Climate and land use change pressures are critical to food production in Social-Ecological Systems (SESs). This study assessed farmers’ perceptions of the pressures of climate and land use changes alongside their impacts on food production in Mhahakanumulla Village Tank Cascade System (MVTCS), a SES maintained by traditional agricultural land use systems in the dry zone of Sri Lanka. This study used both rating and ranking scale questions to quantify farmers’ perceptions. The tobit regression model was employed to evaluate how farmer perception was influenced by socio-economic factors. The results showed that most of the farmers had experienced that the climate of the MVTCS area had changed over time, and they perceived variability of rainfall patterns as the most prominent and influential climate change. The increased cost of production, wildlife damage, and land degradation were ranked by the farmers as the most impactful factors of food production due to climate change. The farmers rated deforestation and land clearing as the most influential and impactful changes in land use, while wildlife damage and land degradation ranked as the highest impacts on food production due to land use changes. Among the socio-economic determinants, training and income/profit positively influenced farmer perceptions of the severity of both climate and land use change. The level of farmer’s adaptation to climate change had a negative association with their perception of the severity of climate change. Household size negatively influenced the perceptions of the severity of climate change while positively influencing perceptions of land use change impacts. Among the spatial determinants, farm size and downstream locations of MVTCS positively influenced perceptions of the severity of both climate and land use change. Thus, the effectiveness of adaptation strategies towards climate and land use change pressures depends on how well they are understood by the farmers. The study findings provide helpful insights for formulating localized land use policies and climate change adaptation strategies in these globally important landscapes with a combination of both top-down and bottom-up approaches.

Changes in farming systems are dominated by changes in global climate and local environment, apart from the non-climatic drivers. Given the challenges in partitioning the contribution of climatic and non-climatic factors to the changes in farming systems, this paper aims to assess the types and changes of coastal farming systems, the farmer perceptions of the causes of the changes in farming systems, and the relationship between the infuencing factors and perceptions. A structured interview schedule was used to collect data from 381 randomly selected coastal households during September–October 2018. The random forest classifcation model was applied to estimate the relative importance of the farmers' characteristics on their perception of causes of changes in farming systems. This study reveals that the coastal farmers had mostly semi-subsistence type of mixed farming systems, which were going through dynamic changes in terms of their sizes and number of farmers. In general, the participation in rice, vegetables, and livestock farming was decreasing but increasing in fsheries, forestry, and fruit farming. Most (95.5%) of the farmers had to change at least one of the farming enterprises over the past decade (2009–2018) compared with the previous decade (1999–2008). About two-thirds of the farmers perceived that climate change had caused changes in their farming systems. Compared with the eastern coasts, the farmers in the western coasts tended to blame climate change to a higher extent for the effect on their agricultural activities. The random forest model outputs imply that the farmers who are younger in age and with less formal education, larger family, and smaller farmland should be supported with scientific knowledge on causes of changes in farming systems. This could help them more aware of climate change issues related to agriculture and increase their enthusiasm to take part in adaptive changes in farming systems.

Climate and agriculture affect each other in a reciprocal fashion. In agrarian countries like Bangladesh, agricultural activities are mostly defined by seasonal climatic cycles. Failure of agricultural adaptations to keep pace with climate change and variabilities have the potential to impact food production, and eventually, food security. Exploration of agricultural impacts of climate change in coastal areas of Bangladesh, one of the globally top ranked climate vulnerable countries, was the guiding focus of this study. Literature review, farmer interviews, agricultural office visits, government organizational databases, global climate model ensembles, and tide gauge records were used to collect primary and secondary information. Field data was collected from randomly selected 381 farmers from 10 selected subdistricts across the coastal areas of Bangladesh during September–October 2018. A wide range of statistical and econometric approaches were applied to reveal the complex relationship between farmers’ perception, climatic data, farming variables and socioeconomic characteristics.

Farming decisions in relation to adaptations under climate change largely depend on perception of climate change, and their feasibility is linked to the accuracy of their perceptions. Last 30- year (1988–2017) average temperature shows 0.45 °C spatial differences among the visited subdistricts. Yearly precipitation gradient could be >100 cm from the drier western to the wetter eastern coasts. While monthly averages of coastal temperature had increased except in early winter (October–December), while pre-monsoon and November rainfall had decreased with an increase in monsoon precipitation. Onset of monsoon rainfall was found to be delayed in the coastal areas. The farmers, in general, mentioned a warmer temperature and less rainfall in the recent decade (2009–2018) compared with the past decade (1999–2008). Their perceptions were mostly consistent with meteorological records though the observed decrease in winter temperature and the change in rainfall in some locations did not match with their perceptions. About one-third (30%) of the farmers accurately identified the changes in annual rainfall and temperature (annual, summer and winter average). Cluster analysis flagged 58.8% of the farmers as weak perception group. However, 41.2% of them were found in the moderate perception group characterized by younger age, better education, smaller family size, richer economic status, larger farm size, more affiliation with non-farm jobs, users of more communication media, closer to the marketplaces, and more distant from the sea. Thus, they were comparatively economically better-off than the weak perception group.

Farm productivity had a mean value of 1.98 in terms of revenue-cost ratio as reported by the farmers during the interviews based on the previous cropping year. Over one in ten (11%) of the farmers opined that their farm productivity had currently declined compared with the past. Majority (64%) of the farmers thought that this decline was due to climate change and its consequences, such as changes in temperature, precipitation, floods, droughts, and salinity. Outputs of the logistic regression shows that the farmers with greater level of education, more awareness of climate change, less communication with extension agents, stronger belief in decreased cyclone and salinity, and weaker belief in decreased flood had perceived that climate change was responsible for the decrease in their farm productivity. The farmers identified dry season soil salinity, coastal inundations and floods were the climate change induced issues that had adversely affected crop productivity.

To keep the farm productivity at desired levels, the farmers had adopted on average 10–11 farm management practices out of the 22 selected adaptation options. Two-thirds (67%) of the farmers mentioned that they had changed the farm management practices because of climate change. The farmers performed the crop-related adaptations more than the livestock, fisheries or general agricultural adaptations. According to the discriminant function analysis, the farmers with stronger belief in climatic impacts on their farm management were younger in age, had higher level of education, more involvement with non-farm jobs, greater affiliation with farmrelated organizations, more awareness of climate change, and greater accuracy of perception of changes in climatic variables.

Similar to the changes in farm management practices, 64% of the farmers had changed their farming systems due to climate change. In recent years (2009–2018) compared with the previous years (1999–2008), three farm enterprises, namely rice, vegetables, and livestock, had decreased, while three others, namely fisheries, forestry, and fruit farming, had increased. The random forest algorithm has identified that larger family size had negative effect, while age, education, and cultivated land had positive effects on the probability of believing that climate change had impacted their changes in farming systems. The farmers who more accurately perceived the changes in temperature, rainfall and cyclones and had better awareness of climate change agreed in greater magnitude that their farming systems had changed due to the influence of climate change.

Farmer opinions highlight the adverse effects of salinity intrusion, temperature, rainfall, floods, and cyclones on their agricultural activities. Therefore, we modelled coastal inundation in Bangladesh by semi-empirical approach using downscaled and bias corrected 28 global climate models. Singular spectrum analysis was undertaken to separate the trends from the time series of temperature and tide gauge data for the period of 1980–2100. The model shows that sea level is likely to rise at a rate of 6.69–9.88 mm/year which would result in up to 1.15 m sealevel rise by 2100 inundating at least 2098 km2 of the coast which has =1 m elevation. Though this inundated part is located mostly outside the river and coastal embankments, saline water intrusion and groundwater contamination are likely to increase in a changing climate.

During historical disasters (ten selected flood and cyclone events) between 1970 and 2017, coastal areas had lost 12.10% of crop production which was 2.54% higher than the non-coast. Temperature and precipitation affect crop yield and production in two ways—through their trends and variabilities. The mixed effects model reveals that these variables explain 12% of the variance in crop production. Climate trends and variabilities are likely to reduce crop yield, respectively, by 2.75 and 2.91%, which equates to 2.4 million metric tons of crop loss per year.

Farmers’ concerns and data-driven analysis establish the fact that coastal agriculture is increasingly under climatic threats and in more precarious conditions than the inland agriculture. Climatic impacts on coastal farming cannot be stopped but there is scope to keep it viable under climate change. This study suggests that economically worse-off farmers should be attended and communicated with updated climate change information by extension agents to enhance their adaptation actions. Failure of around one-third of the farmers to detect the climatic impacts on farm productivity, farm management and farming systems implies that they need to be updated with farm-related climatic knowledge to motivate them to adopt agricultural adaptations. Enhancing involvement of the farmers with agricultural extension associations is likely to improve their climate change awareness and perception of changes in climatic variables. Limited capacity of the farmers to keep the coastal farming sustainable warrants for external support. For example, maintenance of river and coastal embankments should continue to be the first priority of coastal agricultural planning. This research provides information and insights of climatic impacts on coastal farms from both farmer and empirical perspectives, which are necessary for agricultural policy formulation. Information generated through this study is expected to help policymakers and extension agents to formulate and implement coastal agricultural development programmes in Bangladesh. Researchers and academicians could benefit from the approaches and methods used here to apply in various socioeconomic and ecological constellations.

This dataset was created during a study assessing the impacts of climate change on climate/land suitability and the quality of tea in Sri Lanka, specifically Camellia sinensis (L) O. Kuntze. Data for chemical analysis of tea biochemicals were collected through sample collection during field visits over the years from 2018 to 2020 and subsequent chemical analysis. Data for climate modelling were collected from online databases, Departments of Climate and Meteorology of Sri Lanka. Data for geospatial analysis were gathered from shapefiles, Department of Agriculture Sri Lanka. This data was then used to model climate/land and tea quality.