving breeding programs, introducing new varieties, and enhancing agricultural techniques to boost yield.

Keywords: agriculture, sesamum indicum L., production trends, export market, yield, Somalia

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Authors: Elsadig Omer, Dora Szlatenyi, Sándor Csenki, Ivan Czako, Vince Láng

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Sustainable soil management is essential to keep agricultural soils healthy. In recent years, the EU-supported project (Interreg) has led to the advancement of agroforestry, regenerative agriculture, agroecology, and precision agriculture. However, the distribution of best practices remains fragmented, limiting their regional or global influence. The goal of Soi4Nature is to understand the characteristics of soil and the many management techniques that influence soil health. Additionally, to support regenerative agriculture to improve the productivity and health of soils in the Pannonian region while also conserving the environment and local soils. Various farming approaches will be gathered, tested, and distributed to improve the soil and ecological functions of the agricultural land in the research region.The proposed research, Soil4Nature, aims to address interconnected soil and farming issues through a comprehensive approach. This research will collect, harmonize, analyze, and integrate data from real-world experiments. It will also facilitate collaboration among farmers, agricultural technology experts, and socio-economic research centers in Hungary and Slovakia to promote sustainable soil and farm management practices. In its final stage, Soil4Nature will develop methodologies for upscaling and disseminating its findings effectively. The study will culminate in creating an interactive digital farm dashboard, enabling visual and user-friendly communication of results to non-technical users.

Keywords: sustainable agriculture, reduced tillage, soil management, adoption practices, conservation agriculture

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Authors: Hattie Makumbe, Affoue, Sephora

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Magnesium (Mg) is a critical macronutrient in plant physiology, playing a central role in photosynthesis, enzyme activation, and energy metabolism. As the core atom in the chlorophyll molecule, magnesium directly influences chlorophyll biosynthesis and overall plant health. This study investigated the effects of varying Magnesium sulfate concentrations from very low (100 mg/L), low (150 mg/L), medium (200 mg/L), high (250 mg/L) concentrations, on chlorophyll accumulation in watercress. Watercress (Nasturtium officinale),is a fast-growing, nutrient-rich leafy vegetable from the Brassicaceae family. The main objective of the study was to determine the optimal Mg application level that maximizes the chlorophyll content without causing nutrient imbalances. The experiment was conducted under controlled environmental conditions using a split-plotdesign. Watercresswasgrown hydroponically using nutrient film techniques throughout key stages of vegetative growth. Chlorophyll content was assessed using Soil Plant Analysis Development (SPAD) meter readings and confirmed through HPLC and spectrophotometric analysis of chlorophyll extracts.Results revealed that all Mg treatments led to significant increases in chlorophyll content compared to the untreated control group. The most significantconcentration occurred at 200 mg/L and 250 mg/L concentrations, with 200 mg/L emerging as the most efficient level for promoting chlorophyll accumulation without physiological stress. Enhanced chlorophyll levels positively correlated with improved leaf coloration; deep green color and overall vigor, suggesting greater photosynthetic efficiency and biomass potential.This study demonstrates that increased magnesium fertilization can substantially improve chlorophyll concentration and physiological performance in watercress. These findings offer valuable guidance for growers seeking to optimize yield and quality of Magnesium-sulphate through targeted nutrient management. Future research should explore magnesium fertilization effects on biomass accumulation and interactions with other macronutrients.

Keywords: watercress, hydroponics, controlled environment agriculture, fertiliser

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Authors: El-Shazly Mohamed Hegazuy

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SF_RE was derived from its basic Darcian-Richard's. It was Darcian -Richard's, modified Richard's, stress from of Richad's, then silicon-like-soil water hydraulic capacitance of Richard's. HASPs were derived, nominated, and discussed. Each boundary condition was set according to DSWEM. The auther considered that total soil water energy modes are diagnostic because each of which needs a special managerial practice to touch the target function of better soil tilth. FORTRAN code and DD.SWEM algorithm were achieved. As the studied variables, HASP, are newly born from SF_RE. It is recommended to use the statistical modeling to correlate them each to other and to the water and nutrients' stresses reduction functions. HASP.α W_Up , HASP .α N_Up. ISSI_MOD, the conceptually based model, has led finally to a physical model that solar radiation supports the continuity of life on planet earth under the current changes of global climate. Silicon is made up the skeleton of PANGEA. This is the hidden reason stands behind the all positive never negative functions of the second abundant element in the earth's crust. A maternal like relationship stands between star sun and its planet earth. A code of optimality coming from star sun each sunshine is used by silicon in releasing the plants' abiotic stresses of global climatic extremes. Silicon acts as antenna receives the incoming electromagnetic radiation from the star sun, executes the code of optimality to let the life on planet earth continue, and does the good for the best for all the other agro-ecosystem components. The plants' bio signals, the soil geo-signal, and the canopy atmospheric signal all are under the control of the maternal relationship between solar radiation and the shadow of the skeleton of PANGAEA.

Keywords: soil stress index, soil water hydraulic capacitance, silicon-like- soil water hydraulic capacitance, root distribution, gaining value, HASP

Procedia PDF Downloads 24

Authors: Olawuyi O. J., Olayinka F. A., Ogundipe V. O., Azeez K. A., Hameed B. A., Azeez A. A.

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The growing global demand for sustainable food production in the face of climate change necessitates the identification of crop species that are both nutritionally valuable and resilient to environmental stresses. This study evaluated the drought response of ten Vigna radiata (L.) R. Wilczek accessions (TVr131, TVr110, TVr25, TVr80, TVr118, TVr161, TVr168, TVr87, TVr100, and TVr57) over a two-week period, with control plants maintained under regular irrigation. Significant genotypic variation (p < 0.05) was observed across vegetative and agronomic traits from seedling to maturity. Among the accessions, TVr168 exhibited superior performance in plant height and leaflet width, while TVr131 recorded the highest number of leaflets and showed strong yield performance under drought conditions. TVr87 had the greatest leaflet length, whereas TVr25 showed delayed germination and poor performance across all growth stages. Positive correlations were observed between leaflet number and other vegetative traits, such as leaflet length, leaflet width, plant height, stem diameter, and canopy spread, indicating coordinated vegetative responses under water-limited conditions. Correlation analysis of yield-related traits revealed strong positive associations between seed weight and seed colour (r = 0.98), pod colour at maturity (r = 0.63), and pod curvature (r = 0.48), suggesting that these traits contribute jointly to reproductive success under drought. Pod weight was positively correlated with pod colour at maturity (r = 0.68), whereas pod pubescence showed significant negative correlations with both pod curvature (r = –0.65) and pod weight (r = –0.79), highlighting potential trade-offs between protective structures and yield. These correlations provide useful targets for indirect selection in mungbean improvement programs. Principal Component Analysis (PCA) revealed that the first principal component accounted for the greatest variance in morphological (72.37%), agronomic (26.99%), and yield traits (31.13%). These findings underscore the potential of specific accessions, particularly TVr131 and TVr168 and associated traits for enhancing drought resilience and productivity in mungbean. Chromosomal analysis, conducted at the International Institute of Tropical Agriculture (IITA), supports ongoing efforts toward genetic characterization and breeding for climate-resilient legume crops.

Keywords: vigna radiata, drought stress, morphological traits, yield performance, genetic variability, mungbean accessions, genotype x environment interaction

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Authors: K. U. Thanisha, B. R. Siddharth, B. Saron

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In the context of climate change and increasing resource constraints, precision agriculture has become a crucial approach for enhancing crop yield and sustainability. This research presents a crop recommendation system that integrates real-time soil moisture data from solar-powered IoT sensors with environmental and climatic parameters to suggest optimal crops. The system employs a machine learning model that dynamically adapts to real-time field conditions, bridging the gap between static data-driven models and real-world variability. Leveraging renewable solar energy for powering sensors promotes environmental sustainability and supports off-grid agricultural applications. Experimental simulations and prototype implementation demonstrate improved prediction accuracy, ecological viability, and the potential for scalable deployment in rural settings.

Keywords: machine learning, random forest, sustainable agriculture, Iot

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Authors: Amina Belguendouz, Benamar Benmahioul

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This research evaluated the feasibility of regenerating whole potato plants in vitro from two cultivars, Desiree and Spunta, using culture media enriched with ash derived from plant waste. The results confirmed the successful induction of caulogenesis and rhizogenesis, highlighting the influence of the mineral composition of the ash-based media on organogenic responses. The meristematic tissues used as explants demonstrated high organogenetic potential, marking a crucial step in the micropropagation process aimed at producing healthy, uniform plantlets. Subsequently, microtuber induction was assessed on both hormone-free Murashige and Skoog (MS) medium and MS supplemented with naphthaleneacetic acid (NAA), under three photoperiod regimes (0, 8, and 16 hours of light). While hormone-free MS medium promoted superior shoot and root development, the addition of NAA enhanced microtuber formation in terms of both rate and duration. Photoperiod was a determining factor in microtuberization, with 8- and 16-hour light exposures yielding significantly greater tuber weights and diameters compared to cultures maintained in complete darkness. These findings underscore the potential of integrating agro-waste-derived components in sustainable in vitro propagation protocols for potato.

Keywords: varieties, ash, microtuberization, Solanum tuberosum L.

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Authors: Charles Harry Nthewa, Keston Njira, Joseph Chimungu, Patson Nalivata, Austin Phiri

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Soil degradation, nutrient depletion, and declining organic matter challenge smallholder farmers in semi-arid regions of Malawi. Legume-based intercropping and residue incorporation offer nature-based solutions to enhance soil fertility and climate resilience. This study assessed the effects of cropping systems and residue management on soil carbon storage and nutrient dynamics in cowpea (Vigna unguiculata) and pigeon pea (Cajanus cajan) intercropping systems under semi-arid conditions. A field experiment conducted at Chinguluwe and Lunzu EPAs tested five cropping systems: sole cowpea, sole pigeon pea, sole sorghum, cowpea–pigeon pea intercrop, and a residue-removed control. Treatments followed a random complete block design. Crop residues were analyzed for nitrogen (N), phosphorus (P), potassium (K), and organic carbon (OC), while soil samples from 0–20 cm and 20–40 cm depths were analyzed for soil organic carbon (SOC), ammonium (NH₄⁺), nitrate (NO₃⁻), total nitrogen (TN), and phosphorus (P). Intercropping significantly improved soil nitrogen through biological nitrogen fixation, enhanced SOC, and increased P availability, especially with residue incorporation. Cowpea residues, rich in nitrogen, decomposed rapidly, while pigeon pea contributed more biomass, supporting long-term SOC buildup. Residue incorporation increased NH₄⁺, NO₃⁻, TN, P, and SOC, with the highest benefits in intercropped plots. In contrast, residue removal, particularly in sole sorghum plots, led to nutrient decline. These findings recommend cowpea–pigeon pea intercropping with residue incorporation as a low-cost, sustainable approach to boost soil health and productivity, especially in resource-constrained, drought prone areas.

Keywords: cropping systems, intercropping, residue incorporation, soil organic carbon (SOC), nutrient dynamics

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Authors: Lidia Dzierzbicka-Głowacka, Dawid Dybowski, Maciej Janecki, Artur Nowicki, Jan Jadczyszyn, Tamara Jadczyszyn, Stefan Pietrzak, Aleksander Mach

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The aim of the EnviAgri project is to improve farm management, resulting in increased production efficiency and enhanced environmental quality through the reduction of nutrient losses and the limitation of carbon dioxide emissions into the atmosphere. An added value will be the improvement of agriculture’s public image, which is currently perceived as one of the main contributors to environmental degradation. The ongoing degradation of the natural environment and its impact on climate warming has been a cause for concern for many years. Agricultural activity is viewed as a significant source of pollution emissions. Therefore, the Common Agricultural Policy is directly or indirectly involved in the implementation of climate policy and actions aimed at protecting the natural environment. A significant portion of all funds within the CAP is linked to efforts aimed at mitigating the negative effects of agricultural production. Agriculture should not be an exception when it comes to ICT. Therefore, a cross-cutting objective of the Common Agricultural Policy is “Knowledge, innovation, and digitalization.” The innovative feature of the project is the EnviAgri Model, which integrates several functional modules—a tool that enables analyses and forecasting, and supports decision-making critical to the management of farms and agricultural areas where activity is currently conducted or could potentially be conducted. It allows for the assessment of the impact of such activities on components of the natural environment, such as air, soil, surface water, groundwater, and coastal waters. Through this, it enables the analysis and optimization of the organization and processes of agricultural and processing production on the farm. A key innovation of the project, is the development of EnviAgri Model’s computational modules as web applications for:  determining ammonia emissions at the farm level,  preparing a balance of soil organic matter in the farm,  estimating nitrate leaching from soils for individual fields on the farm under Poland's soil and climatic conditions,  estimating greenhouse gas emissions,  assessing the risk of phosphorus leaching in surface waters, and  assessing the risk of agricultural drought for specific crops in surface waters—based on real-time and forecasted values of the Climatic Water Balance and a soil drought susceptibility category map. Another innovation is the development and integration of the Interactive Meteo-Soil Map of Poland module with the computational module in such a way that it enables not only the analysis of past events and the generation of suggestions within the current production cycle, but also the ability to run simulations under userdefined hypothetical external conditions for the next production cycle. EnviAgri will provide information that serves as a reliable basis for decision-making throughout the entire agricultural cycle—from the earliest concepts, through planning and implementation, to operation. The publication is financed by the National Center for Research and Development (Poland) under the Gospostrateg IX strategic program (No. GOSPOSTRATEG9/000T/22/P).

Keywords: agriculture, environmental and climate protection, low-emission economy, numerical modeling, production efficiency

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Authors: Harshida A. Gamit, Natarajan Amaresan

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Climate change exacerbates environmental stressors, such as heat and ultraviolet (UV) radiation, which significantly impact crop growth and productivity. This study explores the role of UVB-resistant methylotrophic bacteria in alleviating the adverse effects of heat and UV radiation on mung bean (Vigna radiata L.), a critical legume crop, under both controlled and field conditions. A total of 55 methylotrophic isolates were obtained from desert plants, with 15 strains demonstrating resistance to UVB radiation for up to 4 hours. These strains, identified as Methylorubrum and Methylobacterium species, exhibited consistent plant growth-promoting (PGP) traits, including the production of IAA-like substances, siderophores, and ACC deaminase activity, even under UVB stress. Seed priming with these methylotrophs significantly improved seed germination, root and shoot elongation, and chlorophyll content, while enhancing antioxidant properties such as superoxide dismutase, peroxidase, and phenylalanine ammonia lyase activities. Notably, the methylotrophic treatment mitigated UVB-induced oxidative stress, reducing cellular damage and enhancing the physiological performance of mung bean plants. The community-level physiological profile (CLPP) analysis of treated plants confirmed increased microbial activity, further supporting the beneficial effects of the bacterial inoculation. In field trials, methylotrophs showed a remarkable improvement in mung bean growth, pod formation, and yield attributes, with increases in pod numbers (25.44–32.78) and yield (10.81–23.63 q/ha) compared to untreated controls under both UVB shielded and non-shielded conditions. The biochemical analysis of the methylotrophs revealed the presence of carotenoids, flavonoids, and other antioxidants, which are likely responsible for their UVB protection mechanisms. The adaptive morphological changes, such as biofilm formation and altered cell shape, were also observed in response to UVB exposure, highlighting the robustness of these bacteria under stress. This study underscores the potential of methylotrophic bacteria as sustainable biotechnological solutions to enhance crop resilience against UVB radiation and heat stress, promoting agricultural sustainability and food security in the face of climate change.

Keywords: climate change, UV radiation, plant-microbe interaction, sustainable agriculture

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Authors: Samarth Bista

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Rice (Oryza sativa) is a staple food crop for more than half of the world's population, and its cultivation is crucial for food security, particularly in Asia. Zinc (Zn) is a crucial micronutrient for optimal rice growth, yet its deficiency, particularly in nutrient-poor soils, poses a significant challenge to achieving high yields. A field experiment was conducted in Rautahat, Nepal, to assess the impact of different zinc fertilizer application methods on the growth and yield of spring rice (Oryza sativa L. var. Hardinath Hybrid-1) with the aim of identifying the most effective zinc application strategy for enhancing rice production. In this experiment, significant variation was observed with various methods of zinc application on the growth and yield of spring rice. The results showed that treatment with combined soil application and foliar spray produced the highest plant height (97.11 cm) and the greatest number of tillers per hill (19.58). Similar treatment had the longest panicle length (26.50 cm) while the control had the shortest panicle length (23.2750 cm). From the field experiment with different treatments, soil application and foliar spray recorded the highest thousand-grain weight (21.50 g), grain yield (6.41 ton/ha), and straw yield (15.53 ton/ha). Foliar spray also showed strong performance across most parameters, ranking second in terms of plant height, grain yield, and straw yield, highlighting its potential as a supplementary application method. The results suggest that a combination of soil application and foliar spray is the most effective zinc fertilization strategy for improving the growth and yield of spring rice, as it enhances nutrient use efficiency by ensuring both immediate uptake and sustained availability. This integrated approach offers a promising solution for addressing zinc deficiencies in rice fields, leading to better crop performance and increased food production.

Keywords: growth parameters, spring time, yield, zinc fertilizer application

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Authors: Rafael F. del Castillo, Sonia Trujillo-Argueta

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Insects are among the most successful organisms on Earth, and their presence in crop fields causes significant damage, accounting for approximately 18% of global agricultural losses. Chemical insecticides remain widely used for pest control due to their effectiveness. However, the increasing resistance of insects, coupled with health risks and environmental pollution of soils and watersheds, highlights the urgent need for alternative solutions. In the face of climate change, pollution mitigation, and growing food demands, a paradigm shift in agroecosystem management strategies is imperative. Studying the natural habitats where insect plagues originate offers valuable insights for designing sustainable pest control strategies. The natural and semi-natural ecosystems of the Central Valleys of Oaxaca, Mexico—dominated by dry forests and scrublands—harbor insect species that are significant crop pests in other regions. For example, The cicada Quesada gigas (Homoptera), known for causing severe damage to commercial plantations such as Schizolobium amazonicum (parica), citrus, and coffee, commonly inhabits native trees like the guaje (Leucaena esculenta), which produces edible pods. The grasshopper Sphenarium purpurascens, a pest of maize, beans, alfalfa, and squash crops, functions as a generalist folivore in these native ecosystems.The stink bug Nezara viridula, notorious for damaging soybeans and legumes, is occasionally found in its natural habitat. In their native ecosystems, these species do not exhibit pest behavior, likely due to the presence of natural predators such as birds, insectivorous insects, and spiders like Diguetia sp. Remarkably, in Oaxaca, Quesada and Sphenarium are traditionally consumed as alternative protein sources, and their collection may contribute to pest control. Preliminary findings suggest that agricultural practices leading to ecosystem simplification, particularly those that reduce predator populations, are key drivers of pest outbreaks. Conversely, the biodiversity of natural ecosystems and cultural practices, such as the consumption of insects as food, appear to play vital roles in pest regulation and merit further research.

Keywords: Quesada gigas, Sphenarium purpurascens, Nezara viridula, agricultural pests, native ecosystems, edible insects

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Authors: Minas Ververis, Emanuel Lekakis, Panagiotis Kekelis, Aphrodite Theofilidou, Vassilis Aschonitis

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The increasing pressure on Mediterranean agricultural systems from climate change, water scarcity, and the need for sustainable resource management has prompted a shift toward digital and climate-smart solutions. Emerging crops like stevia (Stevia rebaudiana), a plant cultivated for its natural sweeteners, present an opportunity for sustainable intensification due to its low chemical input requirements and growing market demand. However, its agronomic performance is highly dependent on targeted irrigation and fertilization management. Current practices often rely on generic recommendations, which can lead to yield instability, reduced glycoside content, and increased susceptibility to fungal diseases. For this cause a crop-specific decision support system was specifically designed to optimize irrigation and nutrient application in stevia cultivation. NITREOS Stevia is tailored to the physiological characteristics and agronomic needs of stevia, integrating Sentinel-2 satellite imagery, numerical weather predictions, and crop growth models to estimate crop water and nutrient requirements in real time. Through remote sensing-based monitoring of vegetation indices, water balance modeling, and fertilization simulations, NITREOS Stevia delivers daily, field-specific recommendations for input management. Importantly, the system eliminates the need for continuous in-field sensors or manual sampling, making it accessible and scalable for medium-sized farming operations. The system was tested in six pilot fields, cultivated with stevia, in the region of Fthiotida, Central Greece, over two growing seasons. Each field was divided into two sections: one managed using conventional practices and the other guided by NITREOS Stevia recommendations. Agronomic and environmental performance indicators were measured, including plant biomass, glycoside concentration, input use efficiency, disease incidence, and overall yield. In addition, environmental parameters such as soil moisture and nutrient loss were monitored through laboratory analysis of plant tissues. Results from the operational use of NITREOS were promising. Fields managed under the NITREOS guided practices showed improved water and nitrogen use efficiency, slightly higher leaf biomass, and good steviol glycoside content. Yield data collected from NITREOS-managed plots indicated a consistency and even slightly increase in leaf biomass compared to conventionally managed counterparts, while also demonstrating a reduction in irrigation water use, more than 10% in some cases. Fertilizer application was optimized through the integration of satellite-derived vegetation indices and field-calibrated nutrient models, leading to more efficient nitrogen uptake. The system’s spatial recommendations, based on management zones defined by Sentinel-2 imagery, enabled variable-rate fertilization adapted to intra-field variability. Seasonal analysis of plant tissue and soil data further supported the accuracy of model-based recommendations and confirmed improved nutrient use efficiency. These results affirm the potential of NITREOS as a technically robust and agronomically relevant tool for improving stevia crop management under Mediterranean conditions.

Keywords: precision agriculture, stevia cultivation, irrigation management, remote sensing

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Authors: Emanuel Lekakis, Minas Ververis, Vassilis Aschonitis

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Rice cultivation in Greece is concentrated in lowland areas of Central Macedonia, including the Axios Delta and other floodplain ecosystems protected under Ramsar and NATURA 2000 frameworks. These regions face growing environmental pressures due to nitrogen leaching and surface runoff associated with conventional fertilization in flooded paddy systems. The continuous submergence of rice fields and the frequent renewal of irrigation water result in significant nitrogen losses through leaching, volatilization, and surface discharge. These processes reduce nitrogen use efficiency and contribute to eutrophication and degradation of sensitive aquatic ecosystems. The NITREOS Aquatic was developed to address this challenge by providing a digital decision support tool tailored to the specific requirements of nitrogen management in rice paddies. The platform integrates multi-temporal Sentinel-2 satellite imagery, numerical weather prediction models, and field-based sampling to generate zone-specific fertilizer recommendations. These are delivered via a user interface that allows producers to visualize spatial variability within fields and apply site-specific fertilization using variable rate technology (VRT). The system was tested in four pilot rice fields in the region of Chalastra, Thessaloniki, over two cultivation cycles. Two fields were managed conventionally and served as controls, while two others were managed with NITREOS-generated prescriptions. The same cultivation practices were applied across all fields except for fertilization. Satellite imagery from three prior growing seasons was used to define management zones, which were then matched to targeted N-P-K application rates. Soil and plant tissue sampling was conducted to calibrate and validate nutrient uptake and output models. In-season monitoring was complemented by end-of-season yield estimation and environmental assessment, including nitrogen balance analysis and footprint evaluation. The NITREOS-guided fields demonstrated reductions in total fertilizer use and improved spatial accuracy of nutrient application, leading to better nitrogen uptake and lower nutrient losses. Preliminary data suggest a reduction in nitrogen runoff and an increase in yield stability compared to conventional fields. NITREOS Aquatic fills a critical gap in the application of targeted agriculture to paddy systems, where water management complicates nutrient delivery. By minimizing environmental risks and increasing input efficiency, the platform supports both agronomic performance and compliance with environmental directives. Its successful deployment under real-world conditions demonstrates its potential for broader use in similar agro-ecological zones and contributes to the ongoing digital transition of sustainable rice farming in Europe. Acknowledgment: This work was implemented by the Operational Group NITREOS AQUATIC (Μ16SΥΝ2-00027) and co-funded by the European Union and Greece under the RDP 2014-2020.

Keywords: precision agriculture, rice cultivation, nitrogen leaching, variable rate fertilization, remote sensing

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Authors: Emanuel Lekakis, Minas Ververis, Panagiotis Kekelis, Aphrodite Theofilidou, Vassilis Aschonitis

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Cotton is a strategically important crop for Greek agriculture, accounting for over 80% of the total EU production and supporting tens of thousands of producers and workers across the value chain. Despite its economic value, fertilization practices in cotton farming remain largely empirical and unstandardized, leading to inefficient input use, increased production costs, and severe environmental degradation. To address this issue, a scalable, cost-effective digital farming tool was developed for optimizing nitrogen, phosphorus, and potassium fertilization as well as irrigation in cotton cultivation. The system leverages satellite remote sensing, meteorological modeling, and crop-specific algorithms to support farmers with site-specific input recommendations. NITREOS Cotton was piloted in four cotton fields in the municipality of Chalkidona, Central Macedonia, over two full growing seasons. Each pair of adjacent fields included one control plot managed under conventional practices and one experimental plot where fertilization and irrigation followed NITREOS Cotton recommendations. Sentinel-2 satellite data from a three-year time series was used to define within-field management zones. These zones served as the basis for spatially variable fertilization maps, calculated via the NITREOS user interface based on satellite-derived vegetation indices and nutrient demand modeling. Additional calibration was conducted through soil sampling and analysis. The NITREOS managed plots demonstrated reduced variability in plant growth, improved fertilizer use efficiency, and consistent yields. A reduction of 10% to 15% in input use was observed in fertilization and irrigation, depending on field conditions and zone-specific requirements. The system also supported the generation of accurate yield estimates and better planning of harvest logistics. Environmental benefits were observed in the form of lower nitrate leaching risk and a reduced carbon and water footprint. As a digital farming tool developed specifically for cotton in Greece, NITREOS Cotton demonstrates that high-precision input management can be achieved using satellite data and digital advisory systems without requiring hardware-intensive infrastructure. Its successful deployment under operational conditions confirms its potential for wider adoption across cotton-producing regions, promoting sustainable intensification and supporting the long-term resilience of Greek cotton production.

Keywords: digital farming, cotton, variable rate application, satellite imagery

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Authors: Panagiotis Kekelis, Aphrodite Theofilidou, Vassilis Aschonitis

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Sewage sludge is the byproduct of wastewater treatment plants, and its production in the European Union (EU) exceeds 10 million metric tons (dry weight). Conventional disposal methods, such as incineration and landfilling, pose environmental and economic challenges (e.g., greenhouse gas emissions and contamination). Consequently, the agricultural reuse of sewage sludge has gained attention as a sustainable alternative due to its high dispersion in large areas, leveraging its nutrient-rich composition (e.g., nitrogen, phosphorus, and organic matter) to enhance soil fertility and carbon sequestration. However, its safe application requires stringent regulation to mitigate risks from contaminants like heavy metals and pathogens, underscoring the control of its use based on the latest existing regulatory framework to balance agricultural benefits with environmental protection. In the context of the EU’s ambitious climate targets, such as carbon neutrality by 2050 under the European Green Deal, sewage sludge recycling represents a pragmatic strategy to align waste circularity with carbon farming initiatives. This study aims to present an integrated approach for controlling the amount of sewage sludge dose in agricultural fields in Greece, considering the physical-chemical properties of soil and sewage sludge and the maximum permissible limits that have been set by the respective Greek regulatory framework (Υ.Α. ΥΠΕΝ/ΔΔΑ/41828/630/2023 - ΦΕΚ 2692/Β` 21.4.2023). The approach is based on an algorithm that performs a two-step procedure: (a) an evaluation of measured contaminants' concentrations (e.g., heavy metals, pathogens, organic pollutants) in the sewage sludge based on their maximum permissible limits and (b) a computational procedure of the mixing process of soil and sewage sludge for a specific depth of incorporation. The procedure estimates the dose of sewage sludge and the final soil organic carbon content of the mixture, considering its maximum permissible threshold by optimizing the ratio of soil organic carbon vs. total nitrogen (C/N) based on a target optimum value (e.g., C/N=10-12). The reason for selecting the C/N ratio as a basic target parameter is that it regulates the balance between organic matter stability/mineralization and nutrient release for plant growth without excessive loss through leaching and volatilization or nitrogen immobilization by soil microbes. The algorithm was developed in EXCEL software, and the optimization is performed using the tool ‘SOLVER’ based on the SIMPLEX method. The algorithm was developed and used in the context of the ΙΛΥΣ (ILYS) project in operational agricultural fields in northern Greece, considering the variation of soil properties per field based on zones that were delineated using remote sensing techniques. This work was implemented by the Operational Group ΙΛΥΣ (ILYS) (Μ16SΥΝ2-00258) and co-funded by the European Union and Greece under the RDP 2014-2020.

Keywords: carbon to nitrogen ratio, dose optimization algorithm, sewage sludge, soil amendment, soil organic carbon

Procedia PDF Downloads 22

Authors: Sushmita Kalika-Singh

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Cercospora beticola, the causal agent of Cercospora leaf spot (CLS), is the most economically damaging foliar pathogen of sugarbeet (Beta vulgaris), capable of reducing yields by up to 40% in heavily infested fields. In the United States, CLS management primarily relies on fungicide applications, but the continued use of site-specific chemistries, particularly demethylation inhibitors (DMIs), has led to reduced sensitivity and resistance development in field populations. Monitoring the sensitivity of C. beticola to commonly used fungicides is essential to inform sustainable disease management strategies and preserve fungicide efficacy. This study assessed the in vitro sensitivity of 40 C. beticola isolates to five fungicide active ingredients: prothioconazole (41%, in Proline®), copper hydroxide (37.5%, in Champ® WG), mancozeb (37%, in Manzate® Max), and triphenyltin hydroxide (80%, in Supertin®). Stock solutions were prepared based on the percentage of active ingredient and amended into CV8 agar medium across a range of concentrations (0.01–800 µg/mL). Under sterile conditions, 5 mm agar plugs were excised from 14-day-old cultures and transferred to fungicide-amended plates. Plates were incubated at 22 ± 2°C in the dark, and mycelial growth was measured after 14 days using two perpendicular diameter readings per colony. The study followed a randomized complete block design and was conducted in three experiments based on the expected effective concentration (EC₅₀) range for each fungicide. In the initial experiment, mancozeb and triphenyltin hydroxide were tested at concentrations of 0.01, 0.1, 1, and 10 µg/mL. EC₅₀ values were successfully calculated for both fungicides and found to be 8 µg/mL for mancozeb (Manzate® Max) and 2 µg/mL for triphenyltin hydroxide (Supertin®). For copper hydroxide and prothioconazole EC₅₀ values could not be calculated using lower concentration ranges, prompting a second and third round of assays. Concentrations of 25, 50, 100, 200, 400, and 800 µg/mL were used to reassess sensitivity to these fungicides. The resulting EC₅₀ values were 70 µg/mL for copper hydroxide (Champ® WG), 218 µg/mL for Headline®, and 496 µg/mL for prothioconazole (Proline®), indicating markedly lower sensitivity among the C. beticola isolates. These findings demonstrate that C. beticola isolates remain sensitive to triphenyltin hydroxide and mancozeb, which yielded low EC₅₀ values, whereas significantly higher EC₅₀ values were observed for copper hydroxide and prothioconazole. These results suggest decreased sensitivity or emerging resistance to certain fungicides widely used in sugarbeet production. Given the critical role of fungicides in CLS management, these findings underscore the need for rotating chemistries with different modes of action and adopting integrated disease management strategies to slow resistance development. Overall, this study provides important baseline sensitivity data for key fungicides used against C. beticola and contributes to ongoing efforts to guide fungicide resistance monitoring and improve disease control strategies in sugarbeet production systems.

Keywords: cercsopora beticola, sensitivity, fungicides, sugar beet

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Authors: Ouradi Linda, Iddir Mohamed El Amine, Trik Hartani, Mounia Baha

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The aim of this study was to investigate the possibility of predicting Alollobophora chlorotica (earthworm) density through apparent electrical conductivity (ECa) on cultivated soils. Soils were sampled at 30 locations within the study area. The EM was measured using a mobile electromagnetic induction (EM38) sensor. The relationship between A.chlorotica density and ECa was analyzed with linear regression and geostatistical analysis. Further research is needed to fully evaluate the potential of ECa measurements for predicting A.chlorotica density in tilled soil. The results also show a strong correlation between ECa, and A.chlorotica density (R² > 0.89). The Wilcoxon test shows a non-significant difference between measured CaCO₃ and those predicted by ECa (p > 0.05). This result confirms the ECa technique as a useful tool to evaluate the spatial variability of soil earthworm density. All of these results suggest that the ECa represents a real possibility for reducing the number of soil samplings, by guaranteeing the reliability of the estimates of real values of earthworm density and their surface spatial distribution. ECa could therefore be a useful tool to spatialize and predict the distribution of earthworm density in soil cultivated.

Keywords: apparent electrical conductivity, earthworm, Alollobophora chlorotica, Algeria

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Authors: Ebby Soita, Laura Brohm, Michal Segoli, Carmi Korine, Noam Weiss, Jessica Schäckermann

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The overdependence on chemical pesticides in modern agriculture has resulted in significant environmental challenges, including the degradation of ecosystems, loss of biodiversity, and the development of pest resistance. These negative impacts have increasingly highlighted the urgent need for more sustainable and ecologically sound alternatives to traditional pest management practices. This is especially critical in hyper-arid regions where agricultural ecosystems are already vulnerable to environmental stressors. In this study, we explored the potential of using desert wildflowers to support biological pest control within date plantations in the Southern Arava region of Israel. Our objective was to assess whether certain desert-adapted wildflower species could attract beneficial insects that are natural enemies of agricultural pests such as the lesser date moth, thus contributing both to pest management and biodiversity conservation. We established flower strips composed of twenty desert wildflower species adjacent to date plantations. Flower phenology was monitored to determine the timing and duration of blooming events for each species. Insect visitation was assessed through systematic suction sampling and visual observations conducted at regular intervals throughout the flowering period. Beneficial insects, including parasitoid wasps, ladybugs, hoverflies, and bees, were identified and recorded to quantify their abundance and distribution across different wildflower species. Our findings demonstrated that Erucaria micocarpa exhibited the earliest blooming among the planted species and attracted the highest proportion of beneficial insects, accounting for 49% of the total insect visitors observed. Diplotaxis acris was the second most attractive species, contributing 24% of beneficial insect visitation, followed by Rumex cyprius at 19%. Bees, both solitary and social species, dominated the visitation patterns across all flower strips, indicating their strong preference for the desert wildflowers planted. Other groups, including ladybugs, hoverflies, and parasitoid wasps, were also frequently observed. These results underscore the selective attractiveness of specific desert wildflower species to beneficial insects and highlight the role that floral resource availability plays in shaping insect community dynamics in agricultural landscapes. This study provides strong evidence that integrating native desert wildflowers into agricultural systems can enhance the presence of natural enemies of pests, offering a promising and ecologically sustainable alternative to chemical pesticide use in dryland farming. Moreover, the promotion of plant and insect biodiversity through such practices supports broader conservation goals and contributes to the ecological resilience of farming systems. Future research will expand on these findings by investigating the seasonal stability of insect visitation patterns, analyzing the floral volatile compounds responsible for insect attraction, and assessing the reproductive success and effectiveness of natural enemies in pest suppression. These insights will help guide the development of practical recommendations for farmers and policymakers aiming to implement sustainable, biodiversity-friendly agricultural practices in arid and semi-arid environments.

Keywords: agricultural biodiversity, biological pest control, desert wildflowers, sustainable farming

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Authors: Ogah Bliss Idoko, Nakajima Mitsutoshi, Marcos Antonio Das Veves

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In response to the unsafe use of synthetic pesticides, a stable, sustained-release neem-based biopesticide was formulated using extracts from neem (Azadirachta indica) kernels and leaves. Oil, defatted kernel extract (rich in limonoids), and crude leaf extract were combined into an oil-in-water nanoemulsion. Defatting was done via ultrasonication in hexane, followed by methanol extraction. Dual-solvent methods were assessed for optimal yield of azadirachtin. The quality and content of kernel and leaf extracts were validated through HPLC and GC-MS. High-shear and high-pressure homogenization produced a stable nanoemulsion with a mean droplet diameter of 165 nm, which remained nearly constant for over one month. Stability was confirmed using reverse HPLC, laser droplet size analyzer, and a vibro-viscometer. The formulation showed complete lethality against maize weevils (Sitophilus zeamais) within 12 h. Additionally, the formulation reduced azadirachtin photodegradation under daylight, suggesting a protective effect. This observation supports the development of a new method for accurately analyzing bioactive compounds in crude leaf extracts.

Keywords: biopesticide, crop protection, nanoemulsion, IPM, food safety, food security

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