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Article

Sustainability of Agricultural and Forestry Systems: Resource Footprint Approach

by
Yannay Casas-Ledón
1,2,*,
Javiera Silva
1,
Sebastián Larrere
1 and
Yenisleidy Martínez-Martínez
1
1
Environmental Engineering Department, Faculty of Environmental Sciences-EULA Center, University of Concepción, Victor Lamas 1290, Casilla 160-C, Concepción 4070386, Chile
2
Water Research Center for Agriculture and Mining (CRHIAM), ANID Fondap, Victoria 1295, Concepción 4070411, Chile
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(23), 10173; https://doi.org/10.3390/su162310173
Submission received: 15 October 2024 / Revised: 7 November 2024 / Accepted: 18 November 2024 / Published: 21 November 2024
(This article belongs to the Section Resources and Sustainable Utilization)
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Abstract

:
Land management is critical for the conservation of natural resources, particularly in agroforestry systems which rely heavily on land productivity and availability. Optimizing land utilization is critical for sustainable biomass production and is a key component of achieving effective, long-term sustainable land management. This study assesses the resource efficiency of agroforestry production systems with a novel exergy-based indicator (ΔEF). The indicator was used in the Biobío and Ñuble regions to assess the resource balance between six agricultural and two forestry production systems. The ΔEF values ranged from positive to negative, with positive values indicating better resource usage and negative values suggesting the opposite. Eucalyptus globulus had higher ΔEF values (18.06–19.5 MJex/m2.yr) than Pinus radiata (−2.71 to −1.47 MJex/m2.yr), indicating better sustainability due to its high biomass yields and lower harvesting period and resource consumption. Sugar beet, wheat, and potatoes were the most sustainable (8.57–154.6 MJex/m2.yr) because of their high yields and less intensive harvesting methods. Disparities in biomass yield, potential net primary production (NPPpot), and land management intensity drive differences in ΔEF across regions. Our findings enhance the understanding of local and non-local resource efficiency in agroforestry systems, revealing significant drivers to encourage more sustainable land management practices.

Graphical Abstract

1. Introduction

Land degradation is a complex issue primarily caused by the poor management of natural capital, such as soil, water, and vegetation [1]. The increasing demand for natural resources, particularly land for agricultural and forestry activities, limits land availability [2]. Factors like climate change, population growth, and urbanization can increase land productivity by increasing water and agrochemical use. However, increasing land use intensity can enhance crop yields without acquiring more land, but it also exerts significant environmental pressure, contributing to land degradation and adverse ecosystem impacts [1,3,4]. Therefore, efficient land use is crucial to avoid environmental damage, preserve natural resources, and promote a sustainable production system.
The efficiency of a production system is determined by the ratio of useful output produced to the total input used [5]. In the context of agroforestry systems, it measures the amount of biomass produced per input unit (e.g., local and non-local resources); in other words, it reflects how effective resources are converted into the desired product. Evaluating efficiency in agricultural and forestry systems is a complex task due to the challenges of accurately accounting for all outputs—such as harvest residues and losses, which are often overlooked in crop productivity estimations—and inputs, particularly natural resources, some of which are difficult to account comprehensively [2]. Despite these challenges, significant efforts have been made to assess and monitor resource use efficiency in both natural and human-managed systems by integrating the exergy concept with ecological principles, providing a more holistic approach for measuring the intensity and efficiency of land use [6,7].
In this context, net primary production (NPP)-exergy based indicators have been proposed to account for the consumption of land resources in life cycle assessments, as demonstrated by Alvarenga et al. [8]. Site-dependent characterization factors were developed in the above study for land as a resource for natural and human-made systems at different spatial scales (grid, region, country, continent), which allowed a more complete and consistent spatially differentiated accounting of land resources. Along the same line, Taelman et al. [9] developed an exergy-based indicator based on the human appropriation of NPP (HANPPex) to assess land use impacts on ecosystem health. Additionally, Alvarenga et al. [2] provided a new method for analyzing the natural resource balance of terrestrial biomass production systems using exergy, integrating the total biomass produced and the cumulative consumption of non-local resources (e.g., fertilizer, pesticides, fuel, etc.) for a human-made biomass production system. Moreover, the authors emphasized that the proposed indicator offered a more comprehensive representation of the natural resource balance in biomass production systems (agricultural and forestry) than existing methodologies (ΔNPPLC and NEV), becoming a more accurate approach for assessing the resource footprint of biomass production systems. Furthermore, the indicator is highly sensitive to the main product output, which is directly influenced by biomass production practices which vary by country and location. As a result, the development of sustainable biomass production strategies necessitates data that reflect site-specific characteristics at the subnational level.
In Chile, few studies have concentrated on the efficiency of land use in agroforestry systems. A study conducted by Martínez Martínez et al. [10] evaluated the effects of land use alterations on biomass productivity in the Biobío and Ñuble regions, employing an NPP-exergy indicator. The authors investigated variations in NPP-based exergy dynamics in connection to land use transitions in South-central Chile between 2000 and 2014. Their findings demonstrated that the spread of exotic tree plants increased the NPPEX. However, the study only assessed the yearly biomass available in ecosystems (including above- and below-ground NPP) at the time, based on land cover type, without considering the harvested NPP component or accounting for biomass changes in relation to the region’s potential natural NPP in the absence of human intervention. More recently, Casas-Ledón et al. [11] endeavored to evaluate land use efficiency in the Biobío and Ñuble regions from 2007 to 2014. Their studies revealed that croplands and forest plantations demonstrated the highest degrees of human appropriation of net primary production (HANPPex), indicating substantial land use intensity and biomass harvest as the main factor affecting human intervention on land resources. Nonetheless, these studies were primarily concerned with assessing the change in land cover type, without discriminating between the harvesting of specific crops (e.g., wheat crop) and specialized forestry management. Furthermore, high-biomass harvesting is caused by agricultural and forestry operations that consume non-local resources such as agrochemicals, water, and fuel, which are not included in impact evaluations. Thus, in terms of sustainability, a biomass production system requires both local natural inputs (land, sun irradiation, etc.) and non-local resources (fertilizers, irrigation). To gain a better understanding of the sustainability of a biomass production system, we analyzed the natural resource balance, which included the total biomass output from land, non-local resources, and potential annual natural biomass production.
The aforementioned research indicates that the efficiency and sustainability of agricultural and forestry systems are significant concerns related to land use in the Biobío and Ñuble regions. The assessment thus far has concentrated exclusively on the primary biomass product collected or removed, neglecting the consideration of any non-local resources utilized in its production. Consequently, to achieve a more thorough understanding of the sustainability of agroforestry biomass production systems, it is important to evaluate the complete resource balance. This study seeks to evaluate the sustainability and efficiency of agroforestry systems in the Biobío and Ñuble regions utilizing an innovative exergy-based indicator (ΔEF) that accounts for the whole resource footprint, encompassing both local and non-local inputs. This comprehensive resource balance strategy seeks to improve the sustainable management of land and resources, providing critical insights for enhancing the sustainability of agricultural and forestry systems.

2. Materials and Methods

2.1. Study Area

The biomass production systems considered in this work involved agricultural and forestry systems across two regions in Chile’s south-central zone, specifically the Biobío and Ñuble regions. Both regions are characterized by their remarkable crop and forest plantation land cover. Biobío has a cropland cover of approximately 13.6% and forest plantations on around 36.9% of the total surface, while Ñuble boasts a crop and plantation land cover of 29.6% and 29.4% (Figure 1).
This research focused on six agricultural production systems (maize, oat, wheat, rapeseed, sugar beet, and potato), selected due to their relevance to the harvested surface area in each region. For instance, in the Biobío region, wheat accounted for 31% of the total crop land harvested area, maize for 23%, oat for 13%, rapeseed for 6.3%, potato for 5%, and sugar beet for 3%. Meanwhile, the Ñuble region predominantly harvests cereal crops such as wheat (57%), oat (27%), and maize (23%), followed by sugar beet (9%), rice (6%), potato (4%), and rapeseed (3%) [12]. For forestry systems, Pinus radiata and Eucalyptus globulus commercial plantations were considered, which represented the higher harvested area for both regions (13–14% for Pinus radiata and 5–8% for Eucalyptus globulus) [13].

2.2. Resource Exergy Footprint Indicator

The net annual exergy production (ΔEF) indicator was used for measuring sustainability. This method was proposed by Alvarenga et al. [2], and it gives insight into natural resource efficiency.
The ΔEF is estimated based on the difference between the total annual produced biomass (A), the cumulative exergy consumption embodied in non-local resources (C), and the natural potential of biomass production (B), as described in Equation (1).
∆EF = A − (B + C)
All parameters involved in Equation (1) are expressed in MJex/m2.yr. The description of each parameter and the methodology used for estimating them can be seen below.

2.3. Total Annual Produced Biomass (A)

The total produced biomass component includes the main crop harvested, above-ground residues, below-ground biomass, and what has been destroyed during harvest in the course of a year [14]. In this study, information regarding the biomass harvested was obtained from annual regional statistics provided by ODEPA [12], which included regional harvested surface (ha) and yields (ton/ha) per crops. The total above-ground crop residues and below-ground crop residues (e.g., roots killed during harvest) were estimated based on crop harvest factors (HFs) and below/above-ground ratios (Rs), as described in Equations (2) and (3):
Crop residueabove = Yieldcrop × HFcrop
Crop residuebelow = Yieldcrop × (1 + HFcrop)/R
where Yieldcrop is the average crop yield in ton dry matter (DM)/ha. The crop yields were based on average values between 2018 and 2022 for both regions and all agricultural systems. Historical data related to crop yields, disaggregated by region, were not available before 2018 due to the fact that the actual Ñuble region was only created in 2018, separating it from the old Biobío region.
No regional and specific pre-harvest biomass losses related to consumption by herbivores, losses by weed, and during crop growth were available for our study cases. Therefore, a factor loss (0.14) reported by Haberl et al. [15] was considered, which was the same for all crops.
Crop residues above and below the ground were expressed as tons of DM per hectare. The moisture content (MC) of each crop was utilized to calculate the yield crop total weight (metric ton) in dry matter. Finally, these data were transformed into carbon-based (kgC/m2.yr) metrics, assuming a carbon content/dry matter factor depending on crop variety (Table S1) [16].
Meanwhile, the total biomass from forest plantation resources included above- and below-ground biomass, where the above-ground biomass comprised harvested, residual, and remnant components. The harvested biomass (kgC/m2.yr) was estimated for each plantation species according to Equation (4):
Ah-forest = Vh × D × CF
where Vh is the total annual harvested volume (m3) per ha for each plantation species, D is the wood density (kg/m3), and CF is the carbon fraction (kgC/kg dry matter (DM)). The biomass residual component (Aresidual) was also estimated using Equation (4), knowing the volume of branches pruned and/or discarded in the different stages of forestry management (pruning, thinning, and final harvest). The biomass remnant component (Aremnant) was estimated as the difference between the above-ground biomass minus the biomass harvested and residual, according to Equation (5):
Aremnant = (MAI × BEF × D × CF) − Ah-forest − Aresidual
where MAI is the mean annual increment (m3/m2.yr), and BEF is the biomass expansion factor (dimensionless). Data about forestry management practices in Chile, forest biomass production, and input data for all estimations were collected from different national sources, including the Chilean Forest Institute (INFOR) [13,17,18]. More information about forest data and specific parameters can be found in the Supplementary Materials (Table S2).
The exergy content of the biomass was estimated using the exergy-to-energy (β) ratio method [19], which is dependent on biomass chemical composition (content of carbon, oxygen, nitrogen, and hydrogen) and its LHV. The chemical composition of the main product and above-ground residue was collected from Phyllis2 database (https://phyllis.nl/Browse/Standard/ECN-Phyllis, accessed on 7 September 2023) (Tables S3–S6). The group contribution method was used when only biochemical composition was provided, particularly for potatoes’ and sugar beets’ above-ground biomass. For below-ground residues, the chemical exergy content per dry matter (MJex/kgDM) was assumed to be the average of the above-ground biomass chemical exergy content, due to the lack of specific data.
The specific data required to estimate the above-ground and below-ground biomass were taken from Casas-Ledón et al. [11], shown in the Supplementary Materials (Table S1). The chemical composition of the crops and the description of each exergy method are also available in the Supplementary Materials (Section S3).

2.4. Natural Potential of Biomass (B)

The natural potential of biomass (B) constitutes an estimation of biomass production in a region without human interventions. For this study, B expressed in MJex/m2.yr was extrapolated from a regionalized net primary productivity map provided by Casas-Ledón et al. [11]. It was estimated using the Miami vegetation model, which produces reliable NPP patterns [20] based on easy-access mean annual temperature (BT) and total annual precipitation (BP) data, as described in Equations (6)–(8). For this reason, despite being pioneered along with other NPP estimation methods, it remains an attractive tool for this purpose [21,22].
BT = 3000 × (1 + exp (1.1315 − 0.119 × T))−1
BP = 3000 × (1 − exp (−0.000664 × P))
B = min (BT, BP)
In the above, T is the mean annual temperature (°C), and P is the total annual precipitation (mm).
The potential primary production for a given location (pixel) is defined as the minimum values of the calculated temperature-limited (BT) and precipitation-limited (BP) biomass accumulation, expressed in terms of carbon-based (kgC/m2.yr) metrics, assuming a carbon content/dry matter factor of 50%. The B layers were translated into exergy terms (MJex/m2.yr) using Alvarenga et al. [2]’s biomass-to-exergy conversion factor (β = 42.9 MJex/kgC).
The average potential primary productivity (B) was associated with the land use types via the superposition of B and the land use maps using ArcGIS 10.4 software. Data on crop georeferentiation at the community level were provided by the Territorial Statistical Consultation System of the Office of Agrarian Policies (ODEPA), but not at the pixel level. Therefore, communal average B values of each crop were considered in this work. The average B values by crop and forestry systems, disaggregated by region, are listed in Table 1.

2.5. Exergy Embodied in Non-Local Resources (C)

Non-local resources refer to all resources consumed during crop harvesting, including fertilizers, pesticides, irrigation, and fuels for agricultural machines (e.g., sowing and harvesting). The cumulative exergy consumption (C) expressed in terms of (MJex/m2.yr) for each non-local resource was evaluated using Equation (9), computed by multiplying the resource amount consumed (mi) in the “j” biomass production system, the exergy characterization factor (CFi) for each resource, and the total produced biomass A (kg biomass/m2.yr) in each “j” biomass production system (e.g., wheat production).
Cj = Σ(mi × CFi)j × Aj
Firstly, the specific amount of non-local resources (mi) (e.g., kg pesticides/kg total biomass) consumed during crop harvesting was required, which was extracted from regional agriculture statistics [23].
Afterward, the cumulative consumption of non-local resources along the production chain, considering the cradle-to-gate approach (Figure 2), was accounted through the ecoinvent database v.2.2 [24] for all crops. Finally, these data were transformed in exergy terms using CFi (e.g., MJex/kg pesticides), extracted via the Cumulative Exergy Extraction from Natural Environment (CEENE) method [25]. Renewable resources, fossil fuels, nuclear energy, metal ores, minerals, water resources, and atmospheric resources were the categories considered during this step. The land occupation category was excluded to avoid double accounting the same factors covered by potential primary production (B). A similar assumption was made by Alvarenga et al. [8].

3. Results and Discussion

3.1. Resource Balance Analysis

As shown in Figure 3, there are marked differences in the ΔEF for various crops and regions. The proposed indicator depicted positive values for forest plantations of Eucaliptus globulus (18.06 to 19.5 MJex/m2.yr) in both regions. In contract, Pinus radiata plantations displayed negative values (−2.71 to −1.47 MJex/m2.yr). Agricultural crops such as sugar beet, wheat, and potatoes showed positive values (8.57 to 154.6 MJex/m2.yr) regardless of the region, while the rest of crops (maize, oat, and rapeseed) had a negative net balance (−2.48 to −25.7 MJex/m2.yr). A positive ΔEF signifies a more sustainable production system, as the biomass generated by the anthropogenic system (agricultural and forestry) exceeds the local (B) and non-local resources utilized (C) for its production. A negative number (ΔEF < 0) indicates that the use of non-local resources exceeds the biomass generated by human-made systems (agricultural and forestry), resulting in a less sustainable system.
The exergy balance disparities between Eucalyptus globulus and Pinus radiata in forest plantations mostly hinged on biomass yields, biomass composition, harvesting period, and the utilization of local versus non-local resources. The total net primary production (NPP) of Eucalyptus globulus plantations was approximately 1.65 times greater than that of Pinus radiata, primarily due to Eucalyptus globulus exhibiting a higher mean annual increment (MAI = 22.75 m3/ha.yr) and biomass expansion factor (BEF = 1.77), along with shorter harvest durations (10–12 years), in contrast to Pinus radiata (MAI = 21 m3/ha.yr and BEF = 1.54, 18–28 years, respectively). Regarding non-local resource consumption, this parameter did not influence greatly the exergy balance since forest management for both species was less intensive, not requiring large fuel volumes for pruning and thinning activities. In addition, the highest agrochemical consumption occurred during the first year, post sowing, specifically during the fertilization and weed control phases, to ensure optimal plantation development. Regarding Pinus radiata, it is essential to acknowledge that, although the exergy balance yielded negative values, their magnitude was negligible (almost zero), signifying a tiny disparity between total biomass production and total resource consumption.
The negative resource balance for Pinus radiata indicates that current management practices extract less resources from the ecosystem than can be replenished naturally and through silviculture management. This performance is the result of existing silvicultural regimes—pulp-oriented and clear wood-oriented—in radiata pine plantations [26]. The effective management of competing vegetation during the forest establishment phase is crucial for long-term forest productivity, guaranteeing elevated tree survival rates, optimal growth, and uniformity at canopy closure [27]. While modern silvicultural techniques are optimized for biomass quality in both the pulpwood and clear wood industries, the greatest opportunity for improvement lies in the enhancement of weed control strategies during plantation setup. Specifically, reducing or replacing pesticide usage through methods such as mulching, manual weeding, or integrated pest control might decrease dependence on herbicides [27]. However, modifications in pesticide dosage or replacement with alternative herbicides are heavily contingent upon local variables (e.g., soil composition, climatic factors, and regional biodiversity), potentially leading to a diminished biomass yield.
In addition, this study identified uncertainties associated with NPP models and the completeness and accuracy of regional data for each biomass production system, specifically regarding biomass yields and the consumption of non-local resources (e.g., agrochemicals, fuels) during harvesting seasons. This latest component was derived from regional statistics, which were the result of current forest management and agricultural practices (i.e., mechanized and non-mechanized harvesting). Consequently, the primary uncertainties arising from these investigations were predominantly associated with NPP estimation.
The ΔEF indicator is very sensitive to the potential primary productivity (B) value, which depends on the estimation of potential NPP models, and the assumptions made during the assessment of various NPP components and data collection. The model employed for computing NPP (e.g., CASA, Chikugo model, LPJ) significantly influences ΔEF variability. This study utilized the Miami NPP model, which relies solely on mean annual temperature and precipitation as the limiting factors for vegetation growth. Consequently, the potential NPP predictions derived herein may overstate the ΔEF values. In this sense, the study reported by Casas-Ledón et al. [11] indicated that the potential NPP values were overestimated by a factor of 1.4 in comparison to the LPJ estimations for the examined regions. Sun et al. [20] published analogous results, noting that climate-based models, such as the Miami model, exhibit significant overestimation in comparison to the CASA model. Similarly, Yang et al. [28] employed various NPP estimation models to assess carbon stock in China, including MODIS data, the Miami model, and the Thornthwaite Memorial model. The authors determined that the validation model utilizing MODIS (R2 = 0.39) surpassed both the Miami model (R2 = 0.27) and the Thornthwaite Memorial model (R2 = 0.28), attaining a superior R2 value and lower root mean square error (RMSE) and mean absolute error (MAE). Consequently, climatic productivity models, such as the Miami model, are consistently employed to assess potential productivity as the maximum regional output [22]. Accordingly, the employment of more precise NPP models, such as the dynamic global vegetation model (LP) [29], the satellite-based model (MODIS), and the terrestrial ecosystem model (CASA), may yield lower potential biomass productivity values, indicating alterations in ΔEF values and their magnitude. For example, the net exergy balance for the management of Pinus radiata and maize, which have previously shown negative values, could yield a positive value. Consequently, methodologies for addressing fluctuations in vegetation density should be explored to enhance the precision of computed potential NPP values and, subsequently, the ΔEF index.
In the context of crops, positive ΔEF values were significantly affected by a substantial biomass output relative to a diminished resource use. The notable values of the ΔEF for sugar beet and wheat, in comparison to other crops, could be attributed to their substantial biomass yields (sugar beet: 106.6–111.7 ton/ha; wheat: 43.4–54.1 ton/ha), which were approximately 5–12 times greater than the average yields of other crops (Table S1), as well as the intensity of the harvesting practices employed. The harvesting of sugar beet was conducted through a fully mechanized process that enhanced efficiency, productivity, and land management, resulting in increased crop production on a reduced land footprint. On the other hand, the negative ΔEF values of agricultural crops were highly influenced by large potential NPP values, which exceeded the total biomass production by 15%, on average.
From a resource use efficiency perspective, Ñuble exhibited a more sustainable forestry and agricultural production system than the Biobío region, as evidenced by superior ΔEF values resulting from elevated biomass yields, with the exception of sugar beet, which performed better in Biobío than in Ñuble.
Figure 4 presents the contribution of each parameter involved in the ΔEF indicator, disaggregated by region and crop type. The resource consumption associated with each crop shows minimal variation between Biobío and Ñuble. This could be related to the absence of a detailed site-specific inventory of agrochemicals, fuel, and water (non-local resources) consumed during crop harvesting per region. Accordingly, the overall values per meter squared (1 m2) of harvested land were used for estimating these variables in the exergy balance. Fuel consumption refers to the amount of fossil resources utilized throughout the life cycle of all agrochemicals applied in the production of each crop and forest species. This parameter accounted for approximately 86.9% of the total indirect consumption of non-local resources in agricultural systems but was nearly negligible (1.7% of the total) in forest production systems. As illustrated in Figure 4, its value varies among crops, as it depends on the quantity of agrochemicals required for each crop and the type of agrochemical used, since each has its own production chain and associated environmental impacts. For instance, maize and rapeseed cultivation both required several agrochemicals (fertilizers and herbicides). However, the agrochemicals used in maize production exhibited a broader exergy content range (11–208 MJex/kg) and were applied in larger quantities (1–1200 kg/ha.yr) than those used in rapeseed cultivation (0.7–208 MJex/kg and 0.2–514 kg/ha.yr, respectively), due to the specific agrochemical types and their life cycle value chain. Consequently, maize cultivation was associated with a higher fuel consumption compared to rapeseed.
Differences in local resource consumption (B) and resource production were observed between regions. The Biobío region exhibited a higher average (34.76 MJex/m2.yr) compared to Ñuble (33.70 MJex/m2.yr), regardless of forestry or agricultural systems (Table 1). This result can be rationalized by the Miami NPP model’s dependence on mean annual temperature and precipitation. In this case, temperature resulted to be the limiting factor, determining the potential.
The Ñuble region exhibited an average total biomass production of 82.31 MJex/m2.yr, which was 1.06 times higher than the value recorded for Biobío (77.33 MJex/m2.yr). Similarly, Figure 4 highlights notable differences in the total resource (biomass) produced across crop types and forest species. The primary factor influencing total resource production was the biomass harvest of the main product, which accounted for approximately 80% of the total value and was dependent on crop yields and agricultural practices. Notably, sugar beet and wheat exhibited the highest biomass harvests among all crops in both regions, exceeding the average biomass harvest of the remaining crops by 85–86.2% and 78.3–81.9%, respectively. This outcome is attributable to the significantly higher yields of sugar beet (106.6–111.7 ton/ha) and wheat (43.4–54.1 ton/ha) compared to the other crops. Regarding forest plantations, the Eucalyptus globulus species showed a 1.3 times higher biomass production than Pinus radiata while only representing 35% of the total crop biomass harvested (averaged values).
Previous analyses demonstrated that the ΔEF indicator assesses the efficiency of resource utilization for biomass production, encompassing all biomass engaged in the balance, both above and below ground. Nevertheless, not all biomass generated is integrated into the socio-economic system. For instance, above-ground residues are typically disregarded and often burned in the field due to their low costs, resulting in the squandered opportunity to transform them into high-value products and exacerbating greenhouse gas emissions from the agricultural sector [30]. A comparable scenario arises with subterranean residue. Although it constitutes 3.4% of the total biomass output, its recovery and utilization pose challenges; yet, it might significantly enhance soil organic matter, thereby benefiting the environment. Conversely, above-ground residues may represent a significant fraction of biomass in specific cases (e.g., potatoes: 36.8%; oat: 28.9%; rapeseed: 22.1%), offering the possibility of leveraging their potential for generating added-value products, such as compost [31], biogas-based energy [32], biochar for soil amendment and carbon storage [33], and bio-oil as a basis for biorefinery pathways [32].

3.2. Final Remarks

According to our results, the ΔEF has been shown to be a dynamic indicator that significantly contributes to the analysis and evaluation of the complete resource consumption chain connected with agricultural and forestry systems through exergy-based methodologies. It showed considerable sensitivity to biomass yields and land use intensity, and it elucidated the primary factors influencing resource efficiency in biomass production systems. Consequently, the ΔEF may provide empirical information for formulating resource management strategies intended to enhance natural resource utilization and enact regulations for the more sustainable management of anthropogenic systems. However, the suggested indicator solely focused on the quantity of resources used in forestry management and agricultural techniques, without considering the environmental impact associated with the two productive systems. For example, agricultural activities have dramatically impacted the quality of surface water sources in the Biobío Basin and Ñuble River, owing to the application of pesticides and fertilizers [34,35]. Consequently, water pollution is anticipated to exacerbate water scarcity in numerous locations, heightening conflict and competition among water users. Between 2002 and 2035, the population of Ñuble and Biobío is predicted to increase by around 18.3% to 15.3% [36], putting significant demand on local and non-local resources and impacting the environment to provide food security. Moreover, the shift to organic agricultural practices may diminish reliance on agrochemicals and their pollution of water resources; yet, the output of organic farming is inferior to that of chemically intensive fertilization systems [37]. To facilitate an economically viable transition to sustainable agriculture, policies are required to bridge the yield gap between ecologically and chemically intensive agricultural systems [38].
Within the forestry system, Eucalyptus globulus plantations were more sustainable than Pinus radiata, with a net exergy balance approaching the average value observed in the agricultural system (excluding sugar beet production). However, forestry management practices can lead to significant negative environmental and social impacts at the local scale. In this sense, Braun et al. [39] found negative impacts on plant biodiversity associated with the effect of forestry plantations, registering a significant reduction in species richness, native species, and endemic species in the central zone of Chile. Simultaneously, there is evidence of habitat fragmentation [40], increased land erosion [41] and a loss in the morpho-sedimentary control capacity of the Biobío River Basin [42,43]. Additionally, Casas-Ledón et al. [44] demonstrated that the silviculture stage presented major environmental impacts in all impact categories (abiotic depletion, acidification, eutrophication, toxicities, etc.) of the bioenergy pathway with integrated biomass gasification and internal combustion engines, mainly caused by agrochemical use.
Recently, the replacement of native forests with forest plantations has emerged as a significant forerunner to the proliferation of megafires by altering the terrain and increasing its vulnerability to fire outbreaks [45]. Both study regions exhibit a significant prevalence of forest plantation land cover, accounting for 36.9% of the total in Biobio and 29.4% in Ñuble. The configuration of these landscapes predisposed them to the megafires that transpired in the south-central region of Chile in 2017 and 2023, a region which has undergone significant transformation into extensive exotic forestry plantations over the past 40 years—covering over 520,000 hectares in 2017 and 450,000 hectares in 2023 [45]. Moreover, significant societal influence is exerted on densely inhabited regions next to landscapes altered by extensive fires [46]. Megafires can alter the local climate, ecosystem processes, nutrient cycles, and the emission rates of gasses and particulate matter in the short and medium term, contingent upon the extent and intensity of the fires [47].
The agricultural techniques and forestry management practices previously addressed have significantly impacted the environment, ecology, and society at the local level. Consequently, it is advisable to augment sustainability evaluations with additional metrics or methodologies employed in environmental impact assessments, such as life cycle assessments. In this context, LCAs offer a thorough methodology for assessing environmental impacts, providing a well-informed foundation for decision making aimed at developing more sustainable biomass production systems.

4. Conclusions

This study examined the sustainability of agricultural and forestry production systems in the Biobío and Ñuble regions by analyzing the overall consumption of resources using exergy-based methods. Our research improves our understanding of resource use efficiency (natural and non-local) and the main components affecting resource efficiency in biomass production chains. These endeavors offer vital support in developing strategies to advance sustainable farming and forestry practices.
The findings underscore the fact that local environmental factors, including biomass yields and potential primary production values, are crucial in assessing the sustainability of agricultural and forestry systems. The ΔEF indicator underscores substantial discrepancies in resource efficiency among various biomass systems and regions. This illustrates that sustainability is contingent upon regional factors, and measures for enhancing resource efficiency must consider these local environmental dynamics. Crops, including sugar beet, wheat, and potatoes, demonstrated favorable exergy balances due to high biomass yields and efficient harvesting techniques, making them more sustainable. Moreover, Eucalyptus globulus demonstrated superior resource efficiency compared to Pinus radiata, attributable to its increased biomass output and shortened harvesting cycle.
Our findings indicate that the resource exergy footprint indicator solely considers the total resources consumed, without considering the environmental effects throughout the life cycle of the systems. Hence, future research must focus on incorporating environmental consequences into these analyses to obtain a better understanding of the impact and effectiveness of land use.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/su162310173/s1: Table S1: Region-specific agricultural parameters for the estimation of total biomass harvested. Crop moisture content (MC), below/above-ground ratios (Rs), and harvest factors (HFs); Table S2: Region-specific forestry species parameters for the estimation of total biomass harvested; Table S3: Elementary analysis of main products extracted from the Phillys Database; Table S4: Chemical composition of above-ground residue for each crop and its chemical exergy content (MJex/kg biomass); Table S5: Biochemical composition of potatoes and standard chemical exergy of contribution groups; Table S6: Composition of sugar beet leaves on dry mass basis (wt.% dry); Table S7: Polygalacturonic acid standard chemical exergy using the group contribution method; and Table S8: Glucan standard chemical exergy using the group contribution method. References [48,49,50,51,52] are cited in the Supplementary Materials.

Author Contributions

Conceptualization, Y.C.-L.; methodology, Y.C.-L., J.S. and Y.M.-M.; investigation, Y.C.-L., J.S., S.L. and Y.M.-M.; data curation, Y.C.-L., J.S. and Y.M.-M.; writing—original draft preparation, Y.C.-L. and Y.M.-M.; writing—review and editing, Y.C.-L.; visualization, Y.M.-M.; supervision, Y.C.-L.; funding acquisition, Y.C.-L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Research and Development Agency (ANID), ANID/CONICYT FONDECYT, with grant number 11170302, ANID/FONDAP/15130015, and ANID/FONDAP/1523A0001.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

No applicable.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Study area and land cover for each region.
Figure 1. Study area and land cover for each region.
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Figure 2. System boundaries for exergy balance of agricultural/forestry systems.
Figure 2. System boundaries for exergy balance of agricultural/forestry systems.
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Figure 3. Exergy balance (ΔEF, expressed in MJex/m2.yr) of forestry and agricultural systems in the Biobío and Ñuble regions.
Figure 3. Exergy balance (ΔEF, expressed in MJex/m2.yr) of forestry and agricultural systems in the Biobío and Ñuble regions.
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Figure 4. Parameters of the net exergy balance per biomass production systems and regions.
Figure 4. Parameters of the net exergy balance per biomass production systems and regions.
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Table 1. Average potential primary productivity (B) by crop and forestry system, disaggregated by region.
Table 1. Average potential primary productivity (B) by crop and forestry system, disaggregated by region.
Crops/ForestPotential Primary Productivity (B) (MJex/m2.yr)Potential Primary Productivity (B) (MJex/m2.yr)
Biobío RegionÑuble Region
Maize34.8433.51
Wheat34.4033.54
Oat34.3933.74
Rapeseed35.4533.87
Sugar beet35.0434.25
Potatoes34.4233.30
Eucalyptus globulus34.2732.89
Pinus radiada34.3333.09
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Casas-Ledón, Y.; Silva, J.; Larrere, S.; Martínez-Martínez, Y. Sustainability of Agricultural and Forestry Systems: Resource Footprint Approach. Sustainability 2024, 16, 10173. https://doi.org/10.3390/su162310173

AMA Style

Casas-Ledón Y, Silva J, Larrere S, Martínez-Martínez Y. Sustainability of Agricultural and Forestry Systems: Resource Footprint Approach. Sustainability. 2024; 16(23):10173. https://doi.org/10.3390/su162310173

Chicago/Turabian Style

Casas-Ledón, Yannay, Javiera Silva, Sebastián Larrere, and Yenisleidy Martínez-Martínez. 2024. "Sustainability of Agricultural and Forestry Systems: Resource Footprint Approach" Sustainability 16, no. 23: 10173. https://doi.org/10.3390/su162310173

APA Style

Casas-Ledón, Y., Silva, J., Larrere, S., & Martínez-Martínez, Y. (2024). Sustainability of Agricultural and Forestry Systems: Resource Footprint Approach. Sustainability, 16(23), 10173. https://doi.org/10.3390/su162310173

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