نشریه هواشناسی کشاورزی
سال یازدهم شماره 2 (پاییز و زمستان 1402)

Climate change scenarios, in terms of water resources and environment, do not predict a prosperous future for Iran. The main solutions for managing climate change are based on mitigation of greenhouse gas emissions and adaption to it. Adaption to climate change is more beneficial and practical for arid and semi-arid regions, especially for developing countries. According to the IPCC 2022 report, Iran is, unfortunately, one of the few countries that have not taken effective actions at the strategic, political and planning levels in order to mitigate climate change. It seems that the involvement of the country's legislative/executive bodies in the issue of climate change adaptation is the most effective way to overcome the existing problems and shortcomings, and this is despite the fact that the draft of the 7th Development Plan seems to be silent on this matter and no separate chapter has been assigned to it. Since the climate change foresight is one of the main requirements of planning, the proposal and approval of the Iran's Integrated Climate Change Law, which includes the national action plan to promote adaptation to climate change, can make it possible to achieve the goals of the plan. Adaptation to climate change also creates new research issues in many fields, including agriculture and food security. Fortunately, this issue is included in the 7th Development Program Bill, with the aim of improving the productivity and effectiveness of research, emphasizing on academic research, including postgraduate theses. Evaluation of research topics is done in the communication line of the Ideas and Needs System: https://nan.ac. Studies related to Community-Based Adaptation to Climate Change, which is almost neglected in national studies, is also a topic that can be considered in this research field.

Considering the climate suitability in cropping pattern selection is vital in maintaining food security. In this study, the climate suitability of East Azerbaijan Province, north east of Iran, for the cultivation of strategic crops of wheat and barley during four decades 1980 to 2000s was evaluated. Land units were classified based on the covered area of each synoptic weather station using Thiessen method. Climate suitability for rainfed and irrigated wheat and barley was evaluated according to their climatic requirements using the square root-parametric method. Results revealed that during the recent four decades, the climate suitability class for irrigated studied crops was very suitable across the province, but in case of rainfed cultivation, various suitability classes including moderate to unsuitable were observed in different regions. The results confirmed that despite of some unfavorable effects of climate change on suitability of region for both crops cultivation, these impacts might be compensated, to some extent, by technology or management improvements. According to this study, determination of an optimum cropping pattern, adjusting agronomic practices and changing of rainfed wheat to barley in this province may be recommended.

The reference evapotranspiration (ET0) is an important factor for determining the plant water requirements and irrigation scheduling, which is usually estimated by widely accepted equation of Penman Monteith FAO-56 method. The aim of this study was to evaluate the performance of artificial neural networks (ANNs), random forest (RF) and support vector machine (SVM) methods for estimating the daily ET0 in Alborz province. A ten- year-data (2010-2020) of five meteorological synoptic stations of namely MeshkinDasht, Hashtgerd, Eshtehard, Taleghan and Karaj were used for estimation of ETo. The obtained values were compared with the provided data of a national project entitled Crop Water Requirement System. According to the results, the best agreement was found in Meshkin Dasht and Karaj stations. Besides, among the applied approaches, the ANNs method had the highest accuracy comparing to other methods. The values of EF and NRMSE in the ANNs method were determined 0.96 and 0.11, respectively for both training and testing steps in Meshkin Dasht station. While, these values for the RF method were determined 0.96 and 0.11, for training stage and 0.95 and 0.12 for testing stage, respectively. The obtained results in the Karaj station showed that EF and NRMSE in the ANNs method were 0.96 and 0.11, respectively for the training and 0.95 and 0.12 for the testing stage. These values for the RF method were 0.96 and 0.12 for the training stage respectively and 0.95 and 0.13 for the testing. Considering the higher accuracy of the ANNs and RF methods, these approaches can be recommended for estimating the daily ET0 across Alborz province.

The aim of this study was to evaluate the possible changes of yield and related welfare of two major groups of cereals and vegetable crops as a result of two climatic variables (rainfall and temperature) variations in Khuzestan province. For estimation of crop response production function of selected study crops, meteorological data including rainfall, minimum temperature, maximum temperature and relative humidity of 13 stations across the province for the years 1991 to 2019 were collected. Crop response production functions for different climate zones of the province were determined and possible effects of climatic variation on producer / consumer welfare were assessed. According to the results, the welfare of the cereal crops, the producer welfare surplus is higher than the consumer welfare surplus. A decreasing trend in welfare was observed under climate change scenarios. Also, the obtained results of projected welfare of the vegetables crops showed that the consumer welfare surplus is higher than the producer welfare surplus. In general, the welfare surplus of cereals is higher than that of summer vegetable crops and hence as a adaption strategy, cereal cultivation may be recommended.

Most of the regions of the globe have faced changes in the precipitation regime and CO2 concentration with the increase in temperature (especially the minimum temperature). Considering that two-thirds of Iran is considered to be arid and semi-arid regions, which will cause climate changes and successive droughts, and this process will affect agricultural ecosystems and their performance. The aim of this research is to simulate the effects of climate change on growth, biomass yield and corn grain under climate change conditions in Shushtar and Safi Abad cities in the northern part of Khuzestan province in 2020-2050. For this purpose, the production data of precipitation, minimum temperature, maximum temperature and sunshine hours of the LARS-WG statistical model using HADCM3 atmospheric general circulation model and under emission scenarios A2 and B1 were used in the periods of 2020-2050. AquaCrop model was used to simulate plant performance and growth. Before use, the AquaCrop model was calibrated and verified by field data collected in the region. Then, the values of seed yield and biomass in future periods were simulated for the treatment of 50% of the water requirement of the plant. According to the results, under A2 emission scenarios, the evaluation results of the LARS-WG model showed that it had a suitable forecast for the climate parameters and the simulation of the future growing season. And the increase in minimum and maximum temperature will be the average increase in precipitation. The length of the growth period for each station will decrease with the increase of GDD according to the climatic parameters of the region under the influence of climate change. Biomass and grain yield will also increase by one to two tons in different horizons and scenarios assuming the current planting date and full irrigation remain constant.

In this study, the trend of precipitation and temperature variables in basin scale across Iran was evaluated using ministry of energy weather stations data. Initially, among the total 2,142 climatology and 1,230 evaporation stations corseponding data of those with a minimum of 40 years record and a maximum of 3 months of missing data were selected, resulting in 476 and 116 climatology and evaporation stations, respectively. The study period was from water year 1973-74 to 2013-14. The monthly, seasonal, and annual precipitation and temperature data of the selected stations were spatially interpolated in different catchements across the country using the Thiessen method. The Mann-Kendall test was then employed to detect trends of times series. Considering the significance levels of 90, 95, and 99 percent, the results were analyzed at each station, second-degree basin, first-degree basin, and across the entire country. The findings of this research indicate an increasing trend in autumn precipitation for the northern part of the country (13 percent of stations), a decreasing trend in winter precipitation for 33 percent of stations, no significant trend in spring precipitation, and an increasing trend in summer precipitation. Additionally, an increasing trend was observed in all temperature time series. By comparing the changes over the last 5 and 10 years of study period (ending by 2013-2014) with the long term 40-years average, a significant decrease in precipitation in most regions of the country, was observed, this accompanied by an increase in the mean temperature, suggests a substantial reduction in available water resources in study period.

This study aimed to develop an extension-education model of farmers adaption to climate change using an exploratory mixed method approach. In the qualitative part, the grounded theory method based on 18 semi-structured interviews was used. In the quantitative part of this study, the structural equation modeling was applied. By using stratified random sampling, the perceptions of 350 experts and middle level managers of organizations related to climate change were studied. The findings of the qualitative part of the research revealed 223 primary codes, 72 secondary codes, 46 concept codes, 30 sub-categories and six main categories. The identified model was evaluated by structural equation modeling and Smart pls2 software package. Findings showed that man-made activities in agriculture and industry have the highest effect and power in predicting farmers' adaptive capacity to climate change.