04807nam a2200829 a 450000100080000000500110000800800410001910000200006024500910008026001760017152023990034765300200274665300240276665300270279065300180281765300230283565300250285865300270288370000230291070000190293370000260295270000200297870000200299870000160301870000210303470000240305570000260307970000180310570000260312370000160314970000170316570000200318270000220320270000170322470000180324170000170325970000230327670000260329970000240332570000210334970000220337070000220339270000220341470000230343670000240345970000200348370000210350370000220352470000170354670000160356370000190357970000150359870000170361370000170363070000220364770000140366970000180368370000190370170000270372070000160374770000230376370000210378670000230380770000220383070000180385270000160387070000150388670000200390170000220392170000180394370000160396121011952025-07-10       2019    bl uuuu     u00u1 u    #d1 aSAMUEL-ROSA, A.  aBringing together Brazilian soil scientists to share soil data.h[electronic resource]  aIn: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: proceedings... Viçosa, MG: SBCS, 2019. v. 1, p. 63-64. WCSS 2018.c2018  aIn the Amazon region, types of soil known as Archaeological Black Lands (ABL) present anthropic horizon A and are associated with prolonged human occupation by indigenous societies from the pre-Columbian period, where chemical and physical attributes have better quality than other types of soil in the Amazon, setting a large organic carbon reservoir. However, the conversion of these natural ecosystems into cultivated environments make emerge changes in soil carbon dynamics, often leading to a decline in soil organic carbon content. Therefore, our aim was to use data mining techniques to generate predictive models for the effect of soil use on carbon stock in natural and transformed areas of Archaeological Black Lands. We carried out our experiment in Manicoré and Apuí, Amazonas State, Brazil. After field data collection and laboratory analysis, we obtained a set of data consisting of 21 attributes, 20 predictive attributes consisting of 13 soil physical attributes, 6 soil chemical attributes, 1 soil use related attribute, and 1 response variable, referring to soil carbon stock (SCS), which is the classification target. Due to the large number of attributes, we performed a selection procedure to eliminate attributes of low correlation to the response variable. For data classification, we used the binary induction technique of the decision tree through software Weka 3.8. The results obtained showed that for the depth of 0.00-0.05 and 0.05-0.10 m, the best selected subset was determined using the Wrapper method for attribute selection. In the depth of 0.00-0.05 m, we generated a model of 79% accuracy containing only six rules, including, as the most important classification attribute was soil use. For the depth of 0.05-0.10 m, we generated an eight-rule decision tree of 74% accuracy including sand as the most important attribute. In this context, we highlight the Wrapper method efficiency to select subsets of predictive attributes, capable to generate more understandable decision trees, using a smaller number of attributes in the classification process, making it faster and with a lower computational cost. In addition, data mining techniques were efficient at providing predictive models capable to assist the decision-making process on possible management practices with the potential to conserve or increase soil carbon stock in archeological black lands.  aAnthropic soils  aÁrvore de decisão  aData mining techniques  aDecision tree  aEstoque de carbono  aMineração de dados  aSoil management system1 aDALMOLIN, R. S. D.1 aGUBIANI, P. I.1 aOLIVEIRA, S. R. de M.1 aTEIXEIRA, W. G.1 aVIANA, J. H. M.1 aRIBEIRO, E.1 aTORNQUIST, C. G.1 aANJOS, L. H. C. dos1 aSOUZA, J. J. E. L. de1 aOTTONI, M. V.1 aMEDEIROS, P. S. C. de1 aGRIS, D. J.1 aROSIN, N. A.1 aBUENO, J. M. M.1 aSANTOS, H. G. dos1 aWEBER, E. J.1 aFLORES, C. A.1 aCOSTA, E. M.1 aOLIVEIRA, R. P. de1 aFILIPPINI ALBA, J. M.1 aPEDROSO NETO, J. C.1 aPEDRON, F. de A.1 aCAVIGLIONE, J. H.1 aVALLADARES, G. S.1 aMIRANDA, C. S. S.1 aDEMATTÊ, J. A. M.1 aMARQUES JÚNIOR, J.1 aSIQUEIRA, D. S.1 aAQUINO, R. E. de1 aSILVERO, N. E. Q.1 aGENÚ, A. M.1 aBROETTO, T.1 aCANCIAN, L. C.1 aMIGUEL, P.1 aZALAMENA, J.1 aDOTTO, A. C.1 aALMEIDA, J. A. de1 aREICHERT.1 aCURCIO, G. R.1 aCOLLIER, L. S.1 aCARVALHO JUNIOR, W. de1 aFONTANA, A.1 aOLIVEIRA, A. P. de1 aVOGELMANN, E. S.1 aMALLMANN, F. J. K.1 aVASQUES, G. de M.1 aLEPSCH, I. F.1 aFINK, J. R.1 aKER, J. C.1 aSILVA, L. S. da1 aFREITAS, P. L. de1 aBIELUCZYK, B.1 aTIECHER, T.