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Abstract

The state of Tripura, with favourable agro-climatic conditions, offers high potential for diversified horticultural crop production. Despite initiatives like the Horticulture Technology Mission, a notable adoption gap remains. This study (2021–22) in four districts—West Tripura, Khowai, Dhalai, and South Tripura—used an ex-post facto design. One high- and one low-performing RD block for pineapple, turmeric, potato, ginger, and green chilli were purposively selected per district. From 10 villages, 600 farmers were randomly chosen, and data collected via a pre-tested interview schedule.

Most respondents (65.67%) were aged 30–50 years; 44.50% belonged to Scheduled Tribes, and 37.0% had secondary education. Medium scientific orientation (64.17%) and market orientation (76.83%) dominated. Adoption gap was significantly associated with age, caste, education, landholding, income, extension contact, scientific orientation, and market orientation. Pineapple recorded the highest mean adoption gap (71.98%), followed by ginger (70.98%), turmeric (67.11%), green chilli (63.08%), and potato (50.64%). Major gaps occurred in insect-pest and disease management (up to 93.66%), water, and fertilizer management. Economic losses per hectare were Rs. 280,800 (ginger), Rs. 226,350 (pineapple), Rs. 127,500 (green chilli), Rs. 126,150 (potato), and Rs. 41,700 (turmeric). Benefit–cost ratio (BCR) analysis showed potato (2.48), green chilli (2.12), and pineapple (1.98) as more viable, while turmeric (1.38) gave marginal returns. The study underscores the need for crop-specific, stage-wise training, stronger extension services, and efficient input delivery to reduce adoption gaps, improve productivity, and ensure sustainable horticulture in Tripura.

Introduction

India has established itself as a global leader in the cultivation of diverse fruits and vegetables. Over recent years, the horticulture sector has recorded notable growth, with total annual production reaching 354.74 million tonnes and the cultivated area expanding to 29.27 million hectares (MoA & FW 2024). Recognized as the “fruit and vegetable basket” of the world, the country ranks second only to China in overall fruit and vegetable output. Current estimates indicate a production of 114.51 million metric tonnes of fruits and 219.67 million metric tonnes of vegetables. The nation’s diverse climatic zones and varied physiographic features provide highly favorable conditions for the cultivation of a broad spectrum of horticultural crops, including fruits, vegetables, flowers, nuts, spices, and plantation crops. Strategic emphasis on horticulture has brought about remarkable advancements in technology adoption, production efficiency, and the year-round availability of horticultural commodities (Roy et al. 2013; Lotha & Jha 2022).Today, horticultural crops contribute about 33% to the agriculture Gross Value Added (GVA) and cover approximately 25% of the total agricultural exports of the country (MoA and FW 2024). The corporate sector has increasingly demonstrated interest in horticulture, recognizing its economic potential and market opportunities. In the coming decades, significant changes in consumer preferences for both fresh and processed fruits and vegetables are anticipated. Diversification of produce and value addition are expected to remain central themes driving the growth and competitiveness of Indian horticulture in the 21st century. At present, the country’s primary export destinations for horticultural products are the South Asian and Middle Eastern markets (DoA & FW 2025).

The North Eastern (NE) region of India encompasses a geographical area of approximately 26.20 million hectares, accounting for nearly 8% of the nation’s total land area, and is home to around 45.50 million people (Das et al. 2024). Of this total area, roughly 35% comprises plains, while the remaining 65% is hilly terrain. The region’s climatic conditions are highly conducive to the cultivation of a wide range of fruits, vegetables, spices, and other horticultural crops (DoA & FW 2025). Its diverse agro-climatic zones, varied soil profiles, and abundant rainfall provide exceptional potential for producing fruits, vegetables, flowers, plantation crops, tubers, rhizomes, and numerous crops of medicinal and economic importance. Furthermore, the NE region boasts a rich genetic diversity in vegetable crops, encompassing both indigenous tropical varieties and temperate species (Vaid 2020).

The state of Tripura is endowed with the favourable agro-climatic conditions for growing diverse horticultural crops including vegetables, fruits and spices (Das and Majumder 2022). Horticulture Technology Mission has taken many initiatives to promote higher productivity. This development has brought new technology and investment, which has impacted production, productivity and availability of horticultural produce (Sengar and Rani 2020) and Tripura exports about 3.4 crores rupee worth of horticultural produce every year (Directorate of Economics and Statistics 2024). The complexity of modern agricultural technologies often poses challenges for farmers, making it difficult for them to fully comprehend and recall all required operations. Consequently, this hampers the adoption of recommended packages of practices (Kalet & Ruston, 2019). Additional constraints include limited awareness, unavailability or high cost of inputs, adherence to traditional methods, inadequate skills, and reluctance to embrace innovations. As emphasized by Tegegne (2017), technologies yield no benefit unless they are effectively adopted by the farming community. In many cases, recommended techniques fail to reach farmers, and those that do often undergo distortion or partial adoption, preventing farmers from achieving optimal yields. This situation has resulted in a considerable adoption gap in crop production at the farm level (Liu et al. 2018). Complete acceptance of agricultural technologies is rare, with adoption gaps identified as a significant barrier to enhancing production in several states (Goudappa et al. 2012; Gupta et al., 2021). Bridging this gap between research recommendations and on-field adoption is essential for improving agricultural productivity (Kulkarni & Jahagirdar 2015). However, as noted by Sharma and Sidhu (2013), technological transformation in agriculture is a multifaceted process and often more challenging than commonly perceived. In case of the new technologies developed by the agricultural universities and research institutes, it has been observed that either the same has not reached to the farmers' field or farmers are reluctant to use this technology. Tripura is also one of the major producers of horticultural crops like potato, green chilli, pineapple, guava, ginger and turmeric have immense economic importance in local, regional, national and international market. In Tripura few districts lead in the area and production of these horticultural crops whereas other districts are lagging behind. Though, government of Tripura has recommended suitable package of practices for almost all horticultural crops but there exists a wide gap in the area and production of these crops among the districts. Keeping these points in view, the present study titled “Adoption Gaps and Economic Implications in Horticultural Crop Cultivation: A Study of Farmers in Tripura” were undertaken with following specific objectives to know, socio - economic, personal & psychological characteristics of the respondents and their influence, status of knowledge and adoption gaps at farmer’s level with respect to recommended package of practices of the selected horticultural crops and estimated economic loss due to adoption gaps

Methodology

Study Location and Research Methodology

Ex-post-facto research design was used in the present research. There are eight districts in Tripura. Out of these, the districts having highest and lowest area and production in relation to selected horticultural crops namely, pineapple, turmeric, potato, ginger and green chilli were selected. Based on these criteria, West Tripura (23.8330° N, 91.2697° E), South Tripura (23.2317° N, 91.5596° E), Dhalai (23.8467° N, 91.9099° E) and Khowai (24.0689° N, 91.6038° E) districts were selected for the present study. From these selected districts, one RD block having highest and the other RD block having lowest area and production was selected purposively. Thus, Ambssa and Teliamura RD blocks were selected under Dhalai and Khowai districts respectively for the pineapple as well as turmeric crops. Similarly, Rajnagar and Mohanpur RD blocks were selected under South Tripura and West Tripura districts respectively for the potato crop. Further Satchand and Manu RD blocks were selected under South Tripura and Dhalai districts respectively for the ginger crop. Rajnagar and Padmabil RD blocks were selected under South Tripura and Khowai districts respectively for the green chilli crop for the present study.

A list of all villages growing pineapple, turmeric, potato, ginger and green chilli under the selected RD blocks was prepared. This list was further divided in two groups of villages having high and low area and production in relation to the above crops. Further, one village from the high and other from the low group in terms of the area and production of the respective crops were selected. Thus a total ten villages namely Bagmara, Kulai RF, Demcharra, Gayamanibari, Tuichindrai, Hawaibari, Manubazar, Barpathari, Chittamara and Bamutia were selected. There were altogether 11474 households in Bagmara, Kulai RF, Demcharra, Gayamanibari, Tuichindrai, Hawaibari, Manubazar, Barpathari, Chittamara and Bamutia villages. Out of this about 75 per cent of the households (7105) were engaged in cultivating pineapple, turmeric, potato, ginger and green chilli which constituted the universe. Thus 600 farmers were selected as sample respondents by selecting 60 farmers randomly from each of these selected villages (Figure 1).

                                                                           Fig.1: Layout of sampling design.

Data Collection

A pre-tested interview schedule directed towards the objectives of the study was used as the tools for data collection. The schedule was prepared with references from similar research materials from within and outside the institution. Research schedule was prepared in consultation with the experts in the field of agricultural extension by justifying objectives of the study. The schedule was divided into five parts. The first part consisted of the general information; second part consisted of socio-economic characteristics of the respondents. The third part consisted of status of selected horticultural crop cultivation and the fourth and fifth parts consisted of attitude and constraints faced by the respondents. Judges rating for statements as well as relevant scales were included so that data collected with the help of the developed schedule was reliable. Before the actual interview, a pilot study with a preliminary .interview was conducted in the selected villages; a sample of 20 respondents which did not constitute the respondents sample was selected for pre-testing the schedule. Based on the pre-tested results, few difficulties and ambiguous questions were deleted from the draft schedule. For the present study primary data was collected through personal interview by the researcher and secondary data were collected from various publications, magazines, relevant text books and other sources.

Empirical measurement of Socio - economic, personal, psychological variables

The empirical measurement of socio - economic, personal, psychological variables are shown in Table 1.

Table1 Empirical measurement criteria of the independent variables

Adoption model

To examine the determinants influencing the adoption of the “Recommended Package of Practices” among selected horticultural crop growers, two probit regression models were employed. The dependent variable Y_t is binary in nature, taking the value 1 if the farmer adopted the recommended practices, and 0 otherwise. The conceptual framework posits that adoption decisions are shaped by various factors, including demographic attributes of the farm household, access to human resources, availability of physical and social capital, psychological disposition, and economic orientation. Grounded in decision theory, it is assumed that farmers aim to maximize their utility U_t which is a function of the aforementioned socio-economic characteristics and the perceived benefits associated with adoption.

Let, U₁ and U₀ represent the utility derived from adopting and not adopting the recommended practices, respectively. A farmer will choose to adopt if the difference in utility, denoted by the latent variable is greater than zero. Conversely, if the adoption decision will not be made, assuming all other factors remain constant.

By expressing the socio-economic factors as a vector of covariatesthe latent utility difference can be represented in a linear form:

(1)

Accordingly, the probability of adoption can be expressed as:

(2)

and the probability of non-adoption as:

(3)

In this specification, the vector of explanatory variables influences both the latent adoption propensity and the observed binary outcome in the same direction, as established in related empirical literature.

Conceptual Framework

The conceptual framework illustrates the process of technology adoption among farmers, emphasizing the various levels of adoption outcomes—full adoption, partial adoption, and non-adoption or rejection. The adoption pathway begins with the introduction of a new technology and proceeds through three critical stages: knowledge, persuasion, and decision. Initially, farmers gain awareness and understanding of the technology (knowledge), followed by forming an attitude or opinion toward it (persuasion), and finally making a choice to either adopt or reject the technology (decision). This decision-making process is not isolated; it is significantly influenced by a set of interrelated factors categorized into five domains: demographic characteristics, human resources, physical and social capital, psychological attributes, and economic orientation. Demographic factors such as age, education, and experience affect a farmer’s openness to new ideas, while human resources pertain to access to skilled labor and training (Krause et al. 2016). Physical and social capital includes assets like land and irrigation, along with membership in cooperatives or farmer groups. Psychological factors involve risk perception, innovativeness, and confidence, whereas economic orientation reflects the farmer’s financial capacity, income level, and market involvement. Depending on how these influencing factors align with the individual’s perception and ability, the outcome may be full adoption—where the technology is implemented entirely; partial adoption—where only certain aspects are adopted, leaving an adoption gap; or complete rejection—resulting in the widest adoption gap. The framework underscores that the adoption of technology is not merely a technical decision but a dynamic and context-specific process shaped by a farmer's social, economic, and psychological environment (Figure 2).

                            Fig. 2: Conceptual framework to identify technology adoption process & adoption gap.

Analysis of Data

The data collected from the respondents were scored, sorted, tabulated and analysed to calculate frequency, percentage, mean, standard deviation and correlation. Adoption model was developed to find a valuable conclusion.

Knowledge

Knowledge occurs when an individual or other decision making unit is exposed to an innovation’s existence and gains some understanding of how it functions. Knowledge seeking is initiated by an individual and is greatly influenced by one’s predispositions (Ray 2013). In the present study, schedule of knowledge include two aspects and their frequency of use was scored as yes (1) and no (0) and from that knowledge index was also calculated. Based upon the score obtained by the farmers, they were further classified into low, medium and high knowledge group.

Knowledge index was calculated by using the following formula

Where, = Total score obtained by the respondent;

                                 \(\text{Adoption gap (\%)} = \frac{x-y}{x} \times 100\)

Adoption gap

Adoption gap has been defined as the proportion of gap in the adoption of practices recommended and expressed in percentage (Ray et. al., 1995). Following formula was used to find out the technological gap:

$$\text{Knowledge index} (\%) = \frac{S_o}{S_m} \times 100$$

\(Where, x= Total number of recommended practices for each crops, y= Total number of practices actually adopted\)

Results and Discussion

The analysis of data presented in the table revealed that a significant proportion (65.67%) of the selected horticultural farmers belonged to the age group of 35-50 years, followed by 30.50% in the category of above 50 years and 3.83% in the age bracket below 35 years. It was found that 44.50% of the horticultural farmers belonged to Scheduled Tribes (ST), followed by 22.0% Scheduled Castes (SC), 14.67% Other Backward Classes (OBC), 13.33% general category, and 5.50% from other caste groups. The average age of the respondents was recorded at 45.84 years. This trend may be attributed to the fact that farmers in the medium age group tend to possess greater farming experience and exhibit higher motivation for pursuing potato cultivation as a means of livelihood. This dominance of ST farmers could be linked to the prevalence of crops such as pineapple, ginger, and turmeric in the hilly areas of Tripura, predominantly inhabited by tribal communities. Similar observations were reported by Wase (2001), Kafle and Shah (2012), and Chavai et al. (2015). Data further showed that 81.17% of the farmers lived in nuclear families, while the remaining 18.83% resided in joint family structures. This reflects the increasing prevalence of nuclear family setups in the region, consistent with the observations of Singh et al. (2014). Regarding the education, 94.67% of the farmers were literate, with 37.0% having attained secondary-level education. A higher literacy rate may have positively influenced their capacity to adopt recommended agricultural practices, corroborating findings from Arneja et al. (2009) and Chavai et al. (2015). The farming experience data indicated that 66.16% of farmers had moderate experience, followed by 20.67% with high experience and 13.17% with low experience. Moderate experience may have contributed to narrowing the adoption gap in adopting improved practices, consistent with the findings of Kiran (2003) and Raghavendra (2007). 86.16% of farmers had medium level of extension contact, 12.17% had high and 1.67% had low extension contact, possibly due to limited extension activities and farmer participation. These findings are in consonance with that of Singh (2014).

Landholding patterns revealed that 36.17% of farmers were marginal landholders, followed by 27.0% in the semi-medium category, 25.00% smallholders, 10.50% medium, and 1.33% with large holdings. Similar trends were reported by Jaisawal et al. (2013). Income analysis showed that 87.83% of farmers earned less than Rs. 30,000 annually from salaried sources, 87.0% had a similar income level from wages, and 44.50% earned between Rs. 30,000 and Rs.70,000 from farming. Additionally, 70.00% of farmers earned below Rs. 30,000 from sources such as rubber plantations and animal husbandry, with the average annual income recorded at Rs. 167,283.33. These findings align with those of Kulkarni and Jahagirdar (2015). The Social participation analysis indicated that 61.16% of farmers had low levels of participation, while 31.17% and 7.67% exhibited medium and high levels, respectively. Limited social engagement might be a consequence of restricted exposure to external networks, a finding consistent with Jaisawal et al. (2013).

Scientific orientation analysis revealed that 64.17% of farmers exhibited moderate scientific orientation, 22.0% had low levels, and 13.83% had high levels, which may have driven moderate adoption of advanced farming techniques. These findings align with Kalita and Chabukdhara (2014). Market orientation analysis indicated that 76.83% of farmers preferred selling produce in local markets due to lower transportation costs and inadequate marketing infrastructure, while only 23.17% sold in distant markets like Ambassa, Agartala, Udaipur, Belonia, and Manu.

Table 2 revealed that the independent variables like age and caste had positive and significant association with the dependent variable ‘Adoption gap’ at 1% level of probability. This inferred that technological gap of the farmers were higher in the respondents who were old in age. Similar findings were observed by Hussain et al. (2010). Majority of the farmers belonged to the ST, SC and OBC category. So it may be inferred that these farmers had higher adoption gap than other category of the selected respondents. The variable education, size of land holdings, market orientation, extension contact, sources of information utilized, scientific orientation and knowledge had negative and significant association with the dependent variable ‘adoption gap’ at 1% level of probability. Thus it may be inferred that farmers have high level of education, big land holding size, high marketing orientation, high extension contact, higher utilization of information sources, high degree of scientific orientation and higher level of knowledge in the recommended practices had less technological gap in cultivation of horticultural crops. These findings were similar to the findings of Kumar et al. (2008), Naruka et al. (2010) and Marak and Bandyopadhyay (2015). It was also revealed that the variable annual income had negative and significant association with the variable ‘Adoption gap’ at 5% level of probability. Thus it may be inferred that farmers having higher income had low adoption gap. These findings were similar to the findings of Kumar et al. (2008). Thus, it may be inferred that farmers young in age having moderate education shall be selected for imparting training to reduce the adoption gap. Further specialized training programme may be conducted for the small and marginal farmer having low annual income, marketing orientation, mass media exposure so that their marketing orientation and knowledge is increases and adoption gap is minimized.

\(Table 2 Socio - economic, personal, psychological characteristics of the respondents and their correlation with dependent variable “Adoption gap” (N=600).\)

\(Note: *, ** denote significance at the 5%, and 1% levels respectively; NS = Not Significant\)

Status of knowledge and technology adoption of farmers

It was found from Table 3 that majority (64.50%) of the pineapple growers had medium knowledge level followed by 18.67 per cent of them had low and 16.83 per cent of them had high knowledge level. Further, 79.16 per cent of the turmeric growers had medium knowledge level followed by 14.67 per cent of them had high and 6.17 per of them had low knowledge level respectively. Majority (58.17%) of the potato growers had medium knowledge level followed by 22.83per cent of them had high and 19.0 per cent of them had low knowledge level respectively. In case of ginger growers majority (64.5%) of them had medium knowledge level followed by 21.0 per cent of them had high and 14.5 per cent of them had low knowledge level respectively. In case of green chilli growers majority (65.00 %) of them had medium knowledge level followed by 22.50 per cent of them had high and 12.50 per cent of them had low knowledge level respectively.

\(Table 3 Distribution of respondents based on their knowledge level about the recommended practices of the selected horticultural crops (N=600).\)

Among the selected crops, potato growers exhibited the highest knowledge index at 73.31%, with a greater proportion of respondents in the high knowledge category (22.83%) and the highest mean knowledge score (11.73). Green chilli growers followed with a knowledge index of 55.45%, while turmeric growers recorded a moderate knowledge index of 49.82%. The ginger and pineapple growers exhibited relatively lower knowledge indices, at 46.20% and 44.74%, respectively. The differential knowledge indices across crops suggest that the extent of awareness and understanding of improved production technologies is not uniform among horticultural farmers. The higher knowledge index among potato growers may be attributed to the long-standing exposure to extension services, better organized seed distribution systems, and access to input and market information. Similar observations were made by Prasad et al. (2018), who reported a positive correlation between training exposure and knowledge level among vegetable growers in Himachal Pradesh. The low knowledge index among pineapple and ginger growers could be due to several factors including limited institutional support, inadequate training on scientific practices, and low frequency of extension contacts in remote tribal areas of Tripura. Ranjan et al. (2014) emphasized that geographical isolation and weak extension networks in Northeast India hinder knowledge dissemination.

Technology adoption gap in recommended practices of the selected horticultural crops

The crop-wise regression analysis (Table 4) revealed significant influences of socio-economic, personal, and psychological characteristics on the adoption gap across five selected horticultural crops, i.e pineapple, turmeric, potato, ginger, and green chillies. The adjusted coefficients were interpreted to maintain consistency with the overall correlation trends.

Among the demographic factors, age was positively and significantly associated with the adoption gap for all crops, indicating that older farmers tend to exhibit higher levels of adoption lag. Similarly, caste showed a positive and significant effect across all crops, suggesting that ST farmers exhibited greater adoption gaps, possibly due to their engagement with diverse information but selective adoption practices. In the domain of human resources, a notable negative and statistically significant association was observed between education and the adoption gap for all crops. This finding implies that higher educated farmers exhibited lower adoption gaps, consistent with the idea that education enhances awareness and receptivity to new technologies. Extension contact also exhibited a significantly negative influence, reaffirming that increased interaction with extension agents reduces the adoption gap by improving access to technical knowledge and support. Under physical and social capital, landholding size and annual income showed negative and significant associations with the adoption gap across all the selected crops. This implies that farmers with larger landholdings and higher income levels were more likely to adopt the full package of recommended practices. Social participation was non-significant, suggesting limited influence on the adoption gap despite its relevance in social capital theories. Further, psychological factor and Economic orientation were found negative and significantly associated with the adoption gap in each selected crops. These findings indicate that farmers who are more scientifically inclined, economically motivated, and market-oriented tend to adopt technologies more completely, thus minimizing the adoption gap. The models demonstrated moderate explanatory power, with Pseudo R² values ranging from 0.397 (potato) to 0.489 (pineapple), indicating that a considerable proportion of variance in adoption gap could be explained by the selected variables.

The present study identified key socio-economic, human, and psychological factors influencing the adoption gap across five selected horticultural crops in Tripura. The crop wise regression models aligned with overall correlation trends, offering robust insights into technology adoption behaviour in a hill-agro ecosystem. The positive and significant relationship between age and adoption gap across all crops suggests that older farmers are more reluctant or slower in adopting the full package of recommended practices. This supports earlier findings by Adesina and Baidu-Forson (1995), who observed that older farmers are less likely to adopt new technologies due to risk aversion or low expected returns on investment. Similarly, the positive association of caste with adoption gap reflects underlying social disparities, wherein ST farmers, despite access to information, may show greater selectivity or delay in full adoption, consistent with the caste-based disparities in extension benefits reported by Meena et al. (2019).

The negative association of education with adoption gap across crops affirms that education enhances farmers' ability to understand, evaluate, and implement recommended technologies. This finding corroborates studies by Feder et al. (1985) and Meena et al. (2017), which emphasized the critical role of education in reducing adoption knowledge gaps and enhancing adoption. Notably extension contact was negatively and significantly associated with adoption gap, supporting the foundational work of Rogers (2003), who emphasized interpersonal communication and extension as vital in the diffusion of innovations. Frequent contact with extension agents likely mitigates misconceptions and promotes clarity about recommended practices, as seen in the studies by Babu et al. (2012) in the context of Indian agriculture. The study also revealed that larger landholding and higher income levels were associated with a lower adoption gap. This is in line with findings by Doss (2006), who reported that resource rich farmers are better positioned to bear the costs and risks associated with new technologies. Additionally, income security enhances farmers confidence in experimenting with newer technologies, leading to more comprehensive adoption. Among psychological factor and economic orientation all variables negatively influenced the adoption gap, emphasizing the role of progressive mindset and market linkages in driving technology use. Farmers who are scientifically oriented are more likely to seek knowledge actively and assess the long-term benefits of innovations, as argued by Panneerselvam et al. (2012). Similarly, market-oriented farmers prioritize productivity and profitability, aligning their practices with consumer demand and market dynamics (Barrett 2008). Overall, the findings underscore the multifaceted nature of adoption behaviour and highlight the need for tailored extension strategies, particularly for smallholders, less educated, and resource-poor farmers in hill regions. A shift towards more inclusive and participatory extension models, as advocated by Davis et al. (2004), could bridge the adoption gap more effectively.

Table 4 Socio-economic factors influencing adoption gap for pineapple, turmeric, potato, ginger, green chillies.

\(SE = Standard Error;*, ** denote significance at the 5%, and 1% levels respectively; NS = Not Significant, Coefficient: estimated effect of each explanatory variable on the Adoption gap of the individual selected horticultural crops. These coefficients come from probit regression model\)

The analysis of technology adoption gaps in recommended package of practices for five major horticultural crops in Tripura revealed substantial variations across different cultivation stages and crop types (Table 5). The overall adoption gap ranged from 50.64% in potato, 63.08 % for green chilli, 67.11 % for turmeric, 70.98 % for ginger and 71.98% in case of pineapple cultivation. Among the cultivation practices examined, insect pest and disease management showed the highest adoption gaps across all crops, ranging from 87.5% in green chilli to 93.66% in pineapple. This was closely followed by water management (49.17-87.5%) and manures and fertilizer management, which exhibited particularly high gaps in turmeric (94.33%) and ginger (89.5%) cultivation. Seed/seedling treatment also demonstrated consistently high adoption gaps across all crops (63.83-85.33%).

Conversely, yield-related practices showed the lowest adoption gaps, particularly in turmeric (26.33%) and potato (26.17%), suggesting better farmer compliance with harvest-related recommendations. Post-harvest management practices showed moderate adoption gaps ranging from 42.33% in potato to 65.83% in green chilli.

Table 5 Status of adoption gap (%) in cultivation of recommended package of practices.

The high adoption gaps observed across horticultural crops in this study align with previous findings from developing agricultural systems, where technology transfer remains a persistent challenge (Feder et al. 1985; Rogers 2003). The particularly high gaps in pest and disease management are concerning, as these practices directly impact crop productivity and quality. Similar patterns have been reported in other Indian states, where resource constraints and lack of technical knowledge limit adoption of integrated pest management practices (Birthal et al. 2015; Sharma and Peshin 2016). The excessive adoption gap in water management is particularly problematic given the increasing water scarcity and climate variability in the northeastern region (Deka et al. 2013). This finding corroborates with Namara et al. (2010), who identified water management as a critical constraint in smallholder horticulture systems. The high gaps in fertilizer management, especially in turmeric and ginger suggest inadequate understanding of crop nutrition requirements, which has been identified as a major yield-limiting factor in spice cultivation (Srinivasan et al. 2016). The relatively lower adoption gaps in yield related practices indicate that farmers prioritize activities directly linked to immediate economic returns, supporting the rational farmer hypothesis (Schultz 1964). This selective adoption pattern has been documented in various contexts where farmers adopt practices with visible, short-term benefits while neglecting preventive measures (Mauceri et al. 2007).

Profitability Assessment Through Benefit-cost Ratio Analysis

Table 6 highlights BCR of the selected horticultural crops. The high mean BCR of 2.48 reflects the strong profitability potential of potato farming in the study region. This aligns with findings from Surguja district, Chhattisgarh where large-scale farms recorded a BCR as high as 3.22 in 2022–23 (Mukherjee et al. 2024). In Kangra district, Himachal Pradesh, overall BCR was estimated at 1.88, with larger farms exhibiting slightly better efficiency (Raina et al. 2024). More broadly, across India, average potato BCR values hover between 1.6 to 2.0, depending on farm size and region. These figures support the conclusion that potato remains one of the most financially viable horticultural crops, especially for medium to large farms (Indian Potato Team, 2024).Green chilli’s mean BCR of 2.12 suggests a profitable enterprise. Pineapple’s mean BCR of 1.98 reflects a reasonable return on investment (Raina et al. 2024). Ginger’s moderate mean BCR (1.72) reflects modest returns, consistent with ginger being labor-intensive and vulnerable to disease. Turmeric, with the lowest mean BCR (1.38) and a minimum of 0.52, shows clear signs of unprofitability for some growers. India’s dominance in turmeric production does not eliminate quality issues—many domestic turmeric varieties have low curcumin content, affecting market value and profitability. Variable yields, price volatility, and disease susceptibility further amplify economic risk in turmeric farming (Dhivya et al. 2024).

\(Table 6 Benefit-Cost Ratio of selected horticultural crops in the study area (n = 600).\)

Estimated economic loss due to adoption gaps in the selected horticultural crops

The data presented in Table 7 highlight the economic consequences of existing adoption gaps in selected horticultural crops cultivated by farmers. Yield disparities between current and recommended practices were translated into economic losses per hectare using prevailing market prices.

\(Table 7 Estimated economic loss due to adoption gaps in selected horticultural crops (N = 600).\)

The results demonstrate that ginger cultivation suffers the greatest economic loss per hectare (Rs. 2,80,800), followed by green chilli (Rs. 1,27,500) and potato (Rs. 1,26,150). These figures underscore the economic burden of not adopting recommended agronomic practices, such as scientific nutrient management, improved seed varieties, integrated pest and disease management, and optimal planting methods (Kumar et al. 2017; Zhang et al. 2019). The yield gaps indicate inefficiencies in the current production systems, often arising from limited awareness, resource constraints, and fragmented extension services. As noted by Joshi et al. (2006), technological adoption among smallholders remains uneven due to socio-economic and institutional barriers. Such gaps directly translate into opportunity costs, which, if addressed, could significantly enhance farmers’ incomes and regional agricultural productivity.

These findings reinforce the importance of targeted interventions to reduce yield gaps through localized technology dissemination, capacity-building programs, and market-driven incentives. As highlighted by Birthal et al. (2015), even a modest reduction in the yield gap can lead to substantial gains in both productivity and profitability, particularly in high-value horticultural crops. Furthermore, the economic loss estimates serve as a valuable tool for prioritizing crops and regions that require urgent technological support. The considerable losses in ginger and pineapple production suggest that these crops should be focal points for research-extension linkages and public–private partnerships aimed at technology transfer and input accessibility (Pingali 2012). The analysis is consistent with the economic theory of production efficiency, which posits that deviations from optimal input use and practices lead to sub-optimal output and economic inefficiency (Coelli et al. 2005). Bridging these gaps, therefore, is not merely a technical challenge but a critical economic imperative for sustainable horticultural development.

Conclusions

The comprehensive analysis of the socio-economic, technological, and agronomic dimensions of horticultural crop cultivation in Tripura reveals a multifaceted scenario marked by both potential and critical challenges. A significant proportion of the respondents were middle-aged (35–50 years), literate, and moderately experienced in farming, suggesting a workforce that, while capable, may be constrained by legacy practices and limited exposure to modern agricultural innovations. The predominance of Scheduled Tribe and Scheduled Caste farmers reflects the ethnic landscape of Tripura’s hilly regions, where crops like pineapple, ginger, and turmeric are prominent. A core concern highlighted in the study is the persistent adoption gap among farmers, significantly influenced by variables such as age, caste, and farming experience. Older farmers with traditional approaches and limited information access exhibited higher adoption gaps, especially in areas such as insect pest and disease management, water use, and fertilizer application, where adoption gaps exceeded 85% in some crops. These findings underscore the urgent need for targeted extension interventions tailored to specific demographic and agro-ecological profiles.

Encouragingly, knowledge levels across crops were generally moderate, suggesting that foundational awareness exists but needs reinforcement through structured training and improved extension contact. The negative correlation of education, market orientation, landholding size, and scientific orientation with adoption gaps implies that investments in farmer education, exposure visits, and technology demonstrations could yield substantial returns. Economically, although crops like potato and green chilli exhibited higher profitability and lower adoption gaps, crops such as ginger and pineapple showed considerable yield and income losses due to poor adoption of recommended practices. Estimated economic losses per hectare reached Rs.2.8 lakh for ginger, revealing the high opportunity cost of under-adoption. Finally, the Benefit-Cost Ratio (BCR) analysis further validates the importance of crop-specific strategies promoting high-BCR crops while enhancing support systems for lower-performing ones. The findings advocate for stage-wise, crop-specific, and farmer-centric strategies, including strengthening of input delivery systems, participatory training, and institutional support, to bridge the adoption gap and realize the full economic potential of Tripura’s horticultural sector policy interventions must focus on pest management, water use, and fertilizer application where gaps are most severe. Strengthening extension services through increased field-level staffing, mobile advisories, and local demonstrations is crucial, especially in tribal and remote areas. Targeted support such as subsidized inputs and credit for marginal and ST/SC/OBC farmers, coupled with enhanced input delivery and market linkages, can improve adoption and profitability. Promoting farmer education, scientific orientation, and participatory learning models will foster greater receptiveness to innovations. Linking adoption of recommended practices with incentives, certification schemes, and insurance benefits can further motivate farmers. District-level prioritization based on crop wise economic losses, along with cross-departmental convergence and real time monitoring, will ensure focused implementation. Awareness campaigns and localized success stories should be used to demonstrate tangible economic benefits, thereby driving technology uptake and sustainable horticultural growth in the region.

Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing Interests: The authors have no competing interests.

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