فهرست مطالب امیر حجارپور

In the recent year, due to increasing concerns about the future food security, researches in this field are growing globally and it requires to use proper statistic methods for estimation the amount of yield gap and determination of it’s causes. In dryland management, optimization of vegetative growth before flowering in order to maximize the utilization of available water in the soil is a fundamental principle and consequently management factors such as planting date, seeding rate and amount of fertilizer nitrogen (N) use can determine the amount of vegetative growth before flowering. Boundary line analysis is a statistical method that can quantify the response of yield to an environmental or management factor in a situation in which other parameters are variable. The objectives of this study were to introduce, use and comparison of two methods for fitting boundary line to determine the best managements and simultaneously estimate potentials and calculate wheat yield gap in three county of the Golestan province include Aghala, Gomishan and Kalaleh. One, the common method based on the least squares (LS) method and another method based on linear programming (LP) so that no point is fall above the drawn line. For this study, the required information were collected from 332 wheat field during two growing season of 2014 and 2015 in Aghala, Gomishan and Kalaleh. The investigated management factors included planting date, seeding rate and amount of nitrogen fertilizer (N) use (at the base and the road). To fit a boundary line using LS method, a curve was fitted through the maximum yield points based on the least squares method. However in LP method, a curve was fitted on the top edge of the data so that any point is not fall above the drawn line. For this purpose, the parameters of the function were changed so that the total remaining was at the lowest level, in the condition that no single negative remaining was left. Then, the optimum of each specific management factor was recognized and finally with estimating potential yield and minus the average yield in each county, yield gap was calculated for each factor and in whole. The results show the optimum of each specific management factor to reach the potential yield and optimum planting date, seeding rate and optimum nitrogen (N) use in each county were determined. The average wheat yield in drylands of Aghala, Gomishan and Kalaleh was about 2600 kg ha-1 and also potential yield was about 4700 kg ha-1 according to LS method and 4800 kg ha-1 according to LP method. So there is a yield gap about 2000-2100 kg ha-1 (44-45%). It should be noted that these results are not conclusive and it would be required to several years of testing to achieve the optimal range of each region. LP method doesn’t require dividing independent factor and determining the boundary point, and also the fitted curve done with participate of all the data. In other words, all points have their impacts on the drawn curve and this is the advantage of this method. However the lack of involvement of user experience to choice the ideal points for fitting the curve would be the weakness of this method, it seems that each of these methods in certain situations -depend on the type and distribution of data- can be useful. So user after drawing the scatter plot of data should choice the best method to fit line to the data.

In this study, collecting of management information from about 700 wheat farms in Golestan province was conducted during two growing season of 2013-2014 and 2014-2015. In each of region, potential yields, the optimum crop management and simultaneously the percentage of wheat farms out of the optimal range were identify in both irrigated and rainfed conditions using boundary line analysis. So, the information were analyzed in three part of irrigated, high- and low-yield rainfed conditions. By plotting farm’s yield data scatter, against management factors, highest yields in different levels of input or management factors were selected and a boundary function was fitted to the upper boundary of data points. According to the results, potential yield for irrigated, high- and low-yield rainfed wheat were estimated equal to 6816, 5791 and 3932 kg ha-1 with a yield gap of 42, 31 and 50%, respectively. The optimum ranges of sowing date, seeding rate, plant density, frequency and the amount of nitrogen fertilizer applied, the amount of nitrogen applied after sowing, the amount of phosphorus (P2O5) and potassium fertilizers (K2O) applied and irrigation frequency were determined according to the results. Consider the optimum managements, farmers in each region can shrink the yield gap and reach potential yield result in increasing the amount of wheat production in Golestan province.

In this study, all information about management practices in wheat crop were recorded and measured (including 250 variables) from 684 irrigated and rainfed wheat fields during two growing seasons of 2013-2014 and 2014-2015 in Golestan province, Iran. Wheat production in Golestan province can be divided into three production systems including low- yielding rainfed, high- yielding rainfed and irrigated wheat. This study covered 216 fields in low-yielding rainfed, 119 fields in high- yielding rainfed and 349 fields in irrigated wheat and evaluated using comparative performance analysis (CPA). In each system one production model was identified and according to this model, potential yields of wheat were estimated. The results showed that while the average yield of farmers were 1966, 3985 and 3936 kg kg.ha-1, they could harvest up to 5025, 7954 and 8029 kg.ha-1 in low-yield rainfed, high- yielding rainfed and irrigated farms, respectively, hence there were yield gaps of 60, 50 and 51%, respectively. The most important factors found for these yield gaps in low- yielding rainfed system included: use of no-till planter (32%), seedbed prepartation (17%), foliar fertilization (15%), chisel (15%), herbicide (13%) and nitrogen fertilizer (9%). The most important factors for yield gaps in high- yielding rainfed system included: legume rotation (26%), field size (21%), nitrogen fertilizer (15%), potassium fertilizer (12%), manure fertilizer (11%), subsoiler (10%) and fungicide (5%). However, the most important factors for yield gaps in irrigated wheat system included: irrigation (27%), nitrogen fertilizer (25%), sowing date (20%), use of N8720 cultivar (10%), subsoiler (9%) and using of furrower (8%). It was concluded that with proper management of field and considering the listed yield gaps factors in each system, it would possible to obtain higher yield levels comparing with current yield levels that farners harvest. The amount of yield increased will be about 3000 kg.ha-1 in low- yielding rainfed wheat system and about 4000 kg.ha-1 in high- yielding rainfed wheat and irrigated wheat systems in comparison with current yield levels.

Plant breeders to select their breeding objectives through the physiological and morphological characteristics, require classification of the limitations and capabilities which exists in plants; this issue leads to the concept of the ideotype. Designing plant ideotypes entails appropriate statistical methods. The objective of this study was to introduce a method based on multiple regressions to find ideotype with a case study of sunflower in Gonbad.
Sunflower was chosen as an example. Data were produced using 12 sunflower genotypes in a randomized block design in south of Gonbad city. Using multiple regressions in order to determine the important traits and to show the contribution of each trait in formation of yield. The method identified the relation between yield and all variables in a quantify matter. Also according to the positive or negative correlation between the variables affecting the yield, to designing ideotype, various hypotheses put forward and various aspects of them was examined.
Five important traits in determining sunflower yield were recognized in this study. They were total dry mass production, maximum leaf number, grain weight, the percentage of empty achene and the percentage of oil. Then, the optimal values of each trait were determined by the method. These five variables explained 57% of yield. The results indicate that if the correlation between some traits would be changed, it can be used for the benefit of yield. Regarding negative correlation between oil percentage and total dry mass, several hypotheses were evaluated. If the negative correlation between max. dry matter and oil content is not breakable, the yield of ideotype would have an increasing of 812 (from 2080 to 2892) kg ha-1 in comparison with sunflower hybrids. If with increasing max. dry mater, oil content stay at moderate level, it would be an increasing of 873 (from 2080 to 2953) kg ha-1 in ideotype, and if correlation between max. dry matter and oil content is breakable, it would be an increasing of 999 (from 2080 to 3079) kg ha-1 in ideotype.
The designed ideotype would increase grain yield from an average of 2080 kg ha-1 to 2892-3079 kg ha-1 in the ideotype. It was concluded that the method used in this study, because of concerning the genetic differences between varieties, can be used in determining plant ideotypes in conjunction with other methods and it can guide plant breeders to move through ideotype crops.

Chickpea (Cicer arietinum L.) is cultivated on alarge scale in arid and semiarid environments. Terminal drought and heat stress, among other abiotic and biotic stresses, are the major constraints of yield in most regions of chickpea production. The study of the effects of climate change could help to develop adaptation strategies to promote and stabilize crop yield. This research was aimed to assess adoption strategies in rainfed chickpea in response to Zanjan province’s climate change using a crop simulation model along with providing simulated yield maps using geographical information system (GIS).
To study the effects of climate change and simulation the adaptation strategies, the model of Soltani and Sinclair (Soltani & Sinclair, 2011) was used. This model simulates phenological development, leaf development and senescence, mass partitioning, plant nitrogen balance, yield formation and soil water balance. For each location, a baseline period of daily weather data was available (Table 1). Investigated scenarios were historical climate (control) and future climate scenarios that included both direct effects of doubling CO2 (350 to 700 ppm) and its indirect effects (10% reduced rainfall, 4ºC increase in temperature). The crop model was performed for the different years of baseline period for current and future climate under typical management and cultivar and also under three adaptation strategies in the future climate including Management adaptation (M), Genetic adaptation (G) and a combination of both Management and Genetic adaptation (M & G) as described below (Table 2): Management – In various studies changing the planting dates as the simplest and least-cost adaptation strategy has been emphasized (Luo et al., 2009); hence a shift in planting dates i.e. sowing 15 days in advance was explored in this study to reduce the risk of the late season drought.
Genetics – Changes in genotype have been suggested to be the most promising adaptation option in the world. Earlier maturity cultivars may be needed to match future drier conditions. Thus alternative genotype was a cultivar with 20% lesser of the required biological day from emergence to flowering.
M & G – The third adaptation practice was an attempt to combine both earliness and early sowing date (15 days).
A randomized complete-block design was used for data analysis in which climate condition with considered treatment and years was considered as blocks. When it was necessary, mean comparison was done using a Least Significant Difference (LSD) procedure at 5% level.
The results showed that in future climatic change, mean yield for Zanjan province will reach to 1036 kg.ha-1 with 38.4% increasing which was statistically different compared to current situation (760 kg.ha-1). The possibilities for gathering more benefits of grain yield were tested by changing traditional management and genetics of the locations in the future climate which involved three management options as adaptation strategies (Earlier Sowing, Earlier maturity Cultivars and combination of these two options). Applying earlier sowing date in comparison with conventional sowing date, increased mean yield by 67.7% (1268 kg.ha-1). In addition, applied earlier maturity cultivar led to 1212 kg.ha-1 (63.9% increase in comparison with current cultivar). Results revealed that using earlier maturity cultivars in combination with earlier sowing date will increase mean yield up to 1452 kg.ha-1 (94.5% increase),whichwasthe consequence of a shift in growing season to a wetter part of the year and reduced the risk of late season drought stress. Furthermore, breeding for earliness by reducing the vegetative period would save more water to be used for grain filling. Under these circumstances, according to decreased environmental risks, yield sustainability will increase up to 28.4%.
The Results of this study can also be extended to water-limited regions of chickpea producing with similar climatic and edaphic conditions. New varieties should be released with shorter growth periods than current ones and their sowing dates must be advanced if possible. Other management practices such as conservation tillage or keeping residue on the soil surface in order to save and increase soil water content were not included in this study which is suggested to evaluate the effects of these factors on the yield of crops in the future climate change studies.

Due to increase in the world population and increasing concerns about the future food security, shrinking the yield gap has became a hot area of research in field crops. Estimation the amount of yield gap and determination of it’s causes requires using proper methods. Boundary line analysis is a statistical method that can quantify the response of yield to an environmental or management factor in a situation in which other parameters are variable. Indeed, this method is able to determine the response of yield to a desired factor while other factors are appropriate.The first objective of this study was to introduce a method of boundary line analysis as a practical analysis in yield gap studies of crops. Another one was how to use of this method to determine the best managements and estimate the potential yield and yield gap of wheat in Gorgan.
With sampling of farms and use of this method, we can determine potential yield and the reasons of yield gap and also the importance of each parameter in the yield gap. In this study, we explain how to use this method using the data of 95 wheat farm collected in 2008 and 2009 in Gorgan. Management factors include the amount of nitrogen fertilizer (N) (at the base and road), phosphorus (P2O5), potash (K2O), frequency of irrigation, plant density and planting date.
The results show that while the average yield of farmers is 4700 kg, they can achieve up to 6200 kg ha-1 by improving their agricultural management. For this purpose, the following should be considered: (1) using at least 96 kg of N fertilizer in which 73 kg of it should be used in split application, (2) using at least 31 kg of P2O5 and 40 kg of K2O as a base fertilizer, (3) at least two irrigations, (4) plant density of 182 to 447 in m2, and (5) planting date before the late of November.
Boundary line analysis in yield gap studies could clearly show the responses of yield to management factors and estimate possible yield potential. Interpretation of the analysis’s results is very simple and clear. In addition, it seems that this analysis reduce the need to common experiments and also have provided good clues to design new field experiments. With a good sampling of farms in several years and use the ability of such methods we can find the ways of increasing production. It is strongly recommended that the boundary line analysis use as a practical analysis in yield gap studies of crops

Life Cycle Assessment (LCA) is a method that evaluates all environmental effects of a product during its life cycle. The objective of this study was to introduce LCA to Iranian scientific community and to use it in assessment of environmental impact of different production systems of sugar beet in former Khorasan province, west of Iran, compared to Swiss condition. The data were gathered regarding materials and processes from traditional, semi-mechanized and mechanized production systems in 26 regions of 11 geographic areas of Khorasan (current North, Razavi and South Khorasan provinces), the inventory analysis. Then, all the resources (inputs) and environmental emissions (outputs) were quantified under environmental impact category. The results indicated that there is a significant gap between Khorasan and Swiss (optimum situation) in terms of environmental impacts per each ton of sugar beet production (up to 40 times in some of environmental impact category). Moreover this study demonstrated that mechanized sugar beet production system is more environmental friendly regarding to depletion of non-renewable energy sources, global warming, eutrophication, photochemical oxidation, acidification, land use and ozone layer depletion among various systems of sugar beet production in Khorasan. Contribution analysis indicated that irrigation had the greatest role in creating environmental impacts. Climatic condition of this region and the existence of drought led to spend a lot of energy for irrigation. Providing this energy causes high environmental impacts due to the dependence on fossil fuels.

Nitrogen is one of the most important nutrients in crop production, but it's losses to the environment is harmful. The SUNDIAL model (simulation model of N dynamics in the soil) was used to quantify the cycle of N and it's losses in different production systems of sugar beet (Beta Vulgaris L.) in former Khorasan province, east of Iran. The data were collected from traditional, semi-mechanized and mechanized production systems in 26 regions of 10 geographic areas of Khorasan (Northern, Razavi and Southern Khorasan provinces) to be used in the model as inputs. Results showed that the total N loss from the soil profile in traditional, semi-mechanized and mechanized systems did not differ significantly and were 50, 41 and 45 kg N ha-1, respectively. In average, 59% of total N loss occurs as gaseous losses and 41% as leaching. The most part of the gaseous losses in traditional system occurs via denitrification (63%), but in semi-mechanized (75%) and mechanized (82%) systems volatilization was dominant. The key finding is that while various systems of sugar beet production are similar with respect to N loss per hectare, they are different with respect to N loss per ton of crop yield. The average of N loss in traditional system was significantly more than other systems in terms of kg N ton-1. This implies the possibility of reducing N losses and its environmental effects thorough improving crop management.

Climate change due to increase atmospheric CO2 concentration and other greenhouse gases could have considerable effects on crop production in the future. The aim of this study was climate change impacts on chickpea yield in rainfed condition and generating yield map in Zanjan province، Iran. Investigated scenarios were historical climate (control) and future climate scenario that included both direct effect of doubling CO2 (350 to 700ppm) and its indirect effects (10% reduced rainfall، 4ºC increase in temperature). Simulations were done using a simulation model of chickpea and Geographical Information System (GIS) approach was used to produce yield maps. Grain yield and its variability were evaluated in comparison to current trend. The future climate condition will increase crop yield by 33. 4% (717 to 957 kg ha-1 depending on location)، but yield variability will increase by 21. 2%. Increasing in grain yield was a result of positive effect of doubling CO2 on photosynthesis and resultant decrease in transpiration. The results also showed that increasing temperature was the main reason of higher yield variability. Zanjan station with an increase of 46. 5% in grain yield showed the greatest yield increase under the future climate but yield variability at this location was also increased by 37. 7%. The highest grain yield was simulated for Khodabandeh station under both current (1307 kg ha-1) and the future (1385 kg ha-1) climates.

It is essential to understand crop yield potentials and exploitable gaps to detect yield constraints of crops. Yield gap analysis is a method to evaluate our systems distance from actual capability of a region to produce a given crop which will direct researches and help for cropping patterns design and programming. Moreover، knowledge on a region capability could result in investigating on given crop. Thus، this research was aimed to quantify water-limited potential yield، comparison of actual and potential yield and yield gap analysis of chickpea production in Zanjan province. Also Geographical Information System (GIS) approach was used to produce agroecological zoning maps. Averaged water-limited potential yield in Zanjan province was assessed as 727 kg ha-1 (varied from 559-1075 kg ha-1) using a simple simulation model of chickpea. With considering averaged actual yield as 408 kg ha-1 (average of three years، from 2009-2011)، mean yield gap was evaluated as 43% (varied from 24-59%)، which its average value is equal to 320 kg ha-1 (varies from 157-475 kg ha-1). The most important finding was the possibility of considerable increasing of yield in studied province in comparison with current yield. Our results on yield gap revealed that with covering of 80% potential yield، it is possible to increase yield from 5 to 95% (48% in average). Improvement of sowing، protection and harvesting managements are the main and lowest-cost options to fill the chickpea yield gap in Zanjan Province.

In order to evaluate the effect of row spacing and weed density on growth indices of canola (Brassica napus) in competition with wild mustard (Sinapis arvensis(a field experiment was conducted based on randomized complete block design with factorial arrangement and three replications at Agricultural Research Station of Agricultural Sciences and Natural Resources of Gorgan University, during 2009-2010 growing season. The factors included canola row spacings at 3 levels (12, 24 and 36 cm) and wild mustard densities at 4 levels (0, 4, 8 and 16 plant per m2). Results showed that growth indices of canola (leaf area index (LAI), crop growth rate, relative growth rate and total dry matter) decreased in competition with wild mustard in all row spacings of canola. The most leaf area of canola distributed at higher height of canola in row spacing of 12 cm as compared to 24 and 36 cm. According to the results, it seems that LAI and vertical distribution of canola’s leaf area in canopy are the effective indices in reducing wild mustard biomass and increasing competitive ability of canola in competition with wild mustard.

In recent years, artificial models of weather generator parameters have extensively been applied in hydrological and ecological systems and also in the studies of climatic impact potential on agricultural ecosystems throughout the world. These models have been developed as a statistical technique for generating daily weather data when long-term data are not available. The purpose of this investigation was to evaluate the performance of three models of CLIMGEN, LARS-WG and Weather Man to predict at fine-scale and at meteorological stations’ scale for climatic variables including maximum temperature, minimum temperature, precipitation and solar radiation for the years of 2000-20009 in three regions of Gorgan, Gonbad and Mashhad. Firstly, daily weather data of each station from 1975-1999 for Gorgan and Gonbad and 1961-1999 for Mashhad were implemented in the models and the daily data for years 2000-2009 were obtained. For assessment of the mentioned models, the comparison of statistical indicators such as Root Mean Squared Error (RMSE), Model Efficiency (EF) and Determination Coefficient (R2) were used. The results obtained from comparing the monthly average of produced data during a ten-years period by models showed that temperature variable has been predicted better than other parameters by the three models. LARS-WG in Gorgan and Mashhad and CLIMGEN in Gonbad performed better to simulate the minimum temperature. CLIMGEN in Mediterranean climate of Gorgan and semi-arid climate of Gonbad and Weather Man in dry climate of Mashhad have simulated the maximum temperature better than the other models. Solar radiation variable by LARS-WG model in Mashad and Gonbad regions and by CLIMGEN model in Gorgan has been predicted by a more effective performance. Despite the differences among the outputs of the models, it seems that they can be applied in crop modeling and in the issues of climatic changes.

Climate change as a result of increase in atmospheric CO2 concentration and other greenhouse gases may have major effects on productivity of crops in future. The objective of this study was to evaluate the effect of climate change on growth, yield and water consumption of chickpea under rainfed and irrigated conditions of Kermanshah. The studied scenarios were (1) doubling CO2 concentration (i.e., 700 ppm), (2) 4°C increase in temperature, (3) 2% reduction in the amount of precipitation, and (4) a combination of 700 ppm CO2, 4°C increase in temperature and 2% reduction in precipitation. Simulations were done using SSM-Chickpea model for the mentioned scenarios. Simulation results showed that climate change will cause 89% increase in grain yield under rainfed condition and a 33% decrease under irrigated condition. Although increasing concentration of atmospheric CO2 had directly positive effects on yield of chickpea, but it was found that increased temperature was more important. Water use efficiency enhanced by 81% under rainfed condition, but decreased by 25% under irrigated conditions. In addition, crop life cycle is shortened due to increase in temperature. According to the simulation results, the amount of required water for irrigated chickpea is reduced in the future. It was concluded that due to limitation of agriculture water, irrigated chickpea will be less profitable in the future; although area under irrigated chickpea is very low now.