Evaluation of the Adoption of Potato Production Technology and Identification of Farmer Challenges for Enhancing Food Security in Northeast India

Evaluation of the Adoption of Potato Production Technology and Identification of Farmer Challenges for Enhancing Food Security in Northeast India | 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 Evaluation of the Adoption of Potato Production Technology and Identification of Farmer Challenges for Enhancing Food Security in Northeast India Rajib Das, Kaushal Kumar Jha, Pudhuvai Baveesh, Bhupendra Koul, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7663781/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Potato is a crucial vegetable crop in the North-eastern hilly states of India and tops among most widely consumed staple foods in the region. However, potato productivity remains significantly below the national average, with a yield of 8.55 MT/ha compared to the national figure of 25.79 MT/ha. Despite the availability of advanced technologies, challenges persist regarding data availability, access to technology, and infrastructure in rural areas of Northeast India. Farmers often lack knowledge of efficient irrigation schedules, soil management and crop diversification strategies to combat the climatic variation due to technological barriers. The present study evaluates adoption of recommended potato production technologies and to identify the constraints faced by potato farmers. The research involved data collection from a purposive sample of 480 farmers across Assam, Meghalaya, Nagaland, and Tripura. The study employed percentage analysis to describe the socio-personal characteristics of farmers and to assess the level of adoption or non-adoption of the recommended practices. To identify the determinants of adoption, negative binomial regression was applied. In addition, the constraints experienced by farmers were systematically examined using a severity index and weighted mean score. The results indicated that the most critical practices for potato cultivation were planting time, seed quality/selection, and planting methods, with adoption rates of 73.12%, 57.91%, and 56.66%, respectively. Among the 13 recommended practices, most farmers adopted seven practices. The negative binomial regression analysis revealed that factors such as education and potato farming income had a significant positive effect at the 1% level on adoption rates. Furthermore, the main challenges identified among the nine categories of constraints were production issues, followed by limitations in storage and extension services. Policy interventions should aim to improve access to agricultural extension services, ensuring the timely delivery of inputs, promoting credit accessibility, and offering targeted training programs that focus on recommended agronomic practices, which remain an ideal strategy for North East (NE) India. Additionally, active involvement of farmers in decision-making and the promotion of collective action through groups or cooperatives can play a vital role in improving the dissemination and adoption of new technologies. Food security Negative Binomial Regression Farmer challenges Potato NE India Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Potato ( Solanum tuberosum L.) is a major vegetable crop that traces its origin to the Andes region of South America, where it was domesticated by early farming communities nearly 8,000 years ago, particularly around the areas corresponding to present-day Bolivia and Peru (De jong, 2016 , Ramsay et al., 2023 ). The first recorded European encounter with the potato occurred in 1533 when Spanish conquistador Francisco Pizarro saw it in Peru. The crop's spread in India was limited during the first 300 years following its introduction, with significant production gains only occurring after 1941. This slow adoption was mainly due to the lack of locally adapted varieties and suitable agricultural techniques for sub-tropical climates (Singh & Dutt, 2024 ). Potato, often referred as the “king of vegetables”, ranks as the third most important food crop for human consumption, after rice and wheat (Sharma et al., 2017 ). In the Indian context, it plays a crucial role in addressing nutritional deficiencies among the rapidly expanding population and has been recognized by the FAO as a significant crop for ensuring food security (Devaux et al., 2014 ). Food security is crucial to sustainable development, particularly in areas with rapidly increasing population trends and limited agricultural resources (Camire et al., 2009 ). The potato crop significantly contributes to food security, owing to its high yield potential and nutritional value. They serve as a source of critical nutrients, including carbohydrates, vitamins and minerals, which aid in combating malnutrition, poverty and food scarcity. In the immediate post-independence period (1948–1949), India produced about 1.54 million tonnes of potatoes from 0.234 million hectares, with an average productivity of 6.58 tonnes per hectare (Singh and Dutt, 2024 ). Over the decades, potato cultivation has expanded significantly, and by the 2022–23 season, production had risen to 60.14 million tonnes from 2.3 million hectares, positioning India as the second-largest producer globally after China (Department of Agriculture & Farmers Welfare, Government of India, 2023). Potato contributes approximately 21.90% of the total area under vegetable cultivation, with the highest share of 28.90% among the production of vegetables in India. India ranked third, with an area of 23,32,160 hectares (ha), while it ranked second in terms of production, at 60.14 million tons. In contrast, it ranked 68th with a very low productivity of 25.79 million tons per hectare (MT/ha), the lowest among potato-producing countries. Among the Northeastern (NE) states, Assam has the highest potato production, followed by Meghalaya and Tripura. In contrast, the average productivity was very low at 8.55 MT/ha, as shown in Table 1 (Department of Agriculture & Farmers Welfare, Government of India, 2023). Food security remains a pressing global challenge, particularly in regions such as Northeast India, where diverse geographical and socio-economic factors significantly impact agricultural productivity (Raj et al., 2022 ). Climate change significantly impacts soil health through rising temperatures, altered precipitation patterns, and weather changes, thereby affecting crop yields and food security in the Northeast region (Dikshit & Dikshit, 2013; Datta & Bose, 2020). Environmental adversities impact small landholding farmers, who possess limited resources to adapt to climate variations, potentially exacerbating vulnerabilities in the rural economy of NE India, which is heavily reliant on agriculture. The degradation of soil nutrition and health, influenced by these erratic climate changes, can be mitigated by developing and adapting shifting cultivation practices (Datta et al., 2022 ). Das et al. ( 2021 ) carried out an extensive study on shifting cultivation, integrating climatic parameters and soil characteristics, and employed the Normalized Difference Vegetation Index (NDVI) along with Google Earth Engine (GEE) for the analysis. Such studies help generate crop-suitability maps based on climate and soil, providing essential insights to inform farmers and policymakers about informed decisions regarding land use and cultivation practices (Das et al., 2021 ). Technological barriers also significantly impede the adoption of advanced measures developed to promote sustainability in crop cultivation. Despite the availability of advanced technologies such as GEE and remote sensing, challenges persist regarding data availability, access to technology, and infrastructure in rural areas (Datta et al., 2022 ). Farmers often lack knowledge of efficient irrigation schedules, soil management and crop diversification strategies. This constraint can be fulfilled through farmers’ empowerment with technology transfers, the integration of knowledge tools, and extension training. Agriculture in Northeast India is strongly influenced by local customs, community identities, and cultural elements, with a strong emphasis on traditional and ancestral practices (Dikshit & Dikshit, 2013). The dependence on conventional aspects, although historically significant, may hinder the adoption of contemporary technology and strategies necessary for addressing the current climate challenges. Therefore, consideration of traditions and customs is essential to highlight local involvement in policy-making processes, which are vital for promoting sustainability and achieving food security, taking into account cultural values, practices, and socioeconomic conditions. (Naveen et al., 2024 ). The ongoing impacts of climate change intensify existing vulnerabilities, underscoring the urgent need for technology transfer, soil conservation, and the integration of cultural knowledge into adaptation strategies to enhance resilience in rural farming communities of Northeast India. The Sustainable Development Goal (SDG 2): Zero Hunger underscores the importance of sustainable agriculture and food systems in eradicating hunger and malnutrition. Potato is a versatile and nutritious crop that offers significant potential to boost food security in this region. Therefore, there is an urgent need to channel our efforts towards increasing the productivity level of the state. Enhancing potato productivity in the northeastern states of India to meet the rising demand requires an assessment of adoption intensity, food insecurity and the constraints faced by growers. Such insights are crucial for prioritizing research and extension interventions, ultimately contributing to the reduction of yield gaps at the farm level. This research aims to evaluate the adoption of potato production technologies and identify the constraints faced by farmers in NE India. By understanding these factors, we can develop targeted interventions to enhance potato productivity, improve farmers' livelihoods, and contribute to achieving SDG 2. Therefore, the present study was conducted in four purposively selected districts with the highest production in the northeast, following an ex-post-facto research design. A total of 480 respondents were interviewed using a pre-tested interview schedule to determine the adoption intensity and factors constraining farmers. This study will provide policymakers, agricultural extension workers, and researchers with valuable insights to inform the formulation of effective strategies for sustainable agricultural development in the region. Table 1 Comparison of area, production and productivity of potato in India and North-east India Parameters 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 India Area (ha) 1973.19 2079 2116.93 2179.25 2141.72 2172.99 2051.35 2203.03 2225.75 2332.16 Production (MT) 41555.4 48009 43417.1 48604.6 51310 50189.5 48561.9 56172.5 56175.75 60141.60 Productivity (MT/Ha) 21.06 23.09 20.51 22.3 23.96 23.08 23.67 25.49 25.24 25.79 NE India Area (ha) 140.43 142.21 147.57 149.24 153.93 153.99 155.58 141.63 141.93 143.07 Production (MT) 1153.72 2173.42 1471.17 1235.19 1209.31 1254.44 1238.22 1207.6 1194.72 1224.06 Productivity 8.21 15.28 9.97 8.28 7.86 8.15 7.96 8.53 8.42 8.55 Source: Department of Agriculture and Farmers Welfare, Govt. of India ( https://agriwelfare.gov.in/en/StatHortEst ) and National Horticulture Board 2. Materials and methods 2.1. Study area The study was undertaken in the northeastern region of India, covering the states of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura, and Sikkim. The region accounts for 143.07 thousand hectares under potato cultivation, with a production of 1,224.06 thousand metric tonnes. Despite the relatively low average yield of 8.55 MT per hectare, per capita availability of potatoes in this region exceeds the national average (NHB, 2021). For detailed investigation, four districts with comparatively higher levels of potato production were purposively selected: Lakhimpur in Assam (27.2064° N, 94.1514° E), East Khasi Hills in Meghalaya (25.3682° N, 91.7539° E), Kohima in Nagaland (25.6751° N, 94.1086° E), and South Tripura in Tripura (23.2317° N, 91.5596° E) (Fig. 1). 2.2. Data collection A structured interview schedule was developed to collect data from selected arable farmers in the study area (Supplementary file 1). Before conducting the actual survey, we pre-tested the interview schedule with a total of 40 respondents (different from the actual respondents), comprising 10 farmers each from four selected villages across different northeastern states. The villages included Amguri in the North Lakhimpur block of Lakhimpur district, Assam; Mawlyngkut in the Mawsynram block of East Khasi Hills district, Meghalaya; Viswema in the Jakhama block of Kohima district, Nagaland; and Rajnagar in the Rajnagar block of South Tripura district, Tripura. This selection ensured representation from diverse agro-climatic regions, thereby helping to assess the interview schedule's clarity, relevance, and effectiveness before its final use. This interview schedule covered various aspects, including the socioeconomic profile of the participants and the challenges they face in potato cultivation. The blocks with the highest potato production were chosen purposively for the study, namely North Lakhimpur (Lakhimpur district, Assam), Mawsynram (East Khasi Hills district, Meghalaya), Jakhama (Kohima district, Nagaland), and Rajnagar (South Tripura district, Tripura). Four villages were randomly selected from each block, resulting in 16 villages for the study as indicated in Table 2 . The selected villages were Amguri, Nalkatu, Balijan, Rajgarh (Assam); Mawlyngkut, Chirakatta, Dopho, Mawpen (Meghalaya); Pfuchama, Phesama, Viswema, Khuzama (Nagaland); and Barapathari, Rajnagar, Chittamara, Uttar Krishnapur (Tripura). A village wise list of all the potato growers having experience of more than 3 years in cultivating potatoes was prepared and from that list using circular systematic sampling method we selected the respondents for the study. A total of 480 farmers participated in the study, with 30 farmers chosen from each village to provide valuable insights into the challenges and management practices in potato farming across the region. All 480 interview schedules were completed and returned, achieving a 100% response rate. Table 2 Names of the selected villages for the survey States District Block Villages Assam Lakhimpur North Lakhimpur Amguri, Nalkatu, Balijan, Rajgarh Meghalaya East Khasi Hills Mawsynram Mawlyngkut, Chirakatta, Dopho, Mawpen Nagaland Kohima Jakhama Pfuchama, Phesama, Viswema, Khuzama Tripura South Tripura Rajnagar Barapathari, Rajnagar, Chittamara, Uttar Krishnapur The field survey was carried out during 2021–22 through personal interviews with the selected farmers. Prior to each interview, verbal consent was obtained. The interviews were conducted face-to-face using a standardized schedule, with each session lasting approximately 30–40 minutes. To complement this process, 20 key informant interviews (five per state) and eight group discussions (two per state) were organized with progressive farmers, local officials, market intermediaries, and other stakeholders to gather supplementary insights and validate the findings. 2.3. Variables The dependent variable for this study was the intensity of adoption of recommended potato cultivation practices, quantified by the number of such practices followed by the farmers (Supplementary file 2). The focus is specifically on actual recommendation-based potato farming and management, as these practices are likely to enhance both the quality and quantity of potato production. The independent variables examined include age, gender, family size, education, size of land holdings, income from potatoes, annual income, extension contact, and marketing channel were selected based on their documented significance in influencing agricultural adoption and food security (Datta et al., 2022 ; Naveen et al., 2024 ). These variables are detailed in Table 3 . In developing countries, factors such as age, gender, and family size play a crucial role in agriculture, often affecting access to essential resources, including land, labour, and finances, which are key to farm success. Age reflects farming experience and adaptive behavior, while gender shapes access to resources and decision-making roles. Family size affects labour availability and food consumption patterns, and education enhances awareness and adoption of improved practices. Landholdings and income are key determinants of resource access and livelihood security, while extension contact and marketing channels influence information access and market linkages (Datta & Behera, 2022 ). Education level and size of landholding influence the adoption rate, as education drives the uptake of innovations. Extension contacts and income from potatoes also play a significant role, as seasoned farmers may better assess the costs and benefits of adopting new practices based on past observations. Marketing channels may benefit from economies of scale, which reduce input costs and increase opportunities to sell the produce (Teklewold et al., 2013 ; Laosutsan et al., 2019 ). These variables were selected to comprehensively capture the socio-economic, institutional, and resource-based factors affecting adoption and food security outcomes. Table 3 Selection of variables and their empirical measurement Variables Descriptions Mean (N = 480) SD ARPP Number of recommended package of practices adopted by potato growers 6.44 2.81 Age Chronological years [categorized into three categories 55 years] 47.50 8.90 Gender 1 = Male, 2 = Female 1.60 0.49 Family size Structured schedule [small = Mean + SD] 4.68 1.49 Education Modified scale of Venkataramaiah (1983), [1 = Illiterate, 2 = Primary Education, 3 = Upto Middle School, 4 = Upto Secondary, 5 = Upto Higher Secondary, 6 = Graduation, 7 = PG &Above] 3.88 1.01 Size of land holdings Structured schedule [Marginal ( 10 ha)] 0.44 0.23 Income from potato Structured schedule [Marginal ( 10 ha)] 39018 25721.30 Annual income Structured schedule [Marginal ( 10 ha)] 102583 58490.30 Extension contact Structured schedule [Low = Mean + SD] 2.23 1.05 Marketing channel Structured schedule [1 = Farmers – Consumer, 2 = Farmers – Commission agents – Wholesalers – Retailers – Consumer, 3 = Farmers – Wholesalers – Consumer, 4 = Farmers – Wholesalers – Retailers – Consumer, 5 = Farmers – Village traders – Retailers – Consumer, 6 = Farmers – Village traders – Wholesalers – Retailers – Consumer] 2.59 1.98 SD = Standard Deviation, ARPP = Adoption of Recommended Package of Practices 2.4. Data analysis Collected data was arranged using standard procedures for data entry and cleaning. All data were entered in an Excel sheet and analysed using Excel, SPSS and R-studio (MASS, pscl). This research incorporated both descriptive and inferential methods of analysis. Descriptive techniques, such as percentage computations and tabulation, were employed, while the Severity Index, Food Insecurity Experience Scale and mean scores were used to identify the key constraints faced by farmers in cultivating and management potatoes in the region. 2.5. Econometric model A farmer is regarded as a scientific potato grower when at least some of the recommended practices are adopted in cultivation. The central aim of this study is to examine how different independent variables influence the intensity of adoption of such practices. Since adoption intensity is expressed as a count variable with non-negative integer values, Poisson regression is generally employed for analysis. However, the assumption in Poisson regression that the mean equals the variance is often violated due to over-dispersion, which results in inefficient parameter estimates. To overcome this limitation, the negative binomial regression model is applied, as it is more flexible in handling over-dispersed data and less sensitive to distributional assumptions. With an accurately specified variance function and a known dispersion parameter, the negative binomial model serves as a reliable alternative to Poisson regression for modelling count data (Mishra et al., 2018). The negative binomial regression model is discussed as follows (Cameron, 2005 ): \(\:P\:\left(\frac{{y}_{i}}{{x}_{i}}\right)=\:\frac{{\Gamma\:}\left(\theta\:+{y}_{i}\right){r}_{i}^{\theta\:}(1-{r}_{i}{)}^{{y}_{i}}}{\left(1+{y}_{i}\right)\:{\Gamma\:}\left(\theta\:\right)},\:\) y i = 0, 1, 2, ……, \(\:\theta\:\) >0, r i = \(\:\frac{\theta\:}{(\theta\:+\:{\mu\:}_{i})}\) (a) In equation (a), y denotes the frequency of adoption of recommended practices, x i represents the vector of explanatory variables, Γ(.) refers to the gamma function, θ is the dispersion or precision parameter, and µ i indicates the mean parameter corresponding to the expected adoption intensity. The expressions for the mean and variance are presented in equations (b) and (c), respectively. \(\:{\mu\:}_{i}=E\:\left[\frac{{y}_{i}}{{x}_{i}}\right]=exp\:\left({x}_{i}\beta\:\right),\:\:\:\:\:i=1,\:\dots\:\dots\:..,\:N\) (b) \(\:Var\:[\frac{{y}_{i}}{{x}_{i}}={\mu\:}_{i}[1+(1/\theta\:){\mu\:}_{i}]\) (c) The parameters of the negative binomial regression model are estimated using the maximum likelihood estimation method. 2.6. Severity index In addition to examining the adoption intensity of recommended potato farming practices and the factors influencing their adoption, we aim to rank various challenges faced in potato farming based on a severity index derived from responses to a closed-ended Likert rating scale. We identified these challenges by asking farmers: "What were the major problems they faced while cultivating potatoes? and were pre-structured in nine different categories: production, financial, institutional, situational, infrastructural, technical, extension, marketing, and storage constraints" Farmers were then asked to rank the severity of their problems within each category using a pre-tested interview schedule prepared using the Likert scale, where 1 indicates the least severe issue and 5 indicates the most severe. This scale enables us to rank problems by severity, especially when other ordinal ranking methods are not feasible (Bajgain et al., 2024 ). The severity index is calculated using the following formula: \(\:{I}_{severity}=\sum\:\frac{{S}_{i}{F}_{i}}{N}\) , (d) In this equation (d), I severity denotes the severity index for a given problem. Here, S i represents the severity scale value for the iᵗʰ problem based on a five-point Likert scale, where each response is assigned a weight ranging from 1 to 5. F i indicates the frequency of responses corresponding to a particular scale value for the iᵗʰ problem, while N refers to the total number of respondents. The severity index enables the ranking of potato cultivation problem-related issues faced by farmers, where a higher index value indicates greater severity. This ranking helps both government agencies and farmers prioritize challenges and allocate limited resources effectively to improve the livelihoods of potato growers in NE India (Cameron, 2005 ). 2.7. Food Insecurity Experience Scale (FIES) The Food Insecurity Experience Scale (FIES) is a reliable tool recognized by the FAO (Food and Agriculture Organization) for assessing the severity of food insecurity (Ballard et al., 2013 ; Chettri et al., 2025 ). The FIES has the potential to generate comparable data on food insecurity experiences across populations. In our study, we employed the scale developed by Ballard et al., ( 2013 ) (Table 4 ). Table 4 The eight standard Food Insecurity Experience Scale (FIES) questions (FAO 2016) FIES question: During the past 12 months, was there an instance when, due to insufficient money or resources F1 You were worried you would run out of food because of a lack of money or other resources? F2 You were unable to eat healthy and nutritious food because of a lack of money or other resources? F3 You ate only a few kinds of foods because of a lack of money or other resources? F4 You had to skip a meal because there was not enough money or other resources to get food? F5 You ate less than you thought you should because of a lack of money or other resources? F6 Your household ran out of food because of a lack of money or other resources? F7 You were hungry but did not eat because there was not enough money or other resources for food? F8 You went without eating for a whole day because of a lack of money or other resources? 3. Results and discussion 2.4. Basic characteristics of respondents and socio-personal profile of potato farmers The socio-personal characteristics of potato growers in Northeast India are summarized in Table 5 . A substantial proportion (76.25%) of the respondents were within the age group of 35–55 years, while 14.37% were above 55 years and 9.38% were below 35 years. Comparable age distributions among potato farmers have also been reported in earlier studies (Kumar et al., 2020 ). Regarding gender, 51.46% of the farmers were male, and 48.54% were female. This nearly equal gender distribution suggests that potato farming promotes gender equality in the agricultural labor market, aligning with findings by Sawicka & Hameed ( 2019 ) and Ateka & Mbeche ( 2023 ). Table 5 Distribution of the potato farmers as per their socio-personal characters Socio-personal characters Category Distribution of Farmers Age 55 years 14.37% Gender Male 51.46% Female 48.54% Family Size Small ( 7) 9.38% Education Illiterate 12.50% Primary Education 5.63% Upto Middle School 12.08% Upto Secondary 25.42% Upto Higher Secondary 27.92% Graduation 13.12% Post-Graduation & Above 3.33% Size of Land Holdings Marginal ( 10 ha) 3.96% Income from potato Marginal ( 10 ha) Rs. 47336.53 Annual income Marginal ( 10 ha) Rs. 285770.40 Extension contact Low ( 3) 13.75% Marketing channel Farmers – Consumer 39.79% Farmers – Commission agents – Wholesalers – Retailers – Consumer 18.75% Farmers – Wholesalers – Consumer 17.92% Farmers – Wholesalers – Retailers – Consumer 9.17% Farmers – Village traders – Retailers – Consumer 8.54% Farmers – Village traders – Wholesalers – Retailers – Consumer 5.83% The demographic analysis revealed that the majority of farmers (78.33%) belonged to medium-sized families (4–7 members), a trend consistent with previous studies (Jha et al., 2012; Boruah et al., 2015 ; Shree et al., 2019 ). This family structure may influence agricultural decision-making and labor availability, both of which are crucial for effective farm management and the adoption of new technologies. In terms of educational attainment, most farmers had at least a secondary education, with 27.92% completing higher secondary and 13.12% attaining graduate-level education. The proportion of illiterate farmers (12.50%) was relatively low, indicating a moderate level of literacy in the study area. These educational levels likely influenced the adoption of recommended farming practices, as better-educated farmers tend to have improved access to and a deeper understanding of agricultural innovations. Similar findings have been reported by Jaisawal et al. ( 2013 ), Khode and Palsingh ( 2020 ), and Kumar et al. ( 2020 ), highlighting the positive impact of education on technology adoption. These results underscore the importance of tailored extension approaches, ensuring that training programs are designed to align with farmers' educational backgrounds. Leveraging digital tools, farmer field schools, and interactive training methods could further enhance knowledge dissemination and encourage the widespread adoption of best practices in potato farming. Landholding data revealed that 58.54% of the farmers fell into the marginal category, followed by 15.84% with small holdings, 11.87% with semi-medium holdings, 9.79% with medium holdings, and 3.96% with large holdings. Previous research has noted similar trends (Jaisawal et al.,2013; Khode & Palsingh, 2020 ). Across the region, semi-medium farmers had the highest average income from potato farming (Rs. 55,739.92), followed by medium (Rs. 49,778.13) and large farmers (Rs. 47,336.53). The highest mean annual income was observed among large farmers (Rs. 2,85,770.40), followed by medium (Rs. 2,59,847.70), semi-medium (Rs. 2,30,772.40), small (Rs. 1,61,223.90), and marginal farmers (Rs. 1,19,595.30). These results are consistent with findings by (Kulkarni & Jahagirdar, 2015 ). These trends align with previous research, which indicates that larger landholdings generally contribute to higher overall income, although small and semi-medium farmers may achieve better per-unit land returns. Strengthening support mechanisms for these farmers could help bridge the income gap and promote sustainable potato farming in the region. Most (49.58%) of the farmers possessed medium level of extension contact, with 36.67% having low and 13.75% having high levels. This may be due to less intensive extension activities and lower farmer participation, which may have limited access to crucial farming information. Similar findings were reported by Kumar et al. ( 2020 ). Furthermore, in marketing channels, most farmers preferred direct sales to consumers, followed by the Farmers – Commission agents – Wholesalers – Retailers – Consumer chain (Table 5 ) , which corroborates the results found by Shree et al. ( 2019 ). 2.5. Descriptive statistics The summary of the analysed results is presented in Table 6 . In potato cultivation, 13 key dimensions were identified under which recommended technologies were suggested. The table presents the adoption rates of different potato production technologies. In this study, most farmers were found to adopt 7 out of the 13 recommended practices, reflecting a moderate level of adoption. This observation is in line with the findings of Mishra et al. ( 2017 ), who reported that approximately 69% of potato farmers demonstrated a medium level of adoption of improved production technologies. The most critical practices for potato farmers were planting time, seed selection/quality, and planting methods, with adoption rates of 73.12%, 57.91%, and 56.66%, respectively. Adhering to proper plant protection measures is crucial for ensuring crop safety; however, 76.67% of farmers fail to follow these practices properly (Datta & Behera, 2022 ). Yield improvement and maintaining the appropriate seed size are other crucial aspects of potato farming; yet 69.38% of farmers could not produce what the average Indian farmer produces and did not follow the recommended seed size (67.50%) (Abreham & Sete, 2019 ). Additionally, several other essential practices, such as integrated nutrient management, seed production, water management, and intercultural operations, had low adoption rates, with more than half of the selected farmers not implementing them. Reasons for this low adoption could include a lack of awareness, limited access to resources, insufficient technical knowledge, or financial constraints. Figure 2 illustrates the intensity of adopting recommended practices among potato farmers in Northeast India. On average, a potato farmer adopted seven recommended practices. The result also shows that farmers typically adopted at least two recommended packages during potato cultivation. Table 6 Distribution of respondents based on the adoption of the recommended package of practice (N = 480) Potato cultivation practices Adopter (%) Non-adopter (%) Soil Management 53.96 46.04 Seed selection/ quality of seedlings 57.91 42.09 Seed size 32.50 67.50 Seed preparation 40.21 59.79 Planting time 73.12 26.88 Integrated Nutrient Management 33.95 66.05 Planting method 56.66 43.34 Water management 44.17 55.83 Intercultural operations 48.95 51.05 Plant protection measures 23.33 76.67 Harvesting 50.62 49.38 Yield 30.62 69.38 Seed production 38.96 61.04 The findings highlight (Fig. 3) that plant protection measures are the most critical training need among potato farmers in northeast India, with 55.00% of respondents expressing a need for training in this area. This aligns with the highest Training Importance Score (TIS) of 73.75%, indicating widespread concerns about pest and disease management. Effective plant protection is crucial in the region, where climatic conditions often favor pest infestations and disease outbreaks, which can significantly impact yield and quality. Similarly, a substantial proportion of farmers require training in integrated nutrient management (50.83%) and seed selection and seedling quality (50.00%), emphasizing the need for knowledge on soil fertility management and the use of quality planting materials to enhance productivity. In contrast, planting time had the lowest TIS (14.67%), suggesting that most farmers are already aware of optimal sowing periods or consider it less challenging compared to other agronomic practices. These findings underscore the importance of targeted capacity-building programs focusing on pest control, soil fertility management, and seed quality improvement. Strengthening extension services and providing practical, hands-on training can significantly enhance farmers' ability to adopt recommended practices, ultimately improving potato yields and profitability in the region. 3.3 Econometric results The results from the negative binomial regression model, presented in Table 7 , analyze the factors influencing the adoption of recommended package of practices. It was found that Age has a significant negative coefficient of -0.039, indicating that as farmers get older, they are less likely to adopt the recommended practices, possibly due to a reluctance toward new technologies. Prokopy et al. ( 2008 ) found that age has a negative relationship with adoption, as older farmers are less likely to change practices due to a shorter planning horizon. The variable for gender, with a coefficient of 0.085, is significant at the 10% level, indicating that male farmers are more likely to adopt the recommended package of practices compared to female farmers, a similar result found by Teklewold et al. ( 2020 ). Education shows a clear positive relationship with adoption intensity with a coefficient of 0.0789) which are significant at the 1% level. This indicates that higher educational attainment encourages farmers to adopt recommended practices. Further, it also suggests that higher education levels substantially increase the probability of adoption. This result pertains to the results of Kolady et al., 2021 . Income from potato farming exhibits a positive and significant relationship with adoption intensity, with a coefficient of 0.00000890 at the 1% significance level. Strong positive influence, meaning higher income from potato farming encourages the adoption of recommended practices. The annual income, with a coefficient of 0.000000592, is significant at the 5% level, indicating that farmers with higher annual incomes are more likely to adopt the recommended practices (Ketema et al., 2016 ; Manjunatha et al., 2013 ). The marketing channel’s coefficients are statistically significant at the 1% level. Efficient marketing channels provide better price realization, market access, and timely dissemination of market intelligence, thereby encouraging farmers to adopt recommended practices. Similar results were also identified by Hao et al. ( 2018 ) and Onumah et al. ( 2007 ). Other variables, such as family size, land holdings, and extension contact, were found to be non-significant, indicating that these variables may not have a significant impact on the adoption decision-making process. Table 7 Estimated coefficient and marginal effect from the negative binomial regression model Variable Negative binomial regression Coefficient Std. Error Z-Value P-Value Age -0.039** 0.005 -2.450 0.014 Gender 0.085* 0.058 1.720 0.086 Family Size 0.0254NS 0.030 0.847 0.397 Education 0.0789*** 0.021 3.760 0.000 Size of Land Holding -0.2689NS 0.290 -0.927 0.354 Income from Potato 0.00000890*** 0.00000240 3.708 0.000 Annual Income 0.000000592** 0.00000026 2.278 0.023 Extension Contact 0.0423NS 0.040 1.058 0.290 Marketing Channel 0.0345*** 0.015 2.300 0.021 Wald χ² 7.285 Deviance 26.685 Log-likelihood 79.906 N 480 The dependent variable is the Adoption intensity of recommended potato farming practices. The number in parentheses denotes a robust standard error. In denotes the natural logarithm. *, **, *** denotes significance at 10%, 5% and 1% respectively. 3.4. Constraints faced by the farmers in cultivation and management of potato farming and their severity The challenges faced by potato farmers in northeast India were categorized into nine major sections: production, financial, institutional, situational, infrastructural, technical, extension, marketing, and storage constraints, each encompassing several specific issues. The individual problems within each section were ordinally ranked, and an overall ranking of the nine sections was established based on mean severity scores (Table 8 ). Production constraints emerged as the most significant category, ranked first among the nine based on the overall mean score. The primary issue in this category was the lack of knowledge regarding balanced fertilizer application and the timely availability of fertilizers, which had a severity index of 0.83, consistent with the findings of Debey et al. (2020) and Maulidiyah et al. ( 2024 ). This was followed by inadequate awareness of pest and disease management, with a severity index of 0.70, and the high cost of agricultural inputs, which had a severity index of 0.60 (Atreya & Kafle, 2016 ; Awasthi et al., 2020 ). The storage constraints ranked second overall and remain as a major concern, with the most pressing issue being the absence of proper storage facilities, recording a severity index of 0.88, followed by storage losses with a severity index of 0.43 (Deka et al., 2014 ). Extension constraints occupied the third rank, with inadequate or non-existent government-provided training programs highlighted as the most significant problem, as reflected by a severity index of 0.73. This was followed by a lack of live demonstrations for new agricultural practices, with a severity index of 0.60, and infrequent visits from extension personnel, scoring 0.39 (Katayani et al., 2017 ). Situational constraints were ranked fourth, with the main problem being the distant location of markets, which had a severity index of 0.86, followed by the remoteness of farmland. Ranked fifth, marketing constraints primarily involved the influence of middlemen, with a severity index of 0.46, in addition to issues associated with surplus production. Technical constraints, which ranked sixth, were primarily attributed to a lack of farm mechanization, with a severity index of 0.67, followed by the unavailability of new technologies (severity index of 0.46) and threats from wild animals (severity index of 0.39), a similar result also identified by Vaid, ( 2020 ). Infrastructural constraints, ranked seventh, were dominated by the absence of proper tools and implements, with a severity index of 0.70. Similarly, financial constraints also ranked seventh based on overall mean scores, were characterized by inadequate credit facilities, with a severity index of 0.90, insufficient subsidies (severity index of 0.78), high interest rates (severity index of 0.66), short repayment periods (severity index 0.39), and limited personal financial resources (severity index 0.28). Lastly, institutional constraints were ranked ninth, with the primary issue being a lack of support from agricultural and allied departments, having a severity score of 0.78 (Nzomoi et al., 2007 ). To overcome these challenges, particularly in production, the government should organize adequate training programs to improve farmers' knowledge of pest and disease management, balanced fertilizer application, and integrated nutrient management. Additionally, timely access to quality agricultural inputs at reasonable prices is crucial for building farmers' confidence and promoting large-scale, sustainable potato cultivation. Addressing storage constraints will require establishing adequate warehouse facilities to prevent storage losses and enable farmers to obtain better prices during periods of high market demand. Table 8 Major constraints faced by the potato farmers of northeast India with their severity indices and rank Constraints Weighted Mean Score Rank Severity index Overall Mean Score Overall Rank I. Production Constraints Lack of knowledge on balanced fertilizer application 1.89 I 0.83 1.60 I Lack of knowledge regarding pests and diseases 1.85 II 0.70 High cost of input 1.64 III 0.60 Lack of input (seed, Fertilizer) supply 1.02 IV 0.46 II. Financial Constraints Inadequate credit 1.02 I 0.90 0.37 VIII Inadequate subsidy 0.76 II 0.78 High interest rate 0.26 III 0.66 Insufficient repayment time 0.12 IV 0.39 Lack of own resource 0.08 V 0.28 III. Institutional constraints Lack of support from the agricultural department 0.79 I 0.78 0.33 IX Lack of SHG 0.19 II 0.67 Lack of cooperation 0.16 III 0.46 IV. Situational constraints Distant location of the market 1.14 I 0.86 1.00 IV Distant location of land 1.09 II 0.73 Poor transport facility 0.77 III 0.60 V. Infrastructural constraints Lack of tools and implements 1.08 I 0.70 0.42 VII Lack of availability of land 0.29 II 0.68 Lack of irrigation facility 0.21 III 0.57 Lack of established structure for livestock 0.11 IV 0.42 VI. Technical constraints Lack of mechanization 1.31 I 0.67 0.70 VI Unavailability of new technology 0.75 II 0.46 Wild animal threats 0.24 III 0.39 VII. Extension constraints Inadequate training / No training 1.18 I 0.73 1.14 III No Demonstration for new practices 1.14 II 0.60 No or very few visits of extension personnel 1.09 III 0.39 VIII. Marketing constraints Marketing middleman 1.06 I 0.46 0.99 V Surplus production 0.73 II 0.39 IX. Storage constraints Lack of proper storage facilities 1.91 I 0.88 1.42 II Storage loss 0.94 II 0.43 3.5. Food Insecurity Experience Level The incidence of food insecurity among potato farmers in Northeast India, particularly those from purposively selected states such as Assam, Meghalaya, Nagaland, and Tripura, highlights significant challenges in ensuring consistent access to food. The data (Fig. 4 ) reveals considerable variation between states, with 15.83% of respondents across the region expressing concern about running out of food due to a lack of resources. Meghalaya recorded the highest proportion at 22.5%, while Tripura had the lowest at 9.17%. A notable issue was the inability to access healthy and nutritious food, affecting an average of 27.92% of respondents. This concern was most acute in Meghalaya (30%) and Nagaland (29.17%). Limited food variety emerged as the most frequently reported form of food insecurity, with 30.83% of respondents stating they could only eat a few kinds of food due to resource constraints. This issue was most prevalent in Assam (35%). Global food, nutrition and agriculture agencies project that by 2030, all forms of malnutrition are likely to rise, with nearly half of the world’s population expected to be affected (Ville et al., 2019 ). To mitigate this food insecurity challenge and achieve Sustainable Development Goal (SDG-2) in NE India, technological adoption must be prioritized, thereby increasing crop production and yield. Figure 4 also highlights that 14.37% of respondents in the region skipped meals due to a lack of resources, with Nagaland reporting an alarming 33%, while Tripura had the lowest proportion at 7.5%. Furthermore, 7.29% of respondents indicated that they ate less than they felt they needed, with Assam (10%) being the most affected. Households running out of food due to resource shortages affected 23.54% of respondents, with Assam again reporting the highest prevalence at 27.5%. Hunger without access to food was reported by 5.62% of respondents, with Meghalaya (7.5%) experiencing the highest percentage. The most severe form of food insecurity, going without food for an entire day, was reported by 3.54% of respondents. Tripura had the lowest percentage at 1.67%, indicating relatively better conditions but still highlighting extreme cases of food insecurity (Anantharaman et al., 2016 ). These findings underscore the complexity of food insecurity in Northeast India, which is influenced by socio-economic inequalities, agricultural constraints, and limited access to resources. The high levels of insecurity in Assam and Meghalaya underscore the need for targeted interventions to enhance food availability and affordability. Additionally, the high prevalence of skipped meals in Nagaland highlights structural challenges that require urgent attention. Research on food insecurity emphasizes that it encompasses not only adequate calorie intake but also access to diverse and nutritious food, which is crucial for health and development (Montalvo et al., 2024 ). Addressing these issues calls for comprehensive strategies, including policy reforms to support agriculture, enhanced food distribution networks, and targeted financial assistance for vulnerable populations. 4. Conclusion Potatoes are one of the staple foods in the daily diet of Northeast Indians, and the government is focusing on increasing the production level, as productivity is currently low (8.55 MT/ha) compared to the national average of 25.79 MT/ha. Apart from that, the adoption of recommended potato production technologies needs to be addressed, as the maximum number of farmers adopting the recommended practices stands at 7 out of 13, which is a concern that directly impacts productivity. The majority of farmers adopted recommended practices, including planting time, seed selection and quality of seedlings, and planting methods. Whereas limited adoption was recorded in the case of plant protection measures, yield and seed size. Econometric results using negative binomial regression suggested that gender is significant at the 10% level. Further, for Annual income, the marketing channel is positively significant at the 5% level, but age is negatively significant at the 5% level. Whereas variables like education and income from potatoes are highly and positively significant at a 1% level. Furthermore, among the nine different categories of constraints faced by potato farmers in NE India, production constraints, followed by storage constraints and extension constraints, are the most significant. The findings of this study emphasize the need for policymakers to strengthen institutional support for potato farmers, thereby enhancing the adoption of recommended farming practices. Key strategies could include improving access to agricultural extension services, ensuring timely delivery of inputs, promoting credit accessibility, and offering targeted training programs that focus on recommended agronomic practices. Additionally, encouraging farmer participation in decision-making processes and fostering collective action through farmer groups or cooperatives can enhance the dissemination and adoption of technology. Policy interventions should also aim to address context-specific challenges faced by smallholder farmers, thereby ensuring that technological innovations are both accessible and relevant to diverse farming contexts. While the study provides valuable insights into adoption intensity, it is not without limitations. The sample size, though adequate for local-level analysis, may not fully reflect variations in adoption behavior across different regions or agro-ecological zones. Furthermore, the study's reliance on cross-sectional data restricts the ability to establish long-term trends and causal relationships between adoption and the influencing factors. As a result, the conclusions drawn may be limited by the specific socio-economic and institutional context in which the study was conducted. Addressing these limitations in future studies could strengthen the generalizability of findings. Future research could build upon the findings of this study by expanding the geographic scope and incorporating longitudinal data to gain a deeper understanding of the dynamic nature of adoption behavior over time. Moreover, exploring the potential impact of emerging agricultural technologies, such as precision farming, climate-smart practices, and digital advisory tools, could provide deeper insights into how technological adoption can be enhanced in the potato farming sector. Research focusing on gender dimensions, labor dynamics, and environmental sustainability within the context of technology adoption would also offer a more holistic perspective on the socio-economic and environmental implications of recommended agricultural practices. Declarations Funding : The authors declare that they did not receive support from any organization for the submitted work. Data availability statement : The data will be made available on request. Supplementary Information (SI) : Supplementary file 1 with interview schedule, 2 with the list of independent variables used with reasoning. 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Food security and the Food Insecurity Experience Scale (FIES): ensuring progress by 2030. Food Security, 11, 483–491. https://doi.org/10.1007/s12571-019-00936-9 Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.jpg Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7663781","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527867464,"identity":"cead72af-5a38-4014-a177-8832fd5f0cb4","order_by":0,"name":"Rajib Das","email":"","orcid":"","institution":"Krishi Vigyan Kendra","correspondingAuthor":false,"prefix":"","firstName":"Rajib","middleName":"","lastName":"Das","suffix":""},{"id":527867465,"identity":"a933e2bb-2254-425b-9864-43fd6e62e1e0","order_by":1,"name":"Kaushal Kumar Jha","email":"","orcid":"","institution":"Nagaland 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1","display":"","copyAsset":false,"role":"figure","size":5857287,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7663781/v1/615b916ab0c9556d3b9fd162.png"},{"id":93493946,"identity":"e9edcc35-a4fd-4786-a569-40341acaf0fc","added_by":"auto","created_at":"2025-10-14 12:47:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77483,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663781/v1/3c67b8fe3824425db5434d03.jpg"},{"id":93495809,"identity":"8a136cb5-4840-4483-b8e3-41e642cf3124","added_by":"auto","created_at":"2025-10-14 13:03:27","extension":"jpg","order_by":3,"title":"Figure 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12:18:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12010419,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7663781/v1/13cfe979-b9f8-4cfe-aa51-ca3c450bff23.pdf"},{"id":93493947,"identity":"3cbcd889-0acb-4972-9e81-4c211c9ad80b","added_by":"auto","created_at":"2025-10-14 12:47:27","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":919567,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7663781/v1/de59bba3c9a141eade9a189d.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of the Adoption of Potato Production Technology and Identification of Farmer Challenges for Enhancing Food Security in Northeast India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePotato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L.) is a major vegetable crop that traces its origin to the Andes region of South America, where it was domesticated by early farming communities nearly 8,000 years ago, particularly around the areas corresponding to present-day Bolivia and Peru (De jong, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Ramsay et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The first recorded European encounter with the potato occurred in 1533 when Spanish conquistador Francisco Pizarro saw it in Peru. The crop's spread in India was limited during the first 300 years following its introduction, with significant production gains only occurring after 1941. This slow adoption was mainly due to the lack of locally adapted varieties and suitable agricultural techniques for sub-tropical climates (Singh \u0026amp; Dutt, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Potato, often referred as the \u0026ldquo;king of vegetables\u0026rdquo;, ranks as the third most important food crop for human consumption, after rice and wheat (Sharma et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the Indian context, it plays a crucial role in addressing nutritional deficiencies among the rapidly expanding population and has been recognized by the FAO as a significant crop for ensuring food security (Devaux et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFood security is crucial to sustainable development, particularly in areas with rapidly increasing population trends and limited agricultural resources (Camire et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The potato crop significantly contributes to food security, owing to its high yield potential and nutritional value. They serve as a source of critical nutrients, including carbohydrates, vitamins and minerals, which aid in combating malnutrition, poverty and food scarcity. In the immediate post-independence period (1948\u0026ndash;1949), India produced about 1.54\u0026nbsp;million tonnes of potatoes from 0.234\u0026nbsp;million hectares, with an average productivity of 6.58 tonnes per hectare (Singh and Dutt, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Over the decades, potato cultivation has expanded significantly, and by the 2022\u0026ndash;23 season, production had risen to 60.14\u0026nbsp;million tonnes from 2.3\u0026nbsp;million hectares, positioning India as the second-largest producer globally after China (Department of Agriculture \u0026amp; Farmers Welfare, Government of India, 2023). Potato contributes approximately 21.90% of the total area under vegetable cultivation, with the highest share of 28.90% among the production of vegetables in India. India ranked third, with an area of 23,32,160 hectares (ha), while it ranked second in terms of production, at 60.14\u0026nbsp;million tons. In contrast, it ranked 68th with a very low productivity of 25.79\u0026nbsp;million tons per hectare (MT/ha), the lowest among potato-producing countries. Among the Northeastern (NE) states, Assam has the highest potato production, followed by Meghalaya and Tripura. In contrast, the average productivity was very low at 8.55 MT/ha, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Department of Agriculture \u0026amp; Farmers Welfare, Government of India, 2023). Food security remains a pressing global challenge, particularly in regions such as Northeast India, where diverse geographical and socio-economic factors significantly impact agricultural productivity (Raj et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eClimate change significantly impacts soil health through rising temperatures, altered precipitation patterns, and weather changes, thereby affecting crop yields and food security in the Northeast region (Dikshit \u0026amp; Dikshit, 2013; Datta \u0026amp; Bose, 2020). Environmental adversities impact small landholding farmers, who possess limited resources to adapt to climate variations, potentially exacerbating vulnerabilities in the rural economy of NE India, which is heavily reliant on agriculture. The degradation of soil nutrition and health, influenced by these erratic climate changes, can be mitigated by developing and adapting shifting cultivation practices (Datta et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Das et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) carried out an extensive study on shifting cultivation, integrating climatic parameters and soil characteristics, and employed the Normalized Difference Vegetation Index (NDVI) along with Google Earth Engine (GEE) for the analysis. Such studies help generate crop-suitability maps based on climate and soil, providing essential insights to inform farmers and policymakers about informed decisions regarding land use and cultivation practices (Das et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Technological barriers also significantly impede the adoption of advanced measures developed to promote sustainability in crop cultivation. Despite the availability of advanced technologies such as GEE and remote sensing, challenges persist regarding data availability, access to technology, and infrastructure in rural areas (Datta et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Farmers often lack knowledge of efficient irrigation schedules, soil management and crop diversification strategies. This constraint can be fulfilled through farmers\u0026rsquo; empowerment with technology transfers, the integration of knowledge tools, and extension training. Agriculture in Northeast India is strongly influenced by local customs, community identities, and cultural elements, with a strong emphasis on traditional and ancestral practices (Dikshit \u0026amp; Dikshit, 2013). The dependence on conventional aspects, although historically significant, may hinder the adoption of contemporary technology and strategies necessary for addressing the current climate challenges. Therefore, consideration of traditions and customs is essential to highlight local involvement in policy-making processes, which are vital for promoting sustainability and achieving food security, taking into account cultural values, practices, and socioeconomic conditions. (Naveen et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The ongoing impacts of climate change intensify existing vulnerabilities, underscoring the urgent need for technology transfer, soil conservation, and the integration of cultural knowledge into adaptation strategies to enhance resilience in rural farming communities of Northeast India.\u003c/p\u003e\u003cp\u003eThe Sustainable Development Goal (SDG 2): Zero Hunger underscores the importance of sustainable agriculture and food systems in eradicating hunger and malnutrition. Potato is a versatile and nutritious crop that offers significant potential to boost food security in this region. Therefore, there is an urgent need to channel our efforts towards increasing the productivity level of the state. Enhancing potato productivity in the northeastern states of India to meet the rising demand requires an assessment of adoption intensity, food insecurity and the constraints faced by growers. Such insights are crucial for prioritizing research and extension interventions, ultimately contributing to the reduction of yield gaps at the farm level.\u003c/p\u003e\u003cp\u003eThis research aims to evaluate the adoption of potato production technologies and identify the constraints faced by farmers in NE India. By understanding these factors, we can develop targeted interventions to enhance potato productivity, improve farmers' livelihoods, and contribute to achieving SDG 2. Therefore, the present study was conducted in four purposively selected districts with the highest production in the northeast, following an ex-post-facto research design. A total of 480 respondents were interviewed using a pre-tested interview schedule to determine the adoption intensity and factors constraining farmers. This study will provide policymakers, agricultural extension workers, and researchers with valuable insights to inform the formulation of effective strategies for sustainable agricultural development in the region.\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\u003eComparison of area, production and productivity of potato in India and North-east India\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2013-14\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2014-15\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2015-16\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2016-17\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2017-18\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2018-19\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2019-20\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2020-21\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e2021-22\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e2022-23\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eIndia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eArea (ha)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1973.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2116.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2179.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2141.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2172.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2051.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2203.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e2225.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e2332.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProduction (MT)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41555.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43417.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48604.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e50189.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e48561.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e56172.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e56175.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e60141.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProductivity (MT/Ha)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e23.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e25.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e25.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e25.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eNE India\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eArea (ha)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e142.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e149.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e153.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e153.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e155.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e141.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e141.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e143.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProduction (MT)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1153.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2173.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1471.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1235.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1209.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1254.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1238.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1207.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1194.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e1224.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProductivity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e8.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cem\u003eSource: Department of Agriculture and Farmers Welfare, Govt. of India\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://agriwelfare.gov.in/en/StatHortEst\u003c/span\u003e\u003cspan address=\"https://agriwelfare.gov.in/en/StatHortEst\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003cem\u003eand National Horticulture Board\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study area\u003c/h2\u003e\u003cp\u003eThe study was undertaken in the northeastern region of India, covering the states of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura, and Sikkim. The region accounts for 143.07 thousand hectares under potato cultivation, with a production of 1,224.06 thousand metric tonnes. Despite the relatively low average yield of 8.55 MT per hectare, per capita availability of potatoes in this region exceeds the national average (NHB, 2021). For detailed investigation, four districts with comparatively higher levels of potato production were purposively selected: Lakhimpur in Assam (27.2064\u0026deg; N, 94.1514\u0026deg; E), East Khasi Hills in Meghalaya (25.3682\u0026deg; N, 91.7539\u0026deg; E), Kohima in Nagaland (25.6751\u0026deg; N, 94.1086\u0026deg; E), and South Tripura in Tripura (23.2317\u0026deg; N, 91.5596\u0026deg; E) (Fig.\u0026nbsp;1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Data collection\u003c/h2\u003e\u003cp\u003eA structured interview schedule was developed to collect data from selected arable farmers in the study area (Supplementary file 1). Before conducting the actual survey, we pre-tested the interview schedule with a total of 40 respondents (different from the actual respondents), comprising 10 farmers each from four selected villages across different northeastern states. The villages included Amguri in the North Lakhimpur block of Lakhimpur district, Assam; Mawlyngkut in the Mawsynram block of East Khasi Hills district, Meghalaya; Viswema in the Jakhama block of Kohima district, Nagaland; and Rajnagar in the Rajnagar block of South Tripura district, Tripura. This selection ensured representation from diverse agro-climatic regions, thereby helping to assess the interview schedule's clarity, relevance, and effectiveness before its final use. This interview schedule covered various aspects, including the socioeconomic profile of the participants and the challenges they face in potato cultivation. The blocks with the highest potato production were chosen purposively for the study, namely North Lakhimpur (Lakhimpur district, Assam), Mawsynram (East Khasi Hills district, Meghalaya), Jakhama (Kohima district, Nagaland), and Rajnagar (South Tripura district, Tripura).\u003c/p\u003e\u003cp\u003eFour villages were randomly selected from each block, resulting in 16 villages for the study as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The selected villages were Amguri, Nalkatu, Balijan, Rajgarh (Assam); Mawlyngkut, Chirakatta, Dopho, Mawpen (Meghalaya); Pfuchama, Phesama, Viswema, Khuzama (Nagaland); and Barapathari, Rajnagar, Chittamara, Uttar Krishnapur (Tripura). A village wise list of all the potato growers having experience of more than 3 years in cultivating potatoes was prepared and from that list using circular systematic sampling method we selected the respondents for the study. A total of 480 farmers participated in the study, with 30 farmers chosen from each village to provide valuable insights into the challenges and management practices in potato farming across the region. All 480 interview schedules were completed and returned, achieving a 100% response rate.\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\u003eNames of the selected villages for the survey\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\u003eStates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlock\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVillages\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLakhimpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNorth Lakhimpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAmguri, Nalkatu, Balijan, Rajgarh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeghalaya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEast Khasi Hills\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMawsynram\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMawlyngkut, Chirakatta, Dopho, Mawpen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNagaland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKohima\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJakhama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePfuchama, Phesama, Viswema, Khuzama\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTripura\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSouth Tripura\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRajnagar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBarapathari, Rajnagar, Chittamara, Uttar Krishnapur\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\u003eThe field survey was carried out during 2021\u0026ndash;22 through personal interviews with the selected farmers. Prior to each interview, verbal consent was obtained. The interviews were conducted face-to-face using a standardized schedule, with each session lasting approximately 30\u0026ndash;40 minutes. To complement this process, 20 key informant interviews (five per state) and eight group discussions (two per state) were organized with progressive farmers, local officials, market intermediaries, and other stakeholders to gather supplementary insights and validate the findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Variables\u003c/h2\u003e\u003cp\u003eThe dependent variable for this study was the intensity of adoption of recommended potato cultivation practices, quantified by the number of such practices followed by the farmers (Supplementary file 2). The focus is specifically on actual recommendation-based potato farming and management, as these practices are likely to enhance both the quality and quantity of potato production. The independent variables examined include age, gender, family size, education, size of land holdings, income from potatoes, annual income, extension contact, and marketing channel were selected based on their documented significance in influencing agricultural adoption and food security (Datta et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Naveen et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These variables are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIn developing countries, factors such as age, gender, and family size play a crucial role in agriculture, often affecting access to essential resources, including land, labour, and finances, which are key to farm success. Age reflects farming experience and adaptive behavior, while gender shapes access to resources and decision-making roles. Family size affects labour availability and food consumption patterns, and education enhances awareness and adoption of improved practices. Landholdings and income are key determinants of resource access and livelihood security, while extension contact and marketing channels influence information access and market linkages (Datta \u0026amp; Behera, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Education level and size of landholding influence the adoption rate, as education drives the uptake of innovations. Extension contacts and income from potatoes also play a significant role, as seasoned farmers may better assess the costs and benefits of adopting new practices based on past observations. Marketing channels may benefit from economies of scale, which reduce input costs and increase opportunities to sell the produce (Teklewold et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Laosutsan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These variables were selected to comprehensively capture the socio-economic, institutional, and resource-based factors affecting adoption and food security outcomes.\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\u003eSelection of variables and their empirical measurement\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\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescriptions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;480)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of recommended package of practices adopted by potato growers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChronological years [categorized into three categories\u0026thinsp;\u0026lt;\u0026thinsp;35 years, 35\u0026ndash;55 years, \u0026gt;55 years]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.90\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\u003e1\u0026thinsp;=\u0026thinsp;Male, 2\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStructured schedule [small\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;mean-SD, medium\u0026thinsp;=\u0026thinsp;mean- SD to mean\u0026thinsp;+\u0026thinsp;SD, Large\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;Mean\u0026thinsp;+\u0026thinsp;SD]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModified scale of Venkataramaiah (1983), [1\u0026thinsp;=\u0026thinsp;Illiterate, 2\u0026thinsp;=\u0026thinsp;Primary Education, 3\u0026thinsp;=\u0026thinsp;Upto Middle School, 4\u0026thinsp;=\u0026thinsp;Upto Secondary, 5\u0026thinsp;=\u0026thinsp;Upto Higher Secondary, 6\u0026thinsp;=\u0026thinsp;Graduation, 7\u0026thinsp;=\u0026thinsp;PG \u0026amp;Above]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize of land holdings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStructured schedule [Marginal (\u0026lt;\u0026thinsp;1 ha), Small (1\u0026ndash;2 ha), Semi Medium (2\u0026ndash;4 ha), Medium (4\u0026ndash;10 ha), Large (\u0026gt;\u0026thinsp;10 ha)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome from potato\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStructured schedule [Marginal (\u0026lt;\u0026thinsp;1 ha), Small (1\u0026ndash;2 ha), Semi Medium (2\u0026ndash;4 ha), Medium (4\u0026ndash;10 ha), Large (\u0026gt;\u0026thinsp;10 ha)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25721.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStructured schedule [Marginal (\u0026lt;\u0026thinsp;1 ha), Small (1\u0026ndash;2 ha), Semi Medium (2\u0026ndash;4 ha), Medium (4\u0026ndash;10 ha), Large (\u0026gt;\u0026thinsp;10 ha)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58490.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtension contact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStructured schedule [Low\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;mean-SD, Medium\u0026thinsp;=\u0026thinsp;mean- SD to Mean\u0026thinsp;+\u0026thinsp;SD, High\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;Mean\u0026thinsp;+\u0026thinsp;SD]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarketing channel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStructured schedule [1\u0026thinsp;=\u0026thinsp;Farmers \u0026ndash; Consumer, 2\u0026thinsp;=\u0026thinsp;Farmers \u0026ndash; Commission agents \u0026ndash; Wholesalers \u0026ndash; Retailers \u0026ndash; Consumer, 3\u0026thinsp;=\u0026thinsp;Farmers \u0026ndash; Wholesalers \u0026ndash; Consumer, 4\u0026thinsp;=\u0026thinsp;Farmers \u0026ndash; Wholesalers \u0026ndash; Retailers \u0026ndash; Consumer, 5\u0026thinsp;=\u0026thinsp;Farmers \u0026ndash; Village traders \u0026ndash; Retailers \u0026ndash; Consumer, 6\u0026thinsp;=\u0026thinsp;Farmers \u0026ndash; Village traders \u0026ndash; Wholesalers \u0026ndash; Retailers \u0026ndash; Consumer]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eSD\u0026thinsp;=\u0026thinsp;Standard Deviation, ARPP\u0026thinsp;=\u0026thinsp;Adoption of Recommended Package of Practices\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data analysis\u003c/h2\u003e\u003cp\u003eCollected data was arranged using standard procedures for data entry and cleaning. All data were entered in an Excel sheet and analysed using Excel, SPSS and R-studio (MASS, pscl). This research incorporated both descriptive and inferential methods of analysis. Descriptive techniques, such as percentage computations and tabulation, were employed, while the Severity Index, Food Insecurity Experience Scale and mean scores were used to identify the key constraints faced by farmers in cultivating and management potatoes in the region.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Econometric model\u003c/h2\u003e\u003cp\u003eA farmer is regarded as a scientific potato grower when at least some of the recommended practices are adopted in cultivation. The central aim of this study is to examine how different independent variables influence the intensity of adoption of such practices. Since adoption intensity is expressed as a count variable with non-negative integer values, Poisson regression is generally employed for analysis. However, the assumption in Poisson regression that the mean equals the variance is often violated due to over-dispersion, which results in inefficient parameter estimates. To overcome this limitation, the negative binomial regression model is applied, as it is more flexible in handling over-dispersed data and less sensitive to distributional assumptions. With an accurately specified variance function and a known dispersion parameter, the negative binomial model serves as a reliable alternative to Poisson regression for modelling count data (Mishra et al., 2018). The negative binomial regression model is discussed as follows (Cameron, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e):\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\:\\left(\\frac{{y}_{i}}{{x}_{i}}\\right)=\\:\\frac{{\\Gamma\\:}\\left(\\theta\\:+{y}_{i}\\right){r}_{i}^{\\theta\\:}(1-{r}_{i}{)}^{{y}_{i}}}{\\left(1+{y}_{i}\\right)\\:{\\Gamma\\:}\\left(\\theta\\:\\right)},\\:\\)\u003c/span\u003e\u003c/span\u003e y\u003csub\u003ei\u003c/sub\u003e = 0, 1, 2, \u0026hellip;\u0026hellip;, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:\\)\u003c/span\u003e\u003c/span\u003e \u0026gt;0, r\u003csub\u003ei\u003c/sub\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\theta\\:}{(\\theta\\:+\\:{\\mu\\:}_{i})}\\)\u003c/span\u003e\u003c/span\u003e (a)\u003c/p\u003e\u003cp\u003eIn equation (a), \u003cem\u003ey\u003c/em\u003e denotes the frequency of adoption of recommended practices, \u003cem\u003ex\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e represents the vector of explanatory variables, Γ(.) refers to the gamma function, θ is the dispersion or precision parameter, and \u0026micro;\u003csub\u003ei\u003c/sub\u003e indicates the mean parameter corresponding to the expected adoption intensity. The expressions for the mean and variance are presented in equations (b) and (c), respectively.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}=E\\:\\left[\\frac{{y}_{i}}{{x}_{i}}\\right]=exp\\:\\left({x}_{i}\\beta\\:\\right),\\:\\:\\:\\:\\:i=1,\\:\\dots\\:\\dots\\:..,\\:N\\)\u003c/span\u003e\u003c/span\u003e (b)\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Var\\:[\\frac{{y}_{i}}{{x}_{i}}={\\mu\\:}_{i}[1+(1/\\theta\\:){\\mu\\:}_{i}]\\)\u003c/span\u003e\u003c/span\u003e (c)\u003c/p\u003e\u003cp\u003eThe parameters of the negative binomial regression model are estimated using the maximum likelihood estimation method.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Severity index\u003c/h2\u003e\u003cp\u003eIn addition to examining the adoption intensity of recommended potato farming practices and the factors influencing their adoption, we aim to rank various challenges faced in potato farming based on a severity index derived from responses to a closed-ended Likert rating scale. We identified these challenges by asking farmers: \"What were the major problems they faced while cultivating potatoes? and were pre-structured in nine different categories: production, financial, institutional, situational, infrastructural, technical, extension, marketing, and storage constraints\" Farmers were then asked to rank the severity of their problems within each category using a pre-tested interview schedule prepared using the Likert scale, where 1 indicates the least severe issue and 5 indicates the most severe. This scale enables us to rank problems by severity, especially when other ordinal ranking methods are not feasible (Bajgain et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe severity index is calculated using the following formula:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}_{severity}=\\sum\\:\\frac{{S}_{i}{F}_{i}}{N}\\)\u003c/span\u003e\u003c/span\u003e, (d)\u003c/p\u003e\u003cp\u003eIn this equation (d), I\u003csub\u003eseverity\u003c/sub\u003e denotes the severity index for a given problem. Here, \u003cem\u003eS\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e represents the severity scale value for the \u003cem\u003eiᵗʰ\u003c/em\u003e problem based on a five-point Likert scale, where each response is assigned a weight ranging from 1 to 5. \u003cem\u003eF\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e indicates the frequency of responses corresponding to a particular scale value for the \u003cem\u003eiᵗʰ\u003c/em\u003e problem, while \u003cem\u003eN\u003c/em\u003e refers to the total number of respondents. The severity index enables the ranking of potato cultivation problem-related issues faced by farmers, where a higher index value indicates greater severity. This ranking helps both government agencies and farmers prioritize challenges and allocate limited resources effectively to improve the livelihoods of potato growers in NE India (Cameron, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Food Insecurity Experience Scale (FIES)\u003c/h2\u003e\u003cp\u003eThe Food Insecurity Experience Scale (FIES) is a reliable tool recognized by the FAO (Food and Agriculture Organization) for assessing the severity of food insecurity (Ballard et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chettri et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The FIES has the potential to generate comparable data on food insecurity experiences across populations. In our study, we employed the scale developed by Ballard et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe eight standard Food Insecurity Experience Scale (FIES) questions (FAO 2016)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFIES question: During the past 12 months, was there an instance when, due to insufficient money or resources\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYou were worried you would run out of food because of a lack of money or other resources?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYou were unable to eat healthy and nutritious food because of a lack of money or other resources?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYou ate only a few kinds of foods because of a lack of money or other resources?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYou had to skip a meal because there was not enough money or other resources to get food?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYou ate less than you thought you should because of a lack of money or other resources?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYour household ran out of food because of a lack of money or other resources?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYou were hungry but did not eat because there was not enough money or other resources for food?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYou went without eating for a whole day because of a lack of money or other resources?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Basic characteristics of respondents and socio-personal profile of potato farmers\u003c/h2\u003e\u003cp\u003eThe socio-personal characteristics of potato growers in Northeast India are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. A substantial proportion (76.25%) of the respondents were within the age group of 35\u0026ndash;55 years, while 14.37% were above 55 years and 9.38% were below 35 years. Comparable age distributions among potato farmers have also been reported in earlier studies (Kumar et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Regarding gender, 51.46% of the farmers were male, and 48.54% were female. This nearly equal gender distribution suggests that potato farming promotes gender equality in the agricultural labor market, aligning with findings by Sawicka \u0026amp; Hameed (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Ateka \u0026amp; Mbeche (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of the potato farmers as per their socio-personal characters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocio-personal characters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDistribution of Farmers\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;35 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.38%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;55 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e76.25%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;55 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.37%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e51.46%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.54%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eFamily Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmall (\u0026lt;\u0026thinsp;4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.29%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium (4\u0026ndash;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.33%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLarge (\u0026gt;\u0026thinsp;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.38%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.63%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpto Middle School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.08%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpto Secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.42%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpto Higher Secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e27.92%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGraduation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.12%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-Graduation \u0026amp; Above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.33%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eSize of Land Holdings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarginal (\u0026lt;\u0026thinsp;1 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e58.54%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmall (1\u0026ndash;2 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.84%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSemi Medium (2\u0026ndash;4 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.87%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium (4\u0026ndash;10 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.79%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLarge (\u0026gt;\u0026thinsp;10 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.96%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eIncome from potato\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarginal (\u0026lt;\u0026thinsp;1 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRs. 24344.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmall (1\u0026ndash;2 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRs. 33011.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSemi Medium (2\u0026ndash;4 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eRs. 55739.92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium (4\u0026ndash;10 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRs. 49787.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLarge (\u0026gt;\u0026thinsp;10 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRs. 47336.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eAnnual income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarginal (\u0026lt;\u0026thinsp;1 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRs. 119595.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmall (1\u0026ndash;2 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRs. 161223.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSemi Medium (2\u0026ndash;4 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRs. 230772.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium (4\u0026ndash;10 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRs. 259847.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLarge (\u0026gt;\u0026thinsp;10 ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eRs. 285770.40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eExtension contact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow (\u0026lt;\u0026thinsp;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.67%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium (2\u0026ndash;3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e49.58%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (\u0026gt;\u0026thinsp;3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.75%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eMarketing channel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmers \u0026ndash; Consumer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e39.79%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmers \u0026ndash; Commission agents \u0026ndash; Wholesalers \u0026ndash; Retailers \u0026ndash; Consumer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.75%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmers \u0026ndash; Wholesalers \u0026ndash; Consumer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.92%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmers \u0026ndash; Wholesalers \u0026ndash; Retailers \u0026ndash; Consumer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.17%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmers \u0026ndash; Village traders \u0026ndash; Retailers \u0026ndash; Consumer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.54%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmers \u0026ndash; Village traders \u0026ndash; Wholesalers \u0026ndash; Retailers \u0026ndash; Consumer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.83%\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\u003eThe demographic analysis revealed that the majority of farmers (78.33%) belonged to medium-sized families (4\u0026ndash;7 members), a trend consistent with previous studies (Jha et al., 2012; Boruah et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shree et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This family structure may influence agricultural decision-making and labor availability, both of which are crucial for effective farm management and the adoption of new technologies.\u003c/p\u003e\u003cp\u003eIn terms of educational attainment, most farmers had at least a secondary education, with 27.92% completing higher secondary and 13.12% attaining graduate-level education. The proportion of illiterate farmers (12.50%) was relatively low, indicating a moderate level of literacy in the study area. These educational levels likely influenced the adoption of recommended farming practices, as better-educated farmers tend to have improved access to and a deeper understanding of agricultural innovations. Similar findings have been reported by Jaisawal et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Khode and Palsingh (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Kumar et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), highlighting the positive impact of education on technology adoption. These results underscore the importance of tailored extension approaches, ensuring that training programs are designed to align with farmers' educational backgrounds. Leveraging digital tools, farmer field schools, and interactive training methods could further enhance knowledge dissemination and encourage the widespread adoption of best practices in potato farming.\u003c/p\u003e\u003cp\u003eLandholding data revealed that 58.54% of the farmers fell into the marginal category, followed by 15.84% with small holdings, 11.87% with semi-medium holdings, 9.79% with medium holdings, and 3.96% with large holdings. Previous research has noted similar trends (Jaisawal et al.,2013; Khode \u0026amp; Palsingh, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Across the region, semi-medium farmers had the highest average income from potato farming (Rs. 55,739.92), followed by medium (Rs. 49,778.13) and large farmers (Rs. 47,336.53). The highest mean annual income was observed among large farmers (Rs. 2,85,770.40), followed by medium (Rs. 2,59,847.70), semi-medium (Rs. 2,30,772.40), small (Rs. 1,61,223.90), and marginal farmers (Rs. 1,19,595.30). These results are consistent with findings by (Kulkarni \u0026amp; Jahagirdar, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These trends align with previous research, which indicates that larger landholdings generally contribute to higher overall income, although small and semi-medium farmers may achieve better per-unit land returns. Strengthening support mechanisms for these farmers could help bridge the income gap and promote sustainable potato farming in the region.\u003c/p\u003e\u003cp\u003eMost (49.58%) of the farmers possessed medium level of extension contact, with 36.67% having low and 13.75% having high levels. This may be due to less intensive extension activities and lower farmer participation, which may have limited access to crucial farming information. Similar findings were reported by Kumar et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, in marketing channels, most farmers preferred direct sales to consumers, followed by the Farmers \u0026ndash; Commission agents \u0026ndash; Wholesalers \u0026ndash; Retailers \u0026ndash; Consumer chain (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, which corroborates the results found by Shree et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Descriptive statistics\u003c/h2\u003e\u003cp\u003eThe summary of the analysed results is presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. In potato cultivation, 13 key dimensions were identified under which recommended technologies were suggested. The table presents the adoption rates of different potato production technologies. In this study, most farmers were found to adopt 7 out of the 13 recommended practices, reflecting a moderate level of adoption. This observation is in line with the findings of Mishra et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who reported that approximately 69% of potato farmers demonstrated a medium level of adoption of improved production technologies. The most critical practices for potato farmers were planting time, seed selection/quality, and planting methods, with adoption rates of 73.12%, 57.91%, and 56.66%, respectively. Adhering to proper plant protection measures is crucial for ensuring crop safety; however, 76.67% of farmers fail to follow these practices properly (Datta \u0026amp; Behera, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Yield improvement and maintaining the appropriate seed size are other crucial aspects of potato farming; yet 69.38% of farmers could not produce what the average Indian farmer produces and did not follow the recommended seed size (67.50%) (Abreham \u0026amp; Sete, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, several other essential practices, such as integrated nutrient management, seed production, water management, and intercultural operations, had low adoption rates, with more than half of the selected farmers not implementing them. Reasons for this low adoption could include a lack of awareness, limited access to resources, insufficient technical knowledge, or financial constraints.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the intensity of adopting recommended practices among potato farmers in Northeast India. On average, a potato farmer adopted seven recommended practices. The result also shows that farmers typically adopted at least two recommended packages during potato cultivation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of respondents based on the adoption of the recommended package of practice (N\u0026thinsp;=\u0026thinsp;480)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotato cultivation practices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdopter (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-adopter (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil Management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeed selection/ quality of seedlings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeed size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeed preparation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlanting time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e73.12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntegrated Nutrient Management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlanting method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercultural operations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant protection measures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e76.67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHarvesting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeed production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.04\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\u003eThe findings highlight (Fig.\u0026nbsp;3) that plant protection measures are the most critical training need among potato farmers in northeast India, with 55.00% of respondents expressing a need for training in this area. This aligns with the highest Training Importance Score (TIS) of 73.75%, indicating widespread concerns about pest and disease management. Effective plant protection is crucial in the region, where climatic conditions often favor pest infestations and disease outbreaks, which can significantly impact yield and quality. Similarly, a substantial proportion of farmers require training in integrated nutrient management (50.83%) and seed selection and seedling quality (50.00%), emphasizing the need for knowledge on soil fertility management and the use of quality planting materials to enhance productivity. In contrast, planting time had the lowest TIS (14.67%), suggesting that most farmers are already aware of optimal sowing periods or consider it less challenging compared to other agronomic practices.\u003c/p\u003e\u003cp\u003eThese findings underscore the importance of targeted capacity-building programs focusing on pest control, soil fertility management, and seed quality improvement. Strengthening extension services and providing practical, hands-on training can significantly enhance farmers' ability to adopt recommended practices, ultimately improving potato yields and profitability in the region.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Econometric results\u003c/h2\u003e\u003cp\u003eThe results from the negative binomial regression model, presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, analyze the factors influencing the adoption of recommended package of practices. It was found that Age has a significant negative coefficient of -0.039, indicating that as farmers get older, they are less likely to adopt the recommended practices, possibly due to a reluctance toward new technologies. Prokopy et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) found that age has a negative relationship with adoption, as older farmers are less likely to change practices due to a shorter planning horizon. The variable for gender, with a coefficient of 0.085, is significant at the 10% level, indicating that male farmers are more likely to adopt the recommended package of practices compared to female farmers, a similar result found by Teklewold et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Education shows a clear positive relationship with adoption intensity with a coefficient of 0.0789) which are significant at the 1% level. This indicates that higher educational attainment encourages farmers to adopt recommended practices. Further, it also suggests that higher education levels substantially increase the probability of adoption. This result pertains to the results of Kolady et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e. Income from potato farming exhibits a positive and significant relationship with adoption intensity, with a coefficient of 0.00000890 at the 1% significance level. Strong positive influence, meaning higher income from potato farming encourages the adoption of recommended practices. The annual income, with a coefficient of 0.000000592, is significant at the 5% level, indicating that farmers with higher annual incomes are more likely to adopt the recommended practices (Ketema et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Manjunatha et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The marketing channel\u0026rsquo;s coefficients are statistically significant at the 1% level. Efficient marketing channels provide better price realization, market access, and timely dissemination of market intelligence, thereby encouraging farmers to adopt recommended practices. Similar results were also identified by Hao et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Onumah et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Other variables, such as family size, land holdings, and extension contact, were found to be non-significant, indicating that these variables may not have a significant impact on the adoption decision-making process.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated coefficient and marginal effect from the negative binomial regression model\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eNegative binomial regression\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.039**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.085*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily Size\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0254NS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0789***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSize of Land Holding\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2689NS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.354\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncome from Potato\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00000890***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00000240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual Income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000000592**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00000026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExtension Contact\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0423NS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarketing Channel\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0345***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWald χ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog-likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe dependent variable is the Adoption intensity of recommended potato farming practices. The number in parentheses denotes a robust standard error. In denotes the natural logarithm. *, **, *** denotes significance at 10%, 5% and 1% respectively.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Constraints faced by the farmers in cultivation and management of potato farming and their severity\u003c/h2\u003e\u003cp\u003eThe challenges faced by potato farmers in northeast India were categorized into nine major sections: production, financial, institutional, situational, infrastructural, technical, extension, marketing, and storage constraints, each encompassing several specific issues. The individual problems within each section were ordinally ranked, and an overall ranking of the nine sections was established based on mean severity scores (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProduction constraints emerged as the most significant category, ranked first among the nine based on the overall mean score. The primary issue in this category was the lack of knowledge regarding balanced fertilizer application and the timely availability of fertilizers, which had a severity index of 0.83, consistent with the findings of Debey et al. (2020) and Maulidiyah et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This was followed by inadequate awareness of pest and disease management, with a severity index of 0.70, and the high cost of agricultural inputs, which had a severity index of 0.60 (Atreya \u0026amp; Kafle, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Awasthi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The storage constraints ranked second overall and remain as a major concern, with the most pressing issue being the absence of proper storage facilities, recording a severity index of 0.88, followed by storage losses with a severity index of 0.43 (Deka et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Extension constraints occupied the third rank, with inadequate or non-existent government-provided training programs highlighted as the most significant problem, as reflected by a severity index of 0.73. This was followed by a lack of live demonstrations for new agricultural practices, with a severity index of 0.60, and infrequent visits from extension personnel, scoring 0.39 (Katayani et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSituational constraints were ranked fourth, with the main problem being the distant location of markets, which had a severity index of 0.86, followed by the remoteness of farmland. Ranked fifth, marketing constraints primarily involved the influence of middlemen, with a severity index of 0.46, in addition to issues associated with surplus production. Technical constraints, which ranked sixth, were primarily attributed to a lack of farm mechanization, with a severity index of 0.67, followed by the unavailability of new technologies (severity index of 0.46) and threats from wild animals (severity index of 0.39), a similar result also identified by Vaid, (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Infrastructural constraints, ranked seventh, were dominated by the absence of proper tools and implements, with a severity index of 0.70. Similarly, financial constraints also ranked seventh based on overall mean scores, were characterized by inadequate credit facilities, with a severity index of 0.90, insufficient subsidies (severity index of 0.78), high interest rates (severity index of 0.66), short repayment periods (severity index 0.39), and limited personal financial resources (severity index 0.28). Lastly, institutional constraints were ranked ninth, with the primary issue being a lack of support from agricultural and allied departments, having a severity score of 0.78 (Nzomoi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). To overcome these challenges, particularly in production, the government should organize adequate training programs to improve farmers' knowledge of pest and disease management, balanced fertilizer application, and integrated nutrient management. Additionally, timely access to quality agricultural inputs at reasonable prices is crucial for building farmers' confidence and promoting large-scale, sustainable potato cultivation. Addressing storage constraints will require establishing adequate warehouse facilities to prevent storage losses and enable farmers to obtain better prices during periods of high market demand.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMajor constraints faced by the potato farmers of northeast India with their severity indices and rank\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstraints\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeighted Mean Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSeverity index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOverall Mean Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI. Production Constraints\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of knowledge on balanced fertilizer application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of knowledge regarding pests and diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh cost of input\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of input (seed, Fertilizer) supply\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eII. Financial Constraints\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInadequate credit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eVIII\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInadequate subsidy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh interest rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsufficient repayment time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of own resource\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIII. Institutional constraints\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of support from the agricultural department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIX\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of SHG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of cooperation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIV. Situational constraints\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistant location of the market\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistant location of land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor transport facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eV. Infrastructural constraints\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of tools and implements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eVII\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of availability of land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of irrigation facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of established structure for livestock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVI. Technical constraints\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of mechanization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnavailability of new technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWild animal threats\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVII. Extension constraints\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInadequate training / No training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Demonstration for new practices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo or very few visits of extension personnel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVIII. Marketing constraints\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarketing middleman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurplus production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIX. Storage constraints\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of proper storage facilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStorage loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Food Insecurity Experience Level\u003c/h2\u003e\u003cp\u003eThe incidence of food insecurity among potato farmers in Northeast India, particularly those from purposively selected states such as Assam, Meghalaya, Nagaland, and Tripura, highlights significant challenges in ensuring consistent access to food. The data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e) reveals considerable variation between states, with 15.83% of respondents across the region expressing concern about running out of food due to a lack of resources. Meghalaya recorded the highest proportion at 22.5%, while Tripura had the lowest at 9.17%. A notable issue was the inability to access healthy and nutritious food, affecting an average of 27.92% of respondents. This concern was most acute in Meghalaya (30%) and Nagaland (29.17%). Limited food variety emerged as the most frequently reported form of food insecurity, with 30.83% of respondents stating they could only eat a few kinds of food due to resource constraints. This issue was most prevalent in Assam (35%). Global food, nutrition and agriculture agencies project that by 2030, all forms of malnutrition are likely to rise, with nearly half of the world\u0026rsquo;s population expected to be affected (Ville et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To mitigate this food insecurity challenge and achieve Sustainable Development Goal (SDG-2) in NE India, technological adoption must be prioritized, thereby increasing crop production and yield.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e also highlights that 14.37% of respondents in the region skipped meals due to a lack of resources, with Nagaland reporting an alarming 33%, while Tripura had the lowest proportion at 7.5%. Furthermore, 7.29% of respondents indicated that they ate less than they felt they needed, with Assam (10%) being the most affected. Households running out of food due to resource shortages affected 23.54% of respondents, with Assam again reporting the highest prevalence at 27.5%. Hunger without access to food was reported by 5.62% of respondents, with Meghalaya (7.5%) experiencing the highest percentage. The most severe form of food insecurity, going without food for an entire day, was reported by 3.54% of respondents. Tripura had the lowest percentage at 1.67%, indicating relatively better conditions but still highlighting extreme cases of food insecurity (Anantharaman et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings underscore the complexity of food insecurity in Northeast India, which is influenced by socio-economic inequalities, agricultural constraints, and limited access to resources. The high levels of insecurity in Assam and Meghalaya underscore the need for targeted interventions to enhance food availability and affordability. Additionally, the high prevalence of skipped meals in Nagaland highlights structural challenges that require urgent attention. Research on food insecurity emphasizes that it encompasses not only adequate calorie intake but also access to diverse and nutritious food, which is crucial for health and development (Montalvo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Addressing these issues calls for comprehensive strategies, including policy reforms to support agriculture, enhanced food distribution networks, and targeted financial assistance for vulnerable populations.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003ePotatoes are one of the staple foods in the daily diet of Northeast Indians, and the government is focusing on increasing the production level, as productivity is currently low (8.55 MT/ha) compared to the national average of 25.79 MT/ha. Apart from that, the adoption of recommended potato production technologies needs to be addressed, as the maximum number of farmers adopting the recommended practices stands at 7 out of 13, which is a concern that directly impacts productivity. The majority of farmers adopted recommended practices, including planting time, seed selection and quality of seedlings, and planting methods. Whereas limited adoption was recorded in the case of plant protection measures, yield and seed size. Econometric results using negative binomial regression suggested that gender is significant at the 10% level. Further, for Annual income, the marketing channel is positively significant at the 5% level, but age is negatively significant at the 5% level. Whereas variables like education and income from potatoes are highly and positively significant at a 1% level. Furthermore, among the nine different categories of constraints faced by potato farmers in NE India, production constraints, followed by storage constraints and extension constraints, are the most significant.\u003c/p\u003e\u003cp\u003eThe findings of this study emphasize the need for policymakers to strengthen institutional support for potato farmers, thereby enhancing the adoption of recommended farming practices. Key strategies could include improving access to agricultural extension services, ensuring timely delivery of inputs, promoting credit accessibility, and offering targeted training programs that focus on recommended agronomic practices. Additionally, encouraging farmer participation in decision-making processes and fostering collective action through farmer groups or cooperatives can enhance the dissemination and adoption of technology. Policy interventions should also aim to address context-specific challenges faced by smallholder farmers, thereby ensuring that technological innovations are both accessible and relevant to diverse farming contexts.\u003c/p\u003e\u003cp\u003eWhile the study provides valuable insights into adoption intensity, it is not without limitations. The sample size, though adequate for local-level analysis, may not fully reflect variations in adoption behavior across different regions or agro-ecological zones. Furthermore, the study's reliance on cross-sectional data restricts the ability to establish long-term trends and causal relationships between adoption and the influencing factors. As a result, the conclusions drawn may be limited by the specific socio-economic and institutional context in which the study was conducted. Addressing these limitations in future studies could strengthen the generalizability of findings. Future research could build upon the findings of this study by expanding the geographic scope and incorporating longitudinal data to gain a deeper understanding of the dynamic nature of adoption behavior over time. Moreover, exploring the potential impact of emerging agricultural technologies, such as precision farming, climate-smart practices, and digital advisory tools, could provide deeper insights into how technological adoption can be enhanced in the potato farming sector. Research focusing on gender dimensions, labor dynamics, and environmental sustainability within the context of technology adoption would also offer a more holistic perspective on the socio-economic and environmental implications of recommended agricultural practices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The authors declare that they did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e: The data will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information (SI)\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSupplementary file 1 with interview schedule, 2 with the list of independent variables used with reasoning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e: \u003cstrong\u003eConceptualization\u003c/strong\u003e: Rajib Das, Kaushal Kumar Jha; \u003cstrong\u003eMethodology\u003c/strong\u003e: Rajib Das, Kaushal Kumar Jha, Pudhuvai Baveesh; \u003cstrong\u003eFormal analysis and investigation\u003c/strong\u003e: Soumitra Sankar Das, Rajib Das; \u003cstrong\u003eWriting - original draft preparation\u003c/strong\u003e: Rajib Das; \u003cstrong\u003eWriting - review and editing\u003c/strong\u003e: Rajib Das, Pudhuvai Baveesh; Kaushal Kumar Jha, Bhupendra Koul, Meerambika Mishra and Muhammad Fazle Rabbee;\u0026nbsp;\u003cstrong\u003eResources\u003c/strong\u003e: Kaushal Kumar Jha, Bhupendra Koul; \u003cstrong\u003eSupervision\u003c/strong\u003e: Kaushal Kumar Jha.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e: All the authors read this article and have no relevant financial or non-financial interests to disclose. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board/Ethics Statement:\u0026nbsp;\u003c/strong\u003eThis study was approved by the Academic Council (AC) of \u0026nbsp;Nagaland University, Nagaland, in the year 2021.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbreham, G., \u0026amp; Sete, Y. 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Food Security, 11, 483\u0026ndash;491. https://doi.org/10.1007/s12571-019-00936-9\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Food security, Negative Binomial Regression, Farmer challenges, Potato, NE India","lastPublishedDoi":"10.21203/rs.3.rs-7663781/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7663781/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePotato is a crucial vegetable crop in the North-eastern hilly states of India and tops among most widely consumed staple foods in the region. However, potato productivity remains significantly below the national average, with a yield of 8.55 MT/ha compared to the national figure of 25.79 MT/ha. Despite the availability of advanced technologies, challenges persist regarding data availability, access to technology, and infrastructure in rural areas of Northeast India. Farmers often lack knowledge of efficient irrigation schedules, soil management and crop diversification strategies to combat the climatic variation due to technological barriers. The present study evaluates adoption of recommended potato production technologies and to identify the constraints faced by potato farmers. The research involved data collection from a purposive sample of 480 farmers across Assam, Meghalaya, Nagaland, and Tripura. The study employed percentage analysis to describe the socio-personal characteristics of farmers and to assess the level of adoption or non-adoption of the recommended practices. To identify the determinants of adoption, negative binomial regression was applied. In addition, the constraints experienced by farmers were systematically examined using a severity index and weighted mean score. The results indicated that the most critical practices for potato cultivation were planting time, seed quality/selection, and planting methods, with adoption rates of 73.12%, 57.91%, and 56.66%, respectively. Among the 13 recommended practices, most farmers adopted seven practices. The negative binomial regression analysis revealed that factors such as education and potato farming income had a significant positive effect at the 1% level on adoption rates. Furthermore, the main challenges identified among the nine categories of constraints were production issues, followed by limitations in storage and extension services. Policy interventions should aim to improve access to agricultural extension services, ensuring the timely delivery of inputs, promoting credit accessibility, and offering targeted training programs that focus on recommended agronomic practices, which remain an ideal strategy for North East (NE) India. Additionally, active involvement of farmers in decision-making and the promotion of collective action through groups or cooperatives can play a vital role in improving the dissemination and adoption of new technologies.\u003c/p\u003e","manuscriptTitle":"Evaluation of the Adoption of Potato Production Technology and Identification of Farmer Challenges for Enhancing Food Security in Northeast India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 12:47:23","doi":"10.21203/rs.3.rs-7663781/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"653f8c40-3cc2-4d60-bfd1-57566a93e9c4","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-10T23:38:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 12:47:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7663781","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7663781","identity":"rs-7663781","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}