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Article

Agroforestry Species Selection for Forest Rehabilitation in the Asia-Pacific Region: A Meta-Analysis on High-Level Taxonomy

by
Wanjie Zhang
1,2,
Kaiwen Su
3,
Qing Wang
2,4,5,*,
Li Yang
6,
Weina Sun
2,
Sailesh Ranjitkar
7,8,9,
Lixin Shen
1,2,
Roeland Kindt
10,
Yuman Ji
1,
Peter Marshall
5,
Pak Sngoun Pisey
11 and
Yousry A. El-Kassaby
5
1
APFNet-Kunming Training Center, Southwest Forestry University, Kunming 650204, China
2
Asia-Pacific Network for Sustainable Forest Management and Rehabilitation, Bei**g 100102, China
3
College of Economics and Management, Southwest Forestry University, Kunming 650204, China
4
Ecological Technical Research Institute (Bei**g) Co., Ltd., CIECC, Bei**g 100037, China
5
Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
6
School of Life Science, Sun Yat-Sen University, Guangzhou 510275, China
7
N.Gene-Solution of Natural Innovation, Kathmandu 44066, Nepal
8
School of Development Studies & Applied Sciences, Lumbini Buddhist University, Devdaha 32907, Nepal
9
Institute of Cooperation and Development, Mid-West University, Lalitpur 44700, Nepal
10
The World Agroforestry, Global Headquarters, Nairobi P.O. Box 30677-00100, Kenya
11
Forest Carbon Credit and Climate Change Office, Department of Forest Industry and International Cooperation, Forestry Administration, Phnom Penh 12101, Cambodia
 
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Author to whom correspondence should be addressed.
Forests 2023, 14(10), 2045; https://doi.org/10.3390/f14102045
Submission received: 28 August 2023 / Revised: 6 October 2023 / Accepted: 10 October 2023 / Published: 12 October 2023
(This article belongs to the Special Issue Ecosystem Degradation and Restoration: From Assessment to Practice)
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Abstract

:
Agroforestry is important for forest management and rehabilitation in the southeast Asia-Pacific Region (APR), where economic issues, intensive land use, deforestation, and forest degradation are common. Species selection is a key process in establishing agroforestry systems. In this study, we reviewed the agroforestry literature across eight economies within the southeast APR, documented the species used, and compared the existing systems to better understand the challenges and opportunities for the region’s agroforestry expansion. We conducted rule and Maptree analyses using 108 species, belonging to 95 genera and 49 families of plants, to unravel the various agroforestry practices in this region. We identified the most common plant families used in agroforestry combinations within each economy. We then divided the economies into three groups based on the most commonly used genera: (1) Thailand, Vietnam, Papua New Guinea and Fiji (Hevea, Oryza, Eucalyptus, Acacia, and Zea); (2) Nepal and Yunnan China (Zea, Leucaena, Morus, and Hevea); and (3) Indonesia and the Philippines (Oryza, Hevea, Zea, and Brassica). Although this study focused on high-level taxonomic classification (family and genus), we believe that this work will fill the current knowledge gaps, offering guidance to economies in the southeast APR regarding species selection and the adoption of sustainable agroforestry practices.

1. Introduction

The Asia-Pacific Region (APR) harbors some of the world’s most valuable and productive forests, which form unique ecosystems with high biodiversity and carbon stocks [1]. Unfortunately, anthropogenic and natural influences have led to the degradation of the APR’s forest ecosystems over the past few decades. Deforestation and forest degradation, particularly in the southeast APR, have compromised the forests’ ability to respond to climate change events by diminishing their health and resiliency [2,3]. Additionally, poverty and rapid population growth have led to extensive agriculture development and exploitation of natural resources, catalyzing forest ecosystem transformations, such as changes in land use [4,5,6]. As a result, conflicts between food security and the protection of forest ecosystems have become prominent [7].
Agroforestry, a composite and sustainable land-use system that integrates trees and shrubs into a farming system, embodies a holistic approach to land management that addresses ecological, economic, and social dimensions, making it an essential tool for sustainable development. It represents a harmonious coexistence of trees or woody plants with crops or livestock on the same piece of land. Agroforestry is described as “trees on a farm” [8] or a “wildlife-friendly” farm [9], and is known to create an agroecological succession [10,11]. Although agroforestry systems have been used for thousands of years [12], research into their effectiveness began only in the 1970s [13]. Agroforestry has contributed to mitigating and adapting to climate change [14,15], managing land [16], reversing the effects of forest degradation, and deforestation [17], providing ecological services, maintaining biodiversity, and increasing community resilience in terms of food security [18] and income diversification [19]. Agroforestry provides farmers with food, fiber, fodder, timber, fuel, medicinal herbs, other household needs, and marketable products [20]. Moreover, agroforestry practices at the forest margins, or “wooded landscape mosaics”, may help in develo** continuity among different forest types [21,22] and are associated with increasing tree cover (e.g., expansion of fruit orchards [23]) and creating wildlife corridors [24]. Furthermore, forest rehabilitation and afforestation using agroforestry on abandoned lands, forest-degraded areas, slopes, and low-yield agriculture lands have increased forest coverage in the southeast APR [25].
The selection of species is crucial in the design of agroforestry systems [26]. Selecting the most suitable species based on site characteristics is the main principle for successful forest management [27], especially for agroforestry [28]. Since tree species within an agroforestry system take several years to yield enough volume for rubber collection [29,30,31,32], a substantial upfront investment is made without anticipated income in the early development years [33]. As there are no “perfect”, “good”, or “bad” species [27], identifying the most “suitable species combinations” for agroforestry is considered essential to simultaneously fulfill the many-fold benefits of this system. Agroforestry species associations impact one another, and the most “suitable species” (tree and crop) combination should have positive impacts [34]. The interdisciplinary nature of agroforestry [13], dealing with complex soil, water, light, and nutrition dynamics [35], and the myriad of possible species combinations complicates proper system development [36]. Therefore, looking for similar patterns among areas with comparable environments and species distributions can be effective in directing agroforestry expansion. Thus, we aim to highlight common agroforestry models in this study due to these gaps in the research. Common agroforestry models are often combinations of long-lived tree species and short-term crop and herb species [13]. Consequently, it is useful to recognize two types of agroforestry: (1) sedentary farming systems, which typically employ intricate blends of native species devised by farmers in response to population pressures on natural forests, rendering traditional shifting cultivation impractical [37,38,39,40,41]; and (2) simplified sedentary farming systems that are developed by researchers using exotic species (often nitrogen-fixing), which pose no threat to other plants and are planted in collaboration with farmers [42,43,44]. Farmer-initiated “Type One agroforestry” has been underreported in scientific literature and is only documented in a few field investigations [31]. Considerable knowledge gaps remain due to the failure to recognize species used in a particular region that are not acknowledged elsewhere [31]. For “Type Two agroforestry”, species selection is often based on a blend of predictive models [28], nature-mimicking techniques [45,46], and local experience and indigenous knowledge. Most agroforestry publications lack the necessary data to facilitate species exchange, and frequently the reasoning behind species selection is unclear.
The objective of the current work was to undertake a systematic review of agroforestry systems in the southeast Asia-Pacific Region. We acknowledge forest farmers from different economies have similarities and differences when choosing plant combinations during the construction of agroforestry. Conducting a literature review and rule analysis is more efficient than gathering individual data, streamlines the process of information collection, and the common standardized reporting simplifies data interpretation. This approach not only allows for broader coverage of the literature by accommodating more studies without accessing raw data but also reduces ethical and privacy concerns, promoting easier collaborative efforts without divulging sensitive data. This review will encompass a comprehensive description of (1) the various types of agroforestry systems present, (2) the most frequently utilized species associations at both the family and genus levels, and (3) the challenges and opportunities for the further development of agroforestry in the region. Throughout this review, we will emphasize and evaluate ecologically- and economically-advantageous species combinations, with the aim of identifying potential opportunities for the expansion of agroforestry practices in the southeast APR. This study focuses on agroforestry in the southeast Asia-Pacific Region; we have conducted our study with rigorous methodologies and ensured that data collection and analysis were carried out to the best of our abilities to enhance the transparency and credibility of our findings. This study aims to contribute to the existing body of knowledge on agroforestry systems in the southeast Asia-Pacific Region.

2. Methodology

2.1. Study Economies

A total of 12 countries (hereinafter referred to as economies) were included in this study: Cambodia, China (Yunnan Province), Fiji, Indonesia, Lao PDR, Malaysia, Myanmar, Nepal, the Philippines, Papua New Guinea (PNG), Thailand, and Vietnam. The Yunnan province (located in southeastern China) was selected due to its proximity to Lao PDR, Myanmar, and Vietnam, with a similar land area, species diversity, and climate. The population in the study region represents approximately 8% of the world population, with a relatively large segment facing severe economic-opportunity-deprivation [47]. The annual precipitation and annual temperature ranged from 1500 to 3142 mm, and 12 to 30 °C, respectively. Indonesia has the largest GDP/year, land area, and population, totaling USD 1320 billion, 187,751,900 ha, and 281,844,000, respectively. In the economic aspect, Thailand has the second largest economy, followed by the Philippines, Vietnam, Nepal, China (Yunnan), PNG, and Fiji. In contrast, Fiji has the smallest economy, with the smallest GDP/year, land area, and population, totaling USD 4.94 billion, 1,827,000 ha and 916,000, respectively (Table 1 and Figure 1).

2.2. Data Collection

To retrieve papers potentially suitable for our meta-analyses, we conducted an extensive literature search over the past 70 years using Google Scholar. We employed a combination of the following keywords: “Agroforest”, “Agroforestry”, “Farm-forestry”, “Agrosilvopastoral”, and specific economy names (e.g., Thailand). We looked for studies that reported species selection, such as tree-tree combination, tree-crop combination, tree-fodder combination, tree-herbal medicine combination, tree-vegetable combination, and multi-layer combinations of different species in agroforestry systems. In addition, any publication with fewer than 10 citations was excluded, as a low number of citations might indicate limited recognition or acceptance within the academic community. As a result, a total of 429 peer-reviewed articles were used in the current work; the initial set of 12 economies was reduced to 8 economies based on the availability of relevant literature.
Once we had a curated list of publications, we embarked on the task of extracting and organizing the most pertinent information. We created a structured data matrix to capture key details from each source. The key details consisted of the name of the economy under study, the citation details of the publication, the specific agroforestry practices described, and the combinations of species. This organized approach not only facilitated a systematic review of the literature but also set the stage for rigorous subsequent analysis, allowing us to draw meaningful insights about the interplay between economy and agroforestry across the 8 economies. The final dataset consisted of a total of 108 species belonging to 95 genera and 49 families (Table S1) that were frequently used in agroforestry applications. All species names were checked using World Flora [48].

2.3. Data Analysis

First, to select the most common species combinations among and within economies, we examined the species associations (possibility of co-occurrence) under current agroforestry models. We employed rule analysis, which is one of the most widely used approaches in data mining [49], using the apriori algorithm [50]. The data used were based on three levels (species, genus, and family level). However, rule analysis at the species or genus level could not provide suitable results as there were a high number of unrelated entities [49] as a result of the high species diversity among the different economies in our study. A top-down research-based domestication approach lead to smaller numbers of selected species than a bottom-up, decentralized, participatory approach at the farm level. Selecting agroforestry species from different families but within a family-level combination also has other benefits, such as the direct flow of benefits to the farmer or community, and a lower risk of narrowing genetic diversity. Therefore, we selected family-level associations for the analysis. The “support” and “lift” of the rule were used to interpret the correlation among families within each economy. The “support” of the rule reveals the conditional probability of co-occurrence within each economy. The “lift” of the rule indicates whether the family associations are independent (=1), dependent (>1) or substitute (<1).
We then grouped these economies based on similar taxa according to the rule analysis findings. We proposed that economies with similar taxa may introduce the same agroforestry models. Considering the risks of introducing these species-level combinations across economies, we needed to look at least at the genus level. Thus, we used Maptree analysis, which integrates clustering with word clouds [51] to evaluate all genera from the predominant families identified through rule analysis. In Maptree, the “k” value serves as a threshold, and for our study it was employed to pinpoint the most frequently used species. We established the “k” value at 15, focusing on the top 15 genera from each economy. The rule analysis revealed variations in species combinations across economies, with fewer than five shared plant families. Due to insufficient data or uncertainty about species, four economies—Cambodia, Malaysia, Myanmar, and Lao PDR—were excluded from the study. All analyses were conducted in RStudio (version 1.4.1717, RStudio, Boston, MA, USA) [52].

3. Results

All “supports” for the selected family pairs in each of the remaining economies were greater than zero, and the “lifts” were greater than one (Figure 2), indicating that those pairs were positively correlated with each other (note that four economies, Cambodia, Malaysia, Myanmar and Lao PDR, were removed from the analysis due to a lack of data). The most common family combinations were: Theaceae-Euphorbiaceae (Yunnan, China); Santalaceae-Leguminosae (Fiji); Sterculiaceae-Leguminosae, Gramineae-Sterculiaceae, Rubiaceae-Piperaceae, Rubiaceae-Sterculiaceae, and Euphorbiaceae-Verbenaceae (Indonesia); Moraceae-Leguminosae, Betulaceae-Zingiberaceae, Moraceae-Musaceae, Leguminosae-Bromeliaceae-Musaceae, and Meliaceae-Zingiberaceae (Nepal); Cruciferae-Solanaceae and Cruciferae-Cucurbitaceae (the Philippines); Rubiaceae-Convolvulaneae, Rubiaceae-Convolvulaneae-Araceae, and Convolvulaneae-Palmae-Sterculiaceae (Papua New Guinea); Euphorbiaceae-Myrtaceae and Dipterocarpaceae-Gramineae (Thailand); and Anacardiaceae-Sterculiaceae, Thymelaeaceae-Bromeliaceae, Thymelaeaceae-Convolvulaneae, Thymelaeaceae-Convolvulaneae and Ephorbiaceae-Thymelaeaceae-Ephorbiaceae (Vietnam) (Figure 2a–h). The most used families were Gentianaceae, Taxodiaceae, and Jualandaceae (Yunnan, China), and Umbelliferae (the Philippines) (Figure 2a,e).
Maptree analysis was used to classify the studied economies into three groups based on the genera used: (1) Thailand (with Hevea, Oryza, Eucalyptus, and nine other genera as the most common), Vietnam (Acacia, Zea, and four other genera), Papua New Guinea (Coffea, Inpomoea, and Theobroma), and Fiji (Santalum and Manihot); (2) Nepal (Zea, Leucaena, Morus, and eighteen other genera) and Yunnan China (Zea, Hevea, Theobroma, and thirteen other genera); and (3) Indonesia (Oryza, Hevea, Zea, and thirteen other genera) and the Philippines (Oryza, Brassica, Zea, and fourteen other genera) (Figure 3).

4. Discussion

The Asia-Pacific Region (APR) is facing unprecedented challenges related to food security and unsustainable forest management and farming practices. Agroforestry is considered one of the most effective methods for addressing these challenges. The 5th Agroforestry Conference concluded that agroforestry was capable of “saving the world” (https://agroforestry2019.cirad.fr/, accessed on 10 January 2021).
Despite being practiced for thousands of years, agroforestry is still considered an innovative system. Clearly, appropriate species selection is crucial for ensuring the success of agroforestry systems. Determining the most effective species combinations requires large-scale screening, which is time-consuming, research-intensive, and resource-dependent [46]. All of the species identified in our literature review are sustainable and economically viable (a list of the literature reviewed is available upon request from QW). Our results do not allow us to judge the resulting agroforestry species patterns as “appropriate” or “inappropriate”, but they do provide “safe” options for future agroforestry expansion. Additionally, the results enable a better understanding of which combinations of genera are economically and ecologically beneficial, refining further testing within specific economies. Species selection of agroforestry can enhance soil quality, ameliorate microclimates, boost productivity, and improve overall forest resilience [53,54].

4.1. “Generalist” Agroforestry Species Combination within Each Economy

The economies that we studied share similar climates (e.g., sub-tropical or tropical) and are geographically adjacent (Table 1). Our results indicate that there are fewer than five common family combinations among the eight economies. This is not surprising as agroforestry species selection in this region is influenced by farmers’ preferences, species distributions, economic values, cultural differences, policy and grant support, landscape, and existing species diversity among economies [55]. However, within each economy, selecting “generalist” agroforestry species may be easier [8]. To promote “generalist” agroforestry species, family-level combinations should be considered. For example, the combination of Theaceae (Camellia sinensis) and Euphorbiaceae (Hevea brasiliensis, Manihot esculenta, Sauropus androgynus) is common in China (Yunnan) [56] (Figure 2, Table S1). Camellia sinensis is one of the most important oil tree species in southern China and is mainly distributed in the Yunnan provenance. Species belonging to Zingiberaceae often have medicinal and seasoning uses. In Nepal, the combination of species belonging to Betulaceae (Alnus nepalensis) and Zingiberaceae (Eletteria cardamomum, Zingiber officinale Roscoe) should be considered. Alnus nepalensis is native to Nepal (Figure 2, Table S1), and is mainly used for timber and local medicinal uses [57,58]. Incorporating indigenous species into agroforestry systems has in situ genetic conservation value [23] and contributes to the integration of local ecosystems, including mitigating soil erosion, rehabilitation of degraded lands, improving water conservation, and replenishment of soil fertility [59]. Sandalwood species (Santalaceae) have been well-studied in Vanuatu, where exploitation of sandalwood provides the primary means of income generation in many villages [60]. In Indonesia, PNG and southern China, sandalwood are also traditionally important plants for their oil-rich fragrant heartwood; artificial plantation has been widely observed in the past two to three decades and interplanting with indigenous species from Leguminosae could act as host plants for sandalwood [61]. The introduction of indigenous species, such as those from the Leguminosae family, in an interplanting agroforestry system along with sandalwood may enhance soil productivity.

4.2. Duplicating Agroforestry Species Combinations within Similar Ecological Regions

Agroforestry species guides have been developed for some areas, and thousands of individual species have been listed in books and other publication materials [62,63,64]. However, agroforestry systems often combine more than two species. The rule analysis we conducted suggested several family combinations. Duplicating agroforestry species combinations among economies within similar ecological regions should be limited to the genera level to help minimize potential risks, such as bio-safety [65]. Our word cloud (Maptree) results classified the eight economies into three groups, which highlighted their geographic connections. In the first group, Thailand, Vietnam, Papua New Guinea, and Fiji are geographic neighbors. The second and third groups featured China (Yunnan), Nepal, Indonesia and the Philippines, with members of both groups separated by only a short geographic distance (Figure 1).
Family combinations within each group should be considered first and target species selected under the recommended genera. For example, within the group of economies comprised of Thailand, Vietnam, PNG and Fiji, target species should be selected from Hevea, Oryza, Eucalyptus, Acacia, Zea, Coffea, Inpomoea, Theobroma, Santalum, Manihot, and others. Euphorbiaceae (e.g., Hevea brasiliensis and Manihot esculenta) and Myrtaceae (e.g., Eucalyptus camalulensis and Eucalyptus torelliana) are the most widely used combination in Thailand and could be considered for introduction to Vietnam, PNG and Fiji. In PNG, Convolvulaceae (Ipomoea batatas) and Rubiaceae (e.g., Coffea arabica and Coffea robusta) are the major combination and might be suitable for more widespread use in the other economies. Nevertheless, the introduction of species that fall outside of the recommended families may continue to present challenges. Cutnut (Barringtonia procera) belongs to Lecythidaceae and is widely used as an agroforestry species for multiple purposes (windbreak, soil stability, etc.) in PNG and the Solomon Islands [66].

4.3. Limitation of High-Level Taxonomy Meta-Analysis

Most agroforestry species-selection research has been based on farmers’ traditional knowledge and practice, experimental centers, or model predictions [28]. The Asia-Pacific Region is one of the most biodiverse regions in the world and is estimated to harbor a large number of plants, some of which are endemic to the region. Although 108 species were identified in our literature review, and several main species combinations on the family and genera levels were revealed, this is still a small number of species compared to the vast species diversity present within the region. For example, research has shown the positive effect of using Hevea brasiliensis in the restoration of slash-and-burn areas, as this species can not only contribute positively to rubber-based agroforestry goals but also has been shown to increase forest cover in central Sumatra [32]. However, the heavy utilization of several genera such as Eucalyptus, Elaeis, and Hevea found in our literature review may lead to negative ecological impacts, such as depletion of groundwater [67], the decline of understory species diversity [68], and acceleration of erosion [69]. Thus, more species need to be explored for expanding agroforestry in the southeast APR. For example, species in agroforestry practice could be influenced by nearby regions, such as combinations of indigenous fruit and nut trees as practiced in the Solomon Islands [30], Pacific Islands and Oceania. Canarium indicum (breadfruit) (Moraceae) and Shorea macrophylla have been widely used in Southeast Asia. Canarium is often seen providing the necessary sun protection for cocoa in Papua New Guinea [70,71]. In all, the selection and integration of diverse tree species within agroforestry systems can not only enhance soil health but also contribute to the preservation of the rich biodiversity in the APR, thus promoting a sustainable and resilient agroecosystem [72,73].

4.4. Practice of Agroforestry with Indigenous Food and Non-Food Plants (Trees and Others) Have Higher Economic Value

The local practices of agroforestry frequently incorporate indigenous knowledge pertaining to the intercrop** of multipurpose species, which serves as a sustainable means of supporting their daily livelihood [74,75]. The practice of agroforestry is characterized not only by the consideration of the time scale for each species but also by the selection of species that possess higher economic value. Several Asian countries have demonstrated innovation in the development of multi-strata agroforestry systems that utilize entirely indigenous species [31], which exhibit dynamic properties. In Indonesia, it is a common practice for newly married couples to establish their own household, usually separate from their parents, often requiring them to find a new place of residence. These newly-wed couples typically began the process of clearing land for agriculture soon after settling in their new homes. In the first few years, they plant crops (dry-land rice) with damars (Shorea javanica), then other crops such as peppers and bananas after the harvest of dry-land rice. Damars reach canopy closure after ten growing seasons, at which time resin collection can begin and then continue for another 40 years, sustaining production. After 40 years, the production of damars will decrease, and low-productive damars will be harvested and replanted. Within this system, damars are canopy species, while crops such as rice, peppers, and bananas are understory species. The plant of understory species will depend on their respective growing seasons [31]. Products produced from agroforestry are sufficient for personal sustenance. Farmers sell any surplus products in nearby markets to generate additional revenue streams, which can be used to purchase other goods and services. Within this system, rice is essential to provide food, and species including damars, peppers, and bananas are also important economic species. Farmers can also increase their incomes by selling goods from economic species. Notably, the price of damar resin is around 2500.00–3500.00 USD/metric ton; the price of other commodities (peppers, bananas) is less costly compared to damar resin, 1–2 USD/kg.
It should be noted that most agroforestry systems are farmer-initiated, thus there are fewer citations in the literature (429 peer-reviewed papers in the current work). With the constant population growth in the APR region, the demand for land to establish their livelihoods increases. To achieve the ambitious goal of “more people, more trees”, increasing crop production is the first step. The yield of wheat and rice in Asia has respectively increased by 281% and 122% from 1961 to 2005 with the “Green Revolution” initiative (FAO). The second step is to “re-boot” the agroforestry system, which is domesticating useful tree species as new crops based on these systems, and the importance of indigenous trees for the livelihoods of people and the ecology of forests and farms, which was initiated as a research program in 1992 [31,76].

4.5. Promoting Agroforestry Research and Policy Is Needed

In Cambodia, Malaysia, Myanmar, and Lao PDR, there is a pressing need for further research in the field of agroforestry. While these four economies were initially included in this study, they were ultimately excluded during the analysis process due to a dearth of sufficient published information on agroforestry. Nonetheless, the scarcity of available information should not be taken to imply that these economies lack agroforestry practices. It is likely that in-depth studies of agroforestry approaches in these economies would yield valuable insights. The relatively low number of publications on agroforestry in these economies may be attributable to the scarcity of universities offering agroforestry-related programs [77].
Additional research is needed to provide scientific guidelines for policymaking, which may include specific techniques and/or species recommendations for agroforestry. To make agroforestry more effective, there needs to be a strong connection between the science of agroforestry and how it is incorporated into public policy to effect positive social and environmental gains [78]. Many economies have successfully established agroforestry systems due to the implementation of sound policies. One such example is China, where the government released “Opinions released by the General Office of the State Council on Accelerating the Development of Understory Economy” in 2012, which pointed that “On the premise of protecting the ecological environment, market orientation is used to scientifically and reasonably utilize forest resources, vigorously promote the construction of professional cooperative organizations and market circulation systems, and focus on strengthening technological services, policy support, and supervision and management”. In Nepal, the government-supported “Community Forestry Program” has included agroforestry as a means to empower local communities to improve their livelihoods and conserve natural resources since the mid-1970s [79]. The main goals of this program are “to empower local communities whilst encouraging environmental conservation benefits on the Himalayan forests”. The FAO/Government Cooperative Program aims to increase capacity for sustainable agroforestry development, such as knowledge of agroforestry systems and how to manage them [80]. However, additional effective policies are required for expanding agroforestry in the southeast APR. Policies that connect agroforestry to payment for environmental services should be encouraged.
The phenomenon of increasing tree cover through agroforestry has been largely overlooked, as it has been “hidden in statistics” [23]. This may be due to the traditional definition of forests, which excludes lands used for agroforestry. For example, according to the definition by FAO, forests are lands with an area greater than 0.5 hectares, trees taller than 5 m, and tree canopy coverage exceeding 10%. However, there are numerous successful examples of agroforestry programs in the southeast APR, such as the upland-agroforestry program in the Philippines [80], some agroforestry projects initiated by the Nepal Agroforestry Foundation (NAF) that promoted sustainable livelihoods in marginal mountain farms [81], and the peatland restoration response in Indonesia [82]. Various organizations, including the Asia-Pacific Network for Sustainable Forest Management and Rehabilitation (APFNet) [25], the World Agroforestry Centre (ICRAF) [83,84,85], the Food and Agriculture Organization (FAO) [86,87], the Center for International Forestry Research (CIFOR) [88], and the Australian Centre for International Agricultural Research (ACIAR) [89] have supported numerous projects aimed at rehabilitating land using agroforestry for several decades.

5. Conclusions

Increasing agroforestry practices on degraded, burned, and abandoned lands should be considered a high priority for the near-future forest rehabilitation. To provide references for agroforestry species combinations, this paper focused on agroforestry species selection in the southeast APR using high-level taxonomy classification. Overall, each economy has its own preference for agroforestry species selection, with fewer than five common plant families, underscoring the need for significant information sharing within the region. Several agroforestry species combinations are proposed at both the family and genus levels. Based on our literature review, we recommend the following: (1) the most common species combinations at the family level could be adopted within each economy; (2) agroforestry species could be replicated at a genera level within similar economic groups; (3) agroforestry research needs to be expanded in Cambodia, Malaysia, Myanmar and Lao PDR; (4) agroforestry systems should include a wider variety of species than is common at present, as it can enhance ecosystem resilience and provide a broader range of benefits to local communities; and (5) the development of these systems should focus on marketable indigenous fruits and nuts. This approach can enhance economic opportunities for local communities while contributing to biodiversity conservation. Through this study, we aim to bridge existing knowledge gaps and provide guidance for economies within the southeast APR in their species selection and implementation of sustainable agroforestry practices. We acknowledge that forest farmers from different economies have similarities and differences when choosing plant combinations during the construction of agroforestry. Local conditions, preferences, and priorities should be considered in future work.

Supplementary Materials

The following supporting information can be downloaded at: https://mdpi.longhoe.net/article/10.3390/f14102045/s1, Table S1: Species list.

Author Contributions

Conceptualization, Q.W. and Y.A.E.-K.; methodology, W.Z. and K.S.; software, Q.W. and L.Y.; validation, W.S., S.R. and L.S.; formal analysis, Q.W.; investigation, R.K., P.M., P.S.P. and Y.J.; resources, W.Z. and L.S.; writing—original draft preparation, W.Z., Q.W., R.K., P.M., P.S.P. and Y.J.; writing—review and editing, all authors; visualization, L.Y.; supervision, Q.W. and Y.A.E.-K. All authors have read and agreed to the published version of the manuscript.

Funding

This research is supported by the project “Collection and Analysis on Forestry Data and Information among the Asia-Pacific economies (2176063)” of Southwest Forestry University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Please contact the corresponding author for sharing the original data.

Acknowledgments

The authors are grateful to Kuo Sun and Jiuheng Xu, Bei**g Forestry University, for their assistance with the literature review and data collection. The study was part of the staff capacity building project supported by the Asia-Pacific Network (APFNet) for Sustainable Forest Management and Rehabilitation. This work was conducted while QW was associated with the Asia-Pacific Network for Sustainable Forest Management and Rehabilitation, Bei**g, China. This work was also supported by the project of “Collection and Analysis on Forestry Data and Information among the Asia-Pacific economies (2176063)”.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 14 02045 g001
Figure 1. Location of the economies studied. Countries included in an agroforestry analysis examining systems used and common species combinations. (Figure 1, referencing the sources in Table 1).
Figure 1. Location of the economies studied. Countries included in an agroforestry analysis examining systems used and common species combinations. (Figure 1, referencing the sources in Table 1).
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Figure 2. Interaction network for families within the 8 economies: China Yunnan (a), Fiji (b), Indonesia (c), Nepal (d), Philippines (e), PNG (f), Thailand (g), and Vietnam (h). The bigger the size of the circles, the larger the “support” value; the darker the color, the larger the “lift” value.
Figure 2. Interaction network for families within the 8 economies: China Yunnan (a), Fiji (b), Indonesia (c), Nepal (d), Philippines (e), PNG (f), Thailand (g), and Vietnam (h). The bigger the size of the circles, the larger the “support” value; the darker the color, the larger the “lift” value.
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Figure 3. Maptree analysis showing the classification of the most common genera among the 8 economies included in this analysis. The size of the circles containing the genera represents the frequency of occurrence of the genera (the larger the circles, the more frequent the genera).
Figure 3. Maptree analysis showing the classification of the most common genera among the 8 economies included in this analysis. The size of the circles containing the genera represents the frequency of occurrence of the genera (the larger the circles, the more frequent the genera).
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Table 1. Basic information for the economies (countries) studied.
Table 1. Basic information for the economies (countries) studied.
Economy (Country)Population 1Forest Coverage
(%) 2		Land Area
(1000 ha) 3		GDP/Year
(Billion USD) 4		Annual Precipitation
(mm) 5		Annual Temperature
(°C) 6
China (Yunnan) ‡46,930,00055.2539,41039.79235212–22
Fiji916,00062.0318274.94259227
Indonesia281,844,00049.39187,7521320270221–33
Nepal30,770,00041.5914,33540.83150025
The Philippines113,964,00023.9929,817404.28234827
Papua New Guinea9,466,00079.2545,28630.63314223–30
Thailand70,183,00038.9751,089495.34162228.9
Vietnam99,699,00046.4831,343408.8182023–27
Total653,772,000 400,8592744.61
1 World population stat (https://populationstat.com/, accessed on 14 September 2023); 2 FAO 2023 (https://www.fao.org/countryprofiles/en/, accessed on 13 September 2023); 3 FAO 2023 (https://www.fao.org/countryprofiles/en/, accessed on 13 September 2023); 4 World Bank 2022 (https://data.worldbank.org/country, accessed on 14 September 2023); 5 FAO Country profile (http://www.fao.org/faostat/en/#country/115, accessed on 10 January 2021); 6 FAO Country profile (http://www.fao.org/faostat/en/#country/115). ‡ Data for China (Yunnan) were accessed from National Bureau of Statistics of China (http://www.stats.gov.cn/, accessed on 14 September 2023), Yunnan Forestry and Grassland Administration (http://lcj.yn.gov.cn/, accessed on 14 September 2023).
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Zhang, W.; Su, K.; Wang, Q.; Yang, L.; Sun, W.; Ranjitkar, S.; Shen, L.; Kindt, R.; Ji, Y.; Marshall, P.; et al. Agroforestry Species Selection for Forest Rehabilitation in the Asia-Pacific Region: A Meta-Analysis on High-Level Taxonomy. Forests 2023, 14, 2045. https://doi.org/10.3390/f14102045

AMA Style

Zhang W, Su K, Wang Q, Yang L, Sun W, Ranjitkar S, Shen L, Kindt R, Ji Y, Marshall P, et al. Agroforestry Species Selection for Forest Rehabilitation in the Asia-Pacific Region: A Meta-Analysis on High-Level Taxonomy. Forests. 2023; 14(10):2045. https://doi.org/10.3390/f14102045

Chicago/Turabian Style

Zhang, Wanjie, Kaiwen Su, Qing Wang, Li Yang, Weina Sun, Sailesh Ranjitkar, Lixin Shen, Roeland Kindt, Yuman Ji, Peter Marshall, and et al. 2023. "Agroforestry Species Selection for Forest Rehabilitation in the Asia-Pacific Region: A Meta-Analysis on High-Level Taxonomy" Forests 14, no. 10: 2045. https://doi.org/10.3390/f14102045

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