Figures and tables in Economic and Environmental Impact Analysis of Pasture-Based Agriculture: A Review of Economic Modelling Approaches

Abstract

Microsimulation is widely used in economics to analyse the distributional effects of policy and the behavioural responses of heterogeneous agents. In agricultural economics literature, farm-level simulation and bioeconomic models have developed in parallel with farm level microsimulation. These models operate at different scales but combine biological processes with economic and policy factors. The modelling literature provides little systematic assessment of how these approaches address economic and environmental outcomes in pasture-based agricultural systems. This paper addresses the gap through a systematic review of 173 peer-reviewed modelling studies published between 2000 and 2024. The analysis traces temporal and geographical trends in the literature, reviews methodological choices, and assesses how economic and environmental outcomes are modelled. The results shows that farm-level simulation approaches account for largest share of the literature, followed by optimisation models, while microsimulation and macro-scale approaches are less common. Most of the studies were at the farm-level and focus mainly on environmental outcomes, particularly land use and greenhouse gas emissions. Policy modelling concentrated on conventional production systems, while organic and low-input systems are underrepresented. Approximately half of the reviewed studies originate from Europe, and macro-level approaches account for less than 10% in literature. The literature exhibits a persistent pattern: models that handle farm-level heterogeneity seldom connect to sectoral outcomes, while those designed for aggregation are seldom applied to pasture-based systems. This limits the evidence base for policies that require both micro-level behavioural responses and macro-level assessment. Closing this gap will require integrated frameworks that couple farm-scale representation with sectoral or economy-wide models.

This article contains 18 figures and 2 tables.

Figures

**Figure 1.** Conceptual framework of pasture-based agriculture.

**Figure 3.** Temporal distribution of modelling studies published Annually between 2000 - 2024.

**Figure 4.** Geographical distribution of modelling studies (Panel A) and production system coverage by region (Panel B).

**Figure 5.** Distribution of production system by geographical region.

**Figure 6.** The drivers in modelling literature for economic and environmental impact analysis of pasture-based agriculture.

**Figure 7.** Drivers that motivated modelling choice.

**Figure 8.** The frequency of modelling approaches used in economic and environmental impact analysis of pasture-based agriculture.

**Figure 9.** Shows the distribution of modelling approaches across geographical regions.

**Figure 10.** Applications of modelling approaches across sustainability domains.

**Figure 11.** Show the distribution of modelling approaches by unit of analysis.

**Figure 12.** Data sources by modelling approach in sustainability assessments of pasture-based agriculture.

**Figure 13.** Software by modelling approach.

**Figure 14.** Modelling behaviour by scale of application: Panel A reports share of behaviour types across all studies; Panel B shows their distribution across spatial scales applied.

**Figure 15.** Validation strategies adopted in the reviewed studies (share of studies, %).

**Figure 16.** Distribution of impact domains across modelling approaches (row-normalised shares).

**Figure 17.** The distribution of sustainability impacts assessed across reviewed studies.

**Figure 18.** Shows the distribution of policy instruments modelled across production.

Tables

Table 1. Search strategy of the review showing databases, strings and exclusion criteria.

Database	Search Field(s)	Search String	Filters Applied
Scopus	TITLE-ABS-KEY	("livestock production systems" OR "pasture-based agriculture" OR "grass-based livestock" OR "ruminant farming") AND ("model" OR "microsimulation" OR "bio-economic model" OR "input-output model" OR "CGE model" OR "system dynamics model" OR "stochastic model" OR "simulation") AND ("economic impact" OR "profitability" OR "economic sustainability" OR "farm income") AND ("environmental impact" OR "GHG emissions" OR "biodiversity" OR "land use change")	Exclude: Conference papers, Subject areas: Agri, Econ, Env Sci
Web of Science	TS (Topic: Title, Abstract, Keywords)	TS=("livestock production systems" OR "pasture-based agriculture" OR "grass-based livestock" OR "ruminant farming") AND TS=("model" OR "microsimulation" OR "bio-economic model" OR "input-output model" OR "CGE model" OR "system dynamics model" OR "stochastic model" OR "simulation") AND TS=("economic impact" OR "profitability" OR "economic sustainability" OR "farm income") AND TS=("environmental impact" OR "GHG emissions" OR "biodiversity" OR "land use change")	Document types: Articles, Review; Subject areas: Agri, Econ, Env
Google Scholar	Full text (title, abstract)	("livestock production systems" OR "pasture-based agriculture" OR "grass-based livestock" OR "ruminant farming") AND ("model" OR "microsimulation" OR "bio-economic model" OR "input-output model" OR "CGE model" OR "system dynamics model" OR "stochastic model" OR "simulation") AND ("economic impact" OR "profitability" OR "economic sustainability" OR "farm income") AND ("environmental impact" OR "GHG emissions" OR "biodiversity" OR "land use change")	Manual screening; Sort by relevance; Year range; Exclude patents & citations

Table 2. Conceptual classification of economic and environmental modelling approaches applied to pasture-based agriculture.

Model Type	Distinctive Applications	Strengths	Weaknesses or Limitations of the approach	Key References and Examples
1. Farm-level simulation and microsimulation	Farm management, environmental impacts, profitability/resource allocation, policy evaluation	Captures unit-level heterogeneity Flexible scenario testing Links micro and macro Can model distributional outcomes	Data intensive Often siloed in different literatures (agricultural vs social policy) Many static applications without dynamic extensions	Taylor et al. (2020); Vogeler et al. (2017); Hynes et al. (2009); O’Donoghue, 2017; Haydarov et al. (2024); Richardson et al. (2014); Reidsma et al. (2018)
2. Optimisation or Programming Models	Resource allocation, efficiency analysis, profitability	Strength in trade-off analysis Identifies efficient solutions under constraints	Typically assumes rational behaviour Often static; limited uncertainty	Moraes et al., 2012; Addis et al. (2021); Finneran et al. (2012); Kamilaris et al. (2020)
3. Econometric or Statistical Models	Drivers of profitability, adoption, efficiency, policy evaluation	Empirically grounded Flexible with data Good for causal inference	Dependent on data availability Limited system feedback Weak predictive power outside sample	Hanrahan et al. (2018); de Oliveira Silva et al. (2017); Cillero and Reaños (2023); Haydarov et al. (2024); Mehrabi et al. (2020)
4. Agent-Based Models	Farmer behaviour Technology adoption Collective dynamics	Capture individual adaptation Learning, and social networks Explicitly represent behavioural decision processes	Few applications Requires rich behavioural data Validation is difficult	Ryan et al. (2018); Bolte et al. (2007); Mooney et al. (2024)
5. Input-Output or Bioeconomic IO (BIO)	Sectoral and economy-wide spillovers Trade Leakage	Captures indirect effects and economy–environment linkages Transparent	Assumes linearity Static multipliers Poor/no behavioural representation	Jin (2023); Zehetmeier et al. (2014); Breure et al. (2024)
6. Computable General Equilibrium	Trade, policy shocks, macroeconomic impacts	Economy-wide perspective Consistent with national accounts	Assumes equilibrium and rationality High data demands Less detail at farm level	Cillero and Reaños (2023); Li and O’Donoghue (2012); van der Linden et al. (2020)
7. System Dynamics 8. Integrated Assessment Models	Long-term trajectories, climate and sustainability policy	Captures feedback loops, cross-domain integration Dynamic	Oversimplification risk Calibration challenges; less detail at micro level	Lieffering et al. (2016); Benavides et al. (2023); Stürck et al. (2018)
9. Machine Learning 10. Hybrid Approaches	Predictive analytics Pattern recognition Linking models	Handles large datasets Flexible integration Emerging use in farm contexts	Often 'black box' Weak interpretability for policy design	McVey et al. (2023); Maseko et al. (2024); Sonea et al. (2023)