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In the light of daunting global sustainability challenges such as climate change, biodiversity loss and food security, improving our understanding of the complex dynamics of the Earth system is crucial. However, large knowledge gaps related to the effects of land management persist, in particular those human-induced changes in terrestrial ecosystems that do not result in land-cover conversions. Here, we review the current state of knowledge of ten common land management activities for their biogeochemical and biophysical impacts, the level of process understanding and data availability. Our review shows that ca. one-tenth of the ice-free land surface is under intense human management, half under medium and one-fifth under extensive management. Based on our review, we cluster these ten management activities into three groups: (i) management activities for which data sets are available, and for which a good knowledge base exists (cropland harvest and irrigation); (ii) management activities for which sufficient knowledge on biogeochemical and biophysical effects exists but robust global data sets are lacking (forest harvest, tree species selection, grazing and mowing harvest, N fertilization); and (iii) land management practices with severe data gaps concomitant with an unsatisfactory level of process understanding (crop species selection, artificial wetland drainage, tillage and fire management and crop residue management, an element of crop harvest). Although we identify multiple impediments to progress, we conclude that the current status of process understanding and data availability is sufficient to advance with incorporating management in, for example, Earth system or dynamic vegetation models in order to provide a systematic assessment of their role in the Earth system. This review contributes to a strategic prioritization of research efforts across multiple disciplines, including land system research, ecological research and Earth system modelling. [ABSTRACT FROM AUTHOR]

Northern Eurasia is the largest terrestrial reservoir of carbon, and its dynamics and interactions with climate are globally significant. We present five independent estimates of the contemporary carbon balance of central Siberia using three different methodologies: a landscape-ecosystem approach (LEA) that amalgamates comprehensive vegetation, soil, hydrological and morphological information into a Geographical Information System, linked to regression-based estimates of carbon flux; two Dynamic Global Vegetation Models (DGVMs); and two atmospheric inversions. Apart from one of the DGVMs, all methods produce estimates of the net biome productivity (NBP) that are consistent both amongst themselves and with a range of other estimates. They indicate the region to be a carbon sink with a NBP of 27.5 ± 7.2 g C m yr, which is equivalent to 352 ± 92 Mt C yr if considered representative for boreal Asia. This is comparable with fossil fuel emissions for the Russian Federation, currently estimated as 427 MtC yr, and implies that boreal Asia does not play the major role in the northern hemisphere land sink, typically estimated to be of magnitude 1.5-2.9 Gt C yr. The LEA and DGVM approaches produce very different partitioning of NBP into its component fluxes. The DGVMs find net primary production (NPP) to be nearly balanced by heterotrophic respiration, disturbance being a relatively small term pushing the system closer to equilibrium. In the LEA, heterotrophic respiration is significantly less than NPP, and disturbance plays a much larger role in the overall carbon balance. The use in the LEA of observationally based estimates of heterotrophic respiration and fire disturbance, along with a more complete description of disturbance fluxes, suggests that the partitioning derived by the LEA is more likely, and that improved process descriptions and constraints by data are needed in the DGVMs. [ABSTRACT FROM AUTHOR]

There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture., (© 2018 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)