As of December 30, 2024, ** economies reported a negative value for their ten year minus two year government bond yield spread: Ukraine with a negative spread of ***** percent; Turkey, with a negative spread of 1332 percent; Nigeria with **** percent; and Russia with **** percent. At this time, almost all long-term debt for major economies was generating positive yields, with only the most stable European countries seeing smaller values. Why is an inverted yield curve important? Often called an inverted yield curve or negative yield curve, a situation where short term debt has a higher yield than long term debt is considered a main indicator of an impending recession. Essentially, this situation reflects an underlying belief among a majority of investors that short term interest rates are about to fall, with the lowering of interest rates being the orthodox fiscal response to a recession. Therefore, investors purchase safe government debt at today's higher interest rate, driving down the yield on long term debt. In the United States, an inverted yield curve for an extended period preceded (almost) all recent recessions. The exception to this is the economic downturn caused by the coronavirus (COVID-19) pandemic – however, the U.S. ten minus two year spread still came very close to negative territory in mid-2019. Bond yields and the coronavirus pandemic The onset of the coronavirus saw stock markets around the world crash in March 2020. This had an effect on bond markets, with the yield of both long term government debt and short term government debt falling dramatically at this time – reaching negative territory in many countries. With stock values collapsing, many investors placed their money in government debt – which guarantees both a regular interest payment and stable underlying value - in contrast to falling share prices. This led to many investors paying an amount for bonds on the market that was higher than the overall return for the duration of the bond (which is what is signified by a negative yield). However, the calculus is that the small loss taken on stable bonds is less that the losses likely to occur on the market. Moreover, if conditions continue to deteriorate, the bonds may be sold on at an even higher price, partly offsetting the losses from the negative yield.

As of July 18, 2025, the major economy with the highest yield on 10-year government bonds was Turkey, with a yield of ** percent. This is due to the risks investors take when investing in Turkey, notably due to high inflation rates potentially eradicating any profits made when using a foreign currency to investing in securities denominated in Turkish lira. Of the major developed economies, United Kingdom had one the highest yield on 10-year government bonds at this time with **** percent, while Switzerland had the lowest at **** percent. How does inflation influence the yields of government bonds? Inflation reduces purchasing power over time. Due to this, investors seek higher returns to offset the anticipated decrease in purchasing power resulting from rapid price rises. In countries with high inflation, government bond yields often incorporate investor expectations and risk premiums, resulting in comparatively higher rates offered by these bonds. Why are government bond rates significant? Government bond rates are an important indicator of financial markets, serving as a benchmark for borrowing costs, interest rates, and investor sentiment. They affect the cost of government borrowing, influence the price of various financial instruments, and serve as a reflection of expectations regarding inflation and economic growth. For instance, in financial analysis and investing, people often use the 10-year U.S. government bond rates as a proxy for the longer-term risk-free rate.

This dataset provides values for 30 YEAR BOND YIELD reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Government Bond Yields: Long Term: Month Avg: EU 27 excl UK data was reported at 3.570 % in Mar 2025. This records an increase from the previous number of 3.320 % for Feb 2025. Government Bond Yields: Long Term: Month Avg: EU 27 excl UK data is updated monthly, averaging 3.500 % from Jan 2001 (Median) to Mar 2025, with 291 observations. The data reached an all-time high of 5.610 % in Jul 2001 and a record low of 0.060 % in Dec 2020. Government Bond Yields: Long Term: Month Avg: EU 27 excl UK data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s European Union – Table EU.M019: Eurostat: Long Term Government Bond Yield: Monthly Average: By Countries.

Reliable estimates of the impacts of climate change on crop production are critical for assessing the sustainability of food systems. Global, regional, and site-specific crop simulation studies have been conducted for nearly four decades, representing valuable sources of information for climate change impact assessments. However, the wealth of data produced by these studies has not been made publicly available. Here, we develop a global dataset by consolidating previously published meta-analyses and data collected through a new literature search covering recent crop simulations. The new global dataset builds on 8703 simulations from 202 studies published between 1984 and 2020. It contains projected yields of four major crops (maize, rice, soybean, and wheat) in 91 countries under major emission scenarios for the 21st century, with and without adaptation measures, along with geographical coordinates, current temperatures, local and global warming levels. This dataset provides a solid basis for a quantitative assessment of the impacts of climate change on crop production and will facilitate the rapidly developing data-driven machine learning applications.

The Global Dataset of Historical Yield (GDHYv1.2+v1.3) offers annual time series data of 0.5-degree grid-cell yield estimates of major crops worldwide for the period 1981-2016. The crops considered in this dataset are maize, rice, wheat and soybean. The unit of yield data is t/ha. The grd-cell yield data were estimated using the satellite-derived crop-specific vegetation index and FAO-reported country yield statistics. Maize and rice have the data for each of two growing seasons (major/secondary). "Winter" and "spring" are used as the growing season categories for wheat. Only "major" growing season is available for soybean. These growing season categories are based on Sacks et al. (2010, DOI: 10.1111/j.1466-8238.2010.00551.x). The geographic distribution of harvested area changes with time in reality, but we used the time-constant data in 2000 (Monfreda et al., 2008, doi:10.1029/2007GB002947). Many missing values are found in the first (1981) and last (2016) years because grid-cell yields are not estimated for these years when growing season spans two calendar years. The data for the period 1981-2010 are the same with the version 1.2 ( https://doi.org/10.20783/DIAS.528). For the period 2011-2016, a newly created version 1.3 using the satellite products that are different with earlier versions was alighned to ensure the continuity of yield time series. This version is therefore called "the alighned version v1.2+v1.3".

By compiling a global dataset of 7,350 yield observations comparing CA with conventional systems from 397 experiments—each comprising at least three years of continuous yield data for the same crop—across 56 countries on all continents, we developed a quantitative framework to assess how CA determines crop productivity resilience to climate extremes.

Greece Government Bond Yield: Average: 3 Years data was reported at 2.690 % pa in Dec 2017. This records a decrease from the previous number of 2.980 % pa for Nov 2017. Greece Government Bond Yield: Average: 3 Years data is updated monthly, averaging 4.355 % pa from Mar 1999 (Median) to Dec 2017, with 198 observations. The data reached an all-time high of 77.650 % pa in Feb 2012 and a record low of 2.080 % pa in Jul 2014. Greece Government Bond Yield: Average: 3 Years data remains active status in CEIC and is reported by Bank of Greece. The data is categorized under Global Database’s Greece – Table GR.M006: Government Bonds Yield: Average.

Key information about Mexico Short Term Government Bond Yield

• Mexico Short Term Government Bond Yield: Month Avg: Mexico: 3 Years was reported at 5.90 % pa in Feb 2025, compared with 6.17 % pa in the previous month.
• Mexico Short Term Government Bond Yield data is updated monthly, available from May 1996 to Feb 2025.
• The data reached an all-time high of 8.61 % pa in Jul 1996 and a record low of 0.40 % pa in Oct 2013.
• Short Term Government Bond Yield is reported by CEIC Data.

A global meta-analysis of yield-scaled N2O emissions and its mitigation efforts for maize, wheat and rice

Author: Zhisheng Yao, Haojie Guo, Yan Wang, Yang Zhan, Tianli Zhang, Rui Wang, Xunhua Zheng, Klaus Butterbach-Bahl Any correspondence has to be send to zhishengyao@mail.iap.ac.cn

Description of the Data and file structure

Note: There are six sheets in the dataset. The variable name, unit, and description for each column of each sheet are shown below. The empty cells in this Excel file mean that data are not available.

Variable List:

Sheet1: Overview

Sheet2: Maize

| Column | Name | Unit | Description | | :--...

The contribution of potatoes to the global food supply is increasing—consumption more than doubled in developing countries between 1960 and 2005. Understanding climate change impacts on global potato yields is therefore important for future food security. Analyses of climate change impacts on potato compared to other major crops are rare, especially at the global scale. Of two global gridded potato modeling studies published at the time of this analysis, one simulated the impacts of temperature increases on potential potato yields; the other did not simulate the impacts of farmer adaptation to climate change, which may offset negative climate change impacts on yield. These studies may therefore overestimate negative climate change impacts on yields as they do not simultaneously include CO2 fertilisation and adaptation to climate change. Here we simulate the abiotic impacts of climate change on potato to 2050 using the GLAM crop model and the ISI-MIP ensemble of global climate models. Simulations include adaptations to climate change through varying planting windows and varieties and CO2 fertilisation, unlike previous global potato modeling studies. Results show significant skill in reproducing observed national scale yields in Europe. Elsewhere, correlations are generally positive but low, primarily due to poor relationships between national scale observed yields and climate. Future climate simulations including adaptation to climate change through changing planting windows and crop varieties show that yields are expected to increase in most cases as a result of longer growing seasons and CO2 fertilisation. Average global yield increases range from 9 to 20% when including adaptation. The global average yield benefits of adaptation to climate change range from 10 to 17% across climate models. Potato agriculture is associated with lower green house gas emissions relative to other major crops and therefore can be seen as a climate smart option given projected yield increases with adaptation.

View data of the effective yield of an index of non-investment grade publically issued corporate debt in the U.S.

Please see the file 'Description.txt' for data outline and reference to the policies of the data sources.Abstract from paperHuman activities have increased the input of nitrogen and phosphorus into riverine systems. These inputs can increase algal growth that degrades aquatic ecosystems. We constructed a global database of loads (kg) and yields (kg ha-1 yr-1) of dissolved and total nitrogen and phosphorus forms for seven years (centred around 2008) in 1421 catchments. Yields were calculated from 640,950 measurements that were checked, filtered and harmonized from readily available sources. We used the yield data to create a georeferenced model to calculate yields of nitrogen and phosphorus forms across 6020 catchments, globally. The database can be used to assess and inform policy to reduce nitrogen and phosphorus losses from land to freshwater, improve nutrient use efficiency on farms, and help calibrate global models being used to explore scenarios such as nutrient management efficiency in a changing climate.

This statistic shows the potential yield from food waste biogas sources worldwide in 2018, broken down by type of feedstock. The energy potential from baking wastes was estimated to reach *** cubic meters per metric ton.

Switzerland Bond Yield: 20 Years data was reported at 0.437 % pa in Oct 2018. This records a decrease from the previous number of 0.485 % pa for Sep 2018. Switzerland Bond Yield: 20 Years data is updated monthly, averaging 3.500 % pa from Jan 1988 (Median) to Oct 2018, with 370 observations. The data reached an all-time high of 6.930 % pa in May 1992 and a record low of -0.198 % pa in Jul 2016. Switzerland Bond Yield: 20 Years data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.M006: Government Bond Yield.

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 and asses the amount of N fertilizer that would be needed to achieve yield potential. Th dataset consists of wheat grain yield and other key crop and soil variables simulated with 12 wheat crop growth models at 34 high-rainfall or irrigated locations from key wheat growing regions in the world. Simulations are for the 1981-2010 baseline period and two mi-century (2040-2069) future climate scenarios (RCP4.5 and RCP8.5). Daily weather data for each of the 34 global locations for the baseline period (1980-2010) were obtained from the AgMERRA climate dataset. Climate projections were taken from five global climate models (GCMs; HadGEM2-ES, MIROC5, MPI-ESM-MR, GFDL-CM3, and GISS-E2-R) drawn from the Fifth Coupled Model Intercomparison Project (CMIP5). Other data include soil characteristics and initial soil conditions, and cultivar information for all locations. For each location, local mean sowing, anthesis and maturity dates were supplied with qualitative information on vernalization requirements and photoperiod sensitivity. Simulations were performed with unlimited soil nitrogen availability and with N fertilizer application rates ranging from 0 to 400 kg N ha-1 in steps of 50 kg N ha-1. Simulations include end-of-season results for 24 crop and soil variables simulated by the 12 wheat crop models. Simulation protocols used by the individual modeling teams are included, with all necessary information to execute them with a crop growth model.

This study investigates the impact of alternative fertilization practices on the yield-scaled global warming potential in almond orchards. Almond production contributes to greenhouse gas emissions due to fertilizer nitrogen (N) use. Field experiments were conducted in an almond orchard using three fertigation practices: Advance Grower Practice (AGP), Pump and Fertilizer (P&F) and High Frequency-Low Concentration (HFLC).The study was conducted in a commercial almond orchard (16 ha, 2015 and 2016 growing seasons) in the San Joaquin Valley (Madera, California; 36◦ 49’ 15.85” N 120◦ 12’1.20 W, elevation 60 m). The trees (ca. 16 years, 73 trees per row) were spaced 5.5 m tree to tree within row and approximately 14.6 m between alternating rows of Nonpareil and Carmel cultivars. The soil type was the Cajon soil series (Mixed, thermic Typic Torripsamments), characterized by loose fine sand with low organic carbon content and low water holding capacity (Web Soil Survey, 2021). The orchard exists in a semiarid, Mediterranean-like climate. All samples were collected in the second and third year after fertigation treatments were initiated.Urea Ammonium Nitrate [UAN; 32% nitrogen (N), composed of 50% urea-N, 25% NH4+-N, and 25% NO3 --N) was delivered through the irrigation system to all three irrigation treatments (i.e. fertigation). Generally, the fertilizer N content was similar across all Pump and Fertigate treatments but the frequency of application varied. Two treatments (, P&F; HFLC, High Frequency-Low Concentration) provided a similar total, annual N targeted to meet the demands for high yielding commercial almond production based on above- and below-ground growth. The third, AGP, applied approximately 30% more N fertilizer than P&F and HFLC because it represented the standard growing practice with respect to timing and quantity of fertilizer application for the local industry. P&F and HFLC reduced the amount of applied N fertilizer by accounting for groundwater N concentrations. Each fertigation treatment was composed of four tree rows, with each of the four replicates placed along one of the two center tree rows.Greenhouse gas emissions from fertigation were sampled using an array of static chambers that were placed to cover the drip zone and within the tree row and alley. At the same time, soil water content, temperature and inorganic N pools were collected. During harvest, almond yields were measured to allow for calculation of yield-scaled global warming potential.

General information

This dataset contains soybean yield projections in Europe under historical (1981-2010) and future climate with moderate (RCP 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The data has been generated by Guilpart et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. All details can be found in this paper. A brief summary is provided below.

Summary of soybean yield projections methodology

Yield projections have been performed using data-driven relationships between climate and soybean yield derived from machine-learning (Random Forest). The Random Forest model was trained using (i) the the global dataset of historical yields updated version (Iizumi et al. 2014a), which includes grid-wise soybean yields worldwide with the grid size of 1.125 degree over 1981-2010, and (ii) the global retrospective meteorological forcing dataset tailored for agricultural application (GRASP, Iizumi et al. 2014b), which covers the period 1961–2010 at the same spatial resolution as yield data, i.e. a grid size of 1.125 degree. Time-detrended soybean yield data was related (using Random Forest) to 35 climate variables defined at a monthly time step over the seven months of the soybean growing season, plus the fraction of irrigated area, i.e. a total of 36 variables. The 35 climate variables are monthly mean daily minimum and maximum temperatures (Tmin and Tmax, degree Celsius), monthly total precipitation (rain, mm month-1), monthly mean daily total solar radiation (solar, MJ m-2 day-1), monthly mean air vapor pressure (VP, hPa). The fitted model showed high R² (higher than 0.9) and low RMSE (0.35 t ha-1) between observed and predicted yields based on cross-validation.

Then, soybean yield projections under historical over whole Europe have been performed using the GRASP climate data, and yield projections under future climate have been performed using 16 climate change scenarios consisting of bias-corrected data of eight Global Circulation Models (GCM; GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, and NorESM1-M, used in the Coupled Model Intercomparison phase 5 (CMIP5) and two Representative Concentration Pathways (RCPs; 4.5 and 8.5 W m-2). Soybean growing season used for projections is April to October. All projections assumed irrigated fraction equals to zero. Projections are shown only on agricultural area (cropland plus pasture), in the year 2000. Soybean yield is expressed in tons per hectare.

Files description

RF_soybean_historical_GRASP_median_1981_2010.nc : random forest projections of soybean yield in Europe for the historical (1981-2010) period using GRASP climate data. This file contains the median yield (in tons per hectare) over 1981-2010.

RF_soybean_rcp45_median_2050_2059.nc : random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.

RF_soybean_rcp45_median_2090_2099.nc : random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.

RF_soybean_rcp85_median_2050_2059.nc : random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.

RF_soybean_rcp85_median_2090_2099.nc : random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.

References

Guilpart N. et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food.

Iizumi T. et al. (2014a) Historical changes in global yields: Major cereal and legume crops from 1982 to 2006. Glob. Ecol. Biogeogr. 23, 346–357.

Iizumi T. et al. (2014b). A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison. J. Geophys. Res. Atmos. Res. 119, 363–384.

A revised version of this dataset has been published: https://doi.org/10.17026/dans-zyu-xkhc. The files of this dataset are therefore no longer accessible.

This dataset contains the underlying data for the study: Kenya public weather processed by the Global Yield Gap Atlas project. Open Journal for Agricultural Research : ODjAR.

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). One part of the activities consists of collecting and processing weather data as an input for crop simulation models in sub-Saharan African countries including Kenya. This publication covers weather data for 10 locations in Kenya. The project looked for good quality weather data in areas where crops are pre-dominantly grown. As locations with good public weather data are sparse in Africa, the project developed a method to generate weather data from a combination of observed and other external weather data. One locations holds actually measured weather data, the other 9 locations show propagated weather data. The propagated weather data consist on TRMM rain data (or NASA POWER if TRMM is not available) and NASA POWER Tmax, Tmin, and Tdew data corrected based on calibrations with short-term (<10 years) observed weather data. sources (Van Wart et.al. 2015).

Crop cultivation potential describes the agronomically possible upper limit to produce individual crops under given agro-climatic, soil and terrain conditions and applying specific management assumptions and agronomic input levels. Soil moisture conditions together with other climate characteristics (radiation and temperature) during different crop development stages are used in an eco-physiological crop growth model to calculate potential biomass production and yield. The constraint-free crop yields calculated in the AEZ biomass model reflect yield potentials with regard to temperature and radiation regimes prevailing in each grid-cell. Maximum biomass and yields depend on the timing of the crop growth cycle (crop calendar) and are separately calculated for irrigated and rain-fed conditions. Crop specific requirements are matched with temperature regimes prevailing in individual grid-cells. Matching is tested for the full range of possible starting dates. For rain-fed conditions the crop calendar resulting in the highest expected (water-limited) yield is selected to represent maximum biomass and agro-climatic potential yield of the respective crop in a particular grid-cell. The estimation of yield losses due to water stress is based on crop-specific water balances. Yield estimation for irrigation conditions assumes that irrigation is scheduled such that no yield-reducing crop water deficits occur during the crop growth cycle. Differences in crop types and production systems are empirically characterized by the concept of Land Utilization Types (LUTs). A LUT comprises technical specifications for crop production within a given socioeconomic setting. Specific LUT attributes include agronomic information, type of the main produce, water supply type, information on typical cultivation practices, and utilization of main produce. GAEZ v4 distinguishes more than 300 crops/LUTs per level of inputs/management, which are separately assessed for rain-fed and irrigated conditions. These LUTs are grouped into 67 crop sub-types and 53 different food, feed, fiber, and bio-energy crops. Theme 3 provides crop-wise information about: (1) Agro-Climatic Yield, (2) Constraint Factors, (3) Growth Cycle Attributes, and (4) Land Utilization Types (LUT) Selection. GAEZ methodology development, data base compilation, production of results and establishing the Data Portal were accomplished in close technical collaboration and with inputs of the International Institute for Applied Systems Analysis (IIASA). For further details, please refer to the GAEZ v4 Model Documentation.