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Article

Agronomic Management, Socioeconomic Factors, and Carbon and Nitrogen Pools in Avocado–Coffee Systems in Huatusco, Veracruz, México

by
Diana Ayala-Montejo
1,2,
Pablo Martínez-Zurimendi
2,3,*,
Juan Fernando Gallardo-Lancho
4,†,
Rufo Sánchez-Hernández
5,
Eduardo Valdés-Velarde
6,*,
Teresita de Jesús Santiago-Vera
1,7 and
Joel Pérez-Nieto
6
1
Ministry of Science, Humanities, Technology and Innovation, Mexico (SECIHTI), Av. Insurgentes Sur 1582, Col. Crédito Constructor, Alcaldía Benito Juárez, Mexico City 03940, Ciudad de Mexico, Mexico
2
Villahermosa Unit, Department of Agriculture, Society and Environment, El Colegio de la Frontera Sur, Carretera Villahermosa-Reforma km 15.5, Ranchería Guineo II Section, Villahermosa 86280, Tabasco, Mexico
3
Institute of Sustainable Forest Management (IUFOR), University of Valladolid-INIA, Av. Madrid 44, 34004 Palencia, Spain
4
Higher Council for Scientific Research (CSIC), IRNASa, 37008 Salamanca, Spain
5
Academic Division of Agricultural Sciences, Universidad Juárez Autónoma de Tabasco, Carret era Villahermosa-Teapa km 25.2 Ranchería la Huasteca II Section, Villahermosa 86298, Tabasco, Mexico
6
Department of Phytotecnics, Chapingo Autonomous University, Carretera México-Texcoco km 38.5, Chapingo, Texcoco 56230, State of Mexico, Mexico
7
Center of Research and Higher Studies in Social Anthropology, San Cristobal de las Casas 14000, Chiapas, Mexico
*
Authors to whom correspondence should be addressed.
†
Retired.
Resources 2025, 14(3), 35; https://doi.org/10.3390/resources14030035
Submission received: 17 December 2024 / Revised: 27 January 2025 / Accepted: 17 February 2025 / Published: 21 February 2025
Browse Figures
Versions Notes

Abstract

:
Avocado cultivation is displacing traditional coffee production in Veracruz, Mexico. This change modifies the floristic composition and agronomic management (AM), both of which are affected by socioeconomic factors and generate variation in edaphic conditions. The objective of this research was to identify the socioeconomic variables that modify the characteristics of the AM of avocado and coffee systems and have repercussions on the carbon and nitrogen pools. Four case studies were analyzed: a renovated coffee polyculture (SRC), severely cleaned coffee polyculture (IPC), avocado–coffee system (ACS), and conventional avocado orchard (CAO). A socioeconomic analysis was carried out with a 30-year economic projection and 56 variables. Three plots were installed where vegetal biomass carbon (VBC) was evaluated; soil samples were taken in theses plots at three depths (in triplicate) to determine soil organic carbon (SOC) and total soil nitrogen (TSN) in each case. A principal component analysis (PCA) was performed. Four economic variables explain 50.9% of the variation in agronomic management and five social variables 50.2%, in both cases where the component is considered. In all systems, the variation in VBC is explained by the agronomic management, while the reported SOC presents an inverse relationship with AM. The ACS presents the higher values in total C in the system, and SOC and TSN pools, while the CAO shows negative impacts concerning soil organic matter quality. The ACS is sensitive to investment, market diversity, education, and external organic residue addition, explaining the variations in AM and the TSN pools.

1. Introduction

In Huatusco, Veracruz (Mexico), there are 6286 ha of agroforestry systems established as traditional coffee polycultures (TCPs); these systems represent 38.0% of the total surface [1]. This type of system is characterized by interspersing coffee trees with various species of tree and shrub plants (native or derived) that function as shade, showing a complex system with a high diversity of plant species that provide food, medicines, and useful raw materials for local subsistence [2,3].
The TCPs, due to their diversity of plant species and the integrated techniques that are implemented in this agronomic management (AM), provide socioeconomic benefits [4], in addition to having the potential to store and capture carbon (C). These TPCs provide sociocultural, socioenvironmental, and edaphoclimatic benefits that allow small producers to survive factors such as marginalization, education, training, and food availability [5]. Agroforestry systems (AFSs) with coffee offer edaphic conditions like those of a cloud forest, so their fertility is better compared to a monoculture, since their structural dynamics generate concentrations of soil organic matter (SOM) and total soil nitrogen (TSN) greater than those of a monoculture [6].
The AFS with coffee located in Veracruz can store between 188 and 255 Mg C ha−1 in the system and an average of 14.2 Mg N ha−1 in the soil [7,8]. Furthermore, it was reported that FAS studies in Mexico are limited to analyzing the interactions between soil organic carbon (SOC) compartments and biomass [9], but without including analysis that integrates socioeconomic variables and the value of productive diversification with other crops, nor the influence of the implemented AM, nor its impact on C and N pools in the TCP.
In the State of Veracruz, TCPs have decreased their productivity due to phytosanitary problems, such as rust (Hemileia vastatrix Berkeley & Broome) and the borer (Hyphotenemus hampei Ferrari) [10]. The lack of labor has also had an impact because economic resources are not sufficient, due to the fluctuations of prices in the international market, which influences the socioeconomic situation based on remittances, family support, and agricultural investment [11].
These socioeconomic conditions promote the abandonment or change of cultivation, which causes the loss of productive diversity, a decrease in storage and recycling of SOM, as well as the affectation of coffee quality such as defective, overripe, or borer (H. hampei)-infested, the dynamics of C in the system, and the total pools of TSN [12,13].
The cultivation of avocados has currently displaced the TCP, given that the State of Veracruz offers edaphoclimatic and eco-topographic conditions for its development [14], and the fruit has high value in the national and international market. Thus, Mexico’s contribution in 2022 was around 75% of the world avocado trade [15]. This change in land use generated the integration of avocados in coffee farms, whose association modifies the floristic composition and AM. As indicated above, both components are a consequence of socioeconomic factors, which alter the contribution and recycling of the organic residues generated, as well as the dynamics and cycles of C and N [15].
It has been reported that an AFS with coffee–avocado–guava has a profitability and productivity like an avocado monoculture [16], so the association of coffee and avocado (ACS) could be an alternative to counteract the total change of the TCP to orchard with a conventional avocado monoculture (CAO), whose AM could contribute to maintaining C and N pools.
Based on the abovementioned, the objective of the research is to identify the socioeconomic variables that modify the characteristics of the AM of the avocado–coffee production system and that subsequently impact the pools of C and N in the plant biomass and soil.

2. Materials and Methods

2.1. Study Area

The study was carried out in Huatusco of Chicuéllar (Veracruz, Mexico; 19°10′25″ N and 96°57′30″ W) at an average altitude of 1300 masl (Figure 1).
The climate is humid semi-warm with average annual precipitation and mean temperature of 2018 mm yr−1 and 16.4 °C, respectively. The soils are Andosols, which are characterized by having an acid pH and high SOM and TSN pools [17]. The dominant soils are humic andosol-type loam, which are characterized by aluminum toxicity, and their high organic matter content is due to the leaf litter produced by coffee plants and shade trees in agroforestry systems [5].
The dominant vegetation is characteristic of a tropical cloud forest or mountain forest where coffee, chayote, sugar cane, and orange crops are managed. The coffee plantations in the region are managed under shade with tree species [18] such as ixpepe (Trema micrantha (L.) Blume), pink cedar (Acrocarpus fraxinifolius Arn.), guarumbo (Cecropia obtusifolia Bertol.), chalahuite (Inga spp.), red cedar (Cedrela odorata L.), oak (Quercus xalapensis Humb. & Bonpl.), and walnut (Junglans regia L.).

2.2. Sample Design

Four systems were selected as case studies, including two types of traditional coffee polycultures (TCPs) that correspond to the system with renovated coffee and the intensively pruned coffee system. Traditional farming techniques are understood as those that are transmitted from farmer to farmer and that producers consider sufficiently proven and reliable. The other systems were avocado associated with coffee (ACS) and a conventional avocado orchard (CAO), which will act as a control. These were evaluated during the 2018–2019 production cycle in the four systems.
The characteristics of each of the evaluated systems are presented below:
  • Conventional avocado orchard (COA). The avocado plants, Hass variety, were 8 years old, with a density of 210 plants ha−1 and a spacing of 6 × 8 m (48 m2 plant−1). Three fertilizations were carried out per year with NPK fertilizer (17:17:17) and urea, 0.5 kg and 1.40 kg of each per plant and three times a year, respectively. Weed control and pruning were carried out three times a year and a brush cutter and chainsaw were used, respectively. To control pests and diseases, Metalaxy M + Mancozeb (RIDONIL) and Thiamethoxan + Lambda-cyhalothrin (ENGEO) were applied quarterly, with a dose of 250 mL pesticides L−1. This system is considered as reference.
  • System with renovated coffee (SRC). This system was 21 years old, with renovation in rows interspersed between old coffee trees; this renovation was carried out three years ago and with the Geisha variety at 1.00 × 1.00 m (1 m2 plant−1). The 21-year-old coffee (Coffea arabica L.) varieties are Typica, Bourbom, Costa Rica, Colombia, Garnica, and Catuaí; as tree components, inga o known locally as “vanillo” (Inga spp.), grevillea (Grevillea robusta A. Cunn. ex R.Br.), and walnut (Junglas spp.) are maintained. These species fulfill the function of providing shade at variable distances, from 6 × 6 to 15 × 15 m; all plants are planted following contour lines. This system had 4000 coffee plants and 40 shade trees ha−1. Management consisted of controlling weeds with a hoe three times a year, adding fertilizer (5.00 kg of vermicompost per plant and 1.00 t ha−1 of lime (each year), with annual pruning of shade trees. Pest and disease control was not carried out.
  • Intensively pruned coffee system (IPC). This system was 21 years old at the time of the evaluation, with four coffee varieties (Borbón, Typica, Colombia, and Caturra) at a spacing of 2.0 × 1.5 m, with “vanillo” (Inga spp.) and walnut (Juglans spp.) as shade trees with variable spacings of 10 × 10 to 15 × 20 m; all plants were installed following contour curves. This system had 2400 coffee plants and 60 trees ha−1. Fertilization was carried out with 5.00 kg of vermicompost and a limestone amendment with 1.00 kg of lime per plant annually. Weed control was carried out with a hoe and shade trees were pruned with a chainsaw three times per year. Pest and disease control was not carried out.
  • Avocado–coffee system (ACS). The avocado plants were 8 years old, and the coffee plants were 2 years old. The avocado variety in the system was Hass with a density of 100 plants ha−1 with a spacing of 10 × 10 m, while the coffee plants were Typica and Costa Rica with a density of 1800 plants ha−1 with a spacing of 2 × 2 m, placing them between the avocado rows. All plants were planted following contour lines. Vermicompost enriched with mycorrhizae was reintroduced at a dose of 15.00 kg per avocado plant and 5.00 kg per coffee plant annually. Weed control was carried out with a brush cutter and pruning once a year with a chainsaw. Pest and disease control was carried out with night light traps, yellow traps, and applications of Bordeaux mixture, a fungicide composed of copper sulfate and hydrated lime, at a dose of one kg of each in 100 L of water (for 100 avocado plants ha−1); applications were carried out once a month; in the case of coffee plants, no control was carried out.

2.3. Experimental Design

Four systems were selected as case studies: two types of traditional coffee polycultures that corresponded to the system with renovated coffee (SRC) and the intensively pruned coffee system (IPC), another with avocado associated with coffee (ACS), and the last consisting of a conventional avocado orchard (CAO); each system corresponds to a different producer. In each system, three square plots of 25 × 25 m were delimited as replicates. Soil samples were taken at three depths (0–10, 10–20, and 20–30 cm) and plant litter was collected in triplicate. As the distance between trees and bushes was variable, soil samples were taken in the alleys, considering the slope, according to the methodology recommended by Masuhara [6]. SOC and TSN pools were quantified in these samples. In each plot, dasometric evaluations (diameter and height) and species identifications were conducted on all species of trees and coffee plants. Allometric equations of C estimates in vegetation biomass were used, considering an arrangement of three randomly distributed plots in each management system. These investigations were conducted in October 2017, 2018, and 2019.

2.4. Vegetation Biomass Variable

The C pool in vegetation biomass (VBC) was obtained by summing the C stored in trees, shrubs, coffee plants, and roots obtained by allometric equations (Table 1). For this purpose, the plant biomass was estimated with evaluations of the diameters and heights of the vegetation in each plot for each system according to the experimental design described in Section 2.3. Later, we proceeded to multiply the total dry biomass by the factor 0.5 [19] to estimate the stored C. In the case of roots, the selection proposed by Cairns [20] was applied.

2.5. Soil Variables

The SOC pool was analyzed in samples taken in triplicate in each of the plots at three depths: 0–10, 10–20, and 20–30 cm, using Shimadzu TOC-L dry equipment (mg C g−1 soil; Tokio, Japan). The samples were transferred to the agroecology laboratory of the Autonomous Chapingo University in Ziploc bags with the corresponding coding. Subsequently, the conversion to Mg C ha−1 was performed using the formula proposed by Ellert and Bettany [24], which includes soil depth and bulk density, which was previously determined using the cylinder method [25].
The TSN pool was analyzed in the same soil samples by the Kjeldahl method [26].

2.6. Socioeconomic and Agronomic Mangement Variables

The four AFSs were treated as case studies in which 56 socioeconomic variables were selected based on the methodologies proposed by Monterroso [27] and Pérez & Grovas [28]; the parameters and description of determinations are shown in Table 1. The variables were grouped into three groups based on their characteristics:
I.
Economical characteristics and productivity (Table 2a);
II.
Social characteristics (Table 2b): technological, education, equity, management, and access;
III.
Agronomic management (Table 2c): interviews were conducted from January 2018 to December 2018. Information was obtained by interviewing one producer for each type of AFS, to obtain general information and a 30-year socioeconomic projection analysis.
Table 2. (a) Characterization of the variables used in group I (economical characteristics and productivity). (b) Characterization of the variables used in group II (social characteristics). (c) Characterization of the variables used in group III (agronomic management).
Table 2. (a) Characterization of the variables used in group I (economical characteristics and productivity). (b) Characterization of the variables used in group II (social characteristics). (c) Characterization of the variables used in group III (agronomic management).
(a)
AreaVariableVariable and Methodology Description
Productivity (y)Coffee yield in the system (P1)A sample of cherries was taken from three coffee plants per variety and per type of system, and the average number and weight per cherry was calculated to determine the yield per plant. The projection per hectare was then made and the value in Mexican pesos was calculated.
Avocado performance in the system (P2)Five fruits were extracted per plant, from 10 random avocado plants per system, which were weighed to estimate the yield per plant, and then the projection per hectare was made.
Coffee cherry quality (P3)The coffee cherry samples were subjected to a test for defective fruits. This consisted of placing the cherries in a container with water and counting the percentage of them that floated. Then, those infested with the coffee cherry borer, those that were overripe, those that were very green, and those that showed other physical damage were separated. The percentage of defective fruits was multiplied by the cost in kg cherry−1 to determine the monetary value of the losses ha−1.
Avocado fruit quality (P4)The avocado samples used to obtain the variable P2 were classified according to their weight to determine their quality, according to the Mexican size standard. The percentage of fruits with super quality was multiplied by the price during 2018 and then projected to one hectare.
Establishment costs (y)Preparation of land (C1) and soil (C2)The cost was estimated based on the number of days used for clearing, drain construction, shoveling, subsoiling, fallowing, tracking, cross-hatching, and other activities performed during the installation of each system.
Coffee plants (C3), avocado plants (C5)It was calculated with the number of plants, which was multiplied by the cost per plant of nearby nurseries.
Renewed coffee plants (C4)The number of coffee plants incorporated to replace adult plants every 10 years, according to the producer’s criteria, was multiplied by the cost per plant of nearby nurseries during 2018.
Labor for system installation (C6)It was estimated based on the number of days worked by the producer to plant a certain number of plants.
Cost of fertilizers and manure (C7); inputs for sanitary control (C8) and weed control (C9)The number of inputs for fertilization, sanitary control, and weeds used per plant and production cycle were projected per hectare and multiplied by the prices of the year 2018 according to the price of nearby nurseries.
Labor for application of fertilizers (C10); inputs for sanitary control (C11), weeds (C12), and cultural work (C13)Maintenance costs were estimated based on the number of workers employed by the producer to apply inputs to a given number of plants.
Cost of chainsaw service (C14), irrigation, with cistern (C15), coffee harvest (C16), and avocado (C17).Service costs or valuation of daily wages used for the maintenance of coffee and avocado plants in the systems.
Economic (z)Benefit/cost ratio (E1)It was determined with information on expenses and income for each system.
Net profit from coffee (E2) and avocado (E3) revenuesThey were calculated with the yield of coffee cherries and avocado fruits, income, and expenses.
Market diversity: number of buyers per year (E4)The number of buyers per production cycle was added.
(b)
AreaVariableVariable and Methodology Description
TechnologicalNumber of capabilities received per year (T1)It was calculated based on the training received during the period 2016–2018 (#training yr−1).
Number of technologies applied on the farm (T2)A tour was conducted with the producer around the entire farm and the number of technologies applied (#technology) was detected.
EducationEducation level (S1)The classification of national education level is considered. (1) Basic: preschool, primary, and secondary; (2) intermediate: high school, technical professional; (3) higher: technical, higher, bachelor’s degree, and postgraduate.
EquityFamily dependency (S2)Number of family members who depend on the farm’s income (#people).
Income dependency level (S3)Percentage of total income for basic expenses (% of total income).
Family labor (T4)Number of family members involved in the workforce (#people).
Age of staff working on the farm (S12)It was carried out based on the working age scale provided by INEGI. (1) 0–14: children; (2) 15–25: early working age; (3) 25–54: maximum working age; (4) 55–64: mature working age; (5) 65 and older: advanced age.
ManagementLinking (S5)Number of affiliations for training in production, management and post-harvest, assisted (#affiliations).
Linking (S6)Number of government programs (#programs).
Linking (S7)Number of organizations they have affiliated with in the market to (#organizations).
Linking (S8)Financing through cooperatives (#financial).
Type of buyer (S9)Type of buyer for coffee (cooperative = COP = 2, intermediary = INT = 1, or final consumer = CF = 3).
Property type (S10)Own farm or orchard = P = 1 or rented = R = 2
Type of organization (S11)It was classified according to the level of organization. Communal = COM (1), independent = IND (2), or association = ASOC (3)
Dependence on external labor (S13)Percentage of services contracted to perform activities in the systems (%).
AccessNumber of agrochemicals (S14)The number of agrochemical supplies close to the systems (#agrochemicals) was counted.
Distance from agrochemicals to plot (S15)The coordinates of the agrochemicals close to the systems were georeferenced and the distance from these to each system was subsequently calculated (km).
Time of agrochemicals to the plot (S16)The time it takes a producer to transport his inputs to the plot was calculated (minutes).
Availability of nurseries with rust-tolerant and productive coffee trees (S17)The coordinates of the agrochemicals close to the systems were georeferenced and the distance from these to each system was subsequently calculated (km).
Availability of nurseries with productive and canker-tolerant avocado (S18)The coordinates of the agrochemicals close to the systems were georeferenced and the distance from these to each system was subsequently calculated (km).
Housing to plot time (S19)The time it takes a producer to travel from his home to the system was calculated (minutes).
(c)
AreaVariableVariable and Methodology Description
Agronomic managementQuantity of pruning residues reincorporated (M1)Percentage of organic residues incorporated into the systems per production cycle (%).
Quantity of reincorporated crop residues (M2)Percentage of coffee pulp waste incorporated into the systems per production cycle (%).
Number of prunings per year (M3)Number of pruning operations carried out per year (#pruning operations yr−1).
Amount of organic fertilizer applied (M4)Number of applications of organic or synthetic fertilizers made during a year. To obtain the average, the number of applications during the period 2016–2018 was asked (#applications yr−1).
Amount of synthetic fertilizer applied (M5)
Plant density of coffee (M6) and avocado (M7)The relationship between the number of plants and the total area per system, projected per hectare (#plants ha−1).
Density of other vegetation (M8) and buffer zone (M9)It was calculated based on the number of plants and the area they occupied (% of total area).
Diversification of coffee varieties (M10)A census was carried out of all the plants detected in each evaluated plot (#varieties ha−1).
#: number. (y) The units were MXN ha−1 N°; (z) the unit was number of buyers.
Estimates of economic variables were analyzed covering a 30-year period, with a discount rate of 10%. For input costs, the average prices of agrochemicals and plants in nearby nurseries during 2018 were taken as a reference, while for income, the average sales value obtained per kg of coffee cherry (fresh coffee fruits) and avocado fruit was used.
To obtain the cost/benefit analysis of each of the evaluated AFSs, the methodology of Gittinger [29] was applied.
The social variables were determined through interviews in the plots, accounting for the technologies used. In addition, the time and distance of access to the plots were taken, and the degree of education and time management capacity of each of the producers was checked.

2.7. Statistical Analysis

The results of the socioeconomic and management variables correspond to the projections of a 30-year production cycle, and the values of VBC, SOC, and TSN correspond to the average of the determinations generated by three repetitions for VBC, and nine for SOC and TSN in each system.
The influence of the socioeconomic variable group on the agronomic management and on the C and N contents of the four case studies was analyzed using the non-linear principal component (PCA) methodology with the statistical software Statgraphics Centurion XVI (16.2.04) with three components (which explained 100% of the total acquisition). A listwise treatment and a normalization of the values were carried out under the non-linear multivariate analysis approach that allows for scaling the variables to different levels of measurement and allows for non-linear relationships between them.

3. Results and Discussion

3.1. Carbon and Nitrogen Pools in the Systems

The C and N results (Table 3) indicate that the VBC values in the SRC, ACS, and CAO are within the average range of 33.3 to 43.69 Mg C ha−1, in agreement with Masuhara [6] and Cristóbal [7]. In the IPC, the low VBC values are attributed to severe pruning; in the other systems, the VBC values are similar.
Soil carbon organic pools reported lower values than those found in the traditional coffee system (212 Mg C ha−1), but higher than commercial coffee plantations (92.6 Mg C ha−1), while TSN values resembled the average of traditional coffee system (14.2 Mg N ha−1), in agreement with what was reported by Masuhara [6] and Cristóbal [7]. This is attributed to the incorporation of organic waste and the application of organic fertilizers [30]. The values of SOC and TSN pools tend to be lower than commercial coffee plantation systems [7].
The ACS showed the highest values in total carbon compared to the IPC system, which showed that the productive diversification of the coffee plantation with avocado influenced the carbon stores, with a range between 63.4% and 49.7% compared to the other systems. This is attributed to the SOC concentrations; despite the fact that these systems (SRC, IPC, and CAO) showed lower values, differences were observed in the C/N ratio, and the ACS presented lower values than the traditional coffee plantation systems. This behavior is similar to the comparisons made by dos Santos et al. [31] for agroforestry systems with coffee, who demonstrated the effect of the type of organic residue as a function of TSN. Similarly, C/N is a quality parameter of organic matter, which decreased in the CAO, distinguishing itself from the IPC and SCR, although they had SOC values similar to the CAO; however, the quality of SOM was maintained in the ACS, which is also attributed to the management of organic waste and the effects of managing avocado as a monoculture [32].

3.2. Economic, Social, and Management Variables in the Systems

The variation in installation and maintenance costs among systems is shown in Table 4. Installation costs in the SRC, IPC, ACS, and CAO exceed the average costs reported by Espinosa [33] for coffee plantations (MXN 26,100 ha−1). The total maintenance cost in the ACS resembles the average reported for coffee systems (2111 MXN ha−1), being lower than the costs of the CAO (3488 MXN ha−1); (Espinosa [33]; Franco [34]). The benefit/cost ratio for the ACS exceeds 20% of the positive opportunity cost value, thus ensuring that productive diversification in this system allows for higher income [28].
Regarding the social variables (Table 5), the number of government programs (S6), number of organizations they have affiliated with in the market (S7), and financing through cooperatives (S8) did not report values, while data on property type (S10), the number of agrochemical supplies close to the systems (S14), and time of agrochemicals to the plot (S16) showed similarities between them, indicating that the link between producers, government programs, and marketing organizations is scarce. Access to agrochemicals and the type of property show that the systems have the same conditions of access and organization. The other variables correspond to access to training by nearby educational institutions for the implementation of technologies that affect agronomic management and, therefore, the investment capacity to hire external labor and purchase inputs [35].
The variables of group III (Table 6) show that the high planting density in the SRC (4000 plants ha−1) and the number of coffee varieties demand greater agronomic management. The ACS presented intermediate values between the CAO and coffee systems (SRC, IPC). This trend indicates that floristic composition is a variable that generates different topological arrangements and strata and, therefore, different types of agronomic management [36].

3.3. Relationship Between Economic, Productivity, Social, Carbon and Nitrogen Variables and Agronomic Management

3.3.1. Relationship Between Economic and Productivity Variables and Agronomic Management

The variation in the agronomic management in the SRC is explained by the positive variation in the values in components 1 and 2 (Table 7) that group the variables of investment in labor and inputs, which confirms that high planting density generates investment costs for its maintenance. The IPC was the one that showed sensitivity to the three groups of variables (component 1, 2, and 3) followed by the ACS, which is attributed to the fact that this producer manages three different coffee buyers. In the ACS, the variation in agronomic management is due to the variables that correspond to the investment capacity to obtain sporadic services and market diversification (component 3) due to the fact that the system has coffee and avocado production and contemplates the investment for emergency irrigation (Table 4; C15). The variation in the agronomic management in the CAO reference system is explained by the group of variables that correspond to the investment in inputs, results that reflect that in a system managed as a monoculture, a greater investment in inputs is required for its maintenance.
The variables of components 1 and 2 explain 83.1% of the variation in the agronomic management variables (Figure 2a). This result coincides with what was reported by Cruz [37], who indicated that investment is important for generating economic benefits, influencing with a positive trend in agronomic management and contributing to an increase in productivity and, therefore, to the income generated in the AFS [38].
Similarly, variables C7, C8, C9, and C14 confirm that investment in agronomic management positively influences avocado and coffee production (P1+P2), a behavior that according to Martinelli [39] occurs in diversified systems, which generate income at different times and, therefore, there is the ability to invest in different periods, a feature that allows for the programming of AM, as is the case in the ACS.
Component 1 includes the variables of investment in labor, performance, and plants (50.9%), while component 2 combines the variables of investment in maintenance inputs (32.3%); the additional 16.8% of the variation in AM is explained by the variables of investment in sporadic services and the diversity of markets (component 3). Therefore, the variation in agronomic management is related to a greater percentage weight of the economic variables that correspond to the investment capacity for the installation of plants and the costs of investment in labor, which influences the performance or production of the systems [39].

3.3.2. Relationship Between Social and Agronomic Management

The system that showed variation in AM by the variables that group the analysis of the three main components (Table 7) was the IPC, which is located as the system with the greatest vulnerability to variations in technology, equity, and access. The variation in AM in the SRC system is explained by the variables of access to material resources, since in this system the high density of planting requires greater family labor and availability of inputs for its maintenance. The variation in AM in the ACS is explained by access to educational resource capacities, which is attributed to the fact that the management of two crops (avocado and coffee within the same system) requires access to training, to organize their labor time and the level of education of the producer. In the CAO reference system, the variation in agronomic management is mainly explained by the variables that group economic needs, which indicates that it is a system with intensive management.
Social variables explain 80.4% of the variation in AM according to components 1 and 2 (Figure 2b). These results indicate that AM variables are sensitive to access and family labor, since from these the producer makes decisions for the AM of the system [40] which affects coffee and avocado production. Likewise, the age of the producer can influence the maintenance labor force, which is consistent with what was mentioned by Pocomucha [41] who indicated that both the family labor force and the working age of the producer influence the management of weeds and, therefore, generate limitations in the management of AFS. On the other hand, the types of buyer capabilities (S9) influence the productive chain, which, according to Figueroa [42], generates interest in producers to invest in various inputs that allow for an ecological balance.
It is confirmed that component 1 (with 50.2%) groups the social variables that correspond to the access to material resources and human resources for the maintenance of the system, while component 2 includes the variables of economic needs (with 30.6%). Variables S1, S11, and S5 explain an additional 19.6% of the variation in agronomic management (component 3), which allows component 3 to explain the variables of access to educational resources. Therefore, the management variation with the highest percentage weight is due to the group social variables that are related to access to material resources and human resources, which influences the producer’s ability to schedule the use of inputs and labor management, and therefore, agronomic management [43]. The variable doses of synthetic fertilizers applied (M5) was not shown to be related to the social variables.

3.3.3. Relationship Between Carbon Pools and Agronomic Management

The group of AM variables (M1, M2, M3, M4, M6, M7, M8, M9, and M10) showed a positive relationship with the group of socioeconomic variables, showing 78.4% of the variation in VBC according to the values of components 1 and 2 (Figure 3a), which shows that C pools in plant biomass are more sensitive than those in soil to AM, because variations in C storage are generated during the aerial phytomass production cycle [15]. Additionally, SOC stores show an inverse relationship with all AM variables since it depends on external inputs and recycling of organic residues generated by plant biomass, which explains such an inverse connection with AM [43]: the more intense the agronomic management of the system, the lower the organic carbon content of the soil.

3.3.4. Relationship Between Nitrogen Pools and Agronomic Management

The group of agronomic management values were selected as M1, M6+M7, M9, and M10. These variables explain 81.0% of the variation in TSN pools, according to the values of components 1 and 2 (Figure 3b). These results indicate that the diversification of the floristic composition contributes to N fixation and recycling of organic residues, which is consistent with Sharma [44], who indicated that TSN levels depend on recycled plant biomass; Ordóñez [40] confirmed that management with diversified strata promotes edaphoclimatic conditions that influence the recycling of various organic materials and, therefore, variations in TSN pools.

4. Conclusions

The polyculture ACS allows for maintaining VBC, SOC, and TSN pools and total C in the system, showing higher values of these parameters than commercial coffee plantations and the CAO; the ACS seems to maintain the resources with less damage. Even more, the quality of SOM (according to the soil C/N ratio) is notoriously depressed in the CAO.
Social variables such as access to training, capacity to organize, labor, education level, and type of buyer, on the one hand, and economic variables such as the costs of investment in fertilizers, weed control, sanitation, and maintenance services, on the other hand, were the ones that best explained the variation in AM in the systems studied. The variation in AM in the ACS is sensitive to variations in investment in sporadic inputs, market diversity, and access to educational resources, variables that affect management with the incorporation of external organic residues generated as organic fertilizers and planting density, thus explaining the variations in TSN pools and VBC, while SOC contents show an inverse relationship with all the agronomic management variables since they depend on external inputs and the recycling of organic residues generated by the plant biomass.

Author Contributions

Conceptualization, D.A.-M. and E.V.-V.; methodology, D.A.-M. and J.P.-N.; formal analysis, R.S.-H. and T.d.J.S.-V.; investigation, D.A.-M.; writing—review and editing, D.A.-M.; supervision, D.A.-M., P.M.-Z. and J.F.G.-L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data are available from the first author (diana.ayala@ecosur.mx), upon reasonable request.

Acknowledgments

Extensive thanks to the community of Tlaxopa, Huatusco, Veracruz (México) and to the coffee and avocado farmer, Luis Alvarado, for allowing this research to be carried out. We thank Manuel Lorenzo Núñez Piedra for the preparation of Figure 1. We acknowledge the comments from three anonymous reviewers on an earlier version of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Principal geographic location of the study town Huatusco of Chicuéllar, Veracruz, Mexico. Source: Own elaboration based on cartographic input from INEGI (https://www.inegi.org.mx/).
Figure 1. Principal geographic location of the study town Huatusco of Chicuéllar, Veracruz, Mexico. Source: Own elaboration based on cartographic input from INEGI (https://www.inegi.org.mx/).
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Figure 2. Principal component analysis. (a) Economic and productivity variables; (b) social variables. Variables S6, S7, S7, S8, S10, S14, and S16 were not considered for the PCA because values were not reported or were similar between the systems.
Figure 2. Principal component analysis. (a) Economic and productivity variables; (b) social variables. Variables S6, S7, S7, S8, S10, S14, and S16 were not considered for the PCA because values were not reported or were similar between the systems.
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Figure 3. Principal component analysis. (a) Agronomic management and carbon variables; (b) agronomic management and nitrogen variables. The M5 variable was not considered for the PCA because there was no relationship between the socioeconomic variables.
Figure 3. Principal component analysis. (a) Agronomic management and carbon variables; (b) agronomic management and nitrogen variables. The M5 variable was not considered for the PCA because there was no relationship between the socioeconomic variables.
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Table 1. Allometric equations determining the biomass of each type of tree vegetation, coffee tree, and avocado.
Table 1. Allometric equations determining the biomass of each type of tree vegetation, coffee tree, and avocado.
Type of SpeciesAllometric EquationsAuthors
Juglans spp.Log10 Y = −0.834 + 2.223 × Log10 (dbh)[21]
Inga spp.Log10 Y = −0.889 + 2.317 × Log10 (d15)[21]
Grevillea robustaLn Y = −2.0082 + 2.3293 × Ln(dbh)[22]
CoffeeLog10 Y = −1.113 + 1.578 × Log10 (d15) + 0.581 × Log10 (H)[21]
Avocado Y = 10(1.12 + 2.62 × Log10 (dbh) + 0.03 × Log10 (H))[23]
Y = biomass (kg), dbh = diameter at breast height or 1.30 m height (cm), d15 = diameter (cm) over 15 cm height, H = total tree height (m).
Table 3. Total C (Mg C ha−1), N (kg N ha−1) pools and soil C/N ratio per system.
Table 3. Total C (Mg C ha−1), N (kg N ha−1) pools and soil C/N ratio per system.
VariablesSRCIPCACSCAOp-ValueLSD
Total C pools in plant biomass (VBC)40 ± 2.6 A16 ± 2.6 B36 ± 2.6 A37 ± 2.6 A0.000711.7
Total C in soil (SOC)60 ± 3.9 B71 ± 3.9 B139 ± 6.1 A74 ± 6.8 B0.000122.0
SOC 0–10 cm28 ± 2.0 B29 ± 2.0 B51 ± 3.4 A32 ± 3.4 B0.000110.9649
SOC 10–20 cm17 ± 1.5 B25 ± 1.5 B47 ± 2.6 A24 ± 2.6 B0.00018.5201
SOC 20–30 cm15 ± 1.6 B17 ± 1.6 B41 ± 2.8 A18 ± 2.8 B0.00019.0768
Total N pools in soil (TSN)4.9 ± 0.40 C5.6 ± 0.37 C11.7 ± 0.65 A7.2 ± 0.65 B0.00012.1
TSN 0–10 cm2.0 ± 0.15 B 2.3 ± 0.15 B4.4 ± 0.26 A44.4 ± 0.26 B0.00010.8249
TSN 10–20 cm1.8 ± 0.18 B 2.1 ± 0.18 B4.0 ± 0.32 A4.0 ± 0.32 B0.00011.0208
TSN 20–30 cm1.1 ± 0.13 C 1.3 ± 0.13 BC3.3 ± 0.22 A3.3 ± 0.22 B 0.00010.7187
Total C in the systems10087175111
Soil C/N12.312.711.910.3
SRC: system with renovated coffee; IPC: intensively pruned coffee system; ACS: avocado–coffee system; CAO: conventional avocado orchard; Tukey’s test (p ≤ 0.05); different letters indicate significant statistical differences; p-value: probability value; LSD: least significant difference; VBC: plant biomass carbon; SOC: soil organic carbon; TSN: soil total nitrogen.
Table 4. Economic and productivity variables in the four systems studied.
Table 4. Economic and productivity variables in the four systems studied.
VariableSRCIPCACSCAO
P1 and P2 (MXN ha−1)55.2325.20170.04316.11
P3 and P4 (MXN ha−1)3.681.9758.75134.35
C1 (MXN ha−1)5.255.2512.0015.00
C2 (MXN ha−1)2.251.503.003 00
C3, C4 and C5 (MXN ha−1)23.6112.6912.813.30
C6 (MXN ha−1)14.5512.3013.506.30
Total installation cost (MXN ha−1)45.6631.7441.3127.60
C7 (MXN ha−1)913362201731
C8 (MXN ha−1)0033141
C9 (MXN ha−1)0017153
C10, C11, C12 and C13 (MXN ha−1)1.212.134821.66
C14 (MXN ha−1)7826263487
C15 (MXN ha−1)00450
C16 and C17 (MXN ha−1)1.512.261.53616
Annual maintenance cost (MXN ha−1)3.715.012.373.79
E1 (Without unity)1.301.062.362.43
E2 and E3 (MXN ha−1)4.714.3412.716.38
E4 (N° buyers)1342
SRC: system with renovated coffee; IPC: intensively pruned coffee system; ACS: avocado–coffee system; CAO: conventional avocado orchard.
Table 5. Social variables in the four systems studied.
Table 5. Social variables in the four systems studied.
VariableUnitsSRCIPCACSCAO
T1#training yr−12122
T2#technology2131
S1Without unity1121
S2#people2444
S3% of total income504080100
S4#people2322
S5#affiliations1121
S6#programs0000
S7#organizations0000
S8#financial0000
S9Without unity3111
S10Without unity1111
S11Without unity1121
S12Without unity4445
S13%551010
S14#agrochemicals4444
S15kilometers7.57.54.44.4
S16Minutes15151515
S17kilometers2525200
S18kilometers004040
S19Minutes15151010
SRC: system with renovated coffee; IPC: intensively pruned coffee system; ACS: avocado–coffee system; CAO: conventional avocado orchard; #: number.
Table 6. Agronomic management variables in the four systems studied.
Table 6. Agronomic management variables in the four systems studied.
VariableUnitsSRCIPCACSCAO
M1%10001000
M2%20000
M3#pruning yr−13532
M4#applications yr−12210
M5#applications yr−10002
M6#plants ha−14000180018000
M7#plants ha−100100275
M8#plants ha−15030200
M9% of total area1015250
M10#varieties ha−14220
SRC: system with renovated coffee; IPC: intensively pruned coffee system; ACS: avocado–coffee system; CAO: conventional avocado orchard; #: number.
Table 7. Grouping of systems by principal component and type of variable for agronomic management.
Table 7. Grouping of systems by principal component and type of variable for agronomic management.
Agronomic Management
Explanatory VariablesSystemComponent 1Component 2Component 3
Economic and productivity
(Group I)		SRC3.241.17−2.30
IPC1.811.612.65
ACS−0.13−4.250.19
CAO−4.931.47−0.54
Social: Technology, education,
equity, management and access
(Group II)		SRC3.55−0.80−2.29
IPC2.311.782.54
ACS−2.23−3.450.97
CAO−3.632.47−1.22
SRC: system with renovated coffee; IPC: intensively pruned coffee system; ACS: avocado–coffee system; CAO: conventional avocado orchard. Numbers in bold correspond to positive correlations
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Ayala-Montejo, D.; Martínez-Zurimendi, P.; Gallardo-Lancho, J.F.; Sánchez-Hernández, R.; Valdés-Velarde, E.; Santiago-Vera, T.d.J.; Pérez-Nieto, J. Agronomic Management, Socioeconomic Factors, and Carbon and Nitrogen Pools in Avocado–Coffee Systems in Huatusco, Veracruz, México. Resources 2025, 14, 35. https://doi.org/10.3390/resources14030035

AMA Style

Ayala-Montejo D, Martínez-Zurimendi P, Gallardo-Lancho JF, Sánchez-Hernández R, Valdés-Velarde E, Santiago-Vera TdJ, Pérez-Nieto J. Agronomic Management, Socioeconomic Factors, and Carbon and Nitrogen Pools in Avocado–Coffee Systems in Huatusco, Veracruz, México. Resources. 2025; 14(3):35. https://doi.org/10.3390/resources14030035

Chicago/Turabian Style

Ayala-Montejo, Diana, Pablo Martínez-Zurimendi, Juan Fernando Gallardo-Lancho, Rufo Sánchez-Hernández, Eduardo Valdés-Velarde, Teresita de Jesús Santiago-Vera, and Joel Pérez-Nieto. 2025. "Agronomic Management, Socioeconomic Factors, and Carbon and Nitrogen Pools in Avocado–Coffee Systems in Huatusco, Veracruz, México" Resources 14, no. 3: 35. https://doi.org/10.3390/resources14030035

APA Style

Ayala-Montejo, D., Martínez-Zurimendi, P., Gallardo-Lancho, J. F., Sánchez-Hernández, R., Valdés-Velarde, E., Santiago-Vera, T. d. J., & Pérez-Nieto, J. (2025). Agronomic Management, Socioeconomic Factors, and Carbon and Nitrogen Pools in Avocado–Coffee Systems in Huatusco, Veracruz, México. Resources, 14(3), 35. https://doi.org/10.3390/resources14030035

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