Review

Coffee agroforestry as an alternative to the implementation of green economy practices in Indonesia: A systematic review

  • Received: 20 March 2023 Revised: 13 June 2023 Accepted: 13 July 2023 Published: 25 July 2023
  • Agroforestry systems, particularly the coffee agroforestry system (CAS), represent a promising approach to achieving sustainable development and promoting a green economy in Indonesia. Our research aims to explore the potential of CAS to provide ecosystem services and socio-economic benefits and address environmental risks in Indonesia. To critically analyze the current state of CAS in Indonesia, its potential for sustainable development, and the challenges that should be addressed to increase its uptake, we conducted a systematic review of published academic literature. Our findings suggest that CAS has the potential to turn a profit in the future thanks to rising global and domestic market demand as well as environmental benefits like reducing carbon emissions and improving resource efficiency. However, several challenges should be addressed to improve coffee quality and sustain green economy activities, including product certification, collaboration with NGOs and business entities, and coordination of government policies at the site level. Overall, this study highlights the importance of CAS as a green economy approach in Indonesia and suggests policy recommendations to support its promotion and adoption. By addressing the challenges and promoting the potential of CAS, sustainable development and green economic growth can be achieved in Indonesia.

    Citation: Nur Arifatul Ulya, Alfonsus Hasudungan Harianja, Apri Laila Sayekti, Astrina Yulianti, Deden Djaenudin, Edwin Martin, Hariyadi Hariyadi, Julian Witjaksono, Leo Rio Ependi Malau, Muhammad Rifqi Tirta Mudhofir, Satria Astana. Coffee agroforestry as an alternative to the implementation of green economy practices in Indonesia: A systematic review[J]. AIMS Agriculture and Food, 2023, 8(3): 762-788. doi: 10.3934/agrfood.2023041

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  • Agroforestry systems, particularly the coffee agroforestry system (CAS), represent a promising approach to achieving sustainable development and promoting a green economy in Indonesia. Our research aims to explore the potential of CAS to provide ecosystem services and socio-economic benefits and address environmental risks in Indonesia. To critically analyze the current state of CAS in Indonesia, its potential for sustainable development, and the challenges that should be addressed to increase its uptake, we conducted a systematic review of published academic literature. Our findings suggest that CAS has the potential to turn a profit in the future thanks to rising global and domestic market demand as well as environmental benefits like reducing carbon emissions and improving resource efficiency. However, several challenges should be addressed to improve coffee quality and sustain green economy activities, including product certification, collaboration with NGOs and business entities, and coordination of government policies at the site level. Overall, this study highlights the importance of CAS as a green economy approach in Indonesia and suggests policy recommendations to support its promotion and adoption. By addressing the challenges and promoting the potential of CAS, sustainable development and green economic growth can be achieved in Indonesia.



    1. Introduction

    The location of interest is the southeast shoulder of the Rio Grande Rift on the boundary between southeast New Mexico and west Texas in the southwest of USA (Figure 1). This shoulder constitutes the west margin of the Great Plains; it is at the southwestern part of the North American Craton [1,2].

    Figure 1. The stations deployed by USArray in the region; the blue dots represent the stations of the TA array and the red dots represent the stations of the XR array.

    The basement of the North American Craton is the Proterozoic Laurentia plate. Southwestern Laurentia constitutes the Mojave, Yavapai, Mazatzal and Grenville Precambrian provinces, ordered by age and located from northwest to southeast [3]. The basement of this region is the source of extensive geological, geophysical and geochemical research, but it remains poorly understood [3,4,5,6,7,8,9]. The fact that it is mostly covered with younger formations and located in the boundary between two countries (Mexico and USA) have contributed to the problem.

    Mineral resources found in this region include rare earth elements, beryllium and molybdenum related to Tertiary igneous activity. Ignimbrites related to the large silicic event that constitutes the Sierra Madre Occidental are present in calderas at west Texas; some examples are Quitman, Eagle and Chinati [10,11,12,13,14]. Laccoliths are located at places such as Cornudas and Sierra Blanca, TX; stratovolcanoes such as Sierra Blanca, NM and the batholith at Capitan in New Mexico, also contain anomalously high concentrations of incompatible elements [12,15,16,17]. The magmas that created these igneous features rich in rare earth elements, beryllium and molybdenum have similar tholeiitic and alkalic composition to those of oceanic-island basalts indicating the possibility of having originated as asthenosphere derived melts in the lithospheric mantle during extension [18].


    2. Data background

    The seismic data used in this study is the Broad-band High-gain Z-component (BHZ) of the seismograms registered by two seismic projects: The transportable array (TA) [19] and the Flex array (XR) [6,20,21]. Both projects were deployed by EarthScope USArray, supported by National Science Foundation (NSF) and are available for download at the official website of the Incorporated Research Institutions for Seismology, IRIS [22]. We use the WILBER3 tool to download the data [23]. The distribution of the seismic stations is listed in Appendices 1 and Appendices 2, and shown in Figure 1. Three events were specially selected to make the analysis: The first was a magnitude 6.4 earthquake off the coast of Jalisco Mexico (17.52° N, 105.46° W) on September 24,2008; 02: 33: 05 UTC. The second was a magnitude 6.5 earthquake off the coast of Northern California (40.67° N, 124.47° W) on January 10,2010; 00: 27: 41 UTC. The third was a magnitude 8.1 earthquake in the Samoa Islands Region (15.5119° S, 171.9369° W) on September 29,2009; 17: 48: 11 UTC. The time window was considered to select the events; the TA array stations were deployed in the area of interest approximately from February 2008 to February 2010; and the Flex array from August 2008 to December 2011. The events were selected based on large event magnitude and teleseismic distances to produce the best amplitude and signal to noise ratio to model the crustal structure [24]. Large Rayleigh wave amplitudes, relatively low attenuations and long propagation paths have contributed significantly to our understanding of the seismotectonics in the region [25]. The frequent occurrence of earthquakes and growing number of seismic stations near the southeast flank of the Rio Grande Rift made possible the study of the area's Rayleigh wave group velocities. This procedure was selected because it allows the collection with relative ease of a dense distribution of paths using stations within or near the area of research [25,26]. The results shed new light on the seismotectonics of the region.

    The geodetic model and isostatic residual gravity anomaly data were downloaded from USGS Mineral Resources On-Line Spatial Data's website [27]. In Figure 6, the contour of the zero isostatic anomaly is shown for reference [28,29]. Shore lines and borderlines are provided by Generic Mapping Tools, GMT [30,31]. The Elevation Model (ETOPO 1) was downloaded from National Oceanic and Atmospheric Administration's National Centers for Environmental Information, NOAA's NCEI [32].


    3. Methodology

    The following procedure is based on cross correlation of filtered surface waves in to specific band-widths to approximate the inter-station empirical Green's functions using inter-station surface wave dispersion curves. This work was performed using transient seismic signals of the three specific events mentioned in the data section [24,33,34,35,36].

    The selected data was processed using the Seismic Analysis Code (SAC) developed by B. Savage and A. Snoke and provided also by IRIS [37,38]. The distance, longitude and latitude of the event and the seismic stations are read from the header of the seismograms. The travel time of the group is calculated using cross correlation. No removal of instrument contribution was necessary because the data was obtained with identical instruments (Streckeisen STS-2 G3 coupled with Quanterra 330 Linear Phase), so they have the same response and same sensitivity; they are also calibrated under the same criteria because they were all deployed by USArray under the same project, EarthScope [21].

    The distance between two stations is measured by the subtraction of the great circle path lengths connecting the event with the two stations. In this procedure we also use ray tracing, illustrated in Figures 2a, 2b and 2c, to choose the specific pair of stations; it elucidates the relative position and the order of the stations. The first station should be near, or directly on the seismic path between the event and the second station.

    Figure 2a. Ray tracing of the events in Jalisco. Blue dots represent stations of TA array and red dots represent stations of XR array. The background colors represent elevation in meters over sea level.
    Figure 2b. Ray tracing of the event in California. Blue dots represent stations of TA array and red dots represent stations of XR array. The background colors represent elevation in meters over sea level.
    Figure 2c. Ray tracing of the event in Samoa. Blue dots represent stations of TA array and red dots represent stations of XR array. The background colors represent elevation in meters over sea level.

    Now that the two stations are identified to be along similar paths and the distance between the two stations is calculated, the seismic travel time between the first and the second station is then calculated using cross correlation. To achieve the cross correlation, the seismograms are loaded into SAC and filtered in the desired frequency band with a specific ban-width. Once the filtering is done, the output signal is corrected and cross-correlated to measure the difference in arrival times; Figure 3 and Figure 4 describe the process graphically [33,34,36].

    Figure 3. This procedure was coded in SAC to obtain the correlation times.
    Figure 4. Seismograms of an event off the coast of Jalisco. 4a: Are the rough seismograms; 4b: are the seismograms after filtered; 4c: the cross correlation; 4d: the square of the cross correlation. The x axis is in seconds and the y axis is relative amplitude.

    The filter is a band-pass Butterworth order six [39,40]. It is applied twice with the desired corners from where we define the group. After the correlation the signal is squared to facilitate the identification of the largest peak (maximum). Note that in Figure 4d the maximum amplitude is seen approximately at 19 seconds. The average velocity is then calculated as the ratio between the difference in distance and the correlation time.

    We follow the same process for different groups spanning the frequencies available from the seismograms; in this part of the process the physical characteristics of the instrument establish the limitations (Nyquist is 20 Hz). The seismic average velocities of the group are plotted versus period to generate the dispersion curves (Figure 5a).

    Figure 5a. Dispersion curves for the event in Jalisco. Horizontal axis represents speed in km/s, the vertical axes, on the left, represents the period in seconds and on the right, represents the approximate depths according to ak135. The line runs from station TA-223A to station TA-W23A approximately from 32° to 35° latitude along –106.25° longitude. Please see Figure 1 to identify the stations involved in the pairs along the line 3.

    The seismograms were filtered at different band-widths with initial period of 10 seconds. The frequency bandwidths span at increments corresponding to multiples of 5/10, 6/10, 7/10 and 8/10 [33,36]. Figure 5a shows dispersion curves for the stations listed with frequency limits and bandwidths calculated for the event in Jalisco. These dispersion curves and the blockmean tool of the GMT software provided the data to make the profile shown in Figure 5b. The approximate depths labeled in the plot of the dispersion curves in Figure 5a and the profile in Figure 5b on their right axes were taken from the inversion of the model ak135 [41,42]. The red dots in the profile of Figure 5b represent the depth of the Moho according to the receiver functions of the EarthScope Automated Receiver Survey, EARS [43].

    Figure 5b. The seismic velocity profile created with the dispersion curves in Figure 5a. Horizontal axis is latitude; vertical axes on the left is period in seconds; on the right is approximate depths; colors are the seismic velocities. The red dots are the approximated depth to the Moho from receiver functions.

    The data obtained from the calculation of the dispersion curves were stored as matrices containing latitude, longitude, velocity and initial frequency (of the frequency band). These matrices were later used to generate the surface plots shown in Figures 7, 8 and 9. All these Figures were made with the matrices corresponding to band widths of fifth of a decade intervals or periods from 10 s to 20 s for the first band, 20 s to 40 s for the second band, 40 s to 80 s for the third band, 80 s to 160 s for the fourth band. The approximate depths of these frequency bands were approximated from inversion of the model ak135 and joint inversions made for LA RISTRA [8,41,42].

    Figure 6. The isostatic residual gravity anomaly of the region. The contour lines denote the zero value of the isostatic anomaly. Some geological structures in the region are identified as: The Diablo Plateau (D), Fort Davis (d), Ouachita (O), Franklyn-Organ Mountains (F), Capitan (C), North Central Basin Platform (B), South Central Basin Platform (b), San Andres Mountains (A), Potrillos Mountains (P), Hueco Bolson (H), Delaware Basin (DB), Tularosa basin (T), Marfa basin (M), Hovey Channel (h), Mesilla basin (m), Sheffield Channel (S) and Salt basin (s). It also shows some of the tertiary REE deposits developed in the region; the dotted lines shows the boundary between Mazatzal and Grenville Precambrian provinces in brown, the alignment visible in the seismic profile for the Jalisco event in purple, the Delaware Basin in black and the Diablo Plateau west boundary in red.
    Figure 7. Images represent seismic group velocities calculated using cross correlation for the event in Jalisco. The group periods span: In 7a from 10 to 20 seconds; in 7b from 20 to 40 seconds; in 7c from 40 to 80 seconds and in 7d from 80 to 160 seconds. The x axis represents longitude, the y axis latitude and the color range is seismic velocities in km/s.
    Figure 8. Images represent seismic group velocities calculated using cross correlation for the event in California. The group periods span: In 8a from 10 to 20 seconds; in 8b from 20 to 40 seconds; in 8c from 40 to 80 seconds and in 8d from 80 to 160 seconds. The x axis represents longitude, the y axis latitude and the color range is seismic velocities in km/s.
    Figure 9. Images represent seismic group velocities calculated using cross correlation for the event in Samoa. The group periods span: In 9a from 10 to 20 seconds; in 9b from 20 to 40 seconds; in 9c from 40 to 80 seconds and in 9d from 80 to 160 seconds. The x axis represents longitude, the y axis latitude and the color range is seismic velocities in km/s.

    ● Between 10 km and 20 km approximated depth for the periods between 10 s and 20 s.

    ● Between 20 km and 50 km approximated depth for the periods between 20 s and 40 s.

    ● Between 50 km and 150 km approximated depth for the periods between 40 s and 80 s.

    ● Between 150 km and 350 km approximated depth for the periods between 80 s and 160 s.

    Figure 6 was created to identify the geological structures that constitute this part of the North American Craton. The geological features are labeled on the top of isostatic anomaly map. The labels are:

    ● Stable structures identified in the region are: The Diablo Plateau (D), Fort Davis Caldera (d), Ouachita (O), Franklin-Organ Mountains (F), Capitan (C), North Central Basin Platform (B), South Central Basin Platform (b), San Andres Mountains (A) and Potrillos Mountains (P) [44].

    ● Some more flexible corridors surrounding these stable structures are the Hueco Bolson (H), Delaware Basin (DB), Tularosa Basin (T), Marfa Basin (M), Hovey Channel (h), Mesilla Basin (m), Sheffield Channel (S) and Salt Basin (s) [44].

    ●The lines shown represent the boundary between Mazatzal and Grenville Precambrian Provinces [3], the Delaware Basin, the west boundary of the Diablo Plateau and the diagonal line running from southeast to northwest passing through the center of the Delaware Basin characterizes the Jalisco event analysis. These lines are also shown in the results (Figures 7 to Figures 9) to correlate with the structures.

    The results of the seismic velocity model in Figures 7 to 9 were plotted using linear Delaunay triangulation in octave [45,46], an open source alternative software of Matlab [47,48]. Figures 10 and 11 were performed for the estimation of the accuracy [49,50]. The resolution of the figures corresponds to a gridding size of 40 × 40 elements, each 1/100 of a degree in area. For the creation of the surface plot of the seismic velocities, first the Voronoi diagram was created, shown in Figure 10; then the area of interest was gridded into meshes of 5 × 5, 10 × 10, 20 × 20, 30 × 30, 40 × 40, 50 × 50 and 60 × 60 area bins and plotted using the Delaunay triangulation. Figure 11 shows the 5 × 5, 10 × 10, 40 × 40 and 60 × 60 meshes that can be compared to the Voronoi figure. The table in Figure 10 shows the number n of the n × n binning and the size of the area a of each bin. This table was created to determine the most appropriate size of the interpolation mesh; the values of the number n versus the size of the pixel as is shown in the plot next to the table.

    Figure 10. Voronoi diagram showing the partitioned plane for the set of positions corresponding to Rayleigh wave velocities calculated for periods ranging from 10 s to 20 s for the event in Jalisco; the table shows the number of side bins n to be calculated per side in a square array of n × n bins of area a that are shown in the plot. The size of the pixel represents a surface in units of degrees square.
    Figure 11. Plot of the 2D + 1 surface plot of the seismic velocities interpolated using Delaunay triangulation with area binned by 5 × 5, 10 × 10, 40 × 40 and 60 × 60 bins.

    4. Results and discussion

    The results shown in Figures 7, 8 and 9 were chosen because their wider bandwidths enhance the effects of notches and extinction that are consequences of the multipath trajectories that characterize surface wave propagation; this effect is more frequent when using narrower frequency bandwidths [24]. As an example, in Figure 5a, there is a gap for the dispersion curve of line 3 pair 1 (l3p1); no acceptable data was available in the range between 10.00 s to 14.29 s. The gap was compensated by the blockmean interpolation algorithm of GMT that was used to generate the vertical profile in Figure 7b and by reducing the resolution between stations 223A and 123A. The choice of narrower frequency bands increases the vertical resolution but we should expect more gaps in the seismic velocity matrices due to destructive interference of the multipath effect of seismic surface wave propagation [24]. To generate the plots in Figures 7 to 9 the grid size was chosen from the analysis made in Figures 10 and 11. Figure 11 shows the unrealistic approach of using a 5 × 5 or a 10 × 10 grid in the plots and show that grids greater than 40 × 40 does not provide further information to resolve the geological structures.

    From the dispersion curves, Tables 1, 2, 3 and surface plots in Figures 7 to 9, the following structures were resolved: The Delaware Basin shows extreme anisotropy with complicated sub-structures that are resolved differently when the seismic velocities are calculated from sources at different distances and azimuths; its seismic velocities, ranges from 2.2 km/s up to 4.7 km/s. The seismic radiation from the event in California was the slowest, followed by that from the event in Jalisco, with the radiation from the event in Samoa being the fastest. For specific values of specific seismic velocities as function of depth and azimuth please refer to Tables 1, 2, 3. The Diablo Plateau showed seismic velocities, ranging from 3.2 km/s up to 4.3 km/s; but, for the Diablo Plateau, the seismic velocities calculated for the event in California are greater than those calculated for the event in Jalisco in opposition to the scenario in the Delaware Basin and the velocities for the event in Samoa were faster in both scenarios; the event in Jalisco resolves the seismic velocities in high correlation with the isostatic gravity anomaly, the event in Samoa somewhat and the event in California not well. The Tularosa Basin and Sheffield Channel show smaller variations in comparison with the Delaware Basin or the Diablo Plateau but still denote the anisotropy of the region; see Figures 7 to 9 and Tables 1 to 3. The area shows completely different images for every structure depending on the azimuth of the propagation of seismic waves leading to the conclusion of high Rayleigh wave anisotropy.

    Table 1. Seismic velocities for the event in Jalisco.
    Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in Jalisco. The seismic velocities are in kilometers per second
    Geol. Feat. Ref. Stat. Azimuth 15±5 km Depth 35±10 km Depth 100±50 km Depth 250±100 km Depth
    Tularosa Basin Z23A -2.4 2.4 3.3 to 3.5 3.8 3.9 to 4.3
    Diablo Plateau 224A -0.2 3.2 to 3.4 3.5 to 3.6 3.8 to 3.9 3.9
    Delaware Basin SC61 6 3.2 to 3.5 3.5 to 3.7 3.9 to 4.1 3.9 to 4.2
    Fort Davis SC61 6 3.3 3.6 3.9 4
    Sheffield Channel SC73 9 3.3 3.4 3.7 3.8
     | Show Table
    DownLoad: CSV
    Table 2. Seismic velocities for the event in California.
    Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in California. The seismic velocities are in kilometers per second
    Geol. Feat. Ref. Stat. Azimuth 15±5 km Depth 35±10 km Depth 100±50 km Depth 250±100 km Depth
    Tularosa Basin Z23A 111 2.7 to 2.9 1.3 to 1.7 3.7 to 3.8 4
    Diablo Plateau 224A 113 3.2 to 3.6 3.7 to 3.5 3.8 3.9 to 4.1
    Delaware Basin SC61 110 2.2 to 3.5 3.5 to 3.7 3.9 to 4 4 to 4.3
    Fort Davis 326A 113 3.2 to 3.4 3.6 3.8 to 3.9 4.1
    Sheffield Channel SC73 112 2.5 to 2.8 2.6 3.6 3.1
     | Show Table
    DownLoad: CSV
    Table 3. Seismic velocities for the event in Samoa.
    Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in Samoa. The seismic velocities are in kilometers per second
    Geol. Feat. Ref. Stat. Azimuth 15±5 km Depth 35±10 km Depth 100±50 km Depth 250±100 km Depth
    Sierra Blanca NM SC19 51 1.1 1.5 1.8 1.8
    Tularosa Basin Z23A 51 3 4.2 4.8 5 to 5.3
    Diablo Plateau 224A 52 4 to 4.3 4.1 to 4.6 4.8 to 5.5 4.9 to 5.4
    Delaware Basin SC61 53 3.7 to 4.7 4.1 to 5.1 4.6 to 6 4.5 to 4.7
    Fort Davis 426A 54 4.1 to 4.2 4.2 to 4.4 5.1 5.1 to 5.9
    Sheffield Channel SC73 54 3.9 2.8 4.9 4.9
     | Show Table
    DownLoad: CSV

    5. Conclusions

    The use of Rayleigh wave cross correlation analysis is a good tool to identify geological structures of regional size in the crust and upper mantle if the vertical broad band (BHZ) seismic data is available. For this work, data was provided by TA and Flex arrays [19,20].

    If you are planning to use Rayleigh wave cross correlation it is not recommended to combine the data from different events for the following reasons:

    ● The results of the seismic velocity calculations for different events have different averages and standard deviations. The contrast of the plot is affected by the standard deviation.

    ● Anisotropy causes huge differences in seismic velocities for surface waves travelling along different azimuths in a specific region. The seismic velocity of a region is a function of the angle of incidence of the seismic radiation.

    ● The uniqueness of the alignment in the strike and slip of the event (focal mechanism) produces specific distribution of stresses; it is elucidated by the moment tensor [51]. The geology reacts differently to different events.

    For these reasons the plot performed for different events leads to completely different images. From observation of the results, the structures are best resolved by radiation patterns that are perpendicular to their boundaries.

    ● Surface waves have some disadvantages.

    ● Extinction when passing through any geological structure with content of liquid or melts. This effect is caused because shear waves do not propagate in liquids; then, melting, partial melting or content of fluid in the geological formation along the path can cause anomalous correlation or extinction. In this area we have this effect when seismic waves pass through the Mogollon-Datil volcanic field; due to this problem the area near the upper Rio Grande Rift is not possible to model for events like California or Jalisco.

    ● Another important problem arising from surface wave propagation is called multi-pathing; this problem can be understood better if we recall the Huygens-Fresnel's wave propagation principle instead of the ray tracing model [24,51]. Correlation between two stations that, according to ray tracing, follow similar paths due to the presence of nearby boundaries striking along the propagation with large differences in seismic velocities, leads to negative time correlations. In other words, if signals arriving in the first station came from nearby geological structures with faster seismic speed than that of the second station, then the time correlation leads to negative values. This can be seen as the gap in the dispersion curves shown for the Jalisco event in Figure 5a.

    ● In another scenario the difference in phase between the two paths superposes destructively causing extinction of the amplitude in the correlation. The reduction or lack of amplitudes in the signal is seen in the dispersion as a discontinuity commonly called notch; the presence of notches causes loss of resolution in the plot [24].

    For the area of this investigation, the plot has lower resolution on the rift than on the plains (in the shoulder of the rift also called flank). For this reason, we decided to use the widest bandwidths to perform the plot shown in Figures 9 to 11.


    Acknowledgments

    The data used in this study were provided by Incorporated Research Institutions for Seismology Data Management Center (IRIS DMC), United States Geological Survey (USGS), National Oceanic and Atmospheric Administration's National Centers for Environmental Information (NOAA's NCEI) and General Mopping Tools (GMT).


    Conflict of interest

    All authors declare no conflicts of interest in this paper.


    Appendix

    Appendix 1. List of stations for network TA.
    NETWORK STATION LAT LON NETWORK STATION LAT LON
    TA 121A 32.5324 –107.7851 TA TASL 34.9454 –106.4565
    TA 122A 32.6995 –107.0005 TA TASM 34.9455 –106.46
    TA 123A 32.6349 –106.2622 TA TASN 34.9455 –106.46
    TA 124A 32.7001 –105.4544 TA TASO 34.9455 –106.46
    TA 125A 32.6588 –104.6573 TA TASP 34.9455 –106.46
    TA 126A 32.6462 –104.0204 TA TVZX 34.0733 –106.9196
    TA 127A 32.6764 –103.3575 TA X21A 34.4457 –107.7857
    TA 128A 32.6213 –102.485 TA X22A 34.5058 –107.0102
    TA 221A 32.0094 –107.7782 TA X23A 34.581 –106.1881
    TA 222A 32.1046 –107.1013 TA X24A 34.5646 –105.4349
    TA 223A 32.0062 –106.4276 TA X25A 34.5271 –104.6621
    TA 224A 32.076 –105.5226 TA X26A 34.5508 –103.8103
    TA 225A 32.1101 –104.8229 TA X27A 34.6469 –103.0974
    TA 226A 32.0618 –104.1014 TA X28A 34.5185 –102.1973
    TA 226B 32.0778 –104.1654 TA Y21A 34.0087 –107.674
    TA 227A 32.012 –103.2924 TA Y22A 33.937 –106.9652
    TA 228A 32.118 –102.5918 TA Y22C 34.0741 –106.9211
    TA 324A 31.4425 –105.4828 TA Y22D 34.0739 –106.921
    TA 325A 31.3711 –104.9712 TA Y22E 34.0742 –106.9208
    TA 326A 31.3165 –103.9786 TA Y22F 34.0741 –106.9209
    TA 327A 31.3691 –103.4923 TA Y23A 33.9315 –106.0549
    TA 328A 31.3818 –102.8097 TA Y24A 33.9257 –105.4361
    TA 425A 30.7862 –104.9857 TA Y25A 33.9229 –104.6928
    TA 426A 30.6689 –104.0293 TA Y26A 33.9232 –103.8246
    TA 427A 30.8498 –103.4018 TA Y27A 33.8839 –103.1633
    TA 428A 30.7263 –102.6847 TA Y28A 33.9086 –102.2479
    TA 526A 30.0609 –104.0898 TA Z21A 33.3086 –107.6712
    TA 527A 30.1456 –103.6119 TA Z22A 33.2555 –106.9639
    TA 528A 30.1615 –102.788 TA Z23A 33.2621 –106.2319
    TA 529A 30.1246 –102.2204 TA Z24A 33.3298 –105.3649
    TA 626A 29.554 –104.1335 TA Z25A 33.2797 –104.7171
    TA 627A 29.4528 –103.3887 TA Z26A 33.2716 –103.9798
    TA 628A 29.4862 –102.8885 TA Z27A 33.315 –103.2145
    TA MSTX 33.9696 –102.7724 TA Z28A 33.2884 –102.3866
     | Show Table
    DownLoad: CSV
    Appendix 2. List of stations for network XR.
    NETWORK STATION LAT LON NETWORK STATION LAT LON
    XR SC04 34.5228 –105.8119 XR SC40 32.9317 –103.54
    XR SC05 34.5715 –105.0554 XR SC41 32.9833 –103.2056
    XR SC06 34.5145 –104.2664 XR SC42 32.8728 –102.8612
    XR SC07 34.1838 –105.6877 XR SC43 32.9426 –102.5369
    XR SC08 34.1567 –105.4697 XR SC44 32.7572 –105.947
    XR SC09 34.1517 –105.0013 XR SC45 32.6337 –105.1552
    XR SC10 34.1937 –104.6666 XR SC46 32.654 –104.3614
    XR SC11 34.2323 –104.2959 XR SC47 32.629 –103.6257
    XR SC12 34.2148 –103.9116 XR SC48 32.6899 –102.905
    XR SC13 34.2135 –103.5269 XR SC49 32.443 –106.064
    XR SC14 33.9682 –105.7695 XR SC50 32.3895 –105.6153
    XR SC15 33.8308 –105.0255 XR SC51 32.3673 –105.1718
    XR SC16 33.8903 –104.3043 XR SC52 32.488 –104.8272
    XR SC17 33.893 –103.5446 XR SC53 32.3766 –104.3192
    XR SC18 33.8774 –102.8409 XR SC54 32.2837 –104.0398
    XR SC19 33.5188 –105.9744 XR SC55 32.1712 –103.6733
    XR SC20 33.6042 –105.5935 XR SC56 32.3554 –103.3986
    XR SC21 33.5975 –105.1655 XR SC57 32.3691 –102.8513
    XR SC22 33.5682 –104.7542 XR SC58 32.2888 –102.5482
    XR SC23 33.5995 –104.3282 XR SC59 31.9694 –105.1481
    XR SC25 33.5806 –103.5482 XR SC60 32.0937 –104.4877
    XR SC26 33.5044 –103.1184 XR SC61 31.9895 –103.6911
    XR SC27 33.5385 –102.8207 XR SC62 32.0119 –102.9373
    XR SC28 33.5662 –102.4915 XR SC63 31.8029 –104.8464
    XR SC29 33.3102 –105.6705 XR SC64 31.6996 –104.4258
    XR SC30 33.2738 –105.17 XR SC65 31.727 –104.0178
    XR SC31 33.259 –104.3415 XR SC66 31.6679 –103.7363
    XR SC32 33.1939 –103.5979 XR SC67 31.7051 –103.3951
    XR SC33 33.2334 –102.8343 XR SC68 31.8027 –102.77
    XR SC34 32.9513 –105.8163 XR SC69 31.6905 –102.588
    XR SC35 32.9369 –105.5153 XR SC70 31.3663 –103.7374
    XR SC36 33.0053 –105.18 XR SC71 31.6463 –103.0655
    XR SC37 32.937 –104.6192 XR SC72 31.1096 –103.6346
    XR SC38 32.9288 –104.3402 XR SC73 30.9611 –102.9875
    XR SC39 33.0286 –103.8453 XR SC74 31.0024 –102.6771
    XR SC75 31.8742 –105.952
     | Show Table
    DownLoad: CSV



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