Abstract. Afforestation has been proposed as a strategy to mitigate the often high greenhouse gas (GHG) emissions from agricultural soils with high organic matter content. However, the carbon dioxide (CO 2 ) and nitrous oxide (N 2 O) fluxes after afforestation can be considerable, depending predominantly on site drainage and nutrient availability. Studies on the full GHG budget of afforested organic soils are scarce and hampered by the uncertainties associated with methodology. In this study we determined the GHG budget of a spruce-dominated forest on a drained organic soil with an agricultural history. Two different approaches for determining the net ecosystem CO 2 exchange (NEE) were applied, for the year 2008, one direct (eddy covariance) and the other indirect (analyzing the different components of the GHG budget), so that uncertainties in each method could be evaluated. The annual tree production in 2008 was 8.3 ± 3.9 t C ha −1 yr −1 due to the high levels of soil nutrients, the favorable climatic conditions and the fact that the forest was probably in its phase of maximum C assimilation or shortly past it. The N 2 O fluxes were determined by the closed-chamber technique and amounted to 0.9 ± 0.8 t C eq ha −1 yr −1 . According to the direct measurements from the eddy covariance technique, the site acts as a minor GHG sink of −1.2 ± 0.8 t C eq ha −1 yr −1 . This contrasts with the NEE estimate derived from the indirect approach which suggests that the site is a net GHG emitter of 0.6 ± 4.5 t C eq ha −1 yr −1 . Irrespective of the approach applied, the soil CO 2 effluxes counter large amounts of the C sequestration by trees. Due to accumulated uncertainties involved in the indirect approach, the direct approach is considered the more reliable tool. As the rate of C sequestration will likely decrease with forest age, the site will probably become a GHG source once again as the trees do not compensate for the soil C and N losses. Also forests in younger age stages have been shown to have lower C assimilation rates; thus, the overall GHG sink potential of this afforested nutrient-rich organic soil is probably limited to the short period of maximum C assimilation.

Organic and low‐input systems are proposed as ways to reduce the environmental impacts of agriculture. Previous studies have shown that yields of organic systems can be ∼19 to 25% lower than conventional systems. An intermediary, low‐input system could be less damaging for the environment than conventional systems, while reducing yield losses in comparison with organic systems. In this study, we performed a meta‐analysis to compare low‐input systems to conventional and organic systems. Our analysis is based on data of cropping system experiments conducted in Europe and North America, and focuses on two important crops, maize (Zea mays L.) and soft winter wheat (Triticum aestivum L.). Pesticide use was greatly reduced for low‐input systems as compared with conventional for the two crops (50% for maize, 70% for wheat on average). Mean mineral N use was also reduced by 36% for maize and 28% for wheat in low‐input relative to conventional. Maize yields in low‐input systems were not different from those in conventional systems, and were higher than yields in organic systems (yield ratio of low‐input vs. organic = 1.24). Wheat yields in low‐input systems were lower than yields in conventional systems (yield ratio of low‐input vs. conventional = 0.88), but were substantially higher than yields in organic systems (yield ratio of low input vs. organic = 1.43). This is one of the first meta‐analyses to assess performance in terms of pesticide use intensity, and yields, with clear evidence emerging that low‐input systems can markedly reduce pesticide application, without strongly reducing crop yields, relative to conventional systems.Core Ideas Maize yields are equivalent for tested low‐input and conventional systems. Maize yields are on average 24% higher for tested low‐input relative to organic. Wheat yield average is 12% lower in tested low‐input than in conventional systems. Wheat yields are on average 43% higher for tested low‐input relative to organic. Mean pesticide use is more than halved in low‐input compared to conventional systems.

Wood ash additions of 3.3 and 6.6 t ha −1 reduced greenhouse gas (GHG) emissions from a spruce forest (Picea abies) on a minerotrophic drained organic soil. Emissions of carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O) were measured using static dark chambers for two years following the ash treatment. The CO 2 emission from the soil was significantly reduced by 17-23% by both doses during 2006-2008. The mechanism behind the reduction could not be related to a direct inhibition of soil C mineralization by the ash. The emission of N 2 O was also significantly reduced by 44 and 46% during the first year, mainly due to reductions in the winter emissions. Similar reductions of 34 and 50% were found in the second year for the low and the high wood ash, respectively. Increased pH of the soil due to the ash additions may have caused the effect. The control and amended soils consumed ambient CH 4 . The low wood ash dose increased the annual net CH 4 uptake rate by 9%, due to an increased winter uptake. No changes in tree growth could be detected over the short 2-year measurement period. The net effect of wood ash application was a reduction in the total GHG emissions during the first two years after the treatment.

Depending on the balance between sink and source processes for C, drained organic forest soil ecosystems can be in balance or act as net sinks or sources of CO 2 to the atmosphere. In order to study the effect of groundwater level and soil temperature on C-flux, the CoupModel was calibrated (climate data, groundwater levels, soil CO 2 flux, net ecosystem fluxes of CO 2 -exchange, sensible heat flux and latent heat flux, forest production etc.) for a drained forest in Sweden. Bayesian calibration techniques were used to elucidate how different parameters and variables were interlinked in C-circulation. The calibrated model reproduced abiotic and biotic variables reasonably well except for root respiration, which was largely underestimated. Bayesian calibration reduced the uncertainties in the model and highlighted the fact that calibrations should be performed with a high number of parameters instead of specific parameter values.

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