The growing global demand for food production places immense pressure on agricultural systems to produce more with fewer resources. Traditional farming practices often rely heavily on chemical fertilizers and pesticides, leading to soil erosion, water resource pollution, and a decline in crop quality. Exploring the plant rhizosphere, which hosts beneficial microorganisms such as rhizobacteria commonly name as plant growth-promoting rhizobacteria (PGPR) presents a sustainable alternative. These microorganisms enhance plant growth and yield by stimulating the production of plant hormones, promoting biofertilization, and providing biocontrol against pathogens. This reduces the need for chemical fertilizers and pesticides. Furthermore, their use supports soil health, minimizes environmental pollution, and preserves soil biodiversity, reducing public health risks associated with chemical residues in food and the environment.

The use of PGPR in Morocco and the Mediterranean region offers a sustainable solution to address challenges related to drought, soil salinity, and nutrient deficiencies, which are prevalent in this region. Native PGPR strains, particularly those with drought and salt tolerance, can enhance plant resilience by improving water use efficiency, nutrient uptake, and stress adaptation mechanisms. Their application in key crops such as wheat, barley, legumes, olives, and medicinal plants aligns with efforts to promote climate-resilient and low-input agriculture.

The aim of this study is to highlight the potential of the PGPR as an innovative solution to enhance the sustainability of agricultural systems while ensuring ecosystem health and food security.

The root systems of edificatory plant species (environment-forming plants) play a key role in land restoration. In arid ecosystems subjected to abiotic and biotic stresses, revegetation strategies use drought-tolerant, native shrubs to restore degraded semi-desert steppe ecosystems and promote their long-term ecological functionality.

These shrubs, including species from the genera Zygophyllum and Nitraria, exhibit distinct root adaptations, such as deep taproots for groundwater access and extensive lateral roots for capturing surface moisture and mitigating soil compaction and nutrient deficiencies.

Understanding the formation of root systems in shrubs from the Zygophyllaceae families, particularly in the semi-desert landscape, is also helpful for informing proper land restoration efforts in arid zones. The growth rate and depth of soil penetration of these shrubs are main factors in the effective restoration of degraded landscapes.

We aimed to investigate the root system formation of theZygophyllum xanthoxylon (Bunge) Maxim. and Nitraria sibirica Pall. shrubs in order to determine their ecological role within the semi-desert landscape zone of Mongolia (Eastern Gobi, 2023–2025), focusing on plants at 1 and 3 years of age. This study demonstrates that Nitraria sibirica and Zygophyllum xanthoxylon exhibit remarkably rapid growth and development of their root systems.

During the first and second year of growth, the root systems of these shrubs penetrated the soil to depths of 100–120 cm, surpassing the aboveground shoot height by a factor of 1.5–2. By the third year, this ratio increased by 3–4 times, indicating significant vertical growth. The ability to rapidly grow and develop a specialized root structure ensures consistent water uptake under drought and moisture-deficient conditions. Furthermore, the depth of root system penetration is strongly influenced by the hydro-physical properties of the edaphic environment.

The olive tree (Olea europaea L.) is one of the most widespread and cultivated trees in the Mediterranean region. The crop faces many challenges in the context of climate change, particularly frequent droughts due to inter-annual variability in the rainfall. Such a situation leads to the emergence of more arid areas, raising questions about the sustainability of the olive crop in the Mediterranean region. Therefore, a deeper knowledge of the mechanisms of drought resilience is crucial for predicting the response of trees to climate change. Objectives: The analysis of the functional and structural leaf-based traits of olive trees can be an effective strategy to (i) classify the current cultivars based on their adaptability to future climatic challenges and (ii) identify the most relevant and easily measurable traits for use in breeding programmes. Methods: A total of 80 olive cultivars were selected from the World Olive Germplasm Bank of Meknes (INRA, Morocco); each cultivar was represented by two trees, and six leaves were taken from each tree. Twelve traits were measured from each leaf, and these twelve traits were divided into three groups: (1) morphological traits, represented by the leaf area (LA), leaf length (LL), leaf width (LW) and leaf shape (LL/LW); (2) ecophysiological traits such as the stomatal density (DS), trichome density (DT) and leaf blade thickness (LT); and (3) structural traits, represented by the dry and fresh leaf weights (DM and FM), leaf mass per area (LMA), specific leaf area (SLA) and leaf dry matter content (LDMAC). The results of an ANOVA and the determination of the coefficient of variation showed that high phenotypic variability was observed for all the traits studied. In addition, the ANOVA showed that all the measured traits can be used for early detection of stress-tolerant varieties, especially the leaf length (LL), leaf shape (LL/LW), leaf thickness (LT) and leaf dry and fresh weights (DM and FM). In addition, investigation of leaves' morphological and structural traits can be used as a simple and rapid way to classify varieties with respect to their response to drought. Furthermore, the European varieties, such as Cobrancosa, Koroneiki, Arbequina and others, were the most stress-tolerant varieties. Conclusions: There was significant variation in the leaves' functional traits among the olive cultivars, with the variability depending on the type of trait and cultivar origin. This suggests that specific traits can be used to identify drought-tolerant cultivars. All the traits can be used in breeding programmes for variety selection, but the leaf shape, leaf width, leaf thickness and dry and fresh weights are the traits that show the most phenotypic variability between varieties. In future, phenotypic plasticity studies could be extended to other local and introduced varieties for comparison with those covered in this study.

The integration of digital technology with agriculture is unlocking a new era of intelligent farm management. The Digital Twin (DT) framework in agriculture has evolved as a major transformation by providing a real-time, dynamic, optimised, and predictive approach between physical agroecosystems and their digital counterparts. The DTs are applied to address the challenges of modern agriculture, where in-situ monitoring, optimisation, and adaptability are critical factors for enhancing sustainability. Therefore, this study explores the design, application, and evolution of DT frameworks, which are developed and customised for agricultural system optimisation. A structured hierarchical design is proposed to integrate agricultural systems with IoT-enabled sensing, AI-driven analytics, and predictive models. This approach allows real-time monitoring, forecasting, and autonomous control across a wide range of agricultural processes. Three case studies from crucial domains like yield forecasting, autonomous machinery coordination, and predictive disease management are considered. The significance of DTs is demonstrated by analysing resource efficiency, environmental impact, and decision-making adaptability on the farm. Finally, challenges for implementing DTs in agriculture, like heterogeneous data sources, model fidelity, computational overhead, and barriers to adoption among smallholder farmers, are explored, and the mitigation strategies using advanced AI frameworks are discussed. The implementation of DTs can become core infrastructure for smart agriculture by enhancing its scalability, interoperability, and adaptability. This study evaluates a fundamental and practical outline by bridging the physical and virtual barriers to make resilient, sustainable, and optimized agricultural operations in real time.

Recently, a lot of attention has been paid to exploring Artificial Intelligence (AI) for analyzing audio and vocal data, offering a wide range of capabilities in precision livestock farming including poultry behavior monitoring. Animal behaviors provide significant insights into the mental and physical well-being of poultry, serving as an important indicator of their health and subjective states. With the world’s population projected to reach 9.5 billion by 2050 and the demand for animal products like eggs, meat, and milk expected to increase by 70% from 2005 levels, it becomes vital to develop automated, precise systems for monitoring poultry behaviors. This achievement is especially important for managing poultry health and welfare efficiently, overcoming the constraints of manual behavioral observations, which are time-consuming. Automated AI-based systems are thus increasingly becoming crucial for monitoring and promoting good welfare within the growing livestock industry.

In this paper, we aim to develop a simple and efficient AI audio-based approach to recognize chickens’ key behaviors such as eating, greeting, foraging, hunting, and tidbitting to improve poultry farming. First, the proposed study performs cepstral and entropy analyses on the chickens’ vocalizations to extract new vocal features. Second, a simple deep unsupervised clustering method is proposed to recognize the behaviors of the chickens. Alternations in recognized behaviors can be indicators of lameness in chickens. Here, we used an open access chicken language dataset consisting of a total of 74 distinct chicken calls with their probable meanings as based on careful observations. Promising results are obtained by the proposed scheme for chicken behavior monitoring, enabling poultry personnel to accurately determine the health and well-being of chickens.

The COVID-19 pandemic, along with other factors such as climate change, limited agricultural resources, energy crises, population growth, and urbanization, has significantly threatened global food security. In response to these challenges, the development of sustainable technologies for growing nutritious crops in controlled environments has emerged as an important strategy for urban agriculture. This study was conducted at the Plant Tissue Culture Laboratory of the Philippine Rootcrops Research and Training Center (PRCRTC), located at the Visayas State University Main Campus, Baybay City, Leyte. The study was carried out to (1) assess the impact of different growing substrates on the growth and quality characteristics of lettuce microgreens and (2) identify the most suitable growing substrate that would generate the highest productivity and nutritional content in lettuce microgreens. The experimental design employed was a Complete Randomized Design (CRD), consisting of five treatments: T₁—soil; T₂—a Coco Fiber Mat; T₃—a jute fiber mat; T₄—Rockwool; and T₅—vermiculite.

This study highlights the advantages of using a soil substrate for growing lettuce microgreens. This substrate provided favorable conditions with regard to its nutrient availability, water retention, and organic matter content, resulting in greater shoot development and a higher dry weight, leaf size, and total yield. However, plants grown with vermiculite and the jute fiber mat demonstrated higher chlorophyll content, suggesting these treatments' potential for promoting enhanced photosynthetic capabilities.

Introduction:
Livestock plays a vital role in Pakistan’s agricultural sector, yet recurrent outbreaks of foot-and-mouth disease (FMD), particularly caused by FMD virus (FMDV) serotype O, result in significant economic losses. Existing inactivated and live attenuated vaccines have failed to provide sufficient protection against these outbreaks. This study aimed to generate a cost-effective, biosafe plant-based vaccine targeting FMDV serotype O antigens.

Methods:
A synthetic gene construct encoding the P1–2A polyprotein and 3C^pro protease of FMDV serotype O was engineered. To enhance immunogenicity, the cholera toxin B subunit (CTB) was genetically fused at the N-terminus of P1–2A via a flexible glycine–serine linker. The bar gene, under the CaMV 35S promoter, served as a selectable marker. The construct was introduced into young tobacco (Nicotiana tabacum) leaves (4–6 weeks old) via biolistic transformation, and transgenic lines were selected on medium containing 1.0 mg/L phosphinothricin (PPT). Wild-type (non-transformed) plants and vector-only transformants were maintained as negative controls.

Results:
Multiple putative transgenic plants were regenerated and confirmed by PCR using P1–2A, 3C^pro, and bar-specific primers. These lines were acclimatized and progressed to seed set. Segregation analysis and germination assays on medium with up to 3.0 mg/L PPT confirmed stable inheritance of the transgene in the T1 generation. Surviving seedlings consistently tested PCR-positive, validating successful transmission and stable integration of the vaccine construct. Controls (wild-type and vector-only) confirmed that the observed PCR amplifications were construct-specific.

Conclusion:
This work demonstrates the generation of genetically stable tobacco plants expressing key immunogenic proteins of FMDV serotype O. The results provide a foundation for subsequent immunological evaluation and highlight the potential of plant-based expression systems in developing cost-effective veterinary vaccines in Pakistan.

Salinity stress is a major constraint to crop productivity in arid and semi-arid regions, highlighting the need for innovative, data-driven methods to evaluate genotype performance under such conditions. This study, conducted at the Sahline experimental station (Tunisia) in collaboration with the Italian agtech firm aSpace, assesses the salinity tolerance among onion (Allium cepa L.) genotypes cultivated in two field plots—one irrigated with saline water and the other with non-saline water. The methodology integrates AI-predicted soil parameters (organic matter, electrical conductivity, pH, and N-P-K contents) with multi-spectral, multi-temporal satellite imagery (Sentinel-2, PROBA-V, Landsat 8, and MODIS) collected from March to June 2025. Key vegetation and salinity indices—including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Bare Soil Index (BSI), and the Normalized Difference Salinity Index (NDSI)—were computed and cluster analysis was performed to map healthy vegetation patches within the trial. Preliminary results at the plot level reveal clear physiological differences due to salinity. NDVI values were consistently lower in the saline plot, starting as early as March (0.0971 vs. 0.2296 in the non-saline plot) and averaging 0.0984 versus 0.2308 across the entire monitoring period. The NDSI, a salinity-specific index, remained consistently higher in the saline plot (mean: 0.1184 vs. 0.0885), confirming persistent salt stress and aligning with the observed spectral vegetation decline. In parallel, the BSI — which reflects bare soil exposure and indirectly indicates poor canopy development — peaked in April in both plots, reaching 0.2266 in the non-saline and 0.2141 in the saline plot. The slightly higher BSI in the saline plot may reflect areas where salinity stress prevented full canopy development. Interestingly, the MSAVI and EVI were slightly higher in the saline plot across months, possibly due to surface reflectance effects or early-stage physiological responses under stress. These trends were consistent over the three-month monitoring period and are dynamically visualized through an ArcGIS web map interface. aSpace’s AI platform enabled rapid, field-scale estimation of soil properties, overcoming the limitations of traditional sampling and providing scalable, high-resolution coverage. The current results demonstrate the value of integrating AI and remote sensing for rapid, non-destructive phenotyping of salinity response. This integrated approach offers a replicable and scalable framework to support smarter, faster, and more precise crop selection strategies and can be extended to assess salinity resilience and responses to other abiotic stresses in marginal environments.

To cope with the new regulations on pastoralism in Benin, herders shifted from mobile livestock herding towards a more sedentary lifestyle. However, sedentary livestock keeping may lead to severe challenges if feeding and animal health, as well as environmental health, are poorly managed. To provide appropriate recommendations for the sustainability of this land-use system, this study assessed the environmental impact of sedentary cattle farms by estimating their greenhouse gas emissions using the Global Livestock Environmental Assessment Model-interactive (GLEAM-i, Online version). Therefore, three sedentary cattle farm types, namely sedentary zebu (SZF), taurine (STF) and crossbreed (SCF), were selected in two vegetation zones (Sudanian in the North (SZ) and Guineo-Congolian (GCZ) in the South of Benin). Irrespective of the farm type, the animals were exclusively fed on natural pasture. A total of 12 cattle herds were surveyed to collect input data (herd structure, demographic parameters, milk production and composition, and weight data) for the GLEAM-i. The fat and protein content of the milk (determined using a milkotester device), live weight and weight at slaughter of animals were entered into GLEAM-i, which automatically determines the emission intensity values per kg of protein produced. The results revealed that CH₄ was the main GHG emitted (88%) followed by CO₂ (6-7%) and N₂O (6%). The highest and lowest total GHG emissions (kgCO₂-eq/year) were recorded in SZF (188,497) and STF (52,003) farms, respectively. With regard to emissions intensity (kgCO₂-eq/kg Protein), emissions varied from 506.59 to 3043.73 for meat and from 588.86 to 3043.73 for milk. Overall, preliminary trends suggest lower intensities for taurine in the GCZ and for zebu in the SZ. However, these results would be more meaningful with larger studies with production conditions, zone effects, and controlled allocation. These would allow for drawing firm recommendations for breeding strategies to reduce GHG emissions in Benin.

Within the UN's Sustainable Development Goal strategy for 2030, the valorisation of by-products and the reduction of inputs to produce high-biovalue food are priorities. Dairy goats contribute significantly to the sustainable conversion of fibrous biomass into high-quality food products for human consumption. At the same time, feed accounts for the highest expenditure on livestock farms. Therefore, incorporating agro-industrial by-products such as white grape pomace into ruminant diets could reduce feeding costs while improving the circular economy. This study evaluated the effects of incorporating white grape pomace silage (WGPS) at 15% of dietary dry matter in the diet of lactating dairy goats on milk yield and composition. The experiment was conducted over an eight-month lactation period and included an economic assessment. Two isoenergetic and isoproteic diets (Control and WGPS) were formulated and randomly assigned to 40 Murciano-Granadina goats, distributed into four groups (2 diets × 2 replicates) balanced by productive performance. At six-week intervals, batch-level samples were collected to assess dry matter (DM) and water intake, while individual measurements were taken for body weight, milk yield, milk composition, and somatic cell count. Variables were analysed using a mixed linear model (PROC GLIMMIX, SAS v9.4), accounting for the effect of the pre-experimental covariate, which proved significant in all cases except for feed efficiency. Goats fed the WGPS diet showed slightly lower DM and water intake and reduced milk urea concentration, without impacting milk yield or body weight. The economic analysis revealed that WGPS inclusion led to an additional margin of EUR 16 per goat over the eight months compared to the control group. It is concluded that white grape pomace silage is a viable forage option for inclusion in dairy goat diets.