جستجوی مقالات مرتبط با کلیدواژه « مدل شبیه سازی » در نشریات گروه « زراعت »

One of the main challenges of modern agriculture in ensuring food security is development of strategies to deal with potential negative impacts and adapt to climate change. To address this challenge, it is crucial to investigate the effects of climatic factors on agricultural production at a spatiotemporal dimension, develop and utilize crop management decision-support tools, and support targeted agronomic research and policy. These endeavors necessitate the availability of accurate and standardized meteorological data.Studying growth degree days and wheat phenology can significantly enhance our understanding of how wheat growth responds to climate change and aid farmers in adapting to and effectively mitigating its influence.

To determine the environmental and management factors affecting the yield of irrigated and rainfed wheat in different regions of North Khorasan province, we investigated the trend of yield changes from 1980 to 2009. Subsequently, we simulated the wheat plant growth stages using the DSSAT model and analyzed the impact of temperature and rainfall changes on yield through panel data analysis. Panel data analysis is a widely used statistical method in social science, epidemiology, and econometrics for analyzing two-dimensional (typically cross-sectional and longitudinal) panel data. This method involves collecting data over time from the same individuals and conducting regression analysis across these two dimensions.

According to the results of this study, 63% of the changes in irrigated wheat yield between the years 1980-2009 can be attributed to environmental factors (temperature and precipitation), while 37% can be attributed to management factors. When comparing environmental parameters, it was observed that the number of temperatures above 30°C (N30TMAX), mean temperature (GSTMEAN), interaction of amount and frequency of precipitation (TPRAT * NPRAT) significantly affect yield (p ≤ 0.05). Bojnord, Shirvan, and Esfarayen regions exhibited significant positive cross-sectional effects in terms of environmental parameters, whereas Farooj, Raz-Jargalan, Maneh Semelghan, and Jajarm regions displayed negative cross-sectional fixed effects.A study examining the critical stages of wheat growth during good years (with high wheat grain yield) and poor years (with low wheat grain yield) revealed that in all weak years, the minimum temperatures fell below the critical level (-11°C). The occurrence of very low temperatures during the early stages of growth and primary leaf production, which is the plant establishment stage, resulted in reduced photosynthesis levels and subsequently severe yield reduction.In all regions and for 100% of the studied years, irrigated wheat in the grain-filling stage experienced temperatures above 30°C, leading to negative cross-sectional effects in Farooj, Raz-Jargalan, Maneh-Semelghan, and Jajarm. The frequency of temperatures above 30°C during the hard dough stage of irrigated wheat was higher than that during the soft dough stage in all regions. Therefore, delaying the planting date from October (the common planting date in the studied areas) would result in conflicts with high temperatures during the soft dough stage and negative temperatures during the primary leaf production stage and plant establishment at the beginning of the growing season, severely reducing yield.

In general, the results of this study demonstrated that implementing effective management methods, particularly selecting the appropriate planting date, can lead to better adaptation of wheat's phenological stages to environmental conditions. This, in turn, has the potential to enhance wheat yield.

In this research, CERES-Maize crop model was calibrated, validated and evaluated for different maize hybrids under climatic conditions of Gonabad, east of Iran. For this purpose, an experiment was carried out based on a split-split plot randomized complete blocks design. Main factor consisted of three irrigation regimes (optimum irrigation, moderate drought stress, and severe drought stress), subplot factor included of four levels of nitrogen fertilizer (0, 120, 180, and 240 kg/ha) and sub-subplot factor consisted of three maize varieties (DC 370, ZP 677, and SC 704). CERES-Maize model simulated maximum leaf area index of maize varieties with high precision. However, in most of situations predicted values underestimated as compared with actual values. In addition, the model predicted kernel yield and biological yield of three mentioned maize varieties, reasonably. Variations of kernel nitrogen yield of maize varieties under different water and nitrogen regimes were simulated very well by the model. Although, in most of treatments model data values were lower than measured data from field experiment. Model simulated the increases of water use efficiency due to decrease of water supply and increase of nitrogen application, reasonably. Yet, the model accuracy for prediction of water use efficiency of maize with increase of drought stress level was slightly decreased and deviation of the two data sets was increased. According to the results of this research, the CERES-Maize model could be suggested for decision making in maize production for climatic regions similar to location of this experiment.

Land evaluation and its consistency with simulation model’s outputs could assist decision making to develop plants for cultivation. In this study land suitability and potential yield of cumin was determined using CUMMOD and sum of values method by climatic and topographic factors, respectively, as a case study in Khorasans provinces. Results revealed that despite of the majority of favorable and completely favorable zones, potential yield was not the same in studied provinces. The CUMMOD results revealed that southern, a part of central and a part of northern areas of northern Khorasan province have considerable capability to produce cumin (2.5 to 3 ton ha-1). Potential yield was in two classified ranges as 2- 2.5 ton ha-1 and < 2 ton ha-1, while, predicted potential yield for central and southern parts of South Khorasan province was <2 ton ha-1 and 2-2.5 ton ha-1 for corresponding northern parts. Slope and aspect for northern, aspect and maximum temperature for Razavi and maximum temperature for southern Khorasan were the most limiting factors. In this study, although limiting factors were detected, but the majority of zones were located in favorable and completely favorable zones, because sum of values method avoids exaggerated effects of some factors. Therefore, results are more realistic. Overall, our results confirmed that the potential yield will decrease along with moving from northern Khorasan province toward South Khorasan province, which stems from shortening of the growing season.

A simple mechanistic model was constructed to evaluate potential dry matter of pearl millet. All needed parameters were calculated based on many field and laboratory experiments and actual data obtained from 86 fields of Millet located at different regions of Khorasan's provinces. The major simulated processes were crop phenology (as a function of temperature and photoperiod function curves), development of leaves (as a function of daily biomass partitioning to leaves and Specific Leaf Area (SLA)), and total dry matter (as a function of intercepted radiation and radiation use efficiency).The model was run based on daily time step and outputs were evaluated by comprising actual and predicted data. Calculated Root Mean Square Error (RMSE) by actual and predicted data indicated that predicted days to flowering biased 2.61 and 3.37 days from mean of actual values for the first and second sowing dates in studied regions, respectively. Determination coefficients of actual data plotted against predicted data revealed that predicted dry matter yield was acceptable in comparison with actual data. Although, the model had an overestimation during two-third of growing season, but low RMSEs (0.6 and 0.74 ton ha-1 for first and last sowing dates, respectively) demonstrated the ability of model to predict dry matter production during growing season of pearl millet (R2=0.98). In this study, the advantages and deficiencies of model, also, have considered.