Bridging Tradition and Modernity: Socio-Climatic Determinants and Farmers’ Perceptions of Agroforestry Adoption in the Hilly Regions of Himachal Pradesh

Bridging Tradition and Modernity: Socio-Climatic Determinants and Farmers’ Perceptions of Agroforestry Adoption in the Hilly Regions of Himachal Pradesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bridging Tradition and Modernity: Socio-Climatic Determinants and Farmers’ Perceptions of Agroforestry Adoption in the Hilly Regions of Himachal Pradesh Pawan Kumar, Yash Pal, Amar Latta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8935206/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract The study investigates the interactions among social, economic, and climatic factors, farmers' decision-making and perceptions regarding the adoption of agroforestry in the Himalayan region, one of the most ecologically delicate and climate-susceptible mountain ecosystems in the world. The study is based upon the primary data collected from the 680 households of Mandi and the Kullu districts of the Indian western Himalayan state Himachal Pradesh through the Multistage random sample technique. A structured interview schedule comprising the socio-economic characteristics, climatic perceptions, and practices related to agroforestry was used in the collection of primary data. The factors that affected the adoption were analysed using descriptive statistics and a logit regression model. The findings of the study show that agroforestry in the study area is still largely traditional and subsistence-oriented, but not market- and science-oriented. Further, marital status, occupation, family size, family type, and perceived social challenge significantly influence agroforestry adaptation. Apart from that, climatic variability and perception of the risk are also critical to determine households' behaviours; more households that often experience floods, hailstorms, and heatwaves tend to adopt tree-based systems as protective and adaptive strategies. At last study highlights the importance of region-specific policies of agroforestry that would incorporate scientific creativity, local experiences and institutional backing. Development of district level agro forestry resource centres, enhancement of capacity-building initiatives, enhancement of financial incentives, and inclusion of agroforestry into climate adaptation and rural livelihood missions are the key elements in improving resiliency and sustainable mountain development. Agroforestry Climate Change Logit Model Western Himalayas Himachal Pradesh Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The Himalayan region, known as the water tower of Asia, is among the most ecologically delicate and crucial mountain habitats in the global (Kandel et al., 2021 ). Himachal Pradesh is a state situated in the western Himalayas (Chauhan et al., 2021) with complicated topography, sharp slopes, and varied agroclimatic regions that have numerous agrarian people who rely mostly on natural resources (Kumar et al., 2021 ). Climate change has grown to be one of the most acute challenges facing the Himalayan environment and people in the past few decades (Dhimal et al., 2021 ). Higher temperatures, inconsistent rainfall and increased instances of severe weather conditions like floods, cloudbursts and landslides have had a drastic impact on the ecological balance of the area. The mountain farming systems that were once stable and relied on the very fragile balance between agriculture, forests and livestock are now under a severe threat due to changing climatic and socio-economic conditions. The past few years have seen natural calamities occurring frequently and intensely in the Himalaya states, especially in Himachal Pradesh (Kumar, 2024). Cloudbursts, flash floods, and heavy rainfall events have become a common occurrence that has probably caused massive destruction of agricultural land, infrastructure, and human settlements (Kumar et al., 2025 ). Figure 1 shows how climate change alters global weather patterns and generates physical/environmental impacts, including soil degradation, reduced water availability, loss of pollinators, and declining crop quality, which further impacts the crop and livestock, such as altered growing seasons, pest outbreaks, reduced productivity, and crop failures in vulnerable regions. Ultimately, these biophysical disruptions lead to socio-economic consequences, including declining farmers’ income, higher production costs, food insecurity, and greater livelihood vulnerability. The empirical data and local observations indicate that the phenomenon of cloudbursts has increased, causing the destruction of crops as well as soil erosion, siltation, and loss of arable land. These climatic disasters are known not only to ruin the agricultural output but also to interfere with the mode of existence of agricultural groups (Scheffran & Battaglini, 2011 ). Economic losses incurred through these incidents have become an order of the day amongst small and marginal farmers who are already in an environment of limited landholdings, poor soil, inaccessible technology, and low access to finance. In most instances, families have had to either diversify their sources of livelihood or temporarily emigrate to other places in order to find other income-earning opportunities. The increasing rate of occurrence of such occurrences has a close association with deforestation and land-use conversion trends in the Himalayan region. The growth in population pressure, development of transportation systems, tourism and uncontrolled development project building of roads, mines and hydro power building have increased deforestation and land degradation (Bhattarai & Conway, 2020). The ecological vulnerability of the region has also been increased by the fact that the vegetation covers have been removed, and this has exposed the slopes to landslides as well as flash floods (Prasad et al., 2013). Destabilisation of the soil through unplanned quarrying and mining has also led to changes in the hydrological cycle as well as an increase in the intensity of runoff. This has led to an increased environmental crisis in the region, with ecological degradation and socioeconomic vulnerability becoming mutually dependent on each other. Against this background of these interconnected problems, the question of how to mitigate the negative effects of climate change and bring the ecological balance back comes into place. Agroforestry, a land-use system that incorporates trees and crops as well as livestock in the same land to increase productivity, biodiversity, and sustainability (Nungula et al., 2024 ), is one of the new adaptive strategies that is being recognised in the world and being discussed more in India (Atapattu et al., 2025 ). The Food and Agriculture Organisation (FAO) define agroforestry as a dynamic and ecologically based natural resource management system that, through tree incorporation on farms and in the agricultural landscape, would diversify and sustain production to greater social, economic, and environmental advantages to the land users regardless of the levels of the users. This definition highlights the fact that agroforestry is multifunctional; it is not just an agglomeration of agriculture and forestry but a scientifically planned system that is meant to introduce ecological restoration and livelihood (Ferreira et al., 2025 ). Agroforestry is a nature-based intervention which, in addition to mitigating and adapting to climate change by enhancing the soil structure, erosion, water conservation and other diversified sources of income (Abebaw et al., 2025 ; Gupta et al., 2023 ). It provides a sound solution to sustainability in land management and livelihood resilience in mountain ecosystems where there are topographic limitations and soils are not stable under intensive agricultural exploitation. The Fig. 2 highlights how agroforestry functions as an effective adaptation strategy to climate change Nevertheless, even with its ecological and economic proven advantages, the implementation of agroforestry in the hilly areas of Himachal Pradesh has been minimal and more or less traditional (Bhardwaj, 2025). The majority of farmers do not apply agroforestry as a contemporary, designed system, but as an established cultural practice through the traditional ecological knowledge (Pant et al., 2025 ). Observations and survey findings on the field reveal that farmers on the farms are mainly applying the trees as fencing, to provide shade, to get firewood and feed the livestock, not necessarily any contemporary model of agroforestry. The current method of agroforestry is deeply rooted in the indigenous knowledge system (IKS) that is carried on through generations, with the focus on the necessity of forest conservation and soil protection. To avoid soil erosion and preserve moisture in farmlands, along with protecting crops against wind and grazing animals (Jafari et al., 2022 ), elder farmers usually share their experiences about the importance of keeping tree cover in the surroundings. Although this conventional model is environmentally friendly, it is not integrated with the science and contemporary management strategies that may make it more productive and sustainable. Lack of modern agroforestry models that are in tandem with the universal definitions and practices advanced within national and international policy systems is one of the biggest challenges witnessed in the region. Himachal Pradesh does not have a properly established system of institutional support infrastructure and technical skills to assist the farmers in adopting a better agroforestry design as compared to the plains or the more developed agricultural areas. Lack of specialised forestry extension services, inadequate training programs, and low awareness levels among farmers on the economic benefits of forestry adoption are some of the reasons that lead to low adoption. Also, the small landholdings, the absence of capital investment, and the delayed returns of the tree-based farming system deter the smallholders from experimenting with the contemporary agroforestry methods. Farmers who have been exposed to agroforestry believe that it is an expensive and extractive process that has low returns with uncertainties, and some are even afraid that the roots of trees might occupy nutrients in the soil, thus, with time, reducing the soil fertility. Socio-economic determinants also assume a final role in influencing the decision of the farmers to adopt agroforestry. Climatic perception and environmental awareness also influence the adoption behaviour. Farmers who feel that there is also a rise in frequency and intensity of extreme climatic conditions like floods, droughts or hailstorms are more likely to realise that it is time to adopt adaptive land-use activities such as agroforestry. In spite of those local developments, the general view of the awareness and institutional backing in terms of fostering agroforestry as a systematic and income-based business shows low awareness. Lack of specific government schemes, technical support, and demonstration sites has impeded knowledge and innovation. The remote and backward farmers are still isolated with no links to forestry experts, agricultural extension agents and market networks that would enable the commercialisation of agroforestry products. This divide highlights the more macro-policy issue, the necessity to unite traditional ecological knowledge and scientific innovation, as well as institutional capacity-building. Enhancing connections among research organisations, forest administrations, and local societies might contribute to the design of geographically accurate agroforestry models that are both ecologically plausible and economically viable. The proposed research will fill these shortcomings by empirically investigating the factors affecting agroforestry adoption and the perception of the farmers towards it. Additionally, also aimed at knowing how the traditional practices can be fortified and updated with awareness, training and policy provisions. By doing that, this paper will add to the larger discussion of the idea of sustainable mountain development, where combining traditional knowledge and contemporary scientific methodology is essential in climate resilience. Agroforestry is not just a method of agriculture, but a fast track to several Sustainable Development Goals (SDGs) such as SDG 1 (No Poverty), SDG 13 (Climate Action) and SDG 15 (Life on Land). Its use can revolutionise rural livelihoods through the improvement of environmental sustainability, lessening climate shock vulnerability, and increasing income diversification. Thus, the creation of a favourable policy environment, capacity-building of the farmers, as well as the creation of regional agroforestry demonstration centres are key to scaling up adoption in the hilly areas of Himachal Pradesh. Also, unpredictable climatic changes, environmental degradation, and the issue of livelihood insecurity in the Himalayas are calling out to be addressed with a lot of aggressiveness and creativity. Agroforestry, properly handled scientifically and adjusted to the regional context, is an opportunity which can be used to bring back the balance between human demands and ecological sustainability. The present research aims to understand the interaction between the social, economic, and climatic factors and the decision-making and perception of farmers towards the adoption of agroforestry, and the ultimate aim is to find out the measures that may help to mitigate the gap between the traditional wisdom and modern-day scientific practice in rural transformation in Himachal Pradesh. 2. Review of Literature Albugami et al. ( 2024 ) investigated networks of cooperation and research trends on climate change and sustainable livelihood using bibliometric techniques. Further, findings demonstrate a rise in research activity since 2007, especially after 2018, with net-worthy contributions from ICAR and contributions from the USA, India, Bangladesh, and Pakistan, suggesting shifting research priorities. Choudhury et al. ( 2022 ) indicate that soil erosion has increased in all climate scenarios, with agroforestry and horticulture systems being the most successful in lowering runoff and soil loss through adaptive management and bio-mulching. Ranjitkar et al. (2017) used the Ecological niche modelling to assess climate change impacts on agroforestry tree distribution in Yunnan, China and identified that the west and southwest regions are suitable for tea alder systems, southern areas for tea-hog plum, and northern zones for walnut-based agroforestry, and further highlights the agroforestry’s adaptive and ecological restoration potential. Verma et al. ( 2023 ) show that poplar-based systems yield the highest biomass, Melia systems excel in carbon sequestration, and bamboo systems enhance soil nutrients. Additionally, these combinations improve soil health, carbon mitigation, and agricultural sustainability in the hill regions of the northwestern Himalayas. Ullah et al. (2025) evaluated how pastoral livelihoods are impacted by extensive afforestation in Pakistan, the Hindu Kush Himalaya. Further, they showed that pastoralists adjusted through migration and crop-livestock integration, and grazing restrictions decreased fodder access, livestock numbers and income. Further, security is at risk due to the ongoing decline, which emphasises the necessity of participatory conservation strategies. Negi et al. ( 2022 ) revealed that improved species survival, significant tree growth, and increased carbon stock from 40.02 to 65.53 Mg ha⁻¹ in a decade, highlighting restoration’s effectiveness for climate resilience and land sustainability in the western Himalayas. Singh et al. ( 2025 ) used the Agricultural Climatic Vulnerability Index in the study to evaluate the climate vulnerability of vegetable production over 51 blocks of Himachal Pradesh. Study highlights that the most vulnerable area is the Balh Valley, while Paonta Sahib is the least. Further, crop profitability is significantly impacted by temperature and rainfall, but in vulnerable areas, diversification, better management, and irrigation increase resilience and productivity. To evaluate the potential for carbon sequestration in the eastern Himalayas, Kurmi et al. ( 2025 ) measure the net ecosystem production. The study reveals that agroforestry has the highest NEP, outperforming natural forests, demonstrating its potent ability to act as a carbon sink and a useful natural solution for mitigating climate change. While Panmei et al. ( 2025 ) identify native bamboo species effective for slope stabilisation and soil conservation, emphasising their cost-effectiveness, sustainability, and role in enhancing resilience aligned with UN SDG 13 goals. Phondani et al. ( 2017 ) investigated the Seasonal biomass consumption patterns for fodder and fuelwood across the Himalayan village. They show higher consumption in winter, with significant variations in energy values. Further, they emphasise agroforestry’s potential for sustainable biomass energy supply and inform policy development for rural energy management. Hazarika et al. ( 2024 ) show that long-term pineapple agroforestry successfully restores land, improves soil sustainability and organic carbon, better nutrient stratification and decreased soil erodibility in the Eastern Himalayan region. Further, Singh et al. (2024) show that alder pineapple systems improve microbial activities and deep carbon storage, and agroforestry considerably restores soil and biomass carbon. Moreover, agroforestry provides a practical method for repairing damaged cropland and reducing carbon emissions. Das ( 2025 ) examines the obstacles and opportunities for the Indian Sundarbans Biosphere Reserve to adopt climate-smart agriculture. They show that saline-tolerant crop diversification and weather-based practices are widely used. And the adoption is driven by education income and awareness, but illiteracy and a lack of resources impede successful implementation. Babu et al. ( 2020 ) indicate that ginger-based systems deteriorate soil health, undisturbed forest and alder-large cardamom systems preserve the highest carbon stocks and microbial activity. Furthermore, the encouraging alder-cardamom agroforestry improves soil sustainability and carbon restoration. Wani et al. ( 2023 ) show that the natural forests and horticulture retain the highest carbon content in temperate soils, which store substantially more carbon than subtropical soils in the northeastern Himalayas. Tamasiga et al. ( 2025 ) highlight that renewable energy increases agricultural sustainability and productivity, but it faces obstacles like resource competition and cost. The study recommended the targeted incentives, capacity building and context-specific strategies for the adoption of renewable energy. Sahoo et al. ( 2025 ) examined the role of Climate Resilient Agriculture in enhancing sustainability and water management. Study revealed that CRA improves groundwater-surface water balance, supports food security, and aligns with SDGs. Furthermore, Northwestern India faces severe water depletion, emphasising the need for technology integration, community participation, and targeted CRA policies for adaptive resilience. Sharma et al. (2023) highlight the biomass and carbon storage potential of agroforestry systems in the Chamba district of Himachal Pradesh. Furthermore, the findings showed the highest total carbon storage in silvopastoral systems and maximum biomass in agro-Horti silviculture. However, the carbon stock increased with altitude, highlighting agroforestry’s key role in carbon sequestration and climate change mitigation. Additionally, the socio-economic vulnerability across four agroforestry systems in Mizoram was assessed using a composite index approach by Thangjam et al. ( 2023 ) findings show that the highest vulnerability is in Borassus- and oil palm-based systems, mainly due to low adaptive capacity and high exposure. Further, the Parkia-based systems were least vulnerable, highlighting the need for targeted adaptation measures and socio-economic resilience building. Jat et al. ( 2025 ) show that integrated watershed management reduces erosion and runoff while increasing soil moisture, organic carbon productivity, and water efficiency. Additionally, adding grasses, bamboo, and agroforestry enhances carbon sequestration, highlighting IWM's contribution to climate resilience, sustainability, and food security. Kundu and Biswas ( 2025 ) show that techniques like crop rotation, agroforestry, organic farming, and biochar enhance soil biodiversity and resilience. However, obstacles like low awareness, high costs, and insufficient policy support prevent sustainable soil management from being widely adopted. 3. Conceptual framework The conceptual framework of the research has been presented in the Fig. 3 and states agro-forestry as a climate resilient land use solution that enhances livelihood security and minimises the negative impacts of climate change, especially heatwaves, floods and water stress, in the Himalayan area (Gupta et al., 2024 ). In delicate mountainous ecosystems such as Himachal Pradesh, agriculture is very delicate to climatic variation, steep slope, soil erosion, as well as low irrigation facilities (Kumar et al., 2021 ). The growing number of heatwaves, unpredictable precipitation, flash floods and reduced water supply have heightened the risk of production and livelihood susceptibility (Banu & Fazal, 2025 ). It is in this context that the framework suggests that the adoption of agroforestry by farmers is determined by the interaction of socio-economic factors, the perception of the risks associated with climate, and resource endowments, and that agroforestry positively influences ecological stability and resilience to livelihood (Ahmad et al., 2023 ). On the first level, the adaptive capacity of farmers is determined by socio-economic factors like landholding size, education, income level, market access and institutional support (Abdul & Kruse, 2017; Mwadzingeni et al., 2022 ). With higher perceived returns and technical awareness, households that have increased access to markets and services related to extending are more inclined to use tree-based systems. At the same time, climate risk perception is significant in the land-use decision-making process. When farmers feel that the heatwaves are increasing, the frequency of floods is rising, rain becomes erratic, and chances of extreme weather might occur in the future, then they are more likely to consider agroforestry as a protective and adaptive measure (Ermolieva et al., 2022 ). The challenges associated with the resource, such as decreasing soil fertility, water scarcity, and decreased crop production, compel farmers even more to diversify and adopt sustainable agriculture systems. These drivers act in aggregate and establish the likelihood of adopting agroforestry. The fundamental element of the framework is the adoption of agroforestry, which refers to the incorporation of trees in the same land unit with crops and or livestock. Agroforestry is a nature-based and climate-sensitive intervention which especially fits mountain agriculture (Deka & Goswami, 2025 ). It makes the environment vulnerable through various ecological processes. Shade is given by the tree canopies, and it helps to control the microclimatic conditions, which lowers the surface temperature and shields crops and livestock against heat stress during extreme heat waves. The existence of deep-rooted trees enriches the soil structure, improves the presence of organic matter, and enhances water penetration, which lessens surface flow and the effects of floods at steep slopes (Jafari et al., 2022 ). Well-established root systems hold the particles of soil together and stop soil erosion, which is a crucial issue in the Himalayan region. Moreover, agroforestry increases soil moisture retention and recharge of underground water, and hence alleviates water stress in dry seasons (Wang et al., 2023 ). They reduce irrigation and mitigate the risk of rainfall variability by shaving off the losses due to evapotranspiration and overall increase the water-use efficiency of systems based on trees, as well as improve the overall agricultural output. Trees, too, help in carbon sequestration, which connects local adaptation to the global climate mitigation measures. These environmental advantages are translated into enhancing livelihood security (Di Sacco et al., 2021 ). Agroforestry also has a multi-income effect because of the production of timber, fruits, fodder, fuelwood, and non-timber forest products; this has lowered the reliance on single-crop production. Diversity of income reduces susceptibility to climatic crises of crop failures and stabilises household incomes. Proper soil fertility and microclimate promote crop productivity and food security. The other effect of agroforestry is the creation of more employment opportunities on the farm and asset accumulation since the trees are long-term capital investments. Agroforestry enhances adaptive capacity and resilience of rural households by decreasing the production risks and volatility of incomes (Zeratsion et al., 2024 ). 4. Research Methodology 4.1. Area & Data The research was conducted in the Kullu and Mandi districts of Himachal Pradesh, which represent diverse agroclimatic and socio-economic conditions of the western Himalayan region. A representative coverage was made by adopting a multistage random sampling technique. Two districts, Kullu and Mandi, were selected purposely in the first stage because they were highly agriculturally dependent and prone to climatic disasters like floods and landslides. In the second stage, two blocks were selected randomly in each district, and subsequently, four villages in each block were selected randomly. Lastly, the selection of households within every village was done randomly, and a total sample of 680 respondents was obtained. They were gathered using the well-formatted interview schedule of the socio-economic, climatic, and agroforestry-related issues. The data were collected and analysed with the help of the relevant statistical tools like percentage, average and also applied the logit model to determine the determinants that affected the adoption of agroforestry. The Fig. 4 shows the sample distribution of the survey across various blocks in the Mandi and Kullu districts. The Kullu, Banjar, Sadar, and Balh blocks have been included. For the district of Kullu, maximum samples were obtained from the Banjar block (166) and the Kullu block (243), totalling 409 samples for the district. On the other hand, in Mandi district, the survey was focused on the Sadar block (84) and the Balh block (186). Overall, data collection was conducted in 680 households across the two districts and four blocks with a fairly balanced distribution between the districts, though the actual blocks surveyed were quite different. 4.2. Model Specification A logit model was used to test the factors that determine the adoption of agroforestry practices by farmers who have farming operations in Himachal Pradesh. The dependent variable is dichotomous in character, as it will reflect whether the respondent practices agroforestry (1 = Yes and 0 = No). The logit model is used to estimate the likelihood of using agroforestry based on several socio-economic and climatic explanatory variables. Mathematically, the model is expressed as: \(\:\text{Logit}\left({P}_{i}\right)=\text{l}\text{n}\left(\frac{{P}_{i}}{1-{P}_{i}}\right)={\beta\:}_{0}+{\beta\:}_{1}{X}_{1}+{\beta\:}_{2}{X}_{2}+\cdots\:+{\beta\:}_{k}{X}_{k}+{\mu\:}_{i}\) Where: \(\:{P}_{i}\) = Probability that the \(\:{i}^{th}The\:\) household adopts agroforestry \(\:\frac{{P}_{i}}{1-{P}_{i}}\) = Odds ratio of adoption versus non-adoption \(\:{\beta\:}_{0}\) = Constant term \(\:{\beta\:}_{k}\) = Coefficients associated with explanatory variables \(\:{X}_{k}\) = Vector of independent variables \(\:{\mu\:}_{i}\) = Error term The independent variables are gender, marital status, occupation, livestock units, household size, family type and agricultural involvement, farming experience, modern input utilisation, training, use of fertilisers, availability in the market, social constraint, and perception towards extreme weather. Maximum likelihood estimation (MLE) was used to estimate the model, and odds ratios were used to interpret the magnitude and direction of the effects. Strong standard errors were used to control the possibility of heteroskedasticity and make a sound statistical inference. 5. Descriptive Analysis The description of the variables used in the study is presented in Table 1 , while the Table 2 represents the descriptive statistics of 680 sampled households on the different socio-economic and perception-related variables regarding agroforestry and climate change adaptation. The variable Adoption of Agroforestry-Practice (y12) has a mean of 0.478, which implies that approximately 47.8 per cent of the respondents are involved in the practice of agroforestry. Table 1 Description of variable Variable Name Symbol Description Unit of Measurement Agroforestry_Practice y12 Yes = 1; No = 0 Binary Gender x5 1 = male; Female = 2 Binary Livestock_Units x39 Number Count Marital_Status x7 1-Married 2 = Unmarried; 3 = Divorce; 4 = Widowed Categorical Unemployment_No x19 Number Binary Modern_Inputs_Use x36 Yes = 1; No = 0 Binary Household_Size x11 Number Count Family_Type x12 1 = Joint; Nuclear Binary Agriculture_Involvement x13 Number Count Farming_Experience_Years x18 Number Count Head_Occupation x19 1 = Agriculturist; 2 = Govt. Employee; 3 = Private Employee; 4 = other Categorical Chemical_Fertilizer_Use x34 Yes = 1; No = 0 Binary Agri_Training_Attended x59 Yes = 1; No = 0 Binary Market_Availability x60 Yes = 1; No = 0 Binary Social_Constraint_Challenges x76 1 = No challenges 2 = Some challenges 3 = Major challenges Categorical Flood_Frequency x81 1 = Rarely 2 = Occasionally 3 = Frequently Categorical Hailstorm_Frequency x82 1 = Rarely 2 = Occasionally 3 = Frequently Categorical Heatwave_Frequency x86 1 = Decreased 2 = No change 3 = Increased Categorical Extreme_Events_Future x89 1 = Unlikely 2 = Uncertain 3 = Very likely Categorical Source : Author Compilation The respondent gender (x5) is also a mean of 1.156, which means that most of the respondents are male, and marital life (x7) has a mean of 1.22, and the respondent is therefore a married person. The average number of people living in the household (x11) is approximately 5.1, indicating that the families are medium-sized. The variable (type of family) (x12) is of type of family (mean 1.43), indicating that nuclear families are the majority. The mean number of unemployed persons (x19) among households is 1.37, and there is some deviation (Std. dev. 0.857). Livestock ownership is small, with the average of livestock ownership units (x39) being 1.09. The mean farming experience of the respondents is 28.27 (x18) years, which shows that Table 2 Descriptive statistics of variables Variable Obs. Mean Std. dev. Min Max Agroforestry_Practice 680 0.478 0.500 0 1 Gender 680 1.156 0.363 1 2 Livestock_Units 680 1.090 1.368 0 12 Marital_Status 680 1.221 0.510 1 4 Unemployment No 680 1.379 0.857 1 4 Modern_Inputs_Use 680 0.404 0.491 0 1 Household_Size 680 5.107 1.956 1 16 Family Type 680 1.435 0.496 1 2 Agriculture_Involvement 680 2.231 1.324 0 10 Farming_Experience_Years 680 28.272 14.440 0 70 Head_Occupation 680 1.379 0.857 1 4 Chemical_Fertilizer_Use 680 0.722 0.448 0 1 Agri_Training_Attended 680 0.231 0.422 0 1 Market_Availability 680 0.716 0.451 0 1 Social_Constraint_Challenges 680 1.849 0.886 1 3 Flood_Frequency 680 1.378 0.628 1 3 Hailstorm_Frequency 680 1.619 0.633 1 3 Heatwave_Frequency 680 2.413 0.735 1 3 Extreme_Events_Future 680 2.687 0.636 1 3 Source : Author Compilation they have a lot of farming experience. On the factor of use of input, 40.4% use modern agricultural inputs (x36) and 72.2% use chemical fertilisers (x34). It is also found that only 23.1% of respondents attended agricultural training programs (x59), and 71.6% indicated a good market for their products in their area (x60). Concerning adaptation and perception of climate, the average score of the facing social constraints in adaptation (x76) is equal to 1.85 (on a 3-point scale), which can be interpreted as moderate levels of perceived constraints. Extreme weather events are also not equal: the mean of floods (x81) is 1.38, hailstorms (x82) 1.62, and heat waves (x86) 2.41, meaning that heat waves are more common than the others. Last but not least, the perception of the fact that extreme weather events will be more prevalent in the future (x89) has a high mean that is equal to 2.69 and represents a solid agreement among respondents on the fact that the risks of climatic changes will increase. 6. Results & Discussion Figure 5 reveals that nearly half of the respondents (47.79%) are practising agroforestry, while a slightly higher proportion (52.21%) did not engage in such practices. This indicates a moderate level of adoption of agroforestry, reflecting growing awareness among farmers regarding sustainable agricultural methods, though a significant portion remains outside its practice. Furthermore, the data show that a large majority of respondents (76.91%) had not attended any agriculture-related training, whereas only 23.09% had received such training. This suggests a considerable gap in agricultural capacity-building initiatives and limited access to technical knowledge and skill development programs. The low participation in training activities may act as a barrier to the wider adoption of innovative and sustainable practices like agroforestry. Therefore, strengthening agricultural extension services and promoting farmer training programs could play a crucial role in enhancing agroforestry adoption and improving overall farm productivity. Figure 6 illustrates the farmers’ perceptions of agroforestry and its role in enhancing climate resilience, measured using a five-point Likert scale ranging from Strongly Disagree to Strongly Agree. A large majority of farmers expressed positive perceptions about the climate resilience benefits of agroforestry. Specifically, around 61% of respondents agreed or strongly agreed that agroforestry helps protect crops from heat waves, while 15% disagreed or strongly disagreed, and 29% remained neutral. Similarly, 62% of farmers agreed or strongly agreed that agroforestry reduces flood impacts, indicating that many recognise its potential to buffer against heavy rainfall and flooding, though about 12% disagreed and 25% were neutral. Regarding agroforestry’s ability to minimise extreme weather losses, 64% of respondents agreed or strongly agreed, while 12% disagreed and 24% were neutral. The perception of drought resilience was even stronger, about 78% of farmers agreed or strongly agreed that agroforestry reduces drought impact, reflecting their confidence in tree-based systems for moisture retention and shade provision, whereas only 9% disagreed. Farmers showed the highest level of agreement on soil-related benefits: nearly 92% agreed or strongly agreed that agroforestry reduces soil erosion and land degradation, and about 93% believed it improves soil moisture and fertility, suggesting strong awareness of agroforestry’s ecological advantages. Finally, approximately 94% of respondents agreed or strongly agreed that agroforestry reduces overall climate risk, indicating a widespread belief that integrating trees into farming systems enhances climate resilience and sustainability. In summary, the results demonstrate overwhelmingly positive perceptions among farmers regarding agroforestry’s role in mitigating adverse climate impacts. Most respondents view agroforestry as an effective strategy for improving soil health, reducing climate-induced losses, and strengthening long-term agricultural resilience. Table 3 Factors Influencing the Adoption of Agroforestry Practices Agroforestry_Practice Coefficient Robust std. Err. Odds ratio Robust std. Err. Gender 0.403 0.267 1.496 0.400 Livestock_Units 0.078 0.088 1.081 0.095 Marital_Status Base category- Married Unmarried -0.540** 0.259 0.583** 0.151 Widowed -1.716* 0.953 0.180* 0.171 Head_Occupation Base Category- Agriculturist Government Employee 0.491* 0.257 1.634* 0.419 Private Employee -0.509 0.823 0.601 0.494 other -0.940** 0.398 0.391** 0.155 1.Modern_Inputs_Use 0.616** 0.243 1.852** 0.450 Household_Size -0.175*** 0.066 0.839*** 0.055 2Family_Type -0.763*** 0.234 0.466*** 0.109 Agriculture_Involvement 0.122 0.093 1.130 0.105 Farming_Experience_Years 0.014* 0.008 1.014* 0.008 Chemical_Fertilizer_Use 0.752*** 0.260 2.121*** 0.552 1.Agri_Training_Attended -0.100 0.269 0.905 0.244 1.Market_Availability 1.481*** 0.263 4.399*** 1.156 Social_Constraint_Challenges Base Category- No challenges Some challenges 0.610** 0.271 1.841** 0.499 Major challenges 0.951*** 0.223 2.589*** 0.577 Flood_Frequency Base Category- Rarely Occasionally 0.142 0.241 1.152 0.278 Frequently 0.813* 0.459 2.254* 1.034 Hailstorm_Frequency Base Category- Rarely Occasionally -0.645*** 0.225 0.525*** 0.118 Frequently -0.305 0.459 0.737 0.339 Heatwave_Frequency Base Category- Decreased No change -0.614** 0.311 0.541** 0.169 Increased -0.551* 0.323 0.576* 0.186 Extreme_Events_Future Base Category- Unlikely Uncertain 1.003** 0.466 2.727** 1.269 Very Likely 1.186*** 0.391 3.275*** 1.279 _cons -2.242*** 0.614 0.106*** 0.065 Number of obs. 680 Wald chi2(25) 152.76 Prob > chi2 0.000 Log pseudolikelihood -357.168 Pseudo R2 0.2412 Table 3 demonstrates the results of the logit model, which estimates the factors that determine the adoption of agroforestry practices within the study locale. In addition, the goodness-of-fit statistics of the binary logistic regression model in general provide more evidence of its reliability and explanatory power. The sample size, which was used in estimating the model, was 680 observations, which is large enough to guarantee a statistically robust model. The Wald chi-square value of 152.76 and 25 degrees of freedom, and a p-value of 0.000 indicate that the explanatory variables in aggregate are very significant in predicting the probability of using agroforestry. This means that the combined effect of the added social and climatic variables is statistically significant on the likelihood of practising agroforestry. The model log pseudolikelihood of -357.168 is the maximum likelihood estimate of the model that can be employed as a measure of model fit. Although the negative value is anticipated in log-likelihood estimation, a less negative value is normally a sign of a better fit. The pseudo R2 is 0.2412, which suggests that about 24.1percent of the variation in likelihood adopting agroforestry practices is accounted for by the group of independent variables that have been incorporated in the model. In general, such statistics indicate that the model is well explained and that the chosen social and climatic factors play an important role in comprehending what factors affect the agroforestry adoption by farmers. This strengthens the strength and reliability of the estimated findings above. The marital status factor, with base category married, the coefficient is -0.540 with an odds ratio of 0.583, which is statistically significant at 5% level of significance, represents the explanation that the unmarried respondents are 41.7 per cent less likely to adopt agroforestry than those who are married. Likewise, widowed respondents with a -1.716 coefficient and odds ratio of 0.180, which is statistically significant at a 10% level of significance, are much less likely (82% less likely) to engage in agroforestry practices. Regarding occupation, as the base category, the other occupations have a coefficient of -0.940 and an odds ratio of 0.391, which is statistically significant at 5% level of significance, depicting that individuals in other occupations are 60.9% less likely to embrace agroforestry. On the other hand, the coefficient and odds ratio of government employees are 0.491 and 1.634, respectively, which are significant at a 10% level of significance, indicating that the employees are more likely to practice agroforestry by a factor of 63.4. Also, the use of modern agricultural inputs has a coefficient of 0.616 and an odds ratio of 1.852, which is statistically significant at 5% level of significance, showing that farmers not utilising the modern input are 85.2 per cent more likely to use agroforestry, as compared to the use of modern agricultural inputs. Likewise, the use of chemical fertiliser, with the coefficient being 0.752 and an odds ratio of 2.12, which is statistically significant at the 1% level of significance, reflects that the familiarity with better agricultural technologies increases the adoption of agroforestry significantly, i.e. more than two times. Secondly, it has the household size, which has a negative relation with a coefficient of -0.175 and odds ratio 0.839, which is stated as a negatively significant relation that is statistically significant at 1% level of significance and therefore indicates that the larger the household size, the less likely it is to adopt agroforestry. On the same note, respondents belonging to joint families have a family type with a coefficient of -0.763 and an odds ratio of 0.466 that is statistically significant at a level of significance of 1%, indicating that a joint family respondent is less likely to adopt agroforestry by 53.4 per cent as compared to a nuclear family respondent. Also, in terms of perceived social challenges, respondents who said that they face some challenges have a coefficient of 0.610 and an odds ratio of 1.841, which is statistically significant at 5% level of significance, indicating that they are 1.84 times more likely to adopt agroforestry. The coefficient of those facing severe challenges of 0.951 and the odds ratio of 2.589, which is statistically significant at 1% level in the significance value, demonstrate that they are 2.59 times as likely to use agroforestry practices. Moreover, the factors related to the climate are also significantly affected by the adoption of agroforestry. The respondents are 47.5% less likely to adopt agroforestry when the coefficient is -0.645, and the odds ratio of 0.525 is statistically significant at 1 percent level of significance. In the meantime, the respondents who did not notice any change in the frequency of heatwaves, with a coefficient of -0.614 and odds of 0.541, statistically significant at 5% level of significance, are 46% less likely to adopt agroforestry than their counterparts who perceived a reduction. Furthermore, future extreme weather events have a significant impact on adoption. Respondents who perceive extreme events to be uncertain have a coefficient of 1.003 and an odds ratio of 2.727, which is statistically significant at the 5% level of significance, and this means that such respondents are 2.73 times more likely to adopt agroforestry. The extreme events, which are much more likely and statistically significant with an odds ratio of 3.275 at a level of significance of 1%, are more than three times more likely to occur, and the climate risk perception has a significant influence on the adaptive land-use decisions. Lastly, the most impactful determinant is market availability, with a coefficient of 1.481 and an odds ratio of 4.399, which is statistically significant at 1% level of significance, and it means that the respondents who have higher market access are 4.4 times more prone to deploy agroforestry practices. 7. Discussion In the present study, conducted across the districts of Kullu and Mandi in Himachal Pradesh, the observed relationship between marital status and the adoption of agroforestry practices can be attributed to the underlying socio-economic and cultural dynamics prevalent in these regions. Agroforestry is more accepted among married respondents since marriage in Himalayan rural areas signifies stability of the households, mutual labour and better social capital. On the contrary, respondents who are unmarried and widowed tend to experience various socio-economic barriers that restrict their activities in agroforestry. At this stage, unmarried people are usually younger and more interested in education or finding a stable job, and therefore, they are less involved in agriculture. Their interests are usually based on securing income without necessarily investing time and resources in long-term land-based activities such as agroforestry. Conversely, widowed respondents tend to have a labour shortage and less access to social or institutional support systems and hence struggle to operate agroforestry systems that need routine maintenance and resources. Moreover, also states that marital and family stability increases the adoption of innovation by increasing resources and reducing perceived risk. Therefore, marital status is a proxy of livelihood security and adaptive capacity in agroforestry decision-making in the socio-cultural setting of Kullu and Mandi. Also, the study further states that the non-agriculture occupations of the respondents are 60.9 per cent unlikely to practice agroforestry since such people have less time and are not as dependent on farming as their main source of livelihood. They are less interested in their on-land activities, which makes them less motivated to invest in the long-term tree-based systems. Government workers, on the other hand, are 63.4 times more likely to be practising agroforestry, perhaps because they have higher and more consistent income, access to more information, and they are more environmentally conscious. The fact that agroforestry is considered an extra and sustainable land management system, but not a primary source of livelihood among government workers. In addition, these families often keep livestock, and the introduction of tree species in farms offers feed, shelter, and alternative income sources. The economic security and exposure to extension services in Kullu and Mandi are important aspects in the adoption of technology because smallholder farming is the dominant practice in these two regions. Therefore, occupation plays an important role in the choice of adoption, and the income stability and exposure to knowledge increase the willingness to invest in sustainable land-use systems such as agroforestry. There is also a strong association between agroforestry adoption and the use of modern farming inputs. The findings suggest that farmers who do not engage in the use of modern inputs are 85.2% more likely to take up agroforestry than farmers who have a high dependency on modern inputs. It is attributable to the reliance of the farmers on the hybrid seeds, and the mechanised tools are usually short-term, yield-driven and focused on the annual crops and on the instant returns. On the contrary, those who are less reliant on modern inputs are more likely to take the more traditional forms due to the absence of knowledge, lack of financial support and other restrictive factors is applied to agroforestry. Moreover, it is because of upholding soil fertility, retention of moisture and livestock feed. The farmer who does not utilise modern agricultural inputs also indicates the adaptive strategies of poorer and resource-constrained households. These farmers tend to be working on marginal or sloppy lands, and they practice agroforestry not out of economic benefits but as a way of subsistence and land protection. In the experiment where soil erosion, landslides, and soil fertility are widespread problems, planting trees in farmland would stabilise the ground, prevent soil erosion, and preserve soil moisture. Also, such trees are important sources of household survival, including the livestock fodder, domestic fuelwood, and small timber for repair or fencing purposes. Additionally, the negative correlation of household size and family type with adopting agroforestry, as the socio-economic and land-use conditions of mountain farming systems are demonstrated by the results as well. The households that possess more people are less likely to use agroforestry by 16.1%, since in these households, the agricultural land is usually divided among the members, hence the small plots they are left with to handle. Such fragmentation only makes it less possible the idea of committing land-based systems based on trees, which are a long-term commitment and space. In addition, it is also true that large families are more concerned with the immediate subsistence requirements, and the need to conserve land in the long run is not of primary importance. Equally, respondents of joint families are 53.4% unlikely to adopt agroforestry as compared to nuclear families. In the hilly area of Himachal, the decision-making in the joint households tends to be collective and conservative, with various priorities amongst members. Sharing resources also plays a critical role in joint families, as it is more difficult to reach a consensus on changes in land-use, such as tree planting. On the contrary, nuclear families have more freedom and autonomy in incorporating new or sustainable practices. Therefore, scarce land and the complexity of decision-making in the hilly setting are the two factors that make larger and joint households adopt less agroforestry. Also, the positive, statistically significant, coefficients of perceived social challenges show that households experiencing moderate and severe perceived social challenges are much more likely to adopt agroforestry. Agroforestry is a risk-buffering as well as a livelihood diversification strategy in the context of Himachal Pradesh, whereby small and fragmented landholdings, off-farm employment and outmigration are widespread. Those farmers who are more socio-economically stressed might find agroforestry as a more viable alternative source of income stability, fuelwood, and fodder, and it lessens their reliance on the external market. Therefore, an increased vulnerability seems to drive the households toward more resilient land-use systems. Climate-related perceptions are also important in influencing adaptive decisions. The farmers who did not realise the changes in the frequency of heatwaves are one-fourth less likely to adopt than those who noticed a fall. The response shows that awareness and perception of climate variability are significant factors that contribute to adapting mountainous ecosystems. Farmers in Himachal Pradesh, where agricultural activities are very susceptible to unpredictable rainfall, temperature changes, and a rising number of extreme events, a farmer who is aware of these threats will be more willing to implement systems of trees that would help in conserving soil, retaining moisture and controlling microclimatic conditions. In contrast, low awareness of climate change slows the importance to apply adaptive measures. This argument is further reinforced by the perception of the occurrence of extreme weather events in the future. The uncertain farmers and farmers who perceive that the extreme events are highly likely, and those who confirm that the likelihood of occurrence is very high, and the probability of utilizing agro forestry is very high. This indicates an adaptation behaviour that is precautionary in the weak Himalayan environment. Agroforestry systems, which combine trees with crops and livestock, alleviate production risks, stop soil erosion on steep slopes and offer diversified production. They are especially appropriate to climate-exposed hill agriculture. Thus, the perception of climate risks has a positive effect on adaptive land-use choices. All in all, the results can be well organised in line with the socio-ecological realities of the area, where agroforestry is operated as a climate adaptation tool and livelihood improvement tool simultaneously. These findings support the idea that vulnerability, risk perception, and economic opportunity are the joint factors that lead to adoption, and the policies should be strong enough to create awareness of climate and enhance market connections to facilitate the sustainable land-use transitions in mountain agriculture. 8. Conclusion and Policy Implications The present research identifies several determinants influencing the adoption of agroforestry practices in the hilly districts of Kullu and Mandi. Agroforestry, as a land-use model that combines trees with crops and livestock, can also increase ecological stability, livelihoods and climate resiliency. Nonetheless, the paper indicates that even though modern agroforestry models have environmental and economic benefits, most of their applications are still traditional. The results indicate that both social and climatic variables play important roles in farmers' decision-making process with regard to the adoption of agroforestry, but institutional, technical and awareness-related gaps still prevent systematic development and commercialisation of the process. The analysis comes up with some social variables which significantly influence the behaviour of adopting agroforestry. The most significant of these were marital status, occupation, household size, family type, and perceived social challenges. Another important point presented in the study is that climatic variability and risk perception of the farmers are key factors that determine the adoption of agroforestry. The adaptive responses of respondents who are frequent victims of floods, hailstorms, and heatwaves differ. Moreover, respondents who assume that extreme climatic events are not definite or have high chances of rising in the future are much more likely to embrace agroforestry. The rising number of cloudbursts, flash floods and landslides experienced in the districts of Kullu and Mandi has increased awareness of the role of vegetation in stabilising slopes and protecting soil. Quite a few farmers said that they had planted trees as a form of protection against soil erosion and field damage, even without the use of an organised agroforestry model. This is an indication of the adaptive and responsive character of agroforestry adoption, with environmental stress being a driving force for land management decisions. Among the research findings, one of the most significant ones is that there is neither a modern nor a scientifically organised agroforestry model which is practised in the study area. Most farmers are involved in traditional agroforestry systems that are mostly subsistence-based as opposed to being market-based. The use of trees is based on the field boundaries, terraces, or farm bunds primarily for soil conservation, feeding of livestock, fencing, and firewood. The purpose of these practices is vital both domestically and ecologically, but does not fulfil the conventional meaning of agroforestry, which underlines combined and cost-effective systems of trees, crops, and livestock. In such areas, agroforestry is not usually viewed by farmers as an economic activity. Rather, it is regarded as a preservative and auxiliary activity to support their livelihood. The study also concludes that households, which lack access to modern agricultural inputs and are poor and resource-constrained, enjoy more chances to indulge in such traditional modes of agroforestry. They depend on what is on the trees to produce what is demanded in the home, as well as help address the effects of land degradation, landslides, and diminishing soil fertility. The study also establishes a gaping institutional and administrative void in the promotion of agroforestry. It has a deficit of specialised forestry institutions, technical experts and extension programs that aim at the promotion of modern agroforestry systems in the region. The farmers do not have access to training, demonstration projects and money that could assist them in implementing agroforestry as an alternative land use. Additionally, there is poor coordination between the forest and agriculture departments that hinders the practice of the integrated policies. Lack of localised knowledge sharing systems and awareness regarding market opportunities does not even allow farmers to convert their conventional farming activities into profit-making ventures. The results highlight the necessity of having a region-specific agroforestry development policy to incorporate scientific innovation, local knowledge, and institutional support. The development of district-level agroforestry resource centres with forestry, soil conservation, and sustainable agriculture experts should be the priority of policymakers. These centres might be used as training, research and demonstration hubs to sensitise farmers about better agroforestry models and their potential to enhance their economic benefits. Particular attention is to be paid to awareness campaigns that will inform farmers on the economic and ecological advantages of agroforestry in the long run. Capacity-building workshops and community-based training programs must be organised in the backward and remote regions, to develop skills on nursery management, intercropping, soil fertility management and sustainable harvesting of tree-based products. Cooperation with local self-help organisations and cooperatives may also assist in branding and marketing native agroforestry products, which would connect the traditional ways with the new market systems. Small and marginal farmers can be motivated by financial incentives (sapling subsidies, micro credit access, insurance, etc.) to adopt them. Moreover, agroforestry inclusion in the climate adaptation and rural livelihood missions may guarantee the cross-sectoral synergy and promote long-term sustainability. The government must also look into rolling out land use planning models that would encourage the planting of trees on private lands, alongside maintaining ecological balance and conservation of biodiversity. 9. Limitations of research and future scope Although the research offers a useful understanding of the factors that precondition the use of agroforestry in Kullu and Mandi districts, it has some limitations. To start with, the study does not span the entire geographical region, as only two districts are considered, which might not be reflective of the agroecological and socio-economic situations in other areas of Himachal Pradesh or other mountainous areas. Second, the research mainly elicits the perception and practice of farmers based on a cross-sectional survey, which might not capture the change over time and the changing effects of climatic variability on the agroforestry choices. Third, the meaning and perception of agroforestry by the respondents were more traditional and subsistence-based, without a technical distinction between modern integrated systems and local systems. In addition, the availability of institutional data and the input of experts was limited, which limited the analysis of the policy and programmatic interventions. In future studies, they ought to take a comparative-longitudinal design in various districts using GIS-based spatial analysis, economic, and impact assessment of agro-forestry on income, biodiversity and soil health. Policy assessment, institutional interconnections, and the effects of training farmers should also have a future focus that will help to design an efficient, regionally appropriate framework of sustainable and commercially viable agro-forestry development. Declarations Funding Declaration The authors declare that no financial support or funding was received for the research, authorship, and/or publication of this article. Author Contribution Pawan Kumar and Yash Pal contributed to defining the problem, developing the research framework, collecting and cleaning the data, conducting the analysis, and interpreting the results. Amar Latta provided the guidance throughout the research process. They contributed to the review of the manuscript, methodological suggestions and substantive feedback that strengthened the clarity, quality, and rigour of the manuscript. All authors reviewed and approved the final version of the manuscript. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 21 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8935206","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603530820,"identity":"b1c5bf47-dcfa-47cc-90b9-c3617c51ae2d","order_by":0,"name":"Pawan Kumar","email":"","orcid":"","institution":"Centre For Development Studies","correspondingAuthor":false,"prefix":"","firstName":"Pawan","middleName":"","lastName":"Kumar","suffix":""},{"id":603530821,"identity":"678f3085-32ee-40a2-8053-4490833e1ec8","order_by":1,"name":"Yash Pal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYHACNiA+wMDHwMD4AMji4SNaC5BkNgBpYSNFC5sEjIsXGBw//uwBw5878mzs7dcqv+bYybAxMD98dAOfljM55gaMbc8M23jOlN2W3ZYMdBibsXEOPi0HctgkGBsOM7ZJ5KTdltzGDNTCwyaNV8v5588kGP4ctgdpKZbcVk+ElhsJZhIMbIcT2yTSjzF+3HaYsBbJG2/MJBLbDicD/cIszbjtOA8bMwG/8J1Pfybx4c9h23729ocff26rtudnb374GJ8WhQNAIgHM5DFg5gHRzHiUg4B8A5zJ/oDxBwHVo2AUjIJRMDIBAEtsSEuckff3AAAAAElFTkSuQmCC","orcid":"","institution":"Central University of Himachal Pradesh","correspondingAuthor":true,"prefix":"","firstName":"Yash","middleName":"","lastName":"Pal","suffix":""},{"id":603530822,"identity":"3540c072-70c2-4713-967a-643c73d406a1","order_by":2,"name":"Amar Latta","email":"","orcid":"","institution":"Shri Guru Ram Rai University","correspondingAuthor":false,"prefix":"","firstName":"Amar","middleName":"","lastName":"Latta","suffix":""}],"badges":[],"createdAt":"2026-02-21 18:38:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8935206/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8935206/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104779980,"identity":"8c728c15-a6cd-463e-923f-cd09334babf1","added_by":"auto","created_at":"2026-03-17 07:48:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":295505,"visible":true,"origin":"","legend":"\u003cp\u003eClimate Change, Causes, Consequences, and Impacts on Agriculture \u003cem\u003eSource\u003c/em\u003e Author Compilations\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8935206/v1/97fb839b94e7baffc7fa1c9a.png"},{"id":104406372,"identity":"6db4f785-1acc-4445-bfa1-e8baf2714ca8","added_by":"auto","created_at":"2026-03-11 12:25:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230636,"visible":true,"origin":"","legend":"\u003cp\u003eAgroforestry as an Adaptation Strategy to Climate Change \u003cem\u003eSource\u003c/em\u003e Author Compilations.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8935206/v1/04c432c58054548798102c14.png"},{"id":104385010,"identity":"c805266e-344a-4317-b976-5f0b4f761c2a","added_by":"auto","created_at":"2026-03-11 08:36:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":442140,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework Linking Agroforestry Adoption with Climate Change Adaptation and Livelihood Security \u003cem\u003eSource\u003c/em\u003e Author Compilations\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8935206/v1/711f90f4d9211cbb8054f9a5.png"},{"id":104405677,"identity":"977b9fbf-5aff-47e1-8454-87c4dbbdc4dd","added_by":"auto","created_at":"2026-03-11 12:23:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75169,"visible":true,"origin":"","legend":"\u003cp\u003eSampling frameworks of the study \u003cem\u003eSource\u003c/em\u003e Author Compilations\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8935206/v1/1a6d8e320303d9cdaccf6ae7.png"},{"id":104385013,"identity":"130335e3-dde1-4d3e-8ef1-34f89f060f71","added_by":"auto","created_at":"2026-03-11 08:36:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92146,"visible":true,"origin":"","legend":"\u003cp\u003eAdoption of Agroforestry and attending Training \u003cem\u003eSource\u003c/em\u003e Author compilation\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8935206/v1/464df9020380e836f33b1b06.png"},{"id":104406043,"identity":"3fd9b789-fe28-48a2-bffe-e719268cf95b","added_by":"auto","created_at":"2026-03-11 12:24:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":162065,"visible":true,"origin":"","legend":"\u003cp\u003eFarmers’ Perception of Agroforestry and Climate Resilience \u003cem\u003eSource\u003c/em\u003e Author compilation\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8935206/v1/09fa8a026c2568144e2ed299.png"},{"id":104785648,"identity":"e972d1b5-1e4a-453f-8762-589eea06e1d6","added_by":"auto","created_at":"2026-03-17 08:12:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2190064,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8935206/v1/db8d78e8-5e17-499e-bf4e-ea9166c4758c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bridging Tradition and Modernity: Socio-Climatic Determinants and Farmers’ Perceptions of Agroforestry Adoption in the Hilly Regions of Himachal Pradesh","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Himalayan region, known as the water tower of Asia, is among the most ecologically delicate and crucial mountain habitats in the global (Kandel et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Himachal Pradesh is a state situated in the western Himalayas (Chauhan et al., 2021) with complicated topography, sharp slopes, and varied agroclimatic regions that have numerous agrarian people who rely mostly on natural resources (Kumar et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Climate change has grown to be one of the most acute challenges facing the Himalayan environment and people in the past few decades (Dhimal et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Higher temperatures, inconsistent rainfall and increased instances of severe weather conditions like floods, cloudbursts and landslides have had a drastic impact on the ecological balance of the area. The mountain farming systems that were once stable and relied on the very fragile balance between agriculture, forests and livestock are now under a severe threat due to changing climatic and socio-economic conditions. The past few years have seen natural calamities occurring frequently and intensely in the Himalaya states, especially in Himachal Pradesh (Kumar, 2024). Cloudbursts, flash floods, and heavy rainfall events have become a common occurrence that has probably caused massive destruction of agricultural land, infrastructure, and human settlements (Kumar et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows how climate change alters global weather patterns and generates physical/environmental impacts, including soil degradation, reduced water availability, loss of pollinators, and declining crop quality, which further impacts the crop and livestock, such as altered growing seasons, pest outbreaks, reduced productivity, and crop failures in vulnerable regions. Ultimately, these biophysical disruptions lead to socio-economic consequences, including declining farmers\u0026rsquo; income, higher production costs, food insecurity, and greater livelihood vulnerability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe empirical data and local observations indicate that the phenomenon of cloudbursts has increased, causing the destruction of crops as well as soil erosion, siltation, and loss of arable land. These climatic disasters are known not only to ruin the agricultural output but also to interfere with the mode of existence of agricultural groups (Scheffran \u0026amp; Battaglini, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Economic losses incurred through these incidents have become an order of the day amongst small and marginal farmers who are already in an environment of limited landholdings, poor soil, inaccessible technology, and low access to finance. In most instances, families have had to either diversify their sources of livelihood or temporarily emigrate to other places in order to find other income-earning opportunities. The increasing rate of occurrence of such occurrences has a close association with deforestation and land-use conversion trends in the Himalayan region. The growth in population pressure, development of transportation systems, tourism and uncontrolled development project building of roads, mines and hydro power building have increased deforestation and land degradation (Bhattarai \u0026amp; Conway, 2020).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ecological vulnerability of the region has also been increased by the fact that the vegetation covers have been removed, and this has exposed the slopes to landslides as well as flash floods (Prasad et al., 2013). Destabilisation of the soil through unplanned quarrying and mining has also led to changes in the hydrological cycle as well as an increase in the intensity of runoff. This has led to an increased environmental crisis in the region, with ecological degradation and socioeconomic vulnerability becoming mutually dependent on each other. Against this background of these interconnected problems, the question of how to mitigate the negative effects of climate change and bring the ecological balance back comes into place.\u003c/p\u003e \u003cp\u003eAgroforestry, a land-use system that incorporates trees and crops as well as livestock in the same land to increase productivity, biodiversity, and sustainability (Nungula et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), is one of the new adaptive strategies that is being recognised in the world and being discussed more in India (Atapattu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Food and Agriculture Organisation (FAO) define agroforestry as a dynamic and ecologically based natural resource management system that, through tree incorporation on farms and in the agricultural landscape, would diversify and sustain production to greater social, economic, and environmental advantages to the land users regardless of the levels of the users. This definition highlights the fact that agroforestry is multifunctional; it is not just an agglomeration of agriculture and forestry but a scientifically planned system that is meant to introduce ecological restoration and livelihood (Ferreira et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Agroforestry is a nature-based intervention which, in addition to mitigating and adapting to climate change by enhancing the soil structure, erosion, water conservation and other diversified sources of income (Abebaw et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It provides a sound solution to sustainability in land management and livelihood resilience in mountain ecosystems where there are topographic limitations and soils are not stable under intensive agricultural exploitation. The Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlights how agroforestry functions as an effective adaptation strategy to climate change\u003c/p\u003e \u003cp\u003eNevertheless, even with its ecological and economic proven advantages, the implementation of agroforestry in the hilly areas of Himachal Pradesh has been minimal and more or less traditional (Bhardwaj, 2025). The majority of farmers do not apply agroforestry as a contemporary, designed system, but as an established cultural practice through the traditional ecological knowledge (Pant et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Observations and survey findings on the field reveal that farmers on the farms are mainly applying the trees as fencing, to provide shade, to get firewood and feed the livestock, not necessarily any contemporary model of agroforestry. The current method of agroforestry is deeply rooted in the indigenous knowledge system (IKS) that is carried on through generations, with the focus on the necessity of forest conservation and soil protection. To avoid soil erosion and preserve moisture in farmlands, along with protecting crops against wind and grazing animals (Jafari et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), elder farmers usually share their experiences about the importance of keeping tree cover in the surroundings. Although this conventional model is environmentally friendly, it is not integrated with the science and contemporary management strategies that may make it more productive and sustainable. Lack of modern agroforestry models that are in tandem with the universal definitions and practices advanced within national and international policy systems is one of the biggest challenges witnessed in the region.\u003c/p\u003e \u003cp\u003eHimachal Pradesh does not have a properly established system of institutional support infrastructure and technical skills to assist the farmers in adopting a better agroforestry design as compared to the plains or the more developed agricultural areas. Lack of specialised forestry extension services, inadequate training programs, and low awareness levels among farmers on the economic benefits of forestry adoption are some of the reasons that lead to low adoption. Also, the small landholdings, the absence of capital investment, and the delayed returns of the tree-based farming system deter the smallholders from experimenting with the contemporary agroforestry methods. Farmers who have been exposed to agroforestry believe that it is an expensive and extractive process that has low returns with uncertainties, and some are even afraid that the roots of trees might occupy nutrients in the soil, thus, with time, reducing the soil fertility. Socio-economic determinants also assume a final role in influencing the decision of the farmers to adopt agroforestry. Climatic perception and environmental awareness also influence the adoption behaviour. Farmers who feel that there is also a rise in frequency and intensity of extreme climatic conditions like floods, droughts or hailstorms are more likely to realise that it is time to adopt adaptive land-use activities such as agroforestry.\u003c/p\u003e \u003cp\u003eIn spite of those local developments, the general view of the awareness and institutional backing in terms of fostering agroforestry as a systematic and income-based business shows low awareness. Lack of specific government schemes, technical support, and demonstration sites has impeded knowledge and innovation. The remote and backward farmers are still isolated with no links to forestry experts, agricultural extension agents and market networks that would enable the commercialisation of agroforestry products. This divide highlights the more macro-policy issue, the necessity to unite traditional ecological knowledge and scientific innovation, as well as institutional capacity-building. Enhancing connections among research organisations, forest administrations, and local societies might contribute to the design of geographically accurate agroforestry models that are both ecologically plausible and economically viable.\u003c/p\u003e \u003cp\u003eThe proposed research will fill these shortcomings by empirically investigating the factors affecting agroforestry adoption and the perception of the farmers towards it. Additionally, also aimed at knowing how the traditional practices can be fortified and updated with awareness, training and policy provisions. By doing that, this paper will add to the larger discussion of the idea of sustainable mountain development, where combining traditional knowledge and contemporary scientific methodology is essential in climate resilience. Agroforestry is not just a method of agriculture, but a fast track to several Sustainable Development Goals (SDGs) such as SDG 1 (No Poverty), SDG 13 (Climate Action) and SDG 15 (Life on Land). Its use can revolutionise rural livelihoods through the improvement of environmental sustainability, lessening climate shock vulnerability, and increasing income diversification. Thus, the creation of a favourable policy environment, capacity-building of the farmers, as well as the creation of regional agroforestry demonstration centres are key to scaling up adoption in the hilly areas of Himachal Pradesh. Also, unpredictable climatic changes, environmental degradation, and the issue of livelihood insecurity in the Himalayas are calling out to be addressed with a lot of aggressiveness and creativity. Agroforestry, properly handled scientifically and adjusted to the regional context, is an opportunity which can be used to bring back the balance between human demands and ecological sustainability. The present research aims to understand the interaction between the social, economic, and climatic factors and the decision-making and perception of farmers towards the adoption of agroforestry, and the ultimate aim is to find out the measures that may help to mitigate the gap between the traditional wisdom and modern-day scientific practice in rural transformation in Himachal Pradesh.\u003c/p\u003e"},{"header":"2. Review of Literature","content":"\u003cp\u003eAlbugami et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) investigated networks of cooperation and research trends on climate change and sustainable livelihood using bibliometric techniques. Further, findings demonstrate a rise in research activity since 2007, especially after 2018, with net-worthy contributions from ICAR and contributions \u003cem\u003efrom\u003c/em\u003e the USA, India, Bangladesh, and Pakistan, suggesting shifting research priorities. Choudhury et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) indicate that soil erosion has increased in all climate scenarios, with agroforestry and horticulture systems being the most successful in lowering runoff and soil loss through adaptive management and bio-mulching. Ranjitkar et al. (2017) used the Ecological niche modelling to assess climate change impacts on agroforestry tree distribution in Yunnan, China and identified that the west and southwest regions are suitable for tea alder systems, southern areas for tea-hog plum, and northern zones for walnut-based agroforestry, and further highlights the agroforestry\u0026rsquo;s adaptive and ecological restoration potential. Verma et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) show that poplar-based systems yield the highest biomass, Melia systems excel in carbon sequestration, and bamboo systems enhance soil nutrients. Additionally, these combinations improve soil health, carbon mitigation, and agricultural sustainability in the hill regions of the northwestern Himalayas. Ullah et al. (2025) evaluated how pastoral livelihoods are impacted by extensive afforestation in Pakistan, the Hindu Kush Himalaya. Further, they showed that pastoralists adjusted through migration and crop-livestock integration, and grazing restrictions decreased fodder access, livestock numbers and income. Further, security is at risk due to the ongoing decline, which emphasises the necessity of participatory conservation strategies. Negi et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) revealed that improved species survival, significant tree growth, and increased carbon stock from 40.02 to 65.53 Mg ha⁻\u0026sup1; in a decade, highlighting restoration\u0026rsquo;s effectiveness for climate resilience and land sustainability in the western Himalayas. Singh et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) used the Agricultural Climatic Vulnerability Index in the study to evaluate the climate vulnerability of vegetable production over 51 blocks of Himachal Pradesh. Study highlights that the most vulnerable area is the Balh Valley, while Paonta Sahib is the least. Further, crop profitability is significantly impacted by temperature and rainfall, but in vulnerable areas, diversification, better management, and irrigation increase resilience and productivity. To evaluate the potential for carbon sequestration in the eastern Himalayas, Kurmi et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) measure the net ecosystem production. The study reveals that agroforestry has the highest NEP, outperforming natural forests, demonstrating its potent ability to act as a carbon sink and a useful natural solution for mitigating climate change. While Panmei et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) identify native bamboo species effective for slope stabilisation and soil conservation, emphasising their cost-effectiveness, sustainability, and role in enhancing resilience aligned with UN SDG 13 goals. Phondani et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) investigated the Seasonal biomass consumption patterns for fodder and fuelwood across the Himalayan village. They show higher consumption in winter, with significant variations in energy values. Further, they emphasise agroforestry\u0026rsquo;s potential for sustainable biomass energy supply and inform policy development for rural energy management. Hazarika et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) show that long-term pineapple agroforestry successfully restores land, improves soil sustainability and organic carbon, better nutrient stratification and decreased soil erodibility in the Eastern Himalayan region. Further, Singh et al. (2024) show that alder pineapple systems improve microbial activities and deep carbon storage, and agroforestry considerably restores soil and biomass carbon. Moreover, agroforestry provides a practical method for repairing damaged cropland and reducing carbon emissions. Das (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) examines the obstacles and opportunities for the Indian Sundarbans Biosphere Reserve to adopt climate-smart agriculture. They show that saline-tolerant crop diversification and weather-based practices are widely used. And the adoption is driven by education income and awareness, but illiteracy and a lack of resources impede successful implementation. Babu et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) indicate that ginger-based systems deteriorate soil health, undisturbed forest and alder-large cardamom systems preserve the highest carbon stocks and microbial activity. Furthermore, the encouraging alder-cardamom agroforestry improves soil sustainability and carbon restoration. Wani et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) show that the natural forests and horticulture retain the highest carbon content in temperate soils, which store substantially more carbon than subtropical soils in the northeastern Himalayas. Tamasiga et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) highlight that renewable energy increases agricultural sustainability and productivity, but it faces obstacles like resource competition and cost. The study recommended the targeted incentives, capacity building and context-specific strategies for the adoption of renewable energy. Sahoo et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) examined the role of Climate Resilient Agriculture in enhancing sustainability and water management. Study revealed that CRA improves groundwater-surface water balance, supports food security, and aligns with SDGs. Furthermore, Northwestern India faces severe water depletion, emphasising the need for technology integration, community participation, and targeted CRA policies for adaptive resilience. Sharma et al. (2023) highlight the biomass and carbon storage potential of agroforestry systems in the Chamba district of Himachal Pradesh. Furthermore, the findings showed the highest total carbon storage in silvopastoral systems and maximum biomass in agro-Horti silviculture. However, the carbon stock increased with altitude, highlighting agroforestry\u0026rsquo;s key role in carbon sequestration and climate change mitigation. Additionally, the socio-economic vulnerability across four agroforestry systems in Mizoram was assessed using a composite index approach by Thangjam et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) findings show that the highest vulnerability is in Borassus- and oil palm-based systems, mainly due to low adaptive capacity and high exposure. Further, the Parkia-based systems were least vulnerable, highlighting the need for targeted adaptation measures and socio-economic resilience building. Jat et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) show that integrated watershed management reduces erosion and runoff while increasing soil moisture, organic carbon productivity, and water efficiency. Additionally, adding grasses, bamboo, and agroforestry enhances carbon sequestration, highlighting IWM's contribution to climate resilience, sustainability, and food security. Kundu and Biswas (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) show that techniques like crop rotation, agroforestry, organic farming, and biochar enhance soil biodiversity and resilience. However, obstacles like low awareness, high costs, and insufficient policy support prevent sustainable soil management from being widely adopted.\u003c/p\u003e"},{"header":"3. Conceptual framework","content":"\u003cp\u003eThe conceptual framework of the research has been presented in the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and states agro-forestry as a climate resilient land use solution that enhances livelihood security and minimises the negative impacts of climate change, especially heatwaves, floods and water stress, in the Himalayan area (Gupta et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In delicate mountainous ecosystems such as Himachal Pradesh, agriculture is very delicate to climatic variation, steep slope, soil erosion, as well as low irrigation facilities (Kumar et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The growing number of heatwaves, unpredictable precipitation, flash floods and reduced water supply have heightened the risk of production and livelihood susceptibility (Banu \u0026amp; Fazal, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It is in this context that the framework suggests that the adoption of agroforestry by farmers is determined by the interaction of socio-economic factors, the perception of the risks associated with climate, and resource endowments, and that agroforestry positively influences ecological stability and resilience to livelihood (Ahmad et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the first level, the adaptive capacity of farmers is determined by socio-economic factors like landholding size, education, income level, market access and institutional support (Abdul \u0026amp; Kruse, 2017; Mwadzingeni et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). With higher perceived returns and technical awareness, households that have increased access to markets and services related to extending are more inclined to use tree-based systems. At the same time, climate risk perception is significant in the land-use decision-making process. When farmers feel that the heatwaves are increasing, the frequency of floods is rising, rain becomes erratic, and chances of extreme weather might occur in the future, then they are more likely to consider agroforestry as a protective and adaptive measure (Ermolieva et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The challenges associated with the resource, such as decreasing soil fertility, water scarcity, and decreased crop production, compel farmers even more to diversify and adopt sustainable agriculture systems. These drivers act in aggregate and establish the likelihood of adopting agroforestry.\u003c/p\u003e \u003cp\u003eThe fundamental element of the framework is the adoption of agroforestry, which refers to the incorporation of trees in the same land unit with crops and or livestock. Agroforestry is a nature-based and climate-sensitive intervention which especially fits mountain agriculture (Deka \u0026amp; Goswami, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It makes the environment vulnerable through various ecological processes. Shade is given by the tree canopies, and it helps to control the microclimatic conditions, which lowers the surface temperature and shields crops and livestock against heat stress during extreme heat waves. The existence of deep-rooted trees enriches the soil structure, improves the presence of organic matter, and enhances water penetration, which lessens surface flow and the effects of floods at steep slopes (Jafari et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Well-established root systems hold the particles of soil together and stop soil erosion, which is a crucial issue in the Himalayan region. Moreover, agroforestry increases soil moisture retention and recharge of underground water, and hence alleviates water stress in dry seasons (Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). They reduce irrigation and mitigate the risk of rainfall variability by shaving off the losses due to evapotranspiration and overall increase the water-use efficiency of systems based on trees, as well as improve the overall agricultural output. Trees, too, help in carbon sequestration, which connects local adaptation to the global climate mitigation measures. These environmental advantages are translated into enhancing livelihood security (Di Sacco et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgroforestry also has a multi-income effect because of the production of timber, fruits, fodder, fuelwood, and non-timber forest products; this has lowered the reliance on single-crop production. Diversity of income reduces susceptibility to climatic crises of crop failures and stabilises household incomes. Proper soil fertility and microclimate promote crop productivity and food security. The other effect of agroforestry is the creation of more employment opportunities on the farm and asset accumulation since the trees are long-term capital investments. Agroforestry enhances adaptive capacity and resilience of rural households by decreasing the production risks and volatility of incomes (Zeratsion et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"4. Research Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Area \u0026amp; Data\u003c/h2\u003e \u003cp\u003eThe research was conducted in the Kullu and Mandi districts of Himachal Pradesh, which represent diverse agroclimatic and socio-economic conditions of the western Himalayan region. A representative coverage was made by adopting a multistage random sampling technique. Two districts, Kullu and Mandi, were selected purposely in the first stage because they were highly agriculturally dependent and prone to climatic disasters like floods and landslides. In the second stage, two blocks were selected randomly in each district, and subsequently, four villages in each block were selected randomly. Lastly, the selection of households within every village was done randomly, and a total sample of 680 respondents was obtained. They were gathered using the well-formatted interview schedule of the socio-economic, climatic, and agroforestry-related issues. The data were collected and analysed with the help of the relevant statistical tools like percentage, average and also applied the logit model to determine the determinants that affected the adoption of agroforestry.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the sample distribution of the survey across various blocks in the Mandi and Kullu districts. The Kullu, Banjar, Sadar, and Balh blocks have been included. For the district of Kullu, maximum samples were obtained from the Banjar block (166) and the Kullu block (243), totalling 409 samples for the district. On the other hand, in Mandi district, the survey was focused on the Sadar block (84) and the Balh block (186). Overall, data collection was conducted in 680 households across the two districts and four blocks with a fairly balanced distribution between the districts, though the actual blocks surveyed were quite different.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Model Specification\u003c/h2\u003e \u003cp\u003eA logit model was used to test the factors that determine the adoption of agroforestry practices by farmers who have farming operations in Himachal Pradesh. The dependent variable is dichotomous in character, as it will reflect whether the respondent practices agroforestry (1\u0026thinsp;=\u0026thinsp;Yes and 0\u0026thinsp;=\u0026thinsp;No). The logit model is used to estimate the likelihood of using agroforestry based on several socio-economic and climatic explanatory variables.\u003c/p\u003e \u003cp\u003eMathematically, the model is expressed as:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{Logit}\\left({P}_{i}\\right)=\\text{l}\\text{n}\\left(\\frac{{P}_{i}}{1-{P}_{i}}\\right)={\\beta\\:}_{0}+{\\beta\\:}_{1}{X}_{1}+{\\beta\\:}_{2}{X}_{2}+\\cdots\\:+{\\beta\\:}_{k}{X}_{k}+{\\mu\\:}_{i}\\)\u003c/span\u003e \u003c/span\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{i}\\)\u003c/span\u003e \u003c/span\u003e= Probability that the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{i}^{th}The\\:\\)\u003c/span\u003e\u003c/span\u003ehousehold adopts agroforestry\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{P}_{i}}{1-{P}_{i}}\\)\u003c/span\u003e \u003c/span\u003e= Odds ratio of adoption versus non-adoption\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e \u003c/span\u003e= Constant term\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{k}\\)\u003c/span\u003e \u003c/span\u003e= Coefficients associated with explanatory variables\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{k}\\)\u003c/span\u003e \u003c/span\u003e= Vector of independent variables\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e \u003c/span\u003e= Error term\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe independent variables are gender, marital status, occupation, livestock units, household size, family type and agricultural involvement, farming experience, modern input utilisation, training, use of fertilisers, availability in the market, social constraint, and perception towards extreme weather.\u003c/p\u003e \u003cp\u003eMaximum likelihood estimation (MLE) was used to estimate the model, and odds ratios were used to interpret the magnitude and direction of the effects. Strong standard errors were used to control the possibility of heteroskedasticity and make a sound statistical inference.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Descriptive Analysis","content":"\u003cp\u003eThe description of the variables used in the study is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, while the Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e represents the descriptive statistics of 680 sampled households on the different socio-economic and perception-related variables regarding agroforestry and climate change adaptation. The variable Adoption of Agroforestry-Practice (y12) has a mean of 0.478, which implies that approximately 47.8 per cent of the respondents are involved in the practice of agroforestry.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of variable\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnit of Measurement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroforestry_Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ey12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;male; Female\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestock_Units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital_Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-Married 2\u0026thinsp;=\u0026thinsp;Unmarried; 3\u0026thinsp;=\u0026thinsp;Divorce; 4\u0026thinsp;=\u0026thinsp;Widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment_No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModern_Inputs_Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold_Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily_Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Joint; Nuclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture_Involvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarming_Experience_Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead_Occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Agriculturist; 2\u0026thinsp;=\u0026thinsp;Govt. Employee; 3\u0026thinsp;=\u0026thinsp;Private Employee; 4\u0026thinsp;=\u0026thinsp;other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemical_Fertilizer_Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgri_Training_Attended\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket_Availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial_Constraint_Challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;No challenges\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;Some challenges\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;Major challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlood_Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Rarely\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;Occasionally\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;Frequently\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHailstorm_Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Rarely\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;Occasionally\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;Frequently\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeatwave_Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Decreased\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;No change\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;Increased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtreme_Events_Future\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Unlikely\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;Uncertain\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;Very likely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author Compilation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe respondent gender (x5) is also a mean of 1.156, which means that most of the respondents are male, and marital life (x7) has a mean of 1.22, and the respondent is therefore a married person. The average number of people living in the household (x11) is approximately 5.1, indicating that the families are medium-sized. The variable (type of family) (x12) is of type of family (mean 1.43), indicating that nuclear families are the majority. The mean number of unemployed persons (x19) among households is 1.37, and there is some deviation (Std. dev. 0.857). Livestock ownership is small, with the average of livestock ownership units (x39) being 1.09. The mean farming experience of the respondents is 28.27 (x18) years, which shows that\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroforestry_Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestock_Units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital_Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModern_Inputs_Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold_Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture_Involvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarming_Experience_Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead_Occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemical_Fertilizer_Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgri_Training_Attended\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket_Availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial_Constraint_Challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlood_Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHailstorm_Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeatwave_Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtreme_Events_Future\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author Compilation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ethey have a lot of farming experience. On the factor of use of input, 40.4% use modern agricultural inputs (x36) and 72.2% use chemical fertilisers (x34). It is also found that only 23.1% of respondents attended agricultural training programs (x59), and 71.6% indicated a good market for their products in their area (x60). Concerning adaptation and perception of climate, the average score of the facing social constraints in adaptation (x76) is equal to 1.85 (on a 3-point scale), which can be interpreted as moderate levels of perceived constraints. Extreme weather events are also not equal: the mean of floods (x81) is 1.38, hailstorms (x82) 1.62, and heat waves (x86) 2.41, meaning that heat waves are more common than the others. Last but not least, the perception of the fact that extreme weather events will be more prevalent in the future (x89) has a high mean that is equal to 2.69 and represents a solid agreement among respondents on the fact that the risks of climatic changes will increase.\u003c/p\u003e"},{"header":"6. Results \u0026 Discussion","content":"\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reveals that nearly half of the respondents (47.79%) are practising agroforestry, while a slightly higher proportion (52.21%) did not engage in such practices. This indicates a moderate level of adoption of agroforestry, reflecting growing awareness among farmers regarding sustainable agricultural methods, though a significant portion remains outside its practice. Furthermore, the data show that a large majority of respondents (76.91%) had not attended any\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eagriculture-related training, whereas only 23.09% had received such training. This suggests a considerable gap in agricultural capacity-building initiatives and limited access to technical knowledge and skill development programs. The low participation in training activities may act as a barrier to the wider adoption of innovative and sustainable practices like agroforestry. Therefore, strengthening agricultural extension services and promoting farmer training programs could play a crucial role in enhancing agroforestry adoption and improving overall farm productivity.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the farmers\u0026rsquo; perceptions of agroforestry and its role in enhancing climate resilience, measured using a five-point Likert scale ranging from Strongly Disagree to Strongly Agree. A large majority of farmers expressed positive perceptions about the climate resilience benefits of agroforestry. Specifically, around 61% of respondents agreed or strongly agreed that agroforestry helps protect crops from heat waves, while 15% disagreed or strongly disagreed, and 29% remained neutral. Similarly, 62% of farmers agreed or strongly agreed that agroforestry reduces flood impacts, indicating that many recognise its potential to buffer against heavy rainfall and flooding, though about 12% disagreed and 25% were neutral.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding agroforestry\u0026rsquo;s ability to minimise extreme weather losses, 64% of respondents agreed or strongly agreed, while 12% disagreed and 24% were neutral. The perception of drought resilience was even stronger, about 78% of farmers agreed or strongly agreed that agroforestry reduces drought impact, reflecting their confidence in tree-based systems for moisture retention and shade provision, whereas only 9% disagreed.\u003c/p\u003e \u003cp\u003eFarmers showed the highest level of agreement on soil-related benefits: nearly 92% agreed or strongly agreed that agroforestry reduces soil erosion and land degradation, and about 93% believed it improves soil moisture and fertility, suggesting strong awareness of agroforestry\u0026rsquo;s ecological advantages. Finally, approximately 94% of respondents agreed or strongly agreed that agroforestry reduces overall climate risk, indicating a widespread belief that integrating trees into farming systems enhances climate resilience and sustainability.\u003c/p\u003e \u003cp\u003eIn summary, the results demonstrate overwhelmingly positive perceptions among farmers regarding agroforestry\u0026rsquo;s role in mitigating adverse climate impacts. Most respondents view agroforestry as an effective strategy for improving soil health, reducing climate-induced losses, and strengthening long-term agricultural resilience.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors Influencing the Adoption of Agroforestry Practices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroforestry_Practice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobust\u003c/p\u003e \u003cp\u003estd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust\u003c/p\u003e \u003cp\u003estd. Err.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestock_Units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital_Status Base category- Married\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.540**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.583**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.716*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.180*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHead_Occupation Base Category- Agriculturist\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment Employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.491*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.634*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate Employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.940**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.391**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.Modern_Inputs_Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.616**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.852**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold_Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.175***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.839***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2Family_Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.763***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.466***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture_Involvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarming_Experience_Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.014*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemical_Fertilizer_Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.752***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.121***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.Agri_Training_Attended\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.Market_Availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.481***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.399***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial_Constraint_Challenges Base Category- No challenges\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.610**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.841**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.951***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.589***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFlood_Frequency Base Category- Rarely\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequently\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.813*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.254*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHailstorm_Frequency Base Category- Rarely\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.645***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.525***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequently\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeatwave_Frequency Base Category- Decreased\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.614**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.541**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.551*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.576*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtreme_Events_Future Base Category- Unlikely\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.727**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Likely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.186***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.275***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e_cons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.242***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of obs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald chi2(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e152.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog pseudolikelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e-357.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.2412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates the results of the logit model, which estimates the factors that determine the adoption of agroforestry practices within the study locale. In addition, the goodness-of-fit statistics of the binary logistic regression model in general provide more evidence of its reliability and explanatory power. The sample size, which was used in estimating the model, was 680 observations, which is large enough to guarantee a statistically robust model. The Wald chi-square value of 152.76 and 25 degrees of freedom, and a p-value of 0.000 indicate that the explanatory variables in aggregate are very significant in predicting the probability of using agroforestry. This means that the combined effect of the added social and climatic variables is statistically significant on the likelihood of practising agroforestry. The model log pseudolikelihood of -357.168 is the maximum likelihood estimate of the model that can be employed as a measure of model fit. Although the negative value is anticipated in log-likelihood estimation, a less negative value is normally a sign of a better fit. The pseudo R2 is 0.2412, which suggests that about 24.1percent of the variation in likelihood adopting agroforestry practices is accounted for by the group of independent variables that have been incorporated in the model. In general, such statistics indicate that the model is well explained and that the chosen social and climatic factors play an important role in comprehending what factors affect the agroforestry adoption by farmers. This strengthens the strength and reliability of the estimated findings above. The marital status factor, with base category married, the coefficient is -0.540 with an odds ratio of 0.583, which is statistically significant at 5% level of significance, represents the explanation that the unmarried respondents are 41.7 per cent less likely to adopt agroforestry than those who are married. Likewise, widowed respondents with a -1.716 coefficient and odds ratio of 0.180, which is statistically significant at a 10% level of significance, are much less likely (82% less likely) to engage in agroforestry practices. Regarding occupation, as the base category, the other occupations have a coefficient of -0.940 and an odds ratio of 0.391, which is statistically significant at 5% level of significance, depicting that individuals in other occupations are 60.9% less likely to embrace agroforestry. On the other hand, the coefficient and odds ratio of government employees are 0.491 and 1.634, respectively, which are significant at a 10% level of significance, indicating that the employees are more likely to practice agroforestry by a factor of 63.4. Also, the use of modern agricultural inputs has a coefficient of 0.616 and an odds ratio of 1.852, which is statistically significant at 5% level of significance, showing that farmers not utilising the modern input are 85.2 per cent more likely to use agroforestry, as compared to the use of modern agricultural inputs. Likewise, the use of chemical fertiliser, with the coefficient being 0.752 and an odds ratio of 2.12, which is statistically significant at the 1% level of significance, reflects that the familiarity with better agricultural technologies increases the adoption of agroforestry significantly, i.e. more than two times. Secondly, it has the household size, which has a negative relation with a coefficient of -0.175 and odds ratio 0.839, which is stated as a negatively significant relation that is statistically significant at 1% level of significance and therefore indicates that the larger the household size, the less likely it is to adopt agroforestry. On the same note, respondents belonging to joint families have a family type with a coefficient of -0.763 and an odds ratio of 0.466 that is statistically significant at a level of significance of 1%, indicating that a joint family respondent is less likely to adopt agroforestry by 53.4 per cent as compared to a nuclear family respondent. Also, in terms of perceived social challenges, respondents who said that they face some challenges have a coefficient of 0.610 and an odds ratio of 1.841, which is statistically significant at 5% level of significance, indicating that they are 1.84 times more likely to adopt agroforestry. The coefficient of those facing severe challenges of 0.951 and the odds ratio of 2.589, which is statistically significant at 1% level in the significance value, demonstrate that they are 2.59 times as likely to use agroforestry practices. Moreover, the factors related to the climate are also significantly affected by the adoption of agroforestry. The respondents are 47.5% less likely to adopt agroforestry when the coefficient is -0.645, and the odds ratio of 0.525 is statistically significant at 1 percent level of significance. In the meantime, the respondents who did not notice any change in the frequency of heatwaves, with a coefficient of -0.614 and odds of 0.541, statistically significant at 5% level of significance, are 46% less likely to adopt agroforestry than their counterparts who perceived a reduction. Furthermore, future extreme weather events have a significant impact on adoption. Respondents who perceive extreme events to be uncertain have a coefficient of 1.003 and an odds ratio of 2.727, which is statistically significant at the 5% level of significance, and this means that such respondents are 2.73 times more likely to adopt agroforestry. The extreme events, which are much more likely and statistically significant with an odds ratio of 3.275 at a level of significance of 1%, are more than three times more likely to occur, and the climate risk perception has a significant influence on the adaptive land-use decisions. Lastly, the most impactful determinant is market availability, with a coefficient of 1.481 and an odds ratio of 4.399, which is statistically significant at 1% level of significance, and it means that the respondents who have higher market access are 4.4 times more prone to deploy agroforestry practices.\u003c/p\u003e"},{"header":"7. Discussion","content":"\u003cp\u003eIn the present study, conducted across the districts of Kullu and Mandi in Himachal Pradesh, the observed relationship between marital status and the adoption of agroforestry practices can be attributed to the underlying socio-economic and cultural dynamics prevalent in these regions. Agroforestry is more accepted among married respondents since marriage in Himalayan rural areas signifies stability of the households, mutual labour and better social capital. On the contrary, respondents who are unmarried and widowed tend to experience various socio-economic barriers that restrict their activities in agroforestry. At this stage, unmarried people are usually younger and more interested in education or finding a stable job, and therefore, they are less involved in agriculture. Their interests are usually based on securing income without necessarily investing time and resources in long-term land-based activities such as agroforestry. Conversely, widowed respondents tend to have a labour shortage and less access to social or institutional support systems and hence struggle to operate agroforestry systems that need routine maintenance and resources. Moreover, also states that marital and family stability increases the adoption of innovation by increasing resources and reducing perceived risk. Therefore, marital status is a proxy of livelihood security and adaptive capacity in agroforestry decision-making in the socio-cultural setting of Kullu and Mandi. Also, the study further states that the non-agriculture occupations of the respondents are 60.9 per cent unlikely to practice agroforestry since such people have less time and are not as dependent on farming as their main source of livelihood. They are less interested in their on-land activities, which makes them less motivated to invest in the long-term tree-based systems. Government workers, on the other hand, are 63.4 times more likely to be practising agroforestry, perhaps because they have higher and more consistent income, access to more information, and they are more environmentally conscious. The fact that agroforestry is considered an extra and sustainable land management system, but not a primary source of livelihood among government workers. In addition, these families often keep livestock, and the introduction of tree species in farms offers feed, shelter, and alternative income sources. The economic security and exposure to extension services in Kullu and Mandi are important aspects in the adoption of technology because smallholder farming is the dominant practice in these two regions. Therefore, occupation plays an important role in the choice of adoption, and the income stability and exposure to knowledge increase the willingness to invest in sustainable land-use systems such as agroforestry. There is also a strong association between agroforestry adoption and the use of modern farming inputs. The findings suggest that farmers who do not engage in the use of modern inputs are 85.2% more likely to take up agroforestry than farmers who have a high dependency on modern inputs. It is attributable to the reliance of the farmers on the hybrid seeds, and the mechanised tools are usually short-term, yield-driven and focused on the annual crops and on the instant returns. On the contrary, those who are less reliant on modern inputs are more likely to take the more traditional forms due to the absence of knowledge, lack of financial support and other restrictive factors is applied to agroforestry. Moreover, it is because of upholding soil fertility, retention of moisture and livestock feed. The farmer who does not utilise modern agricultural inputs also indicates the adaptive strategies of poorer and resource-constrained households. These farmers tend to be working on marginal or sloppy lands, and they practice agroforestry not out of economic benefits but as a way of subsistence and land protection. In the experiment where soil erosion, landslides, and soil fertility are widespread problems, planting trees in farmland would stabilise the ground, prevent soil erosion, and preserve soil moisture. Also, such trees are important sources of household survival, including the livestock fodder, domestic fuelwood, and small timber for repair or fencing purposes. Additionally, the negative correlation of household size and family type with adopting agroforestry, as the socio-economic and land-use conditions of mountain farming systems are demonstrated by the results as well. The households that possess more people are less likely to use agroforestry by 16.1%, since in these households, the agricultural land is usually divided among the members, hence the small plots they are left with to handle. Such fragmentation only makes it less possible the idea of committing land-based systems based on trees, which are a long-term commitment and space. In addition, it is also true that large families are more concerned with the immediate subsistence requirements, and the need to conserve land in the long run is not of primary importance. Equally, respondents of joint families are 53.4% unlikely to adopt agroforestry as compared to nuclear families. In the hilly area of Himachal, the decision-making in the joint households tends to be collective and conservative, with various priorities amongst members. Sharing resources also plays a critical role in joint families, as it is more difficult to reach a consensus on changes in land-use, such as tree planting. On the contrary, nuclear families have more freedom and autonomy in incorporating new or sustainable practices. Therefore, scarce land and the complexity of decision-making in the hilly setting are the two factors that make larger and joint households adopt less agroforestry.\u003c/p\u003e \u003cp\u003eAlso, the positive, statistically significant, coefficients of perceived social challenges show that households experiencing moderate and severe perceived social challenges are much more likely to adopt agroforestry. Agroforestry is a risk-buffering as well as a livelihood diversification strategy in the context of Himachal Pradesh, whereby small and fragmented landholdings, off-farm employment and outmigration are widespread. Those farmers who are more socio-economically stressed might find agroforestry as a more viable alternative source of income stability, fuelwood, and fodder, and it lessens their reliance on the external market. Therefore, an increased vulnerability seems to drive the households toward more resilient land-use systems.\u003c/p\u003e \u003cp\u003eClimate-related perceptions are also important in influencing adaptive decisions. The farmers who did not realise the changes in the frequency of heatwaves are one-fourth less likely to adopt than those who noticed a fall. The response shows that awareness and perception of climate variability are significant factors that contribute to adapting mountainous ecosystems. Farmers in Himachal Pradesh, where agricultural activities are very susceptible to unpredictable rainfall, temperature changes, and a rising number of extreme events, a farmer who is aware of these threats will be more willing to implement systems of trees that would help in conserving soil, retaining moisture and controlling microclimatic conditions. In contrast, low awareness of climate change slows the importance to apply adaptive measures. This argument is further reinforced by the perception of the occurrence of extreme weather events in the future. The uncertain farmers and farmers who perceive that the extreme events are highly likely, and those who confirm that the likelihood of occurrence is very high, and the probability of utilizing agro forestry is very high. This indicates an adaptation behaviour that is precautionary in the weak Himalayan environment. Agroforestry systems, which combine trees with crops and livestock, alleviate production risks, stop soil erosion on steep slopes and offer diversified production. They are especially appropriate to climate-exposed hill agriculture. Thus, the perception of climate risks has a positive effect on adaptive land-use choices.\u003c/p\u003e \u003cp\u003eAll in all, the results can be well organised in line with the socio-ecological realities of the area, where agroforestry is operated as a climate adaptation tool and livelihood improvement tool simultaneously. These findings support the idea that vulnerability, risk perception, and economic opportunity are the joint factors that lead to adoption, and the policies should be strong enough to create awareness of climate and enhance market connections to facilitate the sustainable land-use transitions in mountain agriculture.\u003c/p\u003e"},{"header":"8. Conclusion and Policy Implications","content":"\u003cp\u003eThe present research identifies several determinants influencing the adoption of agroforestry practices in the hilly districts of Kullu and Mandi. Agroforestry, as a land-use model that combines trees with crops and livestock, can also increase ecological stability, livelihoods and climate resiliency. Nonetheless, the paper indicates that even though modern agroforestry models have environmental and economic benefits, most of their applications are still traditional. The results indicate that both social and climatic variables play important roles in farmers' decision-making process with regard to the adoption of agroforestry, but institutional, technical and awareness-related gaps still prevent systematic development and commercialisation of the process. The analysis comes up with some social variables which significantly influence the behaviour of adopting agroforestry. The most significant of these were marital status, occupation, household size, family type, and perceived social challenges. Another important point presented in the study is that climatic variability and risk perception of the farmers are key factors that determine the adoption of agroforestry. The adaptive responses of respondents who are frequent victims of floods, hailstorms, and heatwaves differ. Moreover, respondents who assume that extreme climatic events are not definite or have high chances of rising in the future are much more likely to embrace agroforestry. The rising number of cloudbursts, flash floods and landslides experienced in the districts of Kullu and Mandi has increased awareness of the role of vegetation in stabilising slopes and protecting soil. Quite a few farmers said that they had planted trees as a form of protection against soil erosion and field damage, even without the use of an organised agroforestry model. This is an indication of the adaptive and responsive character of agroforestry adoption, with environmental stress being a driving force for land management decisions. Among the research findings, one of the most significant ones is that there is neither a modern nor a scientifically organised agroforestry model which is practised in the study area. Most farmers are involved in traditional agroforestry systems that are mostly subsistence-based as opposed to being market-based. The use of trees is based on the field boundaries, terraces, or farm bunds primarily for soil conservation, feeding of livestock, fencing, and firewood. The purpose of these practices is vital both domestically and ecologically, but does not fulfil the conventional meaning of agroforestry, which underlines combined and cost-effective systems of trees, crops, and livestock. In such areas, agroforestry is not usually viewed by farmers as an economic activity. Rather, it is regarded as a preservative and auxiliary activity to support their livelihood. The study also concludes that households, which lack access to modern agricultural inputs and are poor and resource-constrained, enjoy more chances to indulge in such traditional modes of agroforestry. They depend on what is on the trees to produce what is demanded in the home, as well as help address the effects of land degradation, landslides, and diminishing soil fertility. The study also establishes a gaping institutional and administrative void in the promotion of agroforestry. It has a deficit of specialised forestry institutions, technical experts and extension programs that aim at the promotion of modern agroforestry systems in the region. The farmers do not have access to training, demonstration projects and money that could assist them in implementing agroforestry as an alternative land use. Additionally, there is poor coordination between the forest and agriculture departments that hinders the practice of the integrated policies. Lack of localised knowledge sharing systems and awareness regarding market opportunities does not even allow farmers to convert their conventional farming activities into profit-making ventures. The results highlight the necessity of having a region-specific agroforestry development policy to incorporate scientific innovation, local knowledge, and institutional support. The development of district-level agroforestry resource centres with forestry, soil conservation, and sustainable agriculture experts should be the priority of policymakers. These centres might be used as training, research and demonstration hubs to sensitise farmers about better agroforestry models and their potential to enhance their economic benefits. Particular attention is to be paid to awareness campaigns that will inform farmers on the economic and ecological advantages of agroforestry in the long run. Capacity-building workshops and community-based training programs must be organised in the backward and remote regions, to develop skills on nursery management, intercropping, soil fertility management and sustainable harvesting of tree-based products. Cooperation with local self-help organisations and cooperatives may also assist in branding and marketing native agroforestry products, which would connect the traditional ways with the new market systems. Small and marginal farmers can be motivated by financial incentives (sapling subsidies, micro credit access, insurance, etc.) to adopt them. Moreover, agroforestry inclusion in the climate adaptation and rural livelihood missions may guarantee the cross-sectoral synergy and promote long-term sustainability. The government must also look into rolling out land use planning models that would encourage the planting of trees on private lands, alongside maintaining ecological balance and conservation of biodiversity.\u003c/p\u003e"},{"header":"9. Limitations of research and future scope","content":"\u003cp\u003eAlthough the research offers a useful understanding of the factors that precondition the use of agroforestry in Kullu and Mandi districts, it has some limitations. To start with, the study does not span the entire geographical region, as only two districts are considered, which might not be reflective of the agroecological and socio-economic situations in other areas of Himachal Pradesh or other mountainous areas. Second, the research mainly elicits the perception and practice of farmers based on a cross-sectional survey, which might not capture the change over time and the changing effects of climatic variability on the agroforestry choices. Third, the meaning and perception of agroforestry by the respondents were more traditional and subsistence-based, without a technical distinction between modern integrated systems and local systems. In addition, the availability of institutional data and the input of experts was limited, which limited the analysis of the policy and programmatic interventions. In future studies, they ought to take a comparative-longitudinal design in various districts using GIS-based spatial analysis, economic, and impact assessment of agro-forestry on income, biodiversity and soil health. Policy assessment, institutional interconnections, and the effects of training farmers should also have a future focus that will help to design an efficient, regionally appropriate framework of sustainable and commercially viable agro-forestry development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eFunding Declaration\u003c/h2\u003e \u003cp\u003eThe authors declare that no financial support or funding was received for the research, authorship, and/or publication of this article.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePawan Kumar and Yash Pal contributed to defining the problem, developing the research framework, collecting and cleaning the data, conducting the analysis, and interpreting the results. Amar Latta provided the guidance throughout the research process. They contributed to the review of the manuscript, methodological suggestions and substantive feedback that strengthened the clarity, quality, and rigour of the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbebaw S E, Yeshiwas E M, Feleke T G (2025) A Systematic Review on the Role of Agroforestry Practices in Climate Change Mitigation and Adaptation. Climate Resilience and Sustainability 4(2): e70018. https://doi.org/10.1002/cli2.70018. \u003c/li\u003e\n\u003cli\u003eAbdul-Razak M, Kruse S (2017) The adaptive capacity of smallholder farmers to climate change in the Northern Region of Ghana. 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Forest Science and Technology 20(1): 47\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Agroforestry, Climate Change, Logit Model, Western Himalayas, Himachal Pradesh","lastPublishedDoi":"10.21203/rs.3.rs-8935206/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8935206/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study investigates the interactions among social, economic, and climatic factors, farmers' decision-making and perceptions regarding the adoption of agroforestry in the Himalayan region, one of the most ecologically delicate and climate-susceptible mountain ecosystems in the world. The study is based upon the primary data collected from the 680 households of Mandi and the Kullu districts of the Indian western Himalayan state Himachal Pradesh through the Multistage random sample technique. A structured interview schedule comprising the socio-economic characteristics, climatic perceptions, and practices related to agroforestry was used in the collection of primary data. The factors that affected the adoption were analysed using descriptive statistics and a logit regression model. The findings of the study show that agroforestry in the study area is still largely traditional and subsistence-oriented, but not market- and science-oriented. Further, marital status, occupation, family size, family type, and perceived social challenge significantly influence agroforestry adaptation. Apart from that, climatic variability and perception of the risk are also critical to determine households' behaviours; more households that often experience floods, hailstorms, and heatwaves tend to adopt tree-based systems as protective and adaptive strategies. At last study highlights the importance of region-specific policies of agroforestry that would incorporate scientific creativity, local experiences and institutional backing. Development of district level agro forestry resource centres, enhancement of capacity-building initiatives, enhancement of financial incentives, and inclusion of agroforestry into climate adaptation and rural livelihood missions are the key elements in improving resiliency and sustainable mountain development.\u003c/p\u003e","manuscriptTitle":"Bridging Tradition and Modernity: Socio-Climatic Determinants and Farmers’ Perceptions of Agroforestry Adoption in the Hilly Regions of Himachal Pradesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 08:36:37","doi":"10.21203/rs.3.rs-8935206/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-20T14:39:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T15:13:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184921826407130968911834001100990240380","date":"2026-04-18T07:03:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T06:14:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235522723706193680468945330343325667463","date":"2026-04-16T06:31:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T02:11:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205436820067359903335430328495557416573","date":"2026-03-15T05:10:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T08:34:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122119110970556374820574133419417965188","date":"2026-03-06T07:34:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T05:43:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T18:40:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T12:28:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2026-02-21T18:31:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3d59e44c-757e-4a22-9e99-c121a4be3916","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T12:41:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 08:36:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8935206","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8935206","identity":"rs-8935206","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}