<div style="text-align: justify;">The present study comprises a sample of 90 fishery growers by following multi stage simple random sampling technique to access the factors influencing the entrepreneurial behavior in Dimapur district of Nagaland. It is well known fact that fishery is an important enterprise in entire north eastern region of India; which comprising of a brother and seven sister states. The study revealed that entrepreneurial behavior has an positive impact due to the majority of fishery growers had medium level of knowledge about improved package of practices of fishery enterprise viz; farm decision making ability, family size, economic motivation, marketing orientation and scientific orientation programme aspects were enhance the knowledge about reorient relevant training programmes giving due cognizance to the influencing factors.&nbsp;</div>

Factors influencing entrepreneurial behavior of fishery growers in Dimapur district of Nagaland

In the present study, the data on 1649 Bharat Merino (BM) lambs; progeny of 144 sires over the year from 2000 to 2014 available at Southern Regional Research Centre (SRRC), Central Sheep and Wool Research Institute, Mannavanur were considered for the analysis. The different economic growth traits used for the analysis were birth weight (BWT), weaning weight (3MWT), 6 month weight (6MWT), 12 month weight (12MWT) and greasy fleece yield (GFY). The overall least-square means for weights (in kg) were 3.28&plusmn;0.02, 19.08&plusmn;0.23, 25.00&plusmn;0.35, 34.79&plusmn;0.59 and 2.13&plusmn;0.07 for BWT, 3MWT, 6MWT, 12MWT and GFY respectively. Different non genetic factors such as year, sex, type of birth and dam&rsquo;s age had significant (P&lt;0.01) influence on BWT and 3MWT. Season influenced the BWT (P&lt;0.05) while it had no significant effect on 3MWT. Six month weight was also significantly (P&lt;0.05) influenced by all non-genetic factors taken under present study. Twelve month weight was significantly affected by year (P&lt;0.01), sex and season (P&lt;0.05). Similarly, analysis of variance showed that the year, sex of the lambs (P&lt;0.01) and season (P&lt;0.05) were important sources of variation on GFY of Bharat Merino sheep. Lambs born single had higher values of BWT and 3MWT than that of the lambs born as twins. The trend of the increase in BWT and 3MWT of the lambs is being observed up to the period when the age of their corresponding dams is 4-5 years. The findings of the present study showed that non - genetic factors play an important role in the expression of genetic potential in the lambs as well as growth performance of Bharat Merino sheep under sub temperate conditions of Kodai hills of Tamil Nadu, India. The genetic parameters estimated of 6MWT indicate that it is most suitable for use as selection criterion. Improvement of body weight of BM sheep seems feasible in selection programs, as some of the related traits are moderately heritable and those traits specially are well correlated, which could suggest that these traits are useful in selection programs.

Influence of genetic and non genetic factors on growth traits of Bharat Merino sheep in sub-temperate climate of Kodai hills of Tamil Nadu, India

Background: Vechur cattle, a native breed of Kerala, are known for their low maintenance requirements, disease resistance and adaptability to tropical climates. Fertility, particularly conception rate, is a crucial factor in livestock productivity and is influenced by both genetic and non-genetic factors. Non-genetic factors such as management practices, nutrition, health, month and year of breeding play a significant role in reproductive performance. Understanding the impact of these variables is essential for optimizing breeding strategies and improving fertility outcomes. This study aims to analyse the effect of non-genetic factors on the conception rate of Vechur cattle maintained at the Vechur Cattle Conservation Project, Centre for Advanced Studies in Animal Genetics and Breeding, Mannuthy. 
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Methods: A total of 868 breeding records spanning 14 years (2011-2024) were collected and analysed. The data included information on breeding time (forenoon or afternoon), month and year of breeding. The conception rate was calculated as the percentage of successful pregnancies relative to total breeding attempts. Statistical analysis was performed using SPSS v27, employing logistic regression to assess the influence of categorical variables on conception rate and chi-square tests to determine significant differences across different years, months and breeding conditions. Bulls used for natural service were periodically evaluated for seminal quality and their conception rates were compared to assess variations in male fertility. 
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Result: The overall conception rate during the study period was 51.40%. Year-wise analysis showed significant variations, with higher conception rates observed in 2012 and 2021, while lower rates were recorded in 2013 and 2024. Monthly analysis revealed that August had a significantly lower conception rate (32.10%) compared to other months. The time of breeding (forenoon vs. afternoon) had no significant effect on conception rate. No significant variation was observed in conception rates across different bulls used for natural service, suggesting consistent seminal quality and fertility across sires. These findings highlight the influence of environmental factors on reproductive performance and emphasize the need to incorporate these variables into breeding and improvement programs for Vechur cattle. 
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Non-genetic Factors Influencing Conception Rate of Vechur Breed of Cattle from Kerala

Background: The income generation in the market from producers to traders and further to vendors are interlinked and depends on multiple factors. In this paper, the supply chain of the swine market in Assam, Meghalaya, and Nagaland of the North Eastern Region of India is analysed with special reference to traders who are involved in the exchange of live animals or pork along with carcasses to make profit.&nbsp; They do not own the animals or the product but act as an intermediary between buyers and sellers. Nagaland traders specifically face a shortage of supply as local producers sell it independently through petty sellers or vendors without passing it through traders. Majority of the local traders have village informers, who inform them about the availability of animals in their respective places. 
 
Methods: An empirical survey was conducted during 2021 among 97 traders in Assam, Meghalaya and Nagaland to identify the key components influencing the traders in the swine business and the significant factor (p&lt;0.05) influencing the income of traders. For the study two statistical methods were applied-Principal component analysis and regression analysis.&nbsp; 
 
Result: Four principal components influencing the traders are market description, out-of-pocket cost, drive to sale, and awareness of the available services. 55 per cent of the change in income is significantly dependent on the quantity of retail cut, stock purchased and total variable cost. It also indicates that many non-economic factors that are subject to research also function in the income flow of traders. Traders are not organized and lack infrastructure support mainly storage, market and credit facility limits the scope of commercialisation in swine business.

Traders in Livestock Sector: Role in Marketing Channel and the Factors Influencing the Income

The birth weight of calves indicates livestock production performance even before birth, significantly impacting calf health and overall farm productivity. This study was conducted on the factors influencing the birth weight of Holstein and Jersey calves born under the same environmental and feeding conditions. Data were collected during 2016-2024 on 635 cows (Holstein, n = 572; Jersey, n = 63) and their calves, including details such as breed, age and parity of the dam, difference between the date of birth and estimated due date (DBE), birth season, sex and birth weight. The calving season was categorized into spring (March-May), summer (June-August), autumn (September-November) andwinter (December-February). Stepwise multiple regression analyses were conducted to determine the impact of these variables on the birth weight of calves. In Holstein calves, DBE (&beta; = 0.350) had the most substantial impact, followed by sex (&beta; = 0.316), age of dam (&beta; = 0.083) andbirth during autumn (&beta; = -0.112) (p&lt;0.05). For Jersey calves, DBE (&beta; = 0.448) was the most significant factor, followed by sex (&beta; = 0.277) and age of dam (&beta; = 0.228). Unlike Holsteins, no seasonal effect was observed in Jerseys. These findings highlight the influence of various factors on birth weight of calves, suggesting that Jersey cattle may possess certain advantages in seasonal birth conditions compared to those of holstein cattle.

Comparison of Influencing Factors on Birth Weight between Holstein and Jersey Calves

Background: The choice of superior herd sires is essential for genetic improvement in dairy cattle, with sperm quality serving as a key factor. Bull semen&rsquo;s superior qualities are essential for accelerating genetic improvement through AI. These characteristics are affected by several factors, including the environment, management techniques, physiological condition and hereditary factors. This study involved in evaluating genetic and non-genetic factors influencing seminal attributes. 
 
Methods:&nbsp;Data on 1,85,413 ejaculates from nine genetic groups viz. Jersey, Holstein Friesian, crossbred Jersey, crossbred Holstein Friesian, Bargur, Kangayam, Pulikulam, Umblachery and Red Sindhi were collected from four frozen semen stations of Tamil Nadu State. Semen production data viz. ejaculate volume (mL), spermatozoa concentration (million per mL), initial motility (percentage), post-thaw motility (percentage), total initial motile spermatozoa and total post-thaw motile spermatozoa were also examined for various genetic and non-genetic factors affecting them using linear mixed model with repeated measures. 
 
Result:&nbsp;The overall estimated marginal means for Volume of ejaculate, Concentration of spermatozoa, Initial motility, Total initial motile spermatozoa per ejaculate, Total doses per ejaculate, Post-thaw motility and Total post-thaw motile spermatozoa per ejaculate were 3.11 &plusmn; 0.11 (mL), 963 &plusmn; 30.60 (106/mL), 79.50 &plusmn; 1.29 (per cent), 1847 &plusmn; 118 (106), 151.00 &plusmn; 7.20, 51.00 &plusmn; 0.42 (per cent) and 1535 &plusmn; 80.20 (106) respectively.The effect of all genetic and non-genetic factors (farms, ejaculates, period, seasons, age) were observed to be significantly influencing the seminal traits considered in the study. However, the estimated marginal means of each trait displayed varied ranges of mean against non-genetic factors.

Genetic and Non-genetic Factors Influencing Semen Production Traits in Dairy Bulls

<div>Background: The model of sustainable agriculture mostly emphasizes on enhancing the efficacy of agrochemical usage by the implementation of Integrated Pest Management (IPM) technology, substitution of various inputs and practices with effective alternative technologies involving low energy utilization and plant/animal integration. Following the sustainable approach towards agriculture, a research has been conducted in the state of Rajasthan to identify the major factors that influence the adoption of IPM technology in Cotton. Cotton being the major crop of Sri Ganganagar and Hanumangarh districts for kharif season had an average area of 2.39 lakh hectares and average production of 7.44 lakh bales that constituted 51 per cent and 52 per cent of total area and production of Rajasthan, respectively during last five years ending 2016-17.&nbsp; 
 
Methods: On the basis of pest management strategy, farmers in the area were classified into three categories viz. farmers following conventional practice, farmers following IPM practice and farmers following both the practices, for the research. The purpose of the study was to identify the factors influencing adoption of IPM technology in the study area. A total of nine metric socio-economic variables of three groups of a total sample of 90 farmers from both the districts were taken for the study for the crop year 2017-18. The data were analyzed through suitable statistics: linear discriminant analysis and two-step cluster analysis. 
 
Result: It was found that &lsquo;years of experience in current practice followed&rsquo;, &lsquo;number of workshops attended for current practice&rsquo;, &lsquo;years of education&rsquo;, &lsquo;years of experience in agriculture&rsquo; and &lsquo;number of trainings attended for current practice&rsquo; are the major variables that discriminated among the three group of practices followed by the farmers of the study area. Among the selected significant discriminating factors, &lsquo;higher education&rsquo;, &lsquo;higher number of workshops attended for current practice&rsquo; and &lsquo;higher number of trainings attended for current practice&rsquo; were found to be the major influencing factors towards the adoption of IPM technology among the cotton growers in the study area. This study would facilitate the stakeholders involved in the dissemination of IPM technology as a measure for sustainable agriculture.</div>

Analyzing the Factors Influencing the Adoption of Integrated Pest Management (IPM) Technology in Cotton in Rajasthan

Background: This study investigates the factors influencing the choice of institutional models in sheep farming, specifically focusing on contract and non-contract models in East Java, Indonesia. The primary objective of this research is to identify the factors that influence farmers&rsquo; decisions when selecting between these two institutional models and to evaluate the differences in characteristics between farmers engaged in contract and non-contract farming. 
 
Methods: In this study, a quantitative approach employing a survey design was utilized for data collection. The survey encompassed 90 sheep farmers across three principal sheep-producing districts in East Java Province, namely Lumajang, Jember and Bondowoso. Data processing was conducted at the University of Jember over the period of November 2023 to April 2024. To examine the dataset, a binary logit model was employed to analyze and identify the determinants influencing farmers&rsquo; decision-making processes. The variables considered included farming experience, educational attainment, age, Cage Capacity, baseline market prices and access to financial resources. 
 
Result: The study&rsquo;s findings reveal that farmers&rsquo; decisions to adopt the contract model are significantly influenced by base price and farming experience. Specifically, farmers with greater experience are more likely to avoid the contract model, whereas those with higher base prices are more inclined to opt for the contract model. Additionally, farmers with higher base prices are more inclined to opt for it. Formal education did not show a significant effect on the choice between contract and non-contract models. This study concludes that ensuring price stability through the contract model can provide risk management benefits for farmers, particularly for those with larger herds or limited market access. The research contributes to the existing literature by offering valuable insights into the factors that influence the selection of institutional models in sheep farming and provides recommendations for policymakers to promote the adoption of advantageous institutional models within the livestock sector.

Factors Influencing Sheep Farmers&rsquo; Decisions to Contract Farming in East Java Province, Indonesia

Background: Agricultural insurance has emerged as an effective climate risk management strategy in many African countries, but its adoption is still low in Benin. This study seeks to understand farmers&cent;access and perception of agricultural insurance, while analyzing the factors that affect its adoption in the municipality of Ou&egrave;ss&egrave; in Benin.&nbsp; 
 
Methods: Using the method of snowball sampling, the research team collected data from 112 household heads, from six major maize-producing villages. With the data from focus group discussions and a structured questionnaire, we performed descriptive statistics and used the logistic regression model to analyze the factors influencing farmers&#39; agricultural insurance adoption.&nbsp; 
 
Result: Our investigations revealed that almost all (95%) of the respondents have received agricultural insurance information at least once and about 94% of them found insurance useful for effective agricultural risk management. However, most of respondents (66.5%) lacked knowledge of loss assessment methods and 52% of them said that the insurance premium was too high for farmers. Most (95%) of the respondents who subscribed to agricultural insurance also received agricultural credit. The binomial logistic regression analysis revealed that adopting agricultural insurance is positively influenced by the farmers&#39; perception of the insurance, educational level, household size andaccess to agricultural credit. Implementing strategies that consider these influencing factors andpromote awareness, while building capacity and trust, could facilitate farmers&#39; adoption of agricultural insurance, especially in countries like Benin, where agricultural insurance is relatively new.

Agricultural Insurance Adoption in the Context of Climate Change: Influencing Factors in Benin (West Africa)

<div style="text-align: justify;">Background: Online purchase has a very important role to play in creating consistent demand for farmers&rsquo; products and linking them directly to final consumers. Preferences of consumers are the most important driver of online purchases that prosper the linkage between farmers and the final consumers and these preferences are vastly influenced by consumers&rsquo; socio-economic characteristics. The current study was conducted to analyse the influence of socio-economic factors on the online purchase of food products in Bangalore city.&nbsp; 
Methods: Individual cross-sectional data were collected using a pre-tested questionnaire from 105 consumers of randomly selected areas of Bangalore city namely, Sahakarnagar, Judicial Layout, Yelahanka, Canara bank layout, Mahadevapura, Sanjaynagar and Jayanagar. Binary logistic regression was applied as the research method to examine the influence of socio-economic characteristics like gender, age, occupation, education, number of family members with income and income (as predictor variables) towards the online purchase of food products (as dependent&nbsp; variable).&nbsp; 
Result: Results indicated that age (negatively), number of family members earning income and monthly income (positively) significantly influenced the online purchase of food products. Other variables like education, gender (in favour of men) and occupation also influenced (positively) the outcome variable but, statistically were not significant.</div>