Mapping Agricultural Risk for Sustainable Rural Development in the Western Balkans: A Systematic Literature Review and 5 × 5 Evidence-Gap Matrix

Murrja, Arif; Shehu, Erion; Kambo, Arben; Çera, Gentjan

doi:10.3390/su18168266

Abstract

Smallholder and family farms in the Western Balkans face intertwined production, market, institutional, human resources and financial risks. This systematic review maps agricultural-risk evidence across seven Western Balkan countries and five enterprise types: crops, livestock, poultry, beekeeping and mixed systems. Following PRISMA 2020, SciSpace was used as a discovery interface for Scopus-indexed literature and was complemented by regional literature compilation, citation tracking and manual verification. From 189 records identified before deduplication, 68 studies published between 2000 and 2025 met the eligibility criteria. Studies were coded by country, enterprise type, risk category and method, and synthesised through a 5 × 5 evidence-gap matrix and complementary risk-overlap analysis. Evidence is concentrated in production risk for crops and livestock, market risk in beekeeping and institutional risk in mixed systems. The main gaps concern human resources risk in crops and poultry and financial risk in poultry. Although 22 studies address multiple risks, only six cover all five major farm risks, mostly descriptively rather than through interaction modelling. The review is limited by uneven regional source visibility and judgement-based evidence coding. The findings support portfolio-based risk management for sustainable rural development rather than isolated hazard responses. The review was not prospectively registered.

Keywords:

agricultural risk; sustainable rural development; smallholder farming; Western Balkans; evidence-gap matrix; risk management; family farms; PRISMA

1. Introduction

Risk is intrinsic to agricultural production because farm outcomes are shaped by biological processes, weather variability, market fluctuations, policy change, household labour conditions and financial exposure. For smallholder and family farms, unmanaged risk has implications that extend beyond individual farm losses, affecting rural poverty, food security, investment behaviour and the long-term resilience of rural territories [1,2,3].

Agricultural-risk research commonly distinguishes five broad categories: production risk, market risk, institutional risk, human resources risk and financial risk. Production risk captures uncertainty in yields and animal performance caused by weather, pests, diseases and other biophysical drivers. Market risk concerns price volatility, input–output price imbalances, unstable market access and weak bargaining power. Institutional risk refers to exposure created by policy, legal and regulatory change. Human resources risk corresponds to the personal-risk category used in much of the foundational literature and includes illness, injury, ageing, succession failure and labour loss. Financial risk reflects credit obligations, cash-flow variability, interest-rate exposure, insurance gaps and limited access to finance [1,2,4].

The global evidence base remains strongly unbalanced. A major review of 3283 studies showed that agricultural-risk research is dominated by production risk [4]. It also showed that only a minority of studies examine more than one risk type, and that analyses covering all five risk categories remain rare and mainly perception-based rather than quantitatively integrative [4]. This imbalance matters because farm households do not experience risk categories in isolation. A single drought can reduce crop yields, raise feed costs for livestock and poultry, reduce liquidity and intensify repayment difficulties for farms using seasonal credit. Similarly, weak regulatory capacity can amplify disease exposure, restrict market access and limit eligibility for support programmes.

The Western Balkans provide a particularly relevant setting for examining these interlinked risks. Albania, Bosnia and Herzegovina, Croatia, Kosovo, Montenegro, North Macedonia and Serbia—referred to in this review as the seven-country Western Balkan framework (WB7)—share a post-socialist trajectory marked by land fragmentation, incomplete market institutions, limited extension capacity and uneven progress toward European Union agricultural standards [5,6,7,8]. Smallholder and family farms dominate the regional agricultural structure, and many households combine crops, livestock, poultry, beekeeping and mixed farming activities within the same livelihood portfolio [9,10].

To address definitional comparability, this review uses “smallholder/family farm” as an operational regional category rather than as a strict EU/FADN economic-size class. Available regional evidence shows very small average holding sizes and high shares of small holdings: average holding size ranges from about 1.16 ha in Albania and 1.85 ha in North Macedonia to around 5.4 ha in Serbia, while holdings below 2 ha account for 63.2% in Kosovo, 73.4% in Montenegro and 48.8% in Serbia; in North Macedonia, 58.2% of holdings are below 1 ha [8]. For Croatia, Eurostat reports that 70.6% of farms were below 5 ha in 2020 [11]. The EU/FADN approach, by contrast, classifies farms by economic size using standard output, not only by physical area or family operation [12]. Accordingly, the term is used here to denote the region’s prevailing small-scale, family-operated and often diversified farm structure, while acknowledging that national statistical definitions are not fully harmonised across all WB7 countries.

In this review, WB7 is used as a geographical framing of the wider Western Balkan region rather than as a political or enlargement category. The analytical focus follows the five-risk typology used in agricultural risk-management literature and examines whether farm risks are studied across production, market, institutional, human resources and financial dimensions. Croatia is included because it belongs to the broader Western Balkan geographical space and shares comparable post-socialist agrarian legacies with neighbouring countries, including land fragmentation, smallholder structures and the transition toward market-oriented family farming [5,6,7,8,10]. The WB7 framing therefore serves to map how the five major farm-risk categories are represented across the wider regional evidence base, without implying political or institutional equivalence among the countries included.

At the same time, Croatia’s EU membership is explicitly recognised as an institutional difference from the other WB7 countries. Its inclusion is therefore justified on regional and structural agrarian grounds, without treating Croatia as institutionally equivalent to the non-EU Western Balkan countries.

Despite this operational diversity, the regional literature has tended to remain fragmented by country, enterprise and risk type. Available studies often focus on one exposure in one sector, such as climate pressure on crop yields, dairy-market disruptions, avian-influenza outbreaks, weak legal compliance in poultry, or market competition in beekeeping and vegetables [13,14,15,16,17,18]. What remains missing is a systematic regional mapping of how the five core risk categories are distributed across the farm enterprise types that structure smallholder agriculture in the Western Balkans.

This review addresses that gap by constructing a 5 × 5 evidence-gap matrix that intersects five risk categories with five enterprise types: crops, livestock, poultry, beekeeping and mixed systems. The aim is not to estimate the prevalence of risk in the farming population, but to diagnose where scholarly attention is concentrated and where it remains thin or absent. The matrix is complemented by a Venn diagram that visualises the extent to which the literature remains fragmented even when studies move beyond a single risk category.

Compared with broad agricultural-risk reviews that classify risk categories at a general level [4], this review adds a regional and enterprise-specific evidence-mapping layer. The 5 × 5 matrix crosses the five major farm-risk categories with five enterprise types, allowing evidence gaps to be identified at the level of specific risk–enterprise intersections rather than only by risk category. It therefore serves as a diagnostic tool for research prioritisation and policy design in the Western Balkan smallholder context.

Three research questions guide the analysis: RQ1: How is the published evidence on agricultural risk in WB7 smallholder farming distributed across the five risk categories and five enterprise types? RQ2: To what extent does the WB7 risk literature adopt an integrated approach that addresses multiple risk categories, including all five categories simultaneously? RQ3: Which risk–enterprise intersections represent the most consequential evidence gaps, and what methodological and policy priorities do they imply for future research and sustainable rural-development policy?

2. Literature Review

This thematic synthesis is organised around the five major risk categories used in the study: production, market, institutional, human resources and financial risk. This organisation follows the conceptual logic of the review and prepares the basis for the later 5 × 5 evidence-gap matrix. The sources discussed in this section are drawn from the eligible WB7 literature identified through the search and verification process and are used to show how each risk category has been treated across crops, livestock, poultry, beekeeping and mixed farming systems.

2.1. Production Risk

Production risk is the most visible category in the WB7 evidence base. In crops, the literature concentrates on climate variability, projected yield decline, floods, landslides, drought, soil and food-safety hazards, and management constraints in cereal, vegetable and fruit systems [13,19,20,21,22,23]. These studies provide strong evidence of exposure to weather and biological shocks, but they rarely connect production losses to downstream market, financial or institutional consequences.

Livestock production risk is linked mainly to disease, reproductive inefficiency, feed scarcity, water shortages and low productivity in small herds. Evidence from cattle, dairy and small-ruminant systems shows that weak biosecurity, limited disease-control capacity and seasonal resource constraints can undermine farm performance [24,25,26,27,28]. These constraints are not purely technical; they also reflect limited capital, advisory support and insurance coverage.

Poultry production risk is concentrated around avian influenza, Salmonella, biosecurity weaknesses and quality constraints. The evidence covers backyard poultry, intensive laying-hen farms and sector-level constraints, but the financial and market consequences of disease exposure remain insufficiently modelled [15,29,30,31,32,33].

Beekeeping production risk is also well documented. Colony losses, Varroa and Nosema infections, pesticide exposure, deforestation, climate change, shifting flowering periods and contamination risks appear repeatedly across Albanian, Kosovar, Bosnian, Serbian, Croatian and North Macedonian evidence [18,34,35,36,37,38,39]. The wider implication is that production risk often carries simultaneous product-safety, income and market-access consequences.

2.2. Market Risk

Market risk in crop production is reflected in input–output price imbalances, international price exposure, weak competitiveness and unstable value-chain conditions. Evidence from cereals, vegetables and raspberries shows that smallholders face compressed margins, weak bargaining power and sensitivity to external market shocks [17,40,41,42,43].

In livestock, market exposure is shaped by food-safety crises, weak bargaining power, low meat prices and structural limitations in value chains. These constraints are particularly relevant for small-ruminant and dairy systems, where production costs and market prices are often misaligned [14,26,44,45].

Beekeeping forms the clearest concentration of market-risk evidence. Across the region, small producers face competition from cheap imported honey, dependence on informal direct sales, limited branding, weak cooperative structures and vulnerability to supply-chain disruptions [18,46,47,48,49,50,51,52,53,54].

Poultry market risk remains thinner than production-risk evidence. Existing studies point to trade barriers, quality issues, consumer confidence losses and incomplete sectoral analysis, but they do not provide a systematic treatment of price volatility, contract conditions or market power for smallholder poultry producers [31,33,55,56].

2.3. Institutional Risk

Institutional risk is particularly important in mixed systems and in sectors exposed to European Union alignment requirements. Regional evidence highlights policy mismatch, weak implementation capacity, land fragmentation, outdated cadastres, corruption in land processes, incomplete factor markets and limited farmer organisation [5,6,7,57,58,59,60,61].

The institutional dimension is not limited to formal regulation. In poultry, weak consultation with professionals, high farm density and limited surveillance can turn legal and biosecurity gaps into production losses [16,30]. In beekeeping, weak pesticide regulation and incomplete food-safety transposition create exposure for producers attempting to access formal or export-oriented markets [37,62,63,64].

Institutional risk is therefore a cross-cutting constraint. When extension services are weak, producers are less able to comply with standards, obtain subsidies, manage disease risk or access insurance products. This creates a pathway through which institutional weakness intensifies production, market and financial vulnerability.

2.4. Human Resources Risk

Human resources risk is the least developed category in the WB7 evidence base. Available studies concentrate on demographic decline, ageing, out-migration, rural poverty, succession failure and labour scarcity rather than on systematic measures of illness, injury, household labour loss or occupational risk [3,65,66,67].

The livestock and beekeeping evidence illustrates the practical consequences of labour and succession constraints. Small-ruminant systems face shepherd shortages; dairy systems exhibit knowledge gaps related to animal welfare; family beekeeping enterprises face succession challenges; and older beekeepers may struggle with digital registration platforms [45,67,68,69,70]. Gendered differences in disaster-risk exposure further indicate that household labour and resilience are unevenly distributed [71].

For crop and poultry enterprises, the evidence is especially limited. A small number of studies include farmer perceptions, workforce health or household-level constraints, but the region still lacks robust evidence on productivity losses associated with ageing, illness, injury, labour disruption or succession failure [32,72].

2.5. Financial Risk

Financial risk is shaped by credit constraints, weak insurance markets, debt exposure, low profitability and rising input costs. Crop studies provide the strongest evidence, documenting low insurance uptake, demand-side barriers, willingness-to-insure determinants, whole-farm revenue-protection options, debt-risk perceptions and emerging premium-assessment models [73,74,75,76,77,78,79,80].

Livestock financial risk is linked to disease-control costs, low milk profitability, limited credit access and low insurance penetration. These constraints reduce the capacity of farms to invest in productivity, animal-health protection and climate resilience [24,81,82,83].

Beekeeping studies identify subsidy gaps, winter-colony-loss costs and input-cost pressures, while poultry financial risk remains one of the thinnest areas in the literature [35,84,85]. The absence of systematic evidence on credit availability, interest exposure and insurance uptake in smallholder poultry is particularly problematic because poultry production risk is well documented but its financial consequences remain poorly understood.

Overall, the thematic literature shows that the WB7 evidence base contains valuable but unevenly distributed knowledge. Production, market and institutional dimensions are more visible in the literature, whereas human resources risk and poultry-sector financial risk remain less developed. This fragmentation justifies the systematic evidence-mapping approach used in the following Section 3, where the reviewed evidence is operationalised through the 5 × 5 risk–enterprise matrix.

3. Materials and Methods

3.1. Conceptual Framework

The review adopts the five-category risk typology established in agricultural risk-management literature and applies it to the enterprise structure of WB7 smallholder agriculture [1,2,4]. The five risk categories are production, market, institutional, human resources and financial risk. Institutional risk is treated as equivalent in scope to legal or regulatory risk. Human resources risk is treated as equivalent to the personal-risk category used in much of the foundational literature.

The analytical contribution is the cross-tabulation of these five risk categories against five enterprise types: crops, livestock, poultry, beekeeping and mixed farming systems. This design produces a 5 × 5 evidence-gap matrix that identifies where research is concentrated and where critical gaps remain. Albania is discussed as an illustrative case because it concentrates several features of the regional smallholder model: very small average farm size, diversified household portfolios, weak insurance penetration and ongoing alignment pressures with European standards [8,9].

This evidence-mapping design was chosen because the regional literature is heterogeneous in methods, sectors and source types. Statistical pooling would therefore be inappropriate, whereas systematic classification can transparently show where evidence is concentrated, thin or missing. Figure 1 summarises this conceptual framework.

Figure 1. Conceptual framework of smallholder agricultural risk in the Western Balkans.

3.2. Search Strategy

The literature search followed PRISMA 2020 guidance and was conducted between January and March 2026 [86]. The main identification stream was conducted through SciSpace, used as a discovery interface for Scopus-indexed literature. This approach was embedded in a multi-source verification protocol rather than treated as a stand-alone retrieval procedure. All records retained from the SciSpace stream were manually checked for bibliographic completeness, thematic relevance, source type and compliance with the eligibility criteria. In addition, each retained record was cross-referenced against Google Scholar, Semantic Scholar and OpenAlex, while records identified through these supplementary platforms were checked against the SciSpace/Scopus-indexed stream and the reference lists of included studies. This bidirectional cross-referencing reduced the risk of platform-specific omission and ensured that no eligible study identified through any search stream or citation chain was lost before final screening and coding.

This systematic literature review was conducted and reported in accordance with the PRISMA 2020 statement [86]. The PRISMA 2020 flow diagram is presented in Figure 2, and a larger version is also provided as Supplementary Figure S1 for readability. The completed PRISMA 2020 checklist is provided as Supplementary File S1. The review was not prospectively registered, and no separate review protocol was publicly deposited.

Figure 2. PRISMA 2020 flow diagram for the systematic review of agricultural risk in WB7 smallholder agriculture.

Five enterprise-specific search rounds were conducted for crops, livestock, poultry, beekeeping and mixed systems. Each search combined three concept blocks: geographic terms covering the seven WB7 countries; risk-related terms covering production, market, institutional, human resources and financial risk; and enterprise-specific vocabulary. Searches were limited to publications from 1995 to 2025.

The final manual verification and screening reconciliation were completed in March 2026. The full enterprise-specific search-string structure, including the geographical, risk-related and enterprise-specific term blocks, is provided in Supplementary Table S1. No formal language restriction was imposed at the screening stage; however, because the main discovery stream relied on Scopus-indexed metadata and international search platforms, possible language and coverage limitations are acknowledged in Section 5.5.

To reduce coverage bias and improve the representation of regional literature, the main search stream was complemented by two supplementary source streams. The first consisted of a pre-existing regional literature compilation assembled through Semantic Scholar, OpenAlex and Google Scholar. The second consisted of targeted citation tracking and manual verification for under-represented enterprise types and risk categories.

3.3. Eligibility Criteria

Studies were retained when they met five criteria. First, they had to focus geographically on one or more WB7 countries. Second, they had to address smallholder, family or small-to-medium farm operations. Third, they had to provide substantive empirical, quantitative or conceptual treatment of at least one of the five risk types. Fourth, they had to focus on crops, livestock, poultry, beekeeping or mixed farming systems. Fifth, they had to be published as peer-reviewed journal articles or as institutional reports from recognised organisations. Studies that mentioned risk only in the introduction or motivation, without analysing it substantively, were excluded, following the earnest-risk-analysis logic used in previous review work [4].

3.4. Screening and Classification

The search identified 189 records before deduplication: 125 from the SciSpace/Scopus-indexed stream and 64 from supplementary methods. After removing 45 duplicates, 144 unique records were screened by title and abstract. Sixty-eight were excluded for lacking a WB7 focus, lacking substantive risk analysis, being off topic, or not addressing smallholder or family farm operations. Seventy-six reports were assessed at full-text level. Eight were excluded, leaving 68 eligible studies: 58 peer-reviewed articles and 10 institutional reports.

Screening and coding were carried out using a structured extraction sheet. Title and abstract screening was conducted by one author, while a randomly selected 20% sample of records was independently checked by a second author to verify consistency. Any discrepancies, where present, were resolved through discussion among the authors. Full-text eligibility assessment and evidence coding were conducted using the structured extraction sheet. Multi-risk and multi-enterprise assignments, particularly in ambiguous cases, were cross-checked to ensure consistent application of the substantive-analysis threshold. For each report, the coding fields included country or regional focus, enterprise type, risk category, method, source type, publication year and justification for inclusion. Ambiguous cases were resolved by returning to the full text and assigning a study to multiple categories only when the relevant risk or enterprise type was substantively analysed rather than merely mentioned. This procedure underpins the complete study-to-matrix mapping reported in Appendix A.

The classification procedure was therefore guided by the predefined five-risk typology and enterprise categories introduced in Section 3.1, while allowing multiple coding only when a study substantively analysed more than one risk or enterprise dimension.

For each eligible study, coding recorded the country or regional focus, enterprise type, risk type, method and justification for inclusion. A study could be assigned to more than one risk category and, where substantively justified, to more than one enterprise type. The matrix should therefore be read as an evidence map rather than as a set of mutually exclusive observations. Cell values represent matrix entries, not independent statistical observations. No chi-square test or correspondence analysis was applied because the classification structure does not satisfy the independence assumptions required for inferential treatment.

4. Results

The results are presented in three steps. Section 4.1 describes the final evidence base by time period and geography; Section 4.2 reports the 5 × 5 evidence-gap matrix; and Section 4.3 adds a complementary risk-overlap analysis to show the extent of multi-risk coverage across the included studies.

4.1. Descriptive Overview

The final database consists of 68 studies published between 2000 and 2025. The evidence base is recent: 51 studies appeared between 2015 and 2025, and 31 appeared between 2020 and 2025. Serbia accounts for the largest share of study locations, followed by Albania, Kosovo, Bosnia and Herzegovina, Croatia, North Macedonia and Montenegro. Twelve studies address the Western Balkans as a regional unit.

The temporal distribution indicates that agricultural-risk research in the WB7 has accelerated during the last decade, but this growth has not produced balanced coverage across countries, risks and enterprise types. The concentration of studies in more recent years also reflects increasing attention to climate, market and institutional pressures affecting smallholder agriculture.

Geographically, the database combines country-specific studies with regional analyses. This mix is important because the evidence-gap matrix is designed to identify not only national concentrations of research, but also regional blind spots that cut across the Western Balkans. The descriptive overview therefore provides the basis for interpreting the matrix as a map of research attention rather than as a measure of risk prevalence in the farming population.

4.2. The 5 × 5 Evidence-Gap Matrix

Table 1 cross-tabulates the 68 eligible studies by enterprise type and risk type. Because studies may address several risks or enterprise types, the totals should not be interpreted as mutually exclusive study counts. The matrix is a descriptive map of scholarly attention across risk–enterprise intersections.

Table 1. Cross-tabulation of eligible studies by enterprise type and risk type. Red cells (n ≤ 2) indicate severe evidence gaps; yellow cells (n = 3–4) indicate thin coverage. Cell values are descriptive matrix entries, not mutually exclusive study counts.

Figure 3 provides two complementary visual readings of the 5 × 5 evidence-gap matrix. Figure 3A shows the overall distribution of risk categories across enterprise types, indicating that crops, livestock, beekeeping and mixed systems have broader evidence coverage than poultry, while human resources risk remains comparatively weak across all enterprises. Figure 3B provides the cell-level interpretation of the matrix. The darker cells show where the WB7 evidence base is concentrated, particularly production risk in crops and livestock, market risk in beekeeping, and institutional risk in mixed systems. In contrast, the lighter cells reveal the weakest intersections, especially human resources risk in crops and poultry and financial risk in poultry.

Figure 3. Descriptive visualisation of the 5 × 5 evidence-gap matrix: (A) stacked risk-category distribution by enterprise type; (B) heatmap of the risk–enterprise matrix.

Together, the stacked distribution and the heatmap show not only the dominance of production and market risk, but also the uneven enterprise-specific structure of the regional evidence base. This visual evidence is especially important because poultry production risk is well documented, yet the literature does not adequately examine how disease, biosecurity or market disruptions translate into credit exposure, insurance needs or investment constraints.

4.3. Complementary Risk-Overlap Analysis

Figure 4 provides a complementary diagnostic tool. It does not replace the 5 × 5 evidence-gap matrix, which remains the main analytical instrument because it includes the enterprise dimension. Instead, the Venn diagram visualises the degree to which the literature remains fragmented across the five risk categories.

Figure 4. Complementary overlap analysis of the five major farm-risk categories among the 68 eligible WB7 studies: (A) refined Venn diagram showing the overlap of the five major farm-risk categories; (B) summary table of the key counts highlighted in the diagram. The central intersection indicates studies addressing all five major farm risks simultaneously.

Twenty-two of the 68 studies address two or more risk types. This suggests that part of the WB7 literature has moved beyond single-risk analysis. However, the overlap remains partial: many studies cover only two, three or four risks, while only six studies address all five major farm risks simultaneously. These all-risk studies are still primarily perception-based, descriptive or sectoral, rather than based on quantitative simulation, econometric impact modelling or whole-farm welfare analysis.

For transparency, the detailed study allocation across the risk-overlap combinations represented in the Venn diagram is reported in Supplementary Table S2.

This result is central to the paper’s argument. Partial multi-risk coverage is a step forward, but it is not sufficient for a genuinely integrated understanding of smallholder exposure. Farm households experience production, market, institutional, human resources and financial risks as interacting components of the same livelihood portfolio, while the literature still tends to count them separately or analyse only subsets of the risk system.

Accordingly, this review distinguishes multi-risk coverage from genuine risk-interaction modelling. Multi-risk coverage refers to studies that mention, classify or score more than one risk category. Risk-interaction modelling would require an explicit analytical link among risks, for example showing how a production shock generates liquidity pressure, how institutional uncertainty affects market access and investment, or how labour disruption interacts with credit and production constraints. Most all-risk studies in the WB7 evidence base fall into the first category rather than the second.

Taken together, the results show a highly uneven evidence structure. The strongest concentrations occur where production, market or institutional risk has been studied in established enterprise contexts, while the weakest intersections concern human resources risk in crops and poultry and financial risk in poultry. The overlap analysis confirms that multi-risk coverage has increased, but genuine modelling of risk interactions remains rare.

5. Discussion

5.1. Asymmetric Evidence and Fragmented Multi-Risk Coverage

The WB7 evidence base reproduces the global asymmetry found in agricultural-risk research: production and market risks receive much greater attention than human resources and financial risks [4]. The regional matrix adds a second layer to this diagnosis by showing that the imbalance also varies by enterprise type. Crops, livestock and beekeeping receive comparatively stronger coverage, while poultry and human resources risk remain underdeveloped.

This pattern aligns with global agricultural-risk scholarship, but the WB7 setting gives it a specific structural meaning. In a region characterised by fragmented holdings, mixed livelihood portfolios and uneven institutional capacity, the dominance of production-oriented research may underestimate how market access, compliance requirements, household labour and finance jointly shape farm resilience.

To the best of our knowledge, directly comparable systematic evidence for the Western Balkans remains limited. Existing regional studies usually examine single countries, sectors or hazards, rather than mapping the five major farm-risk categories across enterprise types. This gap is practically important because agricultural entrepreneurs and farm managers make decisions within diversified portfolios, where production shocks, market access, institutional compliance, household labour capacity and finance interact. Analysing these risks jointly can therefore support more integrated decisions by farmers, advisory services and rural-development policymakers. The finding that only six eligible studies address all five risks simultaneously is consistent with Komarek et al. [4], confirming that fragmented and production-dominated risk analysis is not only a regional limitation but also a wider global pattern in agricultural risk-management research.

The Venn diagram sharpens this interpretation. The issue is not only that many studies focus on a single risk. Even within the multi-risk subset, most studies remain incomplete because they do not cover the full portfolio of farm risk exposure. A study that captures production and market risk, for example, may still miss the institutional conditions that shape compliance, the human resources constraints that determine labour capacity, and the financial limits that shape investment or insurance decisions.

The most populated cells of the matrix should therefore be interpreted as clusters of risk mechanisms, not simply as bibliometric counts. Production risk in crops and livestock reflects the combined pressure of climatic variability, disease exposure, feed and water constraints, weak biosecurity and limited farm-level risk planning. These factors convert biophysical shocks into income instability and investment hesitation. The concentration of market-risk evidence in beekeeping points to a different mechanism. Small producers are exposed to cheap imported honey, informal sales channels, limited collective marketing, weak branding and variable product-quality requirements. Market access and product credibility therefore become central sources of vulnerability. Institutional risk in mixed systems reflects the way land fragmentation, incomplete cadastral and advisory systems, uneven subsidy access and EU-alignment requirements interact across several activities within the same household portfolio. Conversely, the weakest cells are analytically important because they reveal what the literature still does not measure. Ageing, illness, succession and labour scarcity are rarely modelled as productive-risk variables. Poultry-sector financial risk also remains under-analysed, although disease outbreaks, biosecurity costs and market disruptions can create liquidity pressure, insurance needs and constrained reinvestment.

Two gaps appear especially persistent. Poultry financial risk remains under-researched because poultry studies in the region are concentrated on disease, biosecurity and sector-level production constraints, whereas the financial transmission channels of these shocks—liquidity, credit exposure, insurance demand and reinvestment capacity—are rarely measured. Human resources risk remains thin because ageing, labour scarcity, illness and succession are more often treated as rural background conditions than as farm-level risk variables affecting production continuity, investment capacity and resilience.

5.2. The Methodological Ceiling in Regional Risk Analysis

The review identifies a methodological ceiling. Studies covering all five risks are important, but they mostly rely on Likert-scale perception surveys, comparative indices or descriptive sector reviews. They help identify what farmers perceive as important, but they do not estimate the joint welfare effects of simultaneous shocks. The region still lacks panel datasets, whole-farm simulations, portfolio-based insurance modelling and econometric designs capable of capturing interactions among yield shocks, price changes, policy uncertainty, labour disruption and debt exposure.

This limitation has practical consequences. Without integrated risk modelling, policy instruments are likely to remain partial. Insurance products may be calibrated to isolated hazards; extension services may focus on technical production advice while ignoring credit constraints; and subsidy schemes may target single commodities rather than diversified household portfolios. The matrix and Venn diagram together show why risk management in the WB7 should move from hazard-specific responses toward integrated portfolio-based design.

5.3. Policy Implications for Sustainable Rural Development

Three policy implications follow. First, agricultural insurance should be redesigned around the diversified structure of smallholder portfolios. Evidence of low insurance uptake and weak animal-insurance penetration suggests that existing instruments are poorly aligned with the needs of small family farms [73,77,79,82]. Whole-farm revenue protection, bundled multi-peril products and size-sensitive premium subsidies are promising directions, but they require stronger data on joint risk exposure.

Second, institutional risk created by EU alignment requires stronger extension and advisory services. Compliance with food-safety, traceability and production standards can generate modernisation incentives, but it can also create regulatory pressure for farms lacking capital and information. Better advisory systems could reduce this burden by translating regulatory requirements into practical farm-level decisions [5,7,57].

Third, human resources risk should be treated as a core element of rural resilience policy. Demographic decline, labour scarcity, succession failure and ageing are not merely social background conditions; they directly affect production continuity, innovation capacity and the ability of households to respond to shocks [3,45,65,66].

More operationally, these findings suggest that policy packages should combine risk instruments rather than address each risk separately. Insurance subsidies should be accompanied by advisory support, farm-record systems and credit products that recognise diversified household portfolios. Extension programmes should include financial planning, succession planning and compliance support, not only technical production advice. For poultry and other under-studied enterprises, targeted data collection on costs, credit exposure and insurance demand would allow policymakers to design interventions on the basis of measured vulnerability rather than disease or production indicators alone.

5.4. Albania as an Illustrative Case

Albania illustrates why an integrated approach is needed. The country has one of the most fragmented farm structures in the region, with an average farm size close to 1.2 hectares, and smallholder households commonly combine crops, livestock, poultry and beekeeping activities [8,9]. Albanian evidence covers vegetable production risk, market competition, financial risk, legal risk in poultry, beekeeping threats and five-risk perception in apple farms [16,17,23,37,72,76,87]. At the same time, the wider regional matrix also incorporates evidence on policy-related financial exposure, livestock and dairy-sector risk, family poultry, honey production, small-farm resilience, agricultural insurance, short value chains and pandemic-related agri-food uncertainty [88,89,90,91,92,93,94,95,96,97]. Related recent Albanian evidence further examines demographic patterns in apple farmers’ risk perception [98].

These studies provide valuable enterprise-specific evidence, but they also show the limits of fragmented analysis. Albania needs data and methods that connect production shocks, market access, institutional compliance, household labour capacity and financial vulnerability within the same analytical design. Such integration would support better insurance design, more targeted extension services and more realistic rural-development policy.

5.5. Limitations

Several limitations should be noted. First, the search strategy combined SciSpace as a discovery interface for Scopus-indexed literature with regional literature compilation, OpenAlex, Semantic Scholar, Google Scholar, citation tracking and manual bibliographic verification. Even so, the review may still be affected by the general coverage limitations that characterise systematic reviews of regionally fragmented literature. Some studies published in local journals, institutional repositories or national languages may not have been fully visible through the selected search streams. This risk was reduced by combining database discovery with supplementary source tracking, cross-referencing and manual verification. However, it cannot be eliminated entirely in a region where part of the evidence base is dispersed across national outlets and institutional repositories.

The resulting evidence map should therefore be interpreted as a transparent synthesis of the visible and verifiable literature rather than as an exhaustive census of all agricultural-risk evidence produced in the region.

Second, the 5 × 5 classification necessarily simplifies overlapping risk sources. A single weather event may generate production, market and financial consequences at the same time. Assignment of a study to one or more cells therefore reflects analytical judgement rather than a bright-line rule. Third, matrix entries are not independent observations, because the same study may appear in multiple cells. Finally, the share of multi-risk studies should not be overinterpreted as evidence of analytical depth, because many such studies enumerate risk categories without modelling their interaction.

To limit this subjectivity, coding decisions were linked to substantive analytical treatment in the source text and reported through the study-to-matrix mapping in Appendix A.

6. Conclusions

This review provides an enterprise-level evidence-gap map of agricultural-risk research in the Western Balkans. By intersecting five major farm-risk categories with five enterprise types, it shows where the regional literature is concentrated and where important gaps remain. Production risk is the dominant object of study, market risk in beekeeping forms a clear concentration, and institutional risk in mixed systems reflects the regulatory pressures associated with post-socialist restructuring and European alignment. Human resources risk and poultry-sector financial risk remain particularly underdeveloped.

The main finding is that the regional evidence base is not only uneven by risk category, but also uneven across enterprise types. This means that research and policy priorities cannot be inferred from general agricultural-risk categories alone; they must also consider the specific enterprise settings in which smallholder households combine crops, livestock, poultry, beekeeping and mixed farming activities.

The complementary Venn diagram reinforces the central conclusion: the problem is not only the dominance of single-risk studies, but also the incompleteness of many multi-risk studies. Only six of the 68 eligible studies address all five major farm risks simultaneously, and these remain largely descriptive or perception-based. Future research should move from enumerating risks toward modelling their joint occurrence and welfare effects. For policy, the implication is clear: sustainable rural development in the WB7 requires risk-management instruments designed around diversified farm portfolios rather than isolated hazards.

More specifically, future work should prioritise financial-risk modelling in smallholder poultry systems, labour and succession risk in family farms, determinants of agricultural-insurance uptake, panel datasets that follow farms across seasons, and integrated whole-farm models linking production, market, institutional, human resources and financial shocks.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su18168266/s1, The completed PRISMA 2020 checklist is provided as Supplementary File S1, a larger version of the PRISMA 2020 flow diagram is provided as Supplementary Figure S1 for readability, and the enterprise-specific search-string structure is provided as Supplementary Table S1. The complete study-to-matrix coding file is integrated in Appendix A and can also be made available by the corresponding author upon reasonable request.

Author Contributions

Conceptualisation, A.M.; methodology, A.M.; literature search strategy and article listing, E.S.; data curation, A.M. and E.S.; formal analysis, A.M. and A.K.; visualisation and Figure 3, A.K.; writing—original draft preparation, A.M.; writing—review and editing, G.Ç.; critical review, G.Ç.; supervision, A.M.; project administration, A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Agency for Scientific Research and Innovation of Albania (NASRI), under the project “Risk Management in Agriculture: The Need to Foster Entrepreneurial Skills among Albanian Farmers”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The study-level coding data supporting the findings of this review are included in Appendix A and are available from the corresponding author upon reasonable request.

Acknowledgments

During the preparation of this manuscript, the authors used generative AI tools to support reference formatting and verification, language editing, and consolidation of earlier drafts. All outputs were reviewed and edited by the authors, who take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Complete Study-to-Matrix Mapping

This appendix lists all eligible studies included in the systematic review and maps them to the assigned cells in the 5 × 5 risk–enterprise matrix. Country codes: ALB = Albania, BIH = Bosnia and Herzegovina, HRV = Croatia, KOS = Kosovo, MNE = Montenegro, NMK = North Macedonia, SRB = Serbia, WB = Western Balkans. Risk codes: PR = Production, MR = Market, IR = Institutional, PeR = Personal/Human resources, FR = Financial. Enterprise codes: C = Crops, L = Livestock, Po = Poultry, B = Beekeeping, M = Mixed systems.

Table A1. Coding of included studies by country, enterprise type, risk category and method.

Reference	Country	Ent.	Risk(s)	Method	Justification for Inclusion
CROPS
Cerpja & Murrja (2024) [17]	ALB	C	MR	Survey + reg	Quantifies market competition impact on vegetable farm margins in Shkodra; 79% variance explained by competition.
Cejvanović, Franić & Rozman (2006) [88]	BIH	C	IR, FR	Cost–benefit	Cost–benefit analysis of subsidy/customs changes on apple farms; direct measure of policy-shift impact.
Brankov & Matkovski (2022) [41]	WB	C	MR	Comp. analysis	Food self-sufficiency analysis across WB; documents cereal-market exposure during Ukrainian crisis.
Kokot et al. (2020) [75]	SRB	C	FR, PR	Modelling	Models whole-farm revenue-protection instrument for Serbian crop farms; rare quantitative financial-risk study.
Kovljenić, Matkovski & Djokić (2024) [43]	WB	C	MR, IR	Comp. index	Competitiveness analysis of WB cereal sectors; links political instability to market exposure.
Kurtaj, Cerpja & Murrja (2024) [76]	ALB	C	FR	Survey + reg	86% correlation between debt and perceived risk among Albanian vegetable producers.
Latruffe, Davidova & Desjeux (2008) [59]	KOS	C, M	MR, IR, FR	HH survey	Uses AHS data to measure market imperfections and institutional barriers to smallholder commercialisation.
Makas et al. (2017) [40]	BIH	C	MR, FR	Stats	Quantifies input/output price parity problems for Bosnian wheat and maize producers.
Miskic, Njegomir & Stojic (2018) [22]	SRB	C	PR, MR, FR	Survey	Surveys Serbian farm entrepreneurs on production-risk management and related risk-financing tools; covers crops and mixed systems.
Müller & Hofmann (2022) [19]	WB	C, L	PR	Report	Projects yield declines across WB from climate change; covers crops and livestock.
Murrja, Cera et al. (2024) [72]	ALB	C	All 5	Survey + reg	All five risk types on Albanian apple farms; uses matrix analysis and regression.
Murrja, Kurtaj et al. (2023) [23]	ALB	C	PR	Survey + reg	Vegetable farmers’ perception of production risk in Albania; descriptive stats and regression.
Ndregjoni & Doda (2025) [87]	ALB	C	All 5	PCA	PCA on risk perceptions of 300 Korce county smallholders; maps subjective risk weighting.
Novaković et al. (2025) [13]	SRB	C	PR, IR	Eco-efficiency	Links low crop eco-efficiency in Serbia to structural management and policy failures.
Stojanović (2018) [42]	SRB	C	MR	Case study	Price volatility in Serbian raspberry value chain; quantifies impact on farmer bargaining power.
Stojanović, Rakonjac-Antić & Koprivica (2019) [74]	SRB	C	FR, PeR	Logistic reg	Determinants of crop-insurance willingness; age and income as personal/financial risk factors.
Topi et al. (2020) [21]	ALB	C	PR	Lab analysis	Fusarium mycotoxin contamination in Albanian wheat/maize; production risk with food-safety dimension.
Vasiljević, Zarić & Šević (2013) [73]	SRB	C	FR	Survey	Conference paper documenting low agricultural-insurance uptake in Serbia despite government premium subsidies.
LIVESTOCK
Deže et al. (2021) [89]	HRV	L	All 5	Survey	Peer-reviewed Croatian evidence on risk identification and management strategies in milk production; covers internal and external risk sources relevant to the five-risk framework.
FAO (2025) [26]	MNE	L	All 5	Report	Small-ruminant value-chain analysis; covers all risk types for Montenegrin livestock.
Jalic et al. (2022) [81]	BIH	L	FR, PR	Real options	Real-options model for Bosnian dairy; shows low profitability undermines investment viability.
Koleci et al. (2021) [24]	ALB	L	PR, IR, FR	Review	Endemic cattle disease overview in Albania; communal pasture biosecurity failures documented.
Ljumovic, Vukovic & Viduka (2015) [83]	SRB	L	FR	Survey	Demand-side barriers to agricultural-credit access for Serbian organic producers.
Milićević et al. (2025) [90]	SRB	L	All 5	Review	Serbian livestock sector challenges; structural gaps and sustainability pathways. 2025.
Sulejmani & Iseni (2023) [91]	NMK	L	PR, MR, FR	Descriptive	Small-scale dairy-farm sustainability and perspectives in North Macedonia.
Popovic, Radovanov & Dunn (2017) [14]	SRB	L	MR, PR	Case study	Aflatoxin M1 food-scare crisis impact on Serbian dairy market; market-risk transmission mechanism.
Softic et al. (2020) [25]	BIH	L	PR	Farm data	Published evidence on reproductive performance in dairy farms in Bosnia and Herzegovina; relevant to livestock production risk.
Sredojevic & Popovic (2014) [27]	SRB	L	PR, FR, IR	Econ model	Economic model of Serbian sheep farming; feed costs, pasture tenure, and investment barriers.
Vico et al. (2011) [28]	BIH	L	PR, MR	Descriptive	Dairy-production economics in Sarajevo-Romanija region; farm-level performance data.
Xhoxhi & Szucs (2024) [45]	WB	L	MR, PeR	Report	Small-ruminant value chain; weak bargaining power, labour shortages, succession failures.
Zhitia et al. (2022) [67]	KOS	L	PR, PeR	Farm survey	Dairy cow welfare in Kosovo; identifies knowledge gaps as human-capital constraint.
FAO (2021a) [82]	SRB	L, C	FR	Report	Agricultural-insurance system analysis; confirms <4% farm insurance uptake.
POULTRY
Alders et al. (2025) [92]	BIH	Po	PR, IR, PeR	Report	Family poultry in BiH; disease mortality, vaccination gaps, gender roles. 2025.
Bicoku & Zeqiri (2024) [44]	ALB	Po	MR, PR	Stats	Albanian meat-sector dynamics; poultry trade and production trends.
Djurdjevic et al. (2023) [15]	SRB	Po	PR	Epidemiol.	HPAI H5N8 outbreak in Serbian backyard flocks; up to 100% mortality documented.
Djurdjevic et al. (2025) [29]	SRB	Po	PR	Epidemiol.	HPAI H5 surveillance in Serbian wild birds 2016–2025. 2025.
Shtylla Kika et al. (2024) [30]	ALB	Po	PR, IR	Farm survey	Biosecurity evaluation on Albanian laying-hen farms; inadequate external biosecurity.
Koro et al. (2022) [31]	BIH	Po	PR, MR	Lab + survey	Salmonella in Bosnian backyard poultry; disease risk and consumer confidence impact.
Maloku, Kovács & Shkodra (2020) [56]	KOS	Po	MR, IR	Descriptive	Economic assessment of Kosovo egg sector; market and policy discussion.
Murrja, Ndreca et al. (2023) [32]	KOS	Po	PR, PeR	Survey + reg	Production-risk perception in Kosovo intensive chicken farms; temperature extremes, workforce health.
Ndregjoni, Murrja & Prendi (2023) [16]	KOS	Po	IR	Survey + reg	Legal risk in Kosovo intensive chicken production; non-consultation with professionals.
Silovska Nikolova & Gjorgjiev (2025) [55]	NMK	Po	PR, MR	Descriptive	North Macedonian chicken meat production compared to former Yugoslav countries.
Qorri (2022) [33]	ALB	Po	PR, MR, IR	Sector analysis	Albanian poultry-sector analysis; production costs, trade barriers, EU compliance pressure.
Rodic et al. (2010) [85]	SRB	Po	MR, FR, IR	Sector analysis	Serbian poultry-sector constraints; investment barriers, market structure, policy gaps.
BEEKEEPING
Barlovic et al. (2009) [48]	HRV	B	MR	Stats	Croatian beekeeping profitability data; market-price effects on small producers.
Bekić-Šarić et al. (2025) [70]	SRB	B	IR, PeR	Survey	eAgrar platform challenges for Serbian beekeepers; digital barrier for older producers.
Bislimi (2020) [68]	KOS	B	PeR, IR	Interviews	Family entrepreneurship dynamics in Kosovo beekeeping; succession and business motivation.
Cavlin et al. (2023) [51]	SRB	B	MR	Survey	Factors affecting beekeeping business performance in Serbia; competitive environment.
Ćejvanović, Grgić & Maksimović (2011) [84]	BIH	B	FR, MR	Econ model	Economic model for Bosnian beekeeping sustainability; input costs and pricing.
Cvitkovic et al. (2009) [49]	HRV	B	MR, FR	Stats	Croatian beekeeping economics with production forecasts.
Dedej, Delaplane & Gocaj (2000) [46]	ALB	B	PR, MR	Case study	Technical and economic evaluation of Albanian beekeeping; market conditions and viability.
Dimitrieska & Efremova (2022) [53]	NMK	B	MR, IR	Descriptive	Honey promotion problems in North Macedonia; institutional-support gaps.
Đugum, Ličina Sinanović & Jašić (2017) [63]	BIH	B	IR	Policy analysis	Legal approximation for BiH beekeeping sector; transposition scope and institutional adjustment.
Filipovic & Dumendzic (2024) [18]	HRV	B	MR, PR, FR	Case study	Croatian honey market; import competition, climate impacts on yields, producer perspectives.
Frick et al. (2006) [47]	HRV	B	MR, IR	Case study	Cooperative business potential for Croatian beekeepers; market access and institutional support.
Kluwede Aguiar & Sejdaras (2008) [54]	ALB	B	MR, IR	Case study	Albanian beekeepers and EU honey supply-chain integration; Varroa and market challenges.
Korro et al. (2024) [34]	ALB	B	PR	Survey	Varroa/Nosema infection rates and colony losses in Albania 2021–2023.
Kulici et al. (2023) [37]	ALB/KOS	B	PR, IR	Survey	Climate, habitat, and management drivers of bee population decline in Albania and Kosovo.
Kulici (2024) [38]	ALB	B	PR	Survey	Colony loss trends in Albanian parks; 66 questionnaires on colony health.
Nedić, Nikolić & Hopić (2019) [50]	SRB	B	MR, FR	Stats	Economic justification of Serbian honey production; profitability and export data.
Nikolić, Nedić & Djordjević-Milošević (2022) [93]	SRB	B	MR, FR	Cost analysis	Cost-effectiveness of organic honey production in Serbia.
Paraušić et al. (2024) [52]	WB	B	MR	Survey	COVID-19 impacts on short food-supply chains for WB honey producers.
Popovska Stojanov et al. (2021) [35]	NMK	B	PR, FR	Survey	Direct economic impact of winter colony losses in North Macedonia; losses exceed subsidies.
Ramadani et al. (2017) [69]	NMK	B	PeR	Interviews	Beekeeping as family artisan entrepreneurship in North Macedonia.
Šarić, Paraušić & Subić (2023a) [36]	BIH/SRB	B	PR	Survey	Deforestation and pesticide exposure from Bosnian/Serbian beekeeper perspective.
Šarić, Muça et al. (2023b) [62]	WB	B	PR, IR	Survey	Environmental threats to WB beekeeping; weak pesticide regulation identified.
Sopaj et al. (2025) [39]	KOS	B	PR	Lab analysis	Honey and bee pollen as bioindicators of heavy metal exposure in Kosovo.
Vapa-Tankosic et al. (2020) [64]	SRB	B	IR	Survey	R&D investment and technology adoption attitudes among Serbian beekeepers.
MIXED SYSTEMS/CROSS-CUTTING
Bajramovic et al. (2016) [7]	WB	M	IR, MR	Policy analysis	Agricultural policy analysis across all WB countries; persistent policy–smallholder mismatches.
Bogdanov & Cvejic (2011) [65]	SRB	M	MR, FR, PeR	Survey	Poverty and social exclusion in rural Serbia; family-farm vulnerability.
Borychowski et al. (2020) [94]	SRB + CEE	M	All 5	Composite index	Small-family-farm resilience in Central/Eastern Europe; includes Serbia.
Bielza Díaz-Caneja et al. (2008) [95]	WB + EU	M	FR, IR	Report	European Commission/JRC report on agricultural-insurance schemes in EU−27 and candidate countries; relevant to financial and institutional risk-management tools.
FAO (2013) [61]	WB	M	IR	Report	Farmer association status in Western Balkans; institutional risk-mitigation capacity.
FAO (2020) [9]	ALB	M	PR, PeR	Report	Albanian smallholder and family-farm baseline; farm size, ageing, labour data.
FAO (2021b) [96]	WB	M	MR, IR	Report	Smallholder short-value-chain participation across WB and Turkey; 397 interviews.
Jurjevic et al. (2022) [57]	SRB	M	PR, MR, IR, FR	Comp. analysis	Serbian small-farm sustainability vs. EU benchmarks; covers production, market, institutional and financial dimensions.
Mirc et al. (2024) [78]	SRB	M	FR, IR	Econometric	Determinants of timely agricultural loan repayment by Serbian farmers.
Myslimi, Nikolli & Shima (2022) [77]	ALB	M	FR, IR	Survey	Agricultural-insurance development challenges in Albania.
Obradovic & Bacic (2016) [58]	BIH/HRV	M	IR, PR	Analysis	Land tenure and consolidation issues; outdated cadastre, corruption.
Paraušić (2023) [66]	SRB	M	PeR, IR	Survey	Rural youth out-migration; rural-development policy mismatch in Serbia.
Popovic et al. (2020) [97]	WB	M	MR, IR	Survey	COVID-19 pandemic risk impact on agri-food SMEs in Western Balkans.
Reka et al. (2025) [79]	ALB	M	FR, PR	Survey	Agricultural-insurance risk analysis and premium assessment in Albania.
Sauer, Davidova & Latruffe (2012) [60]	KOS	M	MR, PR, IR	Econometric	Technology adoption as response to price/yield risk among Kosovo smallholders.
van ‘t Wout et al. (2018) [20]	WB	M, C, L	PR	Interviews	Disaster-risk-reduction good practices across WB agriculture.
van ‘t Wout, Augustyn & Terzic (2020) [71]	SRB	M	PR, IR, PeR	Case study	Disaster-risk capacity building for female farmers in Serbia.
Volk, Rednak & Erjavec (2012) [5]	WB	M	IR, MR	Policy analysis	WB agriculture and EU integration; unused potential, policy failures.
Volk, Erjavec & Mortensen (2014) [6]	WB	M	IR	Policy analysis	Land as bottleneck of WB agriculture; structural constraints.
World Bank (2009) [3]	WB	M	PeR, IR	Report	Changing face of rural space; succession, out-migration, policy frameworks.
World Bank (2018) [8]	WB	M	PR, MR, FR, IR	Report	Most comprehensive regional agriculture report; productivity, credit, climate data.
Zhang, Zorya & Zivkov (2024) [10]	WB	M, C	PR, IR	Report	Greening agriculture in WB; structural constraints, climate adaptation.

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Figure 1. Conceptual framework of smallholder agricultural risk in the Western Balkans.

Figure 2. PRISMA 2020 flow diagram for the systematic review of agricultural risk in WB7 smallholder agriculture.

Figure 3. Descriptive visualisation of the 5 × 5 evidence-gap matrix: (A) stacked risk-category distribution by enterprise type; (B) heatmap of the risk–enterprise matrix.

Figure 4. Complementary overlap analysis of the five major farm-risk categories among the 68 eligible WB7 studies: (A) refined Venn diagram showing the overlap of the five major farm-risk categories; (B) summary table of the key counts highlighted in the diagram. The central intersection indicates studies addressing all five major farm risks simultaneously.

Table 1. Cross-tabulation of eligible studies by enterprise type and risk type. Red cells (n ≤ 2) indicate severe evidence gaps; yellow cells (n = 3–4) indicate thin coverage. Cell values are descriptive matrix entries, not mutually exclusive study counts.

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Enterprise Type	Production	Market	Institutional	Human Resources	Financial	Total
Crops	10	9	7	2	9	37
Livestock	10	7	5	5	8	35
Poultry	7	4	4	2	1	18
Beekeeping	8	11	7	3	7	36
Mixed systems	7	8	11	5	9	40
Total	42	39	34	17	34