Green manure (GM) boosts sustainable agriculture by enhancing soil fertility and crop yields. However, the dynamics of nitrogen (N) release from different GM residues into soil and their supply patterns to wheat remain unclear. Clarifying residue N mineralization during the growing season and its contribution to soil N fractions and wheat is key to optimizing GM use strategies. An in-situ decomposition experiment using 15N-labeled soybean (SB) and sudangrass (SG) GM was conducted in a wheat field on the Loess Plateau. Soil and wheat samples were collected at different wheat growth stages. We determined GM-N mineralization, wheat N uptake, and yield at maturity. The contribution and recovery rate of residue N to soil total N (STN), particulate organic N (PON), microbial biomass N (MBN), available N (NH4⁺ and NO3⁻), and to the wheat were also measured. Our results showed that the GM-N mineralization rate and amount from SB consistently exceeded those from SG throughout the wheat growth stages. At maturity, compared to the CK, SB and SG treatments increased wheat N uptake by 112% and 47%, and yield by 65.30% and 39.09%, respectively. The contribution of SB-N to wheat was 8.23 times greater than that of SG. During the wheat season, SB exhibited higher soil N distribution and recovery rates than SG. Before green-up, SB showed greater distribution and recovery rates in PON and MBN than SG. However, during jointing and heading stages, SG demonstrated higher recovery rates in PON and MBN than SB. SB residue N was predominantly accumulated in PON and MBN pools in the early growth stage, which may act as transient N reservoirs and potentially contribute to subsequent wheat N uptake. In contrast, SG’s contribution was more consistent with direct N mineralization supplying available N. Correlation analysis revealed a positive relationship between GM-N contributions to soil N fractions and wheat N uptake, which was closely associated with yield formation. Legume residue N is initially retained in PON and MBN pools, and may subsequently contribute to plant N supply during peak wheat demand after green-up, thereby showing greater temporal synchrony. In contrast, the asynchrony of the non-legume residue N supply may be partly explained by intense early-stage microbial N immobilization competition and limited storage in soil organic N. This demonstrates the greater potential of legume GM for enhancing soil fertility and crop yields.
Migration is challenging for birds, especially juveniles, who experience high mortality rates during migration. The challenge is exacerbated in the Anthropocene, contributing to widespread population declines. Conservation efforts focused on increasing juvenile survival could bolster population recovery. Understanding how age structure of the migrant community shifts throughout migration could inform conservation efforts and future questions of migration ecology. However, it is unknown whether the age structure of the migrant community shifts spatially or temporally during migration. To answer these questions, we first analyzed age-related differences in migration speed and timing of departure during fall migration using 6 567 747 banding encounters, as variability in these components of migration could generate shifts in community demographics. We found widespread differences in migration speed (km d-1) with adults being faster than juveniles in most species, and departure timing differences tied to adult molt. Our analyses revealed shifts in community demographics, with the proportion of juveniles within the community decreasing at northerly latitudes throughout migration. We also determined that demographics have shifted over 53 years, with the proportion of juveniles increasing in the north, and decreasing in the south. Our findings contribute to our knowledge of migration ecology, and our understanding of community shifts over time.
Desertification is one of the main threats to high Andean ecosystems, particularly in arid and semi-arid regions subject to increasing climatic and anthropogenic pressures. This study evaluated the spatial-temporal dynamics of desertification in the province of Candarave (Tacna, Peru) by integrating the Remote Sensing-based Desertification Index (RSDI), constructed from a principal component analysis incorporating four biophysical indicators: vegetation greenness, surface moisture, soil grain size, and fraction of solar radiation reflected (albedo), derived from Landsat 5 and 8 satellite images processed in Google Earth Engine. Temporal trends were analyzed using the Mann-Kendall test, while system stability was evaluated using the coefficient of variation, allowing different degrees of stability and environmental degradation to be characterized during the period 2010-2025. The results show that moderate and severe desertification classes predominate in higher altitude areas, covering approximately 92% of the study area, and are characterized by insignificant to weakly significant negative trends associated with high to relatively high temporal volatility. In contrast, stable areas with no significant changes represent 5.3% of the territory, while restoration processes occupy a small proportion, close to 2.7%. The high variability observed in the high Andean sectors is mainly linked to the interaction between reduced water availability, climate variability, and extreme events, as well as anthropogenic pressures, particularly overgrazing and aquifer exploitation. This multitemporal analysis allows us to anticipate the evolution of desertification and highlights the need to strengthen conservation planning in order to reduce the degradation of strategic high Andean ecosystems in the Tacna region.
High-throughput phenotyping (HTP) techniques have brought new opportunities to understand and evaluate key traits in plant breeding programs. Combining multiple measures through time and random regression models permits a more comprehensive understanding of the genetic and environmental effects on trait expression over time. This study aims to understand the genetic basis of biomass accumulation in winter wheat and how this biomass is related to grain yield using unmanned aerial vehicle (UAV)-based vegetation indices. A large panel of 596 soft red winter wheat genotypes was evaluated for agronomic performance in six environments to verify the ability of HTPs to predict grain yield using multivariate genomic prediction and random regression with Legendre polynomials to model growth through time. An additional set of 22 breeding lines was directly measured for above-ground biomass, serving as a ground truth for the HTP-derived biomass estimates. Cumulative vegetation indices were found to be a reliable method to infer biomass accumulation. Vegetation indices capture reliable phenotypes but exhibit low and inconsistent genetic correlation to grain yield, especially when incorporating residual covariance between traits. Predictive abilities of grain yield increased when using vegetation indices as a secondary trait in a multi-trait genomic prediction model, but increases were highly variable across environments and growing stages, which may be confounded by micro-environmental variation and lead to biased estimates of true genetic merit. Our results suggest that UAV-based vegetation indices can be used to understand genetic parameters of biomass accumulation, but wheat breeders should use caution in their use as proxies for grain yield.
Sensemaking plays a critical role in social media, which features ambiguous, equivocal, and dynamic information, views, and opinions. While existing research has focused on sensemaking with respect to text entries related to extreme events, we examine sensemaking with respect to video content on social media. Drawing on sensemaking theories, we investigate the relationships between video features and sensemaking activities. Specifically, we analyze how information control, cue, and noise in YouTube videos affect sensemaking activities on YouTube and Reddit. Our findings reveal both similarities and differences in how these factors relate to sensemaking activities across the two platforms. This research enriches the current literature on social media sensemaking and provides insights for designing videos that better facilitate sensemaking activities and enhance user experiences.
