Abstract Brazil has the most populous and biodiverse semi-arid region in the world (Brazilian Semi-arid - SAB). However, in recent decades, clusters of desertification have emerged, a problem that could intensify from climate change. The objective of this study was to elaborate on the spatial distribution of areas susceptible to climatic desertification in the SAB, considering future climate change scenarios. Understanding this dynamic is essential for SAB's agri-environmental management. Aridity indices and proposition of climate classes for current condition (1970-2000) and future scenarios (2061-2080) of the Intergovernmental Panel on Climate Change (IPCC) were prepared, considering scenarios from Shared Socioeconomic Pathways: Optimistic (SSP 126) and pessimists (SSP 585). The results indicate that by the end of the century, the climate in the SAB should become significantly drier (Kruskal-Wallis = p-value < 0.05), with an intensification of the aridity index in SSP 585. In the scenarios, the expansion of more arid areas over humid climates could reach 56,500 km2 (10%) in SSP 126 and 140,400 km2 (24%) in SSP 585. Consequently, areas with high (622,400 km2 to 706,300 km2) and very high (622,400 km2 to 706,300 km2) are expected to expand. 4,400 to 21,700 km2) susceptibility to climate desertification in the SAB, respectively in scenarios SSPs 126 and 585. Confirming these projections would imply socioeconomic and ecological risks in the SAB. semiarid semi Brazilian Semiarid Semi . SAB) However decades emerged SABs s agrienvironmental agri environmental management 19702000 1970 2000 (1970-2000 20612080 2061 2080 (2061-2080 IPCC (IPCC prepared Pathways 585 585) century KruskalWallis Kruskal Wallis pvalue p value 0.05, 005 0.05 , 0 05 0.05) 56500 56 500 56,50 km 10% 10 (10% 12 140400 140 400 140,40 24% 24 (24% Consequently 622,400 622400 622 (622,40 706300 706 300 706,30 expand 4400 4 4,40 21700 21 700 21,70 1970200 197 200 (1970-200 2061208 206 208 (2061-208 58 00 0.0 5650 5 50 56,5 1 (10 14040 14 40 140,4 2 (24 622,40 62240 62 (622,4 70630 70 30 706,3 440 4,4 2170 21,7 197020 19 20 (1970-20 206120 (2061-20 0. 565 56, (1 1404 140, (2 622,4 6224 6 (622, 7063 7 3 706, 44 4, 217 21, 19702 (1970-2 20612 (2061-2 ( 622, (622 (1970- (2061- (62 (1970 (2061 (6 (197 (206 (19 (20

Resumo O Brasil possui a região semiárida mais populosa e de maior biodiversidade do mundo (Semiárido Brasileiro - SAB). No entanto, nas últimas décadas, núcleos de desertificação têm surgido, um problema que pode intensificar a partir de mudanças climáticas. O objetivo desse estudo foi elaborar a distribuição espacial das áreas suscetíveis à desertificação climática no SAB, considerando cenários futuros de mudanças do clima. O entendimento dessa dinâmica é algo essencial para a gestão agroambiental do SAB. Foram elaborados índices de aridez e proposição de classes climáticas para condição atual (1970-2000) e cenários futuros (2061-2080) do Painel Intergovernamental sobre Mudanças Climáticas (IPCC), levando em conta cenários do Caminhos Socioeconômicos Compartilhados: otimistas (SSP 126) e pessimistas (SSP 585). Os resultados indicam que até o final do século, o clima no SAB deverá tornar-se significativamente mais seco (Kruskal-Wallis = p-value < 0,05), com intensificação do índice de aridez no SSP 585. Nos cenários, a expansão de áreas mais áridas sobre climas úmidos pode alcançar 56.500 km2 (10%) no SSP 126 e 140,400 km2 (24%) no SSP 585. Consequentemente, espera-se expansão das áreas com alta (622,400 km2 a 706,300 km2) e muito alta (4,400 a 21,700 km2) suscetibilidade à desertificação climática no SAB, respectivamente nos cenários SSPs 126 e 585. Confirmando essas projeções, implicaria em riscos socioeconômicos e ecológicos no SAB. Semiárido . SAB) entanto décadas surgido 19702000 1970 2000 (1970-2000 20612080 2061 2080 (2061-2080 IPCC, IPCC , (IPCC) Compartilhados 585 585) século tornarse tornar se KruskalWallis Kruskal Wallis pvalue p value 0,05, 005 0,05 0 05 0,05) 56500 56 500 56.50 km 10% 10 (10% 12 140400 140 400 140,40 24% 24 (24% Consequentemente esperase espera 622,400 622400 622 (622,40 706300 706 300 706,30 4,400 4400 4 (4,40 21700 21 700 21,70 projeções 1970200 197 200 (1970-200 2061208 206 208 (2061-208 (IPCC 58 00 0,0 5650 5 50 56.5 1 (10 14040 14 40 140,4 2 (24 622,40 62240 62 (622,4 70630 70 30 706,3 4,40 440 (4,4 2170 21,7 197020 19 20 (1970-20 206120 (2061-20 0, 565 56. (1 1404 140, (2 622,4 6224 6 (622, 7063 7 3 706, 4,4 44 (4, 217 21, 19702 (1970-2 20612 (2061-2 ( 622, (622 4, (4 (1970- (2061- (62 (1970 (2061 (6 (197 (206 (19 (20

Resumen El avance de los modelos predictivos mediante Machine Learning Algorithms (ML) asociados a datos ambientales permite mejorar los modelos de fragilidad ambiental, que son herramientas fundamentales para la toma de decisiones. Este estudio tuvo como objetivo derivar una predicción de la fragilidad ambiental mediante la prueba de ML asociado con covariables ambientales en el estado de Minas Gerais. Se utilizaron variables físico-ambientales (suelo, geología, clima, relieve) con peso de fragilidad para los atributos y cálculo de la media para obtener un modelo de Fragilidad Ambiental Potencial (PEF). Posteriormente, extrajimos los valores de PEF a una cuadrícula de 4800 puntos, que se utilizó para generar una nueva predicción de ML, llamada PEFML. Esta predicción se basó en la prueba de cinco algoritmos y un conjunto de 105 covariables ambientales. Los resultados indicaron que la predicción PEFML con mejor desempeño fue el modelo Random Forest (R2 0.59 y RMSE 0.47), indicando un predominio del bajo nivel de fragilidad ambiental. Los modelos PEF y PEFML muestran fuertes correlaciones (0,7 Pearson); sin embargo, PEFML tiene correlaciones más fuertes con otros datos ambientales. Por lo tanto, la predicción PEFML es un modelo robusto que captura información de covariables y tiene patrones espaciales coherentes. (ML decisiones Gerais físicoambientales físico suelo, suelo (suelo geología clima relieve PEF. . (PEF) Posteriormente 480 puntos 10 R2 R (R 059 0 59 0.5 0.47, 047 0.47 , 47 0.47) 0,7 07 7 (0, Pearson Pearson) embargo tanto coherentes (PEF 48 1 05 5 0. 04 0.4 4 0, (0 (

Abstract The advancement of predictive models by Machine Learning Algorithms (ML) associated with environmental data enables the improvement of models of environmental fragility, which are essential tools for decision-making. This study aimed to derive a prediction of environmental fragility by testing ML associated with environmental covariates in the state of Minas Gerais. We use physical-environmental variables (soil, geology, climate, relief) with a weight of fragility for the attributes and calculation of the average to obtain a model of Potential Environmental Fragility (PEF). Subsequently, we extracted the PEF values to a 4,800-point grid, which was used to generate a new prediction by ML called PEFML. This prediction was based on testing five algorithms and a set of 105 environmental covariates. The results indicated that the best-performing PEFML prediction was the Random Forest model (R2 0.59 and RMSE 0.47), indicating a predominance of the low environmental fragility level. The PEF and PEFML models have strong correlations (0.7 Pearson); however, PEFML has stronger correlations with other environmental data. Therefore, the PEFML prediction is a robust model that captures information from covariates and has coherent spatial patterns. (ML decisionmaking. decisionmaking decision making. making decision-making Gerais physicalenvironmental physical soil, soil (soil geology climate relief PEF. . (PEF) Subsequently 4,800point 4800point point 4,800 4 800 grid 10 bestperforming best performing R2 R (R 059 0 59 0.5 0.47, 047 0.47 , 47 0.47) level 0.7 07 7 (0. Pearson Pearson) however Therefore patterns (PEF 800point 4800 4,80 80 1 05 5 0. 04 0.4 (0 480 4,8 8 ( 48 4,

Resumo O avanço de modelos preditivos por Algoritmos de Aprendizado de Máquina (ML) associados à dados ambientais possibilita aprimoramento de modelos de fragilidade ambiental, os quais são importantes ferramentas para tomada de decisão. O objetivo desse estudo foi derivar uma predição de fragilidade ambiental, testando ML associados a covariáveis ambientais no estado de Minas Gerais. Utilizamos variáveis físico-ambientais (solo, geologia, clima, relevo) com peso de fragilidade para os atributos e cálculo da média obtendo o modelo de Fragilidade Ambiental Potencial (PEF). Posteriormente, extraímos os valores de PEF para uma grade de 4.800 pontos e usadas para gerar uma nova predição por ML, denominada PEFML. A predição foi com teste de cinco algoritmos e conjunto de 105 covariáveis ambientais. Comparamos os dois modelos de fragilidade ambiental (PEF e PEFML), inclusive com outros dados de riscos/vulnerabilidade/fragilidade. Os resultados indicaram que a predição de PEFML de melhor desempenho foi o modelo Random Forest (R2 0.59 e RMSE 0.47), indicando predomínio do nível fragilidade baixa. Os modelos de fragilidade PEF e PEFML têm forte correlação (0.7 Pearson), porém, PEFML possui correlações mais fortes com outros dados ambientais. Portanto, a predição de PEFML é um modelo robusto que capta informações de covariáveis e possui padrões espaciais coerentes (ML decisão Gerais físicoambientais físico solo, solo (solo geologia clima relevo PEF. . (PEF) Posteriormente 4800 4 800 4.80 10 PEFML, , PEFML) riscosvulnerabilidadefragilidade riscos vulnerabilidade riscos/vulnerabilidade/fragilidade R2 R (R 059 0 59 0.5 0.47, 047 0.47 47 0.47) baixa 0.7 07 7 (0. Pearson, Pearson Pearson) porém Portanto 480 80 4.8 1 05 5 0. 04 0.4 (0 48 8 4. (

ABSTRACT The term Post-Barreiras is a definition for sediments above the deposits of the Barreiras Formation, and the genesis of soils in these environments must be related to sedimentary deposition. Our objective was to apply multi-technique analyses to characterize the sediments and soils to understand pedogenesis in these environments. We analysed sedimentological parameters and the geochronology of sediments. Morphological, chemical, and mineralogical analyses allowed the characterization of the soil. Also, these data supported the analysis of lithological discontinuity. We considered the contents of Ti and Zr, uniformity value, the fraction of organic material, morphology, and palynological analysis. The age of Post-Barreiras sediments is from the Pleistocene, and they have a more significant variation of sedimentological parameters concerning Barreiras Formation layers. In general, the soils are sandy, acidic, have a low level of exchangeable cations. Mineralogy has a predominance of quartz and kaolinite minerals. In the region, there are soils with low morphological variation, classified as Quartzipsamments. In other cases, there are soils with apparent spodic morphology, which is conditioned by four aspects: (i) Podzolization in Post-Barreiras sandy sediments without evidence of lithological discontinuity, forming Bs horizon (Spodosols); (ii) contact zones (Post-Barreiras/Barreiras Formation) with physical, chemical, and morphological evidence of discontinuity, forming Quartzipsamments or Ultisols; (iii) layers of the Barreiras Formation buried by Post-Barreiras sediments and the subsequent podzolization process, forming Bhm horizon (Spodosols); and (iv) destruction of Ultisols clay, forming Bs horizon (Spodosols). The sedimentary association (Post-Barreiras/Barreiras Formation) favors the development of different soils. The contact zones generate a morphological aspect similar to the Spodosols, associated or not with podzolization processes.

ABSTRACT The objective of this study was to map the levels of degradation of pastures and its association with chemical properties of the soil and relief. We selected a sub-basin located in the municipality of Viçosa, Minas Gerais, and we mapped the degradations through Ikonos images that were submitted for classification. Soil samples were collected and analyzed chemically; the results were transformed in spatial data using cokriging, based on variables of the digital elevation model. We associated the maps (cokriging) with data from exposure of the hillside, levels of solar radiation and levels of the pastures’ degradation. The pastures were mainly classified in moderate (44%) and very strong (30%) degradation levels. The areas with mild degradation tend to increase when there is a higher sum of bases, cationic exchange capacity and organic matter. The portions facing North and the increase of solar radiation showed more advanced stages of degradation.

ABSTRACT: Ultisols are the most common soil order in the Brazilian Amazon. The Legal Amazon (LA) has an area of 5 × 106 km2, with few accessible areas, which restricts studies of soils at a detailed level. The pedological properties can be estimated more efficiently using statistical procedures and machine learning techniques, tools which are capable of recognizing patterns in a large soil database. We analyzed the main chemical and physical properties of the B horizons of the Ultisols of the Brazilian Amazon, as well as the spatial variability of the most explanatory properties of these horizons. Physical and chemical data of 1,068 profiles of the RadamBrasil Project were used. A principal component analysis (PCA) was applied and the most explanatory variables were separated by morphostructural units and climate zones. The technique of machine learning was used for spatialization of the explanatory variables based on predictive covariates. In general, the horizons are thick, clay, with a predominance of negative charges, and low levels of exchangeable cations. The variables retained in the PCA were: sum of bases (SB), Al3+, degree of flocculation (Floc), ∆pH, and organic carbon content (C). Areas of greater precipitation have low SB, with higher values in the basement complex (BC) and in areas under the Andean influence. Higher levels of Al3+ and degrees of flocculation were also associated with greater precipitation. However, the soils are predominantly electronegative, showing a kaolinitic mineralogy. The C contents in general were low, with an increase in more humid zones due to the process of mineralization and illuviation (podzolization), and in the BC due to the protection of C by the aggregation of clay. The use of multivariate analysis allowed a better understanding of the Ultisols’ main properties in different morphostructural and climatic domains, and its spatialization facilitated the interpretation of properties and their relationships with environmental characteristics in the Legal Amazon.

Resumo Embora haja alguns trabalhos a partir da década de 80, pouco se sabe sobre a origem e mecanismos de depósitos destes sedimentos. O objetivo deste estudo foi analisar os parâmetros sedimentológicos, morfologia granulométrica e datação dos sedimentos. Foram coletadas amostras de Pós-Barreiras e sedimentos de praia, na qual foram submetidas a dispersão da argila e silte, com uso de NaOH 0,1 mol L-1; a fração areia foi separada em intervalos de 1/2 phi [Φ]. Foi analisado a morfologia dos grãos via microscópio ótico. Empregou-se a datação em amostras pelo método de Luminescência Opticamente Estimulada. Os resultados indicam que os sedimentos Pós-Barreiras foram depositados em ambiente de elevada energia, presumivelmente pela contribuição maior de processos marinhos com possíveis retrabalhamentos eólicos. Os sedimentos de praia frontal apresentaram características de deposição eólica. As datações definiram que a deposição dos sedimentos Pós-Barreiras foi do Pleistoceno tardio até o Holoceno (de 6.9 a 85.4 ap). A posição altitudinal dos sedimentos Pós-Barreiras (40 a 150 m), não pode ser associada a qualquer nível de elevação mar conhecida, o que surge a hipótese de processos de neotectônicos promovendo a elevação.

Abstract Although there is some work from the 80s, little is known about the origin and mechanisms of these sediment deposits Post-Barreiras. The aim of this work was to analyze the sedimentological parameters, particle size and morphology of grains of sand and dating of sedments. samples Post-Barreiras and beach sediments were collected, which were subjected to dispersion of clay and silt, using NaOH 0,1 mol L-1; the sand fraction was separated at intervals of 1/2 phi [Φ]. The morphology of the grain was analyzed through optical microscope. We used the dating of three samples by the method of Optically Stimulated Luminescence. Results indicate that the Post-Barreiras sediments they were deposited at high energy environment, presumably by a higher contribution of marine processes with possible aeolian reworkings. Beach front sediments showed characteristics of aeolian deposition. The dates determined that the deposition of Post-Barreiras sediments was from the late Pleistocene to Holocene epoch (from 6,950 to 85,350ka). The altitudinal position of Post-Barreiras sediments (40-150m) cannot be associated to any known sea elevation level, which then emerges the hypothesis of neotectonic processes promoting elevation.

Resumen Aunque hay algunas obras de los años 80, poco se sabe sobre el origen y los mecanismos de los depósitos de sedimentos la formación Barreiras. El objetivo del presente trabajo fue analizar los parámetros sedimentológico, morfología de los granos y la datación de sedimentos. Se recogieron muestras de sedimentos Pós-Barreiras y sedimentos de la playa, en el que se sometieron a la dispersión de arcilla y limo, utilizando NaOH 0,1 mol L-1; la fracción de arena se separó a intervalos de 1/2 phi [Φ] y la morfología del grano se analizó por medio de microscopio óptico. Utilizó la datación de muestras por el método de luminiscencia ópticamente estimulada. Los resultados indican que los sedimentos Pós-Barreiras fueron depositados en ambiente de alta energía, presumiblemente por el aumento de la contribución de los procesos marinos con posible reelaboración viento. Los sedimentos de la playa delanteros mostraron características eólicas. Las edades determinaron que la deposición de los sedimentos Pós-Barreiras fue a finales del Pleistoceno al Holoceno (de 6.9 a el 85,4 ka). La posición latitudinal de los sedimentos Pós-Barreiras (40 a 150 m) no se puede asociar con cualquier aumento del nivel del mar conocido, posiblemente, la que aparece que procesos neotectónicos han promovido la elevación.