Effects of summer savory (Satureja hortensis L.) and sweet corn (Zea mays L. saccharata) intercropping on crop production and essential oil profiles of summer savory

Abstract

A 2-year field experiment evaluated the effects of sweet corn-summer savory intercropping on crop productivity and essential oil (EO) composition of summer savory. Five cropping patterns of Corn 100%:Savory 0%, C75:S25, C50:S50, C25:S75, and C0:S100 were tested. The highest corn yield (2,440 kg ha−1) was obtained in a corn monoculture, but was not significantly different from C75:S25 or C50:S50. However, in both years the highest savory yield was obtained in S100 (793.3 g m−2 and 816.6 g m−2, respectively). Savory yields decreased as the proportion of corn increased. The land equivalent ratios in C25:S75, C50:S50, and C75:S25 were 1.54 ± 0.07, 1.56 ± 0.03, and 1.35 ± 0.1, respectively. Monocropped savory had the highest EO value followed by C25:S75 and C50:C50. However, no significant differences were found among these three treatments. Gas chromatography-mass spectrometry (GC–MS) analysis showed that the major components were carvacrol (35.88%–42.96%), γ-terpinene (18.45%–20.03%), ρ-cymene (11.77%–12.24%), and α-terpinene (2.75%–3.96%). The highest amount of carvacrol was recorded in C25:S75 (42.96%). This study suggests that intercropping of corn and savory represents an effective sustainable strategy, especially for smallholders, as a way to increase their overall land productivity and to improve the quality of savory’s EO.

Assessing the Wind Power Potential in Naama, Algeria to Complement Solar Energy through Integrated Modeling of the Wind Resource and Turbine Wind Performance

“…Overall, the PCA with its biplot representation in Figure 8 provides valuable insights into the seasonal variability in wind speeds and underscores the importance of the variables meanRho and Wsmeans in this context. Such analyzes are instrumental in understanding complex datasets, particularly in meteorological or environmental studies where multifactorial interactions are common [36,37]. a scatterplot that intricately maps the relationship between two key variables, and it is supplemented by a green line graphically representing their correlation.…”

“…Collectively, these analytical techniques-the scatterplot with its correlation line, the PCA, and the correlation matrix-converge to provide a comprehensive and insightful view of the dynamics governing wind speed variability. Such analyzes are invaluable in meteorological studies, particularly when assessing factors that influence wind patterns over time [37].…”