Space weathering has long been known to alter the chemical and physical properties of the surfaces of airless bodies such as the Moon. The isotopic compositions of moderately volatile elements in lunar regolith samples could serve as sensitive tracers for assessing the intensity and duration of space weathering. In this study, we develop a new quantitative tool to study space weathering and constrain surface exposure ages based on potassium isotopic compositions of lunar soils. We first report the K isotopic compositions of 13 bulk lunar soils and 20 interval soil samples from the Apollo 15 deep drill core (15004-15006). We observe significant K isotope fractionation in these lunar soil samples, ranging from 0.00 %o to + 11.77 %o, compared to the bulk silicate Moon (-0.07 +/- 0.09 %o). Additionally, a strong correlation between soil maturity (Is/FeO) and K isotope fractionation is identified for the first time, consistent with other isotope systems of moderately volatile elements such as S, Cu, Zn, Se, Rb, and Cd. Subsequently, we conduct numerical modeling to better constrain the processes of volatile element depletion and isotope fractionation on the Moon and calculate a new K Isotope Model Exposure Age (KIMEA) through this model. We demonstrate that this KIMEA is most sensitive to samples with an exposure age lower than 1,000 Ma and becomes less effective for older samples. This novel K isotope tool can be utilized to evaluate the surface exposure ages of regolith samples on the Moon and potentially on other airless bodies if calibrated using other methods (e.g., cosmogenic noble gases) or experimental data.

In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. The objective of this research was to use vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery to remotely assign MCs to potato plants grown in trials, representing three different early stages within a multi-year breeding program. The relationships between VIs (GOSAVI - Green Optimized Soil Adjusted Vegetation Index, MCARI2 - Modified Chlorophyll Absorption Index-Improved, NDRE - Normalized Difference Red Edge, NDVI - Normalized Difference Vegetation Index, and OSAVI - Optimized Soil Adjusted Vegetation Index and WDVI - Weighted Difference Vegetation Index) and visual potato canopy status were determined. Further, this study aimed to identify factors that could improve the accuracy (decrease Mean Absolute Error - MAE) of potato MC estimation remotely. Results show that VIs derived from UAV imagery can be effectively used to remotely assign MCs to potato breeding lines, with higher accuracy for the potato B-clones (20 plants per plot) than the A-clones (6 plants per plot). Among the tested VIs, the NDRE allowed for potato MC evaluation with the lowest MAE. Applying NDRE for remote MC estimation using a validation dataset of potato B-clones (100 plants per plot), resulted in an MC estimate with a 0.81 MAE. However, the accuracy of potato MC estimation using UAV image-based methods should be improved by reducing the potato canopy's variability (increasing uniformity) within the plot. This could be achieved by minimizing 1) potato vines bending over the neighboring row, causing vine overlap between plots, and 2) plants damaged by tractor wheels during field operations. En el mejoramiento de la papa, la clase de madurez (CM) es un criterio de selecci & oacute;n crucial porque este es un aspecto cr & iacute;tico de la producci & oacute;n comercial de papa. Actualmente, la clasificaci & oacute;n de los genotipos de papa en MC se realiza visualmente, lo que requiere mucho tiempo y trabajo. El objetivo de esta investigaci & oacute;n fue utilizar & iacute;ndices de vegetaci & oacute;n (VIs) derivados de im & aacute;genes de veh & iacute;culos a & eacute;reos no tripulados (UAV) para asignar de forma remota MCs a plantas de papa cultivadas en ensayos, representando tres etapas tempranas diferentes dentro de un programa de mejoramiento de varios a & ntilde;os. Se determinaron las relaciones entre los VIs (GOSAVI - & Iacute;ndice de Vegetaci & oacute;n Ajustado al Suelo Optimizado Verde, MCARI2 - & Iacute;ndice de Absorci & oacute;n de Clorofila Modificado-Mejorado, NDRE - Borde Rojo de Diferencia Normalizada, NDVI - & Iacute;ndice de Vegetaci & oacute;n de Diferencia Normalizada, y OSAVI - & Iacute;ndice de Vegetaci & oacute;n Ajustado al Suelo Optimizado y WDVI - & Iacute;ndice de Vegetaci & oacute;n de Diferencia Ponderada) y la visualizaci & oacute;n del dosel de la papa. Adem & aacute;s, este estudio tuvo como objetivo identificar factores que podr & iacute;an mejorar la precisi & oacute;n (disminuir el Error Absoluto Medio - MAE) de la estimaci & oacute;n de MC de papa de forma remota. Los resultados muestran que los VI derivados de las im & aacute;genes de UAV se pueden utilizar de manera efectiva para asignar MC de forma remota a las l & iacute;neas de mejoramiento de papa, con mayor precisi & oacute;n para los clones B de papa (20 plantas por parcela) que para los clones A (6 plantas por parcela). Entre los VI probados, el NDRE permiti & oacute; la evaluaci & oacute;n de la MC de papa con el MAE m & aacute;s bajo. La aplicaci & oacute;n de NDRE para la estimaci & oacute;n remota de MC utilizando un conjunto de datos de validaci & oacute;n de clones B de papa (100 plantas por parcela), result & oacute; en una estimaci & oacute;n de MC con un MAE de 0.81. Sin embargo, la precisi & oacute;n de la estimaci & oacute;n de la MC de la papa utilizando m & eacute;todos basados en im & aacute;genes UAV debe mejorarse reduciendo la variabilidad del dosel de la papa (aumentando la uniformidad) dentro de la parcela. Esto podr & iacute;a lograrse minimizando 1) los tallos de papa que se doblan sobre el surco vecino, lo que causa la superposici & oacute;n de follaje entre las parcelas, y 2) las plantas da & ntilde;adas por las ruedas de los tractores durante las operaciones de campo.

Although several management options are adopted to redirect post-fire forest ecosystems towards less vulnerable and more resilient and functional communities, little is known about the interactions among tree stand age, prefire forest management, and slope aspects, and their consequences for plant species and soil properties recovery immediately after severe wildfires. To address this knowledge need, this study evaluates the post-fire changes in species richness and diversity (with a specific focus on regeneration mechanisms and life forms) of regenerating plants as well as the main physico-chemical and biological properties of burned soils with the reciprocal relations. Plant cover and diversity, and many soil properties have been monitored in forests of southeast Spain with mature, middle and young stands, presence of pre-fire treatments or not, and north and south hillslopes about one year after the fire. To this aim, the reciprocal relationships among soil properties and plants were evaluated adopting a combination of statistical techniques (PERMANOVA, Non-metric Multi-Dimensional Scaling, Distance-based Linear Modelling, Distance-based Redundancy Analysis, and Spearman correlation analysis). The damage to soil and vegetation was so high that both plants and pre-fire soil properties slowly recovered. Only a few life forms of vegetation (geophytes and herbaceous chamaephytes) were influenced by the stand age. If combined with soil aspect, stand age resulted in significantly lower germinating species in mature stands and lower resprouters in young stands, both on south hillslopes. Plant diversity was high, and the post-fire regeneration did not change the species richness and evenness. The post-fire changes in soil properties were limited, and only slight small differences in pH and betaglucosidase among stands of different age were found. No evident associations between soil properties and plant diversity were revealed by the low correlation coefficient. The low variance in plant cover and diversity, as well as in soil properties, resulted in a low accuracy of the dbRDA model to reproduce its variability among sites with different pre-fire characteristics.