A. Mosaedi; S. Mohammadi Moghaddam; M. Ghabaei Sough
Abstract
Introduction: Weather features and their variations have an important role in the yield of agricultural products, especially in rain-fed conditions. The main metrological variables that affected yields consist of precipitation, temperature, soil moisture and solar radiation. Also, drought is one of the ...
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Introduction: Weather features and their variations have an important role in the yield of agricultural products, especially in rain-fed conditions. The main metrological variables that affected yields consist of precipitation, temperature, soil moisture and solar radiation. Also, drought is one of the major constraints to production, especially the mid-season drought which occurs during the podand seed formation stages and the terminal drought which occurs during the pod filling stage. The results of investigating the relation between drought indices such as Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Crop Moisture index (CMI) and Z index with crop yields indicated the capability of these indices to estimate variations in crop yields. The objective of this study in the first step is investigation of relations among wheat and barley crop yields with climatic variables and SPI and RDI drought indices based on Principle Component Analysis (PCA) method at Bojnourd, Mashhad and Birjand stations. In addition, by selecting the prominent variables via PCA method, the best models of estimating each crop’s yield based on multivariate regression methods at selected stations were determined.
Materials and Methods: In this study, the relationship between yields of rain-fed wheat and barley with weather variables consisting of minimum, mean and maximum temperature, precipitation, evapotranspiration and drought indices including SPI and RDI were investigated and modeled at Bojnourd, Mashhad and Birjand stations. For this purpose, the values of each variable were calculated for 34 time scales of 1, 2, 3, 4, 6, and 9 months and wet periods (nine 1-month periods, eight 2- month periods, seven 3- month periods, six 4- month periods, two 6- month periods, one wet period (5 or 7-month) and one 9-month period). After that, the main influencing variables were chosen among investigated time periods for each variable by using the method of principal component analysis (PCA). In continuation, the selected variables via PCA technique were used in the multivariate regression methods to create the best model of predicting wheat and barley yields based on each mentioned variable and combination of them. The performance of the established model was evaluated based on Ideal Point Error (IPE) criteria and the best predicting model of wheat and barley was selected for each region.
Results and Discussion: The results showed that applying PCA technique as a powerful statistical tool leads to decrease of the error and inflation of constructed models. This is done by reducing the volume of data and selecting influencing variables. Based on the PCA results by choosing only four components the 90 percent and greater than variation of crop yields are estimated and the first component includes time periods of spring and winter months. Investigation of the results of the best model at the given stations based on IPE criteria show that the constructed models based on variables of SPI index have more accuracy for predicting yields of wheat and barley at station of Bojnourd, at Mashhad station the created models based on a combination of variables and at Birjand station a model based on a combination of variables and a created model according to RDI variables was used that has more accuracy for predicting yields of wheat and barley, respectively. Comparing the estimated and actual values of wheat and barley yields indicate that the correlation coefficients of the models when applied to estimate the yield of wheat and barley at Bojnourd station resulted in 68 and 69 percent, at Mashhad station 89 and 86 percent and at Birjand station 66 and 74 percent, respectively.The performance evaluation graph shown in Fig. 1 can be used to illustrate model performance and to diagnose model bias.
Conclusion: According to the results, a relation between crop yields and combination of metrological variables and drought indices is more positive and stronger than only metrological variables combination. The results showed that the variables of temperature, precipitation and evapotranspiration are to be considered. Also, the evaluation model indicated that the RDI index is more suitable for predicting rain-fed wheat and barley yields.
abolfazl Mosaedi
Abstract
Prediction of precise forage production and proper management strategies requires identifying key climatic factors in different regions. The objective of this research is to compare forage production in different region based on climatic factors and drought indices. The study sites include Arak, Roudshore, ...
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Prediction of precise forage production and proper management strategies requires identifying key climatic factors in different regions. The objective of this research is to compare forage production in different region based on climatic factors and drought indices. The study sites include Arak, Roudshore, Baghic, and Gharahso in Central and Qom provinces. Climatic factors and drought indices include precipitation, temperature, evapotranspiration, standardized precipitation index (SPI), and Reconnaissance Drought Index (RDI). For each climatic variable/or indices, 33 time periods of 1, 2, 3, 4, 6, and 9-month were specified. We have used Principle Component Analysis to decline the number of variables and then, the appropriate time periods were selected. By using stepwise and best subset, the relationship between forage production and each of the climate factors and indices was modeled. To select model, assessment statistics of R, MBE, RMSE, MARE, and IPE were used. Finally, to predict forage production in Roudshore, Baghic, and Gharahso sites, models based on evapotranspiration (RMSE=7.7, r=0.99), RDI (RMSE=3.1, r=0.99) and precipitation (RMSE=4.0, r=0.99) were selected respectively. The best model was based on the combinations of climatic factors and drought indices (RMSE=0.2, r=0.99) for Arak. In general, the relation between forage production and drought condition based on RDI is stronger than its relationship with precipitation and temperature. As we have used precipitation and evapotranspiration simultaneously in RDI, so this index is more precise than SPI.
