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In this study, the effect of six commercial biocontrol strains, Bacillus pumilus INR7, B. megaterium P2, B. subtilis GB03, B. subtilis S, B. subtilis AS and B. subtilis BS and four indigenous strains Achromobacter sp. B124, Pseudomonas geniculate B19, Serratia marcescens B29 and B. simplex B21 and two plant defense inducers, methyl salicylate (Me-SA) and methyl jasmonate (Me-JA) were assessed on suppression of wheat take-all disease. Treatments were applied either as soil drench or sprayed on shoots. In the soil drench method, the highest disease suppression was achieved in treatment with strains INR7, GB03, B19 and AS along with two chemical inducers. Bacillus subtilis S, as the worst treatment, suppressed take-all severity up to 56%. Both chemical inducers and bacterial strains AS and P2 exhibited the highest effect on suppression of take-all disease in the shoot spray method. Bacillus subtilis S suppressed the disease severity up to 49% and was again the worst strain. The efficacy of strains GB03 and B19 decreased significantly in the shoot spray method compared to the soil drench application method. Our results showed that most treatments had the same effect on take-all disease when they were applied as soil drench or sprayed on aerial parts. This means that induction of plant defense was the main mechanism in suppressing take-all disease by the given rhizobacteria. It also revealed that plant growth was reduced when it was treated with chemical inducers. In contrast, rhizobacteria not only suppressed the disease, but also increased plant growth.
The paper presents an analysis of the wheat yields variability in voivodeships of Poland. The main aim of the study is to present several possible indicators for the crop variability in the context of production risk. It is found that ignoring the long-term yield trends leads to a serious overestimation of production risk.
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The paper presents an analysis of wheat yields variability in the voivodeships of Poland. The main aim of the study was to find out what are the statistical relationships between the wheat yield variability and the following factors: arable area, size of wheat production area, share of arable land used for wheat production, land quality and average yield. For that purpose a multiple linear regression was applied. It was found out that the detected spatial autocorrelation of wheat yields variability measured by standard deviations can be explained in 75% by the fitted model. Two of the considered variables showed a significant negative effect on this variability: the logarithm of arable area and the land quality, while the other two: the average wheat yield and the wheat production area displayed a significant positive effect on the variability. The effect of share of arable land used for wheat production itself was not significant.
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