Prerpints.org logo
Preprint
Article

Importance of Soil Health for Coffea spp Cultivation, from a Cooperative Society in Puebla, Mexico

Altmetrics

Downloads

90

Views

29

Comments

0

A peer-reviewed article of this preprint also exists.

Submitted:

18 March 2024

Posted:

19 March 2024

You are already at the latest version

Alerts
Abstract
The cultivation systems of Coffea spp in a cooperative society in Puebla, Mexico, include rustic, traditional polyculture, commercial polyculture, unshaded monoculture and shaded monoculture. In this work, the properties of the soil were analyzed through physical, chemical, and biological analysis to determine its nutritional status. Composite sample analyses were conducted to determine physical, chemical and microbiological parameters (fungi, actinomycetes, mesophilic bacteria, nitrifying and denitrifying bacteria). Leaf nutrients were determined. Rustic was the cropping system with the highest amount of K (0.76 cmol kg-1) in soil and nutrient assimilation in leaf (N= 2.79%, P= 660.01, K= 17297.22 and Fe= 271.24 mg kg-1) (p= 0.001); in addition to presenting high populations of mesophilic bacteria, fungi and actinomycetes (30.16, 0.59 and 0.83 respectively, x10 6 CFU g-1 soil) and very low nitrification and denitrification rates. The principal component analysis (PCA) (>3.25%) indicated that actinomycetes and K in soil favor the assimilation of Fe, K and P. This Coffea spp cultivation system had a lower impact on soil health than the rest of the systems and favored forest ecosystem conservation.
Keywords: 
Subject: Environmental and Earth Sciences  -   Soil Science

1. Introduction

Primary activities in Mexico contribute 2.7% of the gross domestic product (GDP) [1], and the cultivation of Coffea spp occupies twelfth place [2] with a cultivated area of 702,686 ha [3]. Worldwide, Mexico is ranked tenth as a producer of Coffea spp, with Chiapas, Veracruz, and Puebla being the main producers [4].
Since its introduction, the cultivation of Coffea spp has been influenced by sociocultural and environmental factors [5], developing five cultivation systems: rustic or mountain, traditional polyculture, commercial polyculture, unshaded monoculture, and shaded monoculture [6].
Agricultural systems in the cultivation of Coffea spp depend on chemical fertilization [5], which intensifies chemical degradation, especially soil acidification, in addition to modifying pH values, loss of exchangeable bases, reducing microbial activity and causing toxicity from excess aluminum and manganese, coupled with overexploitation, leaching, erosion and soil runoff [7,8,9]. Over time, soil health has been affected by not fulfilling its function in the ecosystem, satisfying the needs of the organisms present [10,11]. Its evaluation allows us to obtain a complete position of the state of the soil through the analysis of its physical, chemical, and biological properties [11].
Physicochemical indicators such as gravimetric humidity, pH, CEC (cation exchange capacity), P, OC (organic C), Nt (total N), K, and micronutrients (Zn, Mn, Cu, Fe) can be considered, as can the biodiversity of micro and macroorganisms involved in the soil's biogeochemical cycles [12].
In the municipality of Cuetzalan del Progreso, Puebla, Mexico, agricultural soils dedicated to the production of Coffea spp are susceptible to changes in humidity and temperature, which hinders high yields in cherry coffee, coupled with the change in land use, expensive inputs, and migration [13] (pp. 84,85). Due to the poverty and marginalization in which the majority of producers find themselves, it is necessary to generate information on the health of the soil in the different cultivation systems of a cooperative society in Puebla, Mexico.
In this context, considering the economic importance of cultivating Coffea spp in Mexico and the impact it can have on soil health, this research proposes to study the properties of the soil through physical, chemical, and biological analysis to determine its nutritional status under different cultivation systems of Coffea spp in a cooperative society in Puebla, Mexico.

2. Materials and Methods

2.1. Sampling Site

Sampling was carried out in 15 plots of producers located in the "Taposontok" cooperative society of the municipality of Cuetzalan del Progreso, Puebla, with a latitude between 19º 57' 00" N and 20º 05' 18" N, and a length between 97º 24' 36" W and 97º 34' 54" W [14]. The altitude varies between 180 and 1,600 m above sea level. The predominant climate is semi-warm and humid, with year-round rain and an average temperature of 20.3 °C [15] (Figure 1).
Sampling was carried out in February 2023. In each cropping system, three plots were selected: unshaded monoculture (without the presence of shade), shaded monoculture (with species introduced for shade), commercial polyculture (with the introduction of plant species for shade and marketing), traditional polyculture (with shade from the forest ecosystem and introduction of valuable species), rustic (with shade from the forest ecosystem) [6]. For the physical and chemical analyses of the soil, a sample composed of a plot from 0 to 30 cm deep was taken, and for biological analysis, rhizospheric soil was sampled in sterile plastic containers. Thus, leaf samples from coffee plantations without pests and diseases. All samples were stored at 4°C until further analysis.

2.2. Physical and Chemical Analysis

The soil samples were dried and sieved through a 2 mm mesh, and the analyses were carried out in accordance with the Official Mexican Standard NOM-021-RECNAT-2000 [16]. The parameters analyzed in soil were humidity (gravimetric method), apparent density (test tube method), pH (electrometric method), EC (electrical conductivity by conductimetric method), OC (organic carbon) and OM (organic matter by method of Walkley and Black), soil texture (Bouyoucos method), exchangeable bases (Ca, Na, Mg, K) and CEC (Cationic Exchange Capacity) (ammonium acetate method). Nt (total nitrogen) (Kjeldahl method) and extractable P (Bray and Kurtz method) were determined in soil and leaves.
Zn, Mn, Cu and Fe were quantified in soil by extraction with diethylenetriamine penta-acetic acid (0.005 M); the solution was shaken for two h at 120 rpm and filtered through the Whatman No. 42 paper. For the determination of Ca, Mg, Na, K, Zn, Mn and Fe in leaf, an extract was prepared with H2O2 (30% w/w) and concentrated HNO3 (analytical grade), a digestion was carried out at 200°C for 10 min in a CEM Mars Xpress microwave digestion; It was subsequently filtered with Whatman No. 42 paper and volumetric to 50 ml.
Finally, 0–8 ml was injected into a flame atomic absorption spectrometer (Agilent 55B AA). An N2O/acetylene flame, a hollow cathode lamp with a current intensity of 10 mA and wavelength of 239.9 nm, was used for Ca quantification. An air/acetylene flame was used for the rest of the elements. For Mg and Cu, a hollow cathode lamp with a current intensity of 4 mA was used; Mg was read at a wavelength of 202.6 nm and Cu at 327.4 nm. A hollow cathode lamp with a current intensity of 5 mA was used to determine Na (330.2 nm), Zn (213.9 nm), Mn (279.5 nm), Fe (372 nm), K in sheet (404.4 nm) and K in soil (769.9 nm).
Calibration curves were made for K in leaf (0, 50, 100 and 250 mg L-1), K in soil (0, 1 and 3 mg L-1), Ca in leaf (0, 50, 100 and 250 mg L-1), Ca in soil (0, 50 and 100 mg L-1) and Cu in soil (0, 2.5 and 5 mg L-1). And for the following elements in both soil and leaf: Mg (0, 5, 10 and 20 mg L-1), Na (0, 50 and 100 mg L-1), Zn (0, 0.5, 1 and 2 mg L-1), Mn (0, 2, 3 and 5 mg L-1) and Fe (0, 10, 15 and 20 mg L-1).

2.3. Microbiological Analysis

The total mesophilic bacterial population was determined by the most probable number (MPN) technique in nutrient broth medium in triplicate of serial dilutions from 10-5 to 10-7 at 48 h, 120 rpm and 30°C [17].
Nitrifying bacteria were quantified in Nitrosomonas medium (g L-1): (NH4)2SO4, 1.7; MgSO4·7H2O, 0.2; CaCl2·2H2O, 0.02; K2HPO4, 0.015; Ferric EDTA, 0.001; pH 7.5; with 1 ml of trace element solution (g L-1): MnCl2·4H2O, 0.02; Na2MoO4·2H2O, 0.01; ZnSO4·7H2O, 0.01; CuSO4·5H2O, 0.002; CoCl2·6H2O, 0.0002. The nitrifying populations were determined using the MPN technique of 10-5 to 10-7 serial dilutions in triplicate for 8 d, 120 rpm and 30°C.
For the denitrifying bacteria, Nitrate reduction Broth Clark medium was used (g L-1): Peptone, 20; KNO3, 2; pH 7.0. Dilutions were made from 10-1 to 10-4 and monitored for 5 d at 30°C; Durham hoods were used for gas production as growth by MPN. The Sodium Salicylate and Sulfanilamide methods quantified the production of nitrates and nitrites to calculate nitrification and denitrification rates [18,19]. For the quantification of fungi and actinomycetes, the pour-plate method was used using malt mineral medium (g L-1): NH4NO3, 7; K2HPO4, 1; KH2PO4, 1; Malta, 0.2; pH 5.6; enriched with 1 ml of micronutrient solution (g L-1): MgSO4·7H2O, 4; FeSO4·7H2O, 0.2; CaCl2·2H2O, 0.2. 100 µL was plated from 10-3 to 10-6 dilutions, incubated in triplicate for 6 d at 30°C, and quantified as Colony Forming Units (CFU) g-1 dry soil [20].

2.4. Statistical Analysis

The data generated were analyzed in the RStudio software version 4.2.2, and the normality of the data was evaluated using the Shapiro-Wilk test with the "stats" package. A Pearson correlation analysis (p= 0.001, 0.01 and 0.05) was performed with the "corrr" package for leaf properties. Subsequently, an analysis of variance (ANOVA) and a Tukey multiple comparison test of means were performed using the "agricolae" package to determine if there were statistically significant differences (p< 0.05) in the physical, chemical and biological properties of the soil and the leaf between the different cultivation methods. Finally, a principal component analysis (PCA) was performed to find the different relationships between the variables using the "prcomp" and "FactoMineR" packages. The optimal number of clusters for the data set was determined considering the methods "kl", "ch", "hartigan", "mcclain", "gamma", "gplus", "tau", "dunn", "sdindex", "sdbw", "cindex", "silhouette", "ball", "ptbiserial", and "frey"; distance measures "euclidean", "maximum", "manhattan", and "canberra" and graphs were generated using the "Elbow", "GAP" and "Silhouette" methods. Once the optimal number of clusters (two) was selected, the k-means algorithm was applied to both the variables and the individuals (cropping systems) [21].

3. Results

3.1. Comparison of Physical, Chemical, and Microbiological Characteristics in soil

The soils of the Coffea spp cultivation systems in the study area have a generally clayey texture, highlighting that rustic presented the Silt Sandy Loam and Silty Loam textures. The pH values range between 3.9 and 4.93, with an apparent density of 0.81 to 0.91 mg cm-3, a humidity percentage range of 40.27 to 66.36%, and an EC range of 0.13 to 0.28 dS (Table 1). For macronutrients, shaded monoculture had the highest percentage of Nt (0.47%, p= 0.001) and OM (10.89%, p= 0.001). However, for K, the rustic system had the highest value (0.76 cmol kg-1, p= 0.001). On the other hand, P was high in unshaded monoculture (7.96 mg kg-1, p = 0.001).
Regarding micronutrients, the rustic system presented significantly lower concentrations (p= 0.001) of Cu (1.05 mg kg-1), Zn (0.50 mg kg-1), and Mn (0.86 mg kg-1) and a higher concentration of Na (1.14 cmol kg-1). The traditional polyculture had the significantly highest values (p= 0.001) of Mg (4.26 cmol kg-1), Zn (2.88 mg kg-1), Mn (8.96 mg kg-1), and Cu (3.45 mg kg-1).
Shaded monoculture presented the highest CEC (18.40 cmol kg-1, p= 0.001) and Ca (6.83 cmol kg-1, p= 0.001). For Fe, the unshaded monoculture system had the highest concentration (140.45 mg kg-1), and significantly lower values (p= 0.001) for Ca (1.93 cmol kg-1), Mg (1.13 cmol kg-1) and CEC (7.88 cmol kg-1). In commercial polyculture, the significantly lowest values (p= 0.001) of Fe (11.46 mg kg-1), OM (4.79 %), K (0.33 cmol kg-1) and Na (0.88 cmol kg-1) were found (Table 1).
Regarding microbial populations, the commercial polyculture had a significantly lower population (p= 0.001) of fungi (0.05 x 106 CFU g-1 soil) and actinomycetes (0.23 x 106 CFU g-1 soil). The rustic system presented the largest population significantly (p= 0.001) of mesophilic bacteria (30.16 x 106 cells g-1 soil), fungi (0.59 x 106 CFU g-1 soil) and actinomycetes (0.83 x 106 CFU g-1 soil); and a lower population of nitrifying bacteria (1.51 x 106 cells g-1 soil) and denitrifying bacteria (0.01 x 106 cells g-1 soil). On the other hand, in unshaded monoculture, the population of mesophilic bacteria (2.46 x 106 cells g-1 soil) was significantly lower (p= 0.001), with a greater population of nitrifying bacteria (336.54 x 106 cells g-1 soil) and denitrifying bacteria (0.03 x 106 cells g-1 soil) (Table 1).
In Figure 2, it was observed that there are significant differences in the nitrification and denitrification rates of the five cultivation systems, with unshaded monoculture being the one that had the highest rate of nitrification (0.506 mg g-1 soil day-1) and denitrification (9.430 mg g-1 soil day-1), while the rustic system presented the lowest nitrification rates (0.051 mg g-1 soil day-1) and nitrite production was not detected (denitrification).

3.2. Nutrient Analysis in Coffea spp Leaves

The quantification of macronutrients in Coffea spp leaves (Table 2), such as P (660.01 mg kg-1) and K (17297.22 mg kg-1) presented the highest values significantly (p= 0.001) in rustic. Thus, as in the content of micronutrients, Zn (12.50 mg kg-1, p= 0.015) and Fe (271.24 mg kg-1, p= 0.001). On the other hand, N (2.98%), Ca (15160.23 mg kg-1) and Mg (5362.29 mg kg-1) were significantly high (p= 0.001) in shaded monoculture and significantly low for rustic, with values of 10967.89 mg kg-1 and 2833.22 mg kg-1 respectively. Rustic also presented the significantly lowest values (p = 0.001) for Mn (33.33 mg kg-1). The Na concentration for the five culture methods did not present statistically significant differences (p= 0.585).
A total correlation analysis (Pearson) (Table 3) was performed for the relationship of nutrients in the leaves of Coffea spp. The significantly positively related macronutrients were P and K (0.7609674, p= 0.00098) and N and K (0.576633, p= 0.02443). The macronutrients that were positively related to the micronutrients were P and Fe (0.8290195, p= 0.00013), K and Fe (0.9139314, p= 0.00000), and P and Mn (-0.54382089, p= 0.03612) were negatively correlated. Micronutrients such as Ca and Mg (0.7383237, p= 0.00167), Na and Mg (0.655838, p= 0.00794) positively correlated.

3.3. Principal Component Analysis (PCA)

The principal components analysis showed a grouping of treatments defining two groupings. It was found that 59.6% of the variability can be explained by two main dimensions, Dim 1 (33.6%) and Dim 2 (26%). Furthermore, it was highlighted that the plots belonging to the rustic system have similar properties to each other, which were grouped in Cluster 1 (20%), and are different from the plots of the unshaded monoculture systems, shaded monoculture, traditional polyculture and commercial polyculture, which were grouped in Cluster 2 (80%). The differences between the Coffea spp cultivation systems are visualized, separating the rustic system, with minimal intervention in its ecosystem, from those with some modification (Figure 3).
The PCA was carried out with all the study variables. The results indicated that the most important variables to explain the variability of the main dimensions (Figure 4) are those with a more outstanding contribution to the average value of 3.25% for Dim 1 and Dim 2 as Fe (leaf) > N (leaf) > Cu > K (soil) > mesophilic bacteria > nitrifying bacteria > actinomycetes > CEC > K (leaf) > Ca > denitrifying bacteria > Na > P (soil) > Zn > P (leaf) > fungus.
In the cultivation system of Coffea spp rustic, it is influenced by Fe (leaf) > K (soil) > actinomycetes > K (leaf) > Na > P (leaf) > fungus. Shaded monoculture showed a relationship with the variables of N (leaf) > mesophilic bacteria > CEC > Ca. Commercial polyculture and unshaded monoculture systems presented a relationship with nitrifying bacteria > denitrifying bacteria > P (soil), and traditional polyculture was only related to Cu > Zn.
For the Coffea spp rustic cultivation system, the variables with the most significant contribution are actinomycetes (8.10%), fungi (5.55%), mesophilic bacteria (5.99%), and macroelements such as K (7.63%). Furthermore, this system favors the assimilation of nutrients such as K (8.27%), P (6.11%), and Fe (9.11%) in the leaf.
The shaded monoculture of Coffea spp is influenced by the CEC (9.50%) due to the amount of Ca (9.66%) found in the soil. It also favors the availability of nutrients such as N (7.28 %).
In traditional polyculture, the micronutrients Cu (6.39%) and Zn (8.82%) are found in a higher proportion and present a negative relationship with Na (4.70%), which may mean competition for Cu.
The commercial polyculture and unshaded monoculture systems presented similar behavior. The high participation of nitrifying (8.57%) and denitrifying (7.82%) bacterial populations related to the loss of N and availability of P (6.70%) in the soil. Fungi and actinomycetes compete for nutrients, and Fe and K are unavailable for Coffea spp plants.

4. Discussion

The conservation of the Coffea spp crop in Mexico is susceptible since it suffers from diseases (rust, cercosporiosis, phoma leaf spot, and bacterial blight) and pests [22]. The different cultivation systems, including rustic, unshaded monoculture, shaded monoculture, commercial polyculture, and traditional polyculture, have negatively affected the harvesting of coffee cherries [3]. Some factors, such as climate, soil fertility, plant nutrition, diseases and pests, can reduce the crop yield and grain quality of Coffea spp [23].
There are reports of soil's physical and chemical characteristics in relation to plant nutrient deficiency, such as the availability of organic matter, water, texture and pH, to mention a few [24,25]. Nutrients are essential in several metabolic pathways that involve defense mechanisms [26], so it is essential to quantify soil nutrients in the study area.
Soil health results from the interactions between physical, chemical, and biological properties that determine its function [27,28]. In the cultivation of Coffea spp, the nutrients that are most demanded are N and K, followed by Ca, P, Mg, S, Fe, Mn, Zn, Cu, and B [29] (pp. 45-47). However, the nutritional requirements of the Coffea spp crop can change depending on factors such as variety, yield, plant age, and crop management [30].
In the study site, the rustic cropping system presented high amounts of K in the soil (0.76 cmol kg-1), considered high according to the Official Mexican Standard NOM-021-RECNAT-2001 (>0.6 cmol kg-1) [16] (p. 35) and the recommended dose of the culture (> 0.4 cmol kg-1) [31]. There is high participation of actinomycete populations (0.83 x106 CFU g-1 soil), fungi (0.59 x106 CFU g-1 soil) and mesophilic bacteria (30.16 x106 cells g-1 soil) [32] involved in the mobilization of K towards the plant (17297.22 mg kg-1) for resistance processes against fungal diseases and photosynthesis [33,34]. The amounts of K in the rustic cropping system are in the optimal production range (15800-21499.99 mg kg-1) [29] (p. 175).
On the other hand, this rustic system presented the most significant amount of assimilable P (660.01 mg kg-1). However, the P is below the optimal range of the crop (1400 -2000 mg kg-1), so the plant is deficient and can negatively affect yield [29,35] (p. 175). The rustic system also presented the highest concentration of Fe (271.24 mg kg-1) in the leaf, which is involved in photosynthesis processes in productive stage crops [37] for an optimal range of 54-121 mg kg-1 [29] (p. 175). Therefore, it can be inferred that rustic system, the soil´s acidity favors the assimilation of Fe.
Other quantified micronutrients, such as Cu, are present in all cropping systems in amounts of 1.05 to 3.45 mg kg-1, suitable for this soil type (1.0–3.0 mg kg-1) [16,31] (p. 40). Cu can be related to plant respiration and photosynthesis, carbohydrate and N metabolism, antioxidant activity and lignification processes [36]. The presence of Na and K in the soil compete with Cu in the edaphic system of the Coffea spp.
The CEC can affect the availability of nutrients in the soil for the plant since it influences the capacity of the soil to retain cations such as Ca, as observed in the shaded monoculture system, where the value of the CEC (18.40 cmol kg-1) is considered medium (15-25 cmol kg-1) [16,31] (p. 35). Also, this cultivation system presented the highest concentration of N by the plant (2.98%), which is considered high according to the recommended dose of the crop (2.36-2.78%) [29] (p.175).
In modified cultivation systems (commercial polyculture and unshaded monoculture), P is low in availability for mesophilic bacterial populations and in the cultivation of Coffea spp. N loss is also due to nitrification and denitrification processes documented in other intensive farming systems [38]. P in all cultivation systems is considered deficient according to the recommended dose for Coffea spp (10 mg kg-1) [31].
Small producers of Coffea spp present the need to change crop management to diversify their production and ensure food sovereignty [39]. Carrying out this diversification under agroecological principles can reduce the use of inputs, increase crop yields, promote sustainability, and conserve biodiversity and soil health [40,41]. However, diversified agroecosystems such as the rustic system do not imply that it is the most productive. There is compensation for its ecosystem services, such as preserving soil health and biodiversity, CO2 capture, improving the landscape, resilience to climate change, water conservation, and maintenance of biogeochemical cycles, mainly [40,42,43].

5. Conclusions

The different cultivation systems of Coffea spp in a cooperative society in Puebla, Mexico, indicated that the rustic cultivation system stands out for conserving soil health due to its relationship with different properties such as the availability of K, Fe, P, and the participation of different microbial populations (mesophilic bacteria, fungi and actinomycetes) that influence the maintenance of the culture of Coffea spp. The rustic system presented low participation of bacterial populations in denitrification processes. This can be attributed to the rustic system's minimal intervention in the forest ecosystem. Only Coffea spp is introduced, and the rest of the plant layer is preserved in its entirety. This minimal modification allows the conservation of soil health and the fulfillment of its functions in the ecosystem, such as diversification of production, resilience against external factors, and maintaining long-term soil fertility. In addition, by guaranteeing the health of the soil, benefits can be obtained in the plant's nutritional status.
The rest of the coffee system (traditional polyculture, commercial polyculture, unshaded monoculture and shaded monoculture) presented a K deficiency mainly in the soil.
Systems conserved under agroecological management can become a sustainable ecosystem for small producers of Coffea spp in third-world countries.

Author Contributions

Conceptualization, A.M.-G. and R.C.-V.; methodology, J.V.T.-F. and C.M.M.-M.; software, C.M.M.-M. and A.M.-G.; validation, A.M.-G., J.V.T.-F. and R.C.-V.; formal analysis, A.M.-G. and R.C.-V.; investigation, C.M.M.-M, A.M.-G., J.V.T.-F. and R.C.-V.; resources, A.M.-G., J.V.T.-F. and R.C.-V.; data curation, A.M.-G.; writing—original draft preparation, C.M.M.-M, A.M.-G. and R.C.-V..; writing—review and editing, C.M.M.-M., A.M.-G. and R.C.-V.; visualization, A.M.-G. and R.C.-V.; supervision, A.M.-G. and R.C.-V.; project administration, R.C.-V.; funding acquisition, A.M.-G. and R.C.-V. 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 presented in this study are available on request from the corresponding author. They are not publicly available because they are part of an ongoing study.

Acknowledgments

This work was supported by the Consejo Nacional de Humanidades, Ciencia y Tecnología (Conahcyt) for the fellowship Grant of Master in Environmental Sciences awarded to Molina-Monteleón C. M. We thank Luna-Bautista C. F. for his availability and support in designing the map of the study area and producers of the "Taposontok" cooperative society of the municipality of Cuetzalan del Progreso, Puebla, Mexico.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Instituto Nacional de Estadística y Geografía. Producto Interno Bruto (PIB). Available online: https://www.inegi.org.mx/contenidos/saladeprensa/boletines/2023/pib_pconst/pib_pconst2023_11.pdf (accessed on 19 February 2024).
  2. Instituto Nacional de Estadística y Geografía. Censo Agropecuario (CA) 2022. Available online: https://www.inegi.org.mx/programas/ca/2022/ (accessed on 19 February 2024).
  3. Servicio de Información Agroalimentaria y Pesquera. Anuario Estadístico de la Producción Agrícola. Available online: https://nube.siap.gob.mx/cierreagricola/ (accessed on 19 February 2024).
  4. Fideicomisos Instituidos en Relación con la Agricultura. Panorama Agroalimentario Café 2022. Available online: https://www.fira.gob.mx/InvYEvalEcon/EvaluacionIF (accessed on 5 February 2024).
  5. Rodríguez-Osoria, O.; Fuentes-Miranda, O.; Vargas-Batis, B. Factores Sociales, Económicos y Ambientales Asociados a Los Ecosistemas Cafetaleros: Una Revisión Bibliográfica. Revista Científica Agroecosistemas 2021, 9, 33–42. [Google Scholar]
  6. Farfán-V, F. Agroforestería y Sistemas Agroforestales Con Café, 1st ed.; FNC-Cenicafé: Manizales, Colombia, 2014; pp. 6–31. ISBN 978-958-8490-16-8. [Google Scholar]
  7. López-Morgado, R.; Díaz-Padilla, G. Diagnóstico, Productividad y Ambiente En Cafetales: Estudios Regionales y de Caso, 1st ed.; INIFAP: Medellín, México, 2020; pp. 252–274. ISBN 978-607-37-1190-6. [Google Scholar]
  8. Márquez-de-la-Cruz, J.E.; Rodríguez-Mendoza, M.D.l.N.; García-Cué, J.L.; Sánchez-Escudero, J.; Tinoco-Rueda, J.Á. Impacto Del Manejo de Agroecosistemas Cafetaleros En La Calidad Del Suelo En Las Cuatro Estaciones Del Año En Tlapacoyan, Veracruz. CIENCIA ergo-sum 2022, 29, 163:1–13. [Google Scholar] [CrossRef]
  9. Vallejo, V.E.; Afanador, L.N.; Alejandra Hernández, M.; Parra, D.C. Efecto de la Implementación de Diferentes Sistemas Agrícolas Sobre la Calidad del Suelo en el Municipio de Cachipay, Cundinamarca, Colombia. Bioagro 2018, 30, 27–38. [Google Scholar]
  10. Zabaloy, M.C. One Health: Soil Health and Its Link with Human Health. Revista Argentina de Microbiología 2021, 53, 275–276. [Google Scholar] [CrossRef]
  11. Burbano-Orjuela, H. La Calidad y Salud Del Suelo Influyen Sobre La Naturaleza y La Sociedad. Revista de la Facultad de Ciencias Económicas y Administrativas 2017, 18, 118–126. [Google Scholar] [CrossRef]
  12. Gómez-Pinzon, P.D.; Ariza-Vargas, L.K.; Avellaneda-Torres, L.M. Changes in Soil Quality Associated with the Implementation of Ecological Agriculture Techniques in Coffee Plants under Different Coverings. Colombia Forestal 2022, 25, 5–20. [Google Scholar] [CrossRef]
  13. Guerrero-Carrera, J. Impacto Potencial Del Cambio Climático Sobre La Producción de Café En La Región Mazateca de Oaxaca y Región de Cuetzalan, Puebla. Doctoral Thesis, Colegio de Postgraduados, Puebla, México, 2020. [Google Scholar]
  14. Ayuntamiento de Cuetzalan del Progreso. Información de Cuetzalan. Available online: https://www.cuetzalan.gob.mx/index.php?q=informacion_de_cuetzalan&n=transparencia (accessed on 20 February 2024).
  15. Instituto Nacional de Estadística y Geografía. Aspectos Geográficos, Puebla 2021. Available online: https://www.inegi.org.mx/contenidos/app/areasgeograficas/resumen/resumen_21.pdf (accessed on 20 February 2024).
  16. Secretaría de Medio Ambiente y Recursos Naturales. Norma Oficial Mexicana NOM-021-RECNAT-2000, Que Establece Las Especificaciones de Fertilidad, Salinidad y Clasificación de Suelos. Estudios, Muestreo y Análisis. Official Journal of the Federation (DOF) 2002. Available online: https://dof.gob.mx/nota_detalle.php?codigo=756861&fecha=07/12/2001#gsc.tab=0 (accessed on 10 February 2024).
  17. Hernández-Flores, L.; Munive-Hernández, J.A.; Sandoval-Castro, E.; Martínez-Carrera, D.; Villegas-Hernández, Ma. C. Efecto de Las Prácticas Agrícolas Sobre Las Poblaciones Bacterianas Suelo En Sistemas de Cultivo En Chihuahua, México. Revista Mexicana De Ciencias Agrícolas 2013, 4, 353–365. [Google Scholar] [CrossRef]
  18. Mora-Ravelo, G.S.; Gavi-Reyes, F.; Peña-Cabriales, J.H.; Peréz-Moreno, J.; Tijerina-Chávez, L.; Vaquera Huerta, H. Desnitrificación de Un Fertilizante de Lenta Liberación y Urea-fosfato Monoamónico Aplicados a Trigo Irrigado Con Agua Residual o de Pozo. Revista Internacional de Contaminación. Ambiental 2007, 23, 25–33. [Google Scholar]
  19. Mora-Ravelo, S.G.; Gavi-Reyes, F.; Pérez-Moreno, J.; Peña-Cabriales, J.J.; Tijerina-Chávez, L.; de-la-Isla-de-Bauer, M.d.L. Dinamic of Bacteria Desnitrificants and Nitrificants in the Rizospheric of Wheat with Slow Release of Fertilizer, Irrigated with Waste or Well Water. Advances in Microbiology 2013, 3, 343–349. [Google Scholar] [CrossRef]
  20. Mauricio-Gutiérrez, A.; Jiménez-Salgado, T.; Tapia-Hernández, A.; Romero-Arenas, O. Degradación de Diésel Por Sustrato Residual de Agaricus Bisporus a Nivel Microcosmos. Revista Mexicana De Ciencias Agrícolas 2022, 13, 223–234. [Google Scholar] [CrossRef]
  21. R Core Team. R: A Language and Environment for Statistical Computing. 2022. Available online: https://www.R-project.org/ (accessed on 21 February 2024).
  22. Krishnan, S.; Matsumoto, T.; Nagai, C.; Falconer, J.; Shriner, S.; Long, J.; Medrano, J.F.; Vega, F.E. Vulnerability of Coffee (Coffea spp.) Genetic Resources in the United States. Genetic Resources and Crop Evolution 2021, 68, 2691–2710. [Google Scholar] [CrossRef]
  23. Pozza, E.A.; Pozza, A.A.A. Coffee Plant Diseases Affected by Nutritional Balance. Coffee Science 2023, 18. [Google Scholar] [CrossRef]
  24. Alves-Flores, R.; Borges, B.M.M.N.; Almeida, H.J.; De-Mello-Prado, R. Growth and Nutritional Disorders of Coffee Cultivated in Nutrient Solutions with Suppressed Macronutrients. Journal of Plant Nutrition 2016, 39, 1578–1588. [Google Scholar] [CrossRef]
  25. Romero-Fernández, A. de J.; González-Chávez, Ma. del C. Á.; Herrera-Cabrera, B.E.; Corona-Sánchez, J.E.; Carrill- González, R. The Coffee-Mango Association Promotes Favorable Soil Conditions for Better-Nourished and Higher-Yielding Plants. Agriculture, Ecosystems & Environment 2023, 354. [Google Scholar] [CrossRef]
  26. Sieiro-Miranda, G.L.; González-Marrero, A.N.; Rodríguez-Lema, E.L.; Rodríguez-Regal, Mérida. Efecto de Los Macroelementos Primarios En La Susceptibilidad a Enfermedades. Centro Agrícola 2020, 47, 66–74. [Google Scholar]
  27. Chaudhari, S.K.; Biswas, P.P.; Kapil, H. Soil Health and Fertility. In Soils of India, 1st ed.; Mishra, B., Ed.; Springer Cham: Cham, Switzerland, 2020; pp. 215–231. ISBN 978-3-030-31082-0. [Google Scholar]
  28. Doran, J.W.; Zeiss, M.R. Soil Health and Sustainability: Managing the Biotic Component of Soil Quality. Applied Soil Ecology 2000, 15, 3–11. [Google Scholar] [CrossRef]
  29. Sadeghian, S. Nutrición Del Café. Consideraciones Para El Manejo de La Fertilidad Del Suelo, 1st ed.; FNC Cenicafé: Manizales, Colombia, 2022; pp. 45-47, 151-152, 174-175. ISBN 978-958-8490-60-1. [Google Scholar]
  30. López-Morgado, R.; Díaz-Padilla, G.; Zamarripa-Colmenero, A. El Sistema Producto Café En México: Problemática y Tecnología de Producción, 1st ed.; INIFAP: Medellín de Bravo, México, 2017; pp. 218–219. ISBN 978-607-37-0926-2. [Google Scholar]
  31. Sadeghian, S. Interpretación de los Resultados de Análisis de Suelo, Soporte para una Adecuada Nutrición de Cafetales. Avances Técnicos Cenicafé 2018, 497, 1–8. [Google Scholar] [CrossRef]
  32. Liang, P.; Jingan, X.; Liying, S. The Effects of Reclaimed Water Irrigation on the Soil Characteristics and Microbial Populations of Plant Rhizosphere. Environmental Science and Pollution Research 2022, 29, 17570–17579. [Google Scholar] [CrossRef]
  33. de-Fátima-Pereira-Silva, P.; de-Resende, M.L.V.; Reichel, T.; de-Lima Santos, M.; dos-Santos-Botelho, D.M.; Ferreira, E.B.; Freitas, N.C. Potassium Phosphite Activates Components Associated with Constitutive Defense Responses in Coffea Arabica Cultivars. Molecular Biotechnology 2023, 65, 1777–1795. [Google Scholar] [CrossRef]
  34. Ramírez-Builes, V.H.; Küsters, J. Calcium and Potassium Nutrition Increases the Water Use Efficiency in Coffee: A Promising Strategy to Adapt to Climate Change. Hydrology 2021, 8, 75. [Google Scholar] [CrossRef]
  35. Scalco, M.S.; Alvarenga, L.A.; Guimarães, R.J.; Dominghetti, A.W.; Colombo, A.; Assis, G.A.; Abreu, G.F. Teores Foliares de Fósforo e Zinco, Produtividade e Crescimento de Café Irrigado. Pesquisa Agropecuária Brasileira 2014, 49, 95–101. [Google Scholar] [CrossRef]
  36. Martinez, H.E.P.; Lacerda, J.S. de; Clemente, J.M.; Silva Filho, J.B. da; Pedrosa, A.W.; Santos, R.H.S.; Cecon, P.R. Production, Chemical Composition, and Quality of Arabic Coffee Subjected to Copper Doses. Pesquisa Agropecuária Brasileira 2018, 53, 443–452. [Google Scholar] [CrossRef]
  37. Covre, A.M.; Partelli, F.L.; Bonomo, R.; Gontijo, I. Micronutrients in the Fruits and Leaves of Irrigated and Non-Irrigated Coffee Plants. Journal of Plant Nutrition 2018, 41, 1119–1129. [Google Scholar] [CrossRef]
  38. Wang, F.; Chen, S.; Wang, Y.; Zhang, Y.; Hu, C.; Liu, B. Long-Term Nitrogen Fertilization Elevates the Activity and Abundance of Nitrifying and Denitrifying Microbial Communities in an Upland Soil: Implications for Nitrogen Loss from Intensive Agricultural Systems. Frontiers in Microbiology 2018, 9, 406506. [Google Scholar] [CrossRef]
  39. Pronti, A.; Coccia, M. Multicriteria Analysis of the Sustainability Performance between Agroecological and Conventional Coffee Farms in the East Region of Minas Gerais (Brazil). Renewable Agriculture and Food Systems 2021, 36, 299–306. [Google Scholar] [CrossRef]
  40. Guzmán-Luna, A.; Bacon, C.M.; Méndez, V.E.; Flores-Gómez, M.E.; Anderzén, J.; Mier-y-Terán-Giménez-Cacho, M.; Hernández-Jonapá, R.; Rivas, M.; Duarte-Canales, H.A.; Benavides-González, Á.N. Toward Food Sovereignty: Transformative Agroecology and Participatory Action Research With Coffee Smallholder Cooperatives in Mexico and Nicaragua. Frontiers in Sustainable Food Systems 2022, 6, 810840. [Google Scholar] [CrossRef]
  41. Lanka, S.V.; Khadaroo, I.; Böhm, S. Agroecology Accounting: Biodiversity and Sustainable Livelihoods from the Margins. Accounting, Auditing & Accountability Journal 2017, 30, 1592–1613. [Google Scholar] [CrossRef]
  42. Allinne, C.; Savary, S.; Avelino, J. Delicate Balance between Pest and Disease Injuries, Yield Performance, and Other Ecosystem Services in the Complex Coffee-Based Systems of Costa Rica. Agriculture, Ecosystems & Environment 2016, 222, 1–12. [Google Scholar] [CrossRef]
  43. van Noordwijk, M.; Coe, R.; Sinclair, F.L.; Luedeling, E.; Bayala, J.; Muthuri, C.W.; Cooper, P.; Kindt, R.; Duguma, L.; Lamanna, C.; et al. Climate Change Adaptation in and through Agroforestry: Four Decades of Research Initiated by Peter Huxley. Mitigation and Adaptation Strategies for Global Change 2021, 26, 18. [Google Scholar] [CrossRef]
Preprints 101681 g001
Figure 1. Sampling sites in the cooperative society, Puebla, Mexico. Cultivation methods: unshaded monoculture, shaded monoculture, commercial polyculture, traditional polyculture, and rustic.
Figure 1. Sampling sites in the cooperative society, Puebla, Mexico. Cultivation methods: unshaded monoculture, shaded monoculture, commercial polyculture, traditional polyculture, and rustic.
Preprints 101681 g001
Preprints 101681 g002
Figure 2. Nitrification and denitrification rates by cropping system in Coffea spp. Different letters indicate significant statistical differences between cultivation systems according to the Tukey test (p< 0.05).
Figure 2. Nitrification and denitrification rates by cropping system in Coffea spp. Different letters indicate significant statistical differences between cultivation systems according to the Tukey test (p< 0.05).
Preprints 101681 g002
Preprints 101681 g003
Figure 3. Principal component analysis (PCA) for the five Coffea spp cultivation systems.
Figure 3. Principal component analysis (PCA) for the five Coffea spp cultivation systems.
Preprints 101681 g003
Preprints 101681 g004
Figure 4. Principal component analysis (PCA) for leaf nutrients, physical, chemical and biological properties of soil. §: Nutrients in Coffea spp leaf. CEC: Cation exchange capacity.
Figure 4. Principal component analysis (PCA) for leaf nutrients, physical, chemical and biological properties of soil. §: Nutrients in Coffea spp leaf. CEC: Cation exchange capacity.
Preprints 101681 g004
Table 1. Physical, chemical, and microbiological analysis in coffee soils.
Table 1. Physical, chemical, and microbiological analysis in coffee soils.
Parameter Unshaded Monoculture Traditional Polyculture Commercial Polyculture Shaded Monoculture Rustic p-value
Textural Class Clay, Silty Clay Silty Loam, Clay Silty Loam, Loam Clay Loam, Clay Silt Sandy Loam, Silty Loam -
pH 3.90 ± 0.49 4.93 ± 0.55 4.58 ± 0.24 4.78 ± 0.52 4.63 ± 0.38 0.128
Bulk Density
(mg cm-3)		 0.85 ± 0.01 0.91 ± 0.05 0.88 ± 0.09 0.81 ± 0.07 0.87 ± 0.08 0.495
Moisture content (%) 58.06 ± 3.75 ab 40.27 ± 1.89 b 66.36 ± 1.48 a 55.72 ± 2.42 ab 48.31 ± 2.77 ab 0.010
EC (dS) 0.25 ± 0.03 a 0.28 ± 0.01 a 0.14 ± 0.02 b 0.24 ± 0.01 a 0.13 ± 0.01 b 0.001
P (mg kg-1) 7.96 ± 0.28 a 0.33 ± 0.05 b 0.68 ± 0.38 b 0.20 ± 0.01 b 0.26 ± 0.02 b 0.001
OM (%) 5.91 ± 0.39 bc 5.46 ± 0.90 c 4.79 ± 0.40 c 10.89 ± 0.40 a 7.14 ± 0.19 b 0.001
OC (%) 3.43 ± 0.23 bc 3.17 ± 0.52 c 2.78 ± 0.23 c 6.31 ± 0.23 a 4.14 ± 0.11 b 0.001
Nt (%) 0.25 ± 0.05 bc 0.15 ± 0.01 c 0.32 ± 0.03 b 0.47 ± 0.07 a 0.30 ± 0.02 b 0.001
Exchangeable Bases
(cmol kg-1)		 K 0.41 ± 0.02 b 0.36 ± 0.02 bc 0.33 ± 0.02 c 0.38 ± 0.02 bc 0.76 ± 0.05 a 0.001
Ca 1.93 ± 0.38 c 6.79 ± 1.10 a 2.96 ± 0.47 bc 6.83 ± 0.35 a 4.12 ± 0.41 b 0.001
Mg 1.13 ± 0.37 c 4.26 ± 0.64 a 1.43 ± 0.18 bc 2.29 ± 0.04 b 1.58 ± 0.08 bc 0.001
Na 1.02 ± 0.02 ab 0.91 ± 0.01 bc 0.88 ± 0.02 c 0.90 ± 0.01 bc 1.14 ± 0.11 a 0.001
CEC (cmol kg-1) 7.88 ± 0.25 c 15.56 ± 1.48 b 15.02 ± 0.31 b 18.40 ± 0.32 a 14.29 ± 0.24 b 0.001
Micronutrients (mg kg-1) Zn 0.66 ± 0.04 b 2.88 ± 0.18 a 0.53 ± 0.21 b 2.55 ± 0.47 a 0.50 ± 0.12 b 0.001
Mn 1.40 ± 0.20 c 8.96 ± 0.20 a 2.05 ± 0.54 bc 3.67 ± 1.47 b 0.86 ± 0.10 c 0.001
Cu 2.48 ± 0.46 b 3.45 ± 0.23 a 3.14 ± 0.08 a 2.80 ± 0.12 ab 1.05 ± 0.05 c 0.001
Fe 140.45 ± 1.95 a 50.50 ± 0.90 b 11.46 ± 0.26 c 51.60 ± 0.20 b 20.79 ± 0.59 c 0.001
Mesophilic Bacteria 2.46 ± 0.07 e 22.56 ± 0.24 c 19.60 ± 0.31 d 27.59 ± 0.34 b 30.16 ± 0.08 a 0.001
Nitrifying Bacteria 336.54 ± 3.00 a 279.45 ± 1.88 b 226.73 ± 2.57 b 133.12 ± 5.72 c 1.51 ± 0.05 d 0.001
Denitrifying Bacteria 0.03 ± 0.01 a 0.02 ± 0.01 b 0.023 ± 0.01 b 0.02 ± 0.01 b 0.01 ± 0.01 c 0.001
Actinomycetes 0.35 ± 0.05 b 0.39 ± 0.01 b 0.23 ± 0.03 c 0.38 ± 0.02 b 0.83 ± 0.07 a 0.001
Fungi 0.28 ± 0.01 c 0.39 ± 0.01 b 0.05 ± 0.01 d 0.26 ± 0.03 c 0.59 ± 0.03 a 0.001
Data represent the mean of three repetitions ± standard deviation. Different letters in the column indicate significant statistical differences between cultivation systems according to the Tukey test (p< 0.05). EC: Electrical conductivity, OM: Organic matter, OC: Organic carbon, Nt: Total nitrogen, CEC: Cation exchange capacity. 1: values x 106 cells g-1 soil dry; 2: values x106 CFU g-1 soil dry.
Table 2. Analysis of microelements in Coffea spp.
Table 2. Analysis of microelements in Coffea spp.
Parameter Unshaded Monoculture Traditional Polyculture Commercial Polyculture Shaded Monoculture Rustic
N (%) 1.20 ± 0.16 c 2.60 ± 0.11 ab 2.30 ± 0.26 b 2.98 ± 0.13 a 2.79 ± 0.12 a
P (mg kg-1) 290.20 ± 5.19 b 156.02 ± 3.65 c 156.02 ± 4.56 c 295.18 ± 4.78 b 660.01 ± 2.80 a
Micronutrients (mg kg-1) K 9659.61 ± 27.63 c 11558.43 ± 27.80 b 8866.94 ± 11.44 c 12029.73 ± 25.15 b 17297.22 ± 23.73 a
Ca 10021.27 ± 10.00 c 14233.60 ± 14.97 b 16233.93 ± 12.88 a 15160.23 ± 17.44 b 10967.89 ± 14.96 c
Mg 2804.05 ± 17.49 c 3745.70 ± 19.14 b 3478.99 ± 20.78 b 5362.29 ± 15.82 a 2833.22 ± 19.99 c
Zn 10.42 ± 0.42 c 12.92 ± 0.42 a 12.92 ± 0.42 a 11.45 ± 0.46 b 12.50 ± 0.43 a
Mn 37.50 ± 0.33 c 53.33 ± 0.30 b 50.73 ± 0.49 b 60.21 ± 0.54 a 33.33 ± 0.40 c
Na 248.88 ± 2.09 244.71 ± 1.49 262.07 ± 3.25 262.07 ± 3.42 248.32 ± 1.67
Fe 100.83 ± 1.83 c 91.66 ± 1.67 c 96.25 ± 1.10 c 144.79 ± 1.12 b 271.24 ± 1.08 a
Data represent the mean of three repetitions ± standard deviation. Different letters in the column indicate significant statistical differences between cultivation systems according to the Tukey test (p= 0.001).
Table 3. Pearson Correlation Matrix of microelements in Coffea spp leaf.
Table 3. Pearson Correlation Matrix of microelements in Coffea spp leaf.
Variables N P Ca Na Mg K Zn Fe Mn
N 1 0.2467462 0.15408777 0.20881933 0.4175778 0.576633 0.46497838 0.4765052 0.15264905
(0.37532) (0.58349) (0.45513) (0.12145) (0.02443)* (0.08074) (0.07254) (0.58705)
P 0.2467462 1 -0.34567145 -0.17495034 -0.3109498 0.7609674 0.31167246 0.8290195 -0.54382089
(0.37532) (0.20696) (0.53287) (0.25929) (0.00098)*** (0.25812) (0.00013)*** (0.03612)*
Ca 0.1540878 -0.3456715 1 0.35947898 0.7383237 -0.3460064 -0.00839027 -0.3072806 0.3478592
(0.58349) (0.20696) (0.18818) (0.00167)** (0.20649) (0.97633) (0.26525) (0.20390)
Na 0.2088193 -0.1749503 0.35947898 1 0.655838 -0.1032731 0.22520867 -0.1655947 0.08639506
(0.45513) (0.53287) (0.18818) (0.00794)** (0.71417) (0.41966) (0.55532) (0.75949)
Mg 0.4175778 -0.3109498 0.73832374 0.65583796 1 -0.2822739 0.35225978 -0.2968784 0.27643548
(0.12145) (0.25929) (0.00167)** (0.00794)** (0.30805) (0.19785) (0.28259) (0.31859)
K 0.576633 0.7609674 -0.34600645 -0.10327305 -0.2822739 1 0.46540082 0.9139314 -0.3976754
(0.02443)* (0.00098)*** (0.20649) (0.71417) (0.30805) (0.08043) (0.00000)*** (0.14212)
Zn 0.4649784 0.3116725 -0.00839027 0.22520867 0.3522598 0.4654008 1 0.3405685 -0.51123327
(0.08074) (0.25812) (0.97633) (0.41966) (0.19785) (0.08043) (0.21419) (0.05145)
Fe 0.4765052 0.8290195 -0.30728059 -0.16559474 -0.2968784 0.9139314 0.34056846 1 -0.49853466
(0.07254) (0.00013)*** (0.26525) (0.55532) (0.28259) (0.00000)*** (0.21419) (0.05855)
Mn 0.1526491 -0.5438209 0.3478592 0.08639506 0.2764355 -0.3976754 -0.51123327 -0.4985347 1
(0.58705) (0.03612)* (0.20390) (0.75949) (0.31859) (0.14213) (0.05145) (0.05855)
*The correlation is significant at a value of p< 0.05. **The correlation is significant for p< 0.01. ***The correlation is significant for p< 0.001. Two-sided significance values are in parentheses.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated