Grasslands have a large share of the world’s land cover and their sustainable management is important for the protection and provisioning of grassland ecosystem services. The question of how to manage grassland sustainably is becoming increasingly important, especially in view of climate change, which on the one hand extends the vegetation period (and thus potentially allows use intensification) and on the other hand causes yield losses due to droughts. Fertilization plays an important role in grassland management and decisions are usually made at farm level. Data on fertilizer application rates are crucial for an accurate assessment of the effects of grassland management on ecosystem services. However, these are generally not available on farm/field scale. To close this gap, we present an agent-based model for Fertilization In Grasslands (FertIG). Based on animal, land-use, and cutting data, the model estimates grassland yields and calculates field-specific amounts of applied organic and mineral nitrogen on grassland (and partly cropland). Furthermore, the model considers different legal requirements (including fertilization ordinances) and nutrient trade among farms. FertIG was applied to a grassland-dominated region in Bavaria, Germany comparing the effects of changes in the fertilization ordinance as well as nutrient trade. The results show that the consideration of nutrient trade improves organic fertilizer distribution and leads to slightly lower Nmin applications. On a regional scale, recent legal changes (fertilization ordinance) had limited impacts. Limiting the maximum applicable amount of Norg to 170 kg N/ha fertilized area instead of farm area as of 2020 hardly changed fertilizer application rates. No longer considering application losses in the calculation of fertilizer requirements had the strongest effects, leading to lower supplementary Nmin applications. The model can be applied to other regions in Germany and, with respective adjustments, in Europe. Generally, it allows comparing the effects of policy changes on fertilization management at regional, farm and field scale.

The model is designed to analyse the effects of mitigation measures on the European brown hare (Lepus europaeus), which is directly affected by ongoing land use change and has experienced widespread decline throughout Europe since the 1960s. As an input, we use two 4×4 km large model landscapes, which were generated by a landscape generator based on real field sizes and crop proportions and differed in average field size and crop composition. The crops grown annually are evaluated in terms of forage suitability, breeding suitability and crop richness for the hare. Six mitigation scenarios are implemented, defined by a 10 % increase in: (1) mixed silphie, (2) miscanthus, (3) grass-clover ley, (4) alfalfa, (5) set-aside, and (6) general crop richness. The model shows that that both landscape configuration and composition have a significant effect on hare population development, which responds particularly strongly to compositional changes.

This NetLogo model simulates the movement and foraging behavior of 20 bird species (classified as urban avoiders or utilizers) across a 20×20m grid landscape representing southern Bogotá, Colombia. Agents follow an optimal foraging strategy (90% of the time) or move randomly (10%), gaining energy from land-cover-dependent food sources and dying if energy falls below their basal metabolic rate. The model compares two landscape scenarios — current land cover (2016) and a proposed scenario incorporating the ‘Media Luna del Sur’ ecological corridor from Bogotá’s 2022–2035 Land Use Plan (POT) — to evaluate whether the proposed green infrastructure improves functional connectivity for avian biodiversity across the urban matrix. Outputs include cumulative patch visitation counts, exported as raster files, used to generate connectivity heatmaps.

Given the importance of experiencing fulfilling and productive workdays, scholars have focused on ways to boost employees’ daily engagement and performance, suggesting this may be accomplished by increasing meaningfulness of their work tasks. However, an alternative approach may be needed in multifaceted work contexts where employees need to complete sets of varied tasks each day, including tasks that lack meaningfulness. To address this issue, we integrate the workday design perspective with research on residual engagement to examine implications of strategically sequencing tasks in terms of their meaningfulness. We theorize that because of a process we refer to as engagement cascade, performing the day’s most highly meaningful tasks earlier and in a concentrated manner will tend to produce higher levels of daily engagement relative to sequences in which the most highly meaningful tasks occur later or are disbursed. We use agent-based modeling to formalize the engagement cascade process and generate specific predictions regarding how daily engagement is impacted by different task meaningfulness sequences. Findings from an intensive longitudinal design study with NASA and ROSCOSMOS crew members (Study 1), an experience sampling field experiment with full-time employees (Study 2), and two controlled laboratory experiments with undergraduates (Study 3 and Supplement to Study 3) provide support for our hypotheses. Specifically, the decreasing task meaningfulness sequence engendered higher levels of daily engagement, and in turn, daily performance, relative to increasing, inverted U-shaped, and U-shaped sequences.

This model describes the consequences of limited vision of agents in harvesting a common resource. We show the vulnerability of cooperation due to reduced visibility of the resource and other agents.

The model simulates the national Campaign-Based Watershed Management program of Ethiopia. It includes three agents (farmers, Kebele/ village administrator, extension workers) and the physical environment that interact with each other. The physical environment is represented by patches (fields). Farmers make decisions on the locations of micro-watersheds to be developed, participation in campaign works to construct soil and water conservation structures, and maintenance of these structures. These decisions affect the physical environment or generate model outcomes. The model is developed to explore conditions that enhance outcomes of the program by analyzing the effect on the area of land covered and quality of soil and water conservation structures of (1) enhancing farmers awareness and motivation, (2) establishing and strengthening micro-watershed associations, (3) introducing alternative livelihood opportunities, and (4) enhancing the commitment of local government actors.

The purpose of the model is to study the dynamical relationship between individual needs and group performance when focusing on self-organizing task allocation. For this, we develop a model that formalizes Deci & Ryan’s self-determination theory (SDT) theory into an ABM creating a framework to study the social dynamics that pertain to the mutual relations between the individual and group level of team performance. Specifically, it aims to answer how the three individual motivations of autonomy, competence, and belonging affect team performance.

Explores how social networks affect implementation of institutional rules in a common pool resource.

We construct an agent-based model to investigate and understand the roles of green attachment, engagement in local ecological investment (i.e., greening), and social feedback.

We model interpersonal dynamics and study behavior in the classroom in the hypothetical case of a single teacher who defines students’ seating arrangements. The model incorporates the mechanisms of peer influence on study behavior, on attitude formation, and homophilous selection in order to depict the interrelated dynamics of networks, behavior, and attitudes. We compare various seating arrangement scenarios and observe how GPA distribution and level of prejudice changes over time.