Rotational tillage could be an advisable attempt to overcome some of the adverse impacts of mono conservation tillage, and it is necessary to assess the feasibility of adoption of rotational tillage for sustaining productivity in the long run. Data from an 8-year site-specific field study conducted on the Loess Plateau were used to estimate the long-term effect of rotational tillage on soil water dynamic, soil properties and winter wheat (Triticum aestivum L.) productivity. Three mono-tillage (No tilling (NT), subsoiling (ST) and ploughing (PT)) and three rotational tillage (NT/ST (NT and ST performed alternately), ST/PT, PT/NT) methods were applied after wheat harvest. Results showed the mean grain weight in the three rotational tillage treatments was 4.5% to 16.9% greater than in NT, and water use efficiency (WUE) was 5.0% to 18.8% greater over the 8 years. Rotational tillage could overcome the increased bulk density and nutrition stratification caused by NT and soil degradation due to PT. NT/ST was the best rotational tillage pattern with the highest grain yield and WUE, best soil property and relatively low mechanical cost in the present study. Here, we demonstrate that rotational tillage can improve wheat yield, WUE and soil properties compared with long-term no tilling and recommend using NT/ST as the optimal tillage pattern in similar ecological regions.

Accurate irrigated cropland mapping is critical for agricultural water management and food security. Existing image-based irrigation mapping workflows primarily rely on vegetation indices and synthetic aperture radar (SAR) backscatter features, which have limited capacity to characterize the temporal evolution of irrigation processes and crop growth conditions. The AlphaEarth Foundation (AEF) model developed by Google DeepMind provides compact embeddings with temporal semantic information learned via self-supervision, yet their utility for irrigation mapping has not been systematically assessed. In this study, a comprehensive assessment of AEF embeddings for irrigated cropland mapping was performed in terms of feature separability, classification performance, and spatiotemporal transferability. Experiments were conducted in two representative irrigated regions: the Guanzhong Plain in China and Kansas in the USA. Class separability of the 64 embedding dimensions was quantified using the Jeffries–Matusita (JM) distance. Then, the AEF embeddings were compared with the Sentinel feature set (Sentinel-2 bands, normalized difference vegetation index(NDVI), enhanced vegetation index(EVI), normalized difference water index(NDWI) and Sentinel-1 vertical transmit vertical receive(VV), vertical transmit horizontal receive(VH)) using K-means clustering and supervised classifiers, including Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Finally, transfer experiments across 2022 and 2024 in the Guanzhong Plain and Kansas were conducted to examine cross-year and cross-region performance. The results showed that AEF embeddings consistently provide stronger class separability in both study areas, with a maximum JM distance of 1.58 (A29). Using AEF embeddings, RF achieved overall accuracies (OA) of 0.95 in the Guanzhong Plain and 0.93 in Kansas, outperforming models based on Sentinel-1/2 bands and indices. Notably, unsupervised K-means clustering on AEF embeddings yielded OA > 0.85, indicating high intrinsic separability between irrigated and rainfed croplands. Transfer experiments further demonstrate stable temporal transfer (cross-year OA > 0.87), whereas cross-region transfer is constrained by differences in irrigation regimes, crop phenology and management practices, resulting in limited spatial generalization (OA~0.3). Overall, this study demonstrates the potential of high-information-density representations from geospatial foundation models for irrigated cropland mapping and provides methodological and technical insights to support transfer learning and operational mapping over large areas.