1 Introduction

Rice cultivation has historically played an important role in the economic and social development of many Southeast Asian countries (SEA). SEA comprises “mainland” (Cambodia, Laos PDR, Myanmar, Thailand, and Vietnam) and Island regions (Malaysia, Indonesia, and the Philippines) that collectively contribute 26% to global rice production and 40% to exports (Yuan et al. 2022). Mainland and island regions are characterized by tropical and subtropical climatic zones with high annual precipitation.

The majority of rice producers in these countries are smallholders with four main types of rice cultivation systems as follows: irrigated, rainfed, deep water, and upland rice (usually on sloping land) (Mutert and Fairhurst 2002). Irrigated rice systems exhibit the highest productivity, followed by rainfed, deep water, and upland rice. In Indonesia, Malaysia, the Philippines, and Vietnam, irrigation systems are more prevalent. Conversely, Cambodia, Laos, Myanmar, and Thailand primarily rely more on rain-fed lowland cultivation (Mutert and Fairhurst 2002). Despite these differences, all these countries face common challenges—balancing the increasing demand for rice with sustainable agricultural practices and addressing the impact of climate change.

According to the IPCC (2007), in the agricultural sector, global paddy rice cultivation contributes approximately 30% and 11% of global methane (CH4) and nitrous oxide (N2O) emissions, respectively. In Southeast Asia, rice cultivation is a major contributor to GHG emissions in the agricultural sector, with an average of 20% of total GHG emissions at the country level, as indicated by national GHG inventory data (Zhang et al. 2024). For instance, in Thailand in 2019, rice cultivation contributed 54.7% of total GHG emissions (Mungkung et al. 2022). Open-field burning of rice straw after harvest releases carbon dioxide (CO2) at 70%, CH4, carbon monoxide (CO) at 7%, and N2O at 2.09% (Singh et al. 2024). This burning process also leads to the depletion of soil organic matter content (Connor et al. 2020). It is estimated that global rice production must increase by 30% by 2050 in order to satisfy the projected rice demand for the growing world population (Yuan et al. 2022). However, growing more rice will eventually result in increased GHG emissions.

In this region, rice can be grown up to three times per year with the use of irrigation (Mutert and Fairhurst 2002). The production of rice poses great challenges with its usage of 34 to 43% of global irrigation water (Surendran et al. 2021). In Asia, irrigation consumes over 80% of freshwater resources, and more than half of that is used for rice irrigation (Surendran et al. 2021). This intensive water usage significantly contributes to area-based water scarcity (Silalertruksa et al. 2017; Mungkung et al. 2019). To address this challenge, the water footprint has been introduced. It serves as a tool to assess the link between agricultural production, water resources, and environmental impacts, with the aim of improving water use efficiency, sustainability, and management (Silalertruksa et al. 2017; Rusli et al. 2018). Over-application of agro-chemical inputs is another major constraint for sustainable rice production in Asia (Terano et al. 2015; Devkota et al. 2019; Flor et al. 2020; Nguyen et al. 2022). In certain countries (Indonesia, Malaysia, the Philippines, Vietnam, and Thailand), rice production is characterized by high levels of agrochemical inputs to achieve self-sufficiency and support exports in rice production (Cho and Zoebisch 2003; Olabisi et al. 2015; Ali et al. 2018; Digal and Placencia 2018; Atieno et al. 2020; Fritz et al. 2021). This has resulted in adverse health effects and has had negative environmental impacts (Sapbamrer 2018).

Rice fields are not just for agricultural productivity but also providers of various ecosystem services. They contribute to cultural (recreation, cultural identity, tourism), regulating (biocontrol, pollination), and provisioning services (soil nutrients) in Southeast Asia (Settele et al 2018). In light of these valuable contributions, it becomes evident that climate change poses a significant threat to these ecosystem services, particularly in SEA, which is recognized as one of the most vulnerable regions to climate change. Those unsustainable farming practices mentioned above lead to environmental degradation and make it even more difficult to mitigate and adapt to climate change. In response to these challenges, sustainable agricultural practices (SAPs) have emerged within rice cultivation systems. These practices mainly include climate-smart agriculture, conservation agriculture, integrated pest management, nutrient management, organic farming, and straw management. SAPs have been shown to be effective in reducing agro-chemical application and the amount of water used, and in increasing crop yield (Seerasarn et al. 2020; Ha and Bac 2021). SAPs in rice cultivation have the potential to achieve several Sustainable Development Goals (SDGs) including zero hunger (SDG 2), clean water and sanitation (SDG 6), responsible consumption and production (SDG 12), climate action (SDG 13), life below water (SDG 14), and life on land (SDG 15). Therefore, there is a need to increase farmers’ uptake of SAPs in Asia and to improve societal benefits.

To date, no comprehensive review has systematically summarized sustainable rice farming practices and identified determinants of adoption in this region. Thus, this study aims to address this gap by providing a critical review that not only examines the methods used in previous studies but also synthesizes their findings, ultimately identifying key research gaps. The objectives of this study are threefold. Firstly, it aims to identify and summarize the most common SAPs for rice cultivation that have been implemented in SEA countries, including a detailed analysis of their sustainability levels, as discussed in Section 3.1. Secondly, it aims to analyze and evaluate the existing literature on the determinants of adoption, including the factors that influence farmers’ decision-making. Lastly, it aims to highlight the methodological approaches used in previous studies and assess their strengths and limitations.

2 Materials and methods

Most systematic review studies on motivation and the factors determining the participation of AES or adoption of SAPs were conducted mainly on a regional or global scale. For example, Serebrennikov et al. (2020) conducted a systematic review and identified factors influencing the adoption of SAPs in Europe. They found that farmers’ environmental and economic attitudes and their sources of information have a strong impact on their adoption of organic farming. Sapbamrer and Thammachai. (2021) conducted a global systematic review of factors influencing farmers’ adoption of organic farming. They found that extension agents, farm associations, and the government are three key drivers for this adoption. Guo et al. (2020) conducted a comprehensive review of the literature on the adoption of sustainable intensification (SI) in Southern African farming systems. They identified nine relevant drivers of the adoption of SI among smallholder farmers including age, education, extension services, gender, household size, income, farming organization membership, size of arable land, and access to credit. Begho et al. (2022) reviewed factors influencing farmers’ adoption of sustainable crop farming practices in South Asia. They discovered that factors such as education, training and extension programs, soil quality, irrigation, income, and access to credit play a significant role in influencing farmers’ decision-making. A systematic review conducted by Jones et al. (2020) highlighted the importance of both financial and non-financial motivations in influencing participation in payment for ecosystem services (PES) programs in the global south. Foguesatto et al. (2020) reviewed the literature on factors influencing the adoption of SAPs worldwide. Their study suggests that farmers’ perceptions are influenced by economic and psychological factors. They discovered the majority of papers they reviewed ignored the inclusion of psychological factors involving farmers’ adoption decisions. Furthermore, the constructs (i.e., farmers’ perception) were poorly measured in those reviewed papers concerning psychological factors.

This review primarily focuses on the voluntary adoption of sustainable practices, regardless of whether they are supported by the government or NGOs. This study is based on identifying factors that motivate or hinder farmers’ independent decision-making about SAPs, rather than evaluating the impact of external interventions. We concentrated on factors found to be statistically significant in predicting SAP adoption. As this study includes research on using multiple methods such as various regression models or structural equation modeling, a comparison of the effect sizes of these influential factors is beyond the scope of this study. In our study, SAPs include approaches that not only enable rice farmers to implement environmentally friendly practices but also contribute to their economic stability and social well-being. These practices include, but are not limited to, methods such as organic farming, the system of rice intensification (SRI), integrated farming (rice with livestock or fish), good agricultural practices (GAP), integrated pest management (IPM), and rice straw management (RSM). Besides giving an overview of common SAPs for rice production, our review focuses on empirical findings on factors driving or limiting the adoption of SAPs in rice production in SEA. These practices are viable for smallholders, allowing them to make the best use of their resources and land.

2.1 Inclusion criteria

While a number of studies on the technical experiment or economic performance of SAPs in rice cultivation exist, they were omitted in this study. This study included articles that (1) analyzed the adoption of sustainable rice cultivation practices such as reducing greenhouse gas emissions from rice production, decreasing irrigation water use, reducing agro-chemical use, and implementing sustainable straw management; (2) applied statistical methods and used primary data for empirical research in SEA countries; (3) published in peer-reviewed journals and proceedings; (4) published between 1993 and 2022; and (5) published in English. In terms of farmer adoption, the vote-counting method was employed to synthesize evidence from multiple studies in order to categorize the findings into three categories: (1) studies reporting positively significant results; (2) studies reporting negatively significant results; (3) studies reporting non-significant results. This method identified whether a specific variable in a factor exhibits a consistent pattern or mixed results across studies (Priya and Singh 2022). However, we recognize the inherent diversity and context-specific nature of studies conducted in SEA, which can affect their comparability. Therefore, we interpret these categorized results with caution. When a variable shows significantly positive results in the majority of the studies, it is considered to have a positive effect on SAP adoption.

2.2 Search methods

We searched relevant articles in several databases including Web of Science, Scopus, and Google Scholar by using the following keywords: “adoption” OR “determinants” OR “factor” plus “attitude” OR “preference” OR “perception” plus “organic rice farming” OR “system of rice intensification” OR “sustainable agriculture practices” OR “integrated pest management” OR “climate-smart” OR “integrated farming” OR “Good agriculture practices” OR “Best management practices” OR “green manure” plus “Cambodia” OR “Indonesia” OR “Laos” OR “Malaysia” OR “Myanmar” OR “Philippines” OR “Thailand” OR “Vietnam” OR “Southeast Asia”.

2.3 Quality assessment

Our systematic review follows the PRISMA guidelines (Moher et al. 2009). The flow diagram in Fig. 1 depicts the study selection procedures. A total of 1341 records were initially identified from Web of Science, Scopus, and Google Scholar. After removing duplicates, 429 articles underwent abstract screening. Out of these, 298 studies were excluded for not being conducted in SEA countries or focusing on unrelated practices. Further, full-text examination led to the exclusion of 33 additional articles due to inappropriate study design or a lack of focus on adoption and rice cultivation. Ultimately, 39 articles met the inclusion criteria for the review.

Fig. 1
figure 1

Diagram outlining steps and results of article screening, adapted from the PRISMA protocol (Moher et al. 2009).

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2.4 Data analysis

The data were presented based on author, year of publication, country, study population, and findings and recommendations. Several studies have identified and categorized the factors influencing the farmers’ decisions to adopt SAPs. Tu et al. (2018) classified the factors affecting adoption of eco-friendly rice production into eight subgroups: (1) socio-demographic characteristics (age, education, experience, gender, and labor); (2) perception of risk; (3) perceived usefulness (benefit, selling price, yield); (4) perceptions about environment pollution and biodiversity; (5) perceived ease of use (technical aspect); (6) farm physical characteristics (farm size and plots); (7) social network (membership in organizations), and (8) financial characteristics (perception of outside support and access to credit). Pham et al. (2021) categorized factors into four groups: (1) plot characteristics (size, ownership, distance, plot problem, quality, land slops); (2) household characteristics (age, education, gender); (3) resource constraints (assets, food expenditure, labor, livestock units index, off-farm income, total cultivated plots); and (4) social capital (political connections, relatives, membership of farmer groups, sharing with peers, contact with extension agents). Priya and Singh (2022) grouped variables affecting general SAPs adoption into 6 categories: (1) social-economic factors (e.g., age, gender, farm income, etc.), (2) biophysical factors (e.g., farm size, location, distance to market, etc.); (3) institutional factors (e.g., training, input subsidies, policy support, etc.); (4) financial factors (e.g., debt/assets, access to credit, crop insurance, etc.); (5) technological factors (access to knowledge, technical assistance, asset owned, etc.); and (6) psychological factors (e.g., intention to adopt, perception, attitude, etc.). According to recent studies (Dessart et al. 2019), behavioral/psychological factors play a significant role in the adoption of SAPs. They grouped them into three clusters from more distal to more proximal: (i) dispositional factors; (ii) social factors; and (iii) cognitive factors. Based on the above-mentioned studies, this study identifies a comprehensive set of six groups for factors affecting SAP adoptions, including (1) socio-demographic characteristics; (2) farm characteristics and farming factors; (3) economic factors; (4) institutional factors; (5) social factors; and (6) behavioral/psychological factors.

In our systematic review, the studies analyzed had significant heterogeneity in methods and measures applied, including the use of structural equation modeling, which did not report the mean and standard deviation data required for traditional effect size calculations. Consequently, we employed a vote-counting method to synthesize the findings and to discern common themes and issues. While vote-counting has limitations, which will be detailed in Section 3.4, and may not capture the full complexity of the studies, it can still provide a useful summary of the findings and offer insights for future research.

3 Results and discussion

This section presents and discusses the results of the systematic review. In terms of the geographical location, seven countries in SEA have relevant publications: Cambodia (1), Indonesia (6), Malaysia (4), Myanmar (1), Philippines (2), Thailand (12), and Vietnam (13) (Table 1). However, no relevant papers were found for Laos. A detailed summary with findings and recommendations of each study is shown in Table A1 in the Appendix. Section 3.1 outlines the most common SAPs implemented in SEA. Section 3.2 presents the factors most frequently examined that affect the adoption. Section 3.3 identifies research gaps, summarizes analysis methods, and discusses limitations.

Table 1 Counting of studies on rice SAPs adoption in SEA countries. KH Cambodia, ID Indonesia, MM Myanmar, MY Malaysia, PH Philippines, TH Thailand, VN Viet Nam, AWD alternate wetting and drying, BMP/GAP best management practices/good agricultural practices CSA climate-smart agriculture, EF eco-friendly, GFT green fertilizer technology, HPRS Hill pond rice system, IF integrated farming, IRL integrated rice and livestock farming, IRF integrated rice and fish, Mixed SAPs mixed sustainable agricultural practices, OF organic farming, RSM rice straw management, SRI system of rice intensification, SLM sustainable land management, 1M5R one must do-five reduction.
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3.1 Rice SAPs adoption in SEA

As shown in Table 1, organic farming adoption was the most studied (n = 9), followed by Climate Smart Agriculture including SRI and AWD (n = 7), integrated farming, integrated rice-fish farming, integrated rice-livestock farming (n = 6), Good Agricultural Practices/Best Management Practices (n = 4), and rice straw management (n = 3). These findings may indirectly indicate the region’s policy priorities. Table 2 presents the sustainability levels of these SAPs and the following section will provide a detailed analysis of each practice.

Table 2 Sustainability levels of agricultural practices in rice cultivation.
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3.1.1 Organic rice farming (OF)

During the 2000s, organic agriculture gained prominence in Southeast Asian countries thanks to the support of international NGOs and development agencies (Castella and Kibler 2015). Adoption of organic agriculture practices can be effective in improving farmers’ livelihood and conserving agro-biodiversity (Limnirankul and Gypmantasiri 2012). By reducing agro-chemical inputs, promoting crop rotation, and vegetative buffer zones, organic agriculture has the potential to regenerate agricultural land, prevent soil degradation, and counteract biodiversity loss (Fritz et al. 2021). According to Neang et al. (2017), in Cambodia, around 85% of farmers are rice producers. Cambodian organic rice farmers have lower social status because OFs are perceived as old-fashioned and only used by “poor” farmers (Neang et al. 2017). There is not enough of a price premium for organic rice to encourage farmers to adopt this practice (Neang et al. 2017). In Indonesia, organic agriculture remains a very small proportion of total agricultural land (0.2 %) despite almost 30 years of civil society initiatives and government efforts to promote OF (Fritz et al. 2021). Sujianto et al. (2022) investigated Indonesian rice farmers’ perception, motivations, and constraints in the adoption of OF and the level of awareness as well as their belief in OF in the future. They conclude that organic rice farmers and conventional farmers have different perceptions of production, quality, health and safety, price and market, environmental concerns, and certification. In Malaysia, the Malaysian Agricultural Research and Development Institute is actively supporting organic farming (Somasundram et al. 2016). Although the government launched the “Go Organic” program in 2001, the program was not successful, since the adoption rate of OF has remained low (less than 0.1 percent) (Ashari et al. 2018). In Thailand, the organic rice sector accounts for 30.4% of total organic products (Kerdsriserm et al. 2016). The Thai government has promoted OF through various strategies including “a crop diversification program,” “financial incentives,” and “training programs.” However, the adoption of OF has been slow (Seerasarn et al. 2020). In Vietnam, rice farming remains economically viable, so the transition to a more environmentally friendly farming method has been relatively slow (van Aalst et al. 2023).

3.1.2 Climate-smart agriculture (CSA)

CSA is sustainable agriculture incorporating resilience concerns, while at the same time, seeking to reduce greenhouse gas emissions (Ha and Bac 2021). Climate-smart agriculture is a way to combine various sustainable methods to address climate challenges faced by specific farming communities. This involves the adoption of high-yield and drought-tolerant varieties, changing schedules for planting dates, and adopting the system of rice intensification (SRI), minimal tillage, and intercropping (Ha and Bac 2021; Duc Truong et al. 2022).

System of rice intensification (SRI)

SRI is the most well-known CSA including a set of rice cultivation practices which produce higher yields and increase water-use efficiency while being environmentally friendly. SRI is particularly effective in increasing rice productivity while reducing production costs, hence enhancing farmer profitability (Ly et al. 2012; Zaman et al. 2017). In rice-producing countries, SRI has been introduced and has been adopted by many farmers in Cambodia, Indonesia, Malaysia, Thailand, and Vietnam (Doi and Mizoguchi 2013; Aris and Fatah 2019; Ha and Bac 2021; Arsil et al. 2022; Ly et al. 2012). SRI includes a low-cost water-saving technique called Alternative Wetting and Drying (AWD) allowing rice farmers to switch from continuous flooding of paddy fields to intermittent flooding, which has the potential to minimize methane emissions (Samoy-Pascual et al. 2021). Mao et al. (2008) conducted a qualitative analysis of SRI adoption in Cambodia and found that the rice yield increased when farmers changed to SRI implementation. Linquist et al. (2015) estimated that AWD can lower the global warming potential of rice production by 45–90%. Several factors influenced the decision to adopt AWD, not only socioeconomic factors, but also the institutional arrangements within the irrigation association, and the biophysical conditions relative to the distance to water sources (Samoy-Pascual et al. 2021). Nguyen and Hung (2022) investigated the adoption of SRI and its impact on rice yield in the upland region of central Vietnam. They found that age negatively affects SRI adoption, while family labor, number of plots, and access to credit positively affect adoption. SRI adoption was found to increase rice yield by 15.1%, and their results suggest a need for coordinated policies to support SRI implantation in mountainous areas, particularly in training farmers to use the technique. Furthermore, Mao et al. (2008) found that low soil fertility, labor shortage, lack of irrigation systems, drainage and water sources, insufficient organic fertilizer, little knowledge of diseases and pest control, and moreover, natural disasters are challenges farmers have to face and may hinder them from practicing SRI.

3.1.3 Integrated farming (IF)

IF is based on the integration of crops and livestock into production systems and maintains a high level of soil fertility and productivity. Moreover, IF seeks to replace external inputs of energy, agrochemicals, and labor with on-farm resources and natural biological cycles and processes (Purnomo et al. 2021). Integrated rice-livestock (IRL) farming involves several resource-saving practices and efficient farming methods that minimize the negative effects of intensive farming and preserve the environment while achieving acceptable profits and sustained levels of production (Widadie and Agustono 2015). Small-scale farmers will need additional technology and management to enhance their self-sufficiency and resource-use efficiency by integrating crop and livestock systems (Widadie and Agustono 2015). The integrated rice–duck farming (IRDF) is also included in this category because it integrates ducks feeding on insects and weeds in paddy rice fields, while at the same time, duck manure is a good fertilizer to nourish the soil. It has served as a model for the Asian sustainable agriculture movement (Suh, 2014). Bunbongkarn (2013) found the factors influencing the adoption of IF are different among farmers in lowland and upland areas. For example, three factors were significantly associated with the adoption in lowland areas, namely participation frequency of integrated farming training programs, income from vegetables, and percentage use of natural fertilizers. For upland areas, the factors are the number of years of experience in practicing IF, the amount of loans for IF, and water adequacy.

Integrated rice–fish (IRF) farming is a more sustainable alternative to rice monoculture, which could reduce pesticide use, increase nutrient recycling, and improve ecological sustainability, while also supporting economic sustainability (Berg 2002). IRF may increase farm income and improve farm productivity (Bosma et al. 2012). Moreover, IRF and IPM are complementary activities and rice–fish farmers should be an important target group for the development and application of the IPM program in the region (Berg 2002).

3.1.4 Good agricultural practices (GAPs) and best management practices (BMPs)

GAPs and BMPs allow sustainable farms to use agro-chemical inputs in moderation, as long as it does not jeopardize their overall sustainability. A report by Premier and Ledger (2006) highlights the Southeast Asian governments’ efforts to address a uniform standard through the development of the Association of Southeast Asian Nations (ASEAN) scheme for Good Agricultural Practice (GAP), a standard applicable to all ASEAN member countries. GAP is the benchmark for a food safety-based plan aiming to satisfy export requirements. This program is designed to certify that GAP-labeled rice is produced according to best practices for (1) farm-level hygienic conditions, (2) management of agricultural equipment and tools, (3) management of inputs, (4) control of production and practices, and (5) control of accounting and documents (Srisopaporn et al. 2015).

In Indonesia, Connor et al. (2021) found that rice farmers can produce rice more sustainably, and their livelihood can be positively improved by national and regional governments’ projects to promote BMPs. In Malaysia, GAP was launched in 2013 to promote sustainable agriculture practices. A study by Terano et al. (2015) found that Malaysian paddy farmers are willing to practice sustainable agriculture based on GAPs. Since 2012, the Thailand Rice Department (TRD) has been advocating for a comprehensive set of BMPs known as the Cost-Reduction Operating Principles (CROP) aiming to increase farmers’ income by cutting down costs while preserving or increasing yields through the “Three Must Do” and “Three Must Reduce”Footnote 1 recommendations. (Stuart et al. 2018). Similar to Thailand’s BMPs, in Vietnam, the “One Must Do, Five Reductions” (1M5R)Footnote 2 program, designed to promote BMPs in lowland rice cultivation, was certified as a national approach by the Ministry of Agriculture and Rural Development in 2013 (Tho et al. 2021).

We found that Integrated Pest Management (IPM) adoption for rice cultivation was usually investigated together with GAPs/BMPs. For example, Terano et al. (2015) examined farmers’ adoption of GAP including IPM, and Dung et al. (2018) studied the factors affecting the adoption of 1M5R and IPM. Integrated Pest Management (IPM) is a crop protection strategy which has the potential to minimize pesticide application while increasing productivity. Pesticide spray reduction could not only benefit the environment but also reduce workdays used for spraying which could lower input costs and thereby result in higher income for farmers. Josue-Canacan (2022) investigated the constraints and motivation in IPM adoption in the Philippines. She found that increasing farm productivity and income were key motivations for farmers to attend training programs whereas lack of time and capital were major constraints. In Indonesia, although IPM was implemented in rice cultivation, Bulkis et al. (2020) found there has actually been an increase in pesticide use among rice farmers in many parts of the country. This has been linked to increasing brown planthopper attacks in various rice-producing areas in Java. Compared to the low IPM adopters, the high IPM adopters earn higher profits (Bulkis et al. 2020).

According to GAP/BMP standards, farmers are allowed to use agro-chemicals but only at certain times of crop growth. Therefore, farmers only need to fulfill basic farming practice requirements that are not always beneficial to the environment and do not mitigate climate change. However, they can still serve as a starting point for promoting SAPs with proper implementation and monitoring. GAPs/BMPs could gradually shift farmers toward more sustainable practices, such as reducing the use of agro-chemicals. As such, they can be viewed as a stepping stone toward a more sustainable and climate-resilient agriculture.

3.1.5 Rice straw management (RSM)

Increasing the rice production will also increase a high amount of additional rice straw residues. A common practice in SEA is burning the straw directly in the field. Farmers favor this method of managing crop residues as it offers several benefits. It helps counteract the immobilization of nitrogen induced by the residues, improves control over diseases and insect infestations, eliminates weed seeds and seedlings, and assists in eradicating rodents (Kaur et al. 2022). However, open-field rice straw burning has not only a negative impact on human health but also emits significant amounts of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) (Romasanta et al. 2016), which increase GHG emissions and air pollution (Connor et al. 2020). In addition to destroying soil organic matter, burning also reduces beneficial soil bacteria (Mandal et al. 2004). Farmers may rationalize rice straw burning, despite the fact that they realize this could lead to high risks for human health and the environment. For example, farmers may think burning is the only option if the fields are difficult to access (Connor et al. 2020).

Keck and Hung (2019) examined in Vietnam two practices: (1) rice residue burning or (2) incorporating rice residue into the soil, and evaluated the associated costs and benefits. Their analysis revealed that while burning residues may have negative ecological consequences, it remains economically rational for farmers. Consequently, they contend that persuading farmers to shift away from this prevalent practice would require financial compensation to cover additional expenses. Connor et al. (2020) investigated several options for rice straw management (Connor et al. 2020), namely rice straw incorporation, rice straw burning, rice straw composting, rice straw compacting, biogas production from rice straw, urea-treated rice straw, and rice straw collection (self-propelled baler, roller baler, loose straw collection). Each of these practices has its own advantages and disadvantages, depending on how well farmers handle the practices. For example, the incomplete decomposition of rice straw produces methane emissions (Wassmann et al. 2000).

3.2 Factors influencing the adoption of SAPs

This review identified a total of 138 variables, including eight socio-demographic characteristics, 53 farm management factors, 18 economic factors, 12 institutional factors, one social factor, and 45 behavioral factors. A detailed list of variables can be found in Table A2 of the Appendix. We only include variables that appear in at least two or more studies in this manuscript because variables that are rarely found in the literature provide less information for policy reference. However, we should include those variables with statistical significance, even if they only appear once in the analysis because such variables as behavioral/psychological factors are emerging in recent studies and require further research (Priya and Singh 2022). Table 3 thus summarizes the 74 key factors out of a total of 137 variables that influence adoption.

Table 3 Factors have statistically significance on the adoption of SAPs. Sig (+) positive significance, Sig (−) negative significance, N-Sig: non-significant; (−) the variable is always not significant; *the variable is always negatively significant; **has a mixed significance; ***the variable is always positively significant.
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3.2.1 Socio-demographic factors

The age of farmers has been used as an essential explanatory variable in most SAP adoption studies, they indicate that young farmers are more likely to adopt new practices (Priya and Singh 2022). In this review, the effect of farmers’ age was examined in 21 papers. Only six studies thereof found this factor to be negatively significantly correlated with adoption, namely concerning younger farmers. Whereas two studies found elderly farmers are more likely to adopt SAP. Moreover, 13 thereof have no statistical significance. Global literature indicates a positive correlation between education level and SAP adoption (Priya and Singh 2022). The association between education level and adoption was assessed in 26 papers. As demonstrated in Table 3, there was a more frequent positive correlation between adoption and education level, meaning that farmers with a higher level of education are more likely to adopt SAPs. For example, education was identified as a crucial predictor for BMP adoption in Myanmar (Wehmeyer et al. 2022). However, there are 10 papers indicating that this factor was not statistically significant. Farming experience was assessed in 16 papers. Half of them report positive statistical significance. Moreover, the effect of gender on adoption was examined in 13 studies, two thereof show positive whereas five thereof show negative effects on adoption. There are 11 studies assessing correlations between the household variable and adoption. Only one shows negative, and three thereof show positive statistical significance, whereas seven thereof did not show any statistical significance.

3.2.2 Farm characteristics and farming factors

It is generally assumed that farmers with larger farm sizes may be more likely to invest in technology improvements (Dung et al. 2018; Song et al. 2020). However, in this review, mixed results have been found as described: there are 25 studies examining the correlation between farm size and adoption, with results differing across studies; eight studies found a positive statistical significance, and 13 thereof had no significance. In many developing countries, land ownership is positively correlated with SAP adoption (Priya and Singh 2022). Land ownership was assessed in ten studies, four of which found this factor to be positively significantly correlated with adoption, and two thereof show a negative effect. The association between the number of farm laborers and adoption was examined in ten papers. However, seven of which show no statistical significance.

3.2.3 Economic factors

A total of 18 economic factors have been identified (Table A2 in Appendix). Most of the economic variables have appeared only once in our review. As mentioned in Section 3.2, those factors that appeared less than twice have been removed, since there is limited evidence for concluding that any of those economic factors can be a major driver of SAP adoption. Thus, only six economic variables remain in Table 3. Having access to credit is often reported as one of the major challenges in SAP adoption (Priya and Singh 2022). In this review, access to credit was assessed by ten studies, five of which showed significant positive effects, and only one revealed a significant negative effect on SAP adoption. Seven studies have investigated the effect of off-farm income on adoption. Only two studies show positive and one negative statistical significance. The association between farm income (per year) and adoption was investigated in five studies. The result shows this had a significantly positive effect on adoption. The higher the farm income, the more likely farmers will adopt the SAPs. Many studies recommended that governments provide incentives to farmers for the conversion to SAPs (Digal and Placencia 2018; Tu et al. 2018; Yanakittkul and Aungvaravong 2020).

3.2.4 Institutional factors

The influence of institutional factors, including membership of cooperatives, farmers’ associations, and seed growers’ associations, has been examined. Among these, only 6 studies reported a statistically significant positive effect on adoption, while the remaining studies found no statistical significance. Access to extension services and information has consistently been identified as an important factor in fostering adoption (Dung et al. 2018; Tran et al. 2019). Our results are in line with previous studies that access to extension impacted positively on adoption. Nine studies investigated the effect of participation in SAP training, with six of them demonstrating a positive statistical significance on adoption. Additionally, participation frequency in integrated farming training was examined by three studies, and the results show that this factor has a positive effect on adoption. Moreover, government support also emerges as a significant factor in integrated rice farming (Purnomos et al. 2021).

3.2.5 Social factors

Tran-Nam and Tiet (2022) suggested that social factors such as peer influences, and social and personal norms are critical components for the adoption of organic farming. In our review, there is only one study that examined one of the social factors, namely whether neighbors practicing SAPs influence the adoption. However, that study found there was no statistical significance; hence, it is not listed in Table 3.

3.2.6 Behavioral/psychological factors

Out of 39 studies reviewed, seven investigated the influence of behavioral/psychological factors on SAP adoption. Although 45 variables were identified as behavioral/psychological factors, the evidence of these influencing factors on SAP adoption is very limited due to only seven papers paying attention to behavioral factors. We include behavioral/psychological variables with statistical significance, even if they appeared only once in the analysis because they are emerging in recent studies and require further research. Knowledge about SAPs was analyzed in seven studies, and knowledge about climate change was analyzed in two studies. Farmers’ attitudes, perceptions of SAPs, and farmers’ knowledge were found to have a positive statistical significance on adoption. Farmers who perceive the benefits of SAPs and have a positive attitude toward them are more likely to adopt SAPs. However, Myanmar farmers perceive GAPs as difficult to apply despite their benefits (Oo and Usami 2020). Support expectations from the government and institutions have impacts on rice straw management practices (Connor et al. 2020). Among the behavioral factors, farmers’ attitudes toward SAPs were found to be a significant predictor of adoption. The review also found that perceived behavioral control, pro-environmental motivations, risk perception, and subjective norm were important factors for SAP adoption, which is consistent with the findings by Adnan et al. (2017), Dessart et al. (2019), and Jones et al. (2020). Understanding the underlying factors that influence farmers’ decision-making and their attitudes toward SAPs is crucial for promoting sustained adoption of these practices. Therefore, more research on investigating the correlation between behavioral/psychological factors and SAP adoption needs to be encouraged.

3.3 Identification of research gaps, analysis, and limitations

There are several research gaps that warrant attention in future studies. First, while the existing literature primarily focused on the adoption of specific sustainable practices, further research is needed to investigate the synergies and trade-offs among different SAPs across all three dimensions of sustainability: environmental, economic, and social. This includes exploring how these practices interact and contribute to the overall sustainability level in rice cultivation. Second, there is a need for more research on the social factors that influence adoption such as social norms and networks, and which social factors interact with other factors such as economic and institutional factors to influence adoption. Third, despite the growing importance of behavioral/psychological factors in adoption studies globally, very few relevant studies have been conducted in Southeast Asian countries, and hence, there remains a significant gap in the literature. Fourth, most studies were conducted in a single country, while there is a need for comparative studies across different countries in Southeast Asia. Such studies can provide insights into the factors promoting or hindering the adoption of specifically targeted SAPs in different contexts.

In our review, we observed that a majority of the studies employed regression analysis (n = 33), with the most common subtype being specified as logit, probit, or multiple linear regression, cox model (n = 1), and tobit regression (n = 1). The remaining articles (n = 4) used structural equation modeling. Additionally, we examined whether the conceptual models used in the studies were derived from established behavioral models. Only five studies explicitly mentioned the application of theoretical behavioral models such as the Diffusion of Innovation (DOI), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), Health Belief Model (HBM), and Value-Belief-Norm (VBN). Some studies categorized farmers into different groups, such as adopter group and non-adopter groups (n = 12), as well as subgroups based on levels of adoption, including overall adoption, partial adoption, discontinued adoption, and continued rejection (n = 1) (Table A1 in Appendix). These classifications allowed for a more nuanced understanding of the adoption patterns among farmers.

It is important to acknowledge the limitations of this systematic review. First, the search was limited to articles published in English, which may have excluded relevant literature published in other languages. Second, while efforts were made to ensure the quality of the studies included, it is possible that some bias or error may have been introduced due to limitations in the study design or implementation of the reviewed papers. Furthermore, it is crucial to address the limitations of the vote-counting method: (1) it can oversimplify the data, potentially leading to a loss of detailed information from individual studies; (2) there is a risk of interpretative bias, as aggregating results may not accurately represent the varied contexts and methodologies of the studies; and (3) it does not account for the magnitude of effects, which is critical in understanding the impact of the studied factors. Despite these limitations in the vote-counting method, it can still provide a foundation for more in-depth analyses and future research directions.

4 Conclusion and recommendations

This systematic review focuses on investigating the increasing empirical studies about SAPs implemented in rice cultivation and factors influencing farmers’ adoption in SEA countries. We found that the adoption of organic farming is the most studied topic in SEA countries, followed by GAPs/BMPs and CSA/SRI. The results suggest that SAPs can be effective in achieving food security, improving rice productivity, reducing agro-chemical inputs, mitigating the impact of climate change, decreasing water consumption for irrigation, and promoting farmer livelihoods. However, the evidence in this review demonstrates that the adoption rate of those SAPs is low in the SEA region.

The factors influencing farmers’ adoption of SAPs in SEA countries exhibit a complex interplay of similarities and differences. To enhance the adoption of SAPs for rice cultivation in SEA, it is essential to learn from the experiences of SEA countries. Organic farming and climate-smart agriculture have been extensively studied in the region, and the government should continue to promote them. Evidence shows that subsidizing organic inputs could increase the likelihood of adoption in Indonesia, the Philippines, and Vietnam. Increasing awareness of farmers and enhancing the extension systems is emphasized in Malaysia, Thailand, and Vietnam. Based on this systematic review, the following recommendations are made to enhance the adoption of SAPs for rice cultivation in Southeast Asia.

4.1 Knowledge exchange and collaborative research

It is important to establish knowledge exchange platforms and collaborative research initiatives that facilitate cross-border sharing of experiences, expertise, and research findings among farmers, researchers, and policymakers across SEA. There is a need to increase awareness and education among farmers and policy makers. In some cases, countries in SEA may prioritize economic development over environmental conservation, leading to a lack of investment in agri-environmental programs. Furthermore, farmers’ knowledge about climate change and sustainable agricultural practices is an important factor that can influence their decision to adopt SAPs and their ability to implement these practices effectively. Therefore, there is an urgent need to enhance farmers’ knowledge through multifaced approaches such as increasing extension services and establishing field schools and information campaigns for farmers. Encouraging farmers’ participation in SAP training and raising the frequency of participation could increase the SAP adoption rate.

Although the adoption of sustainable agricultural practices such as organic farming has been gaining popularity, there is still a lack of understanding on how behavioral/psychological factors influence farmers’ decision-making in Southeast Asian countries, particularly in relation to rice cultivation. In order to promote the adoption of SAPs and ensure the long-term sustainability of rice cultivation, it is important to understand the trade-offs that farmers face when considering these practices. Future research should focus on identifying the factors that influence farmers’ trade-offs between different agricultural practices in rice cultivation. One potential area of investigation is how both psychological factors and the effects of governmental policies and support programs such as economic incentives and non-monetary incentives influence farmers’ decision-making. To address the existing gap of neglecting the exploration of synergies and trade-offs among different SAPs, it is imperative for future research to investigate the interrelationships and potential conflicts between various SAPs in the context of rice cultivation.

4.2 Develop supportive policies

Governments in SEA should develop relevant policies that incentivize the adoption of SAPs by designing comprehensive agri-environmental programs. These programs often require significant resources to implement, and therefore, it is essential to have supportive policies to encourage farmers’ engagement. Governments can provide financial incentives to farmers who adopt SAPs. Although regulations and financial incentives may encourage initial adoption decisions, they may not be sufficient to support long-term changes in farmers’ practices (Defrancesco et al. 2018), especially in Southeast Asian countries where budget limitations may be a challenge. Furthermore, subsidies for any SAPs have been argued as being unsustainable, and farmers may switch back to conventional farming if financial support for SAPs were to be discontinued (Mills et al. 2017; Dessart et al. 2019).

Public policies can play a crucial role in improving farmers’ access to credit as it is an essential factor for the success of farmers and their agricultural businesses. Incentives can be particularly effective when they are designed to address the specific needs and constraints of farmers. For example, in areas where access to credit is limited, providing loans at low-interest rates can help farmers invest in new equipment and inputs necessary for SAP adoption. Our review suggests that policy interventions should focus on enhancing institutional support and economic incentives and on improving access to credit, information, and training.