The case of organic dairy conversion in Norway: Assessment of multivariate neighbourhood effects

Abstract

1. Introduction

In several countries, policymakers are taking action to promote the adoption of organic farming practices as one way to reduce the environmental impacts of farming in addition to addressing growing consumer concerns regarding food safety and health. The example considered in this study is Norway, where policymakers set ambitious goals for the promotion of organic farming, offering a range of subsidies to support conversion. Although the area of organic land and organic farm management has increased in Norway since the beginning of the millennium (Flaten and Lien, 2006; Norwegian Ministry of Agriculture and Food, 2010; Røsnes, 2010), the goal of the government to increase organic production and consumption from 0.5 per cent in 2005 to 15 per cent by 2020 (Norwegian Ministry of Agriculture and Food, 2009) has fallen far short of this goal. In 2018, only 2.2 per cent of the entire food market was organic, and only 4.4 per cent of the total agricultural area was directly under organic cultivation (Landbruksdirektoratet, 2018). Achieving these ambitious goals requires a more effective policy design to promote the adoption of organic farming practices. We should enhance our understanding of farmers’ motives, reservations, and goals in terms of organic production to develop more informed policy approaches.

Existing research studies describe organic conversions from different perspectives, encapsulating it as a problem of optimal timing with an uncertain investment that incurs sunk costs (Musshoff and Hirschauer, 2008), analysing the role of moral and social considerations (Mzoughi, 2011). Further studies assume that potential adopters make a discrete choice based on the possibility of maximisation of expected utility subject to prices, profits, and inputs, devoting elevated importance to economic and financial concerns (Koesling, Flaten, and Lien, 2008; Lewis, Barham, and Robinson, 2011). Some studies categorise farms according to their characteristics (e.g. farmers’ attitudes, age, herd and land sizes, natural resource assets, or risk deliberation) (Läpple and van Rensburg, 2011; Läpple and Kelley, 2013), explaining the choice of conversion through individual farm characteristics. Despite the broad range of concepts and approaches in the literature, there is a common understanding regarding the importance of physical localisation and neighbourhood settings as influential to the emergence of organic hubs (i.e. a high density of organic farms in a small region).

Compelling attempts have been made to identify the influence of location factors in the development of organic hubs that are underpinned by the economic and social concept of the ‘neighbourhood effect’ (Lewis, Barham, and Robinson, 2011; Gosme et al., 2012; Bjørkhaug and Blekesaune, 2013; Wollni and Andersson, 2014). The conceptual framework is based on the hypothesis that organic farming neighbours or closely spatially related farms can help reduce the uncertainty of organic conversion by lowering the fixed costs of learning about the organic system (Lewis, Barham, and Robinson, 2011), increasing the probability of conversion to organic farming. Knowledge transfer regarding organic technology development and joint input purchase may further reduce barriers to conversion. Hence, co-operation in general leads to a smoother gateway to the supply chain.

One limitation of existing theory and empirical studies on the neighbourhood effect is that only positive external spatial effects have been covered (e.g. Lewis, Barham, and Robinson, 2011). In this study, we argue that negative external effects can be similarly influential on farmers’ conversion decisions. Neighbouring farmers’ reversion from organic to conventional farming¹ or neighbouring organic farmers’ exiting farming altogether could serve as a negative example that potentially reduces the likelihood of conversion. Here similar mechanisms of the neighbourhood effect can be expected to act correspondingly to positive external effect. Those negative occurrences could provide knowledge about the disadvantages and challenges of organic production or lead to a weakening of supply chain access in the local neighbourhood.

To our knowledge, this potential deterrence to organic conversion has not yet been studied. For policymakers and extension services that seek to promote the adoption of organic farming, the role of negative examples must be understood to design the right policy strategies and set achievable goals. With the establishment of only positive externality policies, extension services solely focus on promoting conversion by as many farms as possible, irrespective of how many revert at a later stage. However, the existence of potentially negative externalities warrants a more selective approach to the promotion of organic conversion by only farms that have a low likelihood of reversion. Additionally, this approach would justify additional effort and support concentrating on keeping farms within organic production, rather than only focusing on the promotion of adoption as the primary goal.

Although the question of the role of negative examples is relevant more broadly, we restrict our study to Norway and specifically to dairy production as one of the most prominent agricultural activities in Norway. In this case, we can use a unique yearly panel dataset covering the period from 2002 to 2015. The data set covers (almost)² all farms in Norway, furnishing information about farmers’ conversion decisions as well as the geographic location of each farm. Using an econometric panel model, we aim to explain the binary decision of whether a conventional dairy farm converts to organic farming during the observation period from 2002 to 2015. To account for neighbour interaction, we leverage the geographic information in the data to derive assorted variables describing conversion, reversion and exit of neighbouring farmers.

The remainder of the paper is structured into five sections: Section 2 introduces the natural and economic environment for dairy production in Norway; Section 3 details the adopted and extended theory; and Section 4 presents the data, the applied method and the potential endogeneity issues. Finally, Section 5 discusses the results, and Section 6 concludes and highlights the policy-relevant findings.

2. Norwegian case

Norwegian agriculture is characterised by unfavourable natural conditions³ but enjoys great social and governmental appreciation and support. In 2010, the utilised agricultural area (UAA) was approximately 3.1 per cent of the territory of the country (Eurostat, 2019), measuring about 1 million hectares; one of the smallest ratios within the European Economic Area. Weather conditions, coupled with the peculiar morphology of the territory, limit agricultural production. In 2010, the most common holdings specialise in sheep, goats, and other grazing livestock (27 per cent) and dairy (18 per cent), and the rest are specialist cereals, oilseed and protein crops, general field cropping, specialist cattle-rearing, and fattening of the total population of farms (Eurostat, 2012). In terms of economic size units, or the sum of the standard outputs (SO) per hectare of crop and per head of livestock, dairy is the most prominent subsector, accounting for 30 per cent of the SO of all Norwegian farms in 2010 (Eurostat, 2012).

Agricultural policy endeavours to promote a diversified agricultural sector delivering a ‘multifunctional agriculture’ (Potter and Tilzey, 2007; Bjørkhaug and Richards, 2008; Forbord et al., 2014). Another goal is to maintain family farming identities (Forbord et al., 2014) and ensure that the current UAA is sustained. To achieve these objectives, government support for agriculture is significantly higher in Norway than in the EU (OECD, 2017).

Policies targeting organic agriculture came into effect in 2002 and have been renewed several times with an increase in payment rates⁴ and organic conversion grants to encourage farmers to convert to organic production. In 2005, the long-term strategic plan adopted by the government sought to increase organic production and consumption from 0.5 per cent to 15 per cent of overall production and consumption by 2015. That ambition was somewhat reduced in 2009, projecting this desired level by 2020 (Norwegian Ministry of Agriculture and Food, 2009). Organic farming incurs more costs than conventional farming attributable to increased labour intensity, the prohibition of mineral fertiliser and non-organic plant protection and the use of specific technological settings (Flaten et al., 2005). To compensate for these additional costs, organic farms receive significantly higher financial support than conventional producers.

Norwegian agricultural policy divides the country into seven agricultural subsidy zones because of the major differences in longitudinal production conditions. For the organic livestock subsidy, including dairy cows, suckling cows, and other cattle, the organic bonus rates vary between regions, with the northern zones receiving more financial support than the southern zones until 2015. For instance, the organic premium for dairy cows in 2003 was 880 NOK⁵ in the north and 630 NOK in the south on top of the average 2,500 NOK per animal for conventional dairy cows.⁶ The geographic differences in such organic bonuses adjusted from almost 30 per cent in 2003 to no difference in 2015. The same trend can be observed for the subsidy adjustments of suckling cows and other cattle. Moreover, organic dairy farmers receive not only livestock-based subsidies but also regional payments. Payments are provided to arable land, including cultivated pastures, corn, potatoes, green manure production, and other ecological driven land use, the rates of which are not differentiated by region.

Flaten et al. (2006) explored the conversion motives of individual Norwegian dairy farmers and joint dairy farmers⁷ by the year of conversion,⁸ finding evidence that improved food quality and potential professional challenges of organic adoption are the most frequently cited reasons for all groups (early, mid, and late converters). Furthermore, the motivational force of income stability was cited as the least decisive motive. In Norway, since the 1990s,  off-farm income generation has rapidly increased (Fleming and Lien, 2009), and dairy farming is generally a secondary engagement for the majority of Norwegian dairy farmers. This explains the numerous cases of ‘hobby’ farms featuring rather small herds. These findings indicate that any analysis from a strictly economic perspective—for example, coupling potential positive net value to organic conversion—should be expected to have only limited explanatory power in Norway. This dichotomy coincides with Lund and Algers’ (2003) description of Norwegian organic farmers’ motivations consistently being viewed as idealistic and reaching beyond financial concerns.

According to the Norwegian Agriculture Agency (Landbruksdirektoratet, 2018), in 2018, 2.2 per cent of the entire food market was organic, and only 4.4 per cent of the total agricultural area was registered organic. In the same year, the share of organic milk was up to 3.4 per cent of total milk production, whereas Norwegian organic meat production was almost 1 per cent. The government's ambition of increasing organic production up to 15 per cent by 2020 has remained unsuccessful, despite a massive increase in organic livestock and subsidy payments. In June 2018, Norway announced a new national strategy to promote organic farming until 2030. Support for organic farming has remained constant since 2014, but the organic conversion grants were phased out beginning in 2010 and eliminated altogether in 2016. The government's justification for terminating organic conversion grants was to advance the simplification of the support scheme for organic farmers (Pekala, 2019).

In our observation period (2002–2015), the number of all dairy farms decreased by almost 60 per cent, but the number of operating organic dairy farms increased by 45 per cent (Table 1). The general annual statistics demonstrate that the Norwegian dairy sector declined, losing almost 60 per cent of its milk producers between 2003 and 2015. This decrease was a result of the reversion of previous organic dairy farms to conventional dairy farms, which is beyond the scope of this paper. Our focus remains on the aspects of neighbouring conformity in operating organic dairy farms (ΣODF in Table 1), which increases in the first 3 years, stagnates until 2011 and later significantly decreases. The first organic policy boost in 2002 results in 172 newly registered organic dairy farms by 2003. This development appears to be the largest over the examined years. During the stagnation, organic entries and organic reversions remain at about the same pace. However, from 2012, the number of farms converting from conventional farming to organic (ΣEDF) is only a single digit, whereas the sum of organic reversals (ΣRDF + ΣExDF, in Table 1) is approximately 20 farms annually. Considering the subsequent organic policy developments, in 2010, 2012, and 2013, the number of newly established organic dairy farms is even lower at the post-policy time period than prior. Therefore, the first organic governmental intervention seems to have been successful, whereas additional increases to the organic payment rates do not appear to motivate organic conversion.

3. Theory

One theoretical explanation of emerging business trends is often tied to the patterns of the physical localisation of decision-makers; therefore, analysing the influence of regional proximity within the course of micro-behavioural decision factors and the possible inferences between spatially corresponding units is of core interest in agriculture and resource economics literature (Bockstael, 1996; Weiss, 1996; Nelson and Hallerstein, 1997; Anselin, 2001, 2002; Brock and Durlauf, 2001, 2007). Explicitly including the neighbourhood effect in quantifying the social determinants of conversion decisions incorporates the concepts of social learning (Conley and Udry, 2010) and spatial spillover and landscape development (Irwin and Bockstael, 2002). Consequently, spatial processes can be assumed to have a strong influence on farmers’ organic conversion decision. We adopt the theoretical concept of developing neighbourhood relations, relying on the assumption that formerly converted neighbours may be instrumental in swaying the decision of potential converters by reducing the fixed cost of learning and risk, contributing to advanced supply level and in enhancing the competitiveness of the sector. Technology adoption literature explores the causes and effects of wide technological distribution in terms of economic and physical environments, individual characteristics, production uncertainty, and spatial spillover (e.g. Lindner, 1980; Feder and O'Mara, 1982; Tsur, Sternberg, and Hochman, 1990; Adesina and Baidu-Forson, 1995; Purvis et al., 1995; Koundouri, Nauges, and Tzouvelekas, 2006; Oliveira and Martins, 2011). For example, major road networks or locally favourable production conditions may both contribute to spatially clustered behaviour. Economic units may be spatially dependent, as locational attributes influence each other and thus tend to share similar attributes (Goodchild, 1992; Moffitt, 2001). Lewis, Barham, and Robinson (2011) examine spatial spillovers of organic dairy farming in southwestern Wisconsin and determine that the presence of neighbouring organic dairy farms is a statistically and economically significant explanatory measure in the decision to convert. They identify a non-linear relationship in organic conversion between the number of initial organic farms and the expected number of new conversions that drive the clustered technology diffusion process.

Our key hypothesis in this context is that such diffusion processes are influential in two directions. In previous literature, it is primarily considered that new conversions lead to positive externalities (Lamine and Bellon, 2009; Geniaux et al., 2011; Bouttles et al., 2019). In this study, we hypothesise that reversions (or exits of organic farms) can exert similar negative externalities; an aspect that has yet to be considered in the literature to our knowledge. As shown in Table 1, those reversions and exits imply comparatively large relevance in the Norwegian case, yielding a noteworthy study case. The theoretical motivation of including proxies for bad examples assumes that people may learn from others’ mistakes. We hypothesise that such negative examples serve to increase information uncertainty regarding the hurdles and difficulties of organic production, weakening local clusters in terms of the connections to upstream and downstream industries and the possibilities for local knowledge exchange. Overall negative examples are assumed to mirror the positive effects associated with a successful conversion to organic farming.

In our empirical approach, we endeavour to model farmers’ decision quandary on whether to convert a conventional dairy farm to organic dairy at time t. We jointly incorporate the effects of positive and negative neighbourhood examples explicitly. For this, we derive the following determinants for each farm at time t:

1. the number of neighbouring organic farms (which serve as a proxy of good examples);

2. the number of neighbouring reverting farms at t − 1 (which represent bad examples from the organic viewpoint);

3. the number of neighbouring organic dairy exits at t − 1 (which represent bad examples and assimilate a meaning of redundant milk production).

The actual numbers of multivariate neighbour vectors depend on the definition of the radius surrounding each individual farm. Defining this radius is relatively arbitrary and can hardly be based on precise information or theoretical knowledge. We intend to keep the focus on face-to-face information transfer, which assumes close proximity and personal discussions about the technology. For this reason, we calculate two small radii within 3 and 5 km. There are collateral arguments for determining two seemingly similar ranges: (i) the coefficients under different radius settings are comparable and (ii) opens an opportunity for a potential robustness check on the model outcomes.

The applied econometric model is a reduced form discrete choice panel data model with binary probit link to explain the probability of organic dairy conversion. The 13-year dataset creates unbalanced panels of repeated observations of farm-level decisions.

4. Data, method and endogeneity issues

4.1. Data

As noted, our analysis is based on a Norwegian dataset that is unique for two reasons: first, direct payments (DP) are available for virtually any type of crop and animal, and second, the Information Act requires full transparency of all beneficiaries of government spending. Hence, the amount of agricultural land used, herd size, and farmers’ age are made publicly available for each farm that receives DP. Additionally, geographic information, which is administered by the Norwegian Agriculture Authority, is available for research purposes. The data used in this application is therefore a unique georeferenced farm-level population set of Norwegian dairy holdings that received DP. The 14-year unbalanced panel construction includes more than 179 thousand observations including the annual contributions of 55 thousand dairy farms. We record approximately 4 thousand organic dairy farms in the observed period.

On the basis of the geographic information of each farmstead, we calculate the kilometre distances between the dairies, labelling each dairy farms as either conventional, organic, reverting, or exiting in each year, to allow the determination of the dynamic neighbourhood synthesis of individual farms. In this manner, we derive the number of organic, reverting and exiting farms around each conventional dairy (i.e. potential organic convertor) within 3 and 5 km radii.⁹ Organic certification is awarded by a Norwegian certifying agency after fulfilling 3-year certification standards for land and 1-year certification standards for a dairy herd. In the estimation, we consider the year that the conversion decision is made and not the certification date. Reversion from organic to conventional is easily identified in the dataset as farms that no longer receive the additional payments for organic production. Table 2 presents the summary statistics of the neighbour variables within 3 and 5 km radii.

Additionally, farm characteristics such as herd size, cultivated area, and farmers’ age are also available. Table 3 presents the summary statistics of those variables for organic dairy farms. The mean of organic herd size and land is continuously increasing. Both measures nearly doubled over the examination period. However, the slowly increasing but later constant number of organic cows illustrates the lack of organic breakthrough in the Norwegian dairy industry.

4.2. Method

Organic dairy conversion is assumed to be conditional on farm characteristics and on quantified neighbourhood relations. We encode the latter as categorical variables with five levels (0, 1, 2, 3, 4, or more than 4 neighbours) for the number of neighbouring organic farms and neighbouring organic exits and three levels (0, 1, 2, or more than 2) for the number of neighbouring organic reversion. The groups are determined according to the actual number of differentiated neighbours.¹⁰ With the grouped parameter setting, we may correlate the regressors independently and thus hypothesise that

1. as the number of organic neighbours increases, the probability of organic conversion increases;

2. as the number of organic reversions and exiting organic farms increases, the probability of organic conversion decreases.

The applied econometric model is subject to the binary decision of whether a conventional dairy farm converts to organic, where the decision is observed and monitored each year over a 14-year period. The established panel dataset happens to be unbalanced (i.e. differing in the number of observations between groups) because of continuous entries and exits. Therefore, the estimation method has to be shaped for unbalanced panel setting. We follow the technical innovation of Papke and Wooldridge (2008) and use maximum likelihood estimation without the Gaussian quadrature as a pooled probit with bootstrapped standard errors by resampling the cross-sectional units. We use 500 bootstrap replications on the individuals to obtain the bootstrapped cluster-robust standard errors. This treatment of the standard error allows robust inference to any form of heteroscedasticity or temporal correlation across years for each farm (Cameron and Trivedi, 2005; Cameron, Gelbach, and Miller, 2008; Lewis, Barham, and Robinson, 2011). The theoretical and methodological obstacle of accurately including the effect of social interactions is discussed in detail in the following.

4.3. The endogeneity issue

We endeavour to identify the effects of neighbouring behaviour on the decision of organic conversion, differentiating between positive and negative examples in the neighbourhood. Identification of these effects is challenging because of spatially confounding variables and potential unobservable factors that may cause endogeneity issues (Lewis, Barham, and Robinson, 2011; Hsieh and Lee, 2016).

5. Results

Table 4 presents the parameter estimates for the predictor variables. The set of indicator variables, the annual and zonal dummies, are obtained in  Appendix A. The probit model outputs, for both 3 and 5 km radii, show the coefficients, their standard errors and the z-scores, as well as the associated P-values.

Generally, for both radius settings, the results of the factor variables for describing neighbourhood effects and the period-specific variables support the hypothesised tendencies in the decision-making process of organic conversion. Specifically, we find an increasing probability of organic conversion with a higher number of organic neighbours. As the reference level is 0, which implies no organic neighbour, the first contrast predictor vector Organic Neighbour 1 increases the z-score by 0.275 at 3 km and 0.249 at 5 km radius. Organic Neighbours 2, 3, and 4 demonstrate increased positive effects on conversion, confirming the hypothesis that more neighbours increase the likelihood of organic conversion.

In case of considering the 3 km radius, the parameter vector of 4 or more organic neighbours is insignificant because the occurrence of 4 or more than 4 organic neighbours is very small. However, when we increase the observation window from a 3 to 5 km radius, the effect becomes statistically significant.

Furthermore, vectors indicating the number of reverted neighbours show a negative impact on organic conversion, supporting our hypothesis of negative externalities of reversion. In both models, having only one reverting neighbour (Reverted Neighbour 1) has a negative but insignificant effect. When the number of reverted neighbours increases to two, the parameter estimates become statistically significant and show large negative effects on conversion. Interestingly, in absolute terms, the negative effects of reverting neighbours are substantially larger than the positive effects of converting neighbours, indicating that negative examples have a more important effect on conversion compared with the positive effects.

The number of exited organic neighbours shows slightly surprising results. One exited organic farm in the neighbourhood—compared with the zero reference level—shows a positive but insignificant effect on organic conversion. However, in case of more than one organic exit, the parameter estimates become large negative significant numbers in congruence with our hypothesis. Yet (and this is only speculation), we explain the positive effect of one organic exit in the neighbourhood with the likely transmission of organic dairy cows and technology to a conventional dairy farmer in a close surrounding. Furthermore, there may be numerous reasons why organic farmers may decide to shut down their business. Individual reasons may vary between moving, merging, family issues, age, etc., but none of these relates to any system error in the organic dairy industry. However, in case of more than one organic exit at a time, we are entitled to surmise the presence of some unobserved negative external effect that directly makes them exit simultaneously.

Furthermore, as of the farm-specific attributes, our assumption that youth-induced risk-taking ability and potentially higher level of interest in environmental farming promote organic conversion—proxied by farmer's age—seems to be accurate because of the significant and negative coefficients in both models. It indicates that younger farmers are indeed more likely to convert to organic. We find the coefficients of herd and land sizes very small and consider them marginal in the decision process of organic conversion.

The period-specific dummy predictors show a negative tendency. This confirms that the 2005 and 2009 organic farm policy goal is still far from reach, and the Norwegian organic dairy industry is slowly declining. After 2010, this decrease accelerates in both models, which corresponds to the significant decrease in the summary statistics of organic dairy entries in Table 1. The negative and significant parameter coefficients verify that more organic dairy farms exit organic production than conventional farms decide to convert to organic.

The identical model setting for 3 and 5 km radii allows us to analyse the likely differences between the outcome probabilities when the circumstances of farmers’ neighbourhoods change. The model outcomes show analogous results, which confirm the robust specification of the functional form regarding close proximities.

6. Conclusion

This article explores the factors of organic dairy technology adoption in Norway during the years 2003–2015. We find that farmers’ age, herd size, and agricultural land used are less important elements of potential organic conversion than the neighbouring dairy relations. We demonstrate that spatial spillovers across economic agents are influential in the patterns of technology adoption. Although our model supports the findings of previous studies that the presence of organic dairies in close proximity positively affects organic dairy conversion, more remarkably, we find that neighbouring organic reversions and exits introduce potential negative interaction effects that had not been considered in previous literature.

The results offer important implications for policymakers and extension services that intend to promote organic production. Earlier literature only explored the positive interaction effects of conversion. Based on such results, optimal policy design should promote as much conversion as possible, with each conversion creating positive spillovers. Our results concerning the existence of the negative effects of reversion, however, warrants a more nuanced and strategic policy approach. Given that we demonstrated that the negative effect of neighbouring reversion is potentially stronger than the positive effect of neighbouring conversion in absolute terms, policies should not only be focusing on new adaptors but also ensure that existing organic dairy farms remain active and successful. Correspondingly, policies should increase efforts to improve extension and consultation, supporting farmers that are considering conversion to organic farming and helping them to explore whether converting could be successful in their specific situation. Based on this reasoning, our results support the decision by the Norwegian government to terminate organic conversion grants, which may ease conversion but are ill-suited for sustaining organic production in the long run.

Our result highlighted the critical influence that reversions have in terms of external effects on neighbouring farms. Future research should focus on exploring the root causes underlying the decision to revert from organic to conventional farming. This should help policymakers to design new policies or adjust existing approaches to address the identified causes.

Acknowledgements

The authors want to thank Thomas Heckelei and Klaus Mittenzwei for their valuable and helpful comments. The authors are grateful to Grete Stokstad and Svein Olav Krogli from the Norwegian Forest and Landscape Institute (Skog og Landskap, Ås, Norway) for providing the farm coordinates used in the analysis.

Funding

We acknowledge the support of the Research Council of Norway: Space, land and society: challenges and opportunities for production and innovation in agriculture based value chains; grant no. AGRISPACE/23810 and of the European Regional Development Fund ‘Investing in your future’; grant no. GINOP-2.3.2-15-2016-00028. The research for this publication by Hugo Storm is partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2070-390732324, as well as under DFG grant no. STO 1087/1-1.

Endnotes

References

Appendix A