Resource Management Decisions Research Articles

Multispecies Portfolios of U.S. Marine Fisheries: Ecosystem‐Based Fisheries Management Reduces Economic Risk

Fisheries management has focused on single stocks, not directly accounting for species interactions, and usually only considering economic factors in post hoc analysis. This approach has been successfully applied for many species over many years, but may also inadvertently result in greater risks being incurred. We demonstrate a portfolio optimization approach to inform a broader set of fishery concerns as a way to emphasize species interactions and economic considerations in resource management decision making. The approach can use readily available data on landings and revenue to generate easily digestible indicators of risk, namely the risk gap (i.e., the difference between actual and optimal portfolio values). Herein, we calculate portfolio efficiency frontiers that minimize risk for target revenue outcomes and resulting risk gaps for commercial fisheries using the top 25 landed‐value species in six U.S. fisheries regions. Most regions exhibited a risk gap on the order of US$20–50 million, collectively on average over $250 million. Risk gaps can be used as ecosystem‐level indicators to inform managers of the unnecessary risk being assumed for a given level of revenue for a portfolio of fisheries stocks, which can move us towards operational ecosystem‐based fisheries management.

Modelling Climate Effects on Site Productivity and Developing Site Index Conversion Equations for Jack Pine and Trembling Aspen Mixed Stands

Forest site productivity estimates are crucial for making informed forest resource management decisions. These estimates are valuable both for the tree species currently growing in the stands and for those being considered for future stands. Current models are generally designed for pure stands and do not account for the influence of climate on tree growth. Consequently, site index (SI) conversion equations were developed specifically for jack pine (Pinus banksiana Lamb.) and trembling aspen (Populus tremuloides Michx.) trees grown in naturally originated mixed stands. This work involved sampling 186 trees (93 of each species) from 31 even-aged mixed stands (3 trees per species per site) across Ontario, Canada. Stem analysis data from these trees were utilized to develop stand height growth models by incorporating climate variables for each species. The models were developed using a mixed effects modelling approach. The SI of one species was correlated with that of the other species and climate variables to establish SI conversion equations. The effect of climate on site productivity was evaluated by projecting stand heights at four geographic locations (east, center, west, and far west) in Ontario from 2022 to 2100 using the derived stand height growth models. Height projections were made under three emissions scenarios reflecting varying levels of radiative forcing by the end of the century (2.6, 4.5, and 8.5 watts m−2). Climate effects were observed to vary across different regions, with the least and most pronounced effects noted in the central and far western areas, respectively, for jack pine, while effects were relatively similar across all locations for trembling aspen. Stand heights and SIs of jack pine and trembling aspen trees grown in naturally originated mixed stands can be estimated using the height growth models developed here. Similarly, SI conversion equations enable the estimation of the SI for one species based on the SI of another species and environmental variables.

Reactions to Algorithmic Decision-Making in Human Resource Management: A Meta-Analysis

Reactions to Algorithmic Decision-Making in Human Resource Management: A Meta-Analysis

Proposal for a managed aquifer recharge feasibility index for southern Portugal using multi-criteria decision analysis

Water scarcity in the Mediterranean region requires the adoption of new and optimized water resources management tools that allow a progressive climate change adaptation, being one of the main drivers for the employment of alternative water storage measures such as Managed Aquifer Recharge (MAR). To promote the implementation of these measures, this study evaluates the application of a MAR feasibility index mapping to southern Portugal, employing Multi-Criteria Decision Analysis (MCDA). A participatory approach – developed within the AGREEMAR project – aims for the characterization of the intrinsic site suitability, water availability and demand, where the stakeholder inputs are embedded in the weighting computation process.The results obtained demonstrate prevalent moderate MAR feasibility across the entire region (78% of the area) with an increased percentage of the area corresponding to high MAR feasibility (22%) strongly related to highly suitable geological features. A comparative analysis with the Analytical Hierarchical Process (AHP) showed minor high MAR feasibility areas (7%), and feasibility class transitions between the directly above or below classes.A sensitivity analysis, conducted based on a one-at-a-time (OAT) variation of the criteria weights, assessed the impact of the weighting methods on feasibility class changes. For the AGREEMAR method, changes in the weight of 10 of the 28 considered criteria resulted in negligible changes when compared to the original feasibility map, while for AHP 3 out of 28 produced minor changes. The remaining criteria showed important modifications in the feasibility maps, which underlined AGREEMAR method weights provided higher stability in the results.The study provides information about a MAR site selection process procedures, allowing replication. The dissemination of the results is expected to raise awareness of MAR among stakeholders and support scientifically-based water resources management decision-making.

HR Analytics: Leveraging Big Data to Drive Strategic Decision-Making in Human Resource Management

The research paper examines how HR Analytics can be used to leverage big data in making decisions in Human Resource Management (HRM) that lead to a transformation. By collecting, analyzing and interpreting vast amounts of employee data systematically, the HR analytics offers actionable insights for workforce management optimization, productivity enhancement and alignment of human capital with organizational objectives. The study incorporates major theories such as resource-based view and human capital theory, and employs advanced analytical models like regression analysis, logistic regression for turnover prediction and k-means clustering for workforce segmentation. Results show that engagement and training significantly affected performance while effective prediction of turnover was based on metrics of performance and engagement. Clustering shows separate employee groups that promote directed HR strategies. This research highlights the need for data driven HR practices to gain competitive advantage and achieve organizational success. It also emphasizes ethical considerations as well as calls for further research incorporating advanced machine learning techniques, real-time data analytics, and long-term effects of HR Analytics. As a result, this paper presents a solid structure enabling HR practitioners to implement data-driven approaches that enhance an environment of continuous improvement in HRM.

Enhancing hydrological data completeness: A performance evaluation of various machine learning techniques using probabilistic fusion imputer with neural networks for streamflow data reconstruction

The present-day accessibility of streamflow data, particularly in the developing countries, is often marked by a multitude of data shortfalls or distortions. This study investigates the estimate of missing streamflow data using machine learning approaches, including K-nearestneighbour (KNN), Predictive Mean Matching (PMM), Random Forest (RF) and a novel technique of Probabilistic Fusion Imputer with Neural Networks (PROFINN). This study tackles the issue of data insufficiency in such time series considering the inclusion of numerous hydrological parameters by means of RF feature selector, to determine the most significant ones among them. The study explores the efficacy of selected models on various data gaps under different hydrological scenarios, including diverse flow characteristics (mean, high and low flows) and gap lengths (long and short gaps, continuous and discontinuous gaps) and presents a pattern of ranking system that assesses the level of suitability of each technique for various data gaps. This study underscore PROFINN’s remarkable performance across all scenarios, yielding an average Root Mean Square Error (RMSE) of 0.91 and an average Nash-Sutcliffe Efficiency (NSE) value of 0.93 when applied for an intermittent river system of Pamba in Southern Kerala, India. RF follows PROFINN in the imputation of extreme flows as well as long and short gap scenarios. KNN closely follows PROFINN for the imputation of continuous and discontinuous gap scenarios. This study augments the significance of tailored machine learning techniques in enhancing the integrity of hydrological datasets, offering valuable insights for effective decision-making in water resource management and related fields. Furthermore, the success of PROFINN in streamflow data imputation suggests its potential extension to other hydrological research domains, like historical data reconstruction assisting climate change studies and future water resources planning, emphasizing the broader relevance and applicability of this study’s findings.

Using MODFLOW to Model Riparian Wetland Shallow Groundwater and Nutrient Dynamics in an Appalachian Watershed

Simulating shallow groundwater (SGW) flow dynamics and stream–SGW interactions using numerical modeling tools is necessary to develop a mechanistic understanding of water flow systems and improve confidence in water resource management practices. A three-dimensional (3D) SGW flow model was developed for a riparian wetland in a mixed forest and agricultural catchment in West Virginia (WV), Appalachia, USA, using a Modular 3D Groundwater Model (MODFLOW). The MODFLOW simulation was calibrated in steady (R2 = 0.98, ME = −0.21, and RMSE = 0.77), transient state (R2 = 0.97, ME = −0.41, and RMSE = 1.28) and validated (R2 = 0.97, ME = −0.28, and RMSE = 1.05) using observed SGW levels from thirteen nested piezometers under steady and transient states. An experimental MT3D transport scenario was developed to show the lateral transport of NO₃-N from the aquifer to stream cells. Relatively stable SGW head distribution was observed. In the downstream reach, SGW discharge varied from 948 m3/day to 907 m3/day in 2020, with creek seepage ranging from 802 m3/day to 790 m3/day. Similarly, SGW input to the stream ranged from 891 m3/day to 978 m3/day, while creek seepage ranged from 796 m3/day to 800 m3/day in 2021. In upstream reaches, losing stream conditions were observed in January, June, and September 2020 and January to April 2021, while gaining stream conditions prevailed during other months. Thus, an approximately monthly alternating gaining–losing stream condition was observed in the upstream area. An experimental MT3D transport scenario resulted in an advection–dispersion scenario, showing a cumulative loss of 947 g of NO3-N from SGW to the stream. Denitrification accounted for the cumulative loss of 1406 g of NO3-N from SGW, surpassing 639 g of nitrate from the SGW to the stream during the study period. Additionally, particle tracking using MODPATH indicated a long residence time for SGW nutrients, affirming the efficiency of nitrogen transformation through denitrification. This study is among the first to simulate hydrologic and nutrient interactions in riparian wetlands of a mixed land use catchment in the Appalachian region of the northeastern United States. The results better inform water resource management decisions and modeling efforts in the Appalachian region and similar physiographic regions globally.

Identifying the possible driving mechanisms in Precipitation-Runoff relationships with nonstationary and nonlinear theory approaches

Climate change and complex anthropogenic activities have raised significant concerns regarding Precipitation-Runoff Relationships (PRR). Traditional methods, assuming stationary and linear conditions, often fail to adequately capture these intricate links. To address the limitations, we proposed an integrated framework, employing the Driving indices for Precipitation-Runoff relationships within the nonStationary and nonLinear theory approaches (DPRS and DPRL) to identify the possible driving mechanisms in PRR. The framework is validated across five sub-basins (WRB1-WRB5) within the Wei River Basin, known for its high spatiotemporal variability and intense anthropogenic activities. Spatiotemporal dynamics, nonstationary processes, and nonlinear interactions among various factors are assessed, including climate forcing, groundwater, vegetation dynamics, and anthropogenic influences. DPRS and DPRL assessments revealed that baseflow significantly influences PRR but with high uncertainty. Potential evapotranspiration plays a dominant role in driving negative PRR changes in WRB5 (weakening the correlation between precipitation and runoff), while vegetation dynamics negatively affect PRR with lower uncertainty. Anthropogenic influences represented by Impervious Surface Ratio (ISR), Night-Time Light (NTL), and population density (POP) exhibit varying driving levels, with ISR having the strongest and direct impact, closely linked to urbanization processes and scale within the study cases. The mutual validation of DPRS and DPRL confirms the dominance of baseflow in the Wei River Basin, with urbanization contributing to high ISR, NTL, and POP driving levels in WRB2 and WRB3. Afforestation policies intensify vegetation dynamics’ impact in WRB4 and WRB5. This framework extends its utility to analyze various land evapotranspiration and soil moisture content at different depths in the PRR, supported by a physically-based hydrological model. Basin complexity is further employed to validate the reliability of the assessment outcomes. These insights contribute to a more comprehensive understanding of hydrological processes and facilitate informed decisions for sustainable water resource management within the basin.

Geospatial assessment of climate and human pressure on Snow Leopard habitat in the Trans-Himalayan region of Pakistan

Reliable estimates of how human activities may affect wildlife populations are critical for making scientifically sound resource management decisions. A significant issue in estimating the consequences of management, development, or conservation measures is the need to account for a variety of biotic and abiotic factors, such as land use and climate change, that interact over time altering wildlife habitats and populations. The snow leopard Panthera uncia (Schreber, 1775), as a vulnerable species, is extremely sensitive to indirect impacts of climate change. Given that it is highly difficult undertaking conservation measures on the entire range of snow leopards, identifying hotspots for conservation is necessary. This study was conducted in Bagrot and Haramosh valleys, in the Trans-Himalayan region, to evaluate the impacts of climate and human pressure on snow leopard habitat. Hybrid classification of Landsat satellite data for 2010 and 2020 was performed to elucidate land use changes that suggested a decrease in permanent snow by 10 % and 3 % in Haramosh and Bagrot while an increase in settlements cover by 16 % and 23 %, respectively. Life zone comparison for 2010 and 2020 using the Holdridge life zone (HLZ) classification system disclosed a change from three life zones to five life zones in Haramosh, and four life zones to five life zones in Bagrot, caused by a temperature increase of 2°C to 3°C, indicating that the area is becoming more and more suitable for settlements and less favorable for snow leopards. This study underlines again that mountainous regions are more vulnerable to the impacts of climate change. Warming weather is making survival more difficult for snow leopards. Although they are resilient to the direct effects of climate change, indirect impacts like avalanches, flash floods, urbanization, and human-wildlife conflict make them more vulnerable and threaten their survival. Thus, we recommend establishing further protected areas, better controlling illegal wildlife trade, and conducting genetic studies to understand impacts on snow leopards and rangeland management, livelihood improvement, and human-wildlife conflict reductions.

Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis

Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis

Remote sensing and machine learning to improve aerial wildlife population surveys

Technological and methodological advances in remote sensing and machine learning have created new opportunities for advancing wildlife surveys. We assembled a Community of Practice (CoP) to capitalize on these developments to explore improvements to the efficiency and effectiveness of aerial wildlife monitoring from a management perspective. The core objective of the CoP is to organize the development and testing of remote sensing and machine learning methods to improve aerial wildlife population surveys that support management decisions. Beginning in 2020, the CoP collaboratively identified the natural resource management decisions that are informed by wildlife survey data with a focus on waterbirds and marine wildlife. We surveyed our membership to establish 1) what management decisions they were using wildlife count data to inform; 2) how these count data were collected prior to the advent of remote sensing/machine learning methods; 3) the impetus for transitioning to a remote sensing/machine learning methodological framework; and 4) the challenges practitioners face in transitioning to this framework. This paper documents these findings and identifies research priorities for moving toward operational remote sensing-based wildlife surveys in service of wildlife management.

A spectral mixture analysis based framework for estimating and charactering water use efficiency in heterogeneous drylands

Climate change and irrational land utilization have significantly disrupted the function and structure of the dryland ecosystems, leading to tremendously potential effects on the carbon and water cycles of the global terrestrial ecosystems. Ecosystem-scale water use efficiency (WUE) is a crucial metric that defines the ratio of gross primary productivity (GPP) to evapotranspiration (ET), revealing the coupling relationship between such two ecological processes and playing a significant role in water resource management decision-making. However, quantifying WUE in drylands remains a challenge because of their heterogeneous land surface challenging to characterize model parameters using traditional remote sensing techniques. Here, we developed a process-based framework to estimate GPP and ET, as well as WUE based on physical endmember fractions from spectral mixture analysis (SMA) model. And such framework is used to quantify the spatial and temporal evolution of WUE by optimizing the light use efficiency and assisting in the construction of a GV-Cws model for the separation of transpiration and evaporation processes during computation by utilizing the physically fractional vegetation and soil obtained from SMA in the Hexi Corridor of China, a typical dryland ecosystem. The results demonstrate that our framework can accurately estimate GPP and ET in the Hexi Corridor, as indicated by favorable performance in R2 (0.669, 0.705), RMSE (0.383,0.885) and NSE (0.534, 0.690) respectively. Such results surpass the accuracy of both two Moderate Resolution Imaging Spectroradiometer (MODIS) products and Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2)’s ET product. These advantages highlight that our SMA-based framework can provide highly accurate and distinct characterization of surface carbon and water interaction processes. Meanwhile, the WUE of the Hexi Corridor ecosystem exhibited a positive trend from 2001 to 2017 (0.016 gC·m−2·mm−1·a-1), characterized by simultaneous increases in GPP and ET.

Anthropogenic Eutrophication Drives Major Food Web Changes in Mwanza Gulf, Lake Victoria

Discerning ecosystem change and food web dynamics underlying anthropogenic eutrophication and the introduction of non-native species is necessary for ensuring the long-term sustainability of fisheries and lake biodiversity. Previous studies of eutrophication in Lake Victoria, eastern Africa, have focused on the loss of endemic fish biodiversity over the past several decades, but changes in the plankton communities over this same time remain unclear. To fill this gap, we examined sediment cores from a eutrophic embayment, Mwanza Gulf, to determine the timing and magnitude of changes in the phytoplankton and zooplankton assemblages over the past century. Biogeochemical proxies indicate nutrient enrichment began around ~ 1920 CE and led to rapid increases in primary production, and our analysis of photosynthetic pigments revealed three zones: pre-eutrophication (prior to 1920 CE), onset of eutrophication with increases in all pigments (1920–1990 CE), and sustained eutrophication with cyanobacterial dominance (1990 CE–present). Cladoceran remains indicate an abrupt decline in biomass in ~ 1960 CE, in response to the cumulative effects of eutrophication and lake-level rise, preceding the collapse of haplochromine cichlids in the 1980s. Alona and Chydorus, typically benthic littoral taxa, have remained at relatively low abundances since the 1960s, whereas the abundance of Bosmina, typically a planktonic taxon, increased in the 1990s concurrently with the biomass recovery of haplochromine cichlid fishes. Overall, our results demonstrate substantial changes over the past century in the biomass structure and taxonomic composition of Mwanza Gulf phytoplankton and zooplankton communities, providing a historical food web perspective that can help understand the recent changes and inform future resource management decisions in the Lake Victoria ecosystem.

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.

Integration of Airborne Laser Scanning data into forest ecosystem management in Canada: Current status and future directions

Airborne Laser Scanning (ALS) has been the subject of decades of applied research and development in forest management. ALS data are spatially explicit, capable of accurately characterizing vegetation structure and underlying terrain, and can be used to produce value-added products for terrestrial carbon assessments, hydrology, and biodiversity among others. Scientific support for ALS is robust, however its adoption within environmental decision-making frameworks remains inconsistent. Cost continues to be a principal barrier limiting adoption, especially in remote, forested regions, however added challenges such as the need for technical expertise, unfamiliarity of data capabilities and limitations, data management requirements, and processing logistics also contribute. This review examines the current status of the integration of ALS data into forest ecosystem management in a Canadian context. We advocate for continued inter-agency acquisitions leading to integration of ALS into existing natural resource management decision pathways. We gauge the level of uptake thus far, discuss the barriers to operational implementation at provincial scales, and highlight how we believe ALS can support multiple objectives of forest and environmental management in Canada. We speak to potential benefits for supporting inter-agency terrain generation, ecosystem mapping, biodiversity assessments, silvicultural planning, carbon and forest health evaluations, and riparian characterizations. We conclude by providing key considerations for developing capacity using ALS and discuss the technologies future in the context of Canadian forest and environmental management objectives.

Financial Decision-Making in Human Resource Management: Balancing Costs and Benefits

Financial Decision-Making in Human Resource Management: Balancing Costs and Benefits

The Impact of Artificial Intelligence Replacing Humans in Making Human Resource Management Decisions on Fairness: A Case of Resume Screening

A growing number of organizations have used artificial intelligence (AI) to make decisions to replace human resource (HR) workers; yet, the fairness perceptions of the people affected by the decision are still unclear. Given that an organization’s sustainability is significantly influenced by individuals’ perceptions of fairness, this study takes a resume-screening scenario as an example to explore the impact of AI replacing humans on applicants’ perceptions of fairness. This study adopts the method of the online scenario experiment and uses SPSS to analyze the experimental data: 189 and 214 people, respectively, participated in two online scenarios, with two independent variables of decision makers (AI and humans), two dependent variables of procedural and distributive fairness, and two moderating variables of outcome favorability and the expertise of AI. The results show that the applicants tend to view AI screening resumes as less fair than humans. Furthermore, moderating effects exist between the outcome favorability and the expertise of AI. This study reveals the impact of AI substituting for humans in decision-making on fairness. The proposed model can help organizations use AI to screen resumes more effectively. And future research can explore the collaboration between humans and AI to make human resource management decisions.

Life cycle assessment to evaluate the integral water cycle in industrial supply: A real case study

Wastewater recycling technologies are developed in areas where the necessity of water resources cannot be satisfied by natural sources. Nevertheless, nowadays trends and European Union Plans show an increasing interest on using these technologies to reduce environmental impacts. This manuscript aims to address the question of the real environmental results of using these technologies and the differences between each specific case using the Life Cycle Assessment (LCA) methodology. A real case study is analyzed to answer this question: the integral water cycle of a northern of Spain, comparing a traditional water supply system (system I), and an alternative wastewater regeneration plant (system II). System II presents a higher impact for all categories (between 1.2 and 37 times higher), except for land use, where it is reduced by 53 %. These results show a larger impact produced by the alternative system due to higher energy and chemical product consumption. Energy consumption is the main factor causing the highest impact in most of the impact categories for both studied systems, including the one associated to the water resource consumption. It accounts for at least 50 % of the total impact for each system in 7 of the 16 evaluated impact categories. In terms of climate change, energy consumption is not particularly significant in system I, but it is for system II, where it represents around 50 % of that impact. In the categories where the impact is not determined by energy consumption, chemical product consumption and waste and discharge treatment are the most relevant factors. In this sense, this paper highlights the importance of analysing each case specifically and underscores the usefulness of using LCA methodology as a tool to improve decision-making in resource management, with water resources emerging as a crucial focal point.

Deep Learning-Assisted Online Task Offloading for Latency Minimization in Heterogeneous Mobile Edge

With the proliferation of smart devices in recent years, many applications requiring high computing capability and low latency have emerged. Edge computing is one of the promising paradigms to support such applications. Due to the high volatility of edge environments, e.g., frequent movements of mobile devices, varying task sizes, and time-variant channel conditions, we have to make the offloading and resource management decisions on the fly. This paper formulates and studies the problem of online task offloading and resource management in heterogeneous mobile edge environments. The goal of the problem is to minimize the overall system latency. We prove that the problem is NP-hard. Moreover, traditional algorithms needing long decision-making times are insufficient to support applications with high volatility. This paper proposes a deep learning-assisted online algorithm that can make fast decisions. In particular, we design an offline solver for the proposed problem and use a deep neural network to emulate the solver. We conduct extensive simulations to evaluate the proposed approach. Results show that the proposed approach is around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$50,000\times$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$500\times$</tex-math></inline-formula> faster than the commercial Gurobi solver for the optimal solution and the proposed offline approximation solver, respectively. Moreover, the overall latency under the proposed approach is near-optimal.

Exploring farmer choices in Southern Togo: Utilizing a strategy game to understand decision-making in agricultural practices

CONTEXTAgriculture plays a vital role in African countries, driving economic growth, employment, and non-agricultural sector improvements. However, persistent food insecurity and poverty among smallholder families raise concerns. Land fragmentation compounds these issues, limiting agricultural productivity and pushing farmers towards off-farm activities, despite Africa's potential agricultural markets. OBJECTIVEThe study's focus is Donomadé, a remote village in southern Togo. This study pursues a dual objective. Firstly, it investigates how household characteristics, including family size and resource availability, influence farmers' resource management decisions. Secondly, the research employs serious games to comprehend farmers' livelihood strategy choices within constrained real-world-like conditions. METHODSThe iterative and participatory Companion Modelling (ComMod) approach was utilized for the co-design and co-development of a serious game. This involved close collaboration with a local research team from the University of Lomé, a local NGO, and Donomadé village farmers. Various farming systems were characterized, and collective insights into Donomadé's agricultural dynamics were gathered through surveys, interviews, and workshops. RESULTS AND CONCLUSIONSRegardless of their in-game roles, farmers prioritize fulfilling basic family needs, such as food security and education instead of investing in agriculture. The study underscores the significance of household composition and resource availability when designing technology interventions or support mechanisms for farmers. Aligning interventions with farmers' overarching goals and challenges can enhance technology adoption. Furthermore, addressing food security remains crucial for sustainable development, as it not only improves human welfare and alleviates hunger but also forms the foundation for comprehensive solutions in the face of climate change, resource constraints, and population growth. SIGNIFICANCEThis research highlights that heavy investment in agriculture is not the priority for many farmers in southern Togo, despite it being their main source of sustenance and income. Addressing immediate family needs, especially food security, education, and housing, takes precedence over long-term agricultural investments. Given the persistent challenge of food security amidst global uncertainties, addressing these priorities is paramount to stimulate more significant interest in agricultural investment.