Welcome to MELSpace
DSpace is a digital service that collects, preserves, and distributes digital material. Repositories are important tools for preserving an organization's legacy; they facilitate digital preservation and scholarly communication.
Recent Submissions
- Effects of Replacing Concentrate Feed with Carob (Ceratonia siliqua) Pods on Growth Performance, Carcass Characteristics, Meat Quality, and Rumen Fermentation in Assaf LambsAuthor(s): Ghzayel, Soha; Kholif, Ahmed; Díaz-Reyes, Alexey; Abu Aziz, Bassam; Zoabi, Halimeh; Ben Rhouma, Raouia; Hassan, Sawsan; López Puente, Secundino; Kholif, Adel; Parrini, Silvia; Confessore, Andrea; Ammar, Hajer (MDPI, 2026-08-10)Date: 2026-08-10Type: Journal ArticleStatus: Open accessThis study examined the effects of replacing 25% (P25) or 50% (P50) of concentrate dry matter (DM) with sun-dried carob (Ceratonia siliqua L.) pods on growth performance, apparent nutrient digestibility, carcass traits, meat quality, serum biochemistry, and rumen microbiology in growing Assaf lambs. Twenty-four weaned male Assaf lambs (initial body weight [BW] 27.0 ± 0.5 kg; 2.5 months of age) were randomly assigned to three dietary treatments (n = 8 per group) in a completely randomized design and fed for 16 weeks. P50 achieved the highest ANCOVA-adjusted least squares mean final BW (53.0 kg) and average daily gain (ADG) (220.8 g/d), followed by P25 (51.1 kg; 203.3 g/d) and the control (46.2 kg; 160.2 g/d) (p < 0.001). Feed conversion ratio (FCR) improved from 8.99 in the control to 6.16 and 6.11 in P25 and P50, respectively, with no significant difference between the two carob-supplemented groups (p < 0.001). Apparent DM and organic matter (OM) digestibility increased with carob inclusion at both 3 and 6 months of age (p ≤ 0.0001). Cold carcass weight (CCW) was higher in carob-supplemented lambs (p < 0.001), whereas carcass muscle proportion did not differ among treatments (p = 0.688), and carcass fat proportion was higher in P25 than in the control (p = 0.039). Warner–Bratzler shear force declined progressively with carob inclusion (p < 0.001), indicating improved meat tenderness. Serum total protein was highest in P25, whereas blood urea nitrogen (BUN), low-density lipoprotein (LDL), and glutamate oxaloacetate transaminase (GOT) decreased with carob inclusion (p < 0.001). Rumen pH was highest in P25 (6.40), total bacterial and lactic acid bacteria (LAB) counts increased, and protozoa counts declined (p < 0.001). Because carob pods replaced concentrate, rather than being added to an isonitrogenous diet, the combined effects of pods per se, reduced crude protein supply, and altered energy density must all be considered when interpreting the results. These findings support carob pods as a practical, locally available partial substitute for concentrate feed in Assaf lamb production under Mediterranean and Near Eastern conditions.
- Scaling agroforestry land suitability analysis in Odisha, India: a machine learning approachAuthor(s): Singh, Rajkumar; Gakhar, Shalini; Das, Pulakesh; Prakash, A.Jaya; Behera, Mukunda; Biradar, Chandrashekhar; Mudi, Sujoy; Kolluru, Venkatesh; Dogra, Atul; Agrawal, Shiv Kumar; Dhyani, Shiv Kumar (Frontiers, 2026-06-01)Date: 2026-05-31Type: Journal ArticleStatus: Open accessAgroforestry practices are one of the major pillars of Natural Resources Management (NRM), offering substantial benefits by improving environmental conditions, socio-economy, soil health, biodiversity, and climate resilience. Despite multi-dimensional benefits, robust data for regional planning and effective implementation are lacking, particularly in combined with existing land management practices. Further, analyses are often conducted using a set of reference datasets without validating the scalability. This study aimed to address this gap by leveraging a pre-trained machine learning (ML)-based Multi-Criteria Evaluation (MCE) model to assess agroforestry land suitability in Odisha, India, and validating the outcome with reference data from an independent region. Multi-temporal Sentinel-2 data were used to generate Land Use Land Cover (LULC) and cropping intensity maps, where Random Forest (RF) achieved above 94% classification accuracy, outperforming Support Vector Machine (SVM; above 93%). In comparison to traditional expert-defined weights, RF model-derived variable importance was integrated with fuzzy-MCE approach for agroforestry site suitability analysis. A diverse data array, such as topography, soil parameters, climate conditions, and socioeconomic factors, were employed, wherein the proximity variables contributed ∼70% of total weight. Independent validation using field data from another region with similar agricultural practices and socio-economic characteristics showed high mean suitability (>0.87; range 0.71–0.95). Moreover, the generated Receiver Operating Characteristic (ROC) analysis indicated an Area Under the Curve (AUC) of 0.89, exhibits strong model performance and high capability to site suitable agroforestry sites. Intervention-specific analysis indicated that >94% of double- and single-cropped lands, ∼96% of settlement areas, and >90% of permanent fallow and wastelands were found highly suitable for (i) bund and boundary plantations and intercropping, (ii) home gardens, and (iii) block/bulk plantation agroforestry practices, respectively. Further, we deployed the site suitability layer through a web platform. The developed WebGIS portal enables open data access, spatial querying and intervention planning, providing a practical decision-support tool for state-level agroforestry planning and implementation.
- Rhizosphere Microbiome Engineering for Climate-Smart Agriculture: From Synthetic Consortia to Precision Decision SupportAuthor(s): Mahmoud, Nourhan Fouad; Elzayat, Emad M.; Amr, Dina; A. El-Khishin, Dina; Radwan, Khaled Hashem; Youssef, Alaa; Khalaf, Abeer A.; Ahmed, Hoda; Radwan, Eman H.; Tawkaz, Sawsan; Baum, Michael (MDPI, 2026-05-17)Date: 2026-05-17Type: Journal ArticleStatus: Open accessRhizosphere microbiome engineering is a promising approach that can enhance crop resilience and input use efficiency by redirecting plant–microbe–soil interactions toward predictable functions. Here, we review the mechanistic bases underlying rhizosphere assembly and stability, including root exudate-mediated selection, priority effects, keystone taxa, and metabolite-driven signaling, and connect these principles to proposed design rules for microbial inoculants. We present a generalizable Design–Build–Test–Learn (DBTL) framework for engineering synthetic microbial consortia, covering trait-to-module mapping (nutrient acquisition, phytohormone modulation, ACC deaminase activity, stress-protective metabolites, and biocontrol), compatibility screening, minimal yet robust community architectures, and iterative optimization driven by multi-omics and high-throughput phenotyping. Translation to field settings is framed as an engineering challenge defined by formulation and administration limitations, including carrier type, seed coating and encapsulation methods, shelf life, strain invasiveness, and permanence of colonization amid environmental diversity. We also summarize how integrative measurement pipelines (amplicon and shotgun sequencing, transcriptomics, metabolomics, and network or causal analyses) can advance microbiome studies from correlation to actionability. We describe how precision agriculture (sensors, remote sensing, and variable-rate inputs) and AI/ML (split-sample comparisons, transfer learning, and active learning) approaches can accelerate strain discovery, mixture optimization, and adaptive experimentation, driven by the need for stringent controls, metadata-rich reporting, and cross-site comparability. Use cases focus on stress conditions (drought, salinity, thermal extremes, and biotic stress) to demonstrate how microbial functions translate to agronomic outcomes and to highlight critical bottlenecks for reproducible, scalable microbiome products.
- Virulence spectrum of Moroccan Zymoseptoria tritici isolates and efficacy of Septoria tritici blotch resistance genesAuthor(s): Louriki, Sara; Bouhouch, Yassine; Al-Jaboobi, Muamar; Amri, Ahmed; Douira, Allal; Rehman, Sajid (NRC Research Press (Canadian Science Publishing))Date: 2026-05-28Type: Journal ArticleStatus: Timeless limited accessSeptoria tritici blotch (STB) caused by Zymoseptoria tritici is a major biotic stress in temperate wheat-growing regions of Morocco with yield losses reaching up to 40% under favourable conditions. Owing to the development of fungicide resistance, the use of resistant cultivars seems to be the best option for managing this disease. Therefore, knowledge of the pathogen population structure is indispensable for deploying an efficient and effective resistance breeding approach to combat STB. In this study, the response of 33 bread wheat genotypes (representing 16 resistance genes and QTL) was evaluated against 21 Z. tritici isolates under controlled conditions. Among 693 genotype-isolate interactions, 163 isolate-specific resistances (24%) were observed by comparing the means of disease severity data using LSD0.01 and LSD0.05. In addition to resistant cultivar Murga (G25), two cultivars, Cs synthetic (G4) and KK 4500 (G9), exhibited resistance response to almost all isolates with mean disease severity of 4% and 14–15 isolate-specific resistances. This suggests that these genotypes can be utilized in STB resistance breeding programs for Morocco and durable resistance can be achieved by pyramiding identified R genes against Moroccan Z. tritici isolates.
- Diagnosis of yellow rust disease resistance in bread wheat (Triticum aestivum L.) using SSR markersAuthor(s): Shokirova, D. Sh.; Turakulov, Kh. S.; Kholliyev, O. E.; Fayzullaev, Abdulla; Sultonova, D. F.; Ochilov, B. O.; Hudoyberdieva, M. O.; Islomova, SH. A. (SOC ADVANCEMENT BREEDING RESEARCHES ASIA & OCEANIA, 2026-06-01)Date: 2026-06-01Type: Journal ArticleStatus: Open accessYellow rust (Puccinia striiformis f. sp. tritici) is the primary disease affecting wheat (Triticum aestivum L.) crop productivity worldwide. The latest study aimed to evaluate the 68 wheat cultivars and advanced lines for resistance to yellow rust under field conditions using both morphological assessment and SSR molecular markers. The results revealed eight wheat genotypes (Yr15/6 Avocet S, Xisorak, Yr5/6 Avocet S, Triticum spelta, Yr10/6* Avocet S, Yr SP/6* Avocet S, Spaldings Prolific, and Andijon-2**) were distinctly highly resistant. The two SSR markers Barc008 and Gwm140 proved to be the most reliable for detecting resistance, whereas four other markers (Gwm340, Gwm111, Xgwm131, and Gwm251) showed variable results across the genotypes. The integration of morphological and molecular data highlighted the genetic diversity in yellow rust resistance and demonstrated their potential to efficiently screen the wheat germplasm. The results provide a valuable base for selection of resistant parental lines and employment of marker-assisted selection (MAS) in wheat breeding programs aimed at improving the resistance to yellow rust disease.