Nghiên cứu đặc điểm cấu trúc và sinh trưởng của rừng trồng keo lai (acacia auriculiformis acacia mangium) trên các nhóm đất trồng khác nhau ở khu vực huyện định quán, tỉnh đồng nai​

BỘ GIÁO DỤC VÀ ĐÀO TẠO BỘ NÔNG NGHIỆP VÀ PTNT TRƯỜNG ĐẠI HỌC LÂM NGHIỆP TRẦN MINH HOÀNG NGHIÊN CỨU ĐẶC ĐIỂM CẤU TRÚC VÀ SINH TRƯỞNG CỦA RỪNG TRỒNG KEO LAI (Acacia auriculiformis* Acacia mangium) TRÊN CÁC NHÓM ĐẤT TRỒNG KHÁC NHAU Ở KHU VỰC HUYỆN ĐỊNH QUÁN, TỈNH ĐỒNG NAI LUẬN VĂN THẠC SỸ LÂM NGHIỆP Đồng Nai, năm 2017 BỘ GIÁO DỤC VÀ ĐÀO TẠO BỘ NÔNG NGHIỆP VÀ PTNT TRƯỜNG ĐẠI HỌC LÂM NGHIỆP TRẦN MINH HOÀNG NGHIÊN CỨU ĐẶC ĐIỂM CẤU TRÚC VÀ SINH TRƯỞNG CỦA RỪNG TRỒNG KEO LAI (Acacia auriculiformis* Acacia mangium) TRÊN CÁC NHÓM ĐẤT TRỒNG KHÁC NHAU Ở KHU VỰCHUYỆN ĐỊNH QUÁN, TỈNH ĐỒNG NAI CHUYÊN NGÀNH: LÂM HỌC MÃ SỐ: 60.62.02.01 LUẬN VĂN THẠC SỸ LÂM NGHIỆP NGƯỜI HƯỚNG DẪN KHOA HỌC: PGS TS PHẠM THẾ DŨNG Đồng Nai, năm 2017 CỘNG HÒA XÃ HỘI CHỦ NGHĨA VIỆT NAM Độc lập – Tự – Hạnh phúc LỜI CAM ĐOAN Tơi cam đoan, cơng trình nghiên cứu riêng Các số liệu, kết nêu luận văn trung thực chƣa đƣợc cơng bố cơng trình nghiên cứu khác Nếu nội dung nghiên cứu trùng lặp với cơng trình nghiên cứu cơng bố, tơi xin hồn tồn chịu trách nhiệm tn thủ kết luận đánh giá luận văn Hội đồng khoa học Đồng Nai, ngày 05tháng năm 2017 Người cam đoan Trần Minh Hoàng i LỜI CẢM ƠN Sau thời gian đƣợc học tập, tiếp thu kiến thức chun mơn theo chƣơng trình đào tạo Thạc sỹ chuyên ngành Lâm học Trƣờng Đại học Lâm nghiệp, đến khóa học kết thúc Đƣợc cho phép Ban giám hiệu Trƣờng Đại học Lâm nghiệp, thực đề tài: “Nghiên cứu đặc điểm cấu trúc sinh trƣởng rừng trồng Keo lai (Acacia auriculiformis*Acacia mangium) nhóm đất trồng khác khu vực huyện Định Quán, tỉnh Đồng Nai”làm luận văn thạc sỹ khoa học lâm nghiệp Trong q trình thực đề tài, ngồi nỗ lực thân, nhận đƣợc hƣớng dẫn tận tình thầy giáo hƣớng dẫn PGS.TS Phạm Thế Dũng, quan tâm, hỗ trợ Ban giám hiệu,cán Ban Khoa học công nghệ, thầy cô giáo củaTrƣờng Đại học Lâm nghiệp; giúp đỡ nhiệt tình cán bộ, công nhân viên Công ty TNHH MTV Lâm nghiệp La Ngà – Đồng Nai bạn bè đồng nghiệp trình thu thập, xử lý số liệu Để hoàn thành đề tài luận văn này, cho phép tơi đƣợc bày tỏ lịng biết ơn sâu sắc đến Thầy giáo hƣớng dẫn PGS.TS Phạm Thế Dũng, Ban giám hiệu nhà trƣờng, cán Ban Khoa học công nghệ, thầy cô giáo giảng viên trực tiếp truyền thụ kiến thức suốt khóa học Cơ sở 2- Trƣờng Đại học Lâm nghiệp;cán bộ, công nhân viên Công ty TNHH MTV Lâm nghiệp La Ngà – Đồng Nai bạn bè đồng nghiệp đãgiúp đỡ Luận văn kết nghiên cứu tôi, số liệu kết nghiên cứu luận văn hoàn toàn trung thực chƣa đƣợc sử dụng để bảo vệ học vị Các tài liệu tham khảo có nguồn trích dẫn rõ ràng Đồng Nai, ngày 05 tháng năm 2017 Trần Minh Hoàng ii MỤC LỤC Trang LỜI CAM ĐOAN i LỜI CẢM ƠN ii MỤC LỤC iii NHỮNG KÝ HIỆU VÀ CHỮ VIẾT TẮT TRONG LUẬN VĂN v DANH SÁCH CÁC BẢNG vii DANH SÁCH CÁC HÌNH viii ĐẶT VẤN ĐỀ Chƣơng TỔNG QUAN 1.1 Khái quát Keo lai 1.2 Tình hình nghiên cứu cấu trúc rừng 1.2.1 Khái niệm cấu trúc rừng 1.2.2 Tình hình nghiên cứu cấu trúc rừng giới .4 1.2.3 Tình hình nghiên cứu cấu trúc rừng Việt Nam 1.3 Tình hình nghiên cứu sinh trƣởng rừng 1.3.1 Khái niệm sinh trƣởng rừng .8 1.3.2 Tình hình nghiên cứu sinh trƣởng rừng giới 1.3.3 Tình hình nghiên cứu sinh trƣởng rừng Việt Nam .13 1.4 Những nghiên cứu rừng trồng Keo lai 17 1.5 Thảo luận chung .19 Chƣơng 21 MỤC TIÊU, ĐỐI TƢỢNG, PHẠM VI, NỘI DUNG 21 VÀ PHƢƠNG PHÁP NGHIÊN CỨU 21 2.1 Mục tiêu nghiên cứu 21 2.1.1 Mục tiêu tổng quát 21 2.1.2 Mục tiêu cụ thể 21 2.2 Đối tƣợng nghiên cứu 21 2.3 Phạm vi nghiên cứu 21 iii 2.4 Nội dung nghiên cứu 22 2.5 Phƣơng pháp nghiên cứu .22 2.5.1 Phƣơng pháp luận .22 2.5.2 Phƣơng pháp thu thập số liệu 22 2.5.3 Phƣơng pháp xử lý số liệu 24 2.5.4 Cơng cụ tính tốn 27 Chƣơng 28 ĐẶC ĐIỂM TỰ NHIÊN KHU VỰC NGHIÊN CỨU 28 3.1 Vị trí địa lý 28 3.2 Địa hình 29 3.3 Khí hậu - Thủy văn 30 3.4 Tài nguyên đất 31 Chƣơng 33 KẾT QUẢ NGHIÊN CỨU VÀ THẢO LUẬN 33 4.1 Cấu trúc lâm phần rừng trồng Keo lai 33 4.1.1 Cấu trúc đƣờng kính lâm phần 33 4.1.2 Cấu trúc chiều cao lâm phần .40 4.2.2 Sinh trƣởng chiều cao 52 4.3 Khảo sát số nhân tố ảnh hƣởng đến sinh trƣởng rừng Keo lai trồng khu vực huyện Định Quán 57 4.3.1 Ảnh hƣởng tuổi rừng 57 4.3.2 Ảnh hƣởng nhóm đất trồng 61 4.4 Sinh trƣởng thể tích Keo lai 65 4.4.1 Sinh trƣởng thể tích Keo lai trồng nhóm đất đỏ vàng 65 4.4.2 Sinh trƣởng thể tích Keo lai trồng nhóm đất đen 67 KẾT LUẬN VÀ KIẾN NGHỊ .71 Kết luận .71 Kiến nghị 72 TÀI LIỆU THAM KHẢO .73 PHỤ LỤC 78 iv NHỮNG KÝ HIỆU VÀ CHỮ VIẾT TẮT TRONG LUẬN VĂN Ký hiệu, chữ viết tắt A Cv D1.3 D1.3lt Dbq Dmax Dmin Ex Hbq Hmax Hmin Hvn Hvnlt Ln N Pd Pdlt Ph Phlt R S2 Sk Sx Zd Zdlt Zh Zhlt ∆d ∆dlt ∆h ∆hlt vi The mean absolute error (MAE) of 0.0621168 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is less than 0.05, there is an indication of possible serial correlation at the 95.0% confidence level Plot the residuals versus row order to see if there is any pattern that can be seen Simple Regression - D Den vs A Dependent variable: D Den (Duong Kinh Den) Independent variable: A (Tuoi) Squared-Y model: Y = sqrt(a + b*X) Coefficients Parameter Intercept Slope Analysis of Variance Source Model Residual Total (Corr.) Correlation Coefficient = 0.993889 R-squared = 98.7814 percent R-squared (adjusted for d.f.) = 98.5377 percent Standard Error of Est = 11.8259 Mean absolute error = 8.46592 Durbin-Watson statistic = 1.45745 (P=0.0736) Lag residual autocorrelation = -0.0350393 The StatAdvisor The output shows the results of fitting a squared-Y model to describe the relationship between D Den and A The equation of the fitted model is D Den = sqrt(-58.6186 + 44.9939*A) Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between D Den and A at the 95.0% confidence level The R-Squared statistic indicates that the model as fitted explains 98.7814% of the variability in D Den after transforming to a reciprocal scale to linearize the model The correlation coefficient equals 0.993889, indicating a relatively strong relationship between the variables The standard error of the estimate shows the standard deviation of the residuals to be 11.8259 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu OO The mean absolute error (MAE) of 8.46592 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is greater than 0.05, there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level PP Phụ lục KẾT QUẢ TƯƠNG QUAN GIỮA HVN VÀ A TRÊN NỀN ĐẤT ĐỎ VÀNG TỪ CÁC HÀM MẶC ĐỊNH TRONG STATGRAPHICS PLUS CENTURION XV.I Comparison of Alternative Models - H Do Vang vs A Model Square root-Y logarithmic-X Square root-X Squared-Y Double reciprocal Logarithmic-X Multiplicative S-curve model Double square root Squared-Y square root-X Linear Double squared Logarithmic-Y square root-X Square root-Y Squared-Y logarithmic-X Squared-X Reciprocal-X Exponential Square root-Y squared-X Squared-Y reciprocal-X Logarithmic-Y squared-X Reciprocal-Y Reciprocal-Y squared-X Reciprocal-Y square root-X Reciprocal-Y logarithmic-X Square root-Y reciprocal-X Logistic Log probit The StatAdvisor This table shows the results of fitting several curvilinear models to the data Of the models fitted, the square root-Y logarithmic-X model yields the highest R-Squared value with 99.864% This is 2.85721% higher than the currently selected linear model To change models, select the Analysis Options dialog box Simple Regression - H Do Vang vs A Dependent variable: H Do Vang (CCao Do vang) Independent variable: A (Tuoi) QQ Square root-Y logarithmic-X model: Y = (a + b*ln(X))^2 Coefficients Parameter Intercept Slope Analysis of Variance Source Model Residual Total (Corr.) Correlation Coefficient = 0.99932 R-squared = 99.864 percent R-squared (adjusted for d.f.) = 99.8368 percent Standard Error of Est = 0.0383964 Mean absolute error = 0.0260195 Durbin-Watson statistic = 1.85824 (P=0.2187) Lag residual autocorrelation = -0.0171856 The StatAdvisor The output shows the results of fitting a square root-Y logarithmic-X model to describe the relationship between H Do Vang and A The equation of the fitted model is H Do Vang = (1.78707 + 1.38683*ln(A))^2 Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between H Do Vang and A at the 95.0% confidence level The R-Squared statistic indicates that the model as fitted explains 99.864% of the variability in H Do Vang after transforming to an inverse normal scale to linearize the model The correlation coefficient equals 0.99932, indicating a relatively strong relationship between the variables The standard error of the estimate shows the standard deviation of the residuals to be 0.0383964 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 0.0260195 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is greater than 0.05, there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level Simple Regression - H Do Vang vs A Dependent variable: H Do Vang (CCao Do vang) Independent variable: A (Tuoi) RR Square root-X model: Y = a + b*sqrt(X) Coefficients Parameter Intercept Slope Analysis of Variance Source Model Residual Total (Corr.) Correlation Coefficient = 0.998324 R-squared = 99.665 percent R-squared (adjusted for d.f.) = 99.598 percent Standard Error of Est = 0.381911 Mean absolute error = 0.263433 Durbin-Watson statistic = 1.29357 (P=0.0433) Lag residual autocorrelation = 0.190351 The StatAdvisor The output shows the results of fitting a square root-X model to describe the relationship between H Do Vang and A The equation of the fitted model is H Do Vang = -6.94803 + 10.2864*sqrt(A) Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between H Do Vang and A at the 95.0% confidence level The R-Squared statistic indicates that the model as fitted explains 99.665% of the variability in H Do Vang The correlation coefficient equals 0.998324, indicating a relatively strong relationship between the variables The standard error of the estimate shows the standard deviation of the residuals to be 0.381911 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 0.263433 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is less than 0.05, there is an indication of possible serial correlation at the 95.0% confidence level Plot the residuals versus row order to see if there is any pattern that can be seen Simple Regression - H Do Vang vs A Dependent variable: H Do Vang (CCao Do vang) Independent variable: A (Tuoi) SS Squared-Y model: Y = sqrt(a + b*X) Coefficients Parameter Intercept Slope Analysis of Variance Source Model Residual Total (Corr.) Correlation Coefficient = 0.997783 R-squared = 99.5571 percent R-squared (adjusted for d.f.) = 99.4685 percent Standard Error of Est = 10.4282 Mean absolute error = 7.55592 Durbin-Watson statistic = 1.75022 (P=0.1635) Lag residual autocorrelation = -0.014239 The StatAdvisor The output shows the results of fitting a squared-Y model to describe the relationship between H Do Vang and A The equation of the fitted model is H Do Vang = sqrt(-67.8557 + 66.0661*A) Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between H Do Vang and A at the 95.0% confidence level The R-Squared statistic indicates that the model as fitted explains 99.5571% of the variability in H Do Vang after transforming to a reciprocal scale to linearize the model The correlation coefficient equals 0.997783, indicating a relatively strong relationship between the variables The standard error of the estimate shows the standard deviation of the residuals to be 10.4282 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 7.55592 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is greater than 0.05, there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level Simple Regression - H Do Vang vs A Dependent variable: H Do Vang (CCao Do vang) Independent variable: A (Tuoi) TT Double reciprocal model: Y = 1/(a + b/X) Coefficients Parameter Intercept Slope Analysis of Variance Source Model Residual Total (Corr.) Correlation Coefficient = 0.994658 R-squared = 98.9345 percent R-squared (adjusted for d.f.) = 98.7214 percent Standard Error of Est = 0.010563 Mean absolute error = 0.00767671 Durbin-Watson statistic = 1.54799 (P=0.1235) Lag residual autocorrelation = 0.0802324 The StatAdvisor The output shows the results of fitting a double reciprocal model to describe the relationship between H Do Vang and A The equation of the fitted model is H Do Vang = 1/(-0.00265686 + 0.30651/A) Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between H Do Vang and A at the 95.0% confidence level The R-Squared statistic indicates that the model as fitted explains 98.9345% of the variability in H Do Vang The correlation coefficient equals 0.994658, indicating a relatively strong relationship between the variables The standard error of the estimate shows the standard deviation of the residuals to be 0.010563 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 0.00767671 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is greater than 0.05, there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level Simple Regression - H Do Vang vs A Dependent variable: H Do Vang (CCao Do vang) Independent variable: A (Tuoi) UU Logarithmic-X model: Y = a + b*ln(X) Coefficients Parameter Intercept Slope Analysis of Variance Source Model Residual Total (Corr.) Correlation Coefficient = 0.992426 R-squared = 98.4909 percent R-squared (adjusted for d.f.) = 98.189 percent Standard Error of Est = 0.810639 Mean absolute error = 0.577071 Durbin-Watson statistic = 1.31784 (P=0.0506) Lag residual autocorrelation = 0.144064 The StatAdvisor The output shows the results of fitting a logarithmic-X model to describe the relationship between H Do Vang and A The equation of the fitted model is H Do Vang = 2.22554 + 8.72959*ln(A) Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between H Do Vang and A at the 95.0% confidence level The R-Squared statistic indicates that the model as fitted explains 98.4909% of the variability in H Do Vang after transforming to a Y/(1-Y) scale to linearize the model The correlation coefficient equals 0.992426, indicating a relatively strong relationship between the variables The standard error of the estimate shows the standard deviation of the residuals to be 0.810639 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 0.577071 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is greater than 0.05, there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level VV Phụ lục KẾT QUẢ TƯƠNG QUAN GIỮA HVN VÀ A TRÊN NỀN ĐẤT ĐEN TỪ CÁC HÀM MẶC ĐỊNH TRONG STATGRAPHICS PLUS CENTURION XV.I Comparison of Alternative Models - H Den vs A Model Square root-Y logarithmic-X Square root-X Double reciprocal Squared-Y Multiplicative Double square root Logarithmic-X Linear S-curve model Squared-Y square root-X Double squared Logarithmic-Y square root-X Square root-Y Squared-Y logarithmic-X Squared-X Exponential Reciprocal-X Square root-Y squared-X Squared-Y reciprocal-X Logarithmic-Y squared-X Reciprocal-Y Reciprocal-Y squared-X Reciprocal-Y square root-X Reciprocal-Y logarithmic-X Square root-Y reciprocal-X Logistic Log probit The StatAdvisor This table shows the results of fitting several curvilinear models to the data Of the models fitted, the square root-Y logarithmic-X model yields the highest R-Squared value with 99.777% This is 2.47075% higher than the currently selected linear model To change models, select the Analysis Options dialog box WW Simple Regression - H Den vs A Dependent variable: H Den (CCao Den) Independent variable: A (Tuoi) Square root-Y logarithmic-X model: Y = (a + b*ln(X))^2 Coefficients Parameter Intercept Slope Analysis of Variance Source Model Residual Total (Corr.) Correlation Coefficient = 0.998885 R-squared = 99.777 percent R-squared (adjusted for d.f.) = 99.7325 percent Standard Error of Est = 0.0490528 Mean absolute error = 0.0349685 Durbin-Watson statistic = 1.67482 (P=0.1445) Lag residual autocorrelation = 0.0785567 The StatAdvisor The output shows the results of fitting a square root-Y logarithmic-X model to describe the relationship between H Den and A The equation of the fitted model is H Den = (1.81032 + 1.38327*ln(A))^2 Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between H Den and A at the 95.0% confidence level The R-Squared statistic indicates that the model as fitted explains 99.777% of the variability in H Den after transforming to an inverse normal scale to linearize the model The correlation coefficient equals 0.998885, indicating a relatively strong relationship between the variables The standard error of the estimate shows the standard deviation of the residuals to be 0.0490528 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 0.0349685 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is greater than 0.05, there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level XX Simple Regression - H Den vs A Dependent variable: H Den (CCao Den) Independent variable: A (Tuoi) Square root-X model: Y = a + b*sqrt(X) Coefficients Parameter Intercept Slope Analysis of Variance Source Model Residual Total (Corr.) Correlation Coefficient = 0.998073 R-squared = 99.6149 percent R-squared (adjusted for d.f.) = 99.5379 percent Standard Error of Est = 0.414155 Mean absolute error = 0.2743 Durbin-Watson statistic = 1.38198 (P=0.0590) Lag residual autocorrelation = 0.174437 The StatAdvisor The output shows the results of fitting a square root-X model to describe the relationship between H Den and A The equation of the fitted model is H Den = -7.04106 + 10.4015*sqrt(A) Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between H Den and A at the 95.0% confidence level The R-Squared statistic indicates that the model as fitted explains 99.6149% of the variability in H Den The correlation coefficient equals 0.998073, indicating a relatively strong relationship between the variables The standard error of the estimate shows the standard deviation of the residuals to be 0.414155 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 0.2743 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is greater than 0.05, there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level YY Simple Regression - H Den vs A Dependent variable: H Den (CCao Den) Independent variable: A (Tuoi) Double reciprocal model: Y = 1/(a + b/X) Coefficients Parameter Intercept Slope Analysis of Variance Source Model Residual Total (Corr.) Correlation Coefficient = 0.997431 R-squared = 99.4868 percent R-squared (adjusted for d.f.) = 99.3842 percent Standard Error of Est = 0.00683954 Mean absolute error = 0.00493123 Durbin-Watson statistic = 1.33075 (P=0.0662) Lag residual autocorrelation = 0.161953 The StatAdvisor The output shows the results of fitting a double reciprocal model to describe the relationship between H Den and A The equation of the fitted model is H Den = 1/(0.00189169 + 0.286769/A) Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between H Den and A at the 95.0% confidence level The R-Squared statistic indicates that the model as fitted explains 99.4868% of the variability in H Den The correlation coefficient equals 0.997431, indicating a relatively strong relationship between the variables The standard error of the estimate shows the standard deviation of the residuals to be 0.00683954 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 0.00493123 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is greater than 0.05, there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level ZZ Simple Regression - H Den vs A Dependent variable: H Den (CCao Den) Independent variable: A (Tuoi) Squared-Y model: Y = sqrt(a + b*X) Coefficients Parameter Intercept Slope Analysis of Variance Source Model Residual Total (Corr.) Correlation Coefficient = 0.996494 R-squared = 99.3001 percent R-squared (adjusted for d.f.) = 99.1601 percent Standard Error of Est = 13.4947 Mean absolute error = 9.82531 Durbin-Watson statistic = 1.66404 (P=0.1322) Lag residual autocorrelation = 0.0364974 The StatAdvisor The output shows the results of fitting a squared-Y model to describe the relationship between H Den and A The equation of the fitted model is H Den = sqrt(-71.2514 + 67.9246*A) Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between H Den and A at the 95.0% confidence level The R-Squared statistic indicates that the model as fitted explains 99.3001% of the variability in H Den after transforming to a reciprocal scale to linearize the model The correlation coefficient equals 0.996494, indicating a relatively strong relationship between the variables The standard error of the estimate shows the standard deviation of the residuals to be 13.4947 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 9.82531 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is greater than 0.05, there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level AAA Simple Regression - H Den vs A Dependent variable: H Den (CCao Den) Independent variable: A (Tuoi) Multiplicative model: Y = a*X^b Coefficients Parameter Intercept Slope NOTE: intercept = ln(a) Analysis of Variance Source Model Residual Total (Corr.) Correlation Coefficient = 0.993538 R-squared = 98.7117 percent R-squared (adjusted for d.f.) = 98.4541 percent Standard Error of Est = 0.0785593 Mean absolute error = 0.0634264 Durbin-Watson statistic = 0.993983 (P=0.0131) Lag residual autocorrelation = 0.231521 The StatAdvisor The output shows the results of fitting a multiplicative model to describe the relationship between H Den and A The equation of the fitted model is H Den = exp(1.30921 + 0.916671*ln(A)) or ln(H Den) = 1.30921 + 0.916671*ln(A) Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between H Den and A at the 95.0% confidence level The R-Squared statistic indicates that the model as fitted explains 98.7117% of the variability in H Den The correlation coefficient equals 0.993538, indicating a relatively strong relationship between the variables The standard error of the estimate shows the standard deviation of the residuals to be 0.0785593 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu BBB The mean absolute error (MAE) of 0.0634264 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the P-value is less than 0.05, there is an indication of possible serial correlation at the 95.0% confidence level Plot the residuals versus row order to see if there is any pattern that can be seen CCC ... nhiều nghiên cứu rừng trồng Keo lai, nhƣng chƣa có cơng trình nghiên cứu sâu cấu trúc sinh trƣởng rừng trồng Keo lai nhóm đất trồng khác khu vực huyện Định Quán, tỉnh Đồng Nai Vì thế, nghiên cứu. .. rừng trồng Keo lai 02 nhóm đất trồng khác nhau; (3) Khảo sát số nhân tố ảnh hƣởng đến sinh trƣởng rừng trồng Keo lai khu vực nghiên cứu (4) Sinh trƣởng thể tích Keo lai trồng khu vực nghiên cứu. .. nghiệp, thực đề tài: ? ?Nghiên cứu đặc điểm cấu trúc sinh trƣởng rừng trồng Keo lai (Acacia auriculiformis* Acacia mangium) nhóm đất trồng khác khu vực huyện Định Quán, tỉnh Đồng Nai”làm luận văn