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Generating a global gridded tillage dataset

Abstract. Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on type and spatial distribution. This study describes the generation of a global classification of tillage practices and the spatially explicit mapping of crop-specific tillage systems for around the year 2005. Tillage practices differ by the kind of equipment used, soil surface and depth affected, timing, and their purpose within the cropping systems. We classified the broad variety of globally relevant tillage practices into six tillage systems. The identified tillage systems were allocated to crop-specific cropland areas with a resolution of 5 arc-minutes. The allocation rules were based on literature findings and combine area information on crop type, water management regime, field size, water erosion, income, and aridity. We allocated national Conservation Agriculture areas to grid cells via a probability-based downscaling approach for 54 reporting countries. The dynamic definition of the allocation rules and accounting for national statistics, such as the share of Conservation Agriculture per country, also allows for deriving datasets for future global soil management scenarios. We present the mapping of six tillage systems: no-tillage in the context of Conservation Agriculture (1.1 Mkm2), traditional annual (4.01 Mkm2), traditional rotational (0.65 Mkm2), rotational (0.74 Mkm2), reduced (0.15 Mkm2), and conventional annual tillage (4.65 Mkm2). Further we identified a total area of 4.67 Mkm2 ha as potentially suitable area for Conservation Agriculture under assessed current conditions. We elaborate on the results of a sensitivity analysis for our downscale approach as well compare tillage system area results to literature estimates. The presented tillage dataset and source code are accessible via an open-data repository for modeling communities interested in the quantitative assessment of biophysical and biogeochemical impacts of land use and soil management (DOIs: https://doi.org/10.5880/PIK.2018.012 and https://doi.org/10.5880/PIK.2018.013 (Porwollik et al., 2018a, b)).
