Strobilurin fungicides are among the most heavily deployed crop protection chemicals in the world. First patented in 1996, azoxystrobin ushered in a family of related compounds — including pyraclostrobin, kresoxim-methyl and trifloxystrobin — that now protect cereals, fruits, vegetables, turf and ornamentals against an unusually broad spectrum of diseases, from downy and powdery mildews to rusts and blights. Their mode of action is precise: the molecules bind to the quinol oxidation site of cytochrome b, a key component of the mitochondrial respiratory chain, effectively shutting down fungal energy production. But because respiration is such a fundamental process, scientists have long worried that the chemicals could disturb non-target organisms, including the bacteria and archaea that drive nutrient cycling beneath our feet.

To probe this question, the team constructed laboratory mesocosms — 2-kilogram portions of each soil — and treated them with Quadris at four concentrations of the active ingredient azoxystrobin: a zero-dose control, 2.90 mg per kg, 14.65 mg per kg and 35.0 mg per kg. The mesocosms were maintained at 60 percent of their maximum water-holding capacity and incubated in darkness at 22 degrees Celsius for 120 days, preventing the breakdown of the fungicide by sunlight. Samples were then pulled at intervals of one, 30, 60, 90 and 120 days, allowing the researchers to track both the chemical fate of the fungicide and the metabolic response of the bacterial communities over time.

The centrepiece of the study was a technique called community-level physiological profiling, performed with Biolog EcoPlates. These microplates contain 31 different carbon sources, which the researchers grouped into five chemical guilds: carbohydrates, polymers, carboxylic acids, amino acids and amines/amides. By measuring how vigorously soil bacteria oxidised each substrate, the team could compute the average well colour development, or AWCD — a proxy for overall catabolic activity — and map the detailed physiological fingerprint of each community. The approach reveals not just how active the bacteria are, but which biochemical menus they prefer.

The results were striking. Across both soil types, Quadris depressed the overall catabolic activity of bacterial communities, and the decline persisted for at least two months before a partial recovery and, in some treatments, a late-stage stimulation by day 120. Notably, the magnitude of the drop in AWCD was largely independent of both soil type and fungicide concentration, suggesting a robust, systemic effect on bacterial metabolism. At the same time, the fungicide consistently suppressed the bacterial utilisation of amines and boosted that of amino acids in both soils, while its effects on carbohydrates, carboxylic acids and polymers depended closely on the specific soil environment and dose applied.

The two soils told clearly different stories about the fungicide’s environmental fate. Azoxystrobin dissipated far more slowly in the clay loam than in the loamy sand: the half-life ranged from 36.5 to 86.6 days in the sandy soil but stretched from 130.8 to 212.1 days in the clay-rich soil, classifying the compound as low to moderately persistent in the former and highly persistent in the latter. The researchers attribute this to differences in texture, pH and organic matter — the loamy sand was moderately acidic at pH 5.63, while the clay loam was near-neutral at 6.99 — which likely govern how bioavailable the chemical is to microorganisms. A more bioavailable fungicide, they reason, degrades faster but also exerts greater toxicity.

The fungicide also disturbed the soils’ chemistry. Applications lowered pH significantly in the loamy sand — by up to 14 percent at the highest dose — a change the authors link partly to the formation of azoxystrobin acid, the major degradation product, and possibly to other ingredients in the commercial formulation. Nitrogen pools shifted as well: nitrate concentrations rose overall in both soils, by as much as 70 percent at the low dose in the clay loam, while ammonium fell by 15 percent in the loamy sand and 39 percent in the clay loam. Because nitrification and mineralisation underpin nutrient turnover, such shifts could, the authors suggest, disrupt soil quality and productivity over the long term.

Perhaps the most subtle insight came from analysing metabolic diversity. Rather than losing the ability to use particular carbon sources outright — a change in

Subject of Research: Non-target effects of the fungicide Quadris (azoxystrobin) on bacterial functional profiles in loamy sand and clay loam soils

Article Title: Differences in bacterial functional profiles from loamy sand and clay loam textured soils under fungicide Quadris R impact

Article References: Petkova, M., Kenarova, A., Boteva, S., Georgieva, S., Chanev, C., & Radeva, G. (2022). Differences in bacterial functional profiles from loamy sand and clay loam textured soils under fungicide QuadrisR impact. BioRisk, 17, 31-43. https://doi.org/10.3897/biorisk.17.76939

Image Credits: AI Generated

DOI: 10.3897/biorisk.17.76939

Keywords: azoxystrobin, strobilurin fungicides, soil bacteria, soil health, CLPP, Biolog EcoPlates, soil microbiology, pesticide non-target effects, nutrient cycling, soil texture, catabolic activity, mesocosm experiment

The exercise traces its origins to Pillar Five of the Global Soil Partnership, which calls for harmonized methods, measures, and indicators for sustainable soil management. In November 2017, FAO established the Global Soil Laboratory Network, GLOSOLAN, at its Rome headquarters to strengthen soil testing laboratories worldwide. In Spain, the Spanish Society of Soil Science, the University of Zaragoza, and the Agroenvironmental Laboratory of the Government of Aragon adopted the initiative, building an inventory of soil analysis laboratories and launching a series of intercomparison exercises. The first test, run in 2019 and presented in Madrid on 10 April 2019 by SECS president Jaime Porta, exposed significant discrepancies among laboratories and made clear that standardization was urgently needed.

Porta himself is a central figure in the history of Spanish soil analysis. Beginning as an engineer in the State Agricultural Laboratory Network, the so-called García Faure project, he took charge of the Galicia laboratory in 1972, then moved to the Ebro laboratory in Zaragoza, where he automated soil analysis after studying automated systems at the INRA laboratory in Arras, France, and a cooperative laboratory in the Netherlands. He installed Technicon devices, ancestors of modern segmented continuous flow systems, for serial determination of phosphorus and organic matter, and two Granulostas instruments that processed roughly one hundred soil textures per week. His most enduring contribution was in quality control: working in batches with multiple internal controls and establishing sample exchange networks, practices that anticipated formal laboratory quality protocols by two decades.

For the 2021 exercise, two agricultural soil samples were prepared by drying, disaggregation, and sieving at 2 mm, then homogenized in bulk. The samples, named BIPEA, prepared by the French intercomparison organizer, and HUERTO, prepared directly by the Spanish team, were taken from the superficial 30 cm horizon of cultivated soils. Aliquots of approximately 500 g were dispatched to 29 interested Spanish laboratories, of which 26 responded; 17 had participated in the first test, enabling direct comparison. Each laboratory received an Excel sheet specifying units and significant figures, and results were submitted in triplicate, ideally in different batches, under confidentiality codes ranging from 11 to 301 known only to the laboratory and the two organizers.

The statistical machinery followed the WEPAL methodology of the Wageningen evaluating programmes. Rather than trusting a plain mean, the organizers applied robust statistics: the median and deviation from the median were calculated, anomalous data were eliminated through iterations, and a consensus mean and standard deviation emerged from the accepted values. The acceptance interval was set symmetrically at 95% confidence. Each laboratory result was then converted into a Zscore, the distance from the consensus value expressed in consensus standard deviations. Scores with absolute values below 2 were satisfactory, between 2 and 3 questionable, and above 3 unsatisfactory. A combined Zscore, the root mean square across a laboratory’s results for a parameter or for all parameters, summarized overall performance. Because the combined Zscore distributions were not normal, a logarithmic transformation normalized them before one-factor analyses of variance tested for differences between years.

The 2021 results revealed a striking asymmetry between the two samples. HUERTO produced a higher percentage of satisfactory results than BIPEA across most parameters, mainly because its consensus standard deviations were larger, widening the range of acceptable values. For organic matter, 74.3% of HUERTO results were satisfactory against 45.9% for BIPEA; for assimilable potassium, 71.0% versus 49.3%; and for clay, 83.9% versus 77.8%. Assimilable phosphorus and magnesium were the stars of the exercise, with satisfactory rates reaching 94.0% and 90.0% for HUERTO. Sand content proved the most troublesome textural fraction, with only 64.3% and 67.9% satisfactory results, a consequence of very narrow acceptance ranges that punished even small deviations.

Organic matter emerged as the methodological fault line. Most laboratories measured oxidizable organic matter by the Walkley-Black method, which relies on partial carbon oxidation, but two laboratories used calcination and one used the DUMAS procedure, which determines total organic carbon released by combustion. These total-carbon methods systematically overestimate relative to Walkley-Black, and eleven laboratories posted unsatisfactory combined Zscores for organic matter. Four consistently overestimated across all samples, five underestimated only the BIPEA sample, and one laboratory’s pattern of underestimation in BIPEA and overestimation in HUERTO suggested a transcription error with the samples mixed up. Only three laboratories used the alternative methods, too few for separate statistical treatment, but the discrepancy mirrors problems documented in GLOSOLAN and other international exercises, where comparing organic matter values across methods has repeatedly proven difficult.

Assimilable phosphorus told a brighter story. All laboratories responded, and the Olsen method delivered the highest number of satisfactory results for both samples. The improvement was a direct payoff from the 2019 exercise, which had flagged phosphorus as a weak spot and prompted the triplicate design. In the June 2024 BIPEA test, 77% of Olsen phosphorus results were satisfactory within a narrow acceptance range of 0.047 to 0.075 g/kg; the Spanish exercise achieved 84% with slightly wider ranges. Magnesium showed similarly strong performance, with 22 of 25 responding laboratories rated satisfactory. Potassium, by contrast, remained stubbornly problematic, with 9 of 23 laboratories unsatisfactory, and the internationally recognized BIPEA sample again showed greater heterogeneity that the organizers attribute to less homogeneity in the prepared soil rather than to laboratory error alone.

The comparison between 2019 and 2021 added a longitudinal dimension. Of the 17 laboratories analyzed across both exercises, 10 improved their overall combined Zscore, 3 maintained it, and 4 deteriorated. Ratings shifted from 7 unsatisfactory and 4 questionable laboratories in 2019 to 5 unsatisfactory and 7 questionable in 2021. Phosphorus improved from a questionable to a satisfactory global rating, and magnesium moved from questionable to satisfactory, the only parameter showing a statistically significant decrease in Zscore in the ANOVA at P = 0.088. Clay and phosphorus remained satisfactory across both tests, but potassium and sand stayed unsatisfactory, and organic matter regressed from questionable to unsatisfactory. For laboratories 3 and 4, the ANOVA detected significant improvement at P < 0.05, both posting lower combined Zscores in 2021.

The stakes extend well beyond laboratory pride. The CAP Strategic Plan for Spain requires reliable data on the state of agricultural soils, and evaluating organic matter content in Spanish soils is an explicit objective of its environmental monitoring program, which will collect 128,000 samples. Variability among laboratories, the authors warn, makes analytical results a matter of chance, and applying such results can lead to radically wrong fertilizer and conservation decisions, an unacceptable outcome in the national and European context of soil interest. The findings have been communicated to the Ministry of Agriculture, Fisheries and Food, and on 1 October 2024, Order APA/1044/2024 designated the Agroenvironmental Laboratory of the Government of Aragon as the national reference laboratory for agricultural soil fertility analysis, reviving a harmonization effort that lapsed when the Official Commission on Analysis Methods ceased operations in the early 1990s after powers transferred to the Autonomous Regions.

The authors propose annual two-sample intercomparison exercises covering the usual parameters, sharing of analysis methods to standardize determination techniques even where extraction methods remain official, comparative method studies requiring more participating laboratories and more information with results, and expanded coverage of parameters offered by fewer laboratories, from saturated paste and heavy metals to biological parameters and phytosanitary residues. The two-sample format has proven convenient and efficient, and the triplicate requirement, while complicating exercise management, added limited information. The broader lesson resonates internationally: in the 2024 WEPAL exercise, 337 laboratories participated with soil samples and only 10% of values fell outside acceptance ranges, compared with 28% for BIPEA and 15% for HUERTO in the Spanish exercise, though WEPAL participants were pursuing certification. Repetition of proficiency tests, the evidence shows, is itself a driver of improvement, and regular maintenance of these exercises is now framed as an essential tool for the sustainable governance of soil resources.

Subject of Research: Proficiency testing of Spanish soil laboratories for soil fertility analysis quality and harmonization.

Article Title: Proficiency Testing for Soil Fertility Analysis

Article References: Usón Murillo, A., Betrán Aso, J., & Sampériz Sarvisé, M. (2025). Proficiency Testing for Soil Fertility Analysis. Spanish Journal of Soil Science, 15, Article 14838. https://doi.org/10.3389/sjss.2025.14838

Image Credits: AI Generated

DOI: 10.3389/sjss.2025.14838

Keywords: soil fertility, proficiency testing, Zscore, organic matter, Walkley-Black, Olsen phosphorus, GLOSOLAN, laboratory harmonization, soil texture, CAP Strategic Plan, robust statistics, Spain

The research team, led by Paola Gioacchini and Martina Mazzon, took two contrasting agricultural soils, a loamy Cambisol rich in silt and clay and a sandy Arenosol dominated by coarse particles, and mixed in fragments of a commercial biodegradable mulch film made of Mater-Bi, a starch-based polymer produced by Novamont. The fragments, sized between two and five millimeters, were added at three concentrations: 100, 1,000 and 10,000 milligrams per kilogram of dry soil, corresponding to 0.01, 0.1 and 1 percent by weight. The lowest dose mirrors the estimated annual input of biodegradable mulch material in a realistic agronomic setting, while the highest matches the loading rate recommended by the European standard EN 17033 for testing biodegradability. The soils were then incubated at 23 degrees Celsius, held at 60 percent of their water-holding capacity, for a full twelve months, with microbial measurements taken at the start, at six months and at the end of the experiment.

The clearest signal emerged at the highest dose, and it was dramatic. After one year, microbial biomass carbon, a measure of the living microbial community, was 69 percent higher in the loamy soil and 25 percent higher in the sandy soil compared with untreated controls. Because the biodegradable plastic contains roughly 60 percent carbon and no nitrogen, the highest dose delivered a hefty 6,000 milligrams of carbon per kilogram of soil, essentially a feast for soil microbes. At the six-month mark, the effect was already visible, with microbial biomass reaching approximately 583 milligrams per kilogram in the loamy soil and 238 in the sandy soil. Even though microbial biomass declined in all treatments between six and twelve months, the highest-dose treatment remained significantly elevated above both the control and the lower doses, suggesting that a large pulse of plastic-derived carbon can sustain microbial growth for a remarkably long time.

Quantitative PCR of bacterial 16S rRNA genes and fungal target genes revealed a more nuanced, and partly surprising, story. In the sandy soil, the intermediate dose of 1,000 milligrams per kilogram produced the highest bacterial and fungal gene abundance across the two sampling times, outperforming even the highest dose. The researchers suspect this counterintuitive result relates to carbon losses measured in a companion study: at the highest dose, the sandy soil had released extremely large amounts of carbon dioxide early in the incubation, meaning much of the available carbon may have escaped as gas rather than being converted into microbial cells. In the loamy soil, by contrast, microbial responses were strongly time-dependent, with the highest dose producing peak fungal abundance at six months. The prominence of fungi fits with earlier work showing that fungi are key colonizers and degraders of biodegradable plastics, thanks in part to their ability to produce cutinases, enzymes that accelerate polymer breakdown and carbon release.

Beyond the microbes, the plastic fragments physically restructured both soils. At the end of the year, the researchers used wet sieving to separate each soil into three size classes: macroaggregates larger than 250 micrometers, microaggregates between 250 and 50 micrometers, and the free silt-clay fraction below 50 micrometers. The highest plastic dose increased macroaggregate abundance by 32 percent in the loamy soil and 11 percent in the sandy soil, while sharply reducing the free silt-clay fraction, which fell to just 8.9 percent of the loamy soil’s mass compared with 28.5 percent in the untreated control. The likely mechanism is twofold: the plastic fragments can act as nuclei around which mineral particles, microbial biomass and organic matter coalesce into larger aggregates, and the surge in microbial growth supplies the sticky residues and extracellular substances that bind those particles together.

Crucially, the researchers corrected aggregate weights for sand content, since the sandy soil’s aggregates were up to 90 percent sand by weight, allowing a fair comparison between the two textures. This revealed that the loamy soil inherently holds far more real aggregate material, with 78 percent more macroaggregates and 71 percent more microaggregates than the sandy soil. The lower plastic doses produced weaker and more soil-specific effects: in the loamy soil, the two lower doses increased the recovery of microaggregates to about 50 percent of the soil mass, possibly because macroaggregates formed early in the incubation broke down and freed microaggregates previously locked inside them. In the sandy soil, only the highest dose shifted aggregate composition at all, underscoring how coarse-textured soils resist structural change unless the input is large.

The redistribution of aggregates carried carbon and nitrogen with it. The highest plastic dose increased carbon and nitrogen contents in macroaggregates in both soils, and also in the sandy soil’s microaggregates, while the free silt-clay fraction lost both elements. According to the classic aggregate-turnover model of Six and colleagues, this occlusion of fine particles within macroaggregates may actually protect carbon and nitrogen from microbial attack and mineralization, since physical barriers inside aggregates limit oxygen diffusion and enzyme access. The carbon-to-nitrogen ratio narrowed steadily from macroaggregates down to the silt-clay fraction in both soils, a signature of the predominantly microbial origin of organic matter in the finest fractions, where nitrogen-rich microbial residues accumulate and bind chemically to mineral surfaces.

Stable isotopes added a final layer of insight, and an unexpected one. Nitrogen isotope values, expressed as δ15N, rose progressively from macroaggregates to the finest fractions in both soils, indicating extensive microbial recycling of nitrogen in the smaller fractions, although the plastic itself had no effect on this pattern in the loamy soil and only a modest one in the sandy soil. Carbon isotopes told a different story in each soil: in the loamy soil, the silt-clay fraction was enriched in carbon-13, consistent with repeated microbial processing leaving heavier isotopes behind, while in the sandy soil the finest fraction was actually depleted in carbon-13, possibly reflecting rapid mineralization in which microbes preferentially consumed the lighter, labile compounds and left recalcitrant, carbon-13-depleted molecules stabilized on mineral surfaces. These isotopic shifts were driven by soil texture rather than by the plastic, showing that the soil itself, not the added carbon, governs the pathways of transformation and stabilization.

The authors are careful to note the caveats. The strongest responses occurred at 1 percent plastic by weight, an order of magnitude above realistic annual inputs, and the closed incubation system prevented nitrate leaching, which may have masked some nitrogen isotope effects. Yet the fact that even the lowest, field-realistic dose produced detectable changes in microaggregate recovery and associated carbon and nitrogen is a warning worth heeding. As biodegradable mulches are tilled into fields season after season, their residues will accumulate, and whether the effects documented here persist, amplify or fade over years of repeated incorporation remains an open question. The researchers argue that long-term field studies with repeated applications, focused on realistic doses, are now essential before the soil-biodegradable plastic revolution can be declared truly benign for the ground it is meant to protect.

Subject of Research: Effects of biodegradable plastic mulch residues on soil microbial biomass, aggregation, and carbon and nitrogen stabilization in loamy and sandy soils

Article Title: Contrasting responses of loamy and sandy soils to biodegradable plastic residues: microbial biomass, aggregation, and aggregate-associated carbon and nitrogen

Article References: Gioacchini, P., Mazzon, M., Guerrini, S., Alberoni, D., Cupi, J., Di Gioia, D., & Marzadori, C. (2026). Contrasting responses of loamy and sandy soils to biodegradable plastic residues: microbial biomass, aggregation, and aggregate-associated carbon and nitrogen. Frontiers in Soil Science, 6, Article 1950823. https://doi.org/10.3389/fsoil.2026.1950823

Image Credits: AI Generated

DOI: 10.3389/fsoil.2026.1950823

Keywords: biodegradable plastic mulch, soil aggregates, microbial biomass carbon, soil organic carbon, nitrogen cycling, stable isotopes, soil texture, microplastics, soil biogeochemistry, Mater-Bi, qPCR, carbon sequestration

The research team, led by Tong Wang of Shenzhen University, collected soil samples from three sites across a subtropical agricultural district in southern China, capturing three distinct textures: sandy loam, loam, and clay loam. Each sample was characterized for a full suite of physical and chemical properties, including bulk density, organic carbon, pH, electrical conductivity, field capacity, and permanent wilting point. The soils were then packed into pots under controlled laboratory conditions and exposed to seven salinity levels ranging from 2 to 24 decisiemens per meter, a span chosen to mirror real conditions in coastal Guangdong, where routine irrigation water sits near the low end of that range and seawater intrusion during dry periods can push shallow groundwater toward the top.

The salinity solutions were prepared synthetically from sodium chloride and calcium chloride in a fixed 2:1 molar ratio rather than drawn from natural water sources, a deliberate choice that eliminated uncontrolled ionic confounding. Each pot was leached with its designated saline solution until the drainage water matched the irrigation water’s electrical conductivity, ensuring uniform salt distribution. Gypsum blocks installed before treatment were read with a moisture meter that converts direct current to alternating current to prevent electrode polarization, producing readings proportional to soil water content from dry soil to saturation.

Calibration proceeded by oven-drying the soil, saturating it with saline solutions, and letting it air-dry while periodically recording both block resistance and gravimetric moisture determined by weighing. This produced paired measurements of electrical resistance, which ranged from roughly 4 to 244 kilo-ohms, and true volumetric water content across matric suctions from 0.1 to 2.5 bar. From these data the team defined a correction factor, the difference between predicted and measured water content, and set out to model how that correction depends on salinity, suction, and texture simultaneously.

The modeling tool of choice was Response Surface Methodology, or RSM, a statistical framework designed to untangle the main, quadratic, and interactive effects of multiple continuous variables with relatively few experiments. Because salinity in decisiemens per meter and suction in bar occupy incomparable numerical scales, both factors were coded into a standardized dimensionless range between minus one and plus one, giving regression coefficients equal weight. Rather than using a classical designed experiment such as a central composite design, the researchers treated their systematic laboratory matrix as historical data and fit polynomial surfaces directly using Design-Expert software, confirming that the two independent variables remained orthogonal with negligible multicollinearity.

The central technical finding concerns model order. Linear and two-factor-interaction formulations captured general trends but showed significant lack-of-fit, meaning simpler equations could not represent the combined salinity-suction behavior. Quadratic models improved the fit but still left systematic residuals. Only a cubic polynomial, one that includes third-order terms for each factor, achieved non-significant lack-of-fit tests across all three textures, with adjusted R-squared values of 0.93 to 0.95 and predicted R-squared values within 0.03 of them, indicating genuine generalization rather than over-parameterization. The physical rationale is that electrical transport in a saline porous block follows a dual-regime mechanism: bulk electrolytic conduction through free pore water competes with surface conduction along the electrical double layers lining the gypsum matrix, and the transition between these regimes is asymmetric, not the symmetric parabolic decay a quadratic model assumes.

The fitted coefficients reveal how the correction behaves. In loam soil, salinity carried the largest positive linear coefficient at 0.19, with negative quadratic terms signaling diminishing returns at high salinity and suction, and a negative interaction term showing that simultaneous increases in both variables suppress the corrected moisture. In clay loam, salinity dominated even more strongly with a coefficient of 0.248, while interaction terms were not significant, suggesting the fine pore structure buffers suction effects and lets salinity act largely independently. Sandy loam showed the smallest coefficients overall, consistent with its low specific surface area and limited cation exchange capacity. Across all textures, salinity and its square contributed most to the correction, followed by suction and its square.

The performance metrics are striking. The two-factor cubic models achieved R-squared values of 0.97 to 0.98 with root mean square errors between 0.015 and 0.022 cubic centimeters of water per cubic centimeter of soil, and a coefficient of residual mass near zero, indicating negligible systematic bias. Compared with uncorrected raw gypsum block readings, the calibration reduced RMSE by more than 95 percent across all three textures. Accuracy ranked clay loam highest, then loam, then sandy loam, tracking water-holding capacity and the fine-porosity films that preserve conductive pathways around the sensor. Constrained numerical optimization identified the largest required corrections in clay loam, up to 0.064 cubic centimeters per cubic centimeter at 17.6 decisiemens per meter and 2.5 bar suction, with desirability indices near 0.9.

Some of the raw data patterns are physically revealing in their own right. In clay loam at 6 decisiemens per meter, apparent water content stayed nearly flat between 0.36 and 0.39 across the entire suction range, an apparent plateau that the authors attribute not to a lack of real drainage but to a balance between the resistance increase caused by capillary drying and the conductivity boost from ions concentrated in the shrinking pore water. At moderate salinity, the ionic conductivity of remaining pore water partially compensates for drainage-induced resistance; at higher salinity, thinning water films and increased tortuosity progressively limit ionic transport, producing the strongly nonlinear, non-monotonic response that only the cubic surface can capture.

The practical implications extend well beyond one province. Gypsum blocks remain the backbone of low-cost, distributed irrigation monitoring in greenhouses and smallholder schemes worldwide, precisely because they are cheap, robust, and easy to automate. This study demonstrates that their salt vulnerability is a calibration problem, not a reason to abandon the technology, and that a compact polynomial correction, parameterized per soil texture, can restore accuracy comparable to far more expensive instrumentation. The authors are careful to note the limits: the models are empirical, validated only within the tested ranges of salinity, suction, and texture, and should not be extrapolated beyond them. Field-scale validation across additional soils remains the necessary next step, but for saline agricultural regions from coastal China to arid irrigation districts everywhere, the message is that a seventy-year-old sensor, armed with the right mathematics, can still compete at the precision frontier.

Subject of Research: Response surface calibration of gypsum block soil moisture sensors under salinity and matric suction

Article Title: A response surface-based calibration framework for accurate soil water content determination under saline conditions: a case study in Guangdong Province, China

Article References: Wang, T., Tong, R., Chen, Y., & Wu, X. (2026). A response surface-based calibration framework for accurate soil water content determination under saline conditions: a case study in Guangdong Province, China. Environmental Earth Sciences, 85(16), Article 426. https://doi.org/10.1007/s12665-026-13168-w

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13168-w

Keywords: soil water content, gypsum blocks, sensor calibration, soil salinity, matric suction, response surface methodology, Guangdong, irrigation management, soil texture, electrical resistance, saline agriculture, soil physics

The problem the researchers set out to solve is a familiar one in the geosciences. Digital soil mapping, or DSM, uses quantitative models trained on field observations and environmental data to produce continuous, pixel-based maps of soil properties. These products guide precision agriculture, irrigation scheduling, flood risk assessment, groundwater management, and the parameterization of land surface models used in climate simulation. Yet existing DSM products frequently suffer from a subtle but consequential flaw: they regress toward the mean. Random Forest regressors and similar algorithms tend to underestimate high values and overestimate low ones, smoothing away the very spatial variability that makes soils interesting and management-relevant. The result is a map that looks plausible on average but misses the extremes that matter most for real-world decisions.

Traditional soil surveys, which underpin many legacy map products, add their own complications. Polygon-based surveys describe soil properties using low, representative, and high values for each map unit, often approximating distributions with simple triangular shapes that can oversimplify multi-modal reality. Synthetic sampling within map units can even introduce artificial spatial patterns that propagate noise into downstream products. Geostatistical alternatives such as kriging require stationarity assumptions that break down in heterogeneous landscapes, sometimes producing telltale bull’s-eye artifacts around isolated observations. Bayesian updating offers a principled alternative but demands that analysts specify prior distributional forms and likelihood parameters in advance, a burden that grows with every soil property and depth layer.

The IRC framework takes a different route. It begins with a prior probabilistic soil map generated by a pruned hierarchical Random Forest method that leverages National Cooperative Soil Survey data and georeferenced soil taxa information. Each pixel in the prior map stores not a single value but a distribution: a set of candidate soil property values with associated probabilities, with the twelve most probable values retained in the California implementation. When new georeferenced soil profiles become available, from databases such as the World Soil Information Service or from fresh field campaigns, the method aligns them spatially and vertically with the prior predictions and calculates residuals, the differences between what was observed and what was predicted, depth by depth.

What happens next is the heart of the innovation. A Random Forest regressor is trained to learn the relationship between those residuals and a rich feature space combining static environmental covariates, including Sentinel-1 and Sentinel-2 satellite imagery, GOES land surface temperature, and terrain attributes, with dynamic soil covariates that are updated after every iteration. These dynamic features include the depth centroid, the weighted mean of the current property distribution, the top probable values themselves, the probabilities attached to them, and crucially the differences between the modeling layer and the other five depth layers. By correcting one depth layer at a time and feeding the updated values back into the feature space, the algorithm preserves vertical correlations within soil profiles while gradually squeezing bias out of the entire column. The process repeats, with layers selected randomly, until a convergence criterion is met: the median change in residuals must fall below the fifth percentile of the residual distribution seen across the last three iterations.

Physical realism is enforced throughout. If adding a residual pushes a value past a physical bound, for example a sand content above 100 percent, the excess is capped and redistributed proportionally among the other candidate values at that pixel. For particle size fractions, an additional compositional constraint ensures that sand, silt, and clay always sum to 100 percent after correction. The authors acknowledge that modeling the three texture fractions independently rather than as a joint compositional vector may not fully capture their interdependencies, but the constraint scheme keeps posteriors physically realizable, something unconstrained regression-based bias correction cannot guarantee.

The California case study evaluated six properties, sand, silt, and clay content, pH, oven-dry bulk density, and soil organic matter, across six depth intervals reaching two meters, drawing on more than 11,000 observations per property from WoSIS, the National Soil Characterization Database, and new field measurements in the Salinas Valley. The gains were striking. The coefficient of determination for sand rose from 0.35 in the prior map to 0.84 after correction, silt climbed from 0.19 to 0.79, and clay from 0.25 to 0.84. Posterior soil texture predictions achieved a root mean square error below 10 percent mass fraction, a 7 percent relative reduction over the priors. pH predictions reached an R-squared of 0.945. Even the most challenging properties, soil organic matter and bulk density, which shift over time with land use and are notoriously hard to model, moved from near-zero prior R-squared values to 0.61 and 0.70 respectively.

Bootstrap analysis with 1,000 resamples confirmed that the error reductions were statistically significant across nearly all property-depth combinations, the sole exception being deep soil organic matter between 100 and 200 centimeters. The method also restored spatial structure that the priors had suppressed. Directional semi-variograms in the Central Valley showed that posterior predictions better reproduced the observed sill and effective correlation range, meaning the corrected maps recovered real landscape heterogeneity rather than an over-smoothed average. Vertical soil profiles stratified by land use showed the largest gains in wetlands, consistent with the known weakness of polygon-based surveys in ecologically complex, under-sampled environments.

Perhaps most importantly for risk-sensitive applications, the uncertainty estimates themselves improved. The width of the 90 percent prediction intervals shrank across most of California, particularly in agricultural and desert regions for sand content and across the Sierra Nevada for clay, and fan-chart analysis showed the posterior prediction bands shifting toward observed values in the extreme deciles where priors were most overconfident. A direct comparison against a single-pass correction revealed why the iteration matters: for the top 1 percent of deep soil organic matter values, the iterative approach cut mean absolute error by 25.6 percent relative to one-shot correction, with the advantage growing steadily as values became more extreme.

The authors are candid about limitations. Evaluation relied on out-of-bag samples that may sit close to training clusters, so performance estimates could be optimistic where samples are spatially autocorrelated. The method does not yet account for temporal change in dynamic properties, and the conversion of soil organic carbon to organic matter using the traditional van Bemmelen factor introduces its own uncertainty. Computationally, correcting a 1-kilometer map of California takes roughly two hours, but finer resolutions would demand far more resources. Even so, the framework offers something digital soil mapping has lacked: a scalable pathway for maps to evolve. As new soil observations emerge from field campaigns and sensor networks, IRC can assimilate them without rebuilding the underlying model, pointing toward continuously updated, locally refined soil information for the entire contiguous United States.

Subject of Research: Iterative residual correction for improving probabilistic digital soil property maps

Article Title: Improvement of soil properties maps using an iterative residual correction method

Article References: Xu, C., Scudiero, E., Anderson, R., & Chaney, N. (2026). Improvement of soil properties maps using an iterative residual correction method. SOIL, 12(1), 665-687. https://doi.org/10.5194/soil-12-665-2026

Image Credits: AI Generated

DOI: 10.5194/soil-12-665-2026

Keywords: digital soil mapping, iterative residual correction, Random Forest, soil properties, uncertainty quantification, California, soil texture, soil organic matter, machine learning, soil surveys, land surface modeling, precision agriculture

The research focused on Yishi Forest in the Judean foothills, a hot Mediterranean site receiving about 510 millimeters of rain per year, where a thin soil layer of just 5 to 20 centimeters sits atop limestone bedrock. The team, led by Stav Livne-Luzon and Assaf Yaakobi under the supervision of Tamir Klein, compared plots dominated by three functionally distinct native species: the canopy conifer Pinus halepensis, the sub-canopy broadleaf Quercus calliprinos, and the understory woody shrub Pistacia lentiscus. Some plots contained only one dominant species, while others were mixed stands containing all three. Beneath every selected tree, and in the open gaps between canopies, the researchers collected soil from the top 10 centimeters, gathering 104 samples in total across the forest.

The central analytical move was to split soil organic carbon into two fractions with very different personalities. Mineral-associated organic carbon, or MAOC, consists of small organic molecules such as microbial necromass and root exudates that bind to clay and silt particles, shielding them from decomposition for periods that can stretch to millennia. Particulate organic carbon, or POC, is made of larger, tougher plant compounds like lignin and cellulose that resist chemical binding but remain exposed to microbial attack, turning over within decades. Globally, MAOC makes up roughly 65 percent of soil organic carbon, so understanding what controls each fraction is essential for any serious carbon accounting.

The results were unambiguous at the microsite scale. Soils under tree canopies held up to twice as much organic carbon as soils in nearby forest gaps, and Quercus plots stored 10 to 30 percent more carbon than either Pinus or Pistacia plots. Across the site, carbon concentrations ranged from 18.3 to 187.1 grams of carbon per kilogram of soil, with the highest values under oaks and the lowest in a gap within a mixed plot. When the researchers converted concentrations into stocks, accounting for the site’s shallow soils and abundant stones, mixed Quercus plots approached nearly 3 kilograms of carbon per square meter, compared with about 1 kilogram in monospecific gaps and Pistacia plots.

But the real story emerged when the fractions were separated. Trends in total soil carbon tracked the labile POC fraction almost entirely, and POC tended to be higher in mixed stands than in single-species ones. MAOC, by contrast, showed no significant differences among forest types or microsites, because it had effectively run out of room. The mineral-associated fraction displayed a classic saturation pattern, plateauing at around 45 grams of carbon per kilogram of soil, a figure closely matching saturation values of roughly 40 grams reported for European soils. At low-carbon sites, MAOC comprised nearly 80 percent of the soil carbon pool, but at the richest sites its share collapsed as POC came to account for more than 90 percent of the total.

What sets that ceiling? The answer, in this system, is mineralogy. MAOC increased linearly with clay and silt content across the plots, and a 95th-percentile regression yielded a site-specific carbon capacity of 50.3 grams of carbon per kilogram of clay plus silt. That value sits close to the 48 grams estimated for low-activity mineral soils globally, which fits the site’s carbonate-rich geology, since carbonate minerals are generally less reactive than the iron and aluminum oxides found in more volcanic soils. Notably, when the team applied the same calculation to a large dataset of dryland forests worldwide, the capacity came out substantially higher at 70.2 grams, suggesting that at Yishi Forest it is the minerals themselves, not the arid climate, that limit long-term carbon storage.

The microbial dimension of the study produced equally thought-provoking results. Using a high-resolution sequencing approach that targeted six variable regions of the 16S rRNA gene and enriched the reference database with full-length sequences from PacBio long-read sequencing, the team profiled bacterial communities across seasons and microsites. The sequencing revealed 1,531 amplicon sequence variants spanning 20 phyla, along with 413 novel full-length sequences representing bacteria absent from existing databases. Bacterial communities beneath tree canopies turned out to be subsets of the richer communities found in open gaps, implying that the altered soil conditions under trees act as an environmental filter, winnowing the microbial pool down to favored specialists.

Each tree species appeared to recruit its own microbial entourage. Soils under pines hosted proportionally more Proteobacteria, while oak soils were enriched in Firmicutes. Under pine canopies, genera such as Pseudolabrys, Bradyrhizobium, and Tardiphaga increased in abundance, while oak canopies favored Bradyrhizobium, Nocardioides, and a nitrosomonad genus, taxa associated with nitrogen fixation, nitrification, and the degradation of aromatic compounds. Intriguingly, however, neither bacterial richness nor diversity correlated consistently with any carbon pool, and microbial richness dropped out of the final structural equation model entirely. In this ecosystem, at least, the sheer number of microbial species matters less for carbon storage than the physical and chemical stage on which they perform.

That structural equation model, which explained 43 percent of the variation in MAOC and 66 percent in POC, painted a coherent picture of competing controls. Clay and silt content exerted the strongest direct effect on MAOC, while soil properties and microsite dominated POC, with a direct microsite effect hinting that belowground inputs such as root exudation and root turnover differ among species in ways aboveground litter alone cannot explain. Litter density showed a negative direct path to POC that was offset by a positive indirect route through soil properties, leaving no net effect. Taken together, the model confirms that labile and mineral-associated carbon pools answer to different masters.

The practical bottom line is quantified and sobering in equal measure. Mixed forests in this system were estimated to store approximately 6.1 megagrams of carbon per hectare more than monospecific pine stands, with the difference residing almost entirely in the fast-cycling POC pool, particularly where MAOC was already near saturation. As afforestation and forest management are promoted as leading natural climate solutions, the study suggests that planting diverse stands can meaningfully raise soil carbon stocks in dry regions, but that the durable, century-to-millennium fraction of that carbon is ultimately bounded by soil texture. In shallow, carbonate-rich Mediterranean soils approaching their mineralogical limits, the most reliable carbon gains from forest diversity may be real but transient, a finding that planners banking on soils as permanent carbon vaults would do well to heed.

Subject of Research: Tree species composition, microbial communities, and soil texture controls on particulate and mineral-associated soil organic carbon in Mediterranean forest soils

Article Title: Tree-microbe-soil interactions affecting soil organic carbon fractions in Mediterranean forest soils

Article References: Livne-Luzon, S., Yaakobi, A., Yalin, D., Sade, D., Dener, E., Oppenheimer-Shaanan, Y., & Klein, T. (2026). Tree-microbe-soil interactions affecting soil organic carbon fractions in Mediterranean forest soils. Biogeosciences, 23(18), 6763-6779. https://doi.org/10.5194/bg-23-6763-2026

Image Credits: AI Generated

DOI: 10.5194/bg-23-6763-2026

Keywords: soil organic carbon, Mediterranean forests, mineral-associated organic carbon, particulate organic carbon, tree diversity, mixed forests, soil microbiome, carbon sequestration, soil texture, carbon saturation, Pinus halepensis, Quercus calliprinos

The study, published in the journal SOIL, examined greenhouse soils in two intensive vegetable-growing regions of Liaoning Province, where protected agriculture has flourished since the 1970s and 1980s. The team, led by Boyuan Tan of Shenyang Agricultural University, compared loam soils in one region with sandy loam soils in another. Both soil types had received roughly 41 tonnes of cow and pig manure compost per hectare every year, supplying about 11.6 tonnes of carbon per hectare annually, alongside standard chemical fertilizers and drip irrigation. By sampling plots of different ages, the researchers effectively compressed five decades of soil history into a single snapshot.

The headline finding is striking. In the loam, soil organic carbon content stabilized after about twenty years of manure application, settling between 33.1 and 37.0 grams of carbon per kilogram of soil, with a total stock of roughly 77.5 tonnes of carbon per hectare. In the sandy loam, equilibrium arrived almost immediately: within just two years, carbon content plateaued at 13.3 grams per kilogram, a stock of about 33 tonnes per hectare. Under identical manure inputs, the loam ended up storing two to three times more carbon than its sandier counterpart, and the gap widened rather than narrowed as the years accumulated.

Even more sobering was the fate of the carbon itself. The researchers calculated a sequestration rate, expressing how much of the cumulative manure carbon actually remained in the soil. In the sandy loam, nearly 55 percent of the added carbon was retained after two years, but that figure collapsed to just 4 percent after three decades. The loam fared better, holding a sequestration rate of 16.1 percent at the twenty-year mark, yet even there the rate steadily declined, falling to 7.2 percent after nearly fifty years. In other words, the warmer, more humid greenhouse environment, with temperatures often between 18 and 35 degrees Celsius and humidity around 65 to 80 percent, kept microbes digesting organic matter so vigorously that most of the applied carbon simply returned to the atmosphere as carbon dioxide.

To understand why the two soils behaved so differently, the team dissected soil organic carbon into its component pools. Labile fractions, such as easily oxidizable carbon, dissolved organic carbon, and microbial biomass carbon, turn over quickly and respond to management within seasons. Particulate organic carbon, split into coarse and fine fractions, represents partially decomposed plant and manure residues. Mineral-associated organic carbon, by contrast, is the gold standard of soil carbon storage: organic molecules bound to clay and silt particles, protected from decomposition for years or even centuries. The loam held significantly higher amounts of easily oxidizable carbon, particulate carbon, and mineral-associated carbon than the sandy loam, and its mineral-associated pool kept growing for decades, peaking at 21.83 grams per kilogram after nearly fifty years of manure application.

The sandy loam told a very different story. Its mineral-associated carbon plateaued after only four years, hovering between 6.21 and 7.54 grams per kilogram, and its fine particulate carbon actually declined over time, dropping from a peak of 3.08 grams per kilogram at two years to 1.81 grams per kilogram by year thirty. Correlation analysis revealed why: in the loam, total soil organic carbon was strongly and positively linked to every major fraction, suggesting a cooperative pipeline in which labile and particulate carbon were progressively converted into stable mineral-bound forms. In the sandy loam, carbon was positively correlated only with easily oxidizable and mineral-associated carbon, and negatively with fine particulate carbon, hinting that fresh particulate inputs may even have stimulated the loss of existing stable carbon through priming effects.

The molecular details came from solid-state carbon-13 nuclear magnetic resonance spectroscopy, a technique that reveals the chemical architecture of soil organic matter. In the loam, long-term manure application enriched aromatic and carbonyl carbon, functional groups that resist microbial attack, and raised the aromaticity index by nearly 250 percent compared with unfertilized control fields, an overall gain of about 55 percent in carbon stability. The sandy loam moved in the opposite chemical direction: alkyl carbon increased while aromatic carbon declined, shifting the soil toward hydrophobic, waxy compounds rather than genuinely recalcitrant ones. Structural equation modeling then showed that in the loam, particulate carbon fed the mineral-associated pool, whereas in the sandy loam that pathway was essentially broken, leaving easily oxidizable carbon as the only meaningful precursor of stable carbon.

These results illuminate two distinct stabilization mechanisms operating in the same climate and under the same management. Fine-textured loams, rich in clay and silt, offer abundant mineral surfaces that adsorb organic molecules and shield them from microbes, allowing even metabolically active carbon fractions to contribute to long-term storage. Coarse sandy loams, with limited binding sites, can only protect carbon through hydrophobicity, a weaker defense that depends on the continuous replenishment of fresh labile inputs. The authors conclude that the long-term sequestration rate is not determined by the quantity of external inputs alone but by the efficiency with which labile fractions are transformed into mineral-associated organic carbon, an efficiency that is fundamentally governed by soil composition and mineral properties.

For greenhouse growers, the practical message is that manure is not a one-size-fits-all solution. On sandy soils, piling on more manure year after year yields diminishing returns, because the soil simply cannot hold what it is given. Matching application rates to soil texture, and investing in practices that build mineral protection capacity, may achieve more than sheer volume. The study also carries a caution for carbon accounting: greenhouse agriculture, with its accelerated decomposition, may store far less carbon than open-field experiments suggest, meaning that climate benefits attributed to manure application in protected cultivation could be systematically overestimated. The authors note that future work combining long-term experiments with microbial sequencing and mineralogical analysis will be needed to fully untangle how soil texture, mineral chemistry, and microbial communities jointly decide whether applied carbon becomes a lasting asset or a fleeting guest.

Subject of Research: Long-term soil organic carbon accumulation and stabilization under manure application in greenhouse loam and sandy loam soils

Article Title: Differences in organic carbon fractions and stability explain limited accumulation in loam and sandy loam under greenhouse conditions

Article References: Tan, B., Yang, L., Xiao, X., Li, C., Tan, J., An, N., Meng, L., Yi, Y., Qian, F., Li, N., Liu, X., Li, S., & Han, W. (2026). Differences in organic carbon fractions and stability explain limited accumulation in loam and sandy loam under greenhouse conditions. SOIL, 12(2), 855-869. https://doi.org/10.5194/soil-12-855-2026

Image Credits: AI Generated

DOI: 10.5194/soil-12-855-2026

Keywords: soil organic carbon, greenhouse agriculture, manure application, mineral-associated organic carbon, particulate organic carbon, soil texture, carbon sequestration, 13C NMR spectroscopy, loam, sandy loam, soil fertility, carbon stabilization

The research, led by Kalyanjit Sarmah of the Assam Agroforestry Development Board together with colleagues at Assam University, the Assam State Space Application Centre, and Mangaldai College, employed a geographic information system based multi-criteria decision-making approach to evaluate land suitability for banana plants. Rather than treating the district as a uniform banana-friendly zone, the team dissected it into seven thematic layers: soil texture, soil depth, soil pH, soil drainage, soil stoniness, slope, and groundwater status. Each layer captured a different physical dimension of the landscape, and each was weighted according to its influence on banana productivity before being integrated through a weighted overlay technique in ArcMap 10.5.

The weighting scheme is where the agronomic insight lies. Soil texture, drainage, and stoniness received the highest weights of 4, 3, and 2 respectively, because these properties directly control soil aeration, root penetration, and water retention, the three physiological gatekeepers of banana growth. Bananas demand deep, well-drained, friable soils; waterlogged or stony profiles suffocate their root systems and stunt pseudostem development. By contrast, soil depth, pH, slope, and groundwater availability were assigned a weight of 1, reflecting their comparatively uniform and generally favorable distribution across the district. This deliberate emphasis on the most limiting factors, guided by the National Bureau of Soil Survey and Land Use Planning framework and consultations with the State Horticulture Department, ensured the model highlighted genuine constraints rather than averaging away important differences.

Goalpara’s physical geography makes such an analysis essential. The district sits on the southern bank of the Brahmaputra River in the Lower Brahmaputra Valley, within Agro-Ecological Region 15 of the Eastern Himalayan zone. Its climate is humid subtropical, with average maximum temperatures between 24 and 30 degrees Celsius, winter minima between 12 and 24 degrees, and monsoon rainfall from June through September that can push temperatures as high as 36 degrees. Elevations reach 510 meters, and the terrain ranges from flat alluvial plains near the river to undulating uplands toward the Meghalaya border. This heterogeneity means that two villages only a few kilometers apart can present radically different prospects for a banana plantation.

When the weighted layers were combined, the district resolved into four suitability classes: highly suitable, moderately suitable, marginally suitable, and not suitable. The headline finding is that 4,124.61 hectares of Goalpara qualify as highly suitable for banana cultivation, but that land is strikingly concentrated. Matia Circle alone contains 2,476.62 hectares of prime banana territory, followed by Dudhnoi with 1,418.56 hectares, Rangjuli with 154.66 hectares, Lakhipur with 49.31 hectares, and Balijana with just 25.46 hectares. The highly suitable zones share a signature: flat terrain, deep and well-drained alluvial soils, and low stoniness, precisely the conditions the weighting scheme predicted would matter most.

The spatial contrast between circles tells a story about micro-topography and soil management. Marginal and unsuitable areas are constrained by poor drainage, higher soil stoniness, and unfavorable local relief, conditions that raise input costs and depress yields even under otherwise favorable subtropical skies. Most striking is Balijana Circle, which contains approximately 3,838.27 hectares of marginally suitable land. The study’s authors argue this represents a substantial opportunity rather than a dead end: with targeted interventions such as improved drainage systems, better soil management, and appropriate agronomic practices, these marginal lands could be upgraded into higher suitability classes, effectively unlocking a second banana heartland within the district.

The findings align closely with international evidence on what governs banana productivity. Research in Venezuela by Olivares and colleagues demonstrated that physical and biological soil properties are key determinants of banana yields, while a biophysical land evaluation in Andhra Pradesh by Bhaskar and coauthors identified soil depth, texture, drainage, alkalinity, and organic carbon as critical constraints, noting that shallow soils, coarse fragments, and poor drainage significantly limit productivity, particularly in semi-arid conditions. The Goalpara study echoes these conclusions from a humid subtropical setting, reinforcing the idea that soil texture, drainage, and stoniness are near-universal arbiters of banana success across climates.

But the Venezuelan comparison also exposes a gap in the model. Olivares and colleagues showed that Fusarium wilt, including the devastating TR4 strain, is highly sensitive to agro-environmental conditions, especially excessive moisture and poor drainage. The same waterlogged conditions that constrain banana roots in Goalpara could also foster disease outbreaks, meaning a suitability map built solely on soil and terrain parameters may overestimate sustainable cultivation potential. The authors acknowledge this limitation candidly, noting that their model excludes soil biological properties and phytosanitary risks, and they call for future assessments to integrate disease-risk mapping alongside soil suitability to improve the robustness of site selection.

Management factors add another layer of complexity. Agronomic literature, including work by Arvanitoyannis and Mavromatis on banana cultivation practices and studies of crop structure in Mexican maize systems, indicates that genotype selection, planting density, field preparation, and post-harvest handling can explain substantial yield variability, sometimes more than environmental variables themselves. The Goalpara model captures where the land is capable of supporting bananas, but not how cultivar choice or planting strategy might interact with those capabilities. The authors suggest that future work should weave agronomic and management dimensions into the environmental framework, moving from a static land inventory toward a dynamic decision-support tool.

The practical implications are considerable. India produces roughly 29.78 million metric tons of bananas annually, about 29.19 percent of global production according to FAO figures, and Assam alone had 47,900 hectares under banana cultivation in 2010-11. Yet Goalpara, the state’s leading banana-producing district, remains underutilized relative to its potential, with Matia Circle’s vast highly suitable area producing well below capacity. The study also points to land-use strategy: not all land categories are appropriate, and cultivable wastelands such as scrubland and grassland offer the greatest scope for expansion. If the district optimized its suitable land in an initial development phase, the authors estimate it could potentially meet the banana demand of the entire state of Assam without external supply, while strengthening integration with the Daranggiri market. In a region where traditional farming systems are increasingly vulnerable to climate stresses, a data-driven map of where the land says yes, and where it says not without help, may prove as valuable as any new cultivar.

Subject of Research: GIS-based agro-climatic land suitability analysis for banana cultivation in Goalpara district, Assam, India

Article Title: Spatial analysis of agro-climatic land suitability for banana plants in Goalpara district, Assam, North East India

Article References: Sarmah, K., Kashyap, P. J., Sarma, R., & Sarma, P. K. (2026). Spatial analysis of agro-climatic land suitability for banana plants in Goalpara district, Assam, North East India. Discover Soil, 3(1), Article 99. https://doi.org/10.1007/s44378-026-00262-5

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00262-5

Keywords: banana cultivation, land suitability analysis, GIS, multi-criteria decision-making, Goalpara district, Assam, soil texture, soil drainage, weighted overlay, Northeast India, agro-climatic zoning, sustainable agriculture

The scale of the study is what sets it apart. Nigeria spans roughly 923,770 square kilometres and seven distinct agroecological zones, from the Humid Forest in the south, where annual rainfall exceeds 2,000 millimetres, to the Arid/Sahel in the north, where precipitation drops below 500 millimetres. Between 2022 and 2023, field teams following the harmonised Soils4Africa monitoring framework collected samples from 390 primary sampling units, each a 2 by 2 kilometre grid cell, using a hierarchical probabilistic design. Within each unit, subsamples were taken at the centre and at three equidistant points along a two-metre circle in a Y-shaped configuration, capturing micro-scale variability before compositing. Samples came from two depths, 0 to 20 centimetres and 20 to 50 centimetres, and were analysed at the Agricultural Research Council Soil Testing Facility in Pretoria, South Africa, following international GLOSOLAN guidelines. Plant-available phosphorus was measured with the Mehlich-3 extraction, a standard test for tropical and subtropical soils.

To untangle the web of interacting soil properties, the researchers deployed two complementary statistical approaches. First, a Random Forest regression model ranked candidate predictors of available phosphorus, including soil organic carbon, pH, exchangeable acidity, sand, silt, clay, and oxalate-extractable iron and aluminium. The model achieved moderate predictive performance, with an out-of-bag R-squared of 0.32 and a root mean square error of 6.68 milligrams per kilogram. Second, structural equation modelling tested hypothesised direct and indirect pathways among the variables, using standardised composite indices for fine texture, oxide content, and acidity. The structural equation model explained 29 percent of the variation in available phosphorus and, crucially, allowed the team to separate direct effects from indirect ones, such as the way texture influences phosphorus by shaping organic carbon accumulation and oxide content.

The headline result is unambiguous: soil organic carbon was the most important predictor of available phosphorus in both analyses. Organic matter feeds phosphorus into the soil solution through mineralisation and microbial turnover, and decomposition products known as organic ligands compete with phosphate for binding sites on reactive mineral surfaces, effectively prising the nutrient loose. But the relationship is not linear. Partial dependence plots from the Random Forest model showed that predicted available phosphorus rose steadily with soil organic carbon and then levelled off at higher values. This plateau suggests that once organic matter is abundant, other constraints, such as sorption capacity, mineral surface reactivity, or microbial demand for the nutrient, become limiting, so each additional increment of carbon delivers diminishing returns for phosphorus availability.

Texture told a subtler and, at first glance, paradoxical story. Finer-textured soils, rich in clay and silt, were positively associated with both soil organic carbon and oxide contents, reflecting the well-documented capacity of fine particles to stabilise organic matter through aggregation and mineral association. Yet the total association between fine texture and available phosphorus was negative. The explanation lies in surface chemistry: clay-rich soils and their associated iron and aluminium oxides present vast reactive surfaces that adsorb phosphate strongly, holding it out of the soil solution where roots can access it. In other words, fine-textured soils may store larger total pools of phosphorus, but a smaller fraction of that pool remains immediately available to crops. The oxalate-extractable iron and aluminium, which represent poorly crystalline oxide phases, were positively associated with Mehlich-3 extractable phosphorus, indicating that these oxides act both as phosphorus-retaining surfaces and as reservoirs of extractable phosphorus that the Mehlich-3 reagent can partially recover.

Acidity added a further layer of control. The composite acidity index, which combined exchangeable acidity and pH, showed a negative direct association with available phosphorus, consistent with classical soil chemistry. In acidic conditions, aluminium and iron become more reactive, phosphate sorption intensifies, and insoluble phosphorus compounds precipitate out of solution. However, the study found that acidity was a secondary rather than dominant source of phosphorus variability across Nigeria. Most of the sampled soils were only moderately acid, and exchangeable acidity becomes chemically significant mainly where pH is low enough for exchangeable aluminium and hydrogen to matter. The authors caution that acidity-related constraints are therefore likely to be concentrated in the more acidic subset of Nigerian soils rather than operating uniformly across the country.

The spatial patterns revealed by the survey reinforce the mechanistic findings. Soil organic carbon and available phosphorus were greatest in the Humid Forest zone and lowest in the Arid/Sahel, while pH showed the opposite trend and exchangeable acidity peaked in the humid zone. Clay content and oxalate-extractable iron and aluminium generally increased toward more humid and mid-altitude environments, revealing a coordinated gradient in organic matter, soil reaction, texture, and oxide properties. K-means clustering identified three broad soil groups: a low-fertility sandy group with low organic carbon, few oxides, and the least available phosphorus; a moderate-fertility group; and a high-fertility clay-rich group with higher organic carbon, oxides, and phosphorus but also greater acidity. Projected into principal component space, these groups formed a continuum rather than sharply discrete classes, underscoring how smoothly soil properties grade into one another across the landscape.

Land use emerged as a powerful influence as well, affecting every measured soil property, whereas depth effects were confined mainly to soil organic carbon, available phosphorus, sand, and clay, with topsoil richer in carbon and phosphorus than the subsoil. Farming system classifications, from maize-mixed to agropastoral and irrigated systems, were included to represent management intensity, and the absence of significant depth-by-land-use interactions suggests that management effects penetrate the profile in a consistent way. The authors note that the structural equation model is best viewed as a parsimonious pathway model linking measured properties rather than proof of causal mechanisms, and that the remaining 71 percent of unexplained variation likely reflects fertiliser history, crop management, parent material, mineral phosphorus fractions, microbial processes, and short-term moisture dynamics.

The practical implications are considerable. The study argues that soil fertility management in Nigeria should be matched to the dominant local constraint rather than treating tropical soils as a uniform category. Sandy, low-organic-carbon soils, typical of the drier north, need practices that build organic matter and enhance nutrient retention, since their weak holding capacity also raises the risk of phosphorus losses through leaching. Finer-textured, oxide-rich, or more acidic soils require a different prescription: organic inputs paired with appropriate phosphorus fertilisation and, where exchangeable acidity is elevated, liming to reduce aluminium- and iron-associated constraints. None of these practices is new, the researchers emphasise, but their relevance varies systematically with the dominant soil constraint, and the national dataset now provides the evidence base for targeting them across contrasting agroecological zones.

For a country that must feed more than 200 million people amid population growth, intensive agrochemical use, and mounting concerns about nutrient depletion, the message is timely. Phosphorus deficiency constrains crop production across the tropics even where fertiliser is applied, because a large proportion of soil phosphorus occurs in forms inaccessible to plants. By quantifying how organic carbon, texture, oxides, and acidity jointly regulate availability at national scale, the study transforms a set of site-specific observations into a coherent framework for action. It also charts a path for future research: testing whether the same pathways operate with equal strength in topsoil and subsoil, and integrating fertiliser history and microbial processes into the models. What emerges is a picture of phosphorus availability not as the product of any single soil property, but as the outcome of a coordinated dance between biology, physics, and chemistry, one that farmers and policymakers can now follow step by step.

Subject of Research: Controls on plant-available phosphorus by soil organic carbon, texture, iron and aluminium oxides, and acidity across Nigerian agroecosystems

Article Title: Soil organic carbon, texture and oxide-associated processes regulate phosphorus availability across Nigerian agroecosystems

Article References: Mesele, S. A., Nguemezi, C., Jibrin, J. M., Ande, O., Jayeoba, J. O., Steverink-Mosugu, M., & Huising, E. J. (2026). Soil organic carbon, texture and oxide-associated processes regulate phosphorus availability across Nigerian agroecosystems. Plant and Soil. https://doi.org/10.1007/s11104-026-09145-8

Image Credits: AI Generated

DOI: 10.1007/s11104-026-09145-8

Keywords: soil organic carbon, phosphorus availability, Nigeria, tropical soils, iron and aluminium oxides, soil texture, soil acidity, Mehlich-3, Random Forest, structural equation modelling, agroecological zones, soil fertility management

The research team, led by Saurabh Tripathi of Punjab Agricultural University together with colleagues at ICAR institutes in India, synthesized 97 peer-reviewed field studies published between 2000 and 2025, spanning 156 field experiments across diverse climates, soil types, and production systems. Rather than examining a single technology, the researchers compared four broad categories of intervention: fertilizer management, soil amendments, cropping systems, and decision-support systems. Within those groups, they evaluated nine specific practices, including biochar, deep fertilizer placement, legume intercropping, manure application, residue retention, slow-release fertilizers, split applications, and the Nutrient Expert decision-support tool. The analysis focused on three complementary measures of nitrogen use efficiency: agronomic nitrogen use efficiency, which captures yield gained per unit of nitrogen applied; apparent nitrogen recovery efficiency, which measures how much applied nitrogen the plant actually takes up; and partial factor productivity, the yield produced per kilogram of nitrogen input.

The statistical approach was rigorous. Effect sizes were calculated as natural log response ratios and pooled with a multi-level random-effects model that treated each site-year combination as an independent observation while accounting for non-independence among observations from the same study. Publication bias was assessed with funnel plots and Egger’s regression test, and heterogeneity was explored through meta-regression on moderators such as year, region, and soil texture. The headline result: agronomic interventions collectively improved agronomic nitrogen use efficiency by 14.7 percent, apparent recovery efficiency by 18 percent, and partial factor productivity by 4.8 percent compared with conventional farmer practice. Publication-bias tests confirmed the reliability of the overall effects, and moderators explained a large share of between-study variation, up to 71.8 percent for partial factor productivity.

Some interventions stood out dramatically. Deep placement of fertilizer, which puts nitrogen 15 to 35 centimeters below the surface rather than the conventional 5 to 8 centimeters, produced the largest gains in both agronomic efficiency and recovery, boosting agronomic nitrogen use efficiency by 71.7 percent and recovery efficiency by 40.1 percent. The mechanism is elegant: placing nitrogen closer to the active root zone synchronizes nutrient supply with crop demand, minimizes surface volatilization and runoff, and promotes more stable soil-plant nitrogen cycling. These findings align closely with earlier global syntheses, including an analysis of 99 studies showing that deep placement increased grain yields by 13 to 14 percent and nitrogen use efficiency by more than 30 percent while cutting nitrous oxide and ammonia losses, and a tracer-based dataset of 366 studies confirming that deeper or split placement consistently improves nitrogen recovery.

Cropping system diversification proved equally powerful. Legume intercropping, in which maize is grown alongside nitrogen-fixing legumes, raised agronomic efficiency by 57.8 percent and delivered the largest improvement in partial factor productivity. Legumes contribute roughly 125 kilograms of nitrogen per hectare per season through biological fixation, increase associated cereal yields by 30 to 35 percent, and stimulate microbial activity in the soil. Global analyses suggest that strategically expanding legume-based systems could reduce synthetic nitrogen requirements worldwide by 26 percent, a figure that gives the meta-analysis results real planetary significance. Cropping systems as a category showed the highest improvements across all three efficiency indices, with effect sizes of 0.43, 0.31, and 0.29 for agronomic efficiency, recovery efficiency, and partial factor productivity respectively.

Decision-support systems emerged as a quiet revolution. The Nutrient Expert tool, which tailors nitrogen rates and timing to local yield potential, soil conditions, and climate, improved agronomic efficiency by 31.4 percent and recovery by 26 percent. Field validations across Asia and Africa have shown that such site-specific recommendations can increase yields by 4 to 11 percent while reducing nitrogen applications by 15 to 29 percent. Large-scale on-farm trials across more than 1,500 sites have documented consistent income gains and measurable reductions in global warming potential, confirming that these digital advisory tools are scalable, not merely promising in experimental plots.

Not every intervention worked, and the failures are as instructive as the successes. Manure application and residue retention showed statistically non-significant effects on recovery efficiency in the short term, likely because slow organic nitrogen mineralization delays their benefits beyond the experimental window. Biochar showed moderate positive effects on recovery but negligible effects on agronomic efficiency. Most strikingly, applying nitrogen above recommended rates actively backfired: it reduced agronomic efficiency by 17.7 percent, recovery efficiency by 25.7 percent, and partial factor productivity sharply, with an effect size of minus 0.45. Excess nitrogen is lost through volatilization, leaching, denitrification, and microbial immobilization, meaning farmers pay more to pollute more while gaining nothing in yield.

The analysis also revealed that soil texture profoundly modulates which strategy works where. Silty clay loam and loamy clay soils produced the highest agronomic efficiency, while clay soils favored nitrogen retention and recovery but showed lower partial factor productivity. Sandy soils, with high permeability and poor nutrient retention, suffered reduced efficiency across the board due to leaching losses and therefore stand to benefit most from deep placement and slow-release formulations. Silty loams, combining balanced aeration, moisture retention, and nutrient availability, responded most positively across all indices. This texture-dependence explains much of the residual heterogeneity in the global dataset and underscores why one-size-fits-all fertilizer advice fails: the same practice can be transformative on one field and ineffective on another.

The environmental stakes extend far beyond farm economics. Nitrous oxide released through denitrification and nitrification has a 100-year global warming potential roughly 310 times that of carbon dioxide, and an estimated 1.2 kilograms of nitrous oxide-nitrogen is emitted per 100 kilograms of nitrogen fertilizer applied. Ammonia volatilization and secondary formation of fine particulate matter degrade air quality, with cascading impacts on human health and biodiversity. In India, nitrogen use efficiency has actually declined from about 48 percent in the 1960s to 35 percent in 2018, illustrating how intensification without precision erodes efficiency over time. With maize productivity needing to rise an estimated 40 percent by 2050 to feed a growing population, closing the nitrogen efficiency gap is arguably one of the most consequential challenges in agriculture.

The authors frame their findings as a shift in philosophy: from maximizing nitrogen inputs to optimizing them. Conceptually, the efficiency gains followed a clear hierarchy, with placement and synchronization-based strategies outperforming biological integration, which in turn exceeded product-based innovations alone. Aligning nitrogen supply with crop demand in space and time, they conclude, matters more than simply adding technological complexity. The policy implications are concrete: incentivize deep-placement equipment, slow-release fertilizers, and digital advisory tools; blend organic and inorganic nutrient sources; and embed soil texture and climate information into fertilizer recommendations. Future research should prioritize long-term experiments tracking efficiency gains across soil-climate gradients, link them to greenhouse gas and leaching outcomes, and refine decision-support models with root-zone dynamics, microbial interactions, and real-time weather data. For a crop that feeds billions, the message of this synthesis is clear: the nitrogen problem is not a shortage of fertilizer but a mismatch of timing, placement, and context, and it is one that farmers, armed with the right tools, can fix.

Subject of Research: Effects of agronomic management practices on nitrogen use efficiency in global maize production

Article Title: Global meta-analysis shows that agronomic interventions enhance nitrogen use efficiency in maize

Article References: Tripathi, S., Bhagat, R., Paramesha, V., Walia, S. S., Ravisankar, N., Kaur, J., & Bhattacharjee, S. (2026). Global meta-analysis shows that agronomic interventions enhance nitrogen use efficiency in maize. Discover Soil, 3(1), Article 130. https://doi.org/10.1007/s44378-026-00285-y

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00285-y

Keywords: nitrogen use efficiency, maize, meta-analysis, deep fertilizer placement, legume intercropping, decision-support systems, slow-release fertilizers, soil texture, nitrous oxide emissions, sustainable agriculture, nutrient management, Discover Soil