fatemeh yaghoubi; Mohammad Bannayan Aval; Ghorban Ali Asadi
Abstract
Introduction: Estimating crop water requirement, crop yield and their temporal and spatial variability using crop simulation models are essential for analysis of food security, assessing impact of current and future climates on crop yield and yield gap analysis, however it requires long-term historical ...
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Introduction: Estimating crop water requirement, crop yield and their temporal and spatial variability using crop simulation models are essential for analysis of food security, assessing impact of current and future climates on crop yield and yield gap analysis, however it requires long-term historical daily weather data to obtain robust predictions. Depending on the degree of weather variability among years, at least 10–20 years of daily weather data are necessary for reliable estimates of crop yield and its inter-annual variability. In many regions where crops are grown, daily weather data of sufficient quality and duration are not available. In this way, gridded weather databases with complete terrestrial coverage are available which require comprehensive validation before any application. These weather databases typically derived from global circulation computer models, interpolated weather station data or remotely sensed surface data from satellites. The aims of this study were to evaluate differences between grided AgMERRA weather data and ground observed data and quantify the impact of such differences on simulated water requirement and yield of rainfed wheat at 9 different locations in Khorasan Razavi province.
Materials and Methods: AgMERRA dataset (NASA’s Modern-Era Retrospective analysis for Research and Applications) was selected as the girded weather data source for use in this study because it is publically accessible. We evaluated AgMERRA weather data against observed weather data (OWD) from 9 meteorological stations (Torbat Jam, Torbat Heydarieh, Sabzevar, Sarakhs, Ghoochan, Kashmar, Gonabad, Mashhad, and Neyshabour) in Khorasan Razavi province. For each weather variable (solar radiation, maximum temperature, minimum temperature, precipitation, and wind speed), the degree of correlation and agreement between OWD and AgMERRA data for the grid cell in which weather stations were located were evaluated. The intercept (b), slope (m), and coefficient of determination (r2) of the linear regression were calculated to determine the strength and bias of the relationship, while the root mean square error (RMSE) and normalized root mean square error (NRMSE) were computed to measure the degree of agreement between data sources. Crop water requirement or actual crop evapotranspiration (ETc) under standard condition was computed using CROPWAT 8.0. The CSM-CERES-Wheat (Cropping System Model-Crop Environment Resource Synthesis-Wheat) model, included in the Decision Support System for Agrotechnology Transfer (DSSAT v4.6) software package was used to calculate rainfed wheat yield. For each location in this study, rainfed wheat grain yield and water requirement were simulated using ground-observed and AgMERRA weather data and outputs were compared with each other.
Results and Discussion: The results of this study showed that AgMERRA daily maximum and minimum temperature and solar radiation showed strong correlation and good agreement with data from ground weather stations. AgMERRA daily precipitation had low correlation and good agreement (mean r2= 0.34, RMSE= 2.25 mm and NRMSE= 4.94% across the 9 locations) with OWD daily values, but correlation with 15-day precipitation totals were much better (mean r2 >0.7 across the 9 locations). There was reasonable agreement between a number of observed dry and wet days with AgMERRA compared to OWD. Results indicated that coefficient of variation of simulated water requirement and yield using AgMERRA weather data was remarkably similar to the degree of variation observed in simulated water requirement and yield using OWD at all locations (distribution of CVs in simulated water requirement and yield using AgMERRA weather data were within ±5% of the CV calculated for simulated water requirement and yield using observed weather data) except Torbat Jam, Torbat Heydarieh and Gonabad for water requirement and Mashhad, Kashmar and Ghoochan for yield. There was good agreement between long-term average yield simulated with AgMERRA weather data and long-term average yield simulated using observed weather data. For example, the distribution of simulated yields using AgMERRA data was within 10% of the simulated yields using observed data at all locations. Using AgMERRA weather data resulted in simulated crop water requirement that were not in close agreement with crop water requirement simulated with ground station data at two location including Gonabad and Torbat Heydarieh.
Conclusions: These results supported the use of uncorrected AgMERRA daily maximum and minimum temperature and solar radiation in areas that their weather stations only have a few years of daily weather records available or areas without weather station. Considering the advantage of continuous coverage and availability, use of AgMERRA dataset appears to be a promising option for simulation of long-term average yield and water requirement, as well as for assessing impact of climate change on crop production and also estimating the magnitude of existing gaps between yield potential and current average farm yield in Khorasan Razavi province. But they are not very reliable for accurate simulation of water requirement and yield in a specific year and estimate their inter-annual variation.
R. Moazenzadeh
Abstract
Introduction: In two last decades, greenhouse cultivation of different plants has developed among Iranian farmers, approximately 45 percent of national greenhouse cultures consisting of cucumber, tomato and pepper. As huge amounts of agricultural water in Iran are extracted from groundwater resources ...
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Introduction: In two last decades, greenhouse cultivation of different plants has developed among Iranian farmers, approximately 45 percent of national greenhouse cultures consisting of cucumber, tomato and pepper. As huge amounts of agricultural water in Iran are extracted from groundwater resources and a large number of Iranian plains are in critical conditions, and because irrigation is the major consumer of water (95 percent), it must be performed in a scientific manner. One approach to this is to obtain the knowledge of the consumptive use of major crops which is named evapotranspiration (ETc).
Materials and Methods: This research was carried out in a north-south greenhouse belonging to Plant Protection Research Institute, located on northern Tehran, Iran, for estimating greenhouse cucumber evapotranspiration. Trickle irrigation method was used, and meteorological data such as temperature, humidity and solar radiation were measured daily. Physical and chemical measurements were conducted and electric conductivity (EC) and pH values of 3.42 dsm-1 and 7.19, respectively, were recorded. Soil texture and bulk density were measured as to be sandy loam and 1.4 gr cm-3, respectively. In order to measure the actual evapotranspiration, cucumber seeds were also cultured in six similar microlysimeters and irrigation of each microlysimeter was based on FC moisture. If any drained water was available, it was measured. Finally, with measured meteorological characteristics in greenhouse which are suggested to have an effect on ET and were measurable, the best multiple linear regression and artificial neural network were established. The average data from three microlysimeters were used for calibration and that from three other microlysimeters were used for validation set.
Results and Discussion: In the former case, when we used one multiple linear regression with measurable meteorological variables inside the greenhouse to predict cucumber ET for the entire growth period, high and considerable amounts of error occurred, as the difference between measured and predicted values of ET is approximately 2.86 mm day-1 which is noticeable. Overestimation of the cucumber ET in the first and last stages which will result in decreasing water use efficiency and underestimation in blooming and yielding fruit stages, when cucumber is more susceptible to water stress, are the other disadvantages of using one equation for the entire growth period to describe and predict cucumber ET. In contrast, when we divided growth period into four steps, the MLR method’s performance in prediction of ET was improved and the difference mentioned above between measured and predicted values of ET (2.86 mm day-1) decreased to about 1.32 mm day-1. The results showed that measured and predicted values of ET ranged from (0.08 to 4.75) and (0.13 to 4.25) when the whole growth period is considered as one step, respectively. These mentioned values were obtained (0.08 to 1.5) and (0.13 to 1.75); (0.71 to 2.64) and (1.31 to 4.25); (2.18 to 4.75) and (1.69 to 4.13); (1.32 to 2.61) and (2.66 to 3.74) for each of growth period stages, respectively. Also the value of total ET for the entire growth period is measured 273.45 mm and predicted 275.7 and 275.59 mm, when the whole growth period is considered as one step or divided into four stages, respectively. Although dividing the growth period improved ET prediction, the results in the first and especially the third stage are still discussable. Therefore, as with MLR method, the capability of ANN technique was investigated in prediction of cucumber ET. Comparison of measured and predicted values of ET confirms that ANN has better performance than MLR, even when growth period is divided.
Conclusion: Determining cucumber evapotranspiration in the greenhouse was the main objective of this study. For this purpose we used Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques. In MLR, first we used one equation for the entire growth period. The results showed that this single equation is not able to simulate actual ET of cucumber. To overcome this problem, we divided the growth period into four stages and derived a separate equation for each stage. The results showed that this procedure improves prediction of cucumber ET, especially in the second and last stages of growth period. Statistical indices such as RMSE, Ens, PBIAS and PSR, t-statistical results, measured versus predicted ET values, and predicted values of ET in the growth period indicate that ANN technique is not only reliable, but also easier than the MLR technique.
Y. Kooch
Abstract
Introduction: Among the collection of natural resources in the world, soil is considered as one of the most important components of the environment. Protect and improve the properties of this precious resource, requires a comprehensive and coordinated action that only through a deep understanding of ...
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Introduction: Among the collection of natural resources in the world, soil is considered as one of the most important components of the environment. Protect and improve the properties of this precious resource, requires a comprehensive and coordinated action that only through a deep understanding of quantitative (not only recognition of the quality) the origin, distribution and functionality in a natural ecosystem is possible. Many researchers believe that due to the quick reactions of soil organisms to environmental changes, soil biological survey to estimate soil quality is more important than the chemical and physical properties. For this reason, in many studies the nitrogen mineralization and microbial respiration indices are regarded. The aim of the present study were to study the direct and indirect effects of soil physicochemical characteristics on the most important biological indicators (nitrogen mineralization and microbial respiration), which has not been carefully considered up to now. This research is the first study to provide evidence to the future planning and management of soil sciences.
Materials and Methods: For this, a limitation of 20 ha area of Experimental Forest Station of Tarbiat Modares University was considered. Fifty five soil samples, from the top 15 cm of soil, were taken, from which bulk density, texture, organic C, total N, cation exchange capacity (CEC), nitrogen mineralization and microbial respiration were determined at the laboratory. The data stored in Excel as a database. To determine the relationship between biological indices and soil physicochemical characteristics, correlation analysis and factor analysis using principal component analysis (PCA) were employed. To investigate all direct and indirect relationships between biological indices and different soil characteristics, path analysis (path analysis) was used.
Results and Discussion: Results showed significant positive relations between biological indices and clay, organic carbon and total nitrogen, whereas the correlations of the other soil properties (bulk density, silt, sand and CEC) were insignificant. Factor analysis using of principle component analysis showed that the behavior of these two biological indices in the same territory and controlled by the same factors. Path analysis was employed to study the relationship among soil biological indices and the other soil properties. According to results, soil nitrogen mineralization is more imposed by nitrogen (0.98) and organic carbon (0.91) properties as direct and indirect effects respectively. Whereas the values of soil microbial respiration were affected by organic carbon (0.89) and total nitrogen (0.81). It can be claimed that total nitrogen and organic carbon are the most important soil properties in relation to nitrogen mineralization and microbial respiration, respectively. Regarding to the strong relationship between soil organic carbon and nitrogen and also similarly strong relationship between nitrogen and organic carbon mineralization, enhancing nitrogen mineralization is expected by the increase in organic carbon. In this regard, Nourbakhsh, et al. (2002) claimed that nitrogen mineralization is depended to soil organic nitrogen and derived from total nitrogen. In addition, there is a strong relationship between total nitrogen and soil organic carbon. So, the greater amounts of nitrogen mineralization can be related to more accumulation of organic carbon and nitrogen in topsoil (23). This result is in accordance with Wood, et al. (1990) and Norton, et al. (2003) findings (21, 30). Ebrahimi, et al. (2005) stated that if the C/N ratio is more than 30, the process immobility or nitrogen mineralization stopwill be occurred. The ratios between 20 and 30 usually settle and release of mineral nitrogen does not take place, and the balance remains. If the C/N ratio is less than 20 net release of nitrogen in the soil will increase (9).In the present study, the values of soil C/N ratio were less than 20 (mean 15.80), so the process of nitrogen mineralization occurred in the study area. Suitable conditions for microbial activity of soil microorganism's especially adequate supply of organic carbon increased the microbial respiration in the study area. High correlation between the amount of organic carbon and microbial respiration confirmed this claim. However; it seems that the soil organic carbon is driver of microbial respiration rate. This finding is reported by different researchers (6, 7, 15, and 20).
Conclusion: Path analysis as a complementary method of regression analysis and factor analysis using principal component analysis showed that the biological activity of the soil characteristics are directly affected by soil nitrogen (for nitrogen mineralization index) and organic carbon (for microbial respiration index) and other useful features influence them indirectly through strong correlation with the characteristics of nitrogen and organic carbon in soil.
