Tubiello, F.N., Wanner, N., Asprooth, L., Mueller, M, Ignaciuk, A., Khan, A. A. & Rosero Moncayo, J., 2021. Measuring progress towards sustainable agriculture. FAO Statistics Working Paper 21-24. Rome, FAO. https://doi.org/10.4060/cb4549en FAO. 1988. Report of the FAO Council, 94th Session, 1988. FAO, Rome, Italy

Annex 1: Description of the sub-indicators

1. Gross production value per hectare

Formula:

Gross production value per hectare = $\frac{G r o s s P r o d u c t i o n V a l u e A g r i c u l t u r e}{A g r i c u l t u r a l L a n d}$

Numerator (Gross Production Value Agriculture): Value of gross production has been compiled by multiplying gross production in physical terms by output prices at farm gate. Thus, value of production measures production in monetary terms at the farm gate level. Since intermediate uses within the agricultural sector (seed and feed) have not been subtracted from production data, this value of production aggregate refers to the notion of "gross production".

Denominator (Agriculture Land): Land used for cultivation of crops and animal husbandry. The total of area under ''Cropland'' and ''Permanent meadows and pastures.''

Unit of measure: $\frac{C o n s t a n t 2014 - 2016 I $ }{1000 h e c t a r e s}$

Data sources:

Numerator: FAOSTAT Value of Agricultural Production Domain https://www.fao.org/faostat/en/#data/QV

Denominator: FAOSTAT Land Use Domain

https://www.fao.org/faostat/en/#data/RL

2. Gross output diversification

Formula:

Gross output diversification = $1 - \sum ({\frac{G r o s s P r o d u c t i o n {V a l u e}_{c i t}}{G r o s s P r o d u c t i o n V a l u e_{i t}})}^{2}$

C= crop

I = country

t = year

Unit of measure: unitless

Data source:

FAOSTAT Value of Agricultural Production Domain

https://www.fao.org/faostat/en/#data/QV

3. Agricultural value added per worker

Formula:

Agricultural value added per worker $= \frac{V a l u e a d d e d i n a g r i c u l t u r e, f o r e s t r y a n d f i s h e r i e s}{N u m b e r o f p e o p l e e m p l o y e d i n a g r i c u l t u r e}$

This indicator provides information on the output of the agricultural sector by worker engaged. It is a measure of agricultural productivity. The data on the value added in agriculture, forestry and fisheries is extracted from FAOSTAT and then divided by the number of people employed in agriculture (in broad sense) extracted from ILOSTAT for a given year in a given country.

Unit of measure: US$ (2015 prices) per worker

Data source:

FAOSTAT Employment Indicators: Agriculture Domain

http://www.fao.org/faostat/en/#data/OE

4. Cropland nitrogen use efficiency

Formula: The nutrient budget (NB) is calculated as the sum of inputs: mineral fertilizers (MF) multiplied by the fraction of fertilizer applied to cropland (CF), manure applied to soils (MAS), nitrogen deposition (ND), and biological fixation (BF), and seed (SD) minus outputs: crop removal (CR).

Thus: the Nutrient Budget (NB) for country i for nutrient j for year y is calculated as:

NB_i,j,y = sum(MF_i,j,y x CF_i,j,y, MAS_i,j,y , ND_i,j,y , BF_i,j,y , SD_i,j,y) – CR_i,j,y

The Nutrient Use Efficiency (NUE) for country I for nutrient j for year y is calculated as:

NUE_i,j,y = Cri,_j,y/sum(MF_i,j,y x CF_i,j,y, MAS_i,j,y , ND_i,j,y , BF_i,j,y ,SD_i,j,y)

Unit of measure: %

“A global reference database in FAOSTAT of cropland nutrient budgets and nutrient use efficiency: nitrogen, phosphorus and potassium, 1961–2020”

Ludemann, C. (Creator), Wanner, N. (Creator), Chivenge, P. (Creator), Dobermann, A. (Creator), Einarsson, R. (Creator), Grassini, P. (Creator), Gruere, A. (Creator), Jackson, K. (Creator), Lassaletta, L. (Creator), Maggi, F. (Creator), Obli-Laryea, G. (Creator), van Ittersum, M. (Creator), Vishwakarma, S. (Creator), Zhang, X. (Creator) & Tubiello, F. N. (Creator), 2 Jun 2023

DOI: 10.5061/dryad.hx3ffbgkh

Data sources:

Mineral fertilizers:

Data: “Fertilizers by Nutrient” domain in FAOSTAT

http://fenix.fao.org/faostat/internal/en/#data/RFN

Coefficients: The cropland fraction estimates were derived from 4 exisiting datasets

Zou, T., et. al. Global trends of cropland phosphorus use and sustainability challenges. Nature (2022).

Manure applied to soils

Data: “Manure applied to Soils” domain in FAOSTAT

http://fenix.fao.org/faostat/internal/en/#data/GU

Coefficients: OECD Secretariat 1997, USA (Midwest Plan Service 1985) and Europe (Levington Agriculture 1997) and from Sheldrick et al (2003). Statistics Netherlands (2012).

Atmospheric Deposition:

Data: Vishwakarma, Srishti et al. (2022), Quantifying nitrogen deposition inputs to cropland: A national scale dataset from 1961 to 2020, Dryad, Dataset.

Crop Removal:

Data: Primary Crops under the domain “Crops and livestock products”

https://www.fao.org/faostat/en/#data/QCL

Coefficients: Ludemann et al (2022) Global data on crop nutrient concentration and harvest indices

https://doi.org/10.5061/dryad.n2z34tn0x

Biological Fixation :

Data : Primary Crops under the domain “Crops and livestock products”

https://www.fao.org/faostat/en/#data/QCL

Methods: Peoples et al. (2021) and Herridge et al. (2022).

5. Agriculture component of water stress

Formula:

Agriculture component of water stress = $\frac{T F W W }{(T R W R - E F R) } * 100 %$

TFWW: the total freshwater withdrawn (km3 /year (109 m3 /year))

TRWR: the difference between the total renewable freshwater resources km3 /year (109 m3 /year))

EFR: the environmental flow requirements (km3 /year (109 m3 /year))

While for the overall SDG indicator 6.4.2., values below 25% are considered safe (no stress), whereas values over 25% are classified into four different levels of severity, for the agriculture component of the indicator, adjusted thresholds have been determined at 70 percent of these conventional thresholds at aggregate national level, considering that globally, agriculture is responsible for 70 percent of all water withdrawals. Therefore, a water stress level for the agriculture component of below 17.5% is considered safe, a level of between 17.5% and 35% is considered to be low stress, and so on.

Unit of measure: Percentage

Data source: https://www.fao.org/sustainable-development-goals/indicators/642/en/

https://unstats.un.org/sdgs/dataportal

6. GHG emissions intensity in agriculture

Formula $:$

Green House Gas Emissions Intensity $= \frac{E m i s s i o n s (F a r m g a t e)}{V a l u e o f A g r i c u l t u r a l P r o d u c t i o n} * 100 %$

Numerator (Farm gate emissions): Emissions from drained organic soils, cultivation of histosols, inorganic N fertilizers, crop residues, manure deposited on pasture, range and paddock, manure applied to soils, manure management, enteric fermentation, prescribed burning of savanna, burning crop residues, rice cultivation, and on-farm energy use.

Denominator (Value of Agricultural Production): Value of gross production has been compiled by multiplying gross production in physical terms by output prices at farm gate.

Unit of measure: kg CO₂ equivalent per constant 2014-2016 USD

Data source:

FAOSTAT Climate Change: Agrifood system emissions Emissions totals domain

https://www.fao.org/faostat/en/#data/GT

FAOSTAT Value of Agricultural Production Domain

https://www.fao.org/faostat/en/#data/QV

7. Informal employment in agriculture

SDG Indicator 8.3.1 Proportion of informal employment in total employment, disaggregated by the agricultural sector

Informal employment comprises persons who in their main or secondary jobs were in one of the following categories: - Own-account workers, employers and members of producers’ cooperatives employed in their own informal sector enterprises (the characteristics of the enterprise determine the informal nature of their jobs) - Own-account workers engaged in the production of goods exclusively for own final use by their household (e.g. subsistence farming) - Contributing family workers, regardless of whether they work in formal or informal sector enterprises (they usually do not have explicit, written contracts of employment, and are not subject to labour legislation, social security regulations, collective agreements, etc., which determines the informal nature of their jobs) - Employees holding informal jobs, whether employed by formal sector enterprises, informal sector enterprises, or as paid domestic workers by households (employees are considered to have informal jobs if their employment relationship is, in law or in practice, not subject to national labour legislation, income taxation, social protection or entitlement to certain employment benefits) For the purpose of classifying persons into formal or informal employment for this indicator, only the characteristics of the main job are considered.

Unit of measure: Percentage

Data sources: ILO Stat

https://ilostat.ilo.org/topics/informality/

Annex 2: Methods for assessing the current status

Dimension	2.4.1 sub-indicator theme	Proposed Proxy measure	Numerical target
Economic	Land productivity	Gross production value per hectare	No
Economic	Resilience	Gross output diversification	No
Economic		Agricultural value added per worker (link to 2.3.2)	No
Environment	Soil quality	Nitrogen use efficiency	Yes
Environment	Water availability	Agriculture component of water stress (6.4.2 disaggregation)	Yes
Environment	[No equivalent theme]	GHG emissions intensity	No
Social	Decent employment	Proportion of informal employment in agriculture	No

1. Indicators with a numerical target

The current distance to the target is calculated only when a numerical target exists, as follows:

$d_{i t} = \{\begin{matrix} x^{*} - x_{i t}, w h e n t h e d e s i r e d d i r e c t i o n i s a n i n c r e a s e o v e r t i m e \\ {x_{i t} - x}^{*}, w h e n t h e d e s i r e d d i r e c t i o n i s a d e c r e a s e o v e r t i m e \end{matrix})$

Here $x_{i t}$ denotes the numerical value of the generic indicator for country i in year t; while $x^{*}$ is the target value of the generic indicator (to be reached by 2030). This distance measure is 0 for indicators having already reached the target at the time of the assessment.

SDG indicator 6.4.2, agriculture component

For this indicator, thresholds have been determined that are set at 70 percent, the conventional thresholds for the severity levels of water stress at aggregate national level (as per metadata of SDG indicator 6.4.2), considering that globally, agriculture is responsible for 70 percent of all water withdrawals. The current distance to the target for the agriculture component of SDG indicator 6.4.2 is therefore calculated as follows: Where x is the level of water stress attributable to agriculture

Bounds	Color	Meaning	Score
$x \leq$ 17.5 percent	Dark green	Target already met	5
$17.5 < x \leq 35 p e r c e n t$	Light green	Close to the target	4
$35 p e r c e n t < x \leq 52.5 p e r c e n t$	Yellow	Moderate distance to the target	3
$52.5 p e r c e n t < x \leq 70 p e r c e n t$	Orange	Far from the target	2
$x > 70 p e r c e n t$	Red	Very far from the target	1
None	Grey	Missing data	1

Nitrogen Use Efficiency

For the cropland nitrogen use efficiency (NUE), the desired range is between 50% to 90%, based on a scientifically determined optimal target between 65% and 80%^[1]. The assessment of the current status (last available data) will be conducted by calculating the distance to the target as shown below. The cropland NUE value x for country i in year t will be assessed as follows:

Bounds	Color	Meaning	Score
$50 % \leq x_{i, t} \leq 90 %$	Dark green	Target already met	5
$45 % \leq x_{i, t} < 50 %$ $90 % < x_{i, t} \leq 95 %$	Light green	Close to the target	4
$40 % \leq x_{i, t} < 45 %$ $95 % < x_{i, t} \leq 100 %$	Yellow	Moderate distance to the target	3
$35 % \leq x_{i, t} < 40 %$ $100 % < x_{i, t} \leq 105 %$	Orange	Far from target	2
$x_{i, t} < 35 %$ $x_{i, t} > 105 %$	Red	Very far from target	1
None	Grey	Missing data	1

1.2 Indicators without a numerical target

All the other six proxy measures will be treated as indicators without a numerical target, for which the distance to the target cannot be calculated. For analytical purposes, it is useful however to provide a summary picture that describes the current worldwide distribution of the indicator. For this reason, each country will be associated to the corresponding quintile. The quintiles divide the entire distribution of countries into five equal groups, according to their indicator value: the first quintile contains the bottom fifth of the countries on the indicators scale (i.e. the 20 % of the countries with the lowest value), the second quintile represents the second fifth (from 20 % to 40 %) and so on; finally the fifth quintile represents the top 20 % countries, i.e. those with the highest values for the indicator. A country’s quintile categorization will earn it a corresponding score for the purposes of calculating its overall progress towards productive and sustainable agriculture, depending on the normative direction:

With an increasing normative direction

Quintile	Color	Meaning	Score
$q_{80 %} < x_{i t} \leq q_{100 %}$	Dark green	Best performers	5
$q_{60 %} < x_{i t} \leq q_{80 %}$	Light green	Above median performers	4
$q_{40 %} < x_{i t} \leq q_{60 %}$	Yellow	Median performers	3
$q_{20 %} < x_{i t} \leq q_{40 %}$	Orange	Below median performers	2
$q_{0 %} \leq x_{i t} \leq q_{20 %}$	Red	Worst performers	1
None	Grey	Missing data	1

With a decreasing normative direction

Quintile	Color	Meaning	Score
$q_{0 %} \leq x_{i t} \leq q_{20 %}$	Dark green	Best performers	5
$q_{20 %} < x_{i t} \leq q_{40 %}$	Light green	Above median performers	4
$q_{40 %} < x_{i t} \leq q_{60 %}$	Yellow	Median performers	3
$q_{60 %} < x_{i t} \leq q_{80 %}$	Orange	Below median performers	2
$q_{80 %} < x_{i t} \leq q_{100 %}$	Red	Worst performers	1
None	Grey	Missing data	1

Annex 3: Method for assessing trend

The method to assess the trend distinguishes between indicators underpinning targets with and without a numerical yardstick.

2.1 Indicators with a numerical target

For indicators with a fixed numerical target, the trend is assessed by comparing the actual growth since the baseline year, with the growth required to achieve the target. Assuming a geometrical growth over time, the trend is assessed with the following mathematical expression^{^[2]}

$C R = \frac{C A G R_{a}}{C A G R_{r}} = \frac{{(\frac{x_{t}}{x_{t_{0}}})}^{\frac{1}{t - t_{0}}} - 1}{{(\frac{x^{*}}{x_{t_{0}}})}^{\frac{1}{2030 - t_{0}}} - 1}$ :

Against the following thresholds and categories as included in the technical annex of the global SDG Progress Chart:

Level or ratio CR	Color	Assessment category		Score
$x \leq x^{*}$	Dark green		Target already met	5
$C R \geq 0.95$	Light green		On-track to achieve the target	4
$0.5 < C R < 0.95$	Yellow		On-path, but too slow to achieve the target	3
$- 0.10 \leq C R \leq 0.5$	Orange		No improvement (stagnation) since baseline	2
$C R < - 0.10$	Red		Deterioration/movement away from the target (<<)	1
Missing data	Grey		None	1

2.2 Indicators without a numerical target (applies to all the other indicators)

For indicators without a set numerical target, which is the case for most of the suggested indicators in this proposal, it is only possible to assess the actual growth ( $C A G R_{a}$ in the expression above) against two sets of thresholds and categories, which depend on the normative direction of the indicator.

Therefore,

${C A G R}_{a} = {(\frac{x_{t}}{x_{t_{0}}})}^{\frac{1}{t - t_{0}}} - 1$

Different criteria can be used to assess the CAGR, depending on the sign of the normative direction and also on the fact that for some indicators a situation that remains unchanged over time (not increase or not decrease) can be judged positively.

Thresholds and categories when a positive outcome corresponds to an increase of the indicator

Levels of actual growth rate	Color	Assessment category	Score
${C A G R}_{a} > 0.01$	Dark green	Improvement since baseline-year (>>)	5
${0.005 < C A G R}_{a} \leq 0.01$	Light green	Slight improvement since baseline-year (>)	4
${- 0.005 \leq C A G R}_{a} \leq 0.005$	Yellow	No improvement since baseline-year (=)	3
${- 0.01 \leq C A G R}_{a} < - 0.005$	Orange	Slight deterioration since baseline-year (<)	2
${C A G R}_{a} < - 0.01$	Red	Deterioration since baseline-year (<<)	1
Missing data	Grey	None	1

Thresholds and categories when a positive outcome corresponds to a decrease of the indicator

Levels of actual growth rate	Color	Assessment category	Score
${C A G R}_{a} < - 0.01$	Dark green	Improvement since baseline-year (>>)	5
${- 0.01 \leq C A G R}_{a} < - 0.005$	Light green	Slight improvement since baseline-year (>)	4
${- 0.005 \leq C A G R}_{a} \leq 0.005$	Yellow	No improvement since baseline-year (=)	3
${0.005 < C A G R}_{a} \leq 0.01$	Orange	Slight deterioration since baseline-year (<)	2
${C A G R}_{a} > 0.01$	Red	Deterioration since baseline-year (<<)	1
Missing data	Grey	None	1

Countries that have already reached the maximum score for the current status assessment are assigned the maximum score for the trend assessment.

¹

Ludemann et al., 2023, in press https://essd.copernicus.org/preprints/essd-2023-206/essd-2023-206.pdf ↑

²

$t_{0}$ denotes the baseline year, while $t$ indicates the current or considered year for the assessment ↑

Hesin internet-kagin er ikki longur stuðlaður

Rætta metadátur: 2.4.1