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Indonesia has been the largest supplier of palm oil since 2007 and now makes around 56% of the global market. While the existing literature paid major attention to the diverse impacts of oil palm plantation on socioeconomic factors and the environment, less is known on the joint role of biophysical and socioeconomic factors in shaping the temporal and spatial dynamics of oil palm expansion. This research investigates how the benefits and costs of converting other land use/cover (LULC) types to oil palm plantation affects the expansion patterns. It employs spatial panel modeling approach to assess the contributions of biophysical and socioeconomic driving factors. The modeling effort focuses on Sumatra and Kalimantan, two islands which have accounted for more than 90% of oil palm expansion in Indonesia since 1990, with Sumatra holding the majority of the country’s plantations and Kalimantan having the highest growth rate since 2000. The results showed that the expansion in Kalimantan was strongly stimulated by export value of palm oil products, took place in areas with better biophysical suitability and infrastructure accessibility, followed the pecking order sequence that the more productive areas had already been taken by the existing agriculture and plantations, and avoided areas with high environmental values or socioeconomic costs. As demand for palm oil continues to grow and land resources becomes more limited, the expansion in Kalimantan will tend to approach the dynamics in Sumatra, with plantation expanding into remote and fertile area with high conversion cost or legal barriers. Bare ground seems to have served as a clearing-up tactic to meet the procedural requirements of oil palm plantation for sustainable development. The research facilitates the improved projection of areas prone to future expansion and the development of strategies to manage the leading drivers of LULC in Indonesia.

Presentation ID 16217

Presentation ID 16216

China is the largest rice producing and consuming country in the world, accounting for more than 25% of global production and consumption. Rice cultivation is also one of the main sources of anthropogenic methane (CH4) and nitrous oxide (N2O) emissions. The challenge of maintaining food security while reducing greenhouse gas emissions is an important tradeoff issue for both scientists and policy makers. A systematical evaluation of tradeoffs requires attention across spatial scales and over time in order to characterize the complex interactions across agricultural systems components. We couple three well-known models that capture different key agricultural processes in order to improve the tradeoff analysis. These models are the DNDC biogeochemical model of soil denitrification-decomposition processes, the DSSAT crop growth and development model for decision support and agro-technology analysis, and the regional AEZ crop productivity assessment tool based on agro-ecological analysis. The calibration of eco-physiological parameters and model evaluation used the phenology and management records of 1981-2010 at nine agro-meteorological stations spanning the major rice producing regions of China. The eco-physiological parameters were calibrated with the GLUE optimization algorithms of DSSAT and then converted to the counterparts of DNDC. The upscaling of DNDC was carried out within each cropping zone as classified by AEZ. The emissions of CH4 and N2O associated with rice production under different management scenarios were simulated with the DNDC at each site and also each 1010 km grid-cell across each cropping zone. Our results indicate that it is feasible to maintain rice yields while reducing CH4 and N2O emissions through careful management changes. Our simulations indicated that a reduction of fertilizer applications by 5-35% and the introduction of midseason drainage across the nine study sites resulted in reduced CH4 emission by 17-40% and N2O emission by 12-60%, without negative consequences on rice yield.

Land-use change is increasingly driven by global trade. The term “telecoupling” has been gaining ground as a means to describe how human actions in one part of the world can have spatially distant impacts on land and land-use in another. These interactions can, over time, create both direct and spatially distant feedback loops, in which human activity and land use mutually impact one another over great expanses. In this paper, we develop an analytical framework to clarify spatially distant feedbacks in the case of land use and global trade. We use an innovative mix of multi-regional input-output (MRIO) analysis and stochastic actor-oriented models (SAOMs) for analyzing the co-evolution of changes in trade network patterns with those of land use, as embodied in trade. Our results indicate that the formation of trade ties and changes in embodied land use mutually impact one another, and further, that these changes are linked to disparities in countries' wealth. Through identifying this feedback loop, our results support ongoing discussions about the unequal trade patterns between rich and poor countries that result in uneven distributions of negative environmental impacts. Finally, evidence for this feedback loop is present even when controlling for a number of underlying mechanisms, such as countries' land endowments, their geographical distance from one another, and a number of endogenous network tendencies.

Economic globalisation refers to flows of trade and capital among and between countries. A critical view on economic globalisation is that the historical forces influencing how and when a given country becomes integrated into global trade (and the world economy as a whole) condition the potential paths of development open to that country (e.g., Ref. [1]). Here, trade and other relations between countries act as structural mechanisms enabling wealthier, more core countries to maintain favourable terms of trade, which, in turn, negatively impact less developed, more peripheral ones in a variety of ways [2, 3].

Cropland abandonment has emerged as a prevalent phenomenon in the mountainous areas of China.While there is a general understanding that this new trend is driven by the rising opportunity cost of rural labor, rigorous theoretical and empirical analyses are largely absent. This paper first develops a theoretical model to investigate household decisions on farming scale when off-farm labor market is accessible and there is heterogeneity of farmland productivity and distribution. The model is capable of explaining the hidden reasons of cropland abandonment in sloping and agriculturally less-favored locations. The model also unveils the impacts of heterogeneity of household labor on fallow decisions and the efficiency loss due to an imperfect labor market. The model is empirically tested by applying the Probit and Logit estimators to a unique household and land-plot survey dataset which contains 5258 plots of599 rural households in Chongqing, a provincial level municipality, in Southwest China. The survey shows that more than 30% of the sample plots have been abandoned, mainly since 1992. The econometric results are consistent with our theoretical expectations. This work would help policy-makers and stakeholders to identify areas with a high probability of land abandonment and farming practice which is less sustainable in the mountainous areas.