bannayanm

Lentil (Lens culinaris Medic.) is an important grain legume adapted to cool climates. It is cultivated on 155700 hectares in Iran with 94.7 % of this area under rainfed conditions. The average lentil yield in Iran is 1195 and 476 kg per hectare in irrigated and rainfed farms, respectively. Low productivity is due to use of local varieties, which have low yield potential, and poor agronomic management practices applied by the farmers such as limitation or inappropriate distribution of fertilizer. Nitrogen is an essential element for the growth of crops and its deficiency exists almost everywhere. It is the limiting factor in the crop growth more than any other element, unless use the nitrogen as a fertilizer. Despite the numerous advantages of nitrogen fertilizers, excessive consumption of nitrogen can cause pollution of surface and ground water through leaching and erosion and also increases costs. According to an adequate supply of nutrient elements by careful use of fertilizers, especially in poor soils, yield increases and nitrogen use efficiency improves. The objective of this study was evaluation of yield of two lentil cultivars under the influence of nitrogen fertilizer and also, investigation the nitrogen uptake, utilization and use efficiency to determine the best level of nitrogen fertilizer and cultivar for the study area.

The experiment was conducted as split plot based on randomized complete blocks design with three replications at the Agricultural Research Station, Ferdowsi University of Mashhad, during growth season 2015-16. Nitrogen fertilizer (in three levels i.e. 0, 40 and 80 kg per hectare) and cultivar (Birjand and Robat) were in main plots and sub plots, respectively. Nitrogen fertilizer was applied as urea to the plots before sowing. The sowing date was 9th March in 2016. Sampling was done at harvest time and included pod number per plant, seed number per pod, 100 seed weight, seed yield, biological yield and harvest index. Percentage of biomass nitrogen were measured with the Kjeldahl method and the efficiency index calculated by using the following equation:NupE= Noff/ Ns
NutE b= B/ Noff
NutE s= Sw/ Noff
NUE b= B/ Ns
NUEs= Sw/ Ns                                                                                                                            
Where NupE is the nitrogen (N) uptake efficiency, Noff is the N in above ground dry matter, Ns is the soil N supply, NutEb is the N utilization efficiency based on biomass basis, B is the total above ground biomass at harvest, NutEs is the N utilization efficiency based on seed yield, Sw is the seed weight, NUEb is the N use efficiency based on biological yield, NUEs is the N use efficiency based on seed yield. Data were analyzed with the SAS software; obtained averages compared with LSD test at the 5% level.

The results showed that the interaction effect between nitrogen fertilizer and cultivar was significant on yield components, seed and biological yield. 40 kg nitrogen fertilizer per hectare and Birjand cultivar showed that maximum of pod number per plant (33.47), seed (338. 23 kg per hectare) and biological yield (3291.68 kg per hectare). Maximum of seed number per pod and 100 seed weight were obtained in treatment of non-use of nitrogen fertilizer and Birjand cultivar and treatment of 40 kg nitrogen fertilizer per hectare and Robat cultivar, respectively. Interaction effect between nitrogen fertilizer and cultivar was significant on nitrogen content of biomass, nitrogen uptake, utilization and use efficiency based on seed and biological yields. 40 kg nitrogen fertilizer per hectare and Birjand cultivar showed that maximum of nitrogen content of biomass and nitrogen uptake efficiency. The highest nitrogen use efficiency based on seed yield (3.39 kg seed per kg Ns) and biological yield (33.48 kg biomass per kg Ns) were obtained in treatment of non-use of nitrogen fertilizer and Birjand cultivar that the difference was no significant with the treatment of 40 kg nitrogen fertilizer per hectare and Birjand cultivar. Analysis of correlation showed that, yield and nitrogen use efficiency had positive and significant correlations with the pod number per plant and nitrogen uptake efficiency, respectively. Also, there was positive and significant correlation between nitrogen uptake efficiency and yield.

The results of this study indicated that treatment of 40 kg nitrogen fertilizer per hectare and Birjand cultivar are able to achieve maximum yield and nitrogen use efficiency. However, Birjand cultivar is a late cultivar and requires the optimum planting date for cultivation in this region. According to the observed correlations, breeding of this plant should be cultivars that they absorb nitrogen with more efficiently, so that in addition to improving nitrogen use efficiency and reducing environmental pollution also yield increase.

Drought has significantly affected the environmental and socio-economic conditions in Iran. Three drought indices were used for monitoring drought intensity and duration in Kashafrood basin (northeast of Iran). The standardized precipitation Index (SPI), Precipitation Index Percent of Normal (PNPI) and Agricultural Rainfall Index (ARI) were calculated for the base period (1961-1990). All these indices were used to assess future drought in Kashafrood basin under climate change attributed to low and high greenhouse gas emission scenarios (SRES B2 and A2, respectively) for 3 periods (2010-2039, 2040-2069 and 2070-2100). Projected changes in precipitation, temperature and potential evaporation were simulated by statistical downscaling of HadCM3 outputs. Main results showed: (i) slight increase in precipitation means, around 2.2% to 5.4% under A2 scenario and 6.8% and 8.6% under B2 scenario. (ii) Slight increase in maximum temperature, around 4.6 oC to 5.6 oC for A2 scenario and 4.25 oC and 4.6 oC under B2 scenario. (iii) Slight increase in minimum temperature, around 1.6oC to 1.9 oC under A2 scenario and 1oC and 2.23oC under B2 scenario. (iv) Higher drought frequency associated with global warming was demonstrated by all indices for both scenarios. Such an increase in drought frequency would have major implications for natural resources management, water security planning, water demand management strategies, and drought relief payments.IntroductionDrought is a prolonged, abnormally dry period of shortage of water for normal needs. Generally, this occurs when a region receives consistently below average precipitation (Oliver, 2005). Drought is predominantly controlled by air temperature and precipitation (Loukas et al. 2008). The recent GCM-based projections suggest significant changes in temperature and precipitation. Changing of these climate variables under global warming will cause changes in severity and frequency of drought (Li et al., 2009; Kebat et al., 2002). The severity of drought is quantified as drought index. A drought index defines comprehensive information on drought conditions for decision making at water resources and agricultural sector (Hisdal and Tallaksen, 2005). Loukas et al. (2008) evaluated climate change affects drought severity using SPI at multiple time scales such as 1, 3, 6, 9 and 12 months in the region of Thessaly, Greece. They used outputs of CGCM model under two scenarios of emission (A2 and B2) for the assessment of climate change impact on drought for two future time periods of 2020-2050 and 2070-2100. Their results showed that the annual drought severity has increased and A2 scenario showed the most extreme condition. In water-scarce areas such as Iran, agriculture demands the highest portion of available water. Irrigated agriculture provides a large portion of the world’s food supply including Iran’s (FAO, 2003). Global warming will highly affect water resources availability by increasing variability of precipitation, increasing air temperature, and frequency and severity of drought in arid and semiarid regions (Kebat et al., 2002). Therefore, understanding and predicting drought trends are a continuous effort to meet current and future challenges in many regions of the world. This study aims to find the drought trend under climate change conditions using drought indices of SPI, PNPI, and ARI in Kashafrood basin.

This study was conducted in Kashafrood basin, which is located in the northeast of Iran. This basin is located between 35o 40' and 36o 3' north latitude and 58o 2'-60o 8' east longitude. Mean annual temperature is 13.6oC and the total average annual precipitation is 220 mm. To assess the impact of climate change on drought conditions in this basin, we used outputs from the HadCM3 model simulations. The HadCM3 model outputs were downscaled by ASD model (Hessami et al., 2008) which is a statistical downscaling method. We employed:i) Simulations made using A2 and B2 emission scenarios which assume the highest and lowest CO2 emissions when compared to the other scenarios, respectively.ii) HadCM3 model outputs are for the period from 1961-2099. This model is a coupled model incorporating ocean circulation. The resolution of this model is 2.5º latitude3.5º longitude.iii) Calculation of drought conditions and drought trends by employing selected drought indices at future periods of 2010-2039, 2040-2069 and 2070-2099.Results and DiscussionThe results of calibration and validation of the ASD model shows that this model fairly reproduced monthly maximum and minimum temperature. Simulated precipitation was not exactly similar to observed data and the differences between them were relatively large and could thus show basically regional biases. Projected temperature (maximum and minimum) under A2 and B2 scenarios showed a rising trend for all three time periods. Results showed that range of increase of maximum temperature under A2 scenario (2070-2099) for spring is 5.4°C to 5.8°C. This range for summer time is 5.1°C to 5°C, for fall is 4.4°C to 5.8°C and finally for winter time is 5.6°C to 4°C. These ranges for B2 scenario are 4.3°C to 4.6°C, 4.8°C to 4.9°C, 4.1°C to 4.9°C and 4°C to 3.8°C for the same seasons, respectively. Highest values of minimum temperature under A2 scenario (2070-2099) were 2°C to 1.7°C, 1.8°C to 4.1°C, 3.3°C to 1.8°C and 1.8°C to 1.1°C, which will occur in spring, summer, fall and winter, respectively. These values under B2 scenario were 1.9°C to 1.4°C, 1.5°C to 3.7°C, 2.3°C to 0.5°C and 0.3°C to 1°C in spring, summer, and fall and winter times, respectively. The percentage of variation for mean annual precipitation under B2 scenario would be 6.7%, 6.3%, and 4% for 2010-2039, 2040-2069, and 207-2099 in comparison with the base period. These values for A2 scenario were 5%, 3%, and 1.5% for the same time periods, respectively. Analysis of the 1-month SPI and 3-month SPI time series showed that the record minimum SPI observed in third month of 1966. Visual inspection of 6-month and 12-month SPI time series showed that droughts were quite frequent during the 1965-1966, 1970, 1971 and 1962-1967, 1970-1971, and 1989-1990. However, several severe dry periods were revealed, considering only the annual minimum spatially averaged SPI value (during 1970-1971, 1966-1967, 1983-1984 and 1989-1990). The precipitation value when expressed as a percentage of potential evapotranspiration is the ARI. The results of this study demonstrate that, dry months occurred quite frequently up to 40% in the base period (1961-1990). However, these dry months were quite irregular during the year. The longest duration of dry period was observed in 1965 and 1980 (April to December). For drought trend assessment, outputs of HadCM3 model and two scenarios emission (A2 and B2) were used. Annual drought severity increase over the historical years was marginal and statistically insignificant (P > 0.5) for the 2055s, 2085s period under SRES B2 scenario. The results indicated that the drought severity and duration (SPI<0) will increase under A2 and B2 scenarios for 2025s but for 2055s and 2085s, the number of dry months will decrease compared to the base period under both scenarios. ARI trends in the three periods are projected to fall. The extreme drought corresponds to 1966 when about 75% months of year experienced droughts with PNPI less than -0.25. In 1972, most months of this year were in normal class (more than 75%). Under A2 scenario, PNPI values were highest, due to higher temperature and precipitation than B2 scenario; thus A2 scenario resulted the lowest increa of drought periods. ConclusionThis study suggests the increase of temperature and precipitation in Kashafrood basin as a result of increasing carbon dioxide (A2 and B2 emission scenarios) in the atmosphere. The projected annual mean precipitation of Kashafrood exhibited an increasing trend for the study periods. Temperature also will increase about 5.8ºC and 4.9ºC under A2 and B2 scenarios for 2070-2099 periods, respectively. The analysis of the drought indices indicated that meteorological drought frequency will increase in Kashafrood basin.Results for ARI differ from the SPI projections because temperature contributes in calculation of ARI and higher temperatures increase evapotranspiration. The results of PNPI were similar to SPI for historical and future periods. The results indicated that climate change would largely affect drought duration and subsequently the design of future water resources projects. Thus, sustainable water management measures should be planned to mitigate future impacts of droughts on Kashafrood basin.

Leaf area (LA) is a valuable key for plant physiological studies, therefore accurate and simple models for LA determination are important for many experimental comparisons. A greenhouse experiment was conducted from October 2009 to February 2010 in two basil cultivars (Purple Ruffles and Genovese) to estimate LA, leaf dry weight (DW), leaf fresh weight (DW) and leaf dimensions (width-W and length-L). The aim of the work was to establish a non-destructive model of leaf are estimation. Regression analyses of LA versus FW, DW, L and W revealed several models that could be used for estimating the area of individual basil leaves. Among the models, one based on the sum of dimension squares was the most accurate for Genovese [LA = 0.209 (L2 + W2) + 0.25, R2 = 0.895, RMSE = 0.794 and while the product of dimension squares was the most suitable for Purple Ruffles [LA=0.013 (L2W2) +4.963 R2= 0.817, RMSE = 1.170