The yield on US 10 Year Note Bond Yield rose to 4.38% on July 30, 2025, marking a 0.05 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.14 points and is 0.34 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. US 10 Year Treasury Bond Note Yield - values, historical data, forecasts and news - updated on July of 2025.

Graph and download economic data for Interest Rates: Long-Term Government Bond Yields: 10-Year: Main (Including Benchmark) for United States (IRLTLT01USM156N) from Apr 1953 to May 2025 about long-term, 10-year, bonds, yield, government, interest rate, interest, rate, and USA.

The yield on 10 Year TIPS Yield rose to 1.96% on July 30, 2025, marking a 0.04 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.01 points and is 0.08 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. This dataset includes a chart with historical data for the United States 10 Year TIPS Yield.

The yield on Japan 10Y Bond Yield eased to 1.56% on July 31, 2025, marking a 0.01 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.16 points and is 0.52 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Japan 10 Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

The yield on India 10Y Bond Yield eased to 6.37% on July 31, 2025, marking a 0 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.01 points, though it remains 0.54 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. India 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

The yield on China 10Y Bond Yield eased to 1.74% on July 31, 2025, marking a 0 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.10 points, though it remains 0.39 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. China 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

France Government Bond Yield: Monthly Average: 10 Years data was reported at 3.295 % in Apr 2025. This records a decrease from the previous number of 3.478 % for Mar 2025. France Government Bond Yield: Monthly Average: 10 Years data is updated monthly, averaging 4.046 % from Jan 1986 (Median) to Apr 2025, with 472 observations. The data reached an all-time high of 10.704 % in Oct 1987 and a record low of -0.343 % in Aug 2019. France Government Bond Yield: Monthly Average: 10 Years data remains active status in CEIC and is reported by Banque de France. The data is categorized under Global Database’s France – Table FR.M008: Government Bond Yield: Monthly Average.

China Bond Yield: Treasury Bond: 10 Year data was reported at 1.679 % pa in 16 May 2025. This records a decrease from the previous number of 1.680 % pa for 15 May 2025. China Bond Yield: Treasury Bond: 10 Year data is updated daily, averaging 3.254 % pa from Mar 2006 (Median) to 16 May 2025, with 4806 observations. The data reached an all-time high of 4.722 % pa in 20 Nov 2013 and a record low of 1.596 % pa in 06 Feb 2025. China Bond Yield: Treasury Bond: 10 Year data remains active status in CEIC and is reported by China Central Depository & Clearing Co., Ltd. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MF: PBC & CCDC: Treasury Bond and Other Bond Yield: Daily.

The yield on France 10Y Bond Yield rose to 3.36% on July 30, 2025, marking a 0.01 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.11 points and is 0.35 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. France 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

The yield on Germany 10Y Bond Yield eased to 2.68% on July 31, 2025, marking a 0.03 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.11 points and is 0.43 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Germany 10-Year Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

Zambia GRZ Bond Yield: Weighted Average: 10 Years data was reported at 19.500 % pa in Oct 2018. This records a decrease from the previous number of 20.000 % pa for Sep 2018. Zambia GRZ Bond Yield: Weighted Average: 10 Years data is updated monthly, averaging 18.400 % pa from Aug 2007 (Median) to Oct 2018, with 135 observations. The data reached an all-time high of 25.000 % pa in Jan 2017 and a record low of 14.000 % pa in Oct 2010. Zambia GRZ Bond Yield: Weighted Average: 10 Years data remains active status in CEIC and is reported by Bank of Zambia. The data is categorized under Global Database’s Zambia – Table ZM.M004: Treasury Bills and Bonds Yield: Weighted Average.

Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in rivers across the Chesapeake Bay watershed have been calculated using monitoring data from the Chesapeake Bay Nontidal network (NTN) stations for the period 1985 through 2018. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). Yields (represents the mass of constituent transported from a unit area of a given watershed) are used to compare the export loads from one basin to another. Yield results are obtained by dividing the annual load (pounds) of a given constituent by the respective watershed area (acres) from which the constituent was transported. Yield results presented represent the average annual per-acre loads of nitrogen, phosphorus, and suspended sediment exported from each of the Chesapeake Bay nontidal network stations for two possible time periods: 2009-2018 (10 year average yield) and 2014-2018 (5 year average yield). The file containing 10- and 5-year average yields for all applicable NTN monitoring stations is provided in the "Attached Files" section.

The yield on Kenya 10Y Bond Yield rose to 13.53% on July 28, 2025, marking a 0.03 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.09 points, though it remains 3.53 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Kenya 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

Vietnam Government Bond: Yield Spot: Remaining Maturity: 10 years data was reported at 4.997 % pa in 03 Dec 2018. This records a decrease from the previous number of 5.010 % pa for 30 Nov 2018. Vietnam Government Bond: Yield Spot: Remaining Maturity: 10 years data is updated daily, averaging 6.992 % pa from Mar 2013 (Median) to 03 Dec 2018, with 1427 observations. The data reached an all-time high of 11.310 % pa in 20 Sep 2013 and a record low of 4.015 % pa in 05 Mar 2018. Vietnam Government Bond: Yield Spot: Remaining Maturity: 10 years data remains active status in CEIC and is reported by Hanoi Stock Exchange. The data is categorized under Daily Database’s Government & Other Securities – Table VN.DB001: Goverment Bond: Yield Spot.

Yield Editor is a tool which allows the user to select, apply and analyze a variety of automated filters and editing techniques used to process and clean yield data. The software imports either AgLeader advanced or Greenstar text file formats, and exports data in a delimited ASCII format. Yield Editor 2.0.7 includes some of the improvements and updates that users of the software have asked to be included. It provides three major improvements over version 1.0.2. The most important of these is the inclusion of a module for automated selection of many yield filter values, as well as a couple of additional automated filter types. A legend tool has been added which allows for the viewing of multiple data streams. Finally, a command line interface language under development allows for automated batch mode processing of large yield datasets. Yield maps provide important information for developing and evaluating precision management strategies. The high-quality yield maps needed for decision-making require screening raw yield monitor datasets for errors and removing them before maps are made. To facilitate this process, we developed the Yield Editor interactive software which has been widely used by producers, consultants and researchers. Some of the most difficult and time consuming issues involved in cleaning yield maps include determination of combine delay times, and the removal of “overlapped” data, especially near end rows. Our new Yield Editor 2.0 automates these and other tasks, significantly increasing the reliability and reducing the difficulty of creating accurate yield maps. This paper describes this new software, with emphasis on the Automated Yield Cleaning Expert (AYCE) module. Application of Yield Editor 2.0 is illustrated through comparison of automated AYCE cleaning to the interactive approach available in Yield Editor 1.x. On a test set of fifty grain yield maps, AYCE cleaning was not significantly different than interactive cleaning by an expert user when examining field mean yield, yield standard deviation, and number of yield observations remaining after cleaning. Yield Editor 2.0 provides greatly improved efficiency and equivalent accuracy compared to the interactive methods available in Yield Editor 1.x. Resources in this dataset:Resource Title: Yield Editor 2.0.7. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=370&modecode=50-70-10-00 download page: https://www.ars.usda.gov/research/software/download/?softwareid=370&modecode=50-70-10-00

Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in major rivers across the Chesapeake Bay watershed have been calculated using monitoring data from the Chesapeake Bay River Input Monitoring (RIM) Network stations for the period 1985 through 2020. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). Yields (representing the mass of constituent transported from a unit area of a given watershed) are used to compare the export loads from one basin to another. Yield results are obtained by dividing the annual load (pounds) of a given constituent by the respective watershed area (acres) from which the constituent was transported. Yield results presented represent the average annual per-acre loads of nitrogen, phosphorus, and suspended sediment exported from each of the Chesapeake Bay River Input Monitoring stations for two possible time periods: 2011-2020 (10 year average yield) and 2016-2020 (5 year average yield).

The yield on US 30 Year Bond Yield eased to 4.87% on July 31, 2025, marking a 0.03 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.10 points and is 0.59 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. United States 30 Year Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

The yield on Canada 10Y Bond Yield eased to 3.47% on July 29, 2025, marking a 0.06 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.20 points and is 0.24 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Canada 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecasting

Overview

CY-Bench is a dataset and benchmark for subnational crop yield forecasting, with coverage of major crop growing countries of the world for maize and wheat. By subnational, we mean the administrative level where yield statistics are published. When statistics are available for multiple levels, we pick the highest resolution. The dataset combines sub-national yield statistics with relevant predictors, such as growing-season weather indicators, remote sensing indicators, evapotranspiration, soil moisture indicators, and static soil properties. CY-Bench has been designed and curated by agricultural experts, climate scientists, and machine learning researchers from the AgML Community, with the aim of facilitating model intercomparison across the diverse agricultural systems around the globe in conditions as close as possible to real-world operationalization. Ultimately, by lowering the barrier to entry for ML researchers in this crucial application area, CY-Bench will facilitate the development of improved crop forecasting tools that can be used to support decision-makers in food security planning worldwide.

* Crops : Wheat & Maize
* Spatial Coverage : Wheat (29 countries), Maize (38).
See CY-Bench paper appendix for the list of countries.
* Temporal Coverage : Varies. See country-specific data

Data

Data format

The benchmark data is organized as a collection of CSV files, with each file representing a specific category of variable for a particular country. Each CSV file is named according to the category and the country it pertains to, facilitating easy identification and retrieval. The data within each CSV file is structured in tabular format, where rows represent observations and columns represent different predictors related to a category of variable.

Data content

All data files are provided as .csv.

Folder structure

The CY-Bench dataset has been structure at first level by crop type and subsequently by country. For each country, the folder name follows the ISO 3166-1 alpha-2 two-character code. A separate .csv is available for each predictor data and crop calendar as shown below. The csv files are named to reflect the corresponding country and crop type e.g. **variable_croptype_country.csv**.
```
CY-Bench
│
└─── maize
│ │
│ └─── AO
│ │ -- crop_calendar_maize_AO.csv
│ │ -- fpar_maize_AO.csv
│ │ -- meteo_maize_AO.csv
│ │ -- ndvi_maize_AO.csv
│ │ -- soil_maize_AO.csv
│ │ -- soil_moisture_maize_AO.csv
│ │ -- yield_maize_AO.csv
│ │
│ └─── AR
│ -- crop_calendar_maize_AR.csv
│ -- fpar_maize_AR.csv
│ -- ...
│
└─── wheat
│ │
│ └─── AR
│ │ -- crop_calendar_wheat_AR.csv
│ │ -- fpar_wheat_AR.csv
│ │ ...
```

Example : CSV data content for maize in country X

```
X
└─── crop_calendar_maize_X.csv
│ -- crop_name (name of the crop)
│ -- adm_id (unique identifier for a subnational unit)
│ -- sos (start of crop season)
│ -- eos (end of crop season)
│
└─── fpar_maize_X.csv
│ -- crop_name
│ -- adm_id
│ -- date (in the format YYYYMMdd)
│ -- fpar
│
└─── meteo_maize_X.csv
│ -- crop_name
│ -- adm_id
│ -- date (in the format YYYYMMdd)

│ -- tmin (minimum temperature)
│ -- tmax (maximum temperature)
│ -- prec (precipitation)
│ -- rad (radiation)
│ -- tavg (average temperature)
│ -- et0 (evapotranspiration)
│ -- cwb (crop water balance)
│
└─── ndvi_maize_X.csv
│ -- crop_name
│ -- adm_id
│ -- date (in the format YYYYMMdd)
│ -- ndvi
│
└─── soil_maize_X.csv
│ -- crop_name
│ -- adm_id
│ -- awc (available water capacity)
│ -- bulk_density
│ -- drainage_class
│
└─── soil_moisture_maize_X.csv
│ -- crop_name
│ -- adm_id
│ -- date (in the format YYYYMMdd)
│ -- ssm (surface soil moisture)
│ -- rsm ()
│
└─── yield_maize_X.csv
│ -- crop_name
│ -- country_code
│ -- adm_id
│ -- harvest_year
│ -- yield
│ -- harvest_area
│ -- production

Data access

The full dataset can be downloaded directly from Zenodo or using the ```zenodo_get``` library

License and citation

We kindly ask all users of CY-Bench to properly respect licensing and citation conditions of the datasets included.

These raster datasets are the inputs for a spatial water yield model applied to an 11 county area in the state of Florida's panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in the associated publication. The five input datasets required for this water yield analysis are: 1) a model of pine species basal area, named "ARSA_PineBA_10m" 2) a binary depth to water table raster named "DTW_cm_binary2" , and 3) three spatial aridity index raster dataset named "Aridity_Min", "Aridity_Max" and "Aridity_Mean", created from potential evapotranspiration, and precipitation raster datasets. The min max and mean codifiers relate to the range of aridity values found in our dataset of 7 year temporal range, from MODIS PET and PRISM percipitation yearly data. All input and output data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution.