Linseed (flax, Linum usitatissimum) is an oilseed crop valued for its edible oil (35-45% content) rich in omega-3 fatty acids and its fiber. It is grown globally on about 2.27 million hectares yielding roughly 22.39 million tonnes (≈986 kg/ha). In India, linseed is a minor rabi oilseed (~3.38 lakh ha, 1.47 lakh tonnes, ~435 kg/ha), with Maharashtra contributing about 39 thousand ha (10 thousand tonnes, 246 kg/ha) and Marathwada contributing roughly 16 thousand ha (15 thousand tonnes, 312 kg/ha). In Marathwada’s rainfed, often drought-prone environment, improved varieties can significantly boost yield and income. A recently released linseed variety, LSL-93 (by VNMKV Parbhani/ORS Latur in 2019), matures in 90-95 days and outperforms older varieties under rainfed conditions. For example, field trials found LSL-93 yields around 1036 kg/ha, substantially higher than the 875 kg/ha of the traditional variety NL-260 and also higher oil content (≈41% vs 37.5%). Such improvements suggest LSL-93 could help local farmers increase productivity and profitability.
       
Although, the level of adoption of LSL-93 by the farming community and the factors affecting the adoption of LSL-93 are not well understood. In similar studies on safflower, which is another rabi oil crop, recent studies have shown that socio-economic factors, such as education levels of the farmers and agronomic factors, such as the use of recommended seeds and fertilizers, have significant impacts on the adoption of new varieties of safflower. For example, Bhui et al., (2025) found that farm income levels and timely sowing of safflower varieties have positive significant effects on the adoption of new varieties of safflower at the 5-10% level of significance. Similarly, adequate use of seeds and fertilizers were found to have positive impacts on the adoption of new varieties of safflower (Aswathy and Joseph, 2020); (Chodvadiya, 2018), (Siddayya et al., 2016). Similarly, in the context of linseed, it is hypothesized that the adoption of LSL-93 by the farming community would depend on the socio-economic factors of the farmers.
       
This paper examines the adoption of LSL-93 in the Marathwada region’s five districts. A logistic regression approach is employed to analyze how farmers’ adoption of LSL-93 relative to other varieties of linseed or no linseed is related to various variables including farmers’ age, educational level, farm size, extension contact and adherence to best agronomic practices like timely sowing, proper seed rate and fertilization (Sancley, 2022). This research contributes new insights into technology adoption in Marathwada and is expected to contribute useful information to extension programs involved in oilseed technology transfer activities. Unlike other oilseed adoption studies in India, this is the first empirical attempt at examining oilseed technology adoption at the farmer level in the context of a newly released oilseed variety LSL-93 in rainfed regions of Marathwada (Ojiako, 2006; Tibamanya et al., 2021). By including seed rate in the analysis of oilseed technology adoption, a new dimension is added in understanding cost efficiency-driven oilseed technology adoption in rainfed regions characterized by drought-prone conditions (Felix, 2020).
 
Theoretical framework
 
The adoption of improved agricultural technologies is generally described using a utility maximization framework and the theory of innovation diffusion. According to the economic theory of adoption, farmers are expected to adopt a new technology if they are likely to gain higher utility from its adoption than from existing technologies, taking into consideration the risks and costs associated with its adoption (Aswathy and Joseph, 2020; Chodvadiya, 2018). In this framework, the decision to adopt a new technology is largely driven by factors such as resource endowment, information availability and profitability. The Diffusion of Innovations theory suggests that the adoption of a new technology is a dynamic process driven by individual, socio-economic and institutional factors. Farmers with higher income levels and information availability are more likely to adopt new technologies due to their higher risk-taking ability than those with lower resources, who are likely to show resistance to change due to their risk aversion and uncertainty (Siddayya et al., 2016; Shanila et al., 2025). In the case of linseed cultivation in rainfed areas, the adoption of improved varieties such as LSL-93 would not only be driven by higher productivity but would also be influenced by its input use efficiency, cost-effectiveness and adaptability under changing climatic conditions (Felix, 2020).
 
Research gap
 
Despite the development of better linseed varieties, little empirical research has been conducted on the adoption of recently developed varieties such as LSL-93 in rainfed conditions. Most of the earlier studies conducted on the adoption of oilseeds have concentrated on important oilseed crops such as soybean, mustard and safflower, whereas very less attention is devoted to linseed cultivation in drought-prone conditions (Kayande et al., 2024). In earlier studies conducted on the adoption of oilseeds, the role of socio-demographic variables is considered important, but little attention is devoted to the role of input use behavior and cost efficiency in the adoption of new varieties of oilseed (Kumar et al., 2015). Therefore, the present study aims to bridge this gap by exploring the determinants of the adoption of the improved variety of linseed, namely LSL-93, in Marathwada conditions.

Study area and sampling design
 
The research area of the study is Marathwada region of Maharashtra State of India. The districts covered are Parbhani, Beed, Dharashiv, Latur and Chhatrapati Sambhajinagar. The research study was conducted during the agricultural year 2023-24 in the Department of Agricultural Economics, Vasantrao Naik Marathwada Krishi Vidyapeeth (VNMKV), Parbhani. The sampling design for the research study is based on a multi-stage sampling method. The sampling method is based on the following stages: The districts are purposively selected based on the area of linseed cultivation. Then two tehsils are randomly selected from each district. After that, two villages are randomly selected from each tehsil (Patil et al., 2017). Finally, the farmers are randomly selected from the village census data. The sample size of the research study is 240 farmers, out of which 120 are adopters of improved linseed variety LSL-93 and 120 are non-adopters of the improved linseed variety LSL-93. The sample size is fixed based on the requirement of adequate representation and reliability of the research study.
 
Data collection
 
Primary data collection was done through a structured and pre-tested questionnaire. Data on socio-economic factors (age, education, family size), economic factors (income) and farm-level factors (area under linseed cultivation, seed rate) were collected. The choice of these variables was based on their theoretical importance in influencing farmers’ adoption behaviour, especially in terms of availability of resources, risk perception and efficient use of resources.
 
Analytical framework and model specification
 
The decision to adopt the improved variety of linseed, namely, LSL-93, was examined using a binary logistic regression model, which is most suitable for binary-dependent variable-based outcome, where the dependent variable takes two values: 0 and 1, indicating non-adoption and adoption, respectively.
The functional form of the model is expressed as:

 

Where:
P(Y=1)= Probability of adoption of LSL-93.
β₀ = Intercept.
β_i = Coefficients of explanatory variables.
X_i = Explanatory variables.
       
The model parameters were estimated using the maximum likelihood estimation (MLE) technique.
       
The explanatory variables included:
• Age (years) - proxy for experience and risk behaviour.
• Education (years of schooling) - human capital indicator.
• Family size (number of members) - labour availability.
• Total income (₹) - economic capacity.
• Seed rate (kg/ha) - input-use efficiency.
• Area under linseed (ha) - scale of cultivation.
       
Those variables which were found to have multicollinearity or statistically insignificant were excluded from the final model for robustness and simplicity of the model.
 
Model diagnostics
 
The logistic regression model was checked for its adequacy by performing the likelihood ratio test and R² statistics, which showed that the model fits the data adequately. The likelihood ratio test also showed that the explanatory variables included in the model are jointly statistically significant. The model also showed reasonable predictive ability in differentiating between adopters and non-adopters, which indicates that the variables selected are important in explaining adoption behaviour in the study area. The analysis was carried out using SPSS software.

The binary logistic regression model was used to determine the factors that affect the adoption of the improved linseed variety LSL-93, with the results presented in Table 1. The estimated coefficient measures the impact of the regressor variables on the log-odds of adoption, while the statistical significance measures the strength of the impact. The intercept is positive, indicating the general tendency towards adoption under favorable circumstances. However, the intercept does not have an economic implication on its own (Idrisa et al., 2012; Al-Karablieh et al., 2009).


       
Among the regressor variables, the age coefficient is negative and statistically significant at 10 per cent. This suggests that elderly farmers are less likely to adopt the improved linseed variety. This suggests that older farmers are less inclined to adopt new technologies  and inclined towards traditional ways of doing things, as observed by Diro and Mulugeta (2015); Wondale et al. (2016) and Chete (2021). The variable total income is positively related to the adoption decision. This suggests that farmers with greater financial capabilities are more likely to adopt the improved linseed variety, as observed from the result (Barry et al., 2020; Miah et al., 2015; Ifeanyi et al., 2007).
       
The seed rate variable turned out to be a significant factor in determining adoption behavior, as also found in an earlier study by Ambari and Sidar (2021). A negative and significant coefficient at 5 per cent level of significance was obtained. This suggests that a high seed rate has a negative impact on adoption behavior; therefore, farmers should use optimal input management techniques in order to increase adoption of improved varieties (Ambari and Sidar, 2021; Gaikwad et al., 2020). Variables like education level, family size and area under linseed were observed to have positive coefficients; nevertheless, these were statistically non-significant, as suggested in earlier studies by Diro and Mulugeta (2015) and Wondale et al. (2016). These variables may play a role in adoption; however, these variables were not strong enough to independently influence farmers’ decision-making in the context of the present study (Mustapha et al., 2012; Jariko et al., 2011).
       
From the results obtained in the present study, it is evident that farmers’ economic capacity and input use efficiency play a more significant role in determining adoption behavior rather than demographic variables. Farmers’ decision-making is influenced more by cost and economic benefits rather than social variables (Padaria et al., 2009; Sita Devi and Ponnarasi, 2009). These results are in line with the results obtained by various researchers in their adoption behavior studies (Meena et al., 2016; Manhas et al., 2015).
       
The graphical representation of the findings is shown in Fig 1, which provides a visual representation of the extent and direction of influence of the various factors on adoption behavior. The figure clearly indicates that total income has a positive influence on adoption behavior, whereas age and seed rate have a negative impact on adoption behavior (Tandon et al., 2021; Singh and Singh, 2022). The relatively lower magnitude and higher error ranges of education, family size and area under linseed indicate that these factors have a statistically non-significant impact on adoption behavior (Singh et al., 2013). This visual representation also confirms the findings of the present study that economic potential and input efficiency are the driving factors behind adoption behavior in the study area, as also found in earlier studies by Barry et al. (2020) and Miah et al. (2015).


       
The findings of the present study support the findings of earlier studies on oilseeds and other crop adoption studies, where income and input management were found to be significant factors affecting oilseed adoption behavior (Bhui et al., 2025; Gogoi et al., 2024; Vyas et al., 2023). The negative impact of age on oilseed adoption behavior also supports the findings of earlier studies, where younger farmers were found to be more receptive to oilseed technology, as they were less risk-averse and more exposed to extension activities than older farmers (Singh et al., 2012; Kadam and Suryawanshi, 2011).

In this study, binary logistic regression was applied to assess the variables influencing the adoption of the improved linseed variety LSL-93 in drought-prone areas in the Marathwada region. From the study findings, economic and input efficiency variables influenced adoption more than demographic variables. Age was found to have a negative impact on adoption, implying that older farmers were less likely to adopt new varieties. Income had a positive influence on adoption. The seed rate had a negative correlation with adoption, showing that input efficiency was important in improving adoption rates. There is a need to develop programs to sensitize farmers, ensure availability of high-quality seeds at an affordable price, and develop strong institutional and financing frameworks.

The authors gratefully acknowledge the Department of Agricultural Economics, College of Agriculture, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani for providing research facilities and institutional support during this study. The authors also sincerely thank the Seed Processing Unit, VNMKV, Parbhani KVK Chh. Sambhajinagar and ORS Latur for their valuable cooperation and technical support during the research work.
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily reflect the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information presented in this study. No liability is accepted for any direct or indirect consequences arising from the use of the information contained in this article.

The authors declare that there is no conflict of interest regarding the publication of this research paper.