جستجوی مقالات مرتبط با کلیدواژه "ارزیابی مدل" در نشریات گروه "کشاورزی"

One of the effects of droughts on the catchment area is the impact on sediment production. The purpose of this study was to investigate the effect of drought on the amount of sediment production estimated by the sediment gauge curve located at the Chamriz hydrometric station. In this study, using the long-term data of the Chamriz hydrometric station located upstream of the Darudzen dam, the rate of changes in the produced sediment discharge in each year was calculated using different methods of the sediment curve. Then, by evaluating the applied methods, the best method for performing the subsequent analyses was selected (the non-parametric method (CF2)). Then, using the station's meteorological data, two SPI and RDI drought indicators were calculated, and finally, they were compared with the average annual sediment discharge rate. The results showed that by increasing the amount of drought index (SPI) and RDI (toward tiredness), the amount of sediment discharge increased, indicating the positive effect of climate conditions on the sediment yield of a basin. Finally, by examining the state of sediment production in many years of drought, it can be concluded that during the year of the drought, due to the effect of drought on vegetation status, soil moisture, and other factors, in the years after the occurrence Drought has caused a significant increase in sediment deposition compared to years when the drought was normal on the floor, and since then it has been terrible.

Maize (Zea mays L.) is the most important silage plants in the world because of its high yield, high energy and quality forage and low fibr. Due to the severe water shortage in Iran and the high water demand of corn crop, the only way to increase corn production is to increase the yield per unit area and reduce the yield gap by optimizing production management. Therefore, this study was aimed to estimate the potential yield and yield gap in the main forage maize production areas in Iran. For this purpose GYGA protocol method was used. Yield potential based on the data from a 15-year period (2001-2015) was calculated using the SSM-iCrop simulation model. Before use, parameterization and evaluation of the simulation model for the studied plant were performed using data from all over the country. In the major maize production regions of Iran, 27 reference weather stations (RWS) and 11 main climatic zones (CZ) were selected. The average Ya and Yp of maize in Iran was estimated at 49.3 and 85.6 ton ha-1, respectively. Furthermore, in the major climatic zones was 9.9 ton ha-1 yield gap in the country. In fact, forage yield and production in Iran can be increased from current 49 ton ha-1 and 11.2 million ton to 68 ton ha-1 and 15.6 million ton through optimized management and elimination of exploitable yield gap.

The climate changes have an important role in distribution of plant species. Statistical species distribution models (SDMs) are widely used to predict the changes in species distribution under climate change scenarios. In the peresent study, the distribution of Alopecurus textilis in the current and future climate condition (2050) under the influence of climate change and two scenarios of RCP 4.5 and RCP 8.5 with the GCM data series of general circulation models BCC-CSM1-1، CCSM4 and MRI-CGCM3 by using of Five species distribution models such as Generalized Linear Model, Generalized Additive Model, Classification Tree Analyses, Generalized Boosting Model and Random Forest method in Mazandaran province were investigated. For this purpose, the number of 92 species presence data was recorded using Global Positioning System and along with layers of environmental factors including six bioclimatic variables and two physiographic variables were used in modeling. Among the environmental variables, mean temperature of driest quarter, precipitation seasonality, precipitation of warmest quarter and precipitation of coldest quarter had the most impact on the habitat suitability. Modeling evaluation indicated that the random forest and generalized boosting model had better predictions of climatic habitat than other models. Results showed that in comparison with current conditions under the emission scenarios of RCP 4.5 and RCP 8.5 respectively 22.25 and 36.22 percent of the climatic suitable area for this species will be reduced by 2050. Finally, the results of the present study can be an efficient tool for biodiversity protection, ecosystem management, and species re-habitation planning under future climate change scenarios.

One of the reliable approaches to study the effects of management on agricultural production is using crop growth models. In order to determine the effect of different levels of irrigation on the growth and productivity of grainy maize cultivars and validation of the CERES-Maize model, an experiment was conducted as split plot at the experimental field of Campus of Agriculture and Natural Resources, Razi University, Kermanshah, Iran, during 2017-2018. Main-factor was three irrigation regimes (IR) included supplying 130, 100, 70% water requirement (IR130%, IR100% and IR70% respectively), and sub-factor included three maize cultivars (SC704, Simon and BC678). The evaluated traits were development stages, leaf area index, total dry weight, grain yield and daily evapotranspiration. Model validation results showed that nRMSE values of SC704, Simon and BC678 for days to anthesis were 2.8, 1.7 and 1.9%, for days to physiological maturity were 4.2, 4 and 4.6%, for total dry weight were 9.6, 7.0 and 13.4% and for grain yield were 12.9, 5.2 and 6.1% observations, respectively. The nRMSE for daily evapotranspiration was 12.9 to 23.5% observations. By reducing the water content available in the IR70% treatment compared to other irrigation treatments, the simulated cumulative evapotranspiration gradually moved away from the measured evapotranspiration, so that in the water deficit stress treatment, the model simulated the amount of evapotranspiration more than farm conditions. Overall, the results of validation showed that the CERES-Maize model was able to predict response the growth and yield of maize cultivars under different soil moisture conditions with appropriate accuracy, therefore, it can be used to evaluate the impact of different irrigation regimes in the maize fields.

Identification of habitats potential of endemic species can facilitate conservation planning and rehabilitation programs in watershed. The purpose of this study is modelling the distribution of Artemisia aucheri habitats in watershed of Baladeh Nour city, Mazandaran province. For achieve of this objective, environmental map variables (physiographic, soil and climate parameters) was prepared with geographic information systems techniques in cell size of 10 x 10. Also; 60 site as well as the presence or absence of species were recorded by sampling classified – random. For each sampling site were recorded information about the presence or absence of species and environmental variables, and the relationship between species distribution and environmental factors was determined by using logistic regression (LR), and forecast maps the distribution of the Artemisia aucheri Boiss was produced in the study area. According to LR results, between 14 environmental variables: Slope, pH, Sodium Absorption Ratio, Silt and Annual Mean Temperature were the most important environmental factors influencing the distribution of the species. Evaluate the correctness statistical models were performed by using the kappa coefficient and ROC area under the curve plots. Value indices respectively are 0.5 and 0.75 for the logistic regression model; their values indicated the logistic regression method can to create potential habitat map on local scales. This model, therefore, can be applied to recognize potential sites for rangeland reclamation projects.

In the fields around Golestan dam, crop rotation is not performed, putting the soil fertility at risk. This experiment aims at evaluating the nutritional status of the region’s soils and determining the need for fertilizer with emphasis on macronutrients. The fertilizer requirement of these soils in 2017-2018 has been determined, using the QUEFTS model. At first, 30 fields in the Golestan dam region, irrigated by the dam itself, have been chosen. In each field, a small section has been selected as a non-fertilized one. All agronomic practices have been done in accordance with conventional farmers' method. From each field (fertilized and non-fertilized), five one-meter squared quadrats have been used to cut off the crop and the grain yield and non-grain yield, and measure its N, P, and K concentration. Results reveal that the mean value of N, P, and K uptake by shoots have been 54, 19.5, and 98 kg/ha, respectively, with the average grain yield being 4150 kg/ha in farmers' field. For 1000 kg of grain production per hectare, one requires 13, 4.5, and 22 kg of  N, P, and K, respectively. Therefore, according to the proper validation results of the QUEFTS model, it is recommended to apply 325 kg/ha urea fertilizer, 195 kg/ha triple super phosphate fertilizer, and 60 kg/ha potassium sulfate fertilizer to produce about 5000 kg/ha wheat grain in this area.

Due to the rapid urbanization in Iran, different issues have emerged in its major cities such as street flooding, dispersal of dangerous chemicals and flood-related risks due to the lack of a proper drainage network and disorders in the flood channels; and, Mashhad is not exempt from these issues. The aim of this study is to determine the most important parameters affecting (area of impermeable surfaces has the highest impact, followed by the Manning roughness coefficient in permeable areas, the equivalent width, slope, height of storage in impermeable areas, and impermeable areas without surface storage) runoff generation in District 9 of Mashhad city and to evaluate the performance of the SWMM model. The SWMM model was used to simulate runoff and identify critical areas. In order to determine the duration of the design storm, time of concentration was measured and used as a surrogate. Sensitivity analysis showed that among the eight considered parameters, area of impermeable surfaces has the highest impact, followed by the Manning roughness coefficient in permeable areas, the equivalent width, slope, height of storage in impermeable areas, and impermeable areas without surface storage. The model calibration results showed a good agreement between the simulations of the four incidents with a NS higher than 0.5. The obtained NS confirms the SWMM model’s performance and accuracy. The RMSE was measured 0.6, 0.65, 0.58 and 0.91 respectively for the incidents dated 2015/1/10, 2015/2/15, 2015/2/21 and 2015/3/6, as well as 0.61 and 1.14 for the incidents dated 1394/1/7 and 1394/1/11, which suggest an acceptable model performance. Given the results, there is a good agreement between the simulated and actual runoff volumes which is an indication of the good performance of the SWMM model in predicting urban flood and the model could then be used in urban flood management projects and drainage network planning in Mashhad.

The DSSAT model is one of the most general and extensively used process-based crop growth simulation models. This model has been used worldwide to simulate crop biomass, yield, and soil
nitrogenleaching under different management practices and various climatic conditions. Among management agronomic factors, nitrogen fertilizer has a major effect on crops production. However, nitrogen fertilizer limiting causes to decrease crops production, but, high application rates of nitrogen would led to strong environmental consequences. Thus, management of nitrogen fertilizer consumption causes to decrease environmental pollution in the agroecosystems. Therefore, the objectives of the present study were: (1) determination of genetic coefficients and calibration of the CERES-Wheat modelof DSSAT-CSM, (2) evaluation the performances of model forsimulating wheat growth, development and grain yield and (3) simulationof changesof soil and plant nitrogen in different fertilizer nitrogen application rates under Kermanshah climate condition. Two experiments were established based on the randomized complete block design with three replications during 2015-2016. The treatments were included 4 levels of nitrogen fertilizer application
(90, 180, 300 and 360 kg ha-1 urea). The required model inputs describe field management, daily weather condition, soil profile characteristics, and cultivar characteristics. The cultivar coefficients calibrated under optimum conditions (i.e., minimum stress in weather and nutrients). The genetic coefficients (P1V, P1D, P5, G1, G2, G3 and PHINT) of the Pishtaz wheat cultivar were derived using the GenCal software of DSSAT v 4.6 for 300 kg Urea ha-1 treatment (optimum condition of nitrogen fertilizer based on the results of soil library). After model calibration process, the CERES-Wheat model validated by comparing simulated and measured values of wheat cultivars phenologicaldevelopment stages (DVS), leaf area index, total dry weight and grain yield for treatments of 90, 180, 300 and 360 kg Urea ha-1 fertilizer by root mean square error (RMSE), normalized RMSE (nRMSE) and index of agreement (d) by results ofan independent experiment from calibration experiment. The results indicated that the coefficient P1V was 54.45 °C day, the coefficient P1D was set 90.75 days hr-1, the value for P5 was 720 °C day, the value for G1 was 25, the values for G2 was 30
mg day-1, the value for G3 2 g, and the PHINT was 95°C day. The calibration results showed that the CERESWheat model was able to simulate growth, development stages and yield correctly, which indicate high accuracy in calculated genetic coefficients derived using the GenCal software of DSSAT v 4.6. In the simulated and measured conditions, leaf area index, total dry weight and grain yield improved by increasing of nitrogen fertilizer application. In the simulated and observed conditions, the highest grain yields were 7048 and 7874 kg ha-1 in the treatment of 360 kgnitrogen ha-1 and the lowest grain yields were 4006 and 4217 kg ha-1 in the treatment of 360 kgnitrogen ha-1, respectively. The validation results also indicated that the CERES-Wheat model had high ability to predictg growth, development stages and grain yield in the different fertilizer nitrogen application rates.
So that, the RMSE fordevelopment stages were about 3 to 4 days and the nRMSEwere about 7 to 8% of measured average, respectively. The index of agreement (d) for development stages was about 0.99. The RMSE for total dry weight were about 360 to 720 kg ha-1 and the nRMSE were about 5% to 9% of measured average, respectively. The index of agreement (d) for total dry weight were about 0.94 to 0.99. The amount RMSE for grain yield were 304 to 630 kg ha-1 and the nRMSE were 11% to 17% of measured average, respectively. The index of agreement (d) for grain yield ranged from 0.98 to 0.99. The simulation result also indicated that amount of soil NO3 and NH4 increased with nitrogen fertilizer application. The highestsoil NO3 were 41.3, 54.5, 72.1 and 80.9 kg ha-1 in the treatments of 90, 180, 300 and 360 kg Urea ha-1, respectively. The amount of nitrogen leaching increased with rising of nitrogen fertilizer. The nitrogen leaching were 259.3, 276.2, 310.4 and 335.5 kg ha-1 in the treatments of 90, 180, 300 and 360 kg Urea ha-1, respectively. The amount of nitrogen in the wheat biomass improved by increasing nitrogen fertilizer application. The results indicated that the CERES-Wheat calibrated correctly that confirm calculated genetic coefficient for Pishtaz cultivar under Kermanshah climate conditions. The results of validation also showed that the CERES-Wheat model was able to simulate all studied traits wheat cultivars except leaf area index accurately in different fertilizer nitrogen application rates. Excessive nitrogen consumption led to nitrogen leaching and groundwater pollution. Therefore, it is important to know the distribution of various forms of nitrogen and how they move in the soil.

The DSSAT consists of a series of popular and widely used process-based crop growth simulation models. The models have been used worldwide to simulate crop biomass and yield, and soil nitrogen leaching under different management practices and various climatic conditions. The DSSAT has also proven to be a useful tool for selecting improved agricultural practices. Among all management agronomic factors, nitrogen fertilizer and crop species are major effective aspects impacting crops production. Although limited use of nitrogen fertilizer seems likely to reduce crop yield, high application rates of nitrogen causes serious adverse environmental effects. Thus, management of nitrogen fertilizer consumption decreases production costs and environmental pollution in agroecosystems. Therefore, the objectives of the present study were: (1) to determine the genetic coefficients and calibrate the CERES-Maize model (2) to evaluate the performances of the CERES-Maize model in simulating maize cultivars growth, development and grain yield for different fertilizer nitrogen application rates under Kermanshah climate condition.
This experiment was carried out in a split-plot design with 5 levels of nitrogen fertilizer application (0, 138, 238, 350 and 483 kg ha-1 urea) as main plots, 3 current maize cultivars SC-704, BC-678 and Simon as sub plots, and 4 replications in 2014. The required model inputs describe field management, daily weather condition, soil profile characteristics, and cultivar characteristics. The cultivar coefficients were obtained under optimum conditions (i.e., minimum stress in weather and nutrients). The genetic coefficients (P1, P2, P5, G2, G3 and PHINT) of the maize cultivars i.e. SC-704, BC-78 and Simon were determined using the GenCal software of DSSAT v 4.6 for 350 kg Urea ha-1 treatment (optimum nitrogen fertilizer amount based on the results of soil library). Model performance was evaluated by comparing simulated and measured values of maize cultivars phonological development stages (DVS), leaf area index, total dry weight and grain yield for independent nitrogen fertilizer treatment (0, 138, 238 and 483 kg Urea ha-1) by root mean square error (RMSE), normalized RMSE (nRMSE) and index of agreement (d).
The coefficients of P1, P2, P5, G2, G3 and PHINT ranged between 275 to 286 °C day, 0.576 to 1.80 days hr-1, 910 to 950 °C day, 940 to 990 number per plant, 7.0 to 7.9 mg day-1 and 51.70 to 51.97 °C day , respectively, for all cultivars. The CERES-Maize model was able to accurately simulate growth, development stages and yield for maize cultivars. For both simulated and measured conditions, leaf area index, total dry weight and grain yield were improved by increasing the application of nitrogen fertilizer. Simon cultivar had higher simulated (9925 kg.ha-1) and measured (10467 kg.ha-1) grain yield in respect to other cultivars. The validation results also indicated that the CERES-Maize model gave a reliable estimate of growth, development stages and grain yield for maize cultivars in the different fertilizer nitrogen application rates. The value of RMSE and nRMSE for leaf area index of SC-704, BC-78 and Simon cultivars were 0.56, 0.46 and 0.36 and 25.5%, 21.8% and 16.3%, respectively. The index of agreement (d) for leaf area index ranged from 0.94 to 0.98. The RMSE and nRMSE magnitudes for total dry weight of SC-704, BC-78 and Simon cultivars were 440.1, 569.6 and 419.7 and 6.2%, 8.2% and 5.8%, respectively. The index of agreement (d) for total dry weight ranged from 0.94 to 0.95. The RMSE and nRMSE values for SC-704, BC-78 and Simon grain yield were equal to 163.7, 345.2 and 314.4 and 4.3%, 11.4% and 8.1%, respectively. The index of agreement (d) for grain yield ranged from 0.93 to 0.98.
The results indicated that the CERES-Maize was parameterized reliably for three maize cultivars under Kermanshah climate conditions. The results of validation also showed that the CERES-Maize model was able to give an accurate simulation of all studied traits of maize cultivars except leaf area index in different fertilizer nitrogen application rates.

Introduction The increased level of production of dairy cows increased the losses of nitrogen in feces and urine. The first approach to reduce the nitrogen excretion in the urine or feces, or both is the reduction of nitrogen usage in food rations. This approach is achieved when there is no significant effect on the reduction of animals function. Another way to reduce nitrogen excretion in animals is to improve the nitrogen use efficiency. Mathematical models have been used to predict the potential of nitrogen loss in dairy cows. These models are naturally experimental and not based on a specific process. The animal components in these models are based on CNCPS which these systems contain a large number of experimental elements (Empirical models) and are based on some controversial hypothesis. Jonker et al. (1998) presented a model based on the whole animal with a collection of data and showed that this model could predict the excreted nitrogen of urine from ureic nitrogen of milk precisely. However, they showed that when nitrogen loss is due to the quantity of protein, protein degradation, inadequate energy, etc, the model cannot identify and predict. Therefore, there is a need to develop a model to evaluate the biological processes in animals and nutritional changes to determine their exact role in the assessment of environmental pollution. The purpose of Kebreab et al’s model was to develop a dynamic model based on a certain method that has the ability to predict the quantity and nature of nitrogen excretion in dairy cows under different diets, and also the model could be evaluated by other experiments data. The purpose of this study is the evaluation of the model presented by Kebreab et al (2002). The mathematical models have been used to predict the potential for nitrogen excretion in dairy cows.
Materials and methods To evaluate a dynamic model of nitrogen metabolism in dairy cows by Kebreab and et al. (2002), two master's thesis with titles “Effect of NFC on Performance of Holstein Dairy Cow and Expression of its Mathematical Model on Nitrogen Balance” Kamel (2014), and “Effect of cutting time alfalfa hay harvested at early bud with different ratios of forage to concentrate on chemical composition, fermentation Parameters and performance of early lactation Holstein dairy cows” Firuzi (2014) were used. In this study, to evaluate Kebreab et al’s model, 16 Holstein cows in two 4 × 4 Latin square design were used. Cows were fed with 8 different diets. For each experiment, the total of consumed nitrogen and excreted from feces, urine and milk nitrogen were measured. In addition, the concentration of feed component including dry matter, crude protein, NDF, ADF, starch, non-fibrous carbohydrates, ash, calcium, phosphorus, ammonia nitrogen, pH and metabolisable energy were determined.
Results and Discussion Similar to the results obtained by Kebreab et al. (2002), by increasing nitrogen intake, fecal nitrogen excretion also increased linearly. As the result of Kebreab et al. (2002) and Castillo et al (2000), the nitrogen excreted in the urine predictions match better with the observations.
Conclusion In general, the lowest average of percentage error and the difference of RMSPE with its optimum level are for nitrogen excretion of urine and feces, nitrogen secreted in milk, nitrogen excreted in the urine and the nitrogen excreted in the feces, respectively. Therefore it could be concluded that the model presented by Kebrab et al (2002). has the best estimation and prediction in summation of excreted nitrogen from urine and feces while has the least precision in estimation of excreted nitrogen from feces. Although the results did not show a bi-linear relationship between N intake and output, but our results are very similar with Kebreab’s results.

Researches in the field of risk assessment and water resource risk management and drought require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. To enlarge the data length, we can use weather generators. Weather generators models generate weather data with similar statistical characteristics with actual data. In this paper, we have used LARG-WG and ClimGen models to generate 500 data sample for rainfall and temperature time series in the rainfed research station of Sisab in Northern Khorasan. For performance evaluation of these two models, we have used RMSE, MAE, and CD over the 30 years collected data and their corresponding generated data. Also, to compare the statistical similarity of the generated data with the collected data, we have used t-student, F, and X2 tests. With these tests, the similarity of 16 statistical characteristics of the generated data and the collected data has been investigated in the level of confidence 95%. The results showed that for this Station, LARS-WG model can better simulate rainfall data while ClimGen is a better choice for simulating maximum and minimum temperature data.

The future climate change will alter crop-pest interactions and will affect on amount of crops production, therefore, it is essential to predict pest damage in future conditions. Crop growth models are a technique that researchers could use to study possible impacts of climate change on crops. Although several dynamic models have been developed in recent years, most of them are not generally evaluating impacts of climate change on crop-pest interactions. In the present study, an eco-physiological crop model has been developed to meet these objectives. Its general structure relating to basic crop growth and yield is largely based on LINTUL model, and is written in Fortran Simulation Translator (FST) environment. In this modeling, first, we added phonological development stage (DVS) to LINTUL for simulation of winter wheat growth and then the model calibrated for potential production level. Finally, we incorporate effects of sunn pest damages to winter wheat growth and yield. Parameter values were derived from the literature. The model validated against observed values of DVS, total dry weight yield (TDWY) and grain yield (GY) of winter wheat and winter wheat yield loss due to sunn pest taken from a field experiment. Results indicated that the model was able to explain successfully the observed differences in DVS, TDWY and GY. The loss in TDWY and GY due to sunn pest was also explained satisfactorily. Our simulation results showed that life cycle of winter wheat (from 240 to 217) will be shortened due to accelerate development rate under future climate. The impact of rising temperature on average GY of winter wheat in sunn pest densities was negative (about 47%) while the impact of elevated [CO2] was positive (about 36%) on it. Interaction of elevated [CO2] and temperature also had a negative effect (about 18%) on average GY of winter wheat in sunn pest densities. The impact of rising temperature was positive on sunn pest while the impact of elevated [CO2] had not any effects on it. Therefore, it seems that sunn pest damage on winter wheat growth and yield will be increased (about 7%) in the future climate compared to current climate.

Agricultural practices always need accurate information about occurrence of particular developmental stages. Data from planting dates trials conducted over the Kabotar-Abad Agricultural Research Station، Isfahan during 2003-2009 were used to develop models for estimating developmental stages duration of Sofea safflower variety. To model duration of each developmental stage، duration of each developmental stage considered as the dependent variable and various temperature variables، day length and combination of them considered as the independent variables in a stepwise regression procedure. A step of regression was considered appropriate when the highest R2 was accompanied by the significant (p≤ 0. 05) regression coefficient and partial R2. The number of days from planting to emergence، emergence to head visible، emergence to flowering، emergence to ripening and flowering to ripening were affected by planting dates. Developmental stages duration reduced as temperature increased. Day length had the highest effect on emergence to head visible and emergence to flowering durations and reduced as day length increased. Tmin، T2min and T4min were the variables which entered the regression model and explained about 91% of variation of planting to emergence duration. Durations of emergence to head visible and emergence to flowering were explained by DL×Tmax and DL2×T2max in accuracy of 90% and 96%، respectively. T3max and T4max explained the most variance (99. 64%) of emergence to ripening duration. T2min was the only variable which entered the regression model and explained about 63% of variation of flowering to ripening duration. The results of this study showed that the contribution of temperature and day length is not constant in different developmental stages of safflower.