Generating a rule-based global gridded tillage dataset

Abstract

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 the type of tillage and their spatial distribution. This study describes the generation of a classification of tillage practices and presents the spatially explicit mapping of these 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 categories: no-tillage in the context of Conservation Agriculture, traditional annual, traditional rotational, rotational, reduced, and conventional annual tillage. The identified tillage systems were allocated to gridded crop-specific cropland areas with a resolution of 5 arcmin. 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 scaled reported national Conservation Agriculture areas down to grid cells via a probability-based approach for 54 countries. We provide area estimates of the six tillage systems aggregated to global and country scale. We found that 8.67 Mkm2 of global cropland area was tilled intensively at least once a year, whereas the remaining 2.65 Mkm2 was tilled less intensely. Further, we identified 4.67 Mkm2 of cropland as an area where Conservation Agriculture could be expanded to under current conditions. The tillage classification enables the parameterization of different soil management practices in various kinds of model simulations. The crop-specific tillage dataset indicates the spatial distribution of soil management practices, which is a prerequisite to assess erosion, carbon sequestration potential, as well as water, and nutrient dynamics of cropland soils. The dynamic definition of the allocation rules and accounting for national statistics, such as the share of Conservation Agriculture per country, also allow for derivation of datasets for historical and future global soil management scenarios. The resulting tillage system dataset and source code are accessible via an open-data repository (DOIs: https://doi.org/10.5880/PIK.2019.009 and https://doi.org/10.5880/PIK.2019.010, Porwollik et al., 2019a, b).

Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture

“…Most croplands in China, India and Sub-Sahara Africa, where traditional tillage is widely practiced, are therefore likely to benefit from conservation farming, including for climate mitigation. In regions with HI > 100 (cold humid and tropical humid climates, consistent with Porwollik, Rolinski, Heinke, & Müller, 2019; Figures 4a and 5), the shift to no-till, even with residue retention and crop rotation, is likely to result in negative outcomes. In cool, moist environments, decreased soil temperature and waterlogging from mulch cover may have a disadvantage for early crop seed germination and crop growth, which is associated with lower crop yield (Blanco-Canqui & Ruis, 2018;Pittelkow et al, 2015).…”

Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global gridded crop model

“…Indeed, a proper reconstruction of a business-as-usual field management is important to further narrow down the uncertainty in global crop modelling (Folberth et al, 2019). In this study we allocated prevailing field management using a set of environmental-and country-specific indicators, similarly to Porwollik et al (2019). For example, we accounted for conservation agriculture only in countries where this management strategy is likely according to AQUASTAT (FAO, 2016).…”

Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble

“…The same is the case for tillage practices including residue management, which may also vary in time subject to farmers’ management preferences, economic opportunities, and incentives for specific agricultural practices. Albeit spatial data on representative management practices such as tillage systems are in the process of being compiled globally [100] and remote sensing products may allow for spatial attribution of field management practices in the future [101,102], employing an ensemble to bracket uncertainties appears the most robust approach meanwhile. Nevertheless, increasing the spatial resolution will be required to better reflect actually cultivated soils [27] or microclimate [99] at the sub-grid scale.…”

“…Cultivar specifications, however, are difficult to translate between models and will therefore require new data management tools as proposed by Porter et al [113]. Global tillage practices–albeit not relevant for all GGCMs (Table A in S1 File)—are presently being compiled [100] and will likely become publically available in the near future.…”