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Abstract Introduction: Plant functional traits are widely used to predict community productivity. However, they are rarely used to predict the performance (in terms of growth diameter, growth height, survival, and integral response index) of woody species planted in degraded soils. Objective: To evaluate the relationship between the functional traits and the performance of 25 woody species planted in disturbed soils affected by oil extraction activities in Ecuadorian Amazon. Methods: Eighteen permanent sampling plots were established and five 6-month-old seedlings of each 25 species were randomly planted in each plot (125 individuals per plot), at a distance of 4×4 m. Eight quantitative functional traits (leaf size, specific leaf area, leaf nitrogen concentration, leaf phosphorus concentration, leaf minimum unit, leaf dry matter content, stem specific density and leaf tensile strength) were determined for each species. Results: The woody species with high performance shows greater leaf size, specific leaf area and Stem Specific Density than those showing low performance. Leaf nitrogen concentration and stem specific density had a direct relationship with the integral response index. The leaf size, leaf phosphorus concentration, leaf dry matter content and leaf tensile strength showed a negative relationship with the integral response index. Conclusions: Our study demonstrated that the performance of woody species o disturbed soils can be predicted satisfyingly by leaf and stem functional traits, presumably because these traits capture most of environmental and neighborhood conditions.

Resumen Introducción: Los rasgos funcionales de las plantas han sido ampliamente utilizados para predecir la productividad (en términos de crecimiento en diámetro, crecimiento en altura, sobrevivencia e índice de respuesta integral) de las comunidades vegetales. Sin embargo, rara vez han sido utilizados para predecir el desempeño de las especies leñosas plantadas en suelos degradados. Objetivo: Evaluar la relación entre el desempeño y los rasgos funcionales de 25 especies leñosas plantadas en suelos afectados por actividades de extracción de petróleo en la Amazonía ecuatoriana. Métodos: Se establecieron 18 parcelas permanentes de muestreo y en cada parcela se sembraron aleatoriamente cinco plántulas de 6 meses de las 25 especies (125 individuos por parcela), a una distancia de 4×4 m. Se determinaron ocho rasgos funcionales (área foliar, área foliar específica, concentración de nitrógeno foliar, concentración de fósforo foliar, unidad mínima foliar, contenido foliar de materia seca, densidad específica del fuste y fuerza tensil foliar) de cada especie. Resultados: Las especies leñosas con alto desempeño presentaron mayor área foliar, área foliar específica y densidad específica del fuste que las especies de bajo desempeño. La concentración de nitrógeno foliar y la densidad específica del fuste mostraron una relación directa. El área foliar, la concentración de fósforo foliar, el contenido de materia seca foliar y la fuerza tensil foliar presentaron una relación inversa con el Índice de Respuesta Integral. Conclusión: Se demostró que el desempeño de las especies leñosas plantadas en suelos alterados puede predecirse satisfactoriamente por rasgos funcionales de hoja y de tallo, debido posiblemente a que los rasgos influyen en el crecimiento y supervivencia de las especies, y reflejan la mayoría de las condiciones ambientales.

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Regional mapping herbicide sorption to soil is essential for risk assessment. However, conducting analytical quantification of adsorption coefficient (Kd ) in large-scale studies is too costly; therefore, a research question arises on goodness of Kd spatial prediction from sampling. The application of a spatial Bayesian regression (BR) is a newer technique in agricultural and natural resources sciences that allows converting spatially discrete samples into maps covering continuous spatial domains. The objective of this work was to unveil herbicide sorption to soil at a landscape scale by developing a predictive BR model. We integrated a large set of ancillary soil and climate covariables from sites with Kd measurements into a spatial mixed model including site random effects. The models were fitted using glyphosate and atrazine Kd s, determined in 80 and 120 sites, respectively, from central Argentina. For model assessment, measurements of global and point-wise prediction errors were obtained by cross-validation; residual variability was estimated by bootstrap to compare BR with regression kriging. Results showed that the BR spatial predictions outperformed regression kriging. The glyphosate Kd model (root mean square prediction error, 13% of the mean) included aluminum oxides, pH, and clay content, whereas the atrazine Kd model strongly depended on soil organic carbon and clay and on climatic variables related to water availability (root mean square prediction error, 27%). Spatial modeling of a complex edaphic process as herbicide sorption to soils enhanced environmental interpretations. An efficient approach for spatial mapping provides a modern perspective on the study of herbicide sorption to soil.

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This article presents original geospatial data on soil adsorption coefficient (Kd) for two widely used herbicides in agriculture, glyphosate and atrazine. Besides Kds, the dataset includes site-specific soil data: pH, total nitrogen, total organic carbon, Na, K, Ca, Mg, Zn, Mn, Cu, cation exchange capacity, percentage of sand, silt and clay, water holding capacity, aluminum and iron oxides, as well as climatic and topographic variables. The quantification of herbicides soil retention was made on a sample of soils selected by Conditionated Latin Hypercube method to capture the underlying edaphoclimatic variability in Cordoba, Argentina. The glyphosate data presented here has been used to evaluate statistical methods for model-based digital mapping (F. Giannini Kurina, S. Hang, R. Macchiavelli, M. Balzarini, 2019) [1]. The dataset is made publicly available to enable future analyzes on processes that leads the dynamics of both herbicides in soil.