Innovative contract solutions for the Agri-Environmental-Climate Public Goods provision: Which features meet the farmers' approval? Insights from Emilia-Romagna (Italy)

D'Alberto, Riccardo; Raggi, Meri; Viaggi, Davide Article Innovative contract solutions for the Agri-Environmental-Climate Public Goods provision: Which features meet the farmers' approval? Insights from Emilia-Romagna (Italy) Bio-based and Applied Economics (BAE) Provided in Cooperation with: Firenze University Press Suggested Citation: D'Alberto, Riccardo; Raggi, Meri; Viaggi, Davide (2024) : Innovative contract solutions for the Agri-Environmental-Climate Public Goods provision: Which features meet the farmers' approval? Insights from Emilia-Romagna (Italy), Bio-based and Applied Economics (BAE), ISSN 2280-6172, Firenze University Press, Florence, Vol. 13, Iss. 1, pp. 73-101, https://doi.org/10.36253/bae-14016 This Version is available at: https://hdl.handle.net/10419/321789 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 Bio-based and Applied Economics BAE Copyright: © 2024 D’Alberto, R., Raggi, M., & Viaggi, D. Open access, article published by Firenze University Press under CC-BY-4.0 License. Firenze University Press | www.fupress.com/bae Citation: D’Alberto, R., Raggi, M., & V i a g g i , D . ( 2 0 2 4 ). I n n o v a t i v e c o n t r a c t s o l u - tions for the Agri-Environmental-Cli- mate Public Goods provision: Which features meet the farmers’ approval? Insights from Emilia-Romagna (Italy). Bio-based and Applied Economics 13(1): 73-101. doi: 10.36253/bae-14016 Received: November 30, 2022 Accepted: May 05, 2023 Published: May 20, 2024 Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Guest Editors: Stefano Targetti, Andreas Niedermayr, Kati Häfner, Lena Schaller ORCID RD: 0000-0002-7227-7485 MR: 0000-0001-6960-1099 DV: 0000-0001-9503-2977 Innovative contract solutions for the Agri- Environmental-Climate Public Goods provision: Which features meet the farmers’ approval? Insights from Emilia-Romagna (Italy) Riccardo D’Alberto1,*, Meri Raggi2, Davide Viaggi3 1 Dept. of Economics, University of Verona, Via Cantarane 24, 37129 Verona (VR), Italy 2 Dept. of Statistical Sciences “P. Fortunati”, Alma Mater Studiorum University of Bologna, Via Delle Belle Arti 41, 40126 Bologna (BO), Italy 3 Dept. of Agricultural and Food Sciences (DISTAL), Alma Mater Studiorum University of Bologna, Viale Fanin 50, 40127 Bologna (BO), Italy *Corresponding author. E-mail: [email protected] Abstract. The agroecological transition promoted worldwide is supported by the European Union Common Agricultural Policy towards different strategies and policy tools. The agri-environmental schemes, offering farmers the possibility to adopt environment-friendly practices (thus mitigating negative externalities/providing positive ones) represent a straightforward example. However, there is dissatisfaction about their effectiveness and efficiency, while their improvement is envisaged through a flexible mix of new instruments: novel contract solutions fostering result-based payments, collective implementation, involving value chains and land tenure systems coupled to environmental conditionality. This paper investigates how farmers from Emilia- Romagna (Italy) perceive these innovative contract solutions as “easy to understand”, “applicable”, “economic beneficial”, and their willingness to enroll. The applied ordered logistic regression models include socio-demographic characteristics, structural features of the holdings, and the farmers’ preference(s) for 13 individual contract features. Farmers’ perceptions are driven by the previous experience acquired from similar measures, key socio-demographic characteristics/holding structural features, and peculiar contractual elements. Keywords: public goods, result-based, collective approach, value chain, land tenure. JEL codes: Q15, Q20, Q57. 1. INTRODUCTION An agroecological transition1 is being promoted worldwide through the UN 2030 Agenda for Sustainable Development (United Nations, 2015) and 1 Agroecological transition corresponds to a systemic transformation generated by the ecologisation of agriculture and food. It concerns multiple actors among farmers, supply chains, natural resource managers, policymakers, etc. and it is characterized by the fact that a deliberate political intention 74 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 D’Alberto Riccardo et al. in particular in the European Union (EU) through its Common Agricultural Policy (CAP) and the European Green Deal (Baldock and Buckwell, 2021; European Commission, 2019). Among the CAP strategies and policy tools, the most popular instrument is the eco-conditionality embedded in the indirect subsidies (Mamine et al., 2020) which makes the payment conditional on the uptake of a set of actions considered appropriate for reducing negative externalities or improving positive ones (Hanley et al., 2012; White and Hanley, 2016). Complementary to that, the agri-environmental schemes (AESs) funded by the CAP are based on payments to farmers for the uptake of environment-friendly practices and the provision of ecosystem services that go beyond conditionality. AESs are a compulsory element of the EU Member States rural development plans (RDP) design but are voluntary for farmers. Their relevance lies in the mandatory share of funds allocated to co-financing: 30% of CAP Pillar II (supposed to grow in the future). A large body of literature considers AESs, assessing their agri-environmental-climate effects (see Hasler et al., 2022 and the references therein), analyzing their cost-effectiveness and efficiency (Ansell et al., 2016; Bartolini et al., 2021; Blazy et al., 2021; Drechsler et al., 2017; Pacini et al., 2015), estimating the effects on the agricultural holdings structure and productive choices (Arata and Sckokai, 2016; Bertoni et al., 2020; Chabé-Ferret and Subervie, 2013; D’Alberto et al., 2018; Mennig and Sauer, 2020), and detecting the factors that influence farmers’ uptake decision and behavior (Brown et al., 2021; Drechsler, 2021; Gailhard et al., 2015; Raina et al., 2021; Vergamini et al., 2020). Despite this abundant literature and the knowledge on AESs, there is dissatisfaction about their effectiveness and efficiency in delivering agri-environmental-cli- mate public goods (AECPGs2) and in terms of achievements longevity (Biffi et al., 2021; Bullock et al., 2021). Nowadays, AESs are largely dominated by action-based approaches addressing individual farmers, while their improvement is envisaged through a flexible mix of new instruments (Herzon et al., 2018; Olivieri et al., 2021), such as contract solutions fostering result-based payment schemes or collective implementation, and solutions involving value chains and/or implementing new forms of land tenure systems coupled to environmental is willing to bring such a transformation to move towards a more sustainable agricultural and food system (Magrini et al., 2019). 2 These are non-rival, non-excludable goods provided by agriculture and forestry with direct implications in terms of (potential) positive externalities for both climate and environment (e.g., carbon sequestration, air and water quality and quantity, soil restoration/ maintenance, etc.) (Cooper et al., 2009). conditionality. These novel approaches are expected to provide AECPGs in a more efficient and effective way, being compliant with what is envisaged by the Farm to Fork strategy and the EU Biodiversity Strategy for 2030. The former is at the heart of the European Green Deal that aims at making Europe the first climate-neutral continent by 2050. It plans to reduce the environmental and climate footprint of the EU food system by addressing comprehensive challenges in terms of sustainability towards a transition that ensures that the whole food chain has a neutral or positive environmental impact (European Commission, 2020a). The latter strongly supports such a transition by acknowledging that it cannot be successfully achieved without restoring the endangered ecosystems, “bringing nature back to agricultural land” (European Commission, 2020b). Both initiatives strongly support and incentivize the transition to fully sustainable practices. To the best of our knowledge, some of these new incentive approaches have been mainly investigated individually, like the result-based payments – the most studied instrument so far – (Birge et al., 2017; Russi et al., 2016; Sidemo-Holm et al., 2018; Šumrada et al., 2022, 2021; Zabel, 2019) and the collective approaches (El Mokaddem et al., 2016; Narloch et al., 2017; Westerink et al., 2017), while land tenure contracts with environmental clauses and the initiatives along the value chain were seldom addressed by the literature. This paper investigates four novel contract solutions for the AECPGs provision: result-based (RB), collective (Co), value chain (VC), and land tenure (LT) contracts. These contract types are analyzed in terms of farmers’ acceptability and willingness to uptake, by assessing: 1) The farmers’ perception of the easiness of understanding related to the innovative contract solution. 2) The farmers’ perception of the contract’s applicability in the farm. 3) The farmers’ perception of the economic benefit deriving from the contract. 4) The farmers’ willingness to enroll. The preferences concerning these points are explained using the socio-demographic characteristics of the farmers/land managers and the structural features of the agricultural holdings. The paper also focuses on the assessment of the influence that 13 individual features that define the contract solutions can play in determining the farmers’ preferences. Data are collected by means of an online survey carried out within the EU CONSOLE Project3 among the farmers of Emilia-Romagna (Italy). 3 The CONSOLE Project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 817949. For further details: https://console-project.eu. 75 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 Innovative contract solutions for the Agri-Environmental-Climate Public Goods provision The novelty of the paper lies in 1) the investigation of farmers’ perceptions of four new, incentive contract types that combine a flexible mix of new instruments; 2) the inclusion in the modeling exercise (in addition to the socio-demographic characteristics of the farmer as well as the structural features of the agricultural holding) of the information about the farmers’ preferences for several individual features characterizing these instruments; 3) the application of ordered logistic regression that, to the best of our knowledge, has never been applied to analyze farmers’ preferences for AECPGs contracts.4 Ordered logistic regression models are rather solid (Agresti, 2019, 2010), but the so-called partial proportional odds/non-parallel lines modelling approach has only recently attained a cohesive formalization (Williams, 2006; Yee, 2010). The main, recent innovation consisted in their expansion for allowing the relaxation of its key assumption, the “proportionality of the odds” (Williams, 2016). The latter states that a respondent operates a proportional shift when evaluating his/ her preferences for the levels depicted by the categorical outcome variable. In other words, the assumption states that the “distance” in terms of individual’s preferences between a lower level of the categorical outcome variable and a higher one, is proportional for all the levels of such a variable. It has been demonstrated that violations of this assumption frequently occur in practice and they have been nimbly disregarded (Brant, 1990; Long and Freese, 2014; Xu et al., 2022), hence leading to biased and mis-interpretable results (Agresti, 2010). This is not the case of the present work. Indeed, we test the proportionality of the odds and relax the assumption when needed. This relaxation allows for avoiding biased estimates by properly depicting the shift of individual’s preferences among the different levels of the categorical outcome variable, applying the partial proportional odds model when there is no proportionality of the odds about the levels of preference. The results hint at the influence that previous experience (acquired from very similar measures), key sociodemographic characteristics, and structural features of the holding play in driving the farmers’ perceptions of the easiness of understanding, applicability, and economic benefit of the contract solutions, as well as their willingness to enroll. In addition, the above-mentioned perceptions can be influenced by peculiar contractual elements, not only those straightforwardly linked to the identification of the contract type. The paper is structured as follows: section 2 presents the research framework, the case study, the data at hand, 4 A similar application (logit modelling), but targeting AESs is offered by Gailhard and Bojnec (2015). and the statistical method. Section 3 presents the results, while in section 4 we discuss them. Finally, section 5 hosts the conclusions. 2. DATA AND METHODS 2.1 Case study The Emilia-Romagna region is located in Northeastern Italy. The southern part is hilly and includes the mountainous areas of the Apennines, while the southern part of the Po River plain dominates the northern portion of the territory. The plains are characterized by intensive agriculture and arable crops, the hills by vineyards and orchards, and the mountains mainly by grasslands, arable crops, and woods. The plain area is highly urbanized, while the mountainous areas are marginalized and characterized by land abandonment. Data on Emilia-Romagna citizens were collected online, using Qualtrics, from May to July 2021 with a questionnaire promoted on the institutional website of the Emilia-Romagna region dedicated to Agriculture (Regione Emilia-Romagna, 2022a) and on the corresponding official Facebook page (Regione Emilia- Romagna, 2022b), allowing respondents to freely access the Qualtrics link. 559 questionnaires were initiated, of which 305 completely answered questionnaires (55%) are used for the present analysis. Table 1 depicts the main descriptive statistics of the sample. 2.2 Questionnaire overview The survey questionnaire (D’Alberto et al., 2022) is based on two parts: the first collects the socio-demo- graphic characteristics of the respondent and the main characteristics of the agricultural holding he/she manages/owns; the second focuses on the contract solutions. First, we investigated the respondent’s preference(s) for 13 individual features that potentially define a generic environmental programme/contract. Secondly, information on the respondent’s preference about the four contract solutions (RB, Co, VC, LT) was collected, specified in terms of “understandability”, “applicability” in the farm, and “economic benefit”. Finally, the respondent was asked about his/her willingness to enroll. Table 2 depicts the 13 individual contract features with their definitions, built on the findings from the scientific literature review on the subject (Eichhorn et al., 2020) in combination with the insights gathered from the discussion of such findings among (and with) the 76 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 D’Alberto Riccardo et al. Table 1. Descriptive statistics of the sample. Explanatory variable Nr. of observations Percent Q1, Median, Mean, Q3 (Standard Deviation) Gender male 264 86.56 % female 41 13.44 % Age 18-30 29 9.51 % 31-40 42 13.77 % 41-50 67 21.97 % 51-60 104 34.10 % 61-70 41 13.44 % >71 22 7.21 % Educational level primary 74 24.26 % secondary 156 51.15 % university or higher – BA’s, MA’s, Ph.D. or equivalent 75 24.59 % Membership none 149 48.85 % farmers union 108 35.41 % nature conservation/ environmental organization 48 15.74 % Proportion of holding sales – to processor 0 % 213 69.84 % 1-30 % 38 12.46 % 31-60 % 14 4.59 % 61-100 % 40 13.11 % Proportion of holding sales – to private wholesaler/retailer 0 % 139 45.57 % 1-30 % 58 19.02 % 31-60 % 25 8.20 % 61-100 % 83 27.21 % Proportion of holding sales – to cooperatives 0 % 193 63.28 % 1-30 % 21 6.89 % 31-60 % 21 6.89 % 61-100 % 70 22.95 % Proportion of holding sales – direct to final consumer 0 % 228 74.75 % 1-30 % 37 12.13 % 31-60 % 15 4.92 % 61-100 % 25 8.20 % Specialization arable 136 44.59 % horticulture 15 4.92 % permanent 84 27.54 % livestock 32 10.49 % mixed 38 12.46 % (Continued) 77 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 Innovative contract solutions for the Agri-Environmental-Climate Public Goods provision European stakeholders (Viaggi et al., 2020b).5 These features were selected since they potentially characterize, in general, an agri-environmental programme/contract and, at the same time, for being specifically distinctive of one (or more) incentive contract solution. For example, “the payment gets higher, the better your environmental results are” specifically fits to result-based contract solution. However, this contractual element can be part of a collective-based incentive or a solution involving the value chain. Therefore, the features are not explicitly linked to a contract type, while each of them can regard a specific aspect of the contract. Finally, as per the stakeholders’ suggestions and insights, the 13 features help in framing the general idea of the innovative contract solutions in the most understandable way for the EU farmers/land managers, disregarding their experience(s) with the CAP agri-environmental-climate measures (AECMs). The features in Table 2 were presented to the respondent as general attributes of a hypothetical agrienvironmental contract/programme. Before describing RB, Co, VC, and LT contract solutions in detail, the respondent was asked: “How much would the following 5 The literature review found and analyzed 58 existing case studies within and outside the EU. A survey among project partners and stakeholders and a workshop addressing 105 stakeholders from 11 EU Member States and the United Kingdom were held for discussing, selecting, and debating the most promising examples. characteristics of agri-environmental contracts increase or decrease your willingness to enroll to an environmental contract or programme?”. The possible answers (Likert scale) were: 1 = “Decreases my willingness considerably”, 2 = “Somewhat decreases my willingness”, 3 = “No effect on my willingness”, 4 = “Somewhat increases my willingness”, 5 = “Increases my willingness considerably”. Table 3 depicts the descriptions of the four contract solutions offered to the respondent (Viaggi et al., 2020a, 2020b). After each short description of the contract, the respondent was asked: “How do you see this contract type? Do you agree or disagree with the following statements?”. The three statements were: “Easy to understand”, “Applicable for my farm”, and “Potentially economically beneficial for my farm”. The respondent was asked to express an opinion where 1 = “Strongly Disagree”, 2 = “Disagree”, 3 = “Neutral”, 4 = “Agree”, 5 = “Strongly Agree”. Finally, for each specific contract solution (RB, Co, VC, LT) the respondent was asked: “How likely is that you would enroll in a –name– contract type in the future?” (the answers were 1 = “Very Unlikely”, 2 = “Unlikely”, 3 = “Neutral”, 4 = “Likely”, 5 = “Very Likely”). Considering the contract features presented in Table 2, Figure 1 depicts the distribution of the scores that have been given by the respondents to the 13 individual contract features. As per Figure 1, there are individual contract features that relevantly influence, in a positive way, the Explanatory variable Nr. of observations Percent Q1, Median, Mean, Q3 (Standard Deviation) Organic production no 232 76.07 % yes 73 23.93 % Utilized Agricultural Area owned – in hectares 5.5, 18, 62.41, 40 (191.57) Utilized Agricultural Area rented in – in hectares 0, 9, 49.67, 45 (188.81) Direct CAP payments no 60 19.67 % yes 245 80.33 % RDP payments – Euro no 115 62.30 % yes 190 37.70 % Previous experience no 205 67.21 % yes 100 32.79 % Note: Q1 = 1st quartile; Q3 = 3rd quartile. Table 1. (Continued). 78 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 D’Alberto Riccardo et al. willingness to enroll in a hypothetical agri-environmen- tal contract/programme, e.g., “self-chosen measures”, “better results, higher payment”, and “annual compensation”. Namely, respondents stated that each one of these characteristics contribute in increasing considerably their willingness to enroll in an environmental contract/ Table 2. Individual contract features. Contract feature Definition Self-chosen measures In the contract, you are free to decide about the management practices to achieve the specified environmental result(s). Better results, higher payment The payment gets higher, the better your environmental results are. Collective agreement You can collectively agree on environmental targets and measures at landscape-level together with other land managers/forests owners. Common payment You and other land managers (farmers/forests owners) receive a common payment. You jointly agree on the distribution of the payment. Labelled product You sell your holding’s products labelled as environmentally friendly (e.g., animal welfare products, climate friendly products) when following management measures as prescribed in a processor or retailer contract. Paid by customers The contract is not paid by public money, instead the compensation that you get for environmentally friendly production is paid by buyers of your products. Reduced land rent You can lease land with a reduced rent, if you agree to follow environmental management clauses as specified in the lease contract. Self-monitoring You can do the monitoring of the environmental results yourself (e.g., count specific plants). Control by authority The results that you achieve are regularly controlled by the competent authority coming onto your farm, e.g., once per year. Free training or advice You are offered free training and advice that enables you to reach the environmental targets. Sales guarantee You get a sales guarantee from a processor or retailer in return for implementing environmental measures. Annual compensation You get environmental compensation payment on an annual basis. Periodical payment You get half of the environmental payment at the beginning of, e.g., the five-year contract, and half at the end of it. Table 3. Contract solutions descriptions. Contract solution Description Result-based In a result-based contract you receive a payment only for the delivery of environmental or climate results. You are free in your decision about the management practices, e.g., how to contribute to water protection, landscape improvement, biodiversity or to sequester carbon. Selected indicators and scoring systems to monitor environmental or climate results are often used, and they will be exactly defined in the contract. You have access to free advice or training when you participate in this contract, and you can voluntarily engage in the monitoring activity. Collective You become a member of a group of land managers (farmers or foresters) who applies jointly for compensation in order to implement environmental or climate activities, e.g., water protection, carbon sequestration, biodiversity or landscape improvement. A minimum number of group members (e.g., 5) from your region is required to collaborate in order to get a payment. The group members decide about the implementation and locating the measures, and the distribution of the payment. Within the group, peer land managers and advisors share knowledge and support the achievement of the environmental objectives. Value chain As a producer, you are part of the value chain (producer, processor, retailer, distributor). You engage in a contract where you commit to deliver environmental or climate benefits connected to the production of selected products, e.g., by carrying out management measures which contribute to water protection, landscape improvement, biodiversity, or carbon sequestration. Often these products get a special label. You are paid for it by the market, mainly through a premium price paid by the processor or retailer. Land tenure You enter into a land-tenure contract where you commit to give particular attention to environmental aspects beyond legal requirements when producing on the leased land. The landowner accepts a lower lease payment than for comparable land under usual land tenure agreements to compensate your additional efforts. In the contract environmentally friendly management practices on the leased land are prescribed in order to maintain or improve environmental targets, e.g., water protection, landscape and biodiversity improvement or carbon sequestration or alternatively. 79 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 Innovative contract solutions for the Agri-Environmental-Climate Public Goods provision programme. In contrast, a feature like, e.g., “common payment” has a negative influence on the willingness to enroll (i.e., it is expected to somewhat decrease such a willingness). 2.3 Methodological approach: proportional odds and partial proportional logit models The socio-demographic characteristics of the respondents, the characteristics of agricultural holdings, and the scores related to the 13 individual contract features are used as explanatory variables in the models (one for each incentive contract solution) where the ordered response variables are 1) the easiness of understanding, 2) the applicability in the farm, 3) the economic benefit, 4) the willingness to enroll. These outcome variables are ordered categorical variables, based on a Likert scale. They can be treated by the ordered logit model, also called the proportional odds (PO) or parallel lines (PL) model (Mccullagh, 1980; Winship and Mare, 1984). Following the notation of Agresti (2010), let Y be the outcome of interest: an ordinal dependent variable of M categories observed for the i-th individual (i=1,…,N). The generalized ordered logit model can be written as: (1) where j=1,…,M-1. The probabilities that the outcome variable takes on each of the values 1,…,M are equal to: P(Yi=1)=1-g(Xiβ1), P(Yi=j)=g(Xiβj-1)-g(Xiβj), with j=2,…,M-1 (2) P(Yi=M)=g(X_iβM-1). From this generalized framework, special cases can be derived. For example, when M=2, the model in Equation 1) equals the logistic regression, while, for M>2, it Figure 1. Distribution of the scores of the 13 individual contract features. 80 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 D’Alberto Riccardo et al. becomes equal to a series of binary logistic regressions, one for each pair of categories of the dependent variable. The PO/PL model is a further special case that can be written as follows: (3) where j=1,…,M-1. Such a model presents β coefficients that do not vary across the values of j, as it is instead in Equation 1). Therefore, this modelling approach requires that only the α’s do vary across the j values and, hence, it implies that the M-1 regression lines are parallel. This is the key underlying assumption of the PO/PL model, usually called “proportionality of the odds”. It states that the relationship between each pair of outcome levels is the same. Namely, the shift in individual’s preferences from one level of the categorical variable to the higher/lower one is proportional for all the levels of such a variable. It is well-acknowledged that this cannot always occur in practice. The method has been largely applied by several disciplines in different fields (Agresti, 2019), but violations of this fundamental assumption which can frequently occur in practice have been nimbly disregarded (Brant, 1990; Long and Freese, 2014; Xu et al., 2022) leading to biased and mis-interpretable results (Agresti, 2010). Furthermore, this assumption has been discovered to be overly restrictive (Williams, 2016). In fact, the PO/PL model offers two main pros: 1) it can lead to highly interpretable results (Williams, 2016); 2) it benefits from computational efficiency (Agresti, 2010). Although being very sensitive to violations of the proportionality of the odds, by relaxing the assumption, the aforementioned pros can still be of interest in choosing to apply such a modelling strategy. A successful solution for relaxing the assumption is offered by the partial proportional logit model (PPO) or non-parallel lines model (NPL) (Mccullagh and Nelder, 1989; Peterson and Harrell, 1990). This alternative modelling strategy has recently gained attention due to the developments proposed by Williams (2006) and Yee (2010), being a great alternative to the generalized ordered logit model (Williams, 2016). Relaxing the proportionality of the odds can lead to one or more β’s differing across the values of j, while some other coefficients can still be equal. For the sake of clarity, let X1,X2,X3 be three explanatory variables. The model in Equation 3) can be re-written as: (4) where j=1,…,M-1. In the model of Equation 4) the β’s for X1,X2 are the same for all the values of j, while the coefficient for X3 can differ. For the sake of simplicity, the unconstrained PPO model proposed by Peterson and Harrell (1990) and further extended by Lall et al. (2002) is adopted here. This model offers a re-parametrization of the model in Equation 4) such that, for each explanatory variable, we have a coefficient β and M-2 γ coefficients that indicate a deviation from proportionality. Therefore, here we consider PO/PL models as the starting point of the analysis, test the proportionality of the odds, and (when needed) eventually relax such an assumption by adopting a properly specified PPO/NPL model. The choice of which explanatory variables should be included in the model for the outcome variable of interest is based on the following stepwise approach. First, we included in the PO-defined model all the potential explanatory variables. Second, we checked for convergence of the model, discarding the explanatory variables that forced convergence to fail. Third, we have undergone the assessment of the parallel lines assumption as suggested by Long and Freese (2014) and Williams (2016): if the whole model fails the assumption according to the Brant test, a PPO-defined model is run, by relaxing the assumption of proportionality of the odds for the explanatory variables for which the Brant test is statistically significant. Fourth, we attempted to discard the explanatory variables showing non-statistically significant coefficients but keeping them if their discarding lowered the log-like- lihood and the pseudo-R2 of the model, in comparison to the other, newly defined model(s) (i.e., we kept them if the model’s goodness of fit decreased). 3. RESULTS In the following, the estimated odds ratios are presented.6 The results are depicted according to the prescriptions of Craemer (2009) and Williams (2016): when the explanatory variables included in the model meet the parallel lines assumption, the β coefficients are depicted (with the related p-values). In other words, if the coefficients are depicted only for the first category of the 6 For the sake of brevity, only the statistically significant explanatory variables are depicted. Please, refer to the supplementary material for the integral version of the results on the models’ coefficients. Please, note that we present here only the odds ratios of the statistically significant predictors, although the predictors included in the models were all those depicted in the integral version of the tables in the supplementary material. 87 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 Innovative contract solutions for the Agri-Environmental-Climate Public Goods provision – Across the four contract solutions, age has a peculiar (but well-acknowledged in the literature on the subject) role: the older the farmer, the lower the willingness to consider the new contract solution as applicable. The acceptance of contract types is also affected by the perception of individual contract features. As expected, the perceptions of the contractual elements that more evidently characterize each contract solution influence more relevantly the acceptance of farmers about the incentive contract type (e.g., the collective agreement for Co contracts or the reduced land rent for LT contracts). However, there are additional contract features that can play a role in impacting the level of acceptance. For example, with respect to RB contracts, a positive perception of the possibility of freely deciding about the management practices to achieve the specified environmental result(s) can increase the perceived understandability of the contract. Overall, our findings hint at the fact that improved contract solutions can be based on a mix of instruments and that these can be more profitably implemented when tailored to the need of farmers/land managers through a flexible combination of a larger set of different contractual elements contributing to the contract design. 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Land Use Policy 81, 153–159. https:// doi.org/10.1016/j.landusepol.2018.10.035. 90 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 D’Alberto Riccardo et al. SUPPLEMENTARY MATERIAL Tables 4, 5, 6, and 7 of the manuscript depict the odds ratio of the statistically significant explanatory variables included in the models considered. Here, we present the same tables which, instead, do depict the coefficients of the explanatory variables (same referring models). However, the following tables are presented in their integral version (i.e., the following tables depict the estimated models’ coefficients concerning all the explanatory variables that were included in the models, not only the statistically significant ones). Each table is followed by a brief comment about the statistically significant coefficients. The four models in Table 4 are PO/PL models, as per the one depicted in Equation 3) of the manuscript. All the statistically significant variables depicted in Table 4 meet the proportionality of the odds assumption. Higher values of age make it more likely that the respondent will be in the current (or lower) category of easiness of understanding. Being a member of nature conservation/environmental organizations makes it more likely that the respondent will understand the contract more easily. An increase in scoring of self-chosen measures makes it more likely that the respondent will be in a higher category of easiness of understanding. The coefficients of the proportion of holding sales (to cooperatives), organic production, self-chosen measures, and collective agreement positively influence the perceived applicability of RB contracts. Better results, higher payment is the only statistically significant predictor for economic benefit in relation to RB contracts. An increase in the scoring of this contract characteristic makes it more likely that the respondent will be in a higher category of economic benefit. Being older makes it more likely that the respondent will be at the current level (or lower) of the willingness to enroll in the contract. Increases in scoring of self-mon- itoring and periodical payment make it more likely that the respondent will be in a higher category of willingness to enroll. An increase in the scoring of the contract feature free training makes it more likely that the respondent will be in the current (or lower) level of willingness. The models in Table 5 related to the outcome variables easiness of understanding and economic benefit are PPO/NPL models, as per the one depicted in Equation 4) of the manuscript. In contrast, the models for the outcome variables applicability in the farm and willingness to enroll are PO/PL models, as per the one depicted in Equation 3) of the manuscript. Direct CAP payments and collective agreement predictors do fail the test on the proportionality of the odds. Receiving direct CAP payments boosts the understandability of the collective contract solution, above all with respect to the extreme upper levels of the ordinal outcome variable. Collective agreement produces divergent effects on the extreme lower and upper categories. Previous experience suggests that having experienced collective-alike measures makes the collective contract more “easily understandable”. Being older negatively influences the perceived applicability of Co contracts. Being bigger in terms of holding size makes it more likely that the respondent will perceive “applicable” the Co contract. An increase in the scoring of the variables collective agreement and common payment makes it more likely that the respondent will perceive “applicable” the Co contract. Periodical payment is the only statistically significant predictor influencing (negatively) the economic benefit of Co contracts. The willingness to enroll is influenced by age, collective agreement, common payment, and self-monitoring. Being older makes it more likely that the respondent will be in the current (or lower) category of willingness to enroll, while the increase in the scoring of the three contract features has a positive effect. The models in Table 6 are, all, PO/PL models, as per the one depicted in Equation 3) of the manuscript. Being a holding with a share of sales of 1-30% to private wholesalers/retailers makes it less likely that a respondent will be in a higher category of easiness of understanding. Higher values of proportion to holding sales (direct to final consumer) make it more likely that the respondent will be in a higher category (than the current one) of the perceived understandability. Being livestock-specialized holding makes it more likely that the VC contracts are more “easily understandable”. The increase in the amount of rented-in land (in terms of hectares of UAA) makes it more likely that the respondent will easily understand the VC contract, as well as having experienced value chain-alike measures. Being a holding with a share of sales of 1-30% to private wholesalers/retailers (compared to holdings not exposed to such trades) makes it less likely that the respondent will be in a higher level of applicability in the farm. Being a holding exposed for the same share to sales to direct consumers makes it more likely that the respondent will be in a higher category of the applicability of VC contracts. Having experienced value chainalike measures makes it more likely that the respondent will perceive “applicable” the VC contract. Higher scoring of labelled product and control by authority make it more likely that the respondent will be in a higher category of applicability in the farm. 91 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 Innovative contract solutions for the Agri-Environmental-Climate Public Goods provision Table 4. Model for result-based contract solution. Explanatory variable VU vs U, N, L, VL*VU, U vs N, L, VL*VU, U, N vs L, VL*VU, U, N, L vs VL* Easiness of understanding Age (18-30) 31-40 -0.081 (0.859) 41-50 ‡ -0.929 (0.030) 51-60 -0.616 (0.141) 61-70 -0.478 (0.312) >71 -0.358 (0.529) Educational level (primary) secondary 0.072 (0.798) university or higher 0.507 (0.118) Membership (none) farmers union 0.308 (0.191) nature conservation/ environmental org. ‡ 0.691 (0.046) Proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.305 (0.326) 31-60 % -0.276 (0.531) 61-100 % -0.158 (0.573) Self-chosen measures ‡ 0.562 (0.016) Better results, higher payment 0.232 (0.346) Collective agreement 0.280 (0.079) Labelled product 0.097 (0.651) Applicability in the farm Proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.329 (0.299) 31-60 % 0.363 (0.457) 61-100 % -0.136 (0.661) Proportion of holding sales – to cooperatives (0%) 1-30 % 0.685 (0.116) 31-60 % 0.480 (0.340) 61-100 % ‡ 0.818 (0.006) Organic production (no) yes ‡ 0.833 (0.002) Self-chosen measures ‡ 0.530 (0.038) Better results, higher payment 0.404 (0.133) Collective agreement ‡ 0.487 (0.004) Labelled product 0.236 (0.299) Reduced land rent ‡ 0.651 (0.006) Self-monitoring 0.184 (0.368) Control by authority 0.195 (0.299) Free training -0.267 (0.355) Sales guarantee -0.335 (0.257) Annual compensation 0.441 (0.172) Periodical payment 0.271 (0.147) Economic benefit Self-chosen measures 0.440 (0.082) Better results, higher payment ‡ 0.549 (0.036) (Continued) 92 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 D’Alberto Riccardo et al. Age produces a negative effect on economic benefit. Being specialized in livestock makes it more likely that the respondent will be in a higher level of the willingness to enroll in VC contracts. Having experienced value chain-alike measures makes it more likely that the respondent will be in the current (or lower) category of willingness to enroll. An increase in the scoring of paid by customers and control by authority has a positive impact on willingness to enroll. The models in Table 7 are, all but the one for the Explanatory variable VU vs U, N, L, VL*VU, U vs N, L, VL*VU, U, N vs L, VL*VU, U, N, L vs VL* Collective agreement 0.163 (0.304) Labelled product -0.132 (0.557) Reduced land rent 0.027 (0.901) Self-monitoring 0.332 (0.103) Control by authority -0.177 (0.069) Free training -0.068 (0.540) Sales guarantee -0.309 (0.805) Annual compensation 0.320 (0.335) Periodical payment 0.091 (0.623) Willingness to enroll Age (18-30) 31-40 0.297 (0.598) 41-50 -0.096 (0.850) 51-60 -0.410 (0.408) 61-70 -0.437 (0.428) >71 ‡ -1.612 (0.016) Educational level (primary) secondary -0.217 (0.521) university or higher 0.236 (0.547) Membership (none) farmers union -0.290 (0.280) nature conservation/environmental org. 0.546 (0.242) Proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % 0.418 (0.252) 31-60 % -0.077 (0.874) 61-100 % 0.193 (0.610) Previous experience (no) yes 0.041 (0.924) Self-chosen measures 0.246 (0.370) Better results, higher payment 0.322 (0.277) Collective agreement 0.271 (0.180) Labelled product 0.310 (0.213) Reduced land rent 0.142 (0.563) Self-monitoring ‡ 0.506 (0.035) Control by authority -0.055 (0.802) Free training ‡ -0.705 (0.029) Sales guarantee 0.371 (0.228) Annual compensation 0.063 (0.862) Periodical payment ‡ 0.525 (0.012) Note: The reference modality of the explanatory variable is in parentheses. * VU = Very Unlikely, U = Unlikely, N = Neutral, L = Likely, VL = Very Likely; p-values in parentheses; ‡ in bold indicates the 0.05 level of statistical significance. Table 4. (Continued). 93 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 Innovative contract solutions for the Agri-Environmental-Climate Public Goods provision Table 5. Model for collective contract solution. Explanatory variable VU vs U, N, L, VL*VU, U vs N, L, VL*VU, U, N vs L, VL*VU, U, N, L vs VL* Easiness of understanding Membership (none) farmers union -0.085 (0.841) nature conservation/ environmental org. 0.735 (0.496) Specialization (arable) horticulture 0.107 (0.835) permanent -0.228 (0.440) livestock 0.184 (0.638) mixed -0.014 (0.971) Organic production (no) yes -0.176 (0.505) Direct CAP payments (no) yes -1.022 (0.051) 0.559 (0.236) ‡ 1.693 (0.005) ‡ 2.316 (0.046) Previous experience (no) yes ‡ 1.823 (0.000) Self-chosen measures -0.100 (0.693) Better results, higher payment 0.088 (0.744) Collective agreement ‡ 0.744 (0.022) -0.323 (0.236) -0.350 (0.288) ‡ -0.818 (0.023) Labelled product 0.072 (0.751) Paid by customers 0.088 (0.646) Reduced land rent 0.321 (0.140) Self-monitoring 0.255 (0.223) Control by authority -0.069 (0.715) Free training -0.035 (0.900) Sales guarantee -0.038 (0.891) Annual compensation 0.257 (0.432) Periodical payment 0.254 (0.174) Applicability in the farm Age (18-30) 31-40 ‡ -0.946 (0.040) 41-50 ‡ -1.029 (0.014) 51-60 -0.711 (0.075) 61-70 -0.291 (0.521) >71 ‡ -1.116 (0.037) Proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.190 (0.533) 31-60 % 0.658 (0.125) 61-100 % -0.134 (0.629) Utilized Agricultural Area owned – in hectares ‡ -0.001 (0.024) Self-chosen measures 0.220 (0.375) Better results, higher payment -0.004 (0.989) Collective agreement ‡ 0.641 (0.001) Common payment ‡ 0.472 (0.006) Reduced land rent 0.352 (0.105) Self-monitoring 0.281 (0.153) Control by authority -0.107 (0.556) (Continued) 94 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 D’Alberto Riccardo et al. Explanatory variable VU vs U, N, L, VL*VU, U vs N, L, VL*VU, U, N vs L, VL*VU, U, N, L vs VL* Free training 0.087 (0.754) Sales guarantee -0.191 (0.461) Annual compensation 0.109 (0.718) Periodical payment 0.274 (0.130) Economic benefit Age (18-30) 31-40 -0.588 (0.208) 41-50 -0.368 (0.394) 51-60 -0.386 (0.353) 61-70 -0.077 (0.871) >71 -0.758 (0.168) Membership (none) farmers union -0.323 (0.186) nature conservation/ environmental org. 0.209 (0.575) Proportion of holding sales – to processor (0%) 1-30 % 0.389 (0.253) 31-60 % -0.240 (0.657) 61-100 % -0.456 (0.244) Proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % -0.160 (0.633) 31-60 % 0.474 (0.324) 61-100 % -0.268 (0.507) Proportion of holding sales – to cooperatives (0%) 1-30 % -0.227 (0.618) 31-60 % -0.885 (0.071) 61-100 % -0.592 (0.075) Previous experience (no) yes 0.373 (0.335) Self-chosen measures 0.127 (0.617) Better results, higher payment 0.151 (0.561) Collective agreement 0.140 (0.587) 0.301 (0.108) 0.229 (0.395) 0.453 (0.232) Common payment 0.305 (0.085) Labelled product -0.239 (0.314) Paid by customers 0.039 (0.847) Reduced land rent 0.391 (0.077) Self-monitoring 0.361 (0.094) Control by authority 0.192 (0.321) Free training -0.088 (0.760) Sales guarantee 0.003 (0.990) Annual compensation -0.060 (0.843) Periodical payment ‡ 0.389 (0.038) Willingness to enroll Age (18-30) 31-40 -0.802 (0.117) 41-50 ‡ -1.146 (0.016) 51-60 -0.813 (0.075) 61-70 -0.595 (0.251) >71 ‡ -1.711 (0.006) Table 5. (Continued). (Continued) 95 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 Innovative contract solutions for the Agri-Environmental-Climate Public Goods provision outcome variable economic benefit that is a PPO/NPL model (as the one in Equation 4) of the manuscript), PO/ PL models, as per the one depicted in Equation 3) of the manuscript. In Table 7, the predictor self-chosen measures fails to meet the assumption of proportionality of the odds. Being older makes it more likely that the respondent will be in the current (or lower) category of easiness of understanding. Having previously experienced land tenurealike measures makes it more likely that the respondent will be in a higher category of easiness of understanding. An increase in scoring of self-chosen measures makes it more likely that the respondent will be in a higher level of easiness of understanding, while an increase in scoring of sales guarantee makes it more likely that the respondent will be in the current (or lower) category. Being older makes it more likely that the respondent will be in the current (or lower) category of applicability in the farm. Higher values of control by authority makes it more likely that the respondent will consider “applicable” the LT contracts. Being a holding with a share of 31-60% of sales directly to final consumers makes it more likely that the respondent will be in a higher category of the perceived applicability of LT contracts. An increase in scoring of reduced land rent makes it more likely that the respondent will be in a higher category of the ordinal outcome variable. An increase in scoring of control by authority makes it more likely that the respondent will perceive “applicable” the LT contract solution. A negative impact on the perceived economic benefit of LT contracts is generated by the increase in the scoring of sales guarantee. Being older makes it more likely that the respondent will be in the current (or lower) category of willingness to enroll. Being a holding with a share of sales of 1-30% to processors (compared to holdings not exposed to such trades) makes it more likely that the respondent will be in a higher category of willingness to enroll. Being exposed to the sales to private wholesalers/retailers for a share of 31-60% makes it more likely that the respondent will be in the current (or lower) level of willingness, while it is positively impacted by reduced land rent. Explanatory variable VU vs U, N, L, VL*VU, U vs N, L, VL*VU, U, N vs L, VL*VU, U, N, L vs VL* Membership (none) farmers union -0.406 (0.123) nature conservation/ environmental org. 0.033 (0.937) Proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % 0.095 (0.785) 31-60 % -0.137 (0.771) 61-100 % -0.132 (0.709) Direct CAP payments (no) yes -0.264 (0.398) Previous experience (no) yes 0.286 (0.492) Self-chosen measures 0.426 (0.117) Better results, higher payment -0.257 (0.366) Collective agreement ‡ 0.423 (0.039) Common payment ‡ 0.510 (0.007) Labelled product -0.385 (0.126) Paid by customers 0.261 (0.212) Reduced land rent 0.428 (0.070) Self-monitoring ‡ 0.691 (0.003) Control by authority 0.139 (0.488) Free training 0.267 (0.374) Sales guarantee -0.477 (0.107) Annual compensation 0.192 (0.583) Periodical payment 0.325 (0.092) Note: The reference modality of the explanatory variable is in parentheses. * VU = Very Unlikely, U = Unlikely, N = Neutral, L = Likely, VL = Very Likely; p-values in parentheses; ‡ in bold indicates the 0.05 level of statistical significance. Table 5. (Continued). 96 Bio-based and Applied Economics 13(1): 73-101, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-14016 D’Alberto Riccardo et al. Table 6. Model for value chain contract solution. Explanatory variable VU vs U, N, L, VL*VU, U vs N, L, VL*VU, U, N vs L, VL*VU, U, N, L vs VL* Easiness of understanding Membership (none) farmers union 0.276 (0.270) nature conservation/ environmental org. 0.230 (0.551) Proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % ‡ -1.255 (0.000) 31-60 % -0.683 (0.150) 61-100 % 0.162 (0.659) Proportion of holding sales – direct to final consumer (0%) 1-30 % ‡ 0.881 (0.019) 31-60 % ‡ 1.281 (0.025) 61-100 % -0.640 (0.126) Specialization (arable) horticulture 0.096 (0.867) permanent 0.115 (0.711) livestock ‡ 1.020 (0.016) mixed 0.102 (0.781) Utilized Agricultural Area rented in – in hectares ‡ 0.003 (0.046) Direct CAP payments (no) yes -0.171 (0.556) Previous experience (no) yes ‡ 2.075 (0.000) Self-chosen measures 0.022 (0.931) Better results, higher payment -0.171 (0.526) Labelled product -0.126 (0.606) Paid by customers 0.164 (0.422) Reduced land rent 0.289 (0.208) Self-monitoring 0.343 (0.112) Control by authority 0.146 (0.471) Free training -0.036 (0.898) Sales guarantee -0.005 (0.984) Annual compensation 0.328 (0.304) Periodical payment 0.121 (0.526) Applicability in the farm Age (18-30) 31-40 -0.048 (0.926) 41-50 -0.553 (0.247) 51-60 -0.580 (0.201) 61-70 -0.348 (0.508) >71 -0.628 (0.294) Membership (none) farmers union -0.023 (0.930) nature conservation/ environmental org. 0.108 (0.792) Proportion of holding sales – to private wholesaler/retailer (0%) 1-30 % ‡ -0.773 (0.034) 31-60 % 0.337 (0.948) 61-100 % 0.023 (0.951) (Continued)