Status of Agroforestry in Rice-based Mixed Farming Systems in Northen and Southern Bangladesh Ziaul Hoque1, Minhaz Ahmed1, Sharif Ahmed2 and Humnath Bhandari2 Author affiliation 1Bangabandhu Sheikh Mujibur Rahman Agricultural University and 2International Rice Research Institute Published by International Rice Research Institute October, 2024 The Sustainable Intensification of Mixed Farming Systems Initiative aims to provide equitable, transformative pathways for improved livelihoods of actors in mixed farming systems through sustainable intensification within target agroecologies and socio-economic settings. Through action research and development partnerships, the Initiative will improve smallholder farmers' resilience to weather-induced shocks, provide a more stable income and significant benefits in welfare, and enhance social justice and inclusion for 13 million people by 2030. Activities will be implemented in six focus countries globally representing diverse mixed farming systems as follows: Ghana (cereal–root crop mixed), Ethiopia (highland mixed), Malawi: (maize mixed), Bangladesh (rice mixed), Nepal (highland mixed), and Lao People's Democratic Republic (upland intensive mixed/ highland extensive mixed). © 2024 This publication is licensed for use under the Creative Commons Attribution 4.0 International Licence - https://creativecommons.org/licenses/by/4.0. Unless otherwise noted, you are free to share (copy and redistribute the material in any medium or format), adapt (remix, transform, and build upon the material) for any purpose, even commercially, under the following conditions: ATTRIBUTION. The work must be attributed, but not in any way that suggests endorsement by the publisher or the author(s). https://www.cgiar.org/initiative/19-sustainable-intensification-of-mixed-farming-systems https://creativecommons.org/licenses/by/4.0 Contents Abbreviations and acronyms ....................................................................................................... i Summary of the Activity ............................................................................................................... 1 CHAPTER ONE: INTRODUCTION................................................................................................ 4 1.1 Background of the study ........................................................................................................................... 4 1.2 Objectives of the study ................................................................................................................................ 5 CHAPTER TWO: METHODOLOGY .............................................................................................. 6 CHAPTER THREE: FINDINGS OF THE STUDY........................................................................ 10 3.1 Socio-economic Characteristics of the Respondents ............................................................ 10 3.1.1 Socio-demographic characteristics of the farmers in the northern region ..... 10 3.1.2 Socio-demographic characteristics of the respondents in southern region .. 12 3.1.3 Annual family income of the respondent in northern region .................................. 14 3.1.4 Annual family income in southern region ............................................................................ 18 3.2 Status of agroforestry practices ......................................................................................................... 20 3.2.1 Status of agroforestry practices in the northern region.............................................. 20 3.2.2 Status of agroforestry in southern region ........................................................................... 23 3.2.3 Dominant cropping patterns in northern region ........................................................... 26 3.2.4 Dominant cropping systems in southern region ........................................................... 28 3.3 Perception on adoption of agroforestry systems ................................................................... 29 3.3.1 Factors of adoption of agroforestry .......................................................................................... 29 3.3.1.1 Factors of adoption of agroforestry practices in northern region ...................... 29 3.3.1.2 Factors of adoption of agroforestry practices in southern region ..................... 31 3.3.2 Willingness to adopt rice based mixed agroforestry system ................................... 31 3.3.2.1 Willingness to adopt rice based mixed agroforestry system northern region .................................................................................................................................................................... 32 3.3.2.2 Willingness to adopt rice based mixed farming in southern region .............. 33 3.3.3 Perception on rice based mixed agroforestry system ................................................. 34 3.3.3.1 Perception on rice based mixed agroforestry system in northern region ... 34 3.3.3.2 Perception on rice based mixed agroforestry system in southern region . 37 3.3.4. Supports needed to effectively practice agrogorestry .............................................. 38 3.3.4.1 Supports needed for rice based mixed farming in northern region ............... 38 3.3.4.2 Supports Needed in Southern Region ............................................................................. 40 3.4 LULC change ................................................................................................................................................. 42 3.4.1 LULC change in northern region during 2008-2023 ..................................................... 42 3.4.2 LULC transformation during 2008-2023 in northern region ................................. 44 3.4.3 LULC change in southern region during 1999-2024 ................................................... 44 3.4.4 LULC transformation in northern region during 1999-2024 ...................................46 CHAPTER FOUR: CONCLUSION AND RECOMMENDATIONS ......................................... 48 4.1 Conclusion ...................................................................................................................................................... 48 4.2 Recommendations ................................................................................................................................... 48 4.2.1 Recommendations for the Northern Region ................................................................... 48 4.2.2 Recommendations for the Southern Region ...................................................................49 References ...................................................................................................................................... 50 i Abbreviations and acronyms ABC Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) CIMMYT International Maize and Wheat Improvement Center ICARDA International Center for Agricultural Research in the Dry Areas IITA International Institute of Tropical Agriculture ILRI International Livestock Research Institute IRRI International Rice Research Institute IWMI International Water Management Institute SDG Sustainable Development Goals SI-MFS Sustainable Intensification of Mixed Farming Systems Initiative WP Work Package LULC Land Use and Land Cover FGD Focus Group Discussion KII Key Informant Interview NGO Non-Governmental Organization MFA Mixed Farming with Agroforestry GIS Geographic Information System AOI Area of Interest DEM Digital Elevation Model SD Standard Deviation SA Strongly Agree A Agree NO No Opinion DA Disagree SDA Strongly Disagree GO Government Organization 1 Summary of the Activity With the collaboration of International Rice Research Institute (IRRI), the activity was conducted across two major regions: the northern region, encompassing the districts of Rangpur and Nilphamari, and the southern region, focusing on the districts of Patuakhali and Barguna. The activity aimed to evaluate the current status of agroforestry practices, farmers' perceptions and willingness to adopt rice-based mixed agroforestry practices and analyze land use and land cover (LULC) changes over time to inform future agricultural strategies. The study targeted 240 respondents, with 120 farmers from each region. The respondents were selected based on their involvement in rice-based farming systems, with a focus on those who had either adopted or expressed interest in mixed farming practices. To ensure comprehensive data collection, the study employed a combination of quantitative and qualitative methods. An interview schedule was developed, pretested, and finalized to gather detailed information on various aspects of farming practices, including crop cultivation, agroforestry, and farmers' perceptions of mixed farming systems. Data were collected through face- to-face interviews, focus group discussions (FGDs), and key informant interviews (KIIs) with farmers, agricultural extension officers, and local leaders. One of the critical components of the project was the analysis of land use and land cover (LULC) changes in the study regions over time. This was achieved by processing Landsat satellite images from different time periods. For the northern region, Landsat images from 2008, 2013, 2018, and 2023 were used, while for the southern region, images from 1999, 2011, 2017, and 2024 were analyzed. The images were acquired through standard satellite acquisition techniques, ensuring high spatial and temporal resolution. Initial processing of the Landsat images involved several key steps, including atmospheric correction, radiometric correction, band composite creation, mosaic generation, and the application of false color composites to highlight specific land cover features. The final processed images were used to classify land cover into four main categories: agricultural land, mixed vegetation, bare land, and water bodies. The socio-economic profile of respondents in both the northern and southern regions revealed variations in landholding, education, and income levels. In the northern region, most respondents were smallholder farmers with moderate education and diversified income sources. In contrast, the southern region faced more challenges, with smaller landholdings, lower literacy rates, and limited income diversification due to environmental constraints like salinity. Both regions had predominantly male-headed households, though women played vital roles in farming activities. Access to agricultural inputs and extension services varied, influencing their adoption of sustainable practices. 2 Agroforestry practices in both the northern and southern regions displayed notable differences driven by environmental conditions and farming needs. In the northern region, farmers widely adopted rice-based agroforestry systems like Rice-Fruit Tree Agroforestry, silvopasture, and homestead agroforestry. The integration of trees and crops helped improve soil health, diversify income, and enhance climate resilience. In contrast, southern region farmers focused more on homestead-based agroforestry, boundary planting, and roadside plantations due to saline water issues that restrict mixed agroforestry in crop fields. The practices in the south leaned heavily on agroforestry to mitigate salinity impacts and support sustainable livelihoods. The study also examined the dominant cropping patterns in both regions. In the northern region, rice-based cropping systems such as Rice-Potato-Rice, Rice-Maize- Rice, and Rice-Mustard-Rice were prevalent, with varying degrees of success in terms of productivity and income generation. In the southern region, the dominant cropping systems were influenced by salinity, drought, and water scarcity, leading to patterns such as Fallow-Fallow-T. Amon, Watermelon-Fallow-T. Amon, and Mungbean-Fallow-T. Amon. These patterns were shaped by the local environmental conditions and the need to adapt to climate-related stresses. The project assessed the willingness of farmers to adopt rice-based mixed agroforestry systems in both regions. In the northern region, a significant proportion of farmers expressed a strong willingness to try new agroforestry practices, invest time and effort in learning and implementing these practices, and adopt agroforestry to diversify income sources and reduce economic risks. The perception of government support for these practices was also encouraging, with many farmers perceiving valuable and encouraging support for rice-based mixed agroforestry. In the southern region, however, the willingness to adopt rice-based mixed agroforestry was more tempered. While some farmers were open to trying new practices, concerns about conflicts with traditional rice cultivation methods, the potential challenges of integrating trees into rice fields, and the need for substantial government support were more pronounced. The perception of rice-based mixed agroforestry as a means to reduce the environmental impact of farming and conserve natural resources was also less strong in the southern region compared to the northern region. The study also examined the factors influencing the adoption of agroforestry in both regions. In the northern region, diversified income sources and increased productivity were the primary factors driving adoption. Improved soil health, nutrient cycling, and climate resilience were also important considerations, though less so than income diversification and productivity. In the southern region, the primary driver for adopting agroforestry was the potential for diversified income sources, with increased productivity being the second most important factor. Other factors such as improved soil health, nutrient cycling, and erosion control were less significant in the decision-making process of southern farmers. 3 Throughout the project, the role of government and non-governmental organizations (NGOs) in providing support for farmers was emphasized. Farmers in both regions highlighted the need for subsidies, agricultural loans, access to quality seeds and fertilizers, and training in new agricultural techniques. In the southern region, the excavation of canals to store rainwater and deep tubewells for irrigation were seen as crucial supports for improving agricultural productivity in the face of salinity and water scarcity. The Land Use and Land Cover (LULC) analysis conducted in the northern and southern regions over several decades revealed significant transformations influenced by environmental and socio-economic factors. In the northern region, between 2008 and 2023, agricultural land experienced a slight decline as mixed vegetation and water bodies expanded, reflecting a shift towards agroforestry and sustainable farming practices. This shift was driven by efforts to enhance productivity and diversify income sources. In contrast, the southern region, analyzed from 1999 to 2024, witnessed more drastic changes due to salinity, climate shocks, and water scarcity. Agricultural land decreased substantially, giving way to bare land and water bodies, which increased due to the expansion of shrimp farming and the impact of salinity on crop fields. Mixed vegetation also saw moderate growth as farmers adopted homestead-based agroforestry practices to cope with the challenging conditions. Overall, the LULC transformation in both regions indicates a move towards mixed farming and agroforestry, though the extent and nature of changes differ. The northern region shows a gradual transition towards sustainable land use, while the southern region faces more profound environmental challenges, limiting agricultural expansion and necessitating alternative livelihoods. 4 CHAPTER ONE: INTRODUCTION 1.1 Background of the study Agroforestry is a land-use management system that integrates trees and shrubs into agricultural landscapes, creating a multifunctional approach to land use that balances ecological, economic, and social benefits. This practice combines agriculture and forestry, which not only diversifies farm outputs but also enhances ecosystem services (Nair et al., 2021). Agroforestry systems can mitigate soil erosion, enhance water retention, and improve biodiversity, making them suitable for regions facing climate variability. In rice-based mixed farming systems, the integration of agroforestry practices plays a crucial role by diversifying income sources, improving soil fertility, and increasing resilience to extreme weather events. Mixed farming with agroforestry (MFA) is based on the ideas of agroecology, which aims to make agriculture more sustainable and productive while protecting the environment. In order to do this, these systems try to use scientific ideas from agronomy, ecology, sociology, and economics. An MFA is a farming system that combines different farming (cropping, livestock, and forestry) and agroforestry (integrated crop-forestry systems, crop-livestock systems, crop-livestock-forestry systems, and forestry-livestock systems) enterprises. The many processes and interactions between enterprises create opportunities for synergistic resource transfers over space and time. MFA creates synergies that make better use of resources, which leads to more stable profits through diversification and better care for the environment and ecology (Low et al., 2023). With the current climate variability, agroforestation has emerged as one of the potential adaptation strategies, as it improves subsistence farmers' general standard of living through increases in farm productivity, off-farm incomes, wealth, and the environmental conditions of their farms, while reducing their vulnerability to climate change. The economic, environmental, and social advantages created by agroforestry technologies are believed to enhance rural livelihoods and resource sustainability. Similarly, agroforestry practises may be a potentially effective conservation technique for lowering land-use pressure and improving rural lives. It has capacity to provide wood fuel, develop a resilient microclimate and lessen air pollution, improve infiltration and increase water availability, enhance soil properties, provide an adaptive strategy resilient to climate change, and lessen vulnerability for rural residents. By cultivating trees on farms in a sustainable way, agroforestry significantly reduces the demand on forests to produce wood. It has great potential to increase soil organic matter and biological nitrogen fixation, hence enhancing soil fertility (Cavan et al, 2014). Bangladesh is the most densely populated and climate-vulnerable country in the world, with very low per capita arable land (0.06 ha) and forest land (0.02 ha). Bangladesh has several climate-vulnerable zones, including the southern coastal 5 area, which is prone to salt water, the northern zone, which is prone to drought, the central zone, which is prone to floods and erosion, the haor lands in the north-east, and so on. As a result, in the face of growing food demand on limited land resources and susceptibility to climatic catastrophes, successful mixed farming with agroforestation might be one of Bangladesh's viable adaptation alternatives. About 87% of poor people in rural regions rely primarily on tree crop-based production methods for a livelihood (Rana and Moniruzzaman, 2021; Islam et al., 2021). Scientific knowledge and modern technology-based operations for proper resource utilization, on the other hand, are essential for making farming sustainable. Besides, the adoption decision of any innovation by the potential users is influenced by their socio-economic, psychological, and geographical factors. As a result, a socioeconomic study of agroforestry farmers is needed to investigate the perceived benefits of agroforestry, its constraints and challenges, and the status, diversity, and extent of agroforestry practice, which has not yet been investigated. Furthermore, a spatio-temporal analysis of land use and land cover and finding their trajectories of change over time can be performed with the help of GIS and remote sensing techniques by utilizing historical Landsat images, shedding light on how and to what extent other land uses is being transformed to agroforestry practices. This research project was conducted to minimize the existing research gaps by focusing on the spatio-temporal changes of agroforestry-based land use systems, status of agroforestry practices, including types, diversity, and extent of practice, as well as the perceived benefits, constraints, challenges, and future prospects in the Northen and Southern Bangladesh. 1.2 Objectives of the study i. To assess the status, benefits, constraints, challenges, and opportunities of agroforestry practices in rice based mixed farming systems, ii. To analyze and map out the agroforestry-based land use land cover changes and their drivers during the last two decades, and iii. To recommend sociotechnical intervention opportunities in agroforestry practices to improve the mixed farming systems. 6 CHAPTER TWO: METHODOLOGY The study was conducted in two distinct regions of Bangladesh: the northern (districts of Rangpur and Nilphamari) and southern (districts of Patuakhali and Barguna). A total of 240 respondents were selected, with 120 respondents from each region. The respondents were carefully chosen to represent the farming communities involved in mixed farming systems. An interview schedule was developed for data collection. This schedule was pretested in the field to ensure clarity and relevance of the questions, and then finalized for implementation. Data collection methods included in-depth interviews, focus group discussions (FGDs), and key informant interviews (KIIs). These methods were employed to gather both quantitative and qualitative data on farmers’ practices, perceptions, and challenges related to mixed farming and agroforestry systems. Photo 1: FGD with Farmers in Saidpur, Nilphamari. Photo 3: Field visit with KIIs. Photo 2: FGD with Farmers in Kalapara, Patuakhali. 7 Perceptions of the respondents were assessed using a five-point Likert scale ranging from "Strongly Agree" to "Strongly Disagree," with corresponding scores from 5 to 1. This scale was used to measure the respondents' attitudes toward various aspects of agroforestry and mixed farming practices. In addition to the survey data, a land use and land cover (LULC) analysis was conducted to assess changes in land cover over time. For the northern region (Rangpur and Nilphamari), Landsat satellite images from 2008, 2013, 2018, and 2023 were used. In the southern region, satellite images from 1999, 2011, 2017, and 2024 were analyzed. The satellite images were acquired using standard acquisition techniques, ensuring optimal spatial and temporal resolution. The initial processing of the Landsat images involved several critical steps to ensure data accuracy and quality. First, atmospheric correction was performed to remove atmospheric interference such as haze, water vapor, and dust, which can distort the reflectance values of surface features. This was followed by a radiometric correction, which adjusted the pixel values to reflect accurate surface reflectance by correcting sensor-specific errors. Subsequently, the individual image bands were combined to create a band composite, enabling multi-spectral analysis. A mosaic operation was carried out to stitch together adjacent scenes, creating a seamless image that covered the entire area of interest (AOI). The AOI creation step was crucial for focusing the analysis on specific regions of interest within the northern and southern districts. Photo 5: FGD with Farmers in Rangpur Sadar. Photo 4: FGD with Farmers in Amtoli, Barguna. 8 Figure 1: Study area map. False color composites were generated by assigning different spectral bands to the red, green, and blue channels, which helped highlight specific land cover features such as vegetation, water bodies, and urban areas. These false color images were 9 essential for visual interpretation and classification of land cover types. The final step in the image processing workflow involved merging the processed images with auxiliary data such as digital elevation models (DEMs) to enhance the accuracy of land cover classification. Table 1: LULC categorization scheme. Macro class Micro class Agricultural land Cultivated and uncultivated farmland, orchard Mixed vegetation Homestead agroforestry, roadside plantations, social forestry, plantation mangrove, dense vegetation, and other vegetations Bare land Urban builtin, sandy land, unused land Waterbody Rivers, canals, ponds, open waterbody, flowing water etc. The processed images were then analyzed using QGIS software to categorize the land cover into four primary classes: agricultural land, mixed vegetation, bare land, and water bodies. LULC transformation over time was examined to understand the changes in land use patterns and the implications for agricultural practices in both regions. This comprehensive analysis provided a detailed understanding of the environmental dynamics in the study areas and their impact on sustainable farming practices. 10 CHAPTER THREE: FINDINGS OF THE STUDY 3.1 Socio-economic Characteristics of the Respondents 3.1.1 Socio-demographic characteristics of the farmers in the northern region The demographic information of respondent farmers in the Rangpur and Nilphamari regions reveals a detailed picture of the socio-economic characteristics of the farming population. Analyzing this information provides insight into the composition of farmers in these areas and how various factors such as gender, age, education, farming experience, farm size, and experience in rice-based mixed farming play a role in agricultural practices (Table 2). Table 2: Socio-demographic information of the respondent farmer in the northern region. Variable Category Frequency Percent Mean SD Sex Male 95 79.2 Female 25 20.8 Age Less than 30 years 8 6.7 47.36 11.76 30-45 years 66 55.0 Above 45 years 46 38.3 Education Illiterate (0) 19 15.8 7.23 4.65 Primary (1-5) 33 27.5 Secondary (6-10) 31 25.8 Higer secondary (11-12) 25 20.8 Above higher secondary (>12) 12 10 Farming experience Below 10 years 12 10 26.50 10.98 10-25 years 52 43.3 Above 25 years 56 46.7 Farm size Landless farm (Below 0.02 ha) 0 0 0.8537 0.72 Marginal farm (0.02-0.2 ha) 4 3.3 Small farm (0.2-1.0 ha) 85 70.9 Medium farm (1.0-3.0 ha) 28 23.3 Large farm (Above 3.0 ha) 3 2.5 Experience in rice based mixed farming Below 5 years 45 37.5 7.39 6.23 5-10 years 41 34.2 Above 10 years 34 28.3 11 Starting with gender distribution, the data indicates that a significant majority of farmers are male, accounting for 79.2% of the respondents, while females make up 20.8%. This male-dominated farming population reflects broader gender norms in rural Bangladesh, where men are typically more involved in agricultural activities, while women often play a supportive role. However, the presence of female farmers highlights that women are also actively engaged in farming, contributing to household livelihoods. In terms of age, the farmers exhibit a wide range of age groups. The mean age of the respondents is 47.36 years, with a standard deviation of 11.762, indicating a moderate level of variation in age. A majority of the farmers, 55%, fall within the 30-45 years age group, making this the most represented category. Farmers above 45 years constitute 38.3% of the population, while those younger than 30 years represent only 6.7%. This age distribution suggests that farming is primarily undertaken by middle- aged and older individuals, with younger generations less represented, potentially indicating trends of youth migration to urban areas or less involvement in agriculture. Educational attainment among the farmers is also varied, with the mean level of education being 7.23 years, and a standard deviation of 4.655, suggesting differences in educational background. A significant portion of the respondents, 27.5%, have completed primary education (1-5 years), while 25.8% have attained secondary education (6-10 years). Only 10% have pursued education beyond higher secondary levels, and 20.8% have completed higher secondary education (11-12 years). A notable 15.8% of the farmers are illiterate, which underscores the challenges related to access to education in rural areas. The overall educational landscape indicates that many farmers have basic literacy and numeracy skills, but higher education levels are relatively uncommon. Farming experience varies considerably among the respondents, with the mean years of farming experience being 26.50 years, and a standard deviation of 10.977, indicating a substantial range of experience levels. The largest group of farmers, 46.7%, have over 25 years of experience, reflecting a deep familiarity with agricultural practices. Another significant portion, 43.3%, have between 10-25 years of farming experience, while only 10% have less than 10 years of experience. This suggests that farming in Rangpur and Nilphamari is predominantly carried out by individuals with extensive knowledge and experience, which can be beneficial for implementing sustainable and efficient farming practices. Farm size is another important factor in understanding the demographic profile of the farmers. Most respondents, 70.9%, are small-scale farmers, operating on farms ranging from 0.2 to 1.0 hectares, with a mean farm size of 0.8537 hectares and a standard deviation of 0.72206 hectares. Medium farms, sized between 1.0 to 3.0 hectares, account for 23.3% of the farmers, while marginal farms (0.02-0.2 hectares) make up a small 3.3%. Only 2.5% of the respondents operate large farms, above 3.0 hectares. Notably, there are no landless farmers in the sample. This distribution reflects the prevalence of smallholder farming in the region, where most farmers rely 12 on limited land for their livelihoods, which can impact their capacity to invest in and adopt new agricultural technologies. Experience in rice-based mixed farming, a common practice in the region, is also diverse among the respondents. The average number of years of experience in rice- based mixed farming is 7.39 years, with a standard deviation of 6.230 years. A significant proportion of farmers, 37.5%, have less than 5 years of experience in this specific type of farming, while 34.2% have 5-10 years of experience. Farmers with more than 10 years of experience in rice-based mixed farming represent 28.3% of the respondents. This variation in experience levels suggests that while some farmers are relatively new to rice-based mixed farming, others have developed substantial expertise, which could influence their ability to manage complex cropping systems and adopt innovations in agroforestry practices. 3.1.2 Socio-demographic characteristics of the respondents in southern region The table outlines the socio-demographic characteristics of respondents from the southern region of Bangladesh, particularly from Patuakhali and Barguna districts. It provides insights into the distribution of gender, age, education levels, farming experience, farm sizes, and experience in rice-based mixed farming, painting a picture of the agricultural community in this area (Table 3). Table 3: Socio-demographic information of the respondents in southern region. Variable Category Frequency Percent Mean SD Sex Male 111 92.5 Female 09 7.5 Age Less than 30 years 14 11.7 43.94 10.92 30-45 years 77 64.2 Above 45 years 29 24.2 Education Illiterate (0) 27 22.5 6.5 4.73 Primary (1-5) 32 26.7 Secondary (6-10) 29 24.2 Higer secondary (11-12) 22 18.3 Above higher secondary (>12) 10 8.3 Farming experience Below 10 years 31 25.8 19.5 10.81 10-25 years 57 47.5 Above 25 years 32 26.7 Farm size Landless farm (Below 0.02 ha) 0 0 0.77 0.62 Marginal farm (0.02-0.2 ha) 13 10.8 Small farm (0.2-1.0 ha) 75 62.5 Medium farm (1.0-3.0 ha) 30 25 Large farm (Above 3.0 ha) 02 1.7 Experience in rice based No experience 99 82.5 1.72 4.06 13 mixed farming 1-5 years 05 4.2 6-10 years 07 5.8 Above 10 years 09 7.5 The data (Table 3) shows a significant gender imbalance among the respondents, with 92.5% being male (111 respondents) and only 7.5% being female (9 respondents). This reflects the prevalent gender norms in rural Bangladesh, where men are often recognized as the primary agricultural workers, while women's contributions, though significant, are less visible or officially acknowledged. The majority of the respondents are middle-aged, with 64.2% falling within the 30-45 years age bracket. This indicates that the farming population is largely composed of individuals in their prime working years. A smaller proportion, 24.2%, is above 45 years of age, while 11.7% are younger farmers under 30 years old. The mean age of the respondents is 43.94 years, with a standard deviation of 10.92, suggesting that most farmers in the region are in their 40s, indicating a relatively mature farming population with significant experience. Education levels among the respondents are diverse but generally low. A substantial proportion, 22.5%, are illiterate, which indicates limited access to formal education. The largest group, 26.7%, has completed primary education (grades 1-5), followed by 24.2% who have secondary education (grades 6-10). Those with higher secondary education (grades 11-12) make up 18.3%, while only 8.3% of respondents have education beyond the higher secondary level. The mean years of schooling among the respondents is 6.5, with a standard deviation of 4.73, indicating that most farmers have basic education, but relatively few have advanced levels of formal education. The farming experience of the respondents varies significantly, with a large proportion of farmers having considerable experience. The data shows that 47.5% of the respondents have been farming for 10-25 years, indicating a deep understanding of agricultural practices. Another 26.7% have more than 25 years of farming experience, reflecting a long-term commitment to agriculture. Meanwhile, 25.8% have less than 10 years of farming experience, indicating that a quarter of the respondents are either relatively new to farming or have entered the sector more recently. The mean farming experience is 19.5 years, with a standard deviation of 10.81, suggesting that, on average, farmers in this region have a substantial amount of practical experience in agriculture. Most of the respondents operate small farms, with 62.5% owning between 0.2 and 1.0 hectares of land. Medium farms, ranging from 1.0 to 3.0 hectares, account for 25% of the sample, while marginal farms (0.02 to 0.2 hectares) make up 10.8%. Large farms, defined as those with more than 3.0 hectares, are rare, with only 1.7% of the respondents owning such landholdings. The average farm size among respondents 14 is 0.77 hectares, with a standard deviation of 0.62, indicating that most farmers manage relatively small plots of land, which may limit their agricultural output and income potential. Rice-based mixed farming is an important agricultural practice in the southern region, but the data reveals that many respondents have little or no experience with this method. An overwhelming 82.5% of the respondents report having no experience in rice-based mixed farming, suggesting that either this practice is not widely adopted in the region or that it has only recently gained attention. A small percentage, 4.2%, have 1-5 years of experience, while 5.8% have 6-10 years of experience. Only 7.5% of respondents have more than 10 years of experience in rice- based mixed farming. The mean experience in this practice is 1.72 years, with a standard deviation of 4.06, indicating that while some farmers have adopted rice- based mixed farming, most are either new to it or have not yet engaged in this method. 3.1.3 Annual family income of the respondent in Northern Region Table 4 on the annual family income of farmers in Rangpur and Nilphamari reveals the diverse income sources contributing to the total household income. It breaks down the income earned from various agricultural and non-agricultural activities, highlighting the significant role that farming continues to play in rural livelihoods, while also illustrating the contributions from non-farming activities. The income distribution across these sources varies considerably, reflecting the complex economic realities faced by rural families. Table 4: Annual family income (Tk.) in the northern region. Income Heads Minimum Maximum Mean Std. Deviation Rice 0 1000000 139516.67 143153.029 Potato 0 350000 21258.33 42864.746 Mustard 0 90000 4690.00 11534.642 Maize 0 100000 13817.50 19502.470 Pulse 0 195000 2125.00 18215.130 Wheat 0 30000 666.67 3605.163 Tea 0 600000 5000.00 54772.256 Vegetable 0 300000 14041.67 34126.592 Fruits 0 350000 22895.83 54625.382 Other Crops 0 80000 10191.67 16366.237 Trees 0 90000 5050.00 13638.089 Fodder 0 150000 7154.17 18847.775 Fish 0 50000 5583.33 9390.307 Poultry 0 70000 5133.33 7978.630 Livestock 0 1030000 69304.17 118546.241 Dairy Product 0 650000 21904.17 68259.216 Business 0 1200000 58133.33 140667.731 Service 0 600000 78700.00 135091.541 15 Others 0 400000 3416.67 36518.577 Agriculture 36000 1756000 348332.50 298837.345 Non-Agriculture 0 1380000 140250.00 198958.897 Total Annual Family Income 103500 2356000 488582.50 382299.181 Starting with income from rice, which remains a staple crop in Bangladesh, the data shows that rice farming contributes significantly to household income, with a minimum income of Tk. 0 and a maximum of Tk. 1,000,000. On average, farmers earn Tk. 139,516.67 from rice cultivation, with a high standard deviation of Tk. 143,153.029, indicating substantial variation in income among different households. This variation suggests that while rice is a key source of income, the productivity and profitability of rice farming differ widely among farmers, possibly due to differences in land size, farming practices, and market access. Potato farming, another important crop in the region, provides an average income of Tk. 21,258.33, with a maximum income of Tk. 350,000. The standard deviation for potato income is Tk. 42,864.746, reflecting moderate variability in earnings from this crop. Though potato farming generates significantly less income than rice, it still plays an essential role in supplementing household income, particularly for those who diversify their crop production. Mustard farming contributes a relatively small amount to annual income, with an average income of Tk. 4,690.00 and a maximum income of Tk. 90,000. The standard deviation of Tk. 11,534.642 suggests that while mustard is grown by some farmers, it is not a major income source compared to other crops. Similarly, maize farming provides an average income of Tk. 13,817.50, with a maximum income of Tk. 100,000. The standard deviation of Tk. 19,502.470 indicates some variability in maize income, though it remains a secondary crop compared to rice and potato. Pulse cultivation yields an average income of Tk. 2,125.00, with a substantial standard deviation of Tk. 18,215.130, pointing to significant disparities in income from this crop. The maximum income from pulses is Tk. 195,000, suggesting that some farmers may rely more heavily on pulses, while others may grow them only marginally or not at all. Wheat farming contributes even less, with an average income of Tk. 666.67 and a standard deviation of Tk. 3,605.163, highlighting its limited role in the overall income of most households. Tea farming, though practiced by some farmers in the region, also contributes minimally to household income on average, with an average income of Tk. 5,000.00. However, the standard deviation of Tk. 54,772.256 and the maximum income of Tk. 600,000 show that while tea may not be a widespread source of income, it can be highly profitable for those who are engaged in its cultivation. Vegetable farming contributes an average income of Tk. 14,041.67, with a maximum income of Tk. 300,000. The standard deviation of Tk. 34,126.592 indicates variability in earnings from vegetable farming, which can be a significant source of income for some households, especially those engaged in market-oriented vegetable production. Fruit farming provides an average income of Tk. 22,895.83, with a maximum income of Tk. 350,000 and a standard deviation of Tk. 54,625.382, suggesting that fruit 16 production can be a valuable addition to household income, especially for those with suitable land and resources to cultivate fruit crops like Mango, Jackfruit, and Papaya. Other crops contribute an average income of Tk. 10,191.67, with a standard deviation of Tk. 16,366.237, reflecting a modest but still important income source for households that diversify their crop production beyond the major staples. Tree farming provides an average income of Tk. 5,050.00, with a maximum income of Tk. 90,000 and a standard deviation of Tk. 13,638.089. Tree farming, particularly with species like Eucalyptus, serves as an additional income stream, particularly through the sale of timber and fuelwood, though it does not constitute a major portion of overall household income. Income from fodder is also relatively small, with an average income of Tk. 7,154.17 and a standard deviation of Tk. 18,847.775, indicating that while some households may sell fodder, it remains a supplementary source of income. Fish farming contributes an average income of Tk. 5,583.33, with a maximum income of Tk. 50,000 and a standard deviation of Tk. 9,390.307. Although aquaculture is practiced by some households, it does not appear to be a primary income source for most. Poultry farming provides an average income of Tk. 5,133.33, with a standard deviation of Tk. 7,978.630. Like fish farming, poultry rearing offers supplemental income, though its contribution to total household income remains modest. Livestock farming, on the other hand, plays a more significant role, with an average income of Tk. 69,304.17 and a maximum income of Tk. 1,030,000. The high standard deviation of Tk. 118,546.241 indicates wide variation in earnings from livestock, with some households heavily invested in this sector. Dairy production also contributes meaningfully to household income, with an average income of Tk. 21,904.17 and a maximum income of Tk. 650,000. The standard deviation of Tk. 68,259.216 points to variability in income from dairy products, which can be an important source of regular cash flow for households with dairy cattle. Non-agricultural income sources, including business and services, also play a significant role in household economies. Income from business activities averages Tk. 58,133.33, with a maximum income of Tk. 1,200,000 and a standard deviation of Tk. 140,667.731. This suggests that business activities can be highly profitable for some households, providing a substantial portion of their income. Income from services is similarly important, with an average income of Tk. 78,700.00 and a maximum income of Tk. 600,000. The standard deviation of Tk. 135,091.541 highlights the variability in service-related earnings, which can range from formal employment to various forms of skilled and unskilled labor. Income from other non-agricultural activities averages Tk. 3,416.67, with a standard deviation of Tk. 36,518.577, indicating that some households engage in diverse, smaller-scale activities that contribute to their overall income. When looking at the total annual family income, the mean income is Tk. 488,582.50, with a significant standard deviation of Tk. 382,299.181, reflecting wide disparities in household earnings. The minimum total income is Tk. 103,500, while the maximum reaches Tk. 2,356,000, underscoring the economic diversity among rural households in Rangpur and Nilphamari. 17 Income from agriculture averages Tk. 348,332.50, with a standard deviation of Tk. 298,837.345, showing that for most households, farming remains the backbone of their economy. Non-agricultural income, averaging Tk. 140,250.00 with a standard deviation of Tk. 198,958.897, also contributes significantly to total income, highlighting the importance of diversifying income sources to improve household resilience and economic stability. The relative contribution of different income heads to the total annual income of households in Rangpur and Nilphamari highlights the complexity and diversity of rural livelihoods in these regions. The data illustrates that while agriculture remains the foundation of household economies, non-agricultural activities also play a significant role, providing additional income and reducing dependency on farming alone. Understanding the contributions of various income sources offers valuable insights into the economic strategies employed by rural families to sustain their livelihoods. Agriculture, unsurprisingly, dominates the income profile of rural households, contributing an average of Tk. 348,332.50 to the total annual income. This represents a significant portion of the average total income, which stands at Tk. 488,582.50. The importance of agriculture is further emphasized by the diversity of crops and activities that contribute to this income. Among these, rice farming is the single largest contributor, with an average income of Tk. 139,516.67, reflecting its status as a staple crop and a critical source of revenue for rural households. The variability in income from rice, with some households earning as much as Tk. 1,000,000, underscores its significance in the rural economy. For many families, rice farming alone forms the backbone of their income, influencing their financial security and overall well-being. Other crops, though less lucrative than rice, also make notable contributions. For example, potato farming contributes an average of Tk. 21,258.33, while fruit farming adds an average of Tk. 22,895.83. Although these amounts are smaller compared to rice, they are essential in diversifying income sources and reducing the risks associated with reliance on a single crop. The cultivation of vegetables, contributing an average of Tk. 14,041.67, and other crops such as maize, mustard, and pulses, further supports household income, ensuring that families have multiple streams of revenue to draw from throughout the year. Tree farming, though relatively modest in its contribution with an average income of Tk. 5,050.00, plays a strategic role in long-term income generation. The sale of timber and fuelwood from trees like Eucalyptus provides a valuable buffer against economic shocks, particularly during times when crop yields may be low due to weather conditions or market fluctuations. Similarly, the income from livestock and dairy production, averaging Tk. 69,304.17 and Tk. 21,904.17 respectively, offers important supplementary income. These activities not only provide regular cash flow but also enhance food security for the household. While agriculture forms the core of household income, non-agricultural activities contribute significantly to overall earnings, with an average income of Tk. 140,250.00. This underscores the importance of income diversification in rural areas, where 18 reliance on farming alone may expose households to risks such as crop failure, fluctuating market prices, and adverse weather conditions. Income from business activities is a major non-agricultural source, contributing an average of Tk. 58,133.33 to household earnings. This reflects the entrepreneurial spirit of rural families, many of whom engage in small-scale trading, retail businesses, and other enterprises to supplement their farming income. The potential for business income to reach as high as Tk. 1,200,000 further emphasizes its importance for some households, where successful ventures can significantly elevate their economic status. Income from services also plays a crucial role, with an average contribution of Tk. 78,700.00. This includes wages and salaries from formal and informal employment, highlighting the integration of rural households into broader labor markets. The variability in service-related income, with a standard deviation of Tk. 135,091.541, indicates that some households rely heavily on employment for their income, while others may earn less from this source. Other non-agricultural activities, though contributing smaller amounts, also add to the income diversity of households. For example, income from "other" sources, averaging Tk. 3,416.67, reflects the various informal and temporary jobs that rural families may take on, particularly during off-peak agricultural seasons. These additional earnings, while not large, provide a crucial safety net for households, helping them to cope with financial challenges and unexpected expenses. When comparing agricultural and non-agricultural income, it is clear that while farming remains the primary income source, non-agricultural activities play a critical role in enhancing household resilience. The combined contributions from agriculture (Tk. 348,332.50) and non-agricultural sources (Tk. 140,250.00) result in a total average income of Tk. 488,582.50, with agriculture accounting for approximately 71% of the total and non-agriculture contributing the remaining 29%. This balance between agricultural and non-agricultural income reflects the adaptive strategies employed by rural households to navigate the uncertainties of agricultural livelihoods. By engaging in diverse income-generating activities, families can reduce their vulnerability to external shocks and improve their overall financial stability. The inclusion of non-agricultural income also highlights the dynamic nature of rural economies, where farming is increasingly complemented by business activities, employment, and other forms of work. 3.1.4 Annual family income in the southern region The Table 5 provides detailed information on the annual family income of respondents in the southern region of Bangladesh, categorized by various income sources, including agriculture and non-agriculture activities. The data highlights the wide range of income levels across different sectors, reflecting the economic diversity of households in this region. 19 Table 5: Annual family income (Tk.) in the southern region. Income Heads Minimum Maximum Mean Std. Deviation Rice 0 400000 85508.3 73218.1 Maize 0 50000 4716.7 12052.5 Pulse 0 115000 16858.3 20618.7 Wheat 0 20000 166.7 1825.7 Vegetable 0 300000 27583.3 50201.9 Fruits 0 60000 2875.0 7597.9 Other Crops 0 100000 6566.7 14402.2 Trees 0 60000 1716.7 6906.6 Fodder 0 60000 3233.3 9296.9 Fish 0 200000 19983.3 35245.6 Poultry 0 50000 9275.0 12306.3 Livestock 0 300000 44608.3 62672.3 Dairy Product 0 60000 7583.3 18379.6 Business 0 360000 28783.3 70189.3 Service 0 300000 33158.3 68662.1 Income from Agriculture 4000 870000 230675.0 157198.8 Income from Non- Agriculture 0 360000 61941.7 87841.7 Total Annual Family Income 26000 870000 292616.7 160968.8 Income from rice is a significant contributor to the total family income, with a mean income of Tk. 85,508.3 and a standard deviation of Tk. 73,218.1. This indicates that rice farming is a major livelihood for many families, though there is substantial variability in the income earned from rice cultivation, with some households earning up to Tk. 400,000 while others earn nothing from this crop. Income from maize is considerably lower, with an average income of Tk. 4,716.7 and a standard deviation of Tk. 12,052.5. This suggests that maize is a less common or less profitable crop in the region, contributing only a small portion to overall family income. Similarly, wheat contributes very little to household income, with an average income of only Tk. 166.7 and a standard deviation of Tk. 1,825.7, indicating that wheat farming is not a significant income source for most families. In contrast, income from pulse crops shows a higher average income of Tk. 16,858.3, with a relatively large standard deviation of Tk. 20,618.7. This suggests that pulse farming is a more variable source of income, with some families benefiting significantly while others earn little or nothing from this activity. Income from vegetables is another notable contributor, with an average income of Tk. 27,583.3 and a standard deviation of Tk. 50,201.9, reflecting the importance of vegetable farming in the region, though with considerable income disparities among households. Income from fruits is relatively low, with an average of Tk. 2,875.0 and a standard deviation of Tk. 7,597.9. Similarly, income from other crops contributes an average of Tk. 6,566.7, with a standard deviation of Tk. 14,402.2, suggesting that these income 20 sources play a secondary role in the household economy compared to staple crops like rice and vegetables. Income from trees, fodder, and dairy products also contributes modest amounts to the family income, with average incomes of Tk. 1,716.7, Tk. 3,233.3, and Tk. 7,583.3, respectively. The relatively low mean incomes from these sources, coupled with their standard deviations, indicate that while they provide supplemental income, they are not primary sources for most households. Fishing, livestock, and poultry farming show more substantial contributions to the family income. Income from fishing averages Tk. 19,983.3, with a standard deviation of Tk. 35,245.6, indicating that fishing is a valuable income source for many households, though with considerable variability. Livestock farming is another significant contributor, with an average income of Tk. 44,608.3 and a standard deviation of Tk. 62,672.3, reflecting its importance in the household economy. Poultry farming, while contributing less on average, still provides an average income of Tk. 9,275.0, with a standard deviation of Tk. 12,306.3. Income from business and services also plays a notable role in the non-agricultural sector. Business activities contribute an average income of Tk. 28,783.3, with a large standard deviation of Tk. 70,189.3, indicating that some households benefit significantly from business ventures, while others earn much less. Income from services averages Tk. 33,158.3, with a standard deviation of Tk. 68,662.1, suggesting that employment in service sectors provides a relatively stable income for those engaged in it, though again with notable variability. The total annual family income, combining both agricultural and non-agricultural sources, shows a wide range from Tk. 26,000 to Tk. 870,000, with an average income of Tk. 292,616.7 and a standard deviation of Tk. 160,968.8. Income from agriculture is the dominant source, with an average of Tk. 230,675.0, while non-agricultural income contributes an average of Tk. 61,941.7. This data illustrates the central role of agriculture in the household economy of the southern region, while non-agricultural activities provide supplementary income for many families. The wide range and variability in income across different sectors highlight the economic challenges and opportunities faced by rural households in this region. 3.2 Status of agroforestry practices 3.2.1 Status of agroforestry practices in the northern region Figure 2 outlines the various agroforestry practices adopted by respondents in the northern region, specifically focusing on their engagement in different integrated systems. The percentages indicate the proportion of respondents who practice each system. 21 Figure 2: Status of agroforestry practices in the northern region. The most widespread practice is homestead agroforestry, with 100% of respondents engaged in it. This suggests that integrating trees and crops around homesteads is a universal strategy, likely due to its flexibility and ability to combine food, fodder, and timber production in small spaces. Rice-fruit tree agroforestry is another popular practice, adopted by 13.33% of respondents. This system involves the cultivation of fruit trees in rice fields, maximizing land use by producing both staple food and marketable fruit, thus enhancing income diversification. 0 10 20 30 40 50 60 70 80 90 100 Rice-fish Agroforestry or aquaforestry Agroforestry Windbreak or shelterbelts or live hedge Silvopasture in Rice Fields Rice-Fruit Tree Agroforestry Rice-Livestock Agroforestry Multi-storey Agroforestry Perennial Crop Intercropping Agroforestry for Timber and Fuelwood Crop-livestock farming Rice mixed copping Homestead agroforestry Photo 6: Homestead agroforestry. Photo 7: Homestead agroforestry. 22 Agroforestry windbreaks, shelterbelts, or live hedges are practiced by 19.17% of respondents. These structures serve as protective barriers against wind and erosion, while also contributing to ecosystem services like habitat provision and microclimate regulation. Perennial crop intercropping, practiced by 13.33% of respondents, involves the integration of long-lasting crops with seasonal ones, which could contribute to sustainable land management and soil conservation. Rice-fish agroforestry or aquaforestry is practiced by 11.67% of respondents. This system integrates aquaculture with rice cultivation, providing additional sources of protein and income while also utilizing water resources more efficiently. Agroforestry for timber and fuelwood is practiced by 9.17% of respondents, highlighting the importance of trees not only for environmental benefits but also as a source of essential materials for household use or sale. Photo 9: Rice-fruit tree agroforestry. Photo 8: Multistoried Agroforestry system. Photo 10: Eucalyptus based agroforestry system. Photo 11: Agri-silvicultural systems. 23 Silvopasture in rice fields, practiced by 3.33% of respondents, involves combining forestry and grazing in rice fields, which may help to improve soil health and reduce the need for external inputs like fertilizers. Rice mixed cropping and multi-storey agroforestry are each practiced by 4.17% of respondents. Mixed cropping involves growing multiple crops in the same field, while multi-storey agroforestry involves layering different types of vegetation to optimize space and resource use. Rice-livestock agroforestry, practiced by 1.67% of respondents, integrates livestock rearing with rice cultivation, which can enhance nutrient cycling using animal manure while providing additional income streams. Finally, crop-livestock farming, practiced by just 0.83% of respondents, indicates a minimal level of integration between crop cultivation and livestock rearing outside of agroforestry systems. This could suggest that while some farmers pursue mixed farming, it is not widely adopted as a primary practice. 3.2.2 Status of agroforestry in the southern region Agroforestry practices in the southern region of Bangladesh primarily revolve around homestead-based systems, boundary plantings, and the use of the sorjan system, which involves raised beds with crops and lower areas used for fish or other water-tolerant species. These systems are designed to optimize land use in an area where saline water and adverse environmental conditions limit the potential for conventional agriculture. Homestead-based agroforestry is a dominant practice, where farmers plant a variety of trees and crops within the area surrounding their homes. These small-scale systems are designed to meet household needs, providing fruits, vegetables, timber, and sometimes small amounts of cash crops. For example, combinations such as Eucalyptus trees with Chili, Napa shak (a leafy vegetable), and Jute, or Betelnut trees with Papaya and Bottle gourd exemplify how different layers of plant species are utilized within these homestead systems. The arrangement of multiple crops and trees helps optimize space, improve soil fertility, and increase overall farm productivity. Photo 12: Homestead based agrisilvicultural system. 24 Boundary planting is another common agroforestry practice in the region, where trees like Eucalyptus are planted along the edges of fields or roadsides. This approach serves multiple purposes, including acting as windbreaks, providing shade, and offering timber or fuelwood. Boundary planting with species such as Eucalyptus in combination with crops like Maize and Napa shak showcases how farmers aim to create multifunctional systems while coping with environmental constraints. The sorjan system is particularly significant in regions where saline water poses a challenge to crop cultivation. By creating elevated beds, farmers can plant crops on higher ground while using the lower, waterlogged areas for fish farming or growing water-tolerant species. This method helps mitigate the impacts of salinity on crop fields. Examples of the sorjan system include planting Bottle gourd around a pond for fish cultivation, combined with Coconut and Banana trees. This integration of aquaculture with crop and tree cultivation maximizes the use of land that would otherwise be underutilized due to salinity. Aquaforestry is also practiced in the region, especially in areas where water availability permits. This approach integrates fish farming with tree and crop production, like the sorjan system. For instance, a combination of Bottle gourd, a Photo 13: Boundary Planting. Photo 14: Mango based agroforestry in Sorjan system. 25 pond for fish cultivation, and trees such as Coconut and Banana represents a typical aquaforestry system in the region. These systems allow farmers to diversify their income sources while optimizing water use in saline-prone areas. Roadside plantations are another agroforestry practice in the southern region, where trees like Eucalyptus are planted along roads. This helps in soil conservation, provides shade, and acts as a source of timber or fuelwood for the community. Roadside plantations are often managed by local communities or in collaboration with government initiatives to improve green cover and reduce erosion along transportation routes. However, mixed agroforestry in crop fields is rare due to the challenges posed by saline water. The high salinity in certain areas restricts crop production, making it difficult to implement traditional agroforestry systems in open fields. In some places, farmers can only cultivate one rice crop per year due to saline conditions, limiting the feasibility of integrating trees and other crops into their fields. Photo 15: Silvopastoral system. Photo 16: Roadside plantation. 26 3.2.3 Dominant cropping patterns in the northern region The table presents the dominant cropping systems practiced by respondents in the northern region, illustrating both the percentage of respondents who engage in each system and the percentage of the total cultivated area covered by each cropping pattern. This information sheds light on the prevailing agricultural practices in the region, highlighting the diversity of crop rotations used by farmers. Table 6: Dominant cropping systems in the northern region. Cropping system Number % respondent practiced % area covered Rice-Potato-Rice 45 37.5 12.07 Photo 17: Agro-silvo-aquacultural system. Photo 18: Mangrove plantations. 27 Rice-Maize-Rice 30 25 20.96 Rice-Mustard-Rice 50 41.7 14.76 Rice-Tobacco-Rice 44 36.7 12.73 Rice-Fallow-Rice 29 24.2 5.47 Rice-Onion-Rice 9 7.5 29.94 Rice-Wheat-Rice 31 25.8 0.93 Rice-Cauliflower-Rice 4 3.3 1.19 Rice-Chilly-Rice 3 2.5 1.96 The Rice-Mustard-Rice cropping system is the most widely adopted, with 41.7% of respondents practicing this rotation. This system covers 14.76% of the total cultivated area, indicating that mustard is a significant crop in the region. The inclusion of mustard between two rice seasons suggests that farmers are taking advantage of the dry season to grow oilseeds, which are likely to contribute to household income and nutrition. The Rice-Potato-Rice system is also popular, with 37.5% of respondents practicing it. However, it covers a smaller portion of the total cultivated area (12.07%). Potatoes, being a high-value crop, likely offer good returns, making this rotation attractive despite its lower area coverage. The inclusion of potatoes reflects the region's capacity to produce root crops that require intensive management but offer high yields and economic benefits. The Rice-Tobacco-Rice system, practiced by 36.7% of respondents, covers 12.73% of the cultivated area. Tobacco is a cash crop that provides substantial income to farmers, although it may involve higher risks and environmental costs compared to food crops. The rotation with rice suggests that tobacco cultivation is strategically used in the region to maximize income in specific seasons. The Rice-Maize-Rice system is practiced by 25% of respondents but covers a larger portion of the cultivated area (20.96%). Maize, being a staple food and animal feed crop, is critical in the agricultural landscape. The higher area coverage indicates that maize is not only important for food security but also plays a role in diversifying agricultural production in the region. The Rice-Wheat-Rice system, adopted by 25.8% of respondents, covers only 0.93% of the cultivated area. Despite wheat being an essential staple, its low area coverage suggests that other crops may be more profitable or better suited to the local agro- ecological conditions. This pattern may be due to the specific climate requirements of wheat or its competition with other crops like maize or vegetables. The Rice-Fallow-Rice system, practiced by 24.2% of respondents, covers 5.47% of the cultivated area. This system involves leaving the land fallow between rice seasons, which could be due to factors such as water scarcity, labor shortages, or a strategy for soil recovery. While less intensive than other cropping systems, it may reflect a necessary adaptation to local environmental constraints. 28 The Rice-Onion-Rice system, although practiced by only 7.5% of respondents, covers the largest portion of the cultivated area (29.94%). This indicates that onion production is highly profitable or essential for the local economy, leading to significant land allocation for this crop. Onions, as a high-value crop, likely contribute substantially to farmers' income despite being less widely practiced. Less common cropping systems include Rice-Cauliflower-Rice (3.3% of respondents, covering 1.19% of the area), Rice-Chilly-Rice (2.5% of respondents, covering 1.96% of the area), and other minor rotations. These systems involve the integration of vegetables with rice, offering opportunities for income diversification and improved household nutrition, albeit on a smaller scale compared to other cropping systems. 3.2.4 Dominant cropping systems in the southern region The cropping systems in Patuakhali and Barguna districts of the southern region of Bangladesh are significantly influenced by challenging environmental conditions, particularly salinity, drought, water scarcity, and the impacts of climate shocks. These factors restrict agricultural productivity and shape the dominant cropping patterns observed in the region. Table 7: Dominant cropping systems in the southern region. Dominant Cropping systems in Patuakhali and Barguna district 1. Fallow- Fallow – T. Aman 2. Watermelon- Fallow- T. Aman 3. Mungbean- Fallow- T. Aman 4. Graspea- Fallow- T. Aman 5. Maize- Fallow- T. Aman The most common cropping system is the Fallow-Fallow-T. Aman pattern (Table 7). Due to the high salinity levels, especially during the dry season, and the scarcity of freshwater, farmers often leave their land fallow for two seasons and cultivate only T. Aman rice during the monsoon season when rainwater dilutes the salt content in the soil. This system reflects the region's dependency on natural rainfall and the limitations posed by saline water on year-round cultivation. Another prevalent system is the Watermelon-Fallow-T. Aman pattern. Watermelon is a salt-tolerant crop that can be grown during the dry season when salinity is at its peak. Farmers plant watermelon early in the year and then leave the fields fallow during the harshest part of the dry season. Afterward, they plant T. Aman rice during the monsoon. This pattern allows farmers to earn income from watermelon cultivation while still producing rice during the monsoon. The Mungbean-Fallow-T. Aman cropping system is also common in the region. Mungbean, a leguminous crop, is planted after the winter season, taking advantage of the residual moisture in the soil before the dry season sets in. Following the mungbean harvest, the fields remain fallow during the dry season, and farmers plant 29 T. Aman rice with the arrival of the monsoon rains. Mungbean's ability to improve soil fertility by fixing nitrogen is an added benefit in a region where soil health is compromised by salinity. Similarly, the Grasspea-Fallow-T. Aman system is practiced in areas where farmers opt for grasspea, a drought-tolerant crop, during the dry season. This cropping pattern allows farmers to make use of land that would otherwise remain idle due to water scarcity and salinity, with T. Aman rice being planted during the monsoon. Lastly, the Maize-Fallow-T. Aman cropping system represents another adaptation to the region's environmental challenges. Maize, which can tolerate moderate levels of salinity, is grown during the dry season, followed by a fallow period before the monsoon. Once the rains arrive, T. Aman rice is cultivated. This system helps farmers diversify their crops and reduce their dependency on a single crop, thereby spreading economic risk. 3.3 Perception on adoption of agroforestry systems 3.3.1 Factors of adoption of agroforestry 3.3.1.1 Factors of adoption of agroforestry practices in northern region The Table 8 outlines the various factors influencing the adoption of agroforestry practices in the northern region, with potential index scores ranging from 0 to 360. These index scores reflect the weight of each factor based on the respondents' ranking of its importance. The factors are categorized according to their perceived significance, with higher index scores indicating stronger motivations for adopting agroforestry practices. The analysis reveals that some factors, such as diversified income sources and increased productivity, rank much higher in importance, while others, like carbon sequestration and erosion control, have a minimal influence on the decision-making process. Table 8: Factors of adopting agroforestry in northern region. Factors 1st rank 2nd rank 3rd rank Not mentioned Index Diversified Income Sources 58.3 18.3 2.5 20.8 257 Increased Productivity 22.5 51.7 6.7 19.2 213 Improved Soil Health 5.8 14.2 25.8 54.2 86 Nutrient Cycling and Fixation 5.8 5.0 20.8 68.3 58 Erosion Control 0 0.8 5.8 93.3 9 Climate Resilience 5.0 2.5 14.2 78.3 41 Biodiversity Conservation 0.8 1.7 4.2 93.3 12 Improve water use efficiency 0 0 2.5 97.5 3 Carbon Sequestration 0 0.8 0 99.2 2 30 Cultural and Traditional Values 0 0.8 0.8 98.3 3 Reduced Pest Pressure 0 0.8 5.0 94.2 8 Community and Social Benefits 0.8 1.7 10.0 87.5 19 Diversified income sources emerge as the most influential factor, with 58.3% of respondents ranking it as the primary reason for adopting agroforestry practices. Additionally, 18.3% ranked it second, and 2.5% ranked it third, reflecting the widespread recognition that agroforestry can offer multiple income streams. This could include earnings from crops, fruits, timber, livestock, and other products, making agroforestry an attractive option for rural farmers seeking economic stability. The high index score of 257 further underscores the importance of income diversification in motivating farmers to adopt these systems. The second most significant factor is increased productivity, with 22.5% ranking it first, 51.7% ranking it second, and 6.7% placing it in third. This reflects the perception that agroforestry practices can boost overall farm productivity by efficiently utilizing land and resources, leading to higher yields and greater returns. The index score of 213 highlights that productivity gains are a critical incentive for farmers to integrate trees and other elements into their farming systems. Improved soil health is also considered an important factor, though it ranks lower than income and productivity. About 25.8% of respondents ranked it third, while 14.2% and 5.8% placed it second and first, respectively. Improved soil health is recognized as a benefit of agroforestry, as the presence of trees and shrubs can enhance soil structure, increase organic matter, and reduce erosion. However, with an index score of 86, this factor is not as dominant as income or productivity in driving agroforestry adoption. Nutrient cycling and fixation are another benefit of agroforestry, though it ranks lower in importance. Only 5.8% of respondents ranked it as the most important factor, while 20.8% placed it in third place. With an index score of 58, nutrient cycling and fixation are seen as secondary benefits, possibly more relevant to long-term sustainability rather than immediate gains. Other factors such as erosion control, climate resilience, and biodiversity conservation are recognized but are not primary drivers of agroforestry adoption. For example, erosion control was ranked first by none of the respondents, and only 5.8% ranked it third, resulting in a low index score of 9. Similarly, climate resilience was ranked first by 5.0% of respondents, but 78.3% did not mention it at all, leading to an index score of 41. Biodiversity conservation, with an index score of 12, was also not a significant motivator for most farmers. Factors such as improving water use efficiency, carbon sequestration, cultural and traditional values, and reduced pest pressure were even less influential. For example, improving water use efficiency and carbon sequestration had almost no respondents ranking them highly, with index scores of 3 and 2, respectively. This suggests that while these factors may be recognized as the benefits of agroforestry, they are not the primary reasons driving adoption in this region. Lastly, community and social benefits were mentioned by only a small proportion of respondents. Only 0.8% ranked it first, and 10% placed it third, leading to an index score of 19. While agroforestry can offer communal advantages, such as 31 strengthening social bonds and collective resource management, these benefits are not a major motivator for individual farmers. 3.3.1.2 Factors of adoption of agroforestry practices in the southern region In the southern region, various factors influence the adoption of agroforestry, with farmers prioritizing specific benefits while downplaying others (Table 9). The primary driving force for adoption is the diversification of income sources, which 92.5% of respondents ranked as the most important factor, contributing to an index score of 345. This strong preference highlights how agroforestry offers an opportunity to reduce economic risks by generating multiple revenue streams, making it a key motivator for farmers. Table 9: Factors of adopting agroforestry in the southern region. Factors 1st rank 2nd rank 3rd rank No Index Diversified Income Sources 92.5 4.2 1.7 1.7 345 Increased Productivity 4.2 93.3 2.5 0 242 Improved Soil Health 0.8 2.5 71.7 25.0 95 Nutrient Cycling and Fixation 1.7 0.8 20.0 77.5 32 Erosion Control 0 0 0.8 99.2 1 Community and Social Benefits 0 0 3.3 96.7 4 The second most significant factor is the potential for increased productivity. A substantial 93.3% of respondents ranked this as their second priority, resulting in an index score of 242. This indicates that while farmers may view diversified income as their top reason for adopting agroforestry, they also appreciate how it can enhance overall agricultural yields. Improved soil health emerges as a moderate priority for farmers, with 71.7% ranking it as their third most important factor. Although it doesn't have the same level of importance as income diversification or productivity, its index score of 95 demonstrates that maintaining soil fertility through agroforestry practices is still a notable consideration for adoption. However, other environmental and social factors are ranked significantly lower. Nutrient cycling and fixation, for instance, received only minor attention, with an index score of 32, and 77.5% of respondents not even mentioning it. Similarly, erosion control received the lowest consideration, with no respondents ranking it highly, and it achieved a mere index score of 1. Community and social benefits were also not perceived as critical, with only 3.3% of farmers ranking them, resulting in a low index score of 4. These findings reflect that farmers in the southern region prioritize economic and productivity-related factors when adopting agroforestry practices, with environmental and social benefits playing a much smaller role in their decision- making process. 3.3.2 Willingness to adopt rice-based mixed agroforestry system 32 3.3.2.1 Willingness to adopt rice-based mixed agroforestry system Northern region Table 10 presents data on farmers' willingness to adopt rice-based mixed agroforestry systems in the northern region, with an emphasis on different perspectives regarding productivity, learning investment, potential conflicts, income diversification, and government support. The index scores range from 120 to 600, reflecting the strength of agreement among respondents on these statements. Table 10: Willingness on rice-based mixed agroforestry system in the northern region. Sl. Statements SA A NO DA SDA Mean Index I. I am willing to try new agroforestry practices in my rice fields to enhance overall farm productivity 66.7 29.2 1.7 1.7 0.8 4.59 551 II. I am willing to invest the necessary time and effort to learn and implement rice- based mixed agroforestry practices on my farm 29.2 55 10 5.8 0 4.08 489 III. I am concerned that incorporating trees into my rice fields might lead to conflicts with traditional rice cultivation methods 8.3 46.7 33.3 10.8 0.8 3.51 421 IV. I am motivated to adopt rice- based mixed agroforestry to diversify my sources of income and reduce economic risks 37.5 47.5 12.5 2.5 0 4.20 504 V. I perceive valuable and encouraging government support for rice-based mixed agroforestry 80.8 15.8 0.8 2.5 0 4.75 570 SA= Strongly Agree, A= Agree, NO= No Opinion, DA= Disagree, SDA= Strongly Disagree The first statement, which explores farmers' willingness to try new agroforestry practices in their rice fields to boost overall productivity, receives strong support, with 66.7% of respondents strongly agreeing and 29.2% agreeing. This results in a high mean score of 4.59 and an index of 551, indicating a strong inclination towards adopting agroforestry to enhance farm productivity. Similarly, in the second statement, 55% of respondents agree, and 29.2% strongly agree that they are willing to invest time and effort to learn and implement rice- based mixed agroforestry. This demonstrates significant interest in adopting these practices, reflected by a mean score of 4.08 and an index of 489. However, when it comes to the concern that incorporating trees into rice fields might conflict with traditional rice cultivation methods, there is more variation in responses. While 46.7% of respondents agree, and 8.3% strongly agree, a notable 33 33.3% express no opinion, and 10.8% disagree. This concern yields a lower mean score of 3.51 and an index of 421, indicating some reservations about integrating trees with rice farming. The statement on adopting rice-based mixed agroforestry to diversify income sources and reduce economic risks receives significant support, with 47.5% agreeing and 37.5% strongly agreeing. This strong motivation for economic diversification leads to a mean score of 4.20 and an index of 504, reflecting the perceived financial benefits of agroforestry. Lastly, farmers express overwhelming agreement regarding government support for rice-based mixed agroforestry. With 80.8% strongly agreeing and 15.8% agreeing, the mean score reaches 4.75, and the index hits 570, indicating that positive government involvement is a crucial driver for adoption. 3.3.2.2 Willingness to adopt rice-based mixed farming in Southern Region Farmers in the southern region of Bangladesh expressed varying degrees of willingness to adopt rice-based mixed agroforestry systems. According to the survey, while a significant proportion of respondents showed interest in trying new agroforestry practices to enhance overall farm productivity, there was also considerable hesitation (Table 11). Table 11: Willingness on rice-based mixed agroforestry system in the southern region. Statements SA A NO DA SDA Mean Index I am willing to try new agroforestry practices in my rice fields to enhance overall farm productivity 28.3 25.8 26.7 17.5 1.7 3.62 434 I am willing to invest the necessary time and effort to learn and implement rice- based mixed agroforestry practices on my farm 22.5 31.7 26.7 17.5 1.7 3.58 427 I am concerned that incorporating trees into my rice fields might lead to conflicts with traditional rice cultivation methods 11.7 28.3 43.3 16.7 0 3.35 402 I am motivated to adopt rice-based mixed agroforestry to diversify my sources of income and reduce economic risks 11.7 38.4 34.2 15.8 0 3.46 415 I perceive valuable and encouraging government support for rice-based mixed agroforestry 19.2 38.3 30.0 12.5 0 3.64 437 34 About 28.3% of respondents strongly agreed that they were willing to try these practices, while 25.8% agreed, reflecting a moderate level of openness to innovation. However, 26.7% remained neutral, and 17.5% disagreed, indicating that a substantial number of farmers were unsure or resistant to such changes. The mean score for this statement was 3.62, with an index of 434, suggesting a moderate overall willingness. When it came to investing time and effort in learning and implementing these practices, the responses were similar. A total of 22.5% strongly agreed, and 31.7% agreed, showing a willingness to engage with rice-based mixed agroforestry. However, 26.7% of farmers remained neutral, and 17.5% disagreed, reflecting a division in commitment levels. The mean score was 3.58, with an index of 427. Concerns about conflicts with traditional rice cultivation methods were prominent. While 11.7% of respondents strongly agreed that incorporating trees into rice fields might cause issues, a larger portion, 43.3%, remained neutral, indicating uncertainty about potential conflicts. Around 28.3% agreed with this concern, while 16.7% disagreed. The mean score of 3.35 and an index of 402 highlight that concerns about traditional methods still persist among many farmers. Despite these concerns, some farmers recognized the economic benefits of adopting agroforestry. About 11.7% strongly agreed, and 38.4% agreed that they were motivated to adopt rice-based mixed agroforestry to diversify income sources and reduce economic risks. However, 34.2% remained neutral, showing that many farmers are yet to be fully convinced. The mean score of 3.46 and an index of 415 reflect a cautious optimism. The perception of government support played a significant role in farmers' willingness to adopt agroforestry practices. A total of 19.2% of respondents strongly agreed, and 38.3% agreed that government encouragement could be valuable in promoting agroforestry. However, 30% remained neutral, and 12.5% disagreed, indicating that more consistent and visible government support might be necessary to boost confidence among farmers. The mean score for this statement was 3.64, with an index of 437. 3.3.3 Perception on rice-based mixed agroforestry system 3.3.3.1 Perception on rice-based mixed agroforestry system in Northern Region Table 12 presents farmers' perceptions of rice-based mixed agroforestry systems in the northern region, highlighting various aspects of productivity, sustainability, challenges, and support structures. The perception is quantified through an index score, where a higher score reflects stronger agreement with the statement. The potential index range for these statements is from 120 to 600. The first statement, which posits that rice-based mixed agroforestry has the potential to improve farm productivity and income, garners strong support, with 60.8% of respondents strongly agreeing and 34.2% agreeing. This widespread positivity results in a high index score of 545, demonstrating a strong belief in the system's economic and productive potential. 35 Similarly, a high level of agreement is observed regarding the belief that integrating trees and crops in rice-based systems can lead to more sustainable and resilient farming practices. With 62.5% agreeing and 27.5% strongly agreeing, the index reaches 499, underscoring a strong perception of the sustainability benefits. Table 12: Perception on rice-based mixed agroforestry systems in northern region. Sl. Statements SA A NO DA SDA Index 1. Rice-based mixed agroforestry has the potential to improve overall farm productivity and income 60.8 34.2 3.3 1.7 0 545 2. I believe that integrating trees and crops in rice-based systems can lead to more sustainable and resilient farming practices 27.5 62.5 8.3 1.7 0 499 3. Integrating trees and crops in rice-based systems can help conserve soil and prevent erosion 11.7 56.7 25.8 5.8 0 449 4. I see the potential benefits of rice-based mixed agroforestry compensating any challenges or difficulties it may present 10.8 52.5 27.5 8.3 0.8 437 5. I perceive rice-based mixed agroforestry as a way to reduce the environmental impact of farming and conserve natural resources 17.5 60.0 22.5 0 0 474 6. I believe that integrating trees into my rice fields could positively impact the health and quality of my rice crops 41.7 49.2 8.3 0.8 0 518 7. Rice-based mixed agroforestry provides a sustainable way to diversify income sources and reduce economic risks 50.8 43.3 4.2 1.7 0 532 8. Agroforestry practices in rice-based systems may require additional labor and management, but the benefits outweigh the costs 15.8 71.7 10.8 1.7 0 482 9. The presence of trees in rice fields can contribute to a more pleasant microclimate and provide shade for farmers and livestock 12.5 67.5 18.3 0.8 0.8 468 10. Farmers who practice rice-based mixed agroforestry are likely to have better resilience to climate variability and extreme weather events 19.2 55.0 24.2 0.8 0.8 469 11. There is a potential for conflicts between the space needed for trees and rice cultivation in mixed agroforestry systems 6.7 35.8 35 20.8 1.7 390 12. Adopting rice-based mixed agroforestry may require changes in traditional farming practices and beliefs 5.0 46.7 40.0 8.3 0 418 36 13. Access to markets and appropriate pricing mechanisms are important factors influencing the adoption of rice-based mixed agroforestry 43.3 50.0 5.0 1.7 0 522 14. Government policies and support can play a significant role in encouraging farmers to adopt rice-based mixed agroforestry practices 80.8 15.0 1.7 1.7 0.8 567 When asked whether integrating trees and crops could help conserve soil and prevent erosion, a majority (56.7%) agree, although 25.8% express no opinion, indicating some uncertainty. The index score of 449 still reflects a generally positive view of soil conservation through agroforestry practices. Despite challenges, respondents largely see the benefits of rice-based mixed agroforestry compensating for potential difficulties. With 52.5% agreeing and 10.8% strongly agreeing, this sentiment translates into an index score of 437, reflecting an optimistic but cautious outlook on overcoming challenges. Environmental impact is another key area of agreement, with 60% of respondents agreeing and 17.5% strongly agreeing that agroforestry can help reduce the environmental footprint of farming and conserve resources. This leads to an index score of 474, showing a strong appreciation for the environmental benefits. The integration of trees into rice fields is also perceived positively in terms of its impact on rice crop health and quality. A combined 90.9% of respondents agree or strongly agree with this statement, producing a high index score of 518. Diversifying income and reducing economic risks through rice-based agroforestry is widely supported, with 50.8% strongly agreeing and 43.3% agreeing. This sentiment results in an index score of 532, reflecting strong economic motivation for adoption. When considering the labor and management demands of agroforestry, 71.7% agree that the benefits outweigh the costs, contributing to an index score of 482. This shows a recognition of the effort required but an overall positive assessment of the value provided. The creation of a pleasant microclimate and shade through agroforestry is appreciated by respondents, with 67.5% agreeing and 12.5% strongly agreeing. This results in an index score of 468, reflecting a recognition of the environmental and practical benefits for farmers and livestock. Farmers also believe that rice-based mixed agroforestry can increase resilience to climate variability and extreme weather events. With 55% agreeing and 19.2% strongly agreeing, the index score reaches 469, indicating a strong perception of the system's role in climate adaptation. However, there are concerns about potential conflicts between the space needed for trees and rice cultivation, with 35.8% agreeing that these conflicts may arise. The index score of 390 highlights that this concern is significant, though not dominant. Adopting rice-based mixed agroforestry is also seen as potentially requiring changes in traditional farming practices, with 46.7% agreeing and 40% expressing no opinion. The index score of 418 indicates moderate concern about the cultural and traditional impacts of adoption. Access to markets and appropriate pricing mechanisms is recognized as an important factor influencing adoption. With 50% agreeing and 43.3% strongly 37 agreeing, this sentiment results in an index score of 522, underscoring the critical role of economic incentives in driving adoption. Finally, government policies and support are viewed as highly influential, with 80.8% strongly agreeing and 15% agreeing that they play a significant role in encouraging agroforestry adoption. The high index score of 567 reflects the vital importance of government involvement in supporting this practice. 3.3.3.2 Perception on rice-based mixed agroforestry system in Southern Region Farmers' perceptions of rice-based mixed agroforestry systems in the southern region reflect a mixture of optimism and cautious consideration (Table 13). Overall, they recognize the potential benefits of these systems but are also mindful of the challenges they may pose. The belief that rice-based mixed agroforestry has the potential to improve farm productivity and income is widely accepted, with a mean score of 3.96. This high level of agreement suggests that many farmers see agroforestry as a viable strategy for enhancing their agricultural outputs. Similarly, integrating trees and crops in rice- based systems is perceived as a pathway to more sustainable and resilient farming practices, with another strong mean score of 3.96. Farmers also recognize the environmental benefits of agroforestry. The idea that integrating trees and crops can help conserve soil and prevent erosion is supported, with a mean score of 3.86. Many farmers agree that this approach could reduce the environmental impact of farming and conserve natural resources, reflected in a mean score of 3.63. Additionally, the presence of trees in rice fields is believed to contribute to a more pleasant microclimate and provide shade for farmers and livestock, scoring a mean of 3.75. However, not all perceptions are without concerns. While farmers see the benefits, some acknowledge potential challenges. The belief that the benefits of rice-based mixed agroforestry could outweigh the challenges had a lower mean score of 3.36, indicating some uncertainty. The concern about conflicts between space needed for trees and rice cultivation also received a moderate mean score of 3.49, suggesting that spatial competition remains a relevant issue. Despite these challenges, the perception that agroforestry can help diversify income sources and reduce economic risks remains strong, with a mean score of 3.84. Moreover, the farmers agree that additional labor and management may be required, but they generally believe that the benefits outweigh the costs, reflected in a mean score of 3.60. Table 13: Perception on rice-based mixed agroforestry systems in southern region. Statements Mean score (1-5) Rice-based mixed agroforestry has the potential to improve overall farm productivity and income 3.96 I believe that integrating trees and crops in rice-based systems can lead to more sustainable and resilient farming practices 3.96 38 Integrating trees and crops in rice-based systems can help conserve soil and prevent erosion 3.86 I see the potential benefits of rice-based mixed agroforestry compensating any challenges or difficulties it may present 3.36 I perceive rice-based mixed agroforestry as a way to reduce the environmental impact of farming and conserve natural resources 3.63 I believe that integrating trees into my rice fields could positively impact the health and quality of my rice crops 3.72 Rice-based mixed agroforestry provides a sustainable way to diversify income sources and reduce economic risks 3.84 Agroforestry practices in rice-based systems may require additional labor and management, but the benefits outweigh the costs 3.60 The presence of trees in rice fields can contribute to a more pleasant microclimate and provide shade for farmers and livestock 3.75 Farmers who practice rice-based mixed agroforestry are likely to have better resilience to climate variability and extreme weather events 3.57 There is a potential for conflicts between the space needed for trees and rice cultivation in mixed agroforestry systems 3.49 Adopting rice-based mixed agroforestry may require changes in traditional farming practices and beliefs 3.65 Access to markets and appropriate pricing mechanisms are important factors influencing the adoption of rice-based mixed agroforestry 3.95 Government policies and support can play a significant role in encouraging farmers to adopt rice-based mixed agroforestry practices 4.07 The mean score near1indicate Strongly Disagree while 5 indicates Strongly Agree Another significant aspect highlighted by the farmers is the role of government policies and support. With the highest mean score of 4.07, it is clear that farmers view government intervention as crucial in encouraging the adoption of rice-based mixed agroforestry. Additionally, the importance of access to markets and appropriate pricing mechanisms was recognized, with a mean score of 3.95, indicating that economic incentives and infrastructure play a significant role in the adoption process. 3.3.4. Supports needed to effectively practice agroforestry 3.3.4.1 Supports Needed for rice-based mixed farming in Northern Region In northern Bangladesh, the implementation of rice-based mixed agroforestry practices requires various forms of support to be successful. The table indicating the percentage of respondents who identify these supports highlights several critical areas where intervention is needed (Table 14). 39 Table 14: Supports needed for rice based mixed agroforestry practices in northern region. Supports % respondents Subsidies 58.3 Agricultural loan 30.0 Seed 29.2 Farm implements with subsidized price 43.3 Training 58.3 Fertilizer 13.3 Medicine 7.5 Process mills in local area 7.5 Motivation 19.2 Extension service 15.8 One of the most pressing needs identified is for subsidies, which 58.3% of respondents view as essential. Subsidies can significantly lower the financial barriers to adopting agroforestry practices by offsetting initial costs associated with integrating trees and additional crops into rice fields. These costs can include purchasing seedlings, establishing new infrastructure, or implementing new farming techniques. By providing financial assistance, subsidies help to make these practices more accessible and feasible for smallholder farmers who might otherwise struggle with the upfront investment required. Similarly, training is another crucial support identified by 58.3% of respondents. Proper training equips farmers with the knowledge and skills necessary to effectively implement and manage agroforestry systems. This includes understanding how to integrate different species, manage tree-crop interactions, and optimize resource use. Training programs can also offer guidance on best practices for soil management, pest control, and harvesting techniques. By improving farmers’ expertise, training enhances the likelihood of successful adoption and long-term sustainability of rice-based mixed agroforestry practices. Technology, supported by 43.3% of respondents, is also a criti