Spatially-explicit greenhouse gas footprints of agricultural commodities from around the world
Authors: Adlan Chaidir Arsyan , Wicke Birka , Hanssen Steef V. , Hendriks Carlijn 
Published: March 2024

Agriculture and its land use are associated with 22% of global annual anthropogenic greenhouse gas (GHG) emissions [1]. Reducing these emissions requires insight into how much emissions are caused by specific agricultural commodities and where they occur. Commodity-specific GHG footprints are a useful tool in this regard as they enable producers to determine the emission intensity and environmental impact of their products [2]-[3]. Further, they can help identify emission reduction strategies and region-specific mitigation efforts [4]-[6].
Spatially-explicit GHG footprints are particularly useful since they show the geographic distribution of commodities&#8217; emission intensity and allow for the comparison across countries [7]. Several past studies have produced crop-specific footprints but considered emissions solely from land use change and did not include emissions from agricultural practices [8]-[11]. Attribution was mostly conducted at aggregate level such as country and region level [16],[17]. Those studies that employed spatially-explicit attribution methods are characterized by limited geographical coverage [14] and a limited selection of crops [15]. Studies also applied largely different methods for attributing emissions to crops, making comparison across studies not possible.&#160;
The current study aims at filling in this research gap by improving data resolution and the methodology for attributing dynamic land-use emissions to specific crops. We derive global spatially-explicit GHG emission footprints for 161 agricultural crops over the period of 1970 to 2021 at 15 arcmin resolution. We do so by quantifying spatially-explicit land-use emissions related to agriculture and then attributing them to specific agricultural commodities (Fig1). The analysis is conducted using the LUH2 dataset on land use dynamics over time [16] and IMAGE-LPJmL 3.2 for carbon stock data [17]. IMAGE-LPJmL is a dynamic global vegetation model that simulates vegetation dynamics and distribution based on carbon cycle and crop growth model [18], [19]. This allows leveraging advanced data in terms of dynamic, annual, and spatially specific carbon stocks (Tier-3), rather than constant and/or national level carbon stock data (Tier-1) as generally used in the literature.
This study also compares three different emission attribution methods (AM) (Fig2). AM1 uses an annual accounting period, attributing emissions to the land use change committed in the same year. AM2 uses a larger time step and attributes the emissions only to the land use type at the end of the accounting period. These two methods are the two most employed methods in carbon accounting studies. We also propose an alternative approach that reflects the dynamics of land use (AM3); we attribute the emissions based on occupation year of each land use type in the accounting period.
The expected results of this study are crop-specific GHG footprints in terms of land use emissions per production area (tCO2eq/ha) and per crop yield (tCO2eq/ton) at the grid level as well as means and variations per crop and country. Also, variations as a result of different AMs will be presented and its implications for research and application in e.g. corporate emission reporting and target setting will be discussed.&#160;