The research team conducted field experiments during 2020 and 2021, testing leaching quotas ranging from zero to 300 millimeters combined with schedules that varied from a single seedling-stage leaching to multiple leaching events distributed across the growing season. They tracked soil water content and salt-ion concentrations, measured root traits in different soil layers, followed nitrogen accumulation and uptake dynamics through the season, and recorded lint yield, yield components, and fiber quality. To untangle the web of interacting factors, the researchers deployed random forest modeling and exploratory partial least squares structural equation modeling, statistical tools that allowed them to identify which variables actually drove yield and quality rather than merely correlating with them.

The standout treatment, labeled W3S3, combined a moderate leaching quota with a multi-stage schedule, and its results were dramatic. Compared with the non-leached control, W3S3 increased lint yield by 112.3 percent in 2020 and 91.7 percent in 2021. Boll number, the single most important yield component in cotton, rose by 120.1 percent in the first year and 60.3 percent in the second. In an era when agricultural gains of even a few percent are celebrated, a near-doubling of lint yield through irrigation scheduling alone is the kind of result that demands attention from agronomists and water managers worldwide.

What makes the finding scientifically compelling is that the mechanism was not simply salt removal. The root responses were concentrated in the top 42 centimeters of the soil profile, where W3S3 increased surface root length density and root volume density by more than 40 percent and promoted lateral root expansion. This shallow but dense root architecture is precisely what a cotton plant needs under drip irrigation, because drip emitters wet a limited soil sphere near the surface, and roots proliferating in that moist, low-salt zone gain first access to water and dissolved nutrients. The leaching events effectively reshaped the underground environment, transforming a hostile, saline root zone into a hospitable one where fine roots could branch and forage.

Nitrogen dynamics revealed a second layer of the story. W3S3 improved soil moisture, reduced salt-ion pressure on the plants, enhanced both root and shoot nitrogen accumulation, and prolonged the effective period of nitrogen uptake by about 20 days. In cotton, late-season nitrogen uptake is critical for boll filling and fiber development, and salt stress typically shortens this window by accelerating senescence. By keeping the root zone moist and dilute in salts, the multi-stage leaching schedule allowed plants to keep absorbing nitrogen deep into the boll-setting period, effectively extending the crop’s productive lifespan. The extra three weeks of active uptake translated directly into more and heavier bolls.

The study also illuminated the tradeoffs between yield and fiber quality, a tension that has long frustrated cotton breeders and agronomists. Yield was driven primarily by boll number, nitrogen uptake, soil moisture, and sodium concentration in the root zone, while fiber quality was associated with a different set of variables: sodium levels, the root nitrogen-uptake rate, root length, soil moisture, and root surface area. Notably, the W3 leaching quota increased the spinning consistency index and fiber length, meaning that the moderate quota improved quality even where the multi-stage schedule drove the yield gains. This separation of drivers suggests that farmers and researchers can, to a meaningful degree, tune leaching schedules for yield and leaching quotas for quality rather than accepting an unavoidable compromise between the two.

The statistical modeling added mechanistic rigor to these observations. Random forest analysis ranked the importance of candidate drivers, while the structural equation modeling traced pathways linking water and ion redistribution in the root zone to root architecture, nitrogen uptake, and ultimately yield and quality outcomes. Together, the two approaches supported a coherent causal chain: leaching events redistribute water and push salts away from active rooting zones, the improved hydrochemical environment stimulates root growth and lateral branching, the expanded root system sustains nitrogen uptake longer into the season, and the combination of more bolls and better-fed fibers produces the yield and quality gains observed in the field.

The broader context makes these results especially timely. Salt-affected soils constrain agriculture across arid regions on every continent, and water scarcity is intensifying across northwestern China, making it impossible to simply flush soils with generous irrigation as past generations of farmers did. Researchers have documented the tension between water savings and salinization prevention in dryland irrigation, and long-term mulched drip irrigation has been flagged as a potential risk to agricultural sustainability precisely because restricted water application promotes salt accumulation. The Xinjiang study demonstrates that the solution lies not in choosing between water conservation and salt control, but in engineering the temporal distribution of limited leaching water so that each event does maximum work.

There are, of course, caveats and open questions. The experiments spanned two years at sites in northern Xinjiang, and leaching requirements will vary with soil texture, initial salinity, water table depth, and the salinity of the irrigation water itself. Applying 300 millimeters of extra water, even in moderate staged doses, carries a real cost in water-scarce basins, and excessive leaching can mobilize nitrogen below the root zone or raise shallow water tables. The authors’ finding that moderate quotas outperformed extremes suggests a sweet spot, but translating W3S3 into practical recommendations will require local calibration and, ideally, longer-term trials to confirm that the approach prevents salt accumulation over multiple seasons rather than merely redistributing it within a single year.

Even with those qualifications, the study reframes how scientists think about managing saline-alkali agroecosystems. The conclusion that properly timed moderate drip leaching improved cotton performance by sustaining active roots and late-season nitrogen uptake, rather than through salt removal alone, elevates root ecology from a passive consequence of irrigation to an active management target. For the millions of hectares of salt-affected farmland under drip irrigation worldwide, the message is that the invisible architecture of roots, shaped season by season by the rhythm of water moving through soil, may be the most powerful lever available for reconciling high yields, good fiber quality, and the harsh arithmetic of water scarcity.

Subject of Research: Effects of multi-stage drip leaching on root architecture, nitrogen uptake, and yield-quality tradeoffs in saline-alkali cotton fields

Article Title: Root-zone water-ion redistribution links multi-stage drip leaching to root architecture, nitrogen uptake, and yield-quality tradeoffs in saline-alkali cotton agroecosystems

Article References: Xiao, C., Chen, G., Zhang, F., Fan, J., & Zhang, Q. (2026). Root-zone water-ion redistribution links multi-stage drip leaching to root architecture, nitrogen uptake, and yield-quality tradeoffs in saline-alkali cotton agroecosystems. Plant and Soil. https://doi.org/10.1007/s11104-026-09013-5

Image Credits: AI Generated

DOI: 10.1007/s11104-026-09013-5

Keywords: cotton, drip irrigation, soil salinity, leaching, root architecture, nitrogen uptake, lint yield, fiber quality, saline-alkali soil, Xinjiang, water management, agronomy

The research team, led by Enyue Zou and Miao Wang of Northeast Forestry University together with colleagues from Qufu Normal University and the Institute of Applied Ecology of the Chinese Academy of Sciences, worked in a boreal forest dominated by Larix gmelinii, the Dahurian larch. The site sits at the heart of one of Earth’s largest continuous boreal belts, a biome where cold temperatures slow decomposition and keep nitrogen locked away in soil organic matter. Because plant growth in these forests is typically constrained by nitrogen availability, scientists have long assumed that any extra nitrogen arriving from the atmosphere would be snapped up eagerly by trees and soil microbes alike. The new study shows the reality is far more nuanced.

To follow the nitrogen, the researchers used one of the most powerful tools in ecosystem science: isotopic labeling. They applied paired tracers of nitrogen-15, a rare heavy isotope of nitrogen, in the form of both 15NH4+ and 15NO3−. Because the isotope behaves chemically like ordinary nitrogen but can be detected with a mass spectrometer, every atom of labeled nitrogen that enters a leaf, a fine root, or a soil aggregate leaves a measurable fingerprint. The team established four long-term treatments—a control plus low, medium, and high nitrogen addition—and then traced the labeled nitrogen through five distinct pools: leaves, branches, fine roots, coarse roots, and soil.

The first surprise came at the very start of the tracer’s journey. Ammonium-derived nitrogen entered fine roots more rapidly in the early stages, producing a higher short-term uptake rate than its nitrate counterpart. This makes physiological sense: ammonium is already in a reduced form and can be assimilated directly into amino acids inside the root, whereas nitrate must first be reduced by the enzymes nitrate reductase and nitrite reductase before it can be used—a process that costs energy and takes time. Conifers, in particular, have historically been described as ammonium specialists, and the early uptake pattern in these larch trees fits that classical picture.

But the story changed dramatically as time passed. Nitrate-derived nitrogen proved to be the long-distance traveler of the pair. During the middle and later stages of the experiment, especially under medium and high nitrogen addition, nitrate-derived nitrogen was transported more readily to the leaves and branches above ground. This suggests that once nitrate is taken up, it moves through the xylem stream with relative ease, delivering nitrogen to the canopy where it fuels photosynthesis. In other words, the two forms of deposited nitrogen follow fundamentally different routes through the tree: ammonium is captured quickly and processed locally in the roots, while nitrate behaves more like a mobile courier service running from soil to crown.

Perhaps the most consequential finding concerns what long-term nitrogen addition did not do. Contrary to the expectation that chronic fertilization would continuously boost the fine roots’ appetite for nitrogen, the study found no sustained enhancement of fine-root uptake of the labeled tracer. What changed instead was the plumbing. Nitrogen addition promoted the transfer of labeled nitrogen from fine roots—the short-lived, high-turnover organs that do most of the absorbing—into coarse roots and then into aboveground organs. Under elevated nitrogen deposition, the forest essentially rerouted its internal nitrogen traffic, pushing a larger share of newly acquired nitrogen upward through the woody transport network rather than letting it linger below ground.

The soil, meanwhile, played the role of a temporary vault. Immediately after both nitrogen forms were applied, the soil was the major short-term sink, holding the bulk of the labeled nitrogen through microbial immobilization, exchange with mineral surfaces, and physical fixation. This initial retention is critical for the global carbon cycle, because nitrogen retained in soils can influence how much carbon forests store; previous work, including a 2022 analysis in Nature Communications, has linked the retention of deposited ammonium and nitrate to the strength of the forest carbon sink. But the vault does not hold forever. Over the course of the study, accumulation and recovery of the labeled nitrogen in plant pools steadily increased while soil recovery declined—a clear signature of substantial redistribution of deposited nitrogen from the soil into the living biomass.

This shifting balance carries implications that ripple far beyond a single larch stand. Global nitrogen deposition has risen sharply over the past century, particularly across Asia, and models of future forest carbon sequestration depend heavily on assumptions about where deposited nitrogen goes. If soils initially lock up most incoming nitrogen but gradually release it to trees, then the timescale of observation matters enormously: a snapshot taken weeks after deposition tells a very different story than one taken months or years later. The study’s multi-pool, multi-stage design captures exactly this temporal evolution, which is why its authors argue that long-term deposition reshapes not just how much nitrogen trees use, but the relative use of different nitrogen forms and the long-term fate of exogenous nitrogen across the whole plant–soil system.

The work also speaks to a long-running debate in forest ecology. Classic experiments in the 1990s suggested that conifer roots discriminate strongly against nitrate, implying that boreal and temperate conifers rely almost exclusively on ammonium. More recent studies, including work showing that mature conifers can assimilate nitrate as efficiently as ammonium in several plantation settings, have chipped away at that dogma. The new larch data land squarely in the revisionist camp: nitrate is not merely tolerated by these trees, it is preferentially exported to the canopy over time, particularly when nitrogen supply is elevated. The physiological machinery for nitrate uptake, transport, and signaling appears fully operational in this boreal species, and chronic deposition may even upregulate it.

For a warming world, the timing could not be more relevant. Boreal forests store an enormous fraction of terrestrial carbon, and their response to nitrogen deposition is woven into every major Earth system model. By demonstrating that deposited ammonium and nitrate follow distinct pathways—rapid root capture for ammonium, delayed canopy delivery for nitrate—and that years of enrichment reroute nitrogen from soil sinks into wood and leaves, the study provides the kind of mechanistic detail that modelers need to predict how much anthropogenic nitrogen will actually boost carbon sequestration, and how much will simply cycle through soils and back into the atmosphere. The larch forests of northeastern China, it turns out, are not passive sponges for pollution. They are active accountants, balancing a ledger of two very different currencies and deciding, season by season, where every atom should go.

Subject of Research: Fate of ammonium- and nitrate-derived nitrogen in a nitrogen-enriched boreal forest traced with 15N labeling

Article Title: Long-term nitrogen addition alters the uptake, transport, and fate of ammonium- and nitrate-derived nitrogen in a boreal forest

Article References: Zou, E., Wang, M., Liu, G., Huang, B., Yin, L., Liang, C., Xing, Y., Wang, X., & Wang, Q. (2026). Long-term nitrogen addition alters the uptake, transport, and fate of ammonium- and nitrate-derived nitrogen in a boreal forest. Plant and Soil. https://doi.org/10.1007/s11104-026-09077-3

Image Credits: AI Generated

DOI: 10.1007/s11104-026-09077-3

Keywords: nitrogen deposition, boreal forest, Larix gmelinii, 15N tracer, ammonium, nitrate, fine roots, nitrogen uptake, plant-soil system, carbon sequestration, forest ecology, soil nitrogen

The research, published in the Journal of Agriculture and Food Research, grew the semi-dwarf Basmati variety PB 1692 during the kharif seasons of 2022 and 2023 in a split-plot experiment. Five nitrogen regimes were tested against four zinc fertilization strategies, from conventional prilled urea at the full recommended dose of 130 kilograms of nitrogen per hectare to reduced doses supplemented with foliar sprays of liquid nano-urea, nano-zinc oxide, and even a cyanobacterial formulation based on Anabaena torulosa. From 300 plot-level observations, the team compiled 22 variables spanning plant height, tiller density, dry matter production, panicle characteristics, and nitrogen and zinc concentrations and uptake in straw, hull, bran, and white rice kernels.

Those variables became the training ground for five machine learning algorithms: multiple linear regression, support vector regression, k-nearest neighbors, gradient boosting, and random forest. The researchers applied recursive feature elimination with 10-fold cross-validation to select the most informative predictors and used exhaustive grid-search optimization to tune the random forest’s hyperparameters, fixing the number of trees at 500 after confirming error convergence. A strict bias-variance criterion, requiring the difference in R-squared between training and cross-validation to stay below 10 percent, guarded against overfitting, and final performance was confirmed on a held-out test set using Lin’s concordance correlation coefficient.

The random forest emerged as the clear champion. It achieved R-squared values of 0.97 in 2022 and 0.96 in 2023, with root mean square errors of just 0.08 tonnes per hectare and mean absolute errors of 0.063, outperforming support vector regression, which averaged an R-squared of 0.92, and leaving k-nearest neighbors, the weakest performer at R-squared values between 0.73 and 0.80, far behind. Taylor diagrams, which simultaneously display correlation, standard deviation, and error, visually confirmed the random forest’s dominance in both training and prediction stages across both seasons.

More revealing than raw accuracy was what the model considered important. Using two feature-importance metrics, the percentage increase in mean squared error and the increase in node purity, the random forest ranked total nitrogen uptake, grain nitrogen concentration, dry matter production, and total zinc uptake as the dominant drivers of yield. Plant height and tiller counts, traits farmers and agronomists often watch closely, carried relatively little predictive weight. The message is biologically coherent: fertilizer application alone guarantees nothing unless the crop actually captures, assimilates, and remobilizes nitrogen into grain, processes that depend on root architecture, photosynthetic capacity, and source-sink coordination during grain filling.

The field results reinforced that insight. The full recommended dose of 130 kilograms of nitrogen per hectare produced the highest grain yields, 4.79 and 4.52 tonnes per hectare in 2022 and 2023, gains of 25.4 and 28.0 percent over the unfertilized control, along with nitrogen uptake of roughly 120 kilograms per hectare and zinc uptake about 35 percent higher than in unfertilized plots. But the treatment combining 97.5 kilograms of soil nitrogen with two foliar sprays of nano-urea produced statistically equivalent yields, demonstrating that a 25 percent reduction in mineral nitrogen input could maintain productivity when paired with the nanoscale foliar supplement.

Zinc fertilization delivered its own measurable benefits. Foliar sprays of 0.1 percent nano-zinc oxide produced the strongest yield attributes, raising panicle weight by 9.6 percent and grains per panicle by 4.1 percent on a two-year mean basis compared with no zinc, and adding roughly 0.2 tonnes of grain per hectare. Zinc uptake reached nearly 600 grams per hectare under the nano-zinc treatment, a 10.7 percent increase over the control. Because zinc deficiency remains a widespread human nutritional problem, agronomic biofortification of rice grain through such foliar strategies carries significance well beyond the yield column.

The climate accounting added a compelling twist. Using the CCAFS Mitigation Option Tool, the researchers estimated greenhouse gas emissions in carbon dioxide equivalents, incorporating both fertilizer production and field-induced emissions. The 97.5-kilogram nitrogen plus nano-urea treatment cut absolute emissions by 188.9 kilograms of carbon dioxide equivalent per hectare, a 6.55 percent reduction, and lowered emission intensity by 3.22 to 4.62 percent relative to the full recommended dose, all without a yield penalty. Deeper cuts to 65 kilograms of nitrogen, whether with nano-urea or conventional urea, saved more emissions, 377.2 and 323.5 kilograms per hectare respectively, but at a significant cost in grain, defining the agronomic boundary of an acceptable trade-off. Strikingly, the unfertilized control had the worst emission intensity per kilogram of grain, showing that yield and climate goals are inseparable in irrigated rice.

The study’s authors are candid about limitations. Weather variables were excluded because the two experimental seasons were climatically stable, yet weather remains a major determinant of crop performance, and future models should incorporate it. The work spanned only two years and a single rice variety, and several of the most powerful predictors, including total nutrient uptake and biomass, require destructive sampling and laboratory analysis that are impractical for routine farm use. The researchers argue that the next step is to build simplified, transferable frameworks using readily available inputs such as weather, soil data, fertilizer records, and remotely sensed vegetation indices, potentially augmented by drone observations and process-based crop models.

Even with those caveats, the study marks a meaningful advance in precision agriculture. Most machine learning yield studies lean on satellite or meteorological data; this one integrated field-measured agronomic, physiological, and nutrient variables within a single interpretable framework, and the model’s importance rankings aligned with established crop physiology rather than statistical artifacts. For a crop on which billions of people depend, the combination of a random forest that explains 96 to 97 percent of yield variability and a fertilizer strategy that trims nitrogen and emissions without sacrificing harvest offers a rare, quantified win for both food security and the climate.

Subject of Research: Machine learning prediction of rice grain yield and yield-emission trade-offs under nitrogen and zinc fertilization in a two-year field experiment

Article Title: Rice yield prediction using machine learning models and yield-emission trade-offs from field experiment

Article References: Reddy, K. S., Shivay, Y. S., Prasanna, R., Kumar, D., Peramaiyan, P., Mandi, S., Nayak, S., Baral, K., Alekhya, G., Reddy, K. S., & Borate, R. B. (2026). Rice yield prediction using machine learning models and yield-emission trade-offs from field experiment. Journal of Agriculture and Food Research, 31, Article 103309. https://doi.org/10.1016/j.jafr.2026.103309

Image Credits: AI Generated

DOI: 10.1016/j.jafr.2026.103309

Keywords: rice, machine learning, random forest, nitrogen uptake, nano-urea, zinc biofortification, greenhouse gas emissions, yield prediction, precision agriculture, nutrient use efficiency, Basmati rice, sustainable farming

The logic behind the experiment rests on a fundamental constraint of microbial life: stoichiometry. Heterotrophic bacteria—the workhorses of nutrient removal in streams—need carbon, nitrogen, and phosphorus in roughly balanced proportions to build biomass and fuel respiration. Wastewater treatment plant effluents disrupt that balance. They deliver heavy loads of dissolved inorganic nitrogen and soluble reactive phosphorus, but comparatively little bioavailable dissolved organic carbon. The result is a stream in which microbes are, in effect, starving for carbon while awash in nutrients, a condition that constrains their capacity to take up nitrogen and phosphorus and allows those nutrients to travel unimpeded downstream, where they can fuel algal blooms, oxygen depletion, and coastal eutrophication far from the source.

Pineda-Morante and colleagues—including Miquel Ribot, Susana Bernal, Anna Lupon, Sara Castelar, Esperança Gacia, Stephanie N. Merbt, Helena Guasch, and Eugènia Martí at CEAB-CSIC, together with Francesc Sabater at the University of Barcelona—began with a careful stoichiometric diagnosis of the receiving stream. Their analysis suggested that heterotrophic nutrient uptake in this system was constrained by both dissolved organic carbon and dissolved inorganic nitrogen, relative to an abundant supply of soluble reactive phosphorus. In other words, phosphorus was not the limiting resource here; carbon and nitrogen were. That diagnosis shaped the team’s hypothesis: enriching the stream with bioavailable dissolved organic carbon should partially relieve the carbon constraint, stimulate heterotrophic activity, and thereby enhance the uptake of both nitrogen and phosphorus within the stream reach itself.

What followed was a reach-scale enrichment of the kind that has become a gold standard in stream biogeochemistry. The researchers continuously dosed the stream with the brewery-derived dissolved organic matter solution for four days, while collecting hourly water samples from one station upstream of the injection point and two stations downstream. This high-frequency sampling design was essential for capturing a phenomenon that shorter, coarser studies routinely miss: the pronounced day–night rhythm, or diel variability, in stream nutrient processing. Streams are not chemically static. Photosynthesis by benthic algae during daylight hours and respiration by microbes around the clock generate cycles in oxygen, pH, and nutrient demand that can shift the apparent behavior of a reach from hour to hour.

The response to carbon enrichment was rapid and, in places, dramatic. Within the first meters downstream of the injection point, the team observed strong net removal of dissolved organic carbon, coinciding with pronounced oxygen depletion and the visible development of benthic microbial mats. Those mats—dense communities of bacteria and algae colonizing the streambed—were, in effect, the physical signature of a microbial economy suddenly flush with its limiting resource. The influx of carbon triggered a surge in heterotrophic demand, and the microbial community consumed the added carbon quickly enough to draw down oxygen concentrations in the water flowing over the mats, a classic indication of intense aerobic respiration.

Most consequentially for water quality, the stream reach flipped its functional identity. Before enrichment, the reach acted as a net source of dissolved inorganic nitrogen, meaning more nitrogen exited the reach than entered it—a counterintuitive but well-documented behavior in effluent-dominated streams, where internal processes such as mineralization can release more nitrogen than the reach retains. During the enrichment, that pattern reversed: the reach became a net sink for dissolved inorganic nitrogen, driven mainly by enhanced nitrate uptake. Nitrate, the most abundant and mobile form of nitrogen in most effluents, is notoriously difficult for streams to retain, so a carbon-triggered boost in nitrate uptake is precisely the outcome a nutrient manager would hope to see.

Not every nutrient responded equally, however, and the nuances matter. Ammonium uptake showed a weaker, more spatially localized, and more clearly diel-dependent response, waxing and waning with the daily light cycle rather than increasing uniformly. Soluble reactive phosphorus uptake, meanwhile, showed no clear effect of the carbon addition at all. That outcome is consistent with the team’s initial stoichiometric diagnosis: because phosphorus was already abundant relative to carbon and nitrogen in this stream, relieving the carbon limitation did not create additional phosphorus demand. Microbes with ample phosphorus and newly available carbon directed their extra assimilatory capacity toward nitrogen, particularly nitrate, rather than toward phosphorus.

The study also delivers a sobering reality check. Despite the enhanced local uptake within the dosed reach, the whole-reach reduction in nitrogen and phosphorus export remained below fifteen percent relative to the total nutrient loads delivered by the wastewater treatment plant. In other words, even a four-day, deliberately generous carbon subsidy that visibly transformed the streambed could only trim a modest fraction of the nutrient export leaving the system. Effluent-dominated streams, the findings imply, cannot simply metabolize their way out of the nutrient loads they receive; the loads are simply too large relative to the residence time and processing capacity of the water moving through the channel. Any carbon-based attenuation strategy would therefore need to be one component of a broader management approach that also addresses nutrient loads at the treatment plant itself.

Yet the experiment’s most durable contribution may be methodological rather than outcome-based. The authors frame their results as support for using system-specific carbon–nitrogen–phosphorus stoichiometry to guide dissolved-organic-carbon-based nutrient attenuation strategies. Rather than applying a generic carbon-dosing prescription, managers could first diagnose which element limits heterotrophic nutrient processing in a given stream—carbon, nitrogen, or phosphorus—and target the addition accordingly. In the Catalan study stream, the stoichiometric framework correctly anticipated that carbon enrichment would boost nitrate uptake but leave phosphorus uptake largely untouched, a predictive success that suggests the approach could be exported to other effluent-receiving streams with confidence.

The research also highlights how much fine-grained temporal sampling matters for understanding stream function. Because the team sampled hourly across both upstream and downstream stations for four straight days, they could separate the sustained effects of carbon enrichment from the background diel oscillations in uptake that would have confounded a single grab-sample campaign. Ammonium’s light-dependent response, in particular, would likely have been invisible without that cadence. As freshwater systems worldwide face intensifying pressure from urban effluents, seasonal drought, and warming, studies of this kind—integrating ecological stoichiometry, reach-scale manipulation, and high-frequency sensing—offer a template for diagnosing and, where feasible, engineering the microbial machinery that stands between wastewater and the downstream ecosystems those nutrients would otherwise reach. The work was supported by the Spanish Ministry of Science, Innovation, and Universities through multiple national research projects, and the article is published open access, making the full dataset and methods available to researchers and water managers confronting the same carbon–nutrient imbalances elsewhere.

Subject of Research: How dissolved organic carbon enrichment alters microbial nitrogen and phosphorus uptake in a stream receiving wastewater treatment plant effluent

Article Title: Dissolved organic matter addition alters diel nitrogen and phosphorus uptake downstream of a wastewater treatment plant effluent

Article References: Pineda-Morante, D., Ribot, M., Bernal, S., Lupon, A., Castelar, S., Gacia, E., Merbt, S. N., Sabater, F., Guasch, H., & Martí, E. (2026). Dissolved organic matter addition alters diel nitrogen and phosphorus uptake downstream of a wastewater treatment plant effluent. Biogeochemistry. https://doi.org/10.1007/s10533-026-01363-7

Image Credits: AI Generated

DOI: 10.1007/s10533-026-01363-7

Keywords: biogeochemistry, wastewater effluent, dissolved organic carbon, nitrogen uptake, phosphorus uptake, nutrient stoichiometry, diel variability, stream ecology, heterotrophic microbes, nitrate, eutrophication, Mediterranean stream