MLRYR-Rice10 is the 10-m rice product over the Middle and Lower Reaches of the Yangtze River (MLRYR) in China for five consecutive years (2019-2023). The product captures rice distribution under diverse cropping patterns and crop establishment methods. It was generated by the phenological knowledge-guided automatic rice mapping approach using optical and synthetic aperture radar (SAR) data (PHAROS) . The technical details can be seen in the paper (Li et al., 2026). In the MLRYR-Rice10, pixel values are coded as follows: 1 indicates single-season rice, 2 indicates double-season rice, and 0 represents other land-cover types (non-rice). In the new version (Version v2),  we additionally provide the PHAROS-generated rice distribution map for Punjab Province, Pakistan (2024), in which pixels labeled 1 represent rice and 0 represent non-rice.

MLRYR-Rice10 is the 10-m rice product over the Middle and Lower Reaches of the Yangtze River (MLRYR) in China for five consecutive years (2019-2023). The product captures rice distribution under diverse cropping patterns and crop establishment methods. It was generated by the phenological knowledge-guided automatic rice mapping approach using optical and synthetic aperture radar (SAR) data (PHAROS) . The technical details can be seen in the paper (Li et al., 2026). In the MLRYR-Rice10, pixel values are coded as follows: 1 indicates single-season rice, 2 indicates double-season rice, and 0 represents other land-cover types (non-rice).In the new version (Version v2),  we additionally provide a PHAROS-generated rice distribution map for Punjab Province, Pakistan (2024), in which pixels labeled 1 represent rice and 0 represent non-rice.

MLRYR-Rice10 is the 10-m rice product over the Middle and Lower Reaches of the Yangtze River (MLRYR) in China for five consecutive years (2019-2023). The product captures rice distribution under diverse cropping patterns and crop establishment methods. It was generated by the phenological knowledge-guided automatic rice mapping approach using optical and synthetic aperture radar (SAR) data (PHAROS) . The technical details can be seen in the paper (Li et al., 2026). In the MLRYR-Rice10, pixel values are coded as follows: 1 indicates single-season rice, 2 indicates double-season rice, and 0 represents other land-cover types (non-rice).In the new version (Version v2),  we additionally provide a PHAROS-generated rice distribution map for Punjab Province, Pakistan (2024), in which pixels labeled 1 represent rice and 0 represent non-rice.

The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models. The R code and formatted data used for the statistical analyses are included.

The dataset contains 6 growing seasons of a local winter wheat cultivar ‘Wakanui’ at two farms located in the Canterbury Region of New Zealand. The data of the experiment was used in the AgMIP-Wheat Phase 4 project to evaluate the performance of an ensemble of 29 crop models to predict the effect of changing sowing dates and rates on yield and yield components, in a high-yielding environment. The treatments were managed for non-stress conditions. Data include local daily weather, soil characteristics and initial soil N conditions, crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, and yield components), and cultivar information. Simulations include both daily in-season and end-of-season results from 29 wheat crop models.

The dataset reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.

The data set contains a portion of the International Heat Stress Genotype Experiment(IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.