Estimation of CO2 sequestration potential by afforestation in the arid rangelands of Western Australia based on long-term empirical data

Ecological Engineering 133 (2019) 109–120

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Estimation of CO2 sequestration potential by afforestation in the arid rangelands of Western Australia based on long-term empirical data

T

Hideki Suganumaa,b, , Shin-ichi Aikawac, Yuji Sakaid, Hiroyuki Hamanoe, Nobuhide Takahashib, Kiyotaka Taharaf, Satoko Kawarasakig, Hajime Utsugih, Yasuyuki Egashirai, Takuya Kawanishij, Richard J. Harperk, Hiroyuki Tanouchil, Toshinori Kojimam, Yukuo Aben, Masahiro Saitoo, Shigeru Katop, John Lawq, Koichi Yamadag ⁎

a

Kawasaki Environment Research Institute (KERI), Environment Protection Bureau, Kawasaki City, 3-25-13, Tonomachi, Kawasaki, Kawasaski, Kanagawa 210-0821, Japan Faculty of Textile Science and Technology, Shinshu University, 3-15-1, Tokida, Ueda, Nagano 386-8567, Japan c Japan Forest Technology Association, 7 Rokubancho, Chiyoda, Tokyo 102-0085, Japan d School of Advanced Engineering, Kogakuin University, 2665-1, Nakano-machi, Hachioji, Tokyo 192-0015, Japan e ELIIY Power Co. Ltd., 19th Floor, Shin-Osaki Kangyo Building, Osaki 1-6-4, Shinagawa-ku, Tokyo 141-0032, Japan f Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), 16-1, Onogawa, Tsukuba, Ibaraki 305-8569, Japan g Center for Low Carbon Society Strategy, Japan Science and Technology Agency, 5-3, Yonbancho, Chiyoda, Tokyo 102-8666, Japan h Forestry and Forest Products Research Institute (FFPRI), 1, Matsunosato, Tsukuba, Ibaraki 305-8687, Japan i School of Engineering, Tokyo University of Technology, 1404-1, Katakuramachi, Hachioji, Tokyo 192-0982, Japan j Institute of Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa 920-1192, Japan k College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, Western Australia 6150, Australia l Laboratory for Development of Farming and Mountain Area, 423-4 Okura, Toyono, Nagano 389-1102, Japan m NPO Research Institute of Macro-Engineering Practice, Kanda-kosho-Center-Building 6F, 2-3 Kanda-Jimbo-cho, Chiyoda, Tokyo 101-0051, Japan n Emeritus Professor, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-8577, Japan o Former Forestry and Forest Products Research Institute (FFPRI), 1, Matsunosato, Tsukuba, Ibaraki 305-8687, Japan p General Incorporated Foundation ASCO, 17-32 Chome Hikino Minami, Fukuyama City, Hiroshima 721-0945, Japan q South Kalgoorlie Operations, Alacer Gold Corp., Kalgoorlie-Kambalda Highway, Kalgoorlie, Western Australia 6430, Australia b

ARTICLE INFO

ABSTRACT

Keywords: Arid land afforestation Carbon mitigation Eco-hydrology Eucalyptus camaldulensis Hardpan blasting Rain-dependent growth rate

Large-scale afforestation is a key measure to mitigate global warming, however, implementation may result in land-use competition with agriculture. To avoid such competition, carbon mitigation methods using arid and semi-arid areas have been proposed, but to our knowledge there is no report of rates of sequestration based on long-term observations from actual experimentation. In this study (1999–2015), in an arid area near Leonora, Western Australia (annual rainfall: 220 mm year−1; pan evaporation: 3400 mm year−1), carbon sequestration was assessed in above and below ground biomass in Eucalyptus camaldulensis under ambient conditions and with active site amelioration (combination of water harvesting with large mounds and hardpan blasting). The carbon sequestration rate was estimated at 7.92 Mg-CO2-e ha−1 year−1 for a total carbon sink of 230 Mg-CO2-e ha−1. Carbon mitigation may thus be a viable option in arid regions, not only in Western Australia but globally, and can be enhanced with active site engineering.

1. Introduction Total global emissions of CO2 derived from fossil fuel combustion in 2015 were reported to be over 32 Gt-CO2 (IEA/OECD, 2017), and these had increased 57% compared to that in 1990. In addition, total

emissions including other greenhouse gases (GHG) from land use change were the highest in human history, reaching 49 ( ± 4.5) Gt-CO2e year−1 in 2010 (IPCC, 2014). These emissions are causing changes in the climate that will have major impacts across the global economy. Thus, rapid and substantial counter-measures are essential, particularly

⁎ Corresponding author at: Kawasaki Environment Research Institute (KERI), Environment Protection Bureau, Kawasaki City, 3-25-13, Tonomachi, Kawasaki, Kawasaski, Kanagawa 210-0821, Japan. E-mail address: [email protected] (H. Suganuma).

https://doi.org/10.1016/j.ecoleng.2019.04.015 Received 12 August 2018; Received in revised form 4 April 2019; Accepted 14 April 2019 Available online 01 May 2019 0925-8574/ © 2019 Elsevier B.V. All rights reserved.

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given the ambitious carbon mitigation targets of the Paris Agreement (United Nations, 2015). Smith et al. (2014) emphasized the increasing importance of both bioenergy with carbon dioxide capture and storage (BECCS) and afforestation as mitigation measures, however, they also emphasized the challenges of land availability and the scale of afforestation. These were associated with challenges and risks, in particular, land-use competition between afforestation and other land uses, because there is only limited evidence on the potential for large-scale afforestation. This is reiterated in previous reports on the competition between afforestation and agriculture (Burns et al., 1999; Kirschbaum, 2000), water supplies (Jackson et al., 2005) and food production (Smith et al., 2013). To avoid land-use competition, carbon mitigation through either sequestration or bioenergy production using arid and semi-arid areas has been proposed and studied (Barton and Montagu, 2006; Burrows et al., 2002; Conant et al., 2001; Dean et al., 2015; Dener et al., 2006; Eady et al., 2009; Evans et al., 2015; Glenn et al., 1993; Harper et al., 2007, 2010; Howden et al., 2001; Lal, 2001; Moore et al., 2001; Polglase et al., 2013; Ravindranath et al., 2007; Trumper et al., 2008; Witt et al., 2011; Yamada et al., 2003). However, most of these reports were based on environmental conservation and management, or afforestation in semi-arid areas. There is no specific study that has examined long-term afforestation in arid areas based on field measurements. In this study, areas with annual rainfall under 250 mm are regarded as arid, and those from 250 to 500 mm rainfall are regarded as semi-arid. To our knowledge, the only reports of carbon sequestration potential by afforestation in arid areas have been by Yamada et al. (2003) and Shiono et al. (2007), however, their reports were based on shortterm (2 or 5.5 year) experimentation under irrigated conditions. Yamada (2004) extensively analyzed possible carbon mitigation methods within a no regrets policy framework, and concluded that large-scale afforestation in arid areas could sequester a considerable amount of carbon and avoid land use competition. Thus, arid land afforestation could represent an important option for large-scale carbon mitigation, particularly given the extensive global distribution of such land. There is, however, no substantial report of carbon mitigation potential based on long-term afforestation experiments in arid areas under rain-fed conditions. There are thus three objectives of this paper: identification of the most suitable species for afforestation, confirmation of the adequateness of a proposed afforestation method, and estimates of CO2 sequestration potential following afforestation in an arid region, based on empirical data which were derived from long-term (over 15 years) experimentation in Western Australia.

strata (Environment Australia, 2000). From the report of the National Land and Water Resources Audit (2002), and the land cover classification of Suganuma et al. (2006b), the main vegetation of Sturt Meadows is a mixture of Acacia woodland, Acacia shrub land (with small shrubs, e.g. Eremophilla sp.) and bare ground. Forest cover is < 1% of the area and mainly consists of Eucalyptus camaldulensis or A. aneura along the streamlines of some wadis (Suganuma et al., 2006b). The dominant land use of the Murchison region is extensive grazing of cattle and/or sheep. The average grazing pressure of this region is reported as 0.7 AE km−2 for cattle and 6.0 DSE km−2 for sheep (Fisher et al., 2004). AE denotes “animal equivalent” which is 400 kg per head and DSE denotes “dry sheep equivalent” which is 45 kg per head. A variety of minerals, such as iron, nickel and gold, are mined in the Murchison area, but this activity is quite limited in areal extent. The soils of the region are Basic Duric Red-Orthic Tenosols (Isbell, 1996) which are related to Arenosols in the international soil classification (FAO, 2014). A feature of these soils is the presence of the Wiluna hardpan (Bettenay and Churchward, 1974), which consists of laminated layers of silica with ferruginous and calcareous cement, and which is often up to 10 m thick (Teakle, 1950). This type of hardpan is often associated with Mulga woodland (Bettenay and Churchward, 1974) and is typically associated with arid and semi-arid conditions in Australia (Butt, 1983; Stephens, 1971). It is described as a root impeding layer which constrains the rooting depth of plants (Hingston et al., 1998) and the growth of planted trees (Pracilio et al., 2006). 2.2. Afforestation experiment The afforestation experimental site (121°0′50″E, 28°35′20″S) described here was originally established by Yamada et al. (1999, 2003), and is part of a broader study which investigated carbon mitigation and carbon dynamics in this environment (Yamada, 2004). The research area was chosen on the basis of being typical of inland arid environments in terms of rainfall pattern, solar radiation strength and the presence of the Wiluna hardpan (Yamada, 2004). In addition, the landowner was amenable to the application of civil engineering techniques. Before commencing afforestation at this site in July 1999, the area comprised bare ground. The surface soil comprised a reddish brown sandy loam (Hamano et al., 2001), with a thickness of around 15–20 cm, underlain by Wiluna hardpan which ranged in thickness between 7 and 10 m. This hardpan layer and shallow soil restrains the natural vegetation growth in this area. Fig. 1 (a) is an aerial photograph taken in 1999, showing each of the U-shaped water-harvesting mounds surrounding the afforestation plots, which are described later in this section. Fig. 1 (b) is a photograph taken after rainfall from the air, this showing how the water-harvesting mounds capture surface runoff. The experimental site (Fig. 1) initially consisted of 4 afforestation treatments (M, 2, 3 and 4) with an additional one (Treatment 1) added in January 2002, resulting in 5 treatments in total. The treatments, M, 1, 2, 3 and 4 (Fig. 1) denote the Main method (replicated 11 times), Method 1, Method 2, Method 3 and Method 4, respectively. These treatments were arrayed over an area of 15.1 ha, with overall dimensions of 600 m × 400 m. There was approximately 3 m difference in relief between the upper and lower parts of the research site (Fig. 1), which means the surface slope is about 0.5%. Surface runoff is generated in the upper parts as sheet flow and flows into each afforestation plot. Each plot (approximately 50 m wide × 40 m long) was broadly aligned along the contours, as best as field conditions would allow. Fig. 2 shows a brief outline of each experimental treatment. The Main method (M) was the combination of a conventional water-harvesting system with a large mound surrounding each afforestation plot and a hardpan blasting method invented and described by Yamada et al. (2003). Since Wiluna hardpan and scarce rainfall events were considered to restrain vegetation growth and extent in this area

2. Materials and methods 2.1. Research area The research area of this study is at Sturt Meadows (120°58′E, 28°40′S), which is located near Leonora, about 600 km north-east from Perth (the capital of Western Australia). The annual rainfall of Sturt Meadows based on more than 100 years of records by the Australian Bureau of Meteorology data (Weather station directory: http://www. bom.gov.au/climate/data/stations/) is 220 mm (S.D. = 106.3 mm), and it is thus categorized as an arid area. Annual pan evaporation was observed as approximately 3400 mm year−1 (Yamada et al., 1999), and long-term mean annual pan evaporation near the research area (Meekatharra Airport; 118°32.4′E, 26°36.6′S) was reported at 3531 mm year−1 (Roderick and Farquhar, 2004). This area belongs to the Murchison region of the Interim Biogeographic Regionalization of Australia (IBRA) Version 5.1 (Environment Australia, 2000). The Murchison environment is dominated by mulga (Acacia aneura F. Muell) low woodland, often rich in ephemerals, on outcrop hardpan wash plains and fine-textured Quaternary alluvial and eluvial surfaces mantling granitic and greenstone 110

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species selection suitable for arid land afforestation, there were 11 replicates of experimental plots of treatment M in this area. On the other hand, other treatments were established with only one plot each to compare situations between presence and absence of hardpan blasting, or to seek promising alternative water harvesting techniques. Following are the features of treatments 1, 2, 3 and 4. 1. The structure of the afforestation plot in Method 1 was the same as the Main method, but its water catchment area was much larger (The afforestation plot: water catchment area was about 1:5). Comparison between treatment M and Method 1 was to determine the effect of a larger water catchment area on planted tree survival and growth. 2. Method 2 involved water harvesting only, with no hardpan blasting. The land-use ratio of the plot was the same as the Main method. Comparison between treatment M and Method 2 was to determine the effect of presence and absence of hardpan blasting on planted tree survival and growth. 3. Method 3 involved hardpan blasting and another type of water harvesting. Instead of making a large mound surrounding the whole afforestation plot, a small mound was made around each planted tree (micro-catchment). Comparison between treatment M and Method 3 was to determine the effect of the type of water harvesting mound on planted tree survival and growth. 4. Method 4 was a modified version of Method 3, with double hardpan blasting. The hardpan layer of the upper slope (i.e. the water catchment area) was blasted simultaneously with that of the afforestation plot. This treatment was expected to introduce more runoff water into the subsoil which would subsequently pass downslope as throughflow thus preventing evaporation from the surface soil. Comparison between Methods 3 and 4 was to determine the effect of this modified water harvesting technology on planted tree survival and growth. Detailed information of each of the Methods is given in Table 1. Twelve tree species (Acacia aneura Benth. (mulga), A. tetragonophylla F. Muell. (kurara), A. pruinocarpa Tindale (gidgee), A. papyrocarpa Benth. (western myall), Casuarina obesa Miq. (swamp sheoak), Eucalyptus camaldulensis Dehnh. (river gum), E. campaspe S. Moore (silver gimlet), E. griffithsii Maiden (Griffith’s grey gum), E. lesouefii Maiden (Goldfields blackbutt), E. salubris F. Muell. (gimlet), E. stricklandii Maiden (Strickland’s gum), E. torquata Luehm. (coral gum)) were used in this study. All were Western Australian native tree species from the Murchison and Coolgardie IBRA regions. Among these tree species, A. aneura is predominant in natural woodlands, and C. obesa and E. camaldulensis occur along the wadi stream lines in the research area, and thus the number of planted tree seedlings of these three species was higher than the other tree species. Each afforestation plot consisted of several tree species with the tree species allocated randomly. Each tree seedling was planted on each blasted hole at approximately 7 m spacing. Seedlings were planted on August 1999 and were irrigated on an intermittent basis from a local groundwater source from August 1999 to March 2005. Irrigation was applied at a rate equivalent to 5–7 mm rainfall per application (up to four times a month) depending on weather conditions from August 1999 to February 2001, once per month from March 2001 to March 2004 and once per every two months from April 2004 to March 2005. From April 2005, the afforestation site was maintained under a completely rain-fed condition.

Fig. 1. Photographs of the afforestation site. (a) Aerial photograph taken in 1999 (source: Kevron Aerial Survey Pty. Ltd.). (b) Photograph of experimental plots taken from an airplane after rainfall in March 2000.

(Yamada et al., 1999; Yamada, 2004), it was hypothesized that supplying additional water and removing the effect of the hardpan were the keys for successful afforestation (Yamada et al., 2003; Yamada, 2004). Water harvesting is a common technique for growing crops and/ or vegetation in arid and semi-arid areas (Boers and Ben-Asher, 1982). For the blasting, a drilling machine, commonly used in mining, drilled holes to a depth of 3.5 m (about half the thickness of the Wiluna hardpan at this site) and with a spacing of about 7 m (step 1). Ammonium nitrate fuel oil (ANFO) explosives, also commonly used in mine sites, were packed inside the holes (step 2), and then exploded (step 3). From this procedure, conical shaped holes were created and each of them was filled with blasted materials (mainly cracked hardpan) and soil using a bulldozer. Tree seedlings were then planted at a rate of one seedling for each hole. In this method of afforestation, the land-use ratio of afforestation plots of planted trees versus water catchment area for water runoff was set as 1:3 on average. However, the exact land-use ratio was unknown, and varied from about 1:2 to 1:4. Water catchment areas were in the upper part of the afforestation plots, and were less disturbed by the construction works of afforestation plots, which meant they were conserved in their natural condition as much as possible. Because the objectives of this study consisted of confirmation of the adequateness of the afforestation methodology (Treatment M) and

2.3. Analytical methods 2.3.1. Species selection The trees were measured over the period from June 2000 to September 2015. For each surviving tree, tree height, crown diameter for small trees and stem diameter at 0.3 m (D0.3) for large trees were 111

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Main method and Method 1: Water harvesting with hardpan blasting Surface runoff Step 0

Natural soil condition (with thick hardpan layer)

Step 1

Holes drilled, explosives added

Step 2

Hardpan layer blasted and disrupted

Step 3

Fill holes and trees planted

Method 2: Water harvesting Surface runoff

Method 3: Microcatchment with hardpan blasting Hardpan blasting method is the same as Method 2

Surface runoff Small water harvesting mounds (microcatchments) were made for each tree

Method 4: Microcatchment with double hardpan blasting Hardpan blasting method and micro catchment were the same as Method 3

Holes blasted in upper area to enhance groundwater recharge Fig. 2. Experimental design.

measured. Data from the Main method afforestation plots were used to screen appropriate tree species for arid land afforestation. Because A. pruinocarpa and A. papyrocarpa were only planted in the Method 1 plot, these two tree species were subsequently excluded from the study. Since trees with low survival rates are not suitable for afforestation, only data from

species with high survival rates in 2015 were used in further analyses. Statistical analyses (see Section 2.3.4) of the survival ratio and mean tree biomass of highly surviving tree species, were used to determine the appropriate tree species for arid land afforestation.

112

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(2006a) and Shiono et al. (2006), and were developed through destructive sampling of whole trees. Because destructive sampling of below-ground biomass was very difficult, only a limited number of trees were sampled using an excavator and pressurized water. In Table 2, Ws, Wb, Wl, Wr, Wabove, Cpa, H, RMSE and n denote stem biomass, branch biomass, leaf biomass, root biomass, above-ground biomass, crown projection area, tree height, root mean squared error and tree sample number, respectively. These allometric equations for above ground biomass estimation are power functions (Y = aXb), but only E (IV) is a linear function (Y = aX). Below ground biomass was estimated from the root:shoot ratio. Root:shoot ratios in Table 2 were higher than the IPCC (2003) default value (0.25) but still within reasonable values as discussed by Barton and Montagu (2006).

Table 1 Basic information for afforestation plots. Afforestation methods

Afforestation area

Main method

Water harvesting Hardpan blasting

Method 1

Water harvesting with large catchment area Hardpan blasting Water harvesting No hardpan blasting Micro catchment Hardpan blasting Micro catchment Double hardpan blasting

Method 2 Method 3 Method 4

Stand density

[ha]

Planted tree number [trees]

0.26 0.26 0.22 0.25 0.23 0.24 0.21 0.21 0.25 0.26 0.25 0.33

42 42 39 42 42 42 41 42 42 48 48 59

162 161 178 171 184 175 191 199 168 182 189 181

0.25

42

169

0.10

18

172

0.13

18

134

[trees ha−1]

2.3.4. Statistical analyses For statistical analyses, two types of multiple comparison tests were used. One was Tukey’s test of the population means (hereafter, T1 test; α = 0.05), applied after an ANOVA test. The other was Tukey’s test of population rate with an all pairs comparison (hereafter, T2 test; α = 0.05). The T1 and T2 tests were parametric tests and used with the aim of avoiding a decrease in statistical power. These statistical analyses were applied to the data obtained from tree measurements in 2015 (i.e. 16 years after establishment) and were used to assess the difference of mean tree biomass and survival ratio, respectively. From these two statistical tests, tree species and afforestation methodologies were ranked based on significance (α = 0.05). When comparing afforestation methodologies (Treatment M, 1, 2, 3 and 4), the 11 replicates from treatment M were pooled as one group, following assessment of homogeneity by using the T1 test for mean tree biomass and T2 test for survival ratio.

Mean stand density of Main method was 178 trees ha−1 (about 7.5 m spacing). Establishment year of most of the afforestation plots was August 1999, with the Method 1 plot established in January 2002.

2.3.2. Adequateness of afforestation methodology Since E. camaldulensis survived well in every afforestation method (described later in Section 3.1), data from E. camaldulensis trees (Main method, Method 1, 2, 3 and 4) were used to compare the effectiveness of the different afforestation methodologies (such as the effect of the hardpan blasting and the water harvesting technologies) by comparing differences in survival and growth (see Section 2.3.4).

2.4. Estimation of actual GHG removal by sinks in two carbon pools No afforestation plots consisted of a single tree species and therefore biomass (Mg ha−1) or growth rate (Mg ha−1 year−1) of individual tree species cannot be calculated. Instead, we introduced two variables, “predicted plot biomass” (Mg ha−1) and “predicted growth rate” (Mg ha−1 year−1), to allow biomass (Mg ha−1) and growth rate (Mg ha−1 year−1) to be calculated indirectly for single species. Predicted plot biomass was calculated using the following equation:

2.3.3. Biomass estimation For biomass estimation of the trees (with high survival rates in 2015), allometric equations were used in this study as shown in Table 2. These allometric equations were modified from Suganuma et al.

(1)

PB = TB × SR × D × CF

Table 2 Allometric equations. Species

Equation Number

Dependent variable Y (kg)

Independent variable

Coefficient

X

a

S. E.

Significant T

b

S. E.

Significant T

(unit) 2

2

Significant F

R2

RMSE (kg)

n

Acacia aneura

A(I) A(II) A(III) A(IV) A(V)

Ws + Wb Wl Ws + Wb Wl Wr

D0.3 D0.32 Cpa Cpa Wabove

(m ) (m2) (m2) (m2) (kg)

6461 118 1.442 0.526 0.402

1241.8 36.2 0.065 0.094 N/A

< 0.01 < 0.01 < 0.01 < 0.01 N/A

1.221 0.815 1.442 0.847 1

0.041 0.066 0.219 0.080 N/A

< 0.01 < 0.01 < 0.01 < 0.01 N/A

< 0.01 < 0.01 < 0.01 < 0.01 N/A

0.983 0.912 0.982 0.895 N/A

40.9 4.0 28.6 1.9 N/A

17 17 11 15 2

Casuarina sp.

C(I) C(II) C(III) C(IV) C(V)

Ws + Wb Wl Ws + Wb Wl Wr

D0.32 D0.32 Cpa × H Cpa Wabove

(m2) (m2) (m3) (m2) (kg)

8836 325 0.224 0.795 0.471

1973.2 144.4 0.045 0.322 N/A

< 0.01 < 0.05 < 0.01 < 0.05 N/A

1.352 0.897 1.248 1.082 1

0.050 0.094 0.050 0.178 N/A

< 0.01 < 0.01 < 0.01 < 0.01 N/A

< 0.01 < 0.01 < 0.01 < 0.01 N/A

0.988 0.909 0.987 0.821 N/A

33.3 8.2 35.8 11.5 N/A

11 11 10 10 1

Eucalyptus camaldulensis

E(I) E(II) E(III) E(IV) E(V)

Ws + Wb Wl Ws + Wb Wl Wr

D0.32 D0.32 Cpa × H Cpa Wabove

(m2) (m2) (m3) (m2) (kg)

3855 159 0.153 0.601 0.718

672.1 15.4 0.036 0.013 N/A

< 0.01 < 0.01 < 0.01 < 0.01 N/A

1.258 0.889 1.243 1 1

0.040 0.022 0.049 N/A N/A

< 0.01 < 0.01 < 0.01 N/A N/A

< 0.01 < 0.01 < 0.01 < 0.01 N/A

0.992 0.995 0.986 0.995 N/A

32.3 1.7 80.7 2.8 N/A

10 10 11 11 3

Ws, Wb, Wl, Wr, Wabove, Cpa, H, RMSE and n denote stem biomass, branch biomass, leaf biomass, root biomass, above-ground biomass, crown projection area, tree height, root mean squared error and destructive tree sample number, respectively. 113

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where PB, TB, SR and D denote predicted plot biomass (Mg ha−1), mean tree biomass (kg tree−1), survival ratio and mean stand density (tree ha−1), respectively. CF is a mass conversion factor (1 Mg/1000 kg). Predicted growth rate was calculated using the following equation:

PGR = (PBN + 1

low survival ratios (0.50–0.88) in 2000. Following termination of irrigation in 2005 and a prolonged drought from 2007 to 2008, A. aneura, A. tetragonophilla and E. camaldulensis maintained their survival, whereas the remaining seven tree species had considerable decreases in survival. Only three tree species (A. aneura, C. obesa and E. camaldulensis) had high survival rates by September 2015. Changes in mean tree biomass (kg tree−1) and survival ratio of three most successful species (A. aneura, C. obesa and E. camaldulensis) for the Main method (11 plots total) are given in Fig. 4. The results of the T1 and T2 tests are given in Tables 3 and 4. From the results indicated in Fig. 4 and Tables 3 and 4, E. camaldulensis was statistically (P < 0.05) the best species in terms of survival and mean tree biomass, with respective values of 0.91 survival and 413 kg tree−1 biomass (ranging from 105 to 1136 kg tree−1) in 2015. Thus, E. camaldulensis was regarded as the most appropriate tree species for arid land afforestation.

(2)

PBN )/ L

where PGR, PBN, PBN+1 and L denote predicted growth rate (Mg ha−1 year−1), predicted plot biomass at Nth times observation (Mg ha−1), predicted plot biomass at (N + 1)th times observation (Mg ha−1) and observation length (year), respectively. This PGR is compatible to mean annual increment (MAI: Mg ha−1 year−1). In this study, in accordance with the estimation methodology of carbon credits or certified emission reductions for Afforestation/ Reforestation activities under the Clean Development Mechanism (CDM A/R: UNFCCC, 2006), “actual net GHG removals by sinks” was estimated. Among five carbon pools determined by UNFCCC (2006), two carbon pools (above- and below-ground biomass) were calculated in this study. For the evaluation period determined by UNFCCC (2006), long-term afforestation for conserved forest (30 years) was selected in this study. “Actual GHG removal by sinks” was estimated using the following equation with the assumption that the predicted growth rate (Mg ha−1 year−1) during 2000–2015 would persist over the whole 30 year period:

AGRS = PGR × 30 years × 0.5 × (44/12)

E

3.2. Adequateness of adopted afforestation methodologies Fig. 5 shows the changes over time of survival and mean tree biomass for E. camaldulensis for each afforestation method. Results of the T1 and T2 tests are given on Tables 5 and 6 and the small alphabet letters in these tables show the rank based on the statistical difference among afforestation methodologies in 2015. In Table 5, the letter “b” is statistically superior (P < 0.05) to letter “a”, and “ab” denotes statistically intermediate (Not clearly significant). There was no significant difference (P > 0.05) in survival ratio among afforestation methodologies. On the other hand, the mean tree biomass values for the Main method (413 kg tree−1) and Method 1 (405 kg tree−1) were significantly (P < 0.05) higher than Method 2 (113 kg tree−1). This means hardpan blasting positively affected biomass growth of E. camaldulensis. From these results, the combination of conventional waterharvesting with large mounds and hardpan-blasting, which were adopted as the Main method and Method 1, were regarded as adequate for arid land afforestation. No other afforestation methodology was superior to the Main method.

(3)

where AGRS, PGR and E denote “actual GHG removal by sinks” in two carbon pools (Mg-CO2-e ha−1), predicted growth rate (Mg ha−1 year−1) and CO2 emissions by site amelioration methods (Mg-CO2-e ha−1), respectively. CO2 emissions by site amelioration (E) were estimated using a life cycle assessment (LCA) methodology by Kojima and Egashira (2011) after Tahara et al. (2009) as 7.16 Mg-CO2-e ha−1. In addition, 0.5 is the common conversion factor from biomass to carbon (Gifford, 2000a, 2000b) and (44/12) is the ratio between the molecular mass of CO2 and atomic mass of carbon. 2.5. Validation of growth tendency of tree

3.3. Estimation of CO2 sequestration potential

Generally, the trend in tree growth follows an age-dependent sigmoid curve (e.g. Grierson et al., 1992; West and Mattay, 1993), and thus the above estimation is not suitable. However, we found a unique relationship between predicted growth rate (Mg ha−1 year−1) and supplied water (mm year−1) (see Section 3.4). Because of this unique relationship (water dependent growth), the assumption of using Eq. (3) was considered acceptable. In this study, the significance of the obtained regression equation was checked by F-test and t-test (α = 0.05), with the water dependent growth tendency of planted trees under arid condition then reported. 3. Results

The potential value of “actual GHG removal by sinks” from afforestation (AGRS in Eq. (3)) using data from E. camaldulensis in the Main method plots was estimated at 230 Mg-CO2-e ha−1 in two carbon pools (above- and below-ground biomass) over 30 years. The used equations and values are given in Table 7. Fig. 6 shows representative photographs of E. camaldulensis inside the Main method plots. In 2015, after 16 years of growth, the mean tree height of E. camaldulensis planted in the Main method plots was 8.4 m (S.D. = 1.9 m) with the highest tree reaching 12.7 m, and many trees with more than 30 cm diameter at breast height.

3.1. Comparison of tree species performance

3.4. Water dependent growth of E. camaldulensis

Fig. 3 shows the decline in the survival ratio and supplied water (total amount of rainfall and irrigated water: mm year−1) of the ten planted tree species in the Main method between 1999 and 2015. In 2004, 2005, 2011 and 2014, tree measurements were not carried out. Supplied water (Fig. 3) was calculated from rainfall and irrigation for each tree measurement period. For example, the bar for 2006 denotes the summation of rainfall (778 mm) and irrigation (80 mm) for the period from September 2nd 2003 to September 7th 2006 divided by the measurement duration (3.02 years). Blank bar charts for 1999, 2004, 2005, 2011 and 2014 do not mean that there was no rainfall or irrigation. Tree species other than E. camaldulensis and C. obesa had poor root growth even under irrigated conditions, which resulted in relatively

A unique relationship was observed between supplied water (mm year−1) and the predicted growth rate (Mg ha−1 year−1) for E. camaldulensis in the Main method afforestation plots (Fig. 7). In Fig. 7, the X axis is one year equivalent rainfall with irrigation (mm year−1) and the Y axis is the predicted growth rate (Mg ha−1 year−1). The one year equivalent moisture input was calculated from the total rainfall, total irrigation amount and the measurement duration. A logarithmic relationship resulted in the best fit (R2 = 0.60) and indicated that supplied water (mainly rainfall) explained 60% of the variation in growth of E. camaldulensis. The regression equation was significant (F test: P < 0.05), and its coefficient and constant were also significant (ttest: P < 0.05). This relationship indicates that the growth of E. camaldulensis in this environment is strongly dependent on water supply. 114

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1000 900

700 600 500

0.5

400 300

0.25

Supplied water [mm y-1]

Survival ratio [-]

0.75

0

Irrigation

800

200

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Rainfall

A. aneura A. tetragonophylla C. obesa E. camaldulensis E. campaspe

E. griffithsii E. lesoueffii E. salubris

100

E. stricklandii

0

E. torquata

Observation year

Fig. 3. Trends of survival of planted trees and supplied water (Main method). 700

1.00

600

500

E. camaldulensis

400

0.60

C. obesa A. aneura

300

E. camaldulensis

0.40

C. obesa

200

Survival ratio [-]

Mean tree biomass [kg

tree-1]

0.80

A. aneura 0.20

100

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

0.00

Observation year

Fig. 4. Trends in survival and mean tree biomass of Acacia aneura, Casuarina obesa and Eucalyptus camaldulensis. Error bars are the 95% confidence interval of observed data. Table 3 Summary of T1 test of mean tree biomass for species selection.

Total variation Among Factors variation Error variation

Table 4 Summary of T2 test of survival ratio for species selection.

Degree of freedom

Sum of squared deviation

Unbiased estimate of variance

F

P

147 2

6213738.8 2454627.8

1227313.9

47.34

< 0.001

145

3759111.0

25924.9

Comparison of tree species

Mean difference

T

P

E. camaldulensis vs C. obesa E. camaldulensis vs A. aneura C. obesa vs A. aneura

196.5 324.8 128.3

9.35 12.73 4.79

< 0.05 < 0.05 < 0.05

Tree species

n

Mean tree biomass

Rank

A. aneura C. obesa E. camaldulensis

28 51 69

88.4 216.8 413.3

a b c

Comparison of tree species

Ratio difference

t

P

A. aneura vs E. camaldulensis A. aneura vs C. obesa E. camaldulensis vs C. obesa

−0.20 −0.04 0.16

2.81 0.55 2.32

< 0.05 > 0.05 < 0.05

Tree species

n

Survival ratio

Rank

A. aneura C. obesa E. camaldulensis

41 68 76

0.71 0.75 0.91

a a b

P, t and n denote P value, t value and sample number, respectively.

4. Discussion 4.1. Appropriateness of E. camaldulensis for arid land afforestation Given its drought and water-logging tolerance (Marcar et al., 1995), E. camaldulensis was considered to be a suitable species for afforestation, when combined with active site treatments such as hardpan blasting and water harvesting. This resulted in high biomass productivity (413 kg tree−1 and 4.32 Mg ha−1 year−1) and a high survival ratio (0.91). Arid areas experience large fluctuations in weather conditions, especially rainfall (Thomas, 2011), and this was also seen at the

F, T, P and n denote F value, Tukey's T value, P value and sample number, respectively.

115

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700

1.00

600 500

Main method Method 1 Method 2 Method 3 Method 4 Main method Method 1 Method 2 Method 3 Method 4

400 300 200

0.60

0.40

Survival ratio [-]

Mean tree biomass [kg tree-1]

0.80

0.20

100 0

0.00

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Observation year

Fig. 5. Mean tree biomass and survival of E. camaldulensis for each afforestation method. Error bars are the 95% confidence interval for observed data. Table 5 Summary of T1 test of mean tree biomass for adequateness confirmation of afforestation method.

Total variation Among factors variation Error variation

Degree of freedom

Sum of squared deviation

Unbiased estimate of variance

F

100 4

4726223.2 875759.4

218939.9

5.46

< 0.001

96

3850463.8

40109.0

P

Comparison of afforestation method

Mean difference

T

P

Main method vs Method Main method vs Method Main method vs Method Main method vs Method Method 1 vs Method 2 Method 1 vs Method 3 Method 1 vs Method 4 Method 2 vs Method 3 Method 2 vs Method 4 Method 3 vs Method 4

8.2 299.9 100.8 219.9 291.7 92.7 211.7 −199.1 −80.0 119.0

0.19 5.67 1.54 3.65 4.58 1.24 3.03 2.47 1.05 1.39

> 0.05 < 0.05 > 0.05 > 0.05 < 0.05 > 0.05 > 0.05 > 0.05 > 0.05 > 0.05

Afforestation method

n

Mean tree biomass

Rank

Main method Method 1 Method 2 Method 3 Method 4

69 13 8 5 6

413.3 405.1 113.4 312.4 193.4

b b a ab ab

1 2 3 4

Table 6 Summary of T2 test of survival ratio for adequateness confirmation of afforestation method. Comparison of afforestation method

Ratio difference

t

P

Main method vs Method Main method vs Method Main method vs Method Main method vs Method Method 1 vs Method 2 Method 1 vs Method 3 Method 1 vs Method 4 Method 2 vs Method 3 Method 2 vs Method 4 Method 3 vs Method 4

−0.09 0.18 0.07 0.05 0.27 0.17 0.14 −0.11 −0.13 −0.02

1.14 1.87 0.58 0.42 2.04 1.03 0.92 0.63 0.81 0.13

> 0.05 > 0.05 > 0.05 > 0.05 > 0.05 > 0.05 > 0.05 > 0.05 > 0.05 > 0.05

Afforestation method

n

Survival ratio

Rank

Main method Method 1 Method 2 Method 3 Method 4

76 13 11 6 7

0.91 1.00 0.73 0.83 0.86

a a a a a

1 2 3 4

P, t and n denote P value, t value and sample number, respectively. Table 7 List of used and calculated values of Eqs. (1)–(3). Eq. (1) Eq. (2) Eq. (3)

PB = TB × SR × D × CF PGR = (PBN+1 − PBN)/L AGRS = PGR × 30 years × 0.5 × (44/12) − E

Input values of Eq. (1) TB

F, T, P and n denote F value, Tukey's T value, P value and sample number, respectively.

SR

experimental site. During our experimentation period from July 1999 to September 2015, extraordinarily heavy rainfall events and prolonged drought occurred. For example, annual rainfall was 133 and 180 mm year−1 in 2007 and 2008, respectively and plots with large water harvesting mounds (e.g. Main Method) had temporary waterlogging (Fig. 1 (b)). Despite this, E. camaldulensis maintained its high survival. The tolerance of E. camaldulensis to water-logging would be an advantage against these instances of temporary water-logging in sites where the water harvesting approach is used. Thus, given the ability of E. camaldulensis to establish roots well and its tolerance to both drought and water-logging, it was considered the most appropriate tree species

D CF

Calculated values of Eq. (1)

7.4 kg tree−1 413 kg tree−1 1 0.91 178 trees ha−1 1 Mg/1000 kg

2000 2015 2000 2015

Input values of Eq. (2) PBN PBN+1 L

1.3 66.9 15.19

116

4.32 7.16

1.3 66.9

Mg ha−1 Mg ha−1

2000 2015

Calculated value of Eq. (2) −1

Mg ha Mg ha−1 year

2000 2015

Input values of Eq. (3) PGR E

PB PB

Mg ha−1 year−1 Mg-CO2-e ha−1

PGR

4.32

Mg ha−1 year−1

Calculated value of Eq. (3) AGRS

230

Mg-CO2-e ha−1

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May 2000

March 2001

March 2005

January 2010

September 2016

September 2012

Fig. 6. Photographs of E. camaldulensis from 2000 to 2016 planted in the Main method treatment.

the superiority of E. camaldulensis is overwhelming, particularly after irrigation was terminated. 4.2. Adequateness of the combination of hardpan blasting and conventional water-harvesting and methods For arid land afforestation in Western Australia, hardpan blasting is effective in improving tree growth, and thus application of hardpan blasting and water-harvesting with large mounds was considered adequate. As shown in Fig. 1 (b), water harvesting mounds are functional. By comparing the Main method (0.91 survival and 413 kg tree−1) with Method 2 (0.73 survival and 113 kg tree−1), a significantly positive effect of hardpan blasting on tree growth was confirmed (P < 0.05). These differences in biomass production can be attributable to hardpan blasting, which allows tree roots to grow beyond the shallow surface soil (15–20 cm) to explore deeper soil of about 2 m depth with a conical shaped root systems (Kojima and Egashira, 2011). Shallow surface soils as a consequence of the root impeding layers restricts rooting depth of plants (Hingston et al., 1998) and such hardpan layers constrain woody vegetation elsewhere in Australia (Pracilio et al., 2006). Shallow surface soil stores minimal runoff water whereas in comparison, deep soil can store large amounts of runoff water harvested by mounds which can then infiltrate into deep soil for future uptake by E. camaldulensis. In addition, deep soil water can remain stored for longer time periods and avoid evaporation losses from the soil surface and thus improve overall tree growth and survival in arid areas. Other than hardpan blasting techniques, some engineering techniques with similar water storage characteristics which prevent evaporation and induce stored water in deep soil layers have been described (e.g. Abu-Zreig et al., 2000; Saito et al., 2006). No water harvesting technology superior to conventional water harvesting with large mounds was found in this study. Even micro catchments (Method 3 (0.83 survival and 312 kg tree−1)), which have been reported as superior water harvesting systems for runoff water gathering efficiency (Boers and Ben-Asher, 1982), were not significantly different from the Main method (0.91 survival and 413 kg tree−1). Small mounds were unable to capture all the runoff

Fig. 7. Effect of supplied water on growth rate of E. camaldulensis for the Main method treatment during 2000 to 2015. (Rain-fed (♦) and irrigated + rainfed (◊)).

for arid land afforestation. E. camaldulensis is an endemic tree species in this research area which flourishes along riparian systems as natural vegetation (Suganuma et al., 2006a). It is the most widely distributed of all eucalypts growing predominantly in low rainfall areas along ephemeral water courses, typically in large areas in arid and semi-arid regions (Marcar et al., 1995; Barton and Montagu, 2006). Considering the no regret policy, the wood properties of eucalypts are suitable for fuel and charcoal production, pulp and paper manufacturing, sawn wood (Rockwood et al., 2008), and E. camaldulensis is also suitable for heavy timber construction (Marcar et al., 1995). As Marcar et al. (1995) indicated, E. camaldulensis has a wide range in its natural habitat which is reflected in provenance. In this study, the provenance of tree seedlings was not controlled due to limited available seedling number. The sizes of tree seedlings at planting were not uniform, varying in height from 15 cm to 250 cm, being particularly taller than seedlings of other tree species. These differences may have had an effect compared to other species, however, as shown in Figs. 3 and 4, 117

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water from sudden ephemeral and intermittent heavy rainfall events in this arid region compared with large mounds and this potentially contributed to the difference observed in mean tree biomass. In addition, construction of many micro catchments (small mound) will not be as cost effective compared to making one large mound. Overall, there was no reason to adopt Method 3. There is also no reason to adopt Method 4 (double hardpan blasting), because there was no significant difference between Method 3 and 4. The original assumption of Method 4 was that the hardpan layer of the upper slope (water catchment area) was completely fractured after double hardpan blasting, and thus more rainwater and runoff water would be introduced into the subsoil which would subsequently pass downslope as throughflow. However, the expected effect was not observed in this study. A larger catchment area may have a positive effect on tree growth, with the mean tree biomass of Method 1 (405 kg tree−1) being statistically ranked the same (α = 0.05) as that of Main method (413 kg tree−1) despite the trees of Method 1 being planted 2.5 years later than that of the Main method. The predicted growth rates of Method 1 (5.40 Mg ha−1 year−1) was larger than that of the Main method (4.32 Mg ha−1 year−1). Therefore a larger water catchment area seemed effective in accelerating the growth rate of E. camaldulensis, and this is consistent with the relationship between water supply and tree growth rate (Fig. 7). However, considering the total land-use for afforestation (tree planting area plus water catchment area), a larger water catchment area was not always effective. The land-use ratio of tree planting area versus water catchment area of the Main method is 1:3 on average, and that of Method 1 is approximately 1:5, taking into consideration the slope and complicated terrain features in this study site. The predicted growth rate based on total land-use of the Main method and Method 1 were calculated as 1.08 Mg ha−1 year−1 and 0.90 Mg ha−1 year−1, respectively. Thus, considering total area of land-use, the predicted growth rate of the Main method was considered superior to that of Method 1.

implies that planted E. camaldulensis trees do not reach the senescence phase within 30 years and thus the assumptions used for Eq. (3) are acceptable. Moreover, stand density of natural E. camaldulensis closed forest (≥100 Mg ha−1) varied from 172 to 448 trees ha−1, which were similar to the Main method plots (Mean: 178 trees ha−1, range from 161 to 199 trees ha−1). Secondly, estimated predicted growth rate (4.32 Mg ha−1 year−1; equivalent to 7.92 Mg-CO2-e ha−1 year−1) is comparable with other studies. E. camaldulensis was categorized as a fast growing tree species in Australia (Marcar et al., 1995). In this study, compared to the growth rate of natural vegetation and afforestation in arid and semi-arid zones of Australia (Commonwealth of Australia, 2014; Barton and Montagu, 2006; Burrows et al., 2002; Witt et al., 2011), the estimated predicted growth rate here was considered fast or comparable, though still inferior to that of Pracilio et al. (2006). Whether the estimated predicted growth rate (4.32 Mg ha−1 year−1; equivalent to 7.92 Mg-CO2-e ha−1 year−1) lasts for 30 years is still uncertain and successive observations should be undertaken to confirm this. Generally, tree growth trends follow an age-dependent sigmoid curve (e.g. Grierson et al., 1992), which has low growth in juvenile and senescence phase, and fast constant growth in the full vigor phase. The growth rate of eucalypt species has peak at age 12–20 years (West and Mattay, 1993). Taking into consideration these reported age dependent growth rate of eucalypts, validation of both the value of predicted growth rate and adequateness of using constant growth rate during 30 years in this study is necessary. From data gathered to date, it appears that the predicted growth rate of E. camaldulensis is not strongly age dependent but more influenced by overall moisture input (Fig. 7). By inputting the 30 year average rainfall (1986–2015) of 265 mm year−1 (Bureau of Meteorology of Australia: http://www.bom.gov.au/climate/data/stations/) into the regression equation, predicted growth rate was estimated at 4.31 Mg ha−1 year−1, which was similar to the predicted growth rate of the Main method (4.32 Mg ha−1 year−1). Thus, the estimation methodology in this study with the assumption that predicted growth rate (Mg ha−1 year−1) during 2000–2015 would persist over the whole 30 year period was considered acceptable.

4.3. Adequateness of estimated CO2 sequestration potential

4.4. Further investigation

In this study, estimated CO2 sequestration potential is considered highly reliable, because this value was calculated based on long term observations. Long-term observations from afforestation experiments are much more important than short-term observations for three reasons. Firstly, the large fluctuations of weather conditions in arid areas, especially rainfall (Thomas, 2011). When estimates of carbon sequestration potential are based on short-term observations under highly variable conditions, the resultant data will likely significantly under- or over-estimate longer term potential. Secondly, the growth characteristics of woody plant species which generally vary in a sigmoidal manner with age (e.g. Grierson et al., 1992; West and Mattay, 1993). Thirdly, the generally low increments of carbon sequestration that are likely in arid climates. For example, the associated errors when measured over short periods were explained for soil carbon sequestration (Smith, 2004). Thus, short-term observation data of afforestation experiments are likely to have relatively low reliability. In this study the “actual GHG removal by sinks by afforestation”, which is the CO2 sequestration potential without considering baseline and leakage, was estimated at 230 Mg-CO2-e ha−1 (above- and belowground biomass) after 30 years, which was considered feasible and achievable for following reasons. Firstly, the predicted plot biomass and stand density of the Main method was similar to those of natural closed forests in this region. Estimated “actual GHG removal by sinks via afforestation” (230 MgCO2-e ha−1) was equivalent to a forest biomass of 129 Mg ha−1. This biomass amount was smaller than the largest forest biomass of natural E. camaldulensis closed forest (149 Mg ha−1) estimated from the field observation by Suganuma et al. (2006a) for this research area. This fact

Due to the mortality of trees other than E. camaldulensis, there were many areas with no tree coverage and this unused water would have affected the survival and growth of E. camaldulensis. Therefore, further investigation is required to confirm whether the biomass yields of E. camaldulensis planted as pure-stands and treated with the Main method can achieve the growth rates estimated from this study. When growth trends and water use of E. camaldulensis trees are taken into account, afforestation using the Main method becomes a promising method of GHG mitigation. Our estimated potential of GHG removal (230 Mg CO2e ha−1) was more than 30 times larger than the CO2 emissions from afforestation related works (7.16 Mg CO2-e ha−1: Kojima and Egashira, 2011; Tahara et al. 2009). Given the extent of arid land and the large distribution of the Wiluna hardpan, carbon mitigation by this afforestation method is a promising GHG mitigation measure. 5. Conclusion From our afforestation experimentation, based on long-term empirical data, “actual GHG removal by sinks of afforestation” in two carbon pools (above- and below-ground biomass) over a 30 year period in arid land of Western Australia was estimated at 230 Mg-CO2e ha−1. The preferred tree species for afforestation was the endemic species Eucalyptus camaldulensis. The adequacy of the afforestation methodology, using a combination of hardpan blasting in conjunction with conventional water harvesting by large mounds, was confirmed and there was a clear relationship between biomass growth and carbon 118

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sequestration and moisture inputs. Estimated values (230 MgCO2e ha−1 and 4.32 Mg ha−1 year−1) in this study were considered achievable when compared with reported data. While these findings were applicable to arid areas of Western Australia, with a Wiluna Hardpans, they hold promise for other arid regions in the world. For further confirmation of the results and extrapolation to other regions, additional longer term experimentation is required.

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