In this vignette we will use the example data from the BLueCarbon library to estimate the organic carbon stocks in the first 1 meter of blue carbon soils and the average OC fluxes to this soils in the last 100 years.
load example data
we load and stored them as dataframe. The first dataframe (core_comp) has field measurement data that we will use to estimate soil compaction at core collection. The second dataframe has (bluecarbon_data) laboratory data that we will use to correct core compaction, modelize and estimate organic carbon content in each sample from organic matter content, estimate the stock in the first meter of the core and estimate the average carbon fluxes in the last 100 years.
core compaction estimations
many field methods to extract soil cores can lead to the compaction in the material retrieved (e.g. manual percussion). The compaction percentage can be estimated knowing the diference between the original surface level of the soil and the surface level of the soil withing the sampler after core insertion and before retrieval.
estimate_compaction (core_comp,
core= "core",
sampler_length = "sampler_length",
internal_distance = "internal_distance",
external_distance = "external_distance")
#> Warning in estimate_compaction(core_comp, core = "core", sampler_length =
#> "sampler_length", : Removing cores with missing data: Sm_03_04
#> core sampler_length internal_distance external_distance compaction
#> 1 Sg_01_01 200 35.000000 25.000000 5.7142857
#> 2 Sg_01_02 200 45.000000 35.000000 6.0606061
#> 3 Sg_01_03 200 86.000000 76.000000 8.0645161
#> 4 Sg_02_01 200 10.000000 0.000000 5.0000000
#> 5 Sg_02_02 200 60.000000 50.000000 6.6666667
#> 6 Sg_02_03 200 78.000000 68.000000 7.5757576
#> 7 Sg_03_01 200 52.000000 42.000000 6.3291139
#> 8 Sg_03_02 200 1.000000 1.000000 0.0000000
#> 9 Sg_03_03 200 98.000000 78.000000 16.3934426
#> 10 Sg_04_01 200 21.000000 1.000000 10.0502513
#> 11 Sg_04_02 200 36.000000 16.000000 10.8695652
#> 12 Sg_04_03 100 46.000000 26.000000 27.0270270
#> 13 Sg_05_01 100 90.000000 70.000000 66.6666667
#> 14 Sg_05_02 100 35.000000 15.000000 23.5294118
#> 15 Sg_05_03 100 12.000000 10.000000 2.2222222
#> 16 Sg_05_04 100 13.000000 10.000000 3.3333333
#> 17 Sg_05_05 100 60.000000 40.000000 33.3333333
#> 18 Sg_05_06 100 25.000000 5.000000 21.0526316
#> 19 Mg_01_01 100 80.000000 60.000000 50.0000000
#> 20 Mg_01_02 100 94.000000 74.000000 76.9230769
#> 21 Mg_01_03 100 66.000000 46.000000 37.0370370
#> 22 Sm_01_01 100 32.000000 12.000000 22.7272727
#> 23 Sm_01_02 200 45.000000 25.000000 11.4285714
#> 24 Sm_01_03 200 12.000000 8.000000 2.0833333
#> 25 Sm_02_01 200 5.000000 3.000000 1.0152284
#> 26 Sm_02_02 200 8.000000 7.000000 0.5181347
#> 27 Sm_03_01 200 51.000000 31.000000 11.8343195
#> 28 Sm_03_02 200 64.000000 44.000000 12.8205128
#> 29 Sm_03_03 200 32.000000 12.000000 10.6382979
#> 31 Sg_06_01 100 17.460606 12.460606 5.7117142
#> 32 Sg_06_02 100 13.854545 8.854545 5.4857371
#> 33 Sg_06_03 100 10.248485 5.248485 5.2769605
#> 34 Sg_06_04 100 6.642424 1.642424 5.0834925
#> 35 Sg_07_01 100 3.036364 1.000000 2.0569330
#> 36 Sg_07_02 100 52.000000 47.000000 9.4339623
#> 37 Sg_07_03 100 3.000000 1.000000 2.0202020
#> 38 Sg_08_01 100 65.000000 60.000000 12.5000000
#> 39 Sg_08_02 100 45.000000 40.000000 8.3333333
#> 40 Sg_08_03 100 23.000000 18.000000 6.0975610
#> 41 Sg_08_04 100 32.000000 27.000000 6.8493151
#> 42 Sg_08_05 100 33.000000 28.000000 6.9444444
#> 43 Sg_08_06 100 39.333333 34.333333 7.6142132
#> 44 Sg_08_07 200 44.333333 39.333333 3.1120332
#> 45 Sg_08_08 200 49.333333 44.333333 3.2119914
#> 46 Sg_08_09 200 54.333333 49.333333 3.3185841
#> 47 Sg_09_01 200 59.333333 54.333333 3.4324943
#> 48 Sg_09_02 200 64.333333 20.000000 24.6296296
#> 49 Sg_09_03 200 52.000000 52.000000 0.0000000
#> 50 Sg_09_04 200 6.000000 1.000000 2.5125628
#> 51 Sg_09_05 200 78.000000 73.000000 3.9370079
#> 52 Sg_09_06 200 20.000000 15.000000 2.7027027
#> 53 Sg_09_07 200 31.000000 26.000000 2.8735632
#> 54 Sg_09_08 200 4.000000 1.000000 1.5075377
#> 55 Sg_09_09 200 8.000000 3.000000 2.5380711
#> 56 Sg_10_01 200 22.000000 17.000000 2.7322404
#> 57 Sg_10_02 200 36.000000 31.000000 2.9585799
#> 58 Sm_07_01 200 81.000000 76.000000 4.0322581
#> 59 Sm_07_02 200 1.000000 1.000000 0.0000000
#> 60 Sm_07_03 200 12.000000 7.000000 2.5906736
#> 61 Mg_02_01 200 5.000000 0.000000 2.5000000
#> 62 Sg_11_01 100 8.000000 3.000000 5.1546392
#> 63 Sg_11_02 100 51.000000 46.000000 9.2592593
#> 64 Sg_11_03 100 64.000000 59.000000 12.1951220
#> 65 Sg_11_04 100 6.642424 1.642424 5.0834925
#> 66 Sg_11_05 100 3.036364 2.000000 1.0575139
#> 67 Sg_12_01 100 52.000000 47.000000 9.4339623
#> 68 Sg_12_02 100 3.000000 1.000000 2.0202020
#> 69 Sg_12_03 100 65.000000 60.000000 12.5000000
#> 70 Sm_08_01 100 45.000000 40.000000 8.3333333
#> 71 Sm_08_02 100 23.000000 18.000000 6.0975610
#> 72 Sm_08_03 100 32.000000 27.000000 6.8493151
#> 73 Sm_04_01 100 33.000000 28.000000 6.9444444
#> 74 Sm_04_02 100 39.333333 34.333333 7.6142132
#> 75 Sm_04_03 100 63.000000 58.000000 11.9047619
#> 76 Sm_04_04 100 48.000000 43.000000 8.7719298
#> 77 Sm_06_01 100 3.000000 1.000000 2.0202020
#> 78 Sm_06_01 100 2.000000 1.000000 1.0101010