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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.

A two-year field experiment was conducted to explore the effects of intercropping sweet corn with summer savory on weed growth and crop productivity. Five cropping patterns were set up: sweet corn alone (16 seeds m-2: in rows, 75cm apart), summer savory alone (40 seeds m-2; broadcasted), and three intercropping ratios of 75% sweet corn, 25% summer savory (75%C: 25%S), 50%C: 50%S and 25%C: 75%S, of plant densities used in respective monocultures. When intercropping, weed biomass decreased as the proportion of summer savory increased with a reduction of 48%, 61% and 70 % in 75%C: 25%S, 50%C: 50%S and 25%C: 75%S, respectively, compared to sweet corn alone. In parallel, sweet corn yield was higher under intercropping compared to its monoculture, and increased as the proportion of summer savory decreased with yield increases compared to corn monoculture of 38%, 32% and 15% in the first year and 48%, 23% and 14 % in the second year in 75%C: 25%S, 50%C: 50%S and 25%C: 75%S, respectively. However, the intercropping pattern had the opposite effect on summer savory yield with a significant reduction in yield with an increasing ratio of sweet corn. Our results indicate that intercropping sweet corn with summer savory can increase both weed suppression and yield of sweet corn compared to crop monoculture.

Spatial locational modeling techniques are increasingly used in species distribution modeling. However, the implemented techniques differ in their modeling performance. In this study, we tested the predictive accuracy of three algorithms, namely "random forest (RF)," "support vector machine (SVM)," and "boosted regression trees (BRT)" to prepare habitat suitability mapping of an invasive species, Alhagi maurorum, and its potential biological control agent, Aceria alhagi. Location of this study was in Fars Province, southwest of Iran. The spatial distributions of the species were forecasted using GPS devices and GIS software. The probability values of occurrence were then checked using three algorithms. The predictive accuracy of the machine learning (ML) techniques was assessed by computing the “area under the curve (AUC)” of the “receiver-operating characteristic” plot. When the Aceria alhagi was modeled, the AUC values of RF, BRT and SVM were 0.89, 0.81, and 0.79, respectively. However, in habitat suitability models (HSMs) of Alhagi maurorum the AUC values of RF, BRT and SVM were 0.89, 0.80, and 0.73, respectively. The RF model provided significantly more accurate predictions than other algorithms. The importance of factors on the growth and development of Alhagi maurorum and Aceria alhagi was also determined using the partial least squares (PLS) algorithm, and the most crucial factors were the road and slope. Habitat suitability modeling based on algorithms may significantly increase the accuracy of species distribution forecasts, and thus it shows considerable promise for different conservation biological and biogeographical applications.