For the soil quality index, the pH indicator in CM and CB had values corresponding to one, indicating that the pH of these systems is optimal for coffee cultivation in the area. In contrast, the pH of the rhizosphere of the CS had negative values. According to Cantú et al. (2007), values less than zero denote the worst quality; therefore, the pH of the CS rhizosphere should be corrected with the addition of calcite (CaCO3) or dolomite [CaMg(CO3)2] in order to avoid a misbalance of K+, Ca2+ and Mg2+ ions (Rosas-Patiño et al. 2017). The pH was also used as an indicator to measure the soil quality of different rice and watermelon production systems under greenhouse and field conditions in Shanghai, China. Similarly, Estrada-Herrera et al. (2017) selected this indicator to measure soil quality with different crops (maize, beans, wheat, canary seed, barley, oats, alfalfa, and sorghum) in the Mixteca Alta zone of Oaxaca, Mexico. These studies in weathered soils used pH as an indicator because ion washout can induce soil deficiencies for the crops. The few studies that have evaluated soil quality in coffee cultivation did not consider pH as an indicator; therefore, we could only compare our results with the aforementioned studies.
The CEC soil index was low and very low in the shading and CS systems, respectively. The intensity soil use and low SOM in conventional systems are variables influencing these differences observed (Márquez de la Cruz et al., 2022). The SOM improves the retention of soil nutrients and then fertility. Estrada-Herrera et al. (2017) also used the CEC as an index for soil fertility in associated crops such as corn and bean in Oaxaca, Mexico. These authors referred that low SOM availability and low CEC are common in poor management of conventional crop production systems. Therefore, coffee farmer should apply frequently organic amendments along the coffee production cycle such as chicken manure or guano, and improve the CEC (INTAGRI, 2020a).
The N and K concentration in the soil of the three systems was lower than that required for coffee development in the study area since these nutrients vary strongly in the rhizosphere due to humidity fluctuations. Therefore, N and K levels should be increased by adding manure compost available in the area or harvesting residues and plant debris (Estrada-Herrera et al. 2017). Ca and Mg indicators in the rhizosphere of the CM and CB were ideal for maintaining adequate soil fertility; in contrast, those of the CS were very low quality. The N-K deficit and very low Ca-Mg concentration in the CS rhizosphere are of concern because these macronutrients are essential for optimal coffee development, growth, productivity, yield, and quality (Cardona and Sadeghian, 2005; Sadeghian Khalajabadi, 2011; Romero Fernández et al., 2023). The indicators show that these soils require special attention to improve their quality and fertility, and their management needs to be corrected by supplying fertilizers with the lacking elements. Previous research used macronutrients as soil quality indicators; for example, Valbuena-Calderón et al. (2017) analyzed K and Mg to calculate the soil quality index in coffee under intensive and shaded management systems in southern Colombia. These authors agree that macronutrients are key indicators of soil quality and productivity.
Campos et al. (2007) selected N and Mg as attributes to determine soil quality in the Cofre de Perote National Park, Veracruz, Mexico. However, these soils have different mineralogy than the soils of the present study. Bautista Cruz et al. (2004) used N, K, Ca, and Mg to evaluate the quality of semiarid soils in Oaxaca; they mentioned that Mg was the best indicator of soil quality. These authors suggested that care should be taken when including Mg because soil mineralogy influences its availability. Consequently, many physiological and biochemical processes in plants (e.g., coffee plants) are seriously affected by Mg deficiency, resulting in low growth and yield (Yang et al., 2007). Furthermore, Liu et al. (2014) worked with N and K to determine soil quality and improve rice productivity in southern China.
Soil moisture was low in the CM rhizosphere and very low in the CB and CS rhizosphere. This difference is associated with the soil use management of each system and their respective shade tree. Since mango is a perennial tree, the canopy remains all year round in the CM. In contrast, the banana crop is deciduous, so its vegetation cover is temporary, causing a similar shade behavior to CS and similar wetting and drying cycles. Soil moisture is an important indicator for proper plant development and growth; it increases coffee crop yield and functions as a temperature regulator (Reyna et al., 2011; Romero Fernández et al., 2023). This was consistent with the temperature in CM (22.6 ºC,) which was lower than CB (26.5 ºC) and CS (29.3 ºC) (data not reported). Since permanent shade buffers evapotranspiration and temperature changes, the producer should increase the planting density of the respective shade trees in each system until soil moisture percentages are adequate for coffee cultivation. However, excessive shade favors the appearance of fungi and diseases and is detrimental to coffee (Farfán and Mestre, 2004). Thus, it is necessary to evaluate the characteristics of the plantation and to determine the exact number of trees needed, the distance between them, their topological arrangement, and whether it is necessary to intertwine them over time to maintain shade and reduce abiotic drought stress.
The SC indicator values of the rhizosphere were low in the CB and very low in the CM and CS; however, this situation is common in the coffee area of the Central State of Veracruz, where climate conditions, mainly precipitation, topography, and soil origin, do not favor salt accumulation (Garza Lau et al. 2020). In addition, the low SC of the rhizosphere improves the coffee root’s efficiency at absorbing the nutrients present in the soil solution (Sadeghian Khalajabadi and Zapata 2014). Bautista Cruz et al. (2004) also used SC as a soil quality indicator.
The Fe content in the rhizosphere of CB and CS was excessive, so we recommend not fertilizing with this micronutrient in these two systems. Preventing high Fe concentrations is necessary to avoid interference with the absorption of other elements and, consequently, negative effects on coffee plants. High Fe concentrations inhibit cell division and the elongation of primary roots and, subsequently, the growth of lateral roots (Li et al. 2015). In contrast, Fe in the CM rhizosphere had low quality, so corrective management actions are required to increase it to avoid possible deficiency problems such as chlorosis or yellowing of plant leaves (Aung et al. 2018). In soils with low organic matter and frequent wetting and drying cycles, Fe is oxidized to compounds unavailable to plants, which agrees with our field observations.
The SPR in CB was very high and in CM was high. In contrast, in CS was slow. The SOM in shading coffee systems improves soil resistance and physical properties such as structure. Moreover, vegetal cover from shade trees reduces the impact of erosive forces (Paz and Sanchez, 2007). The number of worms in the shading coffee systems was very low and deficient in CS. This last is the worst quality soil situation due to high land use intensity. Vegetal cover benefits worms in coffee shading systems (Bolaños et al., 2012) as organic residues are the main food source and propitiate temperature and moisture in the soil for the development and reproduction of worms. They indicate soil disturbance and soil system management, and then worms improve soil traits (Andréa, 2010; Gutiérrez-Sarmiento and Cardona, 2014).
Regarding the quality index in the foliar nutrition of coffee, the foliar N and K indicator in CM was high and moderate, respectively. These data explain the very low quality of these soil indicators and suggest that N and K were efficiently absorbed by the plants in the CM system. As mentioned previously, the correct management of fertilization with these indicators needs to be considered to ensure fertility and soil quality in this system. In contrast, the CB system showed very low N and low K quality indicators. In the CS, both indicators were deficient, which was consistent with the deficiency of these macronutrients in the rhizosphere of the two systems. Foliar N was also used as an indicator by Gómez and Jaramillo (2017) in the municipality of Antioquia, Colombia, both for determining soil quality and coffee cup quality.
The foliar Ca was excessive in the coffee plants of the three systems. Thor (2019) indicated that the high Ca concentration in leaf tissue is because, under water stress, this macronutrient accumulates in the leaves rather than in other plant structures. In leaves, Ca has a dual function: enzyme activation and strengthening of the cell wall and membrane structures against pathogen attack. However, more targeted studies on these aspects of nutrition are needed to draw definitive conclusions.
The foliar Fe concentration was very high in CM, high in CS, and moderate in CB. In contrast, the foliar Cu concentration in CM and CS was low and moderate in CB. In this sense, the foliar application of 0.5% CuSO4·5H2O in the CM and CS is recommended. However, it is essential to evaluate the optimal doses before the first leaf application or if the producer has no previous experience with the crop (INTAGRI 2020b).
The chlorophyll b indicator was very high in CM but very low in CB and CS. Coffee plants associated with mango receive less light intensity than the plants in the other systems. Lower light intensity increases the production of photosynthetic pigments by leaf cells, enabling plants to harness light and optimize photosynthetic efficiency (Suárez et al., 2013). Thus, coffee plants in the CM system are physiologically adapted to capture and efficiently use less light, as mentioned by Cambrón-Sandoval et al. (2011). Conversely, Tanaka et al. (2008) indicated that low photosynthetic pigment concentrations could be a symptom of inadequate fertilization or nutritional imbalance. However, other environmental factors, such as temperature and drought, also affect photosynthetic activity. Therefore, calculating the leaf quality index using photosynthetic compounds was very useful because it indirectly measures the physiological and nutritional status of the plants. In addition, measuring photosynthetic compounds is simple, inexpensive, and widely used by several authors in coffee cultivation (López-García et al., 2016; Marín-Garza et al., 2018; Solís Pino et al., 2021; Romero Fernández et al., 2023).
The bean quality index was low in the three coffee growing systems. The bean indicators P, Ca, Mg, Zn, and Cu negatively influenced bean quality. In contrast, K bean concentration positively affected bean quality. Sadeghian and Salamanca (2015) indicated that under hydric or temperature stress conditions, nutrients are redirected to activate specific enzymes instead of translocating to form part of the fruit. Although no nutrient deficiencies were detected visually, future research should consider evaluating enzyme activity in coffee plants to further understand their biochemical status and draw conclusions that are more accurate.
Using PCA in the present investigation proved to be a fast and reliable way to select the minimum data set of the components explaining the greatest variability. PCAs have been widely used to determine quality indexes. For example, Valbuena-Calderón et al. (2017) analyzed 23 soil indicators in coffee cultivation and selected 16 based on a PCA with four components that explained 84% of the total variation. Using 37 soil indicators and 11 foliar indicators, Gómez and Jaramillo (2017) selected 13 and 3 indicators, respectively, with five components explaining 72% of the variability. PCA is also widely used to determine the quality index in other crops of agronomic interest and forestry systems: Chun Juan et al. (2013) evaluated 19 soil indicators in rice and watermelon cultivation (greenhouse and field) and selected 10 with 8 components that explained 70% of the variation using PCA. Finally, Campos et al. (2007) evaluated 13 soil indicators in a fragment of pine forest and corn crops, of which they selected ten that explained 62% of the variation with two components.