To solve many problems such as estimation of average monthly river inflow, it is necessary to consider a time-dependent phenomenon which is involved in several factors. So, a stochastic model has to be obtained to calculate the... more
To solve many problems such as estimation of average monthly river inflow, it is necessary to consider a time-dependent phenomenon which is involved in several factors. So, a stochastic model has to be obtained to calculate the probability of a future amount of inflow. Studying of river behavior and the ability to forecast the future events is a prerequisite for the preparation of optimization models. In the present study, methods for creating, diagnosis and assessing the rate of compatibility with seasonal ARIMA time series models have been provided. It is also supposed that the seasonal classification based on their statistical parameter similarities, could lead to a better normalized series in comparison to the other transformation methods. The discussed methods are suitable for continuous systems. The ARIMA method was used to predict the future amounts of the inflow into the Karaj reservoir by using its previous and present values. For implementing models, first, it is necessary to normalize the observed data with a logical transformation method considering seasonalization. The results showed that the best fitted model is an annual series ARIMA (1, 0, 1) (2, 1, 1)12 with logarithmic transformation for forecasting models. It is also concluded that the 12 month forecast of inflow is better than 24 months in terms of the forecasted values.
A simulation study using the Seasonal Analysis program of the Decision Support Systems for Agrotechnology Transfer (DSSAT 3.5) suite of models was conducted from 2001 to 2003 under a subhumid subtropical climate. The models CERES-Rice and... more
A simulation study using the Seasonal Analysis program of the Decision Support Systems for Agrotechnology Transfer (DSSAT 3.5) suite of models was conducted from 2001 to 2003 under a subhumid subtropical climate. The models CERES-Rice and CERES-Wheat in DSSAT 3.5 were calibrated and validated for transplanted rice, direct seeded rice and wheat crops using the soil and weather parameters of Kharagpur, West Bengal, India. The weather generator program, SIMMETEO, was used to generate future weather scenarios ...
A simulation study using the Seasonal Analysis program of the Decision Support Systems for Agrotechnology Transfer (DSSAT 3.5) suite of models was conducted from 2001 to 2003 under a subhumid subtropical climate. The models CERES-Rice and... more
A simulation study using the Seasonal Analysis program of the Decision Support Systems for Agrotechnology Transfer (DSSAT 3.5) suite of models was conducted from 2001 to 2003 under a subhumid subtropical climate. The models CERES-Rice and CERES-Wheat in DSSAT 3.5 were calibrated and validated for transplanted rice, direct seeded rice and wheat crops using the soil and weather parameters of Kharagpur, West Bengal, India. The weather generator program, SIMMETEO, was used to generate future weather scenarios based on weather data from 9 consecutive years. These weather scenarios were used in the seasonal analysis program to run each treatment combination with 20 replications. The results of both biophysical and economic analyses of the Seasonal Analysis program predicted an application of 120 kg N/ha along with both rice and wheat crop residues at 4 t/ha for rice, whereas the economical analysis, specifically the Mean-Gini analysis, showed that application of 80 kg N/ha along with both...
A simulation study using the Seasonal Analysis program of the Decision Support Systems for Agrotechnology Transfer (DSSAT 3.5) suite of models was conducted from 2001 to 2003 under a subhumid subtropical climate. The models CERES-Rice and... more
A simulation study using the Seasonal Analysis program of the Decision Support Systems for Agrotechnology Transfer (DSSAT 3.5) suite of models was conducted from 2001 to 2003 under a subhumid subtropical climate. The models CERES-Rice and CERES-Wheat in DSSAT 3.5 were calibrated and validated for transplanted rice, direct seeded rice and wheat crops using the soil and weather parameters of Kharagpur, West Bengal, India. The weather generator program, SIMMETEO, was used to generate future weather scenarios based on weather data from 9 consecutive years. These weather scenarios were used in the seasonal analysis program to run each treatment combination with 20 replications. The results of both biophysical and economic analyses of the Seasonal Analysis program predicted an application of 120 kg N/ha along with both rice and wheat crop residues at 4 t/ha for rice, whereas the economical analysis, specifically the Mean-Gini analysis, showed that application of 80 kg N/ha along with both rice and wheat crop residue incorporation at 4 t/ha as the most dominant management options for wheat. The present study revealed that the generated future weather data were reliable and DSSAT could successfully use it to predict the future crop yields under different management practices and select the best one for sustainable production of rice and wheat crops by DSSAT.
A simulation study using the Seasonal Analysis program of the Decision Support Systems for Agrotechnology Transfer (DSSAT 3.5) suite of models was conducted from 2001 to 2003 under a subhumid subtropical climate. The models CERES-Rice and... more
A simulation study using the Seasonal Analysis program of the Decision Support Systems for Agrotechnology Transfer (DSSAT 3.5) suite of models was conducted from 2001 to 2003 under a subhumid subtropical climate. The models CERES-Rice and CERES-Wheat in DSSAT 3.5 were calibrated and validated for transplanted rice, direct seeded rice and wheat crops using the soil and weather parameters of Kharagpur, West Bengal, India. The weather generator program, SIMMETEO, was used to generate future weather scenarios based on weather data from 9 consecutive years. These weather scenarios were used in the seasonal analysis program to run each treatment combination with 20 replications. The results of both biophysical and economic analyses of the Seasonal Analysis program predicted an application of 120 kg N/ha along with both rice and wheat crop residues at 4 t/ha for rice, whereas the economical analysis, specifically the Mean-Gini analysis, showed that application of 80 kg N/ha along with both rice and wheat crop residue incorporation at 4 t/ha as the most dominant management options for wheat. The present study revealed that the generated future weather data were reliable and DSSAT could successfully use it to predict the future crop yields under different management practices and select the best one for sustainable production of rice and wheat crops by DSSAT.
A simulation study using the Seasonal Analysis program of the Decision Support Systems for Agrotechnology Transfer (DSSAT 3.5) suite of models was conducted from 2001 to 2003 under a subhumid subtropical climate. The models CERES-Rice and... more
A simulation study using the Seasonal Analysis program of the Decision Support Systems for Agrotechnology Transfer (DSSAT 3.5) suite of models was conducted from 2001 to 2003 under a subhumid subtropical climate. The models CERES-Rice and CERES-Wheat in DSSAT 3.5 were calibrated and validated for transplanted rice, direct seeded rice and wheat crops using the soil and weather parameters of Kharagpur, West Bengal, India. The weather generator program, SIMMETEO, was used to generate future weather scenarios based on weather data from 9 consecutive years. These weather scenarios were used in the seasonal analysis program to run each treatment combination with 20 replications. The results of both biophysical and economic analyses of the Seasonal Analysis program predicted an application of 120 kg N/ha along with both rice and wheat crop residues at 4 t/ha for rice, whereas the economical analysis, specifically the Mean-Gini analysis, showed that application of 80 kg N/ha along with both rice and wheat crop residue incorporation at 4 t/ha as the most dominant management options for wheat. The present study revealed that the generated future weather data were reliable and DSSAT could successfully use it to predict the future crop yields under different management practices and select the best one for sustainable production of rice and wheat crops by DSSAT.
Crop Simulation Modelling (CSM) has been the most advanced tool to simulate the average productivity of a cropping system. CSM could predict the growth, development and yield of a crop or cropping system under variable management options,... more
Crop Simulation Modelling (CSM) has been the most advanced tool to simulate the average productivity of a cropping system. CSM could predict the growth, development and yield of a crop or cropping system under variable management options, climate and soil environment. For a specific soil and climate, a variable management option was the criterion, which affect the soil-plant environment and varied the yield. Decline or stagnation of yield of rice-wheat rotation has been the major problem in present Indian as well as in Asian Agriculture. Rice-wheat rotation cover nearly 24 million hectare of Indian subcontinent. Except the changes in weather parameters, all the other reasons of yield stagnation are site-specific. In Eastern India, soils are poor in organic matter content with low nutrient reserves and water holding capacity moreover use of recent high yielding varieties need higher rate of inorganic N and irrigation. Limited or no crop residue incorporation along with inappropriate use of inorganic N fertilizers resulted in depletion of soil fertility and ultimately decreasing the yield. Thus a comprehensive study for assessing the effects of management options for sustainability of transplanted rice-wheat system in eastern India was initiated. The study comprised of a three years field experiment along with crop simulation modelling. All data related to soil, crop and weather variables were collected and were used for calibration and validation of CERES-Rice and CERES-Wheat of DSSAT3. 5. Solar radiation, maximum and minimum temperature and rainfall were the weather variables which were mainly used to run the model. Seasonal analysis …
