Crop Forecasting by using Crop-Yield Weather Regression Model* by Dr. M. Rezaul Karim Talukder1 & Dr. M. Sayedur Rahman2 1Team 2 * Leader, EWFIS Project, Ministry of Food. Agricultural Statistics Specialist, EWFIS Project, Ministry of Food. The views expressed in this report are those of the authors and do not necessarily reflect the official position of the Government of Bangladesh. Introduction Crop production in Bangladesh is largely governed by the vagaries of nature. Climate is one of the key components influencing agricultural productivity and has never been stable. The effect of climate variability on agriculture is very significant (Virmani, 1987). Several studies (O’Toole and Jones, 1987; Oldeman, 1980; Satake and Yoshida, 1978) indicate that weather during cropping season strongly influences the crop growth and account for two-third (67%) of the variation in productivity while other factors account for only one-third (33%). Rice is grown in more diverse environmental condition than any other major food crop in the world. The different stages of growth and yields of crops are affected by weather fluctuations that deviate from optimum. Growth and yield of rice are affected by climate factors such as temperature, rainfall, hours of bright sunshine, evapotranspiration and solar radiation (Curry et al., 1990; Yoshida, 1978; 1981). Models for estimating and predicting crop yields require precise knowledge of yield weather relationship. In spite of the adaptation of modern agricultural practices and plant protection measures, favorable weather condition is essential for good harvest. Each phase of agricultural activity from preparatory tillage to plant growth and harvest does have strong dependence for weather (Rahman, 2000a, b; Ahmed et al. 2000). Materials and Methods Modeling Approach There are several methods of arriving at forecasts of production of food corps. These are: (a) monitoring crop conditions on the basis of agro climatic data, (b) making regular survey to assess area, yield and production of crops; and (c) estimating regression models describing quantitative relationship between selected climate/input variables and final yield of the crop. This study used the last approach to estimate yield and production of boro rice by crop-climate regression model. 2 Study Areas and Data Collection Estimation of the model required two sets of data (a) historical crop- yield data; (b) historical data on a number of agro-climatic variables. While yield data for this exercise were the yields of different varieties of Boro rice, agro-climatic data included weekly / periodic data on rainfall, maximum and minimum temperature and solar radiation, input data included price and fertilizer. The agro-climatic parameters that affect crop yield are rainfall, temperature, solar radiation and evapotranspiration. This study was an attempt to determine the nature and extent of relationship of these variables with crop yield at different stages of crop growth. Data relating to these agro-climatic variables were obtained from the BMD, yield and input data from the BBS and DAM. However, the nature of data obtained from the BMD constrained estimation of the model in both spatial and temporal dimensions. Thus for each district/region 21 years (1980-2000) data had been considered in this study. Finally, data were reduced total rainfall for the week, cumulative rainfall of the season up to the week, maximum weekly average temperature, minimum weekly average temperature, average weekly temperature and diurnal temperature range, net solar radiation and water balance index in order to fit into the models used. 3 Model Building Crop-Climate Regression Model Regression analysis is a statistical technique for investigating and modelling the relationship between variables. Applications of regression are numerous and occur in almost every field, including food management, engineering, the physical sciences, economics, life and biological sciences, and the social sciences. In fact, regression analysis may be the most widely used statistical technique. A regression model that involves more than one regressor variable is called a multiple regression model. Fitting and analyzing this model is discussed below. Regression models are used for several purposes, including Data Description Parameter Estimation Prediction and Estimation Control The statistical crop-climate regression model is m n Yi = i+ ij Wj + ik Ik + ui j=1 k=1 Where Yi is the yield of the ith crop, Wj is the jth agro-climatic variable in the production of the ith crop, Ik is the kth input variable in the production of the ith crop, j and k are the coefficient of the relevant variable, o is the constant and ui is the disturbance term. For a particular crop, the explicit form of the equation will be determined by the variables relevant for the crop. In the present exercise, the full 4 model contained ten regressors, which were to explain the yield of Boro rice for the 2001-02 season. The explicit formulation of the multiple regression models was: Y = o + 1 MXT + 2 MNT + 3 AVT + 4 DTR + 5 TRF + 6 CRF + 7 NSR + PR + FERT + TIME + e (1) Where, Y= Yield of Boro rice (Mt/ha). MXT= Maximum temperature (0c) MNT= Minimum temperature (0c) AVT= Average temperature (0c) DTR= Diurnal temperature range (0c) TRF= Total rainfall of the week (mm) CRF= Cumulative rainfall for the season up to the week (mm) NSR= Net solar radiation (cal/cm2/day) PR = Price value (Tk. per quintal) FERT = Fertilizer (‘000 Mt.) TIME= Year e= Stochastic term/ residual term / error term. The method of least squares used to estimate the regression coefficients in equation (1) by MS Excel and SPSS software. The model was estimated for early reproductive stages of Boro rice crop growth, corresponding to week 13 (1st week of April) of the year 2002. For this stage of crop growth, models were estimated for local, HYV and all Boro rice. The results of initial estimates of the model and those of the model finally selected through a series of regression diagnostics are presented in Tables 1 to Table 5 and finally select model using sensitivity analysis to estimate the yield and production scenarios from Table 6 to Table 8. 5 Regression Diagnostics Model Adequacy and Sensitivity Test Since most regression problems involving time series data (Box and Jenkins, 1976) exhibit positive autocorrelation, the hypothesis usually considered in the Durbin-Watson test (Durbin and Watson, 1971). The use and interpretation of a multiple regression model often depends explicitly or implicitly on the estimates of the individual regression coefficients. Several techniques have been proposed for detecting multicollinearity (Farrar and Glauber, 1967; Montgomery and Peck, 1982). These are as follows: Examination of the Correlation Matrix; Variance Inflation Factors; Eigensystem Analysis of X1X. In multiple regression problems certain tests of hypothesis about the model parameters are useful in measuring model adequacy. The test of significance of regression is a test to determine if there is a linear relationship between response variable and any of the regressor variables. Using F-test, we conclude that yield is related to climatic variables. However, this does not necessarily imply that the relationship found is an appropriate one for predicting yield as a function of climatic variables. Further tests of model adequacy are required. Evaluating model adequacy is an important part of a multiple regression problem. R2 is a measure of the reduction in the variability of dependent variable obtained by using the regressor variables. However, a large value of R2 does not necessarily imply that the 6 regression model is a good one. Adding a regressor to the model will always increase R2, regardless of whether or not the additional regressor contributes to the model. Thus it is possible for models with large values of R2 to perform poorly in prediction or estimation. Our focus was on techniques to ensure that the functional form of the model was correct and that the underlying assumptions were not violated. In some applications, theoretical considerations or prior experience can be helpful in selecting the regressors to be used in the model. Building a regression model that includes only a subset of the available regressors involves two conflicting objectives: we would like the model to include as many regressors as possible so that the “information content” in the factors can influence the predicted value of response variable; we want the model to include as few regressors as possible because the variance of the estimated response variable increases as the number of regressors increase. Also, the more regressors there are in a model, the greater the costs of data collection and model maintenance. The process of finding a model that is a compromise between these two objectives is called selecting the “best” regression equation. Experience, professional judgment in the subject matter field and subjective considerations all enter into the variable selection problem (Hocking, 1976). Backward elimination attempts to find good model by working in the opposite direction. That is we begin with a model that includes all K as regressors. Then the partial F-statistic is computed for each regressor as if it were the last variable to enter the model. Backward 7 elimination is often a very good variable selection procedure (Allen, 1974). Analysts, who like to see the effect of including all the candidate regresses, just so that nothing will be missed, particularly favor it. In general if a model is to extrapolate well, good estimates of the individual coefficients are required. When multicollinearity is suspected, the least squares estimates of the regression coefficients may be poor. This may seriously limit the usefulness of the regression model for inference and prediction (Marquardt and Snee, 1975; Mason et al., 1975). The method of least squares used to estimate the regression coefficients in equation (1) by MS Excel and SPSS software. The model was estimated for early reproductive stages of boro rice crop growth, corresponding to weeks 13 (Ist week of April) of the year 2001-02. For this stages of crop growth models were estimated for local, HYV and all Boro rice. The ridge regression technique was applied for sensitivity analysis but not found suitable stable estimate. That’s why finally we had to consider O.L.S model for estimation of yield and production of Boro rice crop. 8 Ridge Regression Simulation Study for Sensitivity Analysis When the method of least squares is applied to non-orthogonal data, poor estimates of the regression coefficients are usually obtained. The variance of the least squares estimates of the regression coefficients may be considerably inflated and the length of the vector of least squared parameter estimates are too long on the average. This implies that the absolute value of the least squares estimates is too large. The Gauss-Markov property referred that the least squares estimator has minimum variance but there is no guarantee that this variance will be small (Montgomery and Peck, 1982). The ridge regression estimates may be computed by using an ordinary least squares computer program and augmenting the data as follows: X XA KI p Y YA OP Where KI P is a pxp diagonal matrix with diagonal elements equal to the square root of the basing parameter and Op is a Px1 vector of zeros. The ridge estimates are then computed from R (X 1A X A ) 1 X 1A YA (X 1 X K1P ) 1 X 1 Y Where K0 is a constant selected by the analyst. Note that when K= o the ridge estimator is the least square estimator. 9 Methods for Choosing Biasing Parameter K Hocrl et al., (1976) have suggested that an appropriate choice for K is 2 K P 1 Where, = standardized regression coefficient and 2 = mean sum square error are found from the least squares estimator of the standardized regression coefficient, and analysis of variance table. The following sequences of estimates of and K Via Simulation; p k0 2 ^ / 2 R (k 0 ) p k1 / R ( K1 ) R(K ) 1 2 R (k 1 ) k2 p / R ( k1 ) / R ( k1 ) . . The procedure is terminated when k i 1 k i The ridge regression technique was applied but was not found suitable for this crop-climate regression model because after a series of iteration the stable estimates were no found. That’s why finally we had to consider O.L.S model for estimation of yield and production of Boro rice crop. 10 Results and Discussions Equations were estimated with respect representing the 23 old districts of Bangladesh. to 23 locations However, for the purpose of demonstrations of the details about the parameter estimates and the regression diagnostics, equation for one district was examined. For this purpose, Mymensingh was chosen on the basis of the fact that total area under Boro in Mymensingh was one of the highest in the country. Table 1, Table 2, and Table 3 show the coefficients and related statistics of the model estimated at the early maturity stage falling in the 13th week of the calendar year and 1st week of April 2002. The local Boro equation for 13 week (Table 1) showed that R2 = .67, implying that 67% of the variability in yield was explained by the variables included in the model. Overall (F-test) regression coefficients were statistically significant and some regression coefficients were statistically significant by using single t-test. Also Durbin-Watson statistic showed that the calculated d value d=2.739 (where tabulated d value dL=1.20, du=1.41) the null hypothesis was not rejected at the 5% level of significance. Thus there was no positive autocorrelation at all. Fig.1& 2 showed that histogram and normal probability plot did not indicate any serious departure from the assumptions. Similar result found by Draper and Smith (1981). These statistical tests on residuals would make relatively confident that including them would not seriously limit the use of the model. 11 The HYV Boro equation for 13 week (Table 2) showed that R2 was .862, implying that 86.2% of the variability in yield was explained by the variables included in the model. Overall (F-test) indicated that regression coefficients were statistically significant and some regression coefficients were statistically significant by using single t-test. Also Durbin-Watson statistic showed that the calculated d value d=1.734 (where tabulated d value dL=1.20, du=1.41). Thus the null hypothesis was not rejected at 5% level of significance, implying that there was no positive autocorrelation. Fig.3& 4 showed that histogram and normal probability plot did not indicate any serious departure from assumptions. Similar result found by Draper and Smith (1981). This statistical test on residual would make one relatively confident that including them would not seriously limit the use of the model. The results of all Boro equation for 13 week (Table 3) showed that R2 = .904, implying that 90.4% of the variability in yield was explained by the variables included in the model. Overall (F-test) indicated that regression coefficients were statistically significant and some regression coefficients were statistically significant by using single t-test. Also Durbin-Watson statistic showed that the calculated d value d=2.358 (where tabulated d value dL=1.20, du=1.41), the null hypothesis was not rejected at 5% level of significance. Thus there was no positive autocorrelation at all. Fig.7 & Fig. 8 shows that histogram and normal probability plot did not indicate any serious departure from the assumptions. Similar result found by Draper and Smith (1981). This statistical test on residual would make one relatively confident that including them would not seriously limit the use of the model. For week 13 the correlation coefficient between MXT & AVT (r=.6527), MXT & DTR (r=.6427), MXT & TRF(r=-.748), MXT & 12 CRF(r=-.618), MNT & AVT(r=.761), MNT 7DTR(r=-.761), TRF&CRF(r=.696), FERT& TIME(r=.73) and PRICE &TIME (r=.85) were highly significant. Collinearity diagnostic test for multicollinearity problems of local, HYV and all Boro for week 13 (Table 4) showed that multicollinearity was present in the data. Also simple correlation of independent variables showed that some variables were highly correlated which indicated that multicollinearity was suspected. The ridge regression simulation study could play a vital role to solve this multicollinearity problems; but in this case, stable estimator could not be found through a long simulation study. The fitted regression model of local Boro for 13 (Table 5) week showed that maximum temperature and time had positive impact on local Boro yield whereas average temperature, diurnal temperature range, total rainfall, net solar radiation, fertilizer and price had negative impact on local Boro yield. Also R2=.67 and F= 3.045 values were significantly improved through variable selection and model adequacy test. Overall (F-test) regression coefficients were statistically highly significant. Among the individual coefficients, the coefficient of the maximum temperature, average temperature, diurnal temperature range, net solar radiation, price and time variable was statistically highly significant. The equation for HYV Boro for 13 week (Table 5) showed that maximum temperature and time had positive impact on HYV Boro yield whereas average temperature, diurnal temperature range, total rainfall, cumulative rainfall, net solar radiation and fertilizer had negative impact on HYV Boro yield. Also R2=.864 and F=9.555 values were significantly improved through variable selection and model adequacy test. Overall (F-test) regression coefficients were statistically 13 highly significant. Among the individual coefficients, the coefficient of the maximum temperature, average temperature, diurnal temperature range and time variable was statistically highly significant. Finally, the equation for all Boro for 13 week (Table 5) showed that maximum temperature and time had positive impact on all Boro yield whereas average temperature, diurnal temperature range, total rainfall, net solar radiation, fertilizer and price had negative impact on all Boro yield. Also R2=.902 and F=13.766 values were significantly improved through variable selection and model adequacy test. Overall (F-test) regression coefficients were statistically highly significant. Among the individual coefficients, the coefficient of the maximum temperature, average temperature, diurnal temperature range and time variable was statistically highly significant. The estimated yields and production of local, HYV and all Boro rice (Table 6, 7 & 8) in 23 districts for 2001-02 from the summary statistics of the equations were calculated by using fitted regression model. It can be seen from Table 6, 7 and 8 that the estimated yields of local, HYV and all Boro rice for the Mymensingh district were 2.0961, 3.283588 and 3.546412 metric tones/ha respectively. When multiplied by the respective area coverage, total production of local, HYV and all Boro rice for the district 13.41567, 616.0011 and 688.0039 thousand metric tones respectively. The predicted production of Boro of other districts is also presented in Tables 6, 7 and 8. The estimated national production of Boro rice, as obtained from the equations, were 335.4826, 11592.01 and 12501.35 thousand metric tones for of local, HYV and all Boro rice respectively. The detail modelling exercises were reported for different rice crop like Boro, Aus and Aman in the report number “EWFIS-15 & 35, 23 and 29 respectively. 14 Concluding Remarks Conclusions The purpose of this study is to set forth the key issues that must be resolved in formulating a national food policy that can help achieve food security. The overall targeted output of the research program is to formulate locally based public policies and strategies for the sustainable development of agricultural markets. The crop rotation system in Bangladesh is very complex. The output of the model showed the potential it had in predicting yield. The results obtained from the analysis provide a picture of predicted production of Boro rice for the 2001-2002 seasons both for individual districts and for the country as a whole. These predictions are made at the early maturity stage of the crop growth cycle. An important consideration would be to judge the reliability of the estimation, particularly in the content of the real world situation. If the findings are judged to be in conformity with the real world environment, they should be providing important clues for advance food planning both at national and regional levels. It may be mentioned that the actual Boro rice production in the country in the immediate past two years were 11027 thousand metric tons in 1999-2000 and 11920 thousand metric tons in 2000-2001. Thus there may be substantial yield losses of Boro in some locations of the country. A comparison of the predicted Boro rice production in week 13 of 2002 with the actual production of the two preceding years shows that while the predicted production increased to the actual production of 2000-2001 by 4.67% respectively. Thus the predicted Boro rice production in 2001-2002 may be taken as a closer approximation of 15 reality, particularly in view of the agroclimatic environments that have prevailed during the crop growth stages. On the basis of the modeling output the following conclusions could be drawn: (1) Delay beyond optimum planting time will reduce Boro production of the country. The model output opened a new horizon in the course of the research work and eventually it was felt necessary to check thoroughly all the driving variables contributing to the production system so as to find out the reasons behind such a low yield. Recommendations Further improvements will make the model a more useful tool for the agriculture sector of Bangladesh. The model could be used to: estimate rainfed yield; investigate the reasons for low yields; assist as a tool to complement the soil and water management research; train agriculture extension personnel in the growth and management of the crop. In future, the model integrate with GIS enables a quick interpretation of the results by the user, as well as a quick definition of alternative scenarios (Fig. 7). The agronomic community, including farmers, land managers, fellow scientists, policy makers and the general public should benefit from this evolving and expanding field. The integrated GIS-Crop model plays a vital role in alleviation of poverty of Bangladesh. The model also helps the government and relevant organizations to identify methods for reducing poverty and 16 achieving sustainable development in agriculture and land use. This integrated GIS-Crop model had four main uses: as a national, regional and local level conservation policy studies; in programme planning and evaluation; in project planning and design; as a research and teaching tool. Stabilizing the price food in the market and continued supply of food grains in the rural markets are the best options for increasing household food security. The second priority option for mitigation household food insecurity was to improving PFDS by making it efficient and effective. Finally suggested to improving rural communication for better transportation of goods and that would certainly benefit the locality as well as community. This study will help identify the types of public policies needed for the development of competitive and efficient agricultural markets that can contribute in reducing rural poverty and promoting agricultural economic growth in the country. 17 Table 1 :Summary statistics of multiple regression model of local Boro for Mymensingh district (Week 13, 2002). Model Constant MXT AVT DTR TRF CRF NSR FERT PRICE TIME Regression t Significant coefficients value value 2.96 2.567 .026 5.544 3.08 .01 -5.584 -3.108 .01 -2.749 -3.074 .011 -.00258 -.971 .353 .0001457 .126 .902 -.00174 -2.322 .040 -.000012 -1.649 .127 -.000063 -2.115 .058 .06121 2.863 .015 R2 F DW .67 2.487** 2.739 ** 1% level of significance. Histog ram Norm al P-P Plot of Re gression Standa rdized Re sidu Depe ndent Variable: YLB O Depe ndent Variable: YLBO 10 1.00 8 .75 6 2 .50 Expected Cum Prob Frequen cy 4 .25 Std. Dev = .74 Mean = 0.0 0 N = 21.00 0 -2.00 -1.50 -1.00 -.50 0.00 .50 1.00 0.00 0.00 1.50 Regress ion Standardi zed Residual .25 .50 .75 1.00 Observed Cum Prob Fig. 1: Histogram for regression standarised local Boro rice. Fig. 2: Normal probability plot of residul for regression standardize residual for local Boro rice. 18 Table 2 Summary statistics of multiple regression model of HYV Boro for Mymensingh district (Week 13, 2002). Model Regression t Significant coefficients value value 1.581 1.628 .132 3.980 2.637 .023 -3.95 -2.624 .024 -1.98 -2.64 .025 -.000533 -.239 .815 -.000529 -.549 .594 -.000717 -1.142 .278 -.00000537 -.881 .397 .000006237 .025 .981 .05066 2.805 .017 .862 7.786** 1.734 Constant MXT AVT DTR TRF CRF NSR FERT PRICE TIME R2 F DW ** 1% level of significance. Histogram Dependent Variable: YHBO 3.5 Norm al P-P Plo t of Regression Stan dardized Residual 3.0 Depe ndent Vari able: YHB O 1.00 2.5 2.0 .75 Expecte d Cum Prob Frequen cy 1.5 1.0 Std. Dev = .74 .5 Mean = 0. 00 N = 21.00 0.0 -1.50 -1.00 -1.25 -.50 -.75 0.00 -.25 .50 .25 1.00 .75 1.25 .50 .25 0.00 0.00 Regress ion Standardize d Residual .25 .50 .75 1.00 Observe d Cum Prob Fig. 3: Histogram of regression standarised residul for HYV Boro rice. Fig. 4: Normal probability plot of regression standardized residual for HYV Boro rice. 19 Table 3: Summary statistics of multiple regression model of All Boro for Mymensingh district (Week 13, 2002). Model Regression t Significant coefficients value value 1.781 1.602 .137 4.373 2.532 .028 -4.362 -2.523 .028 -2.166 -2.573 .028 -.00186 -.731 .48 .000597 .541 .599 -.00109 -1.519 .157 -.0000103 -1.473 .169 -.000031 -1.085 .301 .09282 4.521 .001 .904 11.599** 2.358 Constant MXT AVT DTR TRF CRF NSR FERT PRICE TIME R2 F DW ** 1% level of significance Norm al P-P Pl o t of Regre ssi on Stan dardi zed R esi dua Histog ram Depen dent Varia ble: Y ABO Depe ndent Vari abl e: YAB O 7 1.00 6 .75 5 Frequen cy 3 2 Expecte d Cum Prob 4 .50 Std. Dev = .74 1 Mean = 0.0 0 0 N = 21.00 -1.25 -.75 -1.00 -.25 -.50 .25 0.00 .75 .50 1.25 .25 0.00 1.00 0.00 Regres sion Standard ized Residual .25 .50 .75 1.00 Obs erve d Cum Prob Fig. 5: Histogram for regression standarised residul for all Boro rice. Fig. 6: Normal probability plot of regression standardized residual for all Boro rice. 20 Table 4: Collinearity diagnostic test for multicollnearity of multiple regression model of Boro rice for Mymensingh district (week 13, 2002). Model Constant MXT AVT DTR TRF CRF NSR FERT PRICE TIME Eigen value 8.352 .901 .426 .151 .08669 .04402 .02888 .008953 .0008789 .0000003058 Condition index 1.0000 3.044 4.427 7.427 9.815 13.774 17.086 30.541 97.478 5225.912 21 VIF 8313.705 5000.018 4877.093 3.371 2.573 1.998 2.673 6.008 10.046 Table 5: Summary statistics and estimated values of the O.L.S regression coefficients of the fitted Crop Yield-Weather Regression Model for Boro rice, 2001-2002 season, (Week 13, 2002;early maturity stage) of Mymensingh district. Variables/statistic Regression coefficients/(t-values) Local Boro Const. MXT HYV Boro All Boro 3.01 1.581 1.958 5.583 (3.287) 3.978 (2.758) 4.535 (2.747) -5.625 (-3.321) -2.77 (-3.286) -.00241 (-1.094) -3.947 (-2.742) -1.979 (-2.764) -.00055 (-.271) -.000521 (-.599) -.000722 (-1.261) -.0000054 (-.955) -4.529 (-2.751) -2.251 (-2.747) -.00117 (-.547) MNT AVT DTR TRF CRF NSR FERT PRICE TIME R2 -.001736 (-2.432) -.0000121 (-1.771) -.0000617 (-2.294) .06066 (3.025) .67 .05104 (5.65) .864 -.00103 (-1.498) -.0000109 (-1.641) -.0000259 (-.989) .09054 (4.645) .902 F 3.045 9.555 13.766 d 2.739 1.794 2.358 22 Table 6: Estimated yields and production of local Boro rice in 23 district for 2001-2002 season, as obtained from the Crop YieldsWeather Regression Model (Week 13, 2002; early maturity stage). District Yield per hectare Area of Local Boro (m. tons) Bandarban Barisal Bogra Chittagong Comilla Dhaka Dinajpur Faridpur Jamalpur Jessore Khagrachari Khulna Kishoreganj Kushtia Mymensingh Noakhali Pabna Patuakhali Rajshahi Rangamati Rangpur Sylhet Tangail Total (.000 hectare) 0.413014 1.750407 2.76611 1.850436 1.789772 1.43796 1.050629 1.424805 1.69851 1.991233 1.530155 1.898927 2.011517 1.829548 2.096199 1.44835 1.124639 1.436442 1.78015 2.568856 1.491291 1.212157 1.39182 23 0.089 13.122 1.76 0.2 5.92 8.714 0 12.293 4.43 1.385 0 9.157 34.808 0.242 6.4 1.15 5.21 7.955 3.47 0 4.12 100.145 1.715 222.285 Production of Local Boro (.000 metric tons) 0.036758 22.96884 4.868354 0.370087 10.59545 12.53038 0 17.51513 7.524399 2.757858 0 17.38847 70.01688 0.442751 13.41567 1.665603 5.859369 11.4269 6.177121 0 6.144119 121.3915 2.386971 335.4826 Table 7: Estimated yields and production of HYV Boro rice in 23 district for 2001-2002 season, as obtained from the Crop YieldsWeather Regression Model (Week 13, 2002; early maturity stage). District Yield per hectare (metric tons) Bandarban Barisal Bogra Chittagong Comilla Dhaka Dinajpur Faridpur Jamalpur Jessore Khagrachari Khulna Kishoreganj Kushtia Mymensingh Noakhali Pabna Patuakhali Rajshahi Rangamati Rangpur Sylhet Tangail Total 2.067025 3.258924 2.794693 2.701032 3.285356 3.20296 2.882748 3.629764 3.13164 3.334255 2.38361 2.83159 3.714392 3.086332 3.283588 3.077122 3.017652 1.866792 2.31936 2.840352 3.065608 3.19782 3.1674 24 Area of HYV Boro (.000 hectare) 4.381 103.281 247.57 111.9 280.148 238.151 184.14 174.016 161.635 261.363 6.571 84.393 259.556 77.068 187.6 99.269 165.79 0.6 321.32 6.7 358.745 256.151 147.225 3737.573 Production of HYV Boro (.000 metric tons) 9.055637 336.5849 691.8821 302.2455 920.3859 762.7881 530.8292 631.637 506.1826 871.4509 15.6627 238.9664 964.0927 237.8574 616.0011 305.4628 500.2965 1.120075 745.2568 19.03036 1099.772 819.1248 466.3205 11592.01 Table 8: Estimated yields and production of all Boro rice in 23 district for 2001-2002 Season, as obtained from the Crop Yields-Weather Regression Model (Week 13, 2002;early maturity stage). District Yield per hectare Area of all Boro (.000 hectare) (metric tons) Bandarban Barisal Bogra Chittagong Comilla Dhaka Dinajpur Faridpur Jamalpur Jessore Khagrachari Khulna Kishoreganj Kushtia Mymensingh Noakhali Pabna Patuakhali Rajshahi Rangamati Rangpur Sylhet Tangail Total 2.067025 3.258924 2.800243 2.701032 2.989424 3.164383 2.92866 3.03409 3.016505 3.525775 2.38361 2.723985 3.534677 3.158878 3.546412 3.478304 3.048235 2.232723 3.491757 2.514574 3.251704 2.855149 3.105781 25 4.47 116.403 249.33 112.1 286.068 246.865 184.14 186.309 166.065 262.748 6.571 93.55 294.364 77.31 194 100.419 171 8.555 324.79 6.7 362.865 356.296 148.94 3959.858 Production of all Boro (.000 metric tons) 9.239602 379.3485 698.1846 302.7857 855.1785 781.1754 539.2835 565.2783 500.9359 926.3903 15.6627 254.8288 1040.482 244.2129 688.0039 349.2878 521.2482 19.10095 1134.088 16.84765 1179.93 1017.278 462.575 12501.35 CABINET FPMC MOF EWTC FPMU EWC Assessment Information Memo, Brief EWFIS Source of Information BBS DAM DGF SPARRSO BWDB BMD BARC DAE Others Forecasting Model Statistical Inference & Sensitivity Analysis Modelling Output Integrate Model with GIS GIS Map & Report Fig.7: An Integrated GIS-Crop Production Forecasting Model of Early Warning System for Food Management in Bangladesh. 26