Constraints to agricultural finance in underdeveloped and developing countries: a systematic literature review

ABSTRACT

Agricultural finance plays a pivotal role in facilitating the modernization and commercialization of farming practices and bolstering global food security. However, the provision of sustainable financial services in remote regions poses a significant challenge within the developing and underdeveloped countries. Consequently, farmers often grapple with inadequate access to formal credit sources and essential productive assets due to diverse constraints. Although, several studies have investigated constraints to agriculture finance; the literature is divergent and a holistic framework of constraints to agriculture finance is missing. Therefore, this research explores the constraints to agriculture finance in 31 developing and underdeveloped countries using systematic review approach, and synthesizes a holistic framework of the constraints using Qualitative Evidence Synthesis (QES) method that can be used as dependable framework for conceptualization and policy formation. Moreover, it describes the interplay and mutual reinforcement between the challenges faced by different stakeholders to elaborate how they influence each other in complex ways. Additionally, this study suggests a collective and multi-faceted approach for overcoming barriers to agricultural financing, and highlights the limitations and unintended consequences of agricultural credit and its implications for agricultural sustainability.

KEYWORDS:

• Agricultural finance
• agricultural credit
• constraints
• systematic literature review
• developing and underdeveloped countries

1. Introduction

Hunger and poverty remains the biggest challenge in today’s world (de Janvry & Sadoulet, 2020; Gassner et al., 2019). The less developed parts of the world, in particular, are struggling with the issues of under nutrition and food security (FAO, 2018; Udmale et al., 2020). Agricultural development can play an important role in limiting poverty and ensuring food security particularly in agriculture-based economies (Eichsteller et al., 2022; Haggblade et al., 2010; Maulu et al., 2021).

Agricultural development can be achieved through transition from traditional to contemporary farming methods and technologies. Factors such as agriculture mechanization, increased awareness, and the availability of improved inputs such as certified seeds, fertilizers and pesticides play instrumental roles in facilitating this shift (de Janvry & Sadoulet, 2020; Liu & Wang, 2019). The adoption of these advancements not only enhances productivity, but also contributes significantly to overall rural development (Chaiya et al., 2023).

However, a holistic approach to enhancing agriculture and developing rural economies requires a strong focus on agricultural finance (Lindsjö et al., 2021; McIntosh & Mansini, 2018; Nouman et al., 2013). This is because increasing agricultural production, implementing new farming methods and enhancing the distribution of rural income require availability of sufficient financing (Abhishek et al., 2021; Memon et al., 2016). Farmers often need funds for adopting new technologies and buying inputs like new seed kinds, effective fertilizers and safe pesticides (Fosu-Mensah et al., 2012; Ullah et al., 2022). To meet these needs they have to either utilize their savings or borrow (Omobitan & Khanal, 2022). However, the financial constraints faced by small-scale farmers, coupled with their low income and high cash expenses, necessitate the utilization of agriculture credit as an indispensable source for arranging the required investment (Kumari & Garg, 2023; Nyebar et al., 2023).

In addition, agricultural credit also plays a significant role in promoting agricultural sustainability by enabling farmers to adopt more resilient and environmentally friendly practices, enhance productivity competitiveness and achieve socio economic development (Galdeano-Gómez et al., 2013; Reddy et al., 2020). Agricultural credit is crucial for the modernization and commercialization of farming and efficient utilization of resources to enhance food security and agricultural sustainability (Chaiya et al., 2023; Jaleta et al., 2019).

Loans are required not only for farm operations and modernization, but financing sustainable agricultural technologies also need liquid capital (Arhin et al., 2023; Mapanje et al., 2023; Raza et al., 2023). Moreover, small farmers also need agricultural credit to bridge the gap between their income and expenses since most of the small farmers in rural areas experience consistent low income due to low productivity (Balana et al., 2022; Omobitan & Khanal, 2022; Ullah et al., 2022). Thus, agriculture credit seems to be an effective way to solve the major constraints of the rural economy and enable socio-economic development of small farmers (Maulu et al., 2021; Teye & Quarshie, 2022).

However, the ability of remote regions to offer sustainable financial services is a challenge in developing and underdeveloped countries (Nouman et al., 2013; Weng et al., 2020). The geographical isolation of these remote areas compounds the difficulty in establishing and maintaining accessible financial infrastructure. Limited physical connectivity, coupled with underdeveloped technological frameworks, inhibits the seamless delivery of financial services to rural populations (Falola et al., 2022). Consequently, farmers usually have insufficient access to the formal source of credit and productive assets due to several constraints (Kiros & Meshesha, 2022; Ruml & Parlasca, 2022).

Constraints to agriculture credit significantly impact the farm productivity and efficiency both directly and indirectly. They directly affect the purchasing power of the farmers to acquire inputs and fund operating expenditures in the short run, and farm-related investment decisions in the long run (Omobitan & Khanal, 2022). Similarly, they affect farmers’ risk appetite which in turn affects their technology choice and adoption (Magazzino & Santeramo, 2023; Ruzzante et al., 2021). The poor productivity and inefficiency of the farmers in turn lead to higher farm expenses and lower income that emerges as a critical factor contributing significantly to the perpetuation of poverty in developing and underdeveloped nations. In the regions, where agriculture often serves as a primary livelihood, the inability of poor farmers to access credit impedes their capacity to invest in essential inputs, modernize farming practices and withstand economic shocks (Balana et al., 2022; FAO, 2018; Hu et al., 2019).

The role of constraints to agriculture financing in limiting the farmers’ adoption of innovative technologies and discouraging agriculture productivity is widely acknowledged by recent researches (see, for example, Adenle et al., 2019; Chaiya et al., 2023; Magazzino & Santeramo, 2023; Mapanje et al., 2023; Quddus & Kropp, 2020; Raza et al., 2023; Ruzzante et al., 2021; Yu et al., 2023). The extant literature provides diverse insights on constraints to agriculture financing in different countries. However, the literature is dispersed and divergent. Moreover, a holistic framework of constraints to agriculture finance is missing. To the best of our knowledge, no literature review has systematically and comprehensively assessed the different types of constraints to agriculture finance in developing and under-developed countries. The present study therefore aims to close this research gap. In doing so, it makes several contributions to the existing literature. First, it provides a systematic assessment of the divergent insights to identify key barriers to agricultural finance. Second, it synthesizes a holistic framework of the constraints that could be used as dependable framework for conceptualization and policy formation. Third, it describes the interplay and mutual reinforcement between the challenges faced by different stakeholders to elaborate the interconnected and overlapping nature of these constraints and how they influence each other in complex ways. Fourth, keeping in view the interplay among the factors our study suggests a systematic and multi-faceted approach for overcoming barriers to agricultural credit financing. Finally, it highlights the limitations and unintended consequences of agriculture development and credit and its implications for agricultural sustainability.

2. Review methodology

2.1. Review approach

The current study uses the systematic review approach to synthesize insights from the diverse body of existing literature. Systematic review approach is an established evidence-based methodology that has emerged as a key research method (Hohenstein et al., 2015). It provides collective insights through explicit and systematic collation and synthesis of the insights from the studies that address a clearly formulated question (Nouman et al., 2018; Nouman et al., 2021; Tranfield et al., 2003). Systematic review is a powerful technique because it aims to ‘answer a specific question, to reduce bias in the selection and inclusion of studies, to appraise the quality of the included studies, and to summarize them objectively’ (Petticrew, 2001, p. 99).

The difference between a systematic and a traditional narrative review is that a systematic review uses certain criteria to determine the usefulness of a body of prior research and gathers secondary data and analyse it with repeatable analytical techniques. Only those data are synthesized and recognized in systematic reviews that are closely relevant to the research question(s) that the systematic review tends to answer (Nouman et al., 2021; Nouman & Ullah, 2023). Systematic reviews critically assess research papers and integrate findings in either quantitative or qualitative ways (Paterson, 2012). In addition, such kind of review employs a rigid and open methodology for research synthesis with the intention of reducing the risk to the outcomes of the assessment.

According to Petticrew (2001) and Nouman et al. (2018), high-quality systematic reviews are better as compared to typical narrative reviews in the following aspects:

• systematic reviews constantly endeavour to respond to a precise research question,

• high-quality systematic reviews look for all pertinent studies,

• restricts the reviewer’s selection bias by using specific criteria to determine which papers to include,

• Such reviews rigorously investigate the methods adopted in the studies to judge the quality of the chosen studies. They also consider any potential biases and differences in their findings. Consequently, the conclusions of these reviews are based on methodology sound studies

Given the importance of systematic review, numerous articles in the field of agriculture finance use the systematic review methodology (see, for example, Biscaye et al., 2015; Goodwin et al., 2022; Marr et al., 2016; Zia et al., 2022). The present research follows the same approach to answer the question ‘what are the constraints to agriculture finance in developing and under developed countries?’. Since the objective of this study is to integrate numerous explanations in the existing literature for credit constraints across the world to provide a holistic framework. Systematic review emerges as the most effective technique for the present study because systematic review allows to identify, assess and synthesize diverse evidences (Nouman et al., 2018; Nouman et al., 2021).

2.2. Procedure for locating and selecting relevant studies

The systematic review seeks to find all relevant studies (Armitage & Keeble-Ramsay, 2009; Petticrew, 2001). Additionally, a precise criteria are required to pick the studies that should be included in the sample in order to reduce selection bias (Petticrew, 2001). For the selection of appropriate studies, the PRISMA 2020 framework was adopted. The process is outlined in the PRISMA flow diagram presented in Figure 1, while the details of each step are described in the below subsections.

Figure 1. PRISMA 2020 flow diagram.

2.2.1. Procedure for searching relevant studies

The Web of Science was used as the starting point for searching the relevant studies. Three sets of keywords were used iteratively to identify relevant papers including: (i) ‘credit constraints, agriculture financing, agricultural finance, agriculture credit, agricultural loan’ (ii) ‘barriers, constraints, hurdles’ and (iii) ‘developing countries, less developed countries, poor countries’. Boolean operators (AND, OR) were used to combine keywords from the three categories.

However, by using the aforementioned keywords several thousand sources were listed by Web of Science (n = 28699). The filtering options were used to exclude the irrelevant studies in three phases:

• Phase 1: The custom publication range ‘2010–2022’ was set to exclude the older studies. Consequently, 12,478 studies were filtered out that didn’t fall within the given time frame (n = 16,221).

• Phase 2: The ‘article type’ filter was applied to identify the scholarly articles. Using this filter the ‘articles’ and ‘review articles’ were considered while the other article types were filtered out. This filter helped us in eliminating 521 sources (n = 15,700).

• Phase 3: The results were filtered using the relevant subject areas (considering only business, management, economics and finance). This filter enabled us to eliminate 13,866 from less relevant subject areas (n = 1834).

These three filters enabled us to narrow down our search significantly. However, still we were left with a fairly large number of articles that were eligible for screening (n = 1834). Therefore, a through refinement and selection procedure was adopted to strategically identify the relevant articles as described in the following section.

2.2.2. Procedure for refinement and selection of relevant studies

The following process was adopted for the refinement of the identified studies and selection of relevant studies:

Step 1: Screening the titles. In the first step, the titles of the identified articles (n = 1834) were checked to identify the articles that broadly fall within the scope of the study. As a result, 681 articles were excluded that were clearly unrelated to our topic. However, to be on the safer side those articles were not dropped whose relevance was unclear based on their title alone. After screening the titles we were left with 1153 articles whose titles were somewhat relevant to our topic (n = 1153).

Step 2: Screening the abstracts. In the second step, the abstracts of the remaining 1153 articles were examined to identify the relevant articles. Articles that were not particularly relevant to agriculture finance and/or were not conducted in the context of any developing or under developed country were removed from the sample. In this step 772 articles were dropped, leaving us with a sample of 381 papers that were eligible for retrieval.

Step 4: Retrieving and refining the sample. In the fourth step, the paper sought for retrieval was downloaded. In total 359 were retrieved while 22 articles were dropped because either their full document was not accessible due to different reasons, or only their abstract were written in English language.

Step 5: Refining the sample. The downloaded articles were thoroughly read to identify the studies that explicitly focus on constraints to agriculture finance in the context of any under developed or developing country. Thus, we were left with a sample of 57 articles that were relevant to our research question. Additionally, the scholarly databases including Springer link, Elsevier (Science Direct), Emerald, Taylor & Francis and Wiley Online Library were also searched using the aforementioned keywords searches to identify relevant studies in journals that are not included in the WoS database. The articles identified from these databases were screened using the screening and selection criteria stated in steps 1 through 4. This helped us in identifying 29 new papers, expanding our sample to 86 papers that are relevant to our research question.

2.2.3 Procedure for checking completeness

To make sure we didn’t overlook any relevant articles, we employed backward and forward chaining. Backward chaining was used to find pertinent publications that were cited in the references list of each article selected in step 4. The same process was done for the newly identified papers as well. This helped us to explore the literature from present to past within the selected time frame (i.e. 2010–2022). On the other hand, in the forward chaining, we looked up each individual paper’s citations to determine what other papers had mentioned that specific paper. This process was performed for newly identified articles as well. This procedure, also known as ‘citation searching’, aids in the exploration of literature from the past to the future. Cross-checking using forward and backward chaining enabled us to locate 24 additional articles that met the criteria for selection stated in steps 1 through 4. Thus, after cross-checking our sample grew to 110 relevant research studies meeting the selection criteria.

Table 1 presents the break-up of the 110 studies included in the final sample, highlighting the year-wise and nature-wise classification of the selected publications. Similarly, Table 2 shows the distribution of the chosen studies by country. The selected papers cover the constraints to agriculture finance in 31 countries including 17 developing countries and 14 underdeveloped nations.

Table 1. Year wise distribution of selected studies.

Year	No. of Studies	Nature of Studies	No. of Studies
2010–2012	34	Qualitative	05
2013–2014	14	Quantitative	74
2015–2016	16	Literature Review	07
2017–2018	14	Descriptive	23
2019–2022	32	Books	01
Total	110		110

Table 2. Country wise distribution of selected studies.

Country	Status	No. of Studies	Contributors
Afghanistan	Under Developed	02	(Moahid et al., 2021; Moahid & Maharjan, 2020)
Bangladesh	Under Developed	02	(Islam et al., 2011; Quddus & Kropp, 2020)
Bhutan	Under Developed	01	(Elangbam & Dawa, 2014)
Brazil	Developing	01	(de Castro & Teixeira, 2012)
Burkina Faso	Under Developed	01	(Porgo et al., 2017)
Chile	Developing	01	(Reyes & Lensink, 2011)
China	Developing	17	(Cai et al., 2018; Changsheng et al., 2016; Chen et al., 2020; Chiu et al., 2014; Dong et al., 2012; Hu et al., 2019, 2020; Jia et al., 2010; Khantachavana et al., 2012; Kumar et al., 2013; Li et al., 2013, 2020; Lin, Wang, Gan, Cohen, et al., 2019; Lin, Wang, Gan, Nguyen et al., 2019; Turvey, 2010; Weng et al., 2020; Zhao & Barry, 2014
Democratic Republic Of Congo	Under Developed	02	(Bonnke et al., 2022; Muayila & Tollens, 2012)
Ethiopia	Under Developed	10	(Admasu & Paul, 2010; Ahma et al., 2010; Balana et al., 2022; Gashayie & Singh, 2015; Gebeyehu, 2019; Gurmessa & Ndinda, 2014; Kiros & Meshesha, 2022; McIntosh et al., 2013; Regassa & Melesse, 2023; Tura et al., 2017)
Gambia	Under Developed	01	(Gajigo, 2013)
Ghana	Under Developed	08	(Appiah-Twumasi et al., 2022; Denkyirah et al., 2016; Issahaku et al., 2020; Jumpah et al., 2019; Kuwornu et al., 2012; Sekyi et al., 2017; Teye & Quarshie, 2022; Twumasi et al., 2019)
Guinea	Under Developed	01	(Seck, 2019)
India	Developing	06	(Dhakshana & Rajandran, 2018; Kishore, 2012; Kumar et al., 2013; Sharma et al., 2012; Shee & Turvey, 2012; Yadav & Sharma, 2015)
Indonesia	Developing	01	(Khanal & Regmi, 2017)
Iran	Developing	04	(Ansari et al., 2012; Aref, 2010; Ghambarali et al., 2014; Soltani et al., 2014)
Kenya	Developing	02	(Kiplimo et al., 2015; Muiruri et al., 2010)
Mexico	Developing	02	(Chiu et al., 2014; Khantachavana et al., 2012)
Myanmar	Under Developed	01	(Snoxell & Lyne, 2019)
Nigeria	Developing	11	(Badiru, 2010; Etonihu et al., 2013; Ijioma & Osondu, 2015; Iniovorua et al., 2016; Kehinde & Olatidoye, 2019; Mohammed & Mukhtar, 2015; Ojo & Baiyegunhi, 2020; Olowa & Olowa, 2011; Omolade & Adepoju, 2019; Omonona et al., 2010; Ugwumba & Omojola, 2013)
Pakistan	Developing	17	(Abdullah et al., 2015; Ahmad, 2011; Amanullah et al., 2019; Amanullah & Channa, 2020; Chandio et al., 2017; Chandio & Jiang, 2018; Hassan et al., 2012; Hussain & Thapa, 2012; Jan & Khan, 2012; Khan et al., 2013; Khatun et al., 2014; Mehmood et al., 2017; Rasheed et al., 2016; Saqib et al., 2016; Saqib, Kuwornu, Ahmad, et al., 2018; Saqib, Kuwornu, Panezia, et al., 2018; Ullah et al., 2020)
Peruvian	Developing	01	(Fletschner et al., 2010)
Philippine	Developing	04	(Bathan & Gordoncillo, 2017; Chau et al., 2016; Linh et al., 2019; Phan, 2012)
Rwanda	Under Developed	01	(Daniel Ayalew et al., 2014)
South Africa	Developing	02	(Baiyegunhi et al., 2010; Benjamin, 2012)
Sudan	Under Developed	01	(Ibrahim & Bauer, 2013)
Tanzania	Under Developed	03	(Balana et al., 2022; Jessop et al., 2012; Kinda & Loening, 2010)
Thailand	Developing	03	(Chotewattanakul et al., 2019; Jessop et al., 2012; Menkhoff & Rungruxsirivorn, 2011)
Togo	Under Developed	01	(Ali & Awade, 2019)
Turkey	Developing	01	(Bojnec, 2012)
Uruguay	Developing	01	(Gandelman & Rasteletti, 2017)
Zimbabwe	Developing	01	(Muroiwa et al., 2019)

3. Analysis of the selected articles

Following the approach used by Marston and King (2006) and Nouman et al. (2018), our study analyses the selected publications using established qualitative research methods. For this purpose, each document was carefully examined, and the constraints indicated therein were coded. After identification and coding of the constraints, the qualitative evidence synthesis (QES) method was used to develop the constraints framework, which entails identification, and linking and categorization of themes into groups. According to Paterson (2012) QES is the process of synthesizing or linking the individual research studies, that are related to specific topic, to arrive at a novel or enhanced conceptualization of the phenomenon at hand. The QES entails an interpretative process by which ‘the constituent study texts can be treated as the multivocal interpretation of a phenomenon, just like the voices of different participants in a single qualitative study’ (Zimmer, 2006). QES strives to conceptualize the broader picture by synthesizing the micro details within individual studies similar to connecting the pieces of jigsaw puzzle (Nouman et al., 2018). Therefore, synthesis enhances the generalizability and abstraction of the findings of the individual research studies (Nouman & Ullah, 2023; Sherwood, 1999).

The evidence under QES can be synthesized using a variety of techniques such as meta-analysis, grounded theory, narrative synthesis, framework synthesis and thematic synthesis (Hannes & Lockwood, 2012). In our case, thematic synthesis is used since it follows a highly structured framework for the selection, ordering and tabulation of the primary evidence. The thematic synthesis

uses thematic analysis techniques, as well as adaptations from grounded theory and meta-ethnography, to identify themes across primary research studies. Synthesis component entails an iterative process of inductively grouping themes into overarching categories that capture the similarities, differences, and relationships between the themes. (Paterson, 2012, p. 17)

The analysis and synthesis was done in three stages:

• In the first phase, the 110 articles included in the final sample were thoroughly read and coded. Codes reflect constraints to agriculture finance explicitly appearing in each publication.

• In the second phase, the codes extracted in the first phase were compared and the overlapping themes were either eliminated or merged to avoid redundancy. Finally, we were left with 23 distinct constraints to agriculture financing. Table 3 reports these constraints.

• In the last stage, the linkage between the constraints (identified in the second stage) was established by grouping them into three broader themes namely demand-side constraints, supply-side constraints and infrastructure-related constraints. Consequently, a holistic framework of constraints was formed which reflects a coherent typology of the constraints to agriculture financing. This helped us integrate the diverse and divergent literature and synthesize a holistic conceptualization of the constraints to agriculture financing across 31 different countries.

Table 3. Constraints to agriculture finance.

Constraints	Country	Contributors
Lack of collateral	Africa, China, India, Ghana, Pakistan, Nigeria, Rwanda, Philippine, Ethiopia, Democratic Republic of Congo, Zimbabwe, Cambodia, Mali, Senegal, Tanzania, Thailand, and Tunisia, Kenya, Mexico, Iran, Turkey, Bhutan	(Abdullah et al., 2015; Admasu & Paul, 2010; Ahma et al., 2010; Ahmed et al., 2019; Amanullah et al., 2019; Appiah-Twumasi et al., 2022; Bathan & Gordoncillo, 2017; Benjamin, 2012; Bojnec, 2012; Changsheng et al., 2016; Chau et al., 2016; Chen et al., 2020; Chiu et al., 2014; Daniel Ayalew et al., 2014; Elangbam & Dawa, 2014; Gashayie & Singh, 2015; Ghambarali et al., 2014; Gurmessa & Ndinda, 2014; Hussain & Thapa, 2012; Ijioma & Osondu, 2015; Jessop et al., 2012; Jia et al., 2010; Khantachavana et al., 2012; Lin, Wang, Gan, Nguyen, et al., 2019; Linh et al., 2019; Muayila & Tollens, 2012; Muroiwa et al., 2019; Omolade & Adepoju, 2019; Saqib et al., 2016; Saqib, Kuwornu, Panezia, et al., 2018; Sekyi et al., 2017; Turvey, 2010; Twumasi et al., 2019; Weng et al., 2020; Yadav & Sharma, 2015)
Strict eligibility criteria	Chile, Guinea, China, Peruvian, Africa, Burkina Faso, Rwanda, Philippine, Ethiopia, Pakistan, Bangladesh, Senegal, Mexico, Turkey, Bhutan	(Abdullah et al., 2015; Amanullah et al., 2019; Bathan & Gordoncillo, 2017; Bojnec, 2012; Cai et al., 2018; Changsheng et al., 2016; Chau et al., 2016; Chen et al., 2020; Chiu et al., 2014; Daniel Ayalew et al., 2014; Fletschner et al., 2010; Islam et al., 2011; Khantachavana et al., 2012; Kiros & Meshesha, 2022; Lin, Wang, Gan, Cohen, et al., 2019; McIntosh et al., 2013; Mehmood et al., 2017; Phan, 2012; Porgo et al., 2017; Reyes & Lensink, 2011; Seck, 2019; Weng et al., 2020; Zhao & Barry, 2014)
High interest rate	Chile, Guinea, China, India, Ghana, Pakistan, Nigeria, Philippine, Ethiopia, Democratic Republic of Congo (DRC), Sudan, Bangladesh, Senegal, Tennessee, Afghanistan, Thailand, Africa, Mexico, Iran, Turkey, Bhutan, Myanmar	(Ahmad, 2011; Ahmed et al., 2019; Amanullah et al., 2019; Ansari et al., 2012; Appiah-Twumasi et al., 2022; Awotide et al., 2015; Badiru, 2010; Baiyegunhi et al., 2010; Bojnec, 2012; Chandio & Jiang, 2018; Changsheng et al., 2016; Chiu et al., 2014; Chotewattanakul et al., 2019; Dhakshana & Rajandran, 2018; Dong et al., 2012; Elangbam & Dawa, 2014; Gebeyehu, 2019; Ghambarali et al., 2014; Hassan et al., 2012; Hu et al., 2019, 2020; Hussain & Thapa, 2012; Ibrahim & Bauer, 2013; Ijioma & Osondu, 2015; Iniovorua et al., 2016; Islam et al., 2011; Issahaku et al., 2020; Jumpah et al., 2019; Khantachavana et al., 2012; Khatun et al., 2014; Kiros & Meshesha, 2022; Kishore, 2012; Kumar et al., 2013; Li et al., 2013; Lin, Wang, Gan, Cohen, et al., 2019; Linh et al., 2019; McIntosh et al., 2013; Mehmood et al., 2017; Moahid & Maharjan, 2020; Muayila & Tollens, 2012; Ojo & Baiyegunhi, 2020; Olowa & Olowa, 2011; Omobitan et al., 2019; Omonona et al., 2010; Rasheed et al., 2016; Reyes & Lensink, 2011; Saqib et al., 2016; Saqib, Kuwornu, Ahmad et al., 2018; Seck, 2019; Shee & Turvey, 2012; Turvey, 2010; Twumasi et al., 2019; Ugwumba & Omojola, 2013; Yadav & Sharma, 2015; Zhao & Barry, 2014)
Asymmetric information	Chile, Peruvian, Africa, Uruguay, China, Philippine, Ethiopia, Tanzania, Ghana, Thailand, Mexico, Nigeria, Myanmar, Solomon island	(Baiyegunhi et al., 2010; Balana et al., 2022; Bonnke et al., 2022; Chau et al., 2016; Chen et al., 2020; Chiu et al., 2014; Chotewattanakul et al., 2019; Fletschner et al., 2010; Gandelman & Rasteletti, 2017; Gashayie & Singh, 2015; Hu et al., 2020; Iniovorua et al., 2016; Issahaku et al., 2020; Jia et al., 2010; Khantachavana et al., 2012; Li et al., 2013; Reyes & Lensink, 2011; Tura et al., 2017; Zhao & Barry, 2014)
Lack of competition among financial intuitions	Africa, Gambia, Sudan, China, Zimbabwe, Thailand, Ethiopia, Nigeria, Iran	(Badiru, 2010; Chotewattanakul et al., 2019; Gajigo, 2013; Gebeyehu, 2019; Ibrahim & Bauer, 2013; McIntosh et al., 2013; Muroiwa et al., 2019; Soltani et al., 2014; Zhao & Barry, 2014)
Lack of guarantees	Africa, China, Ghana, Nigeria, Pakistan, Iran, Mexico, Myanmar	(Baiyegunhi et al., 2010; Chandio et al., 2017; Changsheng et al., 2016; Ghambarali et al., 2014; Gurmessa & Ndinda, 2014; Iniovorua et al., 2016; Khantachavana et al., 2012; Saqib, Kuwornu, Ahmad, et al., 2018; Teye & Quarshie, 2022; Twumasi et al., 2019; Weng et al., 2020)
Risk aversion	Africa, Chile, Peruvian, Ghana, Philippine, Ethiopia, China, Senegal, Tanzania, Afghanistan, Thailand, Pakistan, Mexico, Kenya, Solomon island	(Appiah-Twumasi et al., 2022; Belhadi et al., 2021; Bonnke et al., 2022; Chau et al., 2016; Chiu et al., 2014; Fletschner et al., 2010; Gebeyehu, 2019; Jumpah et al., 2019; Khantachavana et al., 2012; McIntosh et al., 2013; Mehmood et al., 2017; Moahid & Maharjan, 2020; Muiruri et al., 2010; Reyes & Lensink, 2011; Seck, 2019; Turvey, 2010; Twumasi et al., 2019; Zhao & Barry, 2014)
Distance to credit sources and poor rural transportation networks	Chile, Pakistan, China, Philippine, Ethiopia, Sudan, Nigeria, Ghana, Africa, Kenya, Bhutan	(Abdullah et al., 2015; Amanullah et al., 2019; Benjamin, 2012; Cai et al., 2018; Chandio et al., 2017; Chandio & Jiang, 2018; Changsheng et al., 2016; Etonihu et al., 2013; Ibrahim & Bauer, 2013; Ijioma & Osondu, 2015; Jumpah et al., 2019; Kiplimo et al., 2015; Ojo & Baiyegunhi, 2020; Rasheed et al., 2016; Regassa & Melesse, 2023; Reyes & Lensink, 2011; Tura et al., 2017; Zhao & Barry, 2014)
Lack of awareness among farmers	Guinea, China, Peruvian, Ghana, Pakistan, Philippine, Nigeria, Bangladesh, Togo, Senegal, Tennessee, Mali, Tanzania, Thailand, and Tunisia, Afghanistan, Kenya, Ethiopia, India, Iran, Bhutan, Myanmar, DRC	(Admasu & Paul, 2010; Ahma et al., 2010; Ali & Awade, 2019; Amanullah et al., 2019; Ansari et al., 2012; Awotide et al., 2015; Badiru, 2010; Bathan & Gordoncillo, 2017; Bonnke et al., 2022; Chandio et al., 2017; Chen et al., 2020; Fletschner et al., 2010; Hassan et al., 2012; Hu et al., 2019; Hussain & Thapa, 2012; Iniovorua et al., 2016; Islam et al., 2011; Jessop et al., 2012; Jumpah et al., 2019; Kinda & Loening, 2010; Kiplimo et al., 2015; Kishore, 2012; Kuwornu et al., 2012; Li et al., 2020; Lin, Wang, Gan, Cohen, et al., 2019; McIntosh et al., 2013; Moahid & Maharjan, 2020; Mohammed & Mukhtar, 2015; Ojo & Baiyegunhi, 2020; Omobitan et al., 2019; Saqib, Kuwornu, Panezia, et al., 2018; Seck, 2019; Sharma et al., 2012; Soltani et al., 2014; Twumasi et al., 2019)
Inefficient value chain and market access	Peruvian, Ethiopia, Pakistan, Kenya, Tanzania, Iran, India, Myanmar	(Admasu & Paul, 2010; Ahma et al., 2010; Ansari et al., 2012; Fletschner et al., 2010; Gashayie & Singh, 2015; Gebeyehu, 2019; Hassan et al., 2012; Kinda & Loening, 2010; Kishore, 2012)
Land tenure status	Ghana, Pakistan, china, Ethiopia, Kenya, Myanmar, Philippine, Sudan	(Admasu & Paul, 2010; Ahmad, 2011; Amanullah et al., 2019; Jan & Khan, 2012; Jia et al., 2010; Muiruri et al., 2010; Sekyi et al., 2017)
Weak relationship between farmers and bank	China, India, Turkey	(Bojnec, 2012; Jia et al., 2010; Kumar et al., 2013; Li et al., 2013; Li et al., 2020)
Lack of suitable financing products	China, Pakistan, Nigeria, Ethiopia, Rwanda, Bangladesh, Tanzania, Ghana, Nigeria, Indonesia, Zimbabwe, Afghanistan, Ethiopia, Iran, Myanmar, Tanzania, Democratic Republic of Congo	(Ahmed et al., 2019; Amanullah et al., 2019; Ansari et al., 2012; Awotide et al., 2015; Bonnke et al., 2022; Gebeyehu, 2019; Jumpah et al., 2019; Khanal & Regmi, 2017; Li et al., 2020; McIntosh et al., 2013; Moahid & Maharjan, 2020; Muroiwa et al., 2019; Omolade & Adepoju, 2019; Omonona et al., 2010; Quddus & Kropp, 2020; Tura et al., 2017; Turvey, 2010)
Lack of insurance facilities	Africa, Philippine, Ethiopia, Zimbabwe, China, Mexico, Iran, Myanmar	(Ansari et al., 2012; Chau et al., 2016; Chiu et al., 2014; Khantachavana et al., 2012; McIntosh et al., 2013; Muroiwa et al., 2019)
Extensive documentation	Chile, Pakistan, Ethiopia, India, Kenya	(Abdullah et al., 2015; Chandio & Jiang, 2018; Dhakshana & Rajandran, 2018; Gashayie & Singh, 2015; Muiruri et al., 2010; Reyes & Lensink, 2011)
Inefficient monitoring policy	Africa, Gambia, Ghana, Nigeria, Pakistan	(Appiah-Twumasi et al., 2022; Gajigo, 2013; Mohammed & Mukhtar, 2015; Olowa & Olowa, 2011)
Complicated procedures	China, Peruvian, Ghana, Pakistan, Burkina Faso, Chile, Nigeria, Ethiopia, Pakistan, Philippine, Tanzania, Tennessee, India, Cambodia	(Abdullah et al., 2015; Ahmad, 2011; Amanullah et al., 2019; Badiru, 2010; Bathan & Gordoncillo, 2017; Chandio & Jiang, 2018; Fletschner et al., 2010; Gebeyehu, 2019; Hussain & Thapa, 2012; Issahaku et al., 2020; Khatun et al., 2014; Kiros & Meshesha, 2022; Lin, Wang, Gan, Nguyen, et al., 2019; Omobitan et al., 2019; Porgo et al., 2017; Saqib et al., 2016; Saqib, Kuwornu, Panezia et al., 2018; Sharma et al., 2012; Tura et al., 2017; Twumasi et al., 2019; Ugwumba & Omojola, 2013; Ullah et al., 2020; Weng et al., 2020; Zhao & Barry, 2014)
Small farm size	India, Philippine, Bangladesh, Tanzania, Africa, Ethiopia, Nigeria, Afghanistan	(Islam et al., 2011; Linh et al., 2019; Moahid & Maharjan, 2020; Oladele & Ward, 2016; Omonona et al., 2010; Regassa & Melesse, 2023; Tura et al., 2017; Yadav & Sharma, 2015)
Lack of investment opportunities	Africa, Gambia, Pakistan, China, Ethiopia, Bhutan, Ghana, Sudan, Indonesia, Kenya, Brazil, Iran, Myanmar	(Amanullah et al., 2019; Elangbam & Dawa, 2014; Gajigo, 2013; Tura et al., 2017; Zhao & Barry, 2014)
Inadequate communication and technology	Pakistan, Democratic Republic of Congo, Ethiopia, Bangladesh, Togo, Africa, Ghana, Nigeria, Turkey, India, Myanmar	(Ali & Awade, 2019; Amanullah et al., 2019; Awotide et al., 2015; Benjamin, 2012; Bojnec, 2012; Gashayie & Singh, 2015; Hassan et al., 2012; Jan & Khan, 2012; Kishore, 2012; Muayila & Tollens, 2012; Quddus & Kropp, 2020; Regassa & Melesse, 2023; Ullah et al., 2020)
Lack of extension services	Tanzania, Myanmar, Ethiopia, Iran, Kenya, Nigeria, Sudan, Ghana, Afghanistan	(Appiah-Twumasi et al., 2022; Ibrahim & Bauer, 2013; Kiplimo et al., 2015; Kiros & Meshesha, 2022; Kuwornu et al., 2012; Moahid et al., 2021; Mohammed & Mukhtar, 2015; Ojo & Baiyegunhi, 2020; Soltani et al., 2014; Tura et al., 2017)
Low repayment ability of farmers	China, India, Pakistan, Nigeria, Ghana, Philippine, Sudan, Bangladesh, Togo, Ghana, Africa, Ethiopia, Zimbabwe, Afghanistan, Thailand	(Admasu & Paul, 2010; Ali & Awade, 2019; Amanullah et al., 2019; Awotide et al., 2015; Bathan & Gordoncillo, 2017; Chandio et al., 2017; Chen et al., 2020; Chotewattanakul et al., 2019; Gashayie & Singh, 2015; Ibrahim & Bauer, 2013; Iniovorua et al., 2016; Islam et al., 2011; Jumpah et al., 2019; Khatun et al., 2014; Kumar et al., 2013; Kuwornu et al., 2012; Lin, Wang, Gan, Cohen, et al., 2019; McIntosh et al., 2013; Moahid & Maharjan, 2020; Muroiwa et al., 2019; Oladele & Ward, 2016; Regassa & Melesse, 2023; Ullah et al., 2020; Weng et al., 2020; Yadav & Sharma, 2015; Zhao & Barry, 2014)
Sluggish and time consuming process	Ghana, Africa, Nigeria, Pakistan, China, Philippine, Ethiopia, Sudan, Zimbabwe, India	(Appiah-Twumasi et al., 2022; Baiyegunhi et al., 2010; Bathan & Gordoncillo, 2017; Chandio et al., 2017; Chandio & Jiang, 2018; Dong et al., 2012; Gashayie & Singh, 2015; Gebeyehu, 2019; Ibrahim & Bauer, 2013; Ijioma & Osondu, 2015; Khatun et al., 2014; Kishore, 2012; Kuwornu et al., 2012; Muroiwa et al., 2019; Oladele & Ward, 2016; Omolade & Adepoju, 2019; Omonona et al., 2010; Rasheed et al., 2016; Saqib, Kuwornu, Panezia, et al., 2018; Twumasi et al., 2019)

4. Holistic framework of constraints to agricultural credit

Table 3 indicates that several distant factors restrain the availability and access to agriculture credit in different developing and under developed countries. All these factors restrain the farmer’s access to agriculture financing in one way or another. However, it is difficult to visualize the big picture because the typology of the constraints is missing. To close this gap, a novel integrated framework of constraints to agriculture financing has been proposed (see Figure 2).

Figure 2. Holistic constraints framework.

The novel integrated framework suggests that there are three major facets of the failure and poor state of agricultural finance in developing and underdeveloped economies. First, there are a number of financial industry-related constraints which restrain the supply of formal credit sources. Second, there are several farmers-rated factors that restrain the farmers’ demand for agriculture financing. Finally, there are some infrastructure-related constraints which restrain the farmers’ access to agriculture finance facilities or result in the failure of the agriculture finance system in the developing and under developed countries.

4.1. Supply-side constraints to agriculture financing

The novel constraints framework suggests that there are several industry-related factors that restrain the supply and availability of agriculture credit. High cost of financing is one of the most dominant and widespread factors restraining farmers’ access to agriculture financing in developing and under developed countries alike as suggested by the highest number of citations in Table 3. The high cost of financing in agriculture is influenced by various factors. The primary factor is the interest rates set by financial institutions (Appiah-Twumasi et al., 2022). Additionally, high transaction fees, collateral requirements and administrative charges imposed by lenders in many countries further increase the overall cost of credit for farmers (Amanullah et al., 2019). Since interest rates are excessively high, particularly in the under developed countries, farmers usually suffer instead of gaining from loans because they find it difficult to reconcile their farming income and the interest rates charged by lending institutions (Awotide et al., 2015). This deters them from seeking credit, or makes it financially burdensome to repay the loans, ultimately limiting their access to the funds necessary for agricultural investments.

Strict eligibility criteria also significantly impact farmers’ access to agricultural credit. Banks and other formal credit institutions have strict credit requirements, making it tough for small and marginal farmers to obtain loans (Abdullah et al., 2015; Chen et al., 2020). The criteria for obtaining credit are stringent in most of the countries, therefore, many farmers fail to meet the requirements, leading to limited or no access to the funds they need to invest in their farming operations (Saqib et al., 2016). This in turn hinders their ability to purchase inputs such as seeds, fertilizers and machinery, as well as access necessary services like irrigation and storage facilities. Strict eligibility criteria often include requirements such as high collateral, strong credit history, land ownership and income stability. Small-scale and marginalized farmers, who lack sufficient collateral or formal land titles, are particularly vulnerable to being excluded from the credit system (Gashayie & Singh, 2015; Muiruri et al., 2010). Additionally, farmers with limited credit history or irregular income streams also struggle to meet the criteria set by lenders.

When applying for loans from formal banks, farmers also encounter the issue of complicated procedures and extensive documentation (Abdullah et al., 2015). Complicated documentation requirements make the loan application process lengthy and time-consuming. Farmers need to gather a wide range of documents, such as land records, income statements, bank statements and various permits or licences. The time and effort required usually discourage farmers from pursuing credit or cause delays in accessing funds (Bathan & Gordoncillo, 2017). Complex documentation often requires a certain level of financial literacy to understand and complete the necessary forms accurately (Chandio & Jiang, 2018). Smallholder farmers, who usually have limited education or financial knowledge, struggle to navigate through the complex requirements (Badiru, 2010).

Complicated documentation drives farmers towards informal lending sources that have simpler requirements but often come with less favourable terms and potential exploitation (Ahmad, 2011). Farmers from marginalized communities, including women farmers or those belonging to minority groups, face additional challenges in fulfilling complex documentation requirements. They usually lack access to property rights, legal identification, or formal financial services, making it harder to meet the criteria for credit. This further exacerbates inequalities in accessing credit and hinders economic empowerment (Bathan & Gordoncillo, 2017). These barriers deter farmers from pursuing formal credit or limit their options and divert them to informal lenders with fewer documentation requirements but higher interest rates.

In addition to complicated documentation, the loan application process is also characterized by sluggish and time-consuming procedures in most of the developing and under developed countries, which is usually completed over the course of several months (Chandio & Jiang, 2018; Dong et al., 2012). Lengthy processes in credit institutions or government agencies lead to delayed approval of loan applications. This delay discourages farmers who often require immediate access to credit to purchase seeds, fertilizers, or other inputs, or to meet urgent financial needs. The time lag is a significant issue because, in agriculture, inputs are gradually converted into outputs through a process called the production cycle. Thus, the longer it takes to obtain credit approval, the more challenging it becomes for farmers to carry out their farming operations effectively.

In addition, the sluggish processes restrict the number of credit options available to farmers (Gebeyehu, 2019). This lack of options limits farmers’ ability to compare interest rates, loan terms and other conditions, potentially resulting in higher borrowing costs or unfavourable loan terms (Ijioma & Osondu, 2015). When farmers perceive the process as time-consuming, burdensome, or uncertain, they often choose to forgo applying for credit altogether. This lack of access to financing for innovation and investment hinders agricultural productivity growth and impedes farmers’ ability to adopt sustainable and efficient practices.

Asymmetric information also has a significant impact on a farmer’s access to credit. Asymmetric information refers to a situation where one party involved in a transaction has more information or knowledge than the other party (Balana et al., 2022). In the context of farmers seeking credit, it typically refers to a situation where lenders have less information about the farmer’s creditworthiness, the quality of their farming operations, or their ability to repay the loan (Bai et al., 2019). Asymmetric information in turn leads to adverse selection, where farmers who are most likely to default on their loans are the ones most likely to seek credit (Fletschner et al., 2010). If lenders cannot accurately assess the risk associated with lending to different farmers, they may become hesitant to provide loans or charge higher interest rates to compensate for the perceived higher risk (Chiu et al., 2014). In addition, asymmetric information also creates moral hazard issues. Once a farmer secures a loan, they may have an incentive to engage in risky behaviour or misallocate the funds if the lender has limited knowledge or control over the farming operations. This risk makes lenders more cautious in granting credit, or induces them to impose stricter conditions on loan agreements (Bonnke et al., 2022).

Inefficient monitoring policies also have implications for farmers’ access to credit (Mohammed & Mukhtar, 2015; Olowa & Olowa, 2011). Monitoring policies are measures put in place by financial institutions and government agencies to assess and supervise borrowers’ activities, ensure compliance with loan terms and minimize risks (Appiah-Twumasi et al., 2022). Monitoring policies provide lenders with mechanisms to track borrowers’ activities and evaluate their performance. In the absence of effective monitoring, lenders perceive higher risks associated with lending to farmers (Gajigo, 2013). This increased risk perception leads to stricter lending criteria, higher interest rates, or even the denial of credit to farmers. In summary, the lack of monitoring policies impacts farmers’ access to credit by increasing risk perception, limiting accountability, hampering risk management, creating inadequate credit history and weakening institutional trust.

Lack of formal financing products specifically designed for agriculture is another significant issue. The industrial sector receives the largest portion of funds in most countries. On the contrary, the share of agriculture financing in the overall financing portfolio of formal financial institutions is very low in most of the less developed and developing countries. Moreover, the credit market of less developed countries is also characterized by low competition among financial institutions (Baiyegunhi et al., 2010). Consequently, farmers usually face fewer choices and limited access to tailored credit products (Amanullah et al., 2019).

Agriculture has unique characteristics and risks that differ from other sectors. Farmers often require financing for seasonal activities (such as purchasing seeds and fertilizers, tending to crops and harvesting) which may not align with the standard repayment schedules of traditional loans. Additionally, agriculture is influenced by factors such as weather conditions, market fluctuations and crop cycles, which can affect the repayment capacity of farmers (Jumpah et al., 2019). The absence of financing products that take these factors into account limits the availability of appropriate credit options for farmers (Bonnke et al., 2022). In most of the developing and under developed countries the farmers have no option but to rely on generic loan products that do not consider the specific needs and challenges of agricultural activities (Moahid et al., 2021). This mismatch between the financing products and the requirements of the agricultural sector usually leads to high default rates, increased financial risk for lenders and reluctance to provide credit to farmers.

4.2. Demand-side constraints

On the demand side, several factors make the formal credit financing a less attractive option for farmers. First and foremost, most of the small scale and poor farmers don’t bother to apply for agriculture finance knowing that lenders will evaluate the creditworthiness and the repayment ability of borrowers before extending credit (Awotide et al., 2015; Chotewattanakul et al., 2019). Since they are usually unable to demonstrate a steady income stream to support loan repayments, they expect that lenders will either be hesitant to provide credit to them or charge higher interest rates to compensate for the perceived risk (Chandio & Jiang, 2018). Farmers with low repayment ability also face restrictions on the amount of credit they can access as lenders are cautious about extending larger loan amounts to borrowers who have a higher likelihood of defaulting (Chen et al., 2020; Linh et al., 2019). Consequently, farmers with low repayment ability find it challenging to access a variety of loan options. Financial institutions typically offer different types of loans tailored to the specific needs of farmers, such as agricultural term loans, crop loans, or livestock loans. However, since most of the small farmers cannot demonstrate the capacity to repay these loans, they usually have limited options available to them, making it harder to meet their financing needs (Bathan & Gordoncillo, 2017; Ibrahim & Bauer, 2013).

Lack of collateral also contributes to farmers’ lower demand for financing from formal sources (Ijioma & Osondu, 2015). Collateral refers to an asset or property that is pledged as security for a loan. In the context of farming, collateral typically includes land, machinery, livestock, or other valuable assets. Lenders often use collateral as a means to assess the borrower’s creditworthiness and mitigate the risk of default (Gurmessa & Ndinda, 2014). Lenders often perceive farmers without collateral as high-risk borrowers (Admasu & Paul, 2010). The lack of collateral not only limits the initial loan approval process but also impacts the loan terms and conditions offered to farmers (Baiyegunhi et al., 2010). In the absence of collateral, lenders typically charge higher interest rates or impose stricter repayment terms, increasing the overall cost of credit for farmers (Bojnec, 2012; Saqib et al., 2016). This discourages farmers from seeking loans and limits their borrowing capacity.

Furthermore, the lack of collateral also affects the size of loans that farmers can access (Weng et al., 2020). Financial institutions typically base the loan amount on the value of the collateral provided. Without sufficient collateral, farmers only qualify for smaller loan amounts, which cannot meet their investment needs or allow for significant improvements in their agricultural activities. Thus, farmers with insufficient collateral are compelled to seek credit from alternative informal sources such as local moneylenders or loan sharks. However, these sources typically charge exorbitant interest rates and impose unfavourable terms, further exacerbating the financial burden on farmers. Consequently, most of the small-scale farmers do not find agricultural financing as a viable and attractive option.

Similarly, lenders also charge higher interest rates due to the increased risk associated with lending money without any guarantees. This makes credit more expensive and less affordable for farmers (Ghambarali et al., 2014). Due to lack of guarantees, lenders are usually hesitant to provide larger loan amounts to farmers. Instead, they prefer to lend smaller sums of money to minimize their risk in case of default (Iniovorua et al., 2016). Consequently, most of the farmers find it difficult to access credit from traditional financial institutions that typically require collateral or guarantees (Saqib, Kuwornu, Ahmad, et al., 2018). This ultimate reduces farmers’ demand for formal financing (Gurmessa & Ndinda, 2014).

A farmer’s risk aversion also has a significant impact on the farmer’s demand for agriculture credit. Risk aversion refers to a farmer’s preference for avoiding or minimizing uncertainty and potential losses (Belhadi et al., 2021). Farmers who are risk-averse tend to be more cautious and reluctant to take on financial obligations or borrow money due to concerns about potential losses or uncertainties in their farming operations (Gebeyehu, 2019). This is because agriculture inherently involves various risks, including weather-related events, pest outbreaks, market fluctuations and unpredictable yields (Twumasi et al., 2019). These risks can affect a farmer’s ability to generate sufficient income to repay loans (Zhao & Barry, 2014). Risk-averse farmers fear the potential inability to meet loan obligations if they face adverse conditions, leading them to be hesitant in seeking credit.

The fear of default and non-bearable financial liabilities results in reduced demand for agriculture credit among risk-averse farmers. They usually prefer to rely on their own savings or limited resources instead of borrowing funds, as this provides a sense of security and minimizes the potential financial burden in case of unfavourable outcomes (Mehmood et al., 2017). Consequently, their investment capacity and ability to expand or improve their farming operations are constrained. Moreover, risk aversion also influences the type of credit products farmers are willing to consider (Belhadi et al., 2021). Risk-averse farmers usually prefer financing options that offer lower interest rates, longer repayment periods, or flexible terms. They are more inclined towards products that provide insurance or risk management components to mitigate potential losses. This preference for lower-risk credit products limits their options, especially if financial institutions have limited offerings or do not cater specifically to the needs of risk-averse farmers.

Land tenancy status also significantly affects a farmer’s demand for agriculture credit. In many cases, farmers who do not have secure land tenure or formal land ownership face challenges in obtaining credit from financial institutions (Chandio & Jiang, 2018). Farmers who are tenants or leaseholders usually have limited control over the land they cultivate, which creates uncertainty for lenders (Gebeyehu, 2019). Financial institutions often consider land ownership or secure land tenure as a form of collateral when evaluating loan applications. They view land ownership as a means of mitigating risk and ensuring that borrowers have tangible assets that can be used to recover the loan in case of default. As a result, farmers without formal land titles or secure tenancy rights are perceived as higher-risk borrowers and struggle to meet the collateral requirements set by lenders (Jia et al., 2010; Sekyi et al., 2017).

Similarly, most of the famers in the under developed countries do farming on small scales. Small-scale farmers usually have lower demand for agriculture financing due to high transaction costs associated with accessing and obtaining agricultural credit including fees, documentation requirements and travel expenses (Amanullah et al., 2019; Issahaku et al., 2020). Small-scale farmers, who generally require smaller loan amounts, often find it economically unviable to bear these costs, thereby reducing their demand for credit (Hu et al., 2020; Kiros & Meshesha, 2022).

Finally, lack of awareness among farmers plays a significant role in reducing demand for agriculture credit (Bonnke et al., 2022; Fletschner et al., 2010; Kinda & Loening, 2010). Different financial institutions, government schemes and agricultural development programmes offer specific credit products tailored to farmers’ needs. However, farmers in the rural areas usually miss out these opportunities due to lack of timely information (Hu et al., 2019). Lack of awareness also leads to poor understanding of the loan application process. Farmers usually do not know the required documents, eligibility criteria, or procedures to follow when applying for credit (Changsheng et al., 2016; Chen et al., 2020). As a result, most of the farmers struggle to fulfil the necessary requirements and face challenges in completing the application accurately and on time.

4.3. Infrastructure related constraints

Infrastructure and agriculture support services have a major impact on improving agricultural output in under developed and developing economies. Inadequate infrastructure poses a significant barrier to productivity and growth because it hampers the availability and distribution of essential agricultural inputs, such as quality seeds, fertilizers and machinery, which are crucial for enhancing productivity (Gashayie & Singh, 2015). According to previous evidences, an inadequate road infrastructure restricts farmers’ ability to reach markets because they usually use small carts pulled by animals or small engines to bring their goods to marketplaces while proper means of transportation are either not available or too costly for small farmers (Fletschner et al., 2010; Jessop et al., 2012; Kiros & Meshesha, 2022).

In addition, inadequate infrastructure in rural areas creates various barriers that hinder farmers from accessing credit and limit the effectiveness of agricultural financing systems in several ways. For example, the lack of accessible infrastructure deters financial institutions from establishing branches or offices in rural areas (Kiplimo et al., 2015). Consequently, in most of the underdeveloped and developing countries, the formal financial intuitions do not have sufficient branches and outreach in the rural areas (Etonihu et al., 2013). Therefore, the rural farmers have to travel to urban areas to access the financial institutions. This limits their ability to visit banks or other lending institutions to apply for loans, provide required documentation, or engage in financial transactions (Cai et al., 2018; Chandio & Jiang, 2018). Poor rural infrastructure, such as poorly maintained roads or lack of transportation networks, makes it further challenging for farmers to reach financial institutions (Cai et al., 2018; Jumpah et al., 2019).

The poor rural infrastructure also leads to inefficient value chain and market access in the rural areas. This is because the poor rural infrastructure restricts farmers’ access to markets and limits their ability to sell their produce at fair prices (Ansari et al., 2012; Zhao & Barry, 2014). Consequently, the limited storage facilities, inadequate transportation networks and unreliable supply chains usually result in post-harvest losses which in turn deter farmers’ income and repayment capacity (Muroiwa et al., 2019). Therefore, the perceived risks associated with market access and value chain inefficiencies make financial institutions hesitant to provide credit to farmers (Zhao & Barry, 2014).

Similarly, insufficient connectivity and limited access to communication and technology infrastructure in the less developed countries also impede farmers’ ability to engage with financial institutions (Ali & Awade, 2019; Awotide et al., 2015; Quddus & Kropp, 2020). Farmers in the rural areas face challenges in accessing information about loan products, interest rates, or application processes. In addition, lack of reliable internet connectivity hinders access to online banking services and limits farmers’ ability to utilize digital platforms for loan applications or payments (Ullah et al., 2020).

Most of the less developed and developing countries also lack adequate agriculture extension services (Moahid et al., 2021; Soltani et al., 2014). Extension services play a crucial role in providing farmers with the necessary information, training and support to improve their agricultural practices, increase productivity and manage risks effectively. Extension services help farmers stay updated on modern farming techniques, new technologies and best practices (Appiah-Twumasi et al., 2022). Consequently, farmers in the poor countries often struggle to adopt innovative methods, improve crop yields, or enhance the quality of their produce. Financial institutions often consider these factors while evaluating a farmer’s creditworthiness (Chiu et al., 2014). Without access to extension services, farmers usually face higher risks, making lenders more cautious about extending credit to them (Kiros & Meshesha, 2022; Ojo & Baiyegunhi, 2020).

The lack of investment opportunities also significantly impacts a farmer’s demand for agriculture credit. Famers in most of the under developed countries are less inclined to seek credit for their farming activities because these countries have limited or unattractive investment options in the agricultural sector (Gajigo, 2013). Investment opportunities in agriculture encompass various aspects, such as acquiring improved seeds, modern farming equipment, irrigation systems, post-harvest infrastructure, or accessing new markets (Amanullah et al., 2019). When farmers perceive a lack of viable opportunities to invest in their farming operations, they question the need for credit or consider it unnecessary (Tura et al., 2017; Zhao & Barry, 2014).

The absence of investment opportunities is influenced by multiple factors. It is due to limited access to markets or inadequate infrastructure that prevents farmers from commercializing their produce effectively (Khanal & Regmi, 2017). Insufficient agricultural extension services and technical support limit farmers’ knowledge and awareness of new technologies or farming practices that could enhance their productivity and profitability. Furthermore, market fluctuations, price volatility, or unfavourable government policies also create uncertainty and discourage farmers from investing (Ahmad, 2011). When farmers perceive high risks or low returns on their investments, they become reluctant to borrow funds and take on additional financial obligations.

The lack of insurance services in the less developed countries also has a significant impact on a farmer’s access to agriculture credit (Muroiwa et al., 2019). Insurance products tailored for agriculture, such as crop insurance or livestock insurance, help in mitigating risks and provide a safety net for farmers. This is because lenders often view agriculture as a high-risk sector due to its vulnerability to various perils such as adverse weather, pests, diseases, or market fluctuations (McIntosh et al., 2013). Without insurance coverage farmers are exposed to these risks. The absence of insurance services raises lenders’ risk perception, which results in stricter lending criteria, higher interest rates, or even loan denial for farmers (Chiu et al., 2014).

5. Interplay and implications

Agriculture, like any other business, requires investment in various areas such as land, seeds, equipment, fertilizers, irrigation systems and labour (Adenle et al., 2019). However, due to several constraints to agriculture financing, farmers usually have limited choices which restricts their borrowing capacity and make it challenging for them to secure sufficient funds to invest in their agricultural operations (Bojnec, 2012; Cai et al., 2018; Chen et al., 2020). The extant literature suggests a large set of abstract explanations for the failure of agricultural finance in the less developed countries. However, the novel constraints framework suggests that these seemingly rather abstract and divergent factors are actually interrelated and all these factors interact together to make an agriculture financing unviable and a less attractive option in underdeveloped and developing countries.

Therefore, it is essential to recognize the interconnected and overlapping nature of these constraints because they often influence each other in complex ways. For example, the demand-side constraints essentially revolve around the challenges faced by farmers in approaching and utilizing credit, while the supply-side constraints focus on the obstacles encountered by financial institutions in providing agricultural credit. However, despite being distinct categories, the demand-side and supply-side constraints have an underlying complex interplay that significantly influences the overall lending landscape.

At the heart of this interplay is a mutual reinforcement between the challenges faced by small-scale farmers and the risk-averse nature of financial institutions. On the demand side, small-scale farmers, who are often constrained by limited resources and income uncertainties, hesitate to apply for loans. This reluctance stems from a genuine concern about their ability to repay, given the inherent risks associated with agriculture, such as crop failure and market volatility. In this context, farmers often perceive the borrowing process as daunting which ultimately leads to a reduced demand for credit.

This diminished demand, however, is not solely a reflection of farmers’ preferences. Rather, it’s influenced by a historical backdrop of financial institutions being hesitant to cater to the needs of small-scale farmers. Financial institutions, being profit-driven entities, usually prioritize lending to more affluent or less risky clients. Thus, perceiving the small-scale and poor farmers as high-risk borrowers, financial institutions often develop a reluctance to design suitable credit products or extend loans to them. Their profit-driven approach in turn further reinforces the farmers’ apprehension and lowers their demand for seeking credit.

Considering this interplay among the factors our study suggests that overcoming barriers to agricultural credit financing requires a systematic and multi-faceted approach involving various stakeholders, including governments, financial institutions, agricultural development organizations and farmers themselves. For instance, it is crucial to promote policies and initiatives that reduce the cost of financing for farmers. This can involve measures such as providing subsidized interest rates, offering financial literacy programmes to improve creditworthiness, establishing agricultural development funds and encouraging competition among financial institutions to drive down borrowing costs. However, reducing costs will not yield the required results in isolation. To improve farmers’ access to credit, it is also essential to streamline credit application and approval procedures, simplify documentation requirements and minimize bureaucratic hurdles. Governments, financial institutions and agricultural organizations can work together to develop efficient credit systems that support timely access to credit for farmers. The reduction of burdensome loan administrative procedures would help increase farmers’ access to credit facilities, agricultural output, and food security (de Janvry & Sadoulet, 2020; Falola et al., 2022).

Similarly, the lack of agricultural financing products often results in limited access to innovative financial instruments. For example, insurance products such as crop insurance or livestock insurance, can help mitigate risks and provide a safety net for farmers (Ansari et al., 2012; Chau et al., 2016). In such a case, it is crucial to develop and promote specialized financing products that may include flexible repayment schedules, loans tied to specific’ agricultural inputs or activities, and risk management tools like insurance, etc. In addition, alternative credit assessment mechanisms should be explored by lending institutions for collateral purposes. These mechanisms should consider factors such as farmers’ credit history, income stability, farming experience, or other forms of non-traditional collateral (Weng et al., 2020). Governments and financial institutions may establish credit guarantee schemes or innovative financing models that mitigate the collateral requirements for farmers (Saqib, Kuwornu, Panezia, et al., 2018).

Our study further implies that it is important to develop financial services that address the specific risks faced by farmers in the developing world. This can include crop insurance, income stabilization programmes and innovative risk-sharing mechanisms that encourage farmers to seek credit for their farming activities (Mehmood et al., 2017; Seck, 2019). Financial literacy programmes and capacity-building initiatives can be used to equip farmers with the knowledge and skills to assess and manage risks effectively in the wake of increasing their confidence in borrowing and utilizing credit to enhance their agricultural operations. However, besides these steps, it is equally important to implement policies and mechanisms that protect the rights of tenants and leaseholders. Strengthening land tenure security and promoting formalization of land rights can provide tenants with a more secure basis for accessing credit without bearing any risk. This can be achieved through legal reforms, establishing land registries, promoting lease agreements and supporting programmes that facilitate access to credit for farmers with informal land tenure.

The lack of proper infrastructure also significantly impacts agricultural financing by limiting access to credit and hindering the overall development of the agricultural sector. This further diminishes the attractiveness of investment opportunities, making it even more challenging for farmers to seek credit to improve their operations (Ahmad, 2011; Appiah-Twumasi et al., 2022; Fletschner et al., 2010). To address infrastructure-related issues, it is crucial to create an enabling environment that promotes attractive investment opportunities in agriculture. This can involve initiatives such as improving market access, developing infrastructure, enhancing extension services and digital infrastructure, supporting research and development, and implementing favourable policies that incentivize agricultural investment. Importantly, the adoption and use of digital financial services should be encouraged to overcome geographical barriers and improve access to financial services in rural areas (Ruzzante et al., 2021; Teye & Quarshie, 2022). Digital financial services may facilitate transactions, reduce costs and provide farmers with convenient access to credit and other financial services.

6. Conclusion

Agricultural finance is a major factor in modernizing agriculture, encouraging resource efficiency and ensuring higher agricultural output. But in order to deliver sustainable financial services to remote areas, agriculture finance faces significant challenges in developing and underdeveloped countries. Consequently, farmers grapple with restricted access to formal credit due to a myriad of constraints. Despite an extensive but dispersed literature on constraints to agricultural finance, a comprehensive framework remains missing, making it difficult to conceptualize and resolve these constraints. In response, this research endeavours to fill this gap by thoroughly investigating the constraints to agricultural finance in developing and underdeveloped countries. Employing a systematic review approach, the study identifies constraints from existing literature and synthesizes these insights into a comprehensive framework using the Qualitative Evidence Synthesis (QES) method.

The novel constraints framework categorizes constraints into three distinct groups namely supply-side constraints, demand-side constraints and infrastructure-related constraints. Moreover, this framework suggests that these seemingly rather abstract and divergent factors are actually interrelated and all these factors interact together to make agriculture financing unviable and a less attractive option. Therefore, it is essential to recognize the interconnected and overlapping nature of these constraints because they often influence each other in complex ways.

Considering this interplay among the factors our study suggests that overcoming barriers to agricultural credit financing requires a systematic and multi-faceted approach involving various stakeholders, including governments, financial institutions, agricultural development organizations and farmers themselves. Through, collective efforts and a holistic approach, stakeholders can contribute to breaking the cycle of limited access to credit for farmers, thereby promoting agricultural productivity, food security and overall economic well-being in developing and undeveloped regions.

7. Limitations and the way forward

This study outlines a holistic approach to conceptualize and resolve constraints to agriculture credit in the wake of fostering agricultural development. However, the efforts for agricultural development have a dual impact on agricultural sustainability. On one hand, the targeted interventions such as access to agricultural credit, technological advancements and improved infrastructure enhance agricultural productivity, elevate livelihoods and contribute to economic growth in rural communities. On the other hand, the potential for unintended consequences arises when development initiatives prioritize short-term gains over long-term sustainability. This is because certain development initiatives such as intensive farming practices, excessive use of agrochemicals and monoculture often lead to environmental degradation, soil erosion, the emission of greenhouse gases and biodiversity loss.

Therefore, striking a balance between increasing agricultural productivity and preserving ecological integrity is paramount. Sustainable agricultural development requires a holistic approach that integrates environmental stewardship, social equity and economic viability to ensure that the benefits of progress are enduring and do not compromise the ability of future generations to meet their needs. Therefore, policymakers, researchers and practitioners must collaborate to design and implement development strategies that align with the principles of agro-ecology, conservation and resilience in the wake of fostering a harmonious coexistence between agricultural progress and long-term sustainability.

In addition, access to credit and financial inclusion is widely used as a tool to advance the socioeconomic sustainability of the farmers. However, credit is a double-edged sword because it often ties poor and small-scale farmers into vicious cycle of unsustainable debt. In the face of unpredictable factors such as crop failures or market fluctuations, small-scale farmers usually struggle to meet repayment obligations which ultimately lead to a perpetuating cycle of debt. Moreover, most of the small-scale farmers in the underdeveloped and developing countries do not have adequate financial literacy. Insufficient understanding of loan terms, interest rates and financial management usually results in mismanagement of borrowed funds. Consequently, farmers face challenges in optimizing the use of credit for activities that could enhance productivity or improve the overall efficiency of their agricultural practices.

To cope with these challenges, innovative participatory financing schemes should be introduced that could enable farmers to seek financing on profit and loss sharing basis. Moreover, the small-scale producers usually need, not only flexible financing, but also support in value chain integration. For this purpose, small-scale farmers need to be connected with markets, processors and retailers so that they can benefit from a more predictable and stable income. This integration can be supported by financing facilities coupled with agriculture development programmes that consider the entire value chain. Policy makers can learn from the experience of Sudanese Islamic Bank¹ introduced a comprehensive package that not only included financing on profit and loss sharing basis but also the provision of necessary inputs, storage facilities, extension services, trainings and marketing advice to be delivered at the farm gate at the right time (Nouman & Ullah, 2023).

Availability of data and material

Data are available upon reasonable request.

Consent for publication

Yes, consent is granted for publication.

Acknowledgements

We thankfully acknowledge the support of all the team members for their valuable discussions. We greatly appreciate the contribution of all authors.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 For detailed case study on the experience of Sudanese Islamic Bank see Nouman and Ullah (2023).