Introduction
Cocoa (Theobroma cacao L.) is a globally important crop cultivated in many parts of humid, tropical Africa, Southeast Asia, and South America (Wessel and Quist-Wessel, Reference Wessel and Quist-Wessel2015). During the last decades, increasing demand for cocoa beans motivated farmers to cultivate the crop in monoculture to increase yield (Andres et al., Reference Andres, Comoé, Beerli, Schneider, Rist, Jacobi and Lichtfouse2016), which has often enhanced deforestation and soil degradation. Typically initial yield after intensification decline over time, as soil nutrients deplete, diseases such as witches’ broom (Moniliophthora perniciosa) increase and soil fertility is exhausted, particularly when sufficient nutrient input is lacking (Andres et al., Reference Andres, Comoé, Beerli, Schneider, Rist, Jacobi and Lichtfouse2016). This often forces farmers to abandon their farms and establish new plantations in existing forests (Arévalo-Gardini et al., Reference Arévalo-Gardini, Canto, Alegre, Loli, Julca and Baligar2015). Such shifting cultivation systems considerably contribute to forest degradation in cocoa producing countries (Arévalo-Gardini et al., Reference Arévalo-Gardini, Canto, Alegre, Loli, Julca and Baligar2015). In West Africa declining soil fertility is among the most limiting factors in cocoa production (Wessel and Quist-Wessel, Reference Wessel and Quist-Wessel2015), which is a major threat to farmers’ livelihoods in countries whose economy is highly reliant on cocoa (Kolavalli and Vigneri, Reference Kolavalli, Vigneri, Chuhan-Pole and Angwafo2011). In Ghana, attempts to prevent declining soil fertility and to boost yields by enhancing farmers’ adoption of chemical inputs have been largely unsuccessful (Gockowski and Sonwa, Reference Gockowski and Sonwa2011). This is due to the relatively high cost of mineral fertilizers and pesticides in addition to lacking knowledge about their efficient utilisation (Wessel and Quist-Wessel, Reference Wessel and Quist-Wessel2015).
Agroforestry technologies have been proposed to counter some of these problems (Alfaro-Flores et al., Reference Alfaro-Flores, Morales-Belpaire and Schneider2015). Cocoa can be easily cultivated under the shade of forest trees and be intercropped with other perennials (Snoeck et al., Reference Snoeck, Lacote, Kéli, Doumbia, Chapuset, Jagoret and Gohet2013) and food crops (Obiri et al., Reference Obiri, Bright, McDonald, Anglaaere and Cobbina2007). Traditional agroforests mimic the forest ecosystem and are known to allow soil fertility restoration (Suárez et al., Reference Suárez, Salazar, Casanoves and Bieng2021). Increased plant diversity within such traditional cocoa agroecosystems may provide a diversification of litter quantity and quality, root architecture, and other physiological traits, which can improve substrate quality for soil microorganisms (da Silva Moço et al., Reference da Silva Moço, da Gama-Rodrigues, da Gama-Rodrigues, Machado and Baligar2009). Furthermore, nutrient losses from erosion and leaching can be reduced by litter covering the soil surface and nutrient pumping capacity of trees with deep roots (Hartemink, Reference Hartemink2005).
To increase the sustainability and profitability of traditional cocoa agroforests, organic certification was introduced in Suhum Municipality, Eastern Region of Ghana in 2005 (Glin et al., Reference Glin, Oosterveer and Mol2015). By certification, agroforesters must prove a mineral fertilizer- and pesticide-free production to benefit from a premium price for their cocoa beans. Certified organic farmers are also supported by the provision of free tree and cocoa seedlings, organic fertilizer and pesticides, mechanised spraying machines, extension service, and training on sustainable agronomic practices. Several studies compared soil fertility of traditional agroforestry systems under conventional and organic management (Alfaro-Flores et al., Reference Alfaro-Flores, Morales-Belpaire and Schneider2015; Haggar et al., Reference Haggar, Barrios, Bolanos, Bolaños, Merlo, Moraga, Munguia, Ponce, Romero, Soto, Staver and Virginio2011; Sauvadet et al., Reference Sauvadet, Van den Meersche, Allinne, Gay, de Melo Virginio Filho, Chauvat, Becquer, Tixier and Harmand2019), however, results have been inconclusive. For example, Haggar et al. (Reference Haggar, Barrios, Bolanos, Bolaños, Merlo, Moraga, Munguia, Ponce, Romero, Soto, Staver and Virginio2011) reported higher soil fertility under organic management than under conventional management in coffee agroforests of Costa Rica. In contrast, in traditional agroforests under cocoa in Bolivia (Alfaro-Flores et al., Reference Alfaro-Flores, Morales-Belpaire and Schneider2015) and coffee in Costa Rica (Sauvadet et al., Reference Sauvadet, Van den Meersche, Allinne, Gay, de Melo Virginio Filho, Chauvat, Becquer, Tixier and Harmand2019) no difference in soil fertility was observed between organic and conventional management. The disparity between the results of Haggar et al. (Reference Haggar, Barrios, Bolanos, Bolaños, Merlo, Moraga, Munguia, Ponce, Romero, Soto, Staver and Virginio2011) and those of Sauvadet et al. (Reference Sauvadet, Van den Meersche, Allinne, Gay, de Melo Virginio Filho, Chauvat, Becquer, Tixier and Harmand2019) was likely due to the high amount of organic fertilizers used by the former. All of these studies were conducted in systems with moderate to high fertilizer inputs ranging from 46 to 300 kg N ha−1, 2 to 205 kg P ha−1, and 44 to 326 kg K ha−1. In West Africa, where most of the world´s cocoa is produced (Wessel and Quist-Wessel, Reference Wessel and Quist-Wessel2015), input levels of farmers are much lower due to their low investment capacities. Due to organic certification being in its developmental stages for West African cocoa, studies comparing soil fertility in traditional cocoa agroforests under conventional and organic systems are rare. Understanding the impact of management systems on soil fertility is of particular importance as it affects nutrient cycling, carbon sequestration, and sustainable yields as key factors to promote sustainable cocoa agroforestry systems. Hence this study compared (a) the soil physical, chemical, and microbial properties under conventional and organic cocoa agroforests in Ghana, (b) the rate of nitrogen (N) mineralisation and potential N mineralised under conventional and organic cocoa agroforests, and (c) the physical and chemical properties regulating microbial properties in these cocoa agroforests.
Materials and methods
Study sites
Our study was conducted in Suhum Municipality, an area of 359 km2 (6°2‘3.84“N and longitude 0°27‘8.64“W) in the Eastern Region of Ghana, West Africa. Suhum Municipality has a long history of cocoa production as it is climatically and ecologically well suited for this crop. Since 2005 farmers in some villages produce certified organic cocoa (Glin et al. Reference Glin, Oosterveer and Mol2015), so that conventionally and organically managed farms exist under similar environmental conditions. Human activities such as agricultural expansion, lumbering, and fuelwood extraction have changed the vegetation of Suhum from natural semi-deciduous forests to secondary forests and regrowth thickets (MOFA, 2017). The area has a bi-modal rainfall pattern, whereby between 2005 and 2020 annual mean rainfall ranged from 1270 to 1651 mm and annual mean temperature from 23 to 32 °C (World Weather Online, 2021). The soils in the study area are classified as Lixisols (IUSS Working Group WRB, 2015), which is the dominating soil type in the humid and subhumid regions of West Africa (Bationo et al. Reference Bationo, Hartemink, Lungu, Naimi, Okoth, Smaling and Thiombiano2006) where most cocoa is grown. Although Lixisols are highly weathered, they are well-suited for cocoa production because of a high saturation with cations, medium to high pH and no aluminium toxicity (Bationo et al., Reference Bationo, Hartemink, Lungu, Naimi, Okoth, Smaling and Thiombiano2006). In the study area two types of Lixisol exist, Ferric and Haplic Lixisols, which were used as one criterion for selecting our study villages. The second selection criterion was to choose a pair of villages, one dominated by certified organic cocoa farms and one dominated by conventional farms, within a 6 km radius. This led to the selection of Nsuta (Haplic Lixisol) and Adimediem (Ferric Lixisol) for organic management whereas Kuano (Haplic Lixisol) and Oboadeka (Haplic Lixisol) represented conventional management.
Organic farms selected for this study (Agbotui et al., Reference Agbotui, Ingold, Wiehle and Buerkert2023) have not received any form of fertilizer in the past two years. Mirids (Distantiella theobroma) were controlled by using Pyrethrum (Pyrethrum 5EW™) at the rate of 400 ml ha−1 once a year between August and September. Black pod disease (caused by Phytophthora palmivora and Phytophthora megakarya) was controlled by integrated system management. Weed control was done by slashing them with cutlasses 2–3 times per year.
Mirids and blackpod disease in conventional farms were controlled using Bifenthrin (Akate Master™; 500 ml ha−1) and Imidacloprid (Confidor™; 150 ml ha−1) twice a year between August and September. It is recommended that fertilization in these farms is done using the compound mineral fertilizer Asaase wura™ (Yara, Ghana; NPK 0–22–8 + 9CaO + 7S + 6MgO) at the rate of 300 kg−1 ha−1 yr−1, but typical farmers only use 50% of this amount. Weed is controlled through a combination of manual weeding and Roundup™ (glyphosate at 225–300 ml ha−1) 2–3 times yearly. Structural characteristics of cocoa agroforests used in this study (Table 1) were typical for the area with a mix of non-N-fixing fruit and timber trees (Asase and Tetteh, Reference Asase and Tetteh2010; Agbotui et al. Reference Agbotui, Mariko and Buerkert2024).
Table 1. Structural characteristics and yield of cocoa agroforests under organic and conventional management in Suhum Municipality, Eastern Region of Ghana
Note: CV = mean coefficient of variation between replicates within management (n = 6).
Soil sampling
Within each village, three farms were selected using farm management information obtained from a farmer survey (Agbotui et al. Reference Agbotui, Mariko and Buerkert2024). Farms with the most productive tree age ranging from 8 to 25 years (Obiri et al., Reference Obiri, Bright, McDonald, Anglaaere and Cobbina2007) were randomly selected ensuring that routine sanitary maintenance such as pruning, weeding, disease, and pest control was guaranteed. Each field was divided into 3–5 sub-plots sized from 1000 to 4000 m2 for sample collection to account for heterogeneity of the topography. Criteria used for sub-plot delineation was natural occurring slopes based on the farmers’ knowledge of their field topography. Within each sub-plot, ten soil samples per depth were taken diagonally in a zig-zag manner at 0–10 cm and 10–30 cm using a soil auger, pooled per sub-plot and depth, and air dried for determination of physico-chemical and microbiological properties.
Soil physico-chemical properties
Bulk density (BD) was measured at each depth using metal cylinders with an inner diameter of 3 cm and a length of 10 cm at a point randomly selected in each field. Soils sampled with the metal cylinders were oven dried at 105 °C until constant weight and soil dry weight was divided by the inner volume of the metal cylinder. After adding 10% H2O2 and 2 M HCl to remove organic matter and carbonate, respectively, soil texture was determined by wet sieving for sand and gravitational sedimentation for silt and clay according to Glendon and Dani (Reference Glendon, Dani, Dane and Topp2017). Air-dry soil samples were sieved to 2 mm mesh prior to further analysis. Total C and N were determined by a Vario Max CN analyser (Elementar Analysensysteme GmbH, Langenselbold, Germany) after soil was grinded with a ball mill and oven dried at 60 °C for 24 h. Soil pH was assessed at a soil to water ratio of 1:2.5 using a glass electrode (pH 3110, Xylem Analytics Germany GmbH, Weilheim, Germany). Electrical conductivity (EC) was determined at a soil to water ratio of 1:5 with a digital conductivity meter (GMH3430, Seneca Germany GmbH, Remscheid, Germany). Carbonate concentration was measured gas volumetrically by adding 10% HCl to soil following the Scheibler method (Loeppert and Suarez, Reference Loeppert, Suarez and Bigham1996). Soil organic carbon (SOC) was calculated as the difference between total C and carbonate C.
Potassium was extracted by mechanical shaking of 10 g soil in 100 mL 0.0125 M CaCl2 for 1 hr. The extracts were filtrated through P-free filters (MN280 ¼, Macherey-Nagel GmbH & Co. KG, Düren, Germany) and K was quantified using the flame photometry (BWB Technologies, Newbury, United Kingdom; Toth and Prince Reference Toth and Prince1949). Available soil P was colorimetrically measured using a spectrophotometer (Hitachi U-2000, Hitachi Ltd. Corp., Tokyo, Japan; Bray and Kurtz Reference Bray and Kurtz1945) in Bray P2 extracts made of 2.5 g soil in 25 mL Bray P2 solution with 0.1N HCl, shaken for 15 min. and centrifuged at 2000 rpm before filtration through P-free filters. For N mineralisation, 20 g air dried soil was incubated at 25 °C and 60% water holding capacity in the dark for 28 days. Ammonium (NH4 +-N) and nitrates (NO3 −-N) were determined at time intervals of 1, 7, 14, and 28 days after distilled water addition. NH4 +-N and NO3 −-N were extracted with 0.0125 M CaCl2 and measured with a Continuous Flow Analyzer (Evolution II, Alliance Instruments GmbH, Freilassing, Germany).
Soil microbial properties
Prior to analysis of microbial properties, soil moisture was adjusted to 60% field capacity and samples were incubated at 25 °C for 7 days in the dark to restore microbial population to mimic those in fresh soils (Zornoza et al., Reference Zornoza, Guerrero, Mataix-Solera, Arcenegui, García-Orenes and Mataix-Beneyto2007). Microbial biomass C (MBC) and N (MBN) were determined by the chloroform fumigation method (Brookes et al., Reference Brookes, Landman, Pruden and Jenkinson1985; Vance et al., Reference Vance, Brookes and Jenkinson1987) in 0.5 M K2SO4 extracts (10 g soil in 40 mL extracting solution). Organic C and total N in the fumigated and non-fumigated soil extracts were measured with a C/N analyzer (Multi N/C 2100s, Analytic Jena GmbH, Jena, Germany). MBC was calculated as E C /k EC , where E C = (extracted organic C from fumigated soil samples) – (extracted organic C from non-fumigated soil samples) and k EC = 0.45 (Wu et al., Reference Wu, Joergensen, Pommerening, Chaussod and Brookes1990). MBN was calculated as E N /k EN , where E N = (extracted organic N from fumigated soil samples) – (extracted organic N from non-fumigated soil samples) and k EN = 0.54 (Brookes et al., Reference Brookes, Landman, Pruden and Jenkinson1985). The microbial quotient was calculated as the proportion of MBC in SOC. In situ soil respiration was measured using a closed chamber system as described by Predotova et al. (Reference Predotova, Gebauer, Diogo, Schlecht and Buerkert2010) in the months of November 2019 and January 2020. The system consisted of a photo-acoustic infrared multi gas analyser (INNOVA 1312-5, Lumasense Technologies A/S, Ballerup, Denmark) connected to a cuvette via a 1.5 m long and 3.3 mm diameter Teflon tube®. The cuvette was made up of 0.3 m diameter and 0.11 m high PVC cylinder combined with a 0.3 m diameter and 0.07 m high base ring pushed 0.05 m into the soil. During measurements, the system was kept closed for 10 minutes and air temperature inside the cuvette was monitored with a data logger (Onset HOBO data logger U12-012, USA). To eliminate carry-over contamination in-between measurements, the cuvette was opened and ventilated for 2 min. before the next measurement. Measurements were carried out in the morning (6–10 am) and afternoon (12–3 pm) to capture diurnal changes of CO2 emissions.
Basal respiration under laboratory conditions was determined using a LGR 915-0011 (Los Gatos Research, Los Gatos, CA, USA) ultra-portable greenhouse gas analyser. To this end, 20 g of pre-incubated moist soil was poured into a 100 ml PET bottle. The bottle was placed into a 1.6 L Mason jar connected to the gas analyser via inlet and outlet tubes of 0.6 m length in a closed chamber system. The gas analyser measured through-flowing air continuously for an accumulation period of 5 min. CO2 flux rates in both the in situ soil and basal respiration were calculated using the ‘gasfluxes’ package of the R software (Fuss et al., Reference Fuss, Hueppi and Pedersen2020). The metabolic quotient (qCO2) was estimated as the ratio of basal respiration and MBC.
Statistical analysis
Data were analysed using nested one-way analysis of variance with management as the fixed factor and Lixisol type as the random factor. Using a first order equation (Nmin = N
0 (1 −
${{\rm{e}}^{{\rm{ - xt}}}}$
)), the potential nitrogen mineralisation (N
0) and nitrogen mineralisation rate constant (X) were estimated (Stanford and Smith, Reference Stanford and Smith1972). The Pearson correlation was used to analyse the relationship between physico-chemical and microbial properties. Redundancy analysis (RDA) was employed to determine the relationships between soil physico-chemical and microbial properties. Data failing the assumptions of ANOVA (normality and homogeneity of variance) were transformed using the ‘bestNormalize’ package in R. All statistical analyses were performed using R 4.0.3 software (R Core Team, 2020).
Results
Average BD in the topsoil of conventional farms was 19% higher than in organic farms but this difference was not significant (Table 2). However average sand content in topsoil under conventional management was 14% greater (p < 0.01) than topsoil under organic management, whereas clay was 1.5 times greater (p < 0.01) under organic management than in conventional management. In the subsoil there was no significant management effect on physical properties.
Table 2. Soil physical properties at different depths of cocoa agroforests under organic and conventional management in Suhum Municipality, Eastern Region of Ghana
Note: CV = mean coefficient of variation between replicates within management (n = 6).
Management significantly affected chemical properties in topsoil, except for extractable P despite 78% higher extractable P in organic than conventional management (Table 3). Average EC in topsoil of conventionally managed farms was two-folds lower (p = 0.03) than their organic counterparts. SOC, total N, SOC/total N, and extractable K in the topsoil were 35%, 30%, 11%, and 47% respectively, lower (p < 0.01) under conventional management than under organic management. In the subsoil both physical and chemical properties were not significantly affected by management.
Table 3. Soil chemical properties at different depths of cocoa agroforests under organic and conventional management in Suhum Municipality, Eastern Region of Ghana
Note: CV = mean coefficient of variation between replicates within management (n = 6).
Before incubation, NH4 +-N concentration was highest in conventionally managed soils, which was 46% greater (p < 0.01) than in organically managed ones (Figure 1A). Day 7 yielded a peak in all management systems, but NH4 +-N concentrations under conventional management was 48% higher (p < 0.05) than in organic counterparts. On day 28, NH4 +-N concentration in organically managed farms was 25% lower (p = 0.03) than their conventional counterparts. The concentrations of NO3 −-N on days 7 and 14 were 1.7- and 1.6-folds respectively, greater (p < 0.01) under organic management than under conventional management (Figure 1B). For both N mineralisation potential and rate, there was no significant difference between management systems in the top- and subsoil (Table 4).
Figure 1. Ammonium (A) and nitrate (B) dynamics of topsoil in cocoa agroforests of villages under organic and conventional management in Suhum Municipality, Eastern Region of Ghana. Soil was incubated under laboratory conditions for 28 days at 25 °C and 60% WHC. Error bars show +/− one standard error of the mean. * and ** indicate p values of 0.05 and 0.01, respectively.
Table 4. Nitrogen mineralisation of soils at different depths from cocoa agroforests under organic and conventional management in Suhum Municipality, Eastern Region of Ghana incubated for 28 days
Note: N
min = N
0 (1 −
${{\rm{e}}^{ - {\rm{xt}}}}$
); Nmin is nitrogen (N) mineralised, N
0 is N mineralisation potential, X is N mineralisation rate constant in days, t is time, and R
2 is goodness of fit of the exponential curve. CV = mean coefficient of variation between replicates within management (n = 6).
For MBC and MBN, the topsoil of farms under organic management were 48% and 57% respectively, greater (p < 0.01) than under conventional management (Table 5). Contrarily the qCO2 of the topsoil under conventional management was 54% higher (p < 0.01) than under organic management. For microbial properties in the subsoil, it was only MBC/SOC that was 34% greater (p < 0.05) under conventional than under organic management. The average in situ soil respiration in November 2019 was 41% greater than in January 2020. In both months, management had no effect on in situ soil respiration (Figure 2).
Table 5. Soil microbial properties at different depths of cocoa agroforests under organic and conventional management in Suhum Municipality, Eastern Region of Ghana
Note: CV = mean coefficient of variation between replicates within management (n = 6).
Figure 2. In situ soil respiration in cocoa agroforests under organic and conventional management in Suhum Municipality, Eastern Region of Ghana. Error bars indicate +/− one standard error of the mean.
Pairwise correlation showed a strong positive relationship for SOC and N with MBC and MBN in topsoils regardless of the management system (Figure 3). The explanatory physico-chemical properties (SOC, pH, BD, and sand) used in the RDA model significantly (p < 0.01) explained 70% of the variation in soil microbial properties (MBC, MBN, and BR) in the topsoil (Figure 4). Together the first two axes explained (p < 0.01) 72% of the variation. The significant explanatory variables were SOC (p < 0.01) which accounted for 64% of the variance, pH (p < 0.05) which accounted for 2.4% of the variance and, BD (p < 0.05) which accounted for 5.2% of the variance in the model. But SOC and pH were highly correlated with the first RDA axis and BD with the second RDA axis. SOC was positively correlated with MBC and MBN, whereas sand was negatively correlated with SOC, MBC, and MBN. On the other hand, basal respiration correlated with BD and soil pH.
Figure 3. Pearson correlation of soil physico-chemical and microbial properties in the topsoils of cocoa agroforests from the Eastern Region of Ghana. × shows no significant difference at P > 0.05. N0 refers to the nitrogen mineralisation potential, BD = bulk density, and BR = basal respiration.
Figure 4. Redundancy analysis biplot using microbial properties as response variables (red arrows) and physico-chemical properties as explanatory variables (blue arrows). Correlations between variables are indicated by angle between arrows i.e. an angle <90° between two arrows imply positive relationship, equal to 90° imply no relationship, and > 90° imply negative relationship. The length of the arrow depicts the strength of association between a variable and the ordination axis in the biplot.
Discussion
Management system’s effect on soil selected physico-chemical properties
Cocoa agroforests are known to have high SOC given high continuous supply of organic material via litterfall and turnover of fine roots (Dawoe et al., Reference Dawoe, Isaac and Quashie-Sam2010). The average SOC of the investigated soil was similar to values reported for cocoa agroforests in the Ashanti Region of Ghana (Dawoe et al., Reference Dawoe, Quashie-Sam and Oppong2014), in Cameroon (Sauvadet et al., Reference Sauvadet, Saj, Freschet, Enock, Becquer, Tixier and Harmand2020), and in Bolivia (Alfaro-Flores et al., Reference Alfaro-Flores, Morales-Belpaire and Schneider2015) ranging from 0.7 to 2.6% and in the lower range of SOC reported for cocoa agroforests in Brazil (Zaia et al., Reference Zaia, Gama-Rodrigues, Gama-Rodrigues, Moço, Fontes, Machado and Baligar2012). SOC positively correlated with pH, EC, total N, and extractable K, as SOC is an important regulator of the cation exchange capacity and nutrient buffering. Niether et al. (Reference Niether, Schneidewind, Fuchs, Schneider and Armengot2019) also reported higher soil N and extractable K concentrations in organic agroforest soils compared with conventional agroforest soils in Bolivia, whereby the latter differences were not significant. Soil organic C and soil nutrient pools represent a balance of inputs and outputs. Decomposition processes lead to CO2 respiration and mineralisation of nutrients, which can leave the soil pool by plant uptake or leaching. While C inputs are coming from dead roots and litterfall, nutrients in organic systems are difficult to replace and the only N source is often coming from legumes (Sauvadet et al., Reference Sauvadet, Saj, Freschet, Enock, Becquer, Tixier and Harmand2020). Despite lacking nutrient input from fertilizers or legumes in the investigated organic farms, total N, extractable P, and K were higher than in conventional farms, which received some mineral fertilizer inputs. While a higher output of plant nutrients by higher yields of cocoa beans, though not significant, in conventional farms is one explanation for this discrepancy, higher leaching losses in the SOC-depleted soils of conventional farms may be another explanatory factor. The composition of shade trees affects C litter quantity and quality, and thus C sequestration as SOC (Sauvadet et al., Reference Sauvadet, Saj, Freschet, Enock, Becquer, Tixier and Harmand2020). Although shade tree composition and density varied between the investigated farms, litterfall quantity was not affected by the management system (Agbotui et al. Reference Agbotui, Mariko and Buerkert2024). Therefore, it is likely that decomposition processes were affected by litter quality and/or the use of pesticides, leading to higher SOC sequestration and higher nutrient contents in organically managed soils. However, SOC often correlates with the clay content of soils, which was significantly higher in the organic soils used in current study. High clay content is known to stabilise SOC by absorbing as well as occluding organic materials within aggregates, thereby protecting it from fast decomposition (Singh et al., Reference Singh, Sarkar, Sarkar, Churchman, Bolan, Mandal, Menon, Purakayastha and Beerling2018).
Management system effect on soil N mineralisation
Typically for cocoa agroforests, the NH4 +-N concentration of all investigated soils were low compared with the NO3 −-N concentration due to the rapid conversion of NH4 +-N into NO3 −-N (Isaac et al. Reference Isaac, Gordon, Thevathasan, Oppong and Quashie-Sam2005). This demonstrates that irrespective of management in the topsoil (0-10 cm) of cocoa agroforests there is no N limitation for nitrification due to high availability of organic matter. In general, the average N mineralisation rate constant in our soils was ten-fold higher than that reported for cocoa agroforests in Brazil (Zaia et al., Reference Zaia, Gama-Rodrigues, Gama-Rodrigues, Moço, Fontes, Machado and Baligar2012). It must be stated that in the current study N mineralisation was determined in a laboratory incubation approach with sieved soils, which can lead to higher mineralisation rates due to the disruption of the natural soil structure (Hassink, Reference Hassink1992). Thus, our values rather represent the potential N mineralisation. However, high clay contents in the Brazilian soil may also explain the observed discrepancy, as soils with high clay content generally have low N mineralisation rates (Soinne et al., Reference Soinne, Keskinen, Räty, Kanerva, Turtola, Kaseva, Nuutinen, Simojoki and Salo2021). A peak in NH4 +-N on day 7 during incubation in soils of both management systems is indicative of a quick onset of ammonification, which is the first step of N mineralisation. The decline of NH4 +-N thereafter and the simultaneous increase in NO3 −-N results from nitrification of NH4 +-N (Islam et al., Reference Islam, Bilkis, Hoque, Uddin, Jahiruddin, Rahman, Rahman, Alhomrani, Gaber and Hossain2021). Temporarily and significantly higher NH4 +-N and lower NO3 −-N in conventional management observed in this study may have resulted from a stimulation of ammonifying bacteria (Demanou et al., Reference Demanou, Monkiédjé, Njiné, Foto, Nola, Togouet and Kemka2004) and an inhibition of nitrifying ones (Zhang et al., Reference Zhang, Wang, Wang, Teng and Xu2017) by pesticides. The overall N mineralisation potential was numerically higher in organically managed top- and subsoil compared with conventionally managed soils, and may indicate a better N availability for cocoa trees under organic management.
Management system effect on soil microbial properties
Average MBC and MBN were close to 125 µg C g−1 and 22 µg N g−1 reported for rice (Oryza sativa)-based agroforestry in India (Kaur et al., Reference Kaur, Gupta and Singh2000), but 79% for MBC and 84% for MBN lower than reported for cocoa agroforests in Bolivia (Alfaro-Flores et al., Reference Alfaro-Flores, Morales-Belpaire and Schneider2015) and Brazil (Zaia et al., Reference Zaia, Gama-Rodrigues, Gama-Rodrigues, Moço, Fontes, Machado and Baligar2012). SOC was strongly correlated to the microbial biomass (Figures 3 and 4), as it serves as an energy and nutrient source, and habitat for soil microorganisms and explains the significantly lower MBC and MBN in the topsoil under conventional management. Several authors have reported an inhibitory effect of pesticides on MBC (Mukherjee et al., Reference Mukherjee, Tripathi, Mukherjee, Bhattacharyya and Chakrabarti2016; Perucci et al., Reference Perucci, Dumontet, Bufo, Mazzatura and Casucci2000), which are toxic to soil microorganisms, especially those that have low ability for pesticide breakdown (Yang et al., Reference Yang, Zhang, Zhu and Zhang2009). Additionally, commonly used pesticides such as glyphosate and endosulfan indirectly affect microbial biomass by reducing soil pH (Bueno de Mesquita, et al. Reference Bueno de Mesquita, Solon, Barfield, Mastrangelo, Tubman, Vincent, Porazinska, Hufft, Shackelford, Suding and Schmidt2023; Manson et al. Reference Manson, Nekaris, Rendell, Budiadi, Imron and Campera2022). This can lower microbial growth efficiency leading to reduced microbial biomass and a decline in SOC accumulation (Malik et al. Reference Malik, Puissant, Buckeridge, Goodall, Jehmlich, Chowdhury, Gweon, Peyton, Mason, van Agtmaal, Blaud, Clark, Whitaker, Pywell, Ostle, Gleixner and Griffiths2018).
In contrast to our findings, Alfaro-Flores et al. (Reference Alfaro-Flores, Morales-Belpaire and Schneider2015) observed no differences in MBC of cocoa agroforests under organic and conventional management in Bolivia. The lack of a management effect in the cited study might be based on the young age (3 years) of the farms and thus the shorter effective duration of pesticides on soil microorganisms. Differences in soil properties between organic and conventional management systems are often observed in the long-term. Average in situ soil respiration of 374 mg m−2 h−1 in our study was close to 302–318 mg m−2 h−1 reported for various land use systems in Ghana (Anokye et al., Reference Anokye, Logah and Opoku2021). In our study, the average in situ soil respiration was approximately 600-times lower than the average basal respiration (214,125 mg m−2 h−1), when estimating the basal respiration on m−2 basis using 10 cm soil depth and 0.98 g cm−3 BD. Disruption of the soil structure during soil sieving for basal respiration determination consequently exposed more organic matter to microbial degradation (Thomson et al., Reference Thomson, Ostle, Mcnamara, Whiteley and Grif2010). In addition, basal respiration was determined under ideal temperature and moisture conditions, which might have led to a higher microbial activity compared with field conditions. The in situ measurements were conducted during the dry season, which is characterised by low precipitation and high maximum temperatures of up to 55°C. This may have inhibited microbial activity. In spite of these differences between in situ soil and basal respiration, both methods detected no management effect on CO2 emission. However, qCO2 was significantly lower in the soils under organic than under conventional management. A higher qCO2 indicates an increased catabolic demand for maintenance energy (Anderson and Domsch, Reference Anderson and Domsch2010; Araújo et al., Reference Araújo, Santos and Monteiro2008). This phenomenon may be caused by the toxicity of pesticides to non-susceptible microbes (Bonfleur et al., Reference Bonfleur, Tornisielo, Regitano and Lavorenti2015) and low SOC (Malik et al. Reference Malik, Puissant, Buckeridge, Goodall, Jehmlich, Chowdhury, Gweon, Peyton, Mason, van Agtmaal, Blaud, Clark, Whitaker, Pywell, Ostle, Gleixner and Griffiths2018).
Pesticides have been found to reduce microbial diversity and enzymatic activity (Wang et al., Reference Wang, Lu, Miller, Liu, Hou, Liang, Zhao, Zhang and Borch2020), although these were not measured in our study. In general, the qCO2 values found in the investigated cocoa agroforests were within the range of 60–218 mg CO2-C g−1 d−1 reported for agroforests in India (Kaur et al., Reference Kaur, Gupta and Singh2000) and Brazil (Notaro et al., Reference Notaro, Medeiros, Duda, Silva and Moura2014). Araújo et al. (Reference Araújo, Santos and Monteiro2008) found that qCO2 was higher in conventional than organic management, which confirms our findings and showed that qCO2 is a sensitive indicator for environmental stress factors of soil microorganisms. This stress leads to soil microbes using more C for respiration instead of growth which reduces nutrient cycling in cocoa agroforests under conventional management.
Conclusion
Our study showed that compared to conventional management, organic management of cocoa agroforests had higher pH, SOC, total N, and microbial biomass C and N. Nitrate was the dominant N form in cocoa agroforests due to the rapid conversion of NH4 +-N into NO3 −-N. Pesticide use might be the cause for temporarily higher NH4 +-N concentrations and lower NO3 −-N concentrations in soils under conventional management, although it only numerically increased potential N mineralisation. The topsoil of conventionally managed soils recorded higher qCO2 than of organically managed ones, which indicates higher C demand for maintenance. Our results show a more intensive nutrient cycling in cocoa agroforests under conventional management.
Acknowledgments
We are indebted to Claudia Thieme-Fricke and Eva Wiegard of the OPATS group and Gabi Dormann of Soil Biology and Plant Nutrition at University of Kassel for their laboratory assistance. Lastly, we are grateful to the Suhum cocoa agroforesters for access to their fields and homes during data collection and to my family and friends for their relentless support throughout this study
Financial support
The German Academic Exchange Service (DAAD) supported this study in the form of a PhD research grant to the first author.
Competing interests
The authors declare they have no competing interest.
Authorship
DKA, MI, and AB designed the experiment. DKA, MI, and RGJ selected the soil fertility indicators. Data analysis was done by DKA and MI. DKA, MI, and RGJ revised the manuscript. AB supervised and helped secured funding.
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