Energetic and Monetary Analysis of Efficiency in Family-Owned Dairy Goat Production Systems in Andalusia (Southern Spain).

Citation: Mena, Y.; Morales-Jerrett, E.; Soler-Montiel, M.; Pérez-Neira, D.; Mancilla-Leytón, J.M. Energetic and Monetary Analysis of Efficiency in Family-Owned Dairy Goat Production Systems in Andalusia (Southern Spain). Animals 2024,14, 104. https:// doi.org/10.3390/ani14010104 Academic Editors: Simona Tarricone, Maria Selvaggi and Maria Antonietta Colonna Received: 14 November 2023 Revised: 21 December 2023 Accepted: 22 December 2023 Published: 27 December 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). animals Article Energetic and Monetary Analysis of Efficiency in Family-Owned Dairy Goat Production Systems in Andalusia (Southern Spain) Yolanda Mena 1,* , Eduardo Morales-Jerrett 1, Marta Soler-Montiel 2, David Pérez-Neira 3 and Juan Manuel Mancilla-Leytón4 1Departamento de Agronomía, Escuela Técnica Superior de Ingeniería Agronómica, Universidad de Sevilla, 41013 Sevilla, Spain; [email protected] 2Departamento de Economía Aplicada, Universidad de Sevilla, 41012 Sevilla, Spain; [email protected] 3Departamento of Economía y Estadística, Universidad de León, 24071 León, Spain; [email protected] 4Departamento de Biología Vegetal y Ecología, Facultad de Biología, Universidad de Sevilla, 41012 Sevilla, Spain; [email protected] *Correspondence: [email protected]; Tel.: +34-954-486-449 Simple Summary: Dairy goat farming in Andalusia is diverse and predominantly family-based and presents particularities in terms of its economic strategies that require a specific approach. For this reason, in the context of climatic and energy crises that have a decisive influence on the activity, it has been considered relevant to deepen the knowledge of the economic and energy profiles of the different goat production systems, filling an information gap that existed. For this purpose, twenty-one farms, representatives of the dairy goat system diversity in Andalusia, were monitored for one year, obtaining technical-economic information that allowed the calculation of different energy and economic indicators. The results of this work demonstrate that, from an energetic point of view, goat farms that base their feeding strategies on grazing and make optimal use of natural resources are more efficient than models based on permanent stabling. This greater environmental sustainability derived from extensification is penalized by the markets, which give priority to intensive models with greater milk production and less food autonomy. Nevertheless, this study demonstrates that extensification strategies are capable of remunerating family labor and can, therefore, be economically viable, provided that support from the Common Agricultural Policy is included in the analyses. Abstract: The family-owned dairy goat sector in Andalusia presents great diversity. Taking into account the particularities of their economic strategies, which are focused on generating net value added and a stable long-term remuneration for family labor, this work aims to expand the scarce existing knowledge on the energetic and economic profiles of the different caprine management systems in a context of climate and energy crisis. For this purpose, twenty-one farms, representatives of the four typologies of the Andalusian dairy goat system, were monitored for one year: pastoral systems, grazing systems with high feed supply, indoor systems with associated crops, and indoor systems without associated crops. Technical-economic information was obtained that allowed the calculation of energy and economic indicators. In terms of socialized output, the differences found were due to the energy derived from milk sales, which was clearly lower in pastoral systems. The higher proportion of energy output obtained from manure with respect to edible products (milk and meat) highlights the importance of the former in energetic terms. High values for external inputs are found in the intensive group (111.22 GJ LSU −1 ), while the lowest results correspond to the pastoral group (36.96 GJ LSU−1). The main external input is the energy proceeding from purchased feed, which accounts for over 79% of the total external energy input in all four groups. The highest energy efficiency corresponds to the pastoral group, which is also the most efficient one in the use of non-renewable energy to produce milk and meat. Additionally, the level of eco-efficiency is higher in pastoral systems. Common Agricultural Policy funds contribute to increasing the remuneration of family work in pastoral systems, assimilating it to the rest of the systems. Therefore, intensification does not imply an absolute monetary advantage in all cases, while extensification can be remunerative for family-owned dairy farming. Animals 2024,14, 104. https://doi.org/10.3390/ani14010104 https://www.mdpi.com/journal/animals Animals 2024,14, 104 2 of 19 Keywords: caprine; eco-efficiency; energy metabolism; feed management; LCA 1. Introduction Dairy goat rearing has great socio-economic and environmental importance in Mediterranean countries because it provides food of organoleptic, functional, and nutritive quality [ 1 ], generates employment and economic resources in marginal areas, reverses depopulation in rural areas [ 2 , 3 ], and promotes a circular economy. In addition, when there is grazing, goat rearing contributes to seed dispersal, biodiversity enhancement, natural fertilization, and forest fire prevention, among other benefits [ 4 ]. These advantages are closely related to the different management systems implemented, particularly in family farms. Being acquainted with and understanding this diversity is, therefore, of increasing scientific relevance [5]. In the last few decades, goat production systems in Europe have experienced important technological advances in management and genetic improvement, which have led to an increase in productivity per animal and farm, the optimization of labor and other resources, and the generation of health-safe products [ 6 ]. These improvements have come along with a significant process of production intensification [ 7 , 8 ], aimed to increase the performance per goat and year, particularly in milk production, because in most cases, milk sales amount to 80% of the farms’ income [ 9 ]. This process has progressively broken the link between this sector and the territory where it is based, with important social and environmental consequences. Despite this intensification and in contrast with what has happened in other sectors, such as the dairy cow sector and, to a lesser extent, the sheep sector, the Spanish dairy goat sector is very diverse in its production systems. This diversity is especially evident in Andalusia, the Spanish southern region, which has a population of 0.99 M goats and produces 51% of the country’s goat milk and 13.26% of the European output [ 10 ]. Practically, the whole production comes from local breeds (Murciano-Granadina, Malagueña, Florida, and Payoya) that are perfectly adapted to their environment and managed under different production systems. The systems range from those where goats remain indoors throughout the year, although they have access to outdoor exercise yards [ 11 , 12 ], to those in which the goats graze all year long and most of the day [ 5 , 13 ]. The farms are generally managed by a family labor force and provide economic sustenance to families who live mostly in disadvantaged areas with very few economic and production alternatives [14]. In Europe, the dairy goat sector has been experiencing economic viability problems for decades, although the situation varies greatly depending on the country [ 6 , 8 , 15 , 16 ]. This economic crisis reduces the ability to improve sustainability, especially in familyowned farms (goat family farms) [ 17 , 18 ], and creates dependence on public support [ 19 ]. Given the difficulty of influencing selling and the main input prices, the focus is on cost reduction [ 20 ]. Feed and labor costs appear as the main costs in all studies, together with the cost of indebtedness linked to fixed-capital investments in modernization and intensification processes [ 21 ]. Some research indicates that reducing feed costs linked to grazing can increase gross margin [ 13 , 22 ]. Other works indicate that, without proper management, the reduction in feed costs either does not exist or can be offset by increased labor costs [ 18 ]. There is also an open debate on the most appropriate indicators to assess the sustainability and economic viability of family farming enterprises [ 23 , 24 ]. In any case, it should be noted that studies on the economic viability of dairy goat farms (farm-level economic viability) in Europe are very scarce, especially those that differentiate between production systems according to their degree of intensification, despite repeated references to the high heterogeneity existing in the activity [ 6 , 8 , 16 ]. In the case of family farms, it is necessary to take into consideration the particularities of their economic strategies, focused on generating net value added and a long-term steady remuneration for the family’s own work, as their way of life through various management systems or styles of farming [ 24 , 25 ]. Animals 2024,14, 104 3 of 19 From the point of view of sustainability, debates on the economic viability of goat rearing are complicated with analyses that allow a better understanding of the dependency on and the efficiency of natural resources. In the current climate change and peak oil context, this is particularly important in relation to the use of energy [ 26 – 28 ]. Thus, the energy transition and climate change adaptation processes are, among other issues, central and cross-sectional elements of the New Green Deal [ 29 ], which urges the scaling up of food-provision systems based on the use of renewable energy, low emission intensity, and high energy efficiency in the production of biomass [ 30 ]. In this new context, efficient agricultural or stockbreeding systems will not only be the most productive ones but also the ones capable of optimizing the input of useful energy and of channeling it, to a larger extent, to the production of usable goods, mostly food and manure, and of derived ecosystem services [31]. The Spanish Plan for Climate Change Adaptation (2021–2030) and the more local Andalusian Plan for Climate Action (2021–2030) are defined on the same terms, indicating the need to increase knowledge on the assessment of global warming risks and impacts on the main crops and livestock species and, on that basis, to improve sustainability and climate change adaptation in rural areas by promoting short marketing channels, bioeconomy, circular economy, and proximity agriculture, among other strategies meant to mitigate climate impact and improve resilience [ 32 , 33 ]. This is why, for sound individual and sectorial decision-making, it is necessary to increase knowledge of the different production systems, not only from an economic perspective but from a biophysical one, based on the use of specific indicators. These indicators should be objectively verifiable, replicable, sensitive to changes in the system, and capable of analyzing their relationship with other indicators. In addition, the information collection process to generate these indicators must be simple and non-costly for the farmers [34]. In the case of dairy goat rearing, the literature has mainly focused on calculating and analyzing greenhouse gas emissions (GHG) through the sector’s carbon footprint [ 17 , 35 – 38 ]. From the point of view of sustainability, some works have shown how organic production can reduce the GHG emissions of the industry [ 39 ], especially if carbon sink is taken into consideration [ 40 , 41 ], while other studies have researched the potential of reducing the emissions associated with grazing [ 42 ]. There are fewer studies that analyze the use of energy in this context [ 43 ]. Some of them quantify energy consumption in goat milk production as part of a wider assessment linked to the life cycle analysis (LCA) methodology [ 44 ]. For instance, Zucali et al. [ 45 ] and other authors have shown how enteric emissions and manure storage are the main hotspots in Italian farms in terms of carbon footprint [ 35 , 40 ]. However, feed and, particularly, purchased fodder are the main hotspots in other impact categories, such as freshwater eutrophication, soil use, and depletion of fossil and renewable mineral resources (/ib). In the same vein, Soares-Cabral et al. [ 46 ] analyze how the partial replacement of soya with grass or hay may be an important driver of reduction in energy consumption. From a methodological point of view, what these works have in common is that they analyze the energy dimension in terms of the farms’ “expenses” or “costs”, but they do not include any efficiency indicators such as, for example, the energy return on energy invested (EROI) [ 47 ]. Recently, some authors have criticized energy analyses as insufficient because they reproduce the economistic cost–benefit logic and have proposed alternative approaches to measure “hidden” energy flows and the energy-related opportunity costs, such as for instance, those linked to the use of manure or to grazing [ 48 , 49 ]. These internal energy flows, which are usually neither accounted for nor analyzed for not being marketoriented, contribute to the maintenance of the structure and functions of agro–silvo–pastoral systems and are, therefore, fundamental in the flow of ecosystem services [ 49 , 50 ]. As far as the authors know, there is only one previous work that has applied this approach to the study of the dairy goat sector at the farm level, focusing on only three farms [ 51 ]. But, it does not consider the monetary dimension of production, and it is therefore difficult to carry out comparative analyses of the results concerning this activity and of the various Animals 2024,14, 104 4 of 19 production models. This work aims to contribute to filling this gap by presenting, in a novel way, the energy and economic profiles of the different management systems used in the dairy goat-rearing sector. The main objective of this work is to analyze, from an energy and monetary perspective, the efficiency of various dairy goat production systems using local breeds, classified according to their degree of intensification. The purpose is to contribute to the generation of knowledge on the economic, social, and environmental dimensions of this activity. For the achievement of this general objective, the authors suggest filling the existing information gap through (i) generating energy indicators using an agroecological approach that takes into account the farms’ internal energy flows; (ii) generating monetary indicators to assess the economic dimension of each production model; and (iii) analyzing the behavior of the different dairy goat production systems in terms of eco-efficiency. This knowledge is necessary to develop dairy goat production systems that are capable of responding appropriately to the current needs of Western societies, not only in regards to the provision of enough quality food but also as an element that can improve the natural environment in which the farms carry out their activity and provide a livelihood for people living in rural areas. 2. Materials and Methods 2.1. Sample Selection and Data Collection Twenty-one representative dairy goat farms using local breeds in Andalusia (southern Spain) were selected and monitored for one year (2018) in order to collect information that allowed the generation of technical and economic indicators following a methodology developed by the authors in previous studies [ 13 , 52 ]. Attending to the criteria established by Morales-Jerrett et al. [ 5 ], the farms were classified into four groups: (i) IS: indoor systems without associated crops (n = 8); (ii) ISC: indoor systems with associated crops (n = 3); (iii) GS: grazing systems with high feed supply (n = 5); and (iv) PS: pastoral systems (n = 5) . Table 1shows the main characteristics of each group of study. The main general data gathered from the four dairy goat production systems can be found in Table S1 and Figure S1 of the Supplementary Materials. Table 1. Main characteristics of the dairy goat production systems analyzed. Production System Main Characteristics Indoor systems without associated crops (IS) - Goats permanently indoors with outdoor exercise yards; - No grazing or crop surfaces associated with the goats; - Food supplied comes from outside the farm; - High consumption of concentrates and fodder; - Highly milk-productive, with de-seasonalized sales. Indoor systems with associated crops (ISC) - Goats permanently indoors with outdoor exercise yards; - Crop surfaces associated with the goats for fodder production; - High consumption of concentrates (from outside the farm) and fodder; - Highly milk-productive, with de-seasonalized sales. Grazing systems with high feed supply (GS) - Variable times of grazing; - Large surfaces dedicated to grazing, whether natural or cultivated; - High external supply of concentrates and even fodder; - Highly milk-productive, certain degree of seasonality. Pastoral systems (PS) - Herds outside the whole year, with variable grazing times; - Large surfaces dedicated to grazing, both natural and cultivated; - Feed from outside the farm is in short supply; - Lower milk production than in other groups marked seasonality; - Usually, multispecies livestock farms. Animals 2024,14, 104 5 of 19 2.2. Energy Assessment 2.2.1. Boundaries, Functional Units, and Analysis Perspective For the energy analysis carried out in this study, a “cradle-to-farm gate” approach was implemented. The main functional unit used was the livestock standard unit (LSU; one adult animal = 0.15 LSU, the rest of animals = 0.11 LSU), with the liter of milk produced used as a complement. The agroecological approach proposed by Pérez-Neira et al. [ 51 ] for the energy analysis of caprine activity is applied here because it contemplates stockbreeding as part of a complex agroecosystem exchanging energy flows with other natural and social systems [ 50 , 53 ]. This approach brings to light and analyses internal energy flows that are not usually taken into consideration despite their contribution to maintaining the structure and functions of the ecosystem and, therefore, sustaining the flow of ecosystem services [49,54]. The analysis was structured around three levels: (1) it incorporated the indirect energy cost of producing and transporting the inputs and capital used during the livestock production process; (2) it took into consideration the energy directly consumed inside the farm; and (3) it included the energy and capital generated by the milk and meat produced, known as socialized output (Figure 1). Given that two of the four types of farms studied here have implemented models in which the goats are permanently indoors, and considering the heterogeneity of the territories in which goats graze in the other two models (Figure S1 of the Supplementary Materials), such variables as unharvested biomass (the biomass that returns to the system by abandonment, without human intervention) and the accumulated biomass of woody species are not included in the analysis, as suggested by Ramos-García et al. [49]. Animals 2024, 14, x FOR PEER REVIEW 5 of 20 - Highly milk-productive, certain degree of seasonality. Pastoral systems (PS) - Herds outside the whole year, with variable grazing times; - Large surfaces dedicated to grazing, both natural and cultivated; - Feed from outside the farm is in short supply; - Lower milk production than in other groups marked seasonality; - Usually, multispecies livestock farms. 2.2. Energy Assessment 2.2.1. Boundaries, Functional Units, and Analysis Perspective For the energy analysis carried out in this study, a “cradle-to-farm gate” approach was implemented. The main functional unit used was the livestock standard unit (LSU; one adult animal = 0.15 LSU, the rest of animals = 0.11 LSU), with the liter of milk produced used as a complement. The agroecological approach proposed by Pérez-Neira et al. [51] for the energy analysis of caprine activity is applied here because it contemplates stockbreeding as part of a complex agroecosystem exchanging energy flows with other natural and social systems [50,53]. This approach brings to light and analyses internal energy flows that are not usually taken into consideration despite their contribution to maintaining the structure and functions of the ecosystem and, therefore, sustaining the flow of ecosystem services [49,54]. The analysis was structured around three levels: (1) it incorporated the indirect energy cost of producing and transporting the inputs and capital used during the livestock production process; (2) it took into consideration the energy directly consumed inside the farm; and (3) it included the energy and capital generated by the milk and meat produced, known as socialized output (Figure 1). Given that two of the four types of farms studied here have implemented models in which the goats are permanently indoors, and considering the heterogeneity of the territories in which goats graze in the other two models (Figure S1 of the Supplementary Materials), such variables as unharvested biomass (the biomass that returns to the system by abandonment, without human intervention) and the accumulated biomass of woody species are not included in the analysis, as suggested by Ramos-García et al. [49]. Figure 1. System boundaries of the energy analysis. 2.2.2. Energy Indicators Ten indicators were selected for the energy analysis. Total energy output (TEO) accounts for livestock-related energy output, whether socialized (milk or meat), re-em- ployed (manure), or part of the livestock structure (increase/decrease in herd weight) Level 1 Level 2 Level 3 Indirect energy Direct energy Socialized output Internal Manure Inputs Livestock External Inputs and Capital (Diesel, NPK, feedstuff, transport, labor, tools, etc.) Socialized output (Milk and Meat) Pasture and crops Figure 1. System boundaries of the energy analysis. 2.2.2. Energy Indicators Ten indicators were selected for the energy analysis. Total energy output (TEO) accounts for livestock-related energy output, whether socialized (milk or meat), re-employed (manure), or part of the livestock structure (increase/decrease in herd weight) (Equation (1)). The cumulative energy demand (CED) measures the use of energy in livestock farms, incorporated in the form of external inputs (concentrates, fertilizers, diesel, machinery, etc.) (EI) or internal inputs (own crops for livestock, grazing, etc.) (II) (Equation (2)). The coefficients used to calculate the energy output (EO) were taken from Moreiras et al. [ 55 ] and Pérez-Neira et al. [ 47 ], while the CED was calculated using the CML-IA methodology (3.07 version) and the Ecoinvent 3.5 and Agribalyse 3.0 databases with SimaPro software (9.3.0.3). TEO = ∑SO ×α(o) + EOr ×α(e) + I/D L ×α(id) (1) CED = ∑EI + II = ∑I(j) ×ß(j) (2) Animals 2024,14, 104 6 of 19 TEO = total energy output (MJ LSU −1 ); SO = socialized output (unit LSU −1 ), which includes milk (l LSU −1 ) and meat (kg LSU −1 ); α (o) = energy coefficient of socialized output (MJ L −1 or kg −1 ); EOr = energy output reuse (manure or milk) (kg LSU −1 ); α (e) = energy coefficient of manure or milk (MJ kg −1 ); I/D L = energy increase/decrease in the number of livestock units (kg LSU −1 ); α (id) = energy coefficient of increase/decrease in livestock (MJ kg −1 ); CED = cumulative energy demand (MJ LSU −1 ); EI = external inputs: concentrates, fodder, electricity, diesel, labor, and other expenses, etc. (MJ LSU −1 ); II = internal inputs: own crops consumed during grazing or indoors, and energy contribution of natural pastures (MJ LSU −1 ); I(j) = Input “j”, where j = feed purchased, fodder, electricity, petrol/diesel, lubricants, phytosanitary material, plastics, tools, fertilizers, seeds, machinery, labor, etc. (unit LSU−1); and ß(j) = energy coefficient of I(j) (MJ unit−1). The final energy return on investment (final EROI) assesses the energy efficiency of livestock systems (Equation (3)), especially in relation to the use of non-renewable energy (NR final EROI) (Equation (4)). The NR EROI food (Equation (5)) focuses on the energy efficiency of producing human food. The external feed dependence (EFD) (Equation (6)) measures the degree of intensification/extensification as the level of dependence/autonomy of a farm in relation to purchased livestock feed. The avoided energy cost of manure (AECM) measures the savings derived from replacing synthetic fertilizers with manure (Equation (7)), while the avoided energy cost of natural pasture consumed during grazing (AECP) does so in relation to the use of natural pastures (Equation (8)). This index is generated by adding to the gross energy provided by natural pasture (ECNP) the energy cost of production required to obtain the same amount of energy (EC of ECNP) from cultivated crops. Complementarily, by applying the concept of opportunity cost, the food/feed EROI (Equation (9)) measures the energy efficiency of transforming edible human food into edible energy in the form of meat and milk. Another relevant concept to be integrated into energy analyses of livestock husbandry is that of “avoided cost”. It allows for identifying the benefits derived from choosing one alternative instead of another. This concept was initially proposed by Environmental Economics as a monetary indicator [ 56 ], and it has been recently reinterpreted from a biophysical perspective by Ecological Economics. The avoided land cost of natural pasture consumed during grazing (ALCP) has been calculated based on the agricultural area required to produce the feed (grains, feed concentrates, and cultivated fodder) that would be necessary to find a substitute, in terms of energy, for the contribution of natural pasture consumed by grazing (Equation (10)). Final EROI = TEO ×CED−1(3) NR Final EROI = TEO ×NR CED−1(4) NR EROIfood = (SO ×α(o))/NR (5) EFD = GEEF ×GERL−1×100 (6) AECM = RM ×Nm×ß(N) (7) AECP = ECNP + EC of ECNP (8) Food/feed EROI = SO ×GEfeedg/c−1(9) ALCP = ECNP ×EYsc−1(10) Final EROI = final energy return on investment; TEO = total energy output (MJ LSU −1 ); CED = cumulative energy demand (MJ LSU −1 ); NR final EROI = non-renewable final EROI; NR CED = non-renewable CED (MJ LSU −1 ), to calculate this indicator, the use of renewable energy was subtracted from the CED; NR EROI food = NR EROI related to food production; SO = socialized output (unit LSU −1 ), which includes milk (L LSU −1 ) and meat (kg LSU −1 ); α (o) = energy coefficient of socialized output (MJ L −1 or kg −1 ); EFD = external feed dependence (%); GEEF = gross energy of external feed (MJ LSU −1 ); Animals 2024,14, 104 7 of 19 GERL = gross energy requirements of the livestock −1 (MJ LSU −1 ); AECM = avoided energy cost of manure (MJ LSU −1 ); RM = reused manure (kg LSU −1 ); Nm = nitrogen contained in manure (kg N kg −1 ); ß(N) = energy coefficient relative to the energy cost of producing nitrogen (MJ kg N −1 ); AECP = avoided energy cost of natural pasture consumed during grazing (MJ LSU or L−1) ; ECNP = energy contribution of natural pasture consumed during grazing (MJ LSU or L−1) ; EC of ECNP = energy cost of production to generate ECNP if it were replaced by crops (MJ LSU or L−1) ; Food/feed EROI = food/feed energy return on investment; SO = socialized output (MJ LSU −1 ); GEfeedg/c = gross energy of grains/feed concentrates with an opportunity cost with regard to human food (MJ LSU−1) ; ALCP = avoided land cost of natural pasture consumed during grazing (ha); and EYsc = energy yield of the substituted crops (MJ ha−1). 2.3. Economic Indicators There is a theoretical-methodological debate on the most appropriate indicators to assess and perform comparative analyses of the economic viability and sustainability of farms. Spicka et al. [ 23 ] suggest the value added and the value added per unit of work because these indicators represent the remuneration of the factors of production (land, labor, and capital) irrespective of their ownership, as unpaid family labor is not deducted. In the case of owned-family farms, Rossi [ 57 ] recommends analyzing the family farm income, defined as the financial reward to all members of the family who work on the farm for their labor, management, and investment, i.e., the remuneration of family labor, land, and capital factors. This question is especially relevant in the analysis of family farms based on the unpaid labor of family members, as farming is a way of living and not only a self-employment strategy [ 24 ]. Thus, the most accurate indicators to assess the economic viability of family farms are the net value added and the family farm income (total and per worker) calculated after the deduction of the cost of paid labor [ 58 ], which represents the return to the farmer family for the use of their own production factors [ 57 ]. The family farm income is equivalent to the net margin when the cost of family labor and other opportunity costs are not subtracted from its calculation. The revenues (Equations (11) and (12)) are milk, goat kids, animals for rearing, and other sources of income, such as manure and aids. The main costs (Equations (13) and (14)) included are feed, health, energy, hired labor, amortizations, and other minor costs. Based on this information, six economic indicators were obtained. The gross value added (GVA) (Equation (15)) and the net value added (NVA) (Equations (16) and (17)) result in resting to the market incomes the intermediate consumption in the first case and also the amortizations in the second. The goat family farm income (GFFI) (Equation (18)) represents the remuneration of the family labor. When aids are taken into account, the goat family farm income cap aid (GFFI CAP aid ) is calculated (Equations (19) and (20)). Furthermore, using the CED, two eco-efficiency indicators were generated: one in relation to total market revenues, the EI1 (Equation (21)), and the other in relation to the goat family farm income with aid (GFFICAP aid), the EI2 (Equation (22)). SI = SM +SGT+ SOG + SAR+ SMmanure (11) AI = DPCAP + RDPCAP (12) IC = FC + HC+ EC + MC (13) HLC = W ×HWD (14) GVA = SI −IC (15) NVA = GVA −AM (16) NVAper worker = NVA ×AWU−1(17) GFFI = SI −(IC + HCL + AM) (18) GFFICAP aid = TI −(IC + HCL + AM) (19) Animals 2024,14, 104 8 of 19 GFFICAP aid per worker= GFFICAP aid ×AWU−1(20) EI1 = SI ×NR CED−1(21) EI2 = GFFICAP aid ×NR CED−1(22) SI = total market incomes (EUR goat −1 ) that include the sales of milk (SM), goat kids (SGK), old goats (SOG), animals for rearing (SAR), and manure (SM manure ); AI =aAid income (EUR goat −1 ) result of adding Direct Payment (DP CAP ) and Rural Development Payment (RDP CAP ) of Common Agrarian Policy; IC = intermediate consumption (EUR goat−1) that includes cost of feed (FC), health (HC), energy (EC), and other minor costs (MC); HLC = hired labor cost (EUR goat −1 ) that is the result of multiply wages (W) by hired working day (HWD); GVA = gross value added (EUR goat −1 ); NVA = net value added (EUR goat−1 or EUR AWU −1 ); GFFI= goat family farm income (EUR goat −1 ); AM = amortizations (EUR goat −1 ); GFFICAP aid = goat family farm income with aid (EUR goat −1 ); GFFIcap aid per worker = estimates the above indicator expressed per worker (EUR AWU −1 ); EI1 and EI2 = eco-efficiency indicators (EUR GJ −1 ); NR CED = non-renewable CED (MJ LSU −1 ), to calculate this indicator the use of renewable energy was subtracted from the CED. 2.4. Statistical Analysis To test for possible significant differences among the results of the four dairy goat production systems, an analysis of variance (ANOVA) was carried out. This was preceded by a test of normality and homoscedasticity. Variables have been log10-transformed for normalization of frequency distribution where necessary. A Tukey test was performed to evaluate significant differences between them (p ≤ 0.05) in two-to-two comparisons. For this purpose, IBM SPSS v 25.0 was used. 3. Results 3.1. Energy Assessment 3.1.1. Energy Outputs and Inputs of the Caprine Activity The main energy outputs of the monitored farms are shown in Table 2. Significant differences in terms of total energy output were not found in two of the elements analyzed: the socialized output (SO) and the energy output reused (EOr). The accumulated biomass and the increase/decrease in the number of animals during one year had no influence on the final results because of their low value. In terms of socialized output (SO), the levels of energy obtained from meat were similar in all groups, and the differences found were due to the energy derived from milk sales (p= 0.041), which was clearly lower in pastoral systems (5.34 GJ LSU −1 , Table 2). When the energy output reused was analyzed, significant differences were found between the indoor system without associated crops (IS) and the rest of the groups (p= 0.030) due to differences in the management of manure. In the systems that included grazing, i.e., grazing systems (GS) and pastoral systems (PS), 74.36% and 95.35%, respectively, of the energy output was related to the use of manure inside the farms, unlike what happened in indoor farms (IS and ISC), where manure was carried outside the farm and represented up to 100% and 91.24%, respectively, of the total biomass energy reused (Table 2). Figure 2represents the proportion of the different energy outputs described in Table 2. Differences between inside and outside use of manure are shown, as is the importance of energy proceeding from milk. This figure is useful to highlight the proportion of energy output obtained from manure (biomass reused outside or inside the farm) and from milk and meat sales. This proportion varies from 63:37 in the IS group to 77:23 in pastoral farms. In the ISC and GS groups, the proportion is, respectively, 68:32 and 67:33. Animals 2024,14, 104 9 of 19 Table 2. Energy outputs of the four dairy goat production systems identified in Andalusia (GJ LSU −1 ). IS: indoor systems without associated crops; ISC: indoor systems with associated crops; GS: grazing systems with high feed supply; and PS: pastoral systems. Energy Outputs IS ISC GS PS p-Value Total energy output (TEO) (a + b + c) 26.20 27.71 28.70 24.73 0.151 (a) Socialized output (SO) (i + ii) 9.70 a8.86 ab 9.38 a5.60 b0.041 (i) Milk 9.52 a8.64 ab 9.15 a5.34 b0.041 (ii) Meat 0.18 0.22 0.23 0.26 0.509 (b) Energy output reused (EOr) (i + ii) 16.41 b18.84 a19.35 a19.16 a0.030 (i) Inside the farm (ia + ib) 0.00 b1.66 b14.90 a18.96 a0.010 (ia) Milk 0.00 b0.00 ab 0.50 ab 0.70 a0.007 (ib) Manure 0.00 b1.66 b14.39 a18.27 a0.010 (ii) Outside the farm (manure) 16.41 a17.19 a4.45 b0.20 b0.006 (c) Increase/decrease in livestock (I/D L) 0.10 0.01 −0.04 −0.04 0.105 a, b Values with different letters on the same row mean significant difference. Animals 2024, 14, x FOR PEER REVIEW 10 of 20 significant differences between groups. Energy from cultivated forages is important in the ISC group (23.17 GJ LSU−1), while the GS and the PS groups present high values of energy linked to grazing surfaces, particularly when they are natural (8.39 and 29.55 GJ LSU−1, respectively) (Table 3). As a result, the proportion of internal and external energy inputs varies according to the system reviewed (see Figure 3). The different goat production systems analyzed have diverse energy metabolisms—(Figure 4). Table 3. Energy inputs of the four dairy goat production systems identified in Andalusia (GJ LSU−1). IS: indoor systems without associated crops; ISC: indoor systems with associated crops; GS: grazing systems with high feed supply; and PS: pastoral systems. Energy Inputs IS ISC GS PS p-Value Cumulative energy demand (CED) (a + b) 111.45 a 97.42 ab 102.53 a 74.27 b 0.001 (a) External inputs (EI) (i + … + vi) 111.22 a 74.25 b 89.24 b 36.90 c 0.000 (i) Electricity 2.58 1.94 2.82 0.89 0.339 (ii) Petrol/gas 2.48 1.79 2.04 0.99 0.511 (iii) Feed purchased 99.01 a 58.83 c 78.78 b 30.82 d 0.000 (iv) Other operational expenses 3.60 7.30 3.43 3.40 0.282 (v) Labor 0.39 0.30 0.47 0.28 0.121 (vi) Machinery 3.16 ab 4.09 a 1.69 ab 0.52 b 0.006 (b) Internal inputs (II) 0.23 c 23.17 b 13.29 b 37.37 a 0.000 (vii) Cultivated forages 0.00 b 23.17 a 0.00 b 1.36 b 0.001 (viii) Cultivated grazing surfaces 0.23 0.00 4.90 6.46 0.076 (ix) Natural grazing surfaces 0.00 c 0.00 c 8.39 b 29.55 a 0.000 Non-renewable CED (i + … + vi) 31.87 a 28.51 a 28.27 a 11.66 b 0.007 a,b,c,d Values with different letters on the same row mean significant difference. Figure 2. Proportion of the components of the total energy output (TEO) in the four dairy goat production systems identified in Andalusia, excluding accumulated biomass and reused milk inside the farm. IS: indoor systems without associated crops; ISC: indoor systems with associated crops; GS: grazing systems with high feed supply; and PS: pastoral systems. Socialized output: milk and meat. Reused biomass: manure used inside and outside the farm (inside and outside the farm in the figure). 36.5 31.2 31.8 21.6 0.7 0.8 0.8 1.1 6.0 51.9 76.6 62.8 62.0 15.5 0.8 0 10 20 30 40 50 60 70 80 90 100 IS ISC GS PS % Milk Meat Inside the farm Outside the farm Figure 2. Proportion of the components of the total energy output (TEO) in the four dairy goat production systems identified in Andalusia, excluding accumulated biomass and reused milk inside the farm. IS: indoor systems without associated crops; ISC: indoor systems with associated crops; GS: grazing systems with high feed supply; and PS: pastoral systems. Socialized output: milk and meat. Reused biomass: manure used inside and outside the farm (inside and outside the farm in the figure). Unlike the outputs, the energy inputs of the different farming systems present significant differences, as shown by the two components of the CED values: external and internal inputs (Table 3). High values for external inputs were found in the IS group (111.22 GJ LSU−1) , while the lower results corresponded to the PS group (36.96 GJ LSU −1 ). The main external input is the energy proceeding from purchased feed, which accounts for more than 79% of the total external energy input in all four groups and for which significant differences were found between systems. No differences were found in the categories of electricity, diesel/gas, labor, and other operational expenses (Table 3). The agroecological approach incorporates into the energy analysis the farm crops used to feed the goats and the natural surfaces used for grazing. Obviously, IS values are close to 0.00, and there are significant differences between groups. Energy from cultivated forages is important in the ISC group (23.17 GJ LSU −1 ), while the GS and the PS groups present high values of energy linked to grazing surfaces, particularly when they are natural (8.39 and 29.55 GJ LSU−1 , respectively) (Table 3). As a result, the proportion of internal and external energy inputs Animals 2024,14, 104 16 of 19 payment schemes, such as eco-schemes, can contribute positively to the maintenance of pastoral goat systems, which are currently in the minority. Supplementary Materials: The following supporting information can be downloaded at https: //www.mdpi.com/article/10.3390/ani14010104/s1, Figure S1: Main characteristics of the surfaces used for goats in systems reserving an area for grazing; Table S1: General characteristics, main outputs obtained, and inputs used during 2018 in the twenty-one farms monitored. IS: indoor systems without associated crops; ISC: indoor systems with associated crops; GS: grazing systems with high feed supply; and PS: pastoral systems. Author Contributions: Conceptualization, Y.M., E.M.-J., D.P.-N., M.S.-M. and J.M.M.-L.; methodology, formal analysis, and data curation, Y.M., E.M.-J., D.P.-N., M.S.-M. and J.M.M.-L.; writing—original draft preparation, Y.M., E.M.-J., D.P.-N. and M.S.-M.; writing—review and editing, Y.M., E.M.-J., D.P.-N., M.S.-M. and J.M.M.-L.; supervision and funding acquisition, Y.M. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Measure 16 of the Andalusian Rural Development Program 2014–2020, included in the grants for the creation and operation of the European Innovation Partnership (EIP-Agri) Operational Groups on Agricultural Productivity and Sustainability, and co-funded by the European Union through the European Agricultural Fund for Rural Development (90%) and the Andalusian Regional Government (10%) (Project GOP21-GR-16-0016). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data is contained within the article. Acknowledgments: The authors want to dedicate this manuscript to the memory of Eduardo Sevilla Guzmán, a friend and outstanding scientist, and a key researcher in the field of agroecology in Spain and Latin America. Special thanks are also due to the farmers and technicians (J. Fernández, O. González, C. Lara, and M. D. 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