A new approach to develop large-scale land-use models using publicly available data

Developing a land-use model for large-scale cases is a topic that has received less attention in the literature, while it is crucial for transportation engineers and urban planners to analyze the effect of various policies in multi-jurisdiction metropolitan areas and to some extent on a statewide scale. While gravity-based models are too simplistic, microsimulation models require extensive data and massive computation. This paper presents a land-use model that can be applied to large-scale geographies using publicly available data and be able to forecast demographic and socioeconomic attributes with reasonable accuracy and acceptable computational time. The proposed model incorporates Putman’s Integrated Transportation–Land-Use Package (ITLUP) and Kockelman’s Gravity-based Land-Use Model (G-LUM) fundamentals with enhanced formulation of newly added variables and structural changes. Considering the nonconvex and nonlinear nature of the proposed model, we utilize an enhanced genetic algorithm for base year calibration. Further, we assess the accuracy of the model with backcasting validation. We utilize the state of Tennessee as the case study area and utilized all open-source data available to the model application. The model results show reasonably accurate estimates of households by size, employment by industry, and land utilization by condition. As applicable, the model outperforms G-LUM by accuracy ( R2 and Percentage of Good Prediction ( PGP)) and error measures (Mean Absolute Percentage Error ( MAPE)). The proposed land-use model has the potential to be applied for medium to large-scale geographies with reasonable accuracy in predicting socioeconomic, demographic, and land condition estimates by using publicly available data.

Which Socio-economic Conditions Drive the Selection of Agroforestry at the Forest Frontier?

Models are essential to assess the socio-economic credentials of new agroforestry systems. In this study, we showcase robust optimisation as a tool to evaluate agroforestry’s potential to meet farmers’ multiple goals. Our modelling approach has three parts. First, we use a discrete land-use model to evaluate two agroforestry systems (alley cropping and silvopasture) and conventional land uses against five socio-economic objectives, focusing on the forest frontier in eastern Panama. Next, we couple the land-use model with robust optimisation, to determine the mix of land uses (farm portfolio) that minimises trade-offs between the five objectives. Here we consider uncertainty to simulate the land-use decisions of a risk-averse farmer. Finally, we assess how the type and amount of agroforestry included in the optimal land-use portfolio changes under different environmental, socio-economic and political scenarios, to explore the conditions that may make agroforestry more attractive for farmers. We identify silvopasture as a promising land use for meeting farmers’ goals, especially for farms with less productive soils. The additional labour demand compared to conventional pasture, however, may prove an important barrier to adoption for farms facing acute labour shortages. The selection of agroforestry responded strongly to changes in investment costs and timber prices, suggesting that cost-sharing arrangements and tax incentives could be effective strategies to enhance adoption. We found alley cropping to be less compatible with farmers’ risk aversion, but this agroforestry system may still be a desirable complement to the land-use portfolio, especially for farmers who are more profit-oriented and tolerant of risk.

SHIFTING CULTIVATION PRACTICES IN BARAK VALLEY, INDIA – POLICY SCENARIOS FROM A SPATIALLY EXPLICIT LAND USE MODEL

Barak valley is a region in north east part of India where the practice of shifting cultivation is quite prevalent. Population growth coupled with the geographic isolation of the area have led to an increased pressure on land and a consequent decline in forest cover. The decrease of forests observed is spatially distributed and dependent on neighborhood rules. Hence, we look towards modelling the land use change to understand the land use changes and the factors affecting them. In this paper, we modify an agent-based land use model for modelling shifting cultivation to determine how various policy changes at a larger scale might affect the shifting cultivation practice in the region at the micro level. We explore scenarios like drastic population increase and availability of irrigation infrastructure in the area. Through the scenario analysis we explore how policies play a role in agriculture patterns and influence land use patterns.

Determining a Carbon Reference Level for a High-Forest-Low-Deforestation Country

Research Highlights: A transparent approach to developing a forest reference emissions level (FREL) adjusted to future local developments in Southern Cameroon is demonstrated. Background and Objectives: Countries with low historical deforestation can adjust their forest reference (emission) level (FREL/FRL) upwards for REDD+ to account for likely future developments. Many countries, however, find it difficult to establish a credible adjusted reference level. This article demonstrates the establishment of a FREL for southern Cameroon adjusted to societal megatrends of strong population—and economic growth combined with rapid urbanization. It demonstrates what can be done with available information and data, but most importantly outlines pathways to further improve the quality of future FREL/FRL’s in light of possibly accessing performance-based payments. Materials and Methods: The virtual FREL encompasses three main elements: Remotely sensed activity data; emission factors derived from the national forest inventory; and the adjustment of the reference level using a land use model of the agriculture sector. Sensitivity analysis is performed on all three elements using Monte Carlo methods. Results: Deforestation during the virtual reference period 2000–2015 is dominated by non-industrial agriculture (comprising both smallholders and local elites) and increases over time. The land use model projections are consistent with this trend, resulting in emissions that are on average 47% higher during the virtual performance period 2020–2030 than during the reference period 2000–2015. Monte Carlo analysis points to the adjustment term as the main driver of uncertainty in the FREL calculation. Conclusions: The available data is suitable for constructing a FREL for periodic reporting to the UNFCCC. Enhanced coherence of input data notably for activity data and adjustment is needed to apply for a performance-based payment scheme. Expanding the accounting framework to include forest degradation and forest gain are further priorities requiring future research.