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LAMPIRAN

136

Lampiran 1. Identitas pemilik lahan dan keterangan kondisi lokasi agroforestri yang menjadi contoh penelitian 1 Desa Pecekelan Lahan milik 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Nama Sastro Silento Kasdi Ibu Dibyo Makhful Ruswanto Cokro Hartono Sucito H. Ami Sucito H. Djalal H. Djalal Mukhotib Warnanto Martojito Hadi Bilan Pak Eko Kartono H. Djalal Mukhotib H Djalal

Luas (m2) 3300 900 400 1800 1100 1400 800 2000 1700 1400 1400 1300 1500 1100 900 600 1100 800 1700 900 500

Tempat Lokasi Gedangan Tanggulasi Gedangan Tanggulasi Gedangan Tanggulasi Gedangan Tanggulasi Gedangan Tanggulasi Pundung-Tanggulasi Gedangan Tanggulasi Koplak-Patean Simanis-Patean Simanis-Patean Koplak-Patean Koplak-Patean Cikal-Patean Kalilusi-Patean Cikal-Patean Sabrang-Tangg Sabrang-Tangg Sabrang-Tangg Kalilusi-Patean Kalilusi-Patean Kalilusi-Patean

Umur 1-2 th 3-4 th 1-2 th 5-6 th 5-6 th 7-8 th 1-2 th 5-6 th 5-6 th 3-4 th 7-8 th 9-10 th 3-4 th 11-12 th 5-6 th 11-12 th 5-6 th 3-4 th 7-8 th 3-4 th 1-2 th

Tanaman kehutanan sengon, suren, pete, kopi sengon, suren, nangka, kopi sengon, waru, cengkeh, kopi sengon, suren, kopi, secang sengon, suren, nangka, kopi sengon, waru, secang, kopi sengon, waru, secang, kopi sengon, waru, secang, kopi sengon, nangka, kopi, secang Sengon, nangka, mahoni, kopi sengon, mahoni, nangka, kopi sengon, mahoni, nangka, kopi sengon, mahoni, nangka, kopi sengon, mahoni, nangka, kopi sengon, mahoni, nangka, kopi sengon, mahoni, nangka, kopi sengon, mahoni, kelapa, kopi sengon, mahoni, kelapa, kopi Sengon, jenitri, mahoni, kopi Sengon, suren, sungkai, kopi sengon, mahoni, jenitri, kopi

Tanaman pertanian

Tanaman kehutanan Sengon, Mahoni, Huru, Calik Angin Sengon, Mahoni, Afrika, Huru, Pocol, Puspa Sengon Sengon, Balsa, Puspa, Mahoni Sengon, Mahoni Mahoni, Sengon, Afrika Sengon, Mahoni, Afrika, Puspa Sengon, Puspa, Mahoni Sengon, Suren, Mahoni, Afrika Sengon, Afrika, Mahoni, Kapuk Randu, Puspa, Akasia Sengon, Mahoni, Afrika Sengon, Mahoni, Afrika, Puspa Mahoni, Sengon, Puspa Sengon, Mahoni, Ramanten, Afrika, Puspa Sengon, Afrika, Puspa, Akasia, Mahoni, Sampang Afrika, Sengon, Akasia, Waru, Puspa, Huru, Mahoni Sengon, Puspa, Huru, Mahoni, Afrika, Sampang Sengon, Afrika, Mahoni, Kapuk Randu, Puspa, Akasia Sengon, Afrika, Mahoni, Puspa, Huru, Calik Angin Sengon, Afrika, Huru, Mahoni

Tanaman pertanian Kopi, Salak, Bambu, Nangka, Nenas, Rambutan Kapulaga, Kopi, Nangka, Pala, Enau, Cengkeh, Mangga, Nenas Kopi, Pisang, Kapulaga Kopi, Nangka, Aren, Rambutan, Petai, Cengkeh, Pisang Nangka, Pisang, Tebu, Kapulaga, Singkong Nenas, Kopi, Enau, Nangka Kopi, Petai, Alpukat, Cengkeh, Jengkol Nenas, Kopi, Cengkeh Kopi, Nangka, Nenas, Manggis, Durian, Alpukat, Tangkil Nenas, Kelapa, Pisang, Jengkol, Alpukat Kopi, Jengkol, Nangka Kopi, Pisang, Nangka Kapulaga, Pisang, Kopi, Limus, Kelapa, Alpukat, Jengkol Kopi, Pepaya, Alpukat, Nangka, Rambutan, Petai Kopi, Jambu Semarang, Cengkeh, Limus, Nangka, Ramanten Enau, Harendong, Cengkeh, Petai, The, Kopi, Nangka, Jengkol Nenas, Kopi, Nangka, Kapulaga, Petai, Manggis, Jengkol Nenas, Kelapa, Pisang, Jengkol, Alpukat Kopi, Nangka, Rambutan, Alpukat, Jengkol, Petai, Nenas Alpukat, Nangka, Kopi

Keterangan tapak Arah jalur Alt (mdpl) lereng (%) Aspek B-T 815 15 U/T U-S 850 35 U/T U-S 820 15 U U-S 790 20 U/T U-S 810 20 U/T U-S 765 15 T U-S 800 30 S U-S 820 28 S/B B-T 840 35 S/B U-S 830 35 T/S U-S 830 20 S/B U-S 800 30 T/S U-S 830 20 S U-S 830 35 U/T U-S 870 40 B B-T 800 15 T U-S 800 15 U/T U-S 820 20 U U-S 850 15 U/B B-T 840 30 U/T U-S 890 25 T/S

Posisi lereng lereng lereng lereng lereng lereng lereng lereng lembah punggung lereng lereng lereng punggung lembah lembah lereng lereng punggung lereng lereng

Keterangan tapak Arah jalur Alt (mdpl) lereng (%) Aspek B-T 810 30 B U-S 825 35 T/S U-S 850 30 S/B U-S 840 35 S B-T 825 40 S/B U-S 830 30 S U-S 860 45 S U-S 855 35 S/B B-T 890 40 S/B B-T 910 25 B B-T 810 35 B B-T 850 30 S B-T 860 15 T U-S 880 30 U/T S-B 900 50 S/B B-T 810 35 S B-T 800 28 T B-T 910 25 B B-T 820 40 T B-T 870 35 S/B

Posisi Lembah Lereng Lereng Lereng Lereng Lereng Lereng Punggung Punggung Lereng Lembah Lereng Punggung Lereng Lereng Lereng Lereng Lereng Lereng Punggung

2 Desa Kertayasa Lahan milik 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Nama Hamidin Sutardi dan Sukmana Suryana Rohman Sadili Bin Suhanta Usup Supriyadi Komarudin Sudinta Yasir Sutisna Rosadi Sarju Engkos Supriyadi Sukarna Sopandi Sudinta Igud Sutisna Pandi Eman

Luas (m2) 600 600 600 1000 800 600 600 800 1500 700 1200 700 400 400 600 700 600 700 600 600

Tempat Lokasi Umur Ci Calung ( Sukaraja ) 3-4 th Sukaraja 5-6 th Sukaraja 5-6 th Ci Guluma ( Padungdungan ) 3-4 th Padungdungan 7-8 th Ci Pesing ( Padungdungan ) 5-6 th Ci Pesing ( Padungdungan ) 7-8 th Padungdungan 1-2 th Padungdungan 3-4 th Padungdungan 1-2 th Ci Calung ( Sukaraja ) 11-12 th Ci Calung ( Sukaraja ) 3-4 th Sukaraja 11-12 th Sukaraja 11-12 th Ci Carenang 5-6 th Ngawitan 7-8 th Leles 9-10 th Padungdungan 1-2 th Leles 3-4 th Palasiang 3-4 th

137

Lampiran 2.

Lokasi Contoh PN U-1 PN U-2 PN U-3 PN U-4 PN U-8 PN U-9 PN U-11 PN U-16 PN U-17 Rataan Std

Lokasi Contoh PC U-1 PC U-2 PC U-4 PC U-6 PC U-7 PC U-8 PC U-9 PC U-10 PC U-12 PC U-14 PC U-19 Rataan Std Ket:

Hasil analisis ciri-ciri fisik dan kimia tanah di lokasi penelitian Desa Pecekelan (Wonosobo) dan Desa Kertayasa (Ciamis)

Kadar Air (%) A B 39% 40% 44% 46% 50% 44% 40% 35% 41% 38% 39% 46% 45% 40% 44% 42% 50% 48% 44% 42% 4% 4%

Bulk Density (g/cm3) A B 1.16 0.98 0.94 0.89 0.78 0.73 1.00 1.11 0.91 0.89 0.96 0.96 1.06 0.96 0.96 0.92 0.84 0.80 0.96 0.92 0.11 0.11

Bulk Density Kadar Air (%) (g/cm3) A B A B 53% 60% 0.66 0.65 61% 59% 0.62 0.71 63% 57% 0.69 0.66 38% 63% 0.90 0.68 58% 58% 0.70 0.68 59% 60% 0.71 0.69 56% 58% 0.81 0.75 66% 60% 0.64 0.69 59% 56% 0.75 0.70 66% 53% 0.56 0.58 58% 58% 0.70 0.68 58% 58% 0.70 0.68 8% 3% 0.09 0.04 A = Kedalaman 0 - 10 cm B = Kedalaman 10 - 20 cm

pH H2O A 4.08 4.67 4.39 4.40 4.89 4.46 4.81 4.40 4.37 4.50 0.25

B 4.69 4.33 4.43 4.37 4.66 4.34 4.66 4.55 4.26 4.48 0.17

pH H2O A 4.82 4.84 5.16 4.96 5.02 5.12 5.16 4.94 4.88 4.81 4.41 4.92 0.21

B 5.11 4.99 5.20 5.26 5.32 5.09 5.30 5.22 5.23 4.63 4.37 5.07 0.30

B 3.52 3.64 3.59 3.49 3.81 3.60 3.71 3.35 3.31 3.56 0.16

C-org (%) A B 2.42 2.19 2.62 2.08 3.23 2.15 1.92 1.50 2.35 1.85 1.96 1.65 1.62 1.27 3.12 2.03 2.75 1.99 2.44 1.86 0.55 0.32

N-total (%) A B 0.23 0.20 0.26 0.38 0.23 0.20 0.20 0.15 0.24 0.19 0.20 0.15 0.16 0.10 0.29 0.20 0.26 0.20 0.23 0.20 0.04 0.08

K-total (ppm) A B 0.40 0.40 0.26 0.15 0.15 0.13 0.10 0.08 0.18 0.09 0.11 0.12 0.10 0.08 0.21 0.10 1.28 0.95 0.31 0.23 0.38 0.29

P-tersedia (Bray1)(ppm) A B 2.8 3.0 3.3 3.5 8.4 3.1 2.8 2.8 4.9 3.0 4.4 3.1 3.6 4.4 3.3 3.6 3.3 3.3 4.1 3.3 1.8 0.5

B 4.27 4.17 4.05 4.30 4.28 4.13 4.23 4.42 4.43 3.77 3.48 4.14 0.29

C-org (%) A B 3.81 2.65 2.92 1.77 2.77 2.27 3.42 2.92 2.35 1.50 3.04 2.50 2.88 2.42 3.73 2.73 3.15 2.65 4.69 3.08 5.92 2.38 3.52 2.44 1.02 0.47

N-total (%) A B 0.28 0.25 0.27 0.17 0.28 0.23 0.22 0.26 0.24 0.17 0.29 0.26 0.24 0.27 0.32 0.26 0.30 0.24 0.44 0.29 0.36 0.22 0.29 0.24 0.06 0.04

K-total (ppm) A B 0.13 0.23 0.77 0.71 0.72 0.26 0.31 0.21 0.74 0.69 0.90 0.62 0.70 0.43 0.31 0.15 0.42 0.45 0.33 0.27 0.62 0.27 0.54 0.39 0.25 0.20

P-tersedia (Bray1)(ppm) A B 8.0 6.4 8.8 7.6 9.2 9.0 9.7 4.3 8.5 8.4 7.2 7.9 8.4 7.6 5.9 5.8 5.9 5.8 6.6 5.3 6.2 7.2 7.7 6.8 1.4 1.4

pH KCl A 3.76 3.63 3.27 3.63 3.93 3.59 3.96 3.53 3.46 3.64 0.22

pH KCl A 4.05 3.78 4.23 3.78 3.96 4.27 4.09 3.78 3.90 3.72 3.55 3.92 0.22

138

Lampiran 2. Lokasi Contoh PN U-1 PN U-2 PN U-3 PN U-4 PN U-8 PN U-9 PN U-11 PN U-16 PN U-17 Rataan Std

Lokasi Contoh PC U-1 PC U-2 PC U-4 PC U-6 PC U-7 PC U-8 PC U-9 PC U-10 PC U-12 PC U-14 PC U-19 Rataan Std Ket:

(Lanjutan)

KTK (me/100g) A B 28.30 25.98 30.10 28.15 21.82 16.22 14.84 12.66 17.46 15.49 16.36 14.18 15.27 14.84 19.64 15.27 23.56 18.33 20.82 17.90 5.59 5.44

Pasir (%) A B 7.49 4.79 3.95 3.53 6.39 2.73 7.54 8.42 11.96 3.04 4.10 2.75 3.38 2.84 4.93 4.36 3.69 3.50 5.94 4.00 2.77 1.81

Debu (%) A B 30.89 28.52 17.19 22.85 17.69 55.08 35.84 33.58 39.46 13.70 33.59 30.72 25.57 28.14 17.20 21.86 19.20 26.45 26.29 28.99 8.87 11.37

KTK (me/100g) Pasir (%) A B A B 26.62 15.10 40.83 45.62 16.28 21.60 34.86 11.98 25.75 22.03 43.10 40.07 25.09 20.15 25.67 25.67 26.98 26.62 17.59 28.10 22.69 22.26 32.55 36.42 25.31 25.75 18.65 25.35 31.86 24.00 27.91 18.50 16.10 18.30 34.22 32.45 31.64 24.22 24.71 25.29 25.13 22.47 28.24 29.70 24.86 22.05 29.85 29.01 5.09 3.31 8.21 9.51 A = Kedalaman 0 - 10 cm B = Kedalaman 10 - 20 cm

Debu (%) A B 27.28 43.72 54.38 68.35 38.32 40.40 48.19 51.98 42.11 43.76 38.71 45.03 45.42 45.56 51.70 54.08 53.29 49.64 41.42 51.32 38.19 43.24 43.55 48.83 8.09 7.78

Liat (%) A 61.62 78.86 75.92 56.62 48.58 62.31 71.05 77.87 77.11 67.77 10.90

B 66.69 73.62 42.19 58.00 83.26 66.53 69.02 73.78 70.05 67.02 11.53

Liat (%) A 31.89 10.76 18.58 26.14 40.30 28.74 35.93 20.39 12.49 33.87 33.57 26.61 9.81

B 10.66 19.67 19.53 22.35 28.14 18.55 29.09 27.42 17.91 23.39 27.06 22.16 5.60

C/N rasio A B 10.52 10.95 10.08 5.47 14.04 10.75 9.60 10.00 9.79 9.74 9.80 11.00 10.13 12.70 10.76 10.15 10.58 9.95 10.59 10.08 1.35 1.95

C-content (ton/ha) A B 56.3 42.9 49.2 37.1 50.2 31.3 38.4 33.4 42.7 32.9 37.6 31.8 34.2 24.3 59.6 37.2 46.2 31.9 46.0 33.7 8.6 5.1

C/N rasio A B 13.61 10.60 10.81 10.41 9.89 9.87 15.55 11.23 9.79 8.82 10.48 9.62 12.00 8.96 11.66 10.50 10.50 11.04 10.66 10.62 16.44 10.82 11.94 10.23 2.28 0.81

C-content (ton/ha) A B 50.1 34.7 36.2 25.2 38.3 30.1 61.6 39.5 33.1 20.4 43.1 34.4 46.4 36.3 47.7 37.7 47.0 37.2 52.8 35.8 83.3 32.3 49.1 33.0 13.9 5.8

139

Lampiran 3.

Daftar peubah untuk penyusunan model alometrik pendugaan biomassa pohon sengon

Dimensi Pohon Contoh Bobot Basah (kg) No.Phn diameter Ttot Tbc Ltajuk Batang Cabang Ranting Tunggak (cm) (m) (m) (m2) 1 18.0 17.0 8.5 20.82 99.93 17.09 12.00 4.50 2 15.5 13.5 9.0 25.50 78.00 9.19 8.00 4.50 3 18.4 13.3 7.8 20.42 84.00 16.50 11.50 6.00 4 15.0 14.5 8.0 21.64 85.00 10.50 10.50 3.00 5 14.0 10.6 7.8 25.73 83.50 13.10 11.00 1.50 6 18.0 17.0 8.5 20.82 99.93 17.09 12.00 0.00 7 16.2 14.5 7.0 34.19 77.50 11.26 10.25 2.50 8 14.8 11.5 7.8 32.76 66.50 12.78 9.00 10.50 9 15.6 14.8 7.8 45.64 82.50 9.35 7.50 0.99 10 9.5 9.5 7.0 11.28 22.50 2.55 1.50 1.00 11 9.5 9.5 6.5 11.49 22.50 1.85 1.75 3.00 12 12.0 14.5 8.5 25.64 38.10 3.53 2.75 0.00 13 27.8 23.0 18.0 121.68 288.50 85.58 36.50 11.25 14 29.0 27.0 14.5 83.28 504.50 81.00 54.50 13.75 15 34.8 27.5 8.8 82.88 580.49 144.50 57.00 22.71 16 22.3 19.3 11.3 61.83 177.43 43.25 18.00 6.50 17 43.8 26.5 13.0 143.07 915.00 162.60 81.00 18.80 18 21.2 15.3 8.3 54.73 136.88 26.75 16.50 0.00 19 28.1 30.5 12.0 96.72 382.50 77.18 33.25 8.78 20 22.6 26.5 10.5 67.89 201.50 40.03 20.50 2.79 21 24.0 19.0 11.5 44.16 197.50 26.65 20.00 4.25 22 22.6 24.0 14.3 58.06 248.50 31.75 26.50 6.41 23 21.4 22.0 11.3 82.07 237.00 44.70 20.00 4.00 24 21.5 20.0 8.0 40.08 168.50 23.15 18.00 3.50 25 24.0 23.0 7.0 65.72 200.00 34.50 24.50 4.90 26 19.0 20.0 11.5 11.64 131.00 12.23 9.50 3.60 27 29.4 22.0 6.3 95.85 391.00 127.25 59.00 15.32 28 6.2 9.5 5.5 8.29 16.23 1.13 1.08 0.33 29 28.5 23.5 9.3 127.61 466.50 126.00 31.00 7.50 30 13.2 15.3 10.5 29.21 48.00 7.98 7.50 0.68

Daun 17.00 10.00 11.50 9.50 7.00 17.00 9.50 11.25 7.50 3.50 3.00 4.00 39.50 38.00 36.00 25.50 74.00 20.25 34.00 26.50 27.00 19.50 19.00 25.00 20.50 10.00 24.00 1.98 42.00 6.00

Total 150.51 109.69 129.50 118.50 116.10 146.01 111.01 110.03 107.84 31.05 32.10 48.38 461.33 691.75 840.70 270.68 1251.40 200.38 535.71 291.32 275.40 332.66 324.70 238.15 284.40 166.33 616.57 20.73 673.00 70.15

Batang 60.44 49.02 51.64 48.56 54.49 63.83 45.44 40.31 55.12 14.10 15.50 24.63 182.20 314.50 377.14 115.24 619.23 82.64 243.52 129.31 119.27 153.58 144.63 106.88 130.64 84.64 241.73 9.96 297.83 30.80

Bobot Kering (kg) Cabang Ranting Tunggak Daun 10.04 5.31 8.67 6.51 7.53 10.12 5.80 8.18 5.91 1.54 0.95 2.03 46.33 49.98 84.11 25.26 104.98 16.56 43.33 24.46 15.24 18.88 26.68 13.94 20.99 7.54 73.90 0.70 71.35 4.06

5.81 3.78 5.35 5.03 5.26 5.92 4.68 4.11 3.66 0.64 0.76 1.31 16.55 26.35 27.63 7.47 41.05 7.56 15.02 8.78 9.16 11.25 7.98 8.44 10.74 4.81 30.75 0.58 16.82 3.33

2.72 2.83 3.69 1.71 0.98 0.00 1.47 6.36 0.66 0.63 2.07 0.00 7.10 8.57 14.76 4.22 12.72 0.00 5.59 1.79 2.57 3.96 2.44 2.22 3.20 2.33 9.47 0.20 4.79 0.43

6.18 4.13 4.26 3.63 2.75 5.49 3.19 3.87 2.76 1.30 1.03 1.53 15.54 13.57 13.12 9.07 27.60 7.03 12.25 10.36 10.31 6.61 6.38 9.46 7.80 3.76 8.89 0.82 15.29 2.04

Total 85.20 65.07 73.62 65.45 71.01 85.36 60.57 62.84 68.11 18.20 20.31 29.49 267.73 412.97 516.75 161.26 805.59 113.79 319.71 174.71 156.55 194.28 188.11 140.94 173.37 103.08 364.75 12.25 406.07 40.67

Btg&tung 63.16 51.85 55.32 50.28 55.47 63.83 46.90 46.67 55.78 14.73 17.57 24.63 189.31 323.08 391.90 119.46 631.95 82.64 249.11 131.11 121.84 157.54 147.07 109.10 133.84 86.97 251.20 10.16 302.62 31.23

136

Lampiran 4.

Daftar peubah untuk penyusunan model hubungan persediaan karbon tegakan dengan struktur tegakan agroforestri

1. AGROFORESTRI DS PECEKELAN

No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

k 932 1,749 13,170 2,045 6,389 1,361 7,377 2,248 5,180 814 558 334 3,868 315 6,031 368 3,123 9,441 223 3,652 4,485

a 0.222 0.171 0.268 0.155 0.242 0.123 0.273 0.179 0.211 0.128 0.109 0.074 0.212 0.051 0.219 0.061 0.179 0.266 0.044 0.202 0.245

CtotAG Cstandkop 11.06 9.26 27.76 25.89 44.41 42.87 30.53 29.44 28.69 26.23 46.91 42.61 27.27 26.65 31.35 28.24 37.83 35.18 35.47 32.25 29.20 27.76 59.99 53.69 26.77 24.57 76.68 72.82 36.87 34.69 85.37 81.15 49.21 46.24 29.76 25.82 71.99 69.24 38.26 34.75 26.78 23.80

Cstand 3.87 19.69 34.40 27.74 22.85 34.79 20.05 23.03 25.70 28.07 22.05 45.29 19.92 63.43 31.33 71.00 42.41 25.28 54.45 26.55 14.71

Chidup 9.30 26.70 43.59 29.74 26.96 42.68 26.73 29.13 35.70 33.34 27.85 53.72 24.77 73.14 35.29 81.15 46.87 27.16 69.26 34.75 23.80

Cstand 26.51 32.95 49.26 19.13 25.99 25.66 38.30 23.61 27.16 8.49 51.31 25.69 54.78 31.32 44.12 31.39 70.79 51.47 23.34 28.36

Chidup 28.70 40.82 54.87 19.73 27.64 30.32 47.17 25.68 31.49 8.53 58.12 28.94 56.12 40.19 45.10 33.04 83.67 52.19 28.37 29.57

2. AGROFORESTRI DS KERTAYASA

No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

k 4,917 2,108 3,863 3,976 8,600 8,437 4,047 2,547 9,703 2,675 2,099 6,249 6,619 888 13,317 9,532 1,341 7,186 4,673 7,665

a 0.246 0.14 0.185 0.22 0.236 0.228 0.194 0.219 0.254 0.226 0.132 0.249 0.18 0.107 0.243 0.234 0.111 0.207 0.252 0.259

CtotAG Cstandkop 31.22 28.41 42.78 40.62 57.52 54.24 20.79 19.70 31.85 26.82 32.15 29.61 50.49 46.66 27.76 25.41 33.19 31.36 9.10 8.49 60.63 57.95 30.60 28.65 58.77 55.78 42.08 40.01 47.19 44.86 35.03 32.39 86.95 83.64 56.17 51.87 28.18 28.08 32.41 28.63

137

Lampiran 5.

Daftar peubah untuk penyusunan model hubungan persediaan karbon tegakan dengan dimensi tegakan agroforestri


No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

CtotAG Cstandkop Cstand 11.06 9.26 3.87 27.76 25.89 19.69 44.41 42.87 34.40 30.53 29.44 27.74 28.69 26.23 22.85 46.91 42.61 34.79 27.27 26.65 20.05 31.35 28.24 23.03 37.83 35.18 25.70 35.47 32.25 28.07 29.20 27.76 22.05 59.99 53.69 45.29 26.77 24.57 19.92 76.68 72.82 63.43 36.87 34.69 31.33 85.37 81.15 71.00 49.21 46.24 42.41 29.76 25.82 25.28 71.99 69.24 54.45 38.26 34.75 26.55 26.78 23.80 14.71

Clive 9.30 26.70 43.59 29.74 26.96 42.68 26.73 29.13 35.70 33.34 27.85 53.72 24.77 73.14 35.29 81.15 46.87 27.16 69.26 34.75 23.80

Age 1.5 3.5 5.5 5.5 5.5 7.5 3.5 5.5 5.5 5.5 7.5 9.5 3.5 11.5 5.5 11.5 7.5 3.5 9.5 3.5 1.5

N

D

488 630 1,800 1,228 919 925 972 811 1,211 690 590 673 935 727 1,178 783 1,197 1,525 722 950 983

8.3 13.5 10.7 10.6 12.2 13.2 9.9 10.5 10.8 14.4 12.5 15.6 10.9 16.9 11.8 17.7 12.6 10.4 16.9 13.0 10.1

BA 2.86 10.51 20.29 14.87 13.01 16.38 11.97 12.31 14.39 14.05 10.17 18.69 11.07 23.52 16.90 27.33 20.23 15.49 22.40 15.67 9.18

Clive 28.70 40.82 54.87 19.73 27.64 30.32 47.17 25.68 31.49 8.53 58.12 28.94 56.12 40.19 45.10 33.04 83.67 52.19 28.37 29.57

Age 3.5 5.5 7.5 3.5 5.5 5.5 7.5 3.5 3.5 1.5 11.5 3.5 9.5 9.5 5.5 7.5 11.5 7.5 3.5 5.5

N 1317 1350 1117 1570 1623 1483 1250 1517 1772 1161 1750 2067 1925 1200 2833 2075 1500 2883 1800 1717

D 10.2 11.5 13.3 9.1 10.0 10.1 11.8 9.4 9.5 7.9 12.2 9.3 12.9 11.2 9.6 9.7 13.1 10.4 8.2 10.2

BA 13.00 17.45 19.23 11.79 14.61 14.75 17.69 12.52 15.74 7.13 25.74 16.08 29.15 15.88 25.26 18.69 30.19 29.47 11.03 17.05


No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

CtotAG Cstandkop Cstand 31.22 28.41 26.51 42.78 40.62 32.95 57.52 54.24 49.26 20.79 19.70 19.13 31.85 26.82 25.99 32.15 29.61 25.66 50.49 46.66 38.30 27.76 25.41 23.61 33.19 31.36 27.16 9.10 8.49 8.49 60.63 57.95 51.31 30.60 28.65 25.69 58.77 55.78 54.78 42.08 40.01 31.32 47.19 44.86 44.12 35.03 32.39 31.39 86.95 83.64 70.79 56.17 51.87 51.47 28.18 28.08 23.34 32.41 28.63 28.36

138

143

Lampiran 6. Hasil pengolahan data model hubungan persediaan karbon tegakan dengan struktur tegakan agroforestri 1. AGROFORESTRI DS PECEKELAN

Descriptive Statistics: k, a, CtotAG, Cstandkop, Cstand, Chidup Variable k a CtotAG Cstandko Cstand Chidup

N 21 21 21 21 21 21

Mean 3508 -0.1730 40.58 37.77 31.27 38.17

Median 2248 -0.1790 35.47 32.25 26.55 33.34

TrMean 3172 -0.1746 39.78 36.99 30.62 37.43

Variable k a CtotAG Cstandko Cstand Chidup

Minimum 223 -0.2730 11.06 9.26 3.87 9.30

Maximum 13170 -0.0440 85.37 81.15 71.00 81.15

Q1 686 -0.2320 28.23 26.06 21.05 26.85

Q3 5606 -0.1160 48.06 44.56 38.60 45.23

StDev 3444 0.0738 18.74 18.05 16.19 17.95

SE Mean 751 0.0161 4.09 3.94 3.53 3.92

Correlations: k, a, CtotAG, Cstandkop, Cstand, Chidup k a CtotAG Cstandko -0.810 0.000 CtotAG -0.318 0.747 0.159 0.000 Cstandko -0.307 0.741 0.998 0.176 0.000 0.000 Cstand -0.288 0.727 0.987 0.987 0.206 0.000 0.000 0.000 Chidup -0.299 0.738 0.998 1.000 0.188 0.000 0.000 0.000 Cell Contents: Pearson correlation P-Value

Cstand

a

0.989 0.000

Regression Analysis: CtotAG versus k, a The regression equation is CtotAG = 87.2 + 0.00453 k + 361 a Predictor Coef SE Coef T P VIF Constant 87.179 5.866 14.86 0.000 k 0.0045306 0.0009837 4.61 0.000 2.9 a 361.13 45.92 7.86 0.000 2.9 S = 8.890 R-Sq = 79.7% R-Sq(adj) = 77.5% PRESS = 1958.16 R-Sq(pred) = 72.12% Analysis of Variance Source DF SS MS F P Regression 2 5600.3 2800.1 35.43 0.000 Residual Error 18 1422.7 79.0 Total 20 7023.0 Source DF Seq SS k 1 712.3 a 1 4887.9 Unusual Observations Obs k CtotAG Fit SE Fit Residual 3 13170 44.41 50.06 6.78 -5.65 11 558 29.20 50.34 2.65 -21.14 16 368 85.37 66.82 3.75 18.55 R denotes an observation with a large standardized residual X denotes an observation whose X value gives it large influence.

St Resid -0.98 X -2.49R 2.30R

144

Regression Analysis: Cstandkop versus k, a The regression equation is Cstandkop = 82.7 + 0.00446 k + 350 a Predictor Constant k a

Coef 82.685 0.0044617 350.00

SE Coef 5.628 0.0009437 44.05

T 14.69 4.73 7.94

P 0.000 0.000 0.000

VIF 2.9 2.9

S = 8.529 R-Sq = 79.9% R-Sq(adj) = 77.7% PRESS = 1790.52 R-Sq(pred) = 72.51% Analysis of Variance Source DF SS MS F P Regression 2 5205.2 2602.6 35.78 0.000 Residual Error 18 1309.3 72.7 Total 20 6514.5 Source DF Seq SS k 1 613.9 a 1 4591.3 Unusual Observations Obs k Cstandko Fit SE Fit Residual St Resid 3 13170 42.87 47.65 6.51 -4.78 -0.87 X 11 558 27.76 47.02 2.54 -19.26 -2.37R 16 368 81.15 62.98 3.59 18.17 2.35R R denotes an observation with a large standardized residual X denotes an observation whose X value gives it large influence.

Regression Analysis: Cstand versus k, a The regression equation is Cstand = 71.4 + 0.00411 k + 315 a Predictor Constant k a

Coef 71.376 0.0041147 315.18

SE Coef 5.124 0.0008592 40.11

T 13.93 4.79 7.86

P 0.000 0.000 0.000

VIF 2.9 2.9

S = 7.766 R-Sq = 79.3% R-Sq(adj) = 77.0% PRESS = 1602.93 R-Sq(pred) = 69.42% Analysis of Variance Source DF SS MS F P Regression 2 4156.9 2078.5 34.47 0.000 Residual Error 18 1085.5 60.3 Total 20 5242.4 Source DF Seq SS k 1 433.6 a 1 3723.4 Unusual Observations Obs k Cstand Fit SE Fit Residual St Resid 3 13170 34.40 41.10 5.92 -6.70 -1.33 X 11 558 22.05 39.32 2.31 -17.27 -2.33R 16 368 71.00 53.66 3.27 17.34 2.46R R denotes an observation with a large standardized residual X denotes an observation whose X value gives it large influence.

Regression Analysis: Chidup versus k, a The regression equation is Chidup = 82.9 + 0.00451 k + 350 a Predictor Coef SE Coef T P VIF Constant 82.910 5.553 14.93 0.000 k 0.0045116 0.0009311 4.85 0.000 2.9 a 349.98 43.47 8.05 0.000 2.9 S = 8.415 R-Sq = 80.2% R-Sq(adj) = 78.0% PRESS = 1750.08 R-Sq(pred) = 72.83%

145

Analysis of Variance Source DF SS Regression 2 5166.9 Residual Error 18 1274.6 Total 20 6441.5 Source DF Seq SS k 1 576.2 a 1 4590.8

MS 2583.5 70.8

F 36.48

P 0.000

Unusual Observations Obs k Chidup Fit SE Fit Residual St Resid 3 13170 43.59 48.53 6.42 -4.94 -0.91 X 11 558 27.85 47.28 2.51 -19.43 -2.42R 16 368 81.15 63.22 3.55 17.93 2.35R R denotes an observation with a large standardized residual X denotes an observation whose X value gives it large influence.


Descriptive Statistics: k, a, CtotAG, Cstand&kopi, Cstand, Clive Variable k a CtotAG Cstand&k Cstand Clive Variable k a CtotAG Cstand&k Cstand Clive

N 20 20 20 20 20 20 Minimum 888 -0.2590 9.10 8.49 8.49 8.53

Mean 5522 -0.2061 40.74 38.16 34.48 38.51 Maximum 13317 -0.1070 86.95 83.64 70.79 83.67

Median 4795 -0.2230 34.11 31.88 29.84 32.27 Q1 2579 -0.2453 30.76 28.16 25.67 28.45

TrMean 5347 -0.2087 39.93 37.28 33.91 37.67 Q3 8244 -0.1813 54.75 50.57 47.97 50.94

StDev 3317 0.0486 17.39 16.84 14.84 16.81

Correlations: k, a, CtotAG, Cstand&kopi, Cstand, Clive k a CtotAG Cstand&k -0.680 0.001 CtotAG -0.162 0.666 0.496 0.001 Cstand&k -0.182 0.680 0.998 0.443 0.001 0.000 Cstand -0.068 0.590 0.985 0.982 0.774 0.006 0.000 0.000 Clive -0.175 0.675 0.999 1.000 0.460 0.001 0.000 0.000 Cell Contents: Pearson correlation P-Value

Cstand

a

0.983 0.000

Regression Analysis: CtotAG versus k, a The regression equation is CtotAG = 101 + 0.00284 k + 370 a Predictor Coef SE Coef T P Constant 101.36 12.39 8.18 0.000 k 0.002841 0.001094 2.60 0.019 a 370.24 74.63 4.96 0.000 S = 11.60 R-Sq = 60.2% R-Sq(adj) = 55.5% Analysis of Variance Source DF SS MS F Regression 2 3461.5 1730.8 12.86 Residual Error 17 2287.4 134.6 Total 19 5748.9

P 0.000

SE Mean 742 0.0109 3.89 3.76 3.32 3.76

146

Source DF Seq SS k 1 150.0 a 1 3311.6 Unusual Observations Obs k CtotAG Fit SE Fit Residual 14 888 42.08 64.27 6.01 -22.19 17 1341 86.95 64.08 5.82 22.87 R denotes an observation with a large standardized residual

St Resid -2.24R 2.28R

Regression Analysis: Cstand&kopi versus k, a The regression equation is Cstand&kopi = 97.3 + 0.00264 k + 358 a Predictor Constant k a S = 11.15

Coef SE Coef T P 97.33 11.91 8.17 0.000 0.002641 0.001051 2.51 0.022 357.87 71.75 4.99 0.000 R-Sq = 60.7% R-Sq(adj) = 56.1%


MS 1636.1 124.4

F 13.16

P 0.000

Unusual Observations Obs k Cstand&k Fit SE Fit Residual 14 888 40.01 61.38 5.78 -21.37 17 1341 83.64 61.15 5.60 22.49 R denotes an observation with a large standardized residual


Regression Analysis: Cstand versus k, a The regression equation is Cstand = 82.8 + 0.00277 k + 308 a Predictor Coef Constant 82.77 k 0.0027653 a 308.38 S = 10.48 PRESS = 2827.04

SE Coef T P 11.20 7.39 0.000 0.0009884 2.80 0.012 67.45 4.57 0.000 R-Sq = 55.4% R-Sq(adj) = 50.1% R-Sq(pred) = 32.45%


MS 1158.5 109.9

F 10.54

P 0.001

Unusual Observations Obs k Cstand Fit SE Fit Residual 14 888 31.32 52.23 5.43 -20.91 17 1341 70.79 52.25 5.26 18.54 R denotes an observation with a large standardized residual

Regression Analysis: Cstand versus k, a The regression equation is Cstand = 82.8 + 0.00277 k + 308 a


147

Predictor Coef Constant 82.77 k 0.0027653 a 308.38 S = 10.48 PRESS = 2827.04

SE Coef T P 11.20 7.39 0.000 0.0009884 2.80 0.012 67.45 4.57 0.000 R-Sq = 55.4% R-Sq(adj) = 50.1% R-Sq(pred) = 32.45%


MS 1158.5 109.9

F 10.54

P 0.001

Unusual Observations Obs k Cstand Fit SE Fit Residual 14 888 31.32 52.23 5.43 -20.91 17 1341 70.79 52.25 5.26 18.54 R denotes an observation with a large standardized residual


Regression Analysis: Clive versus k, a The regression equation is Clive = 97.4 + 0.00267 k + 357 a Predictor Coef SE Coef T P Constant 97.38 11.93 8.16 0.000 k 0.002669 0.001053 2.54 0.021 a 357.15 71.85 4.97 0.000 S = 11.17 R-Sq = 60.5% R-Sq(adj) = 55.8% PRESS = 3265.06 R-Sq(pred) = 39.16% Analysis of Variance Source DF SS MS F P Regression 2 3246.5 1623.3 13.02 0.000 Residual Error 17 2120.0 124.7 Total 19 5366.6 Source DF Seq SS k 1 165.0 a 1 3081.6 Unusual Observations Obs k Clive Fit SE Fit Residual 14 888 40.19 61.54 5.79 -21.35 17 1341 83.67 61.32 5.60 22.35 R denotes an observation with a large standardized residual


148

Lampiran 7 Plot peluang normal sisaan dari persamaan matematik pendugaan potensi persediaan karbon dengan peubah struktur tegakan, (a) Tegakan murni, (b) Tegakan campuran

(a)

(b) (a)

(b)

149

Lampiran 8 Plot tebaran nilai sisaan baku dari persamaan matematik pendugaan potensi persediaan karbon dengan peubah struktur tegakan, (a) Tegakan murni, (b) Tegakan campuran

(a)

(b)

150

Lampiran 9.

Hasil pengolahan data model hubungan persediaan karbon tegakan dengan dimensi tegakan agroforestri


Descriptive Statistics: CtotAG, Cstandkop, Cstand, Clive, Age, N, D, BA Variable CtotAG Cstandko Cstand Clive Age N D BA

N 21 21 21 21 21 21 21 21

Mean 40.58 37.77 31.27 38.17 5.881 949.4 12.500 15.30

Median 35.47 32.25 26.55 33.34 5.500 925.0 12.200 14.87

TrMean 39.78 36.99 30.62 37.43 5.816 928.9 12.447 15.32

Variable CtotAG Cstandko Cstand Clive Age N D BA

Minimum 11.06 9.26 3.87 9.30 1.500 488.0 8.300 2.86

Maximum 85.37 81.15 71.00 81.15 11.500 1800.0 17.700 27.33

Q1 28.23 26.06 21.05 26.85 3.500 706.0 10.550 11.52

Q3 48.06 44.56 38.60 45.23 7.500 1187.5 13.950 19.46

StDev 18.74 18.05 16.19 17.95 2.872 320.4 2.571 5.54

SE Mean 4.09 3.94 3.53 3.92 0.627 69.9 0.561 1.21

Correlations: CtotAG, Cstandkop, Cstand, Clive, Age, N, D, BA CtotAG Cstandko Cstand 0.998 0.000 Cstand 0.987 0.987 0.000 0.000 Clive 0.998 1.000 0.989 0.000 0.000 0.000 Age 0.906 0.906 0.921 0.000 0.000 0.000 N -0.076 -0.067 -0.021 0.743 0.774 0.927 D 0.882 0.875 0.867 0.000 0.000 0.000 BA 0.927 0.930 0.953 0.000 0.000 0.000 Cell Contents: Pearson correlation P-Value

Clive

Age

N

D

0.907 0.000 -0.058 0.804 0.876 0.000 0.934 0.000

-0.187 0.417 0.848 0.000 0.826 0.000

-0.381 0.089 0.250 0.275

0.757 0.000

Cstandko

Best Subsets Regression: CtotAG versus Age, N, D, BA Response is CtotAG

Vars

R-Sq

R-Sq(adj)

C-p

S

1 1 2 2 3 3 4

86.0 82.1 96.1 93.6 96.2 96.1 96.2

85.2 81.2 95.6 92.9 95.5 95.5 95.2

42.0 58.3 1.5 12.0 3.2 3.3 5.0

7.1993 8.1346 3.9092 5.0009 3.9825 3.9947 4.0849

A g B e N D A X X X

X X X X X X X X X X X X X

151

Regression Analysis: CtotAG versus Age, N, D, BA The regression equation is CtotAG = 11.1 + 0.418 Age - 0.0201 N - 0.50 D + 3.42 BA Predictor Constant Age N D BA

Coef 11.10 0.4178 -0.020075 -0.502 3.4219

S = 4.085 PRESS = 441.908 Analysis of Variance Source DF Regression 4 Residual Error 16 Total 20

SE Coef 14.22 0.8234 0.007840 1.259 0.7277

T 0.78 0.51 -2.56 -0.40 4.70

R-Sq = 96.2% R-Sq(pred) = 93.71%

SS 6756.0 267.0 7023.0

P 0.446 0.619 0.021 0.695 0.000

VIF 6.7 7.6 12.6 19.5

R-Sq(adj) = 95.2%

MS 1689.0 16.7

F 101.22

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS Age 1 5765.7 N 1 63.2 D 1 558.1 BA 1 369.0 Unusual Observations Obs Age CtotAG Fit SE Fit Residual 21 1.5 26.780 18.338 1.773 8.442 R denotes an observation with a large standardized residual

St Resid 2.29R

Regression Analysis: CtotAG versus Age, N, BA The regression equation is CtotAG = 5.64 + 0.466 Age - 0.0174 N + 3.19 BA Predictor Constant Age N BA

Coef 5.641 0.4655 -0.017449 3.1874


SE Coef 3.724 0.7943 0.004142 0.4175

T 1.51 0.59 -4.21 7.63

R-Sq = 96.2% R-Sq(pred) = 94.18%

SS 6753.3 269.6 7023.0

P 0.148 0.565 0.001 0.000

VIF 6.6 2.2 6.8

R-Sq(adj) = 95.5%

MS 2251.1 15.9

F 141.93

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS Age 1 5765.7 N 1 63.2 BA 1 924.4 Unusual Observations Obs Age CtotAG Fit SE Fit Residual 21 1.5 26.780 18.447 1.707 8.333 R denotes an observation with a large standardized residual

Regression Analysis: CtotAG versus N, BA The regression equation is CtotAG = 6.60 - 0.0192 N + 3.41 BA

St Resid 2.32R

152

Predictor Constant N BA

Coef 6.605 -0.019199 3.4120


SE Coef 3.280 0.002817 0.1629

T 2.01 -6.81 20.95

R-Sq = 96.1% R-Sq(pred) = 94.66%

SS 6747.9 275.1 7023.0

P 0.059 0.000 0.000

VIF 1.1 1.1

R-Sq(adj) = 95.6%

MS 3373.9 15.3

F 220.78

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS N 1 40.7 BA 1 6707.2 Unusual Observations Obs N CtotAG Fit SE Fit Residual 21 983 26.780 19.054 1.333 7.726 R denotes an observation with a large standardized residual

St Resid 2.10R

Regression Analysis: CtotAG versus BA The regression equation is CtotAG = - 7.38 + 3.13 BA Predictor Coef Constant -7.380 BA 3.1347 S = 7.199 PRESS = 1293.85 Analysis of Variance Source DF Regression 1 Residual Error 19 Total 20

SE Coef T P 4.713 -1.57 0.134 0.2904 10.79 0.000 R-Sq = 86.0% R-Sq(adj) = 85.2% R-Sq(pred) = 81.58% SS 6038.2 984.8 7023.0

MS 6038.2 51.8

F 116.50

P 0.000

No replicates. Cannot do pure error test. Unusual Observations Obs BA CtotAG Fit SE Fit Residual St Resid 1 2.9 11.06 1.59 3.94 9.47 1.57 X X denotes an observation whose X value gives it large influence.

Regression Analysis: CtotAG versus Age The regression equation is CtotAG = 5.81 + 5.91 Age Predictor Coef SE Coef T P Constant 5.810 4.126 1.41 0.175 Age 5.9122 0.6334 9.33 0.000 S = 8.135 R-Sq = 82.1% R-Sq(adj) = 81.2% PRESS = 1579.31 R-Sq(pred) = 77.51% Analysis of Variance Source DF SS MS F P Regression 1 5765.7 5765.7 87.13 0.000 Residual Error 19 1257.2 66.2 Lack of Fit 4 519.6 129.9 2.64 0.075 Pure Error 15 737.7 49.2 Total 20 7023.0 Unusual Observations Obs Age CtotAG Fit SE Fit Residual 11 7.5 29.20 50.15 2.05 -20.95 R denotes an observation with a large standardized residual

St Resid -2.66R

153

Best Subsets Regression: Cstandkop versus Age, N, D, BA Response is Cstandko

Vars

R-Sq

R-Sq(adj)

C-p

S

1 1 2 2 3 3 4

86.5 82.1 96.0 93.4 96.1 96.1 96.2

85.8 81.1 95.6 92.6 95.5 95.4 95.3

40.1 58.7 1.8 13.0 3.3 3.4 5.0

6.8108 7.8442 3.7996 4.9031 3.8472 3.8624 3.9304

A g B e N D A X X X


Regression Analysis: Cstandkop versus Age, N, D, BA The regression equation is Cstandkop = 12.0 + 0.425 Age - 0.0202 N - 0.78 D + 3.41 BA Predictor Constant Age N D BA

Coef 12.00 0.4245 -0.020181 -0.782 3.4117


SE Coef 13.68 0.7923 0.007544 1.212 0.7002

T 0.88 0.54 -2.68 -0.65 4.87

R-Sq = 96.2% R-Sq(pred) = 93.56%

SS 6267.3 247.2 6514.5

P 0.393 0.599 0.017 0.528 0.000

VIF 6.7 7.6 12.6 19.5

R-Sq(adj) = 95.3%

MS 1566.8 15.4

F 101.42

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS Age 1 5345.4 N 1 71.0 D 1 484.2 BA 1 366.8 Unusual Observations Obs Age Cstandko Fit SE Fit Residual 21 1.5 23.800 16.228 1.706 7.572 R denotes an observation with a large standardized residual

Regression Analysis: Cstandkop versus Age, N, BA The regression equation is Cstandkop = 3.50 + 0.499 Age - 0.0161 N + 3.05 BA Predictor Constant Age N BA

Coef 3.500 0.4989 -0.016091 3.0466


SE Coef 3.612 0.7703 0.004017 0.4049

T 0.97 0.65 -4.01 7.52

R-Sq = 96.1% R-Sq(pred) = 93.93%

SS 6260.9 253.6 6514.5

P 0.346 0.526 0.001 0.000

VIF 6.6 2.2 6.8

R-Sq(adj) = 95.4%

MS 2087.0 14.9

F 139.90

P 0.000

St Resid 2.14R

154

No replicates. Cannot do pure error test. Source DF Seq SS Age 1 5345.4 N 1 71.0 BA 1 844.5 Unusual Observations Obs Age Cstandko Fit SE Fit Residual 21 1.5 23.800 16.399 1.656 7.401 R denotes an observation with a large standardized residual

St Resid 2.12R

Regression Analysis: Cstandkop versus N, BA The regression equation is Cstandkop = 4.53 - 0.0180 N + 3.29 BA Predictor Coef Constant 4.533 N -0.017966 BA 3.2872 S = 3.800 PRESS = 370.438 Analysis of Variance Source DF Regression 2 Residual Error 18 Total 20

SE Coef T P VIF 3.188 1.42 0.172 0.002738 -6.56 0.000 1.1 0.1583 20.77 0.000 1.1 R-Sq = 96.0% R-Sq(adj) = 95.6% R-Sq(pred) = 94.31%

SS 6254.6 259.9 6514.5

MS 3127.3 14.4

F 216.62

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS N 1 29.0 BA 1 6225.6

Regression Analysis: Cstandkop versus BA The regression equation is Cstandkop = - 8.55 + 3.03 BA Predictor Coef SE Coef Constant -8.553 4.459 BA 3.0277 0.2747 S = 6.811 PRESS = 1168.00 Analysis of Variance Source DF Regression 1 Residual Error 19 Total 20

T -1.92 11.02

R-Sq = 86.5% R-Sq(pred) = 82.07% SS 5633.1 881.3 6514.5

R-Sq(adj) = 85.8%

MS 5633.1 46.4

No replicates. Cannot do pure error test. Unusual Observations Obs BA Cstandko Fit 1 2.9 9.26 0.11

P 0.070 0.000

F 121.44

SE Fit 3.73

P 0.000

Residual 9.15

X denotes an observation whose X value gives it large influence.

Regression Analysis: Cstandkop versus Age The regression equation is Cstandkop = 4.29 + 5.69 Age Predictor Constant Age

Coef 4.291 5.6926

S = 7.844 PRESS = 1474.38

SE Coef 3.979 0.6108

T 1.08 9.32

R-Sq = 82.1% R-Sq(pred) = 77.37%

P 0.294 0.000

R-Sq(adj) = 81.1%

St Resid 1.61 X

155

Analysis of Variance Source DF Regression 1 Residual Error 19 Lack of Fit 4 Pure Error 15 Total 20

SS 5345.4 1169.1 462.7 706.4 6514.5

MS 5345.4 61.5 115.7 47.1

F 86.87

P 0.000

2.46

0.091

Unusual Observations Obs Age Cstandko Fit SE Fit Residual 11 7.5 27.76 46.99 1.98 -19.23 R denotes an observation with a large standardized residual

Regression Analysis: Cstandkop versus Age, BA The regression equation is Cstandkop = - 6.73 + 2.72 Age + 1.86 BA Predictor Constant Age BA

Coef -6.734 2.7236 1.8619


SE Coef 3.460 0.7233 0.3747

T -1.95 3.77 4.97

R-Sq = 92.4% R-Sq(pred) = 88.70%

SS 6021.5 493.0 6514.5

P 0.067 0.001 0.000

VIF 3.2 3.2

R-Sq(adj) = 91.6%

MS 3010.8 27.4

F 109.93

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS Age 1 5345.4 BA 1 676.1

Regression Analysis: Cstand versus Age, N, D, BA The regression equation is Cstand = 5.31 + 0.843 Age - 0.0146 N - 0.767 D + 2.90 BA Predictor Coef Constant 5.311 Age 0.8433 N -0.014557 D -0.7673 BA 2.9026 S = 2.172 PRESS = 144.921

SE Coef T P VIF 7.561 0.70 0.493 0.4378 1.93 0.072 6.7 0.004168 -3.49 0.003 7.6 0.6695 -1.15 0.269 12.6 0.3869 7.50 0.000 19.5 R-Sq = 98.6% R-Sq(adj) = 98.2% R-Sq(pred) = 97.24%

Analysis of Variance Source DF SS MS Regression 4 5166.9 1291.7 Residual Error 16 75.5 4.7 Total 20 5242.4 No replicates. Cannot do pure error test. Source DF Seq SS Age 1 4448.4 N 1 123.4 D 1 329.6 BA 1 265.5

F 273.89

Regression Analysis: Cstand versus Age, N, BA The regression equation is Cstand = - 3.04 + 0.916 Age - 0.0105 N + 2.54 BA

P 0.000

St Resid -2.53R

156

Predictor Constant Age N BA

Coef -3.037 0.9163 -0.010542 2.5441


SE Coef 2.050 0.4371 0.002279 0.2298

T -1.48 2.10 -4.63 11.07

R-Sq = 98.4% R-Sq(pred) = 97.23%

SS 5160.7 81.7 5242.4

P 0.157 0.051 0.000 0.000

VIF 6.6 2.2 6.8

R-Sq(adj) = 98.2%

MS 1720.2 4.8

F 358.14

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS Age 1 4448.4 N 1 123.4 BA 1 588.9

Regression Analysis: Cstand versus N, BA The regression equation is Cstand = - 1.14 - 0.0140 N + 2.99 BA Predictor Constant N BA

Coef -1.140 -0.013986 2.98607


SE Coef 2.005 0.001722 0.09955

T -0.57 -8.12 30.00

R-Sq = 98.0% R-Sq(pred) = 97.24%

SS 5139.6 102.8 5242.4

P 0.577 0.000 0.000

VIF 1.1 1.1

R-Sq(adj) = 97.8%

MS 2569.8 5.7

F 450.12

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS N 1 2.4 BA 1 5137.2 Unusual Observations Obs N Cstand Fit SE Fit Residual 14 727 63.430 58.924 1.116 4.506 20 950 26.550 32.365 0.523 -5.815 R denotes an observation with a large standardized residual

Regression Analysis: Cstand versus BA The regression equation is Cstand = - 11.3 + 2.78 BA Predictor Coef Constant -11.327 BA 2.7840 S = 5.023 PRESS = 655.988 Analysis of Variance Source DF Regression 1 Residual Error 19 Total 20

SE Coef T P 3.288 -3.44 0.003 0.2026 13.74 0.000 R-Sq = 90.9% R-Sq(adj) = 90.4% R-Sq(pred) = 87.49%

SS 4763.0 479.4 5242.4

MS 4763.0 25.2

F 188.77

P 0.000

St Resid 2.13R -2.49R

157

No replicates. Cannot do pure error test. Unusual Observations Obs BA Cstand Fit SE Fit Residual St Resid 1 2.9 3.87 -3.37 2.75 7.24 1.72 X 3 20.3 34.40 45.16 1.49 -10.76 -2.24R 14 23.5 63.43 54.15 1.99 9.28 2.01R R denotes an observation with a large standardized residual X denotes an observation whose X value gives it large influence.

Regression Analysis: Cstand versus Age The regression equation is Cstand = 0.73 + 5.19 Age Predictor Constant Age

Coef 0.727 5.1930

S = 6.465 PRESS = 1000.74 Analysis of Variance Source DF Regression 1 Residual Error 19 Lack of Fit 4 Pure Error 15 Total 20

SE Coef 3.279 0.5033

T 0.22 10.32

R-Sq = 84.9% R-Sq(pred) = 80.91%

SS 4448.4 794.0 301.1 492.9 5242.4

P 0.827 0.000

R-Sq(adj) = 84.1%

MS 4448.4 41.8 75.3 32.9

F 106.44

P 0.000

2.29

0.108

Unusual Observations Obs Age Cstand Fit SE Fit Residual 11 7.5 22.05 39.67 1.63 -17.62 R denotes an observation with a large standardized residual

St Resid -2.82R

Regression Analysis: Clive versus N, D, BA, Age The regression equation is Clive = 10.2 - 0.0187 N - 0.62 D + 3.32 BA + 0.446 Age Predictor Coef SE Coef T P Constant 10.23 13.01 0.79 0.443 N -0.018733 0.007173 -2.61 0.019 D -0.617 1.152 -0.54 0.599 BA 3.3218 0.6658 4.99 0.000 Age 0.4460 0.7534 0.59 0.562 S = 3.737 PRESS = 381.845 Analysis of Variance Source DF Regression 4 Residual Error 16 Total 20

R-Sq = 96.5% R-Sq(pred) = 94.07% SS 6218.1 223.5 6441.5

VIF 7.6 12.6 19.5 6.7

R-Sq(adj) = 95.7%

MS 1554.5 14.0

F 111.29

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS N 1 21.5 D 1 5489.8 BA 1 701.9 Age 1 4.9 Unusual Observations Obs N Clive Fit SE Fit Residual 21 983 23.800 16.743 1.622 7.057 R denotes an observation with a large standardized residual

Regression Analysis: Clive versus Age, N, BA The regression equation is Clive = 3.51 + 0.505 Age - 0.0155 N + 3.03 BA

St Resid 2.10R

158

Predictor Constant Age N BA

Coef 3.513 0.5047 -0.015501 3.0333


SE Coef 3.421 0.7296 0.003804 0.3835

T 1.03 0.69 -4.07 7.91

R-Sq = 96.5% R-Sq(pred) = 94.42%

SS 6214.0 227.5 6441.5

P 0.319 0.498 0.001 0.000

VIF 6.6 2.2 6.8

R-Sq(adj) = 95.8%

MS 2071.3 13.4

F 154.78

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS Age 1 5293.7 N 1 83.1 BA 1 837.2 Unusual Observations Obs Age Clive Fit SE Fit Residual 21 1.5 23.800 16.878 1.568 6.922 R denotes an observation with a large standardized residual

Regression Analysis: Clive versus N, BA The regression equation is Clive = 4.56 - 0.0174 N + 3.28 BA Predictor Constant N BA

Coef 4.558 -0.017399 3.2768


SE Coef 3.025 0.002598 0.1502

T 1.51 -6.70 21.82

R-Sq = 96.4% R-Sq(pred) = 94.81%

SS 6207.6 233.9 6441.5

P 0.149 0.000 0.000

VIF 1.1 1.1

R-Sq(adj) = 96.0%

MS 3103.8 13.0

F 238.85

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS N 1 21.5 BA 1 6186.1

Regression Analysis: Clive versus BA The regression equation is Clive = - 8.12 + 3.03 BA Predictor Constant BA

Coef -8.115 3.0254


SE Coef 4.292 0.2645

T -1.89 11.44

R-Sq = 87.3% R-Sq(pred) = 83.20%

SS 5624.8 816.8 6441.5

P 0.074 0.000

R-Sq(adj) = 86.7%

MS 5624.8 43.0

F 130.85

P 0.000

St Resid 2.09R

159

No replicates. Cannot do pure error test. Unusual Observations Obs BA Clive Fit SE Fit Residual St Resid 1 2.9 9.30 0.54 3.59 8.76 1.60 X X denotes an observation whose X value gives it large influence.

Regression Analysis: Clive versus Age The regression equation is Clive = 4.86 + 5.67 Age Predictor Constant Age

Coef 4.857 5.6650

S = 7.773 PRESS = 1444.40 Analysis of Variance Source DF Regression 1 Residual Error 19 Lack of Fit 4 Pure Error 15 Total 20

SE Coef 3.943 0.6052

T 1.23 9.36

R-Sq = 82.2% R-Sq(pred) = 77.58%

SS 5293.7 1147.8 444.3 703.5 6441.5

P 0.233 0.000

R-Sq(adj) = 81.2%

MS 5293.7 60.4 111.1 46.9

F 87.63

P 0.000

2.37

0.099

Unusual Observations Obs Age Clive Fit SE Fit Residual 11 7.5 27.85 47.34 1.96 -19.49 R denotes an observation with a large standardized residual

St Resid -2.59R

160


Descriptive Statistics: CtotAG, Cstand&kop, Cstand, Clive, U, N, D, BA Variable CtotAG Cstand&k Cstand Clive U N D BA

N 20 20 20 20 20 20 20 20

Mean 40.74 38.16 34.48 38.51 6.100 1696 10.480 18.12

Median 34.11 31.88 29.84 32.27 5.500 1597 10.150 16.56

TrMean 39.93 37.28 33.91 37.67 6.056 1662 10.467 18.06

Variable CtotAG Cstand&k Cstand Clive U N D BA

Minimum 9.10 8.49 8.49 8.53 1.500 1117 7.900 7.13

Maximum 86.95 83.64 70.79 83.67 11.500 2883 13.300 30.19

Q1 30.76 28.16 25.67 28.45 3.500 1325 9.425 13.40

Q3 54.75 50.57 47.97 50.94 7.500 1894 11.725 23.75

StDev 17.39 16.84 14.84 16.81 2.836 488 1.558 6.55

SE Mean 3.89 3.76 3.32 3.76 0.634 109 0.348 1.47

Correlations: CtotAG, Cstand&kop, Cstand, Clive, U, N, D, BA CtotAG Cstand&k Cstand 0.998 0.000 Cstand 0.985 0.982 0.000 0.000 Clive 0.999 1.000 0.983 0.000 0.000 0.000 U 0.873 0.869 0.849 0.000 0.000 0.000 N 0.163 0.157 0.279 0.493 0.508 0.233 D 0.861 0.852 0.826 0.000 0.000 0.000 BA 0.890 0.884 0.941 0.000 0.000 0.000 Cell Contents: Pearson correlation P-Value

Clive

U

N

D

0.871 0.000 0.158 0.507 0.854 0.000 0.885 0.000

0.060 0.802 0.832 0.000 0.792 0.000

-0.206 0.384 0.530 0.016

0.685 0.001

Cstand&k

Best Subsets Regression: CtotAG versus U, N, D, BA Response is CtotAG Vars

R-Sq

R-Sq(adj)

C-p

S

1 1 2 2 3 3 4

79.3 76.3 92.6 91.2 92.7 92.6 92.7

78.1 74.9 91.7 90.2 91.3 91.2 90.8

26.6 32.9 1.3 4.1 3.1 3.2 5.0

8.1322 8.7065 5.0170 5.4579 5.1366 5.1484 5.2838

B U N D A X X X


Regression Analysis: CtotAG versus U, N, D, BA The regression equation is CtotAG = 2.3 + 0.425 U - 0.0117 N + 0.95 D + 2.53 BA Predictor Coef SE Coef T P Constant 2.30 27.56 0.08 0.935 U 0.4249 0.9728 0.44 0.668 N -0.011689 0.007857 -1.49 0.158 D 0.945 2.716 0.35 0.733 BA 2.5254 0.8506 2.97 0.010

VIF 5.2 10.0 12.2 21.1

161

S = 5.284 R-Sq = 92.7% R-Sq(adj) = 90.8% PRESS = 1341.02 R-Sq(pred) = 76.67% Analysis of Variance Source DF SS MS F P Regression 4 5330.2 1332.5 47.73 0.000 Residual Error 15 418.8 27.9 Total 19 5748.9 No replicates. Cannot do pure error test. Source DF Seq SS U 1 4384.5 N 1 70.3 D 1 629.3 BA 1 246.1 Unusual Observations Obs U CtotAG 13 9.5 58.77 17 11.5 86.95 19 3.5 28.18 R denotes an observation with a

Fit SE Fit Residual 69.64 2.63 -10.87 78.27 4.30 8.68 18.35 2.33 9.83 large standardized residual

St Resid -2.37R 2.82R 2.07R

Regression Analysis: CtotAG versus U, N, BA The regression equation is CtotAG = 11.7 + 0.441 U - 0.0140 N + 2.77 BA Predictor Constant U N BA

Coef 11.712 0.4405 -0.014029 2.7661


SE Coef 5.142 0.9447 0.003954 0.4812

T 2.28 0.47 -3.55 5.75

R-Sq = 92.7% R-Sq(pred) = 87.15%

SS 5326.8 422.2 5748.9

P 0.037 0.647 0.003 0.000

VIF 5.2 2.7 7.2

R-Sq(adj) = 91.3%

MS 1775.6 26.4

F 67.30

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS U 1 4384.5 N 1 70.3 BA 1 872.0 Unusual Observations Obs U CtotAG Fit SE Fit Residual 13 9.5 58.77 69.52 2.54 -10.75 19 3.5 28.18 18.51 2.22 9.67 R denotes an observation with a large standardized residual

Regression Analysis: CtotAG versus N, BA The regression equation is CtotAG = 12.9 - 0.0153 N + 2.97 BA Predictor Constant N BA

Coef 12.919 -0.015308 2.9675

S = 5.017 PRESS = 689.123

SE Coef 4.340 0.002781 0.2071

T 2.98 -5.50 14.33

R-Sq = 92.6% R-Sq(pred) = 88.01%

P 0.008 0.000 0.000

VIF 1.4 1.4

R-Sq(adj) = 91.7%


162

Analysis of Variance Source DF Regression 2 Residual Error 17 Total 19

SS 5321.0 427.9 5748.9

MS 2660.5 25.2

F 105.70

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS N 1 152.1 BA 1 5168.9 Unusual Observations Obs N CtotAG Fit SE Fit Residual 13 1925 58.77 69.95 2.31 -11.18 19 1800 28.18 18.10 1.99 10.08 R denotes an observation with a large standardized residual


Regression Analysis: CtotAG versus BA The regression equation is CtotAG = - 2.10 + 2.36 BA Predictor Coef Constant -2.097 BA 2.3639 S = 8.132 PRESS = 1631.00 Analysis of Variance Source DF Regression 1 Residual Error 18 Total 19

SE Coef T P 5.471 -0.38 0.706 0.2847 8.30 0.000 R-Sq = 79.3% R-Sq(adj) = 78.1% R-Sq(pred) = 71.63%

SS 4558.6 1190.4 5748.9

MS 4558.6 66.1

F 68.93

P 0.000

No replicates. Cannot do pure error test. Unusual Observations Obs BA CtotAG Fit SE Fit Residual 17 30.2 86.95 69.27 3.89 17.68 R denotes an observation with a large standardized residual

St Resid 2.48R

Regression Analysis: CtotAG versus U The regression equation is CtotAG = 8.07 + 5.36 U Predictor Constant U

Coef 8.067 5.3567

S = 8.706 PRESS = 1851.21

SE Coef 4.717 0.7043

T 1.71 7.61

R-Sq = 76.3% R-Sq(pred) = 67.80%

P 0.104 0.000

R-Sq(adj) = 74.9%

Analysis of Variance Source Regression Residual Error Lack of Fit Pure Error Total

DF 1 18 4 14 19

SS 4384.5 1364.4 258.2 1106.2 5748.9

MS 4384.5 75.8 64.6 79.0

F 57.84

P 0.000

0.82

0.535

1 rows with no replicates Unusual Observations Obs U CtotAG Fit SE Fit Residual 14 9.5 42.08 58.96 3.09 -16.88 17 11.5 86.95 69.67 4.27 17.28 R denotes an observation with a large standardized residual


163

Best Subsets Regression: Cstand&kop versus U, N, D, BA Response is Cstand&k Vars

R-Sq

R-Sq(adj)

C-p

S

1 1 2 2 3 3 4

78.1 75.4 91.5 89.6 91.6 91.5 91.6

76.9 74.1 90.5 88.3 90.1 90.0 89.4

23.2 28.1 1.2 4.7 3.0 3.2 5.0

8.0914 8.5746 5.1764 5.7514 5.3063 5.3353 5.4802

B U N D A X X X


Regression Analysis: Cstand&kop versus U, N, D, BA The regression equation is Cstand&kop = 9.7 + 0.41 U - 0.0135 N + 0.08 D + 2.65 BA Predictor Constant U N D BA

Coef 9.69 0.410 -0.013511 0.081 2.6503


SE Coef 28.58 1.009 0.008150 2.817 0.8822

T 0.34 0.41 -1.66 0.03 3.00

R-Sq = 91.6% R-Sq(pred) = 74.72%

SS 4936.0 450.5 5386.5

P 0.739 0.690 0.118 0.978 0.009

VIF 5.2 10.0 12.2 21.1

R-Sq(adj) = 89.4%

MS 1234.0 30.0

F 41.09

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS U 1 4063.1 N 1 60.0 D 1 541.9 BA 1 271.0 Unusual Observations Obs U Cstand&k 13 9.5 55.78 17 11.5 83.64 19 3.5 28.08 R denotes an observation with a


Regression Analysis: Cstand&kop versus U, N, BA The regression equation is Cstand&kop = 10.5 + 0.412 U - 0.0137 N + 2.67 BA Predictor Constant U N BA

Coef 10.493 0.4116 -0.013711 2.6708


SE Coef 5.311 0.9760 0.004085 0.4971

T 1.98 0.42 -3.36 5.37

R-Sq = 91.6% R-Sq(pred) = 85.47%

SS 4936.0 450.5 5386.5

P 0.066 0.679 0.004 0.000

VIF 5.2 2.7 7.2

R-Sq(adj) = 90.1%

MS 1645.3 28.2

F 58.43

P 0.000

St Resid -2.12R 2.64R 2.31R

164

No replicates. Cannot do pure error test. Source DF Seq SS U 1 4063.1 N 1 60.0 BA 1 812.9 Unusual Observations Obs U Cstand&k Fit 13 9.5 55.78 65.86 17 11.5 83.64 75.29 19 3.5 28.08 16.71

SE Fit 2.62 3.28 2.30

Residual -10.08 8.35 11.37

St Resid -2.19R 2.00R 2.38R

R denotes an observation with a large standardized residual

Regression Analysis: Cstand&kop versus N, BA The regression equation is Cstand&kop = 11.6 - 0.0149 N + 2.86 BA Predictor Constant N BA

Coef 11.620 -0.014906 2.8590

S = 5.176 PRESS = 733.881

SE Coef 4.478 0.002870 0.2137

T 2.60 -5.19 13.38

R-Sq = 91.5% R-Sq(pred) = 86.38%


SS 4931.0 455.5 5386.5

P 0.019 0.000 0.000

VIF 1.4 1.4

R-Sq(adj) = 90.5%

MS 2465.5 26.8

F 92.01

P 0.000

No replicates. Cannot do pure error test. Source N BA

DF 1 1

Seq SS 133.3 4797.6

Unusual Observations Obs N Cstand&k Fit SE Fit Residual 13 1925 55.78 66.27 2.38 -10.49 19 1800 28.08 16.32 2.05 11.76 R denotes an observation with a large standardized residual


Regression Analysis: Cstand&kop versus BA The regression equation is Cstand&kop = - 3.00 + 2.27 BA Predictor Constant BA

Coef -3.001 2.2712


SE Coef 5.444 0.2833

T -0.55 8.02

R-Sq = 78.1% R-Sq(pred) = 69.84%

SS 4208.0 1178.5 5386.5

P 0.588 0.000

R-Sq(adj) = 76.9%

MS 4208.0 65.5

F 64.27

P 0.000

No replicates. Cannot do pure error test. Unusual Observations Obs BA Cstand&k Fit SE Fit Residual 17 30.2 83.64 65.57 3.87 18.07 R denotes an observation with a large standardized residual

St Resid 2.54R

165

Regression Analysis: Cstand&kop versus U The regression equation is Cstand&kop = 6.70 + 5.16 U Predictor Constant U

Coef 6.704 5.1566

S = 8.575 PRESS = 1788.29

SE Coef 4.645 0.6937

T 1.44 7.43

R-Sq = 75.4% R-Sq(pred) = 66.80%


SS 4063.1 1323.4 239.4 1084.1 5386.5

P 0.166 0.000

R-Sq(adj) = 74.1%

MS 4063.1 73.5 59.8 77.4

F 55.26

P 0.000

0.77

0.561

1 rows with no replicates Unusual Observations Obs U Cstand&k Fit SE Fit Residual 17 11.5 83.64 66.00 4.21 17.64 R denotes an observation with a large standardized residual

Best Subsets Regression: Cstand versus U, N, D, BA Response is Cstand Vars 1 1 2 2 3 3 4

R-Sq 88.5 72.1 95.1 94.7 95.2 95.1 95.3

R-Sq(adj) 87.8 70.6 94.6 94.1 94.3 94.2 94.0

C-p 20.4 72.0 1.4 2.7 3.0 3.3 5.0

S 5.1756 8.0487 3.4612 3.6052 3.5296 3.5634 3.6395

B U N D A X X X X X X X X X X X X X X X X

Regression Analysis: Cstand versus U, N, D, BA The regression equation is Cstand = - 5.5 - 0.147 U - 0.00693 N + 1.09 D + 2.28 BA Predictor Constant U N D BA

Coef -5.53 -0.1469 -0.006934 1.088 2.2773

S = 3.639 PRESS = 613.785

SE Coef 18.98 0.6701 0.005412 1.870 0.5859

T -0.29 -0.22 -1.28 0.58 3.89

R-Sq = 95.3% R-Sq(pred) = 85.33%

P 0.775 0.829 0.220 0.570 0.001

VIF 5.2 10.0 12.2 21.1

R-Sq(adj) = 94.0%

Analysis of Variance Source DF SS MS Regression 4 3986.62 996.66 Residual Error 15 198.69 13.25 Total 19 4185.31 No replicates. Cannot do pure error test. Source DF Seq SS U 1 3019.26 N 1 219.30 D 1 547.95 BA 1 200.11

F 75.24

P 0.000

St Resid 2.36R

166

Unusual Observations Obs U Cstand Fit SE Fit Residual 3 7.5 49.260 43.876 2.576 5.384 17 11.5 70.790 65.375 2.960 5.415 19 3.5 23.340 15.507 1.607 7.833 R denotes an observation with a large standardized residual

St Resid 2.09R 2.56R 2.40R

Regression Analysis: Cstand versus U, N, BA The regression equation is Cstand = 5.30 - 0.129 U - 0.00963 N + 2.55 BA Predictor Coef SE Coef T Constant 5.298 3.567 1.49 U -0.1289 0.6554 -0.20 N -0.009626 0.002743 -3.51 BA 2.5544 0.3338 7.65 S = 3.563 PRESS = 339.074

R-Sq = 95.1% R-Sq(pred) = 91.90%


SS 3982.1 203.2 4185.3

P 0.157 0.847 0.003 0.000

VIF 5.2 2.7 7.2

R-Sq(adj) = 94.2%

MS 1327.4 12.7

F 104.54

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS U 1 3019.3 N 1 219.3 BA 1 743.6 Unusual Observations Obs U Cstand Fit SE Fit Residual 3 7.5 49.260 42.699 1.559 6.561 19 3.5 23.340 15.694 1.542 7.646 R denotes an observation with a large standardized residual

St Resid 2.05R 2.38R

Regression Analysis: Cstand versus N, BA The regression equation is Cstand = 4.94 - 0.00925 N + 2.50 BA Predictor Constant N BA

Coef 4.944 -0.009252 2.4954

S = 3.461 PRESS = 313.010

SE Coef 2.994 0.001919 0.1429

T 1.65 -4.82 17.47

R-Sq = 95.1% R-Sq(pred) = 92.52%


SS 3981.7 203.7 4185.3

P 0.117 0.000 0.000

VIF 1.4 1.4

R-Sq(adj) = 94.6%

MS 1990.8 12.0

F 166.18

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS N 1 326.6 BA 1 3655.0 Unusual Observations Obs N Cstand 3 1117 49.260 19 1800 23.340

Fit 42.597 15.816

SE Fit 1.429 1.372

Residual 6.663 7.524


167

R denotes an observation with a large standardized residual

Regression Analysis: Cstand versus BA The regression equation is Cstand = - 4.13 + 2.13 BA Predictor Coef Constant -4.131 BA 2.1306 S = 5.176 PRESS = 641.064 Analysis of Variance Source DF Regression 1 Residual Error 18 Total 19

SE Coef T P 3.482 -1.19 0.251 0.1812 11.76 0.000 R-Sq = 88.5% R-Sq(adj) = 87.8% R-Sq(pred) = 84.68% SS 3703.1 482.2 4185.3

MS 3703.1 26.8

F 138.24

P 0.000

No replicates. Cannot do pure error test. Unusual Observations Obs BA Cstand Fit SE Fit Residual 3 19.2 49.26 36.84 1.17 12.42 17 30.2 70.79 60.19 2.47 10.60 R denotes an observation with a large standardized residual


Regression Analysis: Cstand versus U The regression equation is Cstand = 7.37 + 4.45 U Predictor Constant U

Coef 7.366 4.4452

S = 8.049 PRESS = 1519.90

SE Coef 4.361 0.6511

T 1.69 6.83

R-Sq = 72.1% R-Sq(pred) = 63.68%


SS 3019.3 1166.1 155.2 1010.9 4185.3

P 0.108 0.000

R-Sq(adj) = 70.6%

MS 3019.3 64.8 38.8 72.2

F 46.61

P 0.000

0.54

0.711

1 rows with no replicates Unusual Observations Obs U Cstand Fit SE Fit Residual 14 9.5 31.32 49.60 2.85 -18.28 R denotes an observation with a large standardized residual

Best Subsets Regression: Clive versus U, N, D, BA Response is Clive Vars

R-Sq

R-Sq(adj)

C-p

S

1 1 2 2 3 3 4

78.3 75.8 91.7 89.9 91.8 91.7 91.8

77.1 74.5 90.7 88.7 90.3 90.1 89.6

23.8 28.3 1.2 4.5 3.0 3.2 5.0

8.0511 8.4932 5.1227 5.6415 5.2438 5.2761 5.4124

B U N D A X X X


St Resid -2.43R

168

Regression Analysis: Clive versus U, N, D, BA The regression equation is Clive = 7.0 + 0.451 U - 0.0126 N + 0.38 D + 2.55 BA Predictor Constant U N D BA

Coef 6.97 0.4506 -0.012619 0.378 2.5506


SE Coef 28.23 0.9965 0.008049 2.782 0.8713

T 0.25 0.45 -1.57 0.14 2.93

R-Sq = 91.8% R-Sq(pred) = 74.77%

SS 4927.1 439.4 5366.6

P 0.808 0.658 0.138 0.894 0.010

VIF 5.2 10.0 12.2 21.1

R-Sq(adj) = 89.6%

MS 1231.8 29.3

F 42.05

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS U 1 4068.1 N 1 60.0 D 1 548.0 BA 1 251.0 Unusual Observations Obs U Clive 13 9.5 56.12 17 11.5 83.67 19 3.5 28.37 R denotes an observation with a


St Resid -2.15R 2.70R 2.33R

Regression Analysis: Clive versus U, N, BA The regression equation is Clive = 10.7 + 0.457 U - 0.0136 N + 2.65 BA Predictor Coef Constant 10.740 U 0.4568 N -0.013556 BA 2.6470 S = 5.244 PRESS = 766.272

SE Coef T P VIF 5.249 2.05 0.058 0.9645 0.47 0.642 5.2 0.004037 -3.36 0.004 2.7 0.4912 5.39 0.000 7.2 R-Sq = 91.8% R-Sq(adj) = 90.3% R-Sq(pred) = 85.72%

Analysis of Variance Source DF SS MS F P Regression 3 4926.6 1642.2 59.72 0.000 Residual Error 16 440.0 27.5 Total 19 5366.6 No replicates. Cannot do pure error test. Source DF Seq SS U 1 4068.1 N 1 60.0 BA 1 798.5 Unusual Observations Obs U Clive Fit SE Fit Residual 13 9.5 56.12 66.14 2.59 -10.02 19 3.5 28.37 17.14 2.27 11.23 R denotes an observation with a large standardized residual

Regression Analysis: Clive versus N, BA The regression equation is Clive = 12.0 - 0.0149 N + 2.86 BA


169

Predictor Constant N BA

Coef 11.991 -0.014882 2.8558


SE Coef 4.431 0.002840 0.2114

T 2.71 -5.24 13.51

R-Sq = 91.7% R-Sq(pred) = 86.61% SS 4920.4 446.1 5366.6

P 0.015 0.000 0.000

VIF 1.4 1.4

R-Sq(adj) = 90.7%

MS 2460.2 26.2

F 93.75

P 0.000

No replicates. Cannot do pure error test. Source DF Seq SS N 1 133.4 BA 1 4787.0 Unusual Observations Obs N Clive Fit SE Fit Residual 13 1925 56.12 66.59 2.36 -10.47 19 1800 28.37 16.70 2.03 11.67 R denotes an observation with a large standardized residual


Regression Analysis: Clive versus BA The regression equation is Clive = - 2.61 + 2.27 BA Predictor Constant BA

Coef -2.607 2.2690

SE Coef 5.416 0.2819

T -0.48 8.05

P 0.636 0.000

S = 8.051 R-Sq = 78.3% R-Sq(adj) = 77.1% PRESS = 1606.95 R-Sq(pred) = 70.06% Analysis of Variance Source DF SS MS F P Regression 1 4199.8 4199.8 64.79 0.000 Residual Error 18 1166.8 64.8 Total 19 5366.6 No replicates. Cannot do pure error test. Unusual Observations Obs BA Clive Fit SE Fit Residual 17 30.2 83.67 65.89 3.85 17.78 R denotes an observation with a large standardized residual

St Resid 2.51R

Regression Analysis: Clive versus U The regression equation is Clive = 7.04 + 5.16 U Predictor Coef SE Coef T P Constant 7.038 4.601 1.53 0.144 U 5.1598 0.6871 7.51 0.000 S = 8.493 R-Sq = 75.8% R-Sq(adj) = 74.5% PRESS = 1757.54 R-Sq(pred) = 67.25% Analysis of Variance Source DF SS MS F P Regression 1 4068.1 4068.1 56.40 0.000 Residual Error 18 1298.4 72.1 Lack of Fit 4 237.4 59.3 0.78 0.555 Pure Error 14 1061.0 75.8 Total 19 5366.6 1 rows with no replicates Unusual Observations Obs U Clive Fit SE Fit Residual 17 11.5 83.67 66.38 4.17 17.29 R denotes an observation with a large standardized residual

St Resid 2.34R

170

Lampiran 10 Plot peluang normal untuk sisaan dari persamaan matematik pendugaan potensi persediaan karbon dengan peubah tegakan, menggunakan peubah luas bidang dasar dan kerapatan tegakan, (a) Tegakan murni, (b) Tegakan campuran

(a)

(b)

171

Lampiran 11 Plot tebaran nilai sisaan baku dari persamaan matematik pendugaan potensi persediaan karbon dengan peubah tegakan, menggunakan peubah luas bidang dasar dan kerapatan tegakan, (a) Tegakan murni, (b) Tegakan campuran

(a)

(b)

172

Lampiran 12. Hasil pengolahan data model hubungan persediaan karbon tegakan dengan pendekatan fungsi pertumbuhan tegakan agroforestri 1. AGROFORESTRI DS PECEKELAN Model is:CtotAG=b0/(1+b1*exp(-b2*Age)) Dependent variable: CtotAG Independent variables: 1 Loss function: least squares Final value: 661.88554994 R =.95171715 Proportion of variance accounted for: .90576554

Model is: CtotAG=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_pacek Dep. Var. : CtotAG Level of confidence: 95.0% ( alpha=0.050) Estimate Standard t-value p-level Lo. Conf Up. Conf error df = 18 Limit Limit b0 120.8309 28.75442 4.202169 0.000536 60.42013 181.2417 b1 8.0128 1.40634 5.697637 0.000021 5.05820 10.9674 b2 0.2326 0.05049 4.607786 0.000218 0.12657 0.3387

Effect Regression Residual Total Corrected Total Regression vs.Corrected Total

Model is: CtotAG=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_pacek1 Dep. Var. : CtotAG 1 2 3 4 5 Sum of Sqares DF Mean Squares F-value p-value 40941.48 3.00000 13647.16 371.1349 0.000000 661.89 18.00000 36.77 41603.36 21.00000 7023.82 20.00000 40941.48 3.00000 13647.16 38.8597 0.000000

Model is:Cstankp=b0/(1+b1*exp(-b2*Age)) Dependent variable: Cstankp Independent variables: 1 Loss function: least squares Final value: 607.27499943 Proportion of variance accounted for: .90678493 R =.95225255

Model is: Cstandkop=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_pa Dep. Var. : Cstandkop Level of confidence: 95.0% ( alpha=0.050) Estimate Standard t-value p-level Lo. Conf Up. Conf error df = 18 Limit Limit b0 114.1398 26.63614 4.285150 0.000446 58.17939 170.1003 b1 8.5343 1.47467 5.787276 0.000017 5.43616 11.6325 b2 0.2389 0.05095 4.688691 0.000183 0.13186 0.3460


Model is: Cstandkop=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_pa Dep. Var. : Cstandkop 1 2 3 4 5 Sum of Sqares DF Mean Squares F-value p-value 35863.88 3.00000 11954.63 354.3424 0.000000 607.27 18.00000 33.74 36471.16 21.00000 6514.77 20.00000 35863.88 3.00000 11954.63 36.7001 0.000000

173

Model is:Cstand=b0/(1+b1*exp(-b2*Age)) Dependent variable: Cstand Independent variables: 1 Loss function: least squares Final value: 375.08782983 Proportion of variance accounted for: .92844743 R =.96355977

Model is: Cstand=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_pacek Dep. Var. : Cstand Level of confidence: 95.0% ( alpha=0.050) Estimate Standard t-value p-level Lo. Conf Up. Conf error df = 18 Limit Limit b0 92.88910 16.20475 5.732216 0.000020 58.84419 126.9340 b1 9.85634 1.48118 6.654386 0.000003 6.74450 12.9682 b2 0.26748 0.04723 5.663970 0.000023 0.16827 0.3667


Model is: Cstand=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_pacek1 Dep. Var. : Cstand 1 2 3 4 5 Sum of Sqares DF Mean Squares F-value p-value 25396.62 3.00000 8465.539 406.2507 0.000000 375.09 18.00000 20.838 25771.70 21.00000 5242.13 20.00000 25396.62 3.00000 8465.539 32.2981 0.000000

Model is:Clive=b0/(1+b1*exp(-b2*Age)) Dependent variable: Clive Independent variables: 1 Loss function: least squares Final value: 598.2758456 Proportion of variance accounted for: .90715148 R =.952445

Model is: Clive=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_pacek10 Dep. Var. : Clive Level of confidence: 95.0% ( alpha=0.050) Estimate Standard t-value p-level Lo. Conf Up. Conf error df = 18 Limit Limit b0 113.8722 26.46297 4.303078 0.000428 58.27559 169.4689 b1 8.2353 1.41888 5.804074 0.000017 5.25431 11.2162 b2 0.2367 0.05062 4.675644 0.000188 0.13034 0.3431


Model is: Clive=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_pacek10Dep. Var. : Clive 1 2 3 4 5 Sum of Sqares DF Mean Squares F-value p-value 36443.13 3.00000 12147.71 365.4815 0.000000 598.28 18.00000 33.24 37041.40 21.00000 6443.57 20.00000 36443.13 3.00000 12147.71 37.7049 0.000000

174

2. AGROFORESTRI DS KERTAYASA Model is:CtotAG=b0/(1+b1*exp(-b2*Agestand)) Dependent variable: CtotAG Independent variables: 1 Loss function: least squares Final value: 848.32798334 Proportion of variance accounted for: .85242811 R =.92327033


Model is: CtotAG=b0/(1+b1*exp(-b2*Agestand)) (mod_ageyield_pa Dep. Var. : CtotAG 1 2 3 4 5 Sum of Sqares DF Mean Squares F-value p-value 38100.41 3.00000 12700.14 254.5034 0.000000 848.33 17.00000 49.90 38948.74 20.00000 5748.57 19.00000 38100.41 3.00000 12700.14 41.9761 0.000000

Model is: CtotAG=b0/(1+b1*exp(-b2*Agestand)) (mod_ageyield_ Dep. Var. : CtotAG Level of confidence: 95.0% ( alpha=0.050) Estimate Standard t-value p-level Lo. Conf Up. Conf error df = 17 Limit Limit b0 140.9511 88.32140 1.595888 0.128935 -45.3908 327.2929 b1 7.6179 4.01320 1.898216 0.074780 -0.8492 16.0850 b2 0.1746 0.06611 2.640686 0.017170 0.0351 0.3141 Model is:Cstankp=b0/(1+b1*exp(-b2*Age)) Dependent variable: Cstankp Independent variables: 1 Loss function: least squares Final value: 829.30020857 Proportion of variance accounted for: .84603587 R =.91980208

Model is: Cstankp=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_pan Dep. Var. : Cstankp Level of confidence: 95.0% ( alpha=0.050) Estimate Standard t-value p-level Lo. Conf Up. Conf Limit Limit error df = 17 b0 169.9326 168.1670 1.010499 0.326424 -184.869 524.7339 9.8490 8.8360 1.114650 0.280511 -8.793 28.4913 b1 0.1597 0.0669 2.388861 0.028767 0.019 0.3008 b2


Model is: Cstankp=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_pana Dep. Var. : Cstankp 1 2 3 4 5 Sum of Sqares DF Mean Squares F-value p-value 33679.43 3.00000 11226.48 230.1339 0.000000 829.30 17.00000 48.78 34508.73 20.00000 5386.32 19.00000 33679.43 3.00000 11226.48 39.6009 0.000000

Model is:Cstand=b0/(1+b1*exp(-b2*Age)) Dependent variable: Cstand Loss function: least squares Final value: 692.11590224

Independent variables: 1

175

Proportion of variance accounted for: .83462599

R =.91357867

Model is: Cstand=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_panaw Dep. Var. : Cstand Level of confidence: 95.0% ( alpha=0.050) Estimate Standard t-value p-level Lo. Conf Up. Conf error df = 17 Limit Limit b0 97.12680 41.35652 2.348524 0.031202 9.872169 184.3814 b1 6.55069 2.08423 3.142985 0.005932 2.153360 10.9480 b2 0.20081 0.07280 2.758204 0.013435 0.047206 0.3544


Model is: Cstand=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_panaw1 Dep. Var. : Cstand 1 2 3 4 5 Sum of Sqares Mean Squares F-value p-value DF 27273.29 3.00000 9091.097 223.2988 0.000000 692.12 17.00000 40.713 27965.41 20.00000 4185.16 19.00000 27273.29 3.00000 9091.097 41.2723 0.000000

Model is:Clive=b0/(1+b1*exp(-b2*Age)) Dependent variable: Clive Independent variables: 1 Loss function: least squares Final value: 819.83985342 Proportion of variance accounted for: .84721581 R =.92044327

Model is: Clive=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_panaw1 Dep. Var. : Clive Level of confidence: 95.0% ( alpha=0.050) Estimate Standard t-value p-level Lo. Conf Up. Conf error df = 17 Limit Limit b0 156.5041 133.0458 1.176317 0.255678 -124.198 437.2063 b1 8.9576 6.7559 1.325895 0.202416 -5.296 23.2113 b2 0.1642 0.0668 2.458087 0.024998 0.023 0.3052


Model is: Clive=b0/(1+b1*exp(-b2*Age)) (mod_ageyield_panaw1 Dep. Var. : Clive 1 2 3 4 5 Sum of Sqares DF Mean Squares F-value p-value 34210.45 3.00000 11403.48 236.4598 0.000000 819.84 17.00000 48.23 35030.29 20.00000 5366.00 19.00000 34210.45 3.00000 11403.48 40.3776 0.000000

176

Lampiran 13. Hasil pengolahan data pengujian perbandingan penentuan biomassa karbon tegakan dengan menggunakan persamaan alometrik biomassa dan persamaan Ketterings AF Kebun Murni (AF Pecekelan) Regression Analysis: B phnKetr versus Bpohon Allow The regression equation is B phnKetr = - 7.64 + 1.26 Bpohon Allow Predictor Constant Bpohon A S = 5.489

Coef SE Coef T P -7.640 1.261 -6.06 0.000 1.26051 0.01824 69.10 0.000 R-Sq = 98.5% R-Sq(adj) = 98.5%


SS 143851 2229 146080

MS 143851 30

F 4775.01

P 0.000

Unusual Observations Bpohon A B phnKet Fit SE Fit Residual St Resid Obs 88 91.520 103.159 0.811 -11.639 -2.14R 13 33 49.060 33.352 0.804 15.708 2.89R 20 42 32.520 45.692 0.707 -13.172 -2.42R 21 158 206.390 191.130 1.892 15.260 2.96RX 59 85 88.720 100.033 0.783 -11.313 -2.08R 62 150 191.470 180.920 1.753 10.550 2.03RX 70 R denotes an observation with a large standardized residual X denotes an observation whose X value gives it large influence.

Paired T-Test and CI: B phnKetr, Bpohon Allom Paired T for B phnKetr - Bpohon Allow

B phnKetr Bpohon Allow Difference

N 76 76 76

Mean 67.85 59.89 7.96

StDev 44.13 34.74 10.57

SE Mean 5.06 3.99 1.21

95% CI for mean difference: (5.55, 10.38) T-Test of mean difference = 0 (vs not = 0): T-Value = 6.57

AF Kebun Campuran (AF Kertayasa) Regression Analysis: BKetrr versus BAllom The regression equation is BKetrr = - 6.13 + 1.17 BAllom Predictor Constant BAllom S = 5.735

Coef SE Coef T P -6.127 1.869 -3.28 0.002 1.17114 0.02328 50.32 0.000 R-Sq = 98.3% R-Sq(adj) = 98.3%

P-Value = 0.000

177


SS 83259 1447 84706

MS 83259 33

F 2531.79

P 0.000

Unusual Observations Obs BAllom BKetrr Fit SE Fit Residual St Resid 5 135 137.200 151.578 1.694 -14.378 -2.62R 33 146 184.699 164.870 1.927 19.829 3.67R 39 162 196.143 184.135 2.278 12.008 2.28RX 43 130 131.089 146.308 1.604 -15.219 -2.76R R denotes an observation with a large standardized residual X denotes an observation whose X value gives it large influence.

Paired T-Test and CI: BKetrr, BAllom Paired T for BKetrr - BAllom

BKetrr BAllom Difference

N 46 46 46

Mean 77.72 71.59 6.12

StDev 43.39 36.73 8.47

SE Mean 6.40 5.42 1.25

95% CI for mean difference: (3.61, 8.64) T-Test of mean difference = 0 (vs not = 0): T-Value = 4.91

P-Value = 0.000

Lampiran 14. Contoh hasil analisis biaya dan manfaat pengelolaan agroforestri dengan dan tanpa skema perdagangan karbon, dengan pendekatan laju rata-rata persediaan karbon Maksimum Produksi CER Luas lahan agroforestri

Biaya satuan PENDAPATAN Penjualan kayu & hasil pertanian Penerimaan hasil penjarangan Penerimaan hasil tebang habis Penjualan hasil pertanian (kopi) Penjualan CER dari karbon Total Pendapatan Tanpa CER Dengan CER BIAYA PEMB. AGROFORESTRI Penyiapan lahan Pembelian bibit & Penanaman Pemeliharaan tanaman Pemeliharaan & perlindungan Pemupukan Pengelolaan & operasi tahunan Pajak tanah Total Pembangunan Agroforestri BIAYA SKEMA KARBON Biaya desain, pendaftaran & validasi Biaya monitoring Biaya verifikasi & sertifikasi Total Biaya Skema Karbon BIAYA TOTAL Tanpa CER Dengan CER Selisih Pendapatan - Biaya Tanpa CER Dengan CER Pendapatan Total (Terdiskonto) Tanpa CER

Harga CER Suku bunga Biaya transaksi

8,051 ton CO2/thn 450 ha

Satuan

1

15 USD/tonC 10% per Tahun Tetap

2

3

4

591,300,960 3,000 Rp/ha 139,500 Rp/ton C

5

-

6

886,951,440

7

-

326,430,000

326,430,000

326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

326,430,000

326,430,000

326,430,000

1,266,300,960 1,592,730,960

675,000,000 1,001,430,000

1,561,951,440 1,888,381,440

675,000,000 1,001,430,000

Rp/ha Rp/ha Rp/ha Rp/ha Rp/ha Rp/ha/tahun Rp/ha/tahun

840,000,000 1,106,250,000 360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 3,057,750,000

360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 1,111,500,000

360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 1,111,500,000

94,500,000

94,500,000

94,500,000

94,500,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

200,000,000 Rp/30 tahun 200,000,000 Rp/tahun 200,000,000 Rp/5tahun

200,000,000 200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000 200,000,000 400,000,000

200,000,000

200,000,000

3,057,750,000 3,257,750,000

1,111,500,000 1,311,500,000

1,111,500,000 1,311,500,000

477,000,000 677,000,000

477,000,000 877,000,000

477,000,000 677,000,000

477,000,000 677,000,000

789,300,960 915,730,960

198,000,000 124,430,000

1,084,951,440 1,211,381,440

198,000,000 324,430,000

864,900,594

419,121,893

881,680,868

346,381,730

1,866,667 2,458,333 800,000 210,000 610,000 750,000 100,000

(3,057,750,000) (1,111,500,000) (1,111,500,000) (2,931,320,000) (985,070,000) (985,070,000) -

-

-


Biaya satuan

Satuan

1 296,754,545

Dengan CER Biaya Total (Terdiskonto) Tanpa CER Dengan CER NPV Tanpa CER Dengan CER B/C Ratio Tanpa CER Dengan CER Tabel bantu perhitungan: Produksi kayu Volume Penjarangan Volume Tebang habis diameter 20-30 cm up diameter 30 cm up Produksi pertanian Hasil agroforestri kopi Produksi CER (80% dari total) Nilai Penjualan Produksi kayu diameter 20-30 cm up diameter 30 cm up Kopi Harga CER BAHAN SIMULASI Harga CER

Suku Bunga

Biaya Transaksi



2,779,772,727 2,961,590,909

Satuan

Nilai/satuan


2 269,776,860 918,595,041 1,083,884,298

3 4 245,251,690 1,087,856,677

5 6 621,809,241 1,065,942,093

7 513,891,934

835,086,401 985,349,361

325,797,418 462,400,109

296,179,471 544,548,000

269,254,065 382,148,851

244,776,422 347,408,046

539,103,176 625,456,567

122,942,422 77,261,240

612,426,804 683,793,242

101,605,307 166,483,888

(2,779,772,727) (2,664,836,364)

(918,595,041) (814,107,438)

(835,086,401) (740,097,671)

1

2

3

4

m3/ha m3/ha

5

7.73

kg/ha tonC/ha

500 6.50

Rp/m3 Rp/m3 Rp/ha Rp/ton C

100,000 170,000 3,000 139,500

5.20

725,400

5.20

725,400

5.20

500 5.20

725,400

1,314,002 1,500,000 725,400

CER 15 18 21 10 12 14 100

USD/tonC USD/tonC USD/tonC % % % %

4.1 USD/tonCO2 4.9 USD/tonCO2 5.7 USD/tonCO2

15.00 USD/tonC

6

7

11.59

500 5.20

1,500,000 725,400

500 5.20

1,971,003 1,500,000 725,400

500 5.20

1,500,000 725,400




Biaya satuan Satuan 80 % 120 %

1


2

3

4

5

6

7

Lampiran 14. Contoh hasil ana dengan pendekat Maksimum Produksi CER Luas lahan agroforestri Tahun Ke8 PENDAPATAN Penjualan kayu & hasil pertanian Penerimaan hasil penjarangan Penerimaan hasil tebang habis Penjualan hasil pertanian (kopi) Penjualan CER dari karbon Total Pendapatan Tanpa CER Dengan CER BIAYA PEMB. AGROFORESTRI Penyiapan lahan Pembelian bibit & Penanaman Pemeliharaan tanaman Pemeliharaan & perlindungan Pemupukan Pengelolaan & operasi tahunan Pajak tanah Total Pembangunan Agroforestri BIAYA SKEMA KARBON Biaya desain, pendaftaran & validasi Biaya monitoring Biaya verifikasi & sertifikasi Total Biaya Skema Karbon BIAYA TOTAL Tanpa CER Dengan CER Selisih Pendapatan - Biaya Tanpa CER Dengan CER Pendapatan Total (Terdiskonto) Tanpa CER

886,951,440

9

-

10

1,108,689,301

11

-

12

1,108,689,301

13

-

14

15

-

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

1,561,951,440 1,888,381,440

675,000,000 1,001,430,000

1,783,689,301 2,110,119,301

675,000,000 1,001,430,000

1,783,689,301 2,110,119,301

675,000,000 1,001,430,000

675,000,000 1,001,430,000

16

11,865,263,974 675,000,000

12,540,263,974 12,540,263,974

675,000,000 326,430,000 675,000,000 1,001,430,000

840,000,000 1,106,250,000 360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 3,057,750,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000 200,000,000 400,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000 200,000,000 400,000,000

200,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 877,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 877,000,000

3,057,750,000 3,257,750,000

1,084,951,440 1,211,381,440

198,000,000 324,430,000

1,306,689,301 1,233,119,301

198,000,000 324,430,000

1,306,689,301 1,433,119,301

198,000,000 324,430,000

198,000,000 324,430,000

12,063,263,974 11,663,263,974

728,661,875

286,265,892

687,689,440

236,583,382

568,338,380

195,523,456

177,748,597

3,002,039,492

200,000,000

(2,382,750,000) (2,256,320,000) 146,899,667

Lampiran 14. Contoh hasil ana dengan pendekat Maksimum Produksi CER Luas lahan agroforestri Tahun KeDengan CER Biaya Total (Terdiskonto) Tanpa CER Dengan CER NPV Tanpa CER Dengan CER B/C Ratio Tanpa CER Dengan CER Tabel bantu perhitungan: Produksi kayu Volume Penjarangan Volume Tebang habis diameter 20-30 cm up diameter 30 cm up Produksi pertanian Hasil agroforestri kopi Produksi CER (80% dari total) Nilai Penjualan Produksi kayu diameter 20-30 cm up diameter 30 cm up Kopi Harga CER BAHAN SIMULASI Harga CER

Suku Bunga

Biaya Transaksi

8 880,943,879

9 424,704,078

10 813,542,336

11 350,995,106

12 672,349,038

13 290,078,600

14 263,707,818

15 3,002,039,492

16 217,940,345

222,524,020 315,825,496

202,294,564 287,114,088

183,904,149 338,121,465

167,185,590 237,284,370

151,986,900 215,713,064

138,169,909 196,102,785

125,609,008 178,275,259

114,190,008 209,946,827

665,455,490 708,981,317

506,137,854 565,118,382

83,971,328 137,589,990

503,785,291 475,420,871

69,397,792 113,710,736

416,351,480 456,635,975

57,353,547 93,975,815

52,139,588 85,432,559

2,887,849,485 2,792,092,665

8

9

11.59

10

11

14.49

12

13

14

15

(518,555,823) (491,040,972)

16

14.49 95.73 98.79

500 5.20

1,971,003 1,500,000 725,400

500 5.20

1,500,000 725,400

500 5.20

2,463,754 1,500,000 725,400

500 5.20

1,500,000 725,400

500 5.20

2,463,754 1,500,000 725,400

500 5.20

1,500,000 725,400

500 5.20

1,500,000 725,400

500 5.20

9,572,615 16,794,638 1,500,000 725,400

500 5.20

1,500,000 725,400

Lampiran 14. Contoh hasil ana dengan pendekat Maksimum Produksi CER Luas lahan agroforestri Tahun Ke8

9

10

11

12

13

14

15

16

Lampiran 14. Contoh hasil ana dengan pendekat Maksimum Produksi CER Luas lahan agroforestri

17

18

19

20

21

22

23

24

PENDAPATAN Penjualan kayu & hasil pertanian Penerimaan hasil penjarangan Penerimaan hasil tebang habis Penjualan hasil pertanian (kopi) Penjualan CER dari karbon Total Pendapatan Tanpa CER Dengan CER

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 1,001,430,000

675,000,000 1,001,430,000

1,266,300,960 1,592,730,960

675,000,000 1,001,430,000

1,561,951,440 1,888,381,440

675,000,000 1,001,430,000

1,561,951,440 1,888,381,440

675,000,000 1,001,430,000

BIAYA PEMB. AGROFORESTRI Penyiapan lahan Pembelian bibit & Penanaman Pemeliharaan tanaman Pemeliharaan & perlindungan Pemupukan Pengelolaan & operasi tahunan Pajak tanah Total Pembangunan Agroforestri

360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 1,111,500,000

360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 1,111,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000 200,000,000 400,000,000

200,000,000

200,000,000

200,000,000

200,000,000

1,111,500,000 1,311,500,000

1,111,500,000 1,311,500,000

477,000,000 677,000,000

477,000,000 877,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

BIAYA SKEMA KARBON Biaya desain, pendaftaran & validasi Biaya monitoring Biaya verifikasi & sertifikasi Total Biaya Skema Karbon BIAYA TOTAL Tanpa CER Dengan CER Selisih Pendapatan - Biaya Tanpa CER Dengan CER Pendapatan Total (Terdiskonto) Tanpa CER

-

-

591,300,960

-

886,951,440

-

886,951,440

-

(436,500,000) (310,070,000)

(436,500,000) (310,070,000)

(436,500,000) (310,070,000)

789,300,960 915,730,960

198,000,000 124,430,000

1,084,951,440 1,211,381,440

198,000,000 324,430,000

1,084,951,440 1,211,381,440

133,545,152

121,404,683

207,050,326

100,334,449

211,067,390

82,921,032

174,435,859

68,529,779



Suku Bunga

Biaya Transaksi

17 198,127,587

18 180,115,988

19 260,424,239

20 148,856,188

21 255,178,062

22 123,021,643

23 210,890,960

24 101,670,780

219,904,349 259,473,283

199,913,045 235,884,803

77,993,312 110,694,910

70,903,011 130,360,462

64,457,282 91,483,397

58,597,529 83,166,724

53,270,481 75,606,113

48,427,710 68,732,830

(86,359,198) (61,345,696)

(78,508,362) (55,768,815)

129,057,014 149,729,329

29,431,438 18,495,727

146,610,108 163,694,666

24,323,503 39,854,919

121,165,378 135,284,848

20,102,068 32,937,950

17

18

19

20

7.73

500 5.20

1,500,000 725,400

500 5.20

1,500,000 725,400

500 5.20

1,314,002 1,500,000 725,400

21

22

11.59

500 5.20

1,500,000 725,400

500 5.20

1,971,003 1,500,000 725,400

23

24

11.59

500 5.20

1,500,000 725,400

500 5.20

1,971,003 1,500,000 725,400

500 5.20

1,500,000 725,400


17

18

19

20

21

22

23

24


25 PENDAPATAN Penjualan kayu & hasil pertanian Penerimaan hasil penjarangan Penerimaan hasil tebang habis Penjualan hasil pertanian (kopi) Penjualan CER dari karbon Total Pendapatan Tanpa CER Dengan CER

1,108,689,301

26

-

27

1,108,689,301

28

-

29

30

-

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

675,000,000 326,430,000

1,783,689,301 2,110,119,301

675,000,000 1,001,430,000

1,783,689,301 2,110,119,301

675,000,000 1,001,430,000

675,000,000 1,001,430,000

Total

11,865,263,974 675,000,000

9,165,164,885 23,730,527,947 18,225,000,000 9,140,040,000

678,901 1,757,817 1,350,000 677,040

12,540,263,974 12,540,263,974

51,120,692,832 60,260,732,832

3,786,718 4,463,758

1,680,000,000 2,212,500,000 2,160,000,000 2,835,000,000 1,647,000,000 10,125,000,000 1,350,000,000 22,009,500,000

124,444 163,889 160,000 210,000 122,000 750,000 100,000 1,630,333

14,815 429,630 74,074 518,519

1,630,333 2,148,852


94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

BIAYA SKEMA KARBON Biaya desain, pendaftaran & validasi Biaya monitoring Biaya verifikasi & sertifikasi Total Biaya Skema Karbon

200,000,000 200,000,000 400,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000 5,800,000,000 1,000,000,000 7,000,000,000

477,000,000 877,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

22,009,500,000 29,009,500,000

198,000,000 324,430,000

1,306,689,301 1,233,119,301

198,000,000 324,430,000

1,306,689,301 1,433,119,301

198,000,000 324,430,000

198,000,000 324,430,000

16,611,428,858 19,077,898,858

164,627,384

56,636,181

136,055,690

46,806,761

42,551,601

718,664,386

10,806,465,939

BIAYA TOTAL Tanpa CER Dengan CER Selisih Pendapatan - Biaya Tanpa CER Dengan CER Pendapatan Total (Terdiskonto) Tanpa CER

Rataan/ha/th



Suku Bunga

Biaya Transaksi

25 194,755,567

26 84,025,438

27 160,955,014

28 69,442,510

29 63,129,555

30 718,664,386

Rataan/ha/th Total 13,786,841,651

44,025,191 80,943,590

40,022,901 56,803,992

36,384,455 51,639,992

33,076,778 46,945,448

30,069,798 42,677,680

27,336,180 38,797,891

8,645,163,208 10,827,905,359

120,602,193 113,811,977

16,613,280 27,221,446

99,671,234 109,315,022

13,729,983 22,497,063

12,481,803 20,451,875

691,328,206 679,866,496

2,161,302,732 2,958,936,293 1.250 1.273

25

26

14.49

27

28

29

30

14.49 95.73 98.79

500 5.20

2,463,754 1,500,000 725,400

500 5.20

1,500,000 725,400

500 5.20

2,463,754 1,500,000 725,400

500 5.20

1,500,000 725,400

500 5.20

1,500,000 725,400

500 5.20

9,572,615 16,794,638 1,500,000 725,400

70200

4,802,895 6,575,414


25

26

27

28

29

30

Total

Rataan/ha/th

Lampiran 15. Contoh hasil analisis biaya dan manfaat pengelolaan agroforestri dengan dan tanpa skema perdagangan karbon menggunakan pendekatan tdengan pendekatan t-CER Maksimum Produksi CER Luas lahan agroforestri

Harga t-CER Suku bunga Biaya transaksi

8,424 ton CO2/thn 450 ha Biaya satuan Satuan

PENDAPATAN Penjualan kayu & hasil pertanian Penerimaan hasil penjarangan Penerimaan hasil tebang habis Penjualan hasil pertanian (kopi) Penjualan CER dari karbon Total Pendapatan Tanpa CER Dengan CER BIAYA PEMB. AGROFORESTRI Penyiapan lahan Pembelian bibit & Penanaman Pemeliharaan tanaman Pemeliharaan & perlindungan Pemupukan Pengelolaan & operasi tahunan Pajak tanah Total Pembangunan Agroforestri BIAYA SKEMA KARBON Biaya desain, pendaftaran & validasi Biaya monitoring Biaya verifikasi & sertifikasi Total Biaya Skema Karbon BIAYA TOTAL Tanpa CER Dengan CER Selisih Pendapatan - Biaya Tanpa CER Dengan CER Pendapatan Total (Terdiskonto) Tanpa CER Dengan CER

1

3.79 USD/tonC 10% per Tahun Tetap 2

3

4

591,300,960 3,000 Rp/ha 35,257 Rp/ton C

1,866,667 2,458,333 800,000 210,000 610,000 750,000 100,000

5

-

6

886,951,440

7

-

-

-

-

675,000,000 -

675,000,000 437,897,962

675,000,000 -

675,000,000 -

-

-

-

1,266,300,960 1,266,300,960

675,000,000 1,112,897,962

1,561,951,440 1,561,951,440

675,000,000 675,000,000

Rp/ha Rp/ha Rp/ha Rp/ha Rp/ha Rp/ha/tahun Rp/ha/tahun

840,000,000 1,106,250,000 360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 3,057,750,000

200,000,000 Rp/30 tahun 200,000,000 Rp/tahun 200,000,000 Rp/5tahun

200,000,000

360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 1,111,500,000

360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 1,111,500,000

94,500,000

94,500,000

94,500,000

94,500,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000 200,000,000 400,000,000

200,000,000

200,000,000

3,057,750,000 3,257,750,000

1,111,500,000 1,311,500,000

1,111,500,000 1,311,500,000

477,000,000 677,000,000

477,000,000 877,000,000

477,000,000 677,000,000

477,000,000 677,000,000

789,300,960 589,300,960

198,000,000 235,897,962

1,084,951,440 884,951,440

864,900,594 864,900,594

419,121,893 691,022,075

881,680,868 881,680,868

(3,057,750,000) (1,111,500,000) (1,111,500,000) (3,257,750,000) (1,311,500,000) (1,311,500,000) -

-

-

198,000,000 (2,000,000) 346,381,730 346,381,730

Lampiran 15. Contoh hasil analisis biaya dan manfaat pengelolaan agroforestri dengan dan tanpa skema perdagangan karbon menggunakan pendekatan tdengan pendekatan t-CER Maksimum Produksi CER Luas lahan agroforestri

Harga t-CER Suku bunga Biaya transaksi

8,424 ton CO2/thn 450 ha Biaya satuan Satuan

1

Biaya Total (Terdiskonto) Tanpa CER Dengan CER NPV Tanpa CER Dengan CER B/C Ratio Tanpa CER Dengan CER Tabel bantu perhitungan: Produksi kayu Volume Penjarangan Volume Tebang habis diameter 20-30 cm up diameter 30 cm up Produksi pertanian Hasil agroforestri kopi Produksi CER (80% dari total) Nilai Penjualan Produksi kayu diameter 20-30 cm up diameter 30 cm up Kopi Harga t-CER BAHAN SIMULASI Harga CER

Suku Bunga

Biaya Transaksi

2,779,772,727 2,961,590,909

3.79 USD/tonC 10% per Tahun Tetap 2

918,595,041 1,083,884,298

(2,779,772,727) (918,595,041) (2,961,590,909) (1,083,884,298)

Satuan

Nilai/satuan

1

3

4

5

325,797,418 462,400,109

296,179,471 544,548,000

269,254,065 382,148,851

244,776,422 347,408,046

(835,086,401) (985,349,361)

539,103,176 402,500,485

122,942,422 146,474,075

612,426,804 499,532,018

101,605,307 (1,026,316)

2

3

4

5

7.73

500 6.50

Rp/m3 Rp/m3 Rp/ha Rp/ton C

100,000 170,000 3,000 35,257

500

-

-

-

1,314,002 1,500,000 -

t-CER 15 18 21 10 12 14 100 80 120

USD/tonC USD/tonC USD/tonC % % % % % %

4.1 USD/tonCO2 4.9 USD/tonCO2 5.7 USD/tonCO2

7

835,086,401 985,349,361

m3/ha m3/ha

kg/ha tonC/ha

6

3.79 USD/tonC

6

7

11.59

500 27.6

1,500,000 973,107

500

1,971,003 1,500,000 -

500

1,500,000 -

Lampiran 15. Contoh hasil analCER dengan pendekatan Maksimum Produksi CER Luas lahan agroforestri Tahun Ke8 PENDAPATAN Penjualan kayu & hasil pertanian Penerimaan hasil penjarangan Penerimaan hasil tebang habis Penjualan hasil pertanian (kopi) Penjualan CER dari karbon Total Pendapatan Tanpa CER Dengan CER BIAYA PEMB. AGROFORESTRI Penyiapan lahan Pembelian bibit & Penanaman Pemeliharaan tanaman Pemeliharaan & perlindungan Pemupukan Pengelolaan & operasi tahunan Pajak tanah Total Pembangunan Agroforestri BIAYA SKEMA KARBON Biaya desain, pendaftaran & validasi Biaya monitoring Biaya verifikasi & sertifikasi Total Biaya Skema Karbon BIAYA TOTAL Tanpa CER Dengan CER Selisih Pendapatan - Biaya Tanpa CER Dengan CER Pendapatan Total (Terdiskonto) Tanpa CER Dengan CER

886,951,440

9

-

10

1,108,689,301

11

-

12

1,108,689,301

13

-

14

15

-

675,000,000 -

675,000,000 -

675,000,000 860,564,691

675,000,000 -

675,000,000 -

675,000,000 -

675,000,000 -

1,561,951,440 1,561,951,440

675,000,000 675,000,000

1,783,689,301 2,644,253,992

675,000,000 675,000,000

1,783,689,301 1,783,689,301

675,000,000 675,000,000

675,000,000 675,000,000

16

11,865,263,974 675,000,000

12,540,263,974 12,540,263,974

675,000,000 675,000,000 675,000,000

840,000,000 1,106,250,000 360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 3,057,750,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000 200,000,000 400,000,000

200,000,000

200,000,000

200,000,000 200,000,000 400,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 877,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 877,000,000

3,057,750,000 3,257,750,000

198,000,000 (2,000,000)

198,000,000 (2,000,000)

12,063,263,974 11,663,263,974

195,523,456 195,523,456

177,748,597 177,748,597

3,002,039,492 3,002,039,492

1,084,951,440 884,951,440 728,661,875 728,661,875

198,000,000 (2,000,000)

1,306,689,301 1,767,253,992

198,000,000 (2,000,000)

286,265,892 286,265,892

687,689,440 1,019,474,382

236,583,382 236,583,382

1,306,689,301 1,106,689,301 568,338,380 568,338,380

200,000,000

(2,382,750,000) (2,582,750,000) 146,899,667 146,899,667

Lampiran 15. Contoh hasil analCER dengan pendekatan Maksimum Produksi CER Luas lahan agroforestri Tahun Ke8 Biaya Total (Terdiskonto) Tanpa CER Dengan CER NPV Tanpa CER Dengan CER B/C Ratio Tanpa CER Dengan CER Tabel bantu perhitungan: Produksi kayu Volume Penjarangan Volume Tebang habis diameter 20-30 cm up diameter 30 cm up Produksi pertanian Hasil agroforestri kopi Produksi CER (80% dari total) Nilai Penjualan Produksi kayu diameter 20-30 cm up diameter 30 cm up Kopi Harga t-CER BAHAN SIMULASI Harga CER

Suku Bunga

Biaya Transaksi

222,524,020 315,825,496 506,137,854 412,836,378

8

9 202,294,564 287,114,088 83,971,328 (848,195)

9

11.59

10 183,904,149 338,121,465 503,785,291 681,352,917

10

11 167,185,590 237,284,370 69,397,792 (700,988)

11

14.49

12 151,986,900 215,713,064 416,351,480 352,625,317

12

13 138,169,909 196,102,785

14 125,609,008 178,275,259

57,353,547 (579,329)

52,139,588 (526,663)

13

14

15 114,190,008 209,946,827 2,887,849,485 2,792,092,665

15

16 665,455,490 708,981,317 (518,555,823) (562,081,650)

16

14.49 95.73 98.79

500

1,971,003 1,500,000 -

500

1,500,000 -

500 54.24

2,463,754 1,500,000 1,912,366

500

1,500,000 -

500

2,463,754 1,500,000 -

500

1,500,000 -

500

1,500,000 -

500

9,572,615 16,794,638 1,500,000 -

500

1,500,000 -

Lampiran 15. Contoh hasil anal dengan pendekatan Maksimum Produksi CER Luas lahan agroforestri 17 PENDAPATAN Penjualan kayu & hasil pertanian Penerimaan hasil penjarangan Penerimaan hasil tebang habis Penjualan hasil pertanian (kopi) Penjualan CER dari karbon Total Pendapatan Tanpa CER Dengan CER BIAYA PEMB. AGROFORESTRI Penyiapan lahan Pembelian bibit & Penanaman Pemeliharaan tanaman Pemeliharaan & perlindungan Pemupukan Pengelolaan & operasi tahunan Pajak tanah Total Pembangunan Agroforestri BIAYA SKEMA KARBON Biaya desain, pendaftaran & validasi Biaya monitoring Biaya verifikasi & sertifikasi Total Biaya Skema Karbon BIAYA TOTAL Tanpa CER Dengan CER Selisih Pendapatan - Biaya Tanpa CER Dengan CER Pendapatan Total (Terdiskonto) Tanpa CER Dengan CER

-

18

-

19

591,300,960

20

-

21

886,951,440

22

-

23

886,951,440

24

-

675,000,000 -

675,000,000 -

675,000,000 -

675,000,000 437,897,962

675,000,000 -

675,000,000 -

675,000,000 -

675,000,000 -

675,000,000 675,000,000

675,000,000 675,000,000

1,266,300,960 1,266,300,960

675,000,000 1,112,897,962

1,561,951,440 1,561,951,440

675,000,000 675,000,000

1,561,951,440 1,561,951,440

675,000,000 675,000,000

360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 1,111,500,000

360,000,000 94,500,000 274,500,000 337,500,000 45,000,000 1,111,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000 200,000,000 400,000,000

200,000,000

200,000,000

200,000,000

200,000,000

1,111,500,000 1,311,500,000

1,111,500,000 1,311,500,000

477,000,000 677,000,000

477,000,000 877,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

(436,500,000) (636,500,000)

(436,500,000) (636,500,000)

(436,500,000) (636,500,000)

789,300,960 589,300,960

198,000,000 235,897,962

1,084,951,440 884,951,440

133,545,152 133,545,152

121,404,683 121,404,683

207,050,326 207,050,326

100,334,449 165,425,191

211,067,390 211,067,390

82,921,032 82,921,032

198,000,000 (2,000,000) 174,435,859 174,435,859

1,084,951,440 884,951,440 68,529,779 68,529,779

Lampiran 15. Contoh hasil anal dengan pendekatan Maksimum Produksi CER Luas lahan agroforestri 17 Biaya Total (Terdiskonto) Tanpa CER Dengan CER NPV Tanpa CER Dengan CER B/C Ratio Tanpa CER Dengan CER Tabel bantu perhitungan: Produksi kayu Volume Penjarangan Volume Tebang habis diameter 20-30 cm up diameter 30 cm up Produksi pertanian Hasil agroforestri kopi Produksi CER (80% dari total) Nilai Penjualan Produksi kayu diameter 20-30 cm up diameter 30 cm up Kopi Harga t-CER BAHAN SIMULASI Harga CER

Suku Bunga

Biaya Transaksi

18

19

20

21

22

23

24

219,904,349 259,473,283

199,913,045 235,884,803

77,993,312 110,694,910

70,903,011 130,360,462

64,457,282 91,483,397

58,597,529 83,166,724

53,270,481 75,606,113

48,427,710 68,732,830

(86,359,198) (125,928,132)

(78,508,362) (114,480,120)

129,057,014 96,355,416

29,431,438 35,064,729

146,610,108 119,583,993

24,323,503 (245,692)

121,165,378 98,829,747

20,102,068 (203,051)

17

18

19

20

7.73

500

1,500,000 -

500

1,500,000 -

500

1,314,002 1,500,000 -

21

22

11.59

500 27.6

1,500,000 973,107

500

1,971,003 1,500,000 -

23

24

11.59

500

1,500,000 -

500

1,971,003 1,500,000 -

500

1,500,000 -

Lampiran 15. Contoh hasil anal dengan pendekatan Maksimum Produksi CER Luas lahan agroforestri 25 PENDAPATAN Penjualan kayu & hasil pertanian Penerimaan hasil penjarangan Penerimaan hasil tebang habis Penjualan hasil pertanian (kopi) Penjualan CER dari karbon Total Pendapatan Tanpa CER Dengan CER

1,108,689,301

26

-

27

1,108,689,301

28

-

29

30

-

675,000,000 860,564,691

675,000,000 -

675,000,000 -

675,000,000 -

675,000,000 -

1,783,689,301 2,644,253,992

675,000,000 675,000,000

1,783,689,301 1,783,689,301

675,000,000 675,000,000

675,000,000 675,000,000

Total

11,865,263,974 675,000,000

9,165,164,885 23,730,527,947 18,225,000,000 2,596,925,307

678,901 1,757,817 1,350,000 192,365

12,540,263,974 12,540,263,974

51,120,692,832 53,717,618,139

3,786,718 3,979,083

1,680,000,000 2,212,500,000 2,160,000,000 2,835,000,000 1,647,000,000 10,125,000,000 1,350,000,000 22,009,500,000

124,444 163,889 160,000 210,000 122,000 750,000 100,000 1,630,333

14,815 429,630 74,074 518,519

1,630,333 2,148,852


94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

94,500,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

337,500,000 45,000,000 477,000,000

BIAYA SKEMA KARBON Biaya desain, pendaftaran & validasi Biaya monitoring Biaya verifikasi & sertifikasi Total Biaya Skema Karbon

200,000,000 200,000,000 400,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000

200,000,000 5,800,000,000 1,000,000,000 7,000,000,000

477,000,000 877,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

477,000,000 677,000,000

22,009,500,000 29,009,500,000

198,000,000 (2,000,000)

198,000,000 (2,000,000)

16,611,428,858 12,208,354,165

42,551,601 42,551,601

718,664,386 718,664,386

10,806,465,939 11,554,668,482

BIAYA TOTAL Tanpa CER Dengan CER Selisih Pendapatan - Biaya Tanpa CER Dengan CER Pendapatan Total (Terdiskonto) Tanpa CER Dengan CER

198,000,000 (2,000,000) 164,627,384 244,054,062

1,306,689,301 1,767,253,992 56,636,181 56,636,181

198,000,000 (2,000,000) 136,055,690 136,055,690

1,306,689,301 1,106,689,301 46,806,761 46,806,761

Rataan/ha/th

Lampiran 15. Contoh hasil anal dengan pendekatan Maksimum Produksi CER Luas lahan agroforestri 25 Biaya Total (Terdiskonto) Tanpa CER Dengan CER NPV Tanpa CER Dengan CER B/C Ratio Tanpa CER Dengan CER Tabel bantu perhitungan: Produksi kayu Volume Penjarangan Volume Tebang habis diameter 20-30 cm up diameter 30 cm up Produksi pertanian Hasil agroforestri kopi Produksi CER (80% dari total) Nilai Penjualan Produksi kayu diameter 20-30 cm up diameter 30 cm up Kopi Harga t-CER BAHAN SIMULASI Harga CER

Suku Bunga

Biaya Transaksi

26

27

28

29

30

Total

Rataan/ha/th

44,025,191 80,943,590

40,022,901 56,803,992

36,384,455 51,639,992

33,076,778 46,945,448

30,069,798 42,677,680

27,336,180 38,797,891

8,645,163,208 10,827,905,359

120,602,193 163,110,471

16,613,280 (167,811)

99,671,234 84,415,697

13,729,983 (138,687)

12,481,803 (126,079)

691,328,206 679,866,496

2,161,302,732 726,763,124 1.250 1.067

25

26

14.49

27

28

29

30

14.49 95.73 98.79

500 54.24

2,463,754 1,500,000 1,912,366

500

500

500

500

500 73656

1,500,000 -

2,463,754 1,500,000 -

1,500,000 -

1,500,000 -

9,572,615 16,794,638 1,500,000 -

4,802,895 1,615,029

Brown S, Gillespie AJR, Lugo AE Biomass estimation methods for tropical forests with applications to forest inventory data

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