فهرست مطالب m .madadizadeh

Agriculture is a cornerstone of many developing economies, providing food, income, and employment for millions of people. It is also projected to play a vital role in feeding a global population of 9.1 billion people by 2050. However, there are growing concerns about the environmental impact of agriculture, particularly in arid and semi-arid regions like Iran. Managing water and fertilizer usage in agriculture is crucial to ensuring food security and sustainability. However, conducting field experiments to assess the interaction of all factors involved is expensive and time-consuming. This research focuses on optimizing maize production in Kerman province, a region where maize is a major crop. The research is motivated by the need to improve resource management in Iran, where water and fertilizer resources are limited. The APSIM model is used to determine the best management scenario for maize production in Kerman province. APSIM is a crop growth simulation model that can be used to predict the impact of different management practices on crop yield, water use efficiency, and nutrient use efficiency. The use of APSIM in this research provides a cost-effective and time-efficient alternative to conducting extensive field experiments. The results of this research will contribute to the development of sustainable and efficient agricultural practices in Kerman province and similar regions. These regions are characterized by resource constraints, such as limited water and fertilizer availability. The research aimed to simulate the effect of management parameters (planting date and irrigation) on Crop yield and subsequently achieve the optimal management scenario.

The APSIM model was used for simulation in three regions of Bardsir (temperate to cold climate), Jiroft (hot and humid climate), and Orzuye (hot and dry climate). The model requires four series of data: climate, soil, management, and crop data. The required climate data (from 1998 to 2018) including daily maximum and minimum temperatures, length of sunny hours, and daily precipitation were collected and prepared from the synoptic weather stations of the three mentioned regions.The management data set for each of the study regions was prepared in the form of questionnaires and field research from experts of the Agricultural Jihad Organization, the Agricultural Research Center Organization, and prominent farmers in those regions. The crop data includes the plant genetic coefficients of the maize single cross hybrid 704, which were obtained from the calibration of the APSIM model. To optimize planting date and irrigation management in the studied areas, different planting and irrigation date treatments were investigated. In this research, planting date treatments included the conventional planting date of the region, 20 days before the conventional planting date (as early planting date), and 20 days after the conventional planting date (as late planting date). Irrigation treatments included the usual number of irrigations in the region (13 irrigations), less irrigation (11 irrigations), and more irrigation (15 irrigations).

Our results showed that the model successfully simulated maize phenology, especially maturity date, with high accuracy for all fertilizer amounts tested. The model performance in predicting biomass under different nitrogen treatments was also satisfactory, with a minimal difference between observed data and model results. The nRMSE of grain yield in the calibration stage was 11.2% and in the validation stage was 9%. The nRMSE for calibration of the biological yield of SC 704 was 14.8% and for validation was 13.9%. Also, the model was able to simulate phenology with very high accuracy (especially the days to maturity). Overall, the nRMSE of days to flowering was less than 10% and for the days to maturity was less than 5%. Late planting dates consistently showed better performance across regions and irrigation treatments, resulting in significantly increased grain yield compared to conventional and early planting dates. The highest seed yield was obtained with 15 times of irrigation, among the various irrigation treatments. Late planting combined with 15 times of irrigation yielded the best results in Kerman province, particularly in Bardsir, with a yield of 9300 kg ha-1. Optimal moisture and air conditions, along with the cultivation of a late-maturing variety, contributed to the higher seed yield. These findings are consistent with previous research that has confirmed the positive impact of late planting and extended ripening periods on maize yield.

Our results showed that the model simulates the growth and yield of single cross 704 corn in Kerman province well, even after 20 days of late planting. Long-term simulation experiments showed that maize grain yield varied depending on the region, with the highest yield in Bardsir (8317 kg ha-1) and the lowest yield in Jiroft (4735 kg ha-1). The optimum maize grain yield (8872.8 kg ha-1) was obtained by the interaction effect of late planting date and 15 times of irrigation.

Maize (Zea mays L.) is one of the most important cereals after wheat and rice in the tropical and temperate regions of the world. Also, its mean production is 8 ton ha-1. Moreover, the total area of under cultivation is 132572 hectares in Iran. Crop simulation models can play an important role in improving agricultural production systems in many developing countries. Crop models can simulate plant growth processes and grain yield instead of conducting several years of field experiments. On the other hands, crop simulation models should be calibrated and evaluated with independent data sets under different climatic conditions. Therefore, the purpose of this research was evaluation of the APSIM model for simulation of growth, development and yield of maize hybrids in Kerman province under different amounts of nitrogen.

The APSIM model was calibrated and validated using measured data from a two-year field experiment conducted in the 2014 and 2015 growing seasons. The experiment was a factorial arrangement based on a randomized complete block design (RCBD) with three replications conducted at Kerman province in Iran. Four nitrogen rates (0 (control), 92, 220 and 368 kg ha-1) and two maize hybrids (KSC 704 and Maxima) were included in the study. Moreover, inputs of APSIM model were climatic, soil, plant and management data. In order to calibrate the APSIM model, the data of field experiment in the first year (2014) (including flowering date, physiological maturity date, leaf area index, biological yield and grain yield) were included. Moreover, Data from the second experiment (2015) were used to validate the model.

Our results showed that APSIM model accurately predicted phenology (nRMSE=4.5%). But the APSIM model did not capture the effect of nitrogen stress on phenology. At the evaluation step, the model couldn’t accurately predict the maximum leaf area index (nRMSE=26 and 18% for SC 704 and Maxima hybrids, respectively) which led to overestimate of the results. The nRMSE values for the biological yield of SC 704 and Maxima hybrids were 13.9% and 5.7%, respectively. Furthermore, the values of Wilmot agreement index (d) for these SC 704 (0.95) and Maxima (0.99) indicated a close agreement between the field-measured and simulated values. Furthermore, the nRMSE for grain yield simulation of SC 704 and Maxima hybrids were 13.2 and 11.9 percentage, respectively, revealed that the model accurately simulated the grain yield of maize hybrids.

The evaluation of the APSIM model with the experimental data revealed that the model predicted grain yield, biological yield, days to flowering and maturity of maize hybrids reasonably well. This indicates that the model could be applied for assessing various management practices in maize agro-ecosystems under all parts of the semi-arid regions which has the similar characteristics to the study location. On the other hands, the APSIM model couldn’t predict the effect of different nitrogen levels on phenology.