Background and objectives: Sowing date and irrigation are two constrain factors for maize production, thus before sowing, it is necessary to know about crop yield responses to sowing date and water stress. These factors can be simulated using CERES-maize model. This experiment was aimed to calibrate and evaluate the CERES-maize model in Gorgan climatic conditions.Materials and methods: For this purpose, two experiments (each one at two years) were carried out at agricultural research station of Gorgan, Golestan Province. SC.704 as a commercial late-maturing hybrid (MO17×B73), is known as the best maize hybrid is planting in different parts of Iran as well as Golestan province. The first experiment was performed within a three-replicated randomized complete block design during two years (2012 and 2013). The factors included three sowing dates (22 June, 6 July and 21 July) and four irrigation regimes based on maximum allowable depletion (MAD) of the total available soil water (TAW). Also the second experiment was conducted within a four-replicated randomized complete block design during two years (2007 and 2008). The factors included three sowing dates (7 April, 19 April, and 4 May) and two planting densities (6.5 and 8.5 plant m-2). The data from 6.5 plant m-2 was used in this experiment. The model was calibrated using three treatments of optimum irrigation and with nostress in 2012 and two treatments of the second experiment in 2007. Evaluation was done using planting date treatment (2008), planting date and deficit irrigation in 2012 and 2013.Results: The results of the model evaluation indicated that the model simulated phenological stages (anthesis day and maturity day) with high accuracy in different sowing dates and different irrigation regimes. The values of root mean square for anthesis and maturity dates were as 2.5 and 2.9 days, respectively. Therefore, this model could be used in field management planning. Also the results of evaluation base on spring sowing date (2008) and summer sowing dates (2012 and 2013) indicated that the model simulated dry matter and yield of maize with high accuracy. On the other hand, the model simulated dry matter and leaf area index (LAI) trend in different irrigation levels, but hadn’t enough accuracy in terms of model statistical indices.Conclusion: Referring to results of this experiment, the CERES-maize model can be used in Gorgan climatic conditions for simulating the growing stages and grain yield of maize under favorable conditions.

Water deficit is one of the most common constraints to crop productivity in the world and Iran. In order to study effect of drought stress on morphologic traits, yield and yield components of 34 hybrids of corn, an experiment was carried out based of complete randomized block design with three replication under F.C. irrigation and drought stress in Khorasan Razavi Agricultural Research and Natural Resources Institute mashhad,Iran on 2010. Results of analysis of variance showed that in both conditions there are significant different between all hybrids for all traits. In these experiment drought tolerance indices as TOL, MP, GMP, SSI, STI, HARM and also Golden Mean (new index) were calculated. Results of hybrid means comparison showed that in F.C. Irrigation condition S.C500 hybrid and in stress condition N.11 hybrid was better than others in yield trait (13/79 and 5/69, respectively). It seems that Harm, STI, MP and GMP indices have a similar ability to separate drought sensitive and tolerant genotypes. According to cluster analysis (UPGMA method) based on stress tolerance and susceptibility indices and grain yield in both F.C. and stress conditions, hybrids were classified in three groups with low intra- and high extra-group similarities. In conclusion, it can be suggested that H11 and SC250 hybrids should be recommended in Mashhad Plain.

IntroductionGlobal warming directly affects agricultural production and food security (Ainsworth & Ort, 2010). Temperature controls the rate of plant metabolic processes that ultimately affect biomass production and grain yield (Hay & Walker, 1981). Although farmers are not able to control the climatic conditions, management and changes in factors such as irrigation, soil, crop varieties, activities, and technologies used in the cultivation of crops can reduce the harmful effects of climate change (Moradi et al., 2014). One of the reliable approaches to studying the effects of climate change on agricultural production is using crop growth models. The present study was conducted to simulate the effects of climate change on phonological stages and yield of maize and to investigate the possibility of mitigating the negative effects of climate change on maize by changing the sowing date and selecting suitable cultivars as management strategies for adaptation to climate change in Kermanshah region.Materials and Methods The study was conducted in Kermanshah region conditions. The evaluated traits included days from the sowing date to anthesis, days from the sowing date to physiological maturity, total dry weight, and grain yield. In general, the results of the evaluations showed that the CERES-maize model can accurately predict the growth and yield of maize cultivars (SC704, Simon, BC678) in different soil moisture conditions. After ensuring acceptable agreement between simulated values with observed values, the evaluated model was used to study the effects of climate change on maize production in the near future (2021-2050) and the far future (2051-2080) climate change according to RCP4.5 and RCP8.5 scenarios and comparing them with baseline conditions (1981–2010). The average of 17 general circulation models was used to simulate meteorological parameters under climate change conditions. The common sowing date for maize in the Kermanshah region is May 5. In this study sowing dates of May 5 and April 26 as early sowing dates, April 15 as the common sowing dates, and May 25 and June 4 as the late sowing dates, and maize cultivars SC704, BC678, and Simon were considered as adaptation strategies to climate change.Results and DiscussionThe results of simulation showed that regardless of cultivar and sowing date, traits of days from sowing date to anthesis, days from sowing date to physiological maturity, total dry weight and grain yield in the near future climate change in the scenario RCP4.5 varied as -2, -1.5, 2.7, and 3.3% and in the RCP8.5 scenario varied as, -2.9, -2.8, -0.66, and -3.6% compared to baseline conditions, respectively. These values for the RCP4.5 scenario in the far future climate change condition were -5.2, -5.4, -7.1, and -16.2 and for the RCP8.5 scenario were -8.8, -9.8, -23.1, and -45.83%, respectively. In both near and far future climate changes and under both scenarios, by sowing the studied cultivars at late dates (May 15 and May 25) compared to the early dates (April 15 and April 25) and common date (May 5), the length of the developmental period was shorter but other traits such as total dry weight and grain yield were higher. Among the studied cultivars, in both future climate changes and scenarios, the Simon cultivar had the highest grain yield. The highest duration of developmental stages and total dry weight belonged to SC704.ConclusionIn general, if any of the RCP4.5 and RCP8.5 scenarios occur in the near and far future climate change periods, the sowing of the Simon cultivar should be in late dates on May 25 and will have the highest grain yield. Therefore, it can suggest as a suitable strategy to reduce the negative effects of climate change on maize production in the Kermanshah region.

To evaluate the effect of nitrogen and water withholding in vegetative growth stage of corn on efficacy of nicosulfuron and 2,4-D+ MCPA an experiment was conducted as a factorial arrangement in a randomized complete block design with three replications at Tarbiat Modares University in 2017. The first factor contained four treatments including application of nicosulfuron, 2,4-D+ MCPA, weed free and weed infested as control. The second factor consisted of normal irrigation and water withholding from 4 to 8 leaf stage. Third factor contained either urea application or no fertilizer application. Normal irrigation and nitrogen fertilizer application significantly increased nicosulfuron and 2,4-D+ MCPA efficacy in weed control and total weed dry weight decreased to 341 and 361 g.m-2 respectively compared 1370 g.m-2 as weed infested. Nicosulfuron and 2,4-D+ MCPA reduced weed total dry weight significantly under irrigation withholding. Grain yield increased with application of nitrogen and nicosulfuron. Grain yield was higher with application of 2,4-D+ MCPA than nicosulfuron without nitrogen application under irrigation withholding. Application of nicosulfuron with nitrogen application provided satisfactory weed control and increased grain yield.

The CSM-CERES-maize model was calibrated and evaluated by using the field and weather data set of a two-year field experiment previously carried out on silage corn at the Agricultural Research Center in Varamin. The data set included soil, crop, and weather data of a two-year corn experiment with four irrigation levels and three N fertilization levels. Twelve treatments were arranged in a strip-plot design in a randomized complete block with three replications during the 2003 and 2004 growing season. Data collected in the field included weight of biomass, leaf area index, and important physiological dates such as silking, anthesis, grain filling, and harvest maturity during the growing period. The maximum leaf area index and weight of biomass at physiological harvest, along with anthesis and maturity dates were used in the calibration process. The biomass weight during the growing season was used for the model evaluation. The results indicated that GENCALC tool is capable enough to be applied for determining genetic coefficients of maize with acceptable accuracy. The result of calibration of the model produced the following statistics, root mean square errors (RMSE) for anthesis and maturity dates were 1 and 2 days, 980 and 1313 kg ha-1 for weight of biomass and 0.75 and 0.44 for maximum LAI in the years of 2003 and 2004, respectively. Genetic coefficient required by the CSM-CERES maize model including P1, P2, P5, G2, G3, and PHINT were found to be 236 (oC), 0.4 (days/hr), 790 (oC), 833, 8.5 (mg/day), and 55 (oC), respectively. The evaluation of the CSM-CERES-maize showed that the model was able to simulate weight of biomass for the silage maize accurately with root mean square errors (RMSE) ranging from 9 to 1483 kg ha-1 and coefficient of determinations (r2) ranging from 0.86 to 0.99 for all of the 12 treatments for two years. It is concluded that the GENCALC tool was capable of successfully determining the cultivar parameters required by the CSM-CERES-maize model and that the model itself was well calibrated for the data set used in this study.

In order to investigate the effects of seed priming with plant growth promoting rhizobacteria (PGPR) on phyllochron and leaf appearance rate of corn, a factorial experiment based on randomized complete block design with three replications was conducted at the Research Farm of University of Mohaghegh Ardabili in 2008. Factors were: three corn cultivars (SC-404, SC-410 and SC-434) and seed priming with plant growth promoting rhizobacteria in four levels (without priming, seed priming with Azosprilium, seed priming with Azetobacter and seed priming with Azosprilium+Azetobacter). The results showed that phyllochron, time between the appearance of successive leaves on a shoot, was significantly affected by corn cultivar, seed priming with plant growth promoting rhizobacteria and interaction between of seed priming and corn cultivars. Seed priming with Azetobacter decreased the phyllochron but increased rate of leaf appearance. Response of corn cultivars was different to these characteristics.Leaf appearance rate in SC-404 was more than SC-410 and SC-434. The highest leaf appearance rate and the least of time for phyllochron were obtained with seed priming of SC-404 cultivar with Azetobacter. Leaf area index was increased due to seed priming with Azetobacter. In all of priming levels, process of variances leaf area index was similar until 37 days after sowing. From 44-65 days after sowing reached to a maximum level (4.96) and then decreased slowly till 79 days after sowing. This might be related to increasing aging of leaves, shadding and competition between plants for light and other resources. The highest leaf area index obtained in treatment combination of seed priming of SC-404 cultivar with Azetobacter and the least of it was in without priming.Number of leaf per plant and leaf dry matter increased in seed priming with Azetobacter.Thus, it is suggested that in order to increase leaf appearance rate, number of leaf per plant and leaf area index, seeds priming of SC-404 cultivar with Azetobacter be applied in conditions of Ardabil Plain.