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

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.

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.

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.