نشریه هواشناسی کشاورزی
سال یازدهم شماره 1 (بهار و تابستان 1402)

With the current issue, the journal of agricultural meteorology starts its tenth year of publication among Iranian-scientific journals. On the other hand, 2023 is the fiftieth year of the establishment of educational, research and implementation activities in the field of agrometeorology. On this occasion, the editorial topic of this issue is dedicated to a brief introduction of agricultural meteorology and its fifty-year advancements in Iran. Agricultural meteorology entered the circle of applied sciences when in the early 1930s scientists attentions were focused on the physical processes of agricultural soils, crops micrometeorology and other factors affecting crops yield enhancement. Therefore, by the late 1950s, many of the physical laws of physiological processes of agricultural crops, water-soil-plant relationship and crops microclimates were known. The use of meteorological data, from the 1970s onwards, for optimizing of agricultural and horticultural operational managements, entered the practical stage in most countries of the world. From the end of the last century until now, the technology of remote sensing and smart agriculture has been used and developed. In Iran, the organized activities of agricultural meteorology started at the end of 1973, therefore, the following plans were made: 1) training of agricultural meteorology experts, 2) designing and implementing of agricultural meteorology network, and 3) equipping the necessary technical tools for this network. Unfortunately, only the first step was relatively managed, while the other two steps were not put to actions for several years. The objectives of Iranian agricultural meteorology expansion, for this period, were pursued and resumed from 1991 onwards. In the final part of this note, the process of intensifying education, research, advisory services and challenges are mentioned. Currently, the agricultural meteorology postgraduate studies are active in universities of Tehran, Ferdowsi, Sari, Semnan, Buali Sina and Azad up to the doctoral level. Mashhad Climatology Research Institute, which has a positive and older experience, is also considered one of the strong arms of agricultural meteorology in Iran. The Ilam Agro-meteorological research institute is also a relatively new fundamental research center, which established in this field. Based on these findings, the country's educational and research performance in the discussed field would be evaluated positive, but absolutely insufficient.

Solar radiation reaching the earth's surface is one of the most important variables used in solar energy projects, hydrological modeling, evaporation and transpiration, meteorology and agriculture. In this research, the functionality of principal component analysis and linear multivariate regression was investigated in predicting the amount of solar radiation in Yazd station between 2010 to 2014 and 2015 to 2021. Variables of average temperature, minimum temperature, maximum temperature, hours of sunshine, relative humidity and solar radiation are received from the Meteorological Organization on a daily basis and variables of extraterrestrial solar radiation, relative distance from the earth to the sun, solar inclination angle and maximum hours of sunshine are calculated with the existing relations and was chosen as input of Principal component analysis method. The correlation of these parameters with the amount of solar radiation shows the presence of a significant positive relationship at the level of 1% between the amount of solar radiation and extraterrestrial radiation, the ratio of sunny hours, the average temperature and the relative distance from the earth to the sun, and with the solar inclination angle at the level of 5% and was observed a significant negative correlation at the level of 1% with relative humidity values. The results of the analysis of the main components showed that the first two components can be selected as the main components according to the eigenvalues of the parameters and the percentage of variance. The correlation coefficient of the first and second components with solar radiation was 0.893 and -0.168, respectively, and as a result, the first component has a higher correlation with the amount of solar radiation than the second component. The rotated value of loading in the first component shows that the minimum temperature, maximum temperature, average temperature and relative humidity are more correlated with solar radiation and the correlation of other factors (extraterrestrial radiation, solar inclination angle and relative distance from the earth to the sun and the ratio of sunny hours) is low in this component. Investigating the relationship between solar radiation and the values of the main components obtained from PC1 and PC2 using multivariate linear regression showed that the effect of meteorological parameters on solar radiation is significant (p < 0.001) and its R2 is was determined as 0.89%. The evaluation criteria of PCR and MLR methods show the ability of PCA method to estimate solar radiation.

Identifying potential sites for small rainwater harvesting can help to improve crop water productivity especially rainfed cropping systems.In present study, suitable areas for creating small rainwater collection systems in Isfahan province were determined using multi-criteria decision making models including hierarchical analysis process and network analysis process. The selected variables were annual rainfall ( 111 synoptic, climatology , evaporation and rain gauging stations), land slope, soil permeability, vegetation cover, soil fertility, distance from city and village and distance from road. The results showed that in both AHP and ANP methods, Khomeni-Shahr region with about 63% and Mobarake with about 55% of area , have the largest suitable coverage (good and very good condition) for the small rainfall collection systems construction and Aran and Bidgol (4%) and Ardestan (6%) have the lowest percentage of suitable areas, respectively. For the entire province, according to the AHP and ANP methods, about 24.8 and 21.8% of the lands are categorized as good and very good class for the creating small rainwater harvesting systems, respectively. The results also indicated that for both AHP and ANP methods, about 76.3% of lands with good and very good conditions for the construction of these systems are located in dry and semi-arid areas and 23.7% in semi-humid areas. The statistical comparison between the two approaches showed a significant difference between the provided suitable zones for constructing these systems at 0.01 level. The defference may be attributed to assigned weight of input layers.

Considering the spatial variability of climatic conditions across Iran and water scarcity, it is important to investigate whether the heat and cooling requirements of greenhouses are met for their improved and efficient operation and energy management. In this study, using long-term temperature data of selected stations of entire country, the heating and cooling requirements of seven major greenhouse vegetable crops and flowers including cucumbers, tomatoes, peppers, strawberries, chrysanthemums, roses and nations were estimated. The results of this study revealed a significant variation in cooling and heating requirement across Iran. The average values of heating demand for the studied plants are 3322, 4206, 3598, 3598,3059, 3894, and 2322 degree-days respectively. The corresponding mean cooling demand were 1850, 1850, 2052, 2261, 2052, 2476 units respectively.

Prediction of evaporation as a key component of the hydrological cycle is one of the most important issues in water resources management and meteorology studies. In this study, the performance of ARIMA, SARIMA, gene expression programming, multiple linear regression, Monte Carlo and Thomas Fairing models in prediction of monthly evaporation values of Ekbatan Dam station, west of Iran in a 47 years period (1971-2017) were evaluated. For calibration of these models, 40 years data (1971-2010), and for validation, data from 2011-2017 (7-year) were used. The statistical metrics of the correlation coefficient, root mean square error, standard error, the Akaike information criterion, and NSE were selected for evaluation and comparison of models. The results showed that the SARIMA model has more accurate performance in predicting monthly evaporation. The GEP model, ARIMA, and MLR are ranked second to fourth. However, since the GEP model is easier to use than the SARIMA model and requires fewer variables than the SARIMA model, it shows promise to generate faster results, therefore, the GEP models can be the preferred option compared to others.

In this study, the effect of empirical mode decomposition (EMD) and maximal overlap discrete wavelet transform (MODWT) approaches in the processing of average temperature, wind speed and precipitation annual, monthly and single month time scales were evaluated. The selected study stations were Ramsar, Kermanshah, Hamadan, Semnan, Mashhad, Shiraz, Ardabil,Urmia, Zanjan, Yazd, Tabriz, Maragheh and Mianeh. In the time series decomposition using EMD, the trend of combined sub-series and in MODWT, the trend of sub-series was consistent with the Mann-Kendall test values of observed data time series, although, it can also be affected with the value and sign of of Mann-Kendall statistic. The significance level of The Mann-Whitney test in comparison of main annual observed time series with subseries of both approaches was more than 0.05, which indicates the acceptable performance of EMD and MODWT. In general, the comparisons of the sub-series of two approach with the main time series in each station, showed that the corresponding significance level for MODWT approach is higher than EMD. Also, the average of significance level for temperature series greater than those of precipitation and wind speed series. The LSD test at the 95% confidence level indicates good agreement of the observed data time series and combined sub-series.

Land-use changes especially agricultural lands have a significant impact on  the microclimate of a region,natural resrource managrment and land-atmoshptre interactions .Remote sensing is a reliable and precise techniques in generating land-use maps. The aim of this study, is producing to a land use map by and detection of land cover pattern changes including urban,industrial agricultural and fallow land in Zarand region, Kerman province,south of Iran, during 1987 to 2020. In order to classify land use in four above mentioned types, three methods of maximum likelihood, artificial neural network, and support vector machine were used. The support vector machine was found to be the best performing method. In general, the generated land-use maps in the studied years showed an increase of 64,17 and 2 hectares in the agricultural, urban and industrial land uses, respectively. The observed increase in industrial and agricultural lands are quite important in possible changes of cropping pattern and agroclimatic condtion of the region and needs further investigatin in long term environmental management planing.