RESUMO

The present study had the objective to evaluate the effect of blends of KCl and K2SO4 fertilizers and their influence on the yield and the nutritional state of coffee plants, as well as on the chemical composition and quality of the coffee beverage. The experimental design was in randomized blocks with four repetitions and six treatments (T1: 100% KCl; T2: 75% KCl + 25% K2SO4; T3: 50% KCl + 50% K2SO4; T4: 25% KCl + 75% K2SO4; T5: 100% K2SO4; and a control, without application of K). The following analyses were performed: K and Cl content in the leaves and the soil, stocks of Cl in soil, yield, removal of K and Cl with the beans, cup quality of the beverage, polyphenol oxidase activity (PPO), electric conductivity (EC), potassium leaching (KL), the content of phenolic compounds, the content of total sugars (TS), and total titratable acidity (TTA). The stocks of Cl in the soil decreased as the proportion of KCl in the fertilizer was reduced. The fertilization with KCl reduces the cup quality and the activity of the polyphenol oxidase, probably due to the ion Cl. The increase in the application of Cl directly relates to the increase in potassium leaching, electric conductivity, and titratable acidity. Indirectly, these variables indicate damages to the cells by the use of Cl in the fertilizer. The activity of the polyphenol oxidase enzyme and the cup quality indicate that the ion Cl- reduces the quality of the coffee beverage. K content in the leaves was not influenced by the application of blends of K fertilizer while Cl content increased linearly with KCl applied. The application of KCl and K2SO4 blends influenced coffee yield and the optimum proportion was 25% of KCl and 75% of K2SO4. The highest score in the cup quality test was observed with 100% K2SO4.

RESUMO

In Southeast Brazil, the change of grape harvest from wet summer to dry winter through double-pruning management has improved the quality of wines, currently denominated winter wines. In order to better understand the influences of soil, macroclimate, and vineyard management in winter wines, we investigated seven vineyards in the states of Minas Gerais (Três Corações TC, Três Pontas TP, Cordislândia COR, São Sebastião do Paraíso SSP and Andradas AND) and São Paulo (Itobi ITO and Espirito Santo do Pinhal PIN) during three consecutive growing seasons. The vineyards are located in warm temperate zones and grouped in four soil types: Acrudox in TC, AND and SSP, Hapludox in TP; Hapludult in AND and PIN; Eutrudept in ITO. The high clay content (> 35 %) observed in all soil types, associated to low evapotranspiration demand, avoided the occurrence of severe water stress, as observed by the high values of leaf and stem water potential, stomatal conductance, photosynthesis, and transpiration. Differences in vigor were more related to vineyard management and did not affect grape composition. Among vineyards, parameters for berry quality from ITO, such as sugar and acidity, were more associated to high soil sand content and winter temperature. No significant differences were found in anthocyanins and total phenols of berries among vineyards, suggesting that the high thermal range and low precipitation during autumn-winter, historically observed in all municipalities, seemed to be the main factor for improvement of phenolic compounds.

Assuntos

RESUMO

In Southeast Brazil, the change of grape harvest from wet summer to dry winter through double-pruning management has improved the quality of wines, currently denominated winter wines. In order to better understand the influences of soil, macroclimate, and vineyard management in winter wines, we investigated seven vineyards in the states of Minas Gerais (Três Corações TC, Três Pontas TP, Cordislândia COR, São Sebastião do Paraíso SSP and Andradas AND) and São Paulo (Itobi ITO and Espirito Santo do Pinhal PIN) during three consecutive growing seasons. The vineyards are located in warm temperate zones and grouped in four soil types: Acrudox in TC, AND and SSP, Hapludox in TP; Hapludult in AND and PIN; Eutrudept in ITO. The high clay content (> 35 %) observed in all soil types, associated to low evapotranspiration demand, avoided the occurrence of severe water stress, as observed by the high values of leaf and stem water potential, stomatal conductance, photosynthesis, and transpiration. Differences in vigor were more related to vineyard management and did not affect grape composition. Among vineyards, parameters for berry quality from ITO, such as sugar and acidity, were more associated to high soil sand content and winter temperature. No significant differences were found in anthocyanins and total phenols of berries among vineyards, suggesting that the high thermal range and low precipitation during autumn-winter, historically observed in all municipalities, seemed to be the main factor for improvement of phenolic compounds.(AU)

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RESUMO

Arsenic accumulation in the environment poses ecological and human health risks. A greater knowledge about soil total As content variability and its main drivers is strategic for maintaining soil security, helping public policies and environmental surveys. Considering the poor history of As studies in Brazil at the country's geographical scale, this work aimed to generate predictive models of topsoil As content using machine learning (ML) algorithms based on several environmental covariables representing soil forming factors, ranking their importance as explanatory covariables and for feeding group analysis. An unprecedented databank based on laboratory analyses (including rare earth elements), proximal and remote sensing, geographical information system operations, and pedological information were surveyed. The median soil As content ranged from 0.14 to 41.1 mg kg-1 in reference soils, and 0.28 to 58.3 mg kg-1 in agricultural soils. Recursive Feature Elimination Random Forest outperformed other ML algorithms, ranking as most important environmental covariables: temperature, soil organic carbon (SOC), clay, sand, and TiO2. Four natural groups were statistically suggested (As content ± standard error in mg kg-1): G1) with coarser texture, lower SOC, higher temperatures, and the lowest TiO2 contents, has the lowest As content (2.24 ± 0.50), accomplishing different environmental conditions; G2) organic soils located in floodplains, medium TiO2 and temperature, whose As content (3.78 ± 2.05) is slightly higher than G1, but lower than G3 and G4; G3) medium contents of As (7.14 ± 1.30), texture, SOC, TiO2, and temperature, representing the largest number of points widespread throughout Brazil; G4) the largest contents of As (11.97 ± 1.62), SOC, and TiO2, and the lowest sand content, with points located mainly across Southeastern Brazil with milder temperature. In the absence of soil As content, a common scenario in Brazil and in many Latin American countries, such natural groups could work as environmental indicators.

RESUMO

Sulfuric acid digestion analyses (SAD) provide useful information to environmental studies, in terms of the geochemical balance of nutrients, parent material uniformity, nutrient reserves for perennial crops, and mineralogical composition of the soil clay fraction. Yet, these analyses are costly, time consuming, and generate chemical waste. This work aimed at predicting SAD results from portable X-ray fluorescence (pXRF) spectrometry, which is proposed as a “green chemistry” alternative to the current SAD method. Soil samples developed from different parent materials were analyzed for soil texture and SAD, and scanned with pXRF. The SAD results were predicted from pXRF elemental analyses through simple linear regressions, stepwise multiple linear regressions, and random forest algorithm, with and without incorporation of soil texture data. The modeling was developed with 70 % of the data, while the remaining 30 % was used for validation through calculation of R2, adjusted R2, root mean square error, and mean error. Simple linear regression can accurately predict SAD results of Fe2O3 (R2 0.89), TiO2 (R2 0.96), and P2O5 (R2 0.89). Stepwise regressions provided accurate predictions for Al2O3 (R2 0.87) and Ki - molar weathering index (SiO2/Al2O3) (R2 0.74) by incorporating soil texture data, as well as for SiO2 (R2 0.61). Random forest also provided adequate predictions, especially for Fe2O3 (R2 0.95), and improved results of Kr - molar weathering index (SiO2/(Al2O3 + Fe2O3)) (R2 0.66), by incorporation of soil texture data. Our findings showed that the SAD results could be accurately predicted from pXRF data, decreasing costs, time and the production of laboratory waste.(AU)

Assuntos

RESUMO

Sulfuric acid digestion analyses (SAD) provide useful information to environmental studies, in terms of the geochemical balance of nutrients, parent material uniformity, nutrient reserves for perennial crops, and mineralogical composition of the soil clay fraction. Yet, these analyses are costly, time consuming, and generate chemical waste. This work aimed at predicting SAD results from portable X-ray fluorescence (pXRF) spectrometry, which is proposed as a “green chemistry” alternative to the current SAD method. Soil samples developed from different parent materials were analyzed for soil texture and SAD, and scanned with pXRF. The SAD results were predicted from pXRF elemental analyses through simple linear regressions, stepwise multiple linear regressions, and random forest algorithm, with and without incorporation of soil texture data. The modeling was developed with 70 % of the data, while the remaining 30 % was used for validation through calculation of R2, adjusted R2, root mean square error, and mean error. Simple linear regression can accurately predict SAD results of Fe2O3 (R2 0.89), TiO2 (R2 0.96), and P2O5 (R2 0.89). Stepwise regressions provided accurate predictions for Al2O3 (R2 0.87) and Ki - molar weathering index (SiO2/Al2O3) (R2 0.74) by incorporating soil texture data, as well as for SiO2 (R2 0.61). Random forest also provided adequate predictions, especially for Fe2O3 (R2 0.95), and improved results of Kr - molar weathering index (SiO2/(Al2O3 + Fe2O3)) (R2 0.66), by incorporation of soil texture data. Our findings showed that the SAD results could be accurately predicted from pXRF data, decreasing costs, time and the production of laboratory waste.

Assuntos