Our Common Cropland: Quantifying Global Agricultural Land Use from a Consumption Perspective

Understanding teleconnections of regional consumption patterns and global land use supports policy making towards achieving sustainable land use. We present an innovative globally consistent hybrid land-flow accounting method to track biomass flows and embodied land along global supply chains. It uses the large FAOSTAT database, which is, for non-food commodities, complemented with a multi-regional input-output model. We employ the hybrid model globally between 1995 and 2010 and present results for regional markets. Results highlight the growing integration in international markets. In 2010, 31% of cropland cultivation was for export markets compared to 16% in 1995. The higher land demand of livestock-based diets, which account for one third of global cropland use, and differences in land use intensities cause large regional variations in extents and composition of land footprints. The utilization of cropland changed towards a growing importance of the non-food sector accounting for 12% in 2010. Comparing land quality weighted cropland footprints across regions further reveals large differences in the appropriation of available global cropland productivity. Because of large uncertainties and quality differences in the actual use of grassland for feeding ruminants, we propose land quality weighted grassland footprints to discuss the additional land use for ruminant livestock products

Mapping biophysical factors that influence agricultural production and rural vulnerability

This monograph is part of a series of reports that explain how techniques of spatial analysis can be used to investigate poverty and environment links worldwide. It combines rural population distribution data contained in the global rural population database for the year 2000 with methods and results of the "Global agro-ecological assessment for agriculture in the 21st century", in order to estimate the distribution of the world\u27s rural population by agricultural suitability class, land-use category and type of farming system.--Publisher\u27s description

Improving Performance of Agro-ecological Zone (AEZ) Modeling by cross-scale Model Coupling: An Application to Japonica Rice Production in Northeast China

The challenges to food security posed by climate change require unprecedented efforts and ability to simulate and predict the interactions between crop growth dynamics, and the environment and crop management at various scales. This calls for model coupling and fusion efforts, which aims to explore the interaction of agro-ecological processes across different scales. In this research, we proposed a coupling framework between two widely-used crop models, the process-based and site-specific Decision Support System for Agro-technology Transfer (DSSAT) model, and the cropping zone centered Agro-ecological Zone (AEZ) model, with the intention to establish a coupling procedure between them, and to consequently enhance the micro foundation and improve the performance of the AEZ model. The procedure takes three major steps: (1) derive, calibrate and validate the key cultivar parameters using DSSAT, (2) translate these cultivar parameters into AEZ eco-physiological parameters and validate them using AEZ and DSSAT, (3) apply AEZ with these enhanced eco-physiological parameters and compare the new results with the old ones. An illustrative application of this procedure to japonica rice production in Northeast China is carried out for individual year between 1980 and 1999. The application results in a significant improvement in the spatial performance of the AEZ model. Calibration of the crop genetic parameters increase regional average potential yield from 6.5 t/ha, which is substantially lower than the observed yield of 7.3 t/ha in 2000, to 9.3 t/ha. Predicted rice planting areas using the refined AEZ parameterization expands significantly to coincide with the paddy field map of 2000 generated by remote sensing applications. Importantly, the procedure presents a convenient way to update the AEZ model with calibrated genetic parameters, which reflecting observed technological progresses at farm sites