Production technology adoption and electronic market participation intensity of chilli (dry) farmers in India: Application of triple-hurdle model

Kumar, K. Nirmal Ravi et al. Article Production technology adoption and electronic market participation intensity of chilli (dry) farmers in India: Application of triple-hurdle model Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Kumar, K. Nirmal Ravi et al. (2023) : Production technology adoption and electronic market participation intensity of chilli (dry) farmers in India: Application of triple-hurdle model, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-22, https://doi.org/10.1080/23322039.2023.2207939 This Version is available at: https://hdl.handle.net/10419/304067 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Production technology adoption and electronic market participation intensity of chilli (dry) farmers in India: Application of triple-hurdle model K. Nirmal Ravi Kumar, S.N. Mishra, Adinan Bahahudeen Shafiwu, Shailendra Gajanan, Suresh Chandra Babu & A. Sandhya Neelima To cite this article: K. Nirmal Ravi Kumar, S.N. Mishra, Adinan Bahahudeen Shafiwu, Shailendra Gajanan, Suresh Chandra Babu & A. Sandhya Neelima (2023) Production technology adoption and electronic market participation intensity of chilli (dry) farmers in India: Application of triple-hurdle model, Cogent Economics & Finance, 11:1, 2207939, DOI: 10.1080/23322039.2023.2207939 To link to this article: https://doi.org/10.1080/23322039.2023.2207939 © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 21 May 2023. Submit your article to this journal Article views: 1511 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Production technology adoption and electronic market participation intensity of chilli (dry) farmers in India: Application of triple-hurdle model K. Nirmal Ravi Kumar 1 , S.N. Mishra 2 , Adinan Bahahudeen Shafiwu 3 *, Shailendra Gajanan 4 , Suresh Chandra Babu 5 and A. Sandhya Neelima 6 ABOUT THE AUTHORS Dr. K. Nirmal Ravi Kumar is currently Professor & Head (Agril. Economics) in Acharya N.G. Ranga Agricultural University (ANGRAU), Andhra Pradesh, India. He was the recipient of ‘Sri Mocherla Dattatreyulu Gold Medal’ (2013) and ‘State Best Teacher Award’ (2016). Dr. Kumar has written extensively and has to his credit 10 books, and six (6) published articles in reputable journal of higher impact. Dr. S. N. Mishra is presently working as a Professor and Head, Department of Agricultural Economics, College of Agriculture and Honorary Director, CoC Scheme, Bhubaneswar. He has a brilliant academic career and recipient of Gold Medal from Institute of Agricultural Sciences, BHU Varanasi, India. Dr. Adinan B. Shafiwu is a lecturer at the Department of Agriculture and Food Economics, University for Development Studies, Ghana. His research areas include technology adoption, efficiency analysis, food security and welfare studies and he has sixteen (16) published articles in reputed international journals. PUBLIC INTEREST STATEMENT In this paper, we integrate farmers’ adoption decision of a new variety of chilli crop (‘Teja’) along with their electronic market participation decision and e-market participation intensity, based on data from the chilli farming sector in India, where agricultural markets have been modernized through digitization (Kalgudi e-Market). We employed Triple-Hurdle Model (THM) to integrates adoption decision of ‘Teja’ variety of chilli, e-Market Participation Decision and e-market participation intensity thereby, allowing us to make inferences relating to chilli farmers in Andhra Pradesh, India. Our results, showed that the drivers of ‘Teja’ variety adoption, e-market participation, and e-participation intensity include education, reliable extension services, access to seeds of high yielding varieties, market information, and membership in farmer-producer organizations. Added to these, personnel training visits, prompt deliveries of inputs, and prompt payment of sales proceeds are also important in influencing participation and intensities. Also our results showed that the three stochastic decisions of THM are strongly correlated implying that the adoption decision of ‘Teja’ variety of chilli by the farmers influences the e-market participation decision and consequently, e-market participation intensity and these three decisions are sequential. On the basis of the result we recommended for future farm policy and agricultural-research and innovations must recognize the potential that the digital marketing systems have to offer. Such considerations coupled with the provision of market infrastructure including assaying, grading, storage, and market information will promote digital transformation in agricultural value chains in developing countries like India. Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 1 of 22 Received: 09 January 2023 Accepted: 25 April 2023 *Corresponding author: Adinan Bahahudeen Shafiwu, Department of Agricultural and Food Economics, University for Development Studies, Ghana E-mail: [email protected] Reviewing editor: Raoul Fani Djomo Choumbou, Agricultural Economics and Agribusiness, University of Buea, Cameroon Additional information is available at the end of the article © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. Abstract: Agricultural and food system transformation helps increase farm productivity and encourages farmers to participate in updated value chains, adopt newer technologies, thereby helping farmers transform their livelihoods in a sustainable manner. Relatedly, value chain innovations depend on multiple decisions farmers make at various stages of the value chain, adequate participation being a primary factor. In this paper, we integrate farmers’ adoption decision of a new variety of chilli crop (“Teja”) along with their electronic market participation decision and e-market participation intensity, based on data from the chilli farming sector in India, where agricultural markets have been modernized through digitization (Kalgudi e-Market). Thus, the employed Triple-Hurdle Model (THM) integrates adoption decision of “Teja” variety of chilli, e-Market Participation Decision and e-market participation intensity thereby, allowing us to make inferences relating to chilli farmers in Andhra Pradesh, India. Our results, showed that the drivers of “Teja” variety adoption, e-market participation, and e-participation intensity include education, reliable extension services, access to seeds of high yielding varieties, market information, and membership in farmer-producer organizations. Added to these, personnel training visits, prompt deliveries of inputs, and prompt payment of sales proceeds are also important in influencing participation and intensities. Results show that the three stochastic decisions of THM are strongly correlated implying that the adoption decision of “Teja” variety of chilli by the farmers influences the e-market participation decision and consequently, e-market participation intensity and these three decisions are sequential. On the contrary, the decisions viz., e-market participation decision and e-market participation intensity as input buyers and consequent adoption of “Teja” variety of chilli are simultaneous. So, the policy measures that promote production technology interventions (say, “Teja” variety of chilli) will definitely enhance better e-market access of chilli farmers. Accordingly, the breeding programs of the agricultural research stations should enhance the uptake of improved varieties in tune with modern marketing (e-market) technologies. Future farm policy and agricultural-research and innovations must recognize the potential that the digital marketing systems have to offer. Such considerations coupled with the provision of market infrastructure including assaying, grading, storage, and market information will promote digital transformation in agricultural value chains in developing countries like India. Subjects: Agricultural Economics; Statistics for Business, Finance & Economics Keywords: Adoption; production technology; e-market participation decision; market participation intensity; chilli; triple-hurdle mode JEL Classification: C51; C81; M31; Q13; Q18 1. Introduction Agricultural and food system transformation requires the development of efficient and effective value chains where farmers are well integrated into markets with a high level of productivity and the markets serve the consumers effectively by reducing transaction costs for both farmers and the consumers. Indeed, as Singbo et al. (2021), Poole (2017), Abu et al. (2016) and Barrett (2008) demonstrate, farmers’ market participation generates positive economic outcomes, by incentivizing the adoption of improved technology and crop varieties. Existing literature also supports the Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 2 of 22 premise that the adoption of improved crop varieties leads to higher incomes through output marketing in modernized market outlets (Ochieng et al., 2019; Ogutu & Qaim, 2019). In turn, improved access to marketing opportunities and transition towards modern market outlets also intensify the adoption of crop production technologies. Thus, to enhance market-orientation of farmers, it is essential to have effective linkages between farmers and consumers in the food value chains. So, modern outlook of farmers both in terms of adoption of production technologies and modern market outlets participation certainly contribute towards agricultural productivity and profitability. Likewise, recent studies from Africa such as Ebenezer et al. (2019), Gebremedhin et al. (2017), Okoye et al. (2016) and Akrong (2020) note the positive relations between access to technology and greater market participation in the farming sector. Further, the successful transformation of traditional market systems into modern supply chains, such as digitalization, is possible only if the prevalent farming community exhibits sufficient enthusiasm and readiness to adapt to such innovations. The extant research cited above indicates that the relation between farmers’ market participation and their adoption of new technologies is complex. For example, farmers who adopt highyielding varieties of seeds, may still not realize the full benefits from such adoptions, if they fail to participate in modern market systems. Similarly, farmers who are quick in adopting modern marketing systems may not sufficiently realize the maximum gains from such participation, if farm productivity and marketed surpluses are low. Studies from the farm sectors in India (Annemie & Christopher, 2013) and Madagascar (Moser and Barrett, 2006) voice the important concern that, while technology increases farm productivity and economic outcomes, its adoption among all potential users in the population is not guaranteed, because technology diffusion depends upon the pre-existing dynamics and behavioural patterns of the adopters. As Barrett et al. (2012) point out, that while market modernization assists farmers’ economic status, it is important to consider the impact of economic development on generating the needed market innovations. Consequently, economic policies must find the right balance between targeted subsidies and effective extension services. Relatedly, Barrett et al. (2012) also note that agricultural markets have undergone rapid transformations with fast-food chains, supermarkets, and related developments. During the same time, however, small farmer participation in modern value chains has remained low. Further, Barrett et al. (2012) conducted a meta-narrative analysis from five countries to indicate the comparative advantages of farmers who participate in modern value chains, and also the barriers that impede such participation, which mainly arise due to small farmers’ lack of access to farmer groups, supply chains, and cooperatives. Indeed, Barrett (2008) finds compelling evidence from the African farming sector to demonstrate that in almost all instances, macroeconomic and trade-policy tools are least effective in incentivizing market participation among small farmers. However, noticeable gains in farmers’ market participation are generated with targeted interventions, such as those that are specifically directed to farmer organizations, which reduce transaction costs, and improve access to productive assets. Consequently, policies that aim to transform agricultural practices must consider not just farmers’ awareness of modern technologies and crop varieties, but also their capacity to engage in modern transactions within the enhanced supply chain links. Policy proposals that focus solely on encouraging farmers towards new adoption techniques may not necessarily produce their full impact if farmers do not have the necessary complementary support systems in place. Access to and participation in modern market systems are key factors that motivate farmers to adopt newer production methods and crop varieties. However, access to modern supply chains is often inhibited Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 3 of 22 by high transaction costs, such as distance to markets, middlemen fees, market misinformation, lack of knowledge about newer methods of production, pre-existing and often exploitative local network, and institutional links. The economic problem can be posed succinctly by asking the following counterfactual question: how many of the currently non-producing farmers are likely to become potential producers if they could have easier access to modern markets? The answer to this counterfactual question becomes important, from a policy perspective. That is, the potential economic impact of an incomeenhancing farm policy may be overstated if the policy fails to discount the non-participation effects of the non-actors within the population. To adequately model the production-participation decisions, and address the counterfactual question posed above, Burke et al. (2015) and others have developed methods that incorporate sequential decision-making, taking endogeneity and self-selection into account. Specifically, Burke et al. (2015) envisage the household’s economic problem and outcome in different stages, wherein, in the first stage, the economic agent decides to be a producer or a non-producer. In the second stage, the agent decides on whether to participate in the market, say as a seller or a buyer or as both types, given the decision in the first stage. The third stage examines the outcomes in terms of net-gains realized, based on the decisions taken in the first two stages. The three-stage process by Burke et al. (2015), or the Triple-Hurdle Model (THM) extends the Double-Hurdle Model (DHM) framework (Musara et al., 2018), by treating the decision to be producers in the first stage endogenously. From a policy perspective, the THM methodology can shed light on the factors that inhibit production and technology adoption, and simultaneously help explain insufficient participation and the low participation intensity commonly observed in agricultural markets. Our paper employs the THM methodology to examine the adoption-participation outcomes within the chilli farming sector in South India. The main contribution of our paper is two-fold. Firstly, our paper integrates adoption, participation, and intensity decisions and outcomes in updated market supply chains transformed through digital technologies, which have not been considered in previous studies. Secondly, our study is the first to characterize the institutional linkages and performance within updated value chains established in the Indian economy. Indian agricultural sector provides an excellent case study to examine the counterfactual question posed by Burke et al. (2015) since a major portion of the population engages in rural agricultural and allied activities. Moreover, India has also experienced rapid modernization and digitization of its supply chain, thanks to the enhancement of global expo-markets through liberalization. The rest of the paper is organized into five sections. In section two, we describe the institutional background, covering the chilli sector from South India, and the recent digital innovations in farming markets. In section three, we present the THM econometric framework and the estimation strategy. Section four presents the data and discusses the results of the estimation. Section five provides a brief summary and policy conclusions. 2. Problem statement Lack of fair marketing mechanism is one of the major limitations for transacting chillies in Guntur district and also identified as the major constraints to increase production by the farmers (Shaker et al., 2019). According to Financial Express Bureau (2019), chilli farmers in Andhra Pradesh have limited access to e-markets and this prevent them from purchasing quality inputs and hence, in producing both quantity and quality output. This raised the questions of why chilli farmers were not participating in e-markets despite increasing importance for chilli production in Andhra Pradesh and what other factors constrain chilli farmers to participate in the e-market? Though a number of studies have been conducted on determinants and extent e-market participation, they may not be conclusive and apply to the chilli farmers of Andhra Pradesh due the heterogeneity in infrastructure, transaction costs, institutional arrangements and among farmers. Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 4 of 22 Further, no studies correlating the Adoption Decision (AD) of chillies production technology, e-Market Participation Decision (e-MPD) and e-Market Participation Intensity (e-MPI) have been conducted so far in the chilli sector of Andhra Pradesh despite farmers having challenges in accessing the e-market. On the other side, the pace of growth of Kalgudi e-market in terms of number of commodities dealt with; linkages with farmers, traders, processors; digitization of transactions for both inputs and output, etc., in the recent period is really encouraging and this led to analyze the causal relation between production technology adoption (“Teja” variety of chilli) and e-MPI of chilli farmers. So,the current paper exploits the e-transactions in Kalgudi e-market, which was established in the year 2020, in the Guntur district of Andhra Pradesh, South India. This e-market can be considered as a novel innovation that takes the advantages of Information Technology (IT) to grassroots. Within the Kalgudi e-market, the iAgriMarC is the agricultural produce marketing platform, which automates the operations of APMCs. Incidentally, iAgriMarC has been designed and operationalized in multiple States in India, to facilitate business interactions between farmers, traders, and processing firms. The iAgriMarC platform restricts malpractices, increases the effectiveness of marketing administration, and handles produce trade across India with minimum customizations. Up to this point, iAgriMarC has overseen, approximately 40B US transactions among 50,000 traders, and over a million farmers have directly or indirectly benefitted through this innovative platform. Further, the iAgriMarC digital supply chain enables farmers and micro-entrepreneurs to purchase requisite inputs and transact their produce directly to consumers. Farmers have also largely benefited with price discovery, Minimum Support Prices (MSP) enforcement, optimal pricing through e-auctions, transparent purchases and maintenance of farmers’ databases to aid future service delivery. Similarly, traders are also benefitted through online services, payments, connections, price discovery, and increased business hours and business areas. 3. Institution background With the advent of digital agriculture solutions such as access to the internet and the popularization of e-commerce services, electronic markets are gradually gaining popularity in India. e-markets and internet platforms can assist in smooth information transmission. One of the goals towards their establishment is to ensure an adequate supply of desired quality inputs at affordable prices, aggregation of outputs from farmers, and digitization of marketing operations and services, to ensure traceability and realization of remunerative prices for the produce transacted. By and large, e-markets help establish transparency and in removal of trade barriers across geographical boundaries. Indeed, e-market operations correct for information asymmetry within the e-marketing process, by strengthening both backward and forward linkages within the supply chain (Aggarwal et al., 2017; Reindl et al., 2019). e-markets also effectively address major marketing challenges faced by farmers, such as multiple levies (or mandi fees), multiple licenses for trading in Agricultural Produce Market Committees (APMCs), inadequate infrastructure in APMCs, absence of a price discovery mechanism, higher market charges from intermediaries and movement controls. Consequently, e-markets provide an environment, where farmers can freely conduct their transactions and establish greater control over the trade. Their establishment further leads to decongestion of APMC mandies and make the supply chain agile for agricultural commodities. To sum up, e-services help in creating transparency in sale transactions, price discovery, enhancing traceability, provision for quality testing, and reducing overall business risks. e-markets also successfully shield farmers against unethical marketing practices and simultaneously create more flexible marketing processes (Amarender et al., 2019; Reddy, 2016). Consequently, the Kalgudi e-market system can be considered as a network interaction platform that actively engages all stakeholders of agriculture and allied sectors. For example, the e-market Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 5 of 22 platform connects farmers, traders, input dealers, logistics, academia, market facilities, institutional buyers, Farmer-Producer Organizations (FPOs), Non-Government Organizations (NGOs), Government departments, and consumers across the spectrum and generates system-wide positive externalities. The e-platform system thus establishes convergence of all economic agents within an innovative network, with adequate potential to generate value for the entire ecosystem and facilitate further online purchases and sales of requisite inputs and produce. Our paper examines whether the innovative e-market platform performs to its fullest potential. We uncover this issue by examining the participation rates and intensities of farmers in the chilliproducing sector in South India. Chill is one of the major crops in India, and is currently cultivated in roughly 0.70 million hectares (Agricultural Statistics at a Glance, 2019). Our study area is located in the State of Andhra Pradesh, which leads the nation in the production of dry chillies, with an annual output of roughly 0.8 million tons. Our primary data is from a district in Andhra Pradesh, called Guntur, which itself constitutes about 50.32% of the total area under chilli in the state, and is considered the Asia’s largest chilli market. 1 The Kalgudi e-market, mentioned earlier, has also established links with the FPOs in major districts of Andhra Pradesh, our study area. Most of the farmer-members of the e-system cultivate and transact “Teja” variety of chilli through the online platform. Further, the e-market works together with related agricultural extension initiatives in place, such as the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and the Centre for Good Governance (CGG). The factors mentioned above, namely the district’s amount of chilli cultivation, its national reputation in the chilli market, and the participation rate of its farmers in the e-system, motivate us to select Guntur district for our study area. Guntur district is a natural setting to examine the relationship between technology adoption and e-Market Participation Intensity (e-MPI), given the extensive links between farmer-members and the Kalgudi e-market transactions platform in this region. Given the sizeable positive externalities from e-services, and a dominant chilli production sector, one would expect an active e-participation within the chilli production units and the related supply chain. On the contrary, paradoxically, there is insufficient take-up of the Kalgudi e-services platform within the different chilli farming units. The lack of e-participation is particularly noteworthy, given that the traditional market for transactions is often viewed as working unfairly and has been identified as inhibiting production and expansion (Shaker et al., 2019). Lack of access to e-markets is cited as a reason for nonparticipation, and hence, indirectly affects farmers’ adoption and production decisions. The purpose of this paper is to identify a set of factors that link decisions surrounding production and e-participation. To the best of our knowledge, this is the first study of its kind from India that links production technology (“Teja” variety of chilli) and AD to e-MPD and e-MPI. Our study area provides a natural setting to examine the adoption-participation relationships, given the heterogeneity among farmers with respect to infrastructure facilities, transaction costs, and institutional arrangements within the supply chain. Our study sheds light on effective policy proposals that can encourage e-market participation, establish better buyer–seller interactions, promote profitable production adoption decisions, and provide pragmatic promotional awareness campaigns to increase the role of digital value chains in developing and emerging economies. 4. Conceptual framework It is known that enhancing crop productivity through the adoption of improved production technologies presents a credible pathway to economic development of farmers especially through Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 6 of 22 increased participation in modern market outlets (Paul et al., 2022). This led to the use of a TripleHurdle Model to integrate AD of “Teja” variety of chilli, Kalgudi e-MPD and e-MPI. The estimation strategy is from the recent lines of empirical research pioneered by Burke et al. (2015). As Burke et al. (2015) note, for popular and high-value crops like chilli, the initial production decision regarding the adoption of varietal technology is an important additional consideration that can distinguish factors that could induce formerly non-producing farmers to become producing farmers. The proposed framework using a triple-hurdle model (THM) incorporates households’ choice mechanisms in a sequential decision-making framework. THM models are currently adopted in agricultural economics, as in Paul et al. (2022) for Ethiopia, Singbo et al. (2021) for Mali, Ebenezer et al. (2019) and Akrong (2020) for Ghana, Gebremedhin et al. (2017) for Ethiopia, Okoye et al. (2016) for Madagascar and Kondo et al., 2019 for Ghana. Estimation through the THM procedure is based on incorporating the production decision or AD along with e-MPD and e-MPI decisions sequentially, and the underlying methodology is represented in Figure 1. Following, Burke et al. (2015) THM indicates agents’ choices in three stages, where the adoption decision (AD) of the crop is determined in the first stage. In the second stage, the agents decide on market participation (MPD) either as net buyers or net sellers or autarkic and in the third stage, the intensity of e-market participation (MPI) for net buyers and net sellers is determined. Formal expressions that capture the three stages in the THM setup are as follows: Stage 1: y 1 = y 1 (x 1 , ω) Stage 2: y 2 = y 2 (X i , δ) Stage 3: I 1 = I 1 (X a , γ1) N 1 = N 1 (X b ,γ2) N 2 = N 2 (X c , γ3) Figure 1. Illustration of THM. Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 7 of 22 Table 3. THM estimates for e-MPI (bivariate lognormal) Variables Decision 1 Decision 2 Decision 3 (e-MPI) AD e-MPD logTVB logNVR O logNVR IO Coefficient SE Coefficient SE Coefficient SE Coefficient SE Coefficient SE AGE 0.0077 0.0116 0.0392* 0.0189 – – – – – – AGE 2 0.0004 0.0003 0.0156** 0.0051 – – – – – – EDU 0.0066 0.0136 0.2309** 0.0770 0.3052* 0.1502 0.3716** 0.1192 0.4019** 0.1151 FEXP 0.0475** 0.0139 – – – – – – – – LHS 0.0297* 0.0148 – – – – – – – – EXTN 0.4004** 0.1407 3.4447** 1.3437 3.3407* 1.5946 0.5514* 0.2546 3.9514* 1.7946 ATHYV 0.3486** 0.1172 1.9901** 0.3933 1.7766* 0.8145 3.1267** 0.9258 4.8903** 1.4675 PS −0.0215* 0.0104 −0.2033** 0.0599 −0.0293** 0.0097 – – −0.0562** 0.0184 ATMI 0.5240* 0.2507 0.6406** 0.2466 0.4216* 0.1918 0.4010** 0.1462 0.6010** 0.2460 FPO M 0.6554** 0.2491 0.9930** 0.3212 1.4597** 0.4216 0.5032* 0.2474 7.5032** 2.2076 TV – – 0.4308** 0.1206 0.2422** 0.0548 0.0619** 0.0100 0.3619** 0.1002 PDI – – – – 0.3520** 0.1070 – – 0.5492** 0.1823 PPSP – – – – – – 0.0137** 0.0025 0.5137** 0.2052 IMR p – – −4.6146** 1.2143 −7.6857** 2.4938 −7.2346* 3.2979 −5.0984** 1.0979 IMR B – – – – −1.6136** 0.2480 IMRs - - −1.0834** 0.2965 IMR BS - - - - - - −2.6324** 0.5102 CONS 0.4920 0.0754 – – 15.2487 6.3862 13.3857 5.9838 13.3857** 3.9838 LR (χ 2 ) LR χ 2 (10) = 21.62** LR χ 2 (13) = 28.15** – – – R 2 0.21 0.33 0.81** 0.89** 0.82** n 500 427 103 231 93 Note: ** & * - Significant at 1 and 5 percent levels, respectively. Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 14 of 22 farmers realize lower NVR IO , on account of narrowed price differences between purchase of seed and sale of output in e-market and due to low-scale production. The coefficient of IMRp from the first-stage probit estimates is significant in the ordered-probit regression in Stage 2. This implies that the adoption decisions in Stage 1 and the e-market participation decision in Stage 2 are correlated. Further, this result implies that that decisions surrounding adoption of “Teja” variety precedes decisions surrounding e-market participation. Hence, the AD of “Teja” variety influences farmers’ e-MPD and realize remunerative prices. This result is further reinforced by noting that the coefficient of IMRp from the first stage probit is significant in all the three log-normal regressions in Stage 3, representing buyers, sellers and both buyers and sellers. Hence, once the decision to participate in the e-market is made, the adoption of Teja variety influences the net remuneration for all farmer types. This result is also in contrast with those found by Singbo et al. (2021) and Burke et al. (2015), as we considered three categories of farmers in Stage 3 (i.e., input buyers (TVB), output sellers (NVR O ) and both input buyers and output sellers (NVR IO )). Our results for the Indian chilli sector, is therefore important, and lend insight to strategies that policymakers can establish, to improve supply chain management. Similarly, the coefficients of IMR B , IMRs, and IMR BS for TRB, NVR, O and NVR IO of the decisions in Stage 3 are also significant indicating that the three decisions viz., AD, MPD and MPI with reference to TVB, NVR O and NVR IO are strongly correlated. Hence, the decision to adopt “Teja” variety by the farmers influences their e-MPD and consequently, e-MPI with respect to TVB, NVR O and NVR IO . Thus, these three decisions viz., AD, e-MPD and e-MPI for NVR O and NVR IO are sequential. The THM estimation process allows for cross interpretation of factors surrounding the three decisions in the chilli farming sector, AD, e-MPD and e-MPI. For instance, the coefficient of EDU is positive and significant in e-MPD and also across the three log-normal equations. This implies that the likelihood of e-market participation increases with farmers’ education level (EDU). Higher EDU levels are also associated with higher e-participation intensities across buyers and sellers. This finding is consistent with Enete and Igbokwe (2009), who note that higher education enables farmers to understand prevailing market dynamics and the advantages of adopting modern techniques and producing higher yield varieties. Results from Table 3 indicate that complementary services and support systems within the chilli farming sector are important drivers of all decisions surrounding adoption, e-participation and intensity. Farmer-extension services measured via Access to Extension Network (EXTN), Access to High-Yielding Varieties in the e-market (ATHYV), membership in FPO (FPO M ), and access to information in the e-market (ATMI) are all positive and significant across all equations pertaining to the three stages. Previous studies by Alene et al. (2008); Key et al. (2000) had similar insights. The importance of robust extension services and support systems in improving farmers’ economic returns, market participation and production is in keeping with the findings in Barrett (2008), Okoye et al. (2010), Bardhana et al. (2012), Bezu et al. (2014), Shiferaw et al. (2014), Bezabih et al. (2015), Benfica et al. (2017), Tarekegn et al. (2017), Nyein Kyaw et al. (2018), Cornel and Zhang (2021) and Akter et al. (2021). The presence of robust extension services and the resultant positive feedback on adoption is evidenced in the chilli market, with increased likelihood of farmers’ adoption of the “Teja” variety, within the Kalgudi e-platforms. Bardhana et al. (2012) notes similar results for milk producers, with access to milk co-operatives and marketing societies in the Uttarakhand District in Northern India. Likewise, access to improved chilli variety (ATHYV) increases the probability of both e-MPD and e-MPI of farmers in transacting the produce through e-market. The coefficient of ATHYV across all equations are positive and significant. The likelihood of selling “Teja” variety of chilli through e-market (e-MPD) increases by 1.99 percentage points, on an average, if the access to this variety is improves by one percentage point. Further, the importance of the “Teja” variety as the main Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 15 of 22 source of farmers’ income is also area as evidenced by increases in NVR O by 3.13 per cent and NVR IO by 4.89 per cent from e-market. This result highlights the causal relation between improved varietal technology adoption and e-MPI. The findings from THM support Barrett’s (2008) theoretical proposition that the promotion of advanced agricultural technology can act as a catalyst for the market participation of smallholder farmers. Further, from a policy perspective, these findings are highly promising as they suggest that e-services and internet platforms enhance farmers’ market access and generate positive spillover effects for realizing quality agriculture (Akter et al., 2021). Our results are consistent with Bezu et al. (2014) for Malawi, Shiferaw et al. (2014) for Ethiopia and Benfica et al. (2017) all of which indicate that increased productivity of crops is positively associated with market participation. 4 , Similarly, our results also indicate that access to information from the e-market (ATMI) is an important driver of farmers’ production and adoption of new practices in marketing of chillies. In other words, similar to Bezabih et al. (2015), e-market facilitates adoption of modern agricultural inputs, and enables the farmers to select those markets which offer the best returns. Farmer’s membership in FPO also ensure profitable prices for their produce because of improved bargaining power and strengthened backward and forward linkages, and this result is also reinforced by the positive and significant influence of Training Visits of e-personnel (TV) across e-MPD and the log-normal equations. Taken together, these results indicate that the Kalgudi e-market establishes linkages and networks with the local FPOs (see Appendix 1) in the Krishna zone, which facilitates farmers through skill-oriented training programs, exposure visits to e-market, and technical assistance through field visits. The awareness through the e-process creates a trust in the e-market operations while making both production and marketing decisions. Consequently, the strong linkage between e-market and FPOs strongly influences AD, e-MPD, and e-MPI of chilli farmers. These findings further highlight that interventions that facilitate e-MPI and e-MPD would enhance improved chilli (Teja) variety AD. Prompt delivery of inputs and a seamless supply chain also help those farmers who are buyers, given the positive and significant influence of PDI on the log TVB and log NVR IO equations in Table 4. Likewise, Prompt Payment to the farmer from the e-markets (PPSP) drives sellers to enhance their participation intensities, given the positive and significant influence of PPSP in the log NVRo equation. In summary, the THM results yield important insights into farmers’ production and e-market participation decisions in chilli farming, which is an important sector in Indian agriculture. THM estimation processes distinguish factors determining the production from factors affecting e-participation decisions, and identifies those determinants that influence both decisions. 5.2. Robustness testing Finally, we check if the THM estimation employed for our data is robust, by comparing the results to a Double-Hurdle Model (DHM). First, a single-step simultaneous seemingly unrelated regression (SUR) is performed for the variables in the third-stage decision, to test if the three decisions are undertaken simultaneously, without any influence of participation or production strategies, following Bellemare and Barrett (2006) and Burke et al. (2015). The three log-normal SUR equation estimates are in Table 4. Table 5 incorporates the DHM estimation procedure, wherein the first stage presents the results of an ordered probit model to obtain the determinants of e-MPD. The three log-normal equations for TVB, NVRo and NVR IO are estimated in the second-stage after incorporating IMRp to control for selfselection and endogeneity. The log-likelihood value is significant, implying that the SUR system is biased and cannot sufficiently explain e-MPI, and that intensity of e-market transactions must take participation decisions into account, which is consistent with the results obtained by Barrett (2008) and Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 16 of 22 Y N (2018). Finally, results from Table 3 corresponding to decisions in stages 1 and 2 indicate the significance of LR values, implying that THM is a preferred estimation procedure for our data to understand production and e-participation outcomes from the chilli farming sector in South India. 6. Summary and conclusions Chilli (dry) being a promising commercial crop to achieve the transition of farmers towards modern market orientation and profitability, this study analyzed the AD of farmers with respect to “Teja” variety, participation decisions in e-market and consequent higher returns, which in turn made them attractive and widespread adoption of this variety in the study area (Michler et al., 2019; Verkaart et al., 2019). Several conclusions could be drawn from the above analysis. By integrating decisions surround adoption of high yielding variety of variety of chilli, e-market participation, and e-intensity of participation, this paper makes inferences about the study population, where some parts of the population are non-producers of selected chilli variety. The findings show that farmer extension services and a reliable supply chain and payment systems are major influential factors that contribute for causal relation between AD (“Teja” variety) and e-MPI. The model estimates further indicate that for the farmers involved in e-market either as buyers of inputs or sellers of produce, respond positively to adoption, given adequate incentives to e-market participation and support systems in the form of FPOs. The above findings that indicate that relate AD and e-MPI have important policy implications concerning actions that promote extension efforts and e-market access to chilli farmers. Accordingly, emphasis should be in strengthening FPO membership, and in promoting effective implementation of the Farmer Field Schools programme to assist farmers produce more marketable surplus, and encourage the take-up of the market innovations by the non-producers. Table 4. One-step method estimates for e-MPI (bivariate lognormal) by the farmers cultivating “Teja” variety of chilli Variables logTVB logNVR O logNVR IO Coefficient SE Coefficient SE Coefficient SE AGE −0.0022 0.0025 −0.0014 0.0024 −0.0007 0.0030 EDU 0.0029 0.0027 0.0233 0.0125 0.0106** 0.0042 EDU 2 0.0003 0.0001 0.0005 0.0004 0.0030 0.0016 FEXP −0.0002 0.0030 0.0008 0.0027 0.0028 0.0037 LHS 0.0220** 0.0031 0.0201** 0.0028 0.2039** 0.0147 EXTN 0.0140 0.0433 0.2221** 0.0499 0.2181** 0.0667 ATHYV 0.0457** 0.0165 0.1217** 0.0439 0.2324** 0.0784 PS −0.0052* 0.0024 −0.0167 0.0114 −0.0011 0.0034 ATMI 0.0115** 0.0051 0.1623** 0.0588 0.1246* 0.0611 FPO M 0.0294 0.0494 0.3209** 0.0510 0.3791** 0.0632 TV 0.0213** 0.0075 0.3045** 0.0175 0.2196** 0.0520 PDI 0.2248** 0.0480 – – 0.2328** 0.0705 PPSP – – 0.3072** 0.0540 0.2077** 0.0691 CONS 3.7259 0.2236 4.8897 0.2180 4.6110 0.4041 Goodness of fit R 2 0.61 0.72 0.66 n 103 231 93 Note: ** & * - Significant at 1 and 5 per cent levels, respectively. Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 17 of 22 In this study, we hypothesised that commercialisation of chilli (dry) farmers depends not only on production technology (“Teja” variety), but also on the availability of improved marketing technology often designed to increase profitability and productivity. In this context, THM is employed and as a robustness check, we tested the standard DHM against the THM and show that the TH model is preferred. Even our findings further revealed that looking beyond a two-staged MP decision of households add relevant insights into the farmers’ adoption of production technology and e-market decision-making process. This study also emphasizes about adoption of modern variety of chilli (dry) do support a marketoriented development pathway. In line with earlier studies (Michler et al., 2019; Verkaart et al., 2019), this study highlighted that easy access to modern markets will further scale the improved varieties adopted by large number of farmers. As the AD and e-MPI decisions of farmers are sequential, both local research institutes and policy-makers should formulate a comprehensive design to produce and promote sustainable access to improved varieties of chillies, strengthen farmers’ integration with the e-market and agricultural value chain. Until recently, the focus of most agricultural intervention programs in India has traditionally been on the development and release of improved crop varieties without emphasis on market access. Our results argue that access to the e-market technology and robust linkages as primary and crucial qualifications, which enable farmers to choose quality output production and distribution and allow them to sell surplus produce. Relatedly, similar sentiments have been shared by participants in an informal survey conducted with a sample group of e-participants, who are members in chilli FPOs, and have received TVs from the personnel of Kalgudi e-market. Table 5. DHM estimates for e-MPI (bivariate lognormal) Variables Decision 1 Decision 2 (MPI) MPD logTVB logNVR O logNVR IO Coefficient SE Coefficient SE Coefficient SE Coefficient SE AGE 0.0043 0.0100 −0.0020 0.0023 −0.0019 0.0025 −0.0007 0.0030 EDU 0.0460** 0.0114 −0.0031 0.0027 0.0040 0.0026 0.1102 0.3143 EDU 2 0.0053** 0.0017 0.0001 0.0012 0.0000 0.0000 0.0000 0.0000 FEXP 0.0166 0.0119 – – – – – – LHS 0.0104 0.0126 – – – – – – EXTN 0.4156* 0.2071 0.1492* 0.0682 −0.0108 0.0645 0.0076 0.0870 ATHYV 0.4491* 0.2203 0.1569* 0.0753 – – 0.0493 0.1181 PS −0.0319** 0.0089 −0.0072** 0.0022 – – −0.0007 0.0040 ATMI 0.5893 0.4190 0.1334** 0.0511 0.2291** 0.0646 0.1351* 0.0639 FPO M 0.6395** 0.2205 −0.1411 0.0807 0.3126** 0.0683 0.1860** 0.0737 TV – – 0.0311* 0.0150 0.1076** 0.0132 0.0163 0.0209 PDI – – 0.0236 0.0523 – – −0.0273 0.0775 PPSP – – – – 0.2287** 0.0589 0.2215** 0.0775 IMR 1 – – −0.3622** 0.1316 −0.4274** 0.1533 −0.5470 0.3210 CONS – – 3.7520 0.4078 4.7554 0.4408 4.7104 0.6565 Goodness of fit LR (χ 2 ) −41.22** – – – Pseudo R 2 / R 2 0.13 0.63 0.69 0.71 n 427 103 231 93 Note: ** & * - Significant at 1 and 5 per cent levels, respectively. Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 18 of 22 It is clear that even though investment in the provision of public goods is essential, it may not be sufficient to enable all chilli farmers to ensure e-market linkages. Consequently, one option is to mobilize existing services such as, the Agriculture Infrastructure Fund facility from the Government of India to effectively invest in viable projects relating to post-harvest management infrastructure and community farming (FPOs) assets to promote quality production, improve e-market access and to increase value realization for the farmers. The first-best solution in this context is through improving physical infrastructure to support e-market connectivity. Further, the sequential causal relation between AD and e-MPI of chilli farmers established from this study serves as a sustainable pathway towards poverty alleviation in the long run. The findings from this study highlight important implications for future research. Though the analysis may be location-specific, the applied investigation mechanism can be further explored to assess the impact of a good number of modern agricultural (production and marketing) technologies. This study highlighted that poor commercialization of farmers is not only from lack of market access, but may also from production-technology constraints. Further, unless the farmers are well connected to modern markets, the theorised improvements in welfare outcomes based on the adoption of improved production technologies may not hold good. Author details K. Nirmal Ravi Kumar 1 S.N. Mishra 2 Adinan Bahahudeen Shafiwu 3 E-mail: [email protected] Shailendra Gajanan 4 Suresh Chandra Babu 5 A. Sandhya Neelima 6 1 Professor & Head (Agricultural Economics), Agricultural College, Acharya NG Ranga Agricultural University (ANGRAU), Government of Andhra Pradesh, Bapatla, India. 2 Professor & Head (Agricultural Economics), College of Agriculture, Orissa University of Agriculture & Technology, Bhubaneswar 751003, India. 3 Department of Agricultural and Food Economics, University for Development Studies, Ghana. 4 Chair of the Division of Management and Education, Professor of Economics, University of Pittsburgh, PA, USA. 5 Head, Capacity Strengthening, International Food Policy Research Institute (IFPRI), Washington, USA. 6 Ph.D. Student, Department of Agricultural Economics, Agricultural College, Acharya NG Ranga Agricultural University (ANGRAU), Government of Andhra Pradesh, Bapatla, India. Disclosure statement No potential conflict of interest was reported by the authors. Citation information Cite this article as: Production technology adoption and electronic market participation intensity of chilli (dry) farmers in India: Application of triple-hurdle model, K. Nirmal Ravi Kumar, S.N. Mishra, Adinan Bahahudeen Shafiwu, Shailendra Gajanan, Suresh Chandra Babu & A. Sandhya Neelima, Cogent Economics & Finance (2023), 11: 2207939. Notes 1. Recently, other states such as Madhya Pradesh have also become an important supply centres of chillies to Guntur, and Lakshmi (2014) examines the influence of production trends in Madhya Pradesh on Guntur chilli market. 2. The first two FPOs from Guntur district are Agriculture Related Producers Mutually Aided Cooperative Federation Ltd (from Macherla) and Vyavasaya Mariyu Anubanda Raitu Utpatti Darula Sangam (from Pedanandipadu). The second two FPOs from Krishna district are Utpathidala Paraspara Sahayaka Sangam (from Chandarlapadu) and Vetsavai FPO (from Vetsavai). See Appendix 1 for an exhaustive list of FPOs in the Kalgudi e-market. 3. Since all farmers in collected sample data do not adopt “Teja” chilli variety, a probit model is preferred over a tobit model. It is standard to impose at least one justifiable exclusion restriction when estimating the second stage (JM, 2020). 4. Bezu et al. (2014) show that a one percentage point increase in the area planted under modern varieties increases farmers’ income by 0.48 percentage points For Shiferaw et al. (2014) a one percentage point increase in area under improved wheat variety in Ethiopia leads to a marketed surplus of 4.5 kg of wheat. References Abu, B. M., Issahaku, H., & Nkegbe, P. K. (2016). Farm gate versus market centre sales: A multi-crop approach, Agric. Food Econ, 4(21), 1–16. https://doi.org/10.1186/ s40100-016-0065-6 Aggarwal, N., Jain, S., & Narayanan, S. (2017). The Long Road to Transformation of Agricultural Markets in India Lessons from Karnataka. Economic & Political Weekly, 52(41), 47–55. Agricultural Statistics at a Glance. (2019) . Ministry of Agriculture and Farmers Welfare, Department of Agriculture, Cooperation & Farmers Welfare. Government of India. Akrong, R., An economic assessment of the factors that influence smallholder farmer participation in export markets as a case of high value mango markets in Southern Ghana, Unpublished thesis submitted in partial fulfillment of the requirements for the award of the degree of Master of Science in Agricultural Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 19 of 22 and Applied Economics, University of Nairobi, June. 2020 Akter, S., Chindarkar, N., Erskine, W., Spyckerelle, L., Imron, J., & Viana Branco, L. (2021). Increasing smallholder farmers’ market participation through technology adoption in rural Timor-Leste. Asia & the Pacific Policy Studies, 8(2), 1–19. https://doi.org/10. 1002/app5.329 Alene, A. D., Manyong, V. M., Omanya, G., Mignouna, H. D., Bokanga, M., & Odhiambo, G. (2008). Smallholder market participation under transactions costs: Maize supply and fertilizer demand in Kenya. Food Policy, 33(4), 318–328. https://doi.org/10.1016/j.foodpol.2007.12.001 Amarender, R. A., Mehjabeen, & M. (2019). Electronic National Agricultural Markets, Impacts, Problems and Way Forward, IIM Kozhikode Society &. Management Review, 8(2), 143–155. https://doi.org/10.1177/ 2277975218807277 Anderson, B., 2007. Selection models and weak instruments. https://info.umkc.edu/drbanderson/ 2017/06/,2007. Angrist, J. (2001). Estimation of limited dependent variable models with dummy endogenous regressors: Simple strategies for empirical practice. Journal of Business & Economic Statistics, 19(1), 2–16. https:// doi.org/10.1198/07350010152472571 Annemie, M., & Christopher, B. (2013). Measuring Social Networks’ Effects on Agricultural Technology Adoption. American Journal of Agricultural Economics, 95(2), 353–359. Anyisile Kibona, C., & Yuejie, Z. (2021). Factors that influence market participation among traditional beef cattle farmers in the Meatu District of Simiyu Region (Vol. 16, No. 4, pp. e0248576). https://doi.org/10. 1371/journal.pone.0248576 Bardhana, D., Sharmab, M. L., & Saxenac, R. (2012). Market participation behavior of smallholder dairy farmers in Uttarakhand. Agricultural Economics Research Review, 25(2), 243–254. Barrett, C. B. (2008). Smallholder market participation: Concepts and evidence from eastern and southern Africa. Food Policy, 33(4), 299–317. https://doi.org/10. 1016/j.foodpol.2007.10.005 Barrett, C. B., E, B. M., F, B. M., C, M. H., F, N. S. W. T., & Walker, T. F. (2012). Smallholder Participation in Contract Farming: Comparative Evidence from Five Countries. World Development, 40(4), 715–730. https://doi.org/10.1016/j.worlddev.2011.09.006 Bellemare, M. F., & Barrett, C. B. (2006). An ordered Tobit model of market participation: Evidence from Kenya and Ethiopia. American Journal of Agricultural Economics, 88(2), 324–337. https://doi.org/10.1111/j. 1467-8276.2006.00861.x Benfica, R., Boughton, D., Uaiene, R., & Mouzinho, B. (2017). Food crop marketing and agricultural productivity in a high price environment: Evidence and implications for Mozambique. Food Security, 9(6), 1405–1418. https://doi.org/10.1007/s12571-017-0731-x Bezabih, E., Mengistu, K., Jeffreyson, K. M., & Jemal, Y. (2015). Factors affecting market outlet choice of potato producers in Eastern Hararghe Zone, Ethiopia. Journal of Economics and Sustainable Development, 6 (15), 159–172. Bezu, S., Kassie, G. T., Shiferaw, B., & Ricker-gilbert, J. (2014). Impact of improved maize adoption on welfare of farm households in Malawi: A panel data analysis. World Development, 59, 120–131. https:// doi.org/10.1016/j.worlddev.2014.01.023 Burke, W. J., Myers, R. J., & Jayne, T. S. (2015). A triplehurdle model of production and market participation in Kenya’s dairy market. American Journal of Agricultural Economics, 97(4), 1227–1246. https://doi. org/10.1093/ajae/aav009 Bushway, S., Johnson, B. D., & Slocum, L. A. (2007). Is the magic still there? The use of theHeckman two-step correction for selection bias in criminology. Journal of Quantitative Criminology, 23(2), 151–178. https://doi. org/10.1007/s10940-007-9024-4 De Luca, G., & Perotti, V. (2011). Estimation of Ordered Response Models with Sample Selection. The Stata Journal, 11(2), 213–239. https://doi.org/10.1177/ 1536867X1101100204 Enete, A. A., & Igbokwe, E. M. (2009). Cassava market participation decisions on producing households in Africa. Tropicultura, 27(3), 129–136. Financial Express Bureau. (2019). ITC to develop value chain for chilli growers in Andhra Pradesh. October, 19, 2019. Gebremedhin, B., Shiferaw, K., Gizaw, S., Tegegne, A., & Hoekstra, D. (2017). A triple hurdle model of small ruminant production and marketing in the highlands of Ethiopia: Implications for commercial transformation. African Journal of Agricultural and Resource Economics Volume, 12(3), 257–270. Greene, W. H. (2003). Econometric Analysis (5th ed.). Prentice Hall. JM, W. (2020). Introductory Econometrics: A Modern Approach (7th ed.). Cengage. Key, N., Sadoulet, E., & Janvry, A. D. (2000). Transactions costs and agricultural household supply response. American Journal of Agricultural Economics, 82(2), 245–259. https://doi.org/10.1111/0002-9092.00022 Kondo, E., Bruce Sarpong, D., & Egyir, I. S. (2019). Production and market participation decisions of smallholder cowpea producers in the Northern Region of Ghana: A triple hurdle model approach. Proceedings of the 6th African Conference of Agricultural Economists, Abuja, Nigeria https://doi. org/10.22004/ag.econ.295654. Lakshmi, S.B.R. (2014). Spatial and temporal behaviour of arrivals and prices of Chillies in India. Unpublished Ph.D. Thesis, ANGRAU, Leung, S. F., & Yu, S. (1996). On the choice between sample selection and two-part models. Journal of Econometrics, 72(1), 197–229. https://doi.org/10. 1016/0304-4076(94)01720-4 Michler, J. D., Tjernstr¨om, E., Verkaart, S., & Mausch, K. (2019). Money matters: The role of yields and profits in agricultural technology adoption. American Journal of Agricultural Economics, 101(3), 710–731. Moser Christine, M., & Christopher, B. B. (2006). The complex dynamics of smallholder technology adoption: The case of SRI in Madagascar. Agricultural Economics, 35(3). https://doi.org/10.1111/j.15740862.2006.00169.x Musara, J. P., Musemwa, L., Mutenje, M., Mushunje, A., & Pfukwa, C. (2018). Market participation and marketing channel preferences by small scale sorghum farmers in semiarid Zimbabwe. Agrekon, 57(1), 64–77. https://doi.org/10.1080/03031853.2018. 1454334 Nyein Kyaw, N., Ahn, S., & Lee, S. (2018). Soojung Ahn and Sang Hyeon Lee, Analysis of the Factors Influencing Market Participation among Smallholder Rice Farmers in Magway Region, Central Dry Zone of Myanmar. Sustainability, 10(12), 4441. https://doi. org/10.3390/su10124441 Ochieng, J., Knerr, B., George, O., & Emily, O. (2019). Food crops commercialization and household livelihoods: Evidence from rural regions in Central Africa. Agribusiness: An International Journal, 36(2), 318–338. https://doi.org/10.1002/agr.21619 Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 20 of 22 Ogutu, S. O., & Qaim, M. (2019). Commercialization of the small farm sector and multidimensional poverty. World Development, 114, 281–293. https://doi.org/10. 1016/j.worlddev.2018.10.012 Okoye, B. C., Abass, A., Bachwenkizi, B., Asumugha, G., Alenkhe, B., Ranaivoson, R., Randrianarivelo, R., Rabemanantsoa, N., & Ralimanana, I. (2016). Effect of transaction costs on market participation among smallholder cassava farmers in Central Madagascar. Cogent Economics & Finance, 4(1), 1–20. https://doi. org/10.1080/23322039.2016.1143597 Okoye, B. C., Onyenweaku, C. E., & Ukoha, O. O. (2010). An Ordered Probit Model Analysis of Transaction Costs and Market Participation by Small-Holder Cassava Farmers in South-Eastern Nigeria. Nigerian Agricultural Journal, 41(2), 1–9. Paul, M., Jr., Tabe-Ojong, K. M., Woldeyohanes, T. B., & Heckelei, T. (2022). Three hurdles towards commercialisation: Integrating subsistence chickpea producers in the market economy. European Review of Agricultural Economics, 49(3), 668–695. https://doi. org/10.1093/erae/jbab023 Poole, N. (2017). Smallholder Agriculture and Market Participation, Practical Action Publishing. 978-92-5109939-1. https://doi.org/10.3362/9781780449401 Reddy, A. A. (2016). Impact of e-Markets in Karnataka. India, Indian Journal of Agricultural Marketing, 30(2), 31–44. Reindl, M., Gupta, P., Subramanian, S., Kaur, R., & Rani, D. (2019, May). Towards Doubling Cotton Farmer Incomes in Maharashtra. IDH and Techno Serve, 1– 66. https://doi.org/10.13140/RG.2.2.11965.10724/1 Sartori, A. E. (2003). An estimator for some binary-outcome selection models without exclusion restrictions. Political Analysis, 11(2), 111–138. https:// doi.org/10.1093/pan/mpg001 Shaker, B., Madhu, R., Kumar, D. J. H., Chaitanya, D. V., Ranjitha, P. S., Kumar, D. K. R., Jagan, D. P., & Rao, M. (2019). Constraints faced by chilli farmers in production and marketing of dry chilli in Khammam district of Telangana state. Journal of Pharmacognosy and Phytochemistry, 8(5), 2143–2145. Shiferaw, B., Kassie, M., Jaleta, M., & Yirga, C. (2014). Adoption of improved wheat varieties and impacts on household food security in Ethiopia. Food Policy, 44, 272–284. https://doi.org/10.1016/j.foodpol.2013. 09.012 Singbo, A., Badolo, F., Lokossou, J., Affognon, H., Jourdain, L., & Hippolyte, A. (2021). Market participation and technology adoption: An application of a triple-hurdle model approach to improved sorghum varieties in Mali. Scientific African, 13, e00859. https://doi.org/10.1016/j.sciaf.2021.e00859 Siziba, S., Kefasi, N., Diagne, A., Fatunbi, A. O., & Adekunle, A. A. (2011). Determinants of cereal market participation by Sub-Saharan Africa smallholder farmers. Journal of Agriculture and Environmental Sciences Studies, 2(1), 180–193. Tarekegn, K., Haji, J., & Tegegne, B. (2017). Determinants of honey producer market outlet choice in Chena District, southern Ethiopia: A multivariate probit regression analysis. Agricultural and Food Economics, 5(1), 20. https:// doi.org/10.1186/s40100-017-0090-0 Verkaart, S., Mausch, K., Claessens, L., & Giller, K. (2019). A recipe for success? Learning from the rapid adoption of improved chickpea varieties in Ethiopia. International Journal of Agricultural Sustainability, 17 (1), 34–48. https://doi.org/10.1080/14735903.2018. 1559007 W J, B. (2019). Evidence against imposing restrictions on hurdle models as a test for simultaneous versus sequential decision making. American Journal of Agricultural Economics, 101(5), 1473–1481. https:// doi.org/10.1093/ajae/aaz026 Y N, C. (2018). Triple hurdle model with zero expenditures. Quarterly Journal of Econometrics Research, 4(1), 1–9. https://doi.org/10.18488/journal.88.2018.41.1.9 Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 21 of 22 Appendix 1: FPOs-Kalgudi: e-market linkages in Krishna zone of Andhra Pradesh Mandal District Name of FPO Number of Farmers Macherla Guntur Agriculture Related Producers Mutually Aided Cooperative Federation Ltd 3143 Durgi Guntur Vyavasaya Mariyu Anubanda Raitu Utpatti Darula Sangam 2453 Pedanandipadu Guntur Vyavasaya Mariyu Anubanda Raitu Utpatti Darula Sangam 2031 Veldurthy Guntur Agri Related Ppsss Ltd 2002 Bollapalli Guntur Vyavasaya Mariyu Anubanda Raitu Utpatti Darula Sangam 1975 Amaravathi Guntur Agri & Allied Producers Cooperative Society 1885 Chandarlapadu Krishna Utpathidala Paraspara Sahayaka Sangam 2650 Vetsavai Krishna Vetsavai Mandal FPO 1832 Dornala Prakasam Vyavasaya Mariyu Anubanda Utpatti Darula Sangam 2245 Peddakadabur Kurnool Agri And Allied Producers Macs Ltd. 1807 Source: Officials from Kalgudi e-market. Ravi Kumar et al., Cogent Economics & Finance (2023), 11: 2207939 https://doi.org/10.1080/23322039.2023.2207939 Page 22 of 22