Exploring the value of adding a data layer to cooperatives : Megha farmer cooperative case study

By Shefali Girish

Executive Summary

Gender digital divides have aggravated the already existing inequalities in our society. The lack of control by women over their data has hindered their ability to obtain social and economic value of data. This article seeks to establish why adding a data layer to women cooperatives in particular has the power to redistribute the economic benefits of data.

As part of the 17 rooms project by Brookings Institution to develop a roadmap on building gender data cooperatives, we looked at one use case in particular — Megha, which is a women farmer cooperative under the SEWA (Self Employed Women Association) Federation of Gujarat, India. This paper is an analysis of our field visit to Megha cooperative and our early thoughts and hypotheses on how adding a data layer to the cooperative could provide value to its members and what that could look like. The intent of this article is to demonstrate the value of building a gender data cooperative — pooled data can be used to demonstrate creditworthiness to financial service providers and enable increase in access to credit and receive accurate data driven advice and support for women farmers.

The article is structured into two parts. Part I contains a summary of our field visit. Part II has 2 sections — Section 1 seeks to explain why data cooperatives are the most appropriate stewards of data, the impact of cooperatives on empowerment of women and why organizing gender data cooperatives in the context of agriculture is extremely important. Section 2 explains how Megha cooperative currently functions and attempts to explain how data cooperatives can deploy data better to provide value to its members through our hypotheses. Finally, the conclusion provides arguments for why data cooperative is valuable for the women farmer community and lays down the way forward.

Part I : Notes from the Megha cooperative field visit

Aapti field visit to Megha
  • Aapti Institute made a visit to Megha Mandli, which is a women farmer cooperative of the SEWA Cooperative Federation located in Gujarat’s Tapi district. The purpose of the visit was to understand how the cooperative works, its structure, governance, data collection and the challenges they face.
  • Megha Mandli has a total of 1001 members or shareholders. The cooperative works closely with the SEWA Cooperative Federation, who helped initiate this cooperative. The SEWA Federation provides specialised services such as capacity building, training, access to markets, access to credit, business process streamlining, compliance and HR/admin, research and communications.
  • The cooperative has 14 board members who are democratically elected. For every 10 villages, there exists a coordinator between the board and Megha Mandli called Sankalit Sathi. At the community level, the Aagewan (community leader for 1–2 villages) interacts with farmer members at the ground level.
  • Certain decisions will be first decided by the board and then they will conduct general meetings with members of the cooperative.
  • The general meetings happen once in a year and board meetings happen bi-monthly.
  • Responsibilities of board members include discussing the activities of the cooperative and future plans for the cooperative. This decision/discussion is carried forward to the members; the cooperative also supplies inputs such as seeds, fertilizers and small tools/equipment to members to solve issues that arise due to distance and quantity.

The cooperative engages in 2 main types of work -

  1. Organising women farmers and their collective engagement for business related planning. SEWA helps them think through their by-laws until they are registered.
  2. Planning business models — The cooperative mainly works on the input side. They focus on activities such as supplying paddy seed or poultry feed/chicks to the members. For instance, when members engage in poultry farming, the cooperative buys input such as chicks, feed, eggs and sells it to the farmers. This helps farmers save time and money as the cooperative negotiates for lower bulk prices while buying input. The farmers then sell it in the village (output).

Part II : Analysis of Megha visit

Section 1

Data stewardship has emerged as a framework for community-oriented data governance — a structure that can simultaneously unlock data for public good use while not only preserving the rights and interests of the people whose data is in question, but also seeking value for them from the sharing of such data. Cooperatives serve as the ideal form of existing community-oriented institutions to facilitate data stewardship. The principles that ground the cooperative movement (non-monopolistic, self-managed, horizontal, collective benefits) serve as a promising alternative to the extractive logic of the current data ecosystem. This makes data cooperative an ideal steward for data as it is also based on similar principles. Cooperatives also have established governance structures with democratic decision making and are capable of articulating the needs and concerns of their members accurately. Cooperatives can pool data and allow the sharing of data between various stakeholders while being integrated with the data cooperative. There is also potential for cooperatives to exchange data through federated data flows.

Data cooperatives can thus monetise cooperative structures as an optimal form of economic management. The core principles of cooperatives also include supporting the communities they work with, education and training initiatives — which are extremely important for women as it provides access to social protection. This poses many advantages for data cooperatives to be built upon existing cooperatives. Data is collected and organized in structured systems that can be then used for the benefit of the members (including through sharing with third parties). Thus, data acts as a representational tool that brings value by facilitating the objectives of individual members.

Women’s financial journeys are different from men. The Global Financial Inclusion Database has found that women are particularly disadvantaged when it comes to access to financial services. Women's enterprises usually face higher interest rates than their male counterparts and are discriminated against by banks and must change the way in which they seek funds. A pattern of higher credit access by men when compared to women was observed in all surveyed districts across the states of Bihar, West Bengal and Orissa. Women-owned MSMEs in India faced a whopping gap of 70.37 percent in financing. 90% of women entrepreneurs in the country rely on informal sources of financing.

All of this can be attributed to social biases on the part of banks and financial institutions with respect to the credit-worthiness of women-led enterprises. This is despite most research confirming that women are more disciplined than male borrowers and have a better credit profile despite clear social barriers, including a lack of assets (land ownership), lack of collateral, opportunity and technical skills. Moreover, trust is required for women to invest and use new technology — particularly digital financial services, as there are high stakes of personal finances involved. This, in turn, leads to a self-perpetuating cycle of financial exclusion as women are inhibited from applying for loans which leads to limited access to improved inputs and better productivity. When Micro and Small Women Entrepreneurs (MSWEs) are empowered, there is a corresponding larger-scale impact which benefits the development of the society as a whole . This can be attributed to the tendency of female entrepreneurs to reinvest their money in their children’s health, education and so on. Kudumbashree, for instance, is a programme in Kerala which is primarily organised around neighbourhood collectives that comprise women who belong to high risk families and are economically disadvantaged. This is a remarkable example of organising women cooperatives to encourage them to use pooled resources to meet the needs of members. Small regular savings of neighbourhood groups (NHG) are pooled and given out as internal loans to members. These loans serve as buffers that address any immediate financial shocks. Bank linkage programs of Kudumbashree have made access to banks to neighbourhood groups (based on objectively identifiable parameters to rate NHG) possible without any collateral. Cooperative structures can therefore help in increasing the collective bargaining power to gain access to institutional credit and further the goal of financial inclusion

With the prevalence of women engaging in collective enterprises, gender data cooperatives can help in facilitating access to credit and digital financial service providers. The cooperative can help its members understand the advantages of using new products and help ease the process of transitioning to these products and help with the operational requirements associated with using the services. The cooperatives can help in developing and disseminating gender-specific value propositions for Digital financial services (DFS). By adopting a gender lens while providing training and assistance, the cooperatives can also help minimise risks by monitoring unintended adverse effects to women and address concerns of women by providing them recourse. Gender data cooperatives can thus build trust and confidence amongst its members and increase transparency and accountability while empowering women organisations. A cooperative’s membership therefore can lead to an increase in women’s decision making power at household, group and community levels. A remarkable case study is that of P’KWI Farmer cooperative society in North Eastern Uganda which has proven that membership in the cooperative has increased women’s abilities to influence decision making and has significantly increased their economic well being as they gained knowledge and adopted agronomic activities.

Over the last 2 decades, we have witnessed a massive jump in the number of female workers employed in agriculture in India. In 2018 to 2019, 71% female workers were employed in agriculture. This increased participation of women agri-labourers and phenomenon of feminisation is only likely to continue with the increased pressure for men to migrate to urban areas.The dairy cooperatives in Gujarat (AMUL brand) and Shri Mahila Griha Udyog Lijjat Papad are examples of highly successful women leader cooperatives. The problems faced by the farmer community are extremely grave and likely to affect women farmers even more disproportionately — in terms of their right to land, input, market and credit among others. While the contribution of women in the farmer economy is very significant and is likely to increase, they lack voice in decision making and lack access to opportunities. To validate their female agency and to empower them, there is a need to provide services such as capacity building, training and access to credit — all of which women data cooperatives can facilitate while providing women with representation and decision making abilities.

Women are generally underrepresented in formal agricultural value chains, which makes it difficult for female farmers to access agriculture credit. Agriculture credit is a prerequisite for farmers to increase their agricultural output and plays a key role determining the agricultural development of a country. Lack of data on smallholder incomes and credit histories limits the ability of FSPs (Financial Service Providers) to accurately predict cash flows. FSPs are increasingly using data and technology to address the data gaps and conduct risk assessments while creating credit products for the farmers, especially women. This would enable them to design financial products tailored to farmer’s crop cycles.

There are innovative agriculture digital financial service business models that can capture the wealth of farm and farmer data for credit risk assessment and help facilitate data sharing partnerships through APIs. Startups offer loans to unbankable customers using big data (mobile money data, call data records and so on) which is processed by algorithms to predict a person’s likelihood to repay a loan. For instance, Apollo Agriculture offers a short-term input credit product bundled with agronomic information and advice and weather index insurance. The company uses alternative data sources to perform credit risk assessments. They combine farm and farmer data collected by their agents with satellite data to predict value chains, crop yields, crop cycles, housing ownership, animal/livestock ownership and road access. Apollo then applies machine learning to assess creditworthiness and design credit products tailored to farmers’ production cycles. The loan is disbursed and repaid using mobile money. These companies use alternative data to provide credit for farmers which can greatly benefit them and data cooperatives can assist in the organizing, collection and sharing of such data while protecting the privacy rights and interests of the farmers.

Agriculture, as a sector, has a long and rich history of cooperatives. Among the 300 largest cooperatives in the world, about 30% are found in the agriculture and food industry sector, as per International Cooperative Alliance. Agricultural cooperatives throughout the world have played a significant role in organising the small farmers who are responsible for 80% of the world’s food production. These cooperatives tend to be well organised, and are present at different levels, with some acting as a hub for smaller, more localised cooperatives.

Cooperatives allow those who are prone to being exploited and cannot enter the market system to gain strength to do so collectively. They have been the best suited institutions for economic and rural development, especially for women. Women cooperatives have empowered women by enabling access of credit to women at affordable and low interest rates and the consequent promotion of self employment and small scale industries. A study conducted by ICNW states that there is 1 woman member against 3 male members in a cooperative and participation of women, especially in the decision making of the cooperative has significantly increased over the past couple of years.

Cooperatives provide small farmers access to raw materials, advisories on better farming practices and sources of credit. They are also an important factor in obtaining better prices for farmers for their yield, by allowing for a stronger bargaining position. Large scale cooperatives in the agriculture and animal husbandry sector in India also have a very successful track record — AMUL, IFFCO and KRIBHCO being notable examples. These cooperatives are adept with technology adoption and they have large membership bases with a great deal of trust between the members and the cooperative structure. The presence of large cooperatives in the agriculture sector is also witnessed across numerous jurisdictions, making it an ideal vehicle to pilot a roadmap to build data cooperatives.

We believe that data cooperatives, either built on existing cooperatives or newly created, pose many advantages as they are cost effective and provide easier access to data providers and data users. These data cooperatives can act as data custodians to ensure farmers stay in control of their data while taking into account the regional perspective and the associated needs in an already established level of trust.

Data cooperatives can help in pooling of data from various sources, provide support on how to evaluate benefits of farm data use to derive value such as getting access to capital. This can be done by focussing on the direct concerns to farmers, incentivising farmers to use their data and presenting data driven solutions in an understandable and easily identifiable way. Data cooperatives can pave the way to utilise the potential for farmers to use different data sources to create data products that can increase the value of products (for purpose of sale/marketing). For instance, combining data regarding feed consumption, vaccine used, organic fertiliser used, digital equipment performance and so on can help increase the value of the poultry or harvest that is up for sale.The data cooperative will integrate multiple data points to get real time information and recommendations for farmers. The data cooperative can thus act as an intermediary which shares data with data driven apps, agritech companies and ensures that farmers benefit from the value of data. Therefore the data cooperative can undertake the task of marketing their data on conditions set and agreed upon by farmers.

Changes in temperature, frequency and severity of droughts, floods and precipitation cycles have made it very difficult for farmers to harvest their traditional crops, combat proliferation of pests and diseases and retain their nutritional value. Populations in developing countries (that are primarily dependent on agriculture) are more likely to be affected by climate change impacts on agriculture and agriculture in turn contributes largely to climate change due to greenhouse emissions. Small and marginal farmers account for 86% of the total farmers in India and they lack the ability to afford advanced machinery or improve modern farming practices and hence are hampered by low productivity. This has left them very vulnerable and food insecure.

Current data collection efforts are top-down, disparate and the datasets are siloed — farmers have very little to benefit from them. Bottom-up data cooperatives can help reverse current extractivist data practices where farmers can exert more control of their data and make decisions to benefit from such use. Further the diverse environmental conditions and weather patterns in countries like India, is another area where improved data use can greatly benefit farmers. Data can also be used for digital agriculture or smart farming and can provide opportunities for farmers to learn about regenerative practices and understand the impact of their practices to the environment. Data cooperatives with appropriate governance mechanisms can help local farmers collect relevant datasets which could be shared with researcher and agritech companies in return for services and hyperlocal farming advice best suited to their circumstances and needs.

The Birchip cropping group, for instance, aims to improve the lives of farmer communities in Australia by providing support and tools for better farm management practices. The UAWC project promotes collective sustainable models of agriculture and enables women rural communities in Palestine to better respond to their community needs while facing climate change and under territorial occupation.The project assists cooperatives in selecting drought resistant crops and adapting to climate change. It was found that as women participation in the economy increased, it improved women’s influence in local policymaking and they found their voice in their own household and in the public sphere.

Section 2

Currently the relationship between the cooperative and farmers is such that it is a community-based organisation led by women farmers and provides advisory support to farmers on both agricultural and non-agricultural activities.

  1. The cooperative supplies input such as paddy seed, poultry chicks/feeds. They buy from input suppliers at scale and sell it to the farmers according to their demand.
  2. The cooperative however does not involve itself in the output activities of the farmers, i.e., the processing, marketing and sale of their produce.
  3. In our conversation with Megha we realised that they have the desire to scale at the output side but are hindered by the lack of capital to do so.
  4. They have till date not taken any loan including from the SEWA cooperative Bank — this suggests that they are extremely risk averse. This is because the SEWA Cooperative Bank provides loans only to individuals and not cooperatives. The member farmers have taken credit from SEWA during the pandemic for seed purchase.
  5. Unlike Megha, most farmer cooperatives have an increase in share capital and maintain savings. For example — Mulkanoor farmers cooperative, which deals with collecting milk from farmers, processing and selling it — has both group savings and higher share. In the case of Megha, the membership fee is INR 10 with 1001 members/shareholders and they don’t maintain any joint savings (this could either be attributed to a possibility that access to credit and microfinance was something that the cooperative did not want to handle) — which means that there is already a paucity of capital.
  6. Currently the cooperative provides an advisory role with the help of the SEWA Cooperative Federation. They also organise training programmes for farmer-members and enable access to government and SEWA offered social protection services.
  7. The co-operative makes decisions (such as determining the demand for input by farmers) on an estimate and is likely to suffer losses if the estimate is not accurate.

Our hypothesis is that pooling of data through data cooperative structure can help ease the access to capital and hence scale output activity and generate more profits.

Data cooperatives can help deploy data better to provide value to farmers of Megha cooperatives. Our hypotheses are that data cooperatives can manage pooling of data and enable farmers to raise money as a collective and increase access to capital; use the pooled data to provide better data driven insights with the help of AI; help farmers get insights from each other in the community on best practices and negotiate for better services based on farmers needs while protecting their data rights. This section attempts to demonstrate how some of our hypotheses of data cooperatives provide value to farmers.

Hypothesis 1: Data cooperative can manage the pooling of data and help increase access to capital

The data cooperative can collect data from various data sources to build an economic identity for farmers and get aggregate data to demonstrate credit-worthiness of the cooperative to get access to loans. For instance, in the case of small and marginal farmers in Dharwad, it was found that education status, landholdings, irrigation facilities and income levels were the main criteria to assess credit. Therefore many such data points can be used to build an economic identity for each farmer.

Data variables that are loan worthy include — access to reasonable irrigation facilities (irrigated areas have greater demand for institutional credit), health records of farmers and access to hospital facilities, data regarding soil health (Kisan Vigyan Kendra provides for low cost soil tests for micronutrients) and the kinds of crops (whether it is commercial or high value) that ensure consistent return. All these could serve as fundamental building blocks of data for long term loans.

A combination of agricultural (input,produce yield, quality, cash aid for produce) and non-agricultural data (personal data, bills) and gender, family size and occupation will help create an economic identity for farmers and the aggregate data will help get access to credit for cooperatives. Musoni MicroFinance Ltd is an example of a data driven organisation that seeks to serve the unbanked population. They design loan products for farmers with flexible repayment periods. After an analysis of several data points such as KYC, farming data — the product is designed in a way that caters to the farmer’s diverse needs and the crop cycle.

Here are the following ways in which the data layer in Megha can manage pooled data to get access to credit.

Megha supplies input to the farmers by buying it from input suppliers, however they make the payment upfront while buying such input.

  • The proposed data cooperative can collect data on the farmers such as — who receives input, the price they are paid at harvest, whether they repay credit and so on. This collection can happen via community leaders in the village or with the help of digital solutions. For instance, Farmforce — a digital procurement solution allows cooperatives to track interactions with farmers such as input supply. Examples of data points typically captured in the app include farmer ID, crop information, farm geolocation data, farm size, crop cycles and a photo of the farmer.
  • After its collection, the data cooperative can show the aggregated data to FSPs to establish the it’s collective ability to repay credit.
  • The FSP on profiling the creditworthiness of the cooperative, can pay directly to the input supplier for which the cooperative will repay later.
  • The cooperative then recovers the loan amount by deducting it from the farmers output (produce) payments.
  • The data collected could also be used by the cooperative to accurately estimate the actual demand of input of the farmers while buying from the suppliers.

In the case of Megha, the lack of data on farmer activities (output) such as total sale, profits, farmers’ incomes and their creditworthiness limits the ability to predict future cash flows and extend financial products to farmers like loans and insurance.

  • On the output side, The various alternative data collected on farming and farm data can be shared to FSPs for estimating credit rates. The aggregated data can be used to establish creditworthiness and get cash advances/credits from FSPs. The Kisan credit card crop loan, for instance, marks short term credit requirements to include expenses incurred from the cultivation of crops and post-harvest activities.
  • On receiving credit, the data cooperative can disburse and monitor the use of credit for agriculture production. Financing agriculture production using credit improves quality and predictability of output. Providing cash to farmers as per their needs can be an effective incentive for farmers to sell their share to the cooperative.
  • The data cooperative can use the credit to buy the produce of the farmers at the and sell it to a group of agribusiness buyers/retailers or open market

Farmers lack access to credit for their non-agricultural needs

  • The data cooperative can use the combination of farm data, personal and non-agricultural data collected to build an economic identity for individual farmers.
  • This can be accessed by farmers with the help of data cooperatives and used to get individual personal loans. The data cooperative can help digitise and support the entire loan process from application of loan to collection of data, monitoring and repayment.
  • The data cooperative can thus leverage this data to negotiate with FSPs for loans that cover a farmer’s non-agricultural needs that are tailored according to the farmer’s crop cycle.
  • Alternatively, the cooperative as a whole can take out loans for non-agricultural needs and then lend to farmers behaving as a non-profit micro-credit entity.
  • Using the data shared, loans can be designed to fit the crop calendar of farmers. For instance, Advan used a credit scoring model for school loans that combine agriculture and financial data. In this particular case — the loans were disbursed at the start of the school year and during periods of negative cash flow and repay the investments during the harvest months

Hypothesis 2 : Data cooperative can help maintain and digitise savings to increase access to capital

Megha currently does not maintain savings. Irregular cash inflows and regular cash outflows add to the difficulty in managing finances for farmers. This makes savings extremely crucial to meet agricultural consumption and household needs when cash inflows are limited and to cope with financial shocks. To cover expenses, the farmer also would require flexible access to credit, enabling them to make long term investments that increase their productivity, in addition to their savings for the year. Savings increase deposits and both of these factors help in increasing access to capital. For farmers to contribute to greater share and savings, there is a need to establish an extremely high level of trust.

  • The proposed data cooperative can have members contribute equal amounts to a group fund or a linked savings account. This can help increase access to loans.
  • Market-appropriate savings vehicles for all women-owned small and medium enterprises increases productivity and earnings in developing countries. Savings build members’ capital for consumption, emergencies and investment and protect them against borrowing from money lenders — they give access to loans as well as to get insurance from financial service providers. Savings products for women farmers in rural areas become extremely important considering the seasonality of their incomes. Digital savings culture has been proven to enable farmers in both formal and informal value chains to address short term financing needs. Creation of data systems by digitising and linking savings groups for farmers will help capture funds and their transactions with it — thereby building farmers’ financial history.
  • This could also reflect how the savings group operates, generating a record of individual and group balances and transaction history and hence unlocking new opportunities, predominantly access to credit . Data cooperatives can help maintain this and provide assurance to members on security of their savings with a transparent system in place. These voluntary savings can provide flexibility of withdrawing cash at times of need. APIs can be used to integrate the digital wallets of individual farmers with savings accounts. Mobile money enables Digital finance services (DFS) in the agriculture sector — it acts as an alternative to cash payments and acts as an entry point to provide savings and credit products for smallholder farmers
  • For instance, Vodacom Tanzania launched M-Koba in partnership with TPB Bank in 2019. Here, the M-Koba account is managed by the group leader that adds members to the group using mobile numbers. Members send money to the group savings account from their digi wallets — each member can request a group savings loan automatically.

Hypothesis 3: Data cooperative can help manage the pooling to provide data driven expert advice

After a preliminary analysis of samples of data collected that were shared by the cooperative and information gleaned from the interview, we realised that the data collected digitally by the cooperative is not extensive and maybe limited to data regarding seed purchase and sale list.

This is a barrier to getting accurate data-driven expert advice on daily activities of farmers. Currently, such decisions are broadly based on an estimate and on occasions, they have suffered monetary losses for the same. This is likely to reduce if decisions are data-driven and will match the demands of the farmers. The data cooperative can use data management and analytics capabilities.

The data co-operative can also partner with agri-tech companies and ensure responsible data sharing to get information regarding climate, soil quality and geo-location data. The data cooperative can manage such data and analyse it to make informed decisions.

Data sharing is essential for the growth and scale required to close the financing gap for smallholder farmers and to make better informed data-driven decisions with consent of farmers. The data cooperative can enable this by aligning itself at a strategic, operational and commercial level with a common understanding of data protection and processes for its collection

Conclusion

Through a data cooperative, pooled data can be used to demonstrate creditworthiness to financial service providers and enable increase in access to credit and receive accurate data driven advice and support. A data cooperative can enable the transition to digitising data collection and aggregating a variety of data points that could enable an accurate estimation of credit risk. Farmers have both agricultural and non-agricultural needs and building a digital economic identity can help them get access to loans for non-agricultural expenses such as education and rent. Financial service providers also benefit as it reduces their credit risk. This economic identity can be created using a multitude of data points (such as household events, assets or transaction history) to present a more dynamic and holistic picture. Women thus have the ability to control and also create data products that can increase the value of their product.

Data cooperatives also have the potential to negotiate with Financial service providers to ensure that members are not adversely affected when using new forms of services. Existing AI systems perpetuate bias and bring women and marginalised communities into a credit system that is detrimental to them. Data cooperatives can ensure this risk is mitigated. Stewards (such as data cooperatives) by virtue of their links with a diverse community of data producers, can curate, standardise and supply representative datasets to train AI models and reduce the scope for bias. They can facilitate control over one’s data and decide the terms for data use in AI development and demand accountability from data users through grievance redressal mechanisms. ‘High-risk’ applications (such as AI integration for credit scoring) that could result in denial of essential services come with significant compliance requirements. There is potential for data cooperatives to conduct assessment audits and be continuously involved in the evaluation of the social consequences produced by deployment of AI systems amongst its members. Community leaders within the cooperative could also perform the role of human oversight into AI systems and mitigate the discriminatory effects of automated decision making systems.

While making the value proposition to women and demonstrating the gains of data cooperatives and adoption of new technology, it is important to gain their trust. Women need to be sure that technology will work in their best interests and perform its purpose reliably. Especially with digital financial services, women are inclined to take help from agents more often before they feel comfortable using these services. Data cooperatives will help enhance this digital trust by playing the role of intermediary that uses data to achieve the needs of its members and facilitating credit and business management training as required. Data cooperatives can also enhance trust in the DFS provider that the cooperative partners with and enhance trust in the product itself — that the product will work as designed and for the benefit of members. To further this trust, data cooperatives can facilitate training and awareness on how to use these products. The data cooperatives also ensure that the user’s experience with the technology must result in positive expectations with respect to its functionality, usefulness, and reliability. The data cooperative can also ensure that AI systems used are not exploitative and establish grievance redressal processes to resolve service issues in a timely manner.

With the prevalence of women engaged in agriculture in developing countries, data cooperatives can share data and partner with external parties that run agricultural extension programs supporting specific needs of women and are a trusted source of information on a range of topics. As previously noted, trust again plays a critical role in understanding the true advantages and challenges with using the new product or service and data cooperatives can play a major role in enhancing that digital trust.

We believe that data cooperatives can use pooled data in an easily accessible or interoperable format to access credit and invest on behalf of farmers in a more cost efficient manner. The cooperative can be used to test data solutions and gain insights from farmers on various decisions made around agricultural activities. A phased approach to credit, starting with small, relatively simple and short-term products, can introduce farmers to a credit system and help them build credit history that unlocks larger, longer-term credit. The data cooperative model can be used to generate societal value and help groups evolve mechanisms of participative governance and negotiate better.

With Megha — we seek to test our hypothesis and to understand whether adopting the data cooperative model will be helpful to the farmers. We seek to establish a finalised data cooperative design for Megha women farmers cooperative created through a community co-design process and to address essential architecture and governance issues. To achieve this — we will make field visits to understand the willingness of farmers to share their data and most importantly, involve them in the co-designing process of building this data cooperative layer.

If you are interested in learning more about this work or collaborating with us on any of these questions, please get in touch with us at contact@aapti.in.

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