Fungi and plants are rich sources of thousands of secondary metabolites. The genetically coded possibilities for secondary metabolite production, the stimuli of the production, and the special phytotoxins basically determine the microscopic fungi-host plant interactions and the pathogenic lifestyle of fungi. The rewiew introduces plant secondary metabolites usually with antifungal effect as well as the importance of signaling molecules in induced systemic resistance and systemic acquired resistance processes. The review also concerns the mimicing of plant effector molecules like auxins, gibberellins and abscisic acid by fungal secondary metabolites that modulate plant growth or even can subvert the plant defense responses such as programmed cell death to gain nutrients for fungal growth and colonization. It also looks through the special secondary metabolite production and host selective toxins of some significant fungal pathogens and the plant response in form of phytoalexin production. New results coming from genome and transcriptional analyses in context of selected fungal pathogens and their hosts are also discussed.

Adequate chill is of great importance for successful production of deciduous fruit trees. However, temperate fruit trees grown under tropical and subtropical regions may face insufficient winter chill, which has a crucial role in dormancy and productivity. The objective of this review is to discuss the challenges for dormancy and chilling requirements of temperate fruit trees, especially in warm winter regions, under climate change conditions. After defining climate change and dormancy, the effects of climate change on various parameters of temperate fruit trees are described. Then, dormancy breaking chemicals and organic compounds, as well as some aspects of the mechanism of dormancy breaking, are demonstrated. After this, the relationships between dormancy and chilling requirements are delineated and challenging aspects of chilling requirements in climate change conditions and in warm winter environments are demonstrated. Experts have sought to develop models for estimating chilling requirements and dormancy breaking in order to improve the adaption of temperate fruit trees under tropical and subtropical environments. Some of these models and their uses are described in the final section of this review. In conclusion, global warming has led to chill deficit during winter, which may become a limiting factor in the near future for the growth of temperate fruit trees in the tropics and subtropics. With the increasing rate of climate change, improvements in some managing tools (e.g., discovering new, more effective dormancy breaking organic compounds; breeding new, climate-smart cultivars in order to solve problems associated with dormancy and chilling requirements; and improving dormancy and chilling forecasting models) have the potential to solve the challenges of dormancy and chilling requirements for temperate fruit tree production in warm winter fruit tree growing regions.

The Mediterranean part of Syria is affected by soil water erosion due to poor land management. Within this context, the main aim of this research was to track soil erosion and runoff after each rainy storm between September 2013 and April 2014 (rainy season), on two slopes with different gradients (4.7%; 10.3%), under three soil cover types (SCTs): bare soil (BS), metal sieve cover (MC), and strip cropping (SC), in Central Syria. Two statistical multivariate models, the general linear model (GLM), and the random forest regression (RFR) were applied to reveal the importance of SCTs. Our results reveal that higher erosion rate, as well as runoff, were recorded in BS followed by MC, and SC. Accordingly, soil cover had a significant effect (p < 0.001) on soil erosion, and no significant difference was detected between MC and SC. Different combinations of slopes and soil cover had no effect on erosion, at least in this experiment. RFR performed better than GLM in predictions. GLM’s median of mean absolute error was 21% worse than RFR. Nonetheless, 25 repetitions of 2-fold cross-validation ensured the highest available prediction accuracy for RFR. In conclusion, we revealed that runoff, rain intensity and soil cover were the most important factors in erosion.

The aim of this study was to investigate the effect of methyl jasmonate and salicylic acid on 12 quality parameters of apricot fruit in treatments of cold storage (1°C for 7, 14 and 21 days) and shelf-life (4 and 8 days at 25°C after a 21-day cold storage treatment). Fruit quality parameters included 6 physico-chemical characteristics (weight loss, fruit firmness, total soluble solids, acidity, total soluble solids/acidity ratio and juice pH) and 6 sensory properties (skin and flesh colour, texture, taste, visual appearance and overall acceptability). Among physico-chemical characteristics, applications of 0.2 mmol/L methyl jasmonate and 2 mmol/L salicylic acid significantly reduced fruit weight loss, fruit softening and juice pH and maintained soluble solid content and acidity over the whole storage period. Among sensory properties, all parameters were generally increased by methyl jasmonate and salicylic acid treatments compared to water-treated control and the water-treated fruits showed the lowest scores below the acceptability limit except for skin colour. In conclusion, our results suggest using methyl jasmonate and/or salicylic acid to enhance and prolong the storability of apricot fruits during both cold storage and shelf-life period

Gullies reduce both the quality and quantity of productive land, posing a serious threat to sustainable agriculture, hence, food security. Machine Learning (ML) algorithms are essential tools in the identification of gullies and can assist in strategic decision-making relevant to soil conservation. Nevertheless, accurate identification of gullies is a function of the selected ML algorithms, the image and number of classes used, i.e., binary (two classes) and multiclass. We applied Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Random Forest (RF) on a Systeme Pour l’Observation de la Terre (SPOT-7) image to extract gullies and investigated whether the multiclass (m) approach can offer better classification accuracy than the binary (b) approach. Using repeated k-fold cross-validation, we generated 36 models. Our findings revealed that, of these models, both RFb (98.70%) and SVMm (98.01%) outperformed the LDA in terms of overall accuracy (OA). However, the LDAb (99.51%) recorded the highest producer’s accuracy (PA) but had low corresponding user’s accuracy (UA) with 18.5%. The binary approach was generally better than the multiclass approach; however, on class level, the multiclass approach outperformed the binary approach in gully identification. Despite low spectral resolution, the pan-sharpened SPOT-7 product successfully identified gullies. The proposed methodology is relatively simple, but practically sound, and can be used to monitor gullies within and beyond the study region.

Soils in the coastal region of Syria (CRoS) are one of the most fragile components of natural ecosystems. However, they are adversely affected by water erosion processes after extreme land cover modifications such as wildfires or intensive agricultural activities. The main goal of this research was to clarify the dynamic interaction between erosion processes and different ecosystem components (inclination, land cover/land use, and rainy storms) along with the vulnerable territory of the CRoS. Experiments were carried out in five different locations using a total of 15 erosion plots. Soil loss and runoff were quantified in each experimental plot, considering different inclinations and land uses (agricultural land (AG), burnt forest (BF), forest/control plot (F)). Observed runoff and soil loss varied greatly according to both inclination and land cover after 750 mm of rainfall (26 events). In the cultivated areas, the average soil water erosion ranged between 0.14 ± 0.07 and 0.74 ± 0.33 kg/m²; in the BF plots, mean soil erosion ranged between 0.03 ± 0.01 and 0.24 ± 0.10 kg/m². The lowest amount of erosion was recorded in the F plots where the erosion ranged between 0.1 ± 0.001 and 0.07 ± 0.03 kg/m². Interestingly, the General Linear Model revealed that all factors (i.e., inclination, rainfall and land use) had a significant (p < 0.001) effect on the soil loss. We concluded that human activities greatly influenced soil erosion rates, being higher in the AG lands, followed by BF and F. Therefore, the current study could be very useful to policymakers and planners for proposing immediate conservation or restoration plans in a less studied area which has been shown to be vulnerable to soil erosion processes.