Diversity and botanical composition of native species in the Pampa biome in vineyards cultivated on soils with high levels of copper, zinc and manganese and phytoremediation potential.

Viticulture allows the preservation of native species inside vineyards in the Pampa biome. However, phytosanitary treatments in these areas can increase the levels of Cu, Zn and Mn. The study aimed to (i) verify the influence of Cu, Zn and Mn contents in Pampa biome soils; (ii) identify variables related to Cu, Zn and Mn that most contribute to the variation in richness, diversity, and dry matter production of native vegetation, (iii) investigate the phytoremediation potential of species present in vineyards. Botanical composition, Cu, Zn, Mn available in the soil, and plant nutritional composition in two vineyards (V1 and V2) and native field (NF) were evaluated. Vineyards showed higher Cu, Zn and Mn contents in the soil, resulting in the lowest biomass, richness, and diversity of native species. Mn in tissue was the most important variable in explaining the variation in dry matter. Zn in the soil helped to explain the difference in species richness and diversity. P concentration in tissue was important in elucidating the variation in species diversity. Paspalum plicatulum and Paspalum notatum have potential for phytostabilization of metals in vineyards.

Esmeralda Peach (Prunus persica) Fruit Yield and Quality Response to Nitrogen Fertilization.

'Esmeralda' is an orange fleshed peach cultivar primarily used for juice extraction and secondarily used for the fresh fruit market. Fruit yield and quality depend on several local environmental and managerial factors, mainly on nitrogen, which must be balanced with other nutrients. Similar to other perennial crops, peach trees show carryover effects of carbohydrates and nutrients and of nutrients stored in their tissues. The aims of the present study are (i) to identify the major sources of seasonal variability in fruit yield and qu Fruit Tree Department of Federal University of Pelotas (UFPEL), Pelotas 96010610ality; and (ii) to establish the N dose and the internal nutrient balance to reach high fruit yield and quality. The experiment was conducted from 2014 to 2017 in Southern Brazil and it followed five N treatments (0, 40, 80, 120 and 160 kg N ha-1 year-1). Foliar compositions were centered log-ratio (clr) transformed in order to account for multiple nutrient interactions and allow computing distances between compositions. Based on the feature ranking, chilling hours, degree-days and rainfall were the most influential features. Machine learning models k-nearest neighbors (KNN) and stochastic gradient decent (SGD) performed well on yield and quality indices, and reached accuracy from 0.75 to 1.00. In 2014, fruit production did not respond to added N, and it indicated the carryover effects of previously stored carbohydrates and nutrients. The plant had a quadratic response (p < 0.05) to N addition in 2015 and 2016, which reached maximum yield of 80 kg N ha-1. In 2017, harvest was a failure due to the chilling hours (198 h) and the relatively small number of fruits per tree. Fruit yield and antioxidant content increased abruptly when foliar clrCu was >-5.410. The higher foliar P linearly decreased total titratable acidity and increased pulp firmness when clrP > 0.556. Foliar N concentration range was narrow at high fruit yield and quality. The present results have emphasized the need of accounting for carryover effects, nutrient interactions and local factors in order to predict peach yield and nutrient dosage.

Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models.

Brazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers' observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential to reduce production costs and environmental footprint without agronomic loss. Garlic root colonization by arbuscular mycorrhizal fungi likely contributed to P and K uptake. Well-documented data sets and machine learning models could support technology transfer, reduce costs with fertilizers and yield gaps, and sustain the Brazilian garlic production.

When does stratification of a subtropical soil spectral library improve predictions of soil organic carbon content?

More accurate models for the prediction of soil organic carbon (SOC) by visible-near-infrared (Vis-NIR) spectroscopy remains a challenging task, especially when the soil spectral libraries (SSL) is composed of soils with a high pedological variation. One proposition to increase the models accuracy is to reduce the SSL variance, which can be achieved by stratifying the library into sub-libraries. Thus, the main objective of this study was to evaluate whether the stratification of a SSL by environmental, pedological and Vis-NIR spectral criteria results in greater accuracy of spectroscopic models than to general models for prediction of SOC content. The performance of the models was evaluated considering the variance of soil components and sample number. In addition, we tested the effect of two spectral preprocessing techniques and two multivariate calibration methods on spectroscopic modeling. For these purposes, a SSL composed of 2471 samples from Southern Brazil was stratified based on i) physiographic region; ii) land-use/land-cover; iii) soil texture, and iv) spectral class. Two spectral processing techniques: Savitzky-Golay - 1st derivative (SGD) and continuum removed reflectance (CRR) and two multivariate methods (partial least squares regression - PLSR and Cubist) were used to fit the models. The best performances for the global and local models were achieved with the CRR spectral processing associated with the Cubist method. The stratification of the SSL in more homogeneous sample groups by environmental criteria (physiographic regions and land-use/land-cover) improved the accuracy of SOC predictions compared to pedological (soil texture) and Vis-NIR spectral (spectral classes) criteria. The reduction in the number of samples negatively affected the performance of models for sub-libraries with high pedological and spectral variation. Stratification criteria were proposed in a flowchart to assist in decision making in future studies. Our findings suggest the importance of sample balance across environmental, pedological and spectral strata, in order to optimize SOC predictions.

Mapeamento da erodibilidade e erosão potencial do solo em uma bacia hidrográfica de encosta / Mapping of erodibility and potential soil erosion in a hillside watershed

RESUMO O objetivo deste estudo foi estimar o potencial de perda de solo por meio da equação universal de perda de solo e identificar os fatores que controlam o processo erosivo em uma bacia hidrográfica de encosta. O fator erosividade da chuva (R) foi calculado por meio de dados normais de precipitação, obtendo-se, assim, índices mensais de erosão. O fator erodibilidade do solo (K) foi obtido a partir de uma amostragem de solo realizada por meio de uma malha de 340 pontos, na qual os valores foram interpolados pelo método da krigagem ordinária. O fator topográfico (LS) foi gerado a partir do Modelo Digital de Elevação (MDE), já os fatores uso e manejo e práticas conservacionistas (CP) foram obtidos por meio de observações de campo e valores tabelados. As maiores taxas de perdas abrangem 27% da área e se concentram em locais de maiores declividades nos quais predominam solos rasos, como Neossolos Litólicos e Neossolos Regolíticos. O fator LS determinou a magnitude do processo erosivo e o fator CP apresentou a maior relação com o controle das perdas de solo. Os resultados encontrados mostram a importância do fator cobertura do solo, em que medidas de manutenção da vegetação e práticas conservacionistas devem ser adotadas e consideradas pelos gestores ambientais em regiões de encosta com predomínio de solos rasos associados a relevo declivoso.

ABSTRACT The objective of this study was to estimate the soil loss potential through the universal soil loss equation and to identify the factors that control the erosive process in a hillside watershed. Rainfall erosivity factor (R) was calculated through normal precipitation data, thus obtaining monthly erosion indexes. The soil erodibility factor (K) was obtained from a soil sampling in an irregular grid of 340 points, in which the values were interpolated by the ordinary kriging method. The topographic factor (LS) was generated from the Digital Elevation Model (MDE) and the use and management and conservationist practices factors (CP) through field observations and tabulated values. The highest loss rates cover around 27% of the area, and are concentrated in places with higher slopes where shallow soils such as Litolics Neosols and Regolithic Neosols predominate. The LS factor determined the magnitude of the erosive process and the CP factor showed the highest relation with the soil loss control. The results found show the importance of the cover-management factor, in which measures of vegetation maintenance and conservation practices should be adopted and considered by the environmental managers in hillside regions with predominance of shallow soils associated to slope relief.

Modelagem espacial da erosão hídrica do solo associada à sazonalidade agroclimática na região sul do Rio Grande do Sul, Brasil / Spatial modeling of soil water erosion associated with agroclimatic seasonality in the southern region of Rio Grande do Sul, Brazil

RESUMO A erosão hídrica constitui um sério problema de degradação do solo, com impacto em diversas áreas. Sua mensuração é de extrema importância e onerosa. Os modelos empíricos de estimativa de perdas de solo, como a Equação Universal de Perda de Solo Revisada (RUSLE), são utilizados para suprir essa demanda. Consideram-se poucos estudos no Brasil que avaliam o efeito da sazonalidade agroclimática nas estimativas de perda de solo por erosão hídrica em bacias hidrográficas. Dessa forma, o objetivo deste estudo foi avaliar a sazonalidade agroclimática na estimativa de perdas de solo por meio da RUSLE e identificar os fatores que controlam a erosão na Bacia Hidrográfica do Arroio Fragata (BHAF). O fator erosividade da chuva (R) e a média anual de precipitação foram calculados por meio de dados de quatro estações pluviométricas.. O fator erodibilidade do solo (K) foi obtido a partir de informações de solo. O fator topográfico (LS) foi gerado com base no modelo digital de elevação (MDE) e o fator cobertura do solo e práticas conservacionistas (CP) por meio de imagens do satélite Landsat8/OLI. A variação sazonal teve efeito na perda de solo, com maiores taxas de erosão no período de verão e primavera. Perdas de solo entre 5 e 50 Mg ha-1ano-1 foram registradas em 24% da BHAF, associadas a períodos de chuvas mais erosivas, maior declividade e baixa cobertura vegetal. Os fatores da RUSLE com maior contribuição na erosão foram R, LS e CP. A abordagem apresentada pode ser útil para quantificar as perdas de solo em bacias hidrográficas.

ABSTRACT Water erosion is a serious soil degradation problem, with impact in several areas. Its measurement is extremely important and costly. Empirical models of soil loss estimation, such as the revised universal soil loss equation (RUSLE), are used to meet this demand. Few studies in Brazil are considered to evaluate the effect on agroclimatic seasonality in the estimates of soil loss due to water erosion in watersheds. Thus, the objective of this study was to evaluate the agroclimatic seasonality in the estimation of soil losses through RUSLE and to identify the factors that control erosion in the watershed of the Fragata stream. Rainfall erosivity (R) was calculated by means of precipitation data for four seasons and annual average. The soil erodibility factor (K) was obtained from soil information. The topographic factor (LS) was generated from the Digital Elevation Model (MDE) and the soil cover factor and conservationist practices (CP) through Landsat8/OLI satellite images. Seasonal variation had an effect on soil loss, with higher erosion rates in the summer and spring months. Soil losses between 5 and 50 Mg ha-1ano-1 were recorded in 24% of the BHAF, associated with periods of more erosive rainfall, higher slope and low vegetation cover. RUSLE factors with the greatest contribution to erosion were R, LS, and CP. The approach presented can be useful in quantifying soil losses in river basins.

Caracterização de plintitas e petroplintitas em solos da depressão central do Rio Grande do Sul / Plintites and petroplintites characterization in soils from central depression of the Rio Grande do Sul state, Brazil

A presença, composição e distribuição de concreções ferruginosas no solo, denominadas de plintitas e petroplintitas, é condicionada às características ambientais da região, atribuindo propriedades intrínsecas ao solo. O objetivo do estudo foi caracterizar plintitas e petroplintitas de solos da Depressão Central do Rio Grande do Sul através da avaliação de características químicas e mineralógicas dessas feições. Para tanto, procedeu-se à caracterização química, morfológica e granulométrica de três perfis de solo. As concreções ferruginosas presentes em alguns horizontes foram separadas da matriz do solo e submetidas, separadamente, a sucessivos procedimentos de extrações seletivas de Fe e Al. Os horizontes dos solos apresentaram ampla variação de textura e atributos químicos como pH, S, V, m e Matéria Orgânica. Os teores de Feh, Fed e Feo seguiram a tendência: matriz do solo

The presence, composition and distribution of ferruginous concretions in the soil, called plinthite and petroplinthites, are conditioned to the environmental characteristics of the region, attributing intrinsic properties to the soil. The objective of this study was to understand the composition of plinthite and petroplinthites, of soils at the Central Depression of Rio Grande do Sul state, Brazil, by the evaluation of chemical and mineralogical characteristics of these features. For this, chemical, morphological and particle size characterization of three soil profiles, was performed. The ferruginous concretions present in some horizons were separated from the soil matrix and submitted, separately to successive procedures for selective extraction of Fe and Al. The horizons of the soils showed a wide variation of textures and chemical attributes such as pH, S, V, m and MO. The levels of Feh, Feo and Fed followed the trend of the soil matrix