B i o - b a s e d a n d A p p l i e d E c o n o m i c s BAE Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6e172 | DOI: 10.36253/bae-12211 Copyright: © 2022 G. Dono, R. Buttinelli, R. Cortignani. Open access, article published by Firenze University Press under CC-BY-4.0 License. Firenze University Press | www.fupress.com/bae Citation: G. Dono, R. Buttinelli, R. Cortignani (2022). Financial performance of connected Agribusiness activities in Italian agriculture. Bio-based and Applied Economics 11(2): 147-169. doi: 10.36253/bae-12211 Received: October 24, 2021 Accepted: June 6, 2022 Published: August 30, 2022 Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Editor: Simone Cerroni, Fabio Gaetano Santeramo. ORCID GD: 0000-0002-0272-178X RB: 0000-0002-8934-6264 RC: 0000-0002-2685-9783 Paper presented at the 10th AIEAA Conference Financial performance of connected Agribusiness activities in Italian agriculture Gabriele Dono*, Rebecca Buttinelli, Raffaele Cortignani University of Tuscia, Viterbo (Italy) * Corresponding author. E-mail: dono@unitus.it Abstract. The Rural Development Policy combines measures that favour the growth of the productive dimension of farms and their specialization, and measures aimed at supporting diversification paths, with the expansion of the productive functions performed. The evaluation of the economic and financial results of farms engaged in activities of the second type can help to calibrate the intervention between the two options. To this end, we have studied a constant sample of FADN farms in the period 2014-2016, identifying the units engaged in organic farming or other forms of qual- ity production, or engaged in direct sales or processing of their products or, again, in the management of farmhouses. We discuss the condition of financial sustainability of the farms involved in those activities by evaluating their ability to generate cash flows to offset for the depreciation of the farm production system. We used the ratio Free Cash Flow on Equity on Depreciation to compare the results of farms engaged in those activities and farms which are limited to conventional agriculture. The analy- sis of this comparison and of some structural, technical, and economic characteris- tics of the farms involved in those types of activities resulted in various considerations on their characteristics and conditions of financial sustainability. Our attention has focused above all on the financial results of farms within the sectors of Italian agricul- ture in greater financial difficulty. The main objective was, in fact, to verify whether to diversify the farm’s commitment with these activities has contributed to improving the financial sustainability in those agricultural sectors. Various considerations have arisen that can help fine-tune policies to support the types of diversification examined in this study. Keywords: depreciation, Free Cash Flow on Equity, farm financial sustainability, agri- business, organic farming, agricultural products processing, direct sale of agricultural products, quality agricultural products, farmhouses. JEL Codes: Q13, Q14. 1. INTRODUCTION Rural development policy was introduced as the second pillar of the CAP as part of the Agenda 2000 reform. Since then, with the aim of protect- ing rural heritage and creating new jobs, it has also been dedicated to sup- porting multifunctionality and the diversification of agricultural activities. The focus on diversification increased in the 2007-2013 period, with Axis 3 http://creativecommons.org/licenses/by/4.0/legalcode 148 Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Gabriele Dono, Rebecca Buttinelli, Raffaele Cortignani (quality of life in rural areas and diversification of the rural economy), as well as in the 2014-2020 program- ming period, extended up to 2023 and 2025 for many RDP projects, with priorities 2 (Farm Viability and Competitiveness) and 6 (Social inclusion and economic development).1 In the latter period, the Italian Regions allocated 624 million euros, 3.2% of the entire RDP budget, for sub-measures 6.2 (Aid for start-up of non- agricultural activities in rural areas) and 6.4 (Invest- ments to create and develop non-agricultural activities). The budget has been reduced in 2020 over the same period in 2018, linked to the COVID-19 crisis, especially for activities such as agritourism, educational farms. Yet, requests for support for operations related to diversifica- tion have been substantial, making funding insufficient in many cases (ISMEA, 2020). In the period 2010-2019 the trend to diversify agri- cultural activities has notably grown and in 2019 about one fifth of the total value of agricultural production (€ 12.5 billion) came from secondary and support activi- ties. Among others, the first-stage processing of agri- cultural products increased from 1.5 to 2.4 billion euros in the whole period, while direct selling of farm prod- ucts grew by 4.3% in 2018-2019 (CREA PB, 2021; ISTAT, 2020). Farms engaged in related or secondary activities are concentrated in the Centre-North of Italy, which indicates an imbalance in the development of these activities but also a great potential for further expansion. The scientific literature treats the intensification and propagation of these activities as the effect of a change in EU agricultural policies and in the choices of farm- ers seeking to stabilize and supplement their incomes. In this regard, an important line of analysis examines the factors influencing farmers’ decision to diversify or undertake other activities besides conventional agri- culture (Mishra et al., 2004; Rivaroli et al, 2017; Bar- bieri, 2010). A wide debate therefore concerns the influ- ence of the farmer’s age and education, the presence of female labour, the degree of production specializa- tion and the operational size of the farm. McNamara and Weiss (2005) and Meraner et al. (2015) claim that larger farms diversify; in contrast, Mishra et al. (2004) claim that larger farms tend to specialize instead. For tourism-related activities, the influence of other factors is also considered, such as public support or the envi- ronmental characteristics of the area where the farms are located (De Rooij et al., 2014; Boncinelli et al., 2018; Biczkowski et al., 2021). Proximity to urban areas and 1 Focus Area 2A “Improving the economic performance of all farms and facilitating farm restructuring and modernisation” and 6A “Facilitating diversification, creation and development of small enterprises as well as job creation”. consumers is also shown to play a key role, especially in terms of direct selling (Zasada et al., 2015; Pölling and Mergenthaler, 2017). Conversely, it is also highlighted that farms far from urban areas can be pushed to diver- sify due to the lack of alternatives (Bartolini et al., 2014; Arias et al., 2015). At the same time, the repercussions of these activities on the development and social and environmental well-being of one’s own territory are also considered (Arfini et al.,2019a, 2019b; Raimondi et al., 2018; Belletti et al. 2017; Heringa et al., 2012; Lange et al., 2013). The analysis also concerns the production, econom- ic and financial results obtained by the farms that are dedicated to these activities. Studies have investigated the impact of these activities on farm work (Chaplin et al., 2004; Raimondi et al., 2018), on technical efficiency (Lakner et al., 2018, Arru et al., 2019) and on income (Barbieri, 2013; Barnes et al., 2015; Salvioni and Fonta- nella, 2013). Khanal and Mishra (2014) study the finan- cial situation of these farms and state that the income of agritourism families is higher than other agricultural households. According to Joo et al. (2013) agritourism has a positive effect on financial sustainability only on small farms. Salvioni et al. (2020) conclude that diversifi- cation also has a positive impact on the financial perfor- mance of Italian farms. Below we focus our attention on the financial con- dition of the farms engaged in these activities. We study their cash flows which, according to Fazzari et al (1988) and Kaplan and Zingales (2000), measure the firm’s dependence on internal funds, helping to explain its investment choices, the ability to obtain credit and, hence, to finance investments. Our analysis follows the approach of Dono et al. (2021) which frame financial sustainability in the ability to offset the depreciation of the production system with cash flows. Specifically, these authors evaluate the ratio between Free Cash Flow on Equity (FCFE) and the value of depreciation (F/D index) in a constant sample of FADN farms over the period 2014-2016, and show that F/D is higher than 1 in most types of specialized farms, while it is less than 1 in non- specialized types. Dono et al. (2021) examine the financial condition of the ensemble of Italian farms, focussing on the dif- ferent technical-economic orientation sectors. Here we deepen the study of their FADN sample by examining the financial condition in the farms that diversify their activities. In this regard, we focus on first processing and direct selling of farm products, as well as on farmhouse. These activities require more profound changes in entre- preneurial performance, unlike the electricity produc- tion, the provision of farm subcontracting services and 149Financial performance of connected Agribusiness activities in Italian agriculture Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 the land leasing, which are excluded from our analysis. We also consider organic farming that, while managing typical agricultural practices, modifies the classic profile of the farm and its productions, abandoning the conven- tional approach. Finally, we include the supply of quality products that, with the single farm, often involves other units in areas where productions with typical and homo- geneous attributes are made2. For convenience we call Agribusiness the whole of these activities, considering that they are an attempt to search for market niches by enriching the range of goods and services provided to users of the typical farm products. The analysis compares the financial results of the farms conducting these 5 activities with the results of farms that conduct only conventional farming activi- ties, which we call simple farming. We first examine the financial condition of the FADN sample farms involved in at least one of the 5 based on the F/D ratio, named sustainability index, as done by Dono et al. (2021).. Structural, commercial, and economic characteris- tics that can influence the financial results of the farms involved in the 5 agribusiness are hence identified. Com- parisons are made with the financial results of the farms that are limited to conventional agricultural manage- ment. The analysis is exploratory and looks for clues on the contribution of these activities to the financial sus- tainability of Italian farms, focusing on the agricultural sectors that Dono et al. (2021) indicate as in difficult financial conditions. This study is, therefore, preliminary to a modelling, econometric or mathematical program- ming, of the contribution of these activities to the finan- cial sustainability of Italian agriculture. The next paragraph presents the materials and methods, framing the contribution of the financial anal- ysis, the sequence of operations to calculate cash flows and the indicator used to express the financial sustain- ability of farms. The section on the results reports the general characteristics of the sample of farms, with the representativeness and weight of the 5 agribusiness activities on the total. Subsequently, the levels of FCFE and depreciation are described to compare the condition of the farms that only deal with typical agriculture and 2 Dealing with diversification and multifunctionality would require referring to consolidated scientific classifications that generally lead back to the concepts of deepening, expansion and regrounding. (Van der Ploeg et Roep, 2003). Yet, the scientific literature agrees that refer- ring to a unique classification could create confusion and complicate the comparison between results obtained from different studies (Sardone et Monda, 2019; Henke et Salvioni, 2011). Even referring to regulations does not always solve the problem of classification. National accounting divides into support activities and secondary activities contributing to the agriculture production. Eurostat distinguishes Processing of agricultural products and other production of goods and services. Italian legislation is based on Article 2135 of the Civil Code (OECD, 2009). those involved in the 5 agribusinesses. This analysis is conducted by single activity, by technical-economic sec- tor and by size class of farms. The discussion and con- clusion sections follow. 2. MATERIALS AND METHODS. 2.1 General characteristics of the sample of farms We analyse the financial sustainability of Ital- ian farms based on the constant sample of FADN data used by Dono et al. (2021). The FADN was established by the Reg. 79/65/EEC, updated by Reg. CE 1217/2009, and annually collects technical and economic data of a large farms sample following a similar approach in the European Union countries. The more than 86,000 FADN farms represent nearly 5 million farms in the EU, 90% of the Utilized Agricultural Area (UAA) and 90% of Standard Production. Currently the Italian sample is based on about 11,000 farms and covers 95% of the UAA, 97% of the value of Standard Production, 92% of the Work forces and 91% of the Livestock Units. About 1,000 variables are recorded for each farm in the sam- ple, more than 2,500 for the Italian FADN. The FADN sample only includes professional and market-oriented farms and is stratified by region, size class and techni- cal-economic orientation [OTE as Italian acronym, Type of Farm (TF) according to Reg. CE n. 1242/2008, hence- forth TF]. Based on these data, Dono et al. (2021) obtain three years of financial statements (2014-2016) for a con- stant FADN sample consisting of 4.612 Italian farms, for a total of 13,836 observations. Here we divide the FADN sample considering the farms involved in the 5 most diffused agribusiness: namely organic farming (thereafter organic), processing, direct selling (selling), quality production (quality), and farmhouses. Of these groupings, the weight on the total sample of some key variables, structural (Gross Capi- tal, UAA, Working Units) and economic [Gross Saleable Production (GSP), Operating Income], as well as their average value is calculated. This provides a representa- tion of the importance and the operational and eco- nomic dimension of these activities. The next paragraph illustrates how the cash flows for each farm are calculat- ed in each of the three years considered. 2.2 The calculation of cash flows Table 1 shows how the cash flow of each farm is computed. The procedure begins by subtracting the tax component from the Operating Result, then adds depre- 150 Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Gabriele Dono, Rebecca Buttinelli, Raffaele Cortignani ciation, provisions for severance pay and for risks and other expenses. The variation of net working capital, as made up of operating receivables with customers and operating payables with suppliers is hence added, as well as investments, obtained as increase of inventories net of their depreciation. This generates the Cash Flow from Operations. Once the cash flow of the operating activity has been obtained, the financial balance of rela- tionships with the financiers of the farm is considered: where paying interest and principal on debts falling due in the year reduces liquidity, while obtaining new loans increases it. Public aid from the second pillar of the CAP and other national measures also increase liquid- ity, as well as revenue from other current accounts or other income, such as financial assets or divestments. Paying fines and repaying other loans reduces liquid- ity. This sequence generates a monetary liquidity vari- able that still includes payments to work, and the capital resources provided by the farmer. The final cash flow is obtained by subtracting cash withdrawals to pay for the farmer’s resources: Dono et al. (2021) estimated these latter payments at opportunity cost values to obtain the Free Cash Flow to Equity (FCFE). We use the same approach, although it is an approximation as the farmer does not necessarily collect the opportunity cost pay- ments for the resources provided as, moreover, as is the case with the distribution of corporate dividends (Chay & Jungwon Suh, 2009). Financial sustainability is considered as achieved when FCFE is greater than the depreciation of produc- tive capital, even by a margin that can also repay a debt service provided at a subsidized rate. This indicator can be traced back to the financial analysis of the debt of the company that Bonazzi and Iotti apply to the tomato pro- cessing industry, aquaculture, and dairy cattle breeding in Italy (Bonazzi and Iotti, 2014a, 2014b, 2015; Iotti and Bonazzi, 2015). These authors calculate the financial sus- tainability of investment debt by relating its cost to the cash flows generated by various level of the operating activities3. Yet, these indicators can be calculated only in relation to specific investment programs that are in place only in a part of the FADN farms. To carry out a finan- cial sustainability analysis in all cases, as in Dono et al. (2021), we assess whether the final monetary liquidity surplus given by FCFE is sufficient to balance the resid- ual implicit costs, i.e., the depreciation of technologies and provisions for risks or other funds. The index does not check whether the farms will reproduce the initial capital or not. Depreciation, in fact, is calculated at his- torical cost, which in the case of old plants can make the current restoration cost even very different from that associated with depreciation. Furthermore, new market, policy support and production technology conditions may not induce farmers to restore the original system. Thus, the index verifies a minimum sustainability con- dition, defined as weak, which reveals whether farms are generating additional cash flows at the same rate at 3 Bonazzi and Iotti (2014b) consider, among others, the Operating Cash Flow, and the Unlevered Free Cash Flow, which subtracts the invest- ment and adds the divestment to the former. Table 1. FCFE Calculation: formulas and FADN Databases (FDB) used. Income and cash flow items FDB Note Operating income IS - Taxes + Depreciation + Other provisions BS ∆ (employee leaving indemnity fund + other funds) ± ∆ Net working capital BS ∆ (debts + credits + product stock + raw materials stocks) - Investments Cash Flow From Operations (CAFFO) ± Principal portion BS ∆ medium/long term debt - Interest portion IS + Public aid EU second pillar aid and other national aid + Other receipts Free Cash Flow + Compensation to Farmer resources (CAFFE) - Payment to capital BS % of net capital - Compensation to managerial work IS % of gross marketable output - Compensation to manual labor Lab hourly wages for hours of family work Free Cash Flow to Equity (FCFE) (IS) = Income Statement; (BS) = Balance Sheet; (Lab) = Labor file; D = variation over the year. 151Financial performance of connected Agribusiness activities in Italian agriculture Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 which their technological system depreciates. Moreo- ver, unlike the economic valuation indices, the financial components allow this ratio to also embody the invest- ment efforts of farms, as well as their commercial and financial relationships. Dono et al. (2021) calculate the index for the whole sample and for 18 TFs that aggregate the original FADN TFs. The following analysis compares the economic- financial situation of farms dedicated exclusively to agriculture (simple farming), and those involved in at least one of the 5 activities listed above as agribusiness. Specifically, the analysis concerns basic structural, com- mercial, and economic characteristics of the farms in those groups, as well as their values of FCFE, Depre- ciation and FCFE/Depreciation Ratio (F/D), calculated as in Dono et al. (2021). The comparison is carried out within each Type of Farming (TF), whose index values of simple farming are used as reference for assessing the condition of the farms involved in agribusiness. After a first general analysis in the whole sample and in each of its TFs, the farms’ financial results in each of the 3 years of the sample are examined. This generates three groups of different stability in the financial result: agribusiness farms with always better results than simple farming (better); farms with alternating results (alternating); and farms whose results are always worse than simple farm- ing (worse). This aggregation changes the numbers in simple farming and agribusiness because includes in the latter also farms engaged in these activities for only one or two of the three years considered, i.e., that are in the start-up or disinvestment phase. Finally, the results of these three groups are present- ed by size classes to reduce the influence of the opera- tional scale on the comparison between agribusiness and simple farming. These classes are obtained by divid- ing into three equal segments the variation range of the farm’s gross saleable agricultural production (GSP) in each TF. Therefore, for instance, expressed in thousands of euros, the small dairy cattle have a GSP of less than € 1.025, the medium between € 1.025 and € 2.044, the large between € 2.044 and € 3.063. Particular attention is paid to the results in the TFs whose F/D value is below the financial sustainability threshold, to assess whether their agribusiness farms show better results or share this difficult situation. 3. RESULTS Table 2 shows structural and economic features on the constant sample of FADN farms in the period 2014-2016. The two sections of the table distinguish the cases only involved in simple farming and the cases also engaged in the 5 agribusiness. The latter are shown both for their general aggregate (agribusiness), and for each of the 5 activities. The first section of the table reports the percentage of each group for each vari- able that are Gross Capital, Utilized Agricultural Area (UAA), Family Work Units, Gross Saleable Production (GSP), Operating Income and the number of cases. Note that the sum of simple farming and agribusiness is 100, while the sum of the 5 activities exceeds the total of agribusiness, because of cases engaged at the same time in more of these activities. The second section of the table reports the average value of those variables, and the average value of ROI calculated net of from the first pillar CAP payments. The two sections of the table show that, despite an average area analogous to simple farming, the farms with agribusiness operate in average with lower Gross Capi- tal, employ fewer Family Work Units and generate lower Production Values and Operating Income. Differences emerge for organic, whose Production Value is higher than in simple farming. Farmhouses prevails for Gross Capital invested and employed Family Work, although not in terms of Production Value. The ROI values indi- cate that overall, the efficiency of these farms is relatively lower than in the simple farming units. The farmhouse is an exception because it obtains its income, albeit lower, with greater efficiency than simple agriculture. Table 3 presents the results of the FCFE and Depre- ciation, as well as the F/D sustainability index, calcu- lated for the whole of the three years on the individual observations in each group.4 The F/D index of simple farming is higher than the sample average (1.84 vs 1.57) and the entire agribusi- ness (1.15). This worse result of agribusiness is mainly due to the lower cash flow production (-42.1% compared to simple farming) than to a different level of deprecia- tion (-9.5%). Organic is the exception given the +0,31% of FCFE and the -13,2% of Depreciation compared to simple farming. Processing is in a weaker situation but exceeds the financial sustainability threshold of 1.15 used by Dono et al. (2021). The other agribusinesses show average unviable conditions depending on low (selling) or negative FCFE values (quality and farmhouses), and on an average high level of Depreciation ( farmhouses). 4 The table shows the levels of statistical significance of the differences between FCFE values and between Depreciation values but not between F/D values. This happens because the F/D index in the tables are not the average of the farms’ values in the individual groups but the ratios between the sum of the FCFE and Depreciation in each group. This kind of calculation does not change the general relationships among groups but prevents from performing the test of the differences between the values of the different groups. 152 Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Gabriele Dono, Rebecca Buttinelli, Raffaele Cortignani Table 4 shows the number of farms in the sam- ple, the percentage of farms by TF, by simple farming and by each of the 5 agribusiness, and the value of the financial sustainability index for each group. It is noted that the presence of agribusiness farms in many TFs is appreciable: in order, in processing, organic and direct sales. In various TFs the presence of quality (vine- yards), of farmhouse (mixed crops and livestock, dairy cattle) is also relevant. Agribusiness farms have an F / D index value higher than the value in simple farming in several TFs (in bold-italics and in larger font in the table). 5 of the 7 TFs that are below the financial sus- tainability threshold for their farms as a whole, have an F/D index value higher than that threshold for their respective organic farms. Important results are also found for quality (in 3 out of 7 TFs in financial crisis), farmhouse and selling (in 2 out of 7) and processing (in 1 out of 7). No agribusiness exceeds the sustainability threshold in dairy cattle; rather, the F/D of all dairy cattle farms involved in agribusinesses are lower than simple farming. 3.1 Results by stability of financial conditions and by oper- ational size Table 5 compares the results of agribusiness and sim- ple farming based on the stability of the results achieved in the single years of the examined period. As before, the comparison is carried out within the Types of Farm- ing (TF) and considers three groups: agribusiness farms that in all three years achieved better results than simple farming; those with always worse outcomes; those with alternating results. The asterisks in worse and alternat- ing indicate the statistical significance of the differences between their FCFE and Depreciation and the corre- sponding values for better; the asterisks in better refer to the difference with simple farming. Table 2a. Structural and economic features of FADN farms - percentage weight on the total. Gross Capital UAA Working Units GSP Operating Income Number Simple farming 59.0 48.5 50.8 54.1 60.8 48.9 Agribusiness 41.0 51.5 49.2 45.9 39.2 51.1 Organic 14.1 15.9 13.3 20.0 13.2 15.0 Processing 29.2 38.1 37.6 34.5 27.7 39.2 Selling 14.9 19.5 18.1 14.0 12.4 17.6 Quality 5.1 5.7 4.7 5.2 4.5 5.3 Farmhouses 4.6 3.8 4.9 2.5 3.2 4.0 Table 2b: structural and economic features of FADN farms - average value Gross Capital UAA Working Units GSP Operating Income ROI Simple farming 1,032,498 32.2 1.36 117,717 64,829 - 0.040 Agribusiness 703,715 32.7 1.26 95,254 39,931 - 0.056 Organic 832,688 34.4 1.16 141,639 45,878 - 0.060 Processing 659,383 31.5 1.25 93,408 36,805 - 0.059 Selling 750,580 35.9 1.34 84,297 36,625 - 0.039 Quality 844,165 34.5 1.16 103,918 44,375 - 0.055 Farmhouses 1,021,023 30.4 1.61 64,896 41,244 - 0.023 Total sample 885,714 32.4 1.31 106,227 52,094 - 0.047 Source: Our elaboration of FADN data. Table 3. FCFE, Depreciation and financial sustainability index over the entire three-year constant sample. FCFE Depreciation F/D Simple farming 19,643 10,825 1.81 Agribusiness 11,283*** 9,792*** 1.15 Organic 19,703 9,396*** 2.10 Processing 11,312*** 8,708*** 1.30 Selling 10,231*** 10,638* 0.96 Quality -4,440*** 9,494** -0.47 Farmhouses -3,238*** 19,468*** -0.17 Total sample 15,553 9,897 1.57 Difference with Simple farming - statistical significance: *** P = 0.99, ** P = 0.95, * P = 0.90. Source: Our elaboration of FADN data. 153Financial performance of connected Agribusiness activities in Italian agriculture Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 The table shows that 32.4% of organic farms per- forms better than simple farming. The percentage is much lower in other agribusiness, 17-22%, where instead 40% of farms always obtain worse results than simple farming. The gap between the F/D in the three groups is considerable for all activities, with very high average val- ues for better. The average F/D in alternating is close to the sustainability threshold (1.15) in organic and process- ing. In the other activities F/D is less than 1, close to 0 in farmhouses. For each agribusiness high CV values for FCFE and low values for depreciation emerge, suggesting that the differences in F/D mainly depend on the differ- ent values of the cash flows. Table 6 reports the F/D values of the three groups with relevant structural and economic variables at farm level. The latter include depreciable capital, which affects both the denominator of the F/D index, increasing the depreciation value, and its numerator, adding liquid- ity to FCFE. The average value of investments over the three years, which certainly influences the productivity level of other resources in the future, but immediately subtracts liquidity from FCFE. Aid from the CAP II pil- lar adds liquidity to FCFE and includes public funding to support investments as well as agribusiness manage- ment activities. Gross Saleable Production (GSP) reflects the operational size of farms and directly contributes to generating operating income, which adds liquidity to FCFE. The Net change in working capital (ΔWCC) adds, or subtracts, liquidity to FCFE as the result of all com- mercial relationships with customers, suppliers, and banks. Finally, Return on Investments (ROI) calculated net of the CAP aid of the first pillar, as an indicator of farm efficiency. The data are reported for simple farming and agribusiness, and for better, alternating, and worse groups. The asterisks indicate the statistical significance of the differences between better and the groups worse and alternating; between better and simple farming; finally, between total agribusiness and simple farming. The average endowment of depreciable, the value of investments, GSP and ROI of agribusiness are signifi- cantly lower than simple farming. Agribusiness activities are instead more supported by CAP II aid. There is no significant difference in Δ WCC. Differences with simple farming emerge for the indi- vidual groups. Better also displays a significantly lower endowment of depreciable in organic, processing and Table 4. Number of farms (sample), percentage of farms (by TF, simple farming and agribusiness), value of the financial sustainability index (by groupings). Types of farming (TFs) number of farms Sample percentage of farms F/D SIFA ORG PRO DIS QUA FAR SMP SIFA ORG PRO DIS QUA FAR Mixed Crops and Livestock 447 49.4 11.9 38.3 16.1 2.0 10.1 -0.08 0.09 2.75 -0.02 -0.80 1.16 0.02 Extensive Beef Cattle 828 47.9 17.0 40.3 9.4 1.1 4.6 0.10 -0.29 1.23 0.28 -0.25 -1.45 1.51 Mixed Crops 840 39.3 16.2 49.6 15.7 2.6 6.8 0.38 0.43 1.71 0.00 0.36 4.04 -0.50 Mixed Fruits 1.491 53.4 17.2 36.6 9.9 4.4 2.5 0.80 0.49 3.25 2.11 1.95 -1.50 -1.18 Arable Crops 3.039 66.2 7.5 26.1 7.6 1.7 1.9 0.82 0.77 2.71 0.08 0.40 5.63 0.52 Sheep 720 47.4 23.2 33.3 12.1 0.8 2.1 0.87 0.98 0.94 0.78 1.12 6.35 1.23 Dairy Cattle 1.209 67.0 6.4 22.2 8.1 2.2 8.0 1.15 1.71 -0.11 0.52 0.35 -0.98 -2.19 Vineyards 1.683 45.5 9.4 44.4 14.1 9.5 4.2 1.19 -0.29 1.46 2.08 1.25 -3.08 0.49 Mixed Livestock 297 50.5 10.8 39.1 10.1 1.3 4.0 1.42 0.32 9.28 0.37 2.41 7.32 -1.26 Greenhouse Vegetables 126 73.8 7.1 14.3 9.5 2.4 0.0 1.44 1.91 1.67 -0.94 -3.50 2.02 Olive Growing 531 5.3 49.9 86.4 17.1 6.8 5.6 2.08 -4.87 2.49 2.84 2.16 3.35 0.32 Swine 252 77.4 1.2 19.0 5.2 0.0 2.4 2.42 2.54 5.91 1.63 2.29 -7.13 Other 849 65.6 5.8 24.3 9.8 1.4 1.4 2.65 3.62 1.81 1.05 0.79 -2.24 4.90 Poultry 336 74.7 6.3 19.3 4.2 0.3 0.0 3.90 3.80 0.81 4.82 3.92 Citrus Fruits 222 16.2 55.4 61.3 9.9 1.8 0.0 4.12 1.75 5.24 4.94 3.22 0.79 Open Field Vegetables 624 65.5 9.0 26.3 8.3 1.9 2.4 4.48 5.00 1.72 3.41 6.96 2.78 -0.12 Fruits in Shell 114 69.3 20.2 18.4 0.0 0.9 0.0 6.86 4.39 9.12 10.58 32.31 Intensive Beef Cattle 228 82.9 7.0 10.1 3.1 1.3 3.1 7.08 7.78 -0.47 -2.36 -0.30 -5.77 -0.76 Total 13.836 55.4 13.1 34.5 10.2 3.1 3.6 1.57 1.81 2.10 1.30 0.96 -0.47 -0.17 Total sample (SMP), Simple farming (SIFA), organic (ORG), processing (PRO), selling (DIS), quality (QUA), farmhouses (FAR). Source: our elaboration of FADN data. 154 Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Gabriele Dono, Rebecca Buttinelli, Raffaele Cortignani quality activities, while it is higher in farmhouse. The activities in better all make less investments than sim- ple farming, while they benefit from significantly greater CAP II aid. They also display a higher ROI and, more important, positive even net of CAP I aid. Conversely, these activities show non-statistically significant differ- ences for GSP and Δ WCC, even if with positive values and higher than simple farming. Statistically significant differences emerge among the three groups in agribusiness. Alternating and worse show significantly higher endowments of depreciable than better, as well as lower levels of ROI, Δ WCC and GSP5. Since the GSP values in better are close to simple farming, the GSP levels of worse and alternating are also lower than this group’s values. Conversely, alternating, and worse show significantly higher investments levels compared to better; despite this greater commitment, worse receives significantly smaller CAP II aid. An in-depth analysis may concern the position of the better, alternating, and worse groups, in the individ- ual TFs and also by dimensional classes. The next para- graph presents the results of this analysis by focusing on 5 Some variables are distributed in the farms of alternating and worse with a high variability; this makes the differences in their average values compared to better statistically insignificant, although appreciable. the TFs whose F/D value is below the financial sustain- ability threshold. 3.1.1 Stability of financial conditions by TFs and by oper- ational size Tables 7 and 7bis report various information relat- ing to the three financial result groups, better (BET), alternating (ALT) and worse (WOR), in each agribusi- ness and for simple farming in the TFs whose F/D value is below the financial sustainability threshold. Table 7 shows, first, the relevance of the three groups with different financial results in terms of percentage of agribusiness farms placed in them. The ALT group is on average pre-eminent in all cases, and in most of them it is closely followed by WOR. The percentage of farms in BET is close or above WOR only in organic. The table presents in bold-italics and with a larger font the TFs cases whose F/D agribusiness values are greater than simple farming. In all cases, the F/D val- ues of better are well above the financial sustainability threshold and the value of simple farming. Alternating also presents many cases above simple farming, albeit only a few well above the sustainability threshold (qual- ity in sheep and mixed crops - livestock). The F/D values Table 5. farms with better, alternating, or worse results – percentage, F/D index, FCFE and Depreciation, coefficient variation (CV). Statisti- cal significance of differences among the various groups (*). Variables Better Alternating Worse CV = s/m Percentage weight Organic 32.4 37.3 30.3 0.11 Processing 21.8 35.3 42.9 0.32 Selling 21.4 35.0 43.6 0.34 Quality 21.1 37.8 41.1 0.32 Farmhouses 17.3 41.1 41.6 0.42 F/D index Organic 8.54 1.10 -1.45 1.90 Processing 5.98 1.20 -2.14 2.43 Selling 5.89 0.60 -2.02 2.70 Quality 5.76 0.70 -1.95 2.60 Farmhouses 4.51 0.20 -1.71 3.19 FCFE Organic 74,933 *** 8,745 *** -17,859 *** 2.18 Processing 61,763 *** 10,432 *** -16,270 *** 2.13 Selling 64,150 *** 6,577 *** -19,028 *** 2.47 Quality 41,972 *** 6,260 *** -21,997 *** 3.67 Farmhouses 65,614 *** 3,355 *** -39,724 *** 5.43 Depreciation Organic 8,777 *** 8,167 12,343 ** 0.23 Processing 10,325 8,829 ** 7,594 *** 0.15 Selling 10,882 10,763 9,437 0.08 Quality 7,283 *** 9,255 * 11,259 *** 0.21 Statistical significance of differences: *** P = 0.99, ** P = 0.95, * P = 0.90. Source: Our elaboration of FADN data. 155Financial performance of connected Agribusiness activities in Italian agriculture Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 of worse are all below the sustainability threshold and below the average result of simple farming. The ability to generate cash flows (FCFE) appears crucial in determining the F/D result, as suggested by the extent of the values in better, which in all TFs and agribusiness are higher than simple farming. Similar evidence is found in alternating, notably in sheep and mixed crops - livestock. Conversely, FCFE is always neg- ative and inferior to simple farming in worse. Depreciation contributes to determining the value of F/D by increasing both the denominator and the numer- ator of the index. This makes his specific discussion less interesting and, given the exploratory nature of this study, it was decided not to include his data in Table 7 and to make them available in the tables in Appendix A. Table 7bis reports some variables that influence the amount of cash flow. In this case, values of agribusiness that are above simple farming are marked with a bold italic font. The level of gross saleable production (GSP) directly affects operating income, that is one of the main com- ponents of FCFE. In this case it is noted that in most cases in better GSP is higher in agribusiness farms than in simple farming, while the opposite happens for all the farms in worse. Here too we find very high GSP values in the sheep TF in alternating. Furthermore, the ROI, here taken as an indica- tor of efficiency, assumes average positive values in agribusiness farms, while in simple farming it always assumes average negative values. Agribusiness farms have higher ROI also in many TFs in alternating. Yet, in these cases the indicator mainly maintains negative values. Even in worse there are TFs whose ROI is high- er in agribusiness than in simple farming, even if always with a negative sign. Finally, the table shows the investments (INV), which subtract liquidity from FCFE, and the II pillar aid of the Common Agricultural Policy (CAP II), which add it often applying measures to support the former. Those data show that the value of the investments in agribusi- ness is lower than in simple farming in almost all TFs. The opposite happens for alternating and worse, where investments of some TFs are even 6-7 higher than in bet- ter. Above all, it is interesting to note that, despite this investment discrepancy, CAP II aid are mostly greater in better than in alternating and worse. The last in-depth study of this exploration concerns the distribution of agribusiness farms among the three Table 6. Simple farming, total and single agribusinesses in the 3 financial result groups - percentage of farms on total sample, F/D index; per farm 000 € of depreciable, investments, CAP II aid, GSP, DWCC; ROI. % F/D depreciable (€ 000) investments (€ 000) CAP II (€ 000) GSP (€ 000) Δ WCC (€ 000) ROI Simple farming 48.9 1.84 87.0 19.4 2.9 182.5 -0.2 -0.04 Agribusiness 51.1 1.24 76.7*** 13.7*** 5.0*** 101.9*** -0.8 -0.06*** Better Organic 4.9 8.54 54.8 *** 5.2 *** 10.1 *** 176.3 4.5 0.01 *** Processing 8.6 5.98 69.9 *** 7.7 *** 6.4 *** 173.7 2.5 0.01 *** Selling 3.8 5.89 76.5 8.9 *** 6.0 *** 184.1 2.3 0.02 *** Quality 1.1 5.76 49.4 *** 7.9 *** 5.5 *** 144.9 * 0.4 -0.04 Farmhouses 0.7 4.51 140.4 *** 10.6 *** 8.4 *** 162.5 -1.1 0.01 *** Alternating Organic 5.6 1.07 66.0 * 17.0 ** 9.6 102.7 *** -0.6 -0.04 *** Processing 13.9 1.18 77.8 15.3 *** 5.6 105.6 *** -2.1 *** -0.03 *** Selling 6.2 0.61 108.5 *** 16.1 *** 6.4 102.9 *** -1.6 -0.02 *** Quality 2.0 0.68 87.8 *** 23.5 *** 7.0 104.3 * -3.5 -0.02 * Farmhouses 1.6 0.22 202.8 *** 25.4 *** 7.7 97.7 ** -2.4 -0.01 * Worse Organic 4.6 -1.45 114.1 *** 19.8 *** 6.9 *** 64.5 *** -4.1 -0.15 *** Processing 16.8 -2.14 72.6 10.3 * 2.8 *** 46.0 *** -2.1 *** -0.13 *** Selling 7.7 -2.02 96.3 * 12.3 *** 3.2 *** 56.0 *** -3.7 *** -0.08 *** Quality 2.2 -1.95 110.8 *** 34.6 *** 5.9 118.2 -7.2 *** -0.08 Farmhouses 1.7 -1.71 286.2 *** 44.8 *** 5.9 * 69.8 *** -8.3 -0.06 *** Statistical significance of differences: *** P = 0.99, ** P = 0.95, * P = 0.90. Source: Our elaboration of FADN data. 156 Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Gabriele Dono, Rebecca Buttinelli, Raffaele Cortignani GSP size classes and the financial result groups. Table 8 allows this assessment for TFs with F/D values below the financial sustainability threshold (FIST), and for the group above it. The first section of the table shows that most of the agribusiness farms are in the small dimensional class: 98.2% in the TFs with F/D under FIST, 96.9% in the oth- er TFs. Agribusiness farms in BET are always a minority share; in TFs below FIST their percentage is even lower, 20.5%, while the financial result of more than 40% of those farms is worse than simple farming. The second part of the table shows the F/D index values of each group6. A gap emerges between the F/D values in better, which confirm the figures of the pre- vious tables, and those in alternating and worse. The prevalence of cases in the latter groups greatly reduces 6 In the group large the F/D value of alternating is lower than in worse because the comparison is carried out within each TFs, and given the low number of large farms in alternating and worse, this implies com- paring the results of diverse TFs. the average values of F/D both for the total agribusiness and for the small farms. The impact is greater in TFs in financial difficulty, to the point that their F/D index val- ue is below the FIST both in the general average and in small farms. 4. DISCUSSION More than half of our FADN observations man- age activities that we have called agribusiness. Yet, their weight on the sample’s income is much lower, and when compared to simple farming the average operating income of agribusiness is a lower fraction of both gross invested capital and family work units. We found that these activities are carried out on farms that have small- er GSP, and in large part also worse financial results than simple farming in their respective TFs. Yet, a group, albeit a minority, of agribusiness farms achieves better financial results than simple farming; and it is interest- ing to note that most of these farms are classified in TFs Table 7. Percentage of farms, F/D, FCFE in each financial result group in each agribusiness by TF; comparison F/D and FCFE in simple farming. Type of farming (TFs) Farms % Agribusiness F/D FCFE (000 €) ORG PRO DIS QUA FAR SIF ORG PRO DIS QUA FAR SIF ORG PRO DIS QUA FAR Mixed Crops - Livestock BET 34.0 15.8 16.7 15.6 0.09 5.32 5.83 5.01 6.53 0.9 109.2 88.9 35.3 50.0 Extensive Beef Cattle 30.5 21.3 15.4 11.1 26.3 -0.29 4.45 3.63 6.70 2.02 5.32 -2.4 40.6 54.7 45.5 15.2 107.8 Mixed Crops 21.3 13.2 19.7 18.2 5.3 0.43 7.14 7.66 8.05 7.04 28.12 2.8 90.4 56.6 48.9 192.9 26.1 Mixed Fruits 35.9 25.5 23.6 23.1 13.5 0.49 8.24 7.48 10.58 4.61 6.24 4.2 55.4 54.6 99.5 48.0 38.3 Arable Crops 31.0 17.9 16.8 37.3 19.3 0.77 9.36 6.50 5.47 13.9 7.79 6.7 83.7 43.1 57.4 59.5 59.8 Sheep 26.9 13.3 21.8 40.0 0.98 6.42 5.34 4.17 4.50 7.2 52.5 81.6 93.2 261.7 Dairy Cattle 13.0 16.4 19.4 3.7 6.2 1.71 6.41 4.98 6.39 2.32 2.48 38.3 73.9 88.9 79.0 36.1 50.3 Mixed Crops - Livestock ALT 50.9 36.3 40.3 33.3 60.0 0.09 1.18 0.12 0.24 5.66 0.13 0.9 17.1 1.5 4.6 125.8 3.3 Extensive Beef Cattle 43.3 43.1 47.4 44.4 52.6 -0.29 0.39 -0.57 -0.24 -1.60 -0.33 -2.4 3.9 -6.1 -3.8 -21.6 -5.8 Mixed Crops 52.2 44.1 51.5 50.0 63.2 0.43 1.15 0.56 0.09 0.58 0.50 2.8 7.0 2.9 0.6 2.9 3.8 Mixed Fruits 37.9 32.8 34.5 41.5 37.8 0.49 1.02 1.29 -0.42 -1.79 -0.38 4.2 7.7 6.4 -4.1 -23.7 -10.5 Arable Crops 36.2 30.7 29.7 37.3 33.3 0.77 1.20 0.01 0.03 1.23 0.71 6.7 5.4 0.0 0.2 5.0 10.3 Sheep 40.7 40.0 37.9 100.0 20.0 0.98 1.06 1.10 1.09 6.35 1.69 7.2 11.5 14.2 15.3 49.2 62.2 Dairy Cattle 39.0 39.2 32.7 11.1 23.7 1.71 0.79 0.89 0.89 -6.07 -0.74 38.3 15.8 12.4 15.7 -175.2 -15.1 Mixed Crops - Livestock WOR 15.1 48.0 43.1 66.7 24.4 0.09 -4.02 -7.39 -8.29 -5.04 -4.68 0.9 -24.5 -30.8 -38.3 -40.6 -38.3 Extensive Beef Cattle 26.2 35.6 37.2 44.4 21.1 -0.29 -2.69 -4.15 -4.64 -2.99 -6.07 -2.4 -13.8 -17.8 -20.8 -8.8 -11.9 Mixed Crops 26.5 42.7 28.8 31.8 31.6 0.43 -1.82 -2.19 -1.60 -4.04 -0.76 2.8 -25.1 -20.5 -23.3 -9.7 -56.5 Mixed Fruits 26.2 41.7 41.9 35.4 48.6 0.49 -4.46 -3.83 -3.33 -6.52 -2.99 4.2 -12.6 -13.4 -16.7 -48.0 -49.3 Arable Crops 32.8 51.4 53.4 25.5 47.4 0.77 -1.71 -2.30 -2.08 -8.12 -0.39 6.7 -18.9 -14.1 -12.7 -15.2 -10.5 Sheep 32.3 46.7 40.2 40.0 0.98 -0.70 -0.86 -0.66 -0.15 7.2 -16.6 -12.3 -13.5 -21.0 Dairy Cattle 48.1 44.4 48.0 85.2 70.1 1.71 -1.37 -0.85 -0.59 -0.52 -3.17 38.3 -37.7 -21.2 -23.1 -19.0 -59.5 Better (BET, alternating (ALT), worse (WOR), simple farming (SIFA), organic (ORG), processing (PRO), selling (DIS), quality (QUA), farmhouses (FAR). Source: our elaboration of FADN data. 157Financial performance of connected Agribusiness activities in Italian agriculture Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Ta bl e 7b is . G SP , R O I, In ve st m en ts a nd C A P II a id p er fa rm in s im pl e fa rm in g an d in e ac h fin an ci al r es ul t g ro up o f e ac h ag ri bu si ne ss . G SP ( 00 0 €) R O I IN V ( 00 0 €) C A P II ( 00 0 €) SI F O R G PR O D IS Q U A FA R SI F O R G PR O D IS Q U A FA R SI F O R G PR O D IS Q U A FA R SI F O R G PR O D IS Q U A FA R M ix ed C ro ps - L iv es to ck BE T 16 9, 8 31 3, 3 22 4, 3 12 3, 8 10 7, 2 -0 ,0 4 0, 04 0, 04 0, 00 0, 04 23 ,0 9, 6 6, 8 0, 2 2, 8 4, 0 8, 6 4, 7 3, 5 1, 4 Ex te ns iv e B ee f C at tle BE T 10 5, 4 11 6, 4 16 0, 3 98 ,8 38 ,6 37 8, 9 -0 ,0 7 -0 ,0 2 0, 02 0, 02 -0 ,0 3 0, 02 11 ,3 5, 5 8, 1 5, 4 7, 6 16 ,3 4, 5 11 ,1 9, 4 4, 5 11 ,0 8, 5 M ix ed C ro ps BE T 89 ,1 17 2, 5 15 6, 4 14 7, 4 50 1, 1 10 7, 2 -0 ,1 3 0, 06 0, 05 0, 04 -0 ,0 3 0, 01 7, 3 3, 0 1, 3 1, 0 1, 1 1, 2 1, 8 7, 1 3, 5 4, 8 8, 9 7, 4 M ix ed F ru its BE T 95 ,2 15 5, 1 15 5, 3 31 1, 8 12 9, 2 66 ,4 -0 ,0 1 0, 03 0, 03 0, 03 0, 01 0, 04 18 ,7 3, 3 2, 8 5, 2 11 ,4 7, 2 1, 4 8, 0 4, 1 6, 6 5, 3 2, 8 A ra bl e C ro ps BE T 11 5, 2 21 6, 6 12 4, 3 15 5, 8 16 6, 2 93 ,2 -0 ,0 8 0, 04 -0 ,0 2 0, 00 0, 02 0, 01 18 ,4 4, 1 4, 4 8, 8 0, 2 15 ,0 3, 8 12 ,7 7, 9 7, 8 6, 9 14 ,0 Sh ee p BE T 67 ,8 11 8, 9 20 5, 6 24 1, 2 66 2, 4 -0 ,0 3 0, 02 0, 07 0, 05 0, 03 9, 4 6, 6 9, 6 8, 2 20 ,5 5, 5 19 ,8 10 ,6 5, 5 17 ,0 D ai ry C at tle BE T 26 9, 5 18 8, 5 25 1, 5 20 3, 6 22 1, 4 10 7, 6 0, 00 0, 04 0, 04 0, 05 0, 00 0, 00 34 ,2 12 ,3 17 ,5 11 ,8 0, 0 17 ,7 5, 3 10 ,6 8, 5 12 ,2 0, 0 28 ,3 M ix ed C ro ps - L iv es to ck A LT 16 9, 8 86 ,2 85 ,5 10 3, 0 11 8, 0 13 2, 6 -0 ,0 4 0, 00 -0 ,0 1 0, 00 0, 10 0, 01 23 ,0 15 ,2 14 ,5 21 ,4 8, 1 29 ,2 4, 0 10 ,9 7, 0 8, 7 2, 7 10 ,6 Ex te ns iv e B ee f C at tle A LT 10 5, 4 65 ,0 79 ,9 80 ,2 63 ,4 10 3, 1 -0 ,0 7 -0 ,0 4 -0 ,0 3 -0 ,0 3 -0 ,0 3 -0 ,0 2 11 ,3 12 ,7 17 ,1 21 ,5 0, 1 20 ,1 4, 5 12 ,4 5, 5 9, 2 19 ,4 12 ,6 M ix ed C ro ps A LT 89 ,1 95 ,3 68 ,3 72 ,5 51 ,3 40 ,7 -0 ,1 3 -0 ,1 2 -0 ,0 6 -0 ,0 1 -0 ,0 3 0, 01 7, 3 6, 0 8, 4 11 ,1 4, 1 7, 7 1, 8 8, 3 4, 7 6, 7 9, 0 5, 3 M ix ed F ru its A LT 95 ,2 87 ,4 72 ,9 78 ,4 10 8, 4 10 9, 6 -0 ,0 1 -0 ,0 3 -0 ,0 1 -0 ,0 2 -0 ,0 3 0, 00 18 ,7 14 ,5 10 ,7 17 ,6 63 ,3 46 ,4 1, 4 8, 6 3, 8 4, 2 3, 1 2, 2 A ra bl e C ro ps A LT 11 5, 2 61 ,2 60 ,9 67 ,6 56 ,4 11 8, 2 -0 ,0 8 -0 ,0 4 -0 ,0 5 -0 ,0 3 -0 ,0 6 -0 ,0 3 18 ,4 6, 2 10 ,9 7, 2 7, 0 13 ,3 3, 8 8, 8 3, 1 5, 7 4, 4 11 ,0 Sh ee p A LT 67 ,8 80 ,9 10 7, 7 91 ,1 10 8, 0 67 ,4 -0 ,0 3 -0 ,0 1 -0 ,0 2 -0 ,0 2 -0 ,0 3 0, 00 9, 4 13 ,4 22 ,1 12 ,6 1, 2 0, 1 5, 5 12 ,4 12 ,8 14 ,0 7, 3 23 ,3 D ai ry C at tle A LT 26 9, 5 20 9, 3 14 3, 5 15 4, 1 11 6, 2 12 7, 4 0, 00 0, 00 0, 00 0, 00 -0 ,0 2 0, 01 34 ,2 45 ,1 25 ,5 18 ,6 21 0, 1 63 ,4 5, 3 24 ,6 14 ,2 15 ,8 64 ,7 10 ,5 M ix ed C ro ps - L iv es to ck W O R 16 9, 8 60 ,5 38 ,0 32 ,9 45 ,8 15 ,4 -0 ,0 4 -0 ,1 8 -0 ,1 2 -0 ,1 0 -0 ,0 2 -0 ,1 8 23 ,0 11 ,0 13 ,3 21 ,3 26 ,6 28 ,1 4, 0 3, 6 1, 4 1, 4 2, 6 1, 5 Ex te ns iv e B ee f C at tle W O R 10 5, 4 32 ,4 38 ,4 40 ,2 20 ,1 16 ,7 -0 ,0 7 -0 ,1 1 -0 ,0 9 -0 ,1 0 -0 ,1 8 -0 ,1 4 11 ,3 11 ,7 5, 5 8, 5 1, 8 1, 7 4, 5 5, 5 1, 8 2, 8 3, 9 3, 4 M ix ed C ro ps W O R 89 ,1 42 ,7 30 ,6 42 ,9 20 ,4 80 ,8 -0 ,1 3 -0 ,2 4 -0 ,1 7 -0 ,1 1 -0 ,3 9 -0 ,1 9 7, 3 49 ,5 18 ,4 15 ,5 1, 0 59 ,2 1, 8 4, 2 1, 6 0, 8 1, 9 4, 6 M ix ed F ru its W O R 95 ,2 27 ,8 27 ,0 32 ,6 73 ,0 71 ,2 -0 ,0 1 -0 ,2 2 -0 ,1 6 -0 ,1 1 -0 ,0 6 -0 ,0 1 18 ,7 2, 7 4, 2 5, 9 50 ,7 78 ,4 1, 4 2, 9 1, 0 1, 6 1, 2 0, 7 A ra bl e C ro ps W O R 11 5, 2 51 ,1 36 ,5 43 ,1 21 ,2 55 ,4 -0 ,0 8 -0 ,1 3 -0 ,1 4 -0 ,0 8 -0 ,0 6 -0 ,0 7 18 ,4 24 ,3 7, 4 8, 4 3, 1 8, 2 3, 8 5, 5 1, 8 2, 2 1, 1 3, 5 Sh ee p W O R 67 ,8 53 ,6 41 ,7 45 ,6 15 1, 2 -0 ,0 3 -0 ,0 8 -0 ,0 9 -0 ,0 7 -0 ,0 4 9, 4 17 ,9 7, 9 9, 2 37 ,9 5, 5 9, 9 3, 4 6, 6 18 ,8 D ai ry C at tle W O R 26 9, 5 94 ,9 10 2, 5 15 9, 5 23 7, 5 74 ,0 0, 00 -0 ,0 2 -0 ,0 3 -0 ,0 2 -0 ,0 2 -0 ,0 1 34 ,2 42 ,5 22 ,1 30 ,6 67 ,7 76 ,3 5, 3 18 ,1 14 ,3 20 ,3 27 ,0 9, 7 B et te r (B ET , a lte rn at in g (A LT ), w or se ( W O R ), si m pl e fa rm in g (S IF A ), or ga ni c (O R G ), pr oc es si ng ( PR O ), se lli ng ( D IS ), qu al ity ( Q U A ), fa rm ho us es ( FA R ). So uc e: O ur e la bo ra tio n of F A D N d at a. 158 Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Gabriele Dono, Rebecca Buttinelli, Raffaele Cortignani whose average F/D value is below the financial profit- ability threshold. The ability to produce liquidity is the most important factor in determining the financial sus- tainability differential between farms in simple farming and those engaged in the various types of agribusiness. Among the latter, organic shows the best situation, with analogous share of the three groups and the lowest per- centage of farms in worse. Processing remains above the sustainability threshold even in alternating. Selling, qual- ity and farmhouses do not share this condition and show an average unsustainable condition. Farmhouses shows the largest percentage of cases in unstable and worse. The results on the structural and economic variables provided interesting insights into the conditions linked to the different levels of financial sustainability in the various groups. High values of the F/D index are associated with high levels of GSP and ROI. The group better reaches values of 3-4 times higher for these variables than the unstable and worst. Better achieves greater sustainability even than simple farming. Its higher level of GSP sug- gests that the link between the operational dimension and financial difficulties of agribusiness farms should be studied. The literature deals extensively with this topic. Meraner et al. (2015) claim that an increase in the eco- nomic dimension affects the likelihood of undertaking transformation activities. McNamara and Weiss (2005) also argue that as farm size increases, on-farm income diversification is more likely since the decline of mar- ginal yields favours the allocation of farm resources towards more profitable activities. IIbery (1991) and McNally (2001) reach the same conclusion. According to García-Arias et al. (2015) this happens because larg- er farms have more resources to devote to non-typical activities. Lakner et al. (2018) note that in Austria and Switzerland, diversification increases farm production, which in turn strengthens the stability of agricultural production. Instead, diversification negatively affects technical efficiency in some territories, while it improves it in others. Clearly, the commitment to new activities other than simple agriculture also involves profound changes in the corporate structure and organization, as indicated by Salvioni et al. (2020). This suggests that many agribusiness farms might still be in an evolution- ary phase that does not yet allow for significant levels of production, efficiency, and profitability. In our sam- ple the farms engaged in agribusiness are mostly small and most of them obtain worse results of the same type in simple farming. This gap is more marked in TFs in financial difficulty, while in other TFs even small farms with alternating results are financially sustainable. Large farms are in a clear minority, even if in the group better their sustainability is very high. This picture of limited financial sustainability of small farms engaged in agri- business is in partial contrast to the results of the studies cited above. The greater commitment in investments does not correspond to greater public aid. The amount of CAP II payments is, in fact, significantly lower for alternating, worse, and simple farming, despite the greater invest- ments undertaken. This divergence could be due to the time gap between the time of the investment expendi- ture and the reimbursement provided for by the CAP II aid mechanisms, whose payments are linked on project progress. Still, our analysis is based on a three-year time frame; hence, even in the presence of that time gap, it should capture at least a part of the aid associated with these investments. In any case, the condition of greater financial difficulty of the farms in the stage of invest- ments raises the question of the effectiveness of this Table 8. Percentage distribution of agribusiness farms by financial result groups, size class and by groups of TFs, with relative value of the F/D index. % on agribusiness farms F/D value BET ALT WOR TOTAL BET ALT WOR TOTAL F/D < FIST SMALL 19.2 37.6 41.4 98.2 5.68 0.28 -1.76 0.21 MEDIUM 0.9 0.4 0.0 1.4 8.70 4.81 7.67 LARGE 0.4 0.02 0.02 0.4 7.83 -5.07 1.12 6.94 TOTAL 20.5 38.1 41.4 100.0 6.25 0.39 -1.74 0.60 F/D > FIST SMALL 24.5 37.9 34.6 96.9 3.06 1.31 -0.99 1.00 MEDIUM 1.3 1.2 0.1 2.6 3.71 -0.24 0.83 2.34 LARGE 0.4 0.03 0.1 0.5 10.34 1.00 3.18 8.64 TOTAL 26.2 39.1 34.7 100.0 3.75 1.22 -0.89 1.34 Source: our elaboration of FADN data. 159Financial performance of connected Agribusiness activities in Italian agriculture Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 support system, which does not appear to contribute to financial sustainability in that specific phase. The issue needs to be investigated also by examining in detail the composition of the CAP II aid, which includes support for activities that do not require investing in deprecia- ble capital. In any case, this evidence agrees with the conclusions of Boncinelli et al. (2018) that highlight the absence of any relationship between RDP payments and diversification. Higher endowments of depreciable are found pre- cisely in the groups alternating and worse in which the FCFE values are lower or even negative. Instead, for all agribusiness activities, the group better shows lower endowment of these capital, along with lower depre- ciation levels which reduce the denominator of F/D. On the other hand, there is the impact of higher deprecia- tion levels in increasing the numerator of the F/D ratio, favouring greater generation of FCFE. Dono et al. (2021) concluded that in the studied period these capitals show little capacity to increase farms financial sustainability. Our evidence seems to agree with those conclusions, which suggests that in acquiring new capital it will be useful to verify their impact on the productivity. This is mainly true for organic, quality and farmhouse whose endowments of depreciable in worse and alternating are much higher than in better, but negative differences in FCFE are also greater. The literature also deals with the conditions for the development of commercial relationships with buyers, suppliers, and banks: in the cash flows analysis they also contribute to transforming the value of production into greater liquidity through the Δ WCC. In this regard, Pölling and Mergenthaler (2017) claim that in direct sales activities an important role is played by proximity to urban centres, although with differences due to the farm size and technical-economic orientation. Besides, the wide variability in the results agrees with the state- ments of Bauman et al. (2018) on US farms engaged in direct selling that show how results vary under different management conditions, types of market and farm loca- tion areas. Our study did not consider the location of the farm which, especially in disadvantaged areas, far from the most dynamic agricultural markets, could lead to less intensive activities or activities related to tourism, such as farmhouses. In this regard, a third of the farmers interviewed by De Rooij et al. (2014) believes that mul- tifunctionality is best located in areas “without a future for conventional farming”. The literature on farm diversification pays close attention to the performance of the farmhouses. Bagi and Reeder (2012), show that the global net income of the average agritourism farm was small relative to all other farms in 2007. Mastronardi, et al., (2011) claim that the profitability of simple farming, especially when specialized in tree crops, is more than double than in farmhouses. Still, according to Giaccio et al. (2018a; 2018b) farmhouses can increase their income also engag- ing in other activities such as selling, organic and typi- cal foods production, catering and wine tasting services, access to environmental assets, such as forest areas, and provision of leisure services, such as cultural and sport activities. Dries et al. (2012) and Khanal (2020) also claim that there are synergies between structural and farm diversification activities. On the contrary, Khanal and Mishra (2014) affirm that small farms obtain bet- ter results by undertaking both farmhouse and off-farm work7. Giaccio et al. (2018a) show that farm income decreases significantly as the number of family mem- bers employed on the farm increases. At the same time, according to Lupi et al. (2017) farms that employ more (non-family) work are more likely to invest in agritour- ism businesses. We have not explored the links between diverse agribusiness activities due to the limited number of observations with more integrated activities in the stud- ies FADN sample. Our evidence agrees with these results as we found the most difficult situations in farmhouses and quality, which are in less favourable condition even when they get the best results, as in better. Moreo- ver, in the latter activity there is a strong push towards technological innovation that requires great changes and investments that put the farms in difficulty at least momentarily. An example is the Parmigiano Reggiano supply chain, cited by Arfini et al. (2019a). We believe that our conclusions are consistent with these considera- tions, as they depict a very dynamic situation, charac- terized by investments that have not yet reimbursed the costs incurred and are not yet fully operational. 5. CONCLUSION We have found that the studied activities in the examined period constitute a dynamic group in which the farms with negative or unsustainable results are not doomed to bankruptcy. In fact, many of the agribusi- ness farms are engaged in major investments which, on the one hand, subtract liquidity from the cash flow and, 7 Yet, this aspect should be investigated by remembering that the Italian legislation on the subject is very different from that of other countries and the farm is considered an agricultural activity and can only be car- ried out by a farmer “through the use of his own farm in term of the connection of the farming, forestry and livestock raising activities with- in the holding “(Law number 96/2006). 160 Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Gabriele Dono, Rebecca Buttinelli, Raffaele Cortignani on the other, take time to express their potential or even become operational. All this outlines a situation in evolu- tion, perhaps even rapid, therefore, to be verified with fur- ther in-depth studies in a short time. In this perspective, it should also be deepened that farms that approached agribusiness in the middle of the last decade, investing or in a growth phase in this period, have had probably a major stop due to the COVID-19 crisis. This could have strongly affected their growth precisely at the moment of entry into operation of many of their investments. Still, the evidence of financial difficulty faced by agribusiness farms that make more investments sug- gests that it would be useful to modulate financial aid in a different way. This could be partially disbursed at the beginning of the investment process to reduce the financial difficulties associated with its activation. In any case, it is also interesting to explore the perspectives of the group better that, at the moment, is investing less than the others. In particular, it can be asked whether these farms will be able to generate sufficient financial resources to renew their technologies when their capital runs out of payback. The situation of the farms in agribusiness deserves further investigation especially about the situation of the mixed and more extensive TFs that appear to be in financial difficulty according to Dono et al. (2021). It is of interest to deepen the investigation on the possible contribution of agribusiness in improving the finan- cial condition of these TFs. To investigate these aspects, given the scarcity of observations for activities such as quality and farmhouses, it would be desirable to increase the FADN sample, especially of agribusiness relevance. Another aspect of interest concerns the results deriving from the aggregation of these activities: it leads to inves- tigate the issues of the integration of functions along the value chain by farms. 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Between capital investments and capacity building—Development and application of a conceptual framework towards a place-based rural development policy. Land Use Policy. 46. 178-188. 10.1016/j.landusepol.2014.11.023. 163Financial performance of connected Agribusiness activities in Italian agriculture Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 APPENDIX A The following tables report key variables for the three financial result groups (better, alternating, worse) in each agri- business, in each TF. The reported variables are: FCFE, Depreciation (DEPR), F/D, Amortizable Capital (AMOC), Invest- ments (INVES), CAP II aid, Gross saleable production (GSP), Working capital Variation (DWCC), Return on investment (ROI), percentage of farms in the financial result group over the farms of the agribusiness activity in the TF (% N). Organic better than simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock 109,193 20,512 5.32 97,967 9,569 8,579 313,256 36,987 0.04 34.0 Extensive Beef Cattle 40,575 9,127 4.45 17,596 5,544 11,069 116,419 45 -0.02 30.5 Mixed Crops 90,421 12,672 7.14 95,509 2,992 7,090 172,464 553 0.06 21.3 Mixed Fruits 55,440 6,725 8.24 36,501 3,319 7,997 155,074 -380 0.03 35.9 Arable Crops 83,680 8,941 9.36 54,566 4,148 12,742 216,572 4,252 0.04 31.0 Sheep 52,541 8,187 6.42 24,724 6,644 19,846 118,887 977 0.02 26.9 Dairy Cattle 73,903 11,531 6.41 86,511 12,323 10,593 188,461 3,414 0.04 13.0 Vineyards 91,709 11,499 7.98 52,548 8,244 7,651 191,196 3,901 0.04 28.5 Mixed Livestock 261,754 8,091 32.35 84,251 5,194 20,724 333,967 110,174 -0.01 53.1 Greenhouse Vegetables 0.0 Olive Growing 26,066 6,528 3.99 61,104 1,055 8,475 77,641 -182 -0.06 54.3 Swine 0.0 Other 133,535 9,695 13.77 21,941 4,129 2,325 391,676 -23,171 0.05 16.3 Poultry 39,353 0 300 569 2,265 40,998 20,243 0.07 14.3 Citrus Fruits 68,206 6,085 11.21 21,609 1,786 16,400 161,915 66 0.04 34.1 Open Field Vegetables 23,688 2,409 9.83 28,860 576 3,787 99,397 -848 0.05 12.5 Fruits in Shell 172,103 15,461 11.13 123,239 77,055 13,213 277,489 5,340 0.09 39.1 Intensive Beef Cattle 0.0 TOTAL 66,164 8,491 7.79 50,585 5,039 10,724 156,493 5,096 0.03 32.1 Organic alternating over simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock 17,108 14,524 1.18 177,659 15,166 10,894 86,153 4,563 0.00 50.9 Extensive Beef Cattle 3,911 10,062 0.39 46,070 12,727 12,435 65,036 514 -0.04 43.3 Mixed Crops 6,956 6,039 1.15 56,972 6,006 8,263 95,321 1,840 -0.12 52.2 Mixed Fruits 7,679 7,539 1.02 62,409 14,549 8,610 87,370 1,661 -0.03 37.9 Arable Crops 5,383 4,480 1.20 63,378 6,181 8,822 61,184 -1,476 -0.04 36.2 Sheep 11,527 10,850 1.06 68,431 13,368 12,379 80,934 -1,659 -0.01 40.7 Dairy Cattle 15,776 19,906 0.79 159,685 45,105 24,557 209,309 10,287 0.00 39.0 Vineyards 25,842 14,839 1.74 141,147 28,124 5,819 193,831 -8,474 0.01 28.5 Mixed Livestock -68,795 15,394 -4.47 77,632 249,670 36,338 393,577 -2,658 0.00 46.9 Greenhouse Vegetables 122,678 60,799 2.02 419,479 17,667 0 359,083 3,897 0.15 33.3 Olive Growing -5,363 2,672 -2.01 20,878 9,710 5,419 36,509 -1,268 -0.14 39.2 Swine 34,723 5,878 5.91 29,567 2,053 1,985 167,743 4,831 0.01 100.0 Other 20,961 11,101 1.89 48,417 16,005 5,753 233,513 4,194 0.00 36.7 Poultry 26,326 8,207 3.21 45,290 5,827 2,551 204,896 3,705 0.03 42.9 Citrus Fruits 26,762 5,297 5.05 34,080 3,034 9,998 110,320 -2,812 0.01 33.3 Open Field Vegetables 51,623 8,086 6.38 73,211 10,473 12,217 245,748 -8,365 0.03 33.9 Fruits in Shell 39,010 7,066 5.52 33,545 7,974 6,738 110,803 -432 0.00 39.1 Intensive Beef Cattle -7,387 0 0 642 15,644 52,139 -3,383 -0.14 12.5 TOTAL 9,528 8,450 1.13 67,821 17,894 9,961 106,017 -260 -0.02 38.9 164 Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Gabriele Dono, Rebecca Buttinelli, Raffaele Cortignani Organic worse than simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock -24,513 6,102 -4.02 61,924 11,036 3,562 60,453 -7,029 -0.18 15.1 Extensive Beef Cattle -13,761 5,122 -2.69 46,679 11,651 5,525 32,360 152 -0.11 26.2 Mixed Crops -25,102 13,788 -1.82 138,688 49,515 4,201 42,735 28,541 -0.24 26.5 Mixed Fruits -12,582 2,821 -4.46 22,869 2,697 2,878 27,796 -921 -0.22 26.2 Arable Crops -18,884 11,045 -1.71 102,593 24,338 5,509 51,088 8,300 -0.13 32.8 Sheep -16,554 23,816 -0.70 111,819 17,869 9,912 53,587 -2,268 -0.08 32.3 Dairy Cattle -37,685 27,452 -1.37 474,224 42,490 18,110 94,878 -16,880 -0.02 48.1 Vineyards -39,157 8,966 -4.37 96,108 30,606 3,415 64,375 -12,851 -0.05 43.0 Mixed Livestock 0.0 Greenhouse Vegetables -10,333 220 -46.90 894 258 183 47,574 -19 -0.38 66.7 Olive Growing -8,468 374 -22.65 11,419 8,935 3,535 9,704 2,177 -0.92 6.4 Swine 0.0 Other -20,669 11,293 -1.83 80,328 14,388 1,848 90,521 -1,975 -0.07 46.9 Poultry -18,823 17,204 -1.09 202,599 52,103 3,644 33,280 378 0.03 42.9 Citrus Fruits 3,003 7,647 0.39 41,900 1,836 17,079 106,604 -14,695 -0.40 32.5 Open Field Vegetables 2,845 18,155 0.16 138,296 18,838 19,757 189,140 -6,639 -0.03 53.6 Fruits in Shell -1,628 928 -1.75 4,789 125 3,000 14,635 -21 -0.11 21.7 Intensive Beef Cattle -5,473 13,965 -0.39 98,120 16,923 5,106 112,462 5,760 -0.02 87.5 TOTAL -18,154 11,666 -1.56 109,610 20,448 7,465 64,853 -1,511 -0.18 29.0 Processing better than simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock 88,889 15,258 5.83 92,144 6,792 4,745 224,278 27,380 0.04 15.8 Extensive Beef Cattle 54,724 15,065 3.63 80,792 8,082 9,412 160,292 -2,011 0.02 21.3 Mixed Crops 56,630 7,389 7.66 56,618 1,340 3,526 156,402 -1,734 0.05 13.2 Mixed Fruits 54,591 7,294 7.48 48,466 2,822 4,134 155,289 112 0.03 25.5 Arable Crops 43,136 6,641 6.50 51,009 4,379 7,940 124,253 885 -0.02 17.9 Sheep 81,562 15,263 5.34 63,739 9,630 10,553 205,572 -1,497 0.07 13.3 Dairy Cattle 88,913 17,848 4.98 100,811 17,456 8,491 251,492 1,010 0.04 16.4 Vineyards 130,368 20,798 6.27 129,997 20,506 6,817 341,806 5,144 0.05 17.6 Mixed Livestock 67,981 5,452 12.47 27,026 4,052 5,068 135,264 620 -0.01 18.1 Greenhouse Vegetables 110,337 13,041 8.46 71,338 206,941 0 1,227,583 5,195 0.11 5.6 Olive Growing 31,199 7,360 4.24 62,558 2,783 5,216 90,941 696 -0.04 51.6 Swine 247,597 15,465 16.01 241,610 19,139 0 529,816 19,945 0.11 8.3 Other 48,088 4,520 10.64 39,468 3,838 3,098 155,421 1,625 0.05 16.5 Poultry 148,415 6,282 23.62 53,552 1,453 231 235,666 14,281 0.12 26.2 Citrus Fruits 91,413 7,872 11.61 25,833 2,573 19,427 202,941 35 0.07 20.6 Open Field Vegetables 174,734 7,233 24.16 27,483 2,742 1,369 495,664 3,379 0.09 6.7 Fruits in Shell 166,380 15,417 10.79 129,450 78,364 3,473 294,791 2,920 0.14 42.9 Intensive Beef Cattle 0.0 TOTAL 66,637 10,455 6.37 69,550 7,589 6,320 178,136 1,936 0.05 21.0 Processing alternating over simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock 1,509 12,893 0.12 160,760 14,463 7,049 85,496 2,624 -0.01 36.3 Extensive Beef Cattle -6,135 10,803 -0.57 80,844 17,070 5,465 79,945 -1,816 -0.03 43.1 Mixed Crops 2,881 5,140 0.56 44,313 8,425 4,711 68,257 387 -0.06 44.1 Mixed Fruits 6,407 4,980 1.29 36,882 10,723 3,819 72,857 -1,894 -0.01 32.8 Arable Crops 42 6,118 0.01 60,493 10,900 3,119 60,893 1,152 -0.05 30.7 165Financial performance of connected Agribusiness activities in Italian agriculture Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Sheep 14,242 12,995 1.10 74,768 22,056 12,770 107,724 -3,697 -0.02 40.0 Dairy Cattle 12,447 14,022 0.89 89,683 25,468 14,158 143,487 -508 0.00 39.2 Vineyards 25,015 12,530 2.00 117,510 23,602 5,170 181,251 -12,605 0.01 42.8 Mixed Livestock 1,135 8,505 0.13 78,242 2,182 6,276 88,829 -1,986 -0.02 36.2 Greenhouse Vegetables -7,503 5,057 -1.48 85,363 2,561 0 117,605 -7,652 -0.01 66.7 Olive Growing -744 3,262 -0.23 22,515 11,398 3,499 52,342 1,257 -0.09 38.8 Swine 49,173 20,570 2.39 276,229 19,019 2,062 242,757 8,139 0.02 54.2 Other 35,606 11,939 2.98 82,981 12,388 6,543 172,322 5,783 0.02 27.2 Poultry 35,497 7,733 4.59 98,844 2,797 1,534 80,597 1,523 0.00 32.3 Citrus Fruits 27,544 5,886 4.68 65,145 6,392 4,045 114,355 9,471 -0.01 39.0 Open Field Vegetables 57,435 9,656 5.95 109,927 22,290 8,926 221,115 8,717 0.00 34.8 Fruits in Shell 28,272 2,908 9.72 3,838 219 3,750 58,413 -179 0.06 42.9 Intensive Beef Cattle 0.0 TOTAL 11,414 8,762 1.30 76,789 15,010 5,592 107,373 -1,630 -0.01 37.5 Processing worse than simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock -30,766 4,162 -7.39 50,886 13,321 1,401 38,041 -759 -0.12 48.0 Extensive Beef Cattle -17,789 4,285 -4.15 32,949 5,524 1,766 38,418 -1,616 -0.09 35.6 Mixed Crops -20,487 9,369 -2.19 91,870 18,435 1,588 30,619 -3,812 -0.17 42.7 Mixed Fruits -13,401 3,499 -3.83 32,768 4,228 989 27,011 -784 -0.16 41.7 Arable Crops -14,127 6,130 -2.30 62,271 7,419 1,776 36,535 -108 -0.14 51.4 Sheep -12,270 14,348 -0.86 85,963 7,892 3,407 41,673 -1,627 -0.09 46.7 Dairy Cattle -21,153 24,952 -0.85 282,835 22,067 14,251 102,464 -10,187 -0.03 44.4 Vineyards -24,222 6,449 -3.76 51,764 15,298 2,139 53,581 -6,794 -0.11 39.6 Mixed Livestock -23,110 3,962 -5.83 47,854 9,918 2,213 26,977 -1,301 -0.08 45.7 Greenhouse Vegetables -19,794 1,997 -9.91 8,296 406 0 58,705 569 -0.10 27.8 Olive Growing -11,929 1,032 -11.56 13,684 5,385 1,698 12,250 2,167 -0.72 9.6 Swine -38,069 20,761 -1.83 230,148 46,772 2,109 160,185 2,501 -0.06 37.5 Other -17,727 5,854 -3.03 47,832 12,962 862 50,489 -733 -0.08 56.3 Poultry 694 15,298 0.05 201,017 16,952 2,256 36,209 5,410 -0.02 41.5 Citrus Fruits -5,130 4,089 -1.25 29,133 1,843 973 48,743 191 -0.08 40.4 Open Field Vegetables 1,764 9,804 0.18 75,356 3,663 7,373 96,405 -3,077 -0.10 58.5 Fruits in Shell -1,818 70 -25.98 106 25 0 18,359 0 -0.16 14.3 Intensive Beef Cattle -20,623 8,736 -2.36 74,741 10,050 3,116 70,165 -6,733 -0.05 100.0 TOTAL -16,819 7,773 -2.16 73,071 10,756 2,772 46,686 -2,456 -0.13 41.5 Selling better than simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock 35,287 7,037 5.01 74,201 156 3,464 123,840 2,181 0.00 16.7 Extensive Beef Cattle 45,472 6,787 6.70 23,718 5,356 4,513 98,766 9,022 0.02 15.4 Mixed Crops 48,920 6,080 8.05 48,530 983 4,791 147,406 -1,565 0.04 19.7 Mixed Fruits 99,538 9,407 10.58 67,452 5,219 6,639 311,843 -261 0.03 23.6 Arable Crops 57,363 10,489 5.47 100,370 8,764 7,837 155,844 2,277 0.00 16.8 Sheep 93,209 22,328 4.17 116,462 8,212 5,513 241,199 4,961 0.05 21.8 Dairy Cattle 79,037 12,365 6.39 108,252 11,794 12,155 203,553 6,852 0.05 19.4 Vineyards 102,697 18,920 5.43 103,390 25,351 5,488 278,632 -1,037 0.04 19.4 Mixed Livestock 46,498 5,996 7.76 40,998 3,541 2,829 82,530 1,758 -0.01 33.3 Greenhouse Vegetables 0.0 166 Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Gabriele Dono, Rebecca Buttinelli, Raffaele Cortignani Olive Growing 42,654 8,014 5.32 83,858 4,107 5,035 74,964 8,167 -0.08 36.3 Swine 0.0 Other 49,953 4,162 12.00 30,663 1,804 4,469 180,961 -1,480 0.05 16.9 Poultry 161,001 24,526 6.56 259,482 20,481 74 678,060 20,830 0.11 21.4 Citrus Fruits 0.0 Open Field Vegetables 194,439 9,224 21.08 24,137 3,369 1,387 352,758 2,538 0.10 13.5 Fruits in Shell 0.0 Intensive Beef Cattle 0.0 TOTAL 74,090 11,319 6.55 81,060 8,886 5,867 200,362 2,534 0.03 13.4 Selling alternating over simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock 4,607 19,509 0.24 247,712 21,364 8,659 102,997 5,499 0.00 40.3 Extensive Beef Cattle -3,772 15,679 -0.24 143,830 21,522 9,174 80,176 1,901 -0.03 47.4 Mixed Crops 627 7,283 0.09 73,373 11,085 6,722 72,529 1,919 -0.01 51.5 Mixed Fruits -4,144 9,923 -0.42 82,349 17,562 4,156 78,367 1,076 -0.02 34.5 Arable Crops 220 7,754 0.03 92,019 7,242 5,674 67,567 -858 -0.03 29.7 Sheep 15,267 14,061 1.09 83,027 12,610 13,965 91,058 -3,390 -0.02 37.9 Dairy Cattle 15,707 17,615 0.89 115,054 18,623 15,793 154,130 -1,080 0.00 32.7 Vineyards 12,873 14,236 0.90 152,928 25,930 2,989 123,473 -6,559 0.01 42.6 Mixed Livestock 6,127 7,771 0.79 76,646 4,548 5,763 63,373 -689 0.00 46.7 Greenhouse Vegetables -4,362 10,754 -0.41 187,712 25,215 0 168,187 -16,972 0.01 66.7 Olive Growing -6,734 3,398 -1.98 30,025 6,773 5,150 28,368 1,733 -0.18 52.7 Swine 121,918 36,840 3.31 445,716 27,693 0 251,157 28,730 0.03 38.5 Other 32,057 6,662 4.81 64,538 12,007 7,889 157,321 26,134 0.02 25.3 Poultry 32,180 8,342 3.86 86,852 5,633 0 85,363 557 0.07 42.9 Citrus Fruits 29,781 5,581 5.34 108,196 5,901 3,713 171,767 2,741 0.02 59.1 Open Field Vegetables 133,639 9,781 13.66 158,482 24,991 19,066 209,821 21,826 -0.01 25.0 Fruits in Shell 0.0 Intensive Beef Cattle 0.0 TOTAL 9,991 11,094 0.90 112,224 15,716 6,768 97,772 913 -0.01 38.9 Selling worse than simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock -38,334 4,625 -8.29 74,060 21,312 1,353 32,919 2,468 -0.10 43.1 Extensive Beef Cattle -20,787 4,480 -4.64 38,884 8,490 2,845 40,152 -2,870 -0.10 37.2 Mixed Crops -23,301 14,588 -1.60 143,512 15,459 785 42,938 1,432 -0.11 28.8 Mixed Fruits -16,718 5,026 -3.33 52,699 5,905 1,581 32,579 -4,254 -0.11 41.9 Arable Crops -12,714 6,106 -2.08 58,620 8,409 2,195 43,063 1,480 -0.08 53.4 Sheep -13,503 20,539 -0.66 83,448 9,164 6,633 45,650 -1,265 -0.07 40.2 Dairy Cattle -23,088 39,004 -0.59 505,291 30,591 20,348 159,536 -17,221 -0.02 48.0 Vineyards -26,399 6,910 -3.82 63,210 14,062 2,123 71,003 -7,539 -0.06 38.0 Mixed Livestock -15,415 3,513 -4.39 41,835 3,207 1,080 20,246 -436 -0.07 20.0 Greenhouse Vegetables -97,845 8,949 -10.93 90,302 86,673 0 52,271 -789 -0.07 33.3 Olive Growing -12,421 1,736 -7.15 39,410 8,004 2,295 15,975 2,542 -0.23 11.0 Swine -14,009 4,143 -3.38 53,040 472 0 95,932 -14,341 -0.08 61.5 Other -21,773 4,463 -4.88 52,850 13,770 637 65,308 -2,486 -0.07 57.8 Poultry -20,796 4,459 -4.66 62,483 3,657 394 101,142 -6,458 -0.08 35.7 Citrus Fruits -597 5,124 -0.12 46,463 141 1,358 83,520 81 -0.03 40.9 Open Field Vegetables -9,758 6,521 -1.50 56,911 3,699 3,409 51,962 -514 -0.11 61.5 Fruits in Shell 0.0 167Financial performance of connected Agribusiness activities in Italian agriculture Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Intensive Beef Cattle -4,999 16,758 -0.30 166,096 6,388 2,163 126,560 -813 -0.03 100.0 TOTAL -19,564 9,891 -1.98 101,629 12,343 3,594 59,592 -3,133 -0.08 41.5 Quality better than simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock 0.0 Extensive Beef Cattle 15,209 7,518 2.02 2,166 7,601 11,000 38,631 1,766 -0.03 11.1 Mixed Crops 192,871 27,399 7.04 190,015 1,103 8,943 501,130 -3,392 -0.03 18.2 Mixed Fruits 47,970 10,407 4.61 42,141 11,370 5,263 129,228 3,026 0.01 23.1 Arable Crops 59,543 4,271 13.94 30,696 166 6,920 166,162 333 0.02 37.3 Sheep 0.0 Dairy Cattle 36,092 15,578 2.32 36,714 0 0 221,359 27,069 0.00 3.7 Vineyards 26,027 5,422 4.80 26,669 13,031 4,845 75,352 2,660 -0.07 8.1 Mixed Livestock 37,945 5,186 7.32 84,275 3,997 4,880 182,175 22 0.00 100.0 Greenhouse Vegetables 0.0 Olive Growing 28,106 4,756 5.91 31,899 2,606 4,326 79,933 3,562 -0.01 61.1 Swine 0.0 Other 0.0 Poultry 0.0 Citrus Fruits 11,382 1,126 10.11 52 0 6,000 52,523 1,912 -0.31 25.0 Open Field Vegetables 11,133 1,417 7.86 19,101 1,545 0 41,033 29 0.09 16.7 Fruits in Shell 20,227 626 32.31 0 0 0 48,803 -262 0.06 0.0 Intensive Beef Cattle 0.0 TOTAL 46,217 6,872 6.73 41,423 5,199 5,312 132,653 2,189 -0.02 19.5 Quality alternating over simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock 125,751 22,201 5.66 240,691 8,127 2,691 118,021 63,436 0.10 33.3 Extensive Beef Cattle -21,557 13,448 -1.60 41,514 111 19,373 63,367 -11,508 -0.03 44.4 Mixed Crops 2,914 5,055 0.58 47,622 4,142 8,960 51,327 -925 -0.03 50.0 Mixed Fruits -23,707 13,275 -1.79 145,448 63,341 3,119 108,437 8,922 -0.03 41.5 Arable Crops 4,981 4,064 1.23 54,145 7,018 4,438 56,443 2,780 -0.06 37.3 Sheep 49,173 7,746 6.35 49,055 1,221 7,326 107,976 9,335 -0.03 100.0 Dairy Cattle -175,232 28,851 -6.07 280,639 210,096 64,672 116,180 -11,380 -0.02 11.1 Vineyards -914 6,345 -0.14 69,216 27,918 928 78,265 -9,769 0.01 45.0 Mixed Livestock 0.0 Greenhouse Vegetables 122,678 60,799 2.02 419,479 17,667 0 359,083 3,897 0.15 100.0 Olive Growing -6,572 4,817 -1.36 21,661 15,822 6,380 52,196 -59 -0.35 27.8 Swine 0.0 Other 44,401 8,571 5.18 8,025 17,449 0 761,955 3,593 0.08 25.0 Poultry -1,599 0 0 166 0 261,351 3,218 -0.02 0.0 Citrus Fruits 5,680 1,755 3.24 12,835 0 1,500 42,782 -893 0.00 25.0 Open Field Vegetables 112,228 7,696 14.58 65,191 4,999 15,734 264,272 24,885 0.04 41.7 Fruits in Shell 0.0 Intensive Beef Cattle 0.0 TOTAL 2,868 8,917 0.32 85,271 28,871 4,770 103,062 -625 -0.02 39.5 Quality worse than simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock -40,620 8,060 -5.04 116,804 26,607 2,615 45,815 -1,637 -0.02 66.7 Extensive Beef Cattle -8,802 2,948 -2.99 17,765 1,836 3,929 20,100 -665 -0.18 44.4 168 Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Gabriele Dono, Rebecca Buttinelli, Raffaele Cortignani Mixed Crops -9,655 2,392 -4.04 31,950 1,015 1,889 20,353 -489 -0.39 31.8 Mixed Fruits -48,012 7,367 -6.52 68,521 50,730 1,171 72,980 1,249 -0.06 35.4 Arable Crops -15,208 1,874 -8.12 27,857 3,053 1,051 21,169 -4 -0.06 25.5 Sheep 0.0 Dairy Cattle -19,023 36,746 -0.52 538,991 67,693 26,983 237,489 -12,790 -0.02 85.2 Vineyards -42,756 5,680 -7.53 36,530 41,608 927 60,935 -2,871 -0.06 46.9 Mixed Livestock 0.0 Greenhouse Vegetables 0.0 Olive Growing -9,662 163 -59.46 676 135 7,174 9,261 -358 -1.64 11.1 Swine 0.0 Other -79,015 25,764 -3.07 245,432 92,080 1,777 294,073 -64,573 -0.01 75.0 Poultry 0.0 Citrus Fruits -3,535 4,852 -0.73 32,999 4,715 0 92,400 6,772 -0.02 50.0 Open Field Vegetables -4,704 31,944 -0.15 237,877 32,417 4,251 355,549 -3,144 -0.01 41.7 Fruits in Shell 0.0 Intensive Beef Cattle -43,921 7,617 -5.77 36,872 57,418 1,617 165,490 9,123 -0.05 100.0 TOTAL -35,660 11,303 -3.16 124,412 41,558 4,863 101,302 -6,058 -0.21 40.9 Farmhouse better than simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock 49,983 7,658 6.53 84,241 2,807 1,406 107,202 -499 0.04 15.6 Extensive Beef Cattle 107,815 20,280 5.32 107,942 16,347 8,544 378,880 -7,073 0.02 26.3 Mixed Crops 26,066 927 28.12 8,842 1,211 7,410 107,226 -3,954 0.01 5.3 Mixed Fruits 38,252 6,127 6.24 118,149 7,232 2,794 66,355 -1,215 0.04 13.5 Arable Crops 59,830 7,684 7.79 144,572 15,041 13,981 93,245 -4,904 0.01 19.3 Sheep 261,716 58,210 4.50 276,053 20,478 17,014 662,378 6,050 0.03 40.0 Dairy Cattle 50,293 20,315 2.48 188,155 17,720 28,265 107,586 11,629 0.00 6.2 Vineyards 143,257 18,216 7.86 125,046 9,352 3,561 341,652 -50,443 0.07 7.0 Mixed Livestock 44,940 9,433 4.76 137,639 2,510 2,841 44,754 695 0.02 50.0 Greenhouse Vegetables 0.0 Olive Growing 7,107 8,913 0.80 125,718 2,291 6,944 31,462 1,726 -0.03 73.3 Swine 0.0 Other 141,020 18,546 7.60 274,564 12,067 170 245,407 15 0.08 41.7 Poultry 0.0 Citrus Fruits 0.0 Open Field Vegetables 0.0 Fruits in Shell 0.0 Intensive Beef Cattle 0.0 TOTAL 70,640 14,907 4.74 142,460 9,187 8,668 171,327 -2,907 0.03 17.2 Farmhouse alternating over simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock 3,293 25,083 0.13 372,873 29,246 10,591 132,646 3,529 0.01 60.0 Extensive Beef Cattle -5,787 17,719 -0.33 143,984 20,068 12,585 103,071 -3,036 -0.02 52.6 Mixed Crops 3,753 7,523 0.50 98,239 7,717 5,268 40,742 5,328 0.01 63.2 Mixed Fruits -10,534 27,722 -0.38 280,899 46,421 2,188 109,643 5,422 0.00 37.8 Arable Crops 10,323 14,586 0.71 168,903 13,273 11,008 118,242 2,503 -0.03 33.3 Sheep 62,237 36,748 1.69 146,030 92 23,333 67,419 1,860 0.00 20.0 Dairy Cattle -15,110 20,433 -0.74 380,562 63,411 10,528 127,419 12,472 0.01 23.7 Vineyards 15,204 15,159 1.00 201,807 26,725 2,057 110,558 -23,815 0.01 62.0 Mixed Livestock 57 10,535 0.01 97,329 3,796 7,089 25,627 -2,911 -0.01 41.7 169Financial performance of connected Agribusiness activities in Italian agriculture Bio-based and Applied Economics 11(2): 147-169, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12211 Greenhouse Vegetables 0.0 Olive Growing -8,316 1,612 -5.16 4,956 276 2,930 33,794 -120 -0.29 20.0 Swine -8,855 1,853 -4.78 15,738 1,082 357 104,530 -4,545 -0.02 50.0 Other 22,456 1,943 11.56 26,156 9,904 19 159,023 -5,203 0.08 33.3 Poultry 0.0 Citrus Fruits 0.0 Open Field Vegetables 41,056 7,097 5.79 173,061 14,071 138 55,430 16,973 0.10 40.0 Fruits in Shell 0.0 Intensive Beef Cattle 0.0 TOTAL 9,991 11,094 0.90 112,224 15,716 6,768 97,772 913 -0.01 42.1 Farmhouse worse than simple farming FCFE DEPR F/D AMOC INVES CAP II GSP DWCC ROI %N Mixed Crops and Livestock -38,318 8,179 -4.68 144,514 28,072 1,474 15,361 -495 -0.18 24.4 Extensive Beef Cattle -11,946 1,968 -6.07 14,096 1,697 3,379 16,722 -3,706 -0.14 21.1 Mixed Crops -56,520 74,737 -0.76 752,915 59,157 4,630 80,847 -96,498 -0.19 31.6 Mixed Fruits -49,275 16,497 -2.99 244,037 78,449 689 71,227 -10,382 -0.01 48.6 Arable Crops -10,529 27,052 -0.39 424,340 8,171 3,452 55,426 -3,555 -0.07 47.4 Sheep -20,992 144,062 -0.15 337,282 37,894 18,848 151,222 -21,282 -0.04 40.0 Dairy Cattle -59,479 18,757 -3.17 324,263 76,349 9,700 73,979 5,278 -0.01 70.1 Vineyards -39,324 14,267 -2.76 178,816 21,264 1,334 135,022 -8,476 -0.03 31.0 Mixed Livestock -411,154 2,993 -137.37 443,043 396,241 588 7,831 3,001 -0.03 8.3 Greenhouse Vegetables 0.0 Olive Growing -19,688 1,619 -12.16 71,543 35,615 9,272 10,903 11,799 -0.19 6.7 Swine -17,262 1,811 -9.53 9,367 1 0 5,320 -736 -0.27 50.0 Other -23,333 15,840 -1.47 351,705 10,590 0 27,534 -23,596 -0.02 25.0 Poultry 0.0 Citrus Fruits 0.0 Open Field Vegetables -28,544 5,396 -5.29 90,163 20,090 57 30,692 913 -0.02 60.0 Fruits in Shell 0.0 Intensive Beef Cattle -6,447 8,521 -0.76 51,330 13,644 6,124 126,006 -4,896 -0.04 100.0 TOTAL -42,442 25,121 -1.69 305,184 47,696 5,402 72,568 -10,259 -0.09 40.7 Volume 11, Issue 2 - 2022 Firenze University Press Agriculture, food and global value chains: issues, methods and challenges Margherita Scoppola Mapping global value chain participation and positioning in agriculture and food: stylised facts, empirical evidence and critical issues Silvia Nenci1, Ilaria Fusacchia1,2, Anna Giunta1,2, Pierluigi Montalbano3, Carlo Pietrobelli1,4 On the relationships among durum wheat yields and weather conditions: evidence from Apulia region, Southern Italy Marco Tappi*, Gianluca Nardone, Fabio Gaetano Santeramo A choice model-based analysis of diversification in organic and conventional farms Andrea Bonfiglio*, Carla Abitabile, Roberto Henke Financial performance of connected Agribusiness activities in Italian agriculture Gabriele Dono*, Rebecca Buttinelli, Raffaele Cortignani Pesticides, crop choices and changes in well-being Geremia Gios1,*, Stefano Farinelli2, Flavia Kheiraoui3, Fabrizio Martini4, Jacopo Gabriele Orlando5