What is the value of agricultural census data in carbon cycle studies?

• Published in: Journal of Geophysical Research Vol. 116; no. G3
• Main Authors: Chan, Eric C., Lin, John C.
• Format: Journal Article
• Language: English
• Published: Washington Blackwell Publishing Ltd 01.09.2011
• Subjects: Agricultural land; agricultural statistics; Agriculture; Biogeochemistry; Carbon; Carbon cycle; Carbon footprint; Census; cropland; Crops; Environmental accounting; Geobiology; Geophysics; Harvesting; High performance computing; Monte Carlo simulation; Primary production; Spatial distribution; Sustainability reporting; Systems science; uncertainties
• ISSN: 0148-0227; 2169-8953; 2156-2202; 2169-8961
• DOI: 10.1029/2010JG001617

Agricultural census data have been identified as possessing the potential to provide constraints on modeled carbon uptake by croplands at the regional scale. In this study, we build on previous efforts and further assess this potential quantitatively by comparing (1) fractional cropland coverage in southwestern Ontario, Canada, derived from agricultural statistics against three different remotely sensed land cover maps and (2) carbon uptakes determined from agricultural data with simulations generated by a satellite‐data‐driven biospheric model. In addition, we assimilated the census‐data‐derived carbon uptakes with modeled estimates in a Bayesian inverse approach to determine if the crop data can provide constraint, as exhibited by uncertainty reductions, and if so, how much. Uncertainties in census‐data‐derived gross primary production (GPP) estimates are carefully quantified using a Monte Carlo simulation. In general, results from the fractional cropland coverage comparison indicate significant value of the agricultural census data by revealing biases in the spatial distribution of croplands, as found in all three of the satellite land cover products. However, we find that the carbon uptake values derived from crop harvested records are still subject to significant uncertainties that have been underestimated or neglected altogether in past studies. The Monte Carlo simulation suggests that the largest source of uncertainty can be traced to errors in the growth efficiency, followed by harvest production records, and then the harvest index. As a result, attention must be paid to such errors when using the agricultural census data for carbon accounting purposes or to provide constraints to simulations of crop carbon uptake. Key Points Common land cover maps tend to overestimate cropland coverage in SW Ontario Agricultural census data cannot provide tight constraints to carbon models Attention must be paid when using agricultural census data for carbon accounting