Abstract: Highlights • Governance impacts on land use dynamics are modeled at the global scale with an agro-economic dynamic optimization model. • Improved governance performance lowers deforestation, reduces cropland expansion and increases agricultural yield. • Good governance makes a decisive difference in investment for increasing yields in developing regions. • Weak governance increases food prices, particularly in Sub-Saharan Africa and Southeast Asia. • Improving governance performance has significant impacts on poverty reduction. Abstract Deforestation, mainly caused by unsustainable agricultural expansion, results in a loss of biodiversity and an increase in greenhouse gas emissions, as well as impinges on local livelihoods. Countries’ governance performance, particularly with respect to property rights security, exerts significant impacts on land-use patterns by affecting agricultural yield-related technological investment and cropland expansion. This study aims to incorporate governance factors into a recursive agro-economic dynamic model to simulate governance impacts on land-use patterns at the global scale. Due to the difficulties of including governance indicators directly into numerical models, we use lending interest rates as discount rates to reflect risk-accounting factors associated with different governance scenarios. In addition to a reference scenario, three scenarios with high, low and mixed divergent discount rates are formed to represent weak, strong and fragmented governance. We find that weak governance leads to slower yield growth, increased cropland expansion and associated deforestation, mainly in Latin America, Sub-Saharan Africa, South Asia and Southeast Asia. This is associated with increasing food prices, particularly in Sub-Saharan Africa and Southeast Asia. By contrast, strong governance performance provides a stable political and economic situation which may bring down deforestation rates, stimulate investment in agricultural technologies, and induce fairly strong decreases in food prices.

Abstract: Global land-use models have to deal with processes on several spatial scales, ranging from the global scale down to the farm level. The increasing complexity of modern land-use models combined with the problem of limited computational resources represents a challenge to modelers. One solution of this problem is to perform spatial aggregation based on a regular grid or administrative units such as countries. Unfortunately this type of aggregation flattens many regional differences and produces a homogenized map of the world. In this paper we present an alternative aggregation approach using clustering methods. Clustering reduces the loss of information due to aggregation by choosing an appropriate aggregation pattern. We investigate different clustering methods, examining their quality in terms of information conservation. Our results indicate that clustering is always a good choice and preferable compared to grid-based aggregation. Although all the clustering methods we tested delivered a higher degree of information conservation than grid-based aggregation, the choice of clustering method is not arbitrary. Comparing outputs of a model fed with original data and a model fed with aggregated data, bottom-up clustering delivered the best results for the whole range of numbers of clusters tested. (C) 2013 Elsevier B.V. All rights reserved.

Abstract: Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty.