Chloride Applied via Fertilizer Affects Plant Nutrition and Coffee Quality.

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.

Modeling arsenic content in Brazilian soils: What is relevant?

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.

Soybean Resistance to Cercospora sojina Infection Is Reduced by Silicon.

Frogeye leaf spot, caused by Cercospora sojina, is one of the most important leaf diseases of soybean worldwide. Silicon (Si) is known to increase the resistance of several plant species to pathogens. The cultivars Bossier and Conquista, which are susceptible and resistant, respectively, to frogeye leaf spot, supplied and nonsupplied with Si were examined for the activities of defense enzymes and the concentrations of total soluble phenolics (TSP) and lignin-thioglycolic acid (LTGA) derivatives at 8, 14, and 16 days after inoculation (dai) with C. sojina. The importance of cell wall degrading enzymes (CWDE) to the infection process of C. sojina and the effect of Si on their activities were also determined. Soybean plants were grown in hydroponic culture containing either 0 or 2 mM Si (-Si and +Si, respectively) and noninoculated or C. sojina inoculated. Severity of frogeye leaf spot was higher in cultivar Bossier plants than cultivar Conquista and also in the +Si plants compared with their -Si counterparts. Except for the concentrations of TSP and LTGA derivatives, activities of defense enzymes and the CWDE did not change for +Si noninoculated plants regardless of the cultivar. The activities of lipoxygenases, phenylalanine ammonia-lyases, chitinases, and polyphenoloxidases as well as the activities of CWDE decreased for the +Si inoculated plants. The results from this study demonstrated that defense enzyme activities decreased in soybean plants supplied with Si, which compromised resistance to C. sojina infection.