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This study examines how strategic marketing practices influence the performance of small and medium enterprises (SMEs) in Niger State, Nigeria, with a focus on the moderating role of talent management…

This study examines how strategic marketing practices influence the performance of small and medium enterprises (SMEs) in Niger State, Nigeria, with a focus on the moderating role of talent management. SMEs in Nigeria operate in highly competitive and resource-constrained environments, making the integration of effective marketing strategies and human capital management essential for sustained performance. Guided by Strategic Choice Theory and the Resource-Based View, the study investigates the effects of market orientation, innovation orientation, and digital marketing on SME performance while assessing whether talent management enhances these relationships. A descriptive survey design was employed, targeting 793 SMEs registered with the Niger State Board of Internal Revenue, with a sample of 260 selected using the Krejcie and Morgan technique, resulting in 253 valid responses collected through simple random sampling. Reliability of the measurement instrument was confirmed using the Kuder–Richardson Formula 20, with coefficients exceeding 0.70. Data were analysed using descriptive statistics and logistic regression, incorporating interaction terms to capture the moderating influence of talent management. Results reveal that market orientation, innovation orientation, and digital marketing positively and significantly affect SME performance, with innovation orientation showing the strongest impact. Talent management contributes directly to performance and significantly strengthens the effects of strategic marketing practices. The study concludes that the success of SME marketing strategies depends heavily on effective talent management and recommends structured recruitment, targeted training, performance-based incentives, and retention strategies to ensure marketing initiatives translate into measurable performance outcomes. These findings provide empirical evidence on how talent management moderates the effectiveness of strategic marketing practices, offering actionable insights for SME owners, managers, and policymakers seeking to enhance firm performance in developing economies.

What's Changed This release has improved the runtime substantially compared to the previous version (v1.2.2). Added --continue flag to resume interrupted runs from the last completed stage. Vectorise…

What's Changed This release has improved the runtime substantially compared to the previous version (v1.2.2). Added --continue flag to resume interrupted runs from the last completed stage. Vectorised key composition and coverage scoring steps with NumPy/SciPy. Parallelised expensive contig assignment and label propagation steps. Optimised graph-distance queries and BFS traversal. Improved memory efficiency with NumPy-backed feature storage and safer tetramer caching. Fixed "too many open files" errors when writing many FASTA bins by limiting simultaneous open bin files. Refactored the runner into a staged pipeline for easier maintenance and future optimisation. Fix issues with megahit inputs by @xvazquezc in https://github.com/metagentools/MetaCoAG/pull/55 New Contributors @xvazquezc made their first contribution in https://github.com/metagentools/MetaCoAG/pull/55 Full Changelog: https://github.com/metagentools/MetaCoAG/compare/v1.2.2...v1.3.0 Please report any issues and suggestions under MetaCoAG Issues.

These are audio recordings taken by an Eclipse Soundscapes (ES) Data Collector during the week of the April 08, 2024 Total Solar Eclipse.  It was decided to include only raw, unprocessed audio da…

These are audio recordings taken by an Eclipse Soundscapes (ES) Data Collector during the week of the April 08, 2024 Total Solar Eclipse.  It was decided to include only raw, unprocessed audio data files in each site-specific Zenodo record. This decision was so that any researcher can independently verify, reproduce, and extend the analysis performed. As a result, some sites have WAV files with 0 bytes of data or timestamps outside the range of probable recording times. Procedures used by the Eclipse Soundscapes team to process audio data for its purposes are outlined in the Data Management reports located in the Eclipse Soundscapes Zenodo community. Data with 0 bytes of data were included for completeness. When possible, all site-specific files, including the audio files, are included in a zip file for ease of download. If a zip file upload was not possible due to upload or bandwidth limitations, all available audio files are included individually. Data Site location information: Latitude: 39.71116  Longitude:  -83.85999 Local Eclipse Type: Total Solar Eclipse Eclipse Percent (%): 100 WAV files Time & Date Settings: Set with Automated AudioMoth Time Chime (More information on TimeStamp Setting below) Data Collector Start Time Notes: N/A Included Data: Audio files in WAV format with the date and time in UTC within the file name: YYYYMMDD_HHMMSS meaning YearMonthDay_HourMinuteSecondFor example, 20240411_141600.WAV means that this audio file starts on April 11, 2024 at 14:16:00 Coordinated Universal Time (UTC) CONFIG Text file: Includes AudioMoth device setting information, such as sample rate in Hertz (Hz), gain, firmware, etc.  README.md:  Markdown formatted file with information about the recording and recording site. file_list.csv:  A machine and human file that gives the following information on each file in the record: File Name, File Type, Description, File Size in kilobytes, Name of Associated Data Dictionary with the file, calculated SHA-512 Hash of the file as a unique identifier to insure data integrity during transfer and compression. total_eclipse_data.csv:  A machine and human readable file that gives the following information about the site where the audio data recording was taken: ESID#, Latitude, Longitude, Eclipse_type, CoveragePercent, Eclipse Start UTC (1st contact), Totality Start UTC (2nd contact), Totality End UTC (3rd Contact), Eclipse End UTC (4th Contact), Max Eclipse Time UTC License.txt:  A human readable file that explains the terms and conditions under which the data can be used. AudioMoth_Operation_Manual.pdf:  A human readable document that explains the use of an AudioMoth device. The document is current up to the time of the AudioMoth's use in the Eclipse Soundscapes project. file_list_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the file_list.csv file. CONFIG_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the CONFIG.TXT file. eclipse_data_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the total_eclipse_data.csv file. WAV_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the *.WAV files. ES_Data_Management_Pre-Eclipse_Data_Infrastructure_Stage_0.pdf:  PDF document that describes Stage 0 (Pre-Eclipse Infrastructure and Data Stewardship Planning) of the Eclipse Soundscapes (ES) data lifecycle. ES_Data_Management_Receipt_Sorting_and_Metadata_Organization_Stage_1.pdf:  PDF document that describes Stage 1 (Receipt, Sorting, and Metadata Organization) of the Eclipse Soundscapes (ES) data lifecycle. ES_Data_Management_Data_Processing_Stage_2.pdf:  PDF document that describes Stage 2 (Data Processing) of the Eclipse Soundscapes (ES) data Volunteer Scientists. 2023 and 2024 solar eclipse soundscapes audio datalifecycle. ES_Data_Management_Data_Sharing_Stage_3.pdf:  PDF document that describes Stage 3 (Public Data Sharing) of the Eclipse Soundscapes (ES) data lifecycle. Eclipse Information for this location: Eclipse Date: April 08, 2024 Eclipse Start Time (UTC) (1st Contact): 17:53:52 Totality Start Time (UTC) (2nd Contact): [N/A if partial eclipse] 19:10:32 Eclipse Maximum Time [when the most possible amount of the Sun in blocked] (UTC): 19:11:11 Totality End Time (UTC) (3rd Contact): [N/A if partial eclipse] 19:11:50 Eclipse End Time (UTC) (4th Contact): [N/A if partial eclipse] 20:25:52 Audio Data Collection During Eclipse Week ES Data Collectors used AudioMoth devices to record audio data, known as soundscapes, over a 5-day period during the eclipse week: 2 days before the eclipse, the day of the eclipse, and 2 days after. The complete raw audio data collected by the Data Collector at the location mentioned above is provided here. This data may or may not cover the entire requested timeframe due to factors such as availability, technical issues, or other unforeseen circumstances.  ES ID# Information: Each AudioMoth recording device was assigned a unique Eclipse Soundscapes Identification Number (ES ID#). This identifier connects the audio data, submitted via a MicroSD card, with the latitude and longitude information provided by the data collector through an online form. The ES team used the ES ID# to link the audio data with its corresponding location information and then uploaded this raw audio data and location details to Zenodo. This process ensures the anonymity of the ES Data Collectors while allowing them to easily search for and access their audio data on Zenodo.  TimeStamp Information: The ES team and the Data Collectors took care to set the date and time on the AudioMoth recording devices using an AudioMoth time chime before deployment, ensuring that the recordings would have an automatic timestamp. However, participants also manually noted the date and start time as a backup in case the time chime setup failed. The notes above indicate whether the WAV audio files for this site were timestamped manually or with the automated AudioMoth time chime.   Common Timestamp Error: Some AudioMoth devices experienced a malfunction where the timestamp on audio files reverted to a date in 1970 or before, even after initially recording correctly. Despite this issue, the affected data was still included in this ES site's collected raw audio dataset. Latitude & Longitude Information: The latitude and longitude for each site was taken manually by data collectors and submitted to the ES team, either via a web form or on paper. It is shared in Decimal Degrees format. General Project Information: The Eclipse Soundscapes Project is a NASA Volunteer Science project funded by NASA Science Activation that is studying how eclipses affect life on Earth during the October 14, 2023 annular solar eclipse and the April 8, 2024 total solar eclipse. Eclipse Soundscapes revisits an eclipse study from almost 100 years ago that showed that animals and insects are affected by solar eclipses! Like this study from 100 years ago, ES asked for the public's help. ES uses modern technology to continue to study how solar eclipses affect life on Earth!  Eclipse Soundscapes is an enterprise of ARISA Lab, LLC and is supported by NASA award No. 80NSSC21M0008. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Aeronautics and Space Administration.   Eclipse map/figure/table/predictions courtesy of Fred Espenak, NASA/Goddard Space Flight Center, from eclipse.gsfc.nasa.gov.   Eclipse Data Version Definitions {1st digit = year, 2nd digit = Eclipse type (1=Total Solar Eclipse, 9=Annular Solar Eclipse, 0=Partial Solar Eclipse), 3rd digit is unused and in place for future use} 2023.9.0 = Week of October 14, 2023 Annular Eclipse Audio Data, Path of Annularity (Annular Eclipse) 2023.0.0 = Week of October 14, 2023 Annular Eclipse Audio Data, OFF the Path of Annularity (Partial Eclipse) 2024.1.0 = Week of April 8, 2024 Total Solar Eclipse Audio Data, Path of Totality (Total Solar Eclipse) 2024.0.0 =  Week of April 8, 2024 Total Solar Eclipse Audio Data , OFF the Path of Totality (Partial Solar Eclipse) *Please note that this dataset's version number is listed below. Eclipse Soundscapes Data Collector Role Training and Implementation Resources Manual (2023-2024) (Archival Copy) This site-level record includes the Eclipse Soundscapes Data Collector Role Training and Implementation Resources Manual (2023-2024). The manual documents the participant training, device setup procedures, metadata submission requirements, ES ID system, timestamp protocols, data return workflow, and public archiving processes used during the October 14, 2023 annular solar eclipse and the April 8, 2024 total solar eclipse. The manual is preserved for transparency and reproducibility and reflects the procedures under which this dataset was collected and processed. (DOI 10.5281/zenodo.18623442) Data Receipt, Processing, and Analysis Methods All programs supporting Stages 1–3 are openly available in the: Eclipse Soundscapes GitHub repository: https://github.com/ARISA-Lab-LLC/ESCSP Data Management Lifecycle The following section documents the relationship of this record to the full Eclipse Soundscapes (ES) data lifecycle, a multi-stage workflow designed to support large-scale participatory science, long-term data stewardship, open science, and scientific reuse. Each stage addressed a different operational need, beginning before eclipse deployment and continuing through validation, public archiving, and scientific analysis. Together, these stages transformed distributed volunteer-submitted audio recordings into structured, documented, publicly accessible NASA-funded research assets. Stage 0: Pre-Eclipse Infrastructure and Deployment Preparation Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Pre-Eclipse Infrastructure and Deployment Preparation (Stage 0). Zenodo. https://doi.org/10.5281/zenodo.20413370 Stage 0 focused on building the operational foundation required to support geographically distributed eclipse data collection at national scale. This stage included AudioMoth device preparation, accessibility modifications, ES ID # assignment systems, metadata collection workflows, participant training materials, deployment logistics, and planning for downstream data stewardship and archival workflows. The 2023 annular eclipse served as both a scientific investigation and a large-scale operational beta test that informed improvements for the 2024 total solar eclipse campaign. Related Citations and Resources: Severino, M., & Kline, T. (2025, November 24). Eclipse Soundscapes Apprentice Role Curriculum: Solar Eclipses and Multi-Sensory Observing (Informal Education). Zenodo. https://doi.org/10.5281/zenodo.17703003 Severino, M., & Bauer, D. J. (2026). Eclipse Soundscapes Observer Role Training and Resources Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18633602</li> Severino, M., Winter, H., & Bauer, D. J. (2026). Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18623443 Stage 1: Receipt, Sorting, and Metadata Organization Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Receipt, Sorting, and Metadata Organization (Stage 1). Zenodo. https://doi.org/10.5281/zenodo.19471425 Stage 1 transformed returned participant materials into organized, traceable site-level records. This included receiving mailed microSD cards, consolidating participant-submitted metadata, reconciling handwritten and online records, organizing physical audio media by ES ID #, and deriving eclipse timing and coverage information using NASA eclipse prediction datasets. The outputs of Stage 1 established the structured metadata relationships required for downstream validation, processing, archiving, and analysis workflows. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). EPTT (Eclipse Phase Timing Tool) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-Eclipse-Phase-Timing-Tool/li> Espenak, F. (n.d.). Eclipse predictions by Fred Espenak, NASA's GSFC Eclipse Web Site. NASA Goddard Space Flight Center. http://eclipse.gsfc.nasa.gov/eclipse.html Stage 2: Data Processing and Validation Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Data Processing (Stage 2). Zenodo. https://doi.org/10.5281/zenodo.18683402 Stage 2 focused on centralized audio ingestion, validation, timestamp verification, metadata reconciliation, and preparation of datasets for analysis and public sharing. During this stage, returned audio recordings were processed using custom open-source tools developed by the ES team, including ES WAVES and ES AMES. The project implemented scalable infrastructure capable of processing large volumes of participant-submitted microSD cards while preserving all raw audio data without modification. Stage 2 established the validated dataset structure required for long-term preservation and scientific analysis. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). ES WAVES (Eclipse Soundscapes WAV Audio Validation & Extraction Suite) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-ES-WAV-Audio-Validation-Extraction-Suite Winter, H., & Goncalves, J. (2026). ES AMES (Eclipse Soundscapes AudioMoth Metadata Extractor Suite) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-ES-AMES-AudioMoth-Metadata-Extractor-Suite Stage 3: Public Data Sharing and Open Archiving Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Public Audio Data Sharing (Stage 3). Zenodo.https://doi.org/10.5281/zenodo.18683437 Stage 3 transformed validated site-level datasets into publicly archived, DOI-assigned research records published through the Eclipse Soundscapes Zenodo Community. This stage included dataset packaging, metadata standardization, README generation, integrity verification, DOI assignment, and automated repository upload workflows using the Automated Zenodo Upload Software (AZUS). These workflows established the project's long-term open-science infrastructure and ensured that datasets remained findable, accessible, interoperable, reusable, and citable for future scientific and educational use. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). AZUS (Automated Zenodo Upload Software) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/AZUS-Automated-Zenodo-Upload-Software Stage 4: Scientific Analysis and Research Use Stage 4 involves the scientific analysis and interpretation of validated eclipse soundscape datasets. Analysis workflows utilized datasets verified during earlier stages to investigate eclipse-related environmental and animal vocalization changes across hundreds of recording sites. This stage also includes broader scientific interpretation, publication development, and continued reuse of Eclipse Soundscapes datasets and infrastructure for future research, education, and open-science applications. Related Citations and Resources: Pease, B., Gilbert, N., & Severino, M. (2026). Eclipse Soundscapes Preliminary Findings – How Eclipses Affect Nature as determined by Sound (Recorded Webinar). Zenodo.https://doi.org/10.5281/zenodo.18613979 Gilbert, N. A., Pease, B. S., Severino, M., & Winter, H. III. (2026). Photic niche explains avian behavioral responses to solar eclipses. Ecology and Evolution, 16(2), e73090. https://doi.org/10.1002/ece3.73090 Analysis code repository: https://github.com/BrentPease1/eclipse-traits Companion Zenodo record archiving structured analysis scripts and derived outputs: https://doi.org/10.5281/zenodo.15790879[r] Public Archiving, Privacy, and Data Transparency The Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023-2024) includes a detailed explanation of how Eclipse Soundscapes audio data are publicly archived on Zenodo, how participant privacy is protected through the ES ID system, and how transparency and traceability are maintained. It also outlines the criteria for determining which recordings are included in the public archive, as well as the distinction between publicly shared archival data and datasets used for ES-led scientific analyses. Participants and data users can consult this section for full documentation of the project's open science and privacy practices. Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18623443 Citations Individual Site Citation: APA Citation (7th edition) Winter, H., Severino, M., & Volunteer Scientist. (2026). 2024 solar eclipse soundscapes audio data [Audio dataset, ES ID# 347]. Zenodo.{Insert DOI} Collected by volunteer scientists as part of the Eclipse Soundscapes Project. This project is supported by NASA award No. 80NSSC21M0008. Eclipse Community Citation Winter, H., Severino, M., & Volunteer Scientists. 2023 and 2024 solar eclipse soundscapes audio data [Collection of audio datasets]. Eclipse Soundscapes Community, Zenodo. https://zenodo.org/communities/eclipsesoundscapes/ Collected by volunteer scientists as part of the Eclipse Soundscapes Project This project is supported by NASA award No. 80NSSC21M0008.  

This deliverable provides a comprehensive assessment of relative and agricultural drought risks in the Autonomous Province of Vojvodina (APV), Serbia, as part of Phase 1 of the CLIMACHANGE project. Ap…

This deliverable provides a comprehensive assessment of relative and agricultural drought risks in the Autonomous Province of Vojvodina (APV), Serbia, as part of Phase 1 of the CLIMACHANGE project. Applying the standardized and harmonized CLIMAAX Framework, this report identifies key drought hazards, assesses regional vulnerabilities and exposures, and outlines preliminary conclusions. It sets a robust foundation for informed policymaking, targeted adaptation planning, and stakeholder engagement to enhance climate resilience and sustainability in Vojvodina.

Behavioral accounting has emerged as a crucial field in understanding financial decision-making by integrating cognitive psychology with traditional accounting principles. This study examines how beha…

Behavioral accounting has emerged as a crucial field in understanding financial decision-making by integrating cognitive psychology with traditional accounting principles. This study examines how behavioral accounting influences corporate strategies, with a particular focus on mitigating cognitive biases, improving investment decisions, and enhancing financial transparency. The research employs a systematic literature review and statistical correlation analysis of secondary data from 2020 to 2024. Key findings reveal a strong correlation (r = 0.997) between behavioral accounting adoption and improvements in financial decision-making, with a regression analysis indicating that every 1% increase in behavioral accounting adoption leads to a 0.20% improvement in decision efficiency (β = 0.197, R² = 0.986, p < 0.001). Companies that implemented behavioral insights experienced an 8% improvement in decision-making, a 3.5% increase in return on investment (ROI), and a 6% reduction in financial miscalculations. The study concludes that behavioral accounting significantly enhances corporate governance, risk assessment, and financial forecasting, reinforcing its importance in modern financial strategies. Recommendations include integrating behavioral analytics into corporate finance tools, increasing managerial awareness of cognitive biases, and aligning behavioral accounting practices with regulatory frameworks. These findings contribute to advancing behavioral finance theory and its practical applications in corporate decision-making. Keywords: Behavioral accounting, financial decision-making, corporate strategy, cognitive biases, investment efficiency.

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Every response a system makes to a demand consumes something: money, staff recovery, coordination, trust, institutional memory. Systems are rarely destroyed by the demands they cannot meet. They are d…

Every response a system makes to a demand consumes something: money, staff recovery, coordination, trust, institutional memory. Systems are rarely destroyed by the demands they cannot meet. They are destroyed by the accumulated cost of the responses through which they met earlier ones, and that cost stays hidden in the data most systems collect, because output is the last thing to change. An earlier paper in this series developed the supply–demand dialectic, a general relational theory of adaptive change. It specified what each available response consumes. It did not specify what is restored, or how quickly. An account with consumption but no restoration can establish that a pattern of response is unsustainable, but never that one is sustainable. This paper supplies the restoration side. Adaptive reserves are capacities deliberately withheld from present demand. Regeneration is the return of what has been consumed, specified through four independent parameters: how fast a capacity returns, after what delay, to what maximum level, and under what conditions. Adaptive debt is the shortfall that accumulates when a capacity is consumed faster than it regenerates — a present benefit carrying a future obligation, at a rate, with a point beyond which recovery ceases to be available. Depletion is legitimate only where it is visible, bounded, attached to a credible plan of repayment, and justified. Three findings follow. Failure is not gradual: debt crosses a threshold past which no restraint recovers the stock. A system accumulating debt may show rising activity while the capacities funding it fall. And the four conditions of legitimacy are not equally available — ecological systems cannot repay, markets cannot identify the creditor, states cannot enforce a limit, and organizations can satisfy all four and usually do not. Two systems reporting identical results may therefore be in opposite conditions. What separates them is never output. It is always evidence about stocks.

RungeKutta v0.5.23 Diff since v0.5.22 Merged pull requests: Add the two-stage, 3rd order fully implicit tableau IRK3 (#28) (@michakraus)

This deliverable completes Phase 3 of the CLIMACHANGE project by converting the Phase 2 Climate Risk Assessment into a practical climate risk management framework for the Autonomous Province of Vojvod…

This deliverable completes Phase 3 of the CLIMACHANGE project by converting the Phase 2 Climate Risk Assessment into a practical climate risk management framework for the Autonomous Province of Vojvodina. Phase 2 identified relative and agricultural drought as the region’s dominant climate risks, with the highest priority assigned to North Banat (RS124) and Central Banat (RS126), followed by South Banat (RS122) and North Bačka (RS125). West Bačka (RS121) and South Bačka (RS123) were assessed as medium-priority areas, while Srem (RS127) provides an important resilience benchmark. The Phase 3 work therefore focused on deciding what should be done, by whom, through which planning instruments, and how progress should be monitored. The phase combined scientific evidence with structured stakeholder engagement. Seven educational seminars were held between April and June 2026, one in each administrative district of Vojvodina. The seminars presented the Phase 2 results, discussed local risk ownership, collected feedback on existing measures and institutional barriers, and strengthened the capacity of municipal administrations, environmental services, agricultural stakeholders, public utilities, civil protection actors, and other interested parties. Attendance lists, district analyses, a standardised questionnaire, and photo documentation provide the supporting evidence for these activities. Following the district consultation process, the consolidated project results and proposed adaptation recommendations were presented to policy and decision makers at a dedicated final event held in Novi Sad on 19 June 2026. The structured district analyses available for quantitative synthesis contain responses from all seven districts. Because several questionnaire items allowed multiple answers, percentages for risk types and support needs refer to shares of selections, while fixed-choice questions refer to shares of respondents. The aggregated results confirm a clear climate-risk hierarchy: drought represented 44.7% of all hazard selections, heatwaves 25.2%, storms 16.4%, and floods 11.5%. Agriculture was consistently identified as the most vulnerable sector, with water management, infrastructure, public health, protected areas, and forestry emerging as secondary concerns depending on district conditions. The survey also revealed that the principal implementation problem is not the absence of concern, but the gap between awareness of risk and the capacity to act. Across the structured district datasets, 54.5% of respondents did not know whether an updated climate risk assessment existed, 53.7% did not know whether adaptation measures had been integrated into development plans, and 77.2% had no information on adaptation measures already being implemented. A total of 87.8% of respondents considered the public to be either uninformed or only partially informed about climate risks. At the same time, 84.6% supported improving regional and local risk-management plans, indicating strong support among the surveyed stakeholders for further institutional and planning improvements. Based on the original project objectives, the Phase 2 risk assessment and the Phase 3 stakeholder findings, five primary and six additional operational CRM objectives were defined for Vojvodina. The primary objectives address drought losses, water management, soils and ecosystems, monitoring and preparedness, and the integration of climate risk into planning and investment. The additional objectives address awareness, public health, institutional capacity, financial readiness, equity and the transfer of good practices. Integration into decision making is proposed through a Provincial Climate Risk and Adaptation Coordination Group, named provincial and municipal CRM focal points, district-level coordination forums, climate-risk screening of plans and capital investments, annual use of CRA indicators in sectoral programmes and a monitoring dashboard linked to defined indicators. Measures should be phased: governance, data, training, and low-regret actions in the short term; infrastructure modernisation and scalable pilots in the medium term; and systemic transformation of water, agricultural, ecosystem, and urban systems in the long term. Particular attention is required for smallholders, rural and low-income households, women in agriculture, older people, outdoor workers, and municipalities with limited technical and financial capacity. The main conclusion is that Vojvodina has a strong scientific basis and broad support among participating stakeholders for climate risk management, but implementation remains fragmented and under-resourced. The most immediate value of Phase 3 is the creation of a common decision framework that links risk evidence, locally expressed needs, policy instruments, responsible institutions, and measurable indicators. The next step is to convert the proposed portfolio into costed provincial and municipal action plans reflecting district-sensitive priorities, secure financing, establish regular data exchange, and report progress annually. Detailed engineering design, full economic appraisal, and farm-level vulnerability modelling remain outside the scope of this phase and should be undertaken during implementation.