Optimal Level of Straw Addition After the Autumn Harvest for Black Soil Aggregate Stability

Land Degradation and Development, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

ABSTRACT In Northeast China, straw residues are integrated into fields to improve the soil structure and fertility after autumn harvest. However, optimal amount of addition is unclear. To determine whether an increase in correlated with aggregate stability, study focused on black cropland was conducted through field incubation experiment (lasting 150 days) during seasonal freeze–thaw periods, implemented six different treatments: CK (0%), SA1 (1%, i.e., 10 g per kg soil), SA3 (3%), SA5 (5%), SA7 (7%), SA9 (9%). The results revealed that under conditions, stability significantly increased only when ≥ 5%. At this level, enhanced two ways. First, decomposition SOC content, which serves as a binding substance for aggregates promotes formation > 0.25 mm. Second, particles combined form straw‐soil composite macro‐aggregates exhibited high water stability. not positively amount. This because 5% sufficient reach carbon saturation, content showed no significant change further increasing addition. Moreover, excessive led nitrogen limitation slowed down rate but also wasted resources. Therefore, improving These findings provide theoretical basis how rational design return measures, thereby conditions spring sowing seedling emergence China.

Language: Английский

Assessing Land Cover Changes Using the LUCAS Database and Sentinel Imagery: A Comparative Analysis of Accuracy Metrics

Applied Sciences, Journal Year: 2024, Volume and Issue: 15(1), P. 240 - 240

Published: Dec. 30, 2024

Classification of remote sensing images using machine learning models requires a large amount training data. Collecting this data is both labor-intensive and time-consuming. In study, the effectiveness pre-existing reference on land cover gathered as part Land Use–Land Cover Area Frame Survey (LUCAS) database Copernicus program was analyzed. The classification carried out in Google Earth Engine (GEE) Sentinel-2 that were specially prepared to account for phenological development plants. performed SVM, RF, CART algorithms GEE, with an in-depth accuracy analysis conducted custom tool. Attention given reliability different metrics, particular focus widely used (ML) metric “accuracy”, which should not be compared commonly “overall accuracy”, due potential significant artificial inflation accuracy. LUCAS 2018 at Level-1 detail estimated 86%. Using updated dataset, best result achieved RF method, 83%. An overestimation approximately 10% observed when reporting average ACC ML instead overall OA metric.

Language: Английский