ALBERT

Description: The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28). For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05 (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

Description: Land management practices can reduce the environmental impact of agricultural land use and production, improve productivity, and transform cropland into carbon sinks. In our study we assessed the biophysical and biogeochemical impacts and the potential contribution of cover crop practices to sustainable land use. We applied the process-based, global dynamic vegetation model LPJmL (Lund–Potsdam–Jena managed Land) V. 5.0-tillage-cc with a modified representation of cover crops to simulate the growth of grasses on cropland in periods between two consecutive main crops' growing seasons for near-past climate and land use conditions. We quantified simulated responses of agroecosystem components to cover crop cultivation in comparison to bare-soil fallowing practices on global cropland for a period of 50 years. For cover crops with tillage, we obtained annual global median soil carbon sequestration rates of 0.52 and 0.48 t C ha−1 yr−1 for the first and last decades of the entire simulation period, respectively. We found that cover crops with tillage reduced annual nitrogen leaching rates from cropland soils by medians of 39 % and 54 % but also the productivity of the following main crop by an average of 1.6 % and 2 % for the 2 analyzed decades. The largest reductions in productivity were found for rice and modestly lowered ones for maize and wheat, whereas the soybean yield revealed an almost homogenously positive response to cover crop practices replacing bare-soil fallow periods. The obtained simulation results of cover crop with tillage practices exhibit a good ability of the model version to reproduce observed effects reported in other studies. Further, the results suggest that having no tillage is a suitable complementary practice to cover crops, enhancing soil carbon sequestration and the reduction in nitrogen leaching, while reducing potential trade-offs with the main-crop productivity due to their impacts on soil nitrogen and water dynamics. The spatial heterogeneity of simulated impacts of cover crops on the variables assessed here was related to the time period since the introduction of the management practice as well as to environmental and agronomic conditions of the cropland. This study supports findings of other studies, highlighting the substantial potential contribution of cover crop practices to the sustainable development of arable production.

Description: Tillage is a central element in agricultural soil management and has direct and indirect effects onprocesses in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystemmodels, but global assessments are hampered by lack of information on the type of tillage and their spatialdistribution. This study describes the generation of a classification of tillage practices and presents the spatiallyexplicit 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 pur-pose within the cropping systems. We classified the broad variety of globally relevant tillage practices intosix 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 griddedcrop-specific cropland areas with a resolution of 5 arcmin. Allocation rules were based on literature findings andcombine 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 approachfor 54 countries. We provide area estimates of the six tillage systems aggregated to global and country scale. Wefound that 8.67 Mkm2of global cropland area was tilled intensively at least once a year, whereas the remaining2.65 Mkm2was tilled less intensely. Further, we identified 4.67 Mkm2of cropland as an area where ConservationAgriculture could be expanded to under current conditions.The tillage classification enables the parameterization of different soil management practices in various kindsof model simulations. The crop-specific tillage dataset indicates the spatial distribution of soil managementpractices, which is a prerequisite to assess erosion, carbon sequestration potential, as well as water, and nutrientdynamics 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 andfuture global soil management scenarios. The resulting tillage system dataset and source code are accessible viaan 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).