Sericulture, is a significant agricultural activity adopting bivoltine sericulture technologies. Trained farmers, who have received formal education and exposure to modern practices, are generally expected to have a higher level of knowledge and adoption of such technologies. In contrast, untrained farmers, who may rely on traditional methods and local knowledge, tend to exhibit lower adoption rates of newer technologies like Bivoltine sericulture.Very few studies have attempted to understand the impact of training on the yield and quality of silk cocoon production in India. Studies (Rahmathulla et al., 2006) showed that the knowledge level of trainees on sericulture technologies had increased by an average of 57.67 per cent after the training. Similarly, farmers’ cocoon yield increased to an extent of 26.50 per cent after their training in new sericulture technologies (Rahmathulla et al., 2012). Other important findings showed the increasing level of confidence in bivoltine production by the trained farmers and also their change of attitude in risk-taking decisions (Rahmathulla et al., 2009). Therefore, the present study is being proposed to find out how trained farmers could change the productivity and quality of bivoltine cocoons over untrained farmers in the field. This study aims to discriminate the knowledge and adoption levels of bivoltine technologies between trained and untrained sericulture farmers in Tamil Nadu.
 

The study was conducted (2022-2023) in the Coimbatore district of Tamil Nadu, where most farmers rear the bivoltine cocoon, a potential area for bivoltine sericulture. The districts favor and support the environment for mulberry cultivation and silkworm rearing. Farmers accepted and adoption of new sericulture technologies. Out of 10 blocks, 5 blocks selected were purposively selected because more training was conducted in these blocks. 24 villages were selected randomly based on the area under mulberry cultivation. A sample of 150 trained and untrained farmers was selected. Primary data formulated with well-defined objectives based on the interview schedule was prepared. In this, all relevant information was furnished to collect data from respondents in the study area. The study aims to discriminate between two groups. The study statistical tools used for correlation and multiple regression, and the chi-square test.

Discriminate knowledge level bivoltine technologies
 
Discriminate knowledge level of moriculture trained and untrained
 
Table 1 revealed a non-significant relationship with trained farmers’ soil testing and reclamation 0.493, planting spacing 0.828, pruning method 0.536, IPM 0.064, and IDM 0. 077. The result of the study is consistent with Hadimani et al., 2017. Highly significant with mulberry variety 0.00, INM 0.00, IWM 0.04, and harvesting of mulberry at the right time 0.00 in trained farmers. The study results are consistent with Beula Priyadarsini and Vijayakumari. 2013. The untrained farmers’ non-significant planting spacing is 0.045, pruning method 0.640, INM 0.286, IWM 0.07, IPM 0.387, IDM 0.356, and Use of mulberry at the right time 0. 071. The study results are consistent with Hadimani et al., 2017. a highly significant relationship is between soil testing and reclamation 0.005, and mulberry variety 0.001. R-value in mulberry cultivation is highly associated with trained farmers at 0.67 and untrained associated with 0.16. This substantiated similar findings by Ravi Kant et al., 2023 and Beula et al., 2016.


 
Discriminate between silkworm technologies that are trained and those that are untrained
 
Table 2 revealed that the non-significant relationship between trained farmers’ rearing shed 0.625, disinfection 0.631, late age feeding 0.275, moulting care 0.018,IDM 0.846, IPM 0.247, marketing 0.247, cocoon harvesting 0.413 and crop/year 0.480 and untrained farmers rearing shed 0.309,disinfection 0.909, late age feeding 0.334, moulting care 0.162, IDM 0.904, IPM 0.711, marketing 0.547, cocoon harvesting 0.519 and crop/year 0.102. The highly significant trained and untrained farmers 0.000.R-value in silkworm technologies is highly associated with trained farmers at 0.85 and untrained associated with 0.72.

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Discriminate adoption of level bivoltine Technologies
 
Discriminate adoption level of moriculture trained and untrained
 
Table 3 revealed that non-significant relationship between trained farmers’ soil testing and reclamation 0.672, planting spacing 0.019 pruning method 0.527, IPM 0.417, and IDM 0.536 Highly significant with the use of mulberry at the right time 0.000 in trained farmers. The result of the study is consistent with Hadimani et al., (2017); Choudhury et al., (2017) and Deepa  et al. (2007). The untrained farmers’ non -significance to planting spacing 0.650, pruning method 0.364,INM 0.969, IWM 0.082, IPM 0.609,IDM 0.502 and Use of mulberry at the right time 0.529 and highly  significant relationship is soil testing and reclamation 0.005 and mulberry variety 0.001. R-value in mulberry cultivation is highly associated with trained farmers at 0.98 and untrained at 0.64.

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Discriminate adoption level of silkworm technologies trained and untrained
 
Table 4 revealed that the non-significant relationship between the silkworm technologies trained farmers’ late age feeding 0.996, moulting care 0.406, IPM 0.247, marketing 0.605 and untrained farmers’ rearing shed 0.788, IPM 0.245, marketing 0.096 and crop/year 0.102. The highly significant relationship in adopted silkworm.

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Technologies is by trained rearing house, disinfectant, cocoon harvest at the right time, crop/per /year and reared silkworm annum. The result of the study is supported by the findings of Deepa and Sujathamma (2007) and Kumaresan et al., (2009). Untrained farmers had rearing shed, disinfection, moulting care, IDM, cocoon at right time harvest and reared silkworm annum. Cocoons at the right harvest had a direct effect on price during auctioning. The study findings Krishnamurthy, (2012). R-value in silkworm technologies are highly associated with trained farmers at 0.67 and untrained associated with 0.52.
 
Discriminate knowledge level bivoltine technologies
 
The trained farmers in revenue = - 27.297 +2.837 (soil testing) - 41.643 (Mulberry variety) +9.785(Planting space)-6.194(Pruning method) +17.249 (INM)+4.799(IWM)+ 5.687 (IPM)+ 4.061 (IDM)+23532.365 (Harvesting right time) Based on the r2 value independent variables are 98% defined over dependent variables. The above is moderately correlated and has a linear trend. Untrained farmers’ revenue = +989.308 +83.667 (Soil testing) + 24.876 (Mulberry variety) -62.056 (Planting space)-2.849(Pruning method) +123.758 (INM)+48.331(IWM)-40.130 (IPM)- 71.283 (IDM)-31.960 (Harvesting right time). Some kinds of studies were done by Elumalai and Murugesh, 2019; Harish et al., (2022) and Tayade et al., 2016. Based on the r2 value independent variables are 64% defined over dependent variables. The rest of the variables do not show a significant impact over the dependent variable. Fig 1 (a) the trained farmers, histogram showing the bell-shaped curve which represented normal distribution in revenue. Fig 1 (b) the untrained farmers, histogram shows the slight bell-shaped curve which represents the normal distribution in revenue.


       
The trained farmers in Revenue = -17748.771 + 7442.940 (Silk) + 5654.433 (Disinfection) – 20895.473 (Late age feeding) + 20073.881 (Moulting care) + 2084.247 (IDM) + 7730.331 (IPM) – 12176.365 (Marketing) + 967.971 (Crop/Year) +197.335 (Reared of Dfls annual). Based on the r2 value independent variables are 85% defined over dependent variables. The above is moderately correlated and has a linear trend. Among the variables Moulting care and rearing of annual DFLs showed significant impact over the dependent variable. The rest of the variables do not show a significant impact over the dependent variable. Fig 2(a) The histogram shows the bell-shaped curve which represents the normal distribution in yield in trained farmers. 

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The untrained farmers Revenue = 126880.500 - 5106.638 (Silk) - 487.815 (Disinfection) - 8766.105 (Late age feeding) - 8131.983 (Moulting care) - 386.669 (IDM) + 1092.509 (IPM) + 4259.713 (Marketing) -2126.197 (Crop/Year) +78.442 (Reared of Dfls annual). Based on the r2 value independent variables are 72% defined over dependent variables. Moderately correlated. Among the variables, the Rearing of Annual DFLs only shows a significant impact on the dependent variable. The results of the study are supported by the findings of Mani et al., (2006)  and Hiriyanna et al., 2009. The rest of the variables show no significance over the dependent variable. The above Model shows that the intercept has a Positive value and Spacing, Irrigation, Shoot Harvesting, IPM and IDM have shown negative impacts over the dependent value. Soil, variety, INM, IWW, and Reared of Dfls have a positive impact over the depen-dent variable. [Fig 2 (b)] The histogram does not show the bell- shaped curve which represents an abnormal distribution in revenue. The result of the study is supported by the findings of Meenal, (2006) and Lakshmanan et al., 2007.
 
Discriminate adoption level trained and untrained farmers
 
The study revealed that trained farmers Cocoon yield = -27.297 + 2.837 (Soil) - 41.643 (Variety) + 9.785 (Spacing) - 6.194 (Shoot Harvesting) + 17.249 (INM) + 4.799 (IWW) + 5.687 (IPM) + 4.061 (IDM) + 0.834 (Reared of Dfls annual). Based on the r2 value independent variables are 98 % defined over dependent variable. Highly correlated and has a linear trend. Among the variable’s variety, INM and Rearing of DFLs annually have significant impacts on the dependent variable. The above results are in line with the findings of Ravi et al., (2023) and Ovais Ahmed Hajam  et al., 2021. Spacing, irrigation, shoot harvest, IWW, IPM and IDM do not impact significantly over dependent variable. Some kinds of the results were given by Parthasarathi, (2002), IPM in trained farmers was much higher on biological and physical identification of pests and predators. The training positive effect on farmers. The above Model shows that the intercept has a Negative value and variety, Irrigation has shown negative impacts over the dependent value. Soil, Spacing, Shoot Harvesting, INM, IWW, IPM, IDM, and Reared of Dfls have a positive impact over the dependent variable. Fig 3 (a) the trained farmers, histogram showing the bell-shaped curve which represented normal distribution in yield. Fig 3 (b) the untrained farmers, histogram shows a slight bell-shaped curve which represents abnormal distribution in yield.

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The study revealed that untrained farmers Cocoon yield = 989.308 + 83.633 (Soil) + 24.876 (Variety) - 24.876 (Spacing) - 62.056 (Irrigation) - 2.849 (Shoot Harvesting) + 123.758 (INM) + 48.331 (IWW) - 40.130 (IPM) -71.283 (IDM) – 31.960 (Harvesting Mulberry at right time) + 0.016 (Reared of Dfls annual). Based on the r2 value independent variables are 64%. Defined over the dependent variable. Highly correlated and has a linear trend. Among the variables, Garden Spacing shows a significant impact on the dependent variable. The rest of the variables do not show a significant impact over the dependent variable. The above Model shows that the intercept has a Positive value and Spacing, Irrigation, Shoot Harvesting, IPM, CRC garden, and IDM have shown negative impacts over the dependent value. Soil, variety, INM, IWW, and Reared of Dfls have a positive impact over the dependent variable. Fig 4 (a) the trained farmers, histogram shows the bell-shaped curve which represents the normal distribution in yield. Fig 4 (b) The untrained farmers, histogram showed a slight bell-shaped curve which represented an abnormal distribution in yield. The above results are in line with the findings of Harishkumar et al., (2022).

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The trained farmers were highly associated with cocoon production an R-value of 0.890 and untrained farmers had less level adopted in cocoon yield R-value of 0.308.The trained farmers proved that training is essential for high yield  cocoon production. The trained farmers had an R-value of 0.922, and the untrained farmers had 0.552.The cost of production wise trained farmers C:B Radio = 0.59:1 and untrained farmers C:B Radio = 0.68:1.so in clearly known training on yield and return highlights the relationship for trained farmers. The evidence strongly suggests that training is essential in enhancing the knowledge and adoption levels of mulberry cultivation and silkworm technologies. The significant associations observed in trained farmers indicate that well-structured training programs can lead to notable improvements in sericulture practices. Moving forward, it is crucial to prioritize farmer training and capacity-building efforts to maximize the potential for growth and sustainability in sericulture.

The first author is grateful to the Assistant Director of Sericulture, Coimbatore and Technical Service Centers grant to survey the research work in the Coimbatore sericulture fields.
 
Author contribution
 
The first author, a research scholar, conducted the entire set of surveys under the guidance of the second author.
 
Ethical statement
 
Not applicable.

We, the authors of this research paper, declare that we have no conflicts of interest to disclose regarding the research, its findings, and the integrity of the work.