نشریه تحقیقات نوین در گیاه پزشکی
سال دهم شماره 2 (پیاپی 27، تابستان 1400)

Fusarium wilt (yellow) that caused by the fungus Fusarium oxysporum f. sp. phaseoli, is an important disease and can cause severe damage to bean products world wide and cause reduced functional. Since farming techniques to control the disease is not completely effective, cultivars with genetic resistance to the disease are recommended. In order to identify resistant cultivars experiment in that randomized complete block design with three replications and 12 trkeatments, including different cultivars of beans. By method at a concentration suspension inoculation of root drench were inoculated. In root drench method suspension spores of pathogen because of healthy roots a lower percentage of spores could penetrating in roots. So it was far less severe symptoms and can say all cultivars has same statistical group and were resistant and infected cultivars tolerant. Considering the fact that the disease causes yellowing and wilting the factors concentration of chlorophyll a, chlorophyll b, and carotenoid concentrations and chlorophyll total concentrations were measured with evaluating resistant and tolerant cultivars were identified. Accordingly, cultivars Sayad and E9 respectively than other cultivars showed more resistant to the disease. Then the reaction is different bean cultivars to fungal F. oxysporum f.sp. phaseoli were evaluated using specific marker SCAR. According to cultivars Akhtar, Naz, Sadri, Sayad, E9 and WA with primer pairs SU20 have a band of 750 bp and A55 are resistant gene but cultivars Talash, Shekofa, Jegari, Aje, Khomeyn and capsules with specific primer pairs SU20 did not produce band.

The Cucumber Mosaic Virus (CMV) belongs to the Cucumovirus that is one of the most important viruses that infect cucurbit plants in Iran and around the world. This study was accomplished in a completely randomized design by 6 repeats to survey spraying herbal extracts, salicylic acid, and distilled water as the control on resisting cucumber plants against CMV mechanical infection in a greenhouse at the Islamic Azad University of Jahrom Branch in 2015. It was planted 4 seeds of cucumber in each pot, and at the time of inoculation 2 plants were reminded. After treatments, (including spraying of 3% thyme and Rosemary extracts, 0.02% salicylic acid, and distilled water) the plants were inoculated mechanically by the sap of an infected plant. After that, pots were checked for a month. The infection of plants examined by the ELISA test. Data analysis was done using MSTAT-C. Results showed that the herbal extracts significantly were prevented the infection rate of CMV. According to the results in salicylic acid, thyme extract, rosemary extract, and distilled water treatments inoculation rates were 33.3%, 58.3%, 66.6%, and 88.3%, respectively. There wasn’t a statistically significant difference between treatments of salicylic acid and Thyme extract, as well as, spraying with Thyme and Rosemary extracts. Considering the result of this study, it can be concluded that by application of salicylic acid and then Thyme extract, the CMV can be controlled significantly.

Peach powdery mildew (Podosphaera pannosa) is one of the most economically destructive diseases of peach all over the world including Iran. In order to evaluate 11 genotypes of nectarines reaction against powdery mildew, an experiment was conducted in randomized complete block design with three replications during 2017-2018 in Agriculture and Natural Resources Research Station in Meshginshahr. The research was conducted in two conditions of natural infection and artificial inoculation. Cultivars included Independence, Stargold, Sangold, Sangold, Sanking, Giuta, Shabrang Karaj, Ghermeze Paeeze Karaj, Vega, Orion, Jiova and Veinberger. Evaluation of different cultivars of nectarine reaction to powdery mildew was conducted follow 0: Immune, 1: Very resistant 2: Resistant 3: Medium resistant 4: Sensitive and 5: Very sensitive. The results of two-year evaluation showed that there were significant differences between the degrees of resistance among the treatments. Comparison of means showed that the cultivar Veinberger in resistant and Stargold, Sangold, Sangold, Sanking, Giuta, Shabrang Karaj, Ghermeze Paeeze Karaj, Vega, Orion, and Jiova were in medium resistant cultivars. The results of the current study could be used in management of peach powdery mildew.

The experiment was conducted in the agricultural year of 2018-2019 at Moghan Agricultural and Natural Resources Research Center as a split factorial design with a complete random block design in four replications. Treatment of cover crops in seven levels (rye, crimson clover, rapeseed, barley, sedge, mixed rye + sedge and without cover crop) in the main plot (A) and the treatment combination of cover crop management (B) in two levels (1- floor and roller and 2- plowing and leaving on the soil surface) and weed control (C) on two levels (1- using herbicide to control weeds and 2- no weed control) in the sub-plot to the factorial face was arranged. The results showed that the treatment of the mixture of rye cover plant with sedge has reduced the density of weeds by 86.40% compared to the control without cover plant. Also, this treatment has increased corn yield by 58.37% compared to the control without cover crop. Also, the highest percentage of corn yield increase compared to the control (58.37%) and the highest percentage of soil moisture (28.02%) are assigned to this treatment. The rate of water infiltration in the soil related to rye treatment was higher and the lowest rate of water infiltration was in the control treatment. The general results show that planting winter cover plants can increase soil fertility and store more moisture in the soil while managing weeds.

Weeds represent a continuous problem in agricultural production due to their dynamic and resilient nature. Mathematical models offer a significant tool for understanding and predicting the crop yield losses incurred due to weed-crop interference. Weed-crop competition models help to inform weed management decisions, both on a short-term basis to tackle the present weed population and in the long term to plan sustainable weed management strategies. Most competition studies are based on empirical models. Empirical functions are the most commonly used models, which provide information for weed threshold values. The limitations of such models are that they are based on statistical functions and usually do not consider biological insights for crop-weed interference. Crop-weed competition is a complex phenomenon, and to understand this, a detailed mechanistic model offers better insights than an empirical model. Mechanistic or explanatory models take into account all underlying processes or mechanisms and their dependence on each other with respect to time and external drivers. Competition models can be integrated within the framework of a decision support system (DSS). In this review, we present empirical and mechanistic models that are currently in use for studying crop-weed interference.

Site-specific weed management (SSWM) is a strategy of varying weed management within a crop field to match the variation in location, density and composition of the weed population. This concept is based on three facts: 1) weed populations are often irregularly distributed within crop fields, 2) new sensors and platforms together with geospatial technologies (e.g., GPS, GIS) have provided the tools required to detect and map weeds, and 3) new smart sprayers, robots and mechanical cultivators have provided the possibility of careful tailoring of weed management to fit the different conditions found in each field. Site-specific weed management has a real potential to deliver a more productive and sustainable agricultural production based on a more precise and resource-efficient approach. This paper reviews the major conceptual approaches and specifications for the design of site-specific weed management decision support systems (SSWM-DSS), recent advances in the use of remote and ground platforms and sensors for information gathering and processing, and initial experiences translating this information into chemical and physical weed control actuations through decision algorithms and models.