In order to investigate the possibility of quinoa producing in Garmsar, Iran, a factorial experiment conducted in randomized complete block design with three replications in 2018-2019 growing season at Garmsar Agricultural Research Station. The factors were planting date at three levels (March 6th, April 1st and April 6th) and the three genotypes of quinoa (Q26, Q29 and Titicaca). Results showed that the effect of planting date was significant for all studied traits except the harvest index. Also, all studied traits were significantly different in all genotypes. Titicaca planted on March 6th had the highest YIELD (2276 kg.ha-1).The grain YIELD and YIELD components decreased with the delaying the planting date. Compared to early plantings, Latest date, April 6th, led to reduction of all traits, especially grain YIELD (about 50%). The results of simple phenotypic correlation between the studied traits showed that grain YIELD per hectare had the highest correlation with plant YIELD (0.877) and then with leaf area index (0.832), panicle weight (0.815) and number of branches per plant (0.745) that was significant at the 1% probability level.

Rice is one of the most important cereal with great variation, which is a major food for more than half of the world’ s population. MAPping quantitative traits loci is one of the applied and momentous approaches to study YIELD-related traits. In present study 188 F4 rice lines derived from CSR28 and Sadri cross along with the parents were used for genotyping and phenotyping. The objective of this study was to construct high saturation linkage MAP using SNPs markers (Infinium Illumina 6K SNP chip) and QTL identification of YIELD-related traits in 188 F4 population. MAPping of quantitative traits loci led to identify 21 QTLs for studied traits on chromosomes 1, 2, 3, 5, 7, 8 and 10. One QTL (qFG_N-3-1) was identified for filled grain number per plant on chromosome 3. For spikelet fertility, three QTLs (qSpkF_N-2-1, qSpkF_N-3-1 and qSpkF_N-5-1) were MAPped on chromosomes 2, 3 and 5, which explained 29. 28 percentage of phenotypic variation. One QTL (qGY_N-8-1) was detected for grain YIELD on chromosome 8, which was flanked by 9037125 and 9049928 markers. In conclusion, besides the QTLs which is reported for first time, the QTLs were consistent as reported previously, could be introduced as reliable QTLs that is suitable for marker assisted breeding programs.

Identifying the constraining factors of production and YIELD gap is very important. Therefore; this research was performed to identify the production constraining factors of local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the actual and calculated potential YIELD were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The YIELD gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total YIELD gap. The YIELD gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total YIELD variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the YIELD gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, actual YIELD mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha YIELD gap . Mean relative YIELD and relative YIELD gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of YIELD gap and determining the portion of each restricting YIELD variables.

In recent decades, drastic land use changes in Golestan province caused to reduce a substantial amount of Hyrcanian forest. To investigate the changes, land cover MAPs produced using Landsat satellite imagery classification of sensors TM from 1984, 2012 and 2016 respectively used as input data in Land Change Modeler (LCM) to predict land cover changes in 2030. In order to assess the accuracy of modeling, statistics of relative performance characteristic (ROC), ratio Hits/False Alarms and figure of merit was used. In continue to investigate the role of land use changes in water YIELD as one of ecosystem services was discussed. The results show the accuracy of artificial neural network with the ROC equal to 0. 949, the ratio Hits/False Alarms equal to 57 percent and the figure of merit is equal to 11 percent. Land use change modeling results showed that from 1984 to 2012, The most prominent changes were related to reduction of forest cover. This process modeling using artificial neural network showed, from 2016 to 2030 forest cover will be reduced about 30361 hectares. The results of water YIELD study showed that runoff in the area, particularly in the East and North East area has increased. This increase in the amount of runoff occurred as a result of land use change on forest ecosystems to agriculture. Results of this study improve our understanding of hydrological consequences of land-use changes, and provide needed knowledge for effectively developing and managing land-use for sustainability and productivity in the Gorgan-rood watershed.

Brassica napus as an oilseed is a strategic agriculture product in Iran. Knowing the best area for cultivation, helps plan and explore suitable cultivation areas efficiently. At this research, we study 24-Brassica napus farm samples to calculate the actual YIELDs by using GPS set. The independent data are variables introduced such as mean temperature, growing degree-day, mean absolute minimum temperature, mean absolute maximum temperature, mean temperature in sowing time, mean potential evapotranspiration, slope, EC of groundwater, pH of groundwater, and mean relative humidity. We analyzed the consequences of environmental potential on autumn Brassica napus YIELD using Perceptron neural network by multi-layers structure with 3 hidden layers and feed backward algorithm entered to database and Brassica napus cultivation suitability MAP was prepared in geographic information system environment. Results show that T-test between actual and prediction values do not have significant difference in 0.05 level. We calculated Pearson correlation coefficient at 0.98, RMSE and MAE. Thus 243 and 101 Kg/h indicate the ability of neural network methods for agricultural product YIELD predictions.

Today snow and avalanche studies have progressed dramatically throughout the world, and countries are experiencing avalanche phenomena have established advanced research and development centers in this regard. For many years, they have used their research and results. In Iran, despite the presence of snow in some parts of the country, no official observing centers have made in the country. One of the important issues in the avalanche studies are: the avalanche risk MAP that identifies avalanches in snowy and mountainous regions, but this MAP is very different from the avalanche risk MAP. Many domestic researchers find the two MAPs mistakenly the same. In this research, we have tried to determine the difference between avalanche hazard MAP and avalanche risk MAP.

Myocardial Infarction happens when blood stops flowing properly to part of the heart and the heart muscle is injured due to not receiving enough oxygen. Percutaneous coronary intervention (PCI), is a non-surgical procedure used to treat the Myocardial Infarction. When PCI is success, average life expectancy of patient is increased and patient returns to normal life. Every year, part of PCI knowledge, as scientific articles, are provided to medical communities. Extraction of implicit knowledge in papers facilitated access to PCI knowledge, PCI Knowledge MAP shows a comprehensive picture of PCI. Knowledge MAP is a tool for presenting complex process and relation between concepts. This paper base on text mining and using Co-occurrence Analysis method, extracted causal relationships between PCI concepts from abstract of 20 years paper and illustrating these relationships, then Validated PCI MAP by experts, Importance of relationships between concepts is determined Using fuzzy cognitive MAP. in this paper FCM is trained with AHL and DHL algorithms, result indicated PCI has scientific Structure. PCI Fuzzy Knowledge MAP can be used in training, process of PCI changes, and determines the most important factors that influence the success of PCI.