Industrial Pesticides and a Methods Assessment for the Reduction of Associated Risks: A Review

Regarding the increasing growth of the population and importance of food security, Iranian Ministry of Agriculture has prioritized and encouraged greenhouse farming products. One of the developmental challenges of greenhouse farming is the current extensive use of chemical fertilizers. Importantly, as raw agricultural products are the main ingredients on the table of Iranian families  (Iranians generally tend to eat fresh products), the determination of pesticide residues in such products is of utmost importance. The penetration of resistant contaminants into freshwater resources can lead to detrimental effects on humans and the environment. Concerning the importance of environmental protection and the role of chemical pesticides, this study reviews the pesticides used in the agronomic sector and the associated risks of using chemicals to control pests for society, agriculture, freshwater resources, and the environment.Keywords: Environment; Water resources; Agriculture; Health; Biological metho

Incorporating Conditional Uncertainty into Decision-making for Forecasting Actual Evapotranspiration in Semi-arid Area

Background: The long-term effects of climate change in all countries have been able to affect the water management system. Therefore, it is vital to consider the impacts of this phenomenon in sustainable management. A conditional framework has been conducted to predict crop water requirement in semi-arid regions with two climate scenarios of RCP 4.5 and RCP 8.5 considering the IPCC datasets. Methods: Eight models including EC EARTH, CESM, CANESM, HADGEM, GISS E2, GFDLCM2, MIROC ESM and IPSL were implemented to evaluate the extreme points of the evapotranspiration in future.Result: Results showed that GISS E2 and GFDLCM2 models were more accurate to estimate the evapotranspiration. Moreover, in the next two periods for all four parameters in all GCM models, the RCP 8.5 situation was anticipated a better esteem than the RCP 4.5 choice. Comes about appeared that GFDLCM2 and GISS E2 models have more certainty for evapotranspiration. The lowest values during the next two periods 2020-2030 and 2080-2090 and the methods RCP 4.5 and RCP 8.5 for evapotranspiration by GISS E2 model have been obtained. The evapotranspiration alters based on the climate alter models amid the following two periods, distant and close, were inspected for two scenarios, RCP 4.5 and RCP 8.5. The comes about appeared that the RCP 8.5 situation has assessed the four parameters for the following period more than the RCP 4.5 situation.Conclusion: The comes about appeared that the RCP 8.5 situation has assessed the four parameters for the following period more than the RCP 4.5 situation. At that point the changes of the least and most extreme parameters of evapotranspiration for the two outflow scenarios amid the following two close and distant periods were inspected that the comes about appeared that the both scenarios have a nearly steady slant amid both the close and distant prospects and encompasses a slight increment and diminish.Keywords: Climate change; GCM model; Emission scenarios; Streamflow

The Effect of Drought Stress on the Superoxide Dismutase and Chlorophyll Content in Durum Wheat Genotypes

Background: Drought stress is one of the most limiting factors of plant production around the world. So, finding a way for increasing genotypes resistance is so important. Free radicles and other dynamic subordinates of oxygen inactivate chemicals and significant plant cell parts. Superoxide dismutases (SODs) have been distinguished as essential parts in a creature's guard system.Methods: This examination was carried out to examine the SOD movement in 8 durum wheat genotypes from Iran and Azerbaijan under two different conditions in 2015-2016 cropping year. The impact of dry season weight on SOD, chlorophyll content list (CCI), and chlorophyll debasement were examined. Critical contrasts among genotypes and the genotype × climate collaboration among SOD and CCI content were distinguished.Results: The mean examination indicated that the substance of SOD and CCI diminished in susceptible genotypes, while tolerant genotypes SOD and CCI stayed unaltered or increased. For measuring drought tolerance, the stress tolerance index (STI) used. The correlation between STI for Chlorophyll and Chlorophyll CI in drought was significant at 0.01 levels. The pressure resilience list (STI) for SOD and CCI characterized safe and defenseless genotypes into unmistakable gatherings.Conclusion: Hence, these 2 characters can be utilized as a Selection index for screening dry spell safe plant materials.Keywords: Durum wheat, Drought stress, SOD, ST

Investigation of the effect of end season drought stress on morphological on Durum wheat genotypes

Background: Drought stress is one of the most important factors limiting the yield of crops, especially in semi-arid regions of the world. Therefore, studying and investigating plants resistant to these conditions can be useful for the agricultural industry and the country's economy.Methods: To evaluate this capacity overall performance grain durum wheat genotypes in drought conditions and overview a number of the developments related to yield, and a few decided on advanced genotypes, 10 genotypes of Durum wheat within side the 2017-2018 cropping year.Results: The evaluation of variance confirmed great variations among the developments evaluated in phrases of strain and there has been no tension. Also, amongst genotypes in phrases of height, important spike length, grain weight, and there has been a great distinction in yield. Performing evaluation Factor, via evaluation, most important four additives 82.67 percentage of overall running modifications have been justified.Conclusion: The consequences imply the significance of component coefficients traits of overall and fertile tillers, main spike length, 1000-seed weight, and yield decided on genotypes is suitable for dry conditions

Effect of Cold Stress on Germination and Growth of Wheat Cultivars

ABSTRACT This laboratory experiment was carried out in the Agricultural College of the Mohaghegh Ardabili University in 2010. It was conducted by factorial design with two factor and three replications, content 30 grain to per replication. Factor A include three temperature levels (2, 3 and 5° C) and factor B, include five wheat cultivars (Gaspard, MV17, Sardary, Cascogen and Bezostaya) were used in this experiment. Result showed that velocity of seed was lowest in the 2° C and Gaspard, Sardary, Cascogen and Bezostaya have highest velocity of seed, respectively. Therefore, greatest, seed velocities belong to Bezostaya cultivar in the 5° C temperature. Lowest, seed velocity related to MV17 in the 2° C temperature. For the number of roots, Cascogen cultivar with greatest and Gaspard cultivar with lowest of number roots were determined. Sardary cultivar has highest coleoptiles length. In the final result, Bezostaya cultivar was arranged in the first and highest level, between five cultivar, for cold stress characteristics and Sardary cultivar was showed second level, significantly in comparison of the another cultivars

Modified artificial neural network based on developed snake optimization algorithm for short-term price prediction

Short-term prices prediction is a crucial task for participants in the electricity market, as it enables them to optimize their bidding strategies and mitigate risks. However, the price signal is subject to various factors, including supply, demand, weather conditions, and renewable energy sources, resulting in high volatility and nonlinearity. In this study, a novel approach is introduced that combines Artificial Neural Networks (ANN) with a newly developed Snake Optimization Algorithm (SOA) to forecast short-term price signals in the Nord Pool market. The snake optimization algorithm is utilized to optimize both the structure and weights of the neural network, as well as to select relevant input data based on the similarity of price curves and wind production. To evaluate the effectiveness of the proposed technique, experiments have been conducted using data from two regions of the Nord Pool market, namely DK-1 and SE-1, across different seasons and time horizons. The results demonstrate that the proposed technique surpasses two alternative methods based on Particle Swarm Optimization (PSO) and Genetic Algorithms-based Neural Network (PSOGANN) and Gravitational Search Optimization Algorithm-based Neural Network (GSONN), exhibiting superior accuracy and minimal error rates in short-term price prediction. The results show that the average MAPE index of the proposed technique for the DK-1 region is 3.1292%, which is 32.5% lower than the PSOGA method and 47.1% lower than the GSONN method. For the SE-1 region, the average MAPE index of the proposed technique is 2.7621%, which is 40.4% lower than the PSOGA method and 64.7% lower than the GSONN method. Consequently, the proposed technique holds significant potential as a valuable tool for market participants to enhance their decision-making and planning activities

Integrated modeling of food–water–energy nexus for maximizing water productivity

One of the needs of a sustainable decision-making system in agriculture is to determine the role of energy in the food production cycle. Wind energy turbines can be built in agricultural fields for groundwater exploitation and reduce the cost of energy supply for the pumping system. This study was conducted to evaluate the effect of wind energy and economics on sustainable planning of agricultural water resources. A multiobjective framework was developed based on the nondominated sorting principle and water cycle optimizer. Maximization of benefit per cost ratio for the total cropping pattern and minimization of energy consumption for the growing season were addressed as the objectives of the nonlinear problem. The prediction of biomass production was made by simulating a hybrid structure between the soil moisture balance in the root zone area and the development of the canopy cover of each crop. The results showed that the objectives of the problem have been met by irrigation planning using climatic constraints and drought stresses. About 35% of the total water requirement of plants with a higher harvest index (watermelon, melon, etc.) is in the maturing stage of the shade cover. HIGHLIGHTS The role of wind energy variables has been considered in the agricultural yield production.; A multiobjective framework was developed based on the nondominated sorting principle and water cycle optimizer.; The proposed optimization method showed that the total water productivity increased significantly by 38%.

Comparison of Multiple Linear regressions and Artificial Neural Network in Predicting the Yield Using its Components in the Hassle Barley

ABSTRACT In this study 40 genotypes in a randomized complete block design with three replications for two years were planted in the region of Ardabil. The yield related data and its components over the years of the analysis of variance were combined.Results showed that there was a significant difference between genotypes and genotype interaction in the environment. MLR and ANN methods were used to predict yield in barley. The fitted model in a yield predicting linear regression method was as follows: ì Reg = 1.75 + 0.883 X1 + 0.05017X2 +1.984X3. Also, yield prediction based on multi-layer neural network (ANN) using the Matlab Perceptron type software with one hidden layer including 15 neurons and using algorithm after error propagation learning method and hyperbolic tangent function was implemented, in both methods absolute values of relative error as a deviation index in order to estimate and using duad t test of mean deviation index of the two estimates was examined. Results showed that in the ANN technique the mean deviation index of estimation significantly was one-third (1 / 3) of its rate in the MLR, because there was a significant interaction between genotype and environment and its impact on estimation by MLR method .Therefore, when the genotype environment interaction is significant, in the yield prediction in instead of the regression is recommended of a neural network approach due to high yield and more velocity in the estimation to be used