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.

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 order to identify the correlations and relationships between different traits in the bread wheat, 30 genotypes were investigated as sub-factors under the split plot experiment in the completely randomized design in greenhouse and the randomized complete block design in the field with three replications under the normal irrigation and post-anthesis water stress conditions. The results of the canonical correlation analysis showed that at both normal and moisture stress levels, there was a significant correlation between the pair of canonical variables obtained from the phenological traits and the yield components. Under the normal conditions in the greenhouse, to increase the spike density and length, the number of days to 50% flowering can be considered as a suitable selection criterion. According to the results of canonical correlation analysis, under the stressed conditions in greenhouse, to increase the spike length, the number of days to the heading, maturity and 50% of flowering were among the important and effective factors. Under the optimum irrigation conditions in the field, the number of days to maturity was considered as one of the factors influencing the seed weight per spike. The results of canonical correlation analysis under the field stress conditions showed that to increase the 1000-seed weight, the number of days to maturity can be considered an appropriate selection criterion.

This study investigated the impact of different boron doses (Boron-free, 80 L/ha, and 10 L/ha) on the yield and components of yarrow essential oil. The study utilized a randomized block trial design with three replicated field studies conducted between 2017 and 2018 in the Kütahya-Gediz region of Turkey. The essential oil of this species was extracted using the hydro-distillation procedure. The essential oil ratios were 0.66-0.59% in the plants grown without boron, 0.82-0.81% in the plants grown with the 80 L/ha boron dose, and 0.70-0.69% in the plants grown with the 10 L/ha boron dose, based on examinations conducted on dry flowers in 2017-2018. Employing GC-MS/FID analysis, 20 components of the yarrow essential oils were identified. 19 components were detected in the boron-free dose, while 18 were found in the dose 80 L/ha and 10 L/ha of boron. The main components of the essential oil (EO) based on boron doses were determined as follows: α-pinene at 16.25%, 20.50%, and 17.38%; Chrysanthenone at 16.54%, 19.29%, and 20.05%; cis-chrysanthenyl acetate at 11.33%, 15.26%, and 13.29%; and Filifolone at 10.00%, 11.46%, and 11.30%. The ratios of yarrow essential oil and its constituents were influenced positively by the applications of 80 L/ha and 10 L/ha natural boron supplementation. We can recommend applying 80 L/ha boron to the yarrow plant grown for its essential oil to obtain a high essential oil yield.

Straw nursery pots (SNPs), which utilize agricultural residues such as wheat straw and corn stalks as raw materials, can be widely used in the transplanting of seedlings of crops, vegetables, flowers and trees. Plastic nursery pots (PNPs) and direct sowing (DS) were used as controls in evaluating the effects of SNPs on plant growth, dry matter partitioning, yields and economic benefits of transplanted cotton. SNPs significantly increased the rate of emergence, shortened the convalescent period by about 7 d and increased the transplant survival rate by 8.8% compared to PNPs. This led to significantly increased dry matter accumulation: that of reproductive parts for SNP seedlings were 1.5- and 1.8-fold of that for PNPs and DS, respectively. The lint yield using SNPs was 11.5 and 17.5% greater than for PNPs and DS, respectively. Boll number per plant with SNPs was 7.5 and 23.3% greater than for PNPs and DS, respectively; lint weight was not significantly different than with PNPs and was 5.8% greater than for DS. There were no significant differences in lint percentage among the three systems. Further benefit analysis showed that net revenue per hectare from using SNPs in cotton production was US$108 and US$279 greater than for PNP and DS, respectively. This was mainly due to significantly increased output, a reduced number of seedlings needed, nursery pot costs accounting for a relatively small proportion of total investment, and the relatively low labor costs in China.

Background and objectives: Barley (Hordeum vulgare. L) is well adapted to drought and saline conditions as the most important limiting factors for crop production in Iran. This consistency, as well as widespread application in animal feeding, are the reasons for cultivating approximately 1. 77 million hectares of barley, in which, 1. 04 million hectares was attributed to rainfed barley. The previous studies demonstrated that there was a significant difference between the actual and potential yield of crops due to farm management condition. According to the calculated yield loss, the optimized crop field management is necessary to increase agricultural production. This study was aimed to estimate the yield and production gap of barley under rainfed condition as the first step in the terms of the schematization of stable increase in Iran. Materials and Methods: This study is conducted based on the Global Yield Gap Atlas (GYGA) Protocol. As the first step in the implementation of present study, the main rainfed barley harvested areas were determined using GYGA climate zones and the distribution of rainfed barley harvested area maps and the country's meteorological station points layer. After defining the designated climate zones (DCZs) and the reference weather stations (RWSs), the collected data (2000-2014) of agronomic management, meteorological and soil characteristics in each region were employed to estimate the potential yield at the RWSs of rainfed barley as one of components of the yield gap calculation. Estimating barley potential yield under water-limited condition (Yw) was carried out by SSM-iCrop2 during 15 growing seasons. Moreover, the actual yield (Ya) data of rainfed barley was collected at the RWS level as another constituent for yield gap calculation. In the end, the estimated rainfed barley yield gap (Yg) in the RWSs was aggregated to DCZs and finally country-level. Results: In the current study, 38 RWSs within 17 DCZs of rainfed barley harvested areas were identified. The results showed that the average Yw was estimated 2723 kg. ha-1 and the range varied from 1072 to 4002 kg. ha-1. Ya range in the zones were calculated between 390 and 1510 with average of 1009 kg. ha-1. The results illustrated that there was a significant correlation between mean rainfall and maximum temperature during anthesis to harvest maturity period and Yw within 17 DCZs. Hence, with simultaneous increase in rainfall and decrease in average maximum temperature during this phenological period, concomitantly, the Yw value has been amplified. Yg values was estimated between 615 to 3125 kg. ha-1 (equivalent to 53 to 82% of yield gap (%)) with an average of 1714 kg ha-1. Improving the current management conditions to advance toward the attainable yield (Ya) (equivalent to 80% of Yw) in farmers' lands, can increase the average yield of rainfed barley from 1009 to 2178 kg ha-1. Based on the results, the country's production will grow from 1. 05 million tons to 2. 26 million tons in rainfed conditions through increasing yield to the level of attainable yield (80% of potential yield). The rate of barley import from other countries will decrease due to improvement in the production. Conclusion: Our results showed 85 percent of rainfed barley production had been attributed to 17 designated climate zones. Due to the presence of 63% yield gap in rainfed barley fields, by considering 80% of this value as exploitable yield gap, the production can be increased to about 1. 22 million tons which is appreciable for the economical and food security issues in Iran. It is not feasible to achieve the potential yield at the farmer level owing to existing constraints, but approaching the attainable yield by improving field management conditions can be accessible in the current situation.

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.