Abstract Soil water flow, particularly preferential flow (PF), is a critical control on hydrological and biogeochemical processes, including groundwater recharge, contaminant transport, and carbon cycling. However, it remains challenging to predict PF occurrence across large environmental gradients. Here, we developed a deep learning (DL) model to estimate event‐scale soil water flow velocity and the probability of PF occurrence using high‐frequency soil moisture and precipitation data from 33 sites across the National Ecological Observatory Network. The model demonstrated high skill in predicting the binary occurrence of PF (91% F1‐score; 85% accuracy) but the performance was limited in predicting soil water velocity ( R 2 = 0.31). We found that precipitation characteristics (duration, volume, and intensity) were the most important predictors for soil water velocity. Among the non‐precipitation event variables, sand content showed relatively high predictive skill, though differences among non‐event climate variables were generally modest. Lower sand content was associated with increased predicted soil water velocity, a finding that highlights the role of soil structure in producing more non‐uniform flow, which contrasts with traditional uniform flow models. Projecting a reduced DL model under both moderate and high‐emissions future climate scenarios (2060–2099 Representative Concentration Pathways 4.5 and 8.5), we found ∼7.3% increase under RCP4.5 and ∼15% under RCP8.5 of soil water velocities compared to the historical simulation, while modeled likelihood of PF changed little. These findings suggest climate change is not making PF more frequent, but it is making existing PF pathways more efficient with important consequences for associated nutrient and contaminant transport under climate change.

Saturated hydraulic conductivity (k(s)) is a key hydrological property influenced by the size and topology of soil pores, particularly macropores. This study assessed eight (N = 8) published k(s) models that derive pore size distributions (PSD) from water retention (WR) functions, with either exponential (Brooks-Corey function, BC) or sigmoidal (van Genuchten function, VG) shapes. The models are semi-empirical (N = 3) or based on integration of WR-derived PSD (N = 5). Since measurements of k(s) are conflated by both natural variability and the method used to determine its values, models were evaluated with hydraulic properties from 378 samples collected using uniform methodology (HYP-UNI), and separately, with data from 1,734 soils measured by multiple institutions using a range of methods (HYPRES). Macroporosity, defined as the fraction of total porosity that is air-filled at -10 kPa (relative air capacity, RAC), was used to detect soil structure signatures on k(s) predictions. For HYP-UNI, integral-based models, particularly those using the BC function, performed best in soils with RAC >= 5%, but tended to underpredict k(s) when RAC < 5%. Overprediction was less frequent and mainly associated with low total porosity (similar to 40% or lower). Compared to HYP-UNI, model predictions worsened with HYPRES data and semi-empirical models outperformed integral-based approaches. Underprediction in soils with RAC < 5% persisted with HYPRES data but overprediction was not significantly related to porosity. Underprediction in soils with RAC < 5% was pervasive, indicating a limitation in current models that warrants further investigation. Moreover, this study highlights the advantages of using methodologically uniform databases along with high-quality WR-derived PSDs for development/testing of k(s) models.

Integrated grazing systems enhance soil quality; however, little is known about the impact of these systems on the properties and spatial density of soil pores. This study evaluates three-dimensional pore characteristics in a Brazilian Ferralsol under integrated and non-integrated grazing systems. Six soil management systems were studied: continuous grazing (CONT), rotational grazing (ROT), integrated crop-livestock system (ICL), integrated livestock-forest system (ILF), integrated crop-livestock-forest system (ICLF), and native vegetation (NV). Samples from four depths, 0 - 12 cm, 12 - 24 cm, 26 - 38 cm and 84 - 96 cm, were scanned with an X-ray computed tomography instrument at a spatial resolution of 50 mu m. The scanned volumes were reconstructed and segmented. Image analyses included the percentage, size, shape, orientation and connectivity of image-based pores, the fractal dimensions D0, D1, D2, and the parameters Delta alpha, alpha ratio and f(alpha)ratio from multifractal analysis, as well as entropy assessments. Differences between soil management systems and soil depths were compared by one-way ANOVA on ranks. Soil compaction reduced image-based porosity down to 38 cm in all grazing systems in relation to NV. Compaction also altered pore morphology, decreasing triaxial, prolate, oblate, and equant pores while increasing complex-shaped pores. The two most common pore orientation classes were nearhorizontal and inclined, which accounted for over 70 % of the distribution in each system. The only system with negative Euler numbers for all soil layers studied was NV, which implies that grazing reduces pore connectivity when compared to natural vegetation. Pore systems tended to have more clearly defined multifractal properties near the surface than deeper into the soil. Most noticeable differences in pore entropy were found between 26 and 38 cm, being highest in NV, intermediate in integrated grazing systems with trees (ILF and ICLF) and lowest in other grazing systems (CONT, ROT and ICL). The outlook of this work is that soil management strategies for integrated systems must avoid soil compaction by adjusting stocking rates, controlling traffic, maintaining soil cover and diversifying crop rotation.

Identifying and quantifying preferential flow (PF) through soil—the rapid movement of water through spatially distinct pathways in the subsurface—is vital to understanding how the hydrologic cycle responds to climate, land cover, and anthropogenic changes. In recent decades, methods have been developed that use measured soil moisture time series to identify PF. Because they allow for continuous monitoring and are relatively easy to implement, these methods have become an important tool for recognizing when, where, and under what conditions PF occurs. The methods seek to identify a pattern or quantification that indicates the occurrence of PF. Most commonly, the chosen signature is either (1) a nonsequential response to infiltrated water, in which soil moisture responses do not occur in order of shallowest to deepest, or (2) a velocity criterion, in which newly infiltrated water is detected at depth earlier than is possible by nonpreferential flow processes. Alternative signatures have also been developed that have certain advantages but are less commonly utilized. Choosing among these possible signatures requires attention to their pertinent characteristics, including susceptibility to errors, possible bias toward false negatives or false positives, reliance on subjective judgments, and possible requirements for additional types of data. We review 77 studies that have applied such methods to highlight important information for readers who want to identify PF from soil moisture data and to inform those who aim to develop new methods or improve existing ones.

Green Infrastructure (GI) plays a crucial role in reducing stormwater runoff and providing ecological benefits in urban areas. Aggregation is a key process in many soil functions as it influences carbon storage, greenhouse gas emissions, nutrient cycling, hydraulic properties, and biotic activity. In this study, we investigated soil aggregation processes and stability in GI. Soil samples were collected from six bioswale sites in New York City that had two different designs - streetside infiltration swales and enhanced tree pits. The soil samples were taken from the inlet, center, and outlet positions (relative to stormwater input) of each site. These samples were then tested for 1) macro and micro aggregate sizes; 2) distribution of soil organic carbon (SOC) and nitrogen; 3) aggregate stability; and 4) microbial biomass and activity relevant to carbon and nitrogen cycles in macroaggregates. Our results showed that 60% g/g of the soil aggregates at these GI sites were smaller than 2 mm and had high structural stability. Microaggregates between 1-2 mm had the highest SOC and accounted for 60% g/g of all microaggregate size classes. GI aggregates are formed from the breakdown of macroaggregates into intermediate microaggregates. The newly formed microaggregates contained more stable SOC than macroaggregates and bonds within microaggregates were stronger than bonds grouping microaggregates, which is not consistent with a classical model of aggregate formation in natural soils. Microbial biomass and activity were correlated with the carbon and nitrogen content of aggregates and with GI type, allowing for the identification of microbial hot spots. These results suggest that aggregation processes in human-engineered soils included in GI play an important role in sustaining carbon and nitrogen cycles.

Preferential flow (PF) in soil causes the rapid transport of water, nutrients, and contaminants into the subsurface, influencing groundwater recharge and streamflow. Data scarcity has hindered the quantification of PF occurrence and the identification of its drivers across diverse ecoregions. We address this gap by analyzing high-frequency, multi-depth soil moisture data across 17 ecoregions in the USA, using similar to 1,500 sensors at 40 sites. We discovered that PF is widespread, with sites experiencing PF in up to 60% of rainfall events >= 2 mm. Multiple approaches consistently show that PF is more likely to occur with increased peak rainfall intensity, finer textured material, low soil moisture variability, humid climate, and higher net primary productivity. This suggests that PF patterns could shift with projected climate changes, increasing uncertainty in predictions of groundwater recharge, water quality, and streamflow generation.

Understanding soil organic carbon (SOC) response to global change has been hindered by an inability to map SOC at horizon scales relevant to coupled hydrologic and biogeochemical processes. Standard SOC measurements rely on homogenized samples taken from distinct depth intervals. Such sampling prevents an examination of fine-scale SOC distribution within a soil horizon. Visible near-infrared hyperspectral imaging (HSI) has been applied to intact monoliths and split cores surfaces to overcome this limitation. However, the roughness of these surfaces can influence HSI spectra by scattering reflected light in different directions posing challenges to fine-scale SOC mapping. Here, we examine the influence of prescribed surface orientation on reflected spectra, develop a method for correcting topographic effects, and calibrate a partial least squares regression (PLSR) model for SOC prediction. Two empirical models that account for surface slope, aspect, and wavelength and two theoretical models that account for the geometry of the spectrometer were compared using 681 homogenized soil samples from across the United States that were packed into sample wells and presented to the spectrometer at 91 orientations. The empirical approach outperformed the more complex geometric models in correcting spectra taken at non-flat configurations. Topographically corrected spectra reduced bias and error in SOC predicted by PLSR, particularly at slope angles greater than 30 degrees. Our approach clears the way for investigating the spatial distributions of multiple soil properties on rough intact soil samples. A novel topographic correction method reduced the effect of surface orientation on lab-based soil reflectance.A linear empirical approach outperformed two versions of a geometric topographic correction.Bias and error in soil organic carbon predictions were decreased when topographically corrected reflectance spectra were used.Topographic correction advances the use of hyperspectral imaging on intact soil samples displaying natural roughness.

AbstractElectrical conductivity models have been widely used to estimate water content and petrophysical properties of soils in hydrogeophysical studies. However, these models are typically only valid for soils with non‐expandable matrices because they were originally developed for clean sandstone reservoir rocks. Soils containing swelling clays are characterized by matrices that expand/contract upon gaining/losing water. In this laboratory study, we demonstrate that soil matrix changes affect saturation estimation using Archie's laws. A sample of a soil containing a swelling clay was fully saturated with a potassium chloride solution, then left to dry evaporatively at room temperature for 8 days. The complex resistivity of the soil, along with its weight and volume shrinkage, were measured daily during the drying period, and the surface conductivity was calculated based on previous empirical findings. Over the course of the study, the simultaneous evaporation yielded a 33% decrease in volume and caused a nonlinear reduction in saturation with decreasing water content. Accounting for surface conductivity and correcting for saturation using the calculated volume reduction resulted in a power‐law relationship with high R2 values between resistivity and saturation along with reasonable saturation exponents. On the contrary, neglecting either surface conductivity or shrinkage caused similar underestimations of saturation exponents. These results indicate that the application of Archie's second law to soils with swelling clays leads to erroneous predictions of resistivity if saturation values are not corrected for changes in the volume of the soil and surface conductivity is neglected.

The evolution of soil structure in agricultural soils is driven by natural and anthropogenic factors including inherent soil properties, climate and soil management interventions, all acting at different spatial and temporal scales. Although the causal relationships between soil structure and these individual factors are increasingly understood, their relative importance and complex interactive effects on soil structure have so far not been investigated across a geo-climatic region. Here we present the first attempt to identify the relative importance of factors that drive the evolution of soil structure in agricultural soils as well as their direction of effect with a focus on the temperate-boreal zone. This was done using a random forest (RF) approach including soil, climate, time, and site factors as covariates. Relative entropy, as quantified by the Kullback-Leibler (KL) divergence, was used as a quantitative index of soil structure, which is derived from the particle-size distribution and soil water retention data, and integrates the effects of soil structure on pores from the micrometre-scale to large macropores. Our dataset includes 431 intact topsoil and subsoil samples from 89 agricultural sites across Sweden and Norway, which were sampled between 1953 and 2017. The relative importance of covariates for the evolution of soil structure was identified and their non-linear and non-monotonic effects on the KL divergence were investigated through partial dependence analysis. To reveal any differences between topsoils (0–30 cm; n = 174) and subsoils (30–100 cm; n = 257), the same analysis was repeated separately on these two subsets. The covariates were able to explain on average more than 50% of the variation in KL divergence for all soil samples and when only subsoil samples were included. However, the predictions were poorer for topsoil samples (≈ 35%), underlining the complex dynamics of soil structure in agricultural topsoils. Parent material was the most important predictor for the KL divergence, followed by clay content for all soil samples and sampling year for only subsoil samples. Mean annual air temperature ranked third and annual precipitation ranked fourth for subsoil samples. However, it remains unclear whether the effects of climate factors are direct (e.g., freezing and thawing, wetting and drying, rainfall impact) or indirectly expressed through interactions with soil management. The partial dependence analysis revealed a soil organic carbon threshold of around 3% below which soil structure starts to deteriorate. Besides this, our results suggest that subsoil structure in the agricultural land of Sweden deteriorated steadily during the 1950′s to 1970′s, which we attribute to traffic compaction as a consequence of agricultural intensification. We discuss our findings in the light of data bias, laboratory methods and multicollinearity and conclude that the approach followed here gave valuable insights into the drivers of soil structure evolution in agricultural soils of the temperate-boreal zone. Theses insights will be of use to inform soil management interventions that address soil structure or soil properties and functions related to it.

Soil temperature at the land surface (Tsoil) is a key variable for modeling processes that occur belowground. Although Tsoil is correlated to the near-surface air temperature (Tair), these values are often not equal. Evidence confirms that Tsoil - Tair (AT) varies by ecosystem in both positive and negative directions. The goal of this study was to calibrate a semi-empirical model to predict Tsoil from Tair and its interaction with the leaf area index (LAI) at a monthly timescale. The latter variable is introduced to quantify the reduction in solar radiation reaching the land surface using Beer-Lambert's Law with an extinction coefficient k = 0.55. Bootstrap distributions for the three unknown parameters in the model were optimized using monthly Tair and Tsoil measurements from 169 eddy covariance towers in the FLUXNET2015 dataset accompanied by LAI observations at 0.5-km2 resolution from the MODIS satellite. The optimized model had a mean bias error (MBE) of-0.08 degrees C and a root mean square error (RMSE) of 2.26 degrees C. When Tsoil was assumed equal to Tair (AT = 0), these metrics worsened to-1.51 degrees C and 4.09 degrees C, respectively. Data from the US National Ecological Network (MBE/RMSE =-0.51 degrees /2.81 degrees C) and pub-lished global predictions of AT obtained with a machine learning algorithm (MBE/RMSE = 0.73 degrees /1.76 degrees C) validated the optimized model at the global scale. The data confirmed that AT tends to be positive when Tair 0 degrees C, and transitions to negative values when Tair ) 0 degrees C and LAI is greater than 1.89. In the proposed model, LAI values close to 1.89 indicate a 37 % reduction in solar radiation (e -1 in Beer-Lambert's Law). Overall, this work confirms that Tsoil =/ Tair in most ecosystems and that AT is predominantly explained by LAI in non-freezing conditions. Re-calibration is recommended before applying the model in barren ecosystems, or at spatiotem-poral scales different from those in this research.

Soil structure controls key soil functions in both natural and agro-ecosystems. Thus, numerous attempts have been made to develop methods aiming at its characterization. Here we propose an index of soil structure that uses relative entropy to quantify differences in the porosity and pore(void)-size distribution (VSD) between a structured soil derived from soil water retention data and the same soil without structure (a so-called reference soil) estimated from its particle-size distribution (PSD). The difference between these VSDs, which is the result of soil structure, is quantified using the Kullback-Leibler Divergence (KL divergence). We applied the method to soil data from two Swedish field experiments that investigate the long-term effects of soil management (fallow vs. inorganic fertilizer vs. manure) and land use (afforested land vs. agricultural land dominated by grass/clover ley) on soil properties. The KL divergence was larger for the soil receiving regular addition of manure compared with the soils receiving no organic amendments. Furthermore, soils under afforested land showed significantly larger KL divergences compared to agricultural soils near the soil surface, but smaller KL divergences in deeper soil layers, which closely mirrored the distribution of organic matter in the soil profile. Indeed, a significant positive correlation (r = 0.374, p < 0.001) was found between soil organic carbon concentrations and KL divergences across all sites and treatments. Despite challenges related to modelling the VSD of the reference soil without structure, the proposed index proved useful for evaluating differences in soil structure in response to soil management and land-use change and reflected the expected effects of soil organic matter on soil structure. We conclude that relative entropy shows great potential to serve as an easy-to-use index of soil structure, as it only requires widely available data on soil physical and hydraulic properties. Highlights A new index of soil structure is proposed based on relative entropy A method is developed that separates the effects of soil texture and structure on the pore space The index identified soil structural differences in response to land use and soil organic carbon concentrations (SOC) The index shows the potential to serve as an easy-to-use metric of soil structure

Growing livestock populations have intensified the potential for detrimental effects of grazing on grassland soils globally. Grazing management techniques can mitigate these effects but they are livestock-specific and studies on horse grazing are rare. The objective of this work was to compare the effects of rotational grazing (i.e., livestock graze sub-sections of a pasture in sequence) with the continuous approach to grazing on (1) rates of water infiltration (i) at slightly negative pressure potentials (h, - 15, - 10, - 5, - 3.5, - 1 hPa), (2) saturated hydraulic conductivity (Ksat), and (3) bulk density (BD). At a site in New Jersey, USA, one pair of pastures was managed with rotational grazing while another pair experienced continuous grazing. Twelve Standardbred mares were grazed for two years at a stocking rate of 0.52 horses ha- 1. Over that period, i (n = 79, each at 5 h values) and BD (n = 154, from depth ranges 0-10 and 30-40 cm) were measured multiple times and Ksat values were derived from infiltration measurements. Also, the standardized precipitation-evapotranspiration index (SPEI) was calculated with meteorological data from a nearby weather station. We found that: (1) i values inclusive of the largest pores tested (h = - 1 hPa) were greater in rotationally-vs. continuously-grazed fields (geometric means & PLUSMN; GSE were 80.2 & PLUSMN; 1.15 and 42.3 & PLUSMN; 1.13 cm d- 1, respectively), (2) Ksat values were consistent with those of infiltration at h = - 1 hPa but were too variable to isolate the effect of pasture management, and (3) near-surface BD was similar under rotational and continuous grazing (arithmetic means & PLUSMN; SE were 1.32 & PLUSMN; 0.02 and 1.37 & PLUSMN; 0.02 Mg m- 3, respectively). Further, during periods of water surplus (i.e., when SPEI was positive), infiltration was strongly reduced in the smaller soil pores (h = - 10 and - 15 hPa) of the rotationally-grazed fields. This reduction was likely the result of pore colonization and blockage by pasture grass roots growing in these fields. This study suggests that rotational management allows for a larger macropore system than continuous grazing, which could lead to a reduction of water deficits and contribute to the sustainability of grazed ecosystems, with positive effects accruing through time.

Subsoil compaction has become a widespread problem for modern agriculture. Subsoiling is the most common measure to try and deal with this problem. However, despite the widespread use of this type of deep tillage, its effectiveness and sustainability are not without dispute. Previous studies show a great variety of outcomes depending on soil texture, soil moisture content, weather conditions and subsequent field operations, but the effects of the wide variety in available subsoilers have remained understudied. This study evaluated the effectiveness and sustainability of three different subsoilers on a sandy loam soil with a highly compacted upper-subsoil. The tested subsoilers differed in the number of tines, and thus the spacing between them, and several other tine characteristics, like the width of the tine foot and the curvature of the tine. The choice of subsoiler showed a clear impact on soil disruption, fuel consumption and mechanical resistance after one year. Limiting subsoil disturbance by using a subsoiler with less tines seems to have the potential of slowing down recompaction, measured indirectly via penetration resistance, in the upper-subsoil. This would help reduce the frequency of subsoiling, which clearly is a high cost field operation. This experiment highlighted the importance of not over-expanding the working depth of the subsoiler. Not all available subsoilers will be able to disrupt the subsoil down to depths where compaction can regularly be found in Europe and even relatively powerful tractors will be pushed to their limits during these deep tillage operations. Even when effectively breaking open the compacted subsoil layer, no significant effects on potato (Solanum tuberosum L.) yield were observed. Most likely linked to the rather wet conditions prevailing during the tuber bulking period, reducing the potential benefit of opening up the subsoil to root exploration.

Accurate estimation of water content in the soil is a major goal in a variety of agricultural or engineering projects. As soils can have different physical behaviors depending on their water content, it is important to know soil water content at any time during imbibition or drainage. In 1942 G. E. Archie (1907–1978) identified a direct relationship between rock saturation and electrical resistivity. However, the applicability of Archie's law to soils undergoing shrinkage and swelling is unclear. In this laboratory experiment, we conducted electrical conductivity measurements on a soil sample whose volume shrank as it underwent natural evaporation. The loss of water saturation caused the soil to shrink by 33 in total volume. During the 255 hours of the experiment, we accurately measured soil porosity and corrected the degree of saturation. We used these corrected saturation values for calculating Archie's saturation index (Ir) value. Comparing the results of calculated Ir between using the corrected and uncorrected (assuming no shrinkage) saturation, we demonstrate that if the changes in the porosity are not considered, Archie's second law for estimating Ir is unreliable and produces values that are out of the normal range. However, once the shrinkage is considered properly, Archie's laws appear valid for highly swelling clayey soils as the saturation exponent (n) values fall in the normal range for the natural unconsolidated sediments.

Root turnover rates define how frequently plants replace their root systems and input organic matter into soil. Turnover rates are often computed using measurements of total living and dead (standing) root biomass (r) by assuming gross and net production are equivalent during the growing season. This assumption may be inappropriate in grasslands where root lifespans are relatively short, and decomposition substantially offsets growth. The objective of this study was to quantify turnover rates from measurements of r over time assuming growth and decomposition happen simultaneously, and that net r changes (delta r/delta t) decrease linearly as the size of r increases (first-order kinetics). These hypotheses were interpreted with the growth-maintenance respiration paradigm (GMRP) based on whether daily growth is constant (GP) or reduced by the costs of tissue maintenance (MP). The two parameters of the linear GMRP models were inferred using Bayesian methods from 111 growing season records of r versus delta r/delta t from 15 grasslands. Two-level (hierarchical) inferences were setup for the 14 grasslands that had multiple records, assuming parameters from each grassland originated from the same population. For the grassland with one record, a single-level inference was conducted. A total of 89 records, at least one per grassland, substantially supported the GMRP models. Median predicted turnover rates based on production/ decomposition for the GP and MP models were 1.8/1.1 and 1.4/1.2 per growing season, respectively. These estimates were 3 to 7 times faster than those from traditional algorithms that neglect decomposition, suggesting organic matter inputs from roots may be larger than expected in some grasslands, especially where growth occurs almost year-round.

Abstract The simulation of solute transport with models provides a cost-effective and rapid assessment tool. However, both model selection and its parameterization using data obtained from field-scale experiments, it is essential to obtain useful and accurate results. In our work under field conditions, we obtained bromide concentration data of soil water samples from three depths and five water flows to adjust 180 breakthrough curves for estimating solute transport parameters in soil: V, D and dispersivity (λ) and we checked the effect of three previous N treatments in these solute transport parameters. The velocity and dispersion parameters were fitted using the CXTFIT 2.1 model. After selecting the best model using Akaike criteria, we analyzed the distribution of transport parameters estimated using a normality test. The transport parameters were analyzed considering each water flow as a locality, prior fertilization treatments (0N, 100N and 200N) and depth (100, 150 and 190cm respectively) as a repeated measure in space using SAS 9.2. The MIM was the best model to fit the data of relative concentration of Br integrating the soil profile compared to CD and CLT models. In general, transport parameters obtained from adjustment breakthrough curves 180 have a normal distribution. In the analysis of the transport parameters, the velocity was seen to be statistically different between water flows, while the dispersion varied both between flows and between sampling depths and prior fertility treatment. The overall average of the mobile water fraction β was 0.35, indicating preferential flow. The soil structure and the physical non-steady state had a greater impact on the movement of solutes at depth than the steady state. The logarithm of the dispersivity differed statistically by flow and by depth.

There is evidence that indicates that elicitors reduce damage caused by nematodes. Elicitors are compounds that stimulate plant defense promoting secondary metabolism. The aims of this study were to evaluate the soil and previous root crop nematofauna in tomato 'Elpida', and determine the effect on yield and root damage by Nacobbus aberrrans. Soil drench applications were conducted 24 hours pre-transplanting with 1 mL of: salicilic acid 0.50 x 10(-4) M and 1.00 x 10(-4) M, ethylen 0.35 x 10(-3) M and 0.70 x 10(-3) M, jasmonic acid 1.00 x 10(-4) M and 1.00 x 10(-5) M, including untreated plants as control. In the soil, saprophytic, omnivorous and predatory nematodes, as well as Helicotylenchus spp. and N. aberrans, were identified. The previous crop was ungrafted 'Elpida' plants and also grafted on Maxifort, 9184, Multifort and Beaufort. Eggs and N. aberrans J2 were identified in all roots, with significantly lower amounts in Maxifort and 9184. Phytohormones increased the production of fruits with weight higher than 150 g and total yield compared to the control, producing lower galls, galling and reproductive indexes. Salicylic acid 1.00 x 10(-4) M produced lower damage to the roots and a higher total yield and truss yield, followed by ethylene 0.70 x 10(-3) M and jasmonic acid 1.00 x 10(-5) M treatments. Further research is required to provide deeper knowledge of the practical implications of using elicitors and enhance the understanding of their action for N. aberrans control.

RESUMENExisten antecedentes de la reducción de daños por nemátodos con el uso de elicitores, compuestos que estimulan defensas en las plantas, promoviendo el metabolismo secundario.Este trabajo tuvo como objetivos evaluar la nematofauna presente en el suelo y raíces del cultivo antecesor, y medir el efecto sobre el rendimiento y daño en raíces por Nacobbus aberrans en tomate (Solanum lycopersicum)'Elpida', tratado por drench 24 horas previas al trasplante con 1 mL de ácido salicílico 0,50 x 10 -4 M y 1,00 x 10 -4 M, etileno 0,35 x 10 -3 M y 0,70 x 10 -3 M, ácido jasmónico 1,00 x 10 -4 M y 1,00 x 10 -5 M, dejando testigos sin tratar.En el suelo se identificaron nemátodos saprófitos, omnívoros y predatores, Helicotylenchus spp.y N. aberrans.El cultivo antecesor fue tomate 'Elpida' sin injertar e injertado sobre Maxifort, 9184, Multifort y Beaufort, identificándose huevos y N. aberrans J2 en todas las raíces, en cantidad significativamente menor en Maxifort y 9184.Las fitohormonas incrementaron la producción de frutos con peso mayor a 150 g y el rendimiento total respecto al testigo, produciendo menor número de agallas, índice de agallamiento y reproducción.Ácido salicílico 1,00 x 10 -4 M produjo menor daño en raíces y mayor producción total y por racimos, seguido por etileno 0,70 x 10 -3 M y ácido jasmónico 1,00 x 10 -5 M. Es importante continuar investigando aspectos prácticos del uso de elicitores y profundizar en la comprensión de su acción para el control de N. aberrans.