Direct nitrous oxide (N₂O) fluxes from soils under different land use in Brazil—a critical review

We searched English literature for N₂O-N flux data from soils under different land use across Brazil using online databases (Web of Science, Science Direct, Scielo (Brazilian)) and search engines (Google Scholar) between March 2014 and January 2015. Search queries initially included the keywords 'N₂O' AND 'soil' AND 'Brazil', which resulted in large numbers of studies (e.g. 73 studies in the Web of Science). We further specified the search by adding the keyword 'conversion'. Additional specification of the single land use types (AND 'rainforest'/'pasture'/'cropland') or geography (AND 'Amazon'/'Southern Brazil') did not result in additional studies. According to the guidelines of Aiassa et al (2015) on how to proceed on systematic reviews, we made use of personal contacts and contacted research groups in Brazil (EMBRAPA) to improve and expand our search strategies towards Portuguese studies that might have been missed using the Scielo database and due to linguistic difficulties. No time frame was set in terms of the age of the studies—the aim was to gather a good geographic coverage of Brazil. Only tabular values were analyzed; we did not extract data from graphs. Data sets were divided into three categories: (1) data from natural landscape units (Amazon forest, Atlantic forest, and cerrado), (2) land that was converted to pastures, and (3) land under agricultural management and fertilizer application. Since the third category contained long-term (one year or more), as well as short-term experiments (weeks or months), studies within this category were again divided according to the duration of the measurements. Short-term experiments usually presented as cumulative fluxes over the specific time period rather than annual emissions. Nevertheless, we treated short-term experimental data as long-term data if authors extrapolated to annual values (e.g. Metay et al 2007). Similarly, cumulative data resulting from different crops within a rotation (e.g. corn/bean rotation, Cruvinel et al 2011) were extrapolated to annual values, if the whole rotation cycle covered one year. Reported units varied among the studies, thus we converted data sets to identical units (e.g. kg N ha⁻¹ for N₂O fluxes and fertilizer inputs, or g kg⁻¹ for C_org). If N₂O fluxes were not given in annual emissions, but mean daily values were given, reported data were projected to one year. For studies which distinguished between dry and wet seasons, the length of the specific period was used in the extrapolation. Soil types were classified according to the World Reference Base (IUSS 2014). Soil texture, usually expressed as clay content, carbon content, and pH have been shown to influence N₂O-N emissions. Thus, we looked at correlations with available soil properties, as well as with the amount of applied fertilizer. The latter did not include studies including soybean because legumes are treated differently by the IPCC. Forests and cerrados were not included in the regression analysis because information on the specific soil properties were derived from the mineral soil, but not from the overlying humus layer. We differentiated between fertilized and unfertilized plots (usually pastures and croplands). Except for correlations with the amount of applied fertilizer, we only regarded fertilizer-induced emissions (N₂O-N/added N). Data from pastures were ordered according to the time since establishment, as pasture ages turned out to be a meaningful factor in forest areas converted to pastures (e.g. Wick et al 2005, Neill et al 2005).

We used the linear regression method to analyze the relationships between soil properties and N₂O-N emissions. The influence of the applied fertilizer was additionally adapted by a nonlinear model, following the suggestions of Shcherback et al (2014). Relationships were regarded as being statistically significant for a p value of (or smaller than) 0.05. The regression analyses and creation of graphics were conducted using the R software (version 2.15.0).

In general, N₂O emissions from forest soils were higher than emissions from pasture sites (e.g. Verchot et al 1999, Steudler et al 2002, Wick et al 2005, Carmo et al 2012). Reported N₂O-N fluxes from forest soils were positive without exception, but varied from 0.38 up to 16.20 kg N ha⁻¹ yr⁻¹. Rainforests (Amazonas and Atlantic rainforest) differ considerably from cerrado: the highest emission (16.20 kg N₂O-N ha⁻¹ yr⁻¹) was reported from a forest site in the Amazon, whereas the maximum emission from Atlantic forest was much lower (3.42 kg N₂O-N ha⁻¹ yr⁻¹). In contrast, emissions from cerrado sites were exceptionally low (median: 0.14 N₂O-N ha⁻¹ yr⁻¹ with a maximum of 1.19 N₂O-N ha⁻¹ yr⁻¹), and often below the detection limit (<0.6 ng cm⁻² h⁻¹).

Data presented in table 1 includes studies conducted in primary or moderately altered forests. Studies from Verchot et al (1999), Vasconcelos et al (2004), and Coutinho et al (2010) present data from secondary forests of 12, 20, and 34 years after reforestation. Here, annual N₂O-N emissions amount to 0.35, 0.94, and 0.88 kg ha⁻¹, respectively. These results are lower than the median annual emission from primary forests reported in this study and are in the range of annual emissions from pastures.

N₂O-N fluxes from pasture soils varied widely, too, but emissions from pastures younger than 10 years were significantly (p < 0.05) higher than from older pastures (table 1, figure 2). Thus, we differentiated data from pastures of 10 years and younger from those older than 10 years. Neill et al (2005) modeled the behavior of annual N₂O emissions from forests and pasture sites of different ages as an exponential function. When we fitted an exponential function to our data ($y=0.65\;+\;4.15*\mathrm{exp}(-0.10\;x),$ with y = flux of N₂O-N [kg ha⁻¹ yr⁻¹] and $x\;=\;\mathrm{pasture}$ age), we found a similar decrease in N₂O-N emissions with pasture age (figure 2).

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For short-term experiments on pastures under additional fertilization and soil management (table 3), highest emissions were reported from pastures under urine application and tillage management (5.87 and 2.23 kg N ha⁻¹). These studies were not included in the nonlinear regression (figure 2).

Highest annual emissions occurred from crop-pasture rotations (4.26 kg N ha⁻¹ yr⁻¹) and cropland under tillage treatment (2.42 kg N ha⁻¹ yr⁻¹). The overall median from cropland soils was 0.80 kg N ha⁻¹ (table 2).

We could use only 6 studies (15, 16, 22, 26, 34, and 36 in table A1erlaa1347t5) for calculating an emission factor (EF), because these allowed for subtraction of background emissions. Of these, only study 22 (Santos et al 2008) presented long-term measurements focusing on corn and bean cultivation. EFs were 0.24% for corn and 0.13% for bean. EFs from short-term experiments (table 3) ranged from 0.13 to 5.14%, with a median of 0.38%.

Except for pH (R² = 0.21, p = 0.06), correlations with N₂O-N/added N were significant (figures 3(a)–(c) and table 4), but not important. Clay contents covered a range from 13 to 86%, and the linear regression implied only a slightly increasing trend for fertilized plots (0.0009) and a slightly decreasing trend (−0.003) for unfertilized plots with higher clay contents. N₂O-N/added N slightly decreased with increasing pH (−0.03) or carbon content (−0.06). On unfertilized plots, emissions increased with increasing pH (0.41), but emissions decreased with increasing carbon content (−1.38).

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N₂O-N fluxes increased with applied N fertilizer (figure 3(d)). In their global meta-analysis, Shcherbak et al (2014) found that a nonlinear model better described the relationship between N₂O fluxes and fertilization than a linear model. In contrast, we found that a nonlinear model of the Brazilian data did not result in a better description of the relationship between emissions and fertilization compared with a linear model (R² = 0.20* for both linear and nonlinear model). Our nonlinear model ($y=0.93\;+\;1.98\cdot x-0.15\cdot {x}^{2},$ with y = N₂O-N flux [kg ha⁻¹ yr⁻¹] and x = applied fertilizer [kg N ha⁻¹]) does not compare well with that of Shcherbak et al (2014) (figure 3(d)). Besides the different intercept, which is caused by reported N₂O-N fluxes at low and even zero fertilization, our model has a lower slope, due to the low emissions. Table 4 shows more detailed results of the regressions.

4.1.1. Natural vegetation

4.1.2. Pastures

4.1.3. Croplands

4.2.1. Soil texture and structure

4.2.2. Soil chemical properties

Considerable data gaps exist for certain biomes. We found no reported N₂O emissions from the Caatinga and Pantanal biomes. Except for one site, data from croplands were derived from areas that had been established in cerrado areas, which were found to have extremely low emitting soils under natural vegetation. This lack of data hinders our ability to explain the low emissions from croplands, even after fertilizer application, and points out the need for measurements from additional land use types.

Since N₂O emissions exhibit short-termed emission peaks caused by environmental changes, high temporally resolved measurements are needed in order to explain mechanisms. Automated measurements enable continuous data acquisition, but the establishment of such studies is restricted to sites with a power supply and, for certain approaches, flat topography. Therefore, to achieve an adequate spatial measurement distribution across a large nation such as Brazil, we still have to rely on manual measurements that also take into consideration environmental (dry/wet cycles) and human induced (land conversion) changes. Biweekly measurements throughout the year, as done by most authors, are no longer suitable for increasing our understanding of biogeochemical processes. Furthermore, exact knowledge of how N₂O-N emissions change during land conversion is missing and desperately needed, since this time frame may likely account for large emission pulses that need to be accounted for in national budgeting.

Improving the existing understanding of the underlying processes, especially during land conversion can only be ensured by consistent monitoring and frequent measurements. Such monitoring data could provide the basis for further model refinement and allow for spatial and temporal extrapolations. The goal would be to develop regional solutions to improve national inventories. At this point, most process-oriented models have been developed for temperate conditions and application to tropical conditions is challenging. The different hydraulic conditions caused by micro-aggregates need to be considered, since adequate description of the soil moisture is a prerequisite for modelling N₂O-N fluxes from soils.