Agricultural Meteorology
M. Abdollahi Fuzi; B. Bakhtiari; K. Qaderi
Abstract
IntroductionSpring frost is considered an important threat to agricultural products in high and middle latitudes. The damage caused by Late Spring Frosts (LSFs) significantly impacts vulnerable plant organs. This event has caused more economic losses to agriculture than any other climatic hazard in Asia, ...
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IntroductionSpring frost is considered an important threat to agricultural products in high and middle latitudes. The damage caused by Late Spring Frosts (LSFs) significantly impacts vulnerable plant organs. This event has caused more economic losses to agriculture than any other climatic hazard in Asia, North America, and Europe. Also, these phenomena have contributed to low crop yields in Iran. The latest statistics released by the Food and Agriculture Organization of the United Nations (FAO) show that Iran is one of the largest producers of agricultural products and the world’s second-biggest producer of pistachios. Kerman province is one of the significant areas of pistachio production. This province has a large share of the pistachio word area plantation. Spring frost damage to pistachio crops has led to low yields in recent years. A key aspect of studying frost is the ability to accurately estimate its occurrence. In this study, artificial neural network methods have been used to estimate late spring frost in the pistachio crop of Kerman city. Materials and MethodsIn this study, the efficiency of this method was investigated in the estimation of minimum temperature. For this purpose, the daily data of the synoptic station of Kerman city were obtained from Iran Meteorological Organization from 2000 to 2020. Meteorological data including mean, maximum, and minimum temperatures, relative humidity, wind speed, saturated vapor pressure, and sunshine hours were used. Five different combinations of these variables was considered as input variables in artificial neural network method for minimum temperatures modeling. After entering data into network and modeling with each combination, RMSE and R2 values were calculated. Finally, the combination of 8 variables including average and maximum temperature, the minimum temperature the previous day and two days prior, relative humidity, wind speed, saturated vapor pressure, and sunny hours were selected as the most suitable combination of variables. Subsequently, a simulation of minimum temperature values was conducted using 10% of the data. The performance of the methods was evaluated using statistical indices of coefficient of determination (R2), mean square of error (RMSE), Mean Bias Error (MBE), and Coefficient of Nash–Sutcliffe (NSE). Results and DiscussionThe accuracy of an analytical method is the degree of agreement between the test results generated by the method and the true value. Upon examining the models, the M1 model was identified as the best due to its lowest RMSE and higher R². ANN model results were evaluated using various performance measure indicators. The simulated outcome of the model indicated a strong association with actual data, where the correlation coefficient was above 0.95, and the MBE index was zero. Also, the RMSE value was positive and close to zero, and the NSE value was above 0.75. Therefore artificial neural network method had high accuracy. In this study, mean annual minimum temperature was estimated using artificial neural network models (from March 10 to May 20). Comparison between the observed and calculated data showed that these data were in good agreement. Also, the results showed that temperature fluctuations were high between March 10 and March 31. From 2011 to 2017, an almost uniform temperature trend has been observed between March 10 and March 31. However, the years 2000, 2006, and 2020 showed a noticeable decrease in temperature. From 2018 to 2020, this trend of temperature reduction continued. In April, the temperature values were between 7 and 10 degrees Celsius. The years 2001, 2005, 2006, 2009, 2016, and 2019 had a noticeable decrease in temperature. In May, the mean minimum temperature was between 10 and 14 degrees Celsius. Therefore, the probability of frost occurrence in early-flowering cultivars was higher in late March than in April and May. The years 2000, 2004, 2005, 2012, 2015, 2019 and 2020 had the highest number of frost days in the last two decades. ConclusionThe results showed that the artificial neural network method had a high performance in estimating the minimum temperature. The values of the statistical indicators were R2=0.963, RMSE=0.027oC, MBE= 0 and NSE=0.966 respectively. In addition, the ANN method performed well in estimating the number of critical frost days for pistachio crops. The results showed that, although reducing the amount of input data in models decreases their output precision, data-driven methods can still be useful tools for minimum temperature estimation.
Fariba Parnak; .Majid Rahimpour; Kourosh Qaderi
Abstract
Introduction: Soil is a key resource that contributes to the earth system functioning as a control and manages the cycles of water, biota and geochemical and as an important carbon reservoir. Soil organic matter is one of the most important factors in soil quality assessment and having relationship with ...
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Introduction: Soil is a key resource that contributes to the earth system functioning as a control and manages the cycles of water, biota and geochemical and as an important carbon reservoir. Soil organic matter is one of the most important factors in soil quality assessment and having relationship with physical, chemical and biological properties of soil. Carbon sequestration in plant biomass and soils is the simplest and the most economically practical solution to reduce the risks of atmospheric carbon dioxide. Little information is available about the effects of grazing management on sequestration of carbon in Khuzestan Province pastures. Therefore, this study was conducted to evaluate the effects of grazing exclusion on the amount and forms of carbon management and carbon sequestration with economic view in some pasture soils from Peneti Plain of Izeh area and Dimeh regions of Ramhormoz in Khuzestan Province.
Materials and Methods: This study was conducted in two regions including Izeh and Ramhormoz representing different climates, vegetation and soil types of southwestern Iran. We selected two grazing treatments including ungrazed and grazed pastures in each region. The first area includes rangeland ecosystem in Izeh city between 31° 57ʹ 8ʺ to 31° 58ʹ 20ʺ N and 49° 41ʹ 11ʺ to 49° 42ʹ 33ʺ E. The region has a typical temperate continental climate, characterized by dry summers and cold winters. The mean annual rainfall is 623mm. The mean annual temperature (MAT) is 19.2 °C, and the mean monthly air temperature varies from -0.6 °C in January to 42.4 °C in July. The second area (Ramhormoz) is located between 31° 7ʹ 44ʺ to 31° 9ʹ 11ʺ N and 49° 29ʹ 13ʺ to 49° 28ʹ 52ʺ E. The mean annual rainfall is 200 mm and the mean annual temperature (MAT) is 27.2 °C, and the mean monthly air temperature varies from 4.2 °C in January to 51.6 °C in July. For each climate region, grazed and ungrazed sites were located on the same soil series with similar aspect and slope. Then, random soil samples were taken from the surface and subsurface in 15 points. After air drying the soil samples and passing them through a 2 mm sieve, physical, chemical properties of the soils were measured.
Results and Discussion: The soil of both studied regions are non-saline, calcareous, and alkaline and have relatively heavy texture. The results showed that the studied characteristics in four study areas had low and moderate coefficients of variation. This suggests that the contribution of edaphic and environmental factors to explain variation in the data is not high. Also, grazing management has increased soil organic matter of surface and subsurface soil, but despite the increase in organic matter contents of subsurface soils the difference was not statistically significant. The effect of management practices, in order to have a significant effect to lower parts of the soil, it requires a longer period management. Comparing the biomass upon non-grazing (405 and 42 gm-2 in Izeh and Ramhormoz respectively) and grazed (117 and 17 gm-2) areas, indicates a good condition of vegetation in the non-grazing and the effectiveness of enclosure in rehabilitation of pastures in the study area. However, due to more rainfall rates, the amount of biomass produced in Izeh is higher.
Conclusion: The carbon management index in the study areas, as well as the depths of the study is high, indicating recovery of soil carbon and improving its quality. Also, based on carbon sequestration in the study area, non-grazing was one of the most proper and efficient management practices, which improved soil quality. Accordingly, it seems that non-grazing practices should be considered as one of the major programs in renewable natural resources plans. On the other hand, estimation of the economic value of carbon sequestration in the pastures has been remarkable, and increased 17 and 12.7% of the value of carbon sequestration in Izeh and Ramhormoz regions under the management of the exclusion. Therefore, the management of rangelands should be directed to allow for their ecologic performance and capacity considering the environmental economy of rangelands so that in broad terms, the justification for the enhancement and maintenance of the economic equilibrium can be viewed as a guaranty of implementing the range managements resulting in sustained development.
