Estoque de carbono em solos brasileiros e potencial de contribuição para mitigação de emissões de gases de efeito estufa - Cidin, Ana Carolina Martins (2016)
In recent years, Brazil has proposed policies to reduce emissions of greenhouse gases (GEE). In this context, the aim of this study was to estimate the carbon stock (EC) from different Brazilian soils under different agricultural uses and propose strategies that contribute to mitigation of GEE emissions. The research was conducted in four stages: i) organization of a soil database; ii) development of a pedotransfer function (PTF) for the estimation of bulk density (DS) and evaluate the effect on estimate of EC; iii) estimation of the EC; and iv) evaluation of potential EC by Brazilian agriculture. Data from 38.456 soil samples were performed and, after standardization, they formed a database with 10.445 data samples corresponding to 5.823 data for the 0-30 cm layer. These data covered all Brazilian states, all classes of the Brazilian System of Soil Classification and nine types of land use: annual crop in no-tillage system (SPD), annual crop in conventional tillage system, perennial crop, planted forest, integrated crop-livestock (ILP) system, integrated crop-livestock-forest (ILPF) system, pasture, uncovered soil and native vegetation. Many samples had no DS record, then 12 PTF for DS estimation were developed using 974 soil samples. The performance of PTFs was assessed by R2, and in the validation, the accuracy of prediction was measured based on the mean error (ME), the mean absolute error (MAE), and the root mean squared error (RMSE). All functions overestimated DS values and one of them (PTF 5) presented the best performance. The evaluation of the estimated EC was made with 926 samples layer 0-30 cm, using observed data DS (ECobs), estimated data DS from the PTF5 (ECest) and estimated data DS from the null model (ECnull), in which the DS was given by the mean value of DS observed. Based on the calculation of ME, MAE, the RMSE and comparison with ECobs values, it was found that the ECnull values were overestimated and dispersed. It was concluded at this stage that the null model was not a reliable alternative and PTF5 was applied in 4.540 samples from 0-30 cm layer with missing DS. The estimated DS values ranged from 0,10 kg dm-3 at 1,92 kg dm-3 with a mean of 1,39 kg dm-3 and standard deviation of 0,19 kg dm-3. The coefficient of variation was less than 15% configuring a homogeneous data. It was made the EC calculation for 5.823 EC data for the layer 0-30 cm and these values were grouped by types of land use and soil types. In grouping by type of land use, the lowest EC values were observed in annual crop and native vegetation (0,10 Mg ha-1) and the largest maximum values were observed in annual crop, pasture and native vegetation, with 297,3 Mg ha-1, 259,9 Mg ha-1 and 253,6 Mg ha-1 respectively. In grouping by type of soil, it was observed that a minimum value of 0,10 Mg ha-1 in an Argisol while maxima were observed in a Cambisol (297,3 Mg ha-1) and an Argisol (265,8 Mg ha-1). Three scenarios were developed to estimate the potential increase of EC, from changes in management practices and land use. In a scenario where 18% of the areas of annual crops adopt SPD, the increase in EC was 73,6 Gg. If 15 million hectares of degraded pastures were recovered with ILP system in 20% of the area, and ILPF system in 10% of the area, the potential for increasing the EC would be 88.13 Gg. These values could represent an addition of 59,6 Gg of CO2, which could offset the 472,1 Gg CO2 emitted by the Brazilian agricultural sector in 2010, according to estimates by the Ministry of Science, Technology and Innovation. It concludes that: i) the absence of DS data, using a simple PTF is more appropriate than the use of the null model; ii) the detected errors in the estimation of DS by PTF not propagate the EC calculation; iii) the proposed changes would offset emissions from the Brazilian agricultural sector; and iv) the estimates presented highlight the role of the agricultural sector in mitigating GEE emissions.