Exploring the key success factors of farmer producer companies in Gujarat: A farmer's perspective

Introduction

Agriculture was the main occupation when India became independent in 1947; it was also a major source of economic activity. However, it was labor intensive with very low productivity. The green Revolution was attempted in 1960-1970 to make India self-reliant in food production by increasing productivity. However, an economic turnaround for Indian Farmers is still elusive. Indian Farmers are still grappling with the absence of technological expertise, information dissemination, marketing skills, and resources to meet formal market requirements. As a consequence, there is a continuous struggle to generate a remunerative price on a regular basis. Mobilizing farmers for group action to arrange input and collectively sell their products through FPCs is one of the possible alternatives for successful marketing. The fundamental FPC ideology is to collectivize farmers, generate income, and improve productivity. Farmers are frequently unable to extract the maximum value from their produce due to a lack of grassroots organization. Also, the producers' share of consumer rupees is low, implying that a very low fraction of consumer spending is reaching to the farmer or actual producer. The subsequent chains, including middlemen, are more prominent and have a large holding capacity and, therefore, have the capability to exploit the farmer. Primary Producers' organizations or collectivities are being argued to be the only institutions that can protect small farmers from globalization by helping farmers buy or sell better due to economy-of-scale benefits and lower transaction costs with technical help in production and eventually creating and enhancing social capital. But producer organizations still struggle to become successful, and some successful producer organizations continue facing various problems like being effective and competent in the long run. The majority of FPCs in the country primarily start with marketing of input supply services, and once they are successful, they tend to widen their market opportunities by entering into processing and value addition. Around 25% of FPOs are engaged in postharvest processing, and about 20% of FPOs apply organic production methods. The key success factors of Farmer Producer Companies (FPCs) and their role in transforming the agricultural landscape. FPCs have emerged as powerful institutions that empower farmers by enabling collective action, promoting sustainable agriculture practices, and enhancing market access. Understanding the factors contributing to their success is crucial for policymakers, researchers, and agricultural stakeholders to develop effective strategies and policies for fostering the growth and www.extensionjournal.com sustainability of FPCs. Through a systematic review of existing literature, this study identifies and synthesizes the critical success factors that have contributed to the growth and success of FPCs. The findings will provide valuable insights for stakeholders to enhance their understanding of FPCs and guide future endeavors in strengthening agricultural cooperatives (Radadia & Lad, 2023) [16] .

Materials and Methods

The present study was analytical in nature. The study was solely based on the primary data obtained from the farmer member of farmer producer company through structured schedule. There were ten farmer producer company randomly selected. In these, 10-member farmer were randomly selected from each farmer producer company. Thus, in total 100-member farmer were selected spread over ten farmer producer company in Gujarat state the data collected during the period of inquiry was compiled, scrutinized, tabulated, and analyzed as per the objectives of the study by using tabular, frequency, and factor analysis. Mathematically, factor analysis is somewhat similar to multiple regression analysis, in that each variable is expressed as a linear combination of underlying factors. The amount of variance a variable share with all other variables included in the analysis was referred to as communality. The covariation among the variables is described in terms of a small number of common factors plus a unique factor for each variable. These factors were not overtly observed. If the variables are standardized, the factor model may be represented as: Gender distribution indicates that the majority (78%) of respondents are male, while 22% are female. In terms of religion, 86% of the respondents identify as Hindu, followed by 12% who identify as Muslim, and only 2% who identify as Christian. Caste classification shows that the majority (61%) of respondents belong to the general caste, while 28% belong to the OBC category, and 11% belong to the SC/ST caste. Marital status reveals that 94% of the respondents are married, while only 6% are single. Regarding education qualifications, 47% of respondents have completed their studies up to HSC, while 26% have completed their undergraduate studies. The remaining 15% and 12% of respondents have studied up to secondary and primary level, respectively. In order to reduce the number of variables in the study, a factor analysis was performed on the 28 variables. The aim of this analysis was to identify underlying groups of variables that are more easily identifiable. The first step in this process involved assessing whether the data collected from the fieldwork was suitable for factor analysis. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is a widely-used index for evaluating the suitability of factor analysis. The KMO measures the degree of correlation among variables and assesses whether the data is appropriate for factor analysis. Typically, values between 0.5 and 1.0 are considered appropriate for factor analysis.

Additionally, Bartlett's test of sphericity is used to test the hypothesis that the variables in the population are uncorrelated. This means that each variable is correlated with itself but not with other variables. The test evaluates the null hypothesis that the correlation matrix is an identity matrix. A significant Chi-square value indicates that the correlation matrix is not an identity matrix, indicating that factor analysis is appropriate. Both KMO measure of sampling adequacy and Bartlett's test of sphericity are crucial in determining the suitability of factor analysis for a given dataset. These tests provide a robust foundation for further analysis and interpretation of the underlying factors. 0.000

The KMO value obtained in this study is 0.610, indicating that the sample size is adequate and factor analysis is appropriate for the given data. Furthermore, Bartlett's test of sphericity yielded a significant p-value of 0.00, indicating rejection of the null hypothesis and supporting the suitability of factor analysis.

Factor loadings represent the strength and direction of the relationship between a particular variable and the underlying factor. Factor rotation techniques such as varimax are used to simplify the factor structure and highlight relationships that were previously unclear.

Eigenvalues are a critical criterion for selecting the number of significant factors to extract from the analysis. An eigenvalue represents the sum of the squared factor loadings for a particular factor, and a commonly accepted threshold is an eigenvalue greater than or equal to 1.

The percentage of variance explained by each factor is another essential measure in factor analysis. It indicates the www.extensionjournal.com extent to which a particular factor accounts for the total variance in the observed data. Collectively, all the extracted factors provide a comprehensive explanation of the underlying phenomenon. Using principal component analysis, ten factors were extracted from the data, explaining 70.65% of the variance in farmers' attitudes towards the success of farmer producer companies. The eigenvalues of these factors were greater than one, indicating their significance in explaining the underlying phenomenon.

The factor matrix provides the coefficients of variables in terms of factors, known as factor loadings, which indicate the strength of the relationship between each variable and the underlying factor. A large absolute value of a coefficient indicates a strong correlation between the variable and the factor.

To enhance the interpretability of the factors, a Varimax rotation method was used, which is an orthogonal method that reduces the number of variables with high loadings on a single factor. The rotated component matrix with factor loadings can be found in Table 5. This approach allowed for a more straightforward interpretation of the factors, as the rotation redistributed the variance explained by the individual factors, potentially leading to the identification of different factors than those initially extracted. It can be observed from Table 5 that the first factor is highly loaded on two variables, namely "FPC has professional management" (.469) and "FPC has a business plan" (.568). These two variables may be grouped together under one factor as "Management Perspective". This first factor explains 12.52% of the total variance in factors contributing to the success of farmer producer companies among farmer members.

The Second factor is loaded on two variables, including FPC has good knowledgeable staff (.700) and CEO has good experience in business (.701). These variables may club together under one factor as "Management Team". This second factor represents an 8.91% variance in factors contributing to the success of farmer producer companies among farmer members.

The Third factor is loaded on four variables, including Members are allowed to express their feelings out loud without hesitation (.794), Political interference (.678), FPC has a democratic decision-making process (.410) and FPCs all transaction is transparent (.416). These variables may club together under one factor "Group Cohesiveness". This third factor represents a 6.58% variance in factors contributing to the success of farmer producer companies among farmer members.

The Fourth factor is loaded on three variables, including FPC gives better prices for produce (.710), Quick payment to farmers (.521) and FPC procures produce of nonmembers (.573). These variables may club together under one factor "Output services in context to procurement". This fourth factor represents a 6.49% variance in factors contributing to the success of farmer producer companies among farmer members. www.extensionjournal.com

The Fifth factor is loaded on three variables, including Inadequate infrastructure facilities (.780), Credit facilities not available (.741) and Lack of well-developed processing facilities (.686). These variables may club together under one factor "Infrastructure and Credit Facilities". This fifth factor represents a 5.81% variance in factors contributing to the success of farmer producer companies among farmer members.

The Sixth factor is loaded on two variables, including FPC provide input at a reasonable price as compared to the market (.496) and FPC provide quality input at a lower cost (.749). These variables may club together under one factor "Quality with Cost Advantage in Input Services". This Sixth factor represents a 5.74% variance in factors contributing to the success of farmer producer companies among farmer members.

Table 5: Rotated component matrix

The Seventh Factor loaded on three variables, including FPCs members share information with each other (.472), FPCs management interact frequently with members (.977) and FPC's knowledge person frequently contact to the allmember farmer (.977). These variables may club together under one factor "Communication System". This Seventh factor represents a 5.57% variance in factors contributing to the success of farmer producer companies among farmer members.

The Eighth Factor Loaded on three variables, including Information given by FPC are more accurate (.629), FPC delivered timely and useful information (.417) and FPC give www.extensionjournal.com all technical guidance to the member (.786). These variables may club together under one factor "Open Information Sharing". This Eighth factor represents a 5

Success factors 1. Management Perspective:

This refers to the overall mindset of the management team in terms of their approach towards running the FPO. A positive and proactive management perspective can be a key driver of success for FPOs. This involves having a clear vision and strategy for the organization, as well as a focus on innovation and continuous improvement. It also involves a willingness to take calculated risks and make strategic investments that can drive growth and profitability.

Management Team:

The management team of FPOs plays a crucial role in its success. They must have the necessary skills and experience to effectively manage the FPO, including financial management, marketing, and governance. They must also be able to build trust and rapport with the members, and communicate effectively with them.

Group Cohesiveness:

The success of FPOs also depends on the level of cohesiveness among members. This involves building strong relationships and a sense of community among members, as well as a commitment to the common goals of the organization. Effective communication and conflict resolution strategies are also important in building group cohesiveness.

Output Services in Context of Procurement:

The output services provided by FPOs must be of high quality and delivered in a timely manner. Effective management can help ensure that the FPO has the necessary resources and infrastructure to deliver these services, and that they are aligned with the needs and preferences of the buyers. This involves providing access to markets, as well as negotiating contracts and ensuring timely payments to members.

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