Long-term farming systems and climatic variability shape soil fungal diversity and community structure in Kenyan tropical agroecosystems

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However, their responses to contrasting farming systems, nitrogen inputs, crop phenology, and climate variability in tropical agroecosystems remain poorly documented. Long-term datasets from Sub-Saharan Africa are particularly scarce. Methods This study assessed soil fungal diversity and community composition after 15 years of continuous management in the Farming Systems Comparison Trial (SysCom) at two Kenyan sites representing humid highland (Chuka) and semi-arid lowland (Thika) conditions. Four systems were evaluated: Conventional High-input, Conventional Low-input, Organic High-input, and Organic Low-input. Soil samples were collected across major crop growth stages in cereal and potato rotations. Fungal communities were profiled using ITS-based Illumina MiSeq sequencing and analyzed with DADA2 and phyloseq. Diversity metrics, β-diversity, environmental correlations, and differential abundance were assessed in relation to soil chemical properties and climatic variables. Results Farming system, input intensity, nitrogen form, crop stage, and site climate jointly shaped fungal community structure. Chuka’s wetter conditions supported more stable and diverse assemblages, whereas Thika exhibited stronger temporal turnover linked to rainfall variability. Organic systems especially those integrating legumes and mulches harbored richer and more functionally diverse fungal communities dominated by saprotrophic, mycorrhizal, and entomopathogenic genera (e.g., Mortierella , Glomus , Purpureocillium , Beauveria ). Conventional systems contained higher proportions of opportunistic or xerotolerant taxa such as Fusarium , Aspergillus , and Wallemia . Ammonium-N, available P, and soil pH were the strongest abiotic drivers of community assembly. Fungal succession followed crop phenology, with saprotrophs dominating early crop stages and mutualistic and ligninolytic taxa prevailing toward maturity. Conclusions After 15 years, organic low and high-input systems enhanced fungal richness, evenness, and community stability relative to conventional low-input systems. Integration of organic nutrient sources, legumes, mulches, and adaptive water management promotes diverse and resilient fungal communities in tropical agroecosystems. These results highlight the value of ecological intensification for sustaining soil biodiversity and nutrient cycling under increasing climatic uncertainty. Farming system Fungal community dynamics Long-term trial Organic farming Soil fungal diversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Background Soil microorganisms are central to ecosystem functioning, driving organic matter decomposition, nutrient cycling, and soil aggregation (Nannipieri et al. 2017 ; Khatoon et al. 2017 ). Fungi, often form a larger proportion of soil microbial biomass and are particularly important due to their hyphal networks and ability to degrade recalcitrant compounds such as cellulose, lignin, and pectin, decomposition of dead organic matter, recycling nutrients in the ecosystems and forming mutualistic relationships with plants (mycorrhizae) (Ali et al. 2021 ; Rossel et al. 2022 ; Fall et al. 2022 ). Through extracellular enzyme production and exudates, fungi also stabilize soil aggregates, making them key indicators of soil health (Carrillo-Saucedo and Gavito 2020 ). Despite their importance, the impacts of agricultural practices on soil fungal diversity remain less studied than those on bacterial communities, especially in Sub-Saharan Africa (Kazeeroni and Al-Sadi 2016 ). Globally, agricultural soils face degradation from nutrient depletion, erosion, and biodiversity loss, threatening productivity (Bünemann et al. 2018 ; Gomiero 2016 ). In Kenya, declining soil fertility and microbial diversity are major constraints to sustainable agriculture. Conventional farming systems typically rely on synthetic fertilizers, pesticides, and monocultures to maximize yields (Schrama et al. 2018 ), but such practices can increase greenhouse gas emissions, degrade soil structure, and reduce biodiversity (Fuentes-Ponce et al. 2022 ). Mineral fertilizers may also promote pathogenic fungi while suppressing beneficial symbionts such as arbuscular mycorrhizal fungi (Paungfoo-Lonhienne et al. 2015 ; Weng et al. 2022 ). Organic farming provides an alternative approach that emphasizes ecological balance through compost, green manure, crop rotations, and natural pest control. These practices can enhance microbial activity, support more complex soil food webs, and increase antagonistic fungi that suppress plant pathogens (Lupatini et al. 2017 ; Tully and McAskill 2020 ). Although organic yields are often lower initially, long-term studies show that yield gaps can narrow or close once biological equilibria are restored (De Ponti et al. 2012 ; Bonanomi et al. 2016 ; Luo et al. 2018; Bautze et al. 2024 ). However, the long-term impacts of organic and conventional management on soil fungal diversity, particularly in tropical agroecosystems, are poorly documented. Nitrogen (N) management is a major driver of microbial community structure, with responses depending on the form (organic vs. mineral) and application rate (Lin et al. 2022 ). Mineral N inputs can favor opportunistic or pathogenic fungi, while organic N inputs may promote decomposers and mutualists. Crop phenology further influences fungal communities by altering root exudation and nutrient demand, leading to shifts in community composition across vegetative, reproductive, and maturity stages (Pajares and Bohannan 2016 ; Hannula et al. 2021 ). These temporal changes are important for understanding plant-microbe interactions and seasonal dynamics. An underexplored part of fungal ecology is heterotrophic nitrification, in which fungi oxidize ammonium and organic N to nitrate. This process contributes to nitrate accumulation, especially in acidic soils, and may be underestimated in nitrogen balance studies (Pajares and Bohannan 2016 ; Zhang et al. 2020 ). As agricultural N inputs increase in sub-Saharan Africa, understanding fungal contributions to N transformations is critical for predicting nutrient availability and environmental impacts. Input intensity defined by the amount and type of amendments also shapes fungal communities. Low-input systems, typical of smallholder farms, often depend on organic residues and legume intercropping, while high-input systems, common in commercial farming, apply nutrients at or above recommended rates (Vanlauwe et al. 2014 ; Kihara et al. 2020 ). High-input fertilization can stimulate copiotrophic fungi and reduce diversity (Francioli et al. 2016 ), while low-input systems may foster more diverse and resilient communities (Lupatini et al. 2017 ). These effects interact with farming system type, crop species, and environmental conditions, yet little is known about their combined influence in East African systems. In Africa, the interaction between farming systems (organic vs. conventional), nitrogen form, input intensity, crop phenology, and seasonality in shaping fungal diversity is still poorly understood. Most existing studies focus on bacteria or on single soil health indicators, leaving critical gaps in our understanding of fungal responses in long-term tropical trials. To address these gaps, we examined soil fungal diversity and community composition in a > 15-year farming systems comparison trial in two contrasting Kenyan agroecological zones, represented by Chuka and Thika. The trial compares organic and conventional systems at high and low input levels, standing for commercial and smallholder management, respectively. Using Illumina MiSeq sequencing of ITS1 and ITS2 regions, this study aimed to: (1) compare fungal diversity and community structure across farming systems and input intensities; (2) assess temporal shifts in fungal communities across crop developmental stages for cereal and potato systems; and (3) find the interactive effects of nitrogen form, crop phenology, input intensity, and seasonality on fungal community’s dynamics. 2. Methods 2.1 Study site The long-term trials were established in 2007 under the program ‘farming systems comparisons in the tropics (SysCom)’( https://systems-comparison.fibl.org/ ). The trials are found at two sites in the sub-humid zones of the central highlands of Kenya: Chuka (0°20.864′ S, 37°38.792′ E) and Thika (1°00.231′ S, 37°04.747′ E). Both sites have a bimodal rainfall pattern, with distinct long and short rainy seasons. Chuka is situated in the upper midland 2 (UM2) agro-ecological zone at an elevation of 1,458 m above sea level, with a mean annual rainfall of 1,050 mm. Thika is in the upper midland 3 (UM3) zone at an elevation of 1,500 m, receiving an average annual rainfall of 900 mm. The mean annual temperature for both sites is 20°C, ranging from 15°C to 27°C. The dominant soils are humic nitisols in Chuka and rhodic nitisols in Thika. 2.2 Farming systems The study evaluated four contrasting farming systems: two high-input systems, conventional high input (Conv-High) and organic high input (Org-High) both representing commercial-scale production with supplemental irrigation during dry spells, and two low-input systems, conventional low input (Conv-Low) and organic low input (Org-Low) representing smallholder, rainfed agriculture. Crop selection in each system was guided by earlier research, while rotation schemes adhered to local farming practices and the guidelines of the Kenyan Institute of Organic Farming (Musyoka 2006 ). 2.3 Experimental design A randomized complete block design (RCBD) with four replications was implemented at each site. Each experimental plot measured 8 × 8 m, with a 6 × 6 m net plot used for sampling and measurements. Trials followed a long-term, two-season, three-year crop rotation framework as described by Adamtey et al. ( 2016 ). 2.4 Crop rotation In 2021, field trials were conducted during both the long and short rainy seasons. In the high-input systems, the rotation comprised baby corn ( Zea mays var. Pan 14) intercropped with greenleaf desmodium ( Desmodium intortum ) during the long rains, followed by potato ( Solanum tuberosum var. Shangi) intercropped with Dolichos beans ( Lablab purpureus ) and D. intortum during the short rains. In the low-input systems, maize ( Zea mays var. H513) intercropped with common bean ( Phaseolus vulgaris var. GLP 92) was planted during the long rains, while the short rains featured potato intercropped with L. purpureus . 2.5 Field management practices Nutrient management strategies were system-specific but calibrated to supply equivalent nitrogen (N) and phosphorus (P) levels within each input category (high or low). In the Conv-Low system, decomposed farmyard manure (FYM) was supplemented with 50 kg ha − ¹ diammonium phosphate (DAP) during the long rains and 100 kg ha − ¹ during the short rains, providing total N and P inputs of 45 and 27 kg ha − ¹ (long rains) and 45 and 50 kg ha − ¹ (short rains), respectively. The Org-Low system followed the same crop combinations but replaced synthetic fertilizers with decomposed FYM, Tithonia diversifolia mulch, and rock phosphate (RP) at 100 kg ha − ¹ (long rains) and 200 kg ha − ¹ (short rains), matching the Conv-Low nutrient levels. In the Conv-High system, inputs included decomposed FYM plus 200 kg ha − ¹ DAP and 100 kg ha − ¹ calcium ammonium nitrate (CAN) during the long rains, and 300 kg ha − ¹ triple super phosphate (TSP) with 200 kg ha − ¹ CAN during the short rains. These applications supplied 113 and 60 kg ha − ¹ N and P during the long rains, and 90 and 100 kg ha − ¹ N and P during the short rains. The Org-High system used compost derived from the same fresh FYM quantity applied in Conv-High, supplemented with T. diversifolia mulch and/or tea, and RP at 364 kg ha − ¹ (long rains) and 581 kg ha − ¹ (short rains), adjusted to match Conv-High nutrient levels. In organic systems, T. diversifolia mulch was applied shortly after germination to supply readily available N. P was applied at planting and during the vegetative growth stage, while N was split between planting, vegetative, and reproductive stages. Synthetic fertilizer rates in conventional systems followed regional recommendations (Muriuki and Qureshi 2001 ). Pest and disease management was guided by bi-weekly scouting and conventional systems used synthetic pesticides and fungicides, while organic systems applied certified biological products available locally. 2.6 Soil sampling and laboratory analysis Soil samples were collected at key physiological growth stages of the crops. For baby corn and maize, sampling occurred at the vegetative, tussling and silking, grain formation, and maturity stages. For potato, samples were collected at the vegetative, flowering, and maturity stages. In each plot, a composite sample was prepared by combining 12 sub-samples collected with an Edelman auger in a zigzag pattern from the topsoil (0–20 cm), being the primary root zone. For soil chemical analysis, replicate-level sampling was maintained across all farming systems, crop stages, and sites. The total number of soil samples analyzed was: 128 samples for the baby corn/ maize season (2 sites × 4 farming systems × 4 replicates × 4 growth stages) and samples for the potato season (2 sites × 4 farming systems × 4 replicates × 3 growth stages). For microbial analysis, replicate-level samples within each farming system were pooled to create a single composite sample per system, growth stage, and site. The resulting sample numbers were: 32 samples for the baby corn/ maize season (2 sites × 4 systems × 4 stages) and 24 samples for the potato season (2 sites × 4 systems × 3 stages). Soil samples for molecular analysis were immediately placed on dry ice in cooler boxes and transported to the International Centre of Insect Physiology and Ecology ( icipe ), Nairobi, Kenya for storage at -80°C until nucleic acid extraction. Samples for chemical analysis were similarly preserved on dry ice and delivered the same day to Crop Nutritional Laboratory Services (CNLS), Nairobi for processing. At CNLS, soil chemical properties were determined using standard protocols as follows. Soil pH was measured potentiometrically (Okalebo et al. 2002 ). Nitrate nitrogen (NO₃ - -N) and ammonium nitrogen (NH₄ ⁺ -N) were quantified spectrophotometrically following (Dahnke and Johnson 1990 ), and (Keeney and Nelson 1982 ), respectively. Total N was measured using the Kjeldahl method (Sapan et al. 1999 ), while total P was determined colorimetrically (Okalebo et al. 2002 ). Available P was extracted using the Olsen method (Okalebo et al. 2002 ). 2.7 Microbial sampling design Fungal DNA sequencing was performed on pooled soil samples from each farming system at every crop growth stage and site. This pooling strategy enabled the detection of broad patterns in fungal community composition across farming systems and crop stages. Soil chemical analyses were conducted at the replicate level. 2.8 Total eDNA extraction and ITS gene sequencing Total DNA was extracted from soil samples using the PureLink Microbiome DNA Purification Kit (Thermo Fisher Scientific, Waltham, USA) following the manufacturer’s instructions. DNA quality, purity, and concentration were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) and confirmed by agarose gel electrophoresis. Purified DNA was shipped on dry ice to Mr DNA Lab, Shallowater, USA for sequencing on the Illumina MiSeq platform. Fungal ITS regions were amplified using the primer pair ITS1-F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2-R (5′-GCTGCGTTCTTCATCGATGC-3′) (Ihrmark et al. 2012 ). Eukaryotic 18S rRNA genes were amplified with the primer EUK1391F (5′-GTACACACCGCCCGTC-3′) and EukBr (5’-TGATCCTTCTGCAGGTTCACCTAC-3’) (Caporaso et al. 2012 ). PCR amplification consisted of 30 cycles of denaturation at 95°C for 30 s, annealing at 53°C for 40 s, extension at 72°C for 1 min, and a final elongation at 72°C for 10 min. Amplicon size and yield were verified on 2% agarose gels. Equimolar amounts of PCR products were indexed with unique barcodes, pooled, and sequenced at Mr DNA Lab. Following sequencing, barcodes and primer sequences were removed. Low-quality reads (< 300 bp after Phred20-based quality trimming), sequences having ambiguous bases, and homopolymer runs exceeding 5 bp were discarded (Reeder and Knight 2010 ). Quality control was performed at MR DNA Laboratory. Raw reads were demultiplexed, and low-quality sequences were removed using an average Phred score cutoff of 30. Adapter and primer sequences were trimmed and reads with ambiguous bases or shorter than 150 bp after trimming were discarded. Paired-end reads were merged with a minimum overlap of 20 bp, and chimeric sequences were identified and removed using the UCHIME algorithm implemented in USEARCH. 2.9 Statistical analysis of soil chemical parameters All soil chemical parameters were first assessed for normality using the Shapiro - Wilk test. Parameters that met the assumptions of normality were subjected to analysis of variance (ANOVA) to assess the effects of farming system. Analyses were conducted separately for each crop development stage using the agricolae package in R version 4.3.1. Post hoc comparisons among the four farming systems (Conventional High, Organic High, Conventional Low, Organic Low) were performed using Tukey’s Honest Significant Difference (HSD) test to determine differences in mean values of soil parameters. The analysis focused on soil pH, total nitrogen (N), nitrate nitrogen (NO₃ ⁻ -N), ammonium nitrogen (NH₄ ⁺ -N), Olsen phosphorus (available P), and total phosphorus (total P), revealing stage-specific trends across farming systems. 2.10 Bioinformatics analysis of ITS gene sequence data Raw paired-end sequences were processed in RStudio (version 4.0.3) (RS Team 2015 ) using the DADA2 pipeline (version 1.18) (Callahan et al. 2016 ) for quality filtering, trimming, denoising, merging, and chimera removal. Sequences were truncated based on quality score profiles and amplicon sequence variants (ASVs) were inferred. Taxonomic assignment was performed using the UNITE fungal ITS reference database (version 8.2) with a 97% similarity threshold (Nilsson et al. 2019 ). The resulting ASV table, taxonomy table, and associated metadata were integrated in the phyloseq package (version 1.16.2) (McMurdie and Holmes 2013 ) for downstream analyses. Taxa prevalence was calculated from absolute abundance data to determine predominant taxa at each sampling stage. Phylum-level sub setting was performed using the subset function in R. Taxa prevalence plots and relative abundance bar plots of the top 20 most abundant fungal classes were generated using ggplot2 (version 3.3.5) (Wickham 2016 ). Alpha diversity was assessed using Shannon, Simpson, and Observed ASV indices computed in Vegan (version 2.5-7) (Oksanen 2015 ) and the phyloseq package, based on the fungal ASV table. Normality of diversity and soil chemical datasets was evaluated with the Shapiro-Wilk test (Hollander et al. 2013 ). Where data were non-normal, the Kruskal-Walli’s rank-sum test (Kruskal and Wallis 1952 ) was used to evaluate differences among sampling stages across farming systems (adjusted p -value < 0.01). Post hoc pairwise comparisons between sites were conducted using Tukey’s HSD test. Beta diversity was calculated using centered log-ratio transformed ASV tables and Bray-Curti’s dissimilarity matrices with the vegdist function in Vegan (Jolliffe 2002 ). Ordination was performed via principal coordinate analysis (PCoA) (McArdle and Anderson 2001 ), and group differences were assessed using permutational multivariate analysis of variance (PERMANOVA) (McArdle and Anderson 2001 ) with 999 permutations. Soil chemical data were analyzed using the stats package in RStudio (version 3.6.2). Environmental drivers of fungal community structure were assessed using redundancy analysis in vegan (Dormann et al. 2013 ). Soil chemical variables were standardized (z-scores) and checked for multicollinearity using the vif function in car (version 3.0–11) (Fox and Weisberg 2019 ). Forward stepwise selection of significant predictors was performed with the ordistep function (1,000 permutations), removing variables with variance inflation factor > 10. The significance of final models and individual predictors was determined using permutation-based ANOVA with 1,000 permutations (Segata et al. 2011 ). Differentially abundant fungal taxa among sampling stages were identified using the linear discriminant analysis (LDA), effect size (LEfSe) algorithm (Cao et al. 2022 ) implemented in MicrobiomeMarker (version 0.0.1). The Kruskal-Walli’s rank-sum test (adjusted p -value < 0.01) was applied at the genus level, followed by pairwise Wilcoxon rank-sum tests (Mann and Whitney 1947 ; Wilcoxon 1992 ) to confirm biological consistency, with an LDA threshold of 2. 3. Results 3.1 Climate variability during the study period Rainfall and relative humidity (RH) exhibited pronounced spatial and seasonal variability, while temperature remained relatively stable across sites (Fig. 1 ). Chuka recorded higher cumulative rainfall than Thika during both seasons (529 mm vs. 289 mm) in the long rains and (499 mm vs. 193 mm) in the short rains with peaks in April - May and November - December. RH closely followed rainfall patterns, ranging from 61–82% at Chuka and 55–75% at Thika. Mean daily temperatures were comparable between sites (16–21°C), lowest in June - July and highest in February. 3.2 Sequence analysis Fungal community diversity varied among farming systems and sites, with organic farming systems consistently supporting higher diversity than conventional systems (Table 1 ). At Chuka, the number of observed amplicon sequence variants (ASVs) ranged from 3,125 in Conv-High to 3,470 in Org-High, while Chao1 estimates (3,859–4,154) indicated a substantial pool of rare taxa. Diversity indices were lowest under Conv-Low (Shannon 19.4; Simpson 5.7), reflecting reduced evenness and richness. In contrast, both organic systems particularly Org-High (Shannon 27.1; Simpson 6.6) showed more even and taxonomically rich communities. At Thika, diversity was also greater under organic farming systems. Observed ASVs ranged from 3,468–3,791, with high Chao1 estimates (4,361–4,613) suggesting abundant low-frequency taxa. The highest Shannon (29.1) and Simpson (6.7) indices occurred under Org-Low, whereas conventional systems exhibited moderate diversity (Shannon 26.2). Across all systems and sites, Ascomycota dominated, followed by Basidiomycota, Chytridiomycota, and Mucoromycota. Organic plots showed relatively greater proportions of Basidiomycota and Chytridiomycota, while Zoopagomycota was uniquely enriched in the Conv-Low treatment. Table 1 Distribution of high-quality ITS sequences, observed ASVs, diversity indices, and dominant fungal phyla across Chuka and Thika. Summary of sequence quality metrics, alpha-diversity indices, and taxonomic composition of fungal communities at the Chuka and Thika sites Site Farming systems High quality sequences Observed ASVs Shannon Simpson Chao Most abundant phyla Chuka Conv-High 73,904 3,125 20.94 5.66 3,927.34 Ascomycota Basidiomycota Chytridiomycota Chuka Conv-Low 48,403 3,230 19.41 5.66 3,859.40 Ascomycota Chytridiomycota Basidiomycota Chuka Org-High 57,233 3,470 27.12 6.63 4,153.90 Ascomycota Mucoromycota Basidiomycota Chuka Org-Low 50,031 3,190 24.21 6.40 4,112.93 Ascomycota Basidiomycota Chytridiomycota Thika Conv-High 53,176 3,703 26.35 6.46 4,509.85 Ascomycota Mucoromycota Chytridiomycota Thika Conv-Low 92,333 3,663 25.94 6.53 4,526.26 Ascomycota Zoopagomycota Basidiomycota Thika Org-High 42,902 3,791 27.64 6.44 4,613.28 Ascomycota Basidiomycota Chytridiomycota Thika Org-Low 50,425 3,468 29.11 6.74 4,361.36 Ascomycota Chytridiomycota Basidiomycota 3.3 Drivers of fungal community composition across sites At Thika, canonical correspondence analysis (CCA) revealed significant associations between fungal community structure and soil chemical variables (Fig. 2 ). During the long-rain season (season 1), fungal composition was significantly related to soil pH, ammonium-N, and available P ( p < 0.002, R² = 0.1253), which together explained 79.61% of the constrained variance (CCA1 = 52.48%, CCA2 = 27.13; Fig. 2 A). Distinct clustering was observed, with conventional systems aligning with ammonium-N and available P, while organic systems more strongly associated with soil pH. In the short rains season (season 2), soil variables remained significant but explained less variation ( p < 0.044, R² = 0.0317), with all constrained variance captured by CCA1 (100%), and weaker clustering across farming systems (Fig. 2 B). At Chuka, none of the measured soil parameters significantly explained fungal community variation in either season, and hence no CCA plots are presented. 3.4 Dominant fungal phyla across farming systems, sites, and crop stages Across all sites and seasons, Ascomycota dominated the fungal community, accounting for 40–80% of total sequences, followed by Basidiomycota (10–45%), Chytridiomycota (5–20%), and Glomeromycota (up to 15%) (Fig. 3 A - D). At Chuka, Ascomycota remained the prevailing phylum in both seasons, particularly under conventional systems, where its relative abundance peaked (50–70%) at flowering and maturity (Fig. 3 A - B). In contrast, Basidiomycota and Glomeromycota were more abundant in organic systems, especially under Org-Low, which showed elevated Glomeromycota during tasseling/silking and maturity stages. Basidiomycota increased notably under low-input conditions during vegetative growth and grain formation. At Thika, a similar dominance pattern was observed, though organic systems supported greater representation of Basidiomycota, Chytridiomycota, and Mucoromycota, particularly at later crop stages (Fig. 3 C - D). Glomeromycota was enriched under Org-Low, while Mucoromycota showed higher abundance in Org-High plots. In contrast, conventional systems maintained consistently higher proportions of Ascomycota across all stages. 3.5 Fungal alpha-diversity across farming systems at Chuka and Thika At Chuka, fungal richness and diversity were consistently higher under organic farming systems, particularly in Org-High, compared with conventional systems (Fig. 4 A - B). In Season 1, Org-High recorded the greatest richness (520 ASVs) and diversity (Shannon 3.9; Simpson > 0.94), while Conv-High and Conv-Low showed lower diversity (Shannon 3.3–3.5). During Season 2, richness again peaked in Org-High (470 ASVs) but declined in Org-Low (370 ASVs). Evenness remained high in organic plots (Simpson 0.95–1.00) but dropped sharply in Conv-Low (0.50). At Thika, fungal alpha diversity followed similar trends across seasons, with organic systems consistently exhibiting higher richness and evenness (Fig. 4 C - D). In Season 1, observed richness ranged from 92 ASVs in Org-Low to 107 ASVs in Org-High, while Shannon values were highest in Org-High (3.5) and slightly lower but comparable across conventional systems (3.3–3.4). Simpson indices remained uniformly high (> 0.94) but were marginally greater under organic management (0.955–0.960). During Season 2, richness and diversity increased across all systems, again peaking in Org-High (114 ASVs; Shannon 3.6) and remaining lowest in Conv-Low (100 ASVs; Shannon 3.4). Evenness remained high across treatments, though organic plots consistently maintained slightly higher Simpson values (> 0.96). 3.6 Alpha-diversity across sampling stages at Chuka and Thika At Chuka, fungal richness and diversity varied significantly with crop growth stage, showing clear successional trends across both seasons (Fig. 5 A - B). In Season 1, richness was lowest at tasseling/silking (450 ASVs) and increased progressively to 520 ASVs at maturity, with grain formation stages also supporting high diversity. Shannon indices rose from 3.0 to 4.6, while Simpson values increased from 0.88 to 0.96. During Season 2, a similar pattern was observed, with richness ranging from 350 ASVs at flowering to 480 ASVs at maturity. Both Shannon and Simpson indices increased steadily through the growing period (Shannon 0.5 to 1.0; Simpson 0.65 to 0.98). At Thika, fungal alpha diversity also varied markedly across crop growth stages, reflecting dynamic community shifts through the crop cycle (Fig. 5 C - D). In Season 1, richness was highest at grain formation (520 ASVs), moderate at vegetative and tasseling/silking stages (490–495), and lowest at maturity (460). However, Shannon and Simpson indices increased steadily toward maturity (Shannon 4.1; Simpson 0.95). In Season 2, richness peaked earlier at the vegetative stage (550 ASVs) and declined slightly at flowering and maturity (490–500), while diversity and evenness continued to rise toward maturity (Shannon 4.5; Simpson 0.98). 3.7 Beta diversity of fungal communities across farming systems and crop stages Patterns of fungal β-diversity closely mirrored differences in α-diversity and taxonomic composition (Fig. 6 ). Principal Coordinates Analysis (PCoA) based on Bray-Curti’s dissimilarities revealed clear clustering of communities by site, farming system, and crop stage. At Chuka, the first two axes explained 12–18% and 10–15% of variation across the cereal (Fig. 6 A) and potato (Fig. 6 B) seasons, respectively. Low-input systems showed greater dispersion at early growth stages. In contrast, high-input systems formed tighter clusters toward maturity. At Thika, the first two PCoA axes accounted for 24% and 10% of variation during the cereal season (Fig. 6 C) and 16% and12% during the potato season (Fig. 6 D). Communities in low input systems were clearly separated at vegetative and flowering stages. A partial overlap was observed between Org-High and Conv-Low in mid-season. 3.8 Dominant fungal taxa and functional guilds across farming systems Distinct shifts in dominant fungal taxa and guilds were observed across farming systems (Fig. 7 ). Organic systems were enriched in saprotrophic and mutualistic taxa, including Mortierella , Glomus , Periconia , Ceriporia , and Collybia . In contrast, conventional systems harbored higher relative abundances of copiotrophic and opportunistic genera such as Fusarium , Aspergillus , Penicillium , and Cladosporium . Intercrops and mulches further modulated community composition: plots containing Desmodium intortum and Dolichos lablab supported greater abundances of Glomus and Mortierella , while the inclusion of Coriandrum sativum coincided with subtle enrichment of minor phyla. 3.9 Differential abundance of fungal genera across crop developmental stages LEfSe analyses revealed distinct shifts in fungal community composition across sites, crop stages, and seasons (Fig. 7 A - D). At Chuka in Season 1 (Fig. 7 A), several genera Lobulomyces , Flammulina , and Paraconiothyrium dominated across crop stages (log₁₀ abundance > 3.5). Early crop stages were enriched with Blastobotrys and Mucor , whereas Guehomyces and Medicopsis prevailed at maturity. In Season 2 (Fig. 7 B), Rhizophlyctis , Pseudogymnoascus , and Hygrocybe were abundant throughout the cycle, while Ceriporia and Collybia increased at maturity. Periconia was prominent during the vegetative stage, whereas members of Hypocreales and Plectosphaerellaceae were most abundant at flowering. At Thika, Season 1 (Fig. 7 C) communities were dominated by Purpureocillium , Wallemia , Tetraplosphaeria , Gymnopyces , and Spizellomyces . Mid-season there was enrichment of Glomus , Beauveria , and Oliveonia while Ramicandelaber and Phlyctochytrium peaked during grain formation. In Season 2 (Fig. 7 D), Mortierella and Pithya were the most abundant genera, with early-stage enrichment of Cladosporium , Sporidiobolus , and Auricularia , and late-stage dominance by Imaia , Mycena , and Meruliopsis . 4. Discussion Marked spatial and seasonal variation in rainfall and humidity, contrasted with relatively stable temperatures, shaped fungal community dynamics across sites. Chuka, characterized by higher rainfall and humidity, supported more stable and diverse fungal assemblages by sustaining organic matter turnover and reducing desiccation stress. In contrast, Thika’s drier conditions imposed stronger environmental filtering, favoring drought-tolerant and sporulating taxa and leading to greater temporal turnover. These climate-driven differences mirror findings from semi-arid agroecosystems where precipitation gradients modulate microbial diversity and function (Delgado-Baquerizo et al. 2020 ). Management effects on soil fungi must therefore be interpreted within this climatic framework, as moisture-dependent nutrient fluxes and decomposition processes ultimately constrain fungal activity and community stability. Farming systems exerted strong control over fungal diversity and community evenness. Organic systems supported higher ASV richness and Shannon diversity than conventional systems, confirming that organic inputs enhance habitat heterogeneity and substrate quality (Banerjee et al. 2019 ). The dominance of Ascomycota, Basidiomycota, and Chytridiomycota underscores their ecological resilience and core functional roles in soil carbon cycling. Occasional enrichment of Mucoromycota and Zoopagomycota in organic plots likely reflects microhabitats enriched in labile organic matter that favor decomposers and antagonistic taxa (Voříšková et al. 2014 ; Bonfante and Venice 2020 ). These management effects were further modulated by site conditions. Chuka’s humid conditions supported more even, stable communities, whereas Thika’s semi-arid environment showed stronger diversity fluctuations with input intensity and season. This aligns with regional observations where organic systems in Kenya’s highlands sustain higher fungal diversity and resilience (Karanja et al. 2020 ), while arbuscular mycorrhizal (AM) communities in semi-arid zones track moisture and nutrient gradients (Sakha et al. 2025 ). These results demonstrate that climatic and edaphic contexts jointly mediate management impacts on fungal community assembly. In addition to these climatic and management patterns, soil chemical factors exerted a strong influence on fungal composition, particularly at Thika. Here, ammonium-N and available P emerged as key edaphic drivers, consistent with fungal roles in N assimilation, nitrification, and P mobilization (Laughlin et al. 2008 ). Legume intercrops likely enhanced local NH₄ ⁺ availability through biological N fixation, supporting AM fungi and other symbionts. The observed relationships with pH, N, and P align with earlier studies linking soil chemistry to fungal richness and functional guild balance (Francioli et al. 2016 ; Lin et al. 2022 ). By contrast, at Chuka, fungal diversity appeared less constrained by abiotic factors and more by biotic drivers such as root exudation, rhizodeposition, and organic amendments that shape microbial competition and cooperation (Eisenhauer et al. 2017 ; Seitz et al. 2024 ; Zhang et al. 2020 ). This distinction illustrates how nutrient availability and plant-microbe interactions differentially structure fungal communities under humid and semi-arid conditions. Across both sites, the dominance of Ascomycota and Basidiomycota across systems and seasons mirrors global trends in agricultural soils (Rossel et al. 2022 ). However, management clearly modulated their relative abundance. Ascomycota dominated conventional high-input systems, consistent with their copiotrophic traits and tolerance to mineral fertilizers (Xu et al. 2017 ). In contrast, Basidiomycota enrichment under organic systems reflects their lignocellulolytic capacity and roles in organic matter stabilization (Purahong et al. 2016 ). Minor but functionally critical groups, Glomeromycota, Chytridiomycota, Mucoromycota, and Zoopagomycota were more abundant in low-input systems, suggesting that resource limitation fosters specialized guilds. These include nutrient-exchanging AM fungi ( Glomus ), polymer-degrading Chytrids, and predator-antagonistic Zoopagomycetes (Smith and Read 2010 ; Spatafora et al. 2016 ; Roberts et al. 2020 ). Collectively, organic nutrient management supports functionally diverse and ecologically resilient fungal networks capable of buffering environmental stress. Extending beyond taxonomy, alpha-diversity patterns indicated that organic low and high-input systems maintained higher richness and evenness than low-input conventional systems, driven by organic amendments and supplemental irrigation that stabilized fungal populations. Manure, compost, and crop residues provided continuous carbon inputs, while irrigation reduced moisture stress together promoting substrate heterogeneity and hyphal continuity (Xiang et al. 2020 ). Consistent with these alpha-diversity patterns, beta-diversity analyses showed greater community convergence in high-input systems and higher dispersion in low-input systems, particularly at early crop stages. These results suggest that nutrient-rich environments homogenize fungal communities, while resource-limited conditions enhance spatial heterogeneity and compositional turnover a pattern consistent with other long-term experiments (Beillouin et al. 2021 ; Treseder 2013 ). Temporal dynamics further shaped fungal assembly. Community composition shifted predictably with crop developmental stage, reflecting the influence of plant phenology on soil microbial succession. Early vegetative stages favored fast-growing saprotrophs ( Blastobotrys , Mucor ) utilizing root exudates, while reproductive and maturity stages enriched symbiotic and ligninolytic taxa such as Glomus , Periconia , and Ceriporia (Hartmann et al. 2015 ; Pajares and Bohannan 2016 ; Hannula et al. 2021 ). The stronger turnover observed at Thika likely reflects moisture-driven amplification of successional shifts under semi-arid conditions, whereas Chuka’s humid environment promoted greater temporal stability. Differential abundance analyses further clarified these functional trajectories. At Chuka, saprotrophic taxa ( Lobulomyces , Flammulina , Ceriporia ) dominated organic plots, whereas opportunistic Ascomycetes ( Fusidium , Parastagonospora ) were enriched under conventional management consistent with fertilization-driven selection for copiotrophic fungi (Purahong et al. 2016 ). At Thika, enrichment of Purpureocillium , Beauveria , and Glomus in organic systems highlighted functional shifts toward biocontrol and mutualistic guilds. Purpureocillium acts as both an entomopathogen and plant growth promoter (Khan et al. 2012 ; Rigobelo et al. 2024 ), while Beauveria contributes to natural pest suppression (Inglis et al. 2001 ). Glomus enhances P uptake and drought tolerance through AM symbioses (Begum et al. 2019 ; Smith and Read 2010 ). In contrast, Wallemia and Tetraplosphaeria , prevalent in conventional systems, reflect xerophilic or plant-associated taxa adapted to low-moisture environments (Zhao et al. 2024 ). These functional shifts demonstrate that organic inputs not only increase overall diversity but also restructure communities toward taxa that underpin decomposition, nutrient cycling, and biological control, reinforcing ecosystem sustainability. Beyond nutrient inputs, intercropping and mulching also played significant roles in shaping fungal communities. Leguminous intercrops enhanced fungal richness through rhizodeposition and N contributions (Liu et al. 2015 ), while the live mulch Desmodium intortum moderated soil temperature and moisture, sustaining hyphal networks and AM fungal abundance (Midega et al. 2014 ). Coriandrum sativum , introduced primarily for pest management, may have further modulated rhizosphere interactions through secondary metabolites influencing fungal colonization (Aravinthraju et al. 2024 ). These findings demonstrate how integrated plant diversity enhances soil cover, organic inputs, and microclimatic stability, collectively supporting fungal diversity and ecosystem function. Enrichment of mutualistic ( Glomus ), entomopathogenic ( Beauveria , Purpureocillium ), and ligninolytic ( Ceriporia , Collybia ) fungi in organic systems illustrates mechanisms through which ecological intensification improves soil health. AM fungi enhance nutrient uptake, soil aggregation, and drought tolerance (Begum et al. 2019 ), while entomopathogens reduce pest pressure and pesticide dependency (Inglis et al. 2001 ). Ligninolytic taxa accelerate carbon turnover and contribute to soil organic matter renewal (Janusz et al. 2017 ). In sub-Saharan agroecosystems dominated by mineral fertilizers, integrating organic amendments and intercrops can sustain microbial biodiversity, nutrient cycling, and resilience, key principles for climate-adaptive intensification (Tittonell and Giller 2013 ; Vanlauwe et al. 2015 ). Finally, while this study captured robust site- and system-level fungal trends, the pooling of molecular samples limited detection of within-plot variability. However, pooling was necessary to generate representative long-term profiles under resource constraints typical of tropical field research. Future studies integrating replicate-level sequencing, metatranscriptomics, and enzyme assays could more deeply link community composition to functional activity, offering broader insights into how management and climate jointly regulate soil fungal processes. 5. Conclusion This study demonstrates that soil fungal diversity and community composition in Kenyan tropical agroecosystems are jointly shaped by farming system, input intensity, nitrogen form, crop phenology, and climate variability. After over 15 years of continuous management, organic low and high-input systems sustained greater fungal richness, evenness, and community stability than conventional low-input systems, with irrigation and intercrops mitigating seasonal stress effects. Integration of community, soil chemical, and climatic data identified ammonium-N, available phosphorus, and pH as principal edaphic drivers, while legumes, organic amendments, and coriander intercrops enhanced saprotrophic and mycorrhizal guilds. Fungal community succession followed crop phenology, with saprotrophs dominating early stages and mutualistic and ligninolytic taxa prevailing toward maturity. Although pooling of molecular samples limited within-system resolution, the approach effectively captured long-term, system-level trends in fungal assembly. Collectively, these findings show that organic nutrient integration, adaptive water management, and diversified cropping promote stable and functionally diverse fungal communities, reinforcing the ecological foundations of resilient and resource-efficient tropical agriculture. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Funding This research was financially supported by the Biovision Foundation (Grant No. 1040), Coop Sustainability Fund (Grant No. 1040), Liechtenstein Development Service (LED) (Grant No. 1040), and the Swiss Agency for Development and Cooperation (SDC) (Grant No. 1040) through the SysCom Kenya project implemented by the International Centre of Insect Physiology and Ecology ( icipe ). Added institutional support was provided by the Swedish International Development Cooperation Agency (Sida), the Australian Centre for International Agricultural Research (ACIAR), the Government of Norway, the German Federal Ministry for Economic Cooperation and Development (BMZ), and the Government of the Republic of Kenya. The views expressed in this publication do not necessarily reflect those of the funding agencies. Authors’ contributions SWM: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Visualization, Writing - original draft. Acknowledgements The authors thank the management of the Kenya Agricultural and Livestock Research Organization (KALRO) for providing the trial site at Thika, and the management of Kiereni Primary School for offering the trial site at Chuka. We are also grateful to Ms. Jane Makena and Mr. James Karanja for their dedicated management of the field trials and their assistance with data collection. Availability of data and materials The demultiplexed high-quality sequence reads has been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA), Bio Project ID: PRJNA1228344 and study accession number available for download at https://www.ncbi.nlm.nih.gov/sra/PRJNA1228344 . 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J Fungi 27(5):319. https://doi.org/10.3390/jof10050319 Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Annals of Microbiology → Version 1 posted Reviewers agreed at journal 24 Dec, 2025 Reviewers invited by journal 24 Dec, 2025 Editor assigned by journal 10 Dec, 2025 First submitted to journal 08 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Ordination plots showing the relationship between fungal community composition and selected soil chemical properties during (A) the long rains (Season 1) and (B) the short rains (Season 2). Significant soil drivers (pH, ammonium-N, and Olsen P) are indicated by arrows\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8293848/v1/aea08f020fd5c964c80761fb.jpg"},{"id":99314451,"identity":"8b88bdfc-0bfb-4def-a072-a35627c99aac","added_by":"auto","created_at":"2025-12-31 16:21:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":279185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 3\u003c/strong\u003e Relative abundance of dominant fungal phyla across farming systems, crop stages, and seasons at Chuka and Thika.\u003cstrong\u003e \u003c/strong\u003eBar charts showing the relative abundance of major fungal phyla under different farming systems and crop growth stages during two seasons: (A - B) Chuka, Season 1 and Season 2, respectively; (C - D) Thika, Season 1 and Season 2, respectively\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8293848/v1/1084032e12a8304e2df23d7a.jpg"},{"id":99315025,"identity":"23445659-d609-4e8e-a511-cbd7cd695d65","added_by":"auto","created_at":"2025-12-31 16:26:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":112843,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 4 (A - B)\u003c/strong\u003e Alpha-diversity indices of soil fungal communities across farming systems at Chuka.\u003cstrong\u003e \u003c/strong\u003eBoxplots showing fungal alpha-diversity metrics, including observed richness, Shannon, and Simpson indices, under conventional (Conv) and organic (Org) farming systems at high and low input levels during (A) Season 1 and (B) Season 2. Each point represents an individual replicate sample\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8293848/v1/5f0ce9d2b58b4d7e32d4e199.jpg"},{"id":99314349,"identity":"4fff04e3-0e1b-4f13-8427-a2a127689089","added_by":"auto","created_at":"2025-12-31 16:21:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":108227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 4 (C - D)\u003c/strong\u003eAlpha-diversity indices of soil fungal communities across farming systems at Thika.\u003cstrong\u003e \u003c/strong\u003eBoxplots showing fungal alpha-diversity metrics, including observed richness, Shannon, and Simpson indices, under conventional (Conv) and organic (Org) farming systems at high and low input levels during (C) Season 1 and (D) Season 2. Each point represents an individual replicate sample\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8293848/v1/1318b8b9dcda397938f699be.jpg"},{"id":99057045,"identity":"636cecd4-9f86-4c75-885e-860715bde288","added_by":"auto","created_at":"2025-12-26 19:17:32","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":109198,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 5 (A - B) \u003c/strong\u003eAlpha-diversity of soil fungal communities across crop growth stages at Chuka.\u003cstrong\u003e \u003c/strong\u003eBoxplots showing alpha diversity of soil fungal communities during (A) Season 1 and (B) Season 2, assessed using observed richness (ASVs), Shannon, and Simpson indices. Diversity values are presented across crop growth stages: vegetative, tasseling/silking, flowering, grain formation, and maturity. Each point represents a replicate sample\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8293848/v1/cb6a8870304afbe2e6b8ae43.jpg"},{"id":99057050,"identity":"949d7822-91a9-4f4c-8eab-1347cf6062fe","added_by":"auto","created_at":"2025-12-26 19:17:32","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":104215,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 5 (C - D) \u003c/strong\u003eAlpha-diversity of soil fungal communities across crop growth stages at Thika.\u003cstrong\u003e \u003c/strong\u003eBoxplots showing alpha diversity of soil fungal communities during (C) Season 1 and (D) Season 2, assessed using observed richness (ASVs), Shannon, and Simpson indices. Diversity values are presented across crop growth stages: vegetative, tasseling/silking, flowering, grain formation, and maturity. Each point represents a replicate sample\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8293848/v1/0f13e967732ee206afd8bfa2.jpg"},{"id":99314568,"identity":"07cb0b3e-6a8f-4103-9975-16631eadf777","added_by":"auto","created_at":"2025-12-31 16:21:54","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":129739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 6 \u003c/strong\u003ePrincipal coordinates analysis (PCoA) of soil fungal community beta-diversity across farming systems and seasons at Chuka and Thika.\u003cstrong\u003e \u003c/strong\u003eOrdination plots based on Bray-Curti’s dissimilarities showing beta-diversity patterns of soil fungal communities under different farming systems during (A) Season 1 and (B) Season 2 at Chuka, and (C) Season 1 and (D) Season 2 at Thika\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8293848/v1/883dcd772d4d54bf77632737.jpg"},{"id":99057066,"identity":"ce5cd687-1a69-4bdb-bb9c-725957210a35","added_by":"auto","created_at":"2025-12-26 19:17:33","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":326018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 7 \u003c/strong\u003eHeatmap of fungal genera overrepresented across crop growth stages based on linear discriminant analysis effect size (LEfSe).\u003cstrong\u003e \u003c/strong\u003eHeatmaps showing fungal genera significantly enriched at different crop growth stages according to LEfSe analysis. Panels (A - B) represent Chuka during Seasons 1 and 2, while panels (C - D) represent Thika during Seasons 1 and 2. The color scale indicates the log₁₀ abundance of LDA scores, with colors denoting the group in which each taxon was highly enriched\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8293848/v1/7f558dbb153504c1ef137387.jpg"},{"id":107350901,"identity":"6625e8f3-d621-4d11-bece-be7714419200","added_by":"auto","created_at":"2026-04-20 16:06:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2106415,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8293848/v1/bd89b5b9-3f9e-47fe-b45a-06dd445197fc.pdf"}],"financialInterests":"","formattedTitle":"Long-term farming systems and climatic variability shape soil fungal diversity and community structure in Kenyan tropical agroecosystems","fulltext":[{"header":"1. Background","content":"\u003cp\u003eSoil microorganisms are central to ecosystem functioning, driving organic matter decomposition, nutrient cycling, and soil aggregation (Nannipieri et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Khatoon et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Fungi, often form a larger proportion of soil microbial biomass and are particularly important due to their hyphal networks and ability to degrade recalcitrant compounds such as cellulose, lignin, and pectin, decomposition of dead organic matter, recycling nutrients in the ecosystems and forming mutualistic relationships with plants (mycorrhizae) (Ali et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rossel et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fall et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Through extracellular enzyme production and exudates, fungi also stabilize soil aggregates, making them key indicators of soil health (Carrillo-Saucedo and Gavito \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite their importance, the impacts of agricultural practices on soil fungal diversity remain less studied than those on bacterial communities, especially in Sub-Saharan Africa (Kazeeroni and Al-Sadi \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, agricultural soils face degradation from nutrient depletion, erosion, and biodiversity loss, threatening productivity (B\u0026uuml;nemann et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gomiero \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In Kenya, declining soil fertility and microbial diversity are major constraints to sustainable agriculture. Conventional farming systems typically rely on synthetic fertilizers, pesticides, and monocultures to maximize yields (Schrama et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), but such practices can increase greenhouse gas emissions, degrade soil structure, and reduce biodiversity (Fuentes-Ponce et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Mineral fertilizers may also promote pathogenic fungi while suppressing beneficial symbionts such as arbuscular mycorrhizal fungi (Paungfoo-Lonhienne et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Weng et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOrganic farming provides an alternative approach that emphasizes ecological balance through compost, green manure, crop rotations, and natural pest control. These practices can enhance microbial activity, support more complex soil food webs, and increase antagonistic fungi that suppress plant pathogens (Lupatini et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tully and McAskill \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although organic yields are often lower initially, long-term studies show that yield gaps can narrow or close once biological equilibria are restored (De Ponti et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bonanomi et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Luo et al. 2018; Bautze et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the long-term impacts of organic and conventional management on soil fungal diversity, particularly in tropical agroecosystems, are poorly documented.\u003c/p\u003e \u003cp\u003eNitrogen (N) management is a major driver of microbial community structure, with responses depending on the form (organic vs. mineral) and application rate (Lin et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Mineral N inputs can favor opportunistic or pathogenic fungi, while organic N inputs may promote decomposers and mutualists. Crop phenology further influences fungal communities by altering root exudation and nutrient demand, leading to shifts in community composition across vegetative, reproductive, and maturity stages (Pajares and Bohannan \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hannula et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These temporal changes are important for understanding plant-microbe interactions and seasonal dynamics.\u003c/p\u003e \u003cp\u003eAn underexplored part of fungal ecology is heterotrophic nitrification, in which fungi oxidize ammonium and organic N to nitrate. This process contributes to nitrate accumulation, especially in acidic soils, and may be underestimated in nitrogen balance studies (Pajares and Bohannan \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As agricultural N inputs increase in sub-Saharan Africa, understanding fungal contributions to N transformations is critical for predicting nutrient availability and environmental impacts.\u003c/p\u003e \u003cp\u003eInput intensity defined by the amount and type of amendments also shapes fungal communities. Low-input systems, typical of smallholder farms, often depend on organic residues and legume intercropping, while high-input systems, common in commercial farming, apply nutrients at or above recommended rates (Vanlauwe et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kihara et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). High-input fertilization can stimulate copiotrophic fungi and reduce diversity (Francioli et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), while low-input systems may foster more diverse and resilient communities (Lupatini et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These effects interact with farming system type, crop species, and environmental conditions, yet little is known about their combined influence in East African systems.\u003c/p\u003e \u003cp\u003eIn Africa, the interaction between farming systems (organic vs. conventional), nitrogen form, input intensity, crop phenology, and seasonality in shaping fungal diversity is still poorly understood. Most existing studies focus on bacteria or on single soil health indicators, leaving critical gaps in our understanding of fungal responses in long-term tropical trials.\u003c/p\u003e \u003cp\u003eTo address these gaps, we examined soil fungal diversity and community composition in a\u0026thinsp;\u0026gt;\u0026thinsp;15-year farming systems comparison trial in two contrasting Kenyan agroecological zones, represented by Chuka and Thika. The trial compares organic and conventional systems at high and low input levels, standing for commercial and smallholder management, respectively. Using Illumina MiSeq sequencing of ITS1 and ITS2 regions, this study aimed to:\u003c/p\u003e \u003cp\u003e(1) compare fungal diversity and community structure across farming systems and input intensities;\u003c/p\u003e \u003cp\u003e(2) assess temporal shifts in fungal communities across crop developmental stages for cereal and potato systems; and\u003c/p\u003e \u003cp\u003e(3) find the interactive effects of nitrogen form, crop phenology, input intensity, and seasonality on fungal community\u0026rsquo;s dynamics.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study site\u003c/h2\u003e \u003cp\u003eThe long-term trials were established in 2007 under the program \u0026lsquo;farming systems comparisons in the tropics (SysCom)\u0026rsquo;(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://systems-comparison.fibl.org/\u003c/span\u003e\u003cspan address=\"https://systems-comparison.fibl.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The trials are found at two sites in the sub-humid zones of the central highlands of Kenya: Chuka (0\u0026deg;20.864\u0026prime; S, 37\u0026deg;38.792\u0026prime; E) and Thika (1\u0026deg;00.231\u0026prime; S, 37\u0026deg;04.747\u0026prime; E). Both sites have a bimodal rainfall pattern, with distinct long and short rainy seasons. Chuka is situated in the upper midland 2 (UM2) agro-ecological zone at an elevation of 1,458 m above sea level, with a mean annual rainfall of 1,050 mm. Thika is in the upper midland 3 (UM3) zone at an elevation of 1,500 m, receiving an average annual rainfall of 900 mm. The mean annual temperature for both sites is 20\u0026deg;C, ranging from 15\u0026deg;C to 27\u0026deg;C. The dominant soils are humic nitisols in Chuka and rhodic nitisols in Thika.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Farming systems\u003c/h2\u003e \u003cp\u003eThe study evaluated four contrasting farming systems: two high-input systems, conventional high input (Conv-High) and organic high input (Org-High) both representing commercial-scale production with supplemental irrigation during dry spells, and two low-input systems, conventional low input (Conv-Low) and organic low input (Org-Low) representing smallholder, rainfed agriculture. Crop selection in each system was guided by earlier research, while rotation schemes adhered to local farming practices and the guidelines of the Kenyan Institute of Organic Farming (Musyoka \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Experimental design\u003c/h2\u003e \u003cp\u003eA randomized complete block design (RCBD) with four replications was implemented at each site. Each experimental plot measured 8 \u0026times; 8 m, with a 6 \u0026times; 6 m net plot used for sampling and measurements. Trials followed a long-term, two-season, three-year crop rotation framework as described by Adamtey et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Crop rotation\u003c/h2\u003e \u003cp\u003eIn 2021, field trials were conducted during both the long and short rainy seasons. In the high-input systems, the rotation comprised baby corn (\u003cem\u003eZea mays\u003c/em\u003e var. Pan 14) intercropped with greenleaf desmodium (\u003cem\u003eDesmodium intortum\u003c/em\u003e) during the long rains, followed by potato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e var. Shangi) intercropped with Dolichos beans (\u003cem\u003eLablab purpureus\u003c/em\u003e) and \u003cem\u003eD. intortum\u003c/em\u003e during the short rains. In the low-input systems, maize (\u003cem\u003eZea mays\u003c/em\u003e var. H513) intercropped with common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e var. GLP 92) was planted during the long rains, while the short rains featured potato intercropped with \u003cem\u003eL. purpureus\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Field management practices\u003c/h2\u003e \u003cp\u003eNutrient management strategies were system-specific but calibrated to supply equivalent nitrogen (N) and phosphorus (P) levels within each input category (high or low). In the Conv-Low system, decomposed farmyard manure (FYM) was supplemented with 50 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; diammonium phosphate (DAP) during the long rains and 100 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; during the short rains, providing total N and P inputs of 45 and 27 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; (long rains) and 45 and 50 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; (short rains), respectively. The Org-Low system followed the same crop combinations but replaced synthetic fertilizers with decomposed FYM, \u003cem\u003eTithonia diversifolia\u003c/em\u003e mulch, and rock phosphate (RP) at 100 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; (long rains) and 200 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; (short rains), matching the Conv-Low nutrient levels.\u003c/p\u003e \u003cp\u003eIn the Conv-High system, inputs included decomposed FYM plus 200 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; DAP and 100 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; calcium ammonium nitrate (CAN) during the long rains, and 300 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; triple super phosphate (TSP) with 200 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; CAN during the short rains. These applications supplied 113 and 60 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; N and P during the long rains, and 90 and 100 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; N and P during the short rains. The Org-High system used compost derived from the same fresh FYM quantity applied in Conv-High, supplemented with \u003cem\u003eT. diversifolia\u003c/em\u003e mulch and/or tea, and RP at 364 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; (long rains) and 581 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1; (short rains), adjusted to match Conv-High nutrient levels. In organic systems, \u003cem\u003eT. diversifolia\u003c/em\u003e mulch was applied shortly after germination to supply readily available N. P was applied at planting and during the vegetative growth stage, while N was split between planting, vegetative, and reproductive stages. Synthetic fertilizer rates in conventional systems followed regional recommendations (Muriuki and Qureshi \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Pest and disease management was guided by bi-weekly scouting and conventional systems used synthetic pesticides and fungicides, while organic systems applied certified biological products available locally.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Soil sampling and laboratory analysis\u003c/h2\u003e \u003cp\u003eSoil samples were collected at key physiological growth stages of the crops. For baby corn and maize, sampling occurred at the vegetative, tussling and silking, grain formation, and maturity stages. For potato, samples were collected at the vegetative, flowering, and maturity stages. In each plot, a composite sample was prepared by combining 12 sub-samples collected with an Edelman auger in a zigzag pattern from the topsoil (0\u0026ndash;20 cm), being the primary root zone. For soil chemical analysis, replicate-level sampling was maintained across all farming systems, crop stages, and sites. The total number of soil samples analyzed was: 128 samples for the baby corn/ maize season (2 sites \u0026times; 4 farming systems \u0026times; 4 replicates \u0026times; 4 growth stages) and samples for the potato season (2 sites \u0026times; 4 farming systems \u0026times; 4 replicates \u0026times; 3 growth stages).\u003c/p\u003e \u003cp\u003eFor microbial analysis, replicate-level samples within each farming system were pooled to create a single composite sample per system, growth stage, and site. The resulting sample numbers were: 32 samples for the baby corn/ maize season (2 sites \u0026times; 4 systems \u0026times; 4 stages) and 24 samples for the potato season (2 sites \u0026times; 4 systems \u0026times; 3 stages).\u003c/p\u003e \u003cp\u003eSoil samples for molecular analysis were immediately placed on dry ice in cooler boxes and transported to the International Centre of Insect Physiology and Ecology (\u003cem\u003eicipe\u003c/em\u003e), Nairobi, Kenya for storage at -80\u0026deg;C until nucleic acid extraction. Samples for chemical analysis were similarly preserved on dry ice and delivered the same day to Crop Nutritional Laboratory Services (CNLS), Nairobi for processing. At CNLS, soil chemical properties were determined using standard protocols as follows. Soil pH was measured potentiometrically (Okalebo et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Nitrate nitrogen (NO₃\u003csup\u003e-\u003c/sup\u003e-N) and ammonium nitrogen (NH₄\u003csup\u003e⁺\u003c/sup\u003e-N) were quantified spectrophotometrically following (Dahnke and Johnson \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), and (Keeney and Nelson \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), respectively. Total N was measured using the Kjeldahl method (Sapan et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), while total P was determined colorimetrically (Okalebo et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Available P was extracted using the Olsen method (Okalebo et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Microbial sampling design\u003c/h2\u003e \u003cp\u003eFungal DNA sequencing was performed on pooled soil samples from each farming system at every crop growth stage and site. This pooling strategy enabled the detection of broad patterns in fungal community composition across farming systems and crop stages. Soil chemical analyses were conducted at the replicate level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Total eDNA extraction and ITS gene sequencing\u003c/h2\u003e \u003cp\u003eTotal DNA was extracted from soil samples using the PureLink Microbiome DNA Purification Kit (Thermo Fisher Scientific, Waltham, USA) following the manufacturer\u0026rsquo;s instructions. DNA quality, purity, and concentration were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) and confirmed by agarose gel electrophoresis. Purified DNA was shipped on dry ice to Mr DNA Lab, Shallowater, USA for sequencing on the Illumina MiSeq platform. Fungal ITS regions were amplified using the primer pair ITS1-F (5\u0026prime;-CTTGGTCATTTAGAGGAAGTAA-3\u0026prime;) and ITS2-R (5\u0026prime;-GCTGCGTTCTTCATCGATGC-3\u0026prime;) (Ihrmark et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Eukaryotic 18S rRNA genes were amplified with the primer EUK1391F (5\u0026prime;-GTACACACCGCCCGTC-3\u0026prime;) and EukBr (5\u0026rsquo;-TGATCCTTCTGCAGGTTCACCTAC-3\u0026rsquo;) (Caporaso et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). PCR amplification consisted of 30 cycles of denaturation at 95\u0026deg;C for 30 s, annealing at 53\u0026deg;C for 40 s, extension at 72\u0026deg;C for 1 min, and a final elongation at 72\u0026deg;C for 10 min. Amplicon size and yield were verified on 2% agarose gels. Equimolar amounts of PCR products were indexed with unique barcodes, pooled, and sequenced at Mr DNA Lab. Following sequencing, barcodes and primer sequences were removed. Low-quality reads (\u0026lt;\u0026thinsp;300 bp after Phred20-based quality trimming), sequences having ambiguous bases, and homopolymer runs exceeding 5 bp were discarded (Reeder and Knight \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Quality control was performed at MR DNA Laboratory. Raw reads were demultiplexed, and low-quality sequences were removed using an average Phred score cutoff of 30. Adapter and primer sequences were trimmed and reads with ambiguous bases or shorter than 150 bp after trimming were discarded. Paired-end reads were merged with a minimum overlap of 20 bp, and chimeric sequences were identified and removed using the UCHIME algorithm implemented in USEARCH.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis of soil chemical parameters\u003c/h2\u003e \u003cp\u003eAll soil chemical parameters were first assessed for normality using the Shapiro - Wilk test. Parameters that met the assumptions of normality were subjected to analysis of variance (ANOVA) to assess the effects of farming system. Analyses were conducted separately for each crop development stage using the agricolae package in R version 4.3.1. Post hoc comparisons among the four farming systems (Conventional High, Organic High, Conventional Low, Organic Low) were performed using Tukey\u0026rsquo;s Honest Significant Difference (HSD) test to determine differences in mean values of soil parameters. The analysis focused on soil pH, total nitrogen (N), nitrate nitrogen (NO₃\u003csup\u003e⁻\u003c/sup\u003e-N), ammonium nitrogen (NH₄\u003csup\u003e⁺\u003c/sup\u003e-N), Olsen phosphorus (available P), and total phosphorus (total P), revealing stage-specific trends across farming systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Bioinformatics analysis of ITS gene sequence data\u003c/h2\u003e \u003cp\u003eRaw paired-end sequences were processed in RStudio (version 4.0.3) (RS Team \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) using the DADA2 pipeline (version 1.18) (Callahan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for quality filtering, trimming, denoising, merging, and chimera removal. Sequences were truncated based on quality score profiles and amplicon sequence variants (ASVs) were inferred. Taxonomic assignment was performed using the UNITE fungal ITS reference database (version 8.2) with a 97% similarity threshold (Nilsson et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The resulting ASV table, taxonomy table, and associated metadata were integrated in the phyloseq package (version 1.16.2) (McMurdie and Holmes \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) for downstream analyses.\u003c/p\u003e \u003cp\u003eTaxa prevalence was calculated from absolute abundance data to determine predominant taxa at each sampling stage. Phylum-level sub setting was performed using the subset function in R. Taxa prevalence plots and relative abundance bar plots of the top 20 most abundant fungal classes were generated using ggplot2 (version 3.3.5) (Wickham \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlpha diversity was assessed using Shannon, Simpson, and Observed ASV indices computed in Vegan (version 2.5-7) (Oksanen \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and the phyloseq package, based on the fungal ASV table. Normality of diversity and soil chemical datasets was evaluated with the Shapiro-Wilk test (Hollander et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Where data were non-normal, the Kruskal-Walli\u0026rsquo;s rank-sum test (Kruskal and Wallis \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1952\u003c/span\u003e) was used to evaluate differences among sampling stages across farming systems (adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Post hoc pairwise comparisons between sites were conducted using Tukey\u0026rsquo;s HSD test.\u003c/p\u003e \u003cp\u003eBeta diversity was calculated using centered log-ratio transformed ASV tables and Bray-Curti\u0026rsquo;s dissimilarity matrices with the vegdist function in Vegan (Jolliffe \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Ordination was performed via principal coordinate analysis (PCoA) (McArdle and Anderson \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and group differences were assessed using permutational multivariate analysis of variance (PERMANOVA) (McArdle and Anderson \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) with 999 permutations.\u003c/p\u003e \u003cp\u003eSoil chemical data were analyzed using the stats package in RStudio (version 3.6.2). Environmental drivers of fungal community structure were assessed using redundancy analysis in vegan (Dormann et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Soil chemical variables were standardized (z-scores) and checked for multicollinearity using the vif function in car (version 3.0\u0026ndash;11) (Fox and Weisberg \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Forward stepwise selection of significant predictors was performed with the ordistep function (1,000 permutations), removing variables with variance inflation factor\u0026thinsp;\u0026gt;\u0026thinsp;10. The significance of final models and individual predictors was determined using permutation-based ANOVA with 1,000 permutations (Segata et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDifferentially abundant fungal taxa among sampling stages were identified using the linear discriminant analysis (LDA), effect size (LEfSe) algorithm (Cao et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) implemented in MicrobiomeMarker (version 0.0.1). The Kruskal-Walli\u0026rsquo;s rank-sum test (adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was applied at the genus level, followed by pairwise Wilcoxon rank-sum tests (Mann and Whitney \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1947\u003c/span\u003e; Wilcoxon \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) to confirm biological consistency, with an LDA threshold of 2.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Climate variability during the study period\u003c/h2\u003e \u003cp\u003eRainfall and relative humidity (RH) exhibited pronounced spatial and seasonal variability, while temperature remained relatively stable across sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Chuka recorded higher cumulative rainfall than Thika during both seasons (529 mm vs. 289 mm) in the long rains and (499 mm vs. 193 mm) in the short rains with peaks in April - May and November - December. RH closely followed rainfall patterns, ranging from 61\u0026ndash;82% at Chuka and 55\u0026ndash;75% at Thika. Mean daily temperatures were comparable between sites (16\u0026ndash;21\u0026deg;C), lowest in June - July and highest in February.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sequence analysis\u003c/h2\u003e \u003cp\u003eFungal community diversity varied among farming systems and sites, with organic farming systems consistently supporting higher diversity than conventional systems (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At Chuka, the number of observed amplicon sequence variants (ASVs) ranged from 3,125 in Conv-High to 3,470 in Org-High, while Chao1 estimates (3,859\u0026ndash;4,154) indicated a substantial pool of rare taxa. Diversity indices were lowest under Conv-Low (Shannon 19.4; Simpson 5.7), reflecting reduced evenness and richness. In contrast, both organic systems particularly Org-High (Shannon 27.1; Simpson 6.6) showed more even and taxonomically rich communities.\u003c/p\u003e \u003cp\u003eAt Thika, diversity was also greater under organic farming systems. Observed ASVs ranged from 3,468\u0026ndash;3,791, with high Chao1 estimates (4,361\u0026ndash;4,613) suggesting abundant low-frequency taxa. The highest Shannon (29.1) and Simpson (6.7) indices occurred under Org-Low, whereas conventional systems exhibited moderate diversity (Shannon 26.2).\u003c/p\u003e \u003cp\u003eAcross all systems and sites, Ascomycota dominated, followed by Basidiomycota, Chytridiomycota, and Mucoromycota. Organic plots showed relatively greater proportions of Basidiomycota and Chytridiomycota, while Zoopagomycota was uniquely enriched in the Conv-Low treatment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of high-quality ITS sequences, observed ASVs, diversity indices, and dominant fungal phyla across Chuka and Thika. Summary of sequence quality metrics, alpha-diversity indices, and taxonomic composition of fungal communities at the Chuka and Thika sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarming systems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh quality sequences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObserved ASVs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChao\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMost abundant phyla\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChuka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eConv-High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e73,904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3,125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e20.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3,927.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBasidiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChytridiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChuka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eConv-Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e48,403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3,230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e19.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3,859.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChytridiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBasidiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChuka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOrg-High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e57,233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e27.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4,153.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMucoromycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBasidiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChuka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOrg-Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e50,031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3,190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e24.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4,112.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBasidiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChytridiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThika\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eConv-High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e53,176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3,703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e26.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4,509.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMucoromycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChytridiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThika\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eConv-Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e92,333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3,663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e25.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4,526.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZoopagomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBasidiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThika\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOrg-High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e42,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3,791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e27.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4,613.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBasidiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChytridiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThika\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOrg-Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e50,425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3,468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e29.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4,361.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChytridiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBasidiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Drivers of fungal community composition across sites\u003c/h2\u003e \u003cp\u003eAt Thika, canonical correspondence analysis (CCA) revealed significant associations between fungal community structure and soil chemical variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During the long-rain season (season 1), fungal composition was significantly related to soil pH, ammonium-N, and available P (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.002, R\u0026sup2; = 0.1253), which together explained 79.61% of the constrained variance (CCA1\u0026thinsp;=\u0026thinsp;52.48%, CCA2\u0026thinsp;=\u0026thinsp;27.13; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Distinct clustering was observed, with conventional systems aligning with ammonium-N and available P, while organic systems more strongly associated with soil pH. In the short rains season (season 2), soil variables remained significant but explained less variation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.044, R\u0026sup2; = 0.0317), with all constrained variance captured by CCA1 (100%), and weaker clustering across farming systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). At Chuka, none of the measured soil parameters significantly explained fungal community variation in either season, and hence no CCA plots are presented.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Dominant fungal phyla across farming systems, sites, and crop stages\u003c/h2\u003e \u003cp\u003eAcross all sites and seasons, Ascomycota dominated the fungal community, accounting for 40\u0026ndash;80% of total sequences, followed by Basidiomycota (10\u0026ndash;45%), Chytridiomycota (5\u0026ndash;20%), and Glomeromycota (up to 15%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA - D). At Chuka, Ascomycota remained the prevailing phylum in both seasons, particularly under conventional systems, where its relative abundance peaked (50\u0026ndash;70%) at flowering and maturity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA - B). In contrast, Basidiomycota and Glomeromycota were more abundant in organic systems, especially under Org-Low, which showed elevated Glomeromycota during tasseling/silking and maturity stages. Basidiomycota increased notably under low-input conditions during vegetative growth and grain formation.\u003c/p\u003e \u003cp\u003eAt Thika, a similar dominance pattern was observed, though organic systems supported greater representation of Basidiomycota, Chytridiomycota, and Mucoromycota, particularly at later crop stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC - D). Glomeromycota was enriched under Org-Low, while Mucoromycota showed higher abundance in Org-High plots. In contrast, conventional systems maintained consistently higher proportions of Ascomycota across all stages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Fungal alpha-diversity across farming systems at Chuka and Thika\u003c/h2\u003e \u003cp\u003eAt Chuka, fungal richness and diversity were consistently higher under organic farming systems, particularly in Org-High, compared with conventional systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eA - B). In Season 1, Org-High recorded the greatest richness (520 ASVs) and diversity (Shannon 3.9; Simpson\u0026thinsp;\u0026gt;\u0026thinsp;0.94), while Conv-High and Conv-Low showed lower diversity (Shannon 3.3\u0026ndash;3.5). During Season 2, richness again peaked in Org-High (470 ASVs) but declined in Org-Low (370 ASVs). Evenness remained high in organic plots (Simpson 0.95\u0026ndash;1.00) but dropped sharply in Conv-Low (0.50).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt Thika, fungal alpha diversity followed similar trends across seasons, with organic systems consistently exhibiting higher richness and evenness (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eC - D). In Season 1, observed richness ranged from 92 ASVs in Org-Low to 107 ASVs in Org-High, while Shannon values were highest in Org-High (3.5) and slightly lower but comparable across conventional systems (3.3\u0026ndash;3.4). Simpson indices remained uniformly high (\u0026gt;\u0026thinsp;0.94) but were marginally greater under organic management (0.955\u0026ndash;0.960). During Season 2, richness and diversity increased across all systems, again peaking in Org-High (114 ASVs; Shannon 3.6) and remaining lowest in Conv-Low (100 ASVs; Shannon 3.4). Evenness remained high across treatments, though organic plots consistently maintained slightly higher Simpson values (\u0026gt;\u0026thinsp;0.96).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Alpha-diversity across sampling stages at Chuka and Thika\u003c/h2\u003e \u003cp\u003eAt Chuka, fungal richness and diversity varied significantly with crop growth stage, showing clear successional trends across both seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA - B). In Season 1, richness was lowest at tasseling/silking (450 ASVs) and increased progressively to 520 ASVs at maturity, with grain formation stages also supporting high diversity. Shannon indices rose from 3.0 to 4.6, while Simpson values increased from 0.88 to 0.96. During Season 2, a similar pattern was observed, with richness ranging from 350 ASVs at flowering to 480 ASVs at maturity. Both Shannon and Simpson indices increased steadily through the growing period (Shannon 0.5 to 1.0; Simpson 0.65 to 0.98).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt Thika, fungal alpha diversity also varied markedly across crop growth stages, reflecting dynamic community shifts through the crop cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eC - D). In Season 1, richness was highest at grain formation (520 ASVs), moderate at vegetative and tasseling/silking stages (490\u0026ndash;495), and lowest at maturity (460). However, Shannon and Simpson indices increased steadily toward maturity (Shannon 4.1; Simpson 0.95). In Season 2, richness peaked earlier at the vegetative stage (550 ASVs) and declined slightly at flowering and maturity (490\u0026ndash;500), while diversity and evenness continued to rise toward maturity (Shannon 4.5; Simpson 0.98).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Beta diversity of fungal communities across farming systems and crop stages\u003c/h2\u003e \u003cp\u003ePatterns of fungal β-diversity closely mirrored differences in α-diversity and taxonomic composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Principal Coordinates Analysis (PCoA) based on Bray-Curti\u0026rsquo;s dissimilarities revealed clear clustering of communities by site, farming system, and crop stage.\u003c/p\u003e \u003cp\u003eAt Chuka, the first two axes explained 12\u0026ndash;18% and 10\u0026ndash;15% of variation across the cereal (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and potato (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) seasons, respectively. Low-input systems showed greater dispersion at early growth stages. In contrast, high-input systems formed tighter clusters toward maturity.\u003c/p\u003e \u003cp\u003eAt Thika, the first two PCoA axes accounted for 24% and 10% of variation during the cereal season (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) and 16% and12% during the potato season (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Communities in low input systems were clearly separated at vegetative and flowering stages. A partial overlap was observed between Org-High and Conv-Low in mid-season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Dominant fungal taxa and functional guilds across farming systems\u003c/h2\u003e \u003cp\u003eDistinct shifts in dominant fungal taxa and guilds were observed across farming systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Organic systems were enriched in saprotrophic and mutualistic taxa, including \u003cem\u003eMortierella\u003c/em\u003e, \u003cem\u003eGlomus\u003c/em\u003e, \u003cem\u003ePericonia\u003c/em\u003e, \u003cem\u003eCeriporia\u003c/em\u003e, and \u003cem\u003eCollybia\u003c/em\u003e. In contrast, conventional systems harbored higher relative abundances of copiotrophic and opportunistic genera such as \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e, and \u003cem\u003eCladosporium\u003c/em\u003e. Intercrops and mulches further modulated community composition: plots containing \u003cem\u003eDesmodium intortum\u003c/em\u003e and \u003cem\u003eDolichos lablab\u003c/em\u003e supported greater abundances of \u003cem\u003eGlomus\u003c/em\u003e and \u003cem\u003eMortierella\u003c/em\u003e, while the inclusion of \u003cem\u003eCoriandrum sativum\u003c/em\u003e coincided with subtle enrichment of minor phyla.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Differential abundance of fungal genera across crop developmental stages\u003c/h2\u003e \u003cp\u003eLEfSe analyses revealed distinct shifts in fungal community composition across sites, crop stages, and seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eA - D). At Chuka in Season 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), several genera \u003cem\u003eLobulomyces\u003c/em\u003e, \u003cem\u003eFlammulina\u003c/em\u003e, and \u003cem\u003eParaconiothyrium\u003c/em\u003e dominated across crop stages (log₁₀ abundance\u0026thinsp;\u0026gt;\u0026thinsp;3.5). Early crop stages were enriched with \u003cem\u003eBlastobotrys\u003c/em\u003e and \u003cem\u003eMucor\u003c/em\u003e, whereas \u003cem\u003eGuehomyces\u003c/em\u003e and \u003cem\u003eMedicopsis\u003c/em\u003e prevailed at maturity. In Season 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), \u003cem\u003eRhizophlyctis\u003c/em\u003e, \u003cem\u003ePseudogymnoascus\u003c/em\u003e, and \u003cem\u003eHygrocybe\u003c/em\u003e were abundant throughout the cycle, while \u003cem\u003eCeriporia\u003c/em\u003e and \u003cem\u003eCollybia\u003c/em\u003e increased at maturity. \u003cem\u003ePericonia\u003c/em\u003e was prominent during the vegetative stage, whereas members of Hypocreales and Plectosphaerellaceae were most abundant at flowering.\u003c/p\u003e \u003cp\u003eAt Thika, Season 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) communities were dominated by \u003cem\u003ePurpureocillium\u003c/em\u003e, \u003cem\u003eWallemia\u003c/em\u003e, \u003cem\u003eTetraplosphaeria\u003c/em\u003e, \u003cem\u003eGymnopyces\u003c/em\u003e, and \u003cem\u003eSpizellomyces\u003c/em\u003e. Mid-season there was enrichment of \u003cem\u003eGlomus\u003c/em\u003e, \u003cem\u003eBeauveria\u003c/em\u003e, and \u003cem\u003eOliveonia\u003c/em\u003e while \u003cem\u003eRamicandelaber\u003c/em\u003e and \u003cem\u003ePhlyctochytrium\u003c/em\u003e peaked during grain formation. In Season 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eD), \u003cem\u003eMortierella\u003c/em\u003e and \u003cem\u003ePithya\u003c/em\u003e were the most abundant genera, with early-stage enrichment of \u003cem\u003eCladosporium\u003c/em\u003e, \u003cem\u003eSporidiobolus\u003c/em\u003e, and \u003cem\u003eAuricularia\u003c/em\u003e, and late-stage dominance by \u003cem\u003eImaia\u003c/em\u003e, \u003cem\u003eMycena\u003c/em\u003e, and \u003cem\u003eMeruliopsis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMarked spatial and seasonal variation in rainfall and humidity, contrasted with relatively stable temperatures, shaped fungal community dynamics across sites. Chuka, characterized by higher rainfall and humidity, supported more stable and diverse fungal assemblages by sustaining organic matter turnover and reducing desiccation stress. In contrast, Thika\u0026rsquo;s drier conditions imposed stronger environmental filtering, favoring drought-tolerant and sporulating taxa and leading to greater temporal turnover. These climate-driven differences mirror findings from semi-arid agroecosystems where precipitation gradients modulate microbial diversity and function (Delgado-Baquerizo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Management effects on soil fungi must therefore be interpreted within this climatic framework, as moisture-dependent nutrient fluxes and decomposition processes ultimately constrain fungal activity and community stability.\u003c/p\u003e \u003cp\u003eFarming systems exerted strong control over fungal diversity and community evenness. Organic systems supported higher ASV richness and Shannon diversity than conventional systems, confirming that organic inputs enhance habitat heterogeneity and substrate quality (Banerjee et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The dominance of Ascomycota, Basidiomycota, and Chytridiomycota underscores their ecological resilience and core functional roles in soil carbon cycling. Occasional enrichment of Mucoromycota and Zoopagomycota in organic plots likely reflects microhabitats enriched in labile organic matter that favor decomposers and antagonistic taxa (Voř\u0026iacute;škov\u0026aacute; et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bonfante and Venice \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese management effects were further modulated by site conditions. Chuka\u0026rsquo;s humid conditions supported more even, stable communities, whereas Thika\u0026rsquo;s semi-arid environment showed stronger diversity fluctuations with input intensity and season. This aligns with regional observations where organic systems in Kenya\u0026rsquo;s highlands sustain higher fungal diversity and resilience (Karanja et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while arbuscular mycorrhizal (AM) communities in semi-arid zones track moisture and nutrient gradients (Sakha et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These results demonstrate that climatic and edaphic contexts jointly mediate management impacts on fungal community assembly.\u003c/p\u003e \u003cp\u003eIn addition to these climatic and management patterns, soil chemical factors exerted a strong influence on fungal composition, particularly at Thika. Here, ammonium-N and available P emerged as key edaphic drivers, consistent with fungal roles in N assimilation, nitrification, and P mobilization (Laughlin et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Legume intercrops likely enhanced local NH₄\u003csup\u003e⁺\u003c/sup\u003e availability through biological N fixation, supporting AM fungi and other symbionts. The observed relationships with pH, N, and P align with earlier studies linking soil chemistry to fungal richness and functional guild balance (Francioli et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy contrast, at Chuka, fungal diversity appeared less constrained by abiotic factors and more by biotic drivers such as root exudation, rhizodeposition, and organic amendments that shape microbial competition and cooperation (Eisenhauer et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Seitz et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This distinction illustrates how nutrient availability and plant-microbe interactions differentially structure fungal communities under humid and semi-arid conditions.\u003c/p\u003e \u003cp\u003eAcross both sites, the dominance of Ascomycota and Basidiomycota across systems and seasons mirrors global trends in agricultural soils (Rossel et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, management clearly modulated their relative abundance. Ascomycota dominated conventional high-input systems, consistent with their copiotrophic traits and tolerance to mineral fertilizers (Xu et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, Basidiomycota enrichment under organic systems reflects their lignocellulolytic capacity and roles in organic matter stabilization (Purahong et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMinor but functionally critical groups, Glomeromycota, Chytridiomycota, Mucoromycota, and Zoopagomycota were more abundant in low-input systems, suggesting that resource limitation fosters specialized guilds. These include nutrient-exchanging AM fungi (\u003cem\u003eGlomus\u003c/em\u003e), polymer-degrading Chytrids, and predator-antagonistic Zoopagomycetes (Smith and Read \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Spatafora et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Roberts et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Collectively, organic nutrient management supports functionally diverse and ecologically resilient fungal networks capable of buffering environmental stress.\u003c/p\u003e \u003cp\u003eExtending beyond taxonomy, alpha-diversity patterns indicated that organic low and high-input systems maintained higher richness and evenness than low-input conventional systems, driven by organic amendments and supplemental irrigation that stabilized fungal populations. Manure, compost, and crop residues provided continuous carbon inputs, while irrigation reduced moisture stress together promoting substrate heterogeneity and hyphal continuity (Xiang et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsistent with these alpha-diversity patterns, beta-diversity analyses showed greater community convergence in high-input systems and higher dispersion in low-input systems, particularly at early crop stages. These results suggest that nutrient-rich environments homogenize fungal communities, while resource-limited conditions enhance spatial heterogeneity and compositional turnover a pattern consistent with other long-term experiments (Beillouin et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Treseder \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTemporal dynamics further shaped fungal assembly. Community composition shifted predictably with crop developmental stage, reflecting the influence of plant phenology on soil microbial succession. Early vegetative stages favored fast-growing saprotrophs (\u003cem\u003eBlastobotrys\u003c/em\u003e, \u003cem\u003eMucor\u003c/em\u003e) utilizing root exudates, while reproductive and maturity stages enriched symbiotic and ligninolytic taxa such as \u003cem\u003eGlomus\u003c/em\u003e, \u003cem\u003ePericonia\u003c/em\u003e, and \u003cem\u003eCeriporia\u003c/em\u003e (Hartmann et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pajares and Bohannan \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hannula et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The stronger turnover observed at Thika likely reflects moisture-driven amplification of successional shifts under semi-arid conditions, whereas Chuka\u0026rsquo;s humid environment promoted greater temporal stability.\u003c/p\u003e \u003cp\u003eDifferential abundance analyses further clarified these functional trajectories. At Chuka, saprotrophic taxa (\u003cem\u003eLobulomyces\u003c/em\u003e, \u003cem\u003eFlammulina\u003c/em\u003e, \u003cem\u003eCeriporia\u003c/em\u003e) dominated organic plots, whereas opportunistic Ascomycetes (\u003cem\u003eFusidium\u003c/em\u003e, \u003cem\u003eParastagonospora\u003c/em\u003e) were enriched under conventional management consistent with fertilization-driven selection for copiotrophic fungi (Purahong et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). At Thika, enrichment of \u003cem\u003ePurpureocillium\u003c/em\u003e, \u003cem\u003eBeauveria\u003c/em\u003e, and \u003cem\u003eGlomus\u003c/em\u003e in organic systems highlighted functional shifts toward biocontrol and mutualistic guilds. \u003cem\u003ePurpureocillium\u003c/em\u003e acts as both an entomopathogen and plant growth promoter (Khan et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rigobelo et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while \u003cem\u003eBeauveria\u003c/em\u003e contributes to natural pest suppression (Inglis et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). \u003cem\u003eGlomus\u003c/em\u003e enhances P uptake and drought tolerance through AM symbioses (Begum et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Smith and Read \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In contrast, \u003cem\u003eWallemia\u003c/em\u003e and \u003cem\u003eTetraplosphaeria\u003c/em\u003e, prevalent in conventional systems, reflect xerophilic or plant-associated taxa adapted to low-moisture environments (Zhao et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese functional shifts demonstrate that organic inputs not only increase overall diversity but also restructure communities toward taxa that underpin decomposition, nutrient cycling, and biological control, reinforcing ecosystem sustainability.\u003c/p\u003e \u003cp\u003eBeyond nutrient inputs, intercropping and mulching also played significant roles in shaping fungal communities. Leguminous intercrops enhanced fungal richness through rhizodeposition and N contributions (Liu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while the live mulch \u003cem\u003eDesmodium intortum\u003c/em\u003e moderated soil temperature and moisture, sustaining hyphal networks and AM fungal abundance (Midega et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). \u003cem\u003eCoriandrum sativum\u003c/em\u003e, introduced primarily for pest management, may have further modulated rhizosphere interactions through secondary metabolites influencing fungal colonization (Aravinthraju et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings demonstrate how integrated plant diversity enhances soil cover, organic inputs, and microclimatic stability, collectively supporting fungal diversity and ecosystem function.\u003c/p\u003e \u003cp\u003eEnrichment of mutualistic (\u003cem\u003eGlomus\u003c/em\u003e), entomopathogenic (\u003cem\u003eBeauveria\u003c/em\u003e, \u003cem\u003ePurpureocillium\u003c/em\u003e), and ligninolytic (\u003cem\u003eCeriporia\u003c/em\u003e, \u003cem\u003eCollybia\u003c/em\u003e) fungi in organic systems illustrates mechanisms through which ecological intensification improves soil health. AM fungi enhance nutrient uptake, soil aggregation, and drought tolerance (Begum et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while entomopathogens reduce pest pressure and pesticide dependency (Inglis et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Ligninolytic taxa accelerate carbon turnover and contribute to soil organic matter renewal (Janusz et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In sub-Saharan agroecosystems dominated by mineral fertilizers, integrating organic amendments and intercrops can sustain microbial biodiversity, nutrient cycling, and resilience, key principles for climate-adaptive intensification (Tittonell and Giller \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vanlauwe et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, while this study captured robust site- and system-level fungal trends, the pooling of molecular samples limited detection of within-plot variability. However, pooling was necessary to generate representative long-term profiles under resource constraints typical of tropical field research. Future studies integrating replicate-level sequencing, metatranscriptomics, and enzyme assays could more deeply link community composition to functional activity, offering broader insights into how management and climate jointly regulate soil fungal processes.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that soil fungal diversity and community composition in Kenyan tropical agroecosystems are jointly shaped by farming system, input intensity, nitrogen form, crop phenology, and climate variability. After over 15 years of continuous management, organic low and high-input systems sustained greater fungal richness, evenness, and community stability than conventional low-input systems, with irrigation and intercrops mitigating seasonal stress effects. Integration of community, soil chemical, and climatic data identified ammonium-N, available phosphorus, and pH as principal edaphic drivers, while legumes, organic amendments, and coriander intercrops enhanced saprotrophic and mycorrhizal guilds. Fungal community succession followed crop phenology, with saprotrophs dominating early stages and mutualistic and ligninolytic taxa prevailing toward maturity. Although pooling of molecular samples limited within-system resolution, the approach effectively captured long-term, system-level trends in fungal assembly. Collectively, these findings show that organic nutrient integration, adaptive water management, and diversified cropping promote stable and functionally diverse fungal communities, reinforcing the ecological foundations of resilient and resource-efficient tropical agriculture.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was financially supported by the Biovision Foundation (Grant No. 1040), Coop Sustainability Fund (Grant No. 1040), Liechtenstein Development Service (LED) (Grant No. 1040), and the Swiss Agency for Development and Cooperation (SDC) (Grant No. 1040) through the SysCom Kenya project implemented by the International Centre of Insect Physiology and Ecology (\u003cem\u003eicipe\u003c/em\u003e). Added institutional support was provided by the Swedish International Development Cooperation Agency (Sida), the Australian Centre for International Agricultural Research (ACIAR), the Government of Norway, the German Federal Ministry for Economic Cooperation and Development (BMZ), and the Government of the Republic of Kenya. The views expressed in this publication do not necessarily reflect those of the funding agencies.\u003c/p\u003e\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e \u003cp\u003eSWM: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Visualization, Writing - original draft.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank the management of the Kenya Agricultural and Livestock Research Organization (KALRO) for providing the trial site at Thika, and the management of Kiereni Primary School for offering the trial site at Chuka. We are also grateful to Ms. Jane Makena and Mr. James Karanja for their dedicated management of the field trials and their assistance with data collection.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe demultiplexed high-quality sequence reads has been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA), Bio Project ID: PRJNA1228344 and study accession number available for download at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/sra/PRJNA1228344\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/sra/PRJNA1228344\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The metadata, soil chemistry data, input files for QIIME and R analysis scripts were deposited at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.14940274\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.14940274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.5281/zenodo.14940273\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.14940273\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamtey N, Musyoka MW, Zundel C, Cobo JG, Karanja E, Fiaboe KK, Muriuki A, Mucheru-Muna M, Vanlauwe B, Berset E, Messmer MM (2016) Productivity, profitability and partial nutrient balance in maize-based conventional and organic farming systems in Kenya. 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J Fungi 27(5):319. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jof10050319\u003c/span\u003e\u003cspan address=\"10.3390/jof10050319\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amoa","sideBox":"Learn more about [Annals of Microbiology](https://www.springer.com/journal/13213)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/amoa/default.aspx","title":"Annals of Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Farming system, Fungal community dynamics, Long-term trial, Organic farming, Soil fungal diversity","lastPublishedDoi":"10.21203/rs.3.rs-8293848/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8293848/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSoil fungi play central roles in nutrient cycling, organic matter turnover, and plant-soil interactions. However, their responses to contrasting farming systems, nitrogen inputs, crop phenology, and climate variability in tropical agroecosystems remain poorly documented. Long-term datasets from Sub-Saharan Africa are particularly scarce.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study assessed soil fungal diversity and community composition after 15 years of continuous management in the Farming Systems Comparison Trial (SysCom) at two Kenyan sites representing humid highland (Chuka) and semi-arid lowland (Thika) conditions. Four systems were evaluated: Conventional High-input, Conventional Low-input, Organic High-input, and Organic Low-input. Soil samples were collected across major crop growth stages in cereal and potato rotations. Fungal communities were profiled using ITS-based Illumina MiSeq sequencing and analyzed with DADA2 and phyloseq.\u0026nbsp;Diversity metrics, β-diversity, environmental correlations, and differential abundance were assessed in relation to soil chemical properties and climatic variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFarming system, input intensity, nitrogen form, crop stage, and site climate jointly shaped fungal community structure. Chuka\u0026rsquo;s wetter conditions supported more stable and diverse assemblages, whereas Thika exhibited stronger temporal turnover linked to rainfall variability. Organic systems especially those integrating legumes and mulches harbored richer and more functionally diverse fungal communities dominated by saprotrophic, mycorrhizal, and entomopathogenic genera (e.g., \u003cem\u003eMortierella\u003c/em\u003e, \u003cem\u003eGlomus\u003c/em\u003e, \u003cem\u003ePurpureocillium\u003c/em\u003e, \u003cem\u003eBeauveria\u003c/em\u003e). Conventional systems contained higher proportions of opportunistic or xerotolerant taxa such as \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e, and \u003cem\u003eWallemia\u003c/em\u003e. Ammonium-N, available P, and soil pH were the strongest abiotic drivers of community assembly. Fungal succession followed crop phenology, with saprotrophs dominating early crop stages and mutualistic and ligninolytic taxa prevailing toward maturity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAfter 15 years, organic low and high-input systems enhanced fungal richness, evenness, and community stability relative to conventional low-input systems. Integration of organic nutrient sources, legumes, mulches, and adaptive water management promotes diverse and resilient fungal communities in tropical agroecosystems. These results highlight the value of ecological intensification for sustaining soil biodiversity and nutrient cycling under increasing climatic uncertainty.\u003c/p\u003e","manuscriptTitle":"Long-term farming systems and climatic variability shape soil fungal diversity and community structure in Kenyan tropical agroecosystems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-26 19:17:28","doi":"10.21203/rs.3.rs-8293848/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-24T13:35:40+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-24T13:20:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-10T09:33:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Microbiology","date":"2025-12-08T10:34:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amoa","sideBox":"Learn more about [Annals of Microbiology](https://www.springer.com/journal/13213)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/amoa/default.aspx","title":"Annals of Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bd67cbd0-d2a3-441b-8cad-537e6fe5de38","owner":[],"postedDate":"December 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:04:06+00:00","versionOfRecord":{"articleIdentity":"rs-8293848","link":"https://doi.org/10.1186/s13213-026-01852-y","journal":{"identity":"annals-of-microbiology","isVorOnly":false,"title":"Annals of Microbiology"},"publishedOn":"2026-04-13 15:57:55","publishedOnDateReadable":"April 13th, 2026"},"versionCreatedAt":"2025-12-26 19:17:28","video":"","vorDoi":"10.1186/s13213-026-01852-y","vorDoiUrl":"https://doi.org/10.1186/s13213-026-01852-y","workflowStages":[]},"version":"v1","identity":"rs-8293848","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8293848","identity":"rs-8293848","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}