جستجوی مقالات مرتبط با کلیدواژه "regression" در نشریات گروه "کشاورزی"

Legumes are one of the most important sources of protein, minerals, and calories in the diet of people in developing countries. Chickpea is known as one of the most important legumes influenced by management and genetic factors. Accordingly, the current research aimed to assess the effect of planting density on improving economic yield and yield components of rainfed chickpea cultivars in Khorramabad County at the research farm of Lorestan University in 2023. Factorial arrangement of the treatments was set up as a randomized complete block design with three replications. Factors included plant density in five levels (20, 30, 40, 50, and 60 plants per square meter) and cultivars in three levels (Adel, Saeed, and Mansour). Based on the obtained results, the interaction effect of density and cultivar on the distance of the first secondary branch from the ground, the distance of the first pod from the ground, the number of secondary branches, the number of pods per plant, the number of seeds in a pod, and the number of seeds per plant was significant at the level of 1% and was not on economic and biological yields and 100-seed weight. However, the simple effect of density was significant on economic and biological yields at the level of 5%. Also, the simple effect of cultivar was significant on economic and biological yields at the level of 5%. Based on the mean comparison, the highest economic yield (3390.5 kg/ha) was obtained at a density of 50 plants per square meter and the lowest (2678.3 kg/ha) at a density of 20 plants per square meter showing 26.6% superiority. By comparing the regression between economic yield and different densities for various cultivars, the best economic yield of Adel cultivar (3394 kg/ha) was obtained at a density of 44.98 plants per square meter (approximately 45). Also, the maximum economic yield for the Saeed cultivar (3264 kg/ha) was at a density of 59.83 plants per square meter (approximately 60), and for the Mansour cultivar (3430 kg/ha) at a density of 37.85 plants per square meter (approximately 38). Accordingly, the Mansour cultivar with a density of 40 plants per square meter is recommended for the growers in Khorramabad.

The objective of this study was to investigate the relationships between yield and yield components with morphological traits, and to leverage these relationships to select high-yielding cultivars under optimal irrigation and drought stress conditions at the flowering stage.

To identify the correlations and relationships among different traits in bread wheat, 30 genotypes were investigated as sub-factors in a split-plot experiment with a completely randomized design and three replications under conditions of normal irrigation and moisture stress at the beginning of the flowering stage at the research greenhouse of Azerbaijan Shahid Madani University in the 2016-2017 crop season.

Under normal conditions, the number of seeds in a spike (0.89) and under stress conditions, the number of seeds in a spike (0.70) and the number of days from planting to 50% flowering (-0.60) were decisive and influential on the grain yield. The results of canonical correlation analysis showed a significant correlation between the pair of canonical variables obtained from spike traits with grain yield per plant and spike density under both normal and stress levels.

Grain number per spike, spike length, 1000-grain weight, straw yield, spike and shoot density, flag leaf area, and the duration from planting to 50% flowering are the most critical components significantly impacting crop yield enhancement under optimal greenhouse irrigation conditions. Conversely, the critical determinants of grain yield enhancement included grain filling rate, grain filling period, number of days from planting to 50% flowering, root dry weight, straw yield, and grain number per spike when subjected to moisture stress conditions.

Global solar radiation (GSR) is an important parameter for designing and developing different solar energy systems (Tris et al., 1997). The concept of solar radiation means that during a certain period of time, how much solar energy is absorbed by a horizontal surface in an area (Almoroux and Hontoria, 2004; Wang et al., 2011). Iran is a sunny country, and, the optimal utilization of solar energy can be necessary for furure enenrgy development (Jafarkazemi and Mardi, 2019).

In the present study, solar radiation was investigated in five climates. In this way, the two stations of Ramsar and Rasht represent the very humid cool warm climate, the three stations of Kermanshah, Mashhad and Shiraz represent the very hot semi-arid climate, the three stations of Tabriz, Zanjan and Urmia represent the semi-arid climate. Cold-warm, Birjand, Kerman and Isfahan stations were investigated as representatives of warm dry climate and Ahvaz, Booshehr and Bandar Abbas stations as representatives of warm dry temperate climate.Daily measured data of wind speed (U), maximum temperature (Tmax), minimum temperature (Tmin), average air temperature (Tave), average relative humidity (RH), sunshine hour (N) and precipitation (R) As independent variables and the amount of solar radiation (RS) as a dependent variable, for each of the mentioned stations, it was prepared from the National Meteorological Organization during the years 1986 to 2010.After preparing the data and their quality control, solar radiation was estimated using multivariate linear regression fitting under SPSS software. Empirical relationships were calculated for each climate separately.For this purpose, by applying the ranges, restrictions and characteristics of radiation, regression analysis was performed in the Solver environment of Excel software.In order to evaluate the research, the results were measured based on the error measurement indices of the mean squared normal error (nRMSE) and mean bias (MBE).

According to Table (2), the meteorological parameters of maximum, minimum and average temperatures, hours of sunshine and wind have a positive correlation with solar radiation, that is, with the increase of each parameter, the amount of radiation also has an increasing trend, except precipitation and relative humidity, which have a negative correlation with radiation. It shows the reverse relationship between these two parameters with solar radiation. The highest correlation between relative humidity and solar radiation (-0.485) and the lowest correlation between precipitation and radiation with a value of -0.048 is in the hot dry climate. The best regression equations obtained from meteorological parameters using SPSS software for different climates are given in Table (3). in which the amount of error between the available parameters has been calculated for each of the stations.The relationships presented for five climates show that the maximum amount of error based on nRMSE and MBE index in hot dry climate is 11% and -1.15 megajoules per square centimeter per day, respectively. On the other hand, the lowest amount of error based on the two mentioned indicators is in the hot and cold semi-arid climate, which is 8.5% and 0.50 megajoules per square centimeter per day, respectively. To estimate the regression equation in hot and cold semi-arid climates, as the results show, the variables included in the best regression equation in hot and cold semi-arid climates were: maximum temperature, precipitation, minimum temperature, sunshine hour, wind speed and average temperature In hot dry cool climate, the maximum, minimum and average temperature, relative humidity, sunshine hour and wind speed were used. Precipitation, minimum temperature, wind speed and solar hour are used in very hot semi-arid climate, in hot dry climate, maximum and minimum and average temperature, relative humidity, wind speed and solar hour are used, finally in moderately hot dry climate. Precipitation, maximum and average temperature along with wind speed, sunshine hour and relative humidity were used. In very humid, cool, hot, semi-dry, very hot, and dry, moderately hot climates, it is observed that the accuracy of radiation estimation between meteorological parameters is almost within the same interval of change (about 9%). From the MBE index provided between the meteorological parameters, we find that the minimum overestimation value is 0.02 megajoules per square centimeter per day in very humid, cool, hot climates, and the maximum value is (0.50) in the semi-arid, cold, hot climates. be Figure (3) shows the time series graphs of the investigated stations during the relevant statistical period. In general, the estimation of solar radiation using regression equations based on the investigated error measurement indices, which has the least amount of error and, conversely, the highest amount of accuracy, is in the semi-arid, cold, hot climate, because the accuracy is 8.5% based on the nRMSE index. The high accuracy of the equation is used to estimate the radiation, and its MBE value is 0.50, which proves the overestimation of the presented equation.Meteorological parameters were examined on a daily basis to obtain the regression equation in the years 1986 to 2010 in a very humid cool hot climate as mentioned in Table (4), and in the time series graph, the observed radiation values are more than Radiation values areestimates from regression relationships. The very hot semi-arid climate shows that the estimated radiation values have almost a constant range of changes and its maximum value is approximately 2000 megajoules per square centimeter per day, but the observed radiation value is more than this value. According to the diagram and table (4), the semi-arid, cold and hot climate has MBE value of -0.501 MJ/cm2/day among the parameters, and the estimated amount of radiation has not exceeded 2000 MJ/cm2. In some statistical years, the observed value is more than the estimated value. The time series of the hot and cold semi-arid climate is the same as the rest of the climates and it is higher than the mentioned value, and in the cool and hot dry climate, which has MBE with a value of -1.15 MJ/cm2/day, it is among the meteorological parameters. It is also mentioned in Table (4), and in some of the investigated periods, the amount of observations is more than the estimate, and there is a large fluctuation between the amount of radiation estimated and observations, and the estimated amount is up to about 4000 megajoules per square centimeter per day. is Finally, the hot temperate dry climate with MBE, -0.558 megajoules per square centimeter per day indicates the underestimation of solar radiation in this climate.

It is necessary to investigate the energy of solar radiation as it is important and influential parameter on the processes on earth, including water resources. In this research, using the equations obtained from the regression model, the amount of solar radiation reaching the earth's surface was estimated in five different climates in the country, which includes 14 meteorological stations. It was seen that the amount of observed radiation in three climates, semi-arid, very hot, dry, cool, hot, and dry, moderately hot, is lower than the observed amount, and in two climates, very humid, cool, hot, and semi-arid, cold, hot, it is higher than the estimated amount of radiation. According to the regression equations, the amount of error obtained in the hot and cold semi-arid climate has the lowest amount of error and the warm and dry climate has the highest amount of error. The changes in the error percentage of the solar radiation estimation of the other three climates are not large and are about 9%. The meteorological parameters used in the semi-arid, cold, hot climate were temperature, precipitation, sunshine hours, and wind speed. Considering the limitation of measuring solar radiation in the country, the importance of estimating this meteorological parameter is essential in order to use it in the management of water resources by managers.

The improper use of chemical fertilizers in crop production can result in unsafe food sources for consumers. This research focuses on estimating the accumulation of nitrate in tomatoes by analyzing images of tomato tissues. The experiments were conducted using a completely randomized design with four nitrogen levels: 400, 800, 1200, and 1600 kg.ha-1. Fifty samples were randomly selected from each treatment to create images for feature processing and develop a prediction model. The samples were sliced to a consistent thickness, and their images were prepared. The nitrate contents of the same samples were then measured in the laboratory. Color features, including R, G, and B color components, as well as non-color features such as white pixel area (WPA), total slice surface area (TSA), and the ratio of white pixel area to total slice surface area (WPA/TSA), were extracted from the images. The results showed that the nitrate content of the samples increased significantly (P<0.05) in response to the applied nitrogen fertilizer, with measurements of 1.6%, 2.7%, 2.8%, and 3.3%, respectively. Moreover, a strong correlation was found between the color components, WPA, TSA, WPA/TSA, and nitrate accumulation in the samples. Multiple regression and multilayer perceptron neural network (MLP) models were employed to predict the nitrate content. The best subset method was used to build an appropriate regression model. Various topologies and transform functions were applied to identify the best MLP model. The results indicated that an MLP model with a 3-15-1 topology and the lowest mean relative percentage error (MRPE) was the most accurate neural network model. The final regression and neural network models were validated using 60 intact samples. The neural network model achieved a MRPE of approximately 3.5%, demonstrating its precise estimation of nitrate contents compared to the regression model with an MRPE of around 5.2%.

One of the common methods to estimate the yield of crops before their harvest is the use of satellite images and remote sensing technology. One of the efficient methods extracted from remote sensing data to monitor crop yield is the use of spectral indices. This study was carried out to estimate maize yield before its harvest using Landsat 8 satellite images in a part of the lands of Shahid Beheshti agro-industry, Dezful. Eight spectral indices including NDVI, TNDVI, GNDVI, SAVI, OSAVI, NDWI, MNDWI, and NDMI were used in this study. A correlation relationship was formed between the indices and yield with 70% of the data. Then the relationships were evaluated with 30% of the data. MNDWI index had the highest correlation with the field data in December and January, respectively, with a coefficient of determination of 30% and 13%. R2 and RMSE between the observed and predicted yield values by this index for the mentioned months were 72%, 1/71 tons per hectare, and 48%, 1/54 tons per hectare, respectively. Multivariable equations produced for both dates in January and December were obtained with R2 and RMSE, 31%, 0/06 tons per hectare, and 55%, 1/19 tons per hectare, respectively. According to the results, by using the average MNDWI ، SAVI, and NDVI indices in the produced relationships, the errors caused by the spatial changes of yield at the farm level are minimized, and it is possible to estimate the yield before harvest.

The increased demand for cereals that are consumed by humans and livestock can be met through the development of planting drought-tolerant genotypes. Due to the interaction of genotypes × environment, the best genotype in one environment may not be the best in other environments, and therefore, this interaction provides valuable information about the yield of each genotype in different environments and plays an important role in evaluating yield stability. Genetic modification of drought tolerance in crops is one of the most stable and cost-effective approaches to increase production and yield stability. Examining the compatibility and stability of grain yield based on various parametric and non-parametric stability statistics and evaluating tolerance to drought stress based on stress indices in promising barley genotypes of the country's temperate climate are among the goals of this research.

To assess grain yield adaptation and stability and to select high-yielding barley genotypes suitable for terminal drought stress in the temperate climate of Iran, 16 barley genotypes were cultivated during two crop years 2021-2023 in a randomized complete blocks design with three replications in three research stations including Varamin, Birjand, and Yazd under two none-stress and drought stress conditions at the end of the season (12 environments). After determining the grain yield, stress indices, including MP, GMP, TOL, HARM, STI, YI, YSI, RSI, and SSI, and the correlation of each with grain yield were calculated in this study. Stability statistics included Nassar and Huehn’s stability statistics (S(1-6)), Thennarasu’s stability statistics (NP(1-4)), deviation from regression (S²dᵢ), regression coefficient (b), Shukla’s stability variance (σ²ᵢ), environmental variation coefficient (CV), variance component (θᵢ), coefficient of variance (θ(i)), Wricke’s ecovalence (Wᵢ²), and Kang’s sum of ranks (KR). Their relationships were calculated based on Pearson’s correlation. Analysis of variance (ANOVA), mean comparison, and simple correlation were calculated using the SAS-9.0 program, stability statistics were calculated using STABILITYSOFT and principal component analysis (PCA). Stress indices and the correlation of each of these indices with grain yield were calculated using iPASTIC. The three-dimensional distribution diagram of genotypes in the ranges of A, B, C, and D was drawn using Grapher software.

The results of the combined ANOVA indicated the significance of the genotype × environment interaction. According to S(1-2) statistics, G7, G10, G11, and G3, and according to S(3-6) statistics, G7, G3, and G9 were the most stable genotypes. Among the non-parametric Thennarasu’s stability statistics according to the NP(1) criterion of G9, G3, and G5, according to NP(2) G5, G3, and G8, and according to NP(3) and NP(4) criteria, G7, G3, and G9 were recognized as the most stable genotypes. Based on Wricke (W²) and Shukla (σ²) equivalency stability statistics, G3, G9, and G13 were the most stable genotypes. Based on Eberhart and Russell's regression method, G9, G7, and G3 genotypes, with high yields, had general compatibility and good yield stability. Based on Francis and Kannenberg (CVi), genotypes G1, G2, and G15 had the lowest coefficients of environmental variation. Based on the average rank of each genotype in all stress indices (AR), G2, G7, and G3 genotypes were the most tolerant, and G14, G11, and G10 were the most sensitive genotypes to drought stress at the end of seasons, respectively. In the drought stress conditions at the end of the season, grain yield had positive and significant correlations with YI, HM, GMP,  STI, MP, YSI, and RSI indices and negative and significant correlations with the SSI index. In non-stress conditions, grain yield had positive and significant correlations with MP, GMP, STI, HM, and YI indices, but no significant correlations were observed between grain yield and SSI, TOL, YSI, and RSI indices. The PCA revealed that the first and the second principal components explained 69.71% and 30.27% of the variance of the main variables, respectively. The first main component had a positive and high correlation with yield in both stressed and non-stressed environments, as well as MP, STI, GMP, and HM indices. The second component showed a positive and high correlation with grain yield in the non-stressed environment and TOL and SSI indices; it also had negative and high correlations with RSI and YSI indices. Based on the biplot diagram, G3, G7, G8, G9, G12, and G13 presented higher grain yield potential and are more tolerant to drought stress.

In this study, grain yield had negative and significant correlations with NP(3), KR, NP(2), NP(4), S(6), and S(1) statistics, respectively, therefore these statistics can be used for identifying stable genotypes. G3, G7, and G9 with averages of 6732.9, 6730.6, and 6608.1 kg/ha, respectively, not only produced the highest grain yield but also showed the highest grain yield stability and tolerance to terminal drought stress among the studied genotypes based on the total ranking of all studied stability statistics and stress indices. Therefore, they can be used as new cultivars in drought-affected regions in temperate climates or as desirable genetic materials in barley breeding programs for drought tolerance.

Efficient and high-quality information about the current and future state of the forest is needed for sustainable management and basic planning of forest resources. Zagros forests, as one of the most important vegetation areas of Iran, have a very important effect on water supply, soil conservation, climate adjustment and economic and social balance in the whole country, hence, the sustainable protection and management of these forest ecosystems The main concern of researchers and managers in this area has become vegetation. The dominant species of these forests is Iranian oak (Quercus brantii Lindl). According to geographical and environmental conditions, this species has various habitats in the vegetation zone of the middle Zagros. Considering the noble position of the Iranian oak species in the forests of Zagros, the importance of developing more researches regarding this species is essential. The characteristics of tree diameter and height are the most important components needed in forest statistics. These variables are one of the main variables for measuring the appearance characteristics of forest trees and are used in cases such as determining the volume and drawing the height curve. Considering that measuring the height of all forest trees is a long and expensive operation, hence the use of diameter and height models to estimate the height of trees has been developed. The purpose of this research is to investigate the linear and non-linear diameter-height models of Iranian oak species (Q. brantii Lindl) in the high forests of the Middle Zagros in order to find the answer to the question of whether it is possible to estimate the height of Iranian oak high forest trees as There is a function of the diameter of the chest in this vegetative zone or not.

This research was conducted in Sefidkoh protected area of Lorestan. The dominant tree cover of this area is made of oak, like other areas of Zagros, but other plants and trees such as Cratagus persica pojark, Amygdalus sp, Astragalus sp, etc. can be mentioned in it. To carry out this research, by conducting numerous forest tours and getting to know the forests of the region, a stand with an area of approximately five hectares, which had a high forest vegetation structure, was selected. In the selected forest stand, the characteristics of DBH and total height of all Iranian oak trees whose DBH was more than 12.5 cm were counted as 100%. In total, 642 Iranian oak trees were measured in DBH and height. Linear and non-linear models were used to fit the data of DBH and height of trees, which include various models of exponential, power, density-product, growth, sigmoid and other functions. In this research, 80% of the data were used in the modeling process and 20% were used for evaluation, as well as for fitting the data and estimating the indicators of each of the models from the Curve Expert Professional software, which is a software Comprehensive software was used to fit the curves. In the modeling process, diameter data were used as independent variables and height data were used as dependent variables. In the upcoming research, to verify the accuracy of the obtained models, the RMSE, MAEand Bias in absolute and relative terms, as well as the R2 and the AIC was calculated as validation indices.

Based on the descriptive statistics of Iranian oak trees, the average, minimum and maximum DBH was 42.42, 12.5 and 150 cm, respectively, and the height was 6.3, 1.2 and 17 meters, respectively. Based on R2 values, the used models explained 62 to 86% of the total changes in tree height. The results of tree height-diameter modeling showed that Gompertz, MMF and Richard models have the highest explanatory coefficient (0.86, 0.86, 0.86), respectively, and the lowest standard error (307. 1, 1/307, 1/307) and AIC information criterion were (276/04, 277/13, 277/03). The results of the criteria used to validate the used models showed that the Gompertz, MMF and Richard models have RMSE of 22.22, 22.20 and 22.20%, respectively, as well as MAE respectively. 16.69, 16.74 and 16.79 percent were better able to estimate the characteristics of the height of trees. According to the mentioned results, it was found that Gompertz, MMF, and Richard models have a higher ability to estimate the characteristic height of Iranian oak trees compared to other models.

Overall, the results of this research showed that linear and non-linear models have the ability to estimate the height of Iranian oak high forest trees in the growing region of the middle Zagros, and among these models, three non-linear models are Gompertz, MMF and Richard based on evaluation criteria. The performance was more accurate. Therefore, these models can be used in the forest areas of the middle Zagros vegetation zone, which have the same structure and habitat conditions as the studied area. It is suggested to use the generalized models of height and diameter in future researches.

Water and nitrogen are known as the two main limiting factors in achieving maximum yield in agriculture. Therefore, the use of nitrogen fertilizer plays a significant role in increasing agricultural production. However, the indiscriminate use of these fertilizers may be due to their low efficiency. Therefore, investigating the challenges of water scarcity and improper nitrogen use management is crucial. In this study, considering the challenges of water scarcity and improper nitrogen use management, an investigation has been conducted.

This experiment was conducted in the research farm of the Agriculture Faculty of Shirvan Azad University during the agricultural year of 1399-1400. The experiment was carried out in the form of strip plots in three replicates and in a complete randomized block design. Three levels of 50%, 75%, and 100% of plant water requirement were considered as the main plot and five levels of zero (N0), 100 (N100), 200 (N200), 300 (N300), and 400 (N400) kilograms of nitrogen per hectare were considered in the subplots.

Most attributes related to yield components had higher values during the second year in comparison with the first year of the experiment. Moreover, plant height, grain weight, rows per cob and kernels per row in the 100% water requirement were greater compared with the 50% water requirement treatment. As a result of 400 kg/ha nitrogen fertilizer application, plant height, rows per cob, kernels per row, 100-grain weight and biological yield increased by 24.4%, 24.6%, 23.8%, 24% and 24.2% compared with the nitrogen-free treatment, respectively.

Increasing nitrogen fertilizer application leads to an improvement in yield and yield components. The only factor that decreased with increased water supply was water use efficiency. Grain yield under 100% water requirement was 38% more than that of 50% water requirement treatment. The difference between 100% and 50% water requirement treatments in terms of the biological yield was 49.5% in the first and 24% in the second year. Also, increased nitrogen fertilizer application led to increased grain yield. However, no significant difference was observed between 300 and 400 kg/ha nitrogen fertilizer treatments, especially in the second year. Therefore, a 300 kg/ha fertilizer application is recommended to achieve a suitable yield. Fertilizer use efficiency was affected by water requirement so that nitrogen use efficiency under 100% water requirement supply at a given nitrogen fertilizer level (e.g. 100 kg/ha) was significantly higher than that of 50% water requirement conditions. Under 100 kg/ha nitrogen application, nitrogen use efficiency in 100% water requirement treatment was 111.5 and in 50% water requirement treatment was 74.3 kg/ha. Thus, nitrogen use efficiency declined by 33% due to decreased water use. Therefore, it can be concluded that in conditions where sufficient water is not available for irrigation, farmers should not use excessive amounts of nitrogen fertilizer because it has little efficiency and only leads to environmental pollution and capital waste.

Water and soil resources are the most important factors in the production of energy needed for humans. In order to protect water and soil resources, different management methods are applied according to the conditions of each region. The participation of all watershed residents and changing their behavior will be one of the basic tools of any sustainable management plan. Since norms affect the participation and behavior of local communities; the purpose of this research is to investigate the norms affecting the participation and behavior of watershed residents in the implementation of water and soil protection projects.

This research was done by descriptive-analytical method. For this purpose, Benchele watershed in Ravansar city was selected as the study area. A questionnaire was used to collect field data. The statistical population of this research was all heads of households of watershed residents in this area, numbering 569 people. Among them, using Cochran's formula and matching with Morgan's table, 229 people were directly questioned by simple random method and completed the questionnaires. Spearman's correlation tests and stepwise regression were used to analyze the data. The focus of the questions was focused on three independent variables (legal norms, normative beliefs and participatory norms) and two dependent variables (participation and behavior of watershed residents). In the data analysis section, descriptive statistics (frequency, percentage, average, etc.) and inferential statistics (Spearman's correlation test and step-by-step regression) were used. Before performing the regression to check the collinearity test, the degree of collinearity of the independent variables was examined. Also, standardized beta values were used to determine the relative importance of each independent variable in the participation and behavior of watershed residents.

The mean ranking results of each of the items of legal norms affecting the participation and behavior of watershed residents in water and soil protection projects show that the items of "people who damage water and soil resources should be fined" and "existing laws are useful to convince people in the field of protecting water and soil resources" have the greatest and least impact on legal norms with the participation and behavior of watershed residents, respectively. The results of the mean ranking of each of the items of normative beliefs affecting the participation and behavior of watershed residents in water and soil protection projects show that the highest ranking average is related to "society expects me to take care of water resources" and do not destroy the soil" and the lowest rating is related to "experts expect me not to destroy the soil". The mean ranking results of each items of the participation norm effective on the participation and cooperative behavior of watershed residents in water and soil protection projects show that from their point of view, the item "voluntarily participates in the construction of sediment dams" and the statement "I am willing to pay for the implementation of water and soil protection projects", respectively, have the greatest and least impact on the participation and behavior of watershed residents in water and soil protection projects. The results of Spearman's test showed that all three independent variables of legal norms, normative beliefs, and participatory norms have a positive and significant relationship with the participation and behavior of watershed residents. Also, the regression results and beta values showed that, respectively, the indicators of legal norms, normative beliefs, and participatory norms had the highest priority in the participation and behavior of local communities.

The results showed that about 66% of watershed residents have moderate to low participation and cooperative behavior. The mean ranking results of each of the watershed residents' participation and their behavior to participate in water and soil protection projects show that the watershed residents' care of the projects after its completion, participation in the mortar dam construction project Mortar-cement dam has the greatest effect on their participation and cooperative behavior. Also, the items of participation in the construction of gabion dam project and the desire and intention of watershed residents towards water and soil protection had the least effect on the participation and behavior of watershed residents towards water and soil protection. In general, paying attention to the norms, beliefs, behaviors and attitudes of local communities should be on the agenda of managers and planners to pave the way to protect water and soil resources.

The importance of rapid, non-destructive, and accurate estimation of leaf area (LA) in agronomic and physiological studies is well known. The need for such estimates of leaf area in particular for leaves with unusual shapes has led to studies relating leaf dimensions and leaf area. However, a search of literature revealed that little information is available for short day onion (Allium cepa L.). The objective of this study was to develop a statistically validated regression model for leaf area prediction from simple non-destructive measurements for onion cultivated in the South of Iran.

In order to prepare the required plant samples to measure leaf area parameters, an experiment was conducted in the cropping year 2020-2021 in a field of 300 square meters located in Hormozgan province-Rahdad district. The short-day cultivar Takii was used because of its wide planting area in the province. 40 leaves were chosen at random from plants growing in the farm, half were left for validation of the model and the other 20 leaves were subjected to measurement and calibration. The standard method (LICOR LI-3000C) was used for measuring the actual areas of the leaves. Before measuring the leaf area, two main parameters related to leaf length and width were accurately determined for each leaf. To select the best variables and the regression model for estimating leaf area, the fitted simple and multiple linear regression were subjected to the stepwise elimination method. Finally, the accuracy of the selected models was validated using the root mean square error (RMSE) and coefficient of determination (R2).

All linear models for estimation of leaf area with width (W) and length (L) as well as models including both variables were subjected to regression analysis. The results indicated that leaf length (L) is more appropriate variable for estimating onion leaf area and leaf width (W) was not able to properly describe leaf area. Based on the findings of this research leaf area estimation model with both W and L had the best prediction, with RMSE% = 8.64, RMSE= 1.76 and R2= 0.91. (Area= 0.121 + 0.01537 L2 + 0.3225 L×W).

As the understanding of plant growth and development has been increasing, such mathematical models will be very useful tools for the prediction of leaf area for many plants without the use of expensive devices. Thus, the models from the present study will enable researchers of plant growth modeling to predict leaf area non-destructively with the equations developed.Keywords: Leaf Area, Leaf Length, Leaf Width, Regression, Coefficient of determination.

Among the cereal crops, barley is known as the fourth most important crop on a global scale and provides about 50% of the calories needed in the world. This plant can even make up to 70% of calories in less developed countries, especially in Africa and Asia. Terminal drought stress, as one of the consequences of climate change, has significant negative effects on the crop plants’ growth and production. Considering the importance of barley as livestock feed and its role in food industry, development and introduction of drought stress tolerant barley cultivars is very important. To increase the levle of terminal drough tolerance, breeders should combine the improvement of grain yield along with high level of terminal drought tolerance. To reach this goal first step in this way is to select potential germplasm that have genotypic variation for drought tolerance.

Eighteen barley promising genotypes were evaluated for adaptation, grain yield  and yield stability under none-stress and terminal drought stress condtions (withholding irrigation from the 50% spike emergence stage)  using randomized complete blocks design with three replications in three research field stations; Varamin, Birjand and Yazd, Iran, in two cropping seasons (2020-21 and 2021-22). None-stress and stress experiments were carried out separately. In non-stress conditions, full irrigation was applied, and in terminal drough stress conditions, irrigation was withheld at the 50% spike emergence stage. The barley promising genotypes were selected from the advance barley grain yield comparison trial genotypes in the temperate zone stations of the country in the previous cropping season. After determining the grain yield in two conditions of none stress and drought stress, the stress indices including; MP, GMP, TOL, HARM, STI, YI, YSI, RSI and SSI as well as their correlation with grain yield were calculated using the iPASTIC program and the three-dimensional distribution diagram of genotypes in A, B, C and D ranges was drawn using Grapher software. Combined analysis of variance was performed to study genotypic variation and genotype × year interaction effect. Least significant difference (LSD) test was employed for mean comparison at the 5% propbabilty level. Different stability statistics as well as their relationships were calculated using Pearson correlation using STABILITYSOFT program.

The results showed that YI, HM, GMP, STI and MP indices can be used to select barley genotypes adapted and suitable for areas that are prone to terminal drought season stress. In non-stress areas MP, STI, GMP, HM, TOL and SSI indices can be used for selection of suitable and adapted cultivars. According to the biplot diagram, genotypes 2, 3, 11, 12 and 15 had higher grain yield and terminal drough tolerant. Considering mean grain yield, yield stability statistics, and standard deviation of the rank of each of these statistics, genotypes 11, 8, 3, 6 and 15 had higher grain yield stability.

Considering mean grain yield, all stability statistics and stress indices, genotype no. 11 with pedigree of GS679.82/SHYRI//LAUREL/4/CERISE/SHYRI//… /5/MALOUH//Aths/ Lignee686/6/Nik, genotype no. 8 with pedigree  of Anoidium//Alanda/Hamra-01/3/Lignee527/NK1272//JLB70-63/4/Nik, genotype no. 3 with pedigree of 82S:510/3/Arinar/Aths//DS 29/4/Sahra, genotype no. 6 with pedigree of Bgs/Dajia//L.1242/3/(L.B.IRAN/Una8271//Gloria'S'/3/Alm/Una80//....)/4/Sahra and genotype no. 15 with  pedigree of Ashar/Beecher/5/Lignee 527/Chn-01//Gustoe/4/Rhn-08/3/Deir Alla 106//Dl71/Strain 205 as superior genotypes that are suitable for cultivation in none-stress as well as terminal drought stress conditions in temperate agro-climate zone.

Rapeseed with the scientific name (Brassica napus L.) are one of the most important oilseed crops in the world. Considering the country's increasing need to import oil and the importance of rapeseed among oilseeds plants, increasing the yield and percentage of oil is very important. Therefore, identification the traits that increase seed yield, play an important role in the success of breeding programs. Yield is a complex trait that is affected by many factors. direct selection of a variety for yield is not often very effective, so investigating the relationships between grain yield and other traits for indirect selection may be effective. In such a situation, correlations may not clarify the relationships well, therefore, multivariate statistical methods and path analysis can help in understanding the nature of the relationship between traits for direct or indirect selection and improve the efficiency of selection in breeding programs plants become useful.

In this study, eight genotypes of spring rapeseed (SPN-202, SPN-204, SPN-206, SPN-207, SPN-217, SPN-225, SPN-227, SPN-182) along with 56 rapeseed hybrids (F1) were planted in a randomized complete block design with three replications in 2019-2020 crop years in the research farm of Gorgan Agricultural Research Station. During the experiment, phenological, morphological traits, yield and yield components were recorded. The normality of the data was evaluated based on the Kolmogorov-Smirnov method. In order to understand the relationships between traits and to identify the traits affecting on grain yield, at first, correlation coefficients were estimated, then stepwise regression were done. Then, path analysis based on correlation coefficients was used to determine the direct and indirect effects of traits. Finally, all traits were grouped based on principal component analysis.

The results of analysis of variance indicate a significant difference for all studied traits at the probability level of one percent. The coefficient of variation (C.V) for the traits varied from 1.37 (physiological maturity) to 10.31 (number of sub-branches), which indicates that there is sufficient accuracy in conducting the research. The results of correlation evaluation showed that number of pod per lateral branches (0.864), number pods per Plant (0.865), number of lateral branches (0.466), physiological maturing (0.329) and number of grain per pod (0.358) had a positive and significant correlation with grain yield (P<0.01). Stepwise regression analysis showed that the traits of number of pods per plant, number of grain per pod and 1000-grain weight has a decisive role on grain yield. Based on the results of path analysis, number of pods per plant had the most direct effect (0.881) and number of grain per pod had the most indirect effect (0.053) through the number of pods per plant on grain yield. In order the results of principal component analysis, four components were able to explain 75.91% of the changes in the measured data of 64 rapeseed varieties.

Results of the path analysis showed that number of pods per plant, number of grain per pod and 1000-grain weight had a direct positive effect on grain yield. Also, the stepwise regression analysis showed that the stated traits have the highest regression coefficients. Therefore, selection for these traits can be very effective in achieving high performance

Under oxidative stress conditions, the quantity of some biochemical traits, including antioxidants, increases in the plant. Now this question is raised that incremental change in these traits can always increase drought resistance (higher biomass) in drought-stressed conditions? In this regard, conflicting results have been published, which are mostly based on their correlation coefficient. The purpose of this study was a deeper contemplation into these complexities in quinoa; so, the nonlinear, synergistic, and antagonistic relationships of several biochemical traits with biomass were investigated, using multiple nonlinear regression based on stepwise selection. Finally, the resulting regression function was maximized (optimized). The split-plot experiment based on a randomized complete block design was carried out in three replications and two locations (Damghan and Dibaj). Factors were deficit irrigation levels [control (Supplying 100% of the plant's water requirements), moderate deficit irrigation (70%), and severe deficit irrigation (40%)] in the main plots and sodium nitrophenolate spraying levels [the control (spraying tap water on the plant), foliar spraying at the stem elongation stage and foliar spraying at the flowering stage] in the subplots. The data of biomass (dependent variable; Y or drought resistance) and several regressors (superoxide dismutase, anthocyanin, soluble sugars, proline, carotenoid, superoxide dismutase, ascorbate-peroxidase, and catalase; X or independent variables), obtained under severe deficit irrigation conditions, were used for the present analysis. The results of the regression analysis showed that the relation of carotenoid, catalase, anthocyanin, and ascorbate peroxidase with drought resistance was quadratic, however, the mentioned relationship was very different. Over low concentrations (activities), the effect of catalase, anthocyanin, and ascorbate peroxidase was statistically not significant (with increasing concentration, drought resistance showed no change), but over higher concentrations, their impact was decreasing; the effect of ascorbate peroxidase appeared to be increasing just over its higher concentrations. On the contrary, over both low and high concentrations, the effect of carotenoid was positive; the positive effect of higher carotenoid concentrations tended to be slightly stronger than that of low concentrations. Proline and catalase had the highest effect on drought resistance when their increase was considered simultaneously (synergistic relationship). The effect of soluble sugars was negative; particularly, the simultaneous increase in soluble sugars and proline concentrations led to a sharp decrease in drought resistance (antagonistic relationship); hence, it seems that the simultaneous increase of these two traits should not be used for breeding purposes. The results regarding optimization (maximization) of the regression function indicated that a combination of 600 Unit SOD.g-1FW superoxide dismutase, 8.58 μmol ascorbate min-1.mg-1FW ascorbate peroxidase, 2.28 μmol H2O2.min-1FW catalase, 6.65 mg.g-1FW soluble sugars, 2.15 mg.g-1FW anthocyanin, 6.79 mg.g-1FW proline, and 18.8 mg.g-1FW carotenoids could be led to an increase in biomass by 9% higher than the maximum observed value of biomass.

Soil organic carbon (SOC) is one of the most important components of soil physical and chemical properties that have an important role in sustainable production in agriculture and preventing soil degradation and erosion. Data mining approaches and spatial modeling besides machine learning techniques to investigate the amount of soil organic carbon using remote sensing data have been widely considered. The objective of the present study was the evaluation of SOC using the remote sensing technique compared with field methods in some areas of the Gonbad Kavous and Neli forests of Azadshar. The soil samples were collected from the soil surface (0-10 cm depth) to estimate the SOC. Data were categorized into two categories: 70% for training and 30% for validation. Three machine learning algorithms including Random forest (RF), support vector machine, extra tree decision, and XGBoost were used to prepare the organic soil carbon map. In the present study, auxiliary variables for predicting SOC included bands related to Lands 8 OLI and sentinel 2 measurement images, topography, and climate. The results showed that the extraction of the components related to the bands along with the calculation of indicators such as normalized vegetation difference, wetness index, and the MrVBF index as auxiliary variables play an important role in more correct estimation of the amount of soil organic matter. Comparison of different estimation regressions showed that the Sentinel 2 random forest model and in Landsat8 with the values of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MEA) of 0.64, 0.05, and 0.17, respectively, was the best performance ratio compared to other approaches used in the study to estimate the organic carbon content of surface soil in the study area. In general, the results of this study indicated the ability of remote sensing techniques and learning models in the spatial estimation of soil organic carbon. So, this method can be used as an alternative to laboratory methods in determining soil organic carbon.

Seed yield in soybean is a complex trait and is influenced by multiple genetic and environmental factors. In this research, 13 cultivars and advanced genotypes with two control cultivars (Sari and Katul) were cultivated in three warm and humid regions of the north of Iran, including Gorgan, Sari and Mughan, during the two cropping years of 2013 and 2014 in a randomized complete block design with three replications. Mean comparison in different years and locations showed that G15 genotype had the highest seed yield, followed by G2, G4 and G9 genotypes. Correlation coefficients   showed that the number of seeds per m2 (R² = 0.77) and the number of pods per plant (R² = 0.66) had a positive and significant correlation with seed yield, while the correlation between 100 seed weight and seed yield was insignificant (R²=0.02). G2 and G3 genotypes had the highest percentage of charcoal rot and G4 genotype showed the lowest percentage of infection. Regarding the percentage of infection with charcoal rot disease, all the investigated genotypes are below 10% and are considered tolerant genotypes for this disease. GGE biplot analysis indicated that the first and second principal components explained 70.6% of total yield variation. According to which-won-where pattern of GGE biplot, G4, G15, G12 and G7 genotypes had the longest distance from the origin of the biplot and were placed in the group of reactive genotypes to the environment and G2, G3 and G1 genotypes were classified as stable group. Genotype G4 performed well in Mughan1 and Mughan2 whereas, G15 genotype showed the best performance in Sari1 and Gorgan2, and G12 genotype was the best genotype in Sari2 environments. According to the genotypes comparison with ideal genotype, G15, G9, G2 and G1 genotypes had the shortest distance from the ideal genotype and were, hence, included in the group of stable genotypes. The simultaneous GGE biplot of seed yield and stability displayed graphically that G2 genotype had superior performance and broad adaptation to the diverse environments. Sari1(Sari 2013) was the most discriminating and representative environment and is classified as the superior environment and Gorgan 2 (Gorgan 2014) was ranked second. Based on tolerance to charcoal rot disease, average seed yield, as well as different GGE biplot graphs, G2 genotype incorporated both high mean yield and yield stability and can be released as a new soybean variety.

A large part of Iran is located in arid and semi-arid climates. Despite the existing shortcomings, Iranian agriculture is highly dependent on irrigation water. As water is considered as the most important and limiting input of agricultural production in Iran (Zibaie, 2007). Rapeseed (Brassica napus L.) is the third annual oil crop in the world after palm oil and soybeans, which is cultivated for its edible oil and is easily replaced by cereals (FAO, 2013). The main goal in improving agricultural water productivity in the world is to increase agricultural products with less water consumption, thus reducing the share of water in the agricultural sector and allocating more water to other uses and, most importantly, the water needs of the environment (Heydari, 2014). So far, no report has been presented on the evaluation of different irrigation systems in rapeseed cultivation in East Khuzestan. The main purpose of this article was to measure the irrigation water of rapeseed fields in Behbahan city and compare their physical and economic water productivity. In this way, the physical and economic productivity of water under different agricultural management and in irrigation systems can be compared and evaluated.

This project was implemented in the field to determine canola irrigation water under the management of farmers in Behbahan city. In 26 farms, the volume of canola irrigation water (without interfering with their irrigation program) was measured. To determine the volume of irrigation water, first the amount of inflow from the selected water source was measured with a suitable device (WSC flume, meter or ultrasonic flow meter). Linear multivariate regression analysis was used to investigate the effects of the independent variable on the above dependent parameters. Water requirement was calculated based on the FAO Penman-Monteith model using the daily statistics of Behbahan Synoptic Meteorological Station (minimum and maximum daily temperature, minimum and maximum daily humidity, wind speed and maximum sunny hours) and using ETCalculator software. One-way analysis of variance was used to show a statistically significant difference between the means of two or more independent groups. To compare physical and economic productivity indicators of irrigation water, there was a need to resolve the difference. Therefore, the standard Z-Score standardization method was used to resolve the scale difference. SPSS16 software was used for statistical analysis.

The average irrigation water productivity in 26 farms ranged from 0.351 to 1.545 kg per cubic meter and economic water productivity ranged from 6940 to 3719660 Rials per cubic meter. Irrigation water volume with t-statistic (-11.193) and equivalent beta coefficient (-0.056) had a significant negative effect at the level of 1% on the irrigation water productivity and a significant negative effect at level 5% had on the economic water productivity. The method of irrigation of selected farms (surface or sprinkler) had a significant effect at the level of 5% on the physical and economic irrigation water productivity. The calculated scores according to Z-Score showed that 31% of the farms that had sprinkler irrigation system were in good condition in terms of physical and economic productivity of irrigation water and none of the farms in the surface irrigation system were ranked well in terms of irrigation water productivity, but 8% of the irrigation farms had good economic productivity. The results of Pearson correlation coefficient showed that rapeseed yield had a positive and significant correlation at the level of 1% and 0.608 with irrigation water productivity, while yield had no significant correlation with economic water productivity.

Comparison of the results of average traits showed that the implementation of sprinkler irrigation system in the studied farms has increased the physical and economic productivity of irrigation water. The high cost of implementing the irrigation system in agricultural lands has not caused these farms to have lower physical and economic productivity than surface irrigation farms, but instead the cost of implementing the irrigation system during ten years of using this system has led to better management use of irrigation water, increase the physical and economic productivity of irrigation water. This management is mainly implemented by irrigation and drainage companies by shortening the irrigation period and increasing irrigation times. Prevents grain filling and by adjusting the irrigation hour at each irrigation time by moving the sprinklers by irrigation workers, prevents water wastage and deep percolation of water from the root zone and compared to surface irrigation from water losses prevents the end of irrigation furrows. This management, along with reducing labor costs while keeping the canola crop, has reduced costs and increased the physical and economic productivity of irrigation water.

Daily precipitation, which is completely stochastic, is one of the basic components of the water cycle and has an important role in the management of surface and ground water resources in terms of quantity and quality. Indispensable element of drought analysis and flood control research is precipitation. The efficient management of surface water resources directly depends on precipitation. The lack of long-term and reliable data has made it difficult to determine precipitation behavior. In this study, we tried to determine the behavior and pattern of precipitation through human-data interaction called visual data mining approach. One of the new approaches that focuses on the use of visualization and graphics in the analysis of complexities in data is visual data mining. Visual data mining can be thought of as a combination of two disciplines, visualization and data mining. Visual data mining is also closely related to computer graphics, multimedia systems, human-computer interaction, pattern recognition, and high-performance computing. The aim of this study is to analyze Tabriz daily precipitation data and discover the patterns in this data with the help of visual data mining approaches. Discovering these patterns and identifying rainfall behavior will help to manage floods on the one hand and droughts on the other.

In this study, daily precipitation and temperature data of Tabriz Synoptic Station for the last seventy one years (1951-2021) were used to determine precipitation patterns. Tabriz has a generally cold climate as the center of the eastern Azerbaijan province and is surrounded by mountains. Recently, with the increase in data and software, data mining techniques have also started to attract attention. Data alone cannot mean anything. However, graphs consisting of data can give very meaningful information and messages. Visual data mining approach is an effort to bring data to life with different graphics. In this research, various softwares such as R, ArcGIS and Tableau were used for visualization. In addition, ExcelStat was used to check the accuracy of the data and for statistical tests. R statistical language was used to create the data mining structure and to display the data graphically. Then, different diagrams were drawn using the Tableau program. Finally, the drawn diagrams were evaluated and the final graphics were selected. ArcGIS software was used for spatial analysis and map drawing. Also, multiple linear regression method was used to predict precipitation amount and probability of occurrence.

According to the temperature histogram, the long-term average annual temperature in Tabriz varies between 12 and 13 degrees Celsius. Also, according to the precipitation histogram, precipitation over 5 mm in Tabriz varies between 10 and 20 days per year on average. The results obtained showed that there has been a change in precipitation patterns in the last 5 years (2017-2021). Although annual precipitation during these five years is above the 71-year average, it is still below the golden precipitation period (1961 to 1970). The results showed that the intensity of spring rains decreased significantly in Tabriz during the period 2006-2021. However, from 1971 to 1980 and from 2001 to 2010, it was observed that the spring rains were more in terms of both precipitation values and precipitation intensity. However, in the following periods from 2011 to 2021, both precipitation intensity and precipitation values decreased. The results showed that most of the spring precipitation in 1981 was 276.3 mm, making up 73.26% of the total precipitation for that year. According to the findings, precipitation has started to decrease in the spring season in recent periods. Decreased dry grain yield in the Azerbaijan region may be affected by decreased spring precipitation. As a result of this decrease, it is expected that the agricultural economy in the study area will be negatively affected.

In this study, as a first, daily precipitation in Tabriz was investigated with visualized data mining techniques. Thus, interesting findings were obtained with the help of different graphics. The results showed an increase in precipitation in the last 5-6 years. It has also been proven that the temperature behavior during this period shows an increase in average temperature, a confirmation of the increase in global temperature. Although the results of this research showed that visualized data mining is successful in precipitation analysis, it is recommended to conduct more comprehensive studies in this field in the future.

With the development of cities and the increasing population and also the supply of water from outside of basins, an increase in the groundwater level occurs. In recent years, with the expansion of the boundary of the Mashhad city and increasing water needs of this metropolis, a large amount of water has been transferred from the surrounding area and catchment areas adjacent to the city. The transportation of the water and subsequent feeding of the water back into a limited area of the aquifer (in the Mashhad city) led to an increase in the groundwater level. In the present study, to analysis the delay time of the groundwater reaction, the data of the volume of transported water from the Doosti dam and the railway piezometer well data of Mashhad city has been collected from April 2005 to March 2013, then regression analysis has been used. Such analysis will help to manage water transmissions from adjacent areas and control the water level rising in cites. The results indicated that a 4-month delay exists for the observing the effects of the water volume entering the aquifer.