Agriculture is the backbone of many economies across the globe

Because Cadmium (Cd) is harmful to humans, and most non-smokers are exposed to Cd mainly through rice consumption, low-Cd rice breeding is urgently needed. It might not be possible to apply variation created using gene editing technology to breeding directly, so it is important to explore genetic variation in this trait in a natural population. In this study, variation in 4 genes was identified among 3024 accessions from the International Rice Research Institute 3000 Rice Genome Project (IRRI 3K-RGP) and 71 other important varieties, and the relationships between the variants and plant Cd accumulation were validated with hydroponic and pot experiments. Variants in OsNRAMP1, OsNRAMP5, OsLCD, and OsHMA3 were grouped into two, four, three, and two haplotypes, respectively. Fourteen combinations of these haplotypes, which were referred to as Cd-mobile types, were found in the collection. Of these, type 14 was shown to have the greatest potential for low-Cd accumulation, and functional markers for this type were designed. The results of this study provide an important resource for low-Cd rice breeding and highlight an effective strategy for pre-breeding programs.

Euro-Mediterranean croplands are increasingly exposed to co-occurrent climate extremes that threaten productivity. Using satellite-derived Gross Primary Production (2000-2024) across nine major regions, we assessed the resilience of seasonal crop productivity under drought and heat stress. Recovery after winter deficits was rare (3/15 cases, 20%), whereas summer deficits were followed by recovery in the subsequent winter (16/23, 70%). Non-recovery after summer deficits (7/23) consistently coincided with severe prolonged droughts (median affected area ≈80.6%) and positive surface temperature anomalies (median +1.76 °C). By contrast, non-recovery after winter deficits (12/15) was linked either to hot-dry conditions or, less frequently, to cold anomalies with little drought; recovery occurred only when climatic anomalies were weak or absent. These results show that winter conditions set the baseline for annual productivity and constrain summer resilience. Building on this asymmetry, we propose a framework integrating drought-heat indices into early-warning systems and adaptation planning for Euro-Mediterranean agriculture.

Soil particle size fractions (PSF) directly affect the movement and retention of water, nutrients, and air, which are critical for providing optimal conditions for crop growth. This study applies three log-ratio transformation techniques for compositional data: additive log-ratio (ALR), centered log-ratio (CLR), and isometric log-ratio (ILR). Digital soil mapping (DSM) combined environmental covariates (satellite images, terrain features, and climatic data) and machine learning (ML) models (Cubist and random forest (RF)) to create a predictive map for soil particle size distribution across Taiwan. Model accuracy was evaluated using R², root mean squared error (RMSE), Aitchison’s distance (AD), and the right ratio of the predicted soil texture types (RR). The analysis revealed that CLR transformation combined with RF (RF_CLR) had the best performance, with the highest R² values (sand: 0.59, silt: 0.29, clay: 0.51), as well as the lowest RMSE (sand: 16.51%, silt: 10.65%, clay: 8.45%) and AD (0.08). The accuracy of RR (45%) was consistent across different log-ratio transformation methods. The various sampling sizes influence the computational efficiency of the data. Therefore, different sampling sizes were tested for the best-performing combination (RF_ CLR). When the sampling size was less than 50% of the original sampling size (N = 22,000), the prediction performance of PSF showed a significant decline. Our findings can serve as a valuable reference for soil management and crop cultivation planning in Taiwan.

Bare-soil mapping is essential for agricultural monitoring, land-surface characterization, and environmental modelling, supporting applications such as soil organic carbon (SOC) estimation, evapotranspiration retrieval, erosion assessment, and land degradation monitoring. However, accurate detection of exposed soil remains challenging due to spectral confusion with sparse vegetation and crop residues, strong seasonal variability, and the heterogeneous structure of agricultural landscapes. The Harmonized Landsat–Sentinel (HLS) dataset, providing 30 m spatial resolution and a 5-day revisit cycle, offers new opportunities for multi-temporal bare-soil mapping. This study develops an automated workflow combining spectral indices and a machine-learning algorithm (Maximum Entropy, MaxEnt) to map bare soil across agricultural regions using HLS surface reflectance imagery. Multiple indices capturing vegetation–soil contrasts were employed, including NDVI, BSI, NDMI, SAVI/GSAVI, NBR, EVI, and DBSI. High-confidence bare-soil pixels were first identified using a rule-based approach with strict thresholds (e.g., NDVI < 0.2 and BSI > 0.4–0.7), which minimized commission errors and generated reliable presence samples for model calibration. To improve generalization across different day-of-year (DOY) mosaics, these samples were integrated into a presence-background modelling framework using the MaxEnt algorithm (maxnet). Background samples were constrained to non-bare conditions (e.g., NDVI ≥ 0.3). Model performance was evaluated using AUC, Kappa, sensitivity, and specificity, while permutation importance and jackknife analyses quantified predictor contributions. The model achieved an AUC of 0.9 and a Kappa value of 0.7, indicating strong discriminative ability and substantial agreement. NDVI and BSI were identified as the most influential predictors. The resulting products include DOY-specific bare-soil probability maps, binary masks, and aggregated bare-soil frequency maps, providing a robust and scalable framework for long-term agricultural and soil-related applications.