Adapting Rice Farms Pays Off: New Evidence from Thailand and Myanmar

Rice feeds more than half a billion people across Southeast Asia, yet the farmers who grow it are working under increasingly hostile skies. Unpredictable monsoons, searing heat, droughts and floods have turned what was once a predictable seasonal rhythm into a yearly gamble. A new study published in Environmental and Sustainability Indicators offers some of the most detailed household-level evidence yet that when rice farmers adapt, they eat better and earn more. Drawing on surveys of 365 rice-farming households in Thailand and Myanmar, the research quantifies how four low-cost adaptation strategies shape both farm income and food security in two of the region’s most climate-exposed rice bowls.

The research team, led by Thandar Win Maung and Sayamol Charoenratana of Chulalongkorn University together with Nguyen Diem Tien Ho and Peter Michael Rosset, focused on two deliberately contrasting sites: Lat Lum Kaeo District in Thailand’s irrigated central plains and Meiktila Township in Myanmar’s Central Dry Zone. The choice was strategic. Thailand is a mature, leading global rice exporter, while Myanmar, despite producing rice more cost-effectively than most competitors, struggles with unstable prices, fierce competition and disrupted supply chains. Agriculture contributes roughly 30 percent of Myanmar’s GDP and supports more than 30 percent of Thailand’s population, making both countries acutely sensitive to climate-driven yield instability. The World Bank has projected that climate-induced threats could cost Thailand 7 to 14 percent of GDP by 2050 under current scenarios, while the World Food Programme estimates that over 12 million people in Myanmar are acutely food insecure, a crisis worsened by climate shocks and socioeconomic instability.

The study zeroed in on four adaptation practices that farmers themselves reported most often: crop rotation, the use of high-yielding varieties, adjustment of cropping calendars, and modification of planting methods. These are not exotic technologies. They are largely knowledge-based, low-cost adjustments rooted in indigenous farming experience, sometimes combined with government-initiated techniques. Farmers who implemented at least one of these practices during the 2023 rainy season were classified as adapters, while those who reported none were treated as non-adapters. In Lat Lum Kaeo, 62.2 percent of farmers had adopted at least one measure, compared with 51.9 percent in Meiktila. Thai farmers leaned toward more proactive, diversified responses, with 51.1 percent practicing crop rotation and 47.8 percent changing planting methods, while Meiktila farmers relied most heavily on rotation at 39.5 percent and on high-yielding varieties at 30.8 percent.

Before estimating any impacts, the researchers checked whether farmers’ perceptions of climate change matched the meteorological record, and they largely did. Weather station data from 2006 to 2023 show rising temperatures in both districts, with Lat Lum Kaeo warming from an average of 29.25°C to 30.28°C and Meiktila from 27.22°C to 28.28°C. Rainfall trends diverged: Meiktila’s total annual rainfall declined while Lat Lum Kaeo’s increased. When asked about the past decade, 84.3 percent of Meiktila farmers and 63.2 percent of Lat Lum Kaeo farmers perceived rising temperatures, and 77.8 percent of Meiktila farmers reported declining rainfall against 73.5 percent of Thai farmers who saw increases. The alignment between perception and measurement matters, because farmers only adapt when they believe the climate is genuinely shifting.

The methodological core of the study is its handling of self-selection bias, a persistent problem in observational economics. Farmers who adapt may simply be more skilled, better informed or better resourced to begin with, so naively comparing adapters with non-adapters can produce misleading conclusions. The team used a random utility framework to model the adaptation decision, in which a farmer adapts only when the expected net benefit is positive, and then applied propensity score matching to construct a fair comparison. Each of the 209 adapters was matched with three similar non-adopters from the 156-household control group using nearest-neighbour matching with replacement. Diagnostic tests confirmed the approach worked: the pseudo-R-squared fell from 0.122 to 0.026 after matching, the mean standardized difference dropped from 21.3 percent to 10.2 percent, and total bias reduction reached 52.1 percent, indicating that systematic differences between the groups were substantially eliminated.

The headline results are striking. Adaptation raised the probability of being food secure by 19.9 percentage points among adopters, and increased net farm income by 242.22 US dollars per hectare, both significant at the 1 percent level. Food security was measured with the World Food Programme’s Food Consumption Score, which tracks how often households consume different food groups over a week, with households classified as poor, borderline or acceptable. Before matching, the raw gap was even wider: 78 percent of adapters were food secure compared with only 51 percent of non-adapters, and adapters earned an average of 1,097.75 dollars per hectare against 913.92 dollars for non-adapters. Non-parametric Wilcoxon rank-sum tests and 95 percent confidence intervals that excluded zero reinforced the reliability of the positive effects.

Perhaps the most intriguing finding concerns intensity. The distribution of adoption was starkly polarized: 42.7 percent of farmers took no adaptation action at all, while a combined 34.3 percent adopted either three or all four strategies. Farmers who stacked multiple practices consistently outperformed those using fewer or none, with both food security and income rising in a broadly linear fashion as the number of strategies increased. This suggests the practices are complementary rather than substitutes, each additional measure delivering incremental welfare gains. It also carries a warning: the widening gap between non-adopters and multi-strategy users hints at growing inequality if support remains uneven across regions and income groups.

What drives farmers to adapt in the first place? The logistic regression underlying the propensity scores identified five significant determinants. Education was the strongest, positive and significant at the 1 percent level, followed closely by access to climate information. Access to credit was significant at the 5 percent level, while farming experience and road access mattered at the 10 percent level. Notably, access to agricultural extension services and attendance at training, the conventional channels for promoting climate-smart agriculture, were positive but not statistically significant. The authors suggest this may reflect poor extension support, a reliance on traditional knowledge, or training that failed to address appropriate topics or methods, a finding with uncomfortable implications for how adaptation programs are currently designed and delivered.

The policy implications flow directly from these results. Because credit and road infrastructure significantly boost adaptation, the authors argue for policies that ease financial constraints and expand investment in rural transport, which reduces costs, improves market connectivity and cuts postharvest losses. Because extension and training underperformed, they call for a comprehensive review of their quality and curricula, shifting away from generic advice toward specialized, context-specific programs that align with farmers’ practical needs and existing indigenous knowledge. They also recommend strengthening formal and informal education, building farmer networks and communication platforms for peer learning, and involving rural communities directly in adaptation planning to improve local ownership. Governments, the private sector and NGOs, the study concludes, must scale region-specific strategies that integrate formal technologies with farmer-led practices.

The authors are candid about the study’s limits. The cross-sectional design cannot strictly establish temporal ordering between adoption and income, the survey did not record when farmers first adopted practices that are often traditional and long-established, and propensity score matching controls only for observable characteristics, leaving possible bias from unobserved factors such as managerial ability and soil quality. Adoption was self-reported, the data cover selected areas only, and Myanmar’s socio-political instability was not explicitly controlled for even though it may influence both adaptation decisions and food security. Future work, they note, should incorporate longitudinal data and region-specific indicators of political stability. Even with these caveats, the message is clear and consequential for a region where rice is life: adaptation works, it pays, and the farmers who combine multiple strategies stand to gain the most, provided the information, credit and infrastructure reach those currently left behind.

Subject of Research: Impacts of climate adaptation strategies on food security and farm income among rice-farming households in Thailand and Myanmar

Article Title: Rice farming resilience to climate variability: Impacts of adaptation strategies on food security and farm income in Thailand and Myanmar

Article References: Maung, T. W., Charoenratana, S., Ho, N. D. T., & Rosset, P. M. (2026). Rice farming resilience to climate variability: Impacts of adaptation strategies on food security and farm income in Thailand and Myanmar. Environmental and Sustainability Indicators, 32, Article 101560. https://doi.org/10.1016/j.indic.2026.101560

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101560

Keywords: rice farming, climate change adaptation, food security, farm income, Thailand, Myanmar, propensity score matching, crop rotation, high-yielding varieties, climate variability, Southeast Asia, smallholder farmers