1. Introduction
Increased environmental awareness, both in public opinion and in governments of many countries, has prompted, over the last decades, the adoption of incentives for exploiting renewable energy sources. Among these, the production of energy from agricultural products, such as biomass and biofuels, generally termed as bio-energies, has received a certain attention in developed countries. The subsidies provided to foster such productions have raised concerns for many reasons: for their cost, compared to employment and welfare gains [
1], and for the potential competition with traditional allocations of farmland, devoted to food and feed production. The latter aspect, named as the “food, energy and environment trilemma” [
2], poses the problem of a further activity (the production of energy) that engages in direct competition with food and feed production for limited resources like water and agricultural land. As the latter is a fixed, non-renewable factor of production, it is plausible that such competition affects primarily on its allocation and, consequently, on food prices. In this context, the subsidization provided for bioenergy has changed the relative convenience among alternative uses of agricultural land, increasing the relative profitability of crops for energy purposes. For this reason, increasing attention has been paid on potential undesirable effects of such financial incentives on existing supply chains [
3,
4,
5,
6]. Even if this competition may be softened by allocating marginal land for energy production [
7] and/or using dedicated non-agricultural crops [
8], its effects have been perceived as increasingly harmful for other agricultural activities in highly productive areas. Potential negative impacts of bioenergy production may be hampered where the same crop can be allocated to feed both bioenergy plants and livestock. This is, for instance the case of biogas production, which, as a consequence of dedicated subsidization policies, has been developed in highly productive agricultural areas, using increasing amounts of maize silage as feedstock [
9]. As suggested by Bartoli et al. [
10], an energy policy providing incentives for large-sized biogas plants, fed mainly with maize silage, may increase the demand for such crops, increasing its price and, consequently, both feeding costs for livestock farms and land prices. On the other hand, Bartoli et al. [
10] show that an incentive structure more favorable for plants fed mainly with livestock manure—small- and medium-sized—may render biogas production more compatible with other agricultural activity. As the impact of biogas activity may be highly pervasive, its impact on agricultural areas has been explored extensively by means of modelling approaches. On the contrary, contributions focusing on its effect on land allocation and land value and rental prices have been quite limited (see
Section 2 for details). The aim of this paper is to contribute to the current literature about the impact of biogas production on the rental prices. To do so, we examine a case study in a province of Northern Italy, where the biogas industry has increased sharply as a consequence of dedicated policies. The same province is also one of the most productive for field crops and livestock, and we wanted to measure the extent to which the competition between biogas and agricultural activities have impacted land rental prices.
The rest of the paper is organized as follows:
Section 2 is devoted to the literature on determinants of farmland rental price and previous evidence of the effect of biogas on the farmland market; the case study, the data, and the econometric model are presented in
Section 3; results are analytically discussed in
Section 4 while, in conclusion, some reflection on possible policy implications of the research findings are gathered.
2. The Impact of Bioenergy on the Land Market: Evidence from the Literature Review
As we are interested in estimating the impact of biogas production on farmland rental values, the literature review is focused, on one hand, on all potential determinants of land value and, on the other hand, on the previous studies specifically addressing this issue.
Factors affecting the value of agricultural land have been extensively examined. A recent review by Feichtinger and Salhofer [
11], discussing the relationships between farmland value and agricultural payments, classifies the potential determinants of farmland price in internal/agricultural variables and external variables. The first group includes expected income and wealth from agricultural activity and external subsidies. Indeed, all of the authors agree that the first factor affecting the market of farmland is the rationale farmers’ objective of profit maximization. Thus, the willingness to pay for land is directly related to its expected return, which depends on land use capability [
12,
13]. The significant effect of public support to agriculture on land value is the second scientific cornerstone. In particular, researchers focused on the extent to which payments are capitalized into farmland [
14,
15,
16,
17], some of them using spatial econometrics [
18].
As the observation and description of the relationship between external variables and farmland value is complicated, it is more difficult to estimate their impact. Nonetheless, Feichtinger and Salhofer [
11] describe the three aspects that have been considered and measured by researchers and steadily entered in the literature. They refer to the micro and macroeconomic context and the urban competition for land. Microeconomic variables link to competition for land due to its possible alternative use, such as livestock feeding [
19] and manure density [
20], controlling for the location and dimension of the market [
21]. Some adjustments have also been proposed considering the macroeconomic indices of inflation and interest rate, the taxation costs connected to the farmland market and the availability of bank credit [
22]. Obviously the competition for land is especially exerted by non-agricultural activities, whose effect is often controlled using indices of rurality and/or urbanization, such as population density, distance from nearest cities, or relative importance of agriculture in the region [
12] and, more recently, by quantification of non-market amenities of the location [
23].
Specifically, the impact of bioenergy on farmland use and value has been tackled from different perspectives. Some studies propose an approach based on the observation and description of the phenomenon from a qualitative and/or institutional point of view. An extensive general review collecting findings from reports considering European countries has been proposed by Swinnen et al. [
24]. The research suggests that bioenergy is expected to exert an effect on farmland market in Germany, Netherlands, and Sweden. Carrosio [
25,
26] proposes an institutional interpretation of the biogas context in Italy which discusses different scenario analyses deriving from potential changes in actual European bio-energy policies. Other studies present a modelling approach. For example, Johansson and Azar [
27] simulate the effects of a new climate policy scenario in the US supporting biomass per bioenergy productions and calculate that the price of the farmland rental price could increase five times by 2100. Mela and Canali [
9] calculate the land required for energy production depending on the substrate, thus suggesting different degrees of competition per different energy policy scenario. Furthermore, papers by Ostermeyer and Schönau [
28] and Appel et al. [
29] are based on agent-based modeling whose results predict that biogas may be more profitable for larger and more competitive farms at the social cost of increasing the value of land, thus decreasing the market power of smaller farmers. Finally, there are limited contributions that apply econometric methods to measure the effect of biogas on farmland rental price. On the contrary, we are not aware of any research that uses biogas plant characteristics as independent variables, while the following papers enter this information as control parameters of the models. In the research by Kilian et al. [
16] an analysis on Bavarian cross-sectional data on land rental prices are presented. The installed kW per hectare at the municipality level explains the increased demand for land for energy crops resulting in positive significance on the utilized agricultural area (UAA) rental price. Emmann et al. [
30] present results from a survey conducted on 246 German farmers, estimating that the presence of biogas plants in a 10 km radius has a weak significance over the maximum land rental price, while the installed biogas power seems to have no effect on farmland price. Lastly, Hüttel and Odening [
31] used the data from public bids for farmland allocation entering the installed kW per hectare in an agrarian region in the model and finding a positive correlation with rental value.
4. Results and Discussion
Considering the focus of the analysis, five different HP models have been used in order to test the stability in sign, significance, and magnitude of parameter estimates of OLS.
Table 2 shows the variability of land rental prices due to biogas plants and livestock density (as they compete for land use), agricultural land use, year of signature of the contract, and location of the rented land. The parameter estimates of other control variables are reported in
Appendix D. The five specifications have the same combination of control variables and differ for groups of biogas variables. Model 1 and Model 2 report biogas variables at municipality and agrarian region level, respectively. Model 3 includes significant biogas variables of Model 1 only, and Model 4 includes all significant variables at both municipal and agrarian region levels. Finally, all of the biogas variables enter Model 5. As the regression outcomes are stable in terms of the coefficients’ values and significance of the explanatory variable across the five specifications, we will comment on the results of Model 5 because it is more comprehensive.
According to Model 5’s results, the regression has run on 812 land rental contracts against the 2063 available, because the econometric package dropped all of those observations missing at least one of the variables considered in the model.
Table 2 shows that the land rental price is positively influenced by the livestock density and the agricultural land use for energy crops, forage alternation, vegetables, and plant nursery. The first two years of contracts considered show the same relationship, while the agrarian region of pertinence of the rent contract does not influence the dependent variable. Among the control variables, the dimension of rented area and the presence of Common Agricultural Policy (CAP) subsidies affect the rental land price significantly and positively. Considering the farming type, the livestock farm tenant is willing to pay more for land rent (from 106.3 to 709.5 €/ha) compared to dairy farms (reference). A notable exception is represented by specialist goat farms that probably rent marginal lands for grazing. Among the non-significant control variables are the length of the contract, the share of UAA within the municipality (rurality), the presence of rural building in the contract, the standard output of the tenant, the presence of second crops, and the type of land owner.
Considering the focus of this paper, the effects of biogas plant characteristics on the dependent variable are the most relevant findings of the empirical analysis. Results show the significant relation between the number of plants and the average installed kW per plant in the municipality and agrarian region of the contract. Furthermore, the land rental price is linked to the kW per hectare of the agricultural utilized area in the agrarian region, which is not confirmed at the municipal level. As explained, these variables are used to approximate the effect of technology of the biogas plants (plant’s average size, pp), the demand for maize silage for bioenergy production with respect to the number of plants (number of plants, pl, and nominal capacity, kW), and the transportation costs of the energy crops from fields to plants (municipal vs. agrarian region location of the rented land; _m and _r suffixes, respectively).
Model 5 shows that biogas plants have a non-linear type effect on the rental land price according to the power (
pl and kW), the technologies used for the production of biogas (
pp), and the distance between the plants and the rented land. These trends, shown in the
Figure 2,
Figure 3,
Figure 4,
Figure 5 and
Figure 6, are in line with the expected results with regards to the growing branches of the functions estimated. In fact, it does make sense that land rental prices rise as the demand for land for energy crops (maize silage) increases.
On the contrary, the depressive effects of biogas on the price of land rent may be counterintuitive and worth careful comment. In particular,
Figure 2 shows that the rent in the agrarian region increases to a value of 0.8 kW/ha installed power per hectare, then it starts to decrease. This bell-shaped trend presumably derives from a sequence of interconnected events: increasing levels of kW installed per hectare fosters the competition for energy crops, forcing producers to search for feedstock from more and more distant areas and pushing up the cost of bioenergy input procurement [
10,
36]. Beyond a certain threshold of installed power density, the competition for local land becomes economically unviable and leads producers to shift from maize silage (which is bulky) to other biogas feedstocks. Such alternative inputs may be local by-products deriving from agricultural, food, agro-industrial, and forestry industries (as allowed by Ministerial Decree 6 July 2012 [
37]) and/or imported biomass, which would explain the downward trend of rental prices for high kW/ha values. This hypothesis could also explain the decreasing trend described in
Figure 3.
The descending branches of the u-shaped curves, conversely, represents a somehow unexpected effect. It may be explained by the possibility of postponement of manure spreading which reduces the demand for land, decreasing its rental value, as in the
Figure 4. This phenomenon is linked to the biogas technology applied and is more pronounced in large areas as agricultural regions (
Figure 5) where we observed only a negative linear effect. A similar explanation may also apply to
Figure 6, which relates the rental land price and the number of existing biogas plants in the agrarian region. In particular, the price of land decreases to an average density of 24 plants because of the delay of the timing of manure spreading. With the increase of the number of plants, it starts to increase, as the effect of competition for arable land becomes prevalent. The fact that the graph goes into the negative range is not surprising, since these plots are used to describe the "pure" effect of the variable on the rental amount.
Furthermore, we interpreted the positive and significant relationships between land rental price and livestock pressure per hectares, land utilization for forage alternation, arable and energy crops as a proof for competition between food, feed, and fuel. This would confirm the relevance of the “environment trilemma” for social and economic sustainability of bioenergy that need to exploit agricultural land to be produced. Additionally, we observe a statistically significant increase in rental prices for contracts signed in 2011 and 2012, compared to 2010 (reference year). On the contrary, in 2013 and 2014 such an effect is not significant. This may be the result of a change in biogas policy that occurred exactly at the end of 2012. In particular, the Decree of 6 July 2012 [
37] established a new subsidization scheme aimed at providing more incentives for smaller sized plants and for those using agro-food by-products (the mentioned plants were in the following size ranges: 1–300 kW, 301–600 kW, and over 601 kW [
35]). Therefore, while until the end of 2012 mainly large-sized plants were running using maize silage, contributing for more pressure on land rental prices, from 2013 new policies downturned the previous trend.
Finally, it is worth noting that coefficients of determination
R2 of the model account for the 23.6% of the variance, which means that the combination of dependent variables included in the model explain a limited amount of variability in the dependent variable, as compared to previous studies. Such low values in
R2 may be due to the lack of information on soil quality, as explained in
Section 3.3. Furthermore, this limitation must be considered together with the peculiarities of the agricultural land market. In fact, many drivers of land rental prices are difficult to be considered in the model because of their nature. For example, the effect of human capital of both tenants and land owners may capture information on characteristics of the relationships between the two parties. The presence and nature of special clauses or verbal agreements do not enter the model because they are often non-reported in the contract and, thus, out of reach for the researcher. All these factors considered, we are still moderately optimistic in evaluating the reliability of the OLS results because of the stability shown by different specifications and the accordance with expected results.
5. Conclusions and Policy Implications
In recent years, agricultural economists published a fair amount of studies dealing with the analysis of the drivers of the agricultural land market. Obviously, these studies are constrained by data availability and quality, thus researchers from different countries may possess some relevant information and lack some others. The consequence is two-fold. On the one hand, the findings that come from different data and econometric models suffer for repeatability and cannot be formally confirmed (nor rejected). On the other hand, any scientifically-grounded and sound study on the land market may potentially shed new light on this topic and significantly contribute to the accumulation of specific knowledge on this field.
The present paper focused on the relationship between land rental prices and biogas production (in terms of installed power, number and size of plants). We start from the simple observation that biogas plants use some crops as feedstock to produce energy, thus representing a new source of competition for land access. This is expected to affect the price of land rental. Specifically, the rationale would be predicting a positive relation between installed kilowatt of biogas per agricultural area and land rental price. Nonetheless, this expected phenomenon may take different shapes and intensities, as the demand of bioenergy crops (and then the impact on land price) depends on a variety of features [
39], likes plant size, feedstock mix, and the transportation costs of bioenergy crops from fields to plants [
10].
We measured this phenomenon applying a hedonic price model in a case study area in Northern Italy that was previously proven to be relevant for biogas plant installation [
9]. We found that installed power per hectare, number of plants, and average plant size significantly affect land rental prices in a nonlinear fashion. Furthermore, such effects take different shapes according to the territorial level to which the biogas feature operates (municipal or agrarian region). Such detected nonlinear effects suggest that technologies and distances between the biogas plants and the rural area devoted to energy crop production matter in determining the land rental price.
These findings may be considered socially and scientifically relevant because we measured the thresholds for a significant effect of biogas plants characteristics on land rental prices, which could be very useful for a bioenergy regulatory plan design. Considering that a policy-maker may be interested in protecting food and feed production against excess competition exerted by bioenergy crops, we identified a way to calculate the levels of kW per hectares and/or the best biogas plant’s dimensions that help to reach this objective. Consequently, these results and the procedure could be applied to set the parameters to incentivize the most efficient bioenergy policy in rural areas. Furthermore, we confirm previous evidence on the outcome of different biogas incentive schemes; as pointed out by Bartoli et al. [
10], subsidizing large-sized plants (that use mainly energy crops) would lead to increased competition for land, while when small- and medium-sized plants (using prevalently manure) are incentivized, such pressure on agricultural land would be reduced. Our findings are in line with this hypothesis, as we estimated an increase in land rental prices only in those years (2010 and 2011) where the large-sized plants were more subsidized, while such an effect is not significant over the subsequent years (2013 and 2014) with the withdrawal of the old policy scheme.
For example, considering our case study, the findings indicate that the smallest and largest biogas plants give the best social performance, i.e., they impact less on land rental prices. This seems to be the basis for an optimal win-win policy based in incentivizing (1) small plants using livestock manures, which helps breeders maintain adequate environmental standards without increasing their feed cost; and (2) large plants using agro-food by-products, that need professionals to be consulted to optimize the recycle of food waste restoring the nitrogen and phosphorus cycles. These considerations are in line with evidence provided by biophysical economists on the feasibility of maize-based bioenergy. The researchers, using non-monetary metrics, proved, in fact, that the bioenergy industry is unsustainable when using agricultural commodities as energy inputs [
40], while it is a desirable option when using by-products both in social and energy efficiency terms [
41]. Finally, the limitations discussed in
Section 3.3 deserve to be recalled. The lack of information, especially on soil quality, macroeconomic factors and the import of agricultural commodities, suggests that our results omit some relevant effects on land rental prices. From another viewpoint, these shortcomings may be considered valuable recommendations for further studies.