عنوان مقاله [English]
Hyssop (Hyssopus officinalis) is a plant belonging to the genus Mint. The origin of this plant is reported to be Asia Minor and it goes from the Caspian Sea to the Black Sea as well as in the sandy areas of the Mediterranean. Usable parts of hyssop are flowering branches, leaves and seeds. Nowadays, the simulation models of growth and development have been used as suitable tools for acknowledging and analyzing the effect of plant, soil and atmosphere parameters for plants growth and development. Over the last two decades, FAO (Food and Agriculture Organization) has developed and successfully applied the agro-ecological zones (AEZ) methodology and supporting software packages to analysis solutions to various problems of land resources for planning and management for sustainable agricultural development at regional, national and sub-national levels. The issues addressed include linking land-use outputs with other development goals in such areas as food production, food self-sufficiency, cash crop requirements, issues of soil fertility constraints, soil erosion risks and land degradation. This procedure can calculate and present the potential biomass production of any crops under specific climatic condition using climatology parameters. FAO has presented the procedure manual of AEZ Package as a guideline to analyze land suitability for any crops. So, the current research is done to investigate the optimum planting date and forecast the biomass production of Hyssop using the AEZ model.
Materials and Methods
The experiment was conducted as a split-plot in a completely randomized block design with six planting dates and three replications in 2017-2018. The main factor was different levels of nitrogen fertilizer: 0, 50, 100 and 150 kg/ha and sub-factor was planting dates between 30-day periods from October 17 to March 25. The data of this experiment were used to calibrate the model of AEZ and the data of two years of planting date experiments in the period 2016-2017 were used to evaluate the model, so that the data collected in the first year were used for calibration and the data of the second year were used for evaluation. The index of Physiological Days (PDays) was used for analyzing photoperiod response of Hyssop during different planting date. The procedure of Soltane et al., (2006) was used to calculate PDays index.
Results and discussion
The AEZ model evaluation results showed that the RMSEn value of the predicted biomass was 10.81%, and the efficiency index (E), the Hyssop adaptation index (D) value, the coefficient of residual mass (CRM) value in the first year for the predicted value was 0.999, 0.98 and 0.06, respectively. The coefficient of determination (R2) was obtained by linear regression analysis of functions between the actual and simulated values in the first year (R2=0.98). The RMSEn, (E), (D) and (CRM) values for the predicted biomass in the second year were 5.96, 0.999, 0.0454 and 0.98%, respectively. These results indicate that the simulated and real values are in good agreement. Consequently, the model simulated the biomass with high accuracy. The results of biomass and yield analyzing using AEZ model indicated limited growth period with in a period in Jiroft station can be occurred from May to September 21, at this period, the plant stops growing and the yield decreases. Based on model estimation values for other related climatology stations: the highest biomass yield for Bam climatology station (2321.9 kg/ha and 23550.2 kg/ha respectively) can be September to early October, the cold stress limitation can be occurred at the planting date from December 1st to February 3th and the heat stress can be occurred on July planting date. In Kahnuj station, limited growth period with high temperature can be occurred from Early May to September 11, the highest biomass performance can (19413.9 kg/ha and 19764.3 kg/ha respectively) be obtained at the planting date from October to early November.
Given the ability of the AEZ model to analyze the hyssop plant production system, which is able to simulate the effect of different c
limatic, climatic, soil, managerial and plant variables on plant growth and yield, this model can be widely used in different regions as He used an important decision-making and management tool in research and executive dimensions.