Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Land Locked Developing Countries (LLDCs)

Land Locked Developing Countries (LLDCs): Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) was 0.3295 in 2023. ▬ Flat

Latest (2023)
0.3295
Change on year
up 1.2%
Rank
26th
of 29 groups
All-time high
0.3295
in 2023
All-time low
0.269
in 2006
Years of data
29
1995–2023

Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Land Locked Developing Countries (LLDCs), 1995–2023

00.10.20.31995200920231995: 0.3171996: 0.3251997: 0.3191998: 0.3121999: 0.3162000: 0.3162001: 0.2982002: 0.2852003: 0.2712004: 0.2712005: 0.2712006: 0.2692007: 0.2792008: 0.2812009: 0.2862010: 0.3042011: 0.3042012: 0.3072013: 0.3052014: 0.2922015: 0.2942016: 0.2912017: 0.2982018: 0.2982019: 0.2962020: 0.2982021: 0.3112022: 0.3262023: 0.329

Source: Food and Agriculture Organization of the United Nations.

Analysis

In 2023, gross fixed capital formation (agriculture, forestry and fishing) in Land Locked Developing Countries (LLDCs) stood at 0.3295. That is the highest value across all 29 years on record.

The figure is up 1.2% on the previous year and up 7.9% over ten years.

Over the whole period, gross fixed capital formation (agriculture, forestry and fishing) in Land Locked Developing Countries (LLDCs) peaked at 0.3295 in 2023 and was at its lowest, 0.269, in 2006.

Land Locked Developing Countries (LLDCs) ranks 26th of 29 groups on this measure, in the bottom quarter.

Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Land Locked Developing Countries (LLDCs), year by year

Annual values for Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) — Agriculture orientation index US$ in Land Locked Developing Countries (LLDCs), 1995 to 2023.
Year Value Change
1995 0.3172
1996 0.325 +2.5%
1997 0.3186 -2.0%
1998 0.3117 -2.2%
1999 0.3157 +1.3%
2000 0.3161 +0.1%
2001 0.2979 -5.7%
2002 0.2848 -4.4%
2003 0.2715 -4.7%
2004 0.2712 -0.1%
2005 0.2707 -0.2%
2006 0.269 -0.6%
2007 0.2792 +3.8%
2008 0.281 +0.6%
2009 0.2863 +1.9%
2010 0.3038 +6.1%
2011 0.3037 -0.0%
2012 0.3066 +1.0%
2013 0.3054 -0.4%
2014 0.2918 -4.5%
2015 0.2941 +0.8%
2016 0.2914 -0.9%
2017 0.2975 +2.1%
2018 0.2983 +0.3%
2019 0.2962 -0.7%
2020 0.2977 +0.5%
2021 0.311 +4.5%
2022 0.3257 +4.7%
2023 0.3295 +1.2%

Averages by decade

DecadeAverage LowestHighest Years
1990s 0.3176 0.3117 0.325 5
2000s 0.2828 0.269 0.3161 10
2010s 0.2989 0.2914 0.3066 10
2020s 0.316 0.2977 0.3295 4

Countries ranked near Land Locked Developing Countries (LLDCs)

  1. 23 Netherlands (Kingdom of the) 1.48 compare
  2. 24 France 1.48 compare
  3. 25 Australia 1.47 compare
  4. 26 Romania 1.41 compare
  5. 27 Poland 1.39 compare
  6. 28 Croatia 1.36 compare
  7. 29 Australia and New Zealand 1.36 compare

See the full ranking of 228 places →

More environment data for Land Locked Developing Countries (LLDCs)

All data for Land Locked Developing Countries (LLDCs) →

Frequently asked questions

What is gross fixed capital formation (agriculture, forestry and fishing) in Land Locked Developing Countries (LLDCs)?
Gross fixed capital formation (agriculture, forestry and fishing) in Land Locked Developing Countries (LLDCs) was 0.3295 in 2023, according to Food and Agriculture Organization of the United Nations.
What is the highest gross fixed capital formation (agriculture, forestry and fishing) recorded in Land Locked Developing Countries (LLDCs)?
The highest recorded value was 0.3295 in 2023.
What is the lowest gross fixed capital formation (agriculture, forestry and fishing) recorded in Land Locked Developing Countries (LLDCs)?
The lowest recorded value was 0.269 in 2006.
How does Land Locked Developing Countries (LLDCs) rank for gross fixed capital formation (agriculture, forestry and fishing)?
Land Locked Developing Countries (LLDCs) ranks 26th out of 29 groups with data for 2023.
Is gross fixed capital formation (agriculture, forestry and fishing) rising or falling in Land Locked Developing Countries (LLDCs)?
Over the last ten years it is up 7.9%. The long-run trend across the full record is flat.
Where does this Land Locked Developing Countries (LLDCs) data come from?
The figures come from Food and Agriculture Organization of the United Nations, published as part of Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) — Agriculture orientation index US$. Statizoid updates them automatically from the source API.

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Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) in Land Locked Developing Countries (LLDCs). Statizoid, drawing on Food and Agriculture Organization of the United Nations. Retrieved 03 September 2026, from https://environment.statizoid.com/stat/gross-fixed-capital-formation-agriculture-forestry-and-fishing-agriculture-orientation-3/land-locked-developing-countries-lldcs/

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About this data

Indicator
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) — Agriculture orientation index US$
Source
Food and Agriculture Organization of the United Nations
Licence
CC BY-NC-SA 3.0 IGO (FAO)
Coverage
228 places, 6,512 data points, 1995–2023
Last refreshed

As part of the FAO Agriculture Capital Stock (ACS) database, the Statistics Division of FAO publishes country-by-country data on physical investment in agriculture, forestry and fishing as measured by the System of National Accounts (SNA) concept of Gross Fixed Capital Formation (GFCF). Additional variables included in the ACS are Net and Gross Capital Stock, Consumption of Fixed Capital, the Agriculture Investment ratio, and the Gross Fixed Capital Formation Agriculture Orientation Index. The FAO Agriculture Capital Stock Database is an analytical database: whenever available, the database integrates official National Accounts data harvested from the UNSD National Accounts Main Aggregates Database (UNSD AMA) and the OECD Annual National Accounts Database (OECD ANA). The database is further supplemented by OECD Structural Analysis database (OECD STAN) and, in a few cases, data from country’s statistics websites. If the full set of official data is not available for any specific country, imputation methods are applied to obtain estimates over the complete time series. Many data points in ACS are estimated and are flagged as such; they do not represent official submissions by Member Countries. With a view of producing internationally comparable net capital stock estimates, the Perpetual Inventory Method (PIM) with a constant geometric depreciation rate is employed to impute missing data. The Perpetual Inventory Method is a well-established economic model to calculate Net Capital Stocks (NCS) and Consumption of Fixed Capital (CFC) from time series of Gross Fixed Capital Formation (GFCF). Specifically, annual measures of the NCS are obtained from cumulating historical series on physical investment flows and deducting the part of assets that are depreciated (the Consumption of Fixed Capital that occurs in every period). In order to implement the PIM, long time series on aggregate GFCF in agriculture, forestry and fishing is required.An attempt is made to rely as much as possible on National Accounts data published by the OECD and UNSD. When country data are partially or fully missing, econometric techniques to impute missing observations are employed. Depending on the pattern of data missingness for the countries, different imputation methods are applied (from among the ARIMAX, PANEL regression, and OLS approaches) for the data series from 1995 to 2022. The values of Agriculture Capital Stock related indicators for 2023, including Agriculture Investment Ratio, Agriculture Orientation Index, Net Capital Stock, Gross Fixed Capital Formation and Consumption of Fixed Capital, are estimated using the Holt-Winters (HW) method (Cipra et al., 1995). The HW method is an exponential smoothing method for forecasting the annual values of economic variables. In this context, the HW method relies on existing (historical) values of the Agriculture Capital Stock. The predicted value is an extrapolation of the historical values to the specified target date, which extends the timeline without considering seasonality in the annual series.All data series in the database are provided both in national currencies and in US dollars as well as in current prices and constant prices with base year 2015.