# Diversification, age, and organic amendments affect microbial and enzymatic activities in soils of Arabica coffee plantations in the tropical lowlands of Colombia

William Andrés Cardona, Joana Falcao Salles, Luis Gabriel Bautista

Montealegre, Barbara Prack Mccormick, Carlos Mario González Baena, Yeinny Carolina Pisco Ortiz, Eric Scopel, Martha Marina Bolaños Benavides, Miguel Andrés Arango Argoti, Pablo Tittonell

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William Andrés Cardona, Joana Falcao Salles, Luis Gabriel Bautista Montealegre, Barbara Prack Mccormick, Carlos Mario González Baena, et al.. Diversification, age, and organic amendments affect microbial and enzymatic activities in soils of Arabica coffee plantations in the tropical lowlands of Colombia. Geoderma Régional, 2025, 41, pp.e00966. 10.1016/j.geodrs.2025.e00966. hal-05405872

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# Diversification, age, and organic amendments affect microbial and enzymatic activities in soils of Arabica coffee plantations in the tropical lowlands of Colombia

William Andrés Cardona<sup>a,b,*</sup>, Joana Falcao Salles<sup>a</sup>, Luis Gabriel Bautista Montealegre<sup>b</sup>, Barbara Prack Mc Cormick<sup>a</sup>, Carlos Mario González Baena<sup>b</sup>, Yeinny Carolina Pisco Ortiz<sup>b</sup>, Eric Scopel<sup>c</sup>, Martha Marina Bolaños Benavides<sup>b</sup>, Miguel Andrés Arango Argoti<sup>b</sup>, Pablo Tittonell<sup>a,c</sup>

## ARTICLE INFO

**Keywords:**

Coffea arabica

Low marginal zone

Soil enzymes

Associated cropping

Soil microbial activity

Basal respiration

## ABSTRACT

Coffee cultivation is essential for tropical agriculture and rural livelihoods in Latin America, Africa, and Asia. However, coffee production in tropical low-altitude regions characterized by acidic soils and elevated temperatures faces significant challenges, and the soil microbiological dynamics under these environments remain poorly understood. This study assessed the impact of agricultural practices and edaphoclimatic variables on soil biological indicators, such as enzymatic activities and basal respiration in coffee fields located below 1000 m altitude in Colombia. Surveys conducted with 40 coffee growers characterized various agronomic practices and socioeconomic factors, and 150 soil samples were collected from their coffee crops of differing ages and associated species. Enzymatic activities were measured using specific substrates, including acid and alkaline phosphatase, arylsulfatase, urease, dehydrogenase, and β-glucosidase. Statistical analyses including constrained ordination, Spearman correlations, and mixed models, were employed to evaluate variations in biological indicators concerning agronomic practices and edaphoclimatic conditions.

Four groups of coffee growers were identified based on their management practices. Livestock-focused growers who utilized organic fertilizers exhibited the highest enzymatic activity, with measurements of acid phosphatase at 365 µg g⁻¹ h⁻¹, alkaline phosphatase at 49 µg g⁻¹ h⁻¹, and arylsulfatase at 137 µg g⁻¹ h⁻¹. In contrast, traditional growers with older plantations recorded the lowest acid phosphatase (215 µg g⁻¹ h⁻¹), alkaline phosphatase (36 µg g⁻¹ h⁻¹), and β-glucosidase (30 µg g⁻¹ h⁻¹). Basal soil respiration was significantly higher in shade-grown coffee compared to sun-grown (43.0 and 12.6 mg CO₂-C kg⁻¹ soil d⁻¹, respectively). Coffee intercropping with Musaceae, Cocoa, and *Inga* species, organic fertilization, and liming enhanced soil biological indicators.

## 1. Introduction

Coffee (*Coffea arabica* L.) is a globally significant tropical crop, cultivated in over 70 countries and supporting millions of farmers (*Poncet et al., 2024*). Colombia, one of the world's leading coffee

producers, dedicates approximately 850,000 ha to coffee cultivation, traditionally in mid- to high-altitude regions (1200–2000 m.a.s.l.) with favorable climatic and soil conditions (USDA, 2024; Jaramillo, 2018). However, rising land-use pressures and increasing demand have driven the expansion of coffee cultivation into marginal, lower-altitude areas

<footnote><sup>a</sup> University of Groningen, the Netherlands</footnote>

<footnote><sup>b</sup> Corporación Colombiana de Investigación Agropecuaria – AGROSAVIA, Colombia</footnote>

<footnote><sup>c</sup> Centre de coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), France</footnote>

<footnote>* Corresponding author at: Groningen Institute of Evolutionary Life Sciences (GELIFES), University of Groningen, Conservation and Microbial Ecology Groups, Nijenborgh 7, 9747 AG Groningen, the Netherlands.</footnote>

<footnote>E-mail addresses: w.a.cardona@rug.nl (W.A. Cardona), j.falcao.salles@rug.nl (J.F. Salles), lbautista@agrosavia.co (L.G.B. Montealegre), b.p.prack.mccormick@rug.nl (B.P.M. Cormick), carlos.baena88@gmail.com (C.M.G. Baena), ypisco@cenipalma.org (Y.C.P. Ortiz), eric.scopel@cirad.fr (E. Scopel), mmbolanos@agrosavia.co (M.M.B. Benavides), marangoa@agrosavia.co (M.A.A. Argoti), p.a.tittonell@rug.nl (P. Tittonell).</footnote>
(below 1000 m.a.s.l.), particularly in regions such as the Meta department, Colombia. These areas are characterized by higher temperatures, acidic soils, and lower organic matter content, posing significant challenges to coffee production (Farfán, 2014). These conditions not only affect plant physiology but also disrupt soil microbial communities, which play a critical role in nutrient cycling, plant health, and long-term soil fertility (Epelde et al., 2014; Sainju et al., 2022).

Sustainable practices such as agroforestry and organic farming are essential for maintaining soil health, biodiversity, and productivity in marginal environments (Doran and Parkin, 2015; De Leijster et al., 2021; Prieto Martinez et al., 2024). Agroforestry systems, for instance, enhance soil organic matter, improve nutrient availability, and mitigate soil constraints (Muche et al., 2022; Dori et al., 2022). In Colombia, coffee is primarily cultivated in full sun; however, in the Amazonian foothills—where most plantations are located below 1200 m altitude and annual rainfall exceeds 3500 mm—adaptive shade management becomes crucial for balancing humidity and promoting coffee growth (Farfán, 2014; Giraldo-Jaramillo et al., 2021). The rapid expansion of coffee cultivation in these marginal ecosystems highlights the urgent need for sustainable soil management strategies to address the dual pressures of environmental stress and agricultural intensification.

Soil quality assessments incorporate physical, chemical, and biological indicators, with soil microorganisms and their enzymatic activities playing a crucial role in nutrient cycling and ecosystem health (Bhaduri et al., 2022). Enzymes such as β-glucosidase, phosphatases, arylsulfatase, and urease facilitate essential carbon and nutrient cycling processes, releasing vital nutrients for plant growth and soil health (Allison, 2006; Cardoso et al., 2013; Dai et al., 2018). These microbial processes are extremely sensitive to management practices and significantly influence soil respiration, nutrient availability, and carbon sequestration (Epelde et al., 2014; Sainju et al., 2022). In coffee systems, evaluating microbial activity and enzyme function is critical for enhancing plant health, soil fertility, and resilience, thereby promoting sustainable production systems.

The Meta department is emerging as a key region for coffee cultivation under non-traditional conditions. However, limited information exists regarding the microbiological and chemical properties of its soils, and their relationship with management practices and environmental stressors. Addressing this knowledge gap is critical for developing sustainable coffee production systems that enhance resilience and productivity in marginal areas.

Climate models predict significant declines in coffee suitability and productivity in low-altitude regions, particularly in areas below 1000 m altitude in countries such as Brazil, Southeast Asia, Nicaragua, and Colombia (Bunn et al., 2015; Magrach and Ghazoul, 2015; Läderach et al., 2017; Ceballos-Sierra and Dall'Erba, 2021). While these projections underscore vulnerabilities, sustainable practices—including shade management, intercropping, and organic fertilization—can enhance soil health, microbial activity, and nutrient cycling, providing adaptive strategies to mitigate the impacts of climate change. Although these projections primarily pertain to Arabica coffee, recent research indicates that Robusta coffee (*Coffea canephora*), which is traditionally cultivated in lowland tropical regions, is also extremely sensitive to temperature fluctuations (Kath et al., 2020; González-Orozco et al., 2024). This finding suggests that both coffee species are susceptible to the effects of climate change, emphasizing the necessity of adaptive and sustainable management practices in lowland coffee production systems.

We hypothesized that coffee systems incorporating fruit trees, shade species, and agroecological practices would demonstrate enhanced soil biological activity compared to monoculture systems, particularly under challenging soil and climate conditions. This study aimed to evaluate the impact of agricultural practices employed by coffee growers in the Amazonian foothills of southern Meta, Colombia, on soil basal respiration and enzymatic activities while also examining the influence of site-specific edaphoclimatic conditions. By integrating the effects of both

management practices and environmental factors, this research seeks to provide a comprehensive understanding of the drivers of soil biological activity in coffee production systems. The findings aim to offer actionable insights for promoting sustainable coffee production in marginal tropical environments, highlighting the importance of considering the interplay between agronomic management and edaphoclimatic conditions for effective soil health management.

## 2. Material and methods

### 2.1. Agroecosystem characteristics

The research was conducted in the southern Meta department, Colombia, specifically in the rural areas of the municipalities of Uribe and Mesetas, where farmers have established Arabica coffee (*Coffea arabica*) farming systems at altitudes below 1000 m (Fig. 1). These areas were selected because they represent coffee production within the low marginal zone and are part of initiatives aimed at promoting and supporting coffee cultivation in regions historically affected by armed conflict. For optimal growth, the coffee plant should be cultivated within an altitudinal range of 1000 to 2000 m above sea level (m.a.s.l), in areas with temperatures ranging from 19.0 to 21.5 °C, and with annual precipitation between 1800 and 2000 mm (approximately 120 mm of water per month) (Jaramillo, 2000). When altitudes fall below 1000 m, temperatures often exceed 21.5 °C, and the soils tend to be acidic with low fertility. These conditions characterize what is known as a low marginal zone for coffee cultivation. Such environments pose significant challenges to plant development and productivity, necessitating adaptive management strategies to sustain coffee production.

Mesetas receive an average annual precipitation of 3022 ± 37 mm, and Uribe receives 3698 ± 134 mm. These averages are based on data collected over the past 30 years (IDEAM, 2024). The months with the lowest rainfall are December, January, and February, indicating two distinct seasons. Relative humidity remains consistently high at 82 % throughout the year. The mean temperature is 22.5 °C in Mesetas and 23.9 °C in Uribe. The soils in this region include Typic Kanhapludults, Typic Dystropepts, Typic Troporthents, and Oxic Dystrudepts in Mesetas municipality, as well as Typic Troporthents, Typic Dystropepts, Typic Tropfluvents, Typic Tropaquepts, Oxyaquic Dystrudepts, and Typic Hapludults in Uribe municipality (IGAC, 2024). These soils are characterized by moderate to elevated levels of weathering and acidity, which lead to the strong binding of soluble phosphorus through interactions with clay fractions, iron oxides, and aluminum hydroxides (IGAC, 2024).

### 2.2. Surveys

Semi-structured interviews were conducted with 40 coffee growers. The survey addressed local coffee production technology as well as social and economic aspects (Appendix A, Supplementary material). The sample size was determined using deterministic random sampling based on farm accessibility, following the recommendations of Rodríguez Osuna (2005) for small, well-defined populations. Surveys were conducted on 23 farms in the Uribe municipality and 17 farms in the Mesetas municipality.

### 2.3. Typification of coffee growers

We classified coffee growers using the methodology and previous results from the EU project ABRIGUE (DeSIRA, 2024). Our approach involved a two-step statistical process to create homogeneous groups of farmers. First, we transformed qualitative variables into non-correlated variables through Multiple Correspondence Analysis (MCA). Next, we grouped individual coffee growers into types using Agglomerative Hierarchical Clustering (AHC), with the MCA results serving as input variables, as described by (Cardona et al., 2024; Cardona et al., 2024a).
<figure><img src="image_3.png" /><figcaption>Fig. 1. Location of coffee growers and soil samplings in Mesetas and Uribe (Meta department, Colombia). Each point represents one soil sample. The green spectrum of colors represents altitudes below 1000 m.</figcaption></figure>

<table><caption>**Table 1**
Typification of coffee growers in the southern Meta, Colombia.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Unit</th><th>Cluster 1</th><th>Cluster 2</th><th>Cluster 3</th><th>Cluster 4</th></tr><tr><th>Traditional coffee growers with old plantations</th><th>Diversified smallholders with aging coffee farms</th><th>Innovative coffee growers with young plantations</th><th>Livestock-focused coffee growers with mature crops</th></tr></thead><tbody><tr><td><strong>Farmers</strong></td><td></td><td>10</td><td>13</td><td>8</td><td>10</td></tr><tr><td>I. Social environment</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Municipality</td><td>%</td><td>Uribe (100.0)</td><td>Uribe (76.9)<br>Mesetas (23.1)</td><td>Uribe (37.5)<br>Mesetas (62.5)</td><td>Mesetas (100.0)</td></tr><tr><td>Farm labor</td><td>%</td><td>Low (90.0)<br>Abundant (10.0)</td><td>Low (69.2)<br>Abundant (23.1)<br>Enough (7.7)</td><td>Low (25.0)<br>Abundant (50.0)<br>Enough (25.0)</td><td>Low (55.6)<br>Abundant (44.4)</td></tr><tr><td>II. Other agricultural activities</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Livestock</td><td>%</td><td>Yes (50.0)<br>No (50.0)</td><td>Yes (53.8)<br>No (46.2)</td><td>Yes (25.0)<br>No (75.0)</td><td>Yes (77.8)<br>No (22.2)</td></tr><tr><td>Vegetable garden</td><td>%</td><td>No (100.0)</td><td>Yes (69.2)<br>No (30.8)</td><td>No (100.0)</td><td>Yes (100.0)</td></tr><tr><td>III. Agronomic practices</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Germinator</td><td>%</td><td>No (100.0)</td><td>No (100.0)</td><td>Yes (62.5)<br>No (37.5)</td><td>Yes (100.0)</td></tr><tr><td>Nursery</td><td>%</td><td>No (100.0)</td><td>No (100.0)</td><td>Yes (87.5)<br>No (12.5)</td><td>Yes (66.7)<br>No (33.3)</td></tr><tr><td>Own seedlings</td><td>%</td><td>Yes (100.0)<br>No (100.0)</td><td>No (100.0)<br>Yes (23.1)<br>No (76.9)</td><td>Yes (100.0)<br>Yes (12.5)<br>No (87.5)</td><td>Yes (100.0)<br>Yes (88.9)<br>No (11.1)</td></tr><tr><td>Soil cover</td><td>%</td><td>Chem-Org (10.0)<br>Chemical (60.0)<br>Organic (30.0)</td><td>Chem-Org (53.8)<br>Chemical (46.2)</td><td>Chem-Org (87.5)<br>Chemical (12.5)</td><td>Chemical (22.2)<br>Organic (66.67)<br>No (11.1)</td></tr><tr><td>IV. Coffee production</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Plant density</td><td>Plants/ha</td><td>4070</td><td>4042</td><td>4346</td><td>3469</td></tr><tr><td>Coffee yield</td><td>kg ha<sup>-1</sup><br>yr<sup>-1</sup></td><td>690</td><td>440</td><td>534</td><td>347</td></tr><tr><td>Coffee age</td><td>Years</td><td>10.5 (Very old)</td><td>7.8 (Old)</td><td>3.5 (Adult)</td><td>6.5 (Adult-Old)</td></tr></tbody></table>

Numbers in parentheses represent the relative frequency of each category within its respective variable. Chem-Org: Chemical-Organic. Very old, old, adult, and adult-old are classifications according to coffee age.
This method, which is widely employed in studies to categorize farms or farming practices (Bhattarai et al., 2017), relies on the AHC algorithm to progressively group individuals based on similarity, measured by a dissimilarity index. At each step, the algorithm pairs individuals with the least dissimilarity, and these pairs are then aggregated using Ward's minimum-variance method, which minimizes the increase in total intra-class inertia. This process resulted in the typification of coffee growers (Table 1 and Fig. S1).

## 2.4. Soil sampling

Sampling was conducted during the rainy season (March–April 2023). Soil samples were collected from each farm, considering the age and type of associated crops for each coffee plantation (Table 2). A minimum of three random samples were obtained from each visited coffee plantation, resulting in a total of 150 samples (nine farms had two coffee plantations of different ages). Subsamples were collected from the top 0–15 cm of soil and combined into composite samples, with each sample consisting of eight individual subsamples. This depth represents the topsoil layer, where most root activity, organic matter input, and microbial processes occur (Ankuda et al., 2024). The collected samples were stored in sealed plastic bags at 4 °C until analysis. Before analysis, the samples were sieved through a 2 mm sieve.

## 2.5. Soil microbial activity – SMA

Soil microbial activity (SMA) was assessed by placing 25 g of soil with a moisture holding capacity of 60 % into airtight incubation containers, which were pre-incubated for one week at 25 °C. Each container had a beaker with 30 mL of 0.05 M NaOH solution to absorb CO₂ produced by soil microorganisms at 25 °C. Measurements of non-neutralized NaOH were recorded over one month. To analyze the absorbed CO₂, 10 mL of 0.05 M BaCl₂ solution and three drops of phenolphthalein indicator were added to the beakers containing the NaOH extracted from the incubation containers. The solution was then titrated with 0.25 M HCl. Beakers without soil, maintained under the same conditions, served as CO₂ blanks. The difference in the volume of HCl consumed between the samples and the blanks during titration was used to estimate the amount of CO₂ released by soil microorganisms (Silva Rodrigues Pinto et al., 2018). The released CO₂ values were utilized to calculate two basal soil respiration indices: cumulative soil respiration (CSR) and basal soil respiration (BSR). Cumulative soil respiration (CSR) represents the respiration rate over a 25-day incubation period, measured in mg CO₂–C kg⁻¹ soil. The soil basal respiration rate (BSR) is defined as the respiration rate on the 25th day of incubation, expressed as mg CO₂–C kg⁻¹ soil d⁻¹ (Yaghoubi Khan-ghahi et al., 2019).

<table><caption>**Table 2**
Associated crops found in coffee fields (n = 150).</caption><thead><tr><th>Associated crop</th><th>Coffee crop sown between</th><th>Relative frequency</th></tr></thead><tbody><tr><td>Avocado (Persea americana)</td><td>Avocado rows</td><td>8 %</td></tr><tr><td>Citrus-trees (Citrus spp.)</td><td>Citrus species rows</td><td>2 %</td></tr><tr><td>Cocoa (Theobroma cacao)</td><td>Cocoa rows</td><td>2 %</td></tr><tr><td>Fruit-trees</td><td>Fruit species around coffee rows: could have avocado, citrus, cocoa, and other fruit species; but there were few trees of each species</td><td>13 %</td></tr><tr><td>Guamo/Ice-cream-bean (Inga spp.)</td><td>Coffee rows with Guamo (Inga edulis) trees around them</td><td>6 %</td></tr><tr><td>Musaceae (Musa spp.)</td><td>Plantain or Banana rows</td><td>11 %</td></tr><tr><td>Shade-grown coffee</td><td>Tree species around coffee rows: there could be trees sowed, or trees left there when the coffee was sowed</td><td>37 %</td></tr><tr><td>Soursop (Annona muricata)</td><td>Soursop rows</td><td>2 %</td></tr><tr><td>Full-sun coffee</td><td>Coffee under monoculture</td><td>19 %</td></tr></tbody></table>

## 2.6. Soil enzymatic activities

Extracellular enzyme analysis involved extracting and measuring p-nitrophenol released when soil samples were incubated at 37 °C and 100 rpm for two hours with a buffered solution containing specific synthetic substrates for the enzymes assessed: β-glucosidase (BG, E.C. 3.2.1.21), acid phosphatase (ACP, E.C. 3.1.3.2), alkaline phosphatase (ALP, E.C. 3.1.3.1), and arylsulfatase (ARS, E.C. 3.1.6.1). Each sample was prepared in 15 mL Falcon tubes containing 0.5 g of soil (particle size <2 mm). Enzyme activities were assayed in duplicate, with one control sample to which substrate was added after incubation; this control value was subtracted from the sample values. The supernatant was centrifuged at 3500 rpm for 10 min, and the p-nitrophenol concentration was measured using a spectrophotometer at 420 nm. This method was adapted from Tabatabai (1994), and the results were expressed as µg g⁻¹ h⁻¹.

A methodology for estimating urease activity in soil (UA, EC 3.5.1.5), as described by Kandeler and Gerber (1988), involves three types of setups: sample, blank, and negative control. For the sample, 5 g of soil was placed in a 100 mL Erlenmeyer flask, mixed with 2.5 mL of 0.08 M urea, covered, and incubated at 37 °C for 2 h with maximum agitation. After incubation, 50 mL of a 1 M KCl/0.01 M HCl solution was added to stop the reaction, followed by 30 min of mechanical shaking. The blank setup follows the same procedure but substitutes distilled water for urea initially, adding the urea only after incubation. The negative control consists of 2.5 mL of 0.08 M urea in an empty flask, which is incubated and stopped as described.

The resulting suspensions were filtered, and 1 mL of the filtrate was diluted with 9 mL of distilled water. Next, 5 mL of a sodium salicylate-nitroprusside/NaOH solution and 2 mL of a 0.1 % sodium dichloro-oisocyanurate solution were added. The optical density of the resulting mixture was measured at 690 nm to quantify the ammonium concentration. Results were expressed in µg NH₄-N g⁻¹ dwt 2 h⁻¹.

The method for assessing dehydrogenase activity (DHA, EC 1.1.1), an intracellular enzyme, involved the use of soluble tetrazolium salts as artificial electron acceptors, specifically triphenyl tetrazolium chloride (TTC) and triphenyl tetrazolium formazan (TTF) (Miras Avalos et al., 2007). After sieving the sampled soil, 5 g were placed in a Falcon tube, and each sample received 5 mL of a 1 to 1.5 % TTC solution. The tubes were shaken and incubated at 37 °C for 24 h. Following incubation, TTF was extracted with 10 mL of methanol by shaking each sample and allowing it to decant. The supernatant was then centrifuged at 3400 rpm for 10 min, and the TTF concentration was measured using a spectrophotometer at a wavelength of 485 nm. Results were expressed in µg TTF g⁻¹ 24 h⁻¹.

Enzymatic activities were expressed per gram of dry soil, calculated based on the gravimetric soil moisture content at the time of analysis. The gravimetric water content was determined by drying the soil samples at 105 °C for 48 h.

## 2.7. Soil chemical and physical properties

The soil pH was measured using a 2:1 solution of deionized water and soil. The particle size distribution (soil type) was determined employing the Bouyoucos method, while textural classification was conducted using the USDA soil texture triangle. Organic carbon content was assessed by oxidizing the soil with potassium dichromate and sulfuric acid (modified Walkley and Black method). The absorbance was measured using UV-visible spectrophotometry at 585 nm, with adjustments made to zero using the calibration curve blank (Manson et al., 2020).
Regarding aluminum and exchangeable acidity, 25 mL of 1 M KCl was added to 2.5 g of soil, which was then mechanically stirred for 10 min. The mixture was filtered and titrated with 0.01 M NaOH, along with three drops of phenolphthalein, until a pale pink color appeared. Exchangeable bases were measured using 1 M ammonium acetate extraction followed by atomic absorption spectrophotometry (Manson et al., 2020). Micronutrients were also determined through atomic absorption spectrophotometry after extraction with Mehllich-1. Boron and sulfur were extracted using monobasic calcium phosphate and analyzed via UV-Vis spectrophotometry. Phosphorus was measured using the Bray II extraction method and analyzed with UV-Vis spectrophotometry (Manson et al., 2020).

## 2.8. Weather variables

Weather variables, including annual temperature (°C), relative humidity (%), and rainfall volume (mm), were generated by interpolating raster surfaces using the kriging method in ArcGIS® Pro 2.9.0 (ESRI) software. This analysis utilized data from the period between 1990 and 2021 (IDEAM, 2024).

## 2.9. Statistical analysis

A constrained ordination analysis was conducted in three stages using a structured methodology. First, a redundancy analysis (RDA) was performed with soil microbial and enzymatic activities as response variables. The explanatory variables included the typification of coffee growers (Table 1), associated crop (Table 2), fertilization type (chemical/organic), coffee area, and liming (yes/no). The type of fertilization was not considered redundant because, unlike the typification of coffee growers, which recorded frequencies of different fertilization types, the RDA dichotomized fertilization into two categories: chemical and organic. Each category was assigned binary levels: 0 for “No” and 1 for “Yes”. All variables were standardized before analysis.

In the subsequent stage, a partial redundancy analysis (partial RDA) was conducted by modifying the RDA model to include soil and climate variables as covariates. Forward selection was independently applied to the response variables to derive a parsimonious set of explanatory variables based on a double-stopping rule, which considered both significance levels ($p < 0.05$) and adjusted $R^2$ values (Blanchet et al., 2008). The RDA and partial RDA analyses were performed using the rda() function in the vegan package in R.

Additionally, redundancy analysis (RDA) and partial redundancy analysis (partial RDA) were employed to assess soil and weather variables as explanatory variables, while agronomic practices were treated as covariates. This methodology aimed to investigate the behavior of response variables—specifically, soil microbial and enzymatic activities—based solely on environmental factors, independent of agronomic practices.

Lastly, canonical variation partitioning (Borcard et al., 2011) was employed to determine the relative contributions of agronomic practices, weather variables, and soil properties in explaining the variation in soil microbial and enzymatic activities. Adjusted $R^2$ values were utilized to obtain unbiased estimates (Peres-Neto et al., 2006). The same agronomic practices identified in the forward selection procedure were applied in this analysis. A parsimonious set of soil properties and weather variables was derived using the same selection procedure to elucidate the variability in soil microbial and enzymatic activities. The analysis was conducted in R using the varpart() function in the vegan package. The significance of the fractions (i.e., explained fractions of variation accounted for by the sum of the canonical axes) was tested by partial redundancy analyses and permutational significance tests (1000 permutations).

This analytical approach was designed to assess both the individual and combined effects of agronomic practices and environmental factors, including soil and weather variables, on soil microbial and enzymatic activities.

We conducted the Kruskal-Wallis test to identify significant global differences among the following groups: a) types of coffee growers, b) coffee-associated crops, and c) soil types. Subsequently, we performed the Wilcoxon test for pairwise comparisons between specific groups when significant differences were observed ($p < 0.05$), using the traditional coffee growers with old plantations cluster, coffee monoculture (full-sun), and loam soil type as reference categories. Differences in soil microbial and enzymatic activities were assessed using generalized linear mixed models (GLMM) and linear mixed models (LMM), incorporating the previously mentioned groups as random effects. Predictor variables (fixed effects) included soil chemical properties. Response variables, such as phosphatases, cumulative soil respiration, basal respiration rate, β-glucosidase, and urease, followed a Gamma distribution with a log link function, while arylsulfatase exhibited a normal distribution. Finally, we performed a Spearman correlation analysis between basal respiration indices and enzymatic activities. Statistical analyses were conducted using R Studio software, utilizing the factoextra, FactoMineR, lm4, rstatix, GGally, and Cluster packages.

# 3. Results

## 3.1. Effect of agronomic practices on soil microbial and enzymatic activities

Using agronomic practices to constrain the ordination of soil microbial and enzymatic activities across various sites through redundancy analysis (RDA), the model accounted for 34.1 % of the total variation ($p < 0.001$). After adjusting for the number of explanatory variables using permutation tests, 11 significant explanatory variables remained in the model ($p < 0.05$) to constrain the response variables. The adjusted $R^2$ indicated that the model explained 25.7 % of the variance in the response variables. The adjusted $R^2$ coefficient in RDA does not have a direct interpretation as it does in ordinary linear regression. Instead, the adjusted $R^2$ in RDA typically represents the proportion of total variance in the response variables explained by the model, while accounting for the number of predictors in the model and the complexity of the experimental design. This adjustment is crucial in preventing the overestimation of explanatory power, particularly in models with numerous predictors. In ecological studies, the adjusted $R^2$ is especially significant as it considers the complexity of the experimental design and mitigates the risk of overestimating explanatory power, which is vital when addressing interconnected environmental and biological factors.

A partial Redundancy Analysis (partial RDA) was employed to control the effects of soil and weather covariates, thereby reducing the unexplained variation present in the initial RDA model. This analysis revealed that agronomic practices accounted for 21.6 % of the variation in soil microbial and enzymatic activities across the sites, while covariates, including weather and soil characteristics, explained 23.4 %. The adjusted model accounted for 18.4 % of the variation in the response variables across the sites and was statistically significant ($p = 0.001$). The partial RDA adjusts for the effects of explanatory variables based on covariates that are not of primary interest and, therefore, are not plotted.

Soil enzymes that are closely related occupy common sites, indicating relationships similar to those between enzymes associated with the carbon and phosphorus cycles, such as β-glucosidase and phosphatases (Fig. 2). Positive correlations were observed between β-glucosidase and phosphatases with coffee associated with species such as Musaceae and Guamo, as well as with organic fertilization. Conversely, dehydrogenase and soil respiration indices exhibited positive correlations with coffee associated with cocoa. Additionally, arylsulfatase was positively correlated with coffee associated with fruit trees (Fig. 2).

Finally, the analysis of the partitioning of variation among explanatory variables and covariates revealed that soil management accounted for 19.7 % ($p = 0.001$) of the variation in the response variables. Soil properties explained 10.2 % ($p = 0.001$), while weather variables
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Effects of explanatory variables (agronomic practices) on soil microbial and enzymatic activities at arabica coffee crops in the 0–15 cm surface layer. ACP (acid phosphatase), ALP (alkaline phosphatase), ARS (arylsulfatase), BG (β-glucosidase), UA (urease), DHA (dehydrogenase), BSR (basal respiration rate), and CSR (cumulative soil respiration). Each point represents one soil sample. Longer arrows mean this variable strongly drives the variation in the community matrix. Arrows pointing in opposite directions have a negative relationship. Arrows pointing in the same direction have a positive relationship.</figcaption></figure>

contributed 0.9 % (p = 0.001) to the variation in soil microbial and enzymatic activities across different sites. Furthermore, agronomic practices and soil variables collectively accounted for 7.1 % of the variation.

## 3.2. Effect of soil properties on soil microbial and enzymatic activities

Using physicochemical soil quality indicators to constrain the ordination of soil microbial and enzymatic activities across various sites with redundancy analysis (RDA), the model accounted for 27.9 % of the total variation (p < 0.001). After adjusting for the number of explanatory variables through permutation tests, seven significant explanatory variables remained in the model (p < 0.05) to constrain the response variables. The adjusted R² indicated that the model explained 16.0 % of the variance in the response variables.

A partial Redundancy Analysis (partial RDA) was utilized to assess the effects of agronomic practices covariates and to minimize the unexplained variation present in the initial RDA model. This analysis indicated that soil properties accounted for only 12.2 % of the variation in soil microbial and enzymatic activity across the sites, whereas the covariates (agronomic practices) explained 32.1 % of this variation. The adjusted model accounted for only 10.0 % of the variation in the response variables across the sites and was statistically significant (p = 0.001).

A positive correlation was observed between β-glucosidase and acid phosphatase with pH (Fig. 3). Conversely, arylsulfatase and urease exhibited a positive correlation with soil acidity. Additionally, dehydrogenase and soil respiration indices were correlated with phosphorus (P) and copper (Cu).

Soil Organic Carbon (SOC) levels were significantly higher in Mesetas, measuring 2.81 g 100 g⁻¹, compared to 1.62 g 100 g⁻¹ in Uribe. Both municipalities exhibited similar pH values, with Mesetas at 4.43 and Uribe at 4.29, indicating extremely acidic conditions. Additionally, soil acidity was comparable between the two regions, measured at 4.51 cmol(+) kg⁻¹ in Mesetas and 4.13 cmol(+) kg⁻¹ in Uribe. Notably, the highest clay content was observed in Uribe, at 30.5 %, in contrast to 24.4 % in Mesetas. Soils in Uribe municipality contained 2.68 mg kg⁻¹ of phosphorus (P), 0.11 mg kg⁻¹ of boron (B), 0.50 mg kg⁻¹ of copper (Cu), and 26.06 mg kg⁻¹ of iron (Fe). In Mesetas municipality, the levels were 1.97 mg kg⁻¹ of phosphorus (P), 0.20 mg kg⁻¹ of boron (B), 0.40 mg kg⁻¹ of copper (Cu), and 50.56 mg kg⁻¹ of iron (Fe). These findings suggest distinct soil properties between the municipalities, which may

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Effects of explanatory variables (soil properties) on soil microbial and enzymatic activities at arabica coffee crops in the 0–15 cm surface layer. ACP (acid phosphatase), ALP (alkaline phosphatase), ARS (arylsulfatase), BG (β-glucosidase), DHA (dehydrogenase), UA (urease), BSR (basal respiration rate), CSR (cumulative soil respiration), and SOC (soil organic carbon). Each point represents one soil sample. Longer arrows mean this variable strongly drives the variation in the community matrix. Arrows pointing in opposite directions have a negative relationship. Arrows pointing in the same direction have a positive relationship.</figcaption></figure>

influence local agricultural practices and soil management strategies.

## 3.3. The dual influence of agronomic practices and soil properties on soil microbial and enzymatic activities

Canonical variation partitioning demonstrated the relative influence of agronomic practices and soil properties on potential microbial and enzyme activities by quantifying the unique and shared proportions of variability attributed to each set of explanatory factors. The adjusted R² indicated that the model accounted for 37.9 % of the variance in the response variables (Table 3).

Redundancy analysis (RDA), which constrained soil microbial and

<caption>**Table 3**</caption>

Partitioning variation in soil microbial and enzymatic activities as a function of agronomic practices and soil properties at arabica coffee crops in the southern Meta, Colombia, in the 0–15 cm surface layer.

<table><thead><tr><th></th><th>Percent<br>variation<br>explained<sup>a</sup></th><th>Explanatory<br>variables<sup>b</sup></th><th>Variables retained</th></tr></thead><tbody><tr><td>Overall<br>model</td><td>37.9***</td><td>19</td><td>Associated Cocoa, Associated<br>Musaceae, Cluster Livestock-<br>Focused Coffee Growers with<br>Mature Crops, Associated<br>Citrus-trees, Chemical<br>Fertilization, Organic<br>Fertilization, Associated<br>Guamo, Liming (Whitewash),<br>Full-sun, Associated Fruit-<br>trees, Cluster Traditional<br>Coffee Diversified</td></tr><tr><td>Agronomic<br>practices</td><td>25.7***</td><td>11</td><td>Smallholders</td></tr><tr><td>Soil<br>properties</td><td>16.0***</td><td>7</td><td>Acidity, P, pH, Soil Organic<br>Carbon, Fe, Cu, B</td></tr></tbody></table>

<footnote><sup>a</sup> Variation explained by the full model (agronomic practices and soil properties) consists of both the respective contributions of each set of explanatory variables as well as their covariation. Significance was assessed using 1000 permutations of either the full RDA model or the appropriate partial redundancy analysis model, with ***p < 0.001.</footnote>

<footnote><sup>b</sup> A parsimonious set of explanatory variables was derived using forward selection based on a double-stopping rule of both an alpha criterion and an adjusted R² within each set of explanatory variables individually.</footnote>
enzymatic activities based on agronomic practices and soil properties, accounted for 50.3 % of the total variation (p < 0.001). Permutation tests, conducted to adjust for the number of explanatory variables, identified 19 significant predictors (p < 0.001) that contributed to the variation in the response variables (Fig. 4).

The results highlight the predictors that contribute to the variance in the soil enzymatic and basal respiration model, along with the associated percentages of explained variance and their significance levels (Table 4). Coffee crops associated with cocoa, Musaceae, and Guamo emerge as the most influential predictors, followed by soil acidity, which shows high statistical significance (p = 0.001) (Table 4). This emphasizes the critical roles of crop associations and soil pH in driving soil microbial activity and nutrient cycling processes.

Key soil chemical properties, including copper (Cu), iron (Fe), pH, and soil organic carbon (SOC), exhibited significant effects. Notably, SOC—a key indicator of soil health—contributed less than anticipated, suggesting that other factors, such as land use or management practices, may be overshadowing its influence in this particular context.

The residual variance of 54.2 % suggests that additional unexplored factors, potentially related to microclimatic conditions or unmeasured biological interactions, significantly influence the system. This underscores the complexity of tropical lowland coffee systems and emphasizes the necessity for further investigation into the sources of unexplained variability.

### 3.4. Variation of soil microbial and enzymatic activities across field types (associated crops and soil textures)

The coffee grower category known as Innovative coffee growers with young plantations exhibited the highest levels of soil enzymatic activities (Fig. 5). However, this group did not demonstrate significant statistical differences in arylsulfatase levels (Fig. 5). The dehydrogenase activity and the basal soil respiration rate did not show significant differences among the types of coffee growers, with mean values of 75.9 ± 0.6 µg TTF g⁻¹ 24 h⁻¹ and 12.4 ± 0.7 mg CO₂-C kg⁻¹ soil d⁻¹, respectively. Additionally, when evaluating coffee crop associations, Musaceae exhibited the highest levels of phosphatases, urease, and β-glucosidase (Table 5). In contrast, coffee crops associated with citrus trees and cocoa recorded the highest levels of arylsulfatase and soil microbial activity (Table 5), as indicated by the basal respiration rate and cumulative soil respiration, respectively. Enzymatic and microbial

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Effects of explanatory variables (jointly using agronomic practices and soil properties) on soil microbial and enzymatic activities at arabica coffee crops in the 0–15 cm surface layer. ACP (acid phosphatase), ALP (alkaline phosphatase), ARS (arylsulfatase), BG (β-glucosidase), UA (urease), BSR (basal respiration rate), CSR (cumulative soil respiration), and DHA (dehydrogenase), and SOC (soil organic carbon). Each point represents one soil sample. Longer arrows mean this variable strongly drives the variation in the community matrix. Arrows pointing in opposite directions have a negative relationship. Arrows pointing in the same direction have a positive relationship.</figcaption></figure>

<caption>**Table 4**</caption>

Significant agronomic practices and soil properties that explain the soil microbial and enzymatic activities at arabica coffee crops in the 0–15 cm surface layer.

<table><thead><tr><th>Model predictors</th><th>Variance (explain %)</th><th>Contribution %</th></tr></thead><tbody><tr><td>Associated crops (Cocoa, Musaceae, Guamo, Avocado, Citrus-trees)</td><td>1.32</td><td>16.5 %</td></tr><tr><td>Acidity, pH</td><td>0.82</td><td>10.3 %</td></tr><tr><td>Soil nutrients (P, Cu, Fe, Zn, S)</td><td>0.77</td><td>9.7 %</td></tr><tr><td>Fertilization (Organic, Chemical)</td><td>0.21</td><td>2.6 %</td></tr><tr><td>Cluster Livestock-Focused Coffee Growers with Mature Crops</td><td>0.20</td><td>2.5 %</td></tr><tr><td>Full-sun</td><td>0.09</td><td>1.1 %</td></tr><tr><td>Whitewash</td><td>0.09</td><td>1.1 %</td></tr><tr><td>Soil Organic Carbon – SOC</td><td>0.08</td><td>1.0 %</td></tr><tr><td>Coffee area</td><td>0.08</td><td>1.0 %</td></tr><tr><td>Residual</td><td>4.34</td><td>54.2 %</td></tr></tbody></table>

soil activities also varied significantly based on soil texture. Specifically, soils classified as loamy sand exhibited the lowest phosphatase levels, while those categorized as sandy loam displayed the lowest arylsulfatase values (Table 5).

The acid phosphatase exhibited approximately 22.2 % variation across various coffee-associated crops and demonstrated about 12.2 % variation among diverse groups of coffee growers. Furthermore, concerning soil type, the acid phosphatase varied by 10.9 %. Regarding the fixed effects, the model estimated increases of 8.6 %, 9.0 %, and 11.4 % in the acid phosphatase enzyme for each unit increase in acidity, phosphorus (P), and copper (Cu), respectively. In contrast, the variability of alkaline phosphatase across various coffee-associated crops accounted for approximately 30.2 %. However, the variability observed in this enzyme among diverse groups of coffee growers was significantly lower, at around 9.6 %. The model estimated increases of 23.0 % and 9.3 % in the alkaline phosphatase enzyme for each unit increase in acidity and boron (B) level, respectively. Conversely, there were decreases of 14.6 %, 11.6 %, and 9.2 % in the alkaline phosphatase enzyme for each unit increase in phosphorus (P), copper (Cu), and boron (B) levels, respectively.

β-glucosidase exhibited a 24.0 % variation across coffee-associated crops, an-8.4 % variation among coffee grower groups, and an-8.2 % variation among soil types. In terms of fixed effects, there was a 16.3 % increase in β-glucosidase activity for each one-unit increase in pH. Similarly, for each percentage increase in soil organic carbon (SOC), there was a 7.5 % increase in the activity of this enzyme. Additionally, enzyme activity increased by 10.3 % for every 1 mg kg⁻¹ rise in iron content. In contrast, a linear model without random effects was utilized to analyze dehydrogenase activity. The analysis indicated that a 10 mg kg⁻¹ increase in phosphorus (P) corresponds to a 22.0 % increase in dehydrogenase activity.

An increase of one unit in pH and acidity corresponded to an increase of approximately 33.3 and 8.5 µg g⁻¹ h⁻¹ in arylsulfatase activity, respectively. Similarly, a one percentage point increase in soil organic carbon (SOC) was associated with an increase of approximately 8.9 µg g⁻¹ h⁻¹ in arylsulfatase activity. In terms of soil type, sandy loam was linked to a decrease of around 25.9 µg g⁻¹ h⁻¹ in arylsulfatase activity compared to clay soil. Lastly, the Livestock-focused coffee growers with mature crops cluster exhibited an increase of approximately 22.2 µg g⁻¹ h⁻¹ in arylsulfatase activity compared to the Innovative coffee growers with young plantations cluster.

Soil urease activity exhibited a 42.3 % variation across coffee-associated crops, a 26.7 % variation among coffee grower groups, and an 11.4 % variation among different soil types. In terms of fixed effects, urease activity increased by 5.2 % for each one-unit increase in soil acidity. Additionally, this enzyme activity increased fivefold for every 1 mg kg⁻¹ rise in boron content.

Concerning soil respiration activity, the cumulative soil respiration measured in soils where coffee was intercropped with cocoa was twice
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Soil enzymatic and microbial activities: a. Acid phosphatase, b. Alkaline phosphatase, c. β-glucosidase, d. Arylsulfatase, e. Urease, and f. Cumulative soil respiration in the different typologies of coffee growers in the 0–15 cm surface layer. Wilcoxon test at 0.05 (*), 0.01 (**), and 0.001 (***) Traditional coffee growers with old plantations as the reference value. Each point represents one soil sample.</figcaption></figure>

as high as the value recorded in coffee monoculture. The basal respiration rate exhibited a significant random effect based on soil type, accounting for approximately 10.7 % of the variability. However, the soil variables did not significantly influence cumulative or basal soil respiration.

Finally, through Spearman's correlation analysis, we identified phosphatases as enzymes that are related to other enzymes and soil respiration indices (Fig. 6).

## 4. Discussion

Since the 1970s, Arabica coffee production has transitioned from traditional shade-grown systems to sun-grown or simplified shade systems. This shift has been driven by the pursuit of higher yields, improved pest control, and reduced labor costs. The intensification of coffee production, characterized by increased planting density and the use of agrochemicals, has resulted in a decline in diverse agroforestry practices. By the 1990s,
<caption>Table 5</caption>

Soil enzymes and basal soil respiration in coffee fields with different associated crops and soil types in southern Meta, Colombia (n = 150) in the 0–15 cm surface layer.

<table><thead><tr><th rowspan="3">Associated crop</th><th colspan="6">Soil enzymatic activity</th><th colspan="2">Soil microbial activity</th></tr><tr><th>Acid Phosphatase</th><th>Alkaline Phosphatase</th><th>β-glucosidase</th><th>Arylsulfatase</th><th>Urease</th><th>Basal Soil Respiration Rate</th><th>Cumulative Soil Respiration</th></tr><tr><th>µg g<sup>-1</sup> h<sup>-1</sup></th><th>µg g<sup>-1</sup> h<sup>-1</sup></th><th>µg g<sup>-1</sup> h<sup>-1</sup></th><th>µg g<sup>-1</sup> h<sup>-1</sup></th><th>µg NH<sub>4</sub>-N g<sup>-1</sup> dwt 2h<sup>-1</sup></th><th>mg CO<sub>2</sub>-C kg<sup>-1</sup> soil d<sup>-1</sup></th><th>mg CO<sub>2</sub>-C kg<sup>-1</sup></th></tr></thead><tbody><tr><td>Avocado (n = 12)</td><td>274.0 ± 44.7 *</td><td>40.8 ± 5.9 <sup>n.s</sup></td><td>34.8 ± 3.7 <sup>n.s</sup></td><td>116.3 ± 8.6 <sup>n.s</sup></td><td>69.0 ± 19.8 *</td><td>14.5 ± 2.4 <sup>n.s</sup></td><td>98.8 ± 17.1 <sup>n.s</sup></td></tr><tr><td>Citrus-trees (n = 3)</td><td>121.6 ± 20.2 ***</td><td>9.2 ± 3.3 ***</td><td>23.3 ± 4.0 *</td><td>138.7 ± 34.7 <sup>n.s</sup></td><td>15.2 ± 1.2 <sup>n.s</sup></td><td>8.2 ± 1.5 <sup>n.s</sup></td><td>69.5 ± 3.9 <sup>n.s</sup></td></tr><tr><td>Cocoa (n = 3)</td><td>182.9 ± 13.0 **</td><td>31.2 ± 3.9 *</td><td>37.1 ± 3.3 <sup>n.s</sup></td><td>85.3 ± 5.4 <sup>n.s</sup></td><td>34.8 ± 6.7 <sup>n.s</sup></td><td>43.0 ± 9.7 ***</td><td>278.4 ± 49.1 ***</td></tr><tr><td>Fruit-trees (n = 20)</td><td>305.7 ± 36.5 <sup>n.s</sup></td><td>39.5 ± 3.5 <sup>n.s</sup></td><td>37.0 ± 1.7 <sup>n.s</sup></td><td>97.4 ± 10.4 <sup>n.s</sup></td><td>16.5 ± 1.7 *</td><td>7.7 ± 0.8 *</td><td>78.3 ± 12.0 *</td></tr><tr><td>Guamo (n = 9)</td><td>223.6 ± 11.5 **</td><td>56.9 ± 4.4 <sup>n.s</sup></td><td>51.4 ± 7.0 <sup>n.s</sup></td><td>128.8 ± 10.8 <sup>n.s</sup></td><td>60.0 ± 5.4 ***</td><td>8.5 ± 1.8 <sup>n.s</sup></td><td>102.7 ± 11.2 *</td></tr><tr><td>Musaceae (n = 16)</td><td>355.4 ± 35.3 *</td><td>66.3 ± 7.5 *</td><td>48.1 ± 4.1 *</td><td>108.7 ± 11.9 <sup>n.s</sup></td><td>77.1 ± 14.0 ***</td><td>16.0 ± 2.8 <sup>n.s</sup></td><td>117.6 ± 9.9 ***</td></tr><tr><td>Shade-grown (n = 55)</td><td>277.5 ± 15.4 <sup>n.s</sup></td><td>37.0 ± 2.7 **</td><td>30.1 ± 2.1 **</td><td>117.3 ± 7.1 <sup>n.s</sup></td><td>42.0 ± 3.9 *</td><td>11.8 ± 0.7 <sup>n.s</sup></td><td>89.5 ± 4.3 *</td></tr><tr><td>Soursop (n = 3)</td><td>171.0 ± 23.2 **</td><td>31.6 ± 3.6 *</td><td>22.0 ± 3.2 *</td><td>102.2 ± 16.1 <sup>n.s</sup></td><td>30.5 ± 3.2 <sup>n.s</sup></td><td>12.1 ± 4.6 <sup>n.s</sup></td><td>149.1 ± 14.6 ***</td></tr><tr><td>Full-sun (n = 29)</td><td>277.0 ± 14.8</td><td>48.8 ± 3.4</td><td>38.2 ± 2.1</td><td>115.0 ± 8.5</td><td>26.9 ± 2.7</td><td>12.6 ± 1.5</td><td>74.9 ± 3.7</td></tr><tr><td>Kruskal-Wallis</td><td>0.001</td><td>0.000017</td><td>0.00024</td><td>0.12</td><td>0.00000013</td><td>0.0041</td><td>0.00000091</td></tr></tbody></table><table><thead><tr><th rowspan="3">Soil Type</th><th colspan="6">Soil enzymatic activity</th><th colspan="2">Soil microbial activity</th></tr><tr><th>Acid Phosphatase</th><th>Alkaline Phosphatase</th><th>β-glucosidase</th><th>Arylsulfatase</th><th>Urease</th><th>Basal Soil Respiration Rate</th><th>Cumulative Soil Respiration</th></tr><tr><th>µg g<sup>-1</sup> h<sup>-1</sup></th><th>µg g<sup>-1</sup> h<sup>-1</sup></th><th>µg g<sup>-1</sup> h<sup>-1</sup></th><th>µg g<sup>-1</sup> h<sup>-1</sup></th><th>µg NH<sub>4</sub>-N g<sup>-1</sup> dwt 2h<sup>-1</sup></th><th>mg CO<sub>2</sub>-C kg<sup>-1</sup> soil d<sup>-1</sup></th><th>mg CO<sub>2</sub>-C kg<sup>-1</sup></th></tr></thead><tbody><tr><td>Clay (n = 21)</td><td>252.2 ± 13.0 <sup>n.s</sup></td><td>48.1 ± 3.4 <sup>n.s</sup></td><td>38.1 ± 3.9 <sup>n.s</sup></td><td>131.4 ± 11.8 <sup>n.s</sup></td><td>52.3 ± 5.8 ***</td><td>13.0 ± 1.7 *</td><td>91.2 ± 6.0 *</td></tr><tr><td>Clay loam (n = 37)</td><td>277.7 ± 15.6 <sup>n.s</sup></td><td>48.1 ± 3.2 <sup>n.s</sup></td><td>38.1 ± 2.0 <sup>n.s</sup></td><td>127.1 ± 6.6 <sup>n.s</sup></td><td>37.5 ± 7.0 <sup>n.s</sup></td><td>11.7 ± 1.3 <sup>n.s</sup></td><td>101.2 ± 7.9 *</td></tr><tr><td>Loamy sand (n = 4)</td><td>146.0 ± 30.2 **</td><td>16.7 ± 6.4 *</td><td>29.4 ± 1.5 <sup>n.s</sup></td><td>132.4 ± 27.1 <sup>n.s</sup></td><td>20.4 ± 4.5 <sup>n.s</sup></td><td>9.2 ± 2.0 <sup>n.s</sup></td><td>72.3 ± 4.0 <sup>n.s</sup></td></tr><tr><td>Sandy clay (n = 1)</td><td>175.1 ± 0.0 <sup>n.s</sup></td><td>43.4 ± 0.0 <sup>n.s</sup></td><td>19.5 ± 0.0 <sup>n.s</sup></td><td>169.9 ± 0.0 <sup>n.s</sup></td><td>56.9 ± 0.0 <sup>n.s</sup></td><td>19.9 ± 0.0 <sup>n.s</sup></td><td>71.9 ± 0.0 <sup>n.s</sup></td></tr><tr><td>Sandy clay loam (n = 37)</td><td>313.1 ± 25.6 <sup>n.s</sup></td><td>38.8 ± 2.8 <sup>n.s</sup></td><td>32.1 ± 2.3 <sup>n.s</sup></td><td>109.7 ± 7.1 <sup>n.s</sup></td><td>50.5 ± 7.6 <sup>n.s</sup></td><td>12.2 ± 1.2 *</td><td>94.7 ± 7.3 <sup>n.s</sup></td></tr><tr><td>Sandy loam (n = 36)</td><td>272.0 ± 22.7 <sup>n.s</sup></td><td>44.5 ± 5.1 <sup>n.s</sup></td><td>35.5 ± 2.7 <sup>n.s</sup></td><td>88.8 ± 7.0 <sup>n.s</sup></td><td>37.9 ± 5.2 <sup>n.s</sup></td><td>15.2 ± 1.9 **</td><td>98.3 ± 10.8 <sup>n.s</sup></td></tr><tr><td>Loam (n = 14)</td><td>293.6 ± 30.5</td><td>41.2 ± 5.5</td><td>41.8 ± 5.2</td><td>112.9 ± 13.3</td><td>29.3 ± 6.0</td><td>7.4 ± 1.1</td><td>77.2 ± 10.8</td></tr><tr><td>Kruskal-Wallis</td><td>0.059</td><td>0.037</td><td>0.38</td><td>0.0022</td><td>0.0062</td><td>0.07</td><td>0.33</td></tr></tbody></table>

Wilcoxon test at 0.05 (*), 0.01 (**), and 0.001 (***). n.s: not significant. The columns highlighted in gray indicate the reference value. The "±" symbol represents the standard error around the mean value.

Wilcoxon test at 0.05 (*), 0.01 (**), and 0.001 (***). n.s: not significant. The columns highlighted in gray indicate the reference value. The "±" symbol represents the standard error around the mean value.

nearly 50 % of shade coffee farms in Latin America had converted to low-shade or sun-grown systems. For instance, traditional shade coffee cultivation experienced a dramatic decline in El Salvador (from 92 % to 24 %) and Nicaragua (from 55 % to 25 %) between 1996 and 2012, while Costa Rica completely replaced traditional systems. In contrast, Colombia maintained stable shade levels of approximately 30 %, and Honduras and

Mexico observed increases in shaded coffee areas (Jha et al., 2014). Intensification often involves the cultivation of resistant varieties with minimal shade and heavy agrochemical application, as evidenced in Guatemala and Chiapas, Mexico, where even organic producers have resorted to synthetic inputs to achieve higher yields and enhance disease control (Perfecto et al., 2019; Valencia et al., 2018).

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Spearman correlation between basal respiration and soil enzymes at 0.05 (*), 0.01 (**), and 0.001 (***). No asterisk = not significant (p > 0.05). β-glucosidase (BG), acid phosphatase (ACP), alkaline phosphatase (ALP), arylsulfatase (ARS), dehydrogenase activity (DHA), Urease (UA), Cumulative soil respiration (CSR), and Basal soil respiration rate (BSR). Each point represents one soil sample.</figcaption></figure>
In Brazil, modern Arabica cultivars often thrive without shade, particularly in regions with optimal climates and soils (19–24°S, 500–1300 m.a.s.l.), yielding more than shaded systems (DaMatta, 2004). However, as coffee cultivation expands into marginal lands characterized by water scarcity, elevated temperatures, and prolonged droughts, effective shade management becomes crucial for maintaining yields and creating suitable growing conditions (DaMatta and Rena, 2002). With climate change intensifying, implementing agronomic adaptation strategies is essential for sustainable coffee production (Camargo, 2010).

Our findings underscore the ecological and agronomic advantages of shade systems, particularly in tropical lowland regions characterized by marginal conditions. The integration of shade trees and diverse crop associations enhances soil health by increasing enzymatic activity and supporting nutrient cycling, thereby bolstering resilience to environmental stressors. Although unshaded systems may perform well in optimal climates, shade-based agroforestry systems are essential in marginal areas, as they mitigate the adverse effects of climate change. These systems improve soil organic matter, enhance water retention, and stabilize microclimates, providing a buffer against erratic weather patterns (Gomes et al., 2020).

By integrating shade trees such as Musaceae and Guamo, our study demonstrates how diversified systems enhance enzymatic activity, improve soil health, and promote nutrient cycling, providing a sustainable alternative to conventional agricultural intensification. While intensification may yield short-term benefits, the long-term ecological and productivity advantages of shade systems highlight the significance of agroecological practices, particularly in marginal growing conditions. Customized management strategies can effectively balance productivity and sustainability, addressing the ongoing simplification of traditional agricultural systems.

## 4.1. Effect of coffee field diversity on soil health

The redundancy analysis (RDA) revealed a clear relationship between basal respiration, enzymatic activities, and agricultural management practices. Coffee crops associated with fruit trees (such as cocoa, Musaceae, and citrus species) and specific agronomic practices, including soil covers and organic fertilization, accounted for most of the variability in soil enzymes and basal respiration (Table 4). Agronomic practices explained a significantly higher proportion of variance compared to soil properties (Table 3), indicating their stronger influence on soil biological processes (Figs. 2 and 4). However, it is essential to incorporate soil and climate variables (e.g., precipitation, temperature, and humidity) as covariates in the RDA model to mitigate confounding effects and gain a better understanding of environmental influences on soil health. Similar findings were reported by Kompala-Baba et al. (2021), who emphasized the impact of soil physicochemical properties on enzyme activity.

Coffee plantations that included Musaceae and Guamo trees exhibited higher activities of β-glucosidase, urease, and phosphatase. In contrast, dehydrogenase activity and soil respiration were more closely associated with cocoa intercropping (Figs. 2 and 4). For instance, acid phosphatase values were significantly higher in coffee-Musaceae systems (355.4 µg g⁻¹ h⁻¹) compared to full-sun systems (277.0 µg g⁻¹ h⁻¹), which aligns with the findings of Gómez-Pinzón et al. (2022). Similarly, Pereira de Oliveira et al. (2024) reported that acid phosphatase activity varied among different coffee growing systems, with the lowest value observed in Catuai coffee grown in full-sun (272.23 µg g⁻¹ dry soil h⁻¹) and the highest value in Catuai coffee crop intercropped with Grevillea robusta (392.16 µg g dry soil h⁻¹). Conversely, Reis dos Santos Bastos et al. (2023) found that the β-glucosidase activity did not exhibit significant differences between monoculture and agroforestry.

Arylsulfatase activity was positively influenced by coffee cultivated alongside fruit trees, Guamo, and citrus species, indicating enhanced sulfur cycling and improved soil fertility (Figs. 2 and 4).

et al. (2022) also reported similar patterns with mandarin and citrus as companion crops.

Cumulative soil respiration was significantly higher in coffee-cocoa systems compared to monocultures, indicating enhanced microbial activity and improved soil health due to increased organic matter and diverse root exudates (Griffiths et al., 2022). The elevated basal respiration rates observed in coffee-cocoa systems (Table 5) further suggest optimal biological activity and robust microbial populations, which support soil fertility and sustainability (Evanylo and McGuinn, 2000). These findings highlight the advantages of agroforestry systems, such as coffee-cocoa associations, in enhancing soil health and resilience, providing a sustainable alternative to monoculture practices.

## 4.2. Effect of soils conditions and fertility management on soil health

We observed a positive relationship between liming and arylsulfatase activity (Fig. 2), which is consistent with the findings of Acosta-Martinez and Tabatabai (2000). They noted that lime application enhances β-glucosidase and arylsulfatase activities by optimizing soil pH. However, coffee growers surveyed in this region typically apply less than 800 kg ha⁻¹ of dolomite lime, primarily during sowing, and often utilize suboptimal lime sources. These practices limit the potential benefits of liming, underscoring the need for improved strategies, such as combining dolomite lime with gypsum or phosphoric rock, to effectively address soil acidity.

Model predictions indicate that increased pH enhances the activities of β-glucosidase and arylsulfatase, underscoring the importance of managing soil acidity for optimal enzyme function. In phosphorus-deficient Ultisols, low phosphorus availability drives an increase in phosphatase secretion by roots and microorganisms to mobilize phosphate, thereby aiding plant adaptation to phosphorus stress (Kai et al., 2002; Richardson and Simpson, 2011). This observation aligns with our findings, which revealed that higher phosphatase activity is associated with acidic soils. Given the non-renewable nature of phosphorus, optimizing phosphorus cycling through microbial processes is critical for sustainable agriculture (Richardson and Simpson, 2011). Elevated levels of acid phosphatase (Table 5) suggest active microbial release of this enzyme to access organic phosphorus, particularly in phosphorus-deficient Oxisols and Ultisols, such as those found in southern Meta, Colombia. Consistent lime application and amendments to reduce soil acidity are essential for enhancing coffee productivity and microbial activity, thereby decreasing reliance on synthetic fertilizers. Soil pH also influences phosphatase activity, with alkaline phosphatase increasing as pH rises and acid phosphatase decreasing (Dick et al., 2000). However, Balota and Chaves (2010) found no significant correlation between enzyme activities and pH, which contrasts with our results that demonstrated strong relationships between pH and the activities of β-glucosidase and phosphatase (Fig. 3).

Organic carbon (SOC) content significantly influences β-glucosidase and urease activities, with higher SOC levels in shaded coffee systems (e. g., fruit trees or forest species) enhancing soil fertility and microbial activity (de Notaro et al., 2014). Hergoualc'h et al. (2012) observed greater carbon stocks in agroforestry systems featuring Inga trees (Guamo) compared to coffee monocultures. Our findings corroborate these results, as coffee crops associated with Guamo and Musaceae exhibited the highest β-glucosidase values (51.4 and 48.1 µg g dry soil h⁻¹, respectively). The levels of acid phosphatase and arylsulfatase in soils of coffee plantations associated with Guamo trees were compared to those recorded in the Brazilian Cerrado by Castro Lopes et al. (2013). This comparison revealed that arylsulfatase levels in the coffee plantations located in southern Meta (Table 5) were substantially higher than the average of 61 mg p-nitrophenol kg⁻¹ soil h⁻¹ observed in the Cerrado.

The positive response of β-glucosidase to pH and soil organic carbon (SOC) highlights its critical role in carbon cycling and organic matter decomposition, emphasizing the importance of maintaining SOC for soil
fertility and sustainability. Similarly, arylsulfatase activity increased with higher pH and SOC, underscoring its significance in sulfur cycling. Sandy loam soils exhibited reduced arylsulfatase activity compared to clay soils, indicating the necessity for tailored soil management practices. By monitoring enzymes such as β-glucosidase and arylsulfatase, researchers and practitioners can more effectively assess soil health, nutrient cycling, and the impact of agricultural practices on long-term sustainability.

Arylsulfatase activity exhibited significant variability across various soil and agronomic factors. Specifically, a 1 % increase in soil organic carbon was associated with an enhancement of approximately 8.9 µg g⁻¹ h⁻¹ in the activity of this enzyme. The higher arylsulfatase activity observed in soils linked to the Livestock-focused coffee growers with mature crops cluster, compared to the Innovative coffee growers with young plantations cluster (Table 1) suggests that the management practices typical of this coffee growers group are particularly effective in maintaining sulfur availability, which is essential for the synthesis of amino acids in plants. These findings underscore the influence of soil properties, soil organic carbon (SOC) levels, and grower practices on sulfur cycling, with potential implications for soil fertility management in coffee cultivation. According to Reis dos Santos Bastos et al. (2023), both the quantity and quality of organic residues significantly affect arylsulfatase activity. The increase in arylsulfatase activity is directly proportional to the organic matter stock and nitrogen content of the litter accumulated in the soil, emphasizing the advantages of agroforestry systems over monoculture. Our findings support the assertion that incorporating tree components is vital for the sustainability of coffee cultivation systems, fostering a more diverse environment that enhances soil biological activity and nutrient cycling (Upson and Burgess, 2013). Soil type also played a critical role, with sandy loam soils exhibiting a decrease of approximately 25.9 µg g⁻¹ h⁻¹ in arylsulfatase activity compared to clay soils. Soil texture emerged as a significant factor, with loamy sand and sandy loam soils demonstrating reduced enzymatic activity, highlighting the necessity for soil management strategies tailored to specific soil types. This is particularly relevant for coffee growers in regions with these soil textures, as targeted interventions, such as organic amendments or mulching, may be essential to enhance enzymatic activity and, consequently, soil fertility.

The highest levels of soil enzymes and microbial activity were observed in soils composed of clay particles (Table 5). Bowles et al. (2014) suggested that soil texture did not significantly account for variations in enzyme activities, attributing this to the similarity of soil types and the limited range of soil textures sampled in their study. In contrast, Hu et al. (2022) reported elevated activities of β-glucosidase, arylsulfatase, and acid phosphatase in forest Ferrasol, which can be attributed to its clayey texture that protects these enzymes from degradation. Clay-rich soils experience reduced fluctuations in temperature, pH, and moisture, thereby promoting soil stability and providing protection for enzymes against denaturation (Rahmanian and Khadem, 2023). Aragão et al. (2023) observed that enzymatic activity is more sensitive to soil texture than microbial biomass, which has practical implications for utilizing enzymatic activity as an indicator of soil quality.

Phosphorus content was closely related to phosphatase activity, with higher phosphorus levels correlating with increased acid phosphatase activity (Kompala-Baba et al., 2021). However, Olander and Vitousek (2000) observed reduced acid phosphatase activity in soils with high total phosphate levels. In this study, soil copper content positively influenced acid phosphatase activity, which contrasts with the findings of Genova et al. (2024), who reported a negative effect. Additionally, boron negatively affected alkaline phosphatase activity, consistent with the results of Bilen et al. (2011).

### 4.3. Consequences for sustainable coffee management in the region

The highest enzymatic activities were observed in Innovative coffee

growers with young plantations and the Livestock-focused coffee growers with mature crops groups, suggesting that practices such as organic amendments, young coffee crops, and diverse crop associations enhance soil biological function (Fig. 5). However, the lack of significant differences in soil respiration indicates that while enzymatic activity improves, microbial respiration remains stable across management practices. Consistent dehydrogenase activity across grower types suggests a baseline level of microbial oxidative activity, less sensitive to agronomic variations. Oliveira and Ferreira (2014) found that organic-mineral compost significantly increased dehydrogenase, β-glucosidase, and phosphatase activities, highlighting β-glucosidase as a key indicator of organic residue additions (Moscatelli et al., 2012). Both coffee grower types, primarily using organic fertilizers, exhibited the highest acid and alkaline phosphatase and arylsulfatase activities (Fig. 5). Using organic residues like wet coffee husk for fertilization stimulates the activity of enzymes involved in organic residue decomposition, such as arylsulfatase, urease, phosphatase, and dehydrogenase (Liang et al., 2014). In this regard, it is important to mention that coffee growers of both groups use wet coffee husk as a source of organic fertilizer.

The findings of this study also have important implications for greenhouse gas (GHG) emissions and climate resilience in lowland coffee systems. Soil microbial and enzymatic activities, influenced by agronomic practices and soil properties, play a key role in carbon and nutrient cycling, which are linked to GHG emissions. Agroforestry systems, particularly those integrating Musaceae and Guamo trees, showed higher β-glucosidase and phosphatase activities, indicating enhanced organic matter decomposition and nutrient cycling. These systems also had higher SOC levels, suggesting greater carbon sequestration potential. This is critical in lowland areas, where elevated temperatures and intense rainfall can accelerate organic matter decomposition and carbon loss.

Organic fertilization, as observed in Livestock-focused coffee growers with mature crops, was associated with higher enzymatic activities and improved soil health. Organic fertilizers can reduce nitrous oxide emissions compared to chemical fertilizers by promoting slower nutrient release and microbial immobilization of nitrogen (Bentzon-Tarp et al., 2023). However, the type and management of organic amendments must be carefully considered to minimize emissions. Agroforestry systems also improve soil structure and water retention, reducing anaerobic conditions that favor methane emissions, particularly in lowland areas prone to waterlogging during heavy rains.

Lowland coffee-growing regions are increasingly vulnerable to extreme weather events, which can severely impact soil health and crop productivity. Agroforestry systems enhance drought resilience by improving soil moisture retention and reducing evapotranspiration (Gómez-Pinzón et al., 2022). Intense rainfall can lead to soil erosion and nutrient leaching, particularly in monoculture systems with poor soil cover. A study in southern Minas Gerais, Brazil, highlighted significant soil and organic carbon losses due to water erosion in coffee-growing areas without adequate soil cover, underscoring the importance of agroforestry in mitigating erosion and preserving soil health (Santana et al., 2024).

Acid phosphatase was the most sensitive indicator of soil changes related to coffee cropping, soil type, and coffee growers typification (Fig. 6). In acidic soils, acid phosphatase demonstrated greater sensitivity than alkaline phosphatase in distinguishing between different soil use types (Kuwano et al., 2014). The sensitivity gradient of enzymatic and microbial activities was as follows: acid phosphatase ≥ arylsulfatase ≥ alkaline phosphatase ≥ urease ≥ β-glucosidase > cumulative soil respiration ≥ dehydrogenase ≥ basal respiration rate. Conversely, Rodrigues et al. (2022) identified arylsulfatase as the most sensitive enzyme, followed by β-glucosidase and acid phosphatase, particularly in response to changes in water regime, lime application, and the presence of Brachiaria.

Different practices employed in coffee plantations significantly impact soil quality, particularly regarding biological parameters. In this
context, our research provides valuable insights into the relationships among soil physicochemical properties, weather conditions, and enzymatic and microbial activities, based on field data that included soil samples from various agricultural management practices. Incorporating trees into agronomic practices is crucial for the sustainability of coffee cultivation systems; such systems may promote long-term soil carbon accumulation because trees in integrated systems enhance enzymatic activity through increased fine root volume, root exudate production, and litter input (Upson and Burgess, 2013). Reducing fertilizer inputs would support a more integrated approach to agroecological intensification, leveraging shade trees to provide ecosystem services such as improving the chemical and biological quality of surface soil, reducing nutrient leaching into deeper soil layers, and enhancing nutrient cycling through abundant leaf litter.

These results underscore the significance of customized agronomic practices that consider crop combinations and soil characteristics to enhance soil health and increase coffee productivity in marginal lowland areas. The findings offer essential insights into the biochemical processes within coffee-growing systems, highlighting the role of soil enzyme activities and their correlation with soil properties and agro-nomic practices. Acid and alkaline phosphatases, which are vital for phosphorus cycling, demonstrated considerable variability based on the associated crops and, to a lesser extent, among different coffee grower groups. The pronounced variability of alkaline phosphatase across crop associations, combined with its lower variability among coffee grower types, indicates that the specific crops integrated with coffee significantly influence soil phosphorus dynamics. This emphasizes the potential of crop diversification as a strategy to improve phosphorus availability, which is critical for the growth of coffee plants in low-fertility lowland soils.

However, to enhance the generalizability of these findings and better understand their broader applicability, future research should consider expanding the study to encompass diverse regions with varying climatic conditions and soil types. Such an approach would allow for a more comprehensive evaluation of the relationships between agronomic practices and soil enzymatic activities across different environmental contexts. While our study accounted for key agronomic practices and soil properties, the spatial and temporal variability of coffee-growing systems may introduce heterogeneity in the results.

Additionally, a more in-depth comparison between organic and conventional farming practices is warranted, focusing specifically on their respective impacts on soil enzyme activities, microbial communities, and crop yield. This comparative analysis would provide valuable insights into the long-term sustainability and effectiveness of different agricultural management strategies. Integrating advanced statistical approaches, such as remote sensing and modeling, could also help validate and scale up the findings to broader spatial and temporal contexts, supporting the development of region-specific recommendations for sustainable coffee management.

## 5. Conclusions

The study demonstrated that diversified agroforestry systems, particularly those integrating coffee with shade trees such as Musaceae, Guamo, and cocoa, significantly improve soil enzymatic activities and microbial respiration. These systems enhance key enzymes involved in nutrient cycling, such as acid phosphatase, β-glucosidase, and arylsulfatase, which are critical for maintaining soil fertility. This highlights the importance of crop diversification in promoting soil health, especially in marginal tropical lowlands where environmental conditions are challenging.

The use of organic fertilizers and liming practices was strongly associated with enhanced soil microbial and enzymatic activities. Organic amendments, such as coffee wet husk, not only increased enzyme activities but also improved soil organic carbon levels, which are essential for long-term soil fertility. Liming, which reduces soil

acidity, was particularly effective in boosting enzyme activities, emphasizing the need to manage soil pH in acidic tropical soils.

The study revealed that soil pH and organic carbon content are critical factors influencing soil enzyme activities. Higher pH levels and increased organic carbon were positively correlated with enhanced enzymatic activity, particularly for acid phosphatase and β-glucosidase. This suggests that maintaining optimal pH and organic matter levels is essential for sustaining microbial processes that drive nutrient cycling in coffee-growing soils.

Soil texture also played a significant role in determining enzymatic activity. Clayey soils exhibited higher levels of enzyme activity compared to sandy soils, due to better protection of enzymes from environmental fluctuations. This finding underscores the importance of tailoring soil management practices to specific soil types to maximize soil health and productivity.

Coffee growers in marginal tropical regions should adopt diversified agroforestry systems by integrating shade trees like Musaceae, Guamo, and cocoa. These systems enhance soil health and improve resilience to climate change by stabilizing microclimates and reducing soil erosion. Prioritizing the use of organic fertilizers and regular liming practices can further improve soil functionality and nutrient cycling, particularly in acidic soils. Tailoring soil management strategies to specific soil types, such as adding organic amendments in sandy soils or maintaining optimal pH in clayey soils, can maximize soil fertility and productivity. These practices offer a sustainable pathway for coffee production in marginal environments, reducing dependency on synthetic inputs and improving long-term soil health. The findings offer actionable insights that extend beyond Colombia to similar tropical lowland regions globally, including Latin America, Africa, and Southeast Asia. By linking management practices to soil health indicators, this research provides valuable guidance for promoting sustainability in coffee cultivation under challenging conditions.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Pablo Tittonell reports financial support was provided by European Union. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgment

This study was conducted with the support of the Colombian Corporation for Agricultural Research – AGROSAVIA, the DeSIRA initiative, and the Ministry of Science, Technology, and Innovation of Colombia – Minciencias.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.geodrs.2025.e00966.

## Data availability

Data will be made available on request.

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